WorldWideScience

Sample records for modeling spatial variability

  1. How to get rid of W: a latent variables approach to modelling spatially lagged variables

    NARCIS (Netherlands)

    Folmer, H.; Oud, J.

    2008-01-01

    In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are

  2. How to get rid of W : a latent variables approach to modelling spatially lagged variables

    NARCIS (Netherlands)

    Folmer, Henk; Oud, Johan

    2008-01-01

    In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are

  3. Modelling the effects of spatial variability on radionuclide migration

    International Nuclear Information System (INIS)

    1998-01-01

    The NEA workshop reflect the present status in national waste management program, specifically in spatial variability and performance assessment of geologic disposal sites for deed repository system the four sessions were: Spatial Variability: Its Definition and Significance to Performance Assessment and Site Characterisation; Experience with the Modelling of Radionuclide Migration in the Presence of Spatial Variability in Various Geological Environments; New Areas for Investigation: Two Personal Views; What is Wanted and What is Feasible: Views and Future Plans in Selected Waste Management Organisations. The 26 papers presented on the four oral sessions and on the poster session have been abstracted and indexed individually for the INIS database. (R.P.)

  4. A Non-Gaussian Spatial Generalized Linear Latent Variable Model

    KAUST Repository

    Irincheeva, Irina

    2012-08-03

    We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.

  5. A Non-Gaussian Spatial Generalized Linear Latent Variable Model

    KAUST Repository

    Irincheeva, Irina; Cantoni, Eva; Genton, Marc G.

    2012-01-01

    We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.

  6. Spatial variability and parametric uncertainty in performance assessment models

    International Nuclear Information System (INIS)

    Pensado, Osvaldo; Mancillas, James; Painter, Scott; Tomishima, Yasuo

    2011-01-01

    The problem of defining an appropriate treatment of distribution functions (which could represent spatial variability or parametric uncertainty) is examined based on a generic performance assessment model for a high-level waste repository. The generic model incorporated source term models available in GoldSim ® , the TDRW code for contaminant transport in sparse fracture networks with a complex fracture-matrix interaction process, and a biosphere dose model known as BDOSE TM . Using the GoldSim framework, several Monte Carlo sampling approaches and transport conceptualizations were evaluated to explore the effect of various treatments of spatial variability and parametric uncertainty on dose estimates. Results from a model employing a representative source and ensemble-averaged pathway properties were compared to results from a model allowing for stochastic variation of transport properties along streamline segments (i.e., explicit representation of spatial variability within a Monte Carlo realization). We concluded that the sampling approach and the definition of an ensemble representative do influence consequence estimates. In the examples analyzed in this paper, approaches considering limited variability of a transport resistance parameter along a streamline increased the frequency of fast pathways resulting in relatively high dose estimates, while those allowing for broad variability along streamlines increased the frequency of 'bottlenecks' reducing dose estimates. On this basis, simplified approaches with limited consideration of variability may suffice for intended uses of the performance assessment model, such as evaluation of site safety. (author)

  7. Panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable

    NARCIS (Netherlands)

    Elhorst, J. Paul

    2001-01-01

    This paper surveys panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable. In particular, it focuses on the specification and estimation of four panel data models commonly used in applied research: the fixed effects model, the random effects model, the

  8. Sparse modeling of spatial environmental variables associated with asthma.

    Science.gov (United States)

    Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

    2015-02-01

    Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

  9. Modelling the Spatial Isotope Variability of Precipitation in Syria

    Energy Technology Data Exchange (ETDEWEB)

    Kattan, Z.; Kattaa, B. [Department of Geology, Atomic Energy Commission of Syria (AECS), Damascus (Syrian Arab Republic)

    2013-07-15

    Attempts were made to model the spatial variability of environmental isotope ({sup 18}O, {sup 2}H and {sup 3}H) compositions of precipitation in syria. Rainfall samples periodically collected on a monthly basis from 16 different stations were used for processing and demonstrating the spatial distributions of these isotopes, together with those of deuterium excess (d) values. Mathematically, the modelling process was based on applying simple polynomial models that take into consideration the effects of major geographic factors (Lon.E., Lat.N., and altitude). The modelling results of spatial distribution of stable isotopes ({sup 18}O and {sup 2}H) were generally good, as shown from the high correlation coefficients (R{sup 2} = 0.7-0.8), calculated between the observed and predicted values. In the case of deuterium excess and tritium distributions, the results were most likely approximates (R{sup 2} = 0.5-0.6). Improving the simulation of spatial isotope variability probably requires the incorporation of other local meteorological factors, such as relative air humidity, precipitation amount and vapour pressure, which are supposed to play an important role in such an arid country. (author)

  10. SPATIAL MODELLING FOR DESCRIBING SPATIAL VARIABILITY OF SOIL PHYSICAL PROPERTIES IN EASTERN CROATIA

    Directory of Open Access Journals (Sweden)

    Igor Bogunović

    2016-06-01

    Full Text Available The objectives of this study were to characterize the field-scale spatial variability and test several interpolation methods to identify the best spatial predictor of penetration resistance (PR, bulk density (BD and gravimetric water content (GWC in the silty loam soil in Eastern Croatia. The measurements were made on a 25 x 25-m grid which created 40 individual grid cells. Soil properties were measured at the center of the grid cell deep 0-10 cm and 10-20 cm. Results demonstrated that PR and GWC displayed strong spatial dependence at 0-10 cm BD, while there was moderate and weak spatial dependence of PR, BD and GWC at depth of 10-20 cm. Semi-variogram analysis suggests that future sampling intervals for investigated parameters can be increased to 35 m in order to reduce research costs. Additionally, interpolation models recorded similar root mean square values with high predictive accuracy. Results suggest that investigated properties do not have uniform interpolation method implying the need for spatial modelling in the evaluation of these soil properties in Eastern Croatia.

  11. Partitioning the impacts of spatial and climatological rainfall variability in urban drainage modeling

    Science.gov (United States)

    Peleg, Nadav; Blumensaat, Frank; Molnar, Peter; Fatichi, Simone; Burlando, Paolo

    2017-03-01

    The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP - Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM - the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the

  12. From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions

    Science.gov (United States)

    Fenicia, Fabrizio; Kavetski, Dmitri; Savenije, Hubert H. G.; Pfister, Laurent

    2016-02-01

    This paper explores the development and application of distributed hydrological models, focusing on the key decisions of how to discretize the landscape, which model structures to use in each landscape element, and how to link model parameters across multiple landscape elements. The case study considers the Attert catchment in Luxembourg—a 300 km2 mesoscale catchment with 10 nested subcatchments that exhibit clearly different streamflow dynamics. The research questions are investigated using conceptual models applied at hydrologic response unit (HRU) scales (1-4 HRUs) on 6 hourly time steps. Multiple model structures are hypothesized and implemented using the SUPERFLEX framework. Following calibration, space/time model transferability is tested using a split-sample approach, with evaluation criteria including streamflow prediction error metrics and hydrological signatures. Our results suggest that: (1) models using geology-based HRUs are more robust and capture the spatial variability of streamflow time series and signatures better than models using topography-based HRUs; this finding supports the hypothesis that, in the Attert, geology exerts a stronger control than topography on streamflow generation, (2) streamflow dynamics of different HRUs can be represented using distinct and remarkably simple model structures, which can be interpreted in terms of the perceived dominant hydrologic processes in each geology type, and (3) the same maximum root zone storage can be used across the three dominant geological units with no loss in model transferability; this finding suggests that the partitioning of water between streamflow and evaporation in the study area is largely independent of geology and can be used to improve model parsimony. The modeling methodology introduced in this study is general and can be used to advance our broader understanding and prediction of hydrological behavior, including the landscape characteristics that control hydrologic response, the

  13. Quantifying measurement uncertainty and spatial variability in the context of model evaluation

    Science.gov (United States)

    Choukulkar, A.; Brewer, A.; Pichugina, Y. L.; Bonin, T.; Banta, R. M.; Sandberg, S.; Weickmann, A. M.; Djalalova, I.; McCaffrey, K.; Bianco, L.; Wilczak, J. M.; Newman, J. F.; Draxl, C.; Lundquist, J. K.; Wharton, S.; Olson, J.; Kenyon, J.; Marquis, M.

    2017-12-01

    In an effort to improve wind forecasts for the wind energy sector, the Department of Energy and the NOAA funded the second Wind Forecast Improvement Project (WFIP2). As part of the WFIP2 field campaign, a large suite of in-situ and remote sensing instrumentation was deployed to the Columbia River Gorge in Oregon and Washington from October 2015 - March 2017. The array of instrumentation deployed included 915-MHz wind profiling radars, sodars, wind- profiling lidars, and scanning lidars. The role of these instruments was to provide wind measurements at high spatial and temporal resolution for model evaluation and improvement of model physics. To properly determine model errors, the uncertainties in instrument-model comparisons need to be quantified accurately. These uncertainties arise from several factors such as measurement uncertainty, spatial variability, and interpolation of model output to instrument locations, to name a few. In this presentation, we will introduce a formalism to quantify measurement uncertainty and spatial variability. The accuracy of this formalism will be tested using existing datasets such as the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign. Finally, the uncertainties in wind measurement and the spatial variability estimates from the WFIP2 field campaign will be discussed to understand the challenges involved in model evaluation.

  14. Variability in results from negative binomial models for Lyme disease measured at different spatial scales.

    Science.gov (United States)

    Tran, Phoebe; Waller, Lance

    2015-01-01

    Lyme disease has been the subject of many studies due to increasing incidence rates year after year and the severe complications that can arise in later stages of the disease. Negative binomial models have been used to model Lyme disease in the past with some success. However, there has been little focus on the reliability and consistency of these models when they are used to study Lyme disease at multiple spatial scales. This study seeks to explore how sensitive/consistent negative binomial models are when they are used to study Lyme disease at different spatial scales (at the regional and sub-regional levels). The study area includes the thirteen states in the Northeastern United States with the highest Lyme disease incidence during the 2002-2006 period. Lyme disease incidence at county level for the period of 2002-2006 was linked with several previously identified key landscape and climatic variables in a negative binomial regression model for the Northeastern region and two smaller sub-regions (the New England sub-region and the Mid-Atlantic sub-region). This study found that negative binomial models, indeed, were sensitive/inconsistent when used at different spatial scales. We discuss various plausible explanations for such behavior of negative binomial models. Further investigation of the inconsistency and sensitivity of negative binomial models when used at different spatial scales is important for not only future Lyme disease studies and Lyme disease risk assessment/management but any study that requires use of this model type in a spatial context. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. Spatial variability in floodplain sedimentation: the use of generalized linear mixed-effects models

    Directory of Open Access Journals (Sweden)

    A. Cabezas

    2010-08-01

    Full Text Available Sediment, Total Organic Carbon (TOC and total nitrogen (TN accumulation during one overbank flood (1.15 y return interval were examined at one reach of the Middle Ebro River (NE Spain for elucidating spatial patterns. To achieve this goal, four areas with different geomorphological features and located within the study reach were examined by using artificial grass mats. Within each area, 1 m2 study plots consisting of three pseudo-replicates were placed in a semi-regular grid oriented perpendicular to the main channel. TOC, TN and Particle-Size composition of deposited sediments were examined and accumulation rates estimated. Generalized linear mixed-effects models were used to analyze sedimentation patterns in order to handle clustered sampling units, specific-site effects and spatial self-correlation between observations. Our results confirm the importance of channel-floodplain morphology and site micro-topography in explaining sediment, TOC and TN deposition patterns, although the importance of other factors as vegetation pattern should be included in further studies to explain small-scale variability. Generalized linear mixed-effect models provide a good framework to deal with the high spatial heterogeneity of this phenomenon at different spatial scales, and should be further investigated in order to explore its validity when examining the importance of factors such as flood magnitude or suspended sediment concentration.

  16. Spatial modelling of marine organisms in Forsmark and Oskarshamn. Including calculation of physical predictor variables

    Energy Technology Data Exchange (ETDEWEB)

    Carlen, Ida; Nikolopoulos, Anna; Isaeus, Martin (AquaBiota Water Research, Stockholm (SE))

    2007-06-15

    GIS grids (maps) of marine parameters were created using point data from previous site investigations in the Forsmark and Oskarshamn areas. The proportion of global radiation reaching the sea bottom in Forsmark and Oskarshamn was calculated in ArcView, using Secchi depth measurements and the digital elevation models for the respective area. The number of days per year when the incoming light exceeds 5 MJ/m2 at the bottom was then calculated using the result of the previous calculations together with measured global radiation. Existing modelled grid-point data on bottom and pelagic temperature for Forsmark were interpolated to create surface covering grids. Bottom and pelagic temperature grids for Oskarshamn were calculated using point measurements to achieve yearly averages for a few points and then using regressions with existing grids to create new maps. Phytoplankton primary production in Forsmark was calculated using point measurements of chlorophyll and irradiance, and a regression with a modelled grid of Secchi depth. Distribution of biomass of macrophyte communities in Forsmark and Oskarshamn was calculated using spatial modelling in GRASP, based on field data from previous surveys. Physical parameters such as those described above were used as predictor variables. Distribution of biomass of different functional groups of fish in Forsmark was calculated using spatial modelling based on previous surveys and with predictor variables such as physical parameters and results from macrophyte modelling. All results are presented as maps in the report. The quality of the modelled predictions varies as a consequence of the quality and amount of the input data, the ecology and knowledge of the predicted phenomena, and by the modelling technique used. A substantial part of the variation is not described by the models, which should be expected for biological modelling. Therefore, the resulting grids should be used with caution and with this uncertainty kept in mind. All

  17. Spatial modelling of marine organisms in Forsmark and Oskarshamn. Including calculation of physical predictor variables

    International Nuclear Information System (INIS)

    Carlen, Ida; Nikolopoulos, Anna; Isaeus, Martin

    2007-06-01

    GIS grids (maps) of marine parameters were created using point data from previous site investigations in the Forsmark and Oskarshamn areas. The proportion of global radiation reaching the sea bottom in Forsmark and Oskarshamn was calculated in ArcView, using Secchi depth measurements and the digital elevation models for the respective area. The number of days per year when the incoming light exceeds 5 MJ/m2 at the bottom was then calculated using the result of the previous calculations together with measured global radiation. Existing modelled grid-point data on bottom and pelagic temperature for Forsmark were interpolated to create surface covering grids. Bottom and pelagic temperature grids for Oskarshamn were calculated using point measurements to achieve yearly averages for a few points and then using regressions with existing grids to create new maps. Phytoplankton primary production in Forsmark was calculated using point measurements of chlorophyll and irradiance, and a regression with a modelled grid of Secchi depth. Distribution of biomass of macrophyte communities in Forsmark and Oskarshamn was calculated using spatial modelling in GRASP, based on field data from previous surveys. Physical parameters such as those described above were used as predictor variables. Distribution of biomass of different functional groups of fish in Forsmark was calculated using spatial modelling based on previous surveys and with predictor variables such as physical parameters and results from macrophyte modelling. All results are presented as maps in the report. The quality of the modelled predictions varies as a consequence of the quality and amount of the input data, the ecology and knowledge of the predicted phenomena, and by the modelling technique used. A substantial part of the variation is not described by the models, which should be expected for biological modelling. Therefore, the resulting grids should be used with caution and with this uncertainty kept in mind. All

  18. Modeling Spatial and Temporal Variability in Ammonia Emissions from Agricultural Fertilization

    Science.gov (United States)

    Balasubramanian, S.; Koloutsou-Vakakis, S.; Rood, M. J.

    2013-12-01

    Ammonia (NH3), is an important component of the reactive nitrogen cycle and a precursor to formation of atmospheric particulate matter (PM). Predicting regional PM concentrations and deposition of nitrogen species to ecosystems requires representative emission inventories. Emission inventories have traditionally been developed using top down approaches and more recently from data assimilation based on satellite and ground based ambient concentrations and wet deposition data. The National Emission Inventory (NEI) indicates agricultural fertilization as the predominant contributor (56%) to NH3 emissions in Midwest USA, in 2002. However, due to limited understanding of the complex interactions between fertilizer usage, farm practices, soil and meteorological conditions and absence of detailed statistical data, such emission estimates are currently based on generic emission factors, time-averaged temporal factors and coarse spatial resolution. Given the significance of this source, our study focuses on developing an improved NH3 emission inventory for agricultural fertilization at finer spatial and temporal scales for air quality modeling studies. Firstly, a high-spatial resolution 4 km x 4 km NH3 emission inventory for agricultural fertilization has been developed for Illinois by modifying spatial allocation of emissions based on combining crop-specific fertilization rates with cropland distribution in the Sparse Matrix Operator Kernel Emissions model. Net emission estimates of our method are within 2% of NEI, since both methods are constrained by fertilizer sales data. However, we identified localized crop-specific NH3 emission hotspots at sub-county resolutions absent in NEI. Secondly, we have adopted the use of the DeNitrification-DeComposition (DNDC) Biogeochemistry model to simulate the physical and chemical processes that control volatilization of nitrogen as NH3 to the atmosphere after fertilizer application and resolve the variability at the hourly scale

  19. Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

    NARCIS (Netherlands)

    Hoffmann, H.; Zhao, G.; Bussel, van L.G.J.

    2015-01-01

    Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield

  20. Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.

    Science.gov (United States)

    Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R

    2015-04-01

    Climate variability, the primary expression of climate change, is one of the most important environmental problems affecting human health, particularly vector-borne diseases. Despite research efforts worldwide, there are few studies addressing the use of information on climate variability for prevention and early warning of vector-borne infectious diseases. Show the utility of climate information for vector surveillance by developing spatial models using an entomological indicator and information on predicted climate variability in Cuba to provide early warning of danger of increased risk of dengue transmission. An ecological study was carried out using retrospective and prospective analyses of time series combined with spatial statistics. Several entomological and climatic indicators were considered using complex Bultó indices -1 and -2. Moran's I spatial autocorrelation coefficient specified for a matrix of neighbors with a radius of 20 km, was used to identify the spatial structure. Spatial structure simulation was based on simultaneous autoregressive and conditional autoregressive models; agreement between predicted and observed values for number of Aedes aegypti foci was determined by the concordance index Di and skill factor Bi. Spatial and temporal distributions of populations of Aedes aegypti were obtained. Models for describing, simulating and predicting spatial patterns of Aedes aegypti populations associated with climate variability patterns were put forward. The ranges of climate variability affecting Aedes aegypti populations were identified. Forecast maps were generated for the municipal level. Using the Bultó indices of climate variability, it is possible to construct spatial models for predicting increased Aedes aegypti populations in Cuba. At 20 x 20 km resolution, the models are able to provide warning of potential changes in vector populations in rainy and dry seasons and by month, thus demonstrating the usefulness of climate information for

  1. Constructing the reduced dynamical models of interannual climate variability from spatial-distributed time series

    Science.gov (United States)

    Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Feigin, Alexander

    2016-04-01

    We suggest a method for empirical forecast of climate dynamics basing on the reconstruction of reduced dynamical models in a form of random dynamical systems [1,2] derived from observational time series. The construction of proper embedding - the set of variables determining the phase space the model works in - is no doubt the most important step in such a modeling, but this task is non-trivial due to huge dimension of time series of typical climatic fields. Actually, an appropriate expansion of observational time series is needed yielding the number of principal components considered as phase variables, which are to be efficient for the construction of low-dimensional evolution operator. We emphasize two main features the reduced models should have for capturing the main dynamical properties of the system: (i) taking into account time-lagged teleconnections in the atmosphere-ocean system and (ii) reflecting the nonlinear nature of these teleconnections. In accordance to these principles, in this report we present the methodology which includes the combination of a new way for the construction of an embedding by the spatio-temporal data expansion and nonlinear model construction on the basis of artificial neural networks. The methodology is aplied to NCEP/NCAR reanalysis data including fields of sea level pressure, geopotential height, and wind speed, covering Northern Hemisphere. Its efficiency for the interannual forecast of various climate phenomena including ENSO, PDO, NAO and strong blocking event condition over the mid latitudes, is demonstrated. Also, we investigate the ability of the models to reproduce and predict the evolution of qualitative features of the dynamics, such as spectral peaks, critical transitions and statistics of extremes. This research was supported by the Government of the Russian Federation (Agreement No. 14.Z50.31.0033 with the Institute of Applied Physics RAS) [1] Y. I. Molkov, E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, "Random

  2. Modeling temporal and spatial variability of leaf wetness duration in Brazil

    Science.gov (United States)

    Alvares, Clayton Alcarde; de Mattos, Eduardo Moré; Sentelhas, Paulo Cesar; Miranda, Aline Cristina; Stape, José Luiz

    2015-05-01

    Leaf wetness duration (LWD) is recognized as a very important conditioner of crops and forests diseases, but clearly, there is a considerable gap in literature on temporal models for prediction of LWD in broad regions from standard meteorological data. The objective of this study was to develop monthly LWD models based on the relationship between hours of relative humidity (RH) ≥ 90 % and average RH for Brazil and based on these models to characterize the temporal and spatial LWD variability across the country. Two different relative humidity databases, being one in an hourly basis (RHh) and another in a monthly basis (RHm), were used. To elaborate the LWD models, 58 automatic weather stations distributed across the country were selected. Monthly LWD maps for the entire country were prepared, and for that, the RHm from the 358 conventional weather stations were interpolated using geostatistical techniques. RHm and LWD showed sigmoidal relationship with determination coefficient above 0.84 and were highly significant ( p LWD monthly models, a very good performance for all months was obtained, with very high precision with r between 0.92 and 0.96. Regarding the errors, mean error showed a slight tendency of overestimation during February (0.29 h day-1), May (0.31 h day-1), July (0.14 h day-1), and August (0.34 h day-1), whereas for the other months, the tendency was of underestimation like January (-0.27 h day-1) and March (-0.25 h day-1). Even as a first approach, the results presented here represent a great advance in the climatology of LWD for Brazil and will allow the development of studies related to crop and forest diseases control plans.

  3. Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa

    Directory of Open Access Journals (Sweden)

    Thandi Kapwata

    2016-11-01

    Full Text Available Malaria is an environmentally driven disease. In order to quantify the spatial variability of malaria transmission, it is imperative to understand the interactions between environmental variables and malaria epidemiology at a micro-geographic level using a novel statistical approach. The random forest (RF statistical learning method, a relatively new variable-importance ranking method, measures the variable importance of potentially influential parameters through the percent increase of the mean squared error. As this value increases, so does the relative importance of the associated variable. The principal aim of this study was to create predictive malaria maps generated using the selected variables based on the RF algorithm in the Ehlanzeni District of Mpumalanga Province, South Africa. From the seven environmental variables used [temperature, lag temperature, rainfall, lag rainfall, humidity, altitude, and the normalized difference vegetation index (NDVI], altitude was identified as the most influential predictor variable due its high selection frequency. It was selected as the top predictor for 4 out of 12 months of the year, followed by NDVI, temperature and lag rainfall, which were each selected twice. The combination of climatic variables that produced the highest prediction accuracy was altitude, NDVI, and temperature. This suggests that these three variables have high predictive capabilities in relation to malaria transmission. Furthermore, it is anticipated that the predictive maps generated from predictions made by the RF algorithm could be used to monitor the progression of malaria and assist in intervention and prevention efforts with respect to malaria.

  4. The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

    Science.gov (United States)

    Szymanowski, Mariusz; Kryza, Maciej

    2017-02-01

    Our study examines the role of auxiliary variables in the process of spatial modelling and mapping of climatological elements, with air temperature in Poland used as an example. The multivariable algorithms are the most frequently applied for spatialization of air temperature, and their results in many studies are proved to be better in comparison to those obtained by various one-dimensional techniques. In most of the previous studies, two main strategies were used to perform multidimensional spatial interpolation of air temperature. First, it was accepted that all variables significantly correlated with air temperature should be incorporated into the model. Second, it was assumed that the more spatial variation of air temperature was deterministically explained, the better was the quality of spatial interpolation. The main goal of the paper was to examine both above-mentioned assumptions. The analysis was performed using data from 250 meteorological stations and for 69 air temperature cases aggregated on different levels: from daily means to 10-year annual mean. Two cases were considered for detailed analysis. The set of potential auxiliary variables covered 11 environmental predictors of air temperature. Another purpose of the study was to compare the results of interpolation given by various multivariable methods using the same set of explanatory variables. Two regression models: multiple linear (MLR) and geographically weighted (GWR) method, as well as their extensions to the regression-kriging form, MLRK and GWRK, respectively, were examined. Stepwise regression was used to select variables for the individual models and the cross-validation method was used to validate the results with a special attention paid to statistically significant improvement of the model using the mean absolute error (MAE) criterion. The main results of this study led to rejection of both assumptions considered. Usually, including more than two or three of the most significantly

  5. Modelling the temporal and spatial distribution of ecological variables in Beibu Gulf

    Science.gov (United States)

    Pan, H.; Huang, L.; Yang, S.; Shi, D.; Pan, W.

    2017-12-01

    Beibu Gulf is an important semi-enclosed gulf located in northern South China Sea. It is rich in natural resources and its coastal rim is undergoing a rapid economic growth in recent years. Study on the spatial and temporal distribution of ecological variables by the influence of physical and biological processes in Beibu Gulf can provide the theoretical basis for the utilization of resources and environmental protection. Based on the MEC three-dimensional hydrodynamic model, a nutrient-phytoplankton-zooplankton-detritus (NPZD) model was applied to simulate the distribution of ecological variables in Beibu Gulf. The result shows that the ecosystem in Beibu Gulf is significantly influenced by dynamic conditions. In autumn and winter, great amount of nutrient-rich water from western Guangdong coastal area passes through Qiongzhou Strait and flows into Beibu Gulf, with about 108.3×103 t of inorganic nitrogen and 3.7×103 t of phosphate annually, leading to phytoplankton bloom. In summer, most of the nutrients come from rivers so high concentrations of nutrients and chlorophyll-a appear on estuaries. The annual net nutrient inputs from South China Sea into Beibu Gulf are 66.6×103 t for inorganic nitrogen and 4.6×103 t for phosphate. Phytoplankton plays an important role in nutrients' refreshment: a) Absorption by the process of photosynthesis is the biggest nutrient sink. b) Cellular release from dead phytoplankton is the biggest source in inorganic budget, making up for 33.4% of nitrogen consumed by photosynthesis while the process of respiration is the biggest source in phosphate budget, making up for 32.4% of phosphorus consumed by photosynthesis. c) Mineralization from detritus is also a considerable supplement of inorganic nutrients. Overall, biological process has more influence than physical process on the nutrient cycle budget in Beibu Gulf. The comparison of the result with remote sensing and in-situ data indicates that the model is able to simulate the

  6. Controls on the spatial variability of key soil properties: comparing field data with a mechanistic soilscape evolution model

    Science.gov (United States)

    Vanwalleghem, T.; Román, A.; Giraldez, J. V.

    2016-12-01

    There is a need for better understanding the processes influencing soil formation and the resulting distribution of soil properties. Soil properties can exhibit strong spatial variation, even at the small catchment scale. Especially soil carbon pools in semi-arid, mountainous areas are highly uncertain because bulk density and stoniness are very heterogeneous and rarely measured explicitly. In this study, we explore the spatial variability in key soil properties (soil carbon stocks, stoniness, bulk density and soil depth) as a function of processes shaping the critical zone (weathering, erosion, soil water fluxes and vegetation patterns). We also compare the potential of a geostatistical versus a mechanistic soil formation model (MILESD) for predicting these key soil properties. Soil core samples were collected from 67 locations at 6 depths. Total soil organic carbon stocks were 4.38 kg m-2. Solar radiation proved to be the key variable controlling soil carbon distribution. Stone content was mostly controlled by slope, indicating the importance of erosion. Spatial distribution of bulk density was found to be highly random. Finally, total carbon stocks were predicted using a random forest model whose main covariates were solar radiation and NDVI. The model predicts carbon stocks that are double as high on north versus south-facing slopes. However, validation showed that these covariates only explained 25% of the variation in the dataset. Apparently, present-day landscape and vegetation properties are not sufficient to fully explain variability in the soil carbon stocks in this complex terrain under natural vegetation. This is attributed to a high spatial variability in bulk density and stoniness, key variables controlling carbon stocks. Similar results were obtained with the mechanistic soil formation model MILESD, suggesting that more complex models might be needed to further explore this high spatial variability.

  7. Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure

    Directory of Open Access Journals (Sweden)

    Mabaso Musawenkosi LH

    2007-09-01

    Full Text Available Abstract Background Several malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana. Results Of 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country. Conclusion We have

  8. A hydrochemical modelling framework for combined assessment of spatial and temporal variability in stream chemistry: application to Plynlimon, Wales

    Directory of Open Access Journals (Sweden)

    H.J. Foster

    2001-01-01

    Full Text Available Recent concern about the risk to biota from acidification in upland areas, due to air pollution and land-use change (such as the planting of coniferous forests, has generated a need to model catchment hydro-chemistry to assess environmental risk and define protection strategies. Previous approaches have tended to concentrate on quantifying either spatial variability at a regional scale or temporal variability at a given location. However, to protect biota from ‘acid episodes’, an assessment of both temporal and spatial variability of stream chemistry is required at a catchment scale. In addition, quantification of temporal variability needs to represent both episodic event response and long term variability caused by deposition and/or land-use change. Both spatial and temporal variability in streamwater chemistry are considered in a new modelling methodology based on application to the Plynlimon catchments, central Wales. A two-component End-Member Mixing Analysis (EMMA is used whereby low and high flow chemistry are taken to represent ‘groundwater’ and ‘soil water’ end-members. The conventional EMMA method is extended to incorporate spatial variability in the two end-members across the catchments by quantifying the Acid Neutralisation Capacity (ANC of each in terms of a statistical distribution. These are then input as stochastic variables to a two-component mixing model, thereby accounting for variability of ANC both spatially and temporally. The model is coupled to a long-term acidification model (MAGIC to predict the evolution of the end members and, hence, the response to future scenarios. The results can be plotted as a function of time and space, which enables better assessment of the likely effects of pollution deposition or land-use changes in the future on the stream chemistry than current methods which use catchment average values. The model is also a useful basis for further research into linkage between hydrochemistry

  9. Anomalous transport in disordered fracture networks: Spatial Markov model for dispersion with variable injection modes

    Science.gov (United States)

    Kang, Peter K.; Dentz, Marco; Le Borgne, Tanguy; Lee, Seunghak; Juanes, Ruben

    2017-08-01

    We investigate tracer transport on random discrete fracture networks that are characterized by the statistics of the fracture geometry and hydraulic conductivity. While it is well known that tracer transport through fractured media can be anomalous and particle injection modes can have major impact on dispersion, the incorporation of injection modes into effective transport modeling has remained an open issue. The fundamental reason behind this challenge is that-even if the Eulerian fluid velocity is steady-the Lagrangian velocity distribution experienced by tracer particles evolves with time from its initial distribution, which is dictated by the injection mode, to a stationary velocity distribution. We quantify this evolution by a Markov model for particle velocities that are equidistantly sampled along trajectories. This stochastic approach allows for the systematic incorporation of the initial velocity distribution and quantifies the interplay between velocity distribution and spatial and temporal correlation. The proposed spatial Markov model is characterized by the initial velocity distribution, which is determined by the particle injection mode, the stationary Lagrangian velocity distribution, which is derived from the Eulerian velocity distribution, and the spatial velocity correlation length, which is related to the characteristic fracture length. This effective model leads to a time-domain random walk for the evolution of particle positions and velocities, whose joint distribution follows a Boltzmann equation. Finally, we demonstrate that the proposed model can successfully predict anomalous transport through discrete fracture networks with different levels of heterogeneity and arbitrary tracer injection modes.

  10. Variability in modeled cloud feedback tied to differences in the climatological spatial pattern of clouds

    Science.gov (United States)

    Siler, Nicholas; Po-Chedley, Stephen; Bretherton, Christopher S.

    2018-02-01

    Despite the increasing sophistication of climate models, the amount of surface warming expected from a doubling of atmospheric CO_2 (equilibrium climate sensitivity) remains stubbornly uncertain, in part because of differences in how models simulate the change in global albedo due to clouds (the shortwave cloud feedback). Here, model differences in the shortwave cloud feedback are found to be closely related to the spatial pattern of the cloud contribution to albedo (α) in simulations of the current climate: high-feedback models exhibit lower (higher) α in regions of warm (cool) sea-surface temperatures, and therefore predict a larger reduction in global-mean α as temperatures rise and warm regions expand. The spatial pattern of α is found to be strongly predictive (r=0.84) of a model's global cloud feedback, with satellite observations indicating a most-likely value of 0.58± 0.31 Wm^{-2} K^{-1} (90% confidence). This estimate is higher than the model-average cloud feedback of 0.43 Wm^{-2} K^{-1}, with half the range of uncertainty. The observational constraint on climate sensitivity is weaker but still significant, suggesting a likely value of 3.68 ± 1.30 K (90% confidence), which also favors the upper range of model estimates. These results suggest that uncertainty in model estimates of the global cloud feedback may be substantially reduced by ensuring a realistic distribution of clouds between regions of warm and cool SSTs in simulations of the current climate.

  11. Impact of Urbanization on Spatial Variability of Rainfall-A case study of Mumbai city with WRF Model

    Science.gov (United States)

    Mathew, M.; Paul, S.; Devanand, A.; Ghosh, S.

    2015-12-01

    Urban precipitation enhancement has been identified over many cities in India by previous studies conducted. Anthropogenic effects such as change in land cover from hilly forest areas to flat topography with solid concrete infrastructures has certain effect on the local weather, the same way the greenhouse gas has on climate change. Urbanization could alter the large scale forcings to such an extent that it may bring about temporal and spatial changes in the urban weather. The present study investigate the physical processes involved in urban forcings, such as the effect of sudden increase in wind velocity travelling through the channel space in between the dense array of buildings, which give rise to turbulence and air mass instability in urban boundary layer and in return alters the rainfall distribution as well as rainfall initiation. A numerical model study is conducted over Mumbai metropolitan city which lies on the west coast of India, to assess the effect of urban morphology on the increase in number of extreme rainfall events in specific locations. An attempt has been made to simulate twenty extreme rainfall events that occurred over the summer monsoon period of the year 2014 using high resolution WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) model to assess the urban land cover mechanisms that influences precipitation variability over this spatially varying urbanized region. The result is tested against simulations with altered land use. The correlation of precipitation with spatial variability of land use is found using a detailed urban land use classification. The initial and boundary conditions for running the model were obtained from the global model ECMWF(European Centre for Medium Range Weather Forecast) reanalysis data having a horizontal resolution of 0.75 °x 0.75°. The high resolution simulations show significant spatial variability in the accumulated rainfall, within a few kilometers itself. Understanding the spatial

  12. Modeling the Impacts of Spatial Heterogeneity in the Castor Watershed on Runoff, Sediment, and Phosphorus Loss Using SWAT: I. Impacts of Spatial Variability of Soil Properties.

    Science.gov (United States)

    Boluwade, Alaba; Madramootoo, Chandra

    2013-01-01

    Spatial accuracy of hydrologic modeling inputs influences the output from hydrologic models. A pertinent question is to know the optimal level of soil sampling or how many soil samples are needed for model input, in order to improve model predictions. In this study, measured soil properties were clustered into five different configurations as inputs to the Soil and Water Assessment Tool (SWAT) simulation of the Castor River watershed (11-km 2 area) in southern Quebec, Canada. SWAT is a process-based model that predicts the impacts of climate and land use management on water yield, sediment, and nutrient fluxes. SWAT requires geographical information system inputs such as the digital elevation model as well as soil and land use maps. Mean values of soil properties are used in soil polygons (soil series); thus, the spatial variability of these properties is neglected. The primary objective of this study was to quantify the impacts of spatial variability of soil properties on the prediction of runoff, sediment, and total phosphorus using SWAT. The spatial clustering of the measured soil properties was undertaken using the regionalized with dynamically constrained agglomerative clustering and partitioning method. Measured soil data were clustered into 5, 10, 15, 20, and 24 heterogeneous regions. Soil data from the Castor watershed which have been used in previous studies was also set up and termed "Reference". Overall, there was no significant difference in runoff simulation across the five configurations including the reference. This may be attributable to SWAT's use of the soil conservation service curve number method in flow simulation. Therefore having high spatial resolution inputs for soil data may not necessarily improve predictions when they are used in hydrologic modeling.

  13. Greenland Ice Sheet seasonal and spatial mass variability from model simulations and GRACE (2003-2012)

    Science.gov (United States)

    Alexander, Patrick M.; Tedesco, Marco; Schlegel, Nicole-Jeanne; Luthcke, Scott B.; Fettweis, Xavier; Larour, Eric

    2016-06-01

    Improving the ability of regional climate models (RCMs) and ice sheet models (ISMs) to simulate spatiotemporal variations in the mass of the Greenland Ice Sheet (GrIS) is crucial for prediction of future sea level rise. While several studies have examined recent trends in GrIS mass loss, studies focusing on mass variations at sub-annual and sub-basin-wide scales are still lacking. At these scales, processes responsible for mass change are less well understood and modeled, and could potentially play an important role in future GrIS mass change. Here, we examine spatiotemporal variations in mass over the GrIS derived from the Gravity Recovery and Climate Experiment (GRACE) satellites for the January 2003-December 2012 period using a "mascon" approach, with a nominal spatial resolution of 100 km, and a temporal resolution of 10 days. We compare GRACE-estimated mass variations against those simulated by the Modèle Atmosphérique Régionale (MAR) RCM and the Ice Sheet System Model (ISSM). In order to properly compare spatial and temporal variations in GrIS mass from GRACE with model outputs, we find it necessary to spatially and temporally filter model results to reproduce leakage of mass inherent in the GRACE solution. Both modeled and satellite-derived results point to a decline (of -178.9 ± 4.4 and -239.4 ± 7.7 Gt yr-1 respectively) in GrIS mass over the period examined, but the models appear to underestimate the rate of mass loss, especially in areas below 2000 m in elevation, where the majority of recent GrIS mass loss is occurring. On an ice-sheet-wide scale, the timing of the modeled seasonal cycle of cumulative mass (driven by summer mass loss) agrees with the GRACE-derived seasonal cycle, within limits of uncertainty from the GRACE solution. However, on sub-ice-sheet-wide scales, some areas exhibit significant differences in the timing of peaks in the annual cycle of mass change. At these scales, model biases, or processes not accounted for by models related

  14. Spatial Scaling of Environmental Variables Improves Species-Habitat Models of Fishes in a Small, Sand-Bed Lowland River.

    Directory of Open Access Journals (Sweden)

    Johannes Radinger

    Full Text Available Habitat suitability and the distinct mobility of species depict fundamental keys for explaining and understanding the distribution of river fishes. In recent years, comprehensive data on river hydromorphology has been mapped at spatial scales down to 100 m, potentially serving high resolution species-habitat models, e.g., for fish. However, the relative importance of specific hydromorphological and in-stream habitat variables and their spatial scales of influence is poorly understood. Applying boosted regression trees, we developed species-habitat models for 13 fish species in a sand-bed lowland river based on river morphological and in-stream habitat data. First, we calculated mean values for the predictor variables in five distance classes (from the sampling site up to 4000 m up- and downstream to identify the spatial scale that best predicts the presence of fish species. Second, we compared the suitability of measured variables and assessment scores related to natural reference conditions. Third, we identified variables which best explained the presence of fish species. The mean model quality (AUC = 0.78, area under the receiver operating characteristic curve significantly increased when information on the habitat conditions up- and downstream of a sampling site (maximum AUC at 2500 m distance class, +0.049 and topological variables (e.g., stream order were included (AUC = +0.014. Both measured and assessed variables were similarly well suited to predict species' presence. Stream order variables and measured cross section features (e.g., width, depth, velocity were best-suited predictors. In addition, measured channel-bed characteristics (e.g., substrate types and assessed longitudinal channel features (e.g., naturalness of river planform were also good predictors. These findings demonstrate (i the applicability of high resolution river morphological and instream-habitat data (measured and assessed variables to predict fish presence, (ii the

  15. Sub-hour solar data for power system modeling from static spatial variability analysis

    Energy Technology Data Exchange (ETDEWEB)

    Hummon, Marissa R.; Ibanez, Eduardo; Brinkman, Gregory; Lew, Debra [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2012-07-01

    High penetration renewable integration studies need high quality solar power data with spatial-temporal correlations that are representative of a real system. For instance, as additional solar power sites are added, the relative amount of variability should decrease due to spatial averaging of localized irradiance fluctuations. This presentation will summarize the research relating sequential point-source sub-hour global horizontal irradiance (GHI) values to static, spatially distributed GHI values. This research led to the development of an algorithm for generating coherent sub-hour datasets that span distances ranging from 10 km to 4,000 km. The algorithm, in brief, generates synthetic GHI values at an interval of one minute, for a specific location, using SUNY/Clean Power Research, satellite-derived, hourly irradiance values for the nearest grid cell to that location and grid cells within 40 km. During each hour, the observed GHI value for the grid cell of interest and the surrounding grid cells is related, via probability distributions, to one of live temporal cloud coverage classifications (class I, II, III, IV, V). Synthesis algorithms are used to select one-minute time step GHI values based on the classification of the grid cell of interest in a particular hour. Three primary statistical measures of the dataset are demonstrated: reduction in ramps as a function of aggregation; coherence of GHI values across sites ranging from 6 to 400 km apart over time scales from one minute to three hours; and ramp magnitude and duration distributions as a function of time of day and day of year. (orig.)

  16. Using a chemistry transport model to account for the spatial variability of exposure concentrations in epidemiologic air pollution studies.

    Science.gov (United States)

    Valari, Myrto; Menut, Laurent; Chatignoux, Edouard

    2011-02-01

    Environmental epidemiology and more specifically time-series analysis have traditionally used area-averaged pollutant concentrations measured at central monitors as exposure surrogates to associate health outcomes with air pollution. However, spatial aggregation has been shown to contribute to the overall bias in the estimation of the exposure-response functions. This paper presents the benefit of adding features of the spatial variability of exposure by using concentration fields modeled with a chemistry transport model instead of monitor data and accounting for human activity patterns. On the basis of county-level census data for the city of Paris, France, and a Monte Carlo simulation, a simple activity model was developed accounting for the temporal variability between working and evening hours as well as during transit. By combining activity data with modeled concentrations, the downtown, suburban, and rural spatial patterns in exposure to nitrogen dioxide, ozone, and PM2.5 (particulate matter [PM] pollution on total nonaccidental mortality for the 4-yr period from 2001 to 2004. It was shown that the time series of the exposure surrogates developed here are less correlated across co-pollutants than in the case of the area-averaged monitor data. This led to less biased exposure-response functions when all three co-pollutants were inserted simultaneously in the same regression model. This finding yields insight into pollutant-specific health effects that are otherwise masked by the high correlation among co-pollutants.

  17. Modeling the spatial distribution of Chagas disease vectors using environmental variables and people´s knowledge.

    Science.gov (United States)

    Hernández, Jaime; Núñez, Ignacia; Bacigalupo, Antonella; Cattan, Pedro E

    2013-05-31

    Chagas disease is caused by the protozoan Trypanosoma cruzi, which is transmitted to mammal hosts by triatomine insect vectors. The goal of this study was to model the spatial distribution of triatomine species in an endemic area. Vector's locations were obtained with a rural householders' survey. This information was combined with environmental data obtained from remote sensors, land use maps and topographic SRTM data, using the machine learning algorithm Random Forests to model species distribution. We analysed the combination of variables on three scales: 10 km, 5 km and 2.5 km cell size grids. The best estimation, explaining 46.2% of the triatomines spatial distribution, was obtained for 5 km of spatial resolution. Presence probability distribution increases from central Chile towards the north, tending to cover the central-coastal region and avoiding areas of the Andes range. The methodology presented here was useful to model the distribution of triatomines in an endemic area; it is best explained using 5 km of spatial resolution, and their presence increases in the northern part of the study area. This study's methodology can be replicated in other countries with Chagas disease or other vectorial transmitted diseases, and be used to locate high risk areas and to optimize resource allocation, for prevention and control of vectorial diseases.

  18. A Stochastic Model of Space-Time Variability of Tropical Rainfall: I. Statistics of Spatial Averages

    Science.gov (United States)

    Kundu, Prasun K.; Bell, Thomas L.; Lau, William K. M. (Technical Monitor)

    2002-01-01

    Global maps of rainfall are of great importance in connection with modeling of the earth s climate. Comparison between the maps of rainfall predicted by computer-generated climate models with observation provides a sensitive test for these models. To make such a comparison, one typically needs the total precipitation amount over a large area, which could be hundreds of kilometers in size over extended periods of time of order days or months. This presents a difficult problem since rain varies greatly from place to place as well as in time. Remote sensing methods using ground radar or satellites detect rain over a large area by essentially taking a series of snapshots at infrequent intervals and indirectly deriving the average rain intensity within a collection of pixels , usually several kilometers in size. They measure area average of rain at a particular instant. Rain gauges, on the other hand, record rain accumulation continuously in time but only over a very small area tens of centimeters across, say, the size of a dinner plate. They measure only a time average at a single location. In making use of either method one needs to fill in the gaps in the observation - either the gaps in the area covered or the gaps in time of observation. This involves using statistical models to obtain information about the rain that is missed from what is actually detected. This paper investigates such a statistical model and validates it with rain data collected over the tropical Western Pacific from ship borne radars during TOGA COARE (Tropical Oceans Global Atmosphere Coupled Ocean-Atmosphere Response Experiment). The model incorporates a number of commonly observed features of rain. While rain varies rapidly with location and time, the variability diminishes when averaged over larger areas or longer periods of time. Moreover, rain is patchy in nature - at any instant on the average only a certain fraction of the observed pixels contain rain. The fraction of area covered by

  19. Modelled estimates of spatial variability of iron stress in the Atlantic sector of the Southern Ocean

    Science.gov (United States)

    Ryan-Keogh, Thomas J.; Thomalla, Sandy J.; Mtshali, Thato N.; Little, Hazel

    2017-09-01

    The Atlantic sector of the Southern Ocean is characterized by markedly different frontal zones with specific seasonal and sub-seasonal dynamics. Demonstrated here is the effect of iron on the potential maximum productivity rates of the phytoplankton community. A series of iron addition productivity versus irradiance (PE) experiments utilizing a unique experimental design that allowed for 24 h incubations were performed within the austral summer of 2015/16 to determine the photosynthetic parameters αB, PBmax and Ek. Mean values for each photosynthetic parameter under iron-replete conditions were 1.46 ± 0.55 (µg (µg Chl a)-1 h-1 (µM photons m-2 s-1)-1) for αB, 72.55 ± 27.97 (µg (µg Chl a)-1 h-1) for PBmax and 50.84 ± 11.89 (µM photons m-2 s-1) for Ek, whereas mean values under the control conditions were 1.25 ± 0.92 (µg (µg Chl a)-1 h-1 (µM photons m-2 s-1)-1) for αB, 62.44 ± 36.96 (µg (µg Chl a)-1 h-1) for PBmax and 55.81 ± 19.60 (µM photons m-2 s-1) for Ek. There were no clear spatial patterns in either the absolute values or the absolute differences between the treatments at the experimental locations. When these parameters are integrated into a standard depth-integrated primary production model across a latitudinal transect, the effect of iron addition shows higher levels of primary production south of 50° S, with very little difference observed in the subantarctic and polar frontal zone. These results emphasize the need for better parameterization of photosynthetic parameters in biogeochemical models around sensitivities in their response to iron supply. Future biogeochemical models will need to consider the combined and individual effects of iron and light to better resolve the natural background in primary production and predict its response under a changing climate.

  20. A new heat flux model for the Antarctic Peninsula incorporating spatially variable upper crustal radiogenic heat production

    Science.gov (United States)

    Burton-Johnson, A.; Halpin, J.; Whittaker, J. M.; Graham, F. S.; Watson, S. J.

    2017-12-01

    We present recently published findings (Burton-Johnson et al., 2017) on the variability of Antarctic sub-glacial heat flux and the impact from upper crustal geology. Our new method reveals that the upper crust contributes up to 70% of the Antarctic Peninsula's subglacial heat flux, and that heat flux values are more variable at smaller spatial resolutions than geophysical methods can resolve. Results indicate a higher heat flux on the east and south of the Peninsula (mean 81 mWm-2) where silicic rocks predominate, than on the west and north (mean 67 mWm-2) where volcanic arc and quartzose sediments are dominant. Whilst the data supports the contribution of HPE-enriched granitic rocks to high heat flux values, sedimentary rocks can be of comparative importance dependent on their provenance and petrography. Models of subglacial heat flux must utilize a heterogeneous upper crust with variable radioactive heat production if they are to accurately predict basal conditions of the ice sheet. Our new methodology and dataset facilitate improved numerical model simulations of ice sheet dynamics. The most significant challenge faced remains accurate determination of crustal structure, particularly the depths of the HPE-enriched sedimentary basins and the sub-glacial geology away from exposed outcrops. Continuing research (particularly detailed geophysical interpretation) will better constrain these unknowns and the effect of upper crustal geology on the Antarctic ice sheet. Burton-Johnson, A., Halpin, J.A., Whittaker, J.M., Graham, F.S., and Watson, S.J., 2017, A new heat flux model for the Antarctic Peninsula incorporating spatially variable upper crustal radiogenic heat production: Geophysical Research Letters, v. 44, doi: 10.1002/2017GL073596.

  1. Modeling temporal and spatial variability of traffic-related air pollution: Hourly land use regression models for black carbon

    Science.gov (United States)

    Dons, Evi; Van Poppel, Martine; Kochan, Bruno; Wets, Geert; Int Panis, Luc

    2013-08-01

    Land use regression (LUR) modeling is a statistical technique used to determine exposure to air pollutants in epidemiological studies. Time-activity diaries can be combined with LUR models, enabling detailed exposure estimation and limiting exposure misclassification, both in shorter and longer time lags. In this study, the traffic related air pollutant black carbon was measured with μ-aethalometers on a 5-min time base at 63 locations in Flanders, Belgium. The measurements show that hourly concentrations vary between different locations, but also over the day. Furthermore the diurnal pattern is different for street and background locations. This suggests that annual LUR models are not sufficient to capture all the variation. Hourly LUR models for black carbon are developed using different strategies: by means of dummy variables, with dynamic dependent variables and/or with dynamic and static independent variables. The LUR model with 48 dummies (weekday hours and weekend hours) performs not as good as the annual model (explained variance of 0.44 compared to 0.77 in the annual model). The dataset with hourly concentrations of black carbon can be used to recalibrate the annual model, resulting in many of the original explaining variables losing their statistical significance, and certain variables having the wrong direction of effect. Building new independent hourly models, with static or dynamic covariates, is proposed as the best solution to solve these issues. R2 values for hourly LUR models are mostly smaller than the R2 of the annual model, ranging from 0.07 to 0.8. Between 6 a.m. and 10 p.m. on weekdays the R2 approximates the annual model R2. Even though models of consecutive hours are developed independently, similar variables turn out to be significant. Using dynamic covariates instead of static covariates, i.e. hourly traffic intensities and hourly population densities, did not significantly improve the models' performance.

  2. Predicting Spatial Distribution of Key Honeybee Pests in Kenya Using Remotely Sensed and Bioclimatic Variables: Key Honeybee Pests Distribution Models

    Directory of Open Access Journals (Sweden)

    David M. Makori

    2017-02-01

    Full Text Available Bee keeping is indispensable to global food production. It is an alternate income source, especially in rural underdeveloped African settlements, and an important forest conservation incentive. However, dwindling honeybee colonies around the world are attributed to pests and diseases whose spatial distribution and influences are not well established. In this study, we used remotely sensed data to improve the reliability of pest ecological niche (EN models to attain reliable pest distribution maps. Occurrence data on four pests (Aethina tumida, Galleria mellonella, Oplostomus haroldi and Varroa destructor were collected from apiaries within four main agro-ecological regions responsible for over 80% of Kenya’s bee keeping. Africlim bioclimatic and derived normalized difference vegetation index (NDVI variables were used to model their ecological niches using Maximum Entropy (MaxEnt. Combined precipitation variables had a high positive logit influence on all remotely sensed and biotic models’ performance. Remotely sensed vegetation variables had a substantial effect on the model, contributing up to 40.8% for G. mellonella and regions with high rainfall seasonality were predicted to be high-risk areas. Projections (to 2055 indicated that, with the current climate change trend, these regions will experience increased honeybee pest risk. We conclude that honeybee pests could be modelled using bioclimatic data and remotely sensed variables in MaxEnt. Although the bioclimatic data were most relevant in all model results, incorporating vegetation seasonality variables to improve mapping the ‘actual’ habitat of key honeybee pests and to identify risk and containment zones needs to be further investigated.

  3. Modelling spatial and temporal variability of hydrologic impacts under climate changes over the Nenjiang River Basin, China

    Science.gov (United States)

    Chen, Hao; Zhang, Wanchang

    2017-10-01

    The Variable Infiltration Capacity (VIC) hydrologic model was adopted for investigating spatial and temporal variability of hydrologic impacts of climate change over the Nenjiang River Basin (NRB) based on a set of gridded forcing dataset at 1/12th degree resolution from 1970 to 2013. Basin-scale changes in the input forcing data and the simulated hydrological variables of the NRB, as well as station-scale changes in discharges for three major hydrometric stations were examined, which suggested that the model was performed fairly satisfactory in reproducing the observed discharges, meanwhile, the snow cover and evapotranspiration in temporal and spatial patterns were simulated reasonably corresponded to the remotely sensed ones. Wetland maps produced by multi-sources satellite images covering the entire basin between 1978 and 2008 were also utilized for investigating the responses and feedbacks of hydrological regimes on wetland dynamics. Results revealed that significant decreasing trends appeared in annual, spring and autumn streamflow demonstrated strong affection of precipitation and temperature changes over the study watershed, and the effects of climate change on the runoff reduction varied in the sub-basin area over different time scales. The proportion of evapotranspiration to precipitation characterized several severe fluctuations in droughts and floods took place in the region, which implied the enhanced sensitiveness and vulnerability of hydrologic regimes to changing environment of the region. Furthermore, it was found that the different types of wetlands undergone quite unique variation features with the varied hydro-meteorological conditions over the region, such as precipitation, evapotranspiration and soil moisture. This study provided effective scientific basis for water resource managers to develop effective eco-environment management plans and strategies that address the consequences of climate changes.

  4. Using stochastic models to incorporate spatial and temporal variability [Exercise 14

    Science.gov (United States)

    Carolyn Hull Sieg; Rudy M. King; Fred Van Dyke

    2003-01-01

    To this point, our analysis of population processes and viability in the western prairie fringed orchid has used only deterministic models. In this exercise, we conduct a similar analysis, using a stochastic model instead. This distinction is of great importance to population biology in general and to conservation biology in particular. In deterministic models,...

  5. A spatial model for a stream networks of Citarik River with the environmental variables: potential of hydrogen (PH) and temperature

    Science.gov (United States)

    Bachrudin, A.; Mohamed, N. B.; Supian, S.; Sukono; Hidayat, Y.

    2018-03-01

    Application of existing geostatistical theory of stream networks provides a number of interesting and challenging problems. Most of statistical tools in the traditional geostatistics have been based on a Euclidean distance such as autocovariance functions, but for stream data is not permissible since it deals with a stream distance. To overcome this autocovariance developed a model based on the distance the flow with using convolution kernel approach (moving average construction). Spatial model for a stream networks is widely used to monitor environmental on a river networks. In a case study of a river in province of West Java, the objective of this paper is to analyze a capability of a predictive on two environmental variables, potential of hydrogen (PH) and temperature using ordinary kriging. Several the empirical results show: (1) The best fit of autocovariance functions for temperature and potential hydrogen (ph) of Citarik River is linear which also yields the smallest root mean squared prediction error (RMSPE), (2) the spatial correlation values between the locations on upstream and on downstream of Citarik river exhibit decreasingly

  6. Modeling temporal and large-scale spatial variability of soil respiration from soil water availability, temperature and vegetation productivity indices

    Science.gov (United States)

    Reichstein, Markus; Rey, Ana; Freibauer, Annette; Tenhunen, John; Valentini, Riccardo; Banza, Joao; Casals, Pere; Cheng, Yufu; Grünzweig, Jose M.; Irvine, James; Joffre, Richard; Law, Beverly E.; Loustau, Denis; Miglietta, Franco; Oechel, Walter; Ourcival, Jean-Marc; Pereira, Joao S.; Peressotti, Alessandro; Ponti, Francesca; Qi, Ye; Rambal, Serge; Rayment, Mark; Romanya, Joan; Rossi, Federica; Tedeschi, Vanessa; Tirone, Giampiero; Xu, Ming; Yakir, Dan

    2003-12-01

    Field-chamber measurements of soil respiration from 17 different forest and shrubland sites in Europe and North America were summarized and analyzed with the goal to develop a model describing seasonal, interannual and spatial variability of soil respiration as affected by water availability, temperature, and site properties. The analysis was performed at a daily and at a monthly time step. With the daily time step, the relative soil water content in the upper soil layer expressed as a fraction of field capacity was a good predictor of soil respiration at all sites. Among the site variables tested, those related to site productivity (e.g., leaf area index) correlated significantly with soil respiration, while carbon pool variables like standing biomass or the litter and soil carbon stocks did not show a clear relationship with soil respiration. Furthermore, it was evidenced that the effect of precipitation on soil respiration stretched beyond its direct effect via soil moisture. A general statistical nonlinear regression model was developed to describe soil respiration as dependent on soil temperature, soil water content, and site-specific maximum leaf area index. The model explained nearly two thirds of the temporal and intersite variability of soil respiration with a mean absolute error of 0.82 μmol m-2 s-1. The parameterized model exhibits the following principal properties: (1) At a relative amount of upper-layer soil water of 16% of field capacity, half-maximal soil respiration rates are reached. (2) The apparent temperature sensitivity of soil respiration measured as Q10 varies between 1 and 5 depending on soil temperature and water content. (3) Soil respiration under reference moisture and temperature conditions is linearly related to maximum site leaf area index. At a monthly timescale, we employed the approach by [2002] that used monthly precipitation and air temperature to globally predict soil respiration (T&P model). While this model was able to

  7. Modelling temporal and large-scale spatial variability of soil respiration from soil water availability, temperature and vegetation productivity indices

    Science.gov (United States)

    Reichstein, M.; Rey, A.; Freibauer, A.; Tenhunen, J.; Valentini, R.; Soil Respiration Synthesis Team

    2003-04-01

    Field-chamber measurements of soil respiration from 17 different forest and shrubland sites in Europe and North America were summarized and analyzed with the goal to develop a model describing seasonal, inter-annual and spatial variability of soil respiration as affected by water availability, temperature and site properties. The analysis was performed at a daily and at a monthly time step. With the daily time step, the relative soil water content in the upper soil layer expressed as a fraction of field capacity was a good predictor of soil respiration at all sites. Among the site variables tested, those related to site productivity (e.g. leaf area index) correlated significantly with soil respiration, while carbon pool variables like standing biomass or the litter and soil carbon stocks did not show a clear relationship with soil respiration. Furthermore, it was evidenced that the effect of precipitation on soil respiration stretched beyond its direct effect via soil moisture. A general statistical non-linear regression model was developed to describe soil respiration as dependent on soil temperature, soil water content and site-specific maximum leaf area index. The model explained nearly two thirds of the temporal and inter-site variability of soil respiration with a mean absolute error of 0.82 µmol m-2 s-1. The parameterised model exhibits the following principal properties: 1) At a relative amount of upper-layer soil water of 16% of field capacity half-maximal soil respiration rates are reached. 2) The apparent temperature sensitivity of soil respiration measured as Q10 varies between 1 and 5 depending on soil temperature and water content. 3) Soil respiration under reference moisture and temperature conditions is linearly related to maximum site leaf area index. At a monthly time-scale we employed the approach by Raich et al. (2002, Global Change Biol. 8, 800-812) that used monthly precipitation and air temperature to globally predict soil respiration (T&P-model

  8. A Conceptual Model for Spatial Grain Size Variability on the Surface of and within Beaches

    Directory of Open Access Journals (Sweden)

    Edith Gallagher

    2016-05-01

    Full Text Available Grain size on the surface of natural beaches has been observed to vary spatially and temporally with morphology and wave energy. The stratigraphy of the beach at Duck, North Carolina, USA was examined using 36 vibracores (~1–1.5 m long collected along a cross-shore beach profile. Cores show that beach sediments are finer (~0.3 mm and more uniform high up on the beach. Lower on the beach, with more swash and wave action, the sand is reworked, segregated by size, and deposited in layers and patches. At the deepest measurement sites in the swash (~−1.4 to −1.6 m NAVD88, which are constantly being reworked by the energetic shore break, there is a thick layer (60–80 cm of very coarse sediment (~2 mm. Examination of two large trenches showed that continuous layers of coarse and fine sands comprise beach stratigraphy. Thicker coarse layers in the trenches (above mean sea level are likely owing to storm erosion and storm surge elevating the shore break and swash, which act to sort the sediment. Those layers are buried as water level retreats, accretion occurs and the beach recovers from the storm. Thinner coarse layers likely represent similar processes acting on smaller temporal scales.

  9. Representation of spatial variability for modelling of flow and transport processes in the Culebra Dolomite at the WIPP site

    International Nuclear Information System (INIS)

    Meigs, L.C.; Beauheim, R.L.

    1997-01-01

    The Waste Isolation Pilot Plant (WIPP) is a proposed repository for transuranic wastes constructed in bedded Permian-acre halite deposits in southeastern New Mexico, USA. Site-characterization studies at the WIPP site identified groundwater flow in the Culebra Dolomite Member of the Rustler Formation as the most likely Geologic pathway for radio nuclide transport to the accessible environment in the event of a breach of the WIPP repository through inadvertent human intrusion. The Culebra is a 7-m-thick, variably fractured dolomite with massive and layers. Detailed studies at all scales demonstrated that the Culebra is a heterogeneous medium. Heterogeneity in Culebra properties was incorporated into numerical simulations used for data interpretation and PA calculations in different ways, depending on the amount of data available, the certainty with which the effects of a given approach could be evaluated, and the purpose of the study. When abundant, spatially distributed data were available, the heterogeneity was explicitly included. For example, a stochastic approach was used to generate numerous, equally likely, heterogeneous transmissivity fields conditioned on head and transmissivity data. In other cases, constant parameter values were applied over the model domain. These constant values were selected and applied in two different ways. In simple cases where a conservative bounding value could be identified that would not lead to unrealistically conservative results, that value was used for all calculations. In more complex cases, parameter distributions were developed and single values of the parameters were sampled from the distributions and applied across the entire model domain for each of the PA Monte Carlo simulations. We are currently working to refine our understanding of the multiple rates of diffusion attributable to small-scale spatial variability

  10. Evaluation of climate model aerosol seasonal and spatial variability over Africa using AERONET

    Science.gov (United States)

    Horowitz, Hannah M.; Garland, Rebecca M.; Thatcher, Marcus; Landman, Willem A.; Dedekind, Zane; van der Merwe, Jacobus; Engelbrecht, Francois A.

    2017-11-01

    The sensitivity of climate models to the characterization of African aerosol particles is poorly understood. Africa is a major source of dust and biomass burning aerosols and this represents an important research gap in understanding the impact of aerosols on radiative forcing of the climate system. Here we evaluate the current representation of aerosol particles in the Conformal Cubic Atmospheric Model (CCAM) with ground-based remote retrievals across Africa, and additionally provide an analysis of observed aerosol optical depth at 550 nm (AOD550 nm) and Ångström exponent data from 34 Aerosol Robotic Network (AERONET) sites. Analysis of the 34 long-term AERONET sites confirms the importance of dust and biomass burning emissions to the seasonal cycle and magnitude of AOD550 nm across the continent and the transport of these emissions to regions outside of the continent. In general, CCAM captures the seasonality of the AERONET data across the continent. The magnitude of modeled and observed multiyear monthly average AOD550 nm overlap within ±1 standard deviation of each other for at least 7 months at all sites except the Réunion St Denis Island site (Réunion St. Denis). The timing of modeled peak AOD550 nm in southern Africa occurs 1 month prior to the observed peak, which does not align with the timing of maximum fire counts in the region. For the western and northern African sites, it is evident that CCAM currently overestimates dust in some regions while others (e.g., the Arabian Peninsula) are better characterized. This may be due to overestimated dust lifetime, or that the characterization of the soil for these areas needs to be updated with local information. The CCAM simulated AOD550 nm for the global domain is within the spread of previously published results from CMIP5 and AeroCom experiments for black carbon, organic carbon, and sulfate aerosols. The model's performance provides confidence for using the model to estimate large-scale regional impacts

  11. Evaluation of climate model aerosol seasonal and spatial variability over Africa using AERONET

    Directory of Open Access Journals (Sweden)

    H. M. Horowitz

    2017-11-01

    Full Text Available The sensitivity of climate models to the characterization of African aerosol particles is poorly understood. Africa is a major source of dust and biomass burning aerosols and this represents an important research gap in understanding the impact of aerosols on radiative forcing of the climate system. Here we evaluate the current representation of aerosol particles in the Conformal Cubic Atmospheric Model (CCAM with ground-based remote retrievals across Africa, and additionally provide an analysis of observed aerosol optical depth at 550 nm (AOD550 nm and Ångström exponent data from 34 Aerosol Robotic Network (AERONET sites. Analysis of the 34 long-term AERONET sites confirms the importance of dust and biomass burning emissions to the seasonal cycle and magnitude of AOD550 nm across the continent and the transport of these emissions to regions outside of the continent. In general, CCAM captures the seasonality of the AERONET data across the continent. The magnitude of modeled and observed multiyear monthly average AOD550 nm overlap within ±1 standard deviation of each other for at least 7 months at all sites except the Réunion St Denis Island site (Réunion St. Denis. The timing of modeled peak AOD550 nm in southern Africa occurs 1 month prior to the observed peak, which does not align with the timing of maximum fire counts in the region. For the western and northern African sites, it is evident that CCAM currently overestimates dust in some regions while others (e.g., the Arabian Peninsula are better characterized. This may be due to overestimated dust lifetime, or that the characterization of the soil for these areas needs to be updated with local information. The CCAM simulated AOD550 nm for the global domain is within the spread of previously published results from CMIP5 and AeroCom experiments for black carbon, organic carbon, and sulfate aerosols. The model's performance provides confidence for using the model to estimate

  12. Kilometric Scale Modeling of the North West European Shelf Seas: Exploring the Spatial and Temporal Variability of Internal Tides

    Science.gov (United States)

    Guihou, K.; Polton, J.; Harle, J.; Wakelin, S.; O'Dea, E.; Holt, J.

    2018-01-01

    The North West European Shelf break acts as a barrier to the transport and exchange between the open ocean and the shelf seas. The strong spatial variability of these exchange processes is hard to fully explore using observations, and simulations generally are too coarse to simulate the fine-scale processes over the whole region. In this context, under the FASTNEt program, a new NEMO configuration of the North West European Shelf and Atlantic Margin at 1/60° (˜1.8 km) has been developed, with the objective to better understand and quantify the seasonal and interannual variability of shelf break processes. The capability of this configuration to reproduce the seasonal cycle in SST, the barotropic tide, and fine-resolution temperature profiles is assessed against a basin-scale (1/12°, ˜9 km) configuration and a standard regional configuration (7 km resolution). The seasonal cycle is well reproduced in all configurations though the fine-resolution allows the simulation of smaller scale processes. Time series of temperature at various locations on the shelf show the presence of internal waves with a strong spatiotemporal variability. Spectral analysis of the internal waves reveals peaks at the diurnal, semidiurnal, inertial, and quarter-diurnal bands, which are only realistically reproduced in the new configuration. Tidally induced pycnocline variability is diagnosed in the model and shown to vary with the spring neap cycle with mean displacement amplitudes in excess of 2 m for 30% of the stratified domain. With sufficiently fine resolution, internal tides are shown to be generated at numerous bathymetric features resulting in a complex pycnocline displacement superposition pattern.

  13. Hydraulic modelling of the spatial and temporal variability in Atlantic salmon parr habitat availability in an upland stream.

    Science.gov (United States)

    Fabris, Luca; Malcolm, Iain Archibald; Buddendorf, Willem Bastiaan; Millidine, Karen Jane; Tetzlaff, Doerthe; Soulsby, Chris

    2017-12-01

    We show how spatial variability in channel bed morphology affects the hydraulic characteristics of river reaches available to Atlantic salmon parr (Salmo salar) under different flow conditions in an upland stream. The study stream, the Girnock Burn, is a long-term monitoring site in the Scottish Highlands. Six site characterised by different bed geometry and morphology were investigated. Detailed site bathymetries were collected and combined with discharge time series in a 2D hydraulic model to obtain spatially distributed depth-averaged velocities under different flow conditions. Available habitat (AH) was estimated for each site. Stream discharge was used according to the critical displacement velocity (CDV) approach. CDV defines a velocity threshold above which salmon parr are not able to hold station and effective feeding opportunities or habitat utilization are reduced, depending on fish size and water temperature. An average value of the relative available habitat () for the most significant period for parr growth - April to May - was used for inter-site comparison and to analyse temporal variations over 40years. Results show that some sites are more able than others to maintain zones where salmon parr can forage unimpeded by high flow velocities under both wet and dry conditions. With lower flow velocities, dry years offer higher values of than wet years. Even though can change considerably across the sites as stream flow changes, the directions of change are consistent. Relative available habitat (RAH) shows a strong relationship with discharge per unit width, whilst channel slope and bed roughness either do not have relevant impact or compensate each other. The results show that significant parr habitat was available at all sites across all flows during this critical growth period, suggesting that hydrological variability is not a factor limiting growth in the Girnock. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

  14. Effects of Uncertainty and Spatial Variability on Seepage into Drifts in the Yucca Mountain Total system Performance Assessment Model

    International Nuclear Information System (INIS)

    Kalinich, D. A.; Wilson, M. L.

    2001-01-01

    Seepage into the repository drifts is an important factor in total-system performance. Uncertainty and spatial variability are considered in the seepage calculations. The base-case results show 13.6% of the waste packages (WPs) have seepage. For 5th percentile uncertainty, 4.5% of the WPs have seepage and the seepage flow decreased by a factor of 2. For 95th percentile uncertainty, 21.5% of the WPs have seepage and the seepage flow increased by a factor of 2. Ignoring spatial variability resulted in seepage on 100% of the WPs, with a factor of 3 increase in the seepage flow

  15. GIS-modelling of the spatial variability of flash flood hazard in Abu Dabbab catchment, Red Sea Region, Egypt

    Directory of Open Access Journals (Sweden)

    Islam Abou El-Magd

    2010-06-01

    Full Text Available In the mountainous area of the Red Sea region in southeastern Egypt, the development of new mining activities or/and domestic infrastructures require reliable and accurate information about natural hazards particularly flash flood. This paper presents the assessment of flash flood hazards in the Abu Dabbab drainage basin. Remotely sensed data were used to delineate the alluvial active channels, which were integrated with morphometric parameters extracted from digital elevation models (DEM into geographical information systems (GIS to construct a hydrological model that provides estimates about the amount of surface runoff as well as the magnitude of flash floods. The peak discharge is randomly varied at different cross-sections along the main channel. Under consistent 10 mm rainfall event, the selected cross-section in middle of the main channel is prone to maximum water depth at 80 cm, which decreases to nearly 30 cm at the outlet due to transmission loss. The estimation of spatial variability of flow parameters within the catchment at different confluences of the constituting sub-catchments can be considered and used in planning for engineering foundations and linear infrastructures with the least flash flood hazard. Such information would, indeed, help decision makers and planning to minimize such hazards.

  16. Estimating greenhouse gas emissions of European cities — Modeling emissions with only one spatial and one socioeconomic variable

    International Nuclear Information System (INIS)

    Baur, Albert H.; Lauf, Steffen; Förster, Michael; Kleinschmit, Birgit

    2015-01-01

    Substantive and concerted action is needed to mitigate climate change. However, international negotiations struggle to adopt ambitious legislation and to anticipate more climate-friendly developments. Thus, stronger actions are needed from other players. Cities, being greenhouse gas emission centers, play a key role in promoting the climate change mitigation movement by becoming hubs for smart and low-carbon lifestyles. In this context, a stronger linkage between greenhouse gas emissions and urban development and policy-making seems promising. Therefore, simple approaches are needed to objectively identify crucial emission drivers for deriving appropriate emission reduction strategies. In analyzing 44 European cities, the authors investigate possible socioeconomic and spatial determinants of urban greenhouse gas emissions. Multiple statistical analyses reveal that the average household size and the edge density of discontinuous dense urban fabric explain up to 86% of the total variance of greenhouse gas emissions of EU cities (when controlled for varying electricity carbon intensities). Finally, based on these findings, a multiple regression model is presented to determine greenhouse gas emissions. It is independently evaluated with ten further EU cities. The reliance on only two indicators shows that the model can be easily applied in addressing important greenhouse gas emission sources of European urbanites, when varying power generations are considered. This knowledge can help cities develop adequate climate change mitigation strategies and promote respective policies on the EU or the regional level. The results can further be used to derive first estimates of urban greenhouse gas emissions, if no other analyses are available. - Highlights: • Two variables determine urban GHG emissions in Europe, assuming equal power generation. • Household size, inner-urban compactness and power generation drive urban GHG emissions. • Climate policies should consider

  17. Estimating greenhouse gas emissions of European cities — Modeling emissions with only one spatial and one socioeconomic variable

    Energy Technology Data Exchange (ETDEWEB)

    Baur, Albert H., E-mail: Albert.H.Baur@campus.tu-berlin.de; Lauf, Steffen; Förster, Michael; Kleinschmit, Birgit

    2015-07-01

    Substantive and concerted action is needed to mitigate climate change. However, international negotiations struggle to adopt ambitious legislation and to anticipate more climate-friendly developments. Thus, stronger actions are needed from other players. Cities, being greenhouse gas emission centers, play a key role in promoting the climate change mitigation movement by becoming hubs for smart and low-carbon lifestyles. In this context, a stronger linkage between greenhouse gas emissions and urban development and policy-making seems promising. Therefore, simple approaches are needed to objectively identify crucial emission drivers for deriving appropriate emission reduction strategies. In analyzing 44 European cities, the authors investigate possible socioeconomic and spatial determinants of urban greenhouse gas emissions. Multiple statistical analyses reveal that the average household size and the edge density of discontinuous dense urban fabric explain up to 86% of the total variance of greenhouse gas emissions of EU cities (when controlled for varying electricity carbon intensities). Finally, based on these findings, a multiple regression model is presented to determine greenhouse gas emissions. It is independently evaluated with ten further EU cities. The reliance on only two indicators shows that the model can be easily applied in addressing important greenhouse gas emission sources of European urbanites, when varying power generations are considered. This knowledge can help cities develop adequate climate change mitigation strategies and promote respective policies on the EU or the regional level. The results can further be used to derive first estimates of urban greenhouse gas emissions, if no other analyses are available. - Highlights: • Two variables determine urban GHG emissions in Europe, assuming equal power generation. • Household size, inner-urban compactness and power generation drive urban GHG emissions. • Climate policies should consider

  18. Spatial variability in the coefficient of thermal expansion induces pre-service stresses in computer models of virgin Gilsocarbon bricks

    International Nuclear Information System (INIS)

    Arregui-Mena, José David; Margetts, Lee; Griffiths, D.V.; Lever, Louise; Hall, Graham; Mummery, Paul M.

    2015-01-01

    In this paper, the authors test the hypothesis that tiny spatial variations in material properties may lead to significant pre-service stresses in virgin graphite bricks. To do this, they have customised ParaFEM, an open source parallel finite element package, adding support for stochastic thermo-mechanical analysis using the Monte Carlo Simulation method. For an Advanced Gas-cooled Reactor brick, three heating cases have been examined: a uniform temperature change; a uniform temperature gradient applied through the thickness of the brick and a simulated temperature profile from an operating reactor. Results are compared for mean and stochastic properties. These show that, for the proof-of-concept analyses carried out, the pre-service von Mises stress is around twenty times higher when spatial variability of material properties is introduced. The paper demonstrates that thermal gradients coupled with material incompatibilities may be important in the generation of stress in nuclear graphite reactor bricks. Tiny spatial variations in coefficient of thermal expansion (CTE) and Young's modulus can lead to the presence of thermal stresses in bricks that are free to expand. - Highlights: • Open source software has been modified to include random variability in CTE and Young's modulus. • The new software closely agrees with analytical solutions and commercial software. • Spatial variations in CTE and Young's modulus produce stresses that do not occur with mean values. • Material variability may induce pre-service stress in virgin graphite.

  19. Spatial variability in the coefficient of thermal expansion induces pre-service stresses in computer models of virgin Gilsocarbon bricks

    Energy Technology Data Exchange (ETDEWEB)

    Arregui-Mena, José David, E-mail: jose.arreguimena@postgrad.manchester.ac.uk [School of Mechanical, Aerospace, and Civil Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL (United Kingdom); Margetts, Lee, E-mail: lee.margetts@manchester.ac.uk [School of Mechanical, Aerospace, and Civil Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL (United Kingdom); Griffiths, D.V., E-mail: d.v.griffiths@mines.edu [Colorado School of Mines, 1500 Illinois St, Golden, CO 80401 (United States); Lever, Louise, E-mail: louise.lever@manchester.ac.uk [Research Computing, University of Manchester, Oxford Road, Manchester, M13 9PL (United Kingdom); Hall, Graham, E-mail: graham.n.hall@manchester.ac.uk [School of Mechanical, Aerospace, and Civil Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL (United Kingdom); Mummery, Paul M., E-mail: paul.m.mummery@manchester.ac.uk [School of Mechanical, Aerospace, and Civil Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL (United Kingdom)

    2015-10-15

    In this paper, the authors test the hypothesis that tiny spatial variations in material properties may lead to significant pre-service stresses in virgin graphite bricks. To do this, they have customised ParaFEM, an open source parallel finite element package, adding support for stochastic thermo-mechanical analysis using the Monte Carlo Simulation method. For an Advanced Gas-cooled Reactor brick, three heating cases have been examined: a uniform temperature change; a uniform temperature gradient applied through the thickness of the brick and a simulated temperature profile from an operating reactor. Results are compared for mean and stochastic properties. These show that, for the proof-of-concept analyses carried out, the pre-service von Mises stress is around twenty times higher when spatial variability of material properties is introduced. The paper demonstrates that thermal gradients coupled with material incompatibilities may be important in the generation of stress in nuclear graphite reactor bricks. Tiny spatial variations in coefficient of thermal expansion (CTE) and Young's modulus can lead to the presence of thermal stresses in bricks that are free to expand. - Highlights: • Open source software has been modified to include random variability in CTE and Young's modulus. • The new software closely agrees with analytical solutions and commercial software. • Spatial variations in CTE and Young's modulus produce stresses that do not occur with mean values. • Material variability may induce pre-service stress in virgin graphite.

  20. SPATIAL VARIABILITY OF PEDOZEMS MECHANICAL IMPEDANCE

    Directory of Open Access Journals (Sweden)

    Zhukov A.V.

    2013-04-01

    Full Text Available We studied the spatial variability of pedozem mechanical impedance in ResearchRemediation Center of the Dnipropetrovsk State Agrarian University in Ordzhonikidze. Thestatistical distribution of the soil mechanical impedance within the studied area is characterized by deviation from the normal law in 0–10 and 30–50 cm layers from the surface. 2D and 3D modeling shows the structural design of the soil as locations of high mechanical impedance which found in the soils with less hardness.

  1. Spatial Variables as Proxies for Modelling Cognition and Decision-Making in Archaeological Settings: A Theoretical Perspective

    Directory of Open Access Journals (Sweden)

    Thomas G. Whitley

    2004-11-01

    Full Text Available In recent years there has been a flourish of archaeological studies focusing on prehistoric cognition or motivation on the basis of GIS-generated interpretations. These have taken two very different forms on either side of the Atlantic. In the empirically driven positivist community of North American researchers, Cultural Resource Management (CRM projects have created a tendency toward using GIS-based archaeological data in the context of so-called 'predictive modelling', or within typically large-scale interpretations of environmental motivations for settlement. This perspective has its origins in the nature of the North American archaeological record, and the development and dominance of processualism. In contrast, the highly complex European archaeological record and the influence of both post-processualism and landscape forms of archaeology have led to a European focus on using GIS as a tool for reconstructing social and cognitive landscapes. Most frequently this has been in the form of visibility and viewshed analyses of henge-type monuments, hill fortifications and their surrounding landscapes. The disconnect between these two dichotomous traditions suggests on the one hand that North American approaches could benefit from methods that generate a more enriching discussion of agency and social theory, while European approaches could benefit from a less speculative form of epistemological argumentation. These ideas may come together through the use of an enhanced discussion of explanation and causality (in keeping with developments in the history and philosophy of science and key tools such as the use of spatial variables as proxies for cognitive decision-making and social agency.

  2. Examining environmental drivers of spatial variability in aflatoxin ...

    African Journals Online (AJOL)

    Examining environmental drivers of spatial variability in aflatoxin accumulation in Kenyan maize: potential utility in risk prediction models. ... however, because of high sampling cost and lack of affordable and accurate analytical methods.

  3. Evaluation of 7Be fallout spatial variability

    International Nuclear Information System (INIS)

    Pinto, Victor Meriguetti

    2011-01-01

    The cosmogenic radionuclide beryllium-7 (Be) is produced in the atmosphere by cosmic particle reactions and is being used as a tracer for soil erosion and climatic processes research. After the production, 7 Be bonds to aerosol particles in the atmosphere and is deposited on the soil surface with other radionuclide species by rainfall. Because of the high adsorption on soil particles and its short half-life of 53.2 days, this radionuclide follows of the erosion process and can be used as a tracer to evaluate the sediment transport that occurs during a single rain event or short period of rain events. A key assumption for the erosion evaluation through this radiotracer is the uniformity of the spatial distribution of the 7 Be fallout. The 7 Be method was elaborated recently and due to its few applications, some assumptions related to the method were not yet properly investigated yet, and the hypothesis of 7 Be fallout uniformity needs to be evaluated. The aim of this study was to evaluate the 7 Be fallout spatial distribution through the rain water 7 Be activity analysis of the first five millimeters of single rain events. The rain water was sampled using twelve collectors distributed on an experimental area of about 300 m2 , located in the campus of Sao Paulo University, Piracicaba. The 7 Be activities were measured using a 53% efficiency gamma-ray spectrometer from the Radioisotope laboratory of CENA. The 7 Be activities in rain water varied from 0.26 to 1.81 Sq.L - 1, with the highest values in summer and lowest in spring. In each one of the 5 single events, the spatial variability of 7 Se activity in rain water was high, showing the high randomness of the fallout spatial distribution. A simulation using the 7 Be spatial variability values obtained here and 7 Se average reference inventories taken from the literature was performed determining the lowest detectable erosion rate estimated by 7 Be model. The importance of taking a representative number of samples to

  4. Short-Term Effects of Climatic Variables on Hand, Foot, and Mouth Disease in Mainland China, 2008-2013: A Multilevel Spatial Poisson Regression Model Accounting for Overdispersion.

    Science.gov (United States)

    Liao, Jiaqiang; Yu, Shicheng; Yang, Fang; Yang, Min; Hu, Yuehua; Zhang, Juying

    2016-01-01

    Hand, Foot, and Mouth Disease (HFMD) is a worldwide infectious disease. In China, many provinces have reported HFMD cases, especially the south and southwest provinces. Many studies have found a strong association between the incidence of HFMD and climatic factors such as temperature, rainfall, and relative humidity. However, few studies have analyzed cluster effects between various geographical units. The nonlinear relationships and lag effects between weekly HFMD cases and climatic variables were estimated for the period of 2008-2013 using a polynomial distributed lag model. The extra-Poisson multilevel spatial polynomial model was used to model the exact relationship between weekly HFMD incidence and climatic variables after considering cluster effects, provincial correlated structure of HFMD incidence and overdispersion. The smoothing spline methods were used to detect threshold effects between climatic factors and HFMD incidence. The HFMD incidence spatial heterogeneity distributed among provinces, and the scale measurement of overdispersion was 548.077. After controlling for long-term trends, spatial heterogeneity and overdispersion, temperature was highly associated with HFMD incidence. Weekly average temperature and weekly temperature difference approximate inverse "V" shape and "V" shape relationships associated with HFMD incidence. The lag effects for weekly average temperature and weekly temperature difference were 3 weeks and 2 weeks. High spatial correlated HFMD incidence were detected in northern, central and southern province. Temperature can be used to explain most of variation of HFMD incidence in southern and northeastern provinces. After adjustment for temperature, eastern and Northern provinces still had high variation HFMD incidence. We found a relatively strong association between weekly HFMD incidence and weekly average temperature. The association between the HFMD incidence and climatic variables spatial heterogeneity distributed across

  5. Short-Term Effects of Climatic Variables on Hand, Foot, and Mouth Disease in Mainland China, 2008–2013: A Multilevel Spatial Poisson Regression Model Accounting for Overdispersion

    Science.gov (United States)

    Yang, Fang; Yang, Min; Hu, Yuehua; Zhang, Juying

    2016-01-01

    Background Hand, Foot, and Mouth Disease (HFMD) is a worldwide infectious disease. In China, many provinces have reported HFMD cases, especially the south and southwest provinces. Many studies have found a strong association between the incidence of HFMD and climatic factors such as temperature, rainfall, and relative humidity. However, few studies have analyzed cluster effects between various geographical units. Methods The nonlinear relationships and lag effects between weekly HFMD cases and climatic variables were estimated for the period of 2008–2013 using a polynomial distributed lag model. The extra-Poisson multilevel spatial polynomial model was used to model the exact relationship between weekly HFMD incidence and climatic variables after considering cluster effects, provincial correlated structure of HFMD incidence and overdispersion. The smoothing spline methods were used to detect threshold effects between climatic factors and HFMD incidence. Results The HFMD incidence spatial heterogeneity distributed among provinces, and the scale measurement of overdispersion was 548.077. After controlling for long-term trends, spatial heterogeneity and overdispersion, temperature was highly associated with HFMD incidence. Weekly average temperature and weekly temperature difference approximate inverse “V” shape and “V” shape relationships associated with HFMD incidence. The lag effects for weekly average temperature and weekly temperature difference were 3 weeks and 2 weeks. High spatial correlated HFMD incidence were detected in northern, central and southern province. Temperature can be used to explain most of variation of HFMD incidence in southern and northeastern provinces. After adjustment for temperature, eastern and Northern provinces still had high variation HFMD incidence. Conclusion We found a relatively strong association between weekly HFMD incidence and weekly average temperature. The association between the HFMD incidence and climatic

  6. Spatial variability in nutrient transport by HUC8, state, and subbasin based on Mississippi/Atchafalaya River Basin SPARROW models

    Science.gov (United States)

    Robertson, Dale M.; Saad, David A.; Schwarz, Gregory E.

    2014-01-01

    Nitrogen (N) and phosphorus (P) loading from the Mississippi/Atchafalaya River Basin (MARB) has been linked to hypoxia in the Gulf of Mexico. With geospatial datasets for 2002, including inputs from wastewater treatment plants (WWTPs), and monitored loads throughout the MARB, SPAtially Referenced Regression On Watershed attributes (SPARROW) watershed models were constructed specifically for the MARB, which reduced simulation errors from previous models. Based on these models, N loads/yields were highest from the central part (centered over Iowa and Indiana) of the MARB (Corn Belt), and the highest P yields were scattered throughout the MARB. Spatial differences in yields from previous studies resulted from different descriptions of the dominant sources (N yields are highest with crop-oriented agriculture and P yields are highest with crop and animal agriculture and major WWTPs) and different descriptions of downstream transport. Delivered loads/yields from the MARB SPARROW models are used to rank subbasins, states, and eight-digit Hydrologic Unit Code basins (HUC8s) by N and P contributions and then rankings are compared with those from other studies. Changes in delivered yields result in an average absolute change of 1.3 (N) and 1.9 (P) places in state ranking and 41 (N) and 69 (P) places in HUC8 ranking from those made with previous national-scale SPARROW models. This information may help managers decide where efforts could have the largest effects (highest ranked areas) and thus reduce hypoxia in the Gulf of Mexico.

  7. Spatial cluster modelling

    CERN Document Server

    Lawson, Andrew B

    2002-01-01

    Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...

  8. Dynamic spatial panels : models, methods, and inferences

    NARCIS (Netherlands)

    Elhorst, J. Paul

    This paper provides a survey of the existing literature on the specification and estimation of dynamic spatial panel data models, a collection of models for spatial panels extended to include one or more of the following variables and/or error terms: a dependent variable lagged in time, a dependent

  9. Estimating greenhouse gas emissions of European cities--modeling emissions with only one spatial and one socioeconomic variable.

    Science.gov (United States)

    Baur, Albert H; Lauf, Steffen; Förster, Michael; Kleinschmit, Birgit

    2015-07-01

    Substantive and concerted action is needed to mitigate climate change. However, international negotiations struggle to adopt ambitious legislation and to anticipate more climate-friendly developments. Thus, stronger actions are needed from other players. Cities, being greenhouse gas emission centers, play a key role in promoting the climate change mitigation movement by becoming hubs for smart and low-carbon lifestyles. In this context, a stronger linkage between greenhouse gas emissions and urban development and policy-making seems promising. Therefore, simple approaches are needed to objectively identify crucial emission drivers for deriving appropriate emission reduction strategies. In analyzing 44 European cities, the authors investigate possible socioeconomic and spatial determinants of urban greenhouse gas emissions. Multiple statistical analyses reveal that the average household size and the edge density of discontinuous dense urban fabric explain up to 86% of the total variance of greenhouse gas emissions of EU cities (when controlled for varying electricity carbon intensities). Finally, based on these findings, a multiple regression model is presented to determine greenhouse gas emissions. It is independently evaluated with ten further EU cities. The reliance on only two indicators shows that the model can be easily applied in addressing important greenhouse gas emission sources of European urbanites, when varying power generations are considered. This knowledge can help cities develop adequate climate change mitigation strategies and promote respective policies on the EU or the regional level. The results can further be used to derive first estimates of urban greenhouse gas emissions, if no other analyses are available. Copyright © 2015 Elsevier B.V. All rights reserved.

  10. Effect of Variable Spatial Scales on USLE-GIS Computations

    Science.gov (United States)

    Patil, R. J.; Sharma, S. K.

    2017-12-01

    Use of appropriate spatial scale is very important in Universal Soil Loss Equation (USLE) based spatially distributed soil erosion modelling. This study aimed at assessment of annual rates of soil erosion at different spatial scales/grid sizes and analysing how changes in spatial scales affect USLE-GIS computations using simulation and statistical variabilities. Efforts have been made in this study to recommend an optimum spatial scale for further USLE-GIS computations for management and planning in the study area. The present research study was conducted in Shakkar River watershed, situated in Narsinghpur and Chhindwara districts of Madhya Pradesh, India. Remote Sensing and GIS techniques were integrated with Universal Soil Loss Equation (USLE) to predict spatial distribution of soil erosion in the study area at four different spatial scales viz; 30 m, 50 m, 100 m, and 200 m. Rainfall data, soil map, digital elevation model (DEM) and an executable C++ program, and satellite image of the area were used for preparation of the thematic maps for various USLE factors. Annual rates of soil erosion were estimated for 15 years (1992 to 2006) at four different grid sizes. The statistical analysis of four estimated datasets showed that sediment loss dataset at 30 m spatial scale has a minimum standard deviation (2.16), variance (4.68), percent deviation from observed values (2.68 - 18.91 %), and highest coefficient of determination (R2 = 0.874) among all the four datasets. Thus, it is recommended to adopt this spatial scale for USLE-GIS computations in the study area due to its minimum statistical variability and better agreement with the observed sediment loss data. This study also indicates large scope for use of finer spatial scales in spatially distributed soil erosion modelling.

  11. Using Field Data and GIS-Derived Variables to Model Occurrence of Williamson's Sapsucker Nesting Habitat at Multiple Spatial Scales.

    Directory of Open Access Journals (Sweden)

    Mark C Drever

    Full Text Available Williamson's sapsucker (Sphyrapicus thyroideus is a migratory woodpecker that breeds in mixed coniferous forests in western North America. In Canada, the range of this woodpecker is restricted to three small populations in southern British Columbia, precipitating a national listing as 'Endangered' in 2005, and the need to characterize critical habitat for its survival and recovery. We compared habitat attributes between Williamson's sapsucker nest territories and random points without nests or detections of this sapsucker as part of a resource selection analysis to identify the habitat features that best explain the probability of nest occurrence in two separate geographic regions in British Columbia. We compared the relative explanatory power of generalized linear models based on field-derived and Geographic Information System (GIS data within both a 225 m and 800 m radius of a nest or random point. The model based on field-derived variables explained the most variation in nest occurrence in the Okanagan-East Kootenay Region, whereas nest occurrence was best explained by GIS information at the 800 m scale in the Western Region. Probability of nest occurrence was strongly tied to densities of potential nest trees, which included open forests with very large (diameter at breast height, DBH, ≥57.5 cm western larch (Larix occidentalis trees in the Okanagan-East Kootenay Region, and very large ponderosa pine (Pinus ponderosa and large (DBH 17.5-57.5 cm trembling aspen (Populus tremuloides trees in the Western Region. Our results have the potential to guide identification and protection of critical habitat as required by the Species at Risk Act in Canada, and to better manage Williamson's sapsucker habitat overall in North America. In particular, management should focus on the maintenance and recruitment of very large western larch and ponderosa pine trees.

  12. Using Field Data and GIS-Derived Variables to Model Occurrence of Williamson's Sapsucker Nesting Habitat at Multiple Spatial Scales.

    Science.gov (United States)

    Drever, Mark C; Gyug, Les W; Nielsen, Jennifer; Stuart-Smith, A Kari; Ohanjanian, I Penny; Martin, Kathy

    2015-01-01

    Williamson's sapsucker (Sphyrapicus thyroideus) is a migratory woodpecker that breeds in mixed coniferous forests in western North America. In Canada, the range of this woodpecker is restricted to three small populations in southern British Columbia, precipitating a national listing as 'Endangered' in 2005, and the need to characterize critical habitat for its survival and recovery. We compared habitat attributes between Williamson's sapsucker nest territories and random points without nests or detections of this sapsucker as part of a resource selection analysis to identify the habitat features that best explain the probability of nest occurrence in two separate geographic regions in British Columbia. We compared the relative explanatory power of generalized linear models based on field-derived and Geographic Information System (GIS) data within both a 225 m and 800 m radius of a nest or random point. The model based on field-derived variables explained the most variation in nest occurrence in the Okanagan-East Kootenay Region, whereas nest occurrence was best explained by GIS information at the 800 m scale in the Western Region. Probability of nest occurrence was strongly tied to densities of potential nest trees, which included open forests with very large (diameter at breast height, DBH, ≥57.5 cm) western larch (Larix occidentalis) trees in the Okanagan-East Kootenay Region, and very large ponderosa pine (Pinus ponderosa) and large (DBH 17.5-57.5 cm) trembling aspen (Populus tremuloides) trees in the Western Region. Our results have the potential to guide identification and protection of critical habitat as required by the Species at Risk Act in Canada, and to better manage Williamson's sapsucker habitat overall in North America. In particular, management should focus on the maintenance and recruitment of very large western larch and ponderosa pine trees.

  13. Spatial variability analysis of combining the water quality and groundwater flow model to plan groundwater and surface water management in the Pingtung plain

    Science.gov (United States)

    Chen, Ching-Fang; Chen, Jui-Sheng; Jang, Cheng-Shin

    2014-05-01

    As a result of rapid economic growth in the Pingtung Plain, the use of groundwater resources has changed dramatically. The groundwater is quite rich in the Pingtung plain and the most important water sources. During the several decades, a substantial amount of groundwater has been pumped for the drinking, irrigation and aquaculture water supplies. However, because the sustainable use concept of groundwater resources is lack, excessive pumping of groundwater causes the occurrence of serious land subsidence and sea water intrusion. Thus, the management and conservation of groundwater resources in the Pingtung plain are considerably critical. This study aims to assess the conjunct use effect of groundwater and surface water in the Pingtung plain on recharge by reducing the amount of groundwater extraction. The groundwater quality variability and groundwater flow models are combined to spatially analyze potential zones of groundwater used for multi-purpose in the Pingtung Plain. First, multivariate indicator kriging (MVIK) is used to analyze spatial variability of groundwater quality based on drinking, aquaculture and irrigation water quality standards, and probabilistically delineate suitable zones in the study area. Then, the groundwater flow model, Processing MODFLOW (PMWIN), is adopted to simulate groundwater flow. The groundwater flow model must be conducted by the calibration and verification processes, and the regional groundwater recovery is discussed when specified water rights are replaced by surface water in the Pingtung plain. Finally, the most suitable zones of reducing groundwater use are determined for multi-purpose according to combining groundwater quality and quantity. The study results can establish a sound and low-impact management plan of groundwater resources utilization for the multi-purpose groundwater use, and prevent decreasing ground water tables, and the occurrence of land subsidence and sea water intrusion in the Pingtung plain.

  14. Spatial Variability of CCN Sized Aerosol Particles

    Science.gov (United States)

    Asmi, A.; Väänänen, R.

    2014-12-01

    The computational limitations restrict the grid size used in GCM models, and for many cloud types they are too large when compared to the scale of the cloud formation processes. Several parameterizations for e.g. convective cloud formation exist, but information on spatial subgrid variation of the cloud condensation nuclei (CCNs) sized aerosol concentration is not known. We quantify this variation as a function of the spatial scale by using datasets from airborne aerosol measurement campaigns around the world including EUCAARI LONGREX, ATAR, INCA, INDOEX, CLAIRE, PEGASOS and several regional airborne campaigns in Finland. The typical shapes of the distributions are analyzed. When possible, we use information obtained by CCN counters. In some other cases, we use particle size distribution measured by for example SMPS to get approximated CCN concentration. Other instruments used include optical particle counters or condensational particle counters. When using the GCM models, the CCN concentration used for each the grid-box is often considered to be either flat, or as an arithmetic mean of the concentration inside the grid-box. However, the aircraft data shows that the concentration values are often lognormal distributed. This, combined with the subgrid variations in the land use and atmospheric properties, might cause that the aerosol-cloud interactions calculated by using mean values to vary significantly from the true effects both temporary and spatially. This, in turn, can cause non-linear bias into the GCMs. We calculate the CCN aerosol concentration distribution as a function of different spatial scales. The measurements allow us to study the variation of these distributions within from hundreds of meters up to hundreds of kilometers. This is used to quantify the potential error when mean values are used in GCMs.

  15. Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability.

    Science.gov (United States)

    Wu, Chih-Da; Chen, Yu-Cheng; Pan, Wen-Chi; Zeng, Yu-Ting; Chen, Mu-Jean; Guo, Yue Leon; Lung, Shih-Chun Candice

    2017-05-01

    This study utilized a long-term satellite-based vegetation index, and considered culture-specific emission sources (temples and Chinese restaurants) with Land-use Regression (LUR) modelling to estimate the spatial-temporal variability of PM 2.5 using data from Taipei metropolis, which exhibits typical Asian city characteristics. Annual average PM 2.5 concentrations from 2006 to 2012 of 17 air quality monitoring stations established by Environmental Protection Administration of Taiwan were used for model development. PM 2.5 measurements from 2013 were used for external data verification. Monthly Normalized Difference Vegetation Index (NDVI) images coupled with buffer analysis were used to assess the spatial-temporal variations of greenness surrounding the monitoring sites. The distribution of temples and Chinese restaurants were included to represent the emission contributions from incense and joss money burning, and gas cooking, respectively. Spearman correlation coefficient and stepwise regression were used for LUR model development, and 10-fold cross-validation and external data verification were applied to verify the model reliability. The results showed a strongly negative correlation (r: -0.71 to -0.77) between NDVI and PM 2.5 while temples (r: 0.52 to 0.66) and Chinese restaurants (r: 0.31 to 0.44) were positively correlated to PM 2.5 concentrations. With the adjusted model R 2 of 0.89, a cross-validated adj-R 2 of 0.90, and external validated R 2 of 0.83, the high explanatory power of the resultant model was confirmed. Moreover, the averaged NDVI within a 1750 m circular buffer (p < 0.01), the number of Chinese restaurants within a 1750 m buffer (p < 0.01), and the number of temples within a 750 m buffer (p = 0.06) were selected as important predictors during the stepwise selection procedures. According to the partial R 2 , NDVI explained 66% of PM 2.5 variation and was the dominant variable in the developed model. We suggest future studies

  16. Spatial Variability of Wet Troposphere Delays Over Inland Water Bodies

    Science.gov (United States)

    Mehran, Ali; Clark, Elizabeth A.; Lettenmaier, Dennis P.

    2017-11-01

    Satellite radar altimetry has enabled the study of water levels in large lakes and reservoirs at a global scale. The upcoming Surface Water and Ocean Topography (SWOT) satellite mission (scheduled launch 2020) will simultaneously measure water surface extent and elevation at an unprecedented accuracy and resolution. However, SWOT retrieval accuracy will be affected by a number of factors, including wet tropospheric delay—the delay in the signal's passage through the atmosphere due to atmospheric water content. In past applications, the wet tropospheric delay over large inland water bodies has been corrected using atmospheric moisture profiles based on atmospheric reanalysis data at relatively coarse (tens to hundreds of kilometers) spatial resolution. These products cannot resolve subgrid variations in wet tropospheric delays at the spatial resolutions (of 1 km and finer) that SWOT is intended to resolve. We calculate zenith wet tropospheric delays (ZWDs) and their spatial variability from Weather Research and Forecasting (WRF) numerical weather prediction model simulations at 2.33 km spatial resolution over the southwestern U.S., with attention in particular to Sam Rayburn, Ray Hubbard, and Elephant Butte Reservoirs which have width and length dimensions that are of order or larger than the WRF spatial resolution. We find that spatiotemporal variability of ZWD over the inland reservoirs depends on climatic conditions at the reservoir location, as well as distance from ocean, elevation, and surface area of the reservoir, but that the magnitude of subgrid variability (relative to analysis and reanalysis products) is generally less than 10 mm.

  17. Modeling spatial variability of sand-lenses in clay till settings using transition probability and multiple-point geostatistics

    DEFF Research Database (Denmark)

    Kessler, Timo Christian; Nilsson, Bertel; Klint, Knud Erik

    2010-01-01

    (TPROGS) of alternating geological facies. The second method, multiple-point statistics, uses training images to estimate the conditional probability of sand-lenses at a certain location. Both methods respect field observations such as local stratigraphy, however, only the multiple-point statistics can...... of sand-lenses in clay till. Sand-lenses mainly account for horizontal transport and are prioritised in this study. Based on field observations, the distribution has been modeled using two different geostatistical approaches. One method uses a Markov chain model calculating the transition probabilities...

  18. Spatial Variability in Column CO2 Inferred from High Resolution GEOS-5 Global Model Simulations: Implications for Remote Sensing and Inversions

    Science.gov (United States)

    Ott, L.; Putman, B.; Collatz, J.; Gregg, W.

    2012-01-01

    Column CO2 observations from current and future remote sensing missions represent a major advancement in our understanding of the carbon cycle and are expected to help constrain source and sink distributions. However, data assimilation and inversion methods are challenged by the difference in scale of models and observations. OCO-2 footprints represent an area of several square kilometers while NASA s future ASCENDS lidar mission is likely to have an even smaller footprint. In contrast, the resolution of models used in global inversions are typically hundreds of kilometers wide and often cover areas that include combinations of land, ocean and coastal areas and areas of significant topographic, land cover, and population density variations. To improve understanding of scales of atmospheric CO2 variability and representativeness of satellite observations, we will present results from a global, 10-km simulation of meteorology and atmospheric CO2 distributions performed using NASA s GEOS-5 general circulation model. This resolution, typical of mesoscale atmospheric models, represents an order of magnitude increase in resolution over typical global simulations of atmospheric composition allowing new insight into small scale CO2 variations across a wide range of surface flux and meteorological conditions. The simulation includes high resolution flux datasets provided by NASA s Carbon Monitoring System Flux Pilot Project at half degree resolution that have been down-scaled to 10-km using remote sensing datasets. Probability distribution functions are calculated over larger areas more typical of global models (100-400 km) to characterize subgrid-scale variability in these models. Particular emphasis is placed on coastal regions and regions containing megacities and fires to evaluate the ability of coarse resolution models to represent these small scale features. Additionally, model output are sampled using averaging kernels characteristic of OCO-2 and ASCENDS measurement

  19. Deciphering factors controlling groundwater arsenic spatial variability in Bangladesh

    Science.gov (United States)

    Tan, Z.; Yang, Q.; Zheng, C.; Zheng, Y.

    2017-12-01

    Elevated concentrations of geogenic arsenic in groundwater have been found in many countries to exceed 10 μg/L, the WHO's guideline value for drinking water. A common yet unexplained characteristic of groundwater arsenic spatial distribution is the extensive variability at various spatial scales. This study investigates factors influencing the spatial variability of groundwater arsenic in Bangladesh to improve the accuracy of models predicting arsenic exceedance rate spatially. A novel boosted regression tree method is used to establish a weak-learning ensemble model, which is compared to a linear model using a conventional stepwise logistic regression method. The boosted regression tree models offer the advantage of parametric interaction when big datasets are analyzed in comparison to the logistic regression. The point data set (n=3,538) of groundwater hydrochemistry with 19 parameters was obtained by the British Geological Survey in 2001. The spatial data sets of geological parameters (n=13) were from the Consortium for Spatial Information, Technical University of Denmark, University of East Anglia and the FAO, while the soil parameters (n=42) were from the Harmonized World Soil Database. The aforementioned parameters were regressed to categorical groundwater arsenic concentrations below or above three thresholds: 5 μg/L, 10 μg/L and 50 μg/L to identify respective controlling factors. Boosted regression tree method outperformed logistic regression methods in all three threshold levels in terms of accuracy, specificity and sensitivity, resulting in an improvement of spatial distribution map of probability of groundwater arsenic exceeding all three thresholds when compared to disjunctive-kriging interpolated spatial arsenic map using the same groundwater arsenic dataset. Boosted regression tree models also show that the most important controlling factors of groundwater arsenic distribution include groundwater iron content and well depth for all three

  20. Spatial variability and trends in Younger Dryas equilibrium line altitudes across the European Alps using a hypsometrically based ELA model: results and implications

    Science.gov (United States)

    Keeler, D. G.; Rupper, S.; Schaefer, J. M.; Finkel, R. C.; Maurer, J. M.

    2016-12-01

    Alpine glaciers constitute an important component of terrestrial paleoclimate records due to, among other characteristics, their high sensitivity to climate change, near global extent, and their integration of myriad climate variables into a single, easily detected signal. Because the glacier equilibrium line altitude (ELA) provides a more explicit representation of climate than many other glacier properties, ELA methods allow for more direct comparisons of multiple glaciers within or between regions. Such comparisons allow for more complete investigations of the ultimate causes of mountain glaciation during specific events. Many studies however tend to focus on a limited number of sites, and employ a large variety of different techniques for ELA reconstruction between studies, making wider climate implications more tenuous. Methods of ELA reconstruction that can be rapidly and consistently applied to an arbitrary number of paleo-glaciers would provide a more accurate portrayal of the changes in climate across a given region. Here we present ELA reconstructions from Egesen Stadial moraines across the European Alps using an ELA model accounting for differences in glacier width, glacier shape, bed topography, ice thickness, and glacier length, including several glaciers constrained to the Younger Dryas using surface exposure dating techniques. We compare reconstructed Younger Dryas ELA values to modern ELA values using the same model, or using end of summer snowline estimates where no glacier is currently present. We further provide uncertainty estimates on the ΔELA using bootstrapped Monte Carlo simulations for the various input parameters. Preliminary results compare favorably to previous glacier studies of the European Younger Dryas, but provide greater context from many glaciers across the region as a whole. Such results allow for a more thorough investigation of the spatial variability and trends in climate during the Younger Dryas across the European Alps, and

  1. Spatial and temporal variability in urban fine particulate matter concentrations

    International Nuclear Information System (INIS)

    Levy, Jonathan I.; Hanna, Steven R.

    2011-01-01

    Identification of hot spots for urban fine particulate matter (PM 2.5 ) concentrations is complicated by the significant contributions from regional atmospheric transport and the dependence of spatial and temporal variability on averaging time. We focus on PM 2.5 patterns in New York City, which includes significant local sources, street canyons, and upwind contributions to concentrations. A literature synthesis demonstrates that long-term (e.g., one-year) average PM 2.5 concentrations at a small number of widely-distributed monitoring sites would not show substantial variability, whereas short-term (e.g., 1-h) average measurements with high spatial density would show significant variability. Statistical analyses of ambient monitoring data as a function of wind speed and direction reinforce the significance of regional transport but show evidence of local contributions. We conclude that current monitor siting may not adequately capture PM 2.5 variability in an urban area, especially in a mega-city, reinforcing the necessity of dispersion modeling and methods for analyzing high-resolution monitoring observations. - Highlights: →Fine particulate matter (PM 2.5 ) hot spots are hard to identify in urban areas. → Literature conclusions about PM 2.5 hot spots depend on study design and methods. → Hot spots are more likely for short-term concentrations at high spatial density. → Statistical methods illustrate local source impacts beyond regional transport. → Dispersion models and high-resolution monitors are both needed to find hot spots. - Fine particulate matter can vary spatially within large urban areas, in spite of the significant contribution from regional atmospheric transport.

  2. Evaluating spatial patterns in hydrological modelling

    DEFF Research Database (Denmark)

    Koch, Julian

    the contiguous United Sates (10^6 km2). To this end, the thesis at hand applies a set of spatial performance metrics on various hydrological variables, namely land-surface-temperature (LST), evapotranspiration (ET) and soil moisture. The inspiration for the applied metrics is found in related fields...... is not fully exploited by current modelling frameworks due to the lack of suitable spatial performance metrics. Furthermore, the traditional model evaluation using discharge is found unsuitable to lay confidence on the predicted catchment inherent spatial variability of hydrological processes in a fully...

  3. SPATIAL VARIABILITY IN THE MUDPRAWN UPOGEBIA AFRICANA ...

    African Journals Online (AJOL)

    A nested sampling design was used to examine the variability in density, biomass, sex ratio and size of the estuarine mudprawn Upogebia africana in six estuaries on the south-east coast of South Africa. The objectives were to test the general hypothesis that there is variability in these variables at the scales of regions, ...

  4. Variable importance in latent variable regression models

    NARCIS (Netherlands)

    Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.

    2014-01-01

    The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable

  5. Crime Modeling using Spatial Regression Approach

    Science.gov (United States)

    Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.

    2018-01-01

    Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.

  6. Accounting for Unresolved Spatial Variability in Large Scale Models: Development and Evaluation of a Statistical Cloud Parameterization with Prognostic Higher Order Moments

    Energy Technology Data Exchange (ETDEWEB)

    Robert Pincus

    2011-05-17

    This project focused on the variability of clouds that is present across a wide range of scales ranging from the synoptic to the millimeter. In particular, there is substantial variability in cloud properties at scales smaller than the grid spacing of models used to make climate projections (GCMs) and weather forecasts. These models represent clouds and other small-scale processes with parameterizations that describe how those processes respond to and feed back on the largescale state of the atmosphere.

  7. Temporal and spatial variability in North Carolina piedmont stream temperature

    Science.gov (United States)

    J.L. Boggs; G. Sun; S.G. McNulty; W. Swartley; Treasure E.; W. Summer

    2009-01-01

    Understanding temporal and spatial patterns of in-stream temperature can provide useful information to managing future impacts of climate change on these systems. This study will compare temporal patterns and spatial variability of headwater in-stream temperature in six catchments in the piedmont of North Carolina in two different geological regions, Carolina slate...

  8. Spatial variability in airborne pollen concentrations.

    Science.gov (United States)

    Raynor, G S; Ogden, E C; Hayes, J V

    1975-03-01

    Tests were conducted to determine the relationship between airborne pollen concentrations and distance. Simultaneous samples were taken in 171 tests with sets of eight rotoslide samplers spaced from one to 486 M. apart in straight lines. Use of all possible pairs gave 28 separation distances. Tests were conducted over a 2-year period in urban and rural locations distant from major pollen sources during both tree and ragweed pollen seasons. Samples were taken at a height of 1.5 M. during 5-to 20-minute periods. Tests were grouped by pollen type, location, year, and direction of the wind relative to the line. Data were analyzed to evaluate variability without regard to sampler spacing and variability as a function of separation distance. The mean, standard deviation, coefficient of variation, ratio of maximum to the mean, and ratio of minimum to the mean were calculated for each test, each group of tests, and all cases. The average coefficient of variation is 0.21, the maximum over the mean, 1.39 and the minimum over the mean, 0.69. No relationship was found with experimental conditions. Samples taken at the minimum separation distance had a mean difference of 18 per cent. Differences between pairs of samples increased with distance in 10 of 13 groups. These results suggest that airborne pollens are not always well mixed in the lower atmosphere and that a sample becomes less representative with increasing distance from the sampling location.

  9. Disturbance History,Spatial Variability, and Patterns of Biodiversity

    Science.gov (United States)

    Bendix, J.; Wiley, J. J.; Commons, M.

    2012-12-01

    The intermediate disturbance hypothesis predicts that species diversity will be maximized in environments experiencing intermediate intensity disturbance, after an intermediate timespan. Because many landscapes comprise mosaics with complex disturbance histories, the theory implies that each patch in those mosaics should have a distinct level of diversity reflecting combined impact of the magnitude of disturbance and the time since it occurred. We modeled the changing patterns of species richness across a landscape experiencing varied scenarios of simulated disturbance. Model outputs show that individual landscape patches have highly variable species richness through time, with the details reflecting the timing, intensity and sequence of their disturbance history. When the results are mapped across the landscape, the resulting temporal and spatial complexity illustrates both the contingent nature of diversity and the danger of generalizing about the impacts of disturbance.

  10. Quantifying and mapping spatial variability in simulated forest plots

    Science.gov (United States)

    Gavin R. Corral; Harold E. Burkhart

    2016-01-01

    We used computer simulations to test the efficacy of multivariate statistical methods to detect, quantify, and map spatial variability of forest stands. Simulated stands were developed of regularly-spaced plantations of loblolly pine (Pinus taeda L.). We assumed no affects of competition or mortality, but random variability was added to individual tree characteristics...

  11. Thermodynamic Model of Spatial Memory

    Science.gov (United States)

    Kaufman, Miron; Allen, P.

    1998-03-01

    We develop and test a thermodynamic model of spatial memory. Our model is an application of statistical thermodynamics to cognitive science. It is related to applications of the statistical mechanics framework in parallel distributed processes research. Our macroscopic model allows us to evaluate an entropy associated with spatial memory tasks. We find that older adults exhibit higher levels of entropy than younger adults. Thurstone's Law of Categorical Judgment, according to which the discriminal processes along the psychological continuum produced by presentations of a single stimulus are normally distributed, is explained by using a Hooke spring model of spatial memory. We have also analyzed a nonlinear modification of the ideal spring model of spatial memory. This work is supported by NIH/NIA grant AG09282-06.

  12. Variability of the raindrop size distribution at small spatial scales

    Science.gov (United States)

    Berne, A.; Jaffrain, J.

    2010-12-01

    Because of the interactions between atmospheric turbulence and cloud microphysics, the raindrop size distribution (DSD) is strongly variable in space and time. The spatial variability of the DSD at small spatial scales (below a few km) is not well documented and not well understood, mainly because of a lack of adequate measurements at the appropriate resolutions. A network of 16 disdrometers (Parsivels) has been designed and set up over EPFL campus in Lausanne, Switzerland. This network covers a typical operational weather radar pixel of 1x1 km2. The question of the significance of the variability of the DSD at such small scales is relevant for radar remote sensing of rainfall because the DSD is often assumed to be uniform within a radar sample volume and because the Z-R relationships used to convert the measured radar reflectivity Z into rain rate R are usually derived from point measurements. Thanks to the number of disdrometers, it was possible to quantify the spatial variability of the DSD at the radar pixel scale and to show that it can be significant. In this contribution, we show that the variability of the total drop concentration, of the median volume diameter and of the rain rate are significant, taking into account the sampling uncertainty associated with disdrometer measurements. The influence of this variability on the Z-R relationship can be non-negligible. Finally, the spatial structure of the DSD is quantified using a geostatistical tool, the variogram, and indicates high spatial correlation within a radar pixel.

  13. Evaluation of spatial variability of metal bioavailability in soils using geostatistics

    DEFF Research Database (Denmark)

    Owsianiak, Mikolaj; Hauschild, Michael Zwicky; Rosenbaum, Ralph K.

    2012-01-01

    Soil properties show signifficant spatial variability at local, regional and continental scales. This is a challenge for life cycle impact assessment (LCIA) of metals, because fate, bioavailability and effect factors are controlled by environmental chemistry and can vary orders of magnitude...... is performed using ArcGIS Geostatistical Analyst. Results show that BFs of copper span a range of 6 orders of magnitude, and have signifficant spatial variability at local and continental scales. The model nugget variance is signifficantly higher than zero, suggesting the presence of spatial variability...

  14. Spatial scales of pollution from variable resolution satellite imaging

    International Nuclear Information System (INIS)

    Chudnovsky, Alexandra A.; Kostinski, Alex; Lyapustin, Alexei; Koutrakis, Petros

    2013-01-01

    The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not adequate for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM 2.5 as measured by the EPA ground monitoring stations was investigated at varying spatial scales. Our analysis suggested that the correlation between PM 2.5 and AOD decreased significantly as AOD resolution was degraded. This is so despite the intrinsic mismatch between PM 2.5 ground level measurements and AOD vertically integrated measurements. Furthermore, the fine resolution results indicated spatial variability in particle concentration at a sub-10 km scale. Finally, this spatial variability of AOD within the urban domain was shown to depend on PM 2.5 levels and wind speed. - Highlights: ► The correlation between PM 2.5 and AOD decreases as AOD resolution is degraded. ► High resolution MAIAC AOD 1 km retrieval can be used to investigate within-city PM 2.5 variability. ► Low pollution days exhibit higher spatial variability of AOD and PM 2.5 then moderate pollution days. ► AOD spatial variability within urban area is higher during the lower wind speed conditions. - The correlation between PM 2.5 and AOD decreases as AOD resolution is degraded. The new high-resolution MAIAC AOD retrieval has the potential to capture PM 2.5 variability at the intra-urban scale.

  15. Coastal upwelling south of Madagascar: Temporal and spatial variability

    Science.gov (United States)

    Ramanantsoa, Juliano D.; Krug, M.; Penven, P.; Rouault, M.; Gula, J.

    2018-02-01

    Madagascar's southern coastal marine zone is a region of high biological productivity which supports a wide range of marine ecosystems, including fisheries. This high biological productivity is attributed to coastal upwelling. This paper provides new insights on the structure, variability and drivers of the coastal upwelling south of Madagascar. Satellite remote sensing is used to characterize the spatial extent and strength of the coastal upwelling. A front detection algorithm is applied to thirteen years of Multi-scale Ultra-high Resolution (MUR) Sea Surface Temperatures (SST) and an upwelling index is calculated. The influence of winds and ocean currents as drivers of the upwelling is investigated using satellite, in-situ observations, and a numerical model. Results reveal the presence of two well-defined upwelling cells. The first cell (Core 1) is located in the southeastern corner of Madagascar, and the second cell (Core 2) is west of the southern tip of Madagascar. These two cores are characterized by different seasonal variability, different intensities, different upwelled water mass origins, and distinct forcing mechanisms. Core 1 is associated with a dynamical upwelling forced by the detachment of the East Madagascar Current (EMC), which is reinforced by upwelling favourable winds. Core 2 appears to be primarily forced by upwelling favourable winds, but is also influenced by a poleward eastern boundary flow coming from the Mozambique Channel. The intrusion of Mozambique Channel warm waters could result in an asynchronicity in seasonality between upwelling surface signature and upwelling favourables winds.

  16. Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes

    Science.gov (United States)

    Ghosh, Subimal; Vittal, H.; Sharma, Tarul; Karmakar, Subhankar; Kasiviswanathan, K. S.; Dhanesh, Y.; Sudheer, K. P.; Gunthe, S. S.

    2016-01-01

    India’s agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC) model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins. PMID:27463092

  17. Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes.

    Directory of Open Access Journals (Sweden)

    Subimal Ghosh

    Full Text Available India's agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins.

  18. Spatial and temporal variability of Mediterranean drought events

    Science.gov (United States)

    Trigo, R.; Sousa, P.; Nieto, R.; Gimeno, L.

    2009-04-01

    The original Palmer Drought Severity Index (PDSI) and a recent adaptation to European soil characteristics, the Self Calibrated PDSI (or scPDSI) proposed by Schrier et al (2005) were used. We have computed monthly, seasonal and annual trends between 1901 and 2000 but also for the first and second halves of the 20th century. Results were represented only when achieving a minimum level of statistical significance (either 5% or 10% using a Mann-Kendall test) and confirm that the majority of the western and central Mediterranean is getting drier in the last decades of the 20th century while Turkey is generally getting wetter (Trigo et al., 2006). The spatio-temporal variability of these indices was evaluated with an EOF analysis, in order to reduce the large dimensionality of the fields under analysis. Spatial representation of the first EOF patterns shows that EOF 1 covers the entire Mediterranean basin (16.4% of EV), while EOF2 is dominated by a W-E dipole (10% EV). The following EOF patterns present smaller scale features, and explain smaller amounts of variance. The EOF patterns have also facilitated the definition of four sub-regions with large socio-economic relevance: 1) Iberia, 2) Italian Peninsula, 3) Balkans and 4) Turkey. The inter-annual variability of the regional spatial droughts indices for each region was analyzed separately. We have also performed an evaluation of their eventual links with large-scale atmospheric circulation indices that affect the Mediterranean basin, namely the NAO, EA, and SCAND. Finally we have evaluated the main sources of moisture affecting two drought prone areas in the western (Iberia) and eastern (Balkans) Mediterranean. This analysis was performed by means of backward tracking the air masses that ultimately reach these two regions using the Lagrangian particle dispersion model FLEXPART (Stohl et al., 1998) and meteorological analysis data from the ECMWF to track atmospheric moisture. This was done for a five-year period (2000

  19. Probabilistic and spatially variable niches inferred from demography

    Science.gov (United States)

    Jeffrey M. Diez; Itamar Giladi; Robert Warren; H. Ronald. Pulliam

    2014-01-01

    Summary 1. Mismatches between species distributions and habitat suitability are predicted by niche theory and have important implications for forecasting how species may respond to environmental changes. Quantifying these mismatches is challenging, however, due to the high dimensionality of species niches and the large spatial and temporal variability in population...

  20. Spatial variability in branchial basket meristics and morphology of ...

    African Journals Online (AJOL)

    We examined spatial variability in meristic and morphological characteristics of the branchial basket of sardine Sardinops sagax collected from four geographical regions around the southern African coast, namely Namibia and the South African west, south and east coasts. Our analysis tested the hypothesis of three putative ...

  1. Measured spatial variability of beach erosion due to aeolian processes.

    NARCIS (Netherlands)

    de Vries, S.; Verheijen, A.H.; Hoonhout, B.M.; Vos, S.E.; Cohn, Nicholas; Ruggiero, P; Aagaard, T.; Deigaard, R.; Fuhrman, D.

    2017-01-01

    This paper shows the first results of measured spatial variability of beach erosion due to aeolian processes during the recently conducted SEDEX2 field experiment at Long Beach, Washington, U.S.A.. Beach erosion and sedimentation were derived using series of detailed terrestrial LIDAR measurements

  2. Quantitative analysis of spatial variability of geotechnical parameters

    Science.gov (United States)

    Fang, Xing

    2018-04-01

    Geotechnical parameters are the basic parameters of geotechnical engineering design, while the geotechnical parameters have strong regional characteristics. At the same time, the spatial variability of geotechnical parameters has been recognized. It is gradually introduced into the reliability analysis of geotechnical engineering. Based on the statistical theory of geostatistical spatial information, the spatial variability of geotechnical parameters is quantitatively analyzed. At the same time, the evaluation of geotechnical parameters and the correlation coefficient between geotechnical parameters are calculated. A residential district of Tianjin Survey Institute was selected as the research object. There are 68 boreholes in this area and 9 layers of mechanical stratification. The parameters are water content, natural gravity, void ratio, liquid limit, plasticity index, liquidity index, compressibility coefficient, compressive modulus, internal friction angle, cohesion and SP index. According to the principle of statistical correlation, the correlation coefficient of geotechnical parameters is calculated. According to the correlation coefficient, the law of geotechnical parameters is obtained.

  3. Variability in the Precision of Children’s Spatial Working Memory

    Directory of Open Access Journals (Sweden)

    Elena M. Galeano Weber

    2018-02-01

    Full Text Available Cognitive modeling studies in adults have established that visual working memory (WM capacity depends on the representational precision, as well as its variability from moment to moment. By contrast, visuospatial WM performance in children has been typically indexed by response accuracy—a binary measure that provides less information about precision with which items are stored. Here, we aimed at identifying whether and how children’s WM performance depends on the spatial precision and its variability over time in real-world contexts. Using smartphones, 110 Grade 3 and Grade 4 students performed a spatial WM updating task three times a day in school and at home for four weeks. Measures of spatial precision (i.e., Euclidean distance between presented and reported location were used for hierarchical modeling to estimate variability of spatial precision across different time scales. Results demonstrated considerable within-person variability in spatial precision across items within trials, from trial to trial and from occasion to occasion within days and from day to day. In particular, item-to-item variability was systematically increased with memory load and lowered with higher grade. Further, children with higher precision variability across items scored lower in measures of fluid intelligence. These findings emphasize the important role of transient changes in spatial precision for the development of WM.

  4. Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.

    Science.gov (United States)

    Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J

    2010-12-01

    Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies

  5. Spatial variability in streambed hydraulic conductivity of contrasting stream morphologies

    DEFF Research Database (Denmark)

    Sebök, Éva; Calvache, Carlos Duque; Engesgaard, Peter Knudegaard

    2015-01-01

    inner bend of the stream, whereas high Kv values were observed at the erosional outer bend and near the middle of the channel. Calculated Kv values were related to the thickness of the organic streambed sediment layer and also showed higher temporal variability than Kh because of sedimentation...... small-scale measurements were taken in December 2011 and August 2012, both in a straight stream channel with homogeneous elevation and downstream of a channel meander with heterogeneous elevation. All streambed attributes showed large spatial variability. Kh values were the highest at the depositional...... and scouring processes affecting the upper layers of the streambed. Test locations at the channel bend showed a more heterogeneous distribution of streambed properties than test locations in the straight channel, whereas within the channel bend, higher spatial variability in streambed attributes was observed...

  6. Human Plague Risk: Spatial-Temporal Models

    Science.gov (United States)

    Pinzon, Jorge E.

    2010-01-01

    This chpater reviews the use of spatial-temporal models in identifying potential risks of plague outbreaks into the human population. Using earth observations by satellites remote sensing there has been a systematic analysis and mapping of the close coupling between the vectors of the disease and climate variability. The overall result is that incidence of plague is correlated to positive El Nino/Southem Oscillation (ENSO).

  7. Competition in spatial location models

    NARCIS (Netherlands)

    Webers, H.M.

    1996-01-01

    Models of spatial competition are designed and analyzed to describe the fact that space, by its very nature, is a source of market power. This field of research, lying at the interface of game theory and economics, has attracted much interest because location problems are related to many aspects of

  8. One perspective on spatial variability in geologic mapping

    Science.gov (United States)

    Markewich, H.W.; Cooper, S.C.

    1991-01-01

    This paper discusses some of the differences between geologic mapping and soil mapping, and how the resultant maps are interpreted. The role of spatial variability in geologic mapping is addressed only indirectly because in geologic mapping there have been few attempts at quantification of spatial differences. This is largely because geologic maps deal with temporal as well as spatial variability and consider time, age, and origin, as well as composition and geometry. Both soil scientists and geologists use spatial variability to delineate mappable units; however, the classification systems from which these mappable units are defined differ greatly. Mappable soil units are derived from systematic, well-defined, highly structured sets of taxonomic criteria; whereas mappable geologic units are based on a more arbitrary heirarchy of categories that integrate many features without strict values or definitions. Soil taxonomy is a sorting tool used to reduce heterogeneity between soil units. Thus at the series level, soils in any one series are relatively homogeneous because their range of properties is small and well-defined. Soil maps show the distribution of soils on the land surface. Within a map area, soils, which are often less than 2 m thick, show a direct correlation to topography and to active surface processes as well as to parent material.

  9. Spatial scales of pollution from variable resolution satellite imaging.

    Science.gov (United States)

    Chudnovsky, Alexandra A; Kostinski, Alex; Lyapustin, Alexei; Koutrakis, Petros

    2013-01-01

    The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not adequate for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM(2.5) as measured by the EPA ground monitoring stations was investigated at varying spatial scales. Our analysis suggested that the correlation between PM(2.5) and AOD decreased significantly as AOD resolution was degraded. This is so despite the intrinsic mismatch between PM(2.5) ground level measurements and AOD vertically integrated measurements. Furthermore, the fine resolution results indicated spatial variability in particle concentration at a sub-10 km scale. Finally, this spatial variability of AOD within the urban domain was shown to depend on PM(2.5) levels and wind speed. Copyright © 2012 Elsevier Ltd. All rights reserved.

  10. Spatial and temporal variability of interhemispheric transport times

    Science.gov (United States)

    Wu, Xiaokang; Yang, Huang; Waugh, Darryn W.; Orbe, Clara; Tilmes, Simone; Lamarque, Jean-Francois

    2018-05-01

    The seasonal and interannual variability of transport times from the northern midlatitude surface into the Southern Hemisphere is examined using simulations of three idealized age tracers: an ideal age tracer that yields the mean transit time from northern midlatitudes and two tracers with uniform 50- and 5-day decay. For all tracers the largest seasonal and interannual variability occurs near the surface within the tropics and is generally closely coupled to movement of the Intertropical Convergence Zone (ITCZ). There are, however, notable differences in variability between the different tracers. The largest seasonal and interannual variability in the mean age is generally confined to latitudes spanning the ITCZ, with very weak variability in the southern extratropics. In contrast, for tracers subject to spatially uniform exponential loss the peak variability tends to be south of the ITCZ, and there is a smaller contrast between tropical and extratropical variability. These differences in variability occur because the distribution of transit times from northern midlatitudes is very broad and tracers with more rapid loss are more sensitive to changes in fast transit times than the mean age tracer. These simulations suggest that the seasonal-interannual variability in the southern extratropics of trace gases with predominantly NH midlatitude sources may differ depending on the gases' chemical lifetimes.

  11. Spatial variability of chemical properties of soil under pasture

    Directory of Open Access Journals (Sweden)

    Samuel Ferreira da Silva

    2016-04-01

    Full Text Available The objective of this study was to analyze the spatial variability of soil chemical attributes under pasture, as well as lime and fertilizer recommendations based on the interpretation of soil chemical analysis from two sampling methods: conventional and systematic depths of 0 to 10 and 10 to 20 cm. The study was conducted at IFES-campus Alegre-ES. Data analysis was performed using descriptive statistics and geostatistics. Results indicate that the spatial method enabled the identification of deficit areas and excessive liming and fertilization, which could not be defined by the conventional method.

  12. Spatial variability of correlated color temperature of lightning channels

    Directory of Open Access Journals (Sweden)

    Nobuaki Shimoji

    Full Text Available In this paper, we present the spatial variability of the correlated color temperature of lightning channel shown in a digital still image. In order to analyze the correlated color temperature, we calculated chromaticity coordinates of the lightning channels in the digital still image. From results, the spatial variation of the correlated color temperature of the lightning channel was confirmed. Moreover, the results suggest that the correlated color temperature and peak current of the lightning channels are related to each other. Keywords: Lightning, Color analysis, Correlated color temperature, Chromaticity coordinate, CIE 1931 xy-chromaticity diagram

  13. Spatial variability of soil CO2 emission in different topographic positions

    Directory of Open Access Journals (Sweden)

    Liziane de Figueiredo Brito

    2010-01-01

    Full Text Available The spatial variability of soil CO2 emission is controlled by several properties related to the production and transport of CO2 inside the soil. Considering that soil properties are also influenced by topography, the objective of this work was to investigate the spatial variability of soil CO2 emission in three different topographic positions in an area cultivated with sugarcane, just after mechanical harvest. One location was selected on a concave-shaped form and two others on linear-shaped form (in back-slope and foot-slope. Three grids were installed, one in each location, containing 69 points and measuring 90 x 90 m each. The spatial variability of soil CO2 emission was characterized by means of semivariance. Spatial variability models derived from soil CO2 emission were exponential in the concave location while spherical models fitted better in the linear shaped areas. The degree of spatial dependence was moderate in all cases and the range of spatial dependence for the CO2 emission in the concave area was 44.5 m, higher than the mean value obtained for the linear shaped areas (20.65 m. The spatial distribution maps of soil CO2 emission indicate a higher discontinuity of emission in the linear form when compared to the concave form.

  14. Spatial and temporal variability of biophysical variables in southwestern France from airborne L-band radiometry

    Directory of Open Access Journals (Sweden)

    E. Zakharova

    2012-06-01

    Full Text Available In 2009 and 2010 the L-band microwave Cooperative Airborne Radiometer for Ocean and Land Studies (CAROLS campaign was performed in southwestern France to support the calibration and validation of the new Soil Moisture and Ocean Salinity (SMOS satellite mission. The L-band Microwave Emission of the Biosphere (L-MEB model was used to retrieve surface soil moisture (SSM and the vegetation optical depth (VOD from the CAROLS brightness temperature measurements. The CAROLS SSM was compared with in situ observations at 11 sites of the SMOSMANIA (Soil Moisture Observing System-Meteorological Automatic Network Integrated Application network of Météo-France. For eight of them, significant correlations were observed (0.51 ≤ r ≤ 0.82, with standard deviation of differences ranging from 0.039 m3 m−3 to 0.141 m3 m−3. Also, the CAROLS SSM was compared with SSM values simulated by the A-gs version of the Interactions between Soil, Biosphere and Atmosphere (ISBA-A-gs model along 20 flight lines, at a resolution of 8 km × 8 km. A significant spatial correlation between these two datasets was observed for all the flights (0.36 ≤ r ≤ 0.85. The CAROLS VOD presented significant spatial correlations with the vegetation water content (VWC derived from the spatial distribution of vegetation types used in ISBA-A-gs and from the Leaf Area Index (LAI simulated for low vegetation. On the other hand, the CAROLS VOD presented little temporal changes, and no temporal correlation was observed with the simulated LAI. For low vegetation, the ratio of VOD to VWC tended to decrease, from springtime to summertime. The ISBA-A-gs grid cells (8 km × 8 km were sampled every 5 m by CAROLS observations, at a spatial resolution of about 2 km. For 83% of the grid cells, the standard deviation of the sub-grid CAROLS SSM was lower than 0.05 m3 m−3. The presence of small water bodies within the

  15. A Structural Equation Approach to Models with Spatial Dependence

    NARCIS (Netherlands)

    Oud, Johan H. L.; Folmer, Henk

    We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it

  16. A structural equation approach to models with spatial dependence

    NARCIS (Netherlands)

    Oud, J.H.L.; Folmer, H.

    2008-01-01

    We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it

  17. A Structural Equation Approach to Models with Spatial Dependence

    NARCIS (Netherlands)

    Oud, J.H.L.; Folmer, H.

    2008-01-01

    We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it

  18. Directional semivariogram analysis to identify and rank controls on the spatial variability of fracture networks

    Science.gov (United States)

    Hanke, John R.; Fischer, Mark P.; Pollyea, Ryan M.

    2018-03-01

    In this study, the directional semivariogram is deployed to investigate the spatial variability of map-scale fracture network attributes in the Paradox Basin, Utah. The relative variability ratio (R) is introduced as the ratio of integrated anisotropic semivariogram models, and R is shown to be an effective metric for quantifying the magnitude of spatial variability for any two azimuthal directions. R is applied to a GIS-based data set comprising roughly 1200 fractures, in an area which is bounded by a map-scale anticline and a km-scale normal fault. This analysis reveals that proximity to the fault strongly influences the magnitude of spatial variability for both fracture intensity and intersection density within 1-2 km. Additionally, there is significant anisotropy in the spatial variability, which is correlated with trends of the anticline and fault. The direction of minimum spatial correlation is normal to the fault at proximal distances, and gradually rotates and becomes subparallel to the fold axis over the same 1-2 km distance away from the fault. We interpret these changes to reflect varying scales of influence of the fault and the fold on fracture network development: the fault locally influences the magnitude and variability of fracture network attributes, whereas the fold sets the background level and structure of directional variability.

  19. Spatial and temporal variability of hyperspectral signatures of terrain

    Science.gov (United States)

    Jones, K. F.; Perovich, D. K.; Koenig, G. G.

    2008-04-01

    Electromagnetic signatures of terrain exhibit significant spatial heterogeneity on a range of scales as well as considerable temporal variability. A statistical characterization of the spatial heterogeneity and spatial scaling algorithms of terrain electromagnetic signatures are required to extrapolate measurements to larger scales. Basic terrain elements including bare soil, grass, deciduous, and coniferous trees were studied in a quasi-laboratory setting using instrumented test sites in Hanover, NH and Yuma, AZ. Observations were made using a visible and near infrared spectroradiometer (350 - 2500 nm) and hyperspectral camera (400 - 1100 nm). Results are reported illustrating: i) several difference scenes; ii) a terrain scene time series sampled over an annual cycle; and iii) the detection of artifacts in scenes. A principal component analysis indicated that the first three principal components typically explained between 90 and 99% of the variance of the 30 to 40-channel hyperspectral images. Higher order principal components of hyperspectral images are useful for detecting artifacts in scenes.

  20. Spatial Models and Networks of Living Systems

    DEFF Research Database (Denmark)

    Juul, Jeppe Søgaard

    When studying the dynamics of living systems, insight can often be gained by developing a mathematical model that can predict future behaviour of the system or help classify system characteristics. However, in living cells, organisms, and especially groups of interacting individuals, a large number...... variables of the system. However, this approach disregards any spatial structure of the system, which may potentially change the behaviour drastically. An alternative approach is to construct a cellular automaton with nearest neighbour interactions, or even to model the system as a complex network...... with interactions defined by network topology. In this thesis I first describe three different biological models of ageing and cancer, in which spatial structure is important for the system dynamics. I then turn to describe characteristics of ecosystems consisting of three cyclically interacting species...

  1. Spatial Variability of Soil Morphorlogical and Physico-Chemical ...

    African Journals Online (AJOL)

    Spatial Variability of Soil Morphorlogical and Physico-Chemical Properties in Ladoke Akintola University of Technology Cashew Plantation, Ogbomoso. ... Colour (AP, B1 B2 and B3), structure (B2 and B3), stoniness (B1, B2 and B3), concretion (AP B1, B2 and B3) and boundary forms (B1, B2 and B3) have extremely ...

  2. In-Situ Spatial Variability Of Thermal Conductivity And Volumetric ...

    African Journals Online (AJOL)

    Studies of spatial variability of thermal conductivity and volumetric water content of silty topsoil were conduct-ed on a 0.6 ha site at Abeokuta, South-Western Nigeria. The thermal conductivity (k) was measured at depths of up to 0.06 m along four parallel profiles of 200 m long and at an average temperature of 25 C, using ...

  3. Spatial variability in subsurface flow and transport: a review

    International Nuclear Information System (INIS)

    Gutjahr, A.L.; Bras, R.L.

    1993-01-01

    Stochastic models of spatial variations as they apply to both saturated and unsaturated flow and transport problems are examined in this paper. Both modeling and data interpretive geostatistical approaches are reviewed and an integrated discussion combining the two approaches given. The probabilistic content is of special interest for reliability and risk calculations for waste management and groundwater pollution studies. (author)

  4. Spatial variability and Cesium-137 inventories in native forest

    International Nuclear Information System (INIS)

    Andrello, A.C.; Appoloni, C.R.

    2004-01-01

    With the nuclear fission discovery and development of nuclear weapons in 1940s, artificial radioisotopes were introduced in the environment. This contamination is due to worldwide fallout by superficial nuclear tests realized from early 1950s to late 1970s by USA, former URSS, UK, France and China. One of theses radioisotopes that have been very studied is cesium-137. Cesium-137 has a half-life of 30.2 years and its biological behavior is similar to the potassium. The behavior in soil matrix, depth distribution, spatial variability and inventories values of cesium-137 has been determinate for several regions of the world. In Brazil, some research groups have worked on this subject, but there are few works published about theses properties of cesium-137. The aim of this paper was study the depth distribution, spatial variability, and inventory of cesium-137 in native forest. Two native forests (Mata 1 and Mata UEL) were sampling in region of Londrina, PR. The results shows that there is a spatial variability of 40% for Mata 1 and 42% for Mata UEL. The depth distribution of cesium-137 for two forests presented a exponential form, characteristic to undisturbed soil. Cesium-137 inventory determinate for Mata 1 was 358 Bq m -2 and for Mata UEL was 320 Bq m -2 . (author)

  5. Spatial and temporal variability in midge (Nematocera) assemblages in shallow Finnish lakes (60-70 deg N) : community-based modelling of past environmental change

    Energy Technology Data Exchange (ETDEWEB)

    Luoto, T.

    2010-07-01

    Multi- and intralake datasets of fossil midge assemblages in surface sediments of small shallow lakes in Finland were studied to determine the most important environmental factors explaining trends in midge distribution and abundance. The aim was to develop palaeoenvironmental calibration models for the most important environmental variables for the purpose of reconstructing past environmental conditions. The developed models were applied to three high-resolution fossil midge stratigraphies from southern and eastern Finland to interpret environmental variability over the past 2000 years, with special focus on the Medieval Climate Anomaly (MCA), the Little Ice Age (LIA) and recent anthropogenic changes. The midge-based results were compared with physical properties of the sediment, historical evidence and environmental reconstructions based on diatoms (Bacillariophyta), cladocerans (Crustacea: Cladocera) and tree rings. The results showed that the most important environmental factor controlling midge distribution and abundance along a latitudinal gradient in Finland was the mean July air temperature (TJul). However, when the dataset was environmentally screened to include only pristine lakes, water depth at the sampling site became more important. Furthermore, when the dataset was geographically scaled to southern Finland, hypolimnetic oxygen conditions became the dominant environmental factor. The results from an intralake dataset from eastern Finland showed that the most important environmental factors controlling midge distribution within a lake basin were river contribution, water depth and submerged vegetation patterns. In addition, the results of the intralake dataset showed that the fossil midge assemblages represent fauna that lived in close proximity to the sampling sites, thus enabling the exploration of within-lake gradients in midge assemblages. Importantly, this within-lake heterogeneity in midge assemblages may have effects on midge-based temperature

  6. Representation of spatial and temporal variability of daily wind speed and of intense wind events over the Mediterranean Sea using dynamical downscaling: impact of the regional climate model configuration

    Directory of Open Access Journals (Sweden)

    M. Herrmann

    2011-07-01

    Full Text Available Atmospheric datasets coming from long term reanalyzes of low spatial resolution are used for different purposes. Wind over the sea is, for example, a major ingredient of oceanic simulations. However, the shortcomings of those datasets prevent them from being used without an adequate corrective preliminary treatment. Using a regional climate model (RCM to perform a dynamical downscaling of those large scale reanalyzes is one of the methods used in order to produce fields that realistically reproduce atmospheric chronology and where those shortcomings are corrected. Here we assess the influence of the configuration of the RCM used in this framework on the representation of wind speed spatial and temporal variability and intense wind events on a daily timescale. Our RCM is ALADIN-Climate, the reanalysis is ERA-40, and the studied area is the Mediterranean Sea.

    First, the dynamical downscaling significantly reduces the underestimation of daily wind speed, in average by 9 % over the whole Mediterranean. This underestimation has been corrected both globally and locally, and for the whole wind speed spectrum. The correction is the strongest for periods and regions of strong winds. The representation of spatial variability has also been significantly improved. On the other hand, the temporal correlation between the downscaled field and the observations decreases all the more that one moves eastwards, i.e. further from the atmospheric flux entry. Nonetheless, it remains ~0.7, the downscaled dataset reproduces therefore satisfactorily the real chronology.

    Second, the influence of the choice of the RCM configuration has an influence one order of magnitude smaller than the improvement induced by the initial downscaling. The use of spectral nudging or of a smaller domain helps to improve the realism of the temporal chronology. Increasing the resolution very locally (both spatially and temporally improves the representation of spatial

  7. Spatial and temporal variability of the Aridity Index in Greece

    Science.gov (United States)

    Nastos, Panagiotis T.; Politi, Nadia; Kapsomenakis, John

    2013-01-01

    The objective of this paper is to study the spatial and temporal variability of the Aridity Index (AI) in Greece, per decade, during the 50-year period (1951-2000). Besides, the projected changes in ensemble mean AI between the period 1961-1990 (reference period) and the periods 2021-2050 (near future) and 2071-2100 (far future) along with the inter-model standard deviations were presented, based on the simulation results, derived from a number of Regional Climatic Models (RCMs), within the ENSEMBLE European Project. The projection of the future climate was done under SRES A1B. The climatic data used, concern monthly precipitation totals and air temperature from 28 meteorological stations (22 stations from the Hellenic National Meteorological Service and 6 stations from neighboring countries, taken from the Monthly Climatic Data for the World). The estimation of the AI was carried out based on the potential evapotranspiration (PET) defined by Thornthwaite (1948). The data processing was done by the application of the statistical package R-project and the Geographical Information Systems (GIS). The results of the analysis showed that, within the examined period (1951-2000), a progressive shift from the "humid" class, which characterized the wider area of Greece, towards the "sub-humid" and "semi-arid" classes appeared in the eastern Crete Island, the Cyclades complex, the Evia and Attica, that is mainly the eastern Greece. The most significant change appears during the period 1991-2000. The future projections at the end of twentieth century, using ensemble mean simulations from 8 RCMs, show that drier conditions are expected to establish in regions of Greece (Attica, eastern continental Greece, Cyclades, Dodecanese, eastern Crete Island and northern Aegean). The inter-model standard deviation over these regions ranges from 0.02 to 0.05 against high values (0.09-0.15) illustrated in western mountainous continental Greece, during 2021-2050. Higher values of inter-model

  8. The Spatial Variability of Beryllium-7 Depth Distribution Study

    International Nuclear Information System (INIS)

    Jalal Sharib; Zainudin Othman; Dainee Nor Fardzila Ahmad Tugi; Noor Fadzilah Yusof; Mohd Tarmizi Ishak

    2015-01-01

    The objective of this paper is to study the spatial variability of 7 Be depth evolution in soil profile at two different sampling sites. The soil samples have been collected by using metal core in bare area in Bangi, Selangor and Timah Tasoh, Perlis , Malaysia. Two composite core samples for each sampling sites has been sectioned into 2 mm increments to a depth of 4 cm and oven dried at 45- 60 degree Celsius and gently desegregated. These two composite spatial samples are passed through a < 2 mm sieve and packed into proper geometry plastic container for 7 Be analysis by using gamma spectrometry with a 24-hour count time. From the findings, the 7 Be content in the soil samples from Bangi, Selangor study area is distributed lower depth penetration into the soil profile than Timah Tasoh, Perlis catchment due to many factors such as precipitation (fallout) and others. However, the spatial variability from both samples study area is also decreases exponentially with depth and is confined within the top few centimeters and similar with other works been reported (Blake et al., (2000) and Walling et al.,(2008). Furthermore, a detailed discussion from this study findings will be in full papers. (author)

  9. Temporal Changes in the Spatial Variability of Soil Nutrients

    Energy Technology Data Exchange (ETDEWEB)

    Hoskinson, Reed Louis; Hess, John Richard; Alessi, Randolph Samuel

    1999-07-01

    This paper reports the temporal changes in the spatial variability of soil nutrient concentrations across a field during the growing season, over a four-year period. This study is part of the Site-Specific Technologies for Agriculture (SST4Ag) precision farming research project at the INEEL. Uniform fertilization did not produce a uniform increase in fertility. During the growing season, several of the nutrients and micronutrients showed increases in concentration although no additional fertilization had occurred. Potato plant uptake did not explain all of these changes. Some soil micronutrient concentrations increased above levels considered detrimental to potatoes, but the plants did not show the effects in reduced yield. All the nutrients measured changed between the last sampling in the fall and the first sampling the next spring prior to fertilization. The soil microbial community may play a major role in the temporal changes in the spatial variability of soil nutrient concentrations. These temporal changes suggest potential impact when determining fertilizer recommendations, and when evaluating the results of spatially varying fertilizer application.

  10. Spatial variability in degassing at Erebus volcano, Antarctica

    Science.gov (United States)

    Ilanko, Tehnuka; Oppenheimer, Clive; Kyle, Philip; Burgisser, Alain

    2015-04-01

    Erebus volcano on Ross Island, Antarctica, hosts an active phonolitic lava lake, along with a number of persistently degassing vents in its summit crater. Flank degassing also occurs through ice caves and towers. The longevity of the lake, and its stable convection, have been the subject of numerous studies, including Fourier transform infrared (FTIR) spectroscopy of the lava lake. Two distinct gas compositions were previously identified in the main lava lake plume (Oppenheimer et al., 2009; 2011): a persistent 'conduit' gas with a more oxidised signature, ascribed to degassing through a permeable magma conduit; and a H2O- and SO2- enriched 'lake' composition that increases and decreases cyclically due to shallow degassing of incoming magma batches. During the past decade of annual field seasons on Erebus, gas compositions have been measured through FTIR spectroscopy at multiple sites around Erebus volcano, including flank degassing through an ice cave (Warren Cave). We present measurements from four such vents, and compare their compositions to those emitted from the main lava lake. Summit degassing involves variable proportions of H2O, CO2, CO, SO2, HF, HCl, OCS. Cyclicity is evident in some summit vents, but with signatures indicative of shallower magmatic degassing than that of the lava lake. By contrast, flank degassing at Warren Cave is dominated by H2O, CO2, and CH4. The spatial variability in gas compositions within the summit crater suggests an alternative origin for 'conduit' and 'lake' degassing to previous models that assume permeability in the main conduit. Rather, the two compositions observed in main lake degassing may be a result of decoupled 'conduit' gas and pulses of magma rising through discrete fractures before combining in the lake floor or the main plume. Smaller vents around the crater thus emit isolated 'lake' or 'conduit' compositions while their combined signature is observed in the lava lake. We suggest that this separation between gas

  11. Observations on the spatial variability of the Prut river discharges

    Directory of Open Access Journals (Sweden)

    Emil-Andrei BRICIU

    2011-06-01

    Full Text Available Liquid and solid discharges of the Prut River were analysed based on measurementsperformed in 7 points from the Romanian national network of water monitoring during aperiod of 30 years. The analyses were performed on flows for the period after theconstruction of the Stânca-Costeşti dam and show the influence of the dam for the entireanalysed time. The analysis from upstream to downstream of the spatial variability of thePrut River annual discharges showed their steady increase downstream and then adecrease in the sector next to Oancea station. A statistical minority of the annualdischarges showed a continuous increase of them until the flowing of Prut into Danube.Knowing that the lower basin of the river is characterized by a low amount of rainfall anda higher evapo(transpiration than the remaining basin, the decreasing flows to the rivermouth is explicable; but the increasing flows to the river mouth cannot be justified, underthese conditions of water balance, than by certain climatological parameters of thermodynamicalnature which generate, with increased frequency, more intense and rich rainfall, with a torrential character. The analyses on couples of three months showed thatthe Oancea flows are higher than the upstream stations (opposite than usual in yearswhen the flows of the upstream hydrometrical stations are lower than the multiannualaverage and that supports the mentioned pluviometrical character. A plausible cause for"Oancea phenomenon" is the increase and the decrease of the sunspots number, whosecycles are relatively well fold on the increase and decrease of annual average flow atOancea hydrometrical station. The strongest increased discharges of the Prut River overthe discharges at the upstream stations occur from May to July (MJJ, the months with thehighest amount of rainfall. Seasonal analysis of MJJ and other couples of 3 monthsshowed that there are also growing flows at Prisăcani station relative to the adjacentstations, but

  12. Spatial and temporal variability of precipitation and drought in Portugal

    Directory of Open Access Journals (Sweden)

    D. S. Martins

    2012-05-01

    Full Text Available The spatial variability of precipitation and drought are investigated for Portugal using monthly precipitation from 74 stations and minimum and maximum temperature from 27 stations, covering the common period of 1941–2006. Seasonal precipitation and the corresponding percentages in the year, as well as the precipitation concentration index (PCI, was computed for all 74 stations and then used as an input matrix for an R-mode principal component analysis to identify the precipitation patterns. The standardized precipitation index at 3 and 12 month time scales were computed for all stations, whereas the Palmer Drought Severity Index (PDSI and the modified PDSI for Mediterranean conditions (MedPDSI were computed for the stations with temperature data. The spatial patterns of drought over Portugal were identified by applying the S-mode principal component analysis coupled with varimax rotation to the drought indices matrices. The result revealed two distinct sub-regions in the country relative to both precipitation regimes and drought variability. The analysis of time variability of the PC scores of all drought indices allowed verifying that there is no linear trend indicating drought aggravation or decrease. In addition, the analysis shows that results for SPI-3, SPI-12, PDSI and MedPDSI are coherent among them.

  13. Rainfall interception and spatial variability of throughfall in spruce stand

    Directory of Open Access Journals (Sweden)

    Dohnal Michal

    2014-12-01

    Full Text Available The interception was recognized as an important part of the catchment water balance in temperate climate. The mountainous forest ecosystem at experimental headwater catchment Liz has been subject of long-term monitoring. Unique dataset in terms of time resolution serves to determine canopy storage capacity and free throughfall. Spatial variability of throughfall was studied using one weighing and five tipping bucket rain gauges. The basic characteristics of forest affecting interception process were determined for the Norway spruce stand at the experimental area - the leaf area index was 5.66 - 6.00 m2 m-2, the basal area was 55.7 m2 ha-1, and the crown closure above individual rain gauges was between 19 and 95%. The total interception loss in both growing seasons analyzed was 34.5%. The mean value of the interception capacity determined was about 2 mm. Throughfall exhibited high variability from place to place and it was strongly affected by character of rainfall. On the other hand, spatial pattern of throughfall in average showed low variability.

  14. Variable selection and model choice in geoadditive regression models.

    Science.gov (United States)

    Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

    2009-06-01

    Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

  15. Empirical spatial econometric modelling of small scale neighbourhood

    Science.gov (United States)

    Gerkman, Linda

    2012-07-01

    The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.

  16. Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas - a review

    Science.gov (United States)

    Cristiano, Elena; ten Veldhuis, Marie-claire; van de Giesen, Nick

    2017-07-01

    In urban areas, hydrological processes are characterized by high variability in space and time, making them sensitive to small-scale temporal and spatial rainfall variability. In the last decades new instruments, techniques, and methods have been developed to capture rainfall and hydrological processes at high resolution. Weather radars have been introduced to estimate high spatial and temporal rainfall variability. At the same time, new models have been proposed to reproduce hydrological response, based on small-scale representation of urban catchment spatial variability. Despite these efforts, interactions between rainfall variability, catchment heterogeneity, and hydrological response remain poorly understood. This paper presents a review of our current understanding of hydrological processes in urban environments as reported in the literature, focusing on their spatial and temporal variability aspects. We review recent findings on the effects of rainfall variability on hydrological response and identify gaps where knowledge needs to be further developed to improve our understanding of and capability to predict urban hydrological response.

  17. Spatial variability and trends of the rain intensity over Greece

    Science.gov (United States)

    Kambezidis, H. D.; Larissi, I. K.; Nastos, P. T.; Paliatsos, A. G.

    2010-07-01

    In this study, the spatial and temporal variability of the mean annual rain intensity in Greece are examined during a 41-year period (1962-2002). The meteorological datasets concern monthly rain amounts (mm) and the respective monthly durations (h) recorded at thirty two meteorological stations of the Hellenic National Meteorological Service, which are uniformly distributed on Greek territory, in order to calculate the mean monthly rain intensity. All the rain time series used in the analysis were tested by the application of the short-cut Bartlett test of homogeneity. The spatial distribution of the mean annual rain intensity is studied using the Kriging interpolation method, while the temporal variability, concerning the mean annual rain intensity trends along with their significance (Mann-Kendall test), is analysed. The findings of the analysis show that statistically significant negative trends (95% confidence level) appear mainly in the west sub-regions of Greece, while statistically significant positive trends (95% confidence level) appear in the wider area of Athens and the complex of Cyclades Islands. Further analysis concerning the seasonal rain intensity is needed, because there are different seasonal patterns, taking into account that, convective rain in Greece occurs mainly within the summer season.

  18. Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review

    OpenAIRE

    E. Cristiano; M.-C. ten Veldhuis; N. van de Giesen

    2017-01-01

    In urban areas, hydrological processes are characterized by high variability in space and time, making them sensitive to small-scale temporal and spatial rainfall variability. In the last decades new instruments, techniques, and methods have been developed to capture rainfall and hydrological processes at high resolution. Weather radars have been introduced to estimate high spatial and temporal rainfall variability. At the same time, new models have been proposed to reproduce hydrological res...

  19. Continuous Spatial Process Models for Spatial Extreme Values

    KAUST Repository

    Sang, Huiyan; Gelfand, Alan E.

    2010-01-01

    process model for extreme values that provides mean square continuous realizations, where the behavior of the surface is driven by the spatial dependence which is unexplained under the latent spatio-temporal specification for the GEV parameters

  20. A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis

    Science.gov (United States)

    Wang, Jun; Wang, Yang; Zeng, Hui

    2016-01-01

    A key issue to address in synthesizing spatial data with variable-support in spatial analysis and modeling is the change-of-support problem. We present an approach for solving the change-of-support and variable-support data fusion problems. This approach is based on geostatistical inverse modeling that explicitly accounts for differences in spatial support. The inverse model is applied here to produce both the best predictions of a target support and prediction uncertainties, based on one or more measurements, while honoring measurements. Spatial data covering large geographic areas often exhibit spatial nonstationarity and can lead to computational challenge due to the large data size. We developed a local-window geostatistical inverse modeling approach to accommodate these issues of spatial nonstationarity and alleviate computational burden. We conducted experiments using synthetic and real-world raster data. Synthetic data were generated and aggregated to multiple supports and downscaled back to the original support to analyze the accuracy of spatial predictions and the correctness of prediction uncertainties. Similar experiments were conducted for real-world raster data. Real-world data with variable-support were statistically fused to produce single-support predictions and associated uncertainties. The modeling results demonstrate that geostatistical inverse modeling can produce accurate predictions and associated prediction uncertainties. It is shown that the local-window geostatistical inverse modeling approach suggested offers a practical way to solve the well-known change-of-support problem and variable-support data fusion problem in spatial analysis and modeling.

  1. Effects on ground motion related to spatial variability

    International Nuclear Information System (INIS)

    Vanmarcke, E.H.

    1987-01-01

    Models of the spectral content and the space-time correlation structure of strong earthquake ground motion are combined with transient random vibration analysis to yield site-specific response spectra that can account for the effect of local spatial averaging of the ground motion across a rigid foundation of prescribed size. The methodology is presented with reference to sites in eastern North America, although the basic approach is applicable to other seismic regions provided the source and attenuation parameters are regionally adjusted. Parameters in the spatial correlation model are based on data from the SMART-I accelerograph array, and the sensitivity of response spectra reduction factors with respect to these parameters is examined. The starting point of the analysis is the Fourier amplitude spectrum of site displacement expresses as a function of earthquake source parameters and source-to-site distance. The bedrock acceleration spectral density function at a point, derived from the displacement spectrum, is modified to account for anelastic attenuation, and where appropriate, for local soil effects and/or local spatial averaging across a foundation. Transient random vibration analysis yields approximate analytical expressions for median ground motion amplitudes and median response spectra of an earthquake defined in terms of its spectral density function and strong motion duration. The methodology is illustrated for three events characterized by their m b magnitude and epicentral distance. The focus in this paper is on the stochastic response prediction methodology enabling explicit accounting for strong motion duration and the effect of local spatial averaging on response spectra. The numerical examples enable a preliminary assessment of the reduction of response spectral amplitudes attributable to local spatial averaging across rigid foundations of different sizes. 36 refs

  2. Spatial Variability of Indicators of Jiaokou Reservoir Under Different Sampling Scales

    Directory of Open Access Journals (Sweden)

    WEI Wen-juan

    2016-12-01

    Full Text Available This research determined total nitrogen, total phosphorus, ammonia nitrogen and potassium permanganate contents in different scales of Jiaokou reservoir with the purpose of exploring the applicability of spatial variability and its characteristic in different sampling scales. The results showed that, compared the sampling scales of 100 m with 200 m, there were some differences among four indicators in the spatial variation, interpolation simulation and spatial distribution. About the testing model fit, the fitting model for the total nitrogen, permanganate index was Gaussian model, the fitting model for total phosphorus, ammonia nitrogen was the spherical model; Combining evaluation of parameters of models and comprehensive evaluation of spatial interpolation, total nitrogen, total phosphorus showed stronger spatial correlation and better interpolation simulation quality on the sampling scales of 200 m, while total phosphorus and permanganate index showed certain advantages on the 100 m scale; On the aspect of spatial distributions, the contents of ammonia nitrogen and potassium permanganate were mainly affected by human factors, the total phosphorus was affected by internal factors of the reservoir, while total nitrogen was closely related to farming activities around reservoir. The above results showed that total nitrogen, ammonia nitrogen were more available for the 200 m scales and total phosphorus, potassium permanganate were more available for the 100 m scales.

  3. Spatial and temporal variability of Aridity Index in Greece

    Science.gov (United States)

    Nastos, Panagiotis; Politi, Nadia; Douvis, Kostas

    2010-05-01

    Drought events have deteriorated in most European regions during the last decades in frequency, duration, or intensity. Besides, increased drying associated with higher temperatures and decreased precipitation have contributed to changes in drought. Drought-affected areas are projected to increase in extent, with the potential for adverse impacts on multiple sectors, e.g. agriculture, water supply, energy production and health, according to IPCC. The objective of this study is the spatial and temporal variability of the Aridity Index (AI) per decade, in Greece during the period 1951-2000, as far as the projections of AI for the period 2051-2100, based on simulations of ensemble regional climate models (RCMs), for A1B SRES scenario. The climatic data used for the analysis concern monthly values of precipitation and air temperature from 28 meteorological stations; 22 stations from the National Hellenic Meteorological Service and 6 stations from neighboring countries. According to the United Nations Environment Programme (UNEP), AI is defined as P/PET, where P is the average annual precipitation and PET is the potential evapotranspiration, estimated by the Thornthwaite method; PET and P must be expressed in same units, e.g., in milimetres. All the meteorological data processing was carried out by the application of Geographical Information System (GIS). The results of the analysis showed that within the examined period a clear shift from "humid" class that characterized the greater area of Greece in 1950's to "sub-humid" and "semi-dry" classes appeared in mainly the eastern regions of Greece, such as eastern Crete Island, Cyclades Islands, Evia and Attica in 1990's. The future projections derived by the simulations of ensemble RCMs indicated that drier conditions are very likely to appear in Greece associated with significant socio-economic consequences. The decreasing precipitation along with the high rates of evapotranspiration, because of increase in the air

  4. Spatial Dimension as a Variable in Quantum Mechanics

    Science.gov (United States)

    Doren, Douglas James

    Several approximation methods potentially useful in electronic structure calculations are developed. These methods all treat the spatial dimension, D, as a variable. In an Introduction, the motivations for these methods are described, with special attention to the semiclassical 1/D expansion. Several terms in this expansion have been calculated for two-electron atoms. The results have qualitative appeal but poor convergence properties when D = 3. Chapter 1 shows that this convergence problem is due to singularities in the energy at D = 1 and a method of removing their effects is demonstrated. Chapter 2 treats several model problems, showing how to identify special dimensions at which the energy becomes singular or the Hamiltonian simplifies. Expansions are developed about these special finite values of D which are quite accurate at low order, regardless of the physical parameters of the Hamiltonian. In Chapter 3, expansions about singular points in the energy at finite values of D are used to resum the 1/D series in cases where its leading orders are not sufficient. This leads to a hybrid expansion which typically improves on both the 1/D and the finite D series. These methods are applied in Chapter 4 to two -electron atoms. The ground state energy of few-electron systems is dominated by the presence of a pole when D = 1. The residue of this pole is determined by the eigenvalue of a simple limiting Schrodinger equation. The limit and first order correction are determined for both unapproximated nonrelativistic two-electron atoms and the Hartree-Fock approximation to them. The hybrid expansion using only the first few terms in the 1/D series determines the energy at arbitrary D, providing estimates accurate to four or five figures when D = 3. Degeneracies between D = 3 states and those in nonphysical dimensions are developed in Chapter 5 which provide additional applications for this series. Chapter 6 illustrates these methods in an application to the H(' -) ion, an

  5. Groundwater Variability Across Temporal and Spatial Scales in the Central and Northeastern U.S.

    Science.gov (United States)

    Li, Bailing; Rodell, Matthew; Famiglietti, James S.

    2015-01-01

    Depth-to-water measurements from 181 monitoring wells in unconfined or semi-confined aquifers in nine regions of the central and northeastern U.S. were analyzed. Groundwater storage exhibited strong seasonal variations in all regions, with peaks in spring and lows in autumn, and its interannual variability was nearly unbounded, such that the impacts of droughts, floods, and excessive pumping could persist for many years. We found that the spatial variability of groundwater storage anomalies (deviations from the long term mean) increases as a power function of extent scale (square root of area). That relationship, which is linear on a log-log graph, is common to other hydrological variables but had never before been shown with groundwater data. We describe how the derived power function can be used to determine the number of wells needed to estimate regional mean groundwater storage anomalies with a desired level of accuracy, or to assess uncertainty in regional mean estimates from a set number of observations. We found that the spatial variability of groundwater storage anomalies within a region often increases with the absolute value of the regional mean anomaly, the opposite of the relationship between soil moisture spatial variability and mean. Recharge (drainage from the lowest model soil layer) simulated by the Variable Infiltration Capacity (VIC) model was compatible with observed monthly groundwater storage anomalies and month-to-month changes in groundwater storage.

  6. Spatial variability of atrazine dissipation in an allophanic soil.

    Science.gov (United States)

    Müller, Karin; Smith, Roger E; James, Trevor K; Holland, Patrick T; Rahman, Anis

    2003-08-01

    The small-scale variability (0.5 m) of atrazine (6-chloro-N2-ethyl-N4-isopropyl-1,3,5-triazine-2,4-diamine) concentrations and soil water contents in a volcanic silt loam soil (Haplic Andosol, FAO system) was studied in an area of 0.1 ha. Descriptive and spatial statistics were used to analyse the data. On average we recovered 102% of the applied atrazine 2 h after the herbicide application (CV = 35%). An increase in the CV of the concentrations with depth could be ascribed to a combination of extrinsic and intrinsic factors. Both variables, atrazine concentrations and soil water content, showed a high horizontal variability. The semivariograms of the atrazine concentrations exhibited the pure nugget effect, no pattern could be determined along the 15.5-m long transects on any of the seven sampling days over a 55-day period. Soil water content had a weak spatial autocorrelation with a range of 6-10 m. The dissipation of atrazine analysed using a high vertical sampling resolution of 0.02 m to 0.2 m showed that 70% of the applied atrazine persisted in the upper 0.02-m layer of the soil for 12 days. After 55 days and 410 mm of rainfall the centre of the pesticide mass was still at a soil depth of 0.021 m. The special characteristics of the soil (high organic carbon content, allophanic clay) had a strong influence on atrazine sorption and mobility. The mass recovery after 55 days was low. The laboratory degradation rate for atrazine, determined in a complementary incubation study and corrected for the actual field temperature using the Arrhenius equation, only accounted for about 35% of the losses that occurred in the field. Results suggest field degradation rates to be more changeable in time and much faster than under controlled conditions. Preferential flow is discussed as a component of the field transport process.

  7. Evaluating spatial and temporal variability in growth and mortality for recreational fisheries with limited catch data

    Science.gov (United States)

    Li, Yan; Wagner, Tyler; Jiao, Yan; Lorantas, Robert M.; Murphy, Cheryl

    2018-01-01

    Understanding the spatial and temporal variability in life-history traits among populations is essential for the management of recreational fisheries. However, valuable freshwater recreational fish species often suffer from a lack of catch information. In this study, we demonstrated the use of an approach to estimate the spatial and temporal variability in growth and mortality in the absence of catch data and apply the method to riverine smallmouth bass (Micropterus dolomieu) populations in Pennsylvania, USA. Our approach included a growth analysis and a length-based analysis that estimates mortality. Using a hierarchical Bayesian approach, we examined spatial variability in growth and mortality by assuming parameters vary spatially but remain constant over time and temporal variability by assuming parameters vary spatially and temporally. The estimated growth and mortality of smallmouth bass showed substantial variability over time and across rivers. We explored the relationships of the estimated growth and mortality with spring water temperature and spring flow. Growth rate was likely to be positively correlated with these two factors, while young mortality was likely to be positively correlated with spring flow. The spatially and temporally varying growth and mortality suggest that smallmouth bass populations across rivers may respond differently to management plans and disturbance such as environmental contamination and land-use change. The analytical approach can be extended to other freshwater recreational species that also lack of catch data. The approach could also be useful in developing population assessments with erroneous catch data or be used as a model sensitivity scenario to verify traditional models even when catch data are available.

  8. The active liquid Earth - importance of temporal and spatial variability

    Science.gov (United States)

    Arheimer, Berit

    2016-04-01

    The Planet Earth is indeed liquid and active - 71 percent of its surface is water-covered and this water never rests. Thanks to the water cycle, our planet's water supply is constantly moving from one place to another and from one form to another. Only 2.5% of the water is freshwater and it exists in the air as water vapor; it hits the ground as rain and snow; it flows on the surface from higher to lower altitudes in rivers, lakes, and glaciers; and it flows in the ground in soil, aquifers, and in all living organisms until it reaches the sea. On its way over the Earth's crust, some returns quickly to vapor again, while some is trapped and exposed to many "fill and spill" situations for a long journey. The variability in the water balance is crucial for hydrological understanding and modelling. The water cycle may appear simple, but magnitudes and rates in fluxes are very different from one place to another, resulting from variable drivers such as solar energy, precipitation and gravity in co-evolution with geology, soil, vegetation and fauna. The historical evolution, the temporal fluxes and diversity in space continue to fascinate hydrological scientists. Specific physical processes may be well known, but their boundary conditions, interactions and rate often remain unknown at a specific site and are difficult to monitor in nature. This results in mysterious features where trends in drivers do not match runoff, like the Sahelian Paradox or discharge to the Arctic Ocean. Humans have always interfered with the water cycle and engineering is fundamental for water regulation and re-allocation. Some 80% of the river flow from the northern part of the Earth is affected by fragmentation of the river channels by dams. In water management, there is always a tradeoff between upstream and downstream activities, not only regarding total water quantities but also for temporal patterns and water quality aspects. Sharing a water resource can generate conflicts but geopolitical

  9. A preliminary characterization of the spatial variability of precipitation at Yucca Mountain, Nevada

    International Nuclear Information System (INIS)

    Hevesi, J.A.; Flint, A.L.; Ambos, D.S.

    1994-01-01

    Isohyetal maps of precipitation and numerical models for simulating precipitation are needed to help characterize natural infiltration at Yucca Mountain, Nevada. A geostatistical analysis of measured precipitation accumulated from storm periods. Precipitation was measured during a 3.8 year period from January 1990 to October, 1993 using a network of precipitation gages. A total of 34 winter-type storms and 12 summer-type storm, categorized using synoptic weather records, were analyzed using the 1st and 2nd statistical moments and sample variograms. Average standardized variograms indicated good spatial correlation for both storm types with only slight differences in the general spatial structure. Coefficients of variation and average relative variograms indicated that summer storms are characterized by greater variability as compared to winter storms. Models were fitted to the average summer and winter standarized variograms for each storm using the mean storm depth and the coefficient of variation as scaling parameters. Isohyetal maps of 4 representative storms were created using the standarized models. Results indicate that standarized models can be used to simulate the spatial distribution of precipitation depth, provided that the 1st and 2nd moments are known or can be estimated, and that identifiable deterministic trends can be included in the models. A single, fixed model representing the spatial variability of precipitation at Yucca Mountain is not recommended

  10. Predictor variable resolution governs modeled soil types

    Science.gov (United States)

    Soil mapping identifies different soil types by compressing a unique suite of spatial patterns and processes across multiple spatial scales. It can be quite difficult to quantify spatial patterns of soil properties with remotely sensed predictor variables. More specifically, matching the right scale...

  11. Soil physics and the water management of spatially variable soils

    International Nuclear Information System (INIS)

    Youngs, E.G.

    1983-01-01

    The physics of macroscopic soil-water behaviour in inert porous materials has been developed by considering water flow to take place in a continuum. This requires the flow region to consist of an assembly of representative elementary volumes, repeated throughout space and small compared with the scale of observations. Soil-water behaviour in swelling soils may also be considered as a continuum phenomenon so long as the soil is saturated and swells and shrinks in the normal range. Macroscale heterogeneity superimposed on the inherent microscale heterogeneity can take many forms and may pose difficulties in the definition and measurement of soil physical properties and also in the development and use of predictive theories of soil-water behaviour. Thus, measurement techniques appropriate for uniform soils are often inappropriate, and criteria for soil-water management, obtained from theoretical considerations of behaviour in equivalent uniform soils, are not applicable without modification when there is soil heterogeneity. The spatial variability of soil-water properties is shown in results from field experiments concerned with water flow measurements; these illustrate both stochastic and deterministic heterogeneity in soil-water properties. Problems of water management of spatially variable soils when there is stochastic heterogeneity appear to present an insuperable problem in the application of theory. However, for soils showing deterministic heterogeneity, soil-water theory has been used in the solution of soil-water management problems. Thus, scaling using similar media theory has been applied to the infiltration of water into soils that vary over a catchment area. Also, the drain spacing to control the water-table height in soils in which the hydraulic conductivity varies with depth has been calculated using groundwater seepage theory. (author)

  12. Controls of Soil Spatial Variability in a Dry Tropical Forest.

    Directory of Open Access Journals (Sweden)

    Sandeep Pulla

    Full Text Available We examined the roles of lithology, topography, vegetation and fire in generating local-scale (<1 km2 soil spatial variability in a seasonally dry tropical forest (SDTF in southern India. For this, we mapped soil (available nutrients, Al, total C, pH, moisture and texture in the top 10 cm, rock outcrops, topography, all native woody plants ≥1 cm diameter at breast height (DBH, and spatial variation in fire frequency (times burnt during the 17 years preceding soil sampling in a permanent 50-ha plot. Unlike classic catenas, lower elevation soils had lesser moisture, plant-available Ca, Cu, Mn, Mg, Zn, B, clay and total C. The distribution of plant-available Ca, Cu, Mn and Mg appeared to largely be determined by the whole-rock chemical composition differences between amphibolites and hornblende-biotite gneisses. Amphibolites were associated with summit positions, while gneisses dominated lower elevations, an observation that concurs with other studies in the region which suggest that hillslope-scale topography has been shaped by differential weathering of lithologies. Neither NO3(--N nor NH4(+-N was explained by the basal area of trees belonging to Fabaceae, a family associated with N-fixing species, and no long-term effects of fire on soil parameters were detected. Local-scale lithological variation is an important first-order control over soil variability at the hillslope scale in this SDTF, by both direct influence on nutrient stocks and indirect influence via control of local relief.

  13. Determining the spatial variability of personal sampler inlet locations.

    Science.gov (United States)

    Vinson, Robert; Volkwein, Jon; McWilliams, Linda

    2007-09-01

    This article examines the spatial variability of dust concentrations within a coal miner's breathing zone and the impact of sampling location at the cap lamp, nose, and lapel. Tests were conducted in the National Institute for Safety and Health Pittsburgh Research Laboratory full-scale, continuous miner gallery using three prototype personal dust monitors (PDM). The dust masses detected by the PDMs were used to calculate the percentage difference of dust mass between the cap lamp and the nose and between the lapel and the nose. The calculated percentage differences of the masses ranged from plus 12% to minus 25%. Breathing zone tests were also conducted in four underground coal mines using the torso of a mannequin to simulate a miner. Coal mine dust was sampled with multi-cyclone sampling cans mounted directly in front of the mannequin near the cap lamp, nose, and lapel. These four coal mine tests found that the spatial variability of dust levels and imprecision of the current personal sampler is a greater influence than the sampler location within the breathing zone. However, a one-sample t-test of this data did find that the overall mean value of the cap lamp/nose ratio was not significantly different than 1 (p-value = 0.21). However, when applied to the overall mean value of the lapel/nose ratio there was a significant difference from 1 (p-value sampling location for coal mine dust samples. But these results suggest that the cap location is slightly more indicative of what is breathed through the nose area.

  14. Spatial and temporal variability of chorus and hiss

    Science.gov (United States)

    Santolik, O.; Hospodarsky, G. B.; Kurth, W. S.; Kletzing, C.

    2017-12-01

    Whistler-mode electromagnetic waves, especially natural emissions of chorus and hiss, have been shown to influence the dynamics of the Van Allen radiation belts via quasi-linear or nonlinear wave particle interactions, transferring energy between different electron populations. Average intensities of chorus and hiss emissions have been found to increase with increasing levels of geomagnetic activity but their stochastic variations in individual spacecraft measurements are usually larger these large-scale temporal effects. To separate temporal and spatial variations of wave characteristics, measurements need to be simultaneously carried out in different locations by identical and/or well calibrated instrumentation. We use two-point survey measurements of the Waves instruments of the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) onboard two Van Allen Probes to asses spatial and temporal variability of chorus and hiss. We take advantage of a systematic analysis of this large data set which has been collected during 2012-2017 over a range of separation vectors of the two spacecraft. We specifically address the question whether similar variations occur at different places at the same time. Our results indicate that power variations are dominated by separations in MLT at scales larger than 0.5h.

  15. Modeling the Variable Heliopause Location

    Science.gov (United States)

    Hensley, Kerry

    2018-03-01

    In 2012, Voyager 1 zipped across the heliopause. Five and a half years later, Voyager 2 still hasnt followed its twin into interstellar space. Can models of the heliopause location help determine why?How Far to the Heliopause?Artists conception of the heliosphere with the important structures and boundaries labeled. [NASA/Goddard/Walt Feimer]As our solar system travels through the galaxy, the solar outflow pushes against the surrounding interstellar medium, forming a bubble called the heliosphere. The edge of this bubble, the heliopause, is the outermost boundary of our solar system, where the solar wind and the interstellar medium meet. Since the solar outflow is highly variable, the heliopause is constantly moving with the motion driven by changes inthe Sun.NASAs twin Voyager spacecraft were poisedto cross the heliopause after completingtheir tour of the outer planets in the 1980s. In 2012, Voyager 1 registered a sharp increase in the density of interstellar particles, indicating that the spacecraft had passed out of the heliosphere and into the interstellar medium. The slower-moving Voyager 2 was set to pierce the heliopause along a different trajectory, but so far no measurements have shown that the spacecraft has bid farewell to oursolar system.In a recent study, ateam of scientists led by Haruichi Washimi (Kyushu University, Japan and CSPAR, University of Alabama-Huntsville) argues that models of the heliosphere can help explain this behavior. Because the heliopause location is controlled by factors that vary on many spatial and temporal scales, Washimiand collaborators turn to three-dimensional, time-dependent magnetohydrodynamics simulations of the heliosphere. In particular, they investigate how the position of the heliopause along the trajectories of Voyager 1 and Voyager 2 changes over time.Modeled location of the heliopause along the paths of Voyagers 1 (blue) and 2 (orange). Click for a closer look. The red star indicates the location at which Voyager

  16. Spatially varying dispersion to model breakthrough curves.

    Science.gov (United States)

    Li, Guangquan

    2011-01-01

    Often the water flowing in a karst conduit is a combination of contaminated water entering at a sinkhole and cleaner water released from the limestone matrix. Transport processes in the conduit are controlled by advection, mixing (dilution and dispersion), and retention-release. In this article, a karst transport model considering advection, spatially varying dispersion, and dilution (from matrix seepage) is developed. Two approximate Green's functions are obtained using transformation of variables, respectively, for the initial-value problem and for the boundary-value problem. A numerical example illustrates that mixing associated with strong spatially varying conduit dispersion can cause strong skewness and long tailing in spring breakthrough curves. Comparison of the predicted breakthrough curve against that measured from a dye-tracing experiment between Ames Sink and Indian Spring, Northwest Florida, shows that the conduit dispersivity can be as large as 400 m. Such a large number is believed to imply strong solute interaction between the conduit and the matrix and/or multiple flow paths in a conduit network. It is concluded that Taylor dispersion is not dominant in transport in a karst conduit, and the complicated retention-release process between mobile- and immobile waters may be described by strong spatially varying conduit dispersion. Copyright © 2010 The Author(s). Journal compilation © 2010 National Ground Water Association.

  17. Multivariate Non-Symmetric Stochastic Models for Spatial Dependence Models

    Science.gov (United States)

    Haslauer, C. P.; Bárdossy, A.

    2017-12-01

    A copula based multivariate framework allows more flexibility to describe different kind of dependences than what is possible using models relying on the confining assumption of symmetric Gaussian models: different quantiles can be modelled with a different degree of dependence; it will be demonstrated how this can be expected given process understanding. maximum likelihood based multivariate quantitative parameter estimation yields stable and reliable results; not only improved results in cross-validation based measures of uncertainty are obtained but also a more realistic spatial structure of uncertainty compared to second order models of dependence; as much information as is available is included in the parameter estimation: incorporation of censored measurements (e.g., below detection limit, or ones that are above the sensitive range of the measurement device) yield to more realistic spatial models; the proportion of true zeros can be jointly estimated with and distinguished from censored measurements which allow estimates about the age of a contaminant in the system; secondary information (categorical and on the rational scale) has been used to improve the estimation of the primary variable; These copula based multivariate statistical techniques are demonstrated based on hydraulic conductivity observations at the Borden (Canada) site, the MADE site (USA), and a large regional groundwater quality data-set in south-west Germany. Fields of spatially distributed K were simulated with identical marginal simulation, identical second order spatial moments, yet substantially differing solute transport characteristics when numerical tracer tests were performed. A statistical methodology is shown that allows the delineation of a boundary layer separating homogenous parts of a spatial data-set. The effects of this boundary layer (macro structure) and the spatial dependence of K (micro structure) on solute transport behaviour is shown.

  18. Spatial variability of POPs in European background air

    Directory of Open Access Journals (Sweden)

    A. K. Halse

    2011-02-01

    Full Text Available Passive air samplers (PAS were deployed at 86 European background sites during summer 2006 in order (i to gain further insight into spatial patterns of persistent organic pollutants (POPs in European background air and, (ii to evaluate PAS as an alternative sampling technique under EMEP (Co-operative programme for monitoring and evaluation of the long-range transmissions of air pollutants in Europe. The samples were analyzed for selected PCBs, HCHs, DDTs, HCB, PAHs and chlordanes, and air concentrations were calculated on the basis of losses of performance reference compounds. Air concentrations of PCBs were generally lowest in more remote areas of northern Europe with elevated levels in more densely populated areas. γ-HCH was found at elevated levels in more central parts of Europe, whereas α-HCH, β-HCH and DDTs showed higher concentrations in the south-eastern part. There was no clear spatial pattern in the concentrations for PAHs, indicative of influence by local sources, rather than long range atmospheric transport (LRAT. HCB was evenly distributed across Europe, while the concentrations of chlordanes were typically low or non-detectable. A comparison of results obtained on the basis of PAS and active air sampling (AAS illustrated that coordinated PAS campaigns have the potential serve as useful inter-comparison exercises within and across existing monitoring networks. The results also highlighted limitations of the current EMEP measurement network with respect to spatial coverage. We finally adopted an existing Lagrangian transport model (FLEXPART as recently modified to incorporate key processes relevant for POPs to evaluate potential source regions affecting observed concentrations at selected sites. Using PCB-28 as an example, the model predicted concentrations which agreed within a factor of 3 with PAS measurements for all except 1 out of the 17 sites selected for this analysis.

  19. Continuous Spatial Process Models for Spatial Extreme Values

    KAUST Repository

    Sang, Huiyan

    2010-01-28

    We propose a hierarchical modeling approach for explaining a collection of point-referenced extreme values. In particular, annual maxima over space and time are assumed to follow generalized extreme value (GEV) distributions, with parameters μ, σ, and ξ specified in the latent stage to reflect underlying spatio-temporal structure. The novelty here is that we relax the conditionally independence assumption in the first stage of the hierarchial model, an assumption which has been adopted in previous work. This assumption implies that realizations of the the surface of spatial maxima will be everywhere discontinuous. For many phenomena including, e. g., temperature and precipitation, this behavior is inappropriate. Instead, we offer a spatial process model for extreme values that provides mean square continuous realizations, where the behavior of the surface is driven by the spatial dependence which is unexplained under the latent spatio-temporal specification for the GEV parameters. In this sense, the first stage smoothing is viewed as fine scale or short range smoothing while the larger scale smoothing will be captured in the second stage of the modeling. In addition, as would be desired, we are able to implement spatial interpolation for extreme values based on this model. A simulation study and a study on actual annual maximum rainfall for a region in South Africa are used to illustrate the performance of the model. © 2009 International Biometric Society.

  20. Using geomorphological variables to predict the spatial distribution of plant species in agricultural drainage networks.

    Science.gov (United States)

    Rudi, Gabrielle; Bailly, Jean-Stéphane; Vinatier, Fabrice

    2018-01-01

    To optimize ecosystem services provided by agricultural drainage networks (ditches) in headwater catchments, we need to manage the spatial distribution of plant species living in these networks. Geomorphological variables have been shown to be important predictors of plant distribution in other ecosystems because they control the water regime, the sediment deposition rates and the sun exposure in the ditches. Whether such variables may be used to predict plant distribution in agricultural drainage networks is unknown. We collected presence and absence data for 10 herbaceous plant species in a subset of a network of drainage ditches (35 km long) within a Mediterranean agricultural catchment. We simulated their spatial distribution with GLM and Maxent model using geomorphological variables and distance to natural lands and roads. Models were validated using k-fold cross-validation. We then compared the mean Area Under the Curve (AUC) values obtained for each model and other metrics issued from the confusion matrices between observed and predicted variables. Based on the results of all metrics, the models were efficient at predicting the distribution of seven species out of ten, confirming the relevance of geomorphological variables and distance to natural lands and roads to explain the occurrence of plant species in this Mediterranean catchment. In particular, the importance of the landscape geomorphological variables, ie the importance of the geomorphological features encompassing a broad environment around the ditch, has been highlighted. This suggests that agro-ecological measures for managing ecosystem services provided by ditch plants should focus on the control of the hydrological and sedimentological connectivity at the catchment scale. For example, the density of the ditch network could be modified or the spatial distribution of vegetative filter strips used for sediment trapping could be optimized. In addition, the vegetative filter strips could constitute

  1. Temporal and spatial variability of global water balance

    Science.gov (United States)

    McCabe, Gregory J.; Wolock, David M.

    2013-01-01

    An analysis of simulated global water-balance components (precipitation [P], actual evapotranspiration [AET], runoff [R], and potential evapotranspiration [PET]) for the past century indicates that P has been the primary driver of variability in R. Additionally, since about 2000, there have been increases in P, AET, R, and PET for most of the globe. The increases in R during 2000 through 2009 have occurred despite unprecedented increases in PET. The increases in R are the result of substantial increases in P during the cool Northern Hemisphere months (i.e. October through March) when PET increases were relatively small; the largest PET increases occurred during the warm Northern Hemisphere months (April through September). Additionally, for the 2000 through 2009 period, the latitudinal distribution of P departures appears to co-vary with the mean P departures from 16 climate model projections of the latitudinal response of P to warming, except in the high latitudes. Finally, changes in water-balance variables appear large from the perspective of departures from the long-term means. However, when put into the context of the magnitudes of the raw water balance variable values, there appears to have been little change in any of the water-balance variables over the past century on a global or hemispheric scale.

  2. Longterm and spatial variability of Aerosol optical properties measured by sky radiometer in Japan sites

    Science.gov (United States)

    Aoki, K.

    2016-12-01

    Aerosols and cloud play an important role in the climate change. We started the long-term monitoring of aerosol and cloud optical properties since 1990's by using sky radiometer (POM-01, 02; Prede Co. Ltd., Japan). We provide the information, in this presentation, on the aerosol optical properties with respect to their temporal and spatial variability in Japan site (ex. Sapporo, Toyama, Kasuga and etc). The global distributions of aerosols have been derived from earth observation satellite and have been simulated in numerical models, which assume optical parameters. However, these distributions are difficult to derive because of variability in time and space. Therefore, Aerosol optical properties were investigated using the measurements from ground-based and ship-borne sky radiometer. The sky radiometer is an automatic instrument that takes observations only in daytime under the clear sky conditions. Observation of diffuse solar intensity interval was made every ten or five minutes by once. The aerosol optical properties were computed using the SKYRAD.pack version 4.2. The obtained Aerosol optical properties (Aerosol optical thickness, Ångström exponent, Single scattering albedo, and etc.) and size distribution volume clearly showed spatial and temporal variability in Japan area. In this study, we present the temporal and spatial variability of Aerosol optical properties at several Japan sites, applied to validation of satellite and numerical models. This project is validation satellite of GCOM-C, JAXA. The GCOM-C satellite scheduled to be launched in early 2017.

  3. Spatial data quality and coastal spill modelling

    International Nuclear Information System (INIS)

    Li, Y.; Brimicombe, A.J.; Ralphs, M.P.

    1998-01-01

    Issues of spatial data quality are central to the whole oil spill modelling process. Both model and data quality performance issues should be considered as indispensable parts of a complete oil spill model specification and testing procedure. This paper presents initial results of research that will emphasise to modeler and manager alike the practical issues of spatial data quality for coastal oil spill modelling. It is centred around a case study of Jiao Zhou Bay in the People's Republic of China. The implications for coastal oil spill modelling are discussed and some strategies for managing the effects of spatial data quality in the outputs of oil spill modelling are explored. (author)

  4. Appropriatie spatial scales to achieve model output uncertainty goals

    NARCIS (Netherlands)

    Booij, Martijn J.; Melching, Charles S.; Chen, Xiaohong; Chen, Yongqin; Xia, Jun; Zhang, Hailun

    2008-01-01

    Appropriate spatial scales of hydrological variables were determined using an existing methodology based on a balance in uncertainties from model inputs and parameters extended with a criterion based on a maximum model output uncertainty. The original methodology uses different relationships between

  5. Spatial and interannual variability in Baltic sprat batch fecundity

    DEFF Research Database (Denmark)

    Haslob, H.; Tomkiewicz, Jonna; Hinrichsen, H.H.

    2011-01-01

    in the central Baltic Sea, namely the Bornholm Basin, Gdansk Deep and Southern Gotland Basin. Environmental parameters such as hydrography, fish condition and stock density were tested in order to investigate the observed variability in sprat fecundity. Absolute batch fecundity was found to be positively related...... to fish length and weight. Significant differences in absolute and relative batch fecundity of Baltic sprat among areas and years were detected, and could partly be explained by hydrographic features of the investigated areas. A non-linear multiple regression model taking into account fish length...... and ambient temperature explained 70% of variability in absolute batch fecundity. Oxygen content and fish condition were not related to sprat batch fecundity. Additionally, a negative effect of stock size on sprat batch fecundity in the Bornholm Basin was revealed. The obtained data and results are important...

  6. The spatial heterogeneity between Japanese encephalitis incidence distribution and environmental variables in Nepal.

    Directory of Open Access Journals (Sweden)

    Daniel E Impoinvil

    Full Text Available To identify potential environmental drivers of Japanese Encephalitis virus (JE transmission in Nepal, we conducted an ecological study to determine the spatial association between 2005 Nepal JE incidence, and climate, agricultural, and land-cover variables at district level.District-level data on JE cases were examined using Local Indicators of Spatial Association (LISA analysis to identify spatial clusters from 2004 to 2008 and 2005 data was used to fit a spatial lag regression model with climate, agriculture and land-cover variables.Prior to 2006, there was a single large cluster of JE cases located in the Far-West and Mid-West terai regions of Nepal. After 2005, the distribution of JE cases in Nepal shifted with clusters found in the central hill areas. JE incidence during the 2005 epidemic had a stronger association with May mean monthly temperature and April mean monthly total precipitation compared to mean annual temperature and precipitation. A parsimonious spatial lag regression model revealed, 1 a significant negative relationship between JE incidence and April precipitation, 2 a significant positive relationship between JE incidence and percentage of irrigated land 3 a non-significant negative relationship between JE incidence and percentage of grassland cover, and 4 a unimodal non-significant relationship between JE Incidence and pig-to-human ratio.JE cases clustered in the terai prior to 2006 where it seemed to shift to the Kathmandu region in subsequent years. The spatial pattern of JE cases during the 2005 epidemic in Nepal was significantly associated with low precipitation and the percentage of irrigated land. Despite the availability of an effective vaccine, it is still important to understand environmental drivers of JEV transmission since the enzootic cycle of JEV transmission is not likely to be totally interrupted. Understanding the spatial dynamics of JE risk factors may be useful in providing important information to the

  7. Models and Inference for Multivariate Spatial Extremes

    KAUST Repository

    Vettori, Sabrina

    2017-12-07

    The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing to the developing research field of Extreme-Value Theory. We initially study the performance of existing parametric and non-parametric estimators of extremal dependence for multivariate maxima. As the dimensionality increases, non-parametric estimators are more flexible than parametric methods but present some loss in efficiency that we quantify under various scenarios. We introduce a statistical tool which imposes the required shape constraints on non-parametric estimators in high dimensions, significantly improving their performance. Furthermore, by embedding the tree-based max-stable nested logistic distribution in the Bayesian framework, we develop a statistical algorithm that identifies the most likely tree structures representing the data\\'s extremal dependence using the reversible jump Monte Carlo Markov Chain method. A mixture of these trees is then used for uncertainty assessment in prediction through Bayesian model averaging. The computational complexity of full likelihood inference is significantly decreased by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through simulation experiments which also compare different likelihood procedures. Finally, we extend the nested logistic representation to the spatial framework in order to jointly model multivariate variables collected across a spatial region. This situation emerges often in environmental applications but is not often considered in the current literature. Simulation experiments show that the new class of multivariate max-stable processes is able to detect both the cross and inner spatial dependence of a number of extreme variables at a relatively low computational cost, thanks to its Bayesian hierarchical

  8. Bayesian Spatial Modelling with R-INLA

    Directory of Open Access Journals (Sweden)

    Finn Lindgren

    2015-02-01

    Full Text Available The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA approach proposed by Rue, Martino, and Chopin (2009 is a computationally effective alternative to MCMC for Bayesian inference. INLA is designed for latent Gaussian models, a very wide and flexible class of models ranging from (generalized linear mixed to spatial and spatio-temporal models. Combined with the stochastic partial differential equation approach (SPDE, Lindgren, Rue, and Lindstrm 2011, one can accommodate all kinds of geographically referenced data, including areal and geostatistical ones, as well as spatial point process data. The implementation interface covers stationary spatial mod- els, non-stationary spatial models, and also spatio-temporal models, and is applicable in epidemiology, ecology, environmental risk assessment, as well as general geostatistics.

  9. Spatial and temporal variability in seasonal snow density

    KAUST Repository

    Bormann, Kathryn J.

    2013-03-01

    Snow density is a fundamental physical property of snowpacks used in many aspects of snow research. As an integral component in the remote sensing of snow water equivalent and parameterisation of snow models, snow density may be used to describe many important features of snowpack behaviour. The present study draws on a significant dataset of snow density and climate observations from the United States, Australia and the former Soviet Union and uses regression-based techniques to identify the dominant climatological drivers for snow densification rates, characterise densification rate variability and estimate spring snow densities from more readily available climate data. Total winter precipitation was shown to be the most prominent driver of snow densification rates, with mean air temperature and melt-refreeze events also found to be locally significant. Densification rate variance is very high at Australian sites, very low throughout the former Soviet Union and between these extremes throughout much of the US. Spring snow densities were estimated using a statistical model with climate variable inputs and best results were achieved when snow types were treated differently. Given the importance of snow density information in many snow-related research disciplines, this work has implications for current methods of converting snow depths to snow water equivalent, the representation of snow dynamics in snow models and remote sensing applications globally. © 2013 Elsevier B.V.

  10. Spatial and temporal variability in seasonal snow density

    KAUST Repository

    Bormann, Kathryn J.; Westra, Seth; Evans, Jason P.; McCabe, Matthew

    2013-01-01

    Snow density is a fundamental physical property of snowpacks used in many aspects of snow research. As an integral component in the remote sensing of snow water equivalent and parameterisation of snow models, snow density may be used to describe many important features of snowpack behaviour. The present study draws on a significant dataset of snow density and climate observations from the United States, Australia and the former Soviet Union and uses regression-based techniques to identify the dominant climatological drivers for snow densification rates, characterise densification rate variability and estimate spring snow densities from more readily available climate data. Total winter precipitation was shown to be the most prominent driver of snow densification rates, with mean air temperature and melt-refreeze events also found to be locally significant. Densification rate variance is very high at Australian sites, very low throughout the former Soviet Union and between these extremes throughout much of the US. Spring snow densities were estimated using a statistical model with climate variable inputs and best results were achieved when snow types were treated differently. Given the importance of snow density information in many snow-related research disciplines, this work has implications for current methods of converting snow depths to snow water equivalent, the representation of snow dynamics in snow models and remote sensing applications globally. © 2013 Elsevier B.V.

  11. Intelligent spatial ecosystem modeling using parallel processors

    International Nuclear Information System (INIS)

    Maxwell, T.; Costanza, R.

    1993-01-01

    Spatial modeling of ecosystems is essential if one's modeling goals include developing a relatively realistic description of past behavior and predictions of the impacts of alternative management policies on future ecosystem behavior. Development of these models has been limited in the past by the large amount of input data required and the difficulty of even large mainframe serial computers in dealing with large spatial arrays. These two limitations have begun to erode with the increasing availability of remote sensing data and GIS systems to manipulate it, and the development of parallel computer systems which allow computation of large, complex, spatial arrays. Although many forms of dynamic spatial modeling are highly amenable to parallel processing, the primary focus in this project is on process-based landscape models. These models simulate spatial structure by first compartmentalizing the landscape into some geometric design and then describing flows within compartments and spatial processes between compartments according to location-specific algorithms. The authors are currently building and running parallel spatial models at the regional scale for the Patuxent River region in Maryland, the Everglades in Florida, and Barataria Basin in Louisiana. The authors are also planning a project to construct a series of spatially explicit linked ecological and economic simulation models aimed at assessing the long-term potential impacts of global climate change

  12. Spatial variability of excess mortality during prolonged dust events in a high-density city: a time-stratified spatial regression approach.

    Science.gov (United States)

    Wong, Man Sing; Ho, Hung Chak; Yang, Lin; Shi, Wenzhong; Yang, Jinxin; Chan, Ta-Chien

    2017-07-24

    Dust events have long been recognized to be associated with a higher mortality risk. However, no study has investigated how prolonged dust events affect the spatial variability of mortality across districts in a downwind city. In this study, we applied a spatial regression approach to estimate the district-level mortality during two extreme dust events in Hong Kong. We compared spatial and non-spatial models to evaluate the ability of each regression to estimate mortality. We also compared prolonged dust events with non-dust events to determine the influences of community factors on mortality across the city. The density of a built environment (estimated by the sky view factor) had positive association with excess mortality in each district, while socioeconomic deprivation contributed by lower income and lower education induced higher mortality impact in each territory planning unit during a prolonged dust event. Based on the model comparison, spatial error modelling with the 1st order of queen contiguity consistently outperformed other models. The high-risk areas with higher increase in mortality were located in an urban high-density environment with higher socioeconomic deprivation. Our model design shows the ability to predict spatial variability of mortality risk during an extreme weather event that is not able to be estimated based on traditional time-series analysis or ecological studies. Our spatial protocol can be used for public health surveillance, sustainable planning and disaster preparation when relevant data are available.

  13. Spherical Process Models for Global Spatial Statistics

    KAUST Repository

    Jeong, Jaehong; Jun, Mikyoung; Genton, Marc G.

    2017-01-01

    Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture

  14. Spatial variability of nitrogen-15 and its relation to the variability of other soil properties

    International Nuclear Information System (INIS)

    Selles, F.; Karamanos, R.E.; Kachanoski, R.G.

    1986-01-01

    The spatial variability of natural 15 N abundance of a cultivated Chernozemic soil and its native prairie counterpart were smaller than that of total N, organic C, and the C/N ratio. Further, the number of samples required to estimate the true mean of total N with a given precision at various probability levels were twofold those required to estimate the true mean of total N with a given precision at various probability levels were twofold those required to determine the mean 15 N abundance of total soil N in the surface horizons may reflect the isotopic composition of the nitrogenous substances entering the soil system or changes in the isotopic composition of soil N due to humification processes, probably induced by variations in topographic and microrelief features of the soil

  15. Variability aware compact model characterization for statistical circuit design optimization

    Science.gov (United States)

    Qiao, Ying; Qian, Kun; Spanos, Costas J.

    2012-03-01

    Variability modeling at the compact transistor model level can enable statistically optimized designs in view of limitations imposed by the fabrication technology. In this work we propose an efficient variabilityaware compact model characterization methodology based on the linear propagation of variance. Hierarchical spatial variability patterns of selected compact model parameters are directly calculated from transistor array test structures. This methodology has been implemented and tested using transistor I-V measurements and the EKV-EPFL compact model. Calculation results compare well to full-wafer direct model parameter extractions. Further studies are done on the proper selection of both compact model parameters and electrical measurement metrics used in the method.

  16. Spatial variability of chemical and physical attributes of dystrophic Red-Yellow Latosol in no tillage

    Directory of Open Access Journals (Sweden)

    João Vidal de Negreiros Neto

    2014-02-01

    Full Text Available Knowledge of spatial variability in chemical and physical properties of the soil is very important, especially for precision agriculture. Geostatistics is seeking to improve techniques that can enable the correct and responsible use of soil. So during the agricultural year 2011/2012 in an area of direct planting the corn crop in the municipality of Gurupi (TO, in the Brazilian Cerrado, aimed to analyze the spatial variability of chemical and physical properties in a Typic Dystrophic tillage. Was installed sampling grid for the collection of soil, with 100 sampling points in an area of 1755m2. The contents of available phosphorus, organic matter, pH (H2O, concentrations of K +, Ca2+, Mg2+, the sum of values and base saturation (BS, V at depths of 0-0.20 m, and resistance to penetration (RP at depths 0-0.05 m, 0.05-0.10 m, 0.10-0.20 m and 0.20-0.40 m and bulk density (Ds. We conducted a descriptive analysis classic, with the aid of statistical software ASSISTAT, and then were modeled semivariograms for all attributes, resulting in their cross-validation and kriging maps. The chemical and physical properties of soil, except the base saturation (V, spatial dependence. Probably the discontinuity of the spatial dependence of Vvalue, is due to fertility management over the years.

  17. Hierarchical modeling and analysis for spatial data

    CERN Document Server

    Banerjee, Sudipto; Gelfand, Alan E

    2003-01-01

    Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics.Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and dat

  18. Location Aggregation of Spatial Population CTMC Models

    Directory of Open Access Journals (Sweden)

    Luca Bortolussi

    2016-10-01

    Full Text Available In this paper we focus on spatial Markov population models, describing the stochastic evolution of populations of agents, explicitly modelling their spatial distribution, representing space as a discrete, finite graph. More specifically, we present a heuristic approach to aggregating spatial locations, which is designed to preserve the dynamical behaviour of the model whilst reducing the computational cost of analysis. Our approach combines stochastic approximation ideas (moment closure, linear noise, with computational statistics (spectral clustering to obtain an efficient aggregation, which is experimentally shown to be reasonably accurate on two case studies: an instance of epidemic spreading and a London bike sharing scenario.

  19. A variable-order fractal derivative model for anomalous diffusion

    Directory of Open Access Journals (Sweden)

    Liu Xiaoting

    2017-01-01

    Full Text Available This paper pays attention to develop a variable-order fractal derivative model for anomalous diffusion. Previous investigations have indicated that the medium structure, fractal dimension or porosity may change with time or space during solute transport processes, results in time or spatial dependent anomalous diffusion phenomena. Hereby, this study makes an attempt to introduce a variable-order fractal derivative diffusion model, in which the index of fractal derivative depends on temporal moment or spatial position, to characterize the above mentioned anomalous diffusion (or transport processes. Compared with other models, the main advantages in description and the physical explanation of new model are explored by numerical simulation. Further discussions on the dissimilitude such as computational efficiency, diffusion behavior and heavy tail phenomena of the new model and variable-order fractional derivative model are also offered.

  20. An alternative to the standard spatial econometric approaches in hedonic house price models

    DEFF Research Database (Denmark)

    Veie, Kathrine Lausted; Panduro, Toke Emil

    Hedonic models are subject to spatially correlated errors which are a symptom of omitted spatial variables, mis-specification or mismeasurement. Methods have been developed to address this problem through the use of spatial econometrics or spatial fixed effects. However, often spatial correlation is...... varying characteristics markedly. This suggests that omitted variable bias may remain an important problem. We advocate for an increased use of sensitivity analysis to determine robustness of estimates to different models of the (omitted) spatial processes....

  1. Fine scale spatial variability of microbial pesticide degradation in soil: scales, controlling factors, and implications

    Directory of Open Access Journals (Sweden)

    Arnaud eDechesne

    2014-12-01

    Full Text Available Pesticide biodegradation is a soil microbial function of critical importance for modern agriculture and its environmental impact. While it was once assumed that this activity was homogeneously distributed at the field scale, mounting evidence indicates that this is rarely the case. Here, we critically examine the literature on spatial variability of pesticide biodegradation in agricultural soil. We discuss the motivations, methods, and main findings of the primary literature. We found significant diversity in the approaches used to describe and quantify spatial heterogeneity, which complicates inter-studies comparisons. However, it is clear that the presence and activity of pesticide degraders is often highly spatially variable with coefficients of variation often exceeding 50% and frequently displays nonrandom spatial patterns. A few controlling factors have tentatively been identified across pesticide classes: they include some soil characteristics (pH and some agricultural management practices (pesticide application, tillage, while other potential controlling factors have more conflicting effects depending on the site or the pesticide. Evidence demonstrating the importance of spatial heterogeneity on the fate of pesticides in soil has been difficult to obtain but modelling and experimental systems that do not include soil’s full complexity reveal that this heterogeneity must be considered to improve prediction of pesticide biodegradation rates or of leaching risks. Overall, studying the spatial heterogeneity of pesticide biodegradation is a relatively new field at the interface of agronomy, microbial ecology, and geosciences and a wealth of novel data is being collected from these different disciplinary perspectives. We make suggestions on possible avenues to take full advantage of these investigations for a better understanding and prediction of the fate of pesticides in soil.

  2. Research into the influence of spatial variability and scale on the parameterization of hydrological processes

    Science.gov (United States)

    Wood, Eric F.

    1993-01-01

    The objectives of the research were as follows: (1) Extend the Representative Elementary Area (RE) concept, first proposed and developed in Wood et al, (1988), to the water balance fluxes of the interstorm period (redistribution, evapotranspiration and baseflow) necessary for the analysis of long-term water balance processes. (2) Derive spatially averaged water balance model equations for spatially variable soil, topography and vegetation, over A RANGE OF CLIMATES. This is a necessary step in our goal to derive consistent hydrologic results up to GCM grid scales necessary for global climate modeling. (3) Apply the above macroscale water balance equations with remotely sensed data and begin to explore the feasibility of parameterizing the water balance constitutive equations at GCM grid scale.

  3. Characterizing spatial and seasonal variability of carbon dioxide ...

    Indian Academy of Sciences (India)

    Day time fluxes were higher during March and October, while in August and January the magnitudes ... and night time water vapour fluxes, but no spatial variation was observed. 1. ..... density with the formation of new leaves after the.

  4. Spatial occupancy models for large data sets

    Science.gov (United States)

    Johnson, Devin S.; Conn, Paul B.; Hooten, Mevin B.; Ray, Justina C.; Pond, Bruce A.

    2013-01-01

    Since its development, occupancy modeling has become a popular and useful tool for ecologists wishing to learn about the dynamics of species occurrence over time and space. Such models require presence–absence data to be collected at spatially indexed survey units. However, only recently have researchers recognized the need to correct for spatially induced overdisperison by explicitly accounting for spatial autocorrelation in occupancy probability. Previous efforts to incorporate such autocorrelation have largely focused on logit-normal formulations for occupancy, with spatial autocorrelation induced by a random effect within a hierarchical modeling framework. Although useful, computational time generally limits such an approach to relatively small data sets, and there are often problems with algorithm instability, yielding unsatisfactory results. Further, recent research has revealed a hidden form of multicollinearity in such applications, which may lead to parameter bias if not explicitly addressed. Combining several techniques, we present a unifying hierarchical spatial occupancy model specification that is particularly effective over large spatial extents. This approach employs a probit mixture framework for occupancy and can easily accommodate a reduced-dimensional spatial process to resolve issues with multicollinearity and spatial confounding while improving algorithm convergence. Using open-source software, we demonstrate this new model specification using a case study involving occupancy of caribou (Rangifer tarandus) over a set of 1080 survey units spanning a large contiguous region (108 000 km2) in northern Ontario, Canada. Overall, the combination of a more efficient specification and open-source software allows for a facile and stable implementation of spatial occupancy models for large data sets.

  5. A method for estimating spatially variable seepage and hydrualic conductivity in channels with very mild slopes

    Science.gov (United States)

    Shanafield, Margaret; Niswonger, Richard G.; Prudic, David E.; Pohll, Greg; Susfalk, Richard; Panday, Sorab

    2014-01-01

    Infiltration along ephemeral channels plays an important role in groundwater recharge in arid regions. A model is presented for estimating spatial variability of seepage due to streambed heterogeneity along channels based on measurements of streamflow-front velocities in initially dry channels. The diffusion-wave approximation to the Saint-Venant equations, coupled with Philip's equation for infiltration, is connected to the groundwater model MODFLOW and is calibrated by adjusting the saturated hydraulic conductivity of the channel bed. The model is applied to portions of two large water delivery canals, which serve as proxies for natural ephemeral streams. Estimated seepage rates compare well with previously published values. Possible sources of error stem from uncertainty in Manning's roughness coefficients, soil hydraulic properties and channel geometry. Model performance would be most improved through more frequent longitudinal estimates of channel geometry and thalweg elevation, and with measurements of stream stage over time to constrain wave timing and shape. This model is a potentially valuable tool for estimating spatial variability in longitudinal seepage along intermittent and ephemeral channels over a wide range of bed slopes and the influence of seepage rates on groundwater levels.

  6. Spatial and temporal variability of guinea grass (Megathyrsus maximus) fuel loads and moisture on Oahu, Hawaii

    Science.gov (United States)

    Lisa M. Ellsworth; Creighton M. Litton; Andrew D. Taylor; J. Boone Kauffman

    2013-01-01

    Frequent wildfires in tropical landscapes dominated by non-native invasive grasses threaten surrounding ecosystems and developed areas. To better manage fire, accurate estimates of the spatial and temporal variability in fuels are urgently needed. We quantified the spatial variability in live and dead fine fuel loads and moistures at four guinea grass (...

  7. Characteristics of Spatial Structural Patterns and Temporal Variability of Annual Precipitation in Ningxia

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    [Objective] The aim was to study the characteristics of the spatial structural patterns and temporal variability of annual precipitation in Ningxia.[Method] Using rotated empirical orthogonal function,the precipitation concentration index,wavelet analysis and Mann-Kendall rank statistic method,the characteristics of precipitation on the spatial-temporal variability and trend were analyzed by the monthly precipitation series in Ningxia during 1951-2008.[Result] In Ningxia,the spatial structural patterns of a...

  8. Spatial variability in alluvium properties at a low-level nuclear waste site

    International Nuclear Information System (INIS)

    Istok, J.D.; Blout, D.O.; Barker, L.; Johnejack, K.R.; Hammermeister, D.P.

    1994-01-01

    Geological and statistical models for the spatial variability of soil properties are needed to predict field-scale water flow and solute transport but only limited information is currently available on unsaturated soils below the root zone. Spatial variability of selected physical and hydrologic properties was quantified for fine- and coarse-grained alluvial deposits at a low-level nuclear waste disposal site on the Nevada Test Site. Gravimetric water content (w), bulk density (ρ b ), saturated hydraulic conductivity (K a ), and particle-size distribution were determined for vertical and horizontal core specimens and bulk samples collected from 183-m-long horizontal transects in two existing waste disposal trenches located on a single alluvial fan. The transects were approximately aligned parallel and perpendicular to the principal direction of sediment transport. Properties were modeled as either normally or lognormally distributed random variables. Sample coefficients of variation were smallest for ρ b and largest for log(K a ); a weak correlation was identified between log(K a ) and the grain-size parameter d 10 . Particle-size distributions for the fine- and coarse-grained materials were different and significant differences in the natural logarithm of saturated hydraulic conductivity, log(K a ), existed between coarse and fine layers in an excavation aligned with the principal direction of alluvium deposition but not in a perpendicular direction. 37 refs., 7 figs., 11 tabs

  9. Spatial and temporal variability of groundwater recharge in Geba basin, Northern Ethiopia

    Science.gov (United States)

    Yenehun, Alemu; Walraevens, Kristine; Batelaan, Okke

    2017-10-01

    WetSpa, a physically based, spatially distributed watershed model, has been used to study the spatial and temporal variation of recharge in the Geba basin, Northern Ethiopia. The model covers an area of about 4, 249 km2 and integrates elevation, soil and land-use data, hydrometeorological and river discharge data. The Geba basin has a highly variable topography ranging from 1000 to 3280 m with an average slope of 12.9%. The area is characterized by a distinct wet and long dry season with a mean annual precipitation of 681 mm and temperatures ranging between 6.5 °C and 32 °C. The model was simulated on daily basis for nearly four years (January 1, 2000 to December 18, 2003). It resulted in a good agreement between measured and simulated streamflow hydrographs with Nash-Sutcliffe efficiency of almost 70% and 85% for, respectively, the calibration and validation. The water balance terms show very strong spatial and temporal variability, about 3.8% of the total precipitation is intercepted by the plant canopy; 87.5% infiltrates into the soil (of which 13% percolates, 2.7% flows laterally off and 84.2% evapotranspired from the root zone), and 7.2% is surface runoff. The mean annual recharge varies from about 45 mm (2003) to 208 mm (2001), with average of 98.6 mm/yr. On monthly basis, August has the maximum (73 mm) and December the lowest (0.1 mm) recharge. The mean annual groundwater recharge spatially varies from 0 to 371 mm; mainly controlled by the distribution of rainfall amount, followed by soil and land-use, and to a certain extent, slope. About 21% of Geba has a recharge larger than 120 mm and 1% less than 5 mm.

  10. Temporal and spatial variability of rainfall distribution and ...

    African Journals Online (AJOL)

    Rainfall and evapotranspiration are the two major climatic factors affecting agricultural production. This study examined the extent and nature of rainfall variability from measured data while estimation of evapotranspiration was made from recorded weather data. Analysis of rainfall variability is made by the rainfall anomaly ...

  11. Interannual and spatial variability of maple syrup yield as related to climatic factors

    Science.gov (United States)

    Houle, Daniel

    2014-01-01

    Sugar maple syrup production is an important economic activity for eastern Canada and the northeastern United States. Since annual variations in syrup yield have been related to climate, there are concerns about the impacts of climatic change on the industry in the upcoming decades. Although the temporal variability of syrup yield has been studied for specific sites on different time scales or for large regions, a model capable of accounting for both temporal and regional differences in yield is still lacking. In the present study, we studied the factors responsible for interregional and interannual variability in maple syrup yield over the 2001–2012 period, by combining the data from 8 Quebec regions (Canada) and 10 U.S. states. The resulting model explained 44.5% of the variability in yield. It includes the effect of climatic conditions that precede the sapflow season (variables from the previous growing season and winter), the effect of climatic conditions during the current sapflow season, and terms accounting for intercountry and temporal variability. Optimal conditions for maple syrup production appear to be spatially restricted by less favourable climate conditions occurring during the growing season in the north, and in the south, by the warmer winter and earlier spring conditions. This suggests that climate change may favor maple syrup production northwards, while southern regions are more likely to be negatively affected by adverse spring conditions. PMID:24949244

  12. Detecting high spatial variability of ice shelf basal mass balance, Roi Baudouin Ice Shelf, Antarctica

    Directory of Open Access Journals (Sweden)

    S. Berger

    2017-11-01

    Full Text Available Ice shelves control the dynamic mass loss of ice sheets through buttressing and their integrity depends on the spatial variability of their basal mass balance (BMB, i.e. the difference between refreezing and melting. Here, we present an improved technique – based on satellite observations – to capture the small-scale variability in the BMB of ice shelves. As a case study, we apply the methodology to the Roi Baudouin Ice Shelf, Dronning Maud Land, East Antarctica, and derive its yearly averaged BMB at 10 m horizontal gridding. We use mass conservation in a Lagrangian framework based on high-resolution surface velocities, atmospheric-model surface mass balance and hydrostatic ice-thickness fields (derived from TanDEM-X surface elevation. Spatial derivatives are implemented using the total-variation differentiation, which preserves abrupt changes in flow velocities and their spatial gradients. Such changes may reflect a dynamic response to localized basal melting and should be included in the mass budget. Our BMB field exhibits much spatial detail and ranges from −14.7 to 8.6 m a−1 ice equivalent. Highest melt rates are found close to the grounding line where the pressure melting point is high, and the ice shelf slope is steep. The BMB field agrees well with on-site measurements from phase-sensitive radar, although independent radar profiling indicates unresolved spatial variations in firn density. We show that an elliptical surface depression (10 m deep and with an extent of 0.7 km × 1.3 km lowers by 0.5 to 1.4 m a−1, which we tentatively attribute to a transient adaptation to hydrostatic equilibrium. We find evidence for elevated melting beneath ice shelf channels (with melting being concentrated on the channel's flanks. However, farther downstream from the grounding line, the majority of ice shelf channels advect passively (i.e. no melting nor refreezing toward the ice shelf front. Although the absolute, satellite

  13. Monitoring spatial-temporal variability of aerosol over Kenya ...

    African Journals Online (AJOL)

    This study sought to investigate the spatial and temporal variations of aerosols over Kenya based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor Aerosol Optical Depth (AOD) data for the period between 2001 and 2012. A Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) ...

  14. Violation of Bell's inequality with continuous spatial variables

    International Nuclear Information System (INIS)

    Abouraddy, Ayman F.; Yarnall, Timothy; Saleh, Bahaa E. A.; Teich, Malvin C.

    2007-01-01

    The Einstein-Podolsky-Rosen (EPR) argument revealed the paradoxical properties of a two-particle system entangled continuously in the spatial parameter. Yet a direct test of quantum nonlocality exhibited by this state, via a violation of Bell's inequality, has not been forthcoming. In this paper, we identify and construct experimental arrangements comprising simple optical components, without nonlinearities or moving parts, that implement operators in the spatial-parity space of single-photon fields that correspond to the familiar Pauli spin operators. We achieve this by first establishing an isomorphism between the single-mode multiphoton electromagnetic-field space spanned by a Fock-state basis and the single-photon multimode electromagnetic-field space spanned by a spatial-eigenmode basis. We then proceed to construct a Hilbert space with a two-dimensional basis of spatial even-odd parity modes. In particular, we describe an arrangement that implements a rotation in the parity space of each photon of an entangled-photon pair, allowing for a straightforward experimental test of Bell's inequality using the EPR state. Finally, the violation of a Bell inequality is quantified in terms of the physical parameters of the two-photon source

  15. Spatial and temporal variability in recruitment of intertidal mussels ...

    African Journals Online (AJOL)

    Intensity of intertidal mussel recruitment was compared across a range of different spatial and temporal scales around the coast of southern Africa between June 1995 and October 1996. Comparison of the east and west coasts revealed significantly higher recruit densities on the west coast, corresponding to larger adult ...

  16. Spatial Variability of Soil Morphorlogical and Physico- Chemical ...

    African Journals Online (AJOL)

    user

    The available moisture of soil was very low thus water holding capacity (WHC) and wilting point (WP) of the soil was ... with spatial distribution of soil properties and its effect on ... Pore size and root .... nutrient and have better stability. Thus.

  17. Spatial variability of heating profiles in windrowed poultry litter

    Science.gov (United States)

    In-house windrow composting of broiler litter has been suggested as a means to reduce microbial populations between flocks. Published time-temperature goals are used to determine the success of the composting process for microbial reductions. Spatial and temporal density of temperature measurement ...

  18. Comparing daily temperature averaging methods: the role of surface and atmosphere variables in determining spatial and seasonal variability

    Science.gov (United States)

    Bernhardt, Jase; Carleton, Andrew M.

    2018-05-01

    The two main methods for determining the average daily near-surface air temperature, twice-daily averaging (i.e., [Tmax+Tmin]/2) and hourly averaging (i.e., the average of 24 hourly temperature measurements), typically show differences associated with the asymmetry of the daily temperature curve. To quantify the relative influence of several land surface and atmosphere variables on the two temperature averaging methods, we correlate data for 215 weather stations across the Contiguous United States (CONUS) for the period 1981-2010 with the differences between the two temperature-averaging methods. The variables are land use-land cover (LULC) type, soil moisture, snow cover, cloud cover, atmospheric moisture (i.e., specific humidity, dew point temperature), and precipitation. Multiple linear regression models explain the spatial and monthly variations in the difference between the two temperature-averaging methods. We find statistically significant correlations between both the land surface and atmosphere variables studied with the difference between temperature-averaging methods, especially for the extreme (i.e., summer, winter) seasons (adjusted R2 > 0.50). Models considering stations with certain LULC types, particularly forest and developed land, have adjusted R2 values > 0.70, indicating that both surface and atmosphere variables control the daily temperature curve and its asymmetry. This study improves our understanding of the role of surface and near-surface conditions in modifying thermal climates of the CONUS for a wide range of environments, and their likely importance as anthropogenic forcings—notably LULC changes and greenhouse gas emissions—continues.

  19. Analysing earthquake slip models with the spatial prediction comparison test

    KAUST Repository

    Zhang, L.; Mai, Paul Martin; Thingbaijam, Kiran Kumar; Razafindrakoto, H. N. T.; Genton, Marc G.

    2014-01-01

    Earthquake rupture models inferred from inversions of geophysical and/or geodetic data exhibit remarkable variability due to uncertainties in modelling assumptions, the use of different inversion algorithms, or variations in data selection and data processing. A robust statistical comparison of different rupture models obtained for a single earthquake is needed to quantify the intra-event variability, both for benchmark exercises and for real earthquakes. The same approach may be useful to characterize (dis-)similarities in events that are typically grouped into a common class of events (e.g. moderate-size crustal strike-slip earthquakes or tsunamigenic large subduction earthquakes). For this purpose, we examine the performance of the spatial prediction comparison test (SPCT), a statistical test developed to compare spatial (random) fields by means of a chosen loss function that describes an error relation between a 2-D field (‘model’) and a reference model. We implement and calibrate the SPCT approach for a suite of synthetic 2-D slip distributions, generated as spatial random fields with various characteristics, and then apply the method to results of a benchmark inversion exercise with known solution. We find the SPCT to be sensitive to different spatial correlations lengths, and different heterogeneity levels of the slip distributions. The SPCT approach proves to be a simple and effective tool for ranking the slip models with respect to a reference model.

  20. Analysing earthquake slip models with the spatial prediction comparison test

    KAUST Repository

    Zhang, L.

    2014-11-10

    Earthquake rupture models inferred from inversions of geophysical and/or geodetic data exhibit remarkable variability due to uncertainties in modelling assumptions, the use of different inversion algorithms, or variations in data selection and data processing. A robust statistical comparison of different rupture models obtained for a single earthquake is needed to quantify the intra-event variability, both for benchmark exercises and for real earthquakes. The same approach may be useful to characterize (dis-)similarities in events that are typically grouped into a common class of events (e.g. moderate-size crustal strike-slip earthquakes or tsunamigenic large subduction earthquakes). For this purpose, we examine the performance of the spatial prediction comparison test (SPCT), a statistical test developed to compare spatial (random) fields by means of a chosen loss function that describes an error relation between a 2-D field (‘model’) and a reference model. We implement and calibrate the SPCT approach for a suite of synthetic 2-D slip distributions, generated as spatial random fields with various characteristics, and then apply the method to results of a benchmark inversion exercise with known solution. We find the SPCT to be sensitive to different spatial correlations lengths, and different heterogeneity levels of the slip distributions. The SPCT approach proves to be a simple and effective tool for ranking the slip models with respect to a reference model.

  1. Unleashing spatially distributed ecohydrology modeling using Big Data tools

    Science.gov (United States)

    Miles, B.; Idaszak, R.

    2015-12-01

    Physically based spatially distributed ecohydrology models are useful for answering science and management questions related to the hydrology and biogeochemistry of prairie, savanna, forested, as well as urbanized ecosystems. However, these models can produce hundreds of gigabytes of spatial output for a single model run over decadal time scales when run at regional spatial scales and moderate spatial resolutions (~100-km2+ at 30-m spatial resolution) or when run for small watersheds at high spatial resolutions (~1-km2 at 3-m spatial resolution). Numerical data formats such as HDF5 can store arbitrarily large datasets. However even in HPC environments, there are practical limits on the size of single files that can be stored and reliably backed up. Even when such large datasets can be stored, querying and analyzing these data can suffer from poor performance due to memory limitations and I/O bottlenecks, for example on single workstations where memory and bandwidth are limited, or in HPC environments where data are stored separately from computational nodes. The difficulty of storing and analyzing spatial data from ecohydrology models limits our ability to harness these powerful tools. Big Data tools such as distributed databases have the potential to surmount the data storage and analysis challenges inherent to large spatial datasets. Distributed databases solve these problems by storing data close to computational nodes while enabling horizontal scalability and fault tolerance. Here we present the architecture of and preliminary results from PatchDB, a distributed datastore for managing spatial output from the Regional Hydro-Ecological Simulation System (RHESSys). The initial version of PatchDB uses message queueing to asynchronously write RHESSys model output to an Apache Cassandra cluster. Once stored in the cluster, these data can be efficiently queried to quickly produce both spatial visualizations for a particular variable (e.g. maps and animations), as well

  2. Effect of land use on the spatial variability of organic matter and nutrient status in an Oxisol

    Science.gov (United States)

    Paz-Ferreiro, Jorge; Alves, Marlene Cristina; Vidal Vázquez, Eva

    2013-04-01

    Heterogeneity is now considered as an inherent soil property. Spatial variability of soil attributes in natural landscapes results mainly from soil formation factors. In cultivated soils much heterogeneity can additionally occur as a result of land use, agricultural systems and management practices. Organic matter content (OMC) and nutrients associated to soil exchange complex are key attribute in the maintenance of a high quality soil. Neglecting spatial heterogeneity in soil OMC and nutrient status at the field scale might result in reduced yield and in environmental damage. We analyzed the impact of land use on the pattern of spatial variability of OMC and soil macronutrients at the stand scale. The study was conducted in São Paulo state, Brazil. Land uses were pasture, mango orchard and corn field. Soil samples were taken at 0-10 cm and 10-20 cm depth in 84 points, within 100 m x 100 m plots. Texture, pH, OMC, cation exchange capacity (CEC), exchangeable cations (Ca, Mg, K, H, Al) and resin extractable phosphorus were analyzed.. Statistical variability was found to be higher in parameters defining the soil nutrient status (resin extractable P, K, Ca and Mg) than in general soil properties (OMC, CEC, base saturation and pH). Geostatistical analysis showed contrasting patterns of spatial dependence for the different soil uses, sampling depths and studied properties. Most of the studied data sets collected at two different depths exhibited spatial dependence at the sampled scale and their semivariograms were modeled by a nugget effect plus a structure. The pattern of soil spatial variability was found to be different between the three study soil uses and at the two sampling depths, as far as model type, nugget effect or ranges of spatial dependence were concerned. Both statistical and geostatistical results pointed out the importance of OMC as a driver responsible for the spatial variability of soil nutrient status.

  3. Crash rates analysis in China using a spatial panel model

    Directory of Open Access Journals (Sweden)

    Wonmongo Lacina Soro

    2017-10-01

    Full Text Available The consideration of spatial externalities in traffic safety analysis is of paramount importance for the success of road safety policies. Yet, the quasi-totality of spatial dependence studies on crash rates is performed within the framework of single-equation spatial cross-sectional studies. The present study extends the spatial cross-sectional scheme to a spatial fixed-effects panel model estimated using the maximum likelihood method. The spatial units are the 31 administrative regions of mainland China over the period 2004–2013. The presence of neighborhood effects is evidenced through the Moran's I statistic. Consistent with previous studies, the analysis reveals that omitting the spatial effects in traffic safety analysis is likely to bias the estimation results. The spatial and error lags are all positive and statistically significant suggesting similarities of crash rates pattern in neighboring regions. Some other explanatory variables, such as freight traffic, the length of paved roads and the populations of age 65 and above are related to higher rates while the opposite trend is observed for the Gross Regional Product, the urban unemployment rate and passenger traffic.

  4. Spatial Variability of Dielectric Properties in Field Soils

    National Research Council Canada - National Science Library

    Hendrickx, J

    2001-01-01

    ... since it directly influences the three other properties The variability of these properties may be such that either potential land mine signatures are overshadowed or false alarms result In this paper...

  5. Comparison of the spatial and temporal variability of drought indices ...

    African Journals Online (AJOL)

    Churchill

    variability of regional water resources. Lake Chad for ... and water resources management question in drought prone regions of Africa .... implementation of contingency plans during the drought .... management (IWRM) by the Lake Chad Basin.

  6. Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review

    Directory of Open Access Journals (Sweden)

    E. Cristiano

    2017-07-01

    Full Text Available In urban areas, hydrological processes are characterized by high variability in space and time, making them sensitive to small-scale temporal and spatial rainfall variability. In the last decades new instruments, techniques, and methods have been developed to capture rainfall and hydrological processes at high resolution. Weather radars have been introduced to estimate high spatial and temporal rainfall variability. At the same time, new models have been proposed to reproduce hydrological response, based on small-scale representation of urban catchment spatial variability. Despite these efforts, interactions between rainfall variability, catchment heterogeneity, and hydrological response remain poorly understood. This paper presents a review of our current understanding of hydrological processes in urban environments as reported in the literature, focusing on their spatial and temporal variability aspects. We review recent findings on the effects of rainfall variability on hydrological response and identify gaps where knowledge needs to be further developed to improve our understanding of and capability to predict urban hydrological response.

  7. A simplified spatial model for BWR stability

    International Nuclear Information System (INIS)

    Berman, Y.; Lederer, Y.; Meron, E.

    2012-01-01

    A spatial reduced order model for the study of BWR stability, based on the phenomenological model of March-Leuba et al., is presented. As one dimensional spatial dependence of the neutron flux, fuel temperature and void fraction is introduced, it is possible to describe both global and regional oscillations of the reactor power. Both linear stability analysis and numerical analysis were applied in order to describe the parameters which govern the model stability. The results were found qualitatively similar to past results. Doppler reactivity feedback was found essential for the explanation of the different regions of the flow-power stability map. (authors)

  8. Spatial and temporal variability of thermohaline properties in the Bay of Koper (northern Adriatic Sea)

    Science.gov (United States)

    Soczka Mandac, Rok; Žagar, Dušan; Faganeli, Jadran

    2013-04-01

    In this study influence of fresh water discharge on the spatial and temporal variability of thermohaline (TH) conditions is explored for the Bay of Koper (Bay). The Bay is subject to different driving agents: wind stress (bora, sirocco), tidal and seiches effect, buoyancy fluxes, general circulation of the Adriatic Sea and discharge of the Rizana and Badaševica rivers. These rivers have torrential characteristics that are hard to forecast in relation to meteorological events (precipitation). Therefore, during episodic events the spatial and temporal variability of TH properties in the Bay is difficult to determine [1]. Measurements of temperature, salinity and turbidity were conducted monthly on 35 sampling points in the period: June 2011 - December 2012. The data were processed and spatial interpolated with an objective analysis method. Furthermore, empirical orthogonal function analysis (EOF) [2] was applied to investigate spatial and temporal TH variations. Strong horizontal and vertical stratification was observed in the beginning of June 2011 due to high fresh water discharge of the Rizana (31 m3/s) and Badaševica (2 m3/s) rivers. The horizontal gradient (ΔT = 6°C) was noticed near the mouth of the Rizana river. Similar pattern was identified for salinity field on the boundary of the front where the gradient was ΔS = 20 PSU. Vertical temperature gradient was ΔT = 4°C while salinity gradient was ΔS = 18 PSU in the subsurface layer at depth of 3 m. Spatial analysis of the first principal component (86% of the total variance) shows uniform temperature distribution in the surface layer (1m) during the studied period. Furthermore, temporal variability of temperature shows seasonal variation with a minimum in February and maximum in August. This confirms that episodic events have a negligible effect on spatial and temporal variation of temperature in the subsurface layer. Further analysis will include application of EOF on the salinity, density and total

  9. Spatial variability of macrobenthic zonation on exposed sandy beaches

    Science.gov (United States)

    Veiga, Puri; Rubal, Marcos; Cacabelos, Eva; Maldonado, Cristina; Sousa-Pinto, Isabel

    2014-07-01

    We analysed the consistence of vertical patterns of distribution (i.e. zonation) for macrofauna at different spatial scales on four intermediate exposed beaches in the North of Portugal. We tested the hypothesis that biological zonation on exposed sandy beaches would vary at the studied spatial scales. For this aim, abundance, diversity and structure of macrobenthic assemblages were examined at the scales of transect and beach. Moreover, the main environmental factors that could potentially drive zonation patterns were investigated. Univariate and multivariate analyses revealed that the number of biological zones ranged from two to three depending on the beach and from indistinct zonation to three zones at the scale of transect. Therefore, results support our working hypothesis because zonation patterns were not consistent at the studied spatial scales. The median particle size, sorting coefficient and water content were significantly correlated with zonation patterns of macrobenthic assemblages. However, a high degree of correlation was not reached when the total structure of the assemblage was considered.

  10. The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models

    Science.gov (United States)

    Koch, Julian; Cüneyd Demirel, Mehmet; Stisen, Simon

    2018-05-01

    The process of model evaluation is not only an integral part of model development and calibration but also of paramount importance when communicating modelling results to the scientific community and stakeholders. The modelling community has a large and well-tested toolbox of metrics to evaluate temporal model performance. In contrast, spatial performance evaluation does not correspond to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study makes a contribution towards advancing spatial-pattern-oriented model calibration by rigorously testing a multiple-component performance metric. The promoted SPAtial EFficiency (SPAEF) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multiple-component approach is found to be advantageous in order to achieve the complex task of comparing spatial patterns. SPAEF, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are applied in a spatial-pattern-oriented model calibration of a catchment model in Denmark. Results suggest the importance of multiple-component metrics because stand-alone metrics tend to fail to provide holistic pattern information. The three SPAEF components are found to be independent, which allows them to complement each other in a meaningful way. In order to optimally exploit spatial observations made available by remote sensing platforms, this study suggests applying bias insensitive metrics which further allow for a comparison of variables which are related but may differ in unit. This study applies SPAEF in the hydrological context using the mesoscale Hydrologic Model (mHM; version 5.8), but we see great potential across disciplines related to spatially distributed earth system modelling.

  11. Influence of bladder and rectal volume on spatial variability of a bladder tumor during radical radiotherapy

    International Nuclear Information System (INIS)

    Pos, Floris J.; Koedooder, Kees; Hulshof, Maarten C.C.M.; Tienhoven, Geertjan van; Gonzalez Gonzalez, Dionisio

    2003-01-01

    Purpose: To assess the spatial variability of a bladder tumor relative to the planning target volume boundaries during radical radiotherapy, and furthermore to develop strategies to reduce spatial variability. Methods and Materials: Seventeen patients with solitary T2-T4N0M0 bladder cancer were treated with a technique delivering 40 Gy/2 Gy in 20 fractions to the whole bladder with a concomitant boost to the bladder tumor of 20 Gy in 1 Gy fractions in an overall time of 4 weeks. CT scans were made weekly, immediately after treatment, and matched with the planning CT scan. Spatial variability of the tumor, as well as bladder volume and rectal diameter, were scored for each patient each week. Results: In 65% of patients, a part of the tumor appeared outside the planning target volume boundaries at least one time during the course of radiotherapy. No consistent relation of this variability with time was found. Bladder volumes and rectal diameters showed marked variability during the course of treatment. A large initial bladder volume and rectal diameter predicted a large volume variation and a large tumor spatial variability. Conclusion: In this study, a margin of 1.5 to 2 cm seemed to be inadequate in 65% of the patients with respect to spatial variability. Bladder volume and rectal diameter were found to be predictive for spatial variability of a bladder tumor during concomitant boost radiotherapy

  12. Influence of bladder and rectal volume on spatial variability of a bladder tumor during radical radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Pos, Floris J; Koedooder, Kees; Hulshof, Maarten C.C.M.; Tienhoven, Geertjan van; Gonzalez Gonzalez, Dionisio

    2003-03-01

    Purpose: To assess the spatial variability of a bladder tumor relative to the planning target volume boundaries during radical radiotherapy, and furthermore to develop strategies to reduce spatial variability. Methods and Materials: Seventeen patients with solitary T2-T4N0M0 bladder cancer were treated with a technique delivering 40 Gy/2 Gy in 20 fractions to the whole bladder with a concomitant boost to the bladder tumor of 20 Gy in 1 Gy fractions in an overall time of 4 weeks. CT scans were made weekly, immediately after treatment, and matched with the planning CT scan. Spatial variability of the tumor, as well as bladder volume and rectal diameter, were scored for each patient each week. Results: In 65% of patients, a part of the tumor appeared outside the planning target volume boundaries at least one time during the course of radiotherapy. No consistent relation of this variability with time was found. Bladder volumes and rectal diameters showed marked variability during the course of treatment. A large initial bladder volume and rectal diameter predicted a large volume variation and a large tumor spatial variability. Conclusion: In this study, a margin of 1.5 to 2 cm seemed to be inadequate in 65% of the patients with respect to spatial variability. Bladder volume and rectal diameter were found to be predictive for spatial variability of a bladder tumor during concomitant boost radiotherapy.

  13. Spatial variability of turbulent fluxes in the roughness sublayer of an even-aged pine forest

    Science.gov (United States)

    Katul, G.; Hsieh, C.-I.; Bowling, D.; Clark, K.; Shurpali, N.; Turnipseed, A.; Albertson, J.; Tu, K.; Hollinger, D.; Evans, B. M.; Offerle, B.; Anderson, D.; Ellsworth, D.; Vogel, C.; Oren, R.

    1999-01-01

    The spatial variability of turbulent flow statistics in the roughness sublayer (RSL) of a uniform even-aged 14 m (= h) tall loblolly pine forest was investigated experimentally. Using seven existing walkup towers at this stand, high frequency velocity, temperature, water vapour and carbon dioxide concentrations were measured at 15.5 m above the ground surface from October 6 to 10 in 1997. These seven towers were separated by at least 100 m from each other. The objective of this study was to examine whether single tower turbulence statistics measurements represent the flow properties of RSL turbulence above a uniform even-aged managed loblolly pine forest as a best-case scenario for natural forested ecosystems. From the intensive space-time series measurements, it was demonstrated that standard deviations of longitudinal and vertical velocities (??(u), ??(w)) and temperature (??(T)) are more planar homogeneous than their vertical flux of momentum (u(*)2) and sensible heat (H) counterparts. Also, the measured H is more horizontally homogeneous when compared to fluxes of other scalar entities such as CO2 and water vapour. While the spatial variability in fluxes was significant (> 15%), this unique data set confirmed that single tower measurements represent the 'canonical' structure of single-point RSL turbulence statistics, especially flux-variance relationships. Implications to extending the 'moving-equilibrium' hypothesis for RSL flows are discussed. The spatial variability in all RSL flow variables was not constant in time and varied strongly with spatially averaged friction velocity u(*), especially when u(*) was small. It is shown that flow properties derived from two-point temporal statistics such as correlation functions are more sensitive to local variability in leaf area density when compared to single point flow statistics. Specifically, that the local relationship between the reciprocal of the vertical velocity integral time scale (I(w)) and the arrival

  14. Spatial Variability and Geostatistical Prediction of Some Soil Hydraulic Coefficients of a Calcareous Soil

    Directory of Open Access Journals (Sweden)

    Ali Akbar Moosavi

    2017-02-01

    Full Text Available Introduction: Saturated hydraulic conductivity and the other hydraulic properties of soils are essential vital soil attributes that play role in the modeling of hydrological phenomena, designing irrigation-drainage systems, transportation of salts and chemical and biological pollutants within the soil. Measurement of these hydraulic properties needs some special instruments, expert technician, and are time consuming and expensive and due to their high temporal and spatial variability, a large number of measurements are needed. Nowadays, prediction of these attributes using the readily available soil data using pedotransfer functions or using the limited measurement with applying the geostatistical approaches has been receiving high attention. The study aimed to determine the spatial variability and prediction of saturated (Ks and near saturated (Kfs hydraulic conductivity, the power of Gardner equation (α, sorptivity (S, hydraulic diffusivity (D and matric flux potential (Фm of a calcareous soil. Material and Methods: The study was carried out on the soil series of Daneshkadeh located in the Bajgah Agricultural Experimental Station of Agricultural College, Shiraz University, Shiraz, Iran (1852 m above the mean sea level. This soil series with about 745 ha is a deep yellowish brow calcareous soil with textural classes of loam to clay. In the studied soil series 50 sampling locations with the sampling distances of 16, 8 , and 4 m were selected on the relatively regular sampling design. The saturated hydraulic conductivity (Ks, near saturated hydraulic conductivity (Kfs, the power of Gardner equation (α, sorptivity (S, hydraulic diffusivity (D and matric flux potential (Фm of the aforementioned sampling locations was determined using the Single Ring and Droplet methods. After, initial statistical processing, including a normality test of data, trend and stationary analysis of data, the semivariograms of each studied hydraulic attributes were

  15. Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery

    Science.gov (United States)

    Moharana, Shreedevi; Dutta, Subashisa

    2016-12-01

    Chlorophyll and nitrogen are the most essential parameters for paddy crop growth. Spectroradiometric measurements were collected at canopy level during critical growth period of rice. Chemical analysis was performed to quantify the total leaf content. By exploiting the ground based measurements, regression models were established for chlorophyll and nitrogen aimed indices with their corresponding crop growth variables. Vegetation index models were developed for mapping these parameters from Hyperion imagery in an agriculture system. It was inferred that the present Simple Ratio (SR) and Leaf Nitrogen Concentration (LNC) indices, which followed a linear and nonlinear relationship respectively, were completely different from published Tian et al. (2011). The nitrogen content varied widely from 1 to 4% and only 2 to 3% for paddy crop using present modified index models and Tian et al. (2011) respectively. The modified LNC index model performed better than the established Tian et al. (2011) model as far as estimated nitrogen content from Hyperion imagery was concerned. Furthermore, within the observed chlorophyll range obtained from the studied rice varieties grown in the rice agriculture system, the index models (LNC, OASVI, Gitelson, mSR and MTCI) performed well in the spatial distribution of rice chlorophyll content from Hyperion imagery. Spatial distribution of total chlorophyll content varied widely from 1.77 to 5.81 mg/g (LNC), 3.0 to 13 mg/g (OASVI), 0.5 to 10.43 mg/g (Gitelson), 2.18 to 10.61 mg/g (mSR) and 2.90 to 5.40 mg/g (MTCI). The spatial information of these parameters will help in proper nutrient management, yield forecasting, and will serve as inputs for crop growth and forecasting models for a precision rice agriculture system.

  16. Spatial scale separation in regional climate modelling

    Energy Technology Data Exchange (ETDEWEB)

    Feser, F.

    2005-07-01

    In this thesis the concept of scale separation is introduced as a tool for first improving regional climate model simulations and, secondly, to explicitly detect and describe the added value obtained by regional modelling. The basic idea behind this is that global and regional climate models have their best performance at different spatial scales. Therefore the regional model should not alter the global model's results at large scales. The for this purpose designed concept of nudging of large scales controls the large scales within the regional model domain and keeps them close to the global forcing model whereby the regional scales are left unchanged. For ensemble simulations nudging of large scales strongly reduces the divergence of the different simulations compared to the standard approach ensemble that occasionally shows large differences for the individual realisations. For climate hindcasts this method leads to results which are on average closer to observed states than the standard approach. Also the analysis of the regional climate model simulation can be improved by separating the results into different spatial domains. This was done by developing and applying digital filters that perform the scale separation effectively without great computational effort. The separation of the results into different spatial scales simplifies model validation and process studies. The search for 'added value' can be conducted on the spatial scales the regional climate model was designed for giving clearer results than by analysing unfiltered meteorological fields. To examine the skill of the different simulations pattern correlation coefficients were calculated between the global reanalyses, the regional climate model simulation and, as a reference, of an operational regional weather analysis. The regional climate model simulation driven with large-scale constraints achieved a high increase in similarity to the operational analyses for medium-scale 2 meter

  17. Nonparametric Bayesian models for a spatial covariance.

    Science.gov (United States)

    Reich, Brian J; Fuentes, Montserrat

    2012-01-01

    A crucial step in the analysis of spatial data is to estimate the spatial correlation function that determines the relationship between a spatial process at two locations. The standard approach to selecting the appropriate correlation function is to use prior knowledge or exploratory analysis, such as a variogram analysis, to select the correct parametric correlation function. Rather that selecting a particular parametric correlation function, we treat the covariance function as an unknown function to be estimated from the data. We propose a flexible prior for the correlation function to provide robustness to the choice of correlation function. We specify the prior for the correlation function using spectral methods and the Dirichlet process prior, which is a common prior for an unknown distribution function. Our model does not require Gaussian data or spatial locations on a regular grid. The approach is demonstrated using a simulation study as well as an analysis of California air pollution data.

  18. Spatial variability in the density, distribution and vectorial capacity of ...

    African Journals Online (AJOL)

    Malaria transmission varies from one area to another and there are also local difference in time and space. The objective of the study was to determine the local variability of entomological parameters namely, mosquito abundance, human biting rate (HBR), sporozoite rate for Plasmodium falciparum and entomological ...

  19. Spatial representation and cognitive modulation of response variability in the lateral intraparietal area priority map.

    Science.gov (United States)

    Falkner, Annegret L; Goldberg, Michael E; Krishna, B Suresh

    2013-10-09

    The lateral intraparietal area (LIP) in the macaque contains a priority-based representation of the visual scene. We previously showed that the mean spike rate of LIP neurons is strongly influenced by spatially wide-ranging surround suppression in a manner that effectively sharpens the priority map. Reducing response variability can also improve the precision of LIP's priority map. We show that when a monkey plans a visually guided delayed saccade with an intervening distractor, variability (measured by the Fano factor) decreases both for neurons representing the saccade goal and for neurons representing the broad spatial surround. The reduction in Fano factor is maximal for neurons representing the saccade goal and steadily decreases for neurons representing more distant locations. LIP Fano factor changes are behaviorally significant: increasing expected reward leads to lower variability for the LIP representation of both the target and distractor locations, and trials with shorter latency saccades are associated with lower Fano factors in neurons representing the surround. Thus, the LIP Fano factor reflects both stimulus and behavioral engagement. Quantitative modeling shows that the interaction between mean spike count and target-receptive field (RF) distance in the surround during the predistractor epoch is multiplicative: the Fano factor increases more steeply with mean spike count further away from the RF. A negative-binomial model for LIP spike counts captures these findings quantitatively, suggests underlying mechanisms based on trial-by-trial variations in mean spike rate or burst-firing patterns, and potentially provides a principled framework to account simultaneously for the previously observed unsystematic relationships between spike rate and variability in different brain areas.

  20. Temporal and Spatial Trend of Climate Variability in Vietnam

    OpenAIRE

    Duc Luong Nguyen

    2014-01-01

    Vietnam’s long coastline, geographic location, and diverse topography and climates contribute to its being one of the most hazard-prone countries of the Asia-Pacific region. Given that a high proportion of the country’s population and economic assets are located in coastal lowlands and deltas, Vietnam has been ranked among the five countries likely to be most affected by global climate change. This paper aims at providing a short overview on the temporal and spatial trends of climate variabil...

  1. The geological basis and the representation of spatial variability in fractured media

    International Nuclear Information System (INIS)

    Mazurek, M.; Gautschi, A.; Zuidema, P.

    1998-01-01

    Spatial variability of features and parameters relevant for contaminant transport modelling occurs on all scales of interest for the quantification of processes that govern solute migration, typically decimeters to hundreds of meters. Two types of spatial variability are distinguished, namely the internal heterogeneity of each individual water-conducting feature (e.g. the complex architecture of a fault) and the larger-scale heterogeneity that results from the groundwater flow through different types of water-conducting features along the flow-path from the repository to the discharge areas. An up-scaling procedure is required to obtain hydraulic parameters and the properties of the overall flow-path, whereas the heterogeneity of many other geologic features (geometry of flow and matrix porosity, mineralogy, etc.) can be fed directly into coupled codes that quantify radionuclide transport. The procedures needed to derive conceptual models integrating geological and hydraulic field measurements and observations at a given site are illustrated by examples from both crystalline and sedimentary rock formations. (author)

  2. Hydrological and environmental variables outperform spatial factors in structuring species, trait composition, and beta diversity of pelagic algae.

    Science.gov (United States)

    Wu, Naicheng; Qu, Yueming; Guse, Björn; Makarevičiūtė, Kristė; To, Szewing; Riis, Tenna; Fohrer, Nicola

    2018-03-01

    There has been increasing interest in algae-based bioassessment, particularly, trait-based approaches are increasingly suggested. However, the main drivers, especially the contribution of hydrological variables, of species composition, trait composition, and beta diversity of algae communities are less studied. To link species and trait composition to multiple factors (i.e., hydrological variables, local environmental variables, and spatial factors) that potentially control species occurrence/abundance and to determine their relative roles in shaping species composition, trait composition, and beta diversities of pelagic algae communities, samples were collected from a German lowland catchment, where a well-proven ecohydrological modeling enabled to predict long-term discharges at each sampling site. Both trait and species composition showed significant correlations with hydrological, environmental, and spatial variables, and variation partitioning revealed that the hydrological and local environmental variables outperformed spatial variables. A higher variation of trait composition (57.0%) than species composition (37.5%) could be explained by abiotic factors. Mantel tests showed that both species and trait-based beta diversities were mostly related to hydrological and environmental heterogeneity with hydrological contributing more than environmental variables, while purely spatial impact was less important. Our findings revealed the relative importance of hydrological variables in shaping pelagic algae community and their spatial patterns of beta diversities, emphasizing the need to include hydrological variables in long-term biomonitoring campaigns and biodiversity conservation or restoration. A key implication for biodiversity conservation was that maintaining the instream flow regime and keeping various habitats among rivers are of vital importance. However, further investigations at multispatial and temporal scales are greatly needed.

  3. Simulating maize yield and bomass with spatial variability of soil field capacity

    Science.gov (United States)

    Ma, Liwang; Ahuja, Lajpat; Trout, Thomas; Nolan, Bernard T.; Malone, Robert W.

    2015-01-01

    Spatial variability in field soil properties is a challenge for system modelers who use single representative values, such as means, for model inputs, rather than their distributions. In this study, the root zone water quality model (RZWQM2) was first calibrated for 4 yr of maize (Zea mays L.) data at six irrigation levels in northern Colorado and then used to study spatial variability of soil field capacity (FC) estimated in 96 plots on maize yield and biomass. The best results were obtained when the crop parameters were fitted along with FCs, with a root mean squared error (RMSE) of 354 kg ha–1 for yield and 1202 kg ha–1 for biomass. When running the model using each of the 96 sets of field-estimated FC values, instead of calibrating FCs, the average simulated yield and biomass from the 96 runs were close to measured values with a RMSE of 376 kg ha–1 for yield and 1504 kg ha–1 for biomass. When an average of the 96 FC values for each soil layer was used, simulated yield and biomass were also acceptable with a RMSE of 438 kg ha–1 for yield and 1627 kg ha–1 for biomass. Therefore, when there are large numbers of FC measurements, an average value might be sufficient for model inputs. However, when the ranges of FC measurements were known for each soil layer, a sampled distribution of FCs using the Latin hypercube sampling (LHS) might be used for model inputs.

  4. A Statistical Toolbox For Mining And Modeling Spatial Data

    Directory of Open Access Journals (Sweden)

    D’Aubigny Gérard

    2016-12-01

    Full Text Available Most data mining projects in spatial economics start with an evaluation of a set of attribute variables on a sample of spatial entities, looking for the existence and strength of spatial autocorrelation, based on the Moran’s and the Geary’s coefficients, the adequacy of which is rarely challenged, despite the fact that when reporting on their properties, many users seem likely to make mistakes and to foster confusion. My paper begins by a critical appraisal of the classical definition and rational of these indices. I argue that while intuitively founded, they are plagued by an inconsistency in their conception. Then, I propose a principled small change leading to corrected spatial autocorrelation coefficients, which strongly simplifies their relationship, and opens the way to an augmented toolbox of statistical methods of dimension reduction and data visualization, also useful for modeling purposes. A second section presents a formal framework, adapted from recent work in statistical learning, which gives theoretical support to our definition of corrected spatial autocorrelation coefficients. More specifically, the multivariate data mining methods presented here, are easily implementable on the existing (free software, yield methods useful to exploit the proposed corrections in spatial data analysis practice, and, from a mathematical point of view, whose asymptotic behavior, already studied in a series of papers by Belkin & Niyogi, suggests that they own qualities of robustness and a limited sensitivity to the Modifiable Areal Unit Problem (MAUP, valuable in exploratory spatial data analysis.

  5. The spatial variability in studies of soil physical condition

    International Nuclear Information System (INIS)

    Madero M, Edgar; Herrera G Oscar A; Castano C, Alirio

    2000-01-01

    The testing procedure was carried out in 1996-2 at the experimental station of the Universidad Nacional de Colombia in Palmira using vertical tillage (by chiseling) in coherent vertisol (typic Haplustert isohiperthermic fine loamy 1%). eight physical properties in depth of 15-25 cm were studied. the sampling methodology for soil physical properties and corn yield accounted the regionalized variable, and the analysis of results was carried out accounting a map of each variable. the results proved that geostatystics is versatile and give accuracy results. it showed in most of the area that vertical tillage was more favorable than conventional tillage to improve coherence (more soil penetrability without degradation) in seedbed zone. it was not found influence over corn yield. soil organic matter; clay and silt had influence over the soil response to mechanical strengths

  6. Spatial variability of physical properties of tropical soil

    International Nuclear Information System (INIS)

    Reichardt, K.; Libardi, P.L.; Queiroz, S.V.; Grohmann, F.

    1976-04-01

    A basic study with objectives of improving the use of soil and water resources under a particular condition and of developing means for controlling the dynamics of soil-water movement are presented. Special emphasis is given to the variability in space of geometric soil properties such as bulk density, particle density and texture in order to make it possible to define representative means which ideed will be usable to describe the movement of water and of salt in the entire field

  7. Global sensitivity analysis for models with spatially dependent outputs

    International Nuclear Information System (INIS)

    Iooss, B.; Marrel, A.; Jullien, M.; Laurent, B.

    2011-01-01

    The global sensitivity analysis of a complex numerical model often calls for the estimation of variance-based importance measures, named Sobol' indices. Meta-model-based techniques have been developed in order to replace the CPU time-expensive computer code with an inexpensive mathematical function, which predicts the computer code output. The common meta-model-based sensitivity analysis methods are well suited for computer codes with scalar outputs. However, in the environmental domain, as in many areas of application, the numerical model outputs are often spatial maps, which may also vary with time. In this paper, we introduce an innovative method to obtain a spatial map of Sobol' indices with a minimal number of numerical model computations. It is based upon the functional decomposition of the spatial output onto a wavelet basis and the meta-modeling of the wavelet coefficients by the Gaussian process. An analytical example is presented to clarify the various steps of our methodology. This technique is then applied to a real hydrogeological case: for each model input variable, a spatial map of Sobol' indices is thus obtained. (authors)

  8. A physically based analytical spatial air temperature and humidity model

    Science.gov (United States)

    Yang, Yang; Endreny, Theodore A.; Nowak, David J.

    2013-09-01

    Spatial variation of urban surface air temperature and humidity influences human thermal comfort, the settling rate of atmospheric pollutants, and plant physiology and growth. Given the lack of observations, we developed a Physically based Analytical Spatial Air Temperature and Humidity (PASATH) model. The PASATH model calculates spatial solar radiation and heat storage based on semiempirical functions and generates spatially distributed estimates based on inputs of topography, land cover, and the weather data measured at a reference site. The model assumes that for all grids under the same mesoscale climate, grid air temperature and humidity are modified by local variation in absorbed solar radiation and the partitioning of sensible and latent heat. The model uses a reference grid site for time series meteorological data and the air temperature and humidity of any other grid can be obtained by solving the heat flux network equations. PASATH was coupled with the USDA iTree-Hydro water balance model to obtain evapotranspiration terms and run from 20 to 29 August 2010 at a 360 m by 360 m grid scale and hourly time step across a 285 km2 watershed including the urban area of Syracuse, NY. PASATH predictions were tested at nine urban weather stations representing variability in urban topography and land cover. The PASATH model predictive efficiency R2 ranged from 0.81 to 0.99 for air temperature and 0.77 to 0.97 for dew point temperature. PASATH is expected to have broad applications on environmental and ecological models.

  9. Spatial and temporal variability of tropospheric ozone over Europe

    Energy Technology Data Exchange (ETDEWEB)

    Scheel, H E; Sladkovic, R [Fraunhofer Inst. (IFU), Garmisch-Partenkirchen (Germany); Ancellet, G [Universite Paris 6 (France). Service d` Aeronomie du CNRS; Areskoug, H [Air Pollution Lab., Inst. of Applied Environmental Research, Stockholm Univ. (Sweden); Beck, J; Waal, L de [RIVM-LLO, Bilthoven (Netherlands); Boesenberg, J; Grabbe, G [Max-Planck-Institut fuer Meteorologie, Hamburg (Germany); Muer, D de [Meteorological Inst. of Belgium (KMI), Brussels (Belgium); Dutot, A L; Etienne, A; Perros, P; Toupance, G [Universite Paris XII-Creteil (France). Lab. de Physico-Chimie de l` Environment; Egelov, A H; Granby, K [National Environmental Research Inst., Roskilde (Denmark); Esser, P; Roemer, M [IMW-TNO, Delft (Netherlands); Ferenczi, Z; Haszpra, L [Institute for Atmospheric Physics, Budapest (Hungary); Geiss, H; Smit, H [Forschungszentrum Juelich (Germany). Inst. fuer Chemie und Dynamik der Geosphaere (ICG-2); Gomiscek, B [Ljubljana Univ. (Slovenia). Faculty of Chemistry and Chemical Technology; Kezele, N; Klasinc, L [Institut Rudjer Boskovic, Zagreb (Croatia); Laurila, T [Finnish Meteorological Inst., Helsinki (Finland). Dept. of Air Quality; Lindskog, A; Mowrer, J [Swedish Environmental Research Inst. (IVL), Goeteborg (Sweden); Nielsen, T [Risoe National Laboratory, Roskilde (Denmark); Schmitt, R [Meteorologie Consult GmbH, Glashuetten (Germany); Simmonds, P [International Science Consultants, Ringwood (United Kingdom); Solberg, S [NILU, Kjeller (Norway); Varotsos, C [Athens Univ. (Greece); TOR Task Group 1

    1998-12-31

    The first section is concerned with the characteristics of the TOR-measurement sites and the data used. It describes the methodologies employed for the selection of data in order to obtain representative ozone concentrations with minimum bias caused by the individual location. The question of representativeness of the O{sub 3} concentrations at the TOR sites was given special attention, since it is a crucial point for all conclusions drawn from the observations. Therefore several studies were focused on this issue. The further sections of the report deal with results on the spatial and seasonal variations of ozone concentrations over Europe. Results obtained from in-situ measurements in the boundary layer/lower free troposphere and from vertical soundings in the free troposphere are regarded separately. Finally, trend estimates are presented for ozone as well as for some of its precursors. (orig./KW)

  10. Spatial and temporal variability of tropospheric ozone over Europe

    Energy Technology Data Exchange (ETDEWEB)

    Scheel, H.E.; Sladkovic, R. [Fraunhofer Inst. (IFU), Garmisch-Partenkirchen (Germany); Ancellet, G. [Universite Paris 6 (France). Service d`Aeronomie du CNRS; Areskoug, H. [Air Pollution Lab., Inst. of Applied Environmental Research, Stockholm Univ. (Sweden); Beck, J.; Waal, L. de [RIVM-LLO, Bilthoven (Netherlands); Boesenberg, J.; Grabbe, G. [Max-Planck-Institut fuer Meteorologie, Hamburg (Germany); Muer, D. de [Meteorological Inst. of Belgium (KMI), Brussels (Belgium); Dutot, A.L.; Etienne, A.; Perros, P.; Toupance, G. [Universite Paris XII-Creteil (France). Lab. de Physico-Chimie de l`Environment; Egelov, A.H.; Granby, K. [National Environmental Research Inst., Roskilde (Denmark); Esser, P.; Roemer, M. [IMW-TNO, Delft (Netherlands); Ferenczi, Z.; Haszpra, L. [Institute for Atmospheric Physics, Budapest (Hungary); Geiss, H.; Smit, H. [Forschungszentrum Juelich (Germany). Inst. fuer Chemie und Dynamik der Geosphaere (ICG-2); Gomiscek, B. [Ljubljana Univ. (Slovenia). Faculty of Chemistry and Chemical Technology; Kezele, N.; Klasinc, L. [Institut Rudjer Boskovic, Zagreb (Croatia); Laurila, T. [Finnish Meteorological Inst., Helsinki (Finland). Dept. of Air Quality; Lindskog, A.; Mowrer, J. [Swedish Environmental Research Inst. (IVL), Goeteborg (Sweden); Nielsen, T. [Risoe National Laboratory, Roskilde (Denmark); Schmitt, R. [Meteorologie Consult GmbH, Glashuetten (Germany); Simmonds, P. [International Science Consultants, Ringwood (United Kingdom); Solberg, S. [NILU, Kjeller (Norway); Varotsos, C. [Athens Univ. (Greece); TOR Task Group 1

    1997-12-31

    The first section is concerned with the characteristics of the TOR-measurement sites and the data used. It describes the methodologies employed for the selection of data in order to obtain representative ozone concentrations with minimum bias caused by the individual location. The question of representativeness of the O{sub 3} concentrations at the TOR sites was given special attention, since it is a crucial point for all conclusions drawn from the observations. Therefore several studies were focused on this issue. The further sections of the report deal with results on the spatial and seasonal variations of ozone concentrations over Europe. Results obtained from in-situ measurements in the boundary layer/lower free troposphere and from vertical soundings in the free troposphere are regarded separately. Finally, trend estimates are presented for ozone as well as for some of its precursors. (orig./KW)

  11. Landscape Modelling and Simulation Using Spatial Data

    Directory of Open Access Journals (Sweden)

    Amjed Naser Mohsin AL-Hameedawi

    2017-08-01

    Full Text Available In this paper a procedure was performed for engendering spatial model of landscape acclimated to reality simulation. This procedure based on combining spatial data and field measurements with computer graphics reproduced using Blender software. Thereafter that we are possible to form a 3D simulation based on VIS ALL packages. The objective was to make a model utilising GIS, including inputs to the feature attribute data. The objective of these efforts concentrated on coordinating a tolerable spatial prototype, circumscribing facilitation scheme and outlining the intended framework. Thus; the eventual result was utilized in simulation form. The performed procedure contains not only data gathering, fieldwork and paradigm providing, but extended to supply a new method necessary to provide the respective 3D simulation mapping production, which authorises the decision makers as well as investors to achieve permanent acceptance an independent navigation system for Geoscience applications.

  12. Temporal and Spatial Variabilities of Japan Sea Surface Temperature and Atmospheric Forcings

    National Research Council Canada - National Science Library

    Chu, Peter C; Chen, Yuchun; Lu, Shihua

    1998-01-01

    ...) and surface air temperature (SAT) data during 1982-1994 and the National Center for Atmospheric Research surface wind stress curl data during 1982-1989 to investigate the Japan Sea SST temporal and spatial variabilities...

  13. Spatial Temporal Modelling of Particulate Matter for Health Effects Studies

    Science.gov (United States)

    Hamm, N. A. S.

    2016-10-01

    Epidemiological studies of the health effects of air pollution require estimation of individual exposure. It is not possible to obtain measurements at all relevant locations so it is necessary to predict at these space-time locations, either on the basis of dispersion from emission sources or by interpolating observations. This study used data obtained from a low-cost sensor network of 32 air quality monitoring stations in the Dutch city of Eindhoven, which make up the ILM (innovative air (quality) measurement system). These stations currently provide PM10 and PM2.5 (particulate matter less than 10 and 2.5 m in diameter), aggregated to hourly means. The data provide an unprecedented level of spatial and temporal detail for a city of this size. Despite these benefits the time series of measurements is characterized by missing values and noisy values. In this paper a space-time analysis is presented that is based on a dynamic model for the temporal component and a Gaussian process geostatistical for the spatial component. Spatial-temporal variability was dominated by the temporal component, although the spatial variability was also substantial. The model delivered accurate predictions for both isolated missing values and 24-hour periods of missing values (RMSE = 1.4 μg m-3 and 1.8 μg m-3 respectively). Outliers could be detected by comparison to the 95% prediction interval. The model shows promise for predicting missing values, outlier detection and for mapping to support health impact studies.

  14. Climatic and physiographic controls of spatial variability in surface water balance over the contiguous United States using the Budyko relationship

    Science.gov (United States)

    Abatzoglou, John T.; Ficklin, Darren L.

    2017-09-01

    The geographic variability in the partitioning of precipitation into surface runoff (Q) and evapotranspiration (ET) is fundamental to understanding regional water availability. The Budyko equation suggests this partitioning is strictly a function of aridity, yet observed deviations from this relationship for individual watersheds impede using the framework to model surface water balance in ungauged catchments and under future climate and land use scenarios. A set of climatic, physiographic, and vegetation metrics were used to model the spatial variability in the partitioning of precipitation for 211 watersheds across the contiguous United States (CONUS) within Budyko's framework through the free parameter ω. A generalized additive model found that four widely available variables, precipitation seasonality, the ratio of soil water holding capacity to precipitation, topographic slope, and the fraction of precipitation falling as snow, explained 81.2% of the variability in ω. The ω model applied to the Budyko equation explained 97% of the spatial variability in long-term Q for an independent set of watersheds. The ω model was also applied to estimate the long-term water balance across the CONUS for both contemporary and mid-21st century conditions. The modeled partitioning of observed precipitation to Q and ET compared favorably across the CONUS with estimates from more sophisticated land-surface modeling efforts. For mid-21st century conditions, the model simulated an increase in the fraction of precipitation used by ET across the CONUS with declines in Q for much of the eastern CONUS and mountainous watersheds across the western United States.

  15. Spatial generalised linear mixed models based on distances.

    Science.gov (United States)

    Melo, Oscar O; Mateu, Jorge; Melo, Carlos E

    2016-10-01

    Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time. © The Author(s) 2013.

  16. Temporal and spatial variabilities of Antarctic ice mass changes inferred by GRACE in a Bayesian framework

    Science.gov (United States)

    Wang, L.; Davis, J. L.; Tamisiea, M. E.

    2017-12-01

    The Antarctic ice sheet (AIS) holds about 60% of all fresh water on the Earth, an amount equivalent to about 58 m of sea-level rise. Observation of AIS mass change is thus essential in determining and predicting its contribution to sea level. While the ice mass loss estimates for West Antarctica (WA) and the Antarctic Peninsula (AP) are in good agreement, what the mass balance over East Antarctica (EA) is, and whether or not it compensates for the mass loss is under debate. Besides the different error sources and sensitivities of different measurement types, complex spatial and temporal variabilities would be another factor complicating the accurate estimation of the AIS mass balance. Therefore, a model that allows for variabilities in both melting rate and seasonal signals would seem appropriate in the estimation of present-day AIS melting. We present a stochastic filter technique, which enables the Bayesian separation of the systematic stripe noise and mass signal in decade-length GRACE monthly gravity series, and allows the estimation of time-variable seasonal and inter-annual components in the signals. One of the primary advantages of this Bayesian method is that it yields statistically rigorous uncertainty estimates reflecting the inherent spatial resolution of the data. By applying the stochastic filter to the decade-long GRACE observations, we present the temporal variabilities of the AIS mass balance at basin scale, particularly over East Antarctica, and decipher the EA mass variations in the past decade, and their role in affecting overall AIS mass balance and sea level.

  17. Handbook of latent variable and related models

    CERN Document Server

    Lee, Sik-Yum

    2011-01-01

    This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables.- Covers a wide class of important models- Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data- Includes illustrative examples with real data sets from business, education, medicine, public health and sociology.- Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.

  18. Spatial Modeling for Resources Framework (SMRF)

    Science.gov (United States)

    Spatial Modeling for Resources Framework (SMRF) was developed by Dr. Scott Havens at the USDA Agricultural Research Service (ARS) in Boise, ID. SMRF was designed to increase the flexibility of taking measured weather data and distributing the point measurements across a watershed. SMRF was developed...

  19. The 3-D global spatial data model foundation of the spatial data infrastructure

    CERN Document Server

    Burkholder, Earl F

    2008-01-01

    Traditional methods for handling spatial data are encumbered by the assumption of separate origins for horizontal and vertical measurements. Modern measurement systems operate in a 3-D spatial environment. The 3-D Global Spatial Data Model: Foundation of the Spatial Data Infrastructure offers a new model for handling digital spatial data, the global spatial data model or GSDM. The GSDM preserves the integrity of three-dimensional spatial data while also providing additional benefits such as simpler equations, worldwide standardization, and the ability to track spatial data accuracy with greater specificity and convenience. This groundbreaking spatial model incorporates both a functional model and a stochastic model to connect the physical world to the ECEF rectangular system. Combining horizontal and vertical data into a single, three-dimensional database, this authoritative monograph provides a logical development of theoretical concepts and practical tools that can be used to handle spatial data mo...

  20. Providing a non-deterministic representation of spatial variability of precipitation in the Everest region

    Directory of Open Access Journals (Sweden)

    J. Eeckman

    2017-09-01

    Full Text Available This paper provides a new representation of the effect of altitude on precipitation that represents spatial and temporal variability in precipitation in the Everest region. Exclusive observation data are used to infer a piecewise linear function for the relation between altitude and precipitation and significant seasonal variations are highlighted. An original ensemble approach is applied to provide non-deterministic water budgets for middle and high-mountain catchments. Physical processes at the soil–atmosphere interface are represented through the Interactions Soil–Biosphere–Atmosphere (ISBA surface scheme. Uncertainties associated with the model parametrization are limited by the integration of in situ measurements of soils and vegetation properties. Uncertainties associated with the representation of the orographic effect are shown to account for up to 16 % of annual total precipitation. Annual evapotranspiration is shown to represent 26 % ± 1 % of annual total precipitation for the mid-altitude catchment and 34% ± 3 % for the high-altitude catchment. Snowfall contribution is shown to be neglectable for the mid-altitude catchment, and it represents up to 44 % ± 8 % of total precipitation for the high-altitude catchment. These simulations on the local scale enhance current knowledge of the spatial variability in hydroclimatic processes in high- and mid-altitude mountain environments.

  1. SPATIAL AND TEMPORAL VARIABILITY OF DRY STEPPES OF EASTERN MONGOLIA

    Directory of Open Access Journals (Sweden)

    Galina Ogureeva

    2011-01-01

    Full Text Available Spatial-temporal structure and coenotic diversity of dry steppes of Eastern Mongolia was identified by analyzing characteristics of naturally occurring vegetation connection to the regional landscape structure. Different types of combinations of plant communities (phytocoenochores were determined in the vegetation structure of the Eastern Steppe Station Tumén-Tsogt (in Sukhebator district. Temporal dynamics of steppe ecosystems was defined from the studies of steppe cover fluctuations in 2008. The coenotic role of eight annual plant species that form synusiae in the steppe communities was shown through analysis of species constancy, projective cover, and activity. Knowledge about the trend of successions and the manifestation of fluctuations in vegetation cover is necessary for the development of science-based system of management options to maintain the number and abundance of different plant groups in plant communities. Monitoring the state of natural ecosystems has a major scientific and practical importance, since steppe ecosystems are the basic component of the pasture’s resources of the country.

  2. Spatial variability in the icthyoplankton structure of a subtropicalhypersaline lagoon

    Directory of Open Access Journals (Sweden)

    Judson da Cruz Lopes da Rosa

    Full Text Available Abstract The Lagoa de Araruama is a hypersaline ecosystem inhabited by distinct fish species, either permanently or during their reproductive season. Over recent years, some significant environmental changes have been observed in this ecosystem related to the sewage runoff, as salinity decrease (from 64 to 41 psu during the last 40 years and nutrients increase. As both changes are thought to affect the ichthyoplankton assemblage, the present study aimed to evaluate all the potential relationships between salinity disruption and fish larvae distribution. Ichtyoplankton samples were collected monthly from January 2010 to March 2011 at eight sites in Araruama Lagoon by means of a WP2 plankton net equipped with a flowmeter. During this period, low egg densities were coincident with high salinity regions, suggesting that adults are avoiding to release their eggs under less favorable environmental conditions to the larvae. The uneven distribution of eggs and larvae inside the lagoon, as revealed by both spatial and temporal analyses lead us to suggest that changes in salinity have influenced the reproductive rhythms of those fish species that depend upon the Lagoa de Araruama.

  3. Spatial patterns of North Atlantic Oscillation influence on mass balance variability of European glaciers

    Directory of Open Access Journals (Sweden)

    B. Marzeion

    2012-06-01

    Full Text Available We present and validate a set of minimal models of glacier mass balance variability. The most skillful model is then applied to reconstruct 7735 individual time series of mass balance variability for all glaciers in the European Alps and Scandinavia. Subsequently, we investigate the influence of atmospheric variability associated with the North Atlantic Oscillation (NAO on the glaciers' mass balances.

    We find a spatial coherence in the glaciers' sensitivity to NAO forcing which is caused by regionally similar mechanisms relating the NAO forcing to the mass balance: in southwestern Scandinavia, winter precipitation causes a correlation of mass balances with the NAO. In northern Scandinavia, temperature anomalies outside the core winter season cause an anti-correlation between NAO and mass balances. In the western Alps, both temperature and winter precipitation anomalies lead to a weak anti-correlation of mass balances with the NAO, while in the eastern Alps, the influences of winter precipitation and temperature anomalies tend to cancel each other, and only on the southern side a slight anti-correlation of mass balances with the NAO prevails.

  4. Generalized latent variable modeling multilevel, longitudinal, and structural equation models

    CERN Document Server

    Skrondal, Anders; Rabe-Hesketh, Sophia

    2004-01-01

    This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models.

  5. Temporal and spatial variability in the aviation NOx-related O3 impact

    International Nuclear Information System (INIS)

    Gilmore, Christopher K; Barrett, Steven R H; Koo, Jamin; Wang, Qiqi

    2013-01-01

    Aviation NO x emissions promote tropospheric ozone formation, which is linked to climate warming and adverse health effects. Modeling studies have quantified the relative impact of aviation NO x on O 3 in large geographic regions. As these studies have applied forward modeling techniques, it has not been possible to attribute O 3 formation to individual flights. Here we apply the adjoint of the global chemistry–transport model GEOS-Chem to assess the temporal and spatial variability in O 3 production due to aviation NO x emissions, which is the first application of an adjoint to this problem. We find that total aviation NO x emitted in October causes 40% more O 3 than in April and that Pacific aviation emissions could cause 4–5 times more tropospheric O 3 per unit NO x than European or North American emissions. Using this sensitivity approach, the O 3 burden attributable to 83 000 unique scheduled civil flights is computed individually. We find that the ten highest total O 3 -producing flights have origins or destinations in New Zealand or Australia. The top ranked O 3 -producing flights normalized by fuel burn cause 157 times more normalized O 3 formation than the bottom ranked ones. These results show significant spatial and temporal heterogeneity in environmental impacts of aviation NO x emissions. (letter)

  6. Analyzing Variability in Landscape Nutrient Loading Using Spatially-Explicit Maps in the Great Lakes Basin

    Science.gov (United States)

    Hamlin, Q. F.; Kendall, A. D.; Martin, S. L.; Whitenack, H. D.; Roush, J. A.; Hannah, B. A.; Hyndman, D. W.

    2017-12-01

    Excessive loading of nitrogen and phosphorous to the landscape has caused biologically and economically damaging eutrophication and harmful algal blooms in the Great Lakes Basin (GLB) and across the world. We mapped source-specific loads of nitrogen and phosphorous to the landscape using broadly available data across the GLB. SENSMap (Spatially Explicit Nutrient Source Map) is a 30m resolution snapshot of nutrient loads ca. 2010. We use these maps to study variable nutrient loading and provide this information to watershed managers through NOAA's GLB Tipping Points Planner. SENSMap individually maps nutrient point sources and six non-point sources: 1) atmospheric deposition, 2) septic tanks, 3) non-agricultural chemical fertilizer, 4) agricultural chemical fertilizer, 5) manure, and 6) nitrogen fixation from legumes. To model source-specific loads at high resolution, SENSMap synthesizes a wide range of remotely sensed, surveyed, and tabular data. Using these spatially explicit nutrient loading maps, we can better calibrate local land use-based water quality models and provide insight to watershed managers on how to focus nutrient reduction strategies. Here we examine differences in dominant nutrient sources across the GLB, and how those sources vary by land use. SENSMap's high resolution, source-specific approach offers a different lens to understand nutrient loading than traditional semi-distributed or land use based models.

  7. A Core Language for Separate Variability Modeling

    DEFF Research Database (Denmark)

    Iosif-Lazăr, Alexandru Florin; Wasowski, Andrzej; Schaefer, Ina

    2014-01-01

    Separate variability modeling adds variability to a modeling language without requiring modifications of the language or the supporting tools. We define a core language for separate variability modeling using a single kind of variation point to define transformations of software artifacts in object...... hierarchical dependencies between variation points via copying and flattening. Thus, we reduce a model with intricate dependencies to a flat executable model transformation consisting of simple unconditional local variation points. The core semantics is extremely concise: it boils down to two operational rules...

  8. Hydrological model uncertainty due to spatial evapotranspiration estimation methods

    Science.gov (United States)

    Yu, Xuan; Lamačová, Anna; Duffy, Christopher; Krám, Pavel; Hruška, Jakub

    2016-05-01

    Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03‧N, 12°40‧E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.

  9. Spatially explicit modeling in ecology: A review

    Science.gov (United States)

    DeAngelis, Donald L.; Yurek, Simeon

    2017-01-01

    The use of spatially explicit models (SEMs) in ecology has grown enormously in the past two decades. One major advancement has been that fine-scale details of landscapes, and of spatially dependent biological processes, such as dispersal and invasion, can now be simulated with great precision, due to improvements in computer technology. Many areas of modeling have shifted toward a focus on capturing these fine-scale details, to improve mechanistic understanding of ecosystems. However, spatially implicit models (SIMs) have played a dominant role in ecology, and arguments have been made that SIMs, which account for the effects of space without specifying spatial positions, have an advantage of being simpler and more broadly applicable, perhaps contributing more to understanding. We address this debate by comparing SEMs and SIMs in examples from the past few decades of modeling research. We argue that, although SIMs have been the dominant approach in the incorporation of space in theoretical ecology, SEMs have unique advantages for addressing pragmatic questions concerning species populations or communities in specific places, because local conditions, such as spatial heterogeneities, organism behaviors, and other contingencies, produce dynamics and patterns that usually cannot be incorporated into simpler SIMs. SEMs are also able to describe mechanisms at the local scale that can create amplifying positive feedbacks at that scale, creating emergent patterns at larger scales, and therefore are important to basic ecological theory. We review the use of SEMs at the level of populations, interacting populations, food webs, and ecosystems and argue that SEMs are not only essential in pragmatic issues, but must play a role in the understanding of causal relationships on landscapes.

  10. Assimilation of temperature and hydraulic gradients for quantifying the spatial variability of streambed hydraulics

    Science.gov (United States)

    Huang, Xiang; Andrews, Charles B.; Liu, Jie; Yao, Yingying; Liu, Chuankun; Tyler, Scott W.; Selker, John S.; Zheng, Chunmiao

    2016-08-01

    Understanding the spatial and temporal characteristics of water flux into or out of shallow aquifers is imperative for water resources management and eco-environmental conservation. In this study, the spatial variability in the vertical specific fluxes and hydraulic conductivities in a streambed were evaluated by integrating distributed temperature sensing (DTS) data and vertical hydraulic gradients into an ensemble Kalman filter (EnKF) and smoother (EnKS) and an empirical thermal-mixing model. The formulation of the EnKF/EnKS assimilation scheme is based on a discretized 1D advection-conduction equation of heat transfer in the streambed. We first systematically tested a synthetic case and performed quantitative and statistical analyses to evaluate the performance of the assimilation schemes. Then a real-world case was evaluated to calculate assimilated specific flux. An initial estimate of the spatial distributions of the vertical hydraulic gradients was obtained from an empirical thermal-mixing model under steady-state conditions using a constant vertical hydraulic conductivity. Then, this initial estimate was updated by repeatedly dividing the assimilated specific flux by estimates of the vertical hydraulic gradients to obtain a refined spatial distribution of vertical hydraulic gradients and vertical hydraulic conductivities. Our results indicate that optimal parameters can be derived with fewer iterations but greater simulation effort using the EnKS compared with the EnKF. For the field application in a stream segment of the Heihe River Basin in northwest China, the average vertical hydraulic conductivities in the streambed varied over three orders of magnitude (5 × 10-1 to 5 × 102 m/d). The specific fluxes ranged from near zero (qz < ±0.05 m/d) to ±1.0 m/d, while the vertical hydraulic gradients were within the range of -0.2 to 0.15 m/m. The highest and most variable fluxes occurred adjacent to a debris-dam and bridge pier. This phenomenon is very likely

  11. Spatial capture-recapture models for search-encounter data

    Science.gov (United States)

    Royle, J. Andrew; Kery, Marc; Guelat, Jerome

    2011-01-01

    1. Spatial capture–recapture models make use of auxiliary data on capture location to provide density estimates for animal populations. Previously, models have been developed primarily for fixed trap arrays which define the observable locations of individuals by a set of discrete points. 2. Here, we develop a class of models for 'search-encounter' data, i.e. for detections of recognizable individuals in continuous space, not restricted to trap locations. In our hierarchical model, detection probability is related to the average distance between individual location and the survey path. The locations are allowed to change over time owing to movements of individuals, and individual locations are related formally by a model describing individual activity or home range centre which is itself regarded as a latent variable in the model. We provide a Bayesian analysis of the model in WinBUGS, and develop a custom MCMC algorithm in the R language. 3. The model is applied to simulated data and to territory mapping data for the Willow Tit from the Swiss Breeding Bird Survey MHB. While the observed density was 15 territories per nominal 1 km2 plot of unknown effective sample area, the model produced a density estimate of 21∙12 territories per square km (95% posterior interval: 17–26). 4. Spatial capture–recapture models are relevant to virtually all animal population studies that seek to estimate population size or density, yet existing models have been proposed mainly for conventional sampling using arrays of traps. Our model for search-encounter data, where the spatial pattern of searching can be arbitrary and may change over occasions, greatly expands the scope and utility of spatial capture–recapture models.

  12. Control of spatial discretisation in coastal oil spill modelling

    OpenAIRE

    Li, Yang

    2007-01-01

    Spatial discretisation plays an important role in many numerical environmental models. This paper studies the control of spatial discretisation in coastal oil spill modelling with a view to assure the quality of modelling outputs for given spatial data inputs. Spatial data analysis techniques are effective for investigating and improving the spatial discretisation in different phases of the modelling. Proposed methods are implemented and tested with experimental models. A new “Automatic Searc...

  13. Latent variable models are network models.

    Science.gov (United States)

    Molenaar, Peter C M

    2010-06-01

    Cramer et al. present an original and interesting network perspective on comorbidity and contrast this perspective with a more traditional interpretation of comorbidity in terms of latent variable theory. My commentary focuses on the relationship between the two perspectives; that is, it aims to qualify the presumed contrast between interpretations in terms of networks and latent variables.

  14. Spatial Durbin model analysis macroeconomic loss due to natural disasters

    Science.gov (United States)

    Kusrini, D. E.; Mukhtasor

    2015-03-01

    Magnitude of the damage and losses caused by natural disasters is huge for Indonesia, therefore this study aimed to analyze the effects of natural disasters for macroeconomic losses that occurred in 115 cities/districts across Java during 2012. Based on the results of previous studies it is suspected that it contains effects of spatial dependencies in this case, so that the completion of this case is performed using a regression approach to the area, namely Analysis of Spatial Durbin Model (SDM). The obtained significant predictor variable is population, and predictor variable with a significant weighting is the number of occurrences of disasters, i.e., disasters in the region which have an impact on other neighboring regions. Moran's I index value using the weighted Queen Contiguity also showed significant results, meaning that the incidence of disasters in the region will decrease the value of GDP in other.

  15. A nonlocal spatial model for Lyme disease

    Science.gov (United States)

    Yu, Xiao; Zhao, Xiao-Qiang

    2016-07-01

    This paper is devoted to the study of a nonlocal and time-delayed reaction-diffusion model for Lyme disease with a spatially heterogeneous structure. In the case of a bounded domain, we first prove the existence of the positive steady state and a threshold type result for the disease-free system, and then establish the global dynamics for the model system in terms of the basic reproduction number. In the case of an unbound domain, we obtain the existence of the disease spreading speed and its coincidence with the minimal wave speed. At last, we use numerical simulations to verify our analytic results and investigate the influence of model parameters and spatial heterogeneity on the disease infection risk.

  16. Evidence of political yardstick competition in France using a two-regime spatial Durbin model with fixed effects

    NARCIS (Netherlands)

    Elhorst, J. Paul; Freret, Sandy

    2009-01-01

    This research proposes a two-regime spatial Durbin model with spatial and time-period fixed effects to test for political yardstick competition and exclude any other explanation that might produce spatial interaction effects among the dependent variable, the independent variables, or the error term.

  17. High-speed limnology: using advanced sensors to investigate spatial variability in biogeochemistry and hydrology.

    Science.gov (United States)

    Crawford, John T; Loken, Luke C; Casson, Nora J; Smith, Colin; Stone, Amanda G; Winslow, Luke A

    2015-01-06

    Advanced sensor technology is widely used in aquatic monitoring and research. Most applications focus on temporal variability, whereas spatial variability has been challenging to document. We assess the capability of water chemistry sensors embedded in a high-speed water intake system to document spatial variability. This new sensor platform continuously samples surface water at a range of speeds (0 to >45 km h(-1)) resulting in high-density, mesoscale spatial data. These novel observations reveal previously unknown variability in physical, chemical, and biological factors in streams, rivers, and lakes. By combining multiple sensors into one platform, we were able to detect terrestrial-aquatic hydrologic connections in a small dystrophic lake, to infer the role of main-channel vs backwater nutrient processing in a large river and to detect sharp chemical changes across aquatic ecosystem boundaries in a stream/lake complex. Spatial sensor data were verified in our examples by comparing with standard lab-based measurements of selected variables. Spatial fDOM data showed strong correlation with wet chemistry measurements of DOC, and optical NO3 concentrations were highly correlated with lab-based measurements. High-frequency spatial data similar to our examples could be used to further understand aquatic biogeochemical fluxes, ecological patterns, and ecosystem processes, and will both inform and benefit from fixed-site data.

  18. Spatial and temporal variability of winds in the Northern European Seas

    DEFF Research Database (Denmark)

    Karagali, Ioanna; Badger, Merete; Hahmann, Andrea N.

    2013-01-01

    the spatial and temporal variability of the near-surface wind field, including the inter- and intra-annual variability for resource assessment purposes. This study demonstrates the applicability of satellite observations as the means to provide information useful for selecting areas to perform higher...

  19. Environmental versus demographic variability in stochastic predator–prey models

    International Nuclear Information System (INIS)

    Dobramysl, U; Täuber, U C

    2013-01-01

    In contrast to the neutral population cycles of the deterministic mean-field Lotka–Volterra rate equations, including spatial structure and stochastic noise in models for predator–prey interactions yields complex spatio-temporal structures associated with long-lived erratic population oscillations. Environmental variability in the form of quenched spatial randomness in the predation rates results in more localized activity patches. Our previous study showed that population fluctuations in rare favorable regions in turn cause a remarkable increase in the asymptotic densities of both predators and prey. Very intriguing features are found when variable interaction rates are affixed to individual particles rather than lattice sites. Stochastic dynamics with demographic variability in conjunction with inheritable predation efficiencies generate non-trivial time evolution for the predation rate distributions, yet with overall essentially neutral optimization. (paper)

  20. Spatial variability of soil erosion and soil quality on hillslopes in the Chinese loess plateau

    International Nuclear Information System (INIS)

    Li, Y.; Lindstrom, M.J.; Zhang, J.; Yang, J.

    2000-01-01

    Soil erosion rates and soil quality indicators were measured along two hillslope transects in the Loess Plateau near Yan'an, China. The objectives were to: (a) quantify spatial patterns and controlling processes of soil redistribution due to water and tillage erosion, and (b) correlate soil quality parameters with soil redistribution along the hillslope transects for different land use management systems. Water erosion data were derived from 137 Cs measurements and tillage erosion from the simulation of a Mass Balance Model along the hillslope transects. Soil quality measurements, i.e. soil organic matter, bulk density and available nutrients were made at the same sampling locations as the 137 Cs measurements. Results were compared at the individual site locations and along the hillslope transect through statistical and applied time series analysis. The results showed that soil loss due to water erosion and soil deposition from tillage are the dominant soil redistribution processes in range of 23-40 m, and soil deposition by water erosion and soil loss by tillage are dominant processes occurring in range of more than 80 m within the cultivated landscape. However, land use change associated with vegetation cover can significantly change both the magnitudes and scale of these spatial patterns within the hillslope landscapes. There is a strong interaction between the spatial patterns of soil erosion processes and soil quality. It was concluded that soil loss by water erosion and deposition by tillage are the main cause for the occurrence of significant scale dependency of spatial variability of soil quality along hillslope transects. (author)

  1. Tannat grape composition responses to spatial variability of temperature in an Uruguay's coastal wine region

    Science.gov (United States)

    Fourment, Mercedes; Ferrer, Milka; González-Neves, Gustavo; Barbeau, Gérard; Bonnardot, Valérie; Quénol, Hervé

    2017-09-01

    Spatial variability of temperature was studied in relation to the berry basic composition and secondary compounds of the Tannat cultivar at harvest from vineyards located in Canelones and Montevideo, the most important wine region of Uruguay. Monitoring of berries and recording of temperature were performed in 10 commercial vineyards of Tannat situated in the southern coastal wine region of the country for three vintages (2012, 2013, and 2014). Results from a multivariate correlation analysis between berry composition and temperature over the three vintages showed that (1) Tannat responses to spatial variability of temperature were different over the vintages, (2) correlations between secondary metabolites and temperature were higher than those between primary metabolites, and (3) correlation values between berry composition and climate variables increased when ripening occurred under dry conditions (below average rainfall). For a particular studied vintage (2013), temperatures explained 82.5% of the spatial variability of the berry composition. Daily thermal amplitude was found to be the most important spatial mode of variability with lower values recorded at plots nearest to the sea and more exposed to La Plata River. The highest levels in secondary compounds were found in berries issued from plots situated as far as 18.3 km from La Plata River. The increasing knowledge of temperature spatial variability and its impact on grape berry composition contributes to providing possible issues to adapt grapevine to climate change.

  2. Spatial interpolation of climate variables in Northern Germany—Influence of temporal resolution and network density

    Directory of Open Access Journals (Sweden)

    C. Berndt

    2018-02-01

    New hydrological insights: Geostatistical techniques provide a better performance for all climate variables compared to simple methods Radar data improves the estimation of rainfall with hourly temporal resolution, while topography is useful for weekly to yearly values and temperature in general. No helpful information was found for cloudiness, sunshine duration, and wind speed, while interpolation of humidity benefitted from additional temperature data. The influences of temporal resolution, spatial variability, and additional information appear to be stronger than station density effects. High spatial variability of hourly precipitation causes the highest error, followed by wind speed, cloud coverage and sunshine duration. Lowest errors occur for temperature and humidity.

  3. Linking spatial and dynamic models for traffic maneuvers

    DEFF Research Database (Denmark)

    Olderog, Ernst-Rüdiger; Ravn, Anders Peter; Wisniewski, Rafal

    2015-01-01

    For traffic maneuvers of multiple vehicles on highways we build an abstract spatial and a concrete dynamic model. In the spatial model we show the safety (collision freedom) of lane-change maneuvers. By linking the spatial and dynamic model via suitable refinements of the spatial atoms to distance...

  4. Evolution of dispersal in spatially and temporally variable environments: The importance of life cycles.

    Science.gov (United States)

    Massol, François; Débarre, Florence

    2015-07-01

    Spatiotemporal variability of the environment is bound to affect the evolution of dispersal, and yet model predictions strongly differ on this particular effect. Recent studies on the evolution of local adaptation have shown that the life cycle chosen to model the selective effects of spatiotemporal variability of the environment is a critical factor determining evolutionary outcomes. Here, we investigate the effect of the order of events in the life cycle on the evolution of unconditional dispersal in a spatially heterogeneous, temporally varying landscape. Our results show that the occurrence of intermediate singular strategies and disruptive selection are conditioned by the temporal autocorrelation of the environment and by the life cycle. Life cycles with dispersal of adults versus dispersal of juveniles, local versus global density regulation, give radically different evolutionary outcomes that include selection for total philopatry, evolutionary bistability, selection for intermediate stable states, and evolutionary branching points. Our results highlight the importance of accounting for life-cycle specifics when predicting the effects of the environment on evolutionarily selected trait values, such as dispersal, as well as the need to check the robustness of model conclusions against modifications of the life cycle. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

  5. Developing a modelling for the spatial data infrastructure

    CSIR Research Space (South Africa)

    Hjelmager, J

    2005-07-01

    Full Text Available The Commission on Spatial Data Standards of the International Cartographic Association (ICA) is working on defining spatial models and technical characteristics of a Spatial Data Infrastructure (SDI). To date, this work has been restricted...

  6. TES ammonia retrieval strategy and global observations of the spatial and seasonal variability of ammonia

    Directory of Open Access Journals (Sweden)

    M. W. Shephard

    2011-10-01

    Full Text Available Presently only limited sets of tropospheric ammonia (NH3 measurements in the Earth's atmosphere have been reported from satellite and surface station measurements, despite the well-documented negative impact of NH3 on the environment and human health. Presented here is a detailed description of the satellite retrieval strategy and analysis for the Tropospheric Emission Spectrometer (TES using simulations and measurements. These results show that: (i the level of detectability for a representative boundary layer TES NH3 mixing ratio value is ~0.4 ppbv, which typically corresponds to a profile that contains a maximum level value of ~1 ppbv; (ii TES NH3 retrievals generally provide at most one degree of freedom for signal (DOFS, with peak sensitivity between 700 and 900 mbar; (iii TES NH3 retrievals show significant spatial and seasonal variability of NH3 globally; (iv initial comparisons of TES observations with GEOS-CHEM estimates show TES values being higher overall. Important differences and similarities between modeled and observed seasonal and spatial trends are noted, with discrepancies indicating areas where the timing and magnitude of modeled NH3 emissions from agricultural sources, and to lesser extent biomass burning sources, need further study.

  7. Alaskan soil carbon stocks: spatial variability and dependence on environmental factors

    Directory of Open Access Journals (Sweden)

    U. Mishra

    2012-09-01

    Full Text Available The direction and magnitude of soil organic carbon (SOC changes in response to climate change depend on the spatial and vertical distributions of SOC. We estimated spatially resolved SOC stocks from surface to C horizon, distinguishing active-layer and permafrost-layer stocks, based on geospatial analysis of 472 soil profiles and spatially referenced environmental variables for Alaska. Total Alaska state-wide SOC stock was estimated to be 77 Pg, with 61% in the active-layer, 27% in permafrost, and 12% in non-permafrost soils. Prediction accuracy was highest for the active-layer as demonstrated by highest ratio of performance to deviation (1.5. Large spatial variability was predicted, with whole-profile, active-layer, and permafrost-layer stocks ranging from 1–296 kg C m−2, 2–166 kg m−2, and 0–232 kg m−2, respectively. Temperature and soil wetness were found to be primary controllers of whole-profile, active-layer, and permafrost-layer SOC stocks. Secondary controllers, in order of importance, were found to be land cover type, topographic attributes, and bedrock geology. The observed importance of soil wetness rather than precipitation on SOC stocks implies that the poor representation of high-latitude soil wetness in Earth system models may lead to large uncertainty in predicted SOC stocks under future climate change scenarios. Under strict caveats described in the text and assuming temperature changes from the A1B Intergovernmental Panel on Climate Change emissions scenario, our geospatial model indicates that the equilibrium average 2100 Alaska active-layer depth could deepen by 11 cm, resulting in a thawing of 13 Pg C currently in permafrost. The equilibrium SOC loss associated with this warming would be highest under continuous permafrost (31%, followed by discontinuous (28%, isolated (24.3%, and sporadic (23.6% permafrost areas. Our high-resolution mapping of soil carbon stock reveals the

  8. Factor Copula Models for Replicated Spatial Data

    KAUST Repository

    Krupskii, Pavel

    2016-12-19

    We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.

  9. Factor Copula Models for Replicated Spatial Data

    KAUST Repository

    Krupskii, Pavel; Huser, Raphaë l; Genton, Marc G.

    2016-01-01

    We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.

  10. Modeling Spatially Unrestricted Pedestrian Traffic on Footbridges

    DEFF Research Database (Denmark)

    Zivanovic, Stana; Pavic, Aleksandar; Ingólfsson, Einar Thór

    2010-01-01

    restricted movement of pedestrians, has kept attracting attention of researchers. However, it is the normal spatially unrestricted pedestrian traffic, and its vertical dynamic loading component, that are most relevant for vibration serviceability checks for most footbridges. Despite the existence of numerous...... design procedures concerned with this loading, the current confidence in its modelling is low due to lack of verification of the models on as-built structures. This is the motivation behind reviewing the existing design procedures for modelling normal pedestrian traffic in this paper and evaluating...

  11. The spatial impact of neighbouring on the exports activities of COMESA countries by using spatial panel models

    Science.gov (United States)

    Hamzalouh, L.; Ismail, M. T.; Rahman, R. A.

    2017-09-01

    In this paper, spatial panel models were used and the method for selecting the best model amongst the spatial fixed effects model and the spatial random effects model to estimate the fitting model by using the robust Hausman test for analysis of the exports pattern of the Common Market for Eastern and Southern African (COMESA) countries. And examine the effects of the interactions of the economic statistic of explanatory variables on the exports of the COMESA. Results indicated that the spatial Durbin model with fixed effects specification should be tested and considered in most cases of this study. After that, the direct and indirect effects among COMESA regions were assessed, and the role of indirect spatial effects in estimating exports was empirically demonstrated. Regarding originality and research value, and to the best of the authors’ knowledge, this is the first attempt to examine exports between COMESA and its member countries through spatial panel models using XSMLE, which is a new command for spatial analysis using STATA.

  12. Towards Quantitative Spatial Models of Seabed Sediment Composition.

    Directory of Open Access Journals (Sweden)

    David Stephens

    Full Text Available There is a need for fit-for-purpose maps for accurately depicting the types of seabed substrate and habitat and the properties of the seabed for the benefits of research, resource management, conservation and spatial planning. The aim of this study is to determine whether it is possible to predict substrate composition across a large area of seabed using legacy grain-size data and environmental predictors. The study area includes the North Sea up to approximately 58.44°N and the United Kingdom's parts of the English Channel and the Celtic Seas. The analysis combines outputs from hydrodynamic models as well as optical remote sensing data from satellite platforms and bathymetric variables, which are mainly derived from acoustic remote sensing. We build a statistical regression model to make quantitative predictions of sediment composition (fractions of mud, sand and gravel using the random forest algorithm. The compositional data is analysed on the additive log-ratio scale. An independent test set indicates that approximately 66% and 71% of the variability of the two log-ratio variables are explained by the predictive models. A EUNIS substrate model, derived from the predicted sediment composition, achieved an overall accuracy of 83% and a kappa coefficient of 0.60. We demonstrate that it is feasible to spatially predict the seabed sediment composition across a large area of continental shelf in a repeatable and validated way. We also highlight the potential for further improvements to the method.

  13. Spatial variability of hydraulic conductivity of an unconfined sandy aquifer determined by a mini slug test

    DEFF Research Database (Denmark)

    Bjerg, Poul Løgstrup; Hinsby, Klaus; Christensen, Thomas Højlund

    1992-01-01

    The spatial variability of the hydraulic conductivity in a sandy aquifer has been determined by a mini slug test method. The hydraulic conductivity (K) of the aquifer has a geometric mean of 5.05 × 10−4 m s−1, and an overall variance of 1n K equal to 0.37 which corresponds quite well to the results...... obtained by two large scale tracer experiments performed in the aquifer. A geological model of the aquifer based on 31 sediment cores, proposed three hydrogeological layers in the aquifer concurrent with the vertical variations observed with respect to hydraulic conductivity. The horizontal correlation......, to be in the range of 0.3–0.5 m compared with a value of 0.42 m obtained in one of the tracer tests performed....

  14. China’s Air Quality and Respiratory Disease Mortality Based on the Spatial Panel Model

    Directory of Open Access Journals (Sweden)

    Qilong Cao

    2017-09-01

    Full Text Available Background: Air pollution has become an important factor restricting China’s economic development and has subsequently brought a series of social problems, including the impact of air pollution on the health of residents, which is a topical issue in China. Methods: Taking into account this spatial imbalance, the paper is based on the spatial panel data model PM2.5. Respiratory disease mortality in 31 Chinese provinces from 2004 to 2008 is taken as the main variable to study the spatial effect and impact of air quality and respiratory disease mortality on a large scale. Results: It was found that there is a spatial correlation between the mortality of respiratory diseases in Chinese provinces. The spatial correlation can be explained by the spatial effect of PM2.5 pollutions in the control of other variables. Conclusions: Compared with the traditional non-spatial model, the spatial model is better for describing the spatial relationship between variables, ensuring the conclusions are scientific and can measure the spatial effect between variables.

  15. China's Air Quality and Respiratory Disease Mortality Based on the Spatial Panel Model.

    Science.gov (United States)

    Cao, Qilong; Liang, Ying; Niu, Xueting

    2017-09-18

    Background : Air pollution has become an important factor restricting China's economic development and has subsequently brought a series of social problems, including the impact of air pollution on the health of residents, which is a topical issue in China. Methods : Taking into account this spatial imbalance, the paper is based on the spatial panel data model PM 2.5 . Respiratory disease mortality in 31 Chinese provinces from 2004 to 2008 is taken as the main variable to study the spatial effect and impact of air quality and respiratory disease mortality on a large scale. Results : It was found that there is a spatial correlation between the mortality of respiratory diseases in Chinese provinces. The spatial correlation can be explained by the spatial effect of PM 2.5 pollutions in the control of other variables. Conclusions : Compared with the traditional non-spatial model, the spatial model is better for describing the spatial relationship between variables, ensuring the conclusions are scientific and can measure the spatial effect between variables.

  16. China’s Air Quality and Respiratory Disease Mortality Based on the Spatial Panel Model

    Science.gov (United States)

    Cao, Qilong; Liang, Ying; Niu, Xueting

    2017-01-01

    Background: Air pollution has become an important factor restricting China’s economic development and has subsequently brought a series of social problems, including the impact of air pollution on the health of residents, which is a topical issue in China. Methods: Taking into account this spatial imbalance, the paper is based on the spatial panel data model PM2.5. Respiratory disease mortality in 31 Chinese provinces from 2004 to 2008 is taken as the main variable to study the spatial effect and impact of air quality and respiratory disease mortality on a large scale. Results: It was found that there is a spatial correlation between the mortality of respiratory diseases in Chinese provinces. The spatial correlation can be explained by the spatial effect of PM2.5 pollutions in the control of other variables. Conclusions: Compared with the traditional non-spatial model, the spatial model is better for describing the spatial relationship between variables, ensuring the conclusions are scientific and can measure the spatial effect between variables. PMID:28927016

  17. Spatial variability of coastal wetland resilience to sea-level rise using Bayesian inference

    Science.gov (United States)

    Hardy, T.; Wu, W.

    2017-12-01

    The coastal wetlands in the Northern Gulf of Mexico (NGOM) account for 40% of coastal wetland area in the United States and provide various ecosystem services to the region and broader areas. Increasing rates of relative sea-level rise (RSLR), and reduced sediment input have increased coastal wetland loss in the NGOM, accounting for 80% of coastal wetland loss in the nation. Traditional models for predicting the impact of RSLR on coastal wetlands in the NGOM have focused on coastal erosion driven by geophysical variables only, and/or at small spatial extents. Here we developed a model in Bayesian inference to make probabilistic prediction of wetland loss in the entire NGOM as a function of vegetation productivity and geophysical attributes. We also studied how restoration efforts help maintain the area of coastal wetlands. Vegetation productivity contributes organic matter to wetland sedimentation and was approximated using the remotely sensed normalized difference moisture index (NDMI). The geophysical variables include RSLR, tidal range, river discharge, coastal slope, and wave height. We found a significantly positive relation between wetland loss and RSLR, which varied significantly at different river discharge regimes. There also existed a significantly negative relation between wetland loss and NDMI, indicating that in-situ vegetation productivity contributed to wetland resilience to RSLR. This relation did not vary significantly between river discharge regimes. The spatial relation revealed three areas of high RSLR but relatively low wetland loss; these areas were associated with wetland restoration projects in coastal Louisiana. Two projects were breakwater projects, where hard materials were placed off-shore to reduce wave action and promote sedimentation. And one project was a vegetation planting project used to promote sedimentation and wetland stabilization. We further developed an interactive web tool that allows stakeholders to develop similar wetland

  18. The quantitative modelling of human spatial habitability

    Science.gov (United States)

    Wise, James A.

    1988-01-01

    A theoretical model for evaluating human spatial habitability (HuSH) in the proposed U.S. Space Station is developed. Optimizing the fitness of the space station environment for human occupancy will help reduce environmental stress due to long-term isolation and confinement in its small habitable volume. The development of tools that operationalize the behavioral bases of spatial volume for visual kinesthetic, and social logic considerations is suggested. This report further calls for systematic scientific investigations of how much real and how much perceived volume people need in order to function normally and with minimal stress in space-based settings. The theoretical model presented in this report can be applied to any size or shape interior, at any scale of consideration, for the Space Station as a whole to an individual enclosure or work station. Using as a point of departure the Isovist model developed by Dr. Michael Benedikt of the U. of Texas, the report suggests that spatial habitability can become as amenable to careful assessment as engineering and life support concerns.

  19. Spatially variable natural selection and the divergence between parapatric subspecies of lodgepole pine (Pinus contorta, Pinaceae).

    Science.gov (United States)

    Eckert, Andrew J; Shahi, Hurshbir; Datwyler, Shannon L; Neale, David B

    2012-08-01

    Plant populations arrayed across sharp environmental gradients are ideal systems for identifying the genetic basis of ecologically relevant phenotypes. A series of five uplifted marine terraces along the northern coast of California represents one such system where morphologically distinct populations of lodgepole pine (Pinus contorta) are distributed across sharp soil gradients ranging from fertile soils near the coast to podzolic soils ca. 5 km inland. A total of 92 trees was sampled across four coastal marine terraces (N = 10-46 trees/terrace) located in Mendocino County, California and sequenced for a set of 24 candidate genes for growth and responses to various soil chemistry variables. Statistical analyses relying on patterns of nucleotide diversity were employed to identify genes whose diversity patterns were inconsistent with three null models. Most genes displayed patterns of nucleotide diversity that were consistent with null models (N = 19) or with the presence of paralogs (N = 3). Two genes, however, were exceptional: an aluminum responsive ABC-transporter with F(ST) = 0.664 and an inorganic phosphate transporter characterized by divergent haplotypes segregating at intermediate frequencies in most populations. Spatially variable natural selection along gradients of aluminum and phosphate ion concentrations likely accounted for both outliers. These results shed light on some of the genetic components comprising the extended phenotype of this ecosystem, as well as highlight ecotones as fruitful study systems for the detection of adaptive genetic variants.

  20. Spatial variability of maximum annual daily rain under different return periods at the Rio de Janeiro state, Brazil

    Directory of Open Access Journals (Sweden)

    Roriz Luciano Machado

    2010-01-01

    Full Text Available Knowledge of maximum daily rain and its return period in a region is an important tool to soil conservation, hydraulic engineering and preservation of road projects. The objective of this work was to evaluate the spatial variability of maximum annual daily rain considering different return periods, at the Rio de Janeiro State. The data set was composed by historical series of 119 rain gauges, for 36 years of observation. The return periods, estimated by Gumbel distribution, were 2, 5, 10, 25, 50 and 100 years. The spatial variability of the return periods was evaluated by semivariograms. All the return periods presented spatial dependence, with exponential and spherical model fitted to the experimental semivariograms. The parameters of the fitted semivariogram model were very similar; however, it was observed the presence of higher nugget effects for semivariograms of longer return periods. The values of maximum annual daily average rain in all the return periods increased from north to south and from countryside to the coast. In the region between the Serra do Mar range and the coast, besides increasing in magnitude, an increase in the spatial variability of the studied values with increasing return periods was also noticed. This behavior is probably caused by the orographic effect. The interpolated maps were more erratic for higher return periods and at the North, Northeast and Coastal Plain regions, in which the installation of new pluviometric stations are recommended.

  1. Internal variability in a regional climate model over West Africa

    Energy Technology Data Exchange (ETDEWEB)

    Vanvyve, Emilie; Ypersele, Jean-Pascal van [Universite catholique de Louvain, Institut d' astronomie et de geophysique Georges Lemaitre, Louvain-la-Neuve (Belgium); Hall, Nicholas [Laboratoire d' Etudes en Geophysique et Oceanographie Spatiales/Centre National d' Etudes Spatiales, Toulouse Cedex 9 (France); Messager, Christophe [University of Leeds, Institute for Atmospheric Science, Environment, School of Earth and Environment, Leeds (United Kingdom); Leroux, Stephanie [Universite Joseph Fourier, Laboratoire d' etude des Transferts en Hydrologie et Environnement, BP53, Grenoble Cedex 9 (France)

    2008-02-15

    Sensitivity studies with regional climate models are often performed on the basis of a few simulations for which the difference is analysed and the statistical significance is often taken for granted. In this study we present some simple measures of the confidence limits for these types of experiments by analysing the internal variability of a regional climate model run over West Africa. Two 1-year long simulations, differing only in their initial conditions, are compared. The difference between the two runs gives a measure of the internal variability of the model and an indication of which timescales are reliable for analysis. The results are analysed for a range of timescales and spatial scales, and quantitative measures of the confidence limits for regional model simulations are diagnosed for a selection of study areas for rainfall, low level temperature and wind. As the averaging period or spatial scale is increased, the signal due to internal variability gets smaller and confidence in the simulations increases. This occurs more rapidly for variations in precipitation, which appear essentially random, than for dynamical variables, which show some organisation on larger scales. (orig.)

  2. The quantitative modelling of human spatial habitability

    Science.gov (United States)

    Wise, J. A.

    1985-01-01

    A model for the quantitative assessment of human spatial habitability is presented in the space station context. The visual aspect assesses how interior spaces appear to the inhabitants. This aspect concerns criteria such as sensed spaciousness and the affective (emotional) connotations of settings' appearances. The kinesthetic aspect evaluates the available space in terms of its suitability to accommodate human movement patterns, as well as the postural and anthrometric changes due to microgravity. Finally, social logic concerns how the volume and geometry of available space either affirms or contravenes established social and organizational expectations for spatial arrangements. Here, the criteria include privacy, status, social power, and proxemics (the uses of space as a medium of social communication).

  3. Modeling mental spatial reasoning about cardinal directions.

    Science.gov (United States)

    Schultheis, Holger; Bertel, Sven; Barkowsky, Thomas

    2014-01-01

    This article presents research into human mental spatial reasoning with orientation knowledge. In particular, we look at reasoning problems about cardinal directions that possess multiple valid solutions (i.e., are spatially underdetermined), at human preferences for some of these solutions, and at representational and procedural factors that lead to such preferences. The article presents, first, a discussion of existing, related conceptual and computational approaches; second, results of empirical research into the solution preferences that human reasoners actually have; and, third, a novel computational model that relies on a parsimonious and flexible spatio-analogical knowledge representation structure to robustly reproduce the behavior observed with human reasoners. Copyright © 2014 Cognitive Science Society, Inc.

  4. Integrating remote sensing and spatially explicit epidemiological modeling

    Science.gov (United States)

    Finger, Flavio; Knox, Allyn; Bertuzzo, Enrico; Mari, Lorenzo; Bompangue, Didier; Gatto, Marino; Rinaldo, Andrea

    2015-04-01

    Spatially explicit epidemiological models are a crucial tool for the prediction of epidemiological patterns in time and space as well as for the allocation of health care resources. In addition they can provide valuable information about epidemiological processes and allow for the identification of environmental drivers of the disease spread. Most epidemiological models rely on environmental data as inputs. They can either be measured in the field by the means of conventional instruments or using remote sensing techniques to measure suitable proxies of the variables of interest. The later benefit from several advantages over conventional methods, including data availability, which can be an issue especially in developing, and spatial as well as temporal resolution of the data, which is particularly crucial for spatially explicit models. Here we present the case study of a spatially explicit, semi-mechanistic model applied to recurring cholera outbreaks in the Lake Kivu area (Democratic Republic of the Congo). The model describes the cholera incidence in eight health zones on the shore of the lake. Remotely sensed datasets of chlorophyll a concentration in the lake, precipitation and indices of global climate anomalies are used as environmental drivers. Human mobility and its effect on the disease spread is also taken into account. Several model configurations are tested on a data set of reported cases. The best models, accounting for different environmental drivers, and selected using the Akaike information criterion, are formally compared via cross validation. The best performing model accounts for seasonality, El Niño Southern Oscillation, precipitation and human mobility.

  5. ASSESSMENT SPATIAL VARIABILITY OF SOIL ERODIBILITY BY USING OF GEOSTATISTIC AND GIS (Case study MEHR watershed of SABZEVAR

    Directory of Open Access Journals (Sweden)

    Ayoubi, S.A

    2005-05-01

    Full Text Available Soil erodibility is one of the key factors on some sediment and soil erosion models such as USLE, MUSLE, RUSLE, AUSLE (USLE modified in LS factor and MMF and represents like K factor and is function of particle distribution, organic mater, soil structure and ermeability. Traditional methods do not take spatial variability and estimate precision of variables in to consideration and amount of them are constant across the whole of soil series .This study was performed to assess spatial variability of soil erodibility and its relevant variables at MEHR watershed from Khorasan province, in northern Iran. Interested network was designed by 110 samples like nested- systematic with distance about 50, 100, 250 and 500 meter across the study area by preparing point map at GIS. Sampling points were identified in field by an Global Positioning system. Soil sampling was done at depth of 0-5cm of ground surface and permeability was studied at depth of 5-30 cm. Some soil properties such as particle distribution and organic mater were measured at laboratory. Particle size distribution was determined by Hydrometer method and Organic matter was measured by wet oxidation approach. Then spatial analysis was done. Variography analysis on soil attributes according to soil erodibility, showed that Gaussian, exponential and spherical models were the most models to predict spatial variability of soil parameters. The range of spatial dependencies was changed from 320 to 3200 m. Soil attribute maps prepared by kriging technique using models parameters. Then soil attributes were composed by Wischmeier (1978 formula in Illwis media to calculate K factor. Amount of soil erodibility changed from 0.13 to 0.91 that it's maximum and minimum was identified in east and southwest of studiedarea. Soil spatial variability pattern, is similar to silt pattern due to high effect of silt on soil rodibility, Also that is partially confirmed with geology map, indicated which soil

  6. Eutrophication Modeling Using Variable Chlorophyll Approach

    International Nuclear Information System (INIS)

    Abdolabadi, H.; Sarang, A.; Ardestani, M.; Mahjoobi, E.

    2016-01-01

    In this study, eutrophication was investigated in Lake Ontario to identify the interactions among effective drivers. The complexity of such phenomenon was modeled using a system dynamics approach based on a consideration of constant and variable stoichiometric ratios. The system dynamics approach is a powerful tool for developing object-oriented models to simulate complex phenomena that involve feedback effects. Utilizing stoichiometric ratios is a method for converting the concentrations of state variables. During the physical segmentation of the model, Lake Ontario was divided into two layers, i.e., the epilimnion and hypolimnion, and differential equations were developed for each layer. The model structure included 16 state variables related to phytoplankton, herbivorous zooplankton, carnivorous zooplankton, ammonium, nitrate, dissolved phosphorus, and particulate and dissolved carbon in the epilimnion and hypolimnion during a time horizon of one year. The results of several tests to verify the model, close to 1 Nash-Sutcliff coefficient (0.98), the data correlation coefficient (0.98), and lower standard errors (0.96), have indicated well-suited model’s efficiency. The results revealed that there were significant differences in the concentrations of the state variables in constant and variable stoichiometry simulations. Consequently, the consideration of variable stoichiometric ratios in algae and nutrient concentration simulations may be applied in future modeling studies to enhance the accuracy of the results and reduce the likelihood of inefficient control policies.

  7. Evolution of learning strategies in temporally and spatially variable environments: a review of theory.

    Science.gov (United States)

    Aoki, Kenichi; Feldman, Marcus W

    2014-02-01

    The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change--coevolutionary, two-timescale, and information decay--are compared and shown to sometimes yield contradictory results. The so-called Rogers' paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers' paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. Copyright © 2013 Elsevier Inc. All rights reserved.

  8. Evolution of learning strategies in temporally and spatially variable environments: A review of theory

    Science.gov (United States)

    Aoki, Kenichi; Feldman, Marcus W.

    2013-01-01

    The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change – coevolutionary, two-timescale, and information decay – are compared and shown to sometimes yield contradictory results. The so-called Rogers’ paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers’ paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. PMID:24211681

  9. Spatially varying coefficient models in real estate: Eigenvector spatial filtering and alternative approaches

    NARCIS (Netherlands)

    Helbich, M; Griffith, D

    2016-01-01

    Real estate policies in urban areas require the recognition of spatial heterogeneity in housing prices to account for local settings. In response to the growing number of spatially varying coefficient models in housing applications, this study evaluated four models in terms of their spatial patterns

  10. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo

    2016-10-01

    Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

  11. A Computational Model of Spatial Development

    Science.gov (United States)

    Hiraki, Kazuo; Sashima, Akio; Phillips, Steven

    Psychological experiments on children's development of spatial knowledge suggest experience at self-locomotion with visual tracking as important factors. Yet, the mechanism underlying development is unknown. We propose a robot that learns to mentally track a target object (i.e., maintaining a representation of an object's position when outside the field-of-view) as a model for spatial development. Mental tracking is considered as prediction of an object's position given the previous environmental state and motor commands, and the current environment state resulting from movement. Following Jordan & Rumelhart's (1992) forward modeling architecture the system consists of two components: an inverse model of sensory input to desired motor commands; and a forward model of motor commands to desired sensory input (goals). The robot was tested on the `three cups' paradigm (where children are required to select the cup containing the hidden object under various movement conditions). Consistent with child development, without the capacity for self-locomotion the robot's errors are self-center based. When given the ability of self-locomotion the robot responds allocentrically.

  12. Spherical Process Models for Global Spatial Statistics

    KAUST Repository

    Jeong, Jaehong

    2017-11-28

    Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of the spatial domain in global data. Over the past few decades, statisticians have developed covariance models that capture the spatial and temporal behavior of these global data sets. Though the geodesic distance is the most natural metric for measuring distance on the surface of a sphere, mathematical limitations have compelled statisticians to use the chordal distance to compute the covariance matrix in many applications instead, which may cause physically unrealistic distortions. Therefore, covariance functions directly defined on a sphere using the geodesic distance are needed. We discuss the issues that arise when dealing with spherical data sets on a global scale and provide references to recent literature. We review the current approaches to building process models on spheres, including the differential operator, the stochastic partial differential equation, the kernel convolution, and the deformation approaches. We illustrate realizations obtained from Gaussian processes with different covariance structures and the use of isotropic and nonstationary covariance models through deformations and geographical indicators for global surface temperature data. To assess the suitability of each method, we compare their log-likelihood values and prediction scores, and we end with a discussion of related research problems.

  13. Latent spatial models and sampling design for landscape genetics

    Science.gov (United States)

    Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.

    2016-01-01

    We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.

  14. The effect of short-range spatial variability on soil sampling uncertainty

    Energy Technology Data Exchange (ETDEWEB)

    Perk, Marcel van der [Department of Physical Geography, Utrecht University, P.O. Box 80115, 3508 TC Utrecht (Netherlands)], E-mail: m.vanderperk@geo.uu.nl; De Zorzi, Paolo; Barbizzi, Sabrina; Belli, Maria [Agenzia per la Protezione dell' Ambiente e per i Servizi Tecnici (APAT), Servizio Laboratori, Misure ed Attivita di Campo, Via di Castel Romano, 100-00128 Roma (Italy); Fajgelj, Ales; Sansone, Umberto [International Atomic Energy Agency (IAEA), Agency' s Laboratories Seibersdorf, A-1400 Vienna (Austria); Jeran, Zvonka; Jacimovic, Radojko [Jozef Stefan Institute, Jamova 39, 1000 Ljubljana (Slovenia)

    2008-11-15

    This paper aims to quantify the soil sampling uncertainty arising from the short-range spatial variability of elemental concentrations in the topsoils of agricultural, semi-natural, and contaminated environments. For the agricultural site, the relative standard sampling uncertainty ranges between 1% and 5.5%. For the semi-natural area, the sampling uncertainties are 2-4 times larger than in the agricultural area. The contaminated site exhibited significant short-range spatial variability in elemental composition, which resulted in sampling uncertainties of 20-30%.

  15. The effect of short-range spatial variability on soil sampling uncertainty.

    Science.gov (United States)

    Van der Perk, Marcel; de Zorzi, Paolo; Barbizzi, Sabrina; Belli, Maria; Fajgelj, Ales; Sansone, Umberto; Jeran, Zvonka; Jaćimović, Radojko

    2008-11-01

    This paper aims to quantify the soil sampling uncertainty arising from the short-range spatial variability of elemental concentrations in the topsoils of agricultural, semi-natural, and contaminated environments. For the agricultural site, the relative standard sampling uncertainty ranges between 1% and 5.5%. For the semi-natural area, the sampling uncertainties are 2-4 times larger than in the agricultural area. The contaminated site exhibited significant short-range spatial variability in elemental composition, which resulted in sampling uncertainties of 20-30%.

  16. Application of Spatial Regression Models to Income Poverty Ratios in Middle Delta Contiguous Counties in Egypt

    Directory of Open Access Journals (Sweden)

    Sohair F Higazi

    2013-02-01

    Full Text Available Regression analysis depends on several assumptions that have to be satisfied. A major assumption that is never satisfied when variables are from contiguous observations is the independence of error terms. Spatial analysis treated the violation of that assumption by two derived models that put contiguity of observations into consideration. Data used are from Egypt's 2006 latest census, for 93 counties in middle delta seven adjacent Governorates. The dependent variable used is the percent of individuals classified as poor (those who make less than 1$ daily. Predictors are some demographic indicators. Explanatory Spatial Data Analysis (ESDA is performed to examine the existence of spatial clustering and spatial autocorrelation between neighboring counties. The ESDA revealed spatial clusters and spatial correlation between locations. Three statistical models are applied to the data, the Ordinary Least Square regression model (OLS, the Spatial Error Model (SEM and the Spatial Lag Model (SLM.The Likelihood Ratio test and some information criterions are used to compare SLM and SEM to OLS. The SEM model proved to be better than the SLM model. Recommendations are drawn regarding the two spatial models used.

  17. Assessing the spatial variability of mountain precipitation in California's Sierra Nevada using the Airborne Snow Observatory

    Science.gov (United States)

    Brandt, T.; Deems, J. S.; Painter, T. H.; Dozier, J.

    2016-12-01

    In California's Sierra Nevada, 10 or fewer winter storms are responsible for most of the annual precipitation, which falls mostly as snow. Presently, surface stations are used to measure the dynamics of mountain precipitation. However, even in places like the Sierra Nevada—one of the most gauged regions in the world—the paucity of surface stations can lead to large errors in precipitation thereby biasing both total water year and short-term streamflow forecasts. Remotely sensed snow depth and water equivalent, at a time scale that resolves storms, might provide a novel solution to the problems of: (1) quantifying the spatial variability of mountain precipitation; and (2) assessing gridded precipitation products that are mostly based on surface station interpolation. NASA's Airborne Snow Observatory (ASO), an imaging spectrometer and LiDAR system, has measured snow in the Tuolumne River Basin in California's Sierra Nevada for the past four years, 2013-2016; and, measurements will continue. Principally, ASO monitors the progression of melt for water supply forecasting, nonetheless, a number of flights bracketed storms allowing for estimates of snow accumulation. In this study we examine a few of the ASO recorded storms to determine both the basin and subbasin orographic effect as well as the spatial patterns in total precipitation. We then compare these results to a number of gridded climate products and weather models including: Daymet, the Parameter-elevation Regressions on Independent Slopes Model (PRISM), the North American Land Data Assimilation System (NLDAS-2), and the Weather Research and Forecasting (WRF) model. Finally, to put each ASO recorded storm into context, we use a climatology produced from snow pillows and the North American Regional Reanalysis (NARR) for 2014-2016 to examine key accumulation events, and classify storms based on their integrated water vapor flux.

  18. Spatial interpolation schemes of daily precipitation for hydrologic modeling

    Science.gov (United States)

    Hwang, Y.; Clark, M.R.; Rajagopalan, B.; Leavesley, G.

    2012-01-01

    Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

  19. Spatial prediction of water quality variables along a main river channel, in presence of pollution hotspots.

    Science.gov (United States)

    Rizo-Decelis, L D; Pardo-Igúzquiza, E; Andreo, B

    2017-12-15

    In order to treat and evaluate the available data of water quality and fully exploit monitoring results (e.g. characterize regional patterns, optimize monitoring networks, infer conditions at unmonitored locations, etc.), it is crucial to develop improved and efficient methodologies. Accordingly, estimation of water quality along fluvial ecosystems is a frequent task in environment studies. In this work, a particular case of this problem is examined, namely, the estimation of water quality along a main stem of a large basin (where most anthropic activity takes place), from observational data measured along this river channel. We adapted topological kriging to this case, where each watershed contains all the watersheds of the upstream observed data ("nested support effect"). Data analysis was additionally extended by taking into account the upstream distance to the closest contamination hotspot as an external drift. We propose choosing the best estimation method by cross-validation. The methodological approach in spatial variability modeling may be used for optimizing the water quality monitoring of a given watercourse. The methodology presented is applied to 28 water quality variables measured along the Santiago River in Western Mexico. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Internal variability of a 3-D ocean model

    Directory of Open Access Journals (Sweden)

    Bjarne Büchmann

    2016-11-01

    Full Text Available The Defence Centre for Operational Oceanography runs operational forecasts for the Danish waters. The core setup is a 60-layer baroclinic circulation model based on the General Estuarine Transport Model code. At intervals, the model setup is tuned to improve ‘model skill’ and overall performance. It has been an area of concern that the uncertainty inherent to the stochastical/chaotic nature of the model is unknown. Thus, it is difficult to state with certainty that a particular setup is improved, even if the computed model skill increases. This issue also extends to the cases, where the model is tuned during an iterative process, where model results are fed back to improve model parameters, such as bathymetry.An ensemble of identical model setups with slightly perturbed initial conditions is examined. It is found that the initial perturbation causes the models to deviate from each other exponentially fast, causing differences of several PSUs and several kelvin within a few days of simulation. The ensemble is run for a full year, and the long-term variability of salinity and temperature is found for different regions within the modelled area. Further, the developing time scale is estimated for each region, and great regional differences are found – in both variability and time scale. It is observed that periods with very high ensemble variability are typically short-term and spatially limited events.A particular event is examined in detail to shed light on how the ensemble ‘behaves’ in periods with large internal model variability. It is found that the ensemble does not seem to follow any particular stochastic distribution: both the ensemble variability (standard deviation or range as well as the ensemble distribution within that range seem to vary with time and place. Further, it is observed that a large spatial variability due to mesoscale features does not necessarily correlate to large ensemble variability. These findings bear

  1. Accounting for spatial effects in land use regression for urban air pollution modeling.

    Science.gov (United States)

    Bertazzon, Stefania; Johnson, Markey; Eccles, Kristin; Kaplan, Gilaad G

    2015-01-01

    In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  2. An Evolutionary Model of Spatial Competition

    DEFF Research Database (Denmark)

    Knudsen, Thorbjørn; Winter, Sidney G.

      This paper sets forth an evolutionary model in which diverse businesses, with diverse offerings, compete in a stylized physical space.  When a business firm attempts to expand its activity, so as to profit further from the capabilities it has developed, it necessarily does so in a "new location...... as well in the new environment as they did in the old; the firm may respond with effort to locate appropriate environments or by modification of its routines.  Tradeoffs are presented between the complexity of a business model and its replication costs,  as well as issues involving response....... Randomly generated firm policies are tested first by a local market environment, and then, if success leads the firm to grow spatially, in a gradually expanding environment.  In the initial experiments reported here, we show that the model generates configurations that reflect features of the exogenous...

  3. Spatial and temporal variability of mean daily wind speeds in the Czech Republic, 1961-2015

    Czech Academy of Sciences Publication Activity Database

    Brázdil, Rudolf; Zahradníček, Pavel; Řezníčková, Ladislava; Tolasz, R.; Štěpánek, Petr; Dobrovolný, Petr

    2017-01-01

    Roč. 72, č. 3 (2017), s. 197-216 ISSN 0936-577X R&D Projects: GA MŠk(CZ) LO1415; GA ČR(CZ) GA15-11805S Institutional support: RVO:67179843 Keywords : mean daily wind speed * spatial variability * temporal variability * wind stilling * Czech Republic Subject RIV: DG - Athmosphere Sciences, Meteorology OBOR OECD: Meteorology and atmospheric sciences Impact factor: 1.578, year: 2016

  4. Groundwater variability across temporal and spatial scales in the central and northeastern U.S.

    OpenAIRE

    Li, B; Rodell, M; Famiglietti, JS

    2015-01-01

    © 2015 Elsevier B.V. Depth-to-water measurements from 181 monitoring wells in unconfined or semi-confined aquifers in nine regions of the central and northeastern U.S. were analyzed. Groundwater storage exhibited strong seasonal variations in all regions, with peaks in spring and lows in autumn, and its interannual variability was nearly unbounded, such that the impacts of droughts, floods, and excessive pumping could persist for many years. We found that the spatial variability of groundwate...

  5. Characterization of Spatial Variability of Hydrogeologic Properties for Unsaturated Flow in the Fractured Rocks at Yucca Mountain, Nevada

    International Nuclear Information System (INIS)

    Zhou, Quanlin; Bodvarsson, Gudmundur S.; Liu, Hui-Hai; Oldenburg, Curtis M.

    2002-01-01

    The spatial variability of layer-scale hydrogeologic properties of the unsaturated zone (UZ) at Yucca Mountain, Nevada, is investigated using inverse modeling. The thick UZ is grouped into five hydrostratigraphic units and further into 35 hydrogeologic layers. For each layer, lateral variability is represented by the variations in calibrated values of layer-scale properties at different individual deep boreholes. In the calibration model, matrix and fracture properties are calibrated for the one-dimensional vertical column at each individual borehole using the ITOUGH2 code. The objective function is the summation of the weighted misfits between the ambient unsaturated flow (represented by measured state variables: water saturation, water potential, and pneumatic pressure) and the simulated one in the one-dimensional flow system. The objective function also includes the weighted misfits between the calibrated properties and their prior information. Layer-scale state variables and prior rock properties are obtained from their core-scale measurements. Because of limited data, the lateral variability of three most sensitive properties (matrix permeability, matrix of the van Genuchten characterization, and fracture permeability) is calibrated, while all other properties are fixed at their calibrated layer-averaged values. Considerable lateral variability of hydrogeologic properties is obtained. For example, the lateral variability of is two to three orders of magnitude and that of and is one order of magnitude. The effect of lateral variability on site-scale flow and transport will be investigated in a future study

  6. Estimating regional spatial and temporal variability of PM(2.5) concentrations using satellite data, meteorology, and land use information.

    Science.gov (United States)

    Liu, Yang; Paciorek, Christopher J; Koutrakis, Petros

    2009-06-01

    Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters meteorologic information to estimate ground-level PM(2.5) concentrations. We developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM(2.5) concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain. The AOD model has a higher predicting power judged by adjusted R(2) (0.79) than does the non-AOD model (0.48). The predicted PM(2.5) concentrations by the AOD model are, on average, 0.8-0.9 microg/m(3) higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM(2.5), meteorologic parameters are major contributors to the better performance of the AOD model. GOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM(2.5) concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM(2.5) spatial patterns related to AOD availability.

  7. Mapping The Temporal and Spatial Variability of Soil Moisture Content Using Proximal Soil Sensing

    Science.gov (United States)

    Virgawati, S.; Mawardi, M.; Sutiarso, L.; Shibusawa, S.; Segah, H.; Kodaira, M.

    2018-05-01

    In studies related to soil optical properties, it has been proven that visual and NIR soil spectral response can predict soil moisture content (SMC) using proper data analysis techniques. SMC is one of the most important soil properties influencing most physical, chemical, and biological soil processes. The problem is how to provide reliable, fast and inexpensive information of SMC in the subsurface from numerous soil samples and repeated measurement. The use of spectroscopy technology has emerged as a rapid and low-cost tool for extensive investigation of soil properties. The objective of this research was to develop calibration models based on laboratory Vis-NIR spectroscopy to estimate the SMC at four different growth stages of the soybean crop in Yogyakarta Province. An ASD Field-spectrophotoradiometer was used to measure the reflectance of soil samples. The partial least square regression (PLSR) was performed to establish the relationship between the SMC with Vis-NIR soil reflectance spectra. The selected calibration model was used to predict the new samples of SMC. The temporal and spatial variability of SMC was performed in digital maps. The results revealed that the calibration model was excellent for SMC prediction. Vis-NIR spectroscopy was a reliable tool for the prediction of SMC.

  8. Documentation of programs that compute 1) static tilts for a spatially variable slip distribution, and 2) quasi-static tilts produced by an expanding dislocation loop with a spatially variable slip distribution

    Science.gov (United States)

    McHugh, Stuart

    1976-01-01

    The material in this report is concerned with the effects of a vertically oriented rectangular dislocation loop on the tilts observed at the free surface of an elastic half-space. Part I examines the effect of a spatially variable static strike-slip distribution across the slip surface. The tilt components as a function of distance parallel, or perpendicular, to the strike of the slip surface are displayed for different slip-versus-distance profiles. Part II examines the effect of spatially and temporally variable slip distributions across the dislocation loop on the quasi-static tilts at the free surface of an elastic half space. The model discussed in part II may be used to generate theoretical tilt versus time curves produced by creep events.

  9. Monitoring temporal and spatial variability in sandeel (Ammodytes hexapterus) abundance with pigeon guillemot (Cepphus columba) diets

    Science.gov (United States)

    Litzow, Michael A.; Piatt, John F.; Abookire, Alisa A.; Prichard, A.K.; Robards, Martin D.

    2000-01-01

    We evaluated pigeon guillemots (Cepphus columba) as monitors of nearshore fish abundance and community composition during 1995-1999 at Kachemak Bay, Alaska. We studied the composition of chick diets at 10 colonies and simultaneously measured fish abundance around colonies with beach seines and bottom trawls. Sandeels (Ammodytes hexapterus) formed the majority of the diet at one group of colonies. Temporal variability in sandeel abundance explained 74% of inter-annual variability in diet composition at these colonies and 93% of seasonal variability. Diets at other colonies were dominated by demersal fish. Among these colonies, 81% of the variability in the proportion of sandeels in diets was explained by spatial differences in sanded abundance. Pigeon guillemots exhibited a non-linear functional response to sandeel abundance in the area where these fish were most abundant. Temporal and spatial variability in demersal fish abundance was not consistently reflected in diets. Spatial differences in the proportion of different demersal fishes in the diet may have been driven by differences in guillemot prey preference. Prey specialization by individual pigeon guillemots was common, and may operate at the colony level. Inter-annual variability in sandeel abundance may have been tracked more accurately because the magnitude of change (11-fold) was greater than that of demersal fish (three-fold). (C) 2000 International Council for the Exploration of the Sea.

  10. A novel spatial performance metric for robust pattern optimization of distributed hydrological models

    Science.gov (United States)

    Stisen, S.; Demirel, C.; Koch, J.

    2017-12-01

    Evaluation of performance is an integral part of model development and calibration as well as it is of paramount importance when communicating modelling results to stakeholders and the scientific community. There exists a comprehensive and well tested toolbox of metrics to assess temporal model performance in the hydrological modelling community. On the contrary, the experience to evaluate spatial performance is not corresponding to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study aims at making a contribution towards advancing spatial pattern oriented model evaluation for distributed hydrological models. This is achieved by introducing a novel spatial performance metric which provides robust pattern performance during model calibration. The promoted SPAtial EFficiency (spaef) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multi-component approach is necessary in order to adequately compare spatial patterns. spaef, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are tested in a spatial pattern oriented model calibration of a catchment model in Denmark. The calibration is constrained by a remote sensing based spatial pattern of evapotranspiration and discharge timeseries at two stations. Our results stress that stand-alone metrics tend to fail to provide holistic pattern information to the optimizer which underlines the importance of multi-component metrics. The three spaef components are independent which allows them to complement each other in a meaningful way. This study promotes the use of bias insensitive metrics which allow comparing variables which are related but may differ in unit in order to optimally exploit spatial observations made available by remote sensing

  11. Influence of Surface Roughness Spatial Variability and Temporal Dynamics on the Retrieval of Soil Moisture from SAR Observations

    Directory of Open Access Journals (Sweden)

    Jesús Álvarez-Mozos

    2009-01-01

    Full Text Available Radar-based surface soil moisture retrieval has been subject of intense research during the last decades. However, several difficulties hamper the operational estimation of soil moisture based on currently available spaceborne sensors. The main difficulty experienced so far results from the strong influence of other surface characteristics, mainly roughness, on the backscattering coefficient, which hinders the soil moisture inversion. This is especially true for single configuration observations where the solution to the surface backscattering problem is ill-posed. Over agricultural areas cultivated with winter cereal crops, roughness can be assumed to remain constant along the growing cycle allowing the use of simplified approaches that facilitate the estimation of the moisture content of soils. However, the field scale spatial variability and temporal variations of roughness can introduce errors in the estimation of soil moisture that are difficult to evaluate. The objective of this study is to assess the impact of roughness spatial variability and roughness temporal variations on the retrieval of soil moisture from radar observations. A series of laser profilometer measurements were performed over several fields in an experimental watershed from September 2004 to March 2005. The influence of the observed roughness variability and its temporal variations on the retrieval of soil moisture is studied using simulations performed with the Integral Equation Model, considering different sensor configurations. Results show that both field scale roughness spatial variability and its temporal variations are aspects that need to be taken into account, since they can introduce large errors on the retrieved soil moisture values.

  12. Galactic models with variable spiral structure

    International Nuclear Information System (INIS)

    James, R.A.; Sellwood, J.A.

    1978-01-01

    A series of three-dimensional computer simulations of disc galaxies has been run in which the self-consistent potential of the disc stars is supplemented by that arising from a small uniform Population II sphere. The models show variable spiral structure, which is more pronounced for thin discs. In addition, the thin discs form weak bars. In one case variable spiral structure associated with this bar has been seen. The relaxed discs are cool outside resonance regions. (author)

  13. The Economics of Storage, Transmission and Drought: Integrating Variable Wind Power into Spatially Separated Electricity Grids

    NARCIS (Netherlands)

    Scora, H.; Sopinka, A.; Kooten, van G.C.

    2012-01-01

    To mitigate the high variability of wind and make it a more viable renewable energy source, observers recommend greater integration of spatially-separated electrical grids, with high transmission lines linking load centers, scattered wind farms and hydro storage sites. In this study, we examine the

  14. Predictivity strength of the spatial variability of phenanthrene sorption across two sandy loam fields

    DEFF Research Database (Denmark)

    Soares, Antonio; Paradelo Pérez, Marcos; Møldrup, Per

    2015-01-01

    Sorption is commonly suggested as the major process underlying the transport and fate of polycyclic aromatic hydrocarbons (PAHs) in soils. However, studies focusing in spatial variability at the field scale in particular are still scarce. In order to investigate the sorption of phenanthrene...

  15. The Weakest Link : Spatial Variability in the Piping Failure Mechanism of Dikes

    NARCIS (Netherlands)

    Kanning, W.

    2012-01-01

    Piping is an important failure mechanism of flood defense structures. A dike fails due to piping when a head difference causes first the uplift of an inland blanket layer, and subsequently soil erosion due to a ground water flow. Spatial variability of subsoil parameters causes the probability of

  16. Prediction of spatially variable unsaturated hydraulic conductivity using scaled particle-size distribution functions

    NARCIS (Netherlands)

    Nasta, P.; Romano, N.; Assouline, S; Vrugt, J.A.; Hopmans, J.W.

    2013-01-01

    Simultaneous scaling of soil water retention and hydraulic conductivity functions provides an effective means to characterize the heterogeneity and spatial variability of soil hydraulic properties in a given study area. The statistical significance of this approach largely depends on the number of

  17. Passive Sampling to Capture the Spatial Variability of Coarse Particles by Composition in Cleveland, OH

    Science.gov (United States)

    Passive samplers deployed at 25 sites for three week-long intervals were used to characterize spatial variability in the mass and composition of coarse particulate matter (PM10-2.5) in Cleveland, OH in summer 2008. The size and composition of individual particles deter...

  18. Effect of Spatial Variability on Maintenance and Repair Decisions for Concrete Structures

    NARCIS (Netherlands)

    Li, Y.

    2004-01-01

    Due to the increasingly number of elder and deteriorating structures, maintenance is becoming a serious and more complex problem in most of the countries. A lot of studies have been carried out in this area for years. However, the fact that a lot of parameters show spatial random variability, which

  19. The variability of the primeval forest's spatial pattern in the Babia Gora National Park

    International Nuclear Information System (INIS)

    Chrobaczek, U.; Jastrzebski, R.; Ziemniewicz, M.; Kaczor, D.; Widlak, M.; Lesiak, M.

    2011-01-01

    This paper analyzes the spatial variability of stand volume, species composition and regeneration in a primeval stand located in the lower maintain belt in the Babia Gora massif. These characteristics were surveyed on 259 circular plots (of a 7.0 m radius) located in a square grid 20 m · 20 m on the total area 10.36 ha. (authors)

  20. Spatial variability of soil carbon, pH, available phosphorous and potassium in organic farm located in Mediterranean Croatia

    Science.gov (United States)

    Bogunović, Igor; Pereira, Paulo; Šeput, Miranda

    2016-04-01

    Soil organic carbon (SOC), pH, available phosphorus (P), and potassium (K) are some of the most important factors to soil fertility. These soil parameters are highly variable in space and time, with implications to crop production. The aim of this work is study the spatial variability of SOC, pH, P and K in an organic farm located in river Rasa valley (Croatia). A regular grid (100 x 100 m) was designed and 182 samples were collected on Silty Clay Loam soil. P, K and SOC showed moderate heterogeneity with coefficient of variation (CV) of 21.6%, 32.8% and 51.9%, respectively. Soil pH record low spatial variability with CV of 1.5%. Soil pH, P and SOC did not follow normal distribution. Only after a Box-Cox transformation, data respected the normality requirements. Directional exponential models were the best fitted and used to describe spatial autocorrelation. Soil pH, P and SOC showed strong spatial dependence with nugget to sill ratio with 13.78%, 0.00% and 20.29%, respectively. Only K recorded moderate spatial dependence. Semivariogram ranges indicate that future sampling interval could be 150 - 200 m in order to reduce sampling costs. Fourteen different interpolation models for mapping soil properties were tested. The method with lowest Root Mean Square Error was the most appropriated to map the variable. The results showed that radial basis function models (Spline with Tension and Completely Regularized Spline) for P and K were the best predictors, while Thin Plate Spline and inverse distance weighting models were the least accurate. The best interpolator for pH and SOC was the local polynomial with the power of 1, while the least accurate were Thin Plate Spline. According to soil nutrient maps investigated area record very rich supply with K while P supply was insufficient on largest part of area. Soil pH maps showed mostly neutral reaction while individual parts of alkaline soil indicate the possibility of penetration of seawater and salt accumulation in the

  1. Characterization factors for terrestrial acidification at the global scale: a systematic analysis of spatial variability and uncertainty.

    Science.gov (United States)

    Roy, Pierre-Olivier; Azevedo, Ligia B; Margni, Manuele; van Zelm, Rosalie; Deschênes, Louise; Huijbregts, Mark A J

    2014-12-01

    Characterization factors (CFs) are used in life cycle assessment (LCA) to quantify the potential impact per unit of emission. CFs are obtained from a characterization model which assess the environmental mechanisms along the cause-effect chain linking an emission to its potential damage on a given area of protection, such as loss in ecosystem quality. Up to now, CFs for acidifying emissions did not cover the global scale and were only representative of their characterization model geographical scope. Consequently, current LCA practices implicitly assume that all emissions from a global supply chain occur within the continent referring to the characterization method geographical scope. This paper provides worldwide 2°×2.5° spatially-explicit CFs, representing the change in relative loss of terrestrial vascular plant species due to an emission change of nitrogen oxides (NOx), ammonia (NH3) and sulfur dioxide (SO2). We found that spatial variability in the CFs is much larger compared to statistical uncertainty (six orders of magnitude vs. two orders of magnitude). Spatial variability is mainly caused by the atmospheric fate factor and soil sensitivity factor, while the ecological effect factor is the dominant contributor to the statistical uncertainty. The CFs provided in our study allow the worldwide spatially explicit evaluation of life cycle impacts related to acidifying emissions. This opens the door to evaluate regional life cycle emissions of different products in a global economy. Copyright © 2014 Elsevier B.V. All rights reserved.

  2. Spatial Stochastic Point Models for Reservoir Characterization

    Energy Technology Data Exchange (ETDEWEB)

    Syversveen, Anne Randi

    1997-12-31

    The main part of this thesis discusses stochastic modelling of geology in petroleum reservoirs. A marked point model is defined for objects against a background in a two-dimensional vertical cross section of the reservoir. The model handles conditioning on observations from more than one well for each object and contains interaction between objects, and the objects have the correct length distribution when penetrated by wells. The model is developed in a Bayesian setting. The model and the simulation algorithm are demonstrated by means of an example with simulated data. The thesis also deals with object recognition in image analysis, in a Bayesian framework, and with a special type of spatial Cox processes called log-Gaussian Cox processes. In these processes, the logarithm of the intensity function is a Gaussian process. The class of log-Gaussian Cox processes provides flexible models for clustering. The distribution of such a process is completely characterized by the intensity and the pair correlation function of the Cox process. 170 refs., 37 figs., 5 tabs.

  3. Monitoring meteorological spatial variability in viticulture using a low-cost Wireless Sensor Network

    Science.gov (United States)

    Matese, Alessandro; Crisci, Alfonso; Di Gennaro, Filippo; Primicerio, Jacopo; Tomasi, Diego; Guidoni, Silvia

    2014-05-01

    In a long-term perspective, the current global agricultural scenario will be characterize by critical issues in terms of water resource management and environmental protection. The concept of sustainable agriculture would become crucial at reducing waste, optimizing the use of pesticides and fertilizers to crops real needs. This can be achieved through a minimum-scale monitoring of the crop physiologic status and the environmental parameters that characterize the microclimate. Viticulture is often subject to high variability within the same vineyard, thus becomes important to monitor this heterogeneity to allow a site-specific management and maximize the sustainability and quality of production. Meteorological variability expressed both at vineyard scale (mesoclimate) and at single plant level (microclimate) plays an important role during the grape ripening process. The aim of this work was to compare temperature, humidity and solar radiation measurements at different spatial scales. The measurements were assessed for two seasons (2011, 2012) in two vineyards of the Veneto region (North-East Italy), planted with Pinot gris and Cabernet Sauvignon using a specially designed and developed Wireless Sensor Network (WSN). The WSN consists of various levels: the Master/Gateway level coordinates the WSN and performs data aggregation; the Farm/Server level takes care of storing data on a server, data processing and graphic rendering. Nodes level is based on a network of peripheral nodes consisting of a sensor board equipped with sensors and wireless module. The system was able to monitor the agrometeorological parameters in the vineyard: solar radiation, air temperature and air humidity. Different sources of spatial variation were studied, from meso-scale to micro-scale. A widespread investigation was conducted, building a factorial design able to evidence the role played by any factor influencing the physical environment in the vineyard, such as the surrounding climate

  4. Theoretical aspects of spatial-temporal modeling

    CERN Document Server

    Matsui, Tomoko

    2015-01-01

    This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter provides up-to-date coverage of particle association measures that underpin the theoretical properties of recently developed random set methods in space and time otherwise known as the class of probability hypothesis density framework (PHD filters). The second chapter gives an overview of recent advances in Monte Carlo methods for Bayesian filtering in high-dimensional spaces. In particular, the chapter explains how one may extend classical sequential Monte Carlo methods for filtering and static inference problems to high dimensions and big-data applications. The third chapter presents an overview of generalized families of processes that extend the class of Gaussian process models to heavy-tailed families known as alph...

  5. The effects of spatial variability of the aggressiveness of soil on system reliability of corroding underground pipelines

    International Nuclear Information System (INIS)

    Sahraoui, Yacine; Chateauneuf, Alaa

    2016-01-01

    In this paper, a probabilistic methodology is presented for assessing the time-variant reliability of corroded underground pipelines subjected to space-variant soil aggressiveness. The Karhunen-Loève expansion is used to model the spatial variability of soil as a correlated stochastic field. The pipeline is considered as a series system for which the component and system failure probabilities are computed by Monte Carlo simulations. The probabilistic model provides a realistic time and space modelling of stochastic variations, leading to appropriate estimation of the lifetime distribution. The numerical analyses allow us to investigate the impact of various parameters on the reliability of underground pipelines, such as the soil aggressiveness, the pipe design variables, the soil correlation length and the pipeline length. The results show that neglecting the effect of spatial variability leads to pessimistic estimation of the residual lifetime and can lead to condemn prematurely the structure. - Highlights: • The role of soil heterogeneity in pipeline reliability assessment has been shown. • The impact of pipe length and soil correlation length has been examined. • The effect of the uncertainties related to design variables has been observed. • Pipe thickness design for homogeneous reliability has been proposed.

  6. Gait variability: methods, modeling and meaning

    Directory of Open Access Journals (Sweden)

    Hausdorff Jeffrey M

    2005-07-01

    Full Text Available Abstract The study of gait variability, the stride-to-stride fluctuations in walking, offers a complementary way of quantifying locomotion and its changes with aging and disease as well as a means of monitoring the effects of therapeutic interventions and rehabilitation. Previous work has suggested that measures of gait variability may be more closely related to falls, a serious consequence of many gait disorders, than are measures based on the mean values of other walking parameters. The Current JNER series presents nine reports on the results of recent investigations into gait variability. One novel method for collecting unconstrained, ambulatory data is reviewed, and a primer on analysis methods is presented along with a heuristic approach to summarizing variability measures. In addition, the first studies of gait variability in animal models of neurodegenerative disease are described, as is a mathematical model of human walking that characterizes certain complex (multifractal features of the motor control's pattern generator. Another investigation demonstrates that, whereas both healthy older controls and patients with a higher-level gait disorder walk more slowly in reduced lighting, only the latter's stride variability increases. Studies of the effects of dual tasks suggest that the regulation of the stride-to-stride fluctuations in stride width and stride time may be influenced by attention loading and may require cognitive input. Finally, a report of gait variability in over 500 subjects, probably the largest study of this kind, suggests how step width variability may relate to fall risk. Together, these studies provide new insights into the factors that regulate the stride-to-stride fluctuations in walking and pave the way for expanded research into the control of gait and the practical application of measures of gait variability in the clinical setting.

  7. Assessing spatial and temporal variability of phytoplankton communities' composition in the Iroise Sea ecosystem (Brittany, France): A 3D modeling approach. Part 2: Linking summer mesoscale distribution of phenotypic diversity to hydrodynamism

    Science.gov (United States)

    Cadier, Mathilde; Sourisseau, Marc; Gorgues, Thomas; Edwards, Christopher A.; Memery, Laurent

    2017-05-01

    Tidal front ecosystems are especially dynamic environments usually characterized by high phytoplankton biomass and high primary production. However, the description of functional microbial diversity occurring in these regions remains only partially documented. In this article, we use a numerical model, simulating a large number of phytoplankton phenotypes to explore the three-dimensional spatial patterns of phytoplankton abundance and diversity in the Iroise Sea (western Brittany). Our results suggest that, in boreal summer, a seasonally marked tidal front shapes the phytoplankton species richness. A diversity maximum is found in the surface mixed layer located slightly west of the tidal front (i.e., not strictly co-localized with high biomass concentrations) which separates tidally mixed from stratified waters. Differences in phenotypic composition between sub-regions with distinct hydrodynamic regimes (defined by vertical mixing, nutrients gradients and light penetration) are discussed. Local growth and/or physical transport of phytoplankton phenotypes are shown to explain our simulated diversity distribution. We find that a large fraction (64%) of phenotypes present during the considered period of September are ubiquitous, found in the frontal area and on both sides of the front (i.e., over the full simulated domain). The frontal area does not exhibit significant differences between its community composition and that of either the well-mixed region or an offshore Deep Chlorophyll Maximum (DCM). Only three phenotypes (out of 77) specifically grow locally and are found at substantial concentration only in the surface diversity maximum. Thus, this diversity maximum is composed of a combination of ubiquitous phenotypes with specific picoplankton deriving from offshore, stratified waters (including specific phenotypes from both the surface and the DCM) and imported through physical transport, completed by a few local phenotypes. These results are discussed in light

  8. Spatial variability and macro‐scale drivers of growth for native and introduced Flathead Catfish populations

    Science.gov (United States)

    Massie, Danielle L.; Smith, Geoffrey; Bonvechio, Timothy F.; Bunch, Aaron J.; Lucchesi, David O.; Wagner, Tyler

    2018-01-01

    Quantifying spatial variability in fish growth and identifying large‐scale drivers of growth are fundamental to many conservation and management decisions. Although fish growth studies often focus on a single population, it is becoming increasingly clear that large‐scale studies are likely needed for addressing transboundary management needs. This is particularly true for species with high recreational value and for those with negative ecological consequences when introduced outside of their native range, such as the Flathead Catfish Pylodictis olivaris. This study quantified growth variability of the Flathead Catfish across a large portion of its contemporary range to determine whether growth differences existed between habitat types (i.e., reservoirs and rivers) and between native and introduced populations. Additionally, we investigated whether growth parameters varied as a function of latitude and time since introduction (for introduced populations). Length‐at‐age data from 26 populations across 11 states in the USA were modeled using a Bayesian hierarchical von Bertalanffy growth model. Population‐specific growth trajectories revealed large variation in Flathead Catfish growth and relatively high uncertainty in growth parameters for some populations. Relatively high uncertainty was also evident when comparing populations and when quantifying large‐scale patterns. Growth parameters (Brody growth coefficient [K] and theoretical maximum average length [L∞]) were not different (based on overlapping 90% credible intervals) between habitat types or between native and introduced populations. For populations within the introduced range of Flathead Catfish, latitude was negatively correlated with K. For native populations, we estimated an 85% probability that L∞ estimates were negatively correlated with latitude. Contrary to predictions, time since introduction was not correlated with growth parameters in introduced populations of Flathead Catfish

  9. Modeling strategic investment decisions in spatial markets

    International Nuclear Information System (INIS)

    Lorenczik, Stefan; Malischek, Raimund

    2014-01-01

    Markets for natural resources and commodities are often oligopolistic. In these markets, production capacities are key for strategic interaction between the oligopolists. We analyze how different market structures influence oligopolistic capacity investments and thereby affect supply, prices and rents in spatial natural resource markets using mathematical programing models. The models comprise an investment period and a supply period in which players compete in quantities. We compare three models, one perfect competition and two Cournot models, in which the product is either traded through long-term contracts or on spot markets in the supply period. Tractability and practicality of the approach are demonstrated in an application to the international metallurgical coal market. Results may vary substantially between the different models. The metallurgical coal market has recently made progress in moving away from long-term contracts and more towards spot market-based trade. Based on our results, we conclude that this regime switch is likely to raise consumer rents but lower producer rents. The total welfare differs only negligibly.

  10. Spatially explicit modelling of cholera epidemics

    Science.gov (United States)

    Finger, F.; Bertuzzo, E.; Mari, L.; Knox, A. C.; Gatto, M.; Rinaldo, A.

    2013-12-01

    Epidemiological models can provide crucial understanding about the dynamics of infectious diseases. Possible applications range from real-time forecasting and allocation of health care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. We apply a spatially explicit model to the cholera epidemic that struck Haiti in October 2010 and is still ongoing. The dynamics of susceptibles as well as symptomatic and asymptomatic infectives are modelled at the scale of local human communities. Dissemination of Vibrio cholerae through hydrological transport and human mobility along the road network is explicitly taken into account, as well as the effect of rainfall as a driver of increasing disease incidence. The model is calibrated using a dataset of reported cholera cases. We further model the long term impact of several types of interventions on the disease dynamics by varying parameters appropriately. Key epidemiological mechanisms and parameters which affect the efficiency of treatments such as antibiotics are identified. Our results lead to conclusions about the influence of different intervention strategies on the overall epidemiological dynamics.

  11. Modeling strategic investment decisions in spatial markets

    Energy Technology Data Exchange (ETDEWEB)

    Lorenczik, Stefan; Malischek, Raimund [Koeln Univ. (Germany). Energiewirtschaftliches Inst.; Trueby, Johannes [International Energy Agency, 75 - Paris (France)

    2014-04-15

    Markets for natural resources and commodities are often oligopolistic. In these markets, production capacities are key for strategic interaction between the oligopolists. We analyze how different market structures influence oligopolistic capacity investments and thereby affect supply, prices and rents in spatial natural resource markets using mathematical programing models. The models comprise an investment period and a supply period in which players compete in quantities. We compare three models, one perfect competition and two Cournot models, in which the product is either traded through long-term contracts or on spot markets in the supply period. Tractability and practicality of the approach are demonstrated in an application to the international metallurgical coal market. Results may vary substantially between the different models. The metallurgical coal market has recently made progress in moving away from long-term contracts and more towards spot market-based trade. Based on our results, we conclude that this regime switch is likely to raise consumer rents but lower producer rents. The total welfare differs only negligibly.

  12. Quantification of the spatial variability of rainfall based on a dense network of rain gauges

    DEFF Research Database (Denmark)

    Pedersen, Lisbeth; Jensen, Niels Einar; Christiansen, Lasse Engbo

    2010-01-01

    The spatial variability of rainfall within a single Local Area Weather Radar (LAWR) pixel of 500 x 500 m is quantified based on data from two locations. The work was motivated by the need to quantify the variability on this scale in order to provide an estimate of the uncertainty of using a single...... from an earlier campaign in 2003. The fact that the 20072008 dataset was almost four times larger than the original dataset from 2003 motivated this extended study. Two methods were used to describe the variability: the coefficient of variation and the spatial correlation structure of the rainfall......% prediction interval for a given rainfall depth is estimated and can be used to address the uncertainty of using a single rain gauge to represent the rainfall within a 500 x 500 m area. (C) 2009 Elsevier B.V. All rights reserved....

  13. Contribution of geodiversity, climate and spatial variables for biodiversity across a gradient of human influence

    Science.gov (United States)

    Tukiainen, Helena; Alahuhta, Janne; Ala-Hulkko, Terhi; Field, Richard; Lampinen, Raino; Hjort, Jan

    2016-04-01

    Implementation of geodiversity may provide new perspectives for nature conservation. The relation between geodiversity and biodiversity has been established in recent studies but remains underexplored in environments with high human pressure. In this study, we explored the effect of geodiversity (i.e. geological, hydrological and geomorphological diversity), climate and spatial variables on biodiversity (vascular plant species richness) in environments with different human impact. The study area ranged trough the boreal vegetation zone in Finland and included altogether 1401 1-km2 grid cells from urban, rural and natural environments. The contribution of environmental variable groups for species diversity in different environments was statistically analyzed with variation partitioning method. According to the results, the contribution of geodiversity decreased and the contribution of climate and spatial variables increased as the land use became more human-induced. Hence, the connection between geodiversity and species richness was most pronounced in natural state environments.

  14. Gaussian Mixture Model of Heart Rate Variability

    Science.gov (United States)

    Costa, Tommaso; Boccignone, Giuseppe; Ferraro, Mario

    2012-01-01

    Heart rate variability (HRV) is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition. This paper proposes a novel method to investigate HRV, namely by modelling it as a linear combination of Gaussians. Results show that three Gaussians are enough to describe the stationary statistics of heart variability and to provide a straightforward interpretation of the HRV power spectrum. Comparisons have been made also with synthetic data generated from different physiologically based models showing the plausibility of the Gaussian mixture parameters. PMID:22666386

  15. Remote sensing of the Canadian Arctic: Modelling biophysical variables

    Science.gov (United States)

    Liu, Nanfeng

    It is anticipated that Arctic vegetation will respond in a variety of ways to altered temperature and precipitation patterns expected with climate change, including changes in phenology, productivity, biomass, cover and net ecosystem exchange. Remote sensing provides data and data processing methodologies for monitoring and assessing Arctic vegetation over large areas. The goal of this research was to explore the potential of hyperspectral and high spatial resolution multispectral remote sensing data for modelling two important Arctic biophysical variables: Percent Vegetation Cover (PVC) and the fraction of Absorbed Photosynthetically Active Radiation (fAPAR). A series of field experiments were conducted to collect PVC and fAPAR at three Canadian Arctic sites: (1) Sabine Peninsula, Melville Island, NU; (2) Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, NU; and (3) Apex River Watershed (ARW), Baffin Island, NU. Linear relationships between biophysical variables and Vegetation Indices (VIs) were examined at different spatial scales using field spectra (for the Sabine Peninsula site) and high spatial resolution satellite data (for the CBAWO and ARW sites). At the Sabine Peninsula site, hyperspectral VIs exhibited a better performance for modelling PVC than multispectral VIs due to their capacity for sampling fine spectral features. The optimal hyperspectral bands were located at important spectral features observed in Arctic vegetation spectra, including leaf pigment absorption in the red wavelengths and at the red-edge, leaf water absorption in the near infrared, and leaf cellulose and lignin absorption in the shortwave infrared. At the CBAWO and ARW sites, field PVC and fAPAR exhibited strong correlations (R2 > 0.70) with the NDVI (Normalized Difference Vegetation Index) derived from high-resolution WorldView-2 data. Similarly, high spatial resolution satellite-derived fAPAR was correlated to MODIS fAPAR (R2 = 0.68), with a systematic

  16. Improving understanding of controls on spatial variability in methane fluxes in Arctic tundra

    Science.gov (United States)

    Davidson, Scott J.; Sloan, Victoria; Phoenix, Gareth; Wagner, Robert; Oechel, Walter; Zona, Donatella

    2015-04-01

    The Arctic is experiencing rapid climate change relative to the rest of the globe, and this increase in temperature has feedback effects across hydrological and thermal regimes, plant community distribution and carbon stocks within tundra soils. Arctic wetlands account for a significant amount of methane emissions from natural ecosystems to the atmosphere and with further permafrost degradation under a warming climate, these emissions are expected to increase. Methane (CH4) is an extremely important component of the global carbon cycle with a global warming potential 28.5 times greater than carbon dioxide over a 100 year time scale (IPCC, 2013). In order to validate carbon cycle models, modelling methane at broader landscape scales is needed. To date direct measurements of methane have been sporadic in time and space which, while capturing some key controls on the spatial heterogeneity, make it difficult to accurately upscale methane emissions to the landscape and regional scales. This study investigates what is controlling the spatial heterogeneity of methane fluxes across Arctic tundra. We combined over 300 portable chamber observations from 13 micro-topographic positions (with multiple vegetation types) across three locations spanning a 300km latitudinal gradient in Northern Alaska from Barrow to Ivotuk with synchronous measurements of environmental (soil temperature, soil moisture, water table, active layer thaw depth, pH) and vegetation (plant community composition, height, sedge tiller counts) variables to evaluate key controls on methane fluxes. To assess the diurnal variation in CH4 fluxes, we also performed automated chamber measurements in one study site (Barrow) location. Multiple statistical approaches (regression tree and multiple linear regression) were used to identify key controlling variables and their interactions. Methane emissions across all sites ranged from -0.08 to 15.3 mg C-CH4 m-2 hr-1. As expected, soil moisture was the main control

  17. Spatial pattern evaluation of a calibrated national hydrological model - a remote-sensing-based diagnostic approach

    Science.gov (United States)

    Mendiguren, Gorka; Koch, Julian; Stisen, Simon

    2017-11-01

    Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds

  18. Spatial-temporal variability of leaf chlorophyll and its relationship with cocoa yield

    Directory of Open Access Journals (Sweden)

    Caique C. Medauar

    Full Text Available ABSTRACT The objective of this study was to evaluate the spatial-temporal variability of leaf chlorophyll index and its relationship with cocoa yield. The experiment was carried out in an experimental area of cocoa production located in Ilhéus, Bahia State, Brazil. Leaf chlorophyll content was measured in September, October, January, February, March and April in the 2014/2015 season, at each sampling point of a regular grid by using a portable chlorophyll meter. Under the same conditions, yield was evaluated and the data were submitted to descriptive statistics and a linear correlation study. Geostatistical analysis was used to determine and quantify the spatial and temporal variability of leaf chlorophyll index and yield. Leaf chlorophyll index varied over the period evaluated, but the months of February, March and April showed no spatial dependence in the study area, indicating absence of temporal stability. Cocoa monthly yield, except in January, presented high spatial variability. Under the conditions of this study, it was not possible to establish a relationship between leaf chlorophyll index and cocoa yield.

  19. A physically based analytical spatial air temperature and humidity model

    Science.gov (United States)

    Yang Yang; Theodore A. Endreny; David J. Nowak

    2013-01-01

    Spatial variation of urban surface air temperature and humidity influences human thermal comfort, the settling rate of atmospheric pollutants, and plant physiology and growth. Given the lack of observations, we developed a Physically based Analytical Spatial Air Temperature and Humidity (PASATH) model. The PASATH model calculates spatial solar radiation and heat...

  20. Spatial variability of soil magnetic susceptibility in an agricultural field located in Eastern Ukraine

    Science.gov (United States)

    Menshov, Oleksandr; Pereira, Paulo; Kruglov, Oleksandr

    2015-04-01

    .05 MS×10-9 m3/kg, and a minimum and a maximum value of 499.33 and 862.27 MS×10-9 m3/kg respectively. The standard deviation was 85.62 and the coefficient of variation 12.48%. This shows that the spatial variability of soil MS was low. The Global Morans I index was of 0.841, a z-score of 7.741 with a p<0.001, indicating that soil MS had a clustered pattern. The variogram results showed that the gaussian model was the the best fitted. The nugget effect was 0.1007. the sill 0.9905 and the nugget/sill ratio of 0.10, which shows that soil MS has a strong spatial dependency. The results of the interpolation tests showed that the errors distribution followed the normal distribution, the average predicted values were similar to the observed and the correlation between these two distributions was high (between 0.85-0.90) in all the cases. The method that predicted better soil MS was LP2 and the less accurate was SK. Soil MS presented high values in the southwestern part and low in the northeast area of the plot. It is clearly observed a increase of soil MS from the top of the slope to the bottom. Acknowledgments RECARE (Preventing and Remediating Degradation of Soils in Europe Through Land Care, FP7-ENV-2013-TWO STAGE), funded by the European Commission; and for the COST action ES1306 (Connecting European connectivity research). References Boyko, T., Scholger, R., Stanjek, H., MAGPROX team (2004) Topsoil magnetic suseptibility mapping as a tool for pollution monitoring: Repetability of in situ measurments. Journal of Applied Geophysics, 55, 249-259. Dankoub, Z., Ayoubi, S., Khademi, H., Sheng-Gao, L. (2012) Spatial distribution of magnetic properties and selected heavy metals in calcareous soils as affected by land use in the Isfahan Region, Central Iran. Pedosphere, 22, 33-47. Girault, F., Poitou, C., Perrier, F., Koirala, B.P., Bhattarai, M. (2011) Soil characterization using patterns of magnetic susceptibility versus effective radimu concentration. Natural Hazards Earth

  1. Snowpack spatial and temporal variability assessment using SMP high-resolution penetrometer

    Science.gov (United States)

    Komarov, Anton; Seliverstov, Yuriy; Sokratov, Sergey; Grebennikov, Pavel

    2017-04-01

    This research is focused on study of spatial and temporal variability of structure and characteristics of snowpack, quick identification of layers based on hardness and dispersion values received from snow micro penetrometer (SMP). We also discuss the detection of weak layers and definition of their parameters in non-alpine terrain. As long as it is the first SMP tool available in Russia, our intent is to test it in different climate and weather conditions. During two separate snowpack studies in plain and mountain landscapes, we derived density and grain size profiles by comparing snow density and grain size from snowpits and SMP measurements. The first case study was MSU meteorological observatory test site in Moscow. SMP data was obtained by 6 consecutive measurements along 10 m transects with a horizontal resolution of approximately 50 cm. The detailed description of snowpack structure, density, grain size, air and snow temperature was also performed. By comparing this information, the detailed scheme of snowpack evolution was created. The second case study was in Khibiny mountains. One 10-meter-long transect was made. SMP, density, grain size and snow temperature data was obtained with horizontal resolution of approximately 50 cm. The high-definition profile of snowpack density variation was acquired using received data. The analysis of data reveals high spatial and temporal variability in snow density and layer structure in both horizontal and vertical dimensions. It indicates that the spatial variability is exhibiting similar spatial patterns as surface topology. This suggests a strong influence from such factors as wind and liquid water pressure on the temporal and spatial evolution of snow structure. It was also defined, that spatial variation of snowpack characteristics is substantial even within homogeneous plain landscape, while in high-latitude mountain regions it grows significantly.

  2. A Study of the Groundwater Level Spatial Variability in the Messara Valley of Crete

    Science.gov (United States)

    Varouchakis, E. A.; Hristopulos, D. T.; Karatzas, G. P.

    2009-04-01

    The island of Crete (Greece) has a dry sub-humid climate and marginal groundwater resources, which are extensively used for agricultural activities and human consumption. The Messara valley is located in the south of the Heraklion prefecture, it covers an area of 398 km2, and it is the largest and most productive valley of the island. Over-exploitation during the past thirty (30) years has led to a dramatic decrease of thirty five (35) meters in the groundwater level. Possible future climatic changes in the Mediterranean region, potential desertification, population increase, and extensive agricultural activity generate concern over the sustainability of the water resources of the area. The accurate estimation of the water table depth is important for an integrated groundwater resource management plan. This study focuses on the Mires basin of the Messara valley for reasons of hydro-geological data availability and geological homogeneity. The research goal is to model and map the spatial variability of the basin's groundwater level accurately. The data used in this study consist of seventy (70) piezometric head measurements for the hydrological year 2001-2002. These are unevenly distributed and mostly concentrated along a temporary river that crosses the basin. The range of piezometric heads varies from an extreme low value of 9.4 meters above sea level (masl) to 62 masl, for the wet period of the year (October to April). An initial goal of the study is to develop spatial models for the accurate generation of static maps of groundwater level. At a second stage, these maps should extend the models to dynamic (space-time) situations for the prediction of future water levels. Preliminary data analysis shows that the piezometric head variations are not normally distributed. Several methods including Box-Cox transformation and a modified version of it, transgaussian Kriging, and Gaussian anamorphosis have been used to obtain a spatial model for the piezometric head. A

  3. Spatial variability of soil potassium in sugarcane areas subjected to the application of vinasse

    Directory of Open Access Journals (Sweden)

    LAÉRCIO A. DE CARVALHO

    2014-12-01

    Full Text Available When deposited on land the vinasse can promote improvement in fertility, however, often fertilizer application occurs in areas considered homogeneous, without taking into account the variability of the soil. The objective of this study was to evaluate the effect of vinasse application on potassium content in two classes of soils cultivated with sugarcane, and characterize the spatial variability of soil using geostatistical techniques. In the 2010 and 2011 crop year, soil samples were collected from an experimental grid at 0-0.2 and 0.2-0.4 m depth in three soils cultivated with sugarcane, totaling 90 samplings in each grid, for the determination of pH, calcium (Ca, magnesium (Mg, potassium (K, phosphorus (P, aluminum (Al and potential acidity (H + Al. The data have been submitted to analysis of descriptive statistics and the K attribute was subjected to geostatistical analysis. The coefficient of variation indicated medium and high variability of K for the three soils. The results showed that the spatial dependence of K increased in depth to FRce and decreased to PHlv, indicating that the attribute could have followed the pattern of distribution of clay in depth. The investigation of the spatial variability of K on the surface and subsurface soils provided the definition of management zones with different levels of fertility, which can be organized into sub-areas for a more efficient management of the resources and the environment.

  4. Field Scale Studies on the Spatial Variability of Soil Quality Indicators in Washington State, USA

    Directory of Open Access Journals (Sweden)

    Jeffrey L. Smith

    2011-01-01

    Full Text Available Arable lands are needed for sustainable agricultural systems to support an ever-growing human population. Soil quality needs to be defined to assure that new land brought into crop production is sustainable. To evaluate soil quality, a number of soil attributes will need to be measured, evaluated, and integrated into a soil-quality index using the multivariable indicator kriging (MVIK procedure. This study was conducted to determine the spatial variability and correlation of indicator parameters on a field scale with respect to soil quality and suitability for use with MVIK. The variability of the biological parameters decreased in the order of respiration > enzyme assays and qCO2 > microbial biomass C. The distribution frequency of all parameters except respiration were normal although the spatial distribution across the landscape was highly variable. The biological parameters showed little correlation with each other when all data points were considered; however, when grouped in smaller sections, the correlations were more consistent with observed patterns across the field. To accurately assess soil quality, and arable land use, consideration of spatial and temporal variability, soil conditions, and other controlling factors must be taken into account.

  5. Spatial variability of detrended soil plow layer penetrometer resistance transect in a sugarcane field

    Science.gov (United States)

    Pérez, Luis D.; Cumbrera, Ramiro; Mato, Juan; Millán, Humberto; Tarquis, Ana M.

    2015-04-01

    Spatial variability of soil properties is relevant for identifying those zones with physical degradation. In this sense, one has to face the problem of identifying the origin and distribution of spatial variability patterns (Brouder et al., 2001; Millán et al., 2012). The objective of the present work was to quantify the spatial structure of soil penetrometer resistance (PR) collected from a transect data consisted of 221 points equidistant. In each sampling, readings were obtained from 0 cm till 70 cm of depth, with an interval of 5 cm (Pérez, 2012). The study was conducted on a Vertisol (Typic Hapludert) dedicated to sugarcane (Saccharum officinarum L.) production during the last sixty years (Pérez et al., 2010). Recently, scaling approach has been applied on the determination of the scaling data properties (Tarquis et al., 2008; Millán et al., 2012; Pérez, 2012). We focus in the Hurst analysis to characterize the data variability for each depth. Previously a detrended analysis was conducted in order to better study de intrinsic variability of the series. The Hurst exponent (H) for each depth was estimated showing a characteristic pattern and differentiating PR evolution in depth. References Brouder, S., Hofmann, B., Reetz, H.F., 2001. Evaluating spatial variability of soil parameters for input management. Better Crops 85, 8-11. Millán, H; AM Tarquís, Luís D. Pérez, Juan Mato, Mario González-Posada, 2012. Spatial variability patterns of some Vertisol properties at a field scale using standardized data. Soil and Tillage Research, 120, 76-84. Pérez, Luís D. 2012. Influencia de la maquinaria agrícola sobre la variabilidad espacial de la compactación del suelo. Aplicación de la metodología geoestadística-fractal. PhD thesis, UPM (In Spanish). Pérez, Luís D., Humberto Millán, Mario González-Posada 2010. Spatial complexity of soil plow layer penetrometer resistance as influenced by sugarcane harvesting: A prefractal approach. Soil and Tillage

  6. A spatial and nonstationary model for the frequency of extreme rainfall events

    DEFF Research Database (Denmark)

    Gregersen, Ida Bülow; Madsen, Henrik; Rosbjerg, Dan

    2013-01-01

    of extreme rainfall events, a statistical model is tested for this purpose. The model is built on the theory of generalized linear models and uses Poisson regression solved by generalized estimation equations. Spatial and temporal explanatory variables can be included simultaneously, and their relative...

  7. Watershed scale spatial variability in dissolved and total organic and inorganic carbon in contrasting UK catchments

    Science.gov (United States)

    Cumberland, S.; Baker, A.; Hudson, N. J.

    2006-12-01

    Approximately 800 organic and inorganic carbon analyses have been undertaken from watershed scale and regional scale spatial surveys in various British catchments. These include (1) a small (urban catchment (Ouseburn, N England); (2) a headwater, lowland agricultural catchment (River Tern, C England) (3) a large UK catchment (River Tyne, ~3000 sq-km) and (4) a spatial survey of ~300 analyses from rivers from SW England (~1700 sq-km). Results demonstrate that: (1) the majority of organic and inorganic carbon is in the dissolved (DOC and DIC) fractions; (2) that with the exception of peat rich headwaters, DIC concentration is always greater than DOC; (3) In the rural River Tern, riverine DOC and DIC are shown to follow a simple end- member mixing between DIC (DOC) rich (poor) ground waters and DOC (DIC) rich (poor) riparian wetlands for all sample sites. (4) In the urbanized Ouseburn catchment, although many sample sites also show this same mixing trend, some tributaries follow a pollutant trend of simultaneous increases in both DOC and DIC. The Ouseburn is part of the larger Tyne catchment: this larger catchment follows the simple groundwater DIC- soil water DOC end member mixing model, with the exception of the urban catchments which exhibit an elevated DIC compared to rural sites. (5) Urbanization is demonstrated to increase DIC compared to equivalent rural catchments; this DIC has potential sources including diffuse source inputs from the dissolution of concrete, point sources such as trade effluents and landfill leachates, and bedrock derived carbonates relocated to the soil dissolution zone by urban development. (6) DIC in rural SW England demonstrates that spatial variability in DIC can be attributed to variations in geology; but that DIC concentrations in the SW England rivers dataset are typically lower than the urbanized Tyne catchments despite the presence of carbonate bedrock in many of the sample catchments in the SW England dataset. (7) Recent

  8. Mapping Submarine Groundwater Discharge - how to investigate spatial discharge variability on coastal and beach scales

    Science.gov (United States)

    Stieglitz, T. C.; Burnett, W. C.; Rapaglia, J.

    2008-12-01

    Submarine groundwater discharge (SGD) is now increasingly recognized as an important component in the water balance, water quality and ecology of the coastal zone. A multitude of methods are currently employed to study SGD, ranging from point flux measurements with seepage meters to methods integrating over various spatial and temporal scales such as hydrological models, geophysical techniques or surface water tracer approaches. From studies in a large variety of hydrogeological settings, researchers in this field have come to expect that SGD is rarely uniformly distributed. Here we discuss the application of: (a) the mapping of subsurface electrical conductivity in a discharge zone on a beach; and (b) the large-scale mapping of radon in coastal surface water to improving our understanding of SGD and its spatial variability. On a beach scale, as part of intercomparison studies of a UNESCO/IAEA working group, mapping of subsurface electrical conductivity in a beach face have elucidated the non-uniform distribution of SGD associated with rock fractures, volcanic settings and man-made structures (e.g., piers, jetties). Variations in direct point measurements of SGD flux with seepage meters were linked to the subsurface conductivity distribution. We demonstrate how the combination of these two techniques may complement one another to better constrain SGD measurements. On kilometer to hundred kilometer scales, the spatial distribution and regional importance of SGD can be investigated by mapping relevant tracers in the coastal ocean. The radon isotope Rn-222 is a commonly used tracer for SGD investigations due to its significant enrichment in groundwater, and continuous mapping of this tracer, in combination with ocean water salinity, can be used to efficiently infer locations of SGD along a coastline on large scales. We use a surface-towed, continuously recording multi-detector setup installed on a moving vessel. This tool was used in various coastal environments, e

  9. Spatial and temporal variability of greenhouse gas emissions from a small and shallow temperate lake

    Science.gov (United States)

    Praetzel, Leandra; Schmiedeskamp, Marcel; Broder, Tanja; Hüttemann, Caroline; Jansen, Laura; Metzelder, Ulrike; Wallis, Ronya; Knorr, Klaus-Holger; Blodau, Christian

    2017-04-01

    Small inland waters (spots" and "hot moments" that could contribute significantly to total emissions. To address this knowledge gap, we determined CO2 and CH4 emissions and dynamics to identify their controlling environmental factors in a polymictic small (1.4 ha) and shallow (max. depth approx. 1.5 m) crater lake ("Windsborn") in the Eifel uplands in south-west Germany. As Lake Windsborn has a small catchment area (8 ha) and no surficial inflows, it serves well as a model system for the identification of factors and processes controlling emissions. In 2015, 2016 and 2017 we measured CO2 and CH4 gas fluxes with different techniques across the sediment/water and water/atmosphere interface. Atmospheric exchange was measured using mini-chambers equipped with CO2 sensors and with an infra-red greenhouse gas analyzer for high temporal resolution flux measurements. Ebullition of CH4 was quantified with funnel traps. Sediment properties were examined using pore-water peepers. All measurements were carried out along a transect covering both littoral and central parts of the lake. Moreover, a weather station on a floating platform in the center of the lake recorded meteorological data as well as CO2 concentration in different depths of the water column. So far, Lake Windsborn seems to be a source for both CO2 and CH4 on an annual scale. CO2 emissions generally increased from spring to summer. Even though CO2 uptake could be observed during some periods in spring and fall, CO2 emissions in the summer exceeded the uptake. CO2 and CH4 emissions also appeared to be spatially variable between littoral areas and the inner lake. Shallow areas turned out to be "hot spots" of CO2 emissions whereas CH4 emissions were - against our expectations - highest in the center of the lake. Moreover, CH4 ebullition contributed substantially to total CH4 emissions. Our results show the importance of spatially and temporally highly resolved long-term measurements of greenhouse gas emissions and

  10. Characterizing spatial variability of air pollution from vehicle traffic around the Houston Ship Channel area

    Science.gov (United States)

    Zhang, Xueying; Craft, Elena; Zhang, Kai

    2017-07-01

    Mobile emissions are a major source of urban air pollution and have been associated with a variety of adverse health outcomes. The Houston Ship Channel area is the home of a large number of diesel-powered vehicles emitting fine particulate matter (PM2.5; ≤2.5 μm in aerodynamic diameter) and nitrogen oxides (NOx). However, the spatial variability of traffic-related air pollutants in the Houston Ship Channel area has rarely been investigated. The objective of this study is to characterize spatial variability of PM2.5 and NOx concentrations attributable to on-road traffic in the Houston Ship Channel area in the year of 2011. We extracted the road network from the Texas Department of Transportation Road Inventory, and calculated emission rates using the Motor Vehicle Emission Simulator version 2014a (MOVES2014a). These parameters and preprocessed meteorological parameters were entered into a Research LINE-source Dispersion Model (RLINE) to conduct a simulation. Receptors were placed at 50 m resolution within 300 m to major roads and at 150 m resolution in the rest of the area. Our findings include that traffic-related PM2.5 were mainly emitted from trucks, while traffic-related NOx were emitted from both trucks and cars. The traffic contributed 0.90 μg/m3 PM2.5 and 29.23 μg/m3 NOx to the annual average mass concentrations of on-road air pollution, and the concentrations of the two pollutants decreased by nearly 40% within 500 m distance to major roads. The pollution level of traffic-related PM2.5 and NOx was higher in winter than those in the other three seasons. The Houston Ship Channel has earlier morning peak hours and relative late afternoon hours, which indicates the influence of goods movement from port activity. The varied near-road gradients illustrate that proximities to major roads are not an accurate surrogate of traffic-related air pollution.

  11. Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect

    Science.gov (United States)

    Li, Lianfa; Wu, Anna H.; Cheng, Iona; Chen, Jiu-Chiuan; Wu, Jun

    2017-10-01

    Monitoring of fine particulate matter with diameter health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R2 of 0.81, with PM10, meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM2.5 concentrations, respectively. The model had a cross-validation R2 of 0.83 for monthly PM2.5 concentrations (N = 8170) and 0.79 for daily PM2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R2 of 0.61 for the daily model when PM10 was replaced by total suspended particulate. Our model can robustly estimate historical PM2.5 concentrations in California when PM2.5 measurements were not available.

  12. Spatial variability of hailfalls in France: an analysis of air mass retro-trajectories

    Science.gov (United States)

    Hermida, Lucía; Merino, Andrés; Sánchez, José Luis; Berthet, Claude; Dessens, Jean; López, Laura; Fernández-González, Sergio; Gascón, Estíbaliz; García-Ortega, Eduardo

    2014-05-01

    Hail is the main meteorological risk in south-west France, with the strongest hailfalls being concentrated in just a few days. Specifically, this phenomenon occurs most often and with the greatest severity in the Midi-Pyrénées area. Previous studies have revealed the high spatial variability of hailfall in this part of France, even leading to different characteristics being recorded on hailpads that were relatively close together. For this reason, an analysis of the air mass trajectories was carried out at ground level and at altitude, which subsequently led to the formation of the hail recorded by these hailpads. It is already known that in the study zone, the trajectories of the storms usually stretch for long distances and are oriented towards the east, leading to hailstones with diameters in excess of 3 cm, and without any change in direction above 3 km. We analysed different days with hail precipitation where there was at least one stone with a diameter of 3 cm or larger. Using the simulations from these days, an analysis of the backward trajectories of the air masses was carried out. We used the HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory Model) to determine the origin of the air masses, and tracked them toward each of the hailpads that were hit during the day studied. The height of the final points was the height of the impacted hailpads. Similarly, the backward trajectories for different heights were also established. Finally, the results show how storms that affect neighbouring hailpads come from very different air masses; and provide a deeper understanding of the high variability that affects the characteristics of hailfalls. Acknowledgements The authors would like to thank the Regional Government of Castile-León for its financial support through the project LE220A11-2. This study was supported by the following grants: GRANIMETRO (CGL2010-15930); MICROMETEO (IPT-310000-2010-22).

  13. Spatial variability in cost and success of revegetation in a Wyoming big sagebrush community.

    Science.gov (United States)

    Boyd, Chad S; Davies, Kirk W

    2012-09-01

    The ecological integrity of the Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle and A. Young) alliance is being severely interrupted by post-fire invasion of non-native annual grasses. To curtail this invasion, successful post-fire revegetation of perennial grasses is required. Environmental factors impacting post-fire restoration success vary across space within the Wyoming big sagebrush alliance; however, most restorative management practices are applied uniformly. Our objectives were to define probability of revegetation success over space using relevant soil-related environmental factors, use this information to model cost of successful revegetation and compare the importance of vegetation competition and soil factors to revegetation success. We studied a burned Wyoming big sagebrush landscape in southeast Oregon that was reseeded with perennial grasses. We collected soil and vegetation data at plots spaced at 30 m intervals along a 1.5 km transect in the first two years post-burn. Plots were classified as successful (>5 seedlings/m(2)) or unsuccessful based on density of seeded species. Using logistic regression we found that abundance of competing vegetation correctly predicted revegetation success on 51 % of plots, and soil-related variables correctly predicted revegetation performance on 82.4 % of plots. Revegetation estimates varied from $167.06 to $43,033.94/ha across the 1.5 km transect based on probability of success, but were more homogenous at larger scales. Our experimental protocol provides managers with a technique to identify important environmental drivers of restoration success and this process will be of value for spatially allocating logistical and capital expenditures in a variable restoration environment.

  14. Confounding of three binary-variables counterfactual model

    OpenAIRE

    Liu, Jingwei; Hu, Shuang

    2011-01-01

    Confounding of three binary-variables counterfactual model is discussed in this paper. According to the effect between the control variable and the covariate variable, we investigate three counterfactual models: the control variable is independent of the covariate variable, the control variable has the effect on the covariate variable and the covariate variable affects the control variable. Using the ancillary information based on conditional independence hypotheses, the sufficient conditions...

  15. Spatial Data Web Services Pricing Model Infrastructure

    Science.gov (United States)

    Ozmus, L.; Erkek, B.; Colak, S.; Cankurt, I.; Bakıcı, S.

    2013-08-01

    most important law with related NSDI is the establishment of General Directorate of Geographic Information System under the Ministry of Environment and Urbanism. due to; to do or to have do works and activities with related to the establishment of National Geographic Information Systems (NGIS), usage of NGIS and improvements of NGIS. Outputs of these projects are served to not only public administration but also to Turkish society. Today for example, TAKBIS data (cadastre services) are shared more than 50 institutions by Web services, Tusaga-Aktif system has more than 3800 users who are having real-time GPS data correction, Orthophoto WMS services has been started for two years as a charge of free. Today there is great discussion about data pricing among the institutions. Some of them think that the pricing is storage of the data. Some of them think that the pricing is value of data itself. There is no certain rule about pricing. On this paper firstly, pricing of data storage and later on spatial data pricing models in different countries are investigated to improve institutional understanding in Turkey.

  16. Seasonal and spatial variability of surface ozone over China: contributions from background and domestic pollution

    Directory of Open Access Journals (Sweden)

    Y. Wang

    2011-04-01

    Full Text Available Both observations and a 3-D chemical transport model suggest that surface ozone over populated eastern China features a summertime trough and that the month when surface ozone peaks differs by latitude and region. Source-receptor analysis is used to quantify the contributions of background ozone and Chinese anthropogenic emissions on this variability. Annual mean background ozone over China shows a spatial gradient from 55 ppbv in the northwest to 20 ppbv in the southeast, corresponding with changes in topography and ozone lifetime. Pollution background ozone (annual mean of 12.6 ppbv shows a minimum in the summer and maximum in the spring. On the monthly-mean basis, Chinese pollution ozone (CPO has a peak of 20–25 ppbv in June north of the Yangtze River and in October south of it, which explains the peaks of surface ozone in these months. The summertime trough in surface ozone over eastern China can be explained by the decrease of background ozone from spring to summer (by −15 ppbv regionally averaged over eastern China. Tagged simulations suggest that long-range transport of ozone from northern mid-latitude continents (including Europe and North America reaches a minimum in the summer, whereas ozone from Southeast Asia exhibits a maximum in the summer over eastern China. This contrast in seasonality provides clear evidence that the seasonal switch in monsoonal wind patterns plays a significant role in determining the seasonality of background ozone over China.

  17. Spatial Statistical and Modeling Strategy for Inventorying and Monitoring Ecosystem Resources at Multiple Scales and Resolution Levels

    Science.gov (United States)

    Robin M. Reich; C. Aguirre-Bravo; M.S. Williams

    2006-01-01

    A statistical strategy for spatial estimation and modeling of natural and environmental resource variables and indicators is presented. This strategy is part of an inventory and monitoring pilot study that is being carried out in the Mexican states of Jalisco and Colima. Fine spatial resolution estimates of key variables and indicators are outputs that will allow the...

  18. Reducing Spatial Data Complexity for Classification Models

    International Nuclear Information System (INIS)

    Ruta, Dymitr; Gabrys, Bogdan

    2007-01-01

    Intelligent data analytics gradually becomes a day-to-day reality of today's businesses. However, despite rapidly increasing storage and computational power current state-of-the-art predictive models still can not handle massive and noisy corporate data warehouses. What is more adaptive and real-time operational environment requires multiple models to be frequently retrained which further hinders their use. Various data reduction techniques ranging from data sampling up to density retention models attempt to address this challenge by capturing a summarised data structure, yet they either do not account for labelled data or degrade the classification performance of the model trained on the condensed dataset. Our response is a proposition of a new general framework for reducing the complexity of labelled data by means of controlled spatial redistribution of class densities in the input space. On the example of Parzen Labelled Data Compressor (PLDC) we demonstrate a simulatory data condensation process directly inspired by the electrostatic field interaction where the data are moved and merged following the attracting and repelling interactions with the other labelled data. The process is controlled by the class density function built on the original data that acts as a class-sensitive potential field ensuring preservation of the original class density distributions, yet allowing data to rearrange and merge joining together their soft class partitions. As a result we achieved a model that reduces the labelled datasets much further than any competitive approaches yet with the maximum retention of the original class densities and hence the classification performance. PLDC leaves the reduced dataset with the soft accumulative class weights allowing for efficient online updates and as shown in a series of experiments if coupled with Parzen Density Classifier (PDC) significantly outperforms competitive data condensation methods in terms of classification performance at the

  19. Reducing Spatial Data Complexity for Classification Models

    Science.gov (United States)

    Ruta, Dymitr; Gabrys, Bogdan

    2007-11-01

    Intelligent data analytics gradually becomes a day-to-day reality of today's businesses. However, despite rapidly increasing storage and computational power current state-of-the-art predictive models still can not handle massive and noisy corporate data warehouses. What is more adaptive and real-time operational environment requires multiple models to be frequently retrained which further hinders their use. Various data reduction techniques ranging from data sampling up to density retention models attempt to address this challenge by capturing a summarised data structure, yet they either do not account for labelled data or degrade the classification performance of the model trained on the condensed dataset. Our response is a proposition of a new general framework for reducing the complexity of labelled data by means of controlled spatial redistribution of class densities in the input space. On the example of Parzen Labelled Data Compressor (PLDC) we demonstrate a simulatory data condensation process directly inspired by the electrostatic field interaction where the data are moved and merged following the attracting and repelling interactions with the other labelled data. The process is controlled by the class density function built on the original data that acts as a class-sensitive potential field ensuring preservation of the original class density distributions, yet allowing data to rearrange and merge joining together their soft class partitions. As a result we achieved a model that reduces the labelled datasets much further than any competitive approaches yet with the maximum retention of the original class densities and hence the classification performance. PLDC leaves the reduced dataset with the soft accumulative class weights allowing for efficient online updates and as shown in a series of experiments if coupled with Parzen Density Classifier (PDC) significantly outperforms competitive data condensation methods in terms of classification performance at the

  20. Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models

    Directory of Open Access Journals (Sweden)

    Kostas Alexandridis

    2013-06-01

    Full Text Available Assessing spatial model performance often presents challenges related to the choice and suitability of traditional statistical methods in capturing the true validity and dynamics of the predicted outcomes. The stochastic nature of many of our contemporary spatial models of land use change necessitate the testing and development of new and innovative methodologies in statistical spatial assessment. In many cases, spatial model performance depends critically on the spatially-explicit prior distributions, characteristics, availability and prevalence of the variables and factors under study. This study explores the statistical spatial characteristics of statistical model assessment of modeling land use change dynamics in a seven-county study area in South-Eastern Wisconsin during the historical period of 1963–1990. The artificial neural network-based Land Transformation Model (LTM predictions are used to compare simulated with historical land use transformations in urban/suburban landscapes. We introduce a range of Bayesian information entropy statistical spatial metrics for assessing the model performance across multiple simulation testing runs. Bayesian entropic estimates of model performance are compared against information-theoretic stochastic entropy estimates and theoretically-derived accuracy assessments. We argue for the critical role of informational uncertainty across different scales of spatial resolution in informing spatial landscape model assessment. Our analysis reveals how incorporation of spatial and landscape information asymmetry estimates can improve our stochastic assessments of spatial model predictions. Finally our study shows how spatially-explicit entropic classification accuracy estimates can work closely with dynamic modeling methodologies in improving our scientific understanding of landscape change as a complex adaptive system and process.

  1. Impact of precipitation spatial resolution on the hydrological response of an integrated distributed water resources model

    DEFF Research Database (Denmark)

    Fu, Suhua; Sonnenborg, Torben; Jensen, Karsten Høgh

    2011-01-01

    Precipitation is a key input variable to hydrological models, and the spatial variability of the input is expected to impact the hydrological response predicted by a distributed model. In this study, the effect of spatial resolution of precipitation on runoff , recharge and groundwater head...... of the total catchment and runoff discharge hydrograph at watershed outlet. On the other hand, groundwater recharge and groundwater head were both aff ected. The impact of the spatial resolution of precipitation input is reduced with increasing catchment size. The effect on stream discharge is relatively low...... was analyzed in the Alergaarde catchment in Denmark. Six different precipitation spatial resolutions were used as inputs to a physically based, distributed hydrological model, the MIKE SHE model. The results showed that the resolution of precipitation input had no apparent effect on annual water balance...

  2. Spatial Variability of Tree Transpiration Along a Soil Drainage Gradient of Boreal Black Spruce Forest

    Science.gov (United States)

    Angstmann, J. L.; Ewers, B. E.; Kwon, H.; Bond-Lamberty, B.; Amiro, B.; Gower, S. T.

    2008-12-01

    Boreal forests are an integral component in obtaining a predictive understanding of global climate change because they comprise 33% of the world's forests and store large amounts of carbon. Much of this carbon storage is a result of peat formation in cold, poorly-drained soils. Transpiration plays a crucial role in the interaction between carbon and water cycles due to stomatal control of these fluxes. The primary focus of this study is to quantify the spatial variability and drivers of tree transpiration in boreal forest stands across a well- to poorly-drained soil drainage gradient. Species composition of this region of boreal forest changes during succession in well-drained soils from being primarily dominated by Picea mariana with co-dominant Pinus banksiana and Populus tremuloides in younger stands to being dominated solely by Picea marianain older stands. Poorly-drained soils are dominated by Picea mariana and change little with succession. Previous work in well-drained stands showed that 1) tree transpiration changed substantially with stand age due to sapwood-to-leaf area ratio dynamics and 2) minimum leaf water potential (Ψ) was kept constant to prevent excessive cavitation. We hypothesized that 1) minimum Ψ would be constant, 2) transpiration would be proportional to the sapwood-to-leaf area ratio across a soil drainage gradient, and 3) spatial relationships between trees would vary depending on stomatal responses to vapor pressure deficit (D). We tested these hypotheses by measuring Ψ of 33 trees and sap flux from 204 trees utilizing cyclic sampling constructed to study spatial relationships. Measurements were conducted at a 42-year-old stand representing maximum tree diversity during succession. There were no significant differences between growing season averaged Ψ in well- (-0.35 and -1.37 for pre-dawn and mid-day respectively) and poorly- drained soil conditions (-0.38 and -1.41 for pre-dawn and mid-day respectively) for Picea mariana. Water use

  3. Comparison of alternative spatial resolutions in the application of a spatially distributed biogeochemical model over complex terrain

    Science.gov (United States)

    Turner, D.P.; Dodson, R.; Marks, D.

    1996-01-01

    Spatially distributed biogeochemical models may be applied over grids at a range of spatial resolutions, however, evaluation of potential errors and loss of information at relatively coarse resolutions is rare. In this study, a georeferenced database at the 1-km spatial resolution was developed to initialize and drive a process-based model (Forest-BGC) of water and carbon balance over a gridded 54976 km2 area covering two river basins in mountainous western Oregon. Corresponding data sets were also prepared at 10-km and 50-km spatial resolutions using commonly employed aggregation schemes. Estimates were made at each grid cell for climate variables including daily solar radiation, air temperature, humidity, and precipitation. The topographic structure, water holding capacity, vegetation type and leaf area index were likewise estimated for initial conditions. The daily time series for the climatic drivers was developed from interpolations of meteorological station data for the water year 1990 (1 October 1989-30 September 1990). Model outputs at the 1-km resolution showed good agreement with observed patterns in runoff and productivity. The ranges for model inputs at the 10-km and 50-km resolutions tended to contract because of the smoothed topography. Estimates for mean evapotranspiration and runoff were relatively insensitive to changing the spatial resolution of the grid whereas estimates of mean annual net primary production varied by 11%. The designation of a vegetation type and leaf area at the 50-km resolution often subsumed significant heterogeneity in vegetation, and this factor accounted for much of the difference in the mean values for the carbon flux variables. Although area wide means for model outputs were generally similar across resolutions, difference maps often revealed large areas of disagreement. Relatively high spatial resolution analyses of biogeochemical cycling are desirable from several perspectives and may be particularly important in the

  4. Panchromatic SED modelling of spatially resolved galaxies

    Science.gov (United States)

    Smith, Daniel J. B.; Hayward, Christopher C.

    2018-05-01

    We test the efficacy of the energy-balance spectral energy distribution (SED) fitting code MAGPHYS for recovering the spatially resolved properties of a simulated isolated disc galaxy, for which it was not designed. We perform 226 950 MAGPHYS SED fits to regions between 0.2 and 25 kpc in size across the galaxy's disc, viewed from three different sight-lines, to probe how well MAGPHYS can recover key galaxy properties based on 21 bands of UV-far-infrared model photometry. MAGPHYS yields statistically acceptable fits to >99 per cent of the pixels within the r-band effective radius and between 59 and 77 percent of pixels within 20 kpc of the nucleus. MAGPHYS is able to recover the distribution of stellar mass, star formation rate (SFR), specific SFR, dust luminosity, dust mass, and V-band attenuation reasonably well, especially when the pixel size is ≳ 1 kpc, whereas non-standard outputs (stellar metallicity and mass-weighted age) are recovered less well. Accurate recovery is more challenging in the smallest sub-regions of the disc (pixel scale ≲ 1 kpc), where the energy balance criterion becomes increasingly incorrect. Estimating integrated galaxy properties by summing the recovered pixel values, the true integrated values of all parameters considered except metallicity and age are well recovered at all spatial resolutions, ranging from 0.2 kpc to integrating across the disc, albeit with some evidence for resolution-dependent biases. These results must be considered when attempting to analyse the structure of real galaxies with actual observational data, for which the `ground truth' is unknown.

  5. Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.

    Science.gov (United States)

    Guo, Yan; Huang, Jingyi; Shi, Zhou; Li, Hongyi

    2015-01-01

    In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9) allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v) as well as other EMI instruments (e.g. DUALEM-421) can be incorporated to conduct Quasi-3D inversions for deeper soil profiles.

  6. Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.

    Directory of Open Access Journals (Sweden)

    Yan Guo

    Full Text Available In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9 allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v as well as other EMI instruments (e.g. DUALEM-421 can be incorporated to conduct Quasi-3D inversions for deeper soil profiles.

  7. Throughfall and its spatial variability beneath xerophytic shrub canopies within water-limited arid desert ecosystems

    Science.gov (United States)

    Zhang, Ya-feng; Wang, Xin-ping; Hu, Rui; Pan, Yan-xia

    2016-08-01

    Throughfall is known to be a critical component of the hydrological and biogeochemical cycles of forested ecosystems with inherently temporal and spatial variability. Yet little is understood concerning the throughfall variability of shrubs and the associated controlling factors in arid desert ecosystems. Here we systematically investigated the variability of throughfall of two morphological distinct xerophytic shrubs (Caragana korshinskii and Artemisia ordosica) within a re-vegetated arid desert ecosystem, and evaluated the effects of shrub structure and rainfall characteristics on throughfall based on heavily gauged throughfall measurements at the event scale. We found that morphological differences were not sufficient to generate significant difference (P < 0.05) in throughfall between two studied shrub species under the same rainfall and meteorological conditions in our study area, with a throughfall percentage of 69.7% for C. korshinskii and 64.3% for A. ordosica. We also observed a highly variable patchy pattern of throughfall beneath individual shrub canopies, but the spatial patterns appeared to be stable among rainfall events based on time stability analysis. Throughfall linearly increased with the increasing distance from the shrub base for both shrubs, and radial direction beneath shrub canopies had a pronounced impact on throughfall. Throughfall variability, expressed as the coefficient of variation (CV) of throughfall, tended to decline with the increase in rainfall amount, intensity and duration, and stabilized passing a certain threshold. Our findings highlight the great variability of throughfall beneath the canopies of xerophytic shrubs and the time stability of throughfall pattern among rainfall events. The spatially heterogeneous and temporally stable throughfall is expected to generate a dynamic patchy distribution of soil moisture beneath shrub canopies within arid desert ecosystems.

  8. Mapping Spatial Variability of Soil Salinity in a Coastal Paddy Field Based on Electromagnetic Sensors

    Science.gov (United States)

    Guo, Yan; Huang, Jingyi; Shi, Zhou; Li, Hongyi

    2015-01-01

    In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9) allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v) as well as other EMI instruments (e.g. DUALEM-421) can be incorporated to conduct Quasi-3D inversions for deeper soil profiles. PMID:26020969

  9. Ocean carbon and heat variability in an Earth System Model

    Science.gov (United States)

    Thomas, J. L.; Waugh, D.; Gnanadesikan, A.

    2016-12-01

    Ocean carbon and heat content are very important for regulating global climate. Furthermore, due to lack of observations and dependence on parameterizations, there has been little consensus in the modeling community on the magnitude of realistic ocean carbon and heat content variability, particularly in the Southern Ocean. We assess the differences between global oceanic heat and carbon content variability in GFDL ESM2Mc using a 500-year, pre-industrial control simulation. The global carbon and heat content are directly out of phase with each other; however, in the Southern Ocean the heat and carbon content are in phase. The global heat mutli-decadal variability is primarily explained by variability in the tropics and mid-latitudes, while the variability in global carbon content is primarily explained by Southern Ocean variability. In order to test the robustness of this relationship, we use three additional pre-industrial control simulations using different mesoscale mixing parameterizations. Three pre-industrial control simulations are conducted with the along-isopycnal diffusion coefficient (Aredi) set to constant values of 400, 800 (control) and 2400 m2 s-1. These values for Aredi are within the range of parameter settings commonly used in modeling groups. Finally, one pre-industrial control simulation is conducted where the minimum in the Gent-McWilliams parameterization closure scheme (AGM) increased to 600 m2 s-1. We find that the different simulations have very different multi-decadal variability, especially in the Weddell Sea where the characteristics of deep convection are drastically changed. While the temporal frequency and amplitude global heat and carbon content changes significantly, the overall spatial pattern of variability remains unchanged between the simulations.

  10. Spatial variability of caesium-137 activities in soils in the Jura mountains

    International Nuclear Information System (INIS)

    Pimou-Heumou, G.; Lucot, E.; Crini, N.; Briot, M.; Badot, P.M.

    2011-01-01

    275 soil samples were taken in the catchment area of the upper part of the Doubs river located in the Jura mountains according to a sampling strategy designed to evaluate the extent of the spatial variability of 137 Cs activities and to identify its main sources. 137 Cs activities ranged between about 1000 and 12000 Bq.m -2 with an average of approximately 3600 Bq.m -2 . The spatial variability of the contamination is high: 137 Cs activity shows statistically significant links with altitude, soil organic matter and land cover, whereas the other studied parameters, i.e. soil type and topographic position, do not constitute significant sources of variation. These results are discussed in terms of evaluation of the radioactive contamination on a regional scale. They show that to be satisfactory, a sampling strategy must necessarily take into account the various types of land cover. (authors)

  11. Spatial variability of soil pH based on GIS combined with geostatistics in Panzhihua tobacco area

    International Nuclear Information System (INIS)

    Du Wei; Wang Changquan; Li Bing; Li Qiquan; Du Qian; Hu Jianxin; Liu Chaoke

    2012-01-01

    GIS and geostatistics were utilized to study the spatial variability of soil pH in Panzhihua tobacco area. Results showed that pH values in this area ranged from 4.5 to 8.3, especially 5.5 to 6.5, and in few areas were lower than 5.0 or higher than 7.0 which can meet the need of high-quality tobacco production. The best fitting model of variogram was exponential model with the nugget/sill of soil pH in 13.61% indicating strong spatial correlation. The change process was 5.40 km and the coefficient of determination was 0.491. The spatial variability of soil pH was mainly caused by structural factors such as cane, topography and soil type. The soil pH in Panzhihua tobacco area also showed a increasing trend of northwest to southeast trend. The pH of some areas in Caochang, Gonghe and Yumen were lower, and in Dalongtan were slightly higher. (authors)

  12. The stock-flow model of spatial data infrastructure development refined by fuzzy logic.

    Science.gov (United States)

    Abdolmajidi, Ehsan; Harrie, Lars; Mansourian, Ali

    2016-01-01

    The system dynamics technique has been demonstrated to be a proper method by which to model and simulate the development of spatial data infrastructures (SDI). An SDI is a collaborative effort to manage and share spatial data at different political and administrative levels. It is comprised of various dynamically interacting quantitative and qualitative (linguistic) variables. To incorporate linguistic variables and their joint effects in an SDI-development model more effectively, we suggest employing fuzzy logic. Not all fuzzy models are able to model the dynamic behavior of SDIs properly. Therefore, this paper aims to investigate different fuzzy models and their suitability for modeling SDIs. To that end, two inference and two defuzzification methods were used for the fuzzification of the joint effect of two variables in an existing SDI model. The results show that the Average-Average inference and Center of Area defuzzification can better model the dynamics of SDI development.

  13. Land agroecological quality assessment in conditions of high spatial soil cover variability at the Pereslavskoye Opolye.

    Science.gov (United States)

    Morev, Dmitriy; Vasenev, Ivan

    2015-04-01

    The essential spatial variability is mutual feature for most natural and man-changed soils at the Central region of European territory of Russia. The original spatial heterogeneity of forest soils has been further complicated by a specific land-use history and human impacts. For demand-driven land-use planning and decision making the quantitative analysis and agroecological interpretation of representative soil cover spatial variability is an important and challenging task that receives increasing attention from private companies, governmental and environmental bodies. Pereslavskoye Opolye is traditionally actively used in agriculture due to dominated high-quality cultivated soddy-podzoluvisols which are relatively reached in organic matter (especially for conditions of the North part at the European territory of Russia). However, the soil cover patterns are often very complicated even within the field that significantly influences on crop yield variability and have to be considered in farming system development and land agroecological quality evaluation. The detailed investigations of soil regimes and mapping of the winter rye yield have been carried in conditions of two representative fields with slopes sharply contrasted both in aspects and degrees. Rye biological productivity and weed infestation have been measured in elementary plots of 0.25 m2 with the following analysis the quality of the yield. In the same plot soil temperature and moisture have been measured by portable devices. Soil sampling was provided from three upper layers by drilling. The results of ray yield detailed mapping shown high differences both in average values and within-field variability on different slopes. In case of low-gradient slope (field 1) there is variability of ray yield from 39.4 to 44.8 dt/ha. In case of expressed slope (field 2) the same species of winter rye grown with the same technology has essentially lower yield and within-field variability from 20 to 29.6 dt/ha. The

  14. Temporal and spatial variability of wind resources in the United States as derived from the Climate Forecast System Reanalysis

    Science.gov (United States)

    Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman

    2015-01-01

    This study examines the spatial and temporal variability of wind speed at 80m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the...

  15. Spatial variability of surface fuels in treated and untreated ponderosa pine forests of the southern Rocky Mountains

    Science.gov (United States)

    Emma Vakili; Chad M. Hoffman; Robert E. Keane; Wade T. Tinkham; Yvette Dickinson

    2016-01-01

    There is growing consensus that spatial variability in fuel loading at scales down to 0.5 m may govern fire behaviour and effects. However, there remains a lack of understanding of how fuels vary through space in wildland settings. This study quantifies surface fuel loading and its spatial variability in ponderosa pine sites before and after fuels treatment in the...

  16. Natural climate variability in a coupled model

    International Nuclear Information System (INIS)

    Zebiak, S.E.; Cane, M.A.

    1990-01-01

    Multi-century simulations with a simplified coupled ocean-atmosphere model are described. These simulations reveal an impressive range of variability on decadal and longer time scales, in addition to the dominant interannual el Nino/Southern Oscillation signal that the model originally was designed to simulate. Based on a very large sample of century-long simulations, it is nonetheless possible to identify distinct model parameter sensitivities that are described here in terms of selected indices. Preliminary experiments motivated by general circulation model results for increasing greenhouse gases suggest a definite sensitivity to model global warming. While these results are not definitive, they strongly suggest that coupled air-sea dynamics figure prominently in global change and must be included in models for reliable predictions

  17. Concordance among different aquatic insect assemblages and the relative role of spatial and environmental variables

    OpenAIRE

    Chunyan Qin; Yong Zhang; Haiyan Yu; Beixin Wang

    2013-01-01

    Indicator groups are often used for biodiversity monitoring and conservation, however, the effectiveness of these groups in representing biodiversity is rarely tested. To explore community congruence among different aquatic insect groups and how this may be affected by spatial factors and environmental variables, we carried out an investigation on aquatic insects in April 2010 in 21 headwater streams within the Dongtiaoxi Basin, China. In total, we recorded 130 species from 92 genera, 44 fami...

  18. Summer temperature and spatial variability of all-cause mortality in Surat city, India

    Directory of Open Access Journals (Sweden)

    S K Rathi

    2017-01-01

    Full Text Available Background: Ample information is available on extreme heat associated mortality for few Indian cities, but scant literature is available on effect of temperature on spatial variability of all-cause mortality for coastal cities. Objective: To assess the effect of daily maximum temperature, relative humidity and heat index on spatial variability of all-cause mortality for summer months (March to May from 2014 to 2015 for the urban population of Surat (coastal city. Materials and Methods: Retrospective analysis of the all-cause mortality data with temperature and humidity was performed on a total of 9,237 deaths for 184 summer days (2014-2015. Climatic and all-cause mortality data were obtained through Tutiempo website and Surat Municipal Corporation respectively. Bivariate analysis performed through SPSS. Observations: Mean daily mortality was estimated at 50.2 ± 8.5 for the study period with a rise of 20% all-cause mortality at temperature ≥ 40°C and rise of 10% deaths per day during extreme danger level (HI: > 54°C days. Spatial (Zone wise analysis revealed rise of 61% all-cause mortality for Southeast and 30% for East zones at temperature ≥ 40°C. Conclusions: All-cause mortality increased on high summer temperature days. Presence of spatial variation in all-cause mortality provided the evidence for high risk zones. Findings may be helpful in designing the interventions at micro level.

  19. Mapping spatial variability of foliar nitrogen in coffee (Coffea arabica L.) plantations with multispectral Sentinel-2 MSI data

    Science.gov (United States)

    Chemura, Abel; Mutanga, Onisimo; Odindi, John; Kutywayo, Dumisani

    2018-04-01

    Nitrogen (N) is the most limiting factor to coffee development and productivity. Therefore, development of rapid, spatially explicit and temporal remote sensing-based approaches to determine spatial variability of coffee foliar N are imperative for increasing yields, reducing production costs and mitigating environmental impacts associated with excessive N applications. This study sought to assess the value of Sentinel-2 MSI spectral bands and vegetation indices in empirical estimation of coffee foliar N content at landscape level. Results showed that coffee foliar N is related to Sentinel-2 MSI B4 (R2 = 0.32), B6 (R2 = 0.49), B7 (R2 = 0.42), B8 (R2 = 0.57) and B12 (R2 = 0.24) bands. Vegetation indices were more related to coffee foliar N as shown by the Inverted Red-Edge Chlorophyll Index - IRECI (R2 = 0.66), Relative Normalized Difference Index - RNDVI (R2 = 0.48), CIRE1 (R2 = 0.28), and Normalized Difference Infrared Index - NDII (R2 = 0.37). These variables were also identified by the random forest variable optimisation as the most valuable in coffee foliar N prediction. Modelling coffee foliar N using vegetation indices produced better accuracy (R2 = 0.71 with RMSE = 0.27 for all and R2 = 0.73 with RMSE = 0.25 for optimized variables), compared to using spectral bands (R2 = 0.57 with RMSE = 0.32 for all and R2 = 0.58 with RMSE = 0.32 for optimized variables). Combining optimized bands and vegetation indices produced the best results in coffee foliar N modelling (R2 = 0.78, RMSE = 0.23). All the three best performing models (all vegetation indices, optimized vegetation indices and combining optimal bands and optimal vegetation indices) established that 15.2 ha (4.7%) of the total area under investigation had low foliar N levels (landscape scale.

  20. Consequences of spatial autocorrelation for niche-based models

    DEFF Research Database (Denmark)

    Segurado, P.; Araújo, Miguel B.; Kunin, W. E.

    2006-01-01

    1.  Spatial autocorrelation is an important source of bias in most spatial analyses. We explored the bias introduced by spatial autocorrelation on the explanatory and predictive power of species' distribution models, and make recommendations for dealing with the problem. 2.  Analyses were based o...

  1. Spatial Econometric data analysis: moving beyond traditional models

    NARCIS (Netherlands)

    Florax, R.J.G.M.; Vlist, van der A.J.

    2003-01-01

    This article appraises recent advances in the spatial econometric literature. It serves as the introduction too collection of new papers on spatial econometric data analysis brought together in this special issue, dealing specifically with new extensions to the spatial econometric modeling

  2. High-resolution spatial databases of monthly climate variables (1961-2010) over a complex terrain region in southwestern China

    Science.gov (United States)

    Wu, Wei; Xu, An-Ding; Liu, Hong-Bin

    2015-01-01

    Climate data in gridded format are critical for understanding climate change and its impact on eco-environment. The aim of the current study is to develop spatial databases for three climate variables (maximum, minimum temperatures, and relative humidity) over a large region with complex topography in southwestern China. Five widely used approaches including inverse distance weighting, ordinary kriging, universal kriging, co-kriging, and thin-plate smoothing spline were tested. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) showed that thin-plate smoothing spline with latitude, longitude, and elevation outperformed other models. Average RMSE, MAE, and MAPE of the best models were 1.16 °C, 0.74 °C, and 7.38 % for maximum temperature; 0.826 °C, 0.58 °C, and 6.41 % for minimum temperature; and 3.44, 2.28, and 3.21 % for relative humidity, respectively. Spatial datasets of annual and monthly climate variables with 1-km resolution covering the period 1961-2010 were then obtained using the best performance methods. Comparative study showed that the current outcomes were in well agreement with public datasets. Based on the gridded datasets, changes in temperature variables were investigated across the study area. Future study might be needed to capture the uncertainty induced by environmental conditions through remote sensing and knowledge-based methods.

  3. An LES study on the spatial variability impact of surface sensible heat flux (SHF) on the convective boundary layer (CBL)

    Science.gov (United States)

    Kang, S. L.; Chun, J.; Kumar, A.

    2015-12-01

    We study the spatial variability impact of surface sensible heat flux (SHF) on the convective boundary layer (CBL), using the Weather Research and Forecasting (WRF) model in large eddy simulation (LES) mode. In order to investigate the response of the CBL to multi-scale feature of the surface SHF field over a local area of several tens of kilometers or smaller, an analytic surface SHF map is crated as a function of the chosen feature. The spatial variation in the SHF map is prescribed with a two-dimensional analytical perturbation field, which is generated by using the inverse transform technique of the Fourier series whose coefficients are controlled, of which spectrum to have a particular slope in the chosen range of wavelength. Then, the CBL responses to various SHF heterogeneities are summarized as a function of the spectral slope, in terms of mean structure, turbulence statistics and cross-scale processes. The range of feasible SHF heterogeneities is obtained from the SHF maps produced by a land surface model (LSM) of the WRF system. The LSM-derived SHF maps are a function of geographical data on various resolutions. Based on the numerical experiment results with the surface heterogeneities in the range, we will discuss the uncertainty in the SHF heterogeneity and its impact on the atmosphere in a numerical model. Also we will present the range of spatial scale of the surface SHF heterogeneity that significantly influence on the whole CBL. Lastly, we will report the test result of the hypothesis that the spatial variability of SHF is more representative of surface thermal heterogeneity than is the latent heat flux over the local area of several tens of kilometers or smaller.

  4. The spatial limitations of current neutral models of biodiversity.

    Directory of Open Access Journals (Sweden)

    Rampal S Etienne

    Full Text Available The unified neutral theory of biodiversity and biogeography is increasingly accepted as an informative null model of community composition and dynamics. It has successfully produced macro-ecological patterns such as species-area relationships and species abundance distributions. However, the models employed make many unrealistic auxiliary assumptions. For example, the popular spatially implicit version assumes a local plot exchanging migrants with a large panmictic regional source pool. This simple structure allows rigorous testing of its fit to data. In contrast, spatially explicit models assume that offspring disperse only limited distances from their parents, but one cannot as yet test the significance of their fit to data. Here we compare the spatially explicit and the spatially implicit model, fitting the most-used implicit model (with two levels, local and regional to data simulated by the most-used spatially explicit model (where offspring are distributed about their parent on a grid according to either a radially symmetric Gaussian or a 'fat-tailed' distribution. Based on these fits, we express spatially implicit parameters in terms of spatially explicit parameters. This suggests how we may obtain estimates of spatially explicit parameters from spatially implicit ones. The relationship between these parameters, however, makes no intuitive sense. Furthermore, the spatially implicit model usually fits observed species-abundance distributions better than those calculated from the spatially explicit model's simulated data. Current spatially explicit neutral models therefore have limited descriptive power. However, our results suggest that a fatter tail of the dispersal kernel seems to improve the fit, suggesting that dispersal kernels with even fatter tails should be studied in future. We conclude that more advanced spatially explicit models and tools to analyze them need to be developed.

  5. DISCOVERY 2010: Spatial and temporal variability in a dynamic polar ecosystem

    Science.gov (United States)

    Tarling, G. A.; Ward, P.; Atkinson, A.; Collins, M. A.; Murphy, E. J.

    2012-01-01

    The Scotia Sea has been a focus of biological- and physical oceanographic study since the Discovery expeditions in the early 1900s. It is a physically energetic region with some of the highest levels of productivity in the Southern Ocean. It is also a region within which there have been greater than average levels of change in upper water column temperature. We describe the results of three cruises transecting the central Scotia Sea from south to north in consecutive years and covering spring, summer and autumn periods. We also report on some community level syntheses using both current-day and historical data from this region. A wide range of parameters were measured during the field campaigns, covering the physical oceanography of the region, air-sea CO 2 fluxes, macro- and micronutrient concentrations, the composition and biomass of the nano-, micro- and mesoplankton communities, and the distribution and biomass of Antarctic krill and mesopelagic fish. Process studies examined the effect of iron-stress on the physiology of primary producers, reproduction and egestion in Antarctic krill and the transfer of stable isotopes between trophic layers, from primary consumers up to birds and seals. Community level syntheses included an examination of the biomass-spectra, food-web modelling, spatial analysis of multiple trophic layers and historical species distributions. The spatial analyses in particular identified two distinct community types: a northern warmer water community and a southern cold community, their boundary being broadly consistent with the position of the Southern Antarctic Circumpolar Current Front (SACCF). Temperature and ice cover appeared to be the dominant, over-riding factors in driving this pattern. Extensive phytoplankton blooms were a major feature of the surveys, and were persistent in areas such as South Georgia. In situ and bioassay measurements emphasised the important role of iron inputs as facilitators of these blooms. Based on seasonal

  6. Model for Atmospheric Propagation of Spatially Combined Laser Beams

    Science.gov (United States)

    2016-09-01

    NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS MODEL FOR ATMOSPHERIC PROPAGATION OF SPATIALLY COMBINED LASER BEAMS by Kum Leong Lee September...MODEL FOR ATMOSPHERIC PROPAGATION OF SPATIALLY COMBINED LASER BEAMS 5. FUNDING NUMBERS 6. AUTHOR(S) Kum Leong Lee 7. PERFORMING ORGANIZATION NAME(S) AND...BLANK ii Approved for public release. Distribution is unlimited. MODEL FOR ATMOSPHERIC PROPAGATION OF SPATIALLY COMBINED LASER BEAMS Kum Leong Lee

  7. Theoretical investigations of the new Cokriging method for variable-fidelity surrogate modeling

    DEFF Research Database (Denmark)

    Zimmermann, Ralf; Bertram, Anna

    2018-01-01

    Cokriging is a variable-fidelity surrogate modeling technique which emulates a target process based on the spatial correlation of sampled data of different levels of fidelity. In this work, we address two theoretical questions associated with the so-called new Cokriging method for variable fidelity...

  8. Characterization of meter-scale spatial variability of riverbed hydraulic conductivity in a lowland river (Aa River, Belgium)

    Science.gov (United States)

    Ghysels, Gert; Benoit, Sien; Awol, Henock; Jensen, Evan Patrick; Debele Tolche, Abebe; Anibas, Christian; Huysmans, Marijke

    2018-04-01

    An improved general understanding of riverbed heterogeneity is of importance for all groundwater modeling studies that include river-aquifer interaction processes. Riverbed hydraulic conductivity (K) is one of the main factors controlling river-aquifer exchange fluxes. However, the meter-scale spatial variability of riverbed K has not been adequately mapped as of yet. This study aims to fill this void by combining an extensive field measurement campaign focusing on both horizontal and vertical riverbed K with a detailed geostatistical analysis of the meter-scale spatial variability of riverbed K . In total, 220 slug tests and 45 standpipe tests were performed at two test sites along the Belgian Aa River. Omnidirectional and directional variograms (along and across the river) were calculated. Both horizontal and vertical riverbed K vary over several orders of magnitude and show significant meter-scale spatial variation. Horizontal K shows a bimodal distribution. Elongated zones of high horizontal K along the river course are observed at both sections, indicating a link between riverbed structures, depositional environment and flow regime. Vertical K is lognormally distributed and its spatial variability is mainly governed by the presence and thickness of a low permeable organic layer at the top of the riverbed. The absence of this layer in the center of the river leads to high vertical K and is related to scouring of the riverbed by high discharge events. Variograms of both horizontal and vertical K show a clear directional anisotropy with ranges along the river being twice as large as those across the river.

  9. Spatial variability of the response to climate change in regional groundwater systems -- examples from simulations in the Deschutes Basin, Oregon

    Science.gov (United States)

    Waibel, Michael S.; Gannett, Marshall W.; Chang, Heejun; Hulbe, Christina L.

    2013-01-01

    We examine the spatial variability of the response of aquifer systems to climate change in and adjacent to the Cascade Range volcanic arc in the Deschutes Basin, Oregon using downscaled global climate model projections to drive surface hydrologic process and groundwater flow models. Projected warming over the 21st century is anticipated to shift the phase of precipitation toward more rain and less snow in mountainous areas in the Pacific Northwest, resulting in smaller winter snowpack and in a shift in the timing of runoff to earlier in the year. This will be accompanied by spatially variable changes in the timing of groundwater recharge. Analysis of historic climate and hydrologic data and modeling studies show that groundwater plays a key role in determining the response of stream systems to climate change. The spatial variability in the response of groundwater systems to climate change, particularly with regard to flow-system scale, however, has generally not been addressed in the literature. Here we simulate the hydrologic response to projected future climate to show that the response of groundwater systems can vary depending on the location and spatial scale of the flow systems and their aquifer characteristics. Mean annual recharge averaged over the basin does not change significantly between the 1980s and 2080s climate periods given the ensemble of global climate models and emission scenarios evaluated. There are, however, changes in the seasonality of groundwater recharge within the basin. Simulation results show that short-flow-path groundwater systems, such as those providing baseflow to many headwater streams, will likely have substantial changes in the timing of discharge in response changes in seasonality of recharge. Regional-scale aquifer systems with flow paths on the order of many tens of kilometers, in contrast, are much less affected by changes in seasonality of recharge. Flow systems at all spatial scales, however, are likely to reflect

  10. The trade-off between spatial and temporal variabilities in reciprocal upper-limb aiming movements of different durations.

    Directory of Open Access Journals (Sweden)

    Frederic Danion

    Full Text Available The spatial and temporal aspects of movement variability have typically been studied separately. As a result the relationship between spatial and temporal variabilities remains largely unknown. In two experiments we examined the evolution and covariation of spatial and temporal variabilities over variations in the duration of reciprocal aiming movements. Experiments differed in settings: In Experiment 1 participants moved unperturbed whereas in Experiment 2 they were confronted with an elastic force field. Different movement durations-for a constant inter-target distance-were either evoked by imposing spatial accuracy constraints while requiring participants to move as fast as possible, or prescribed by means of an auditory metronome while requiring participants to maximize spatial accuracy. Analyses focused on absolute and relative variabilities, respectively captured by the standard deviation (SD and the coefficient of variation (CV = SD/mean. Spatial variability (both SDspace and CVspace decreased with movement duration, while temporal variability (both SDtime and CVtime increased with movement duration. We found strong negative correlations between spatial and temporal variabilities over variations in movement duration, whether the variability examined was absolute or relative. These findings observed at the level of the full movement contrasted with the findings observed at the level of the separate acceleration and deceleration phases of movement. During the separate acceleration and deceleration phases both spatial and temporal variabilities (SD and CV were found to increase with their respective durations, leading to positive correlations between them. Moreover, variability was generally larger at the level of the constituent movement phases than at the level of the full movement. The general pattern of results was robust, as it emerged in both tasks in each of the two experiments. We conclude that feedback mechanisms operating to

  11. The effects of environmental variability and spatial sampling on the three-dimensional inversion problem.

    Science.gov (United States)

    Bender, Christopher M; Ballard, Megan S; Wilson, Preston S

    2014-06-01

    The overall goal of this work is to quantify the effects of environmental variability and spatial sampling on the accuracy and uncertainty of estimates of the three-dimensional ocean sound-speed field. In this work, ocean sound speed estimates are obtained with acoustic data measured by a sparse autonomous observing system using a perturbative inversion scheme [Rajan, Lynch, and Frisk, J. Acoust. Soc. Am. 82, 998-1017 (1987)]. The vertical and horizontal resolution of the solution depends on the bandwidth of acoustic data and on the quantity of sources and receivers, respectively. Thus, for a simple, range-independent ocean sound speed profile, a single source-receiver pair is sufficient to estimate the water-column sound-speed field. On the other hand, an environment with significant variability may not be fully characterized by a large number of sources and receivers, resulting in uncertainty in the solution. This work explores the interrelated effects of environmental variability and spatial sampling on the accuracy and uncertainty of the inversion solution though a set of case studies. Synthetic data representative of the ocean variability on the New Jersey shelf are used.

  12. A Model for Positively Correlated Count Variables

    DEFF Research Database (Denmark)

    Møller, Jesper; Rubak, Ege Holger

    2010-01-01

    An α-permanental random field is briefly speaking a model for a collection of non-negative integer valued random variables with positive associations. Though such models possess many appealing probabilistic properties, many statisticians seem unaware of α-permanental random fields...... and their potential applications. The purpose of this paper is to summarize useful probabilistic results, study stochastic constructions and simulation techniques, and discuss some examples of α-permanental random fields. This should provide a useful basis for discussing the statistical aspects in future work....

  13. Accounting for and predicting the influence of spatial autocorrelation in water quality modeling

    Science.gov (United States)

    Miralha, L.; Kim, D.

    2017-12-01

    Although many studies have attempted to investigate the spatial trends of water quality, more attention is yet to be paid to the consequences of considering and ignoring the spatial autocorrelation (SAC) that exists in water quality parameters. Several studies have mentioned the importance of accounting for SAC in water quality modeling, as well as the differences in outcomes between models that account for and ignore SAC. However, the capacity to predict the magnitude of such differences is still ambiguous. In this study, we hypothesized that SAC inherently possessed by a response variable (i.e., water quality parameter) influences the outcomes of spatial modeling. We evaluated whether the level of inherent SAC is associated with changes in R-Squared, Akaike Information Criterion (AIC), and residual SAC (rSAC), after accounting for SAC during modeling procedure. The main objective was to analyze if water quality parameters with higher Moran's I values (inherent SAC measure) undergo a greater increase in R² and a greater reduction in both AIC and rSAC. We compared a non-spatial model (OLS) to two spatial regression approaches (spatial lag and error models). Predictor variables were the principal components of topographic (elevation and slope), land cover, and hydrological soil group variables. We acquired these data from federal online sources (e.g. USGS). Ten watersheds were selected, each in a different state of the USA. Results revealed that water quality parameters with higher inherent SAC showed substantial increase in R² and decrease in rSAC after performing spatial regressions. However, AIC values did not show significant changes. Overall, the higher the level of inherent SAC in water quality variables, the greater improvement of model performance. This indicates a linear and direct relationship between the spatial model outcomes (R² and rSAC) and the degree of SAC in each water quality variable. Therefore, our study suggests that the inherent level of

  14. A Spatial Model of the Mere Exposure Effect.

    Science.gov (United States)

    Fink, Edward L.; And Others

    1989-01-01

    Uses a spatial model to examine the relationship between stimulus exposure, cognition, and affect. Notes that this model accounts for cognitive changes that a stimulus may acquire as a result of exposure. Concludes that the spatial model is useful for evaluating the mere exposure effect and that affective change does not require cognitive change.…

  15. Spatial variability of harmful algal blooms in Milford Lake, Kansas, July and August 2015

    Science.gov (United States)

    Foster, Guy M.; Graham, Jennifer L.; Stiles, Tom C.; Boyer, Marvin G.; King, Lindsey R.; Loftin, Keith A.

    2017-01-09

    Cyanobacterial harmful algal blooms (CyanoHABs) tend to be spatially variable vertically in the water column and horizontally across the lake surface because of in-lake and weather-driven processes and can vary by orders of magnitude in concentration across relatively short distances (meters or less). Extreme spatial variability in cyanobacteria and associated compounds poses unique challenges to collecting representative samples for scientific study and public-health protection. The objective of this study was to assess the spatial variability of cyanobacteria and microcystin in Milford Lake, Kansas, using data collected on July 27 and August 31, 2015. Spatially dense near-surface data were collected by the U.S. Geological Survey, nearshore data were collected by the Kansas Department of Health and Environment, and open-water data were collected by U.S. Army Corps of Engineers. CyanoHABs are known to be spatially variable, but that variability is rarely quantified. A better understanding of the spatial variability of cyanobacteria and microcystin will inform sampling and management strategies for Milford Lake and for other lakes with CyanoHAB issues throughout the Nation.The CyanoHABs in Milford Lake during July and August 2015 displayed the extreme spatial variability characteristic of cyanobacterial blooms. The phytoplankton community was almost exclusively cyanobacteria (greater than 90 percent) during July and August. Cyanobacteria (measured directly by cell counts and indirectly by regression-estimated chlorophyll) and microcystin (measured directly by enzyme-linked immunosorbent assay [ELISA] and indirectly by regression estimates) concentrations varied by orders of magnitude throughout the lake. During July and August 2015, cyanobacteria and microcystin concentrations decreased in the downlake (towards the outlet) direction.Nearshore and open-water surface grabs were collected and analyzed for microcystin as part of this study. Samples were collected in the

  16. Variability of apparently homogeneous soilscapes in São Paulo state, Brazil: I. spatial analysis

    Directory of Open Access Journals (Sweden)

    M. van Den Berg

    2000-06-01

    Full Text Available The spatial variability of strongly weathered soils under sugarcane and soybean/wheat rotation was quantitatively assessed on 33 fields in two regions in São Paulo State, Brazil: Araras (15 fields with sugarcane and Assis (11 fields with sugarcane and seven fields with soybean/wheat rotation. Statistical methods used were: nested analysis of variance (for 11 fields, semivariance analysis and analysis of variance within and between fields. Spatial levels from 50 m to several km were analyzed. Results are discussed with reference to a previously published study carried out in the surroundings of Passo Fundo (RS. Similar variability patterns were found for clay content, organic C content and cation exchange capacity. The fields studied are quite homogeneous with respect to these relatively stable soil characteristics. Spatial variability of other characteristics (resin extractable P, pH, base- and Al-saturation and also soil colour, varies with region and, or land use management. Soil management for sugarcane seems to have induced modifications to greater depths than for soybean/wheat rotation. Surface layers of soils under soybean/wheat present relatively little variation, apparently as a result of very intensive soil management. The major part of within-field variation occurs at short distances (< 50 m in all study areas. Hence, little extra information would be gained by increasing sampling density from, say, 1/km² to 1/50 m². For many purposes, the soils in the study regions can be mapped with the same observation density, but residual variance will not be the same in all areas. Bulk sampling may help to reveal spatial patterns between 50 and 1.000 m.

  17. Robust Exponential Synchronization for a Class of Master-Slave Distributed Parameter Systems with Spatially Variable Coefficients and Nonlinear Perturbation

    Directory of Open Access Journals (Sweden)

    Chengdong Yang

    2015-01-01

    Full Text Available This paper addresses the exponential synchronization problem of a class of master-slave distributed parameter systems (DPSs with spatially variable coefficients and spatiotemporally variable nonlinear perturbation, modeled by a couple of semilinear parabolic partial differential equations (PDEs. With a locally Lipschitz constraint, the perturbation is a continuous function of time, space, and system state. Firstly, a sufficient condition for the robust exponential synchronization of the unforced semilinear master-slave PDE systems is investigated for all admissible nonlinear perturbations. Secondly, a robust distributed proportional-spatial derivative (P-sD state feedback controller is desired such that the closed-loop master-slave PDE systems achieve exponential synchronization. Using Lyapunov’s direct method and the technique of integration by parts, the main results of this paper are presented in terms of spatial differential linear matrix inequalities (SDLMIs. Finally, two numerical examples are provided to show the effectiveness of the proposed methods applied to the robust exponential synchronization problem of master-slave PDE systems with nonlinear perturbation.

  18. Spatial variability of NDVI at different seasons in the Community of Madrid (Spain)

    Science.gov (United States)

    Sotoca, Juan J. Martin; Saa-Requejo, Antonio; Borondo, Javier; Tarquis, Ana M.

    2015-04-01

    Agricultural drought quantification is one of the most important tasks in the characterization process of this natural hazard and its implications in crop insurance. Recently, several vegetation indexes based on remote-sensing data (VI) has been applied to quantify it (Dalezios et al, 2012). VIs are obtained combining several frequency bands that represent the relationship between photosynthesis and absorbed/reflected radiation. The most widely used VI is the Normalized Difference Vegetation Index (NDVI). It is based on the principle that healthy vegetation mainly absorbs visible light and reflects the near-infrared frequency band. Drought can be highly localized, and several authors have recognized the critical role of soil moisture and its spatial variability in agricultural losses (Anderson et al., 2011). Therefore, it is important to delimit locations within a homogeneous area that will share main NDVI statistics and in which the same threshold value can be applied to define drought event. In order to do so, we have applied for the first time in this context the method of singularity maps (Cheng and Agterberg, 1996) commonly used in localization of mineral deposits. The NDVI singularity maps calculated in each season through 2011/2012 are showed and discussed (Martín-Sotoca, 2014). References Anderson, M:C:, C. R. Hain, B. Wardlow, J. R. Mecikalski and W. P. Kustas (2011) Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States. J. Climate, 24, 2025-2044. Dalezios, N.R., A. Blanta, N.V. Spyropoulos and A.M. Tarquis (2012) Risk identification of agricultural drought for sustainable Agroecosystems. Nat. Hazards Earth Syst. Sci., 14, 2435-2448. Cheng, Q. and F.P. Agterberg (1996) Multifractal modeling and spatial statistics. Math. Geol., 28, 1-16. Martín-Sotoca, J.J. (2014) Estructura Espacial de la Sequía en Pastos y sus Aplicaciones en el Seguro Agrario. Master Thesis, UPM (In Spanish

  19. APPLICATION OF SPATIAL MODELLING APPROACHES, SAMPLING STRATEGIES AND 3S TECHNOLOGY WITHIN AN ECOLGOCIAL FRAMWORK

    Directory of Open Access Journals (Sweden)

    H.-C. Chen

    2012-07-01

    Full Text Available How to effectively describe ecological patterns in nature over broader spatial scales and build a modeling ecological framework has become an important issue in ecological research. We test four modeling methods (MAXENT, DOMAIN, GLM and ANN to predict the potential habitat of Schima superba (Chinese guger tree, CGT with different spatial scale in the Huisun study area in Taiwan. Then we created three sampling design (from small to large scales for model development and validation by different combinations of CGT samples from aforementioned three sites (Tong-Feng watershed, Yo-Shan Mountain, and Kuan-Dau watershed. These models combine points of known occurrence and topographic variables to infer CGT potential spatial distribution. Our assessment revealed that the method performance from highest to lowest was: MAXENT, DOMAIN, GLM and ANN on small spatial scale. The MAXENT and DOMAIN two models were the most capable for predicting the tree's potential habitat. However, the outcome clearly indicated that the models merely based on topographic variables performed poorly on large spatial extrapolation from Tong-Feng to Kuan-Dau because the humidity and sun illumination of the two watersheds are affected by their microterrains and are quite different from each other. Thus, the models developed from topographic variables can only be applied within a limited geographical extent without a significant error. Future studies will attempt to use variables involving spectral information associated with species extracted from high spatial, spectral resolution remotely sensed data, especially hyperspectral image data, for building a model so that it can be applied on a large spatial scale.

  20. A Biophysical Neural Model To Describe Spatial Visual Attention

    International Nuclear Information System (INIS)

    Hugues, Etienne; Jose, Jorge V.

    2008-01-01

    Visual scenes have enormous spatial and temporal information that are transduced into neural spike trains. Psychophysical experiments indicate that only a small portion of a spatial image is consciously accessible. Electrophysiological experiments in behaving monkeys have revealed a number of modulations of the neural activity in special visual area known as V4, when the animal is paying attention directly towards a particular stimulus location. The nature of the attentional input to V4, however, remains unknown as well as to the mechanisms responsible for these modulations. We use a biophysical neural network model of V4 to address these issues. We first constrain our model to reproduce the experimental results obtained for different external stimulus configurations and without paying attention. To reproduce the known neuronal response variability, we found that the neurons should receive about equal, or balanced, levels of excitatory and inhibitory inputs and whose levels are high as they are in in vivo conditions. Next we consider attentional inputs that can induce and reproduce the observed spiking modulations. We also elucidate the role played by the neural network to generate these modulations

  1. Potential for tree rings to reveal spatial patterns of past drought variability across western Australia

    Science.gov (United States)

    O'Donnell, Alison J.; Cook, Edward R.; Palmer, Jonathan G.; Turney, Chris S. M.; Grierson, Pauline F.

    2018-02-01

    Proxy records have provided major insights into the variability of past climates over long timescales. However, for much of the Southern Hemisphere, the ability to identify spatial patterns of past climatic variability is constrained by the sparse distribution of proxy records. This is particularly true for mainland Australia, where relatively few proxy records are located. Here, we (1) assess the potential to use existing proxy records in the Australasian region—starting with the only two multi-century tree-ring proxies from mainland Australia—to reveal spatial patterns of past hydroclimatic variability across the western third of the continent, and (2) identify strategic locations to target for the development of new proxy records. We show that the two existing tree-ring records allow robust reconstructions of past hydroclimatic variability over spatially broad areas (i.e. > 3° × 3°) in inland north- and south-western Australia. Our results reveal synchronous periods of drought and wet conditions between the inland northern and southern regions of western Australia as well as a generally anti-phase relationship with hydroclimate in eastern Australia over the last two centuries. The inclusion of 174 tree-ring proxy records from Tasmania, New Zealand and Indonesia and a coral record from Queensland did not improve the reconstruction potential over western Australia. However, our findings suggest that the addition of relatively few new proxy records from key locations in western Australia that currently have low reconstruction skill will enable the development of a comprehensive drought atlas for the region, and provide a critical link to the drought atlases of monsoonal Asia and eastern Australia and New Zealand.

  2. Scaling local species-habitat relations to the larger landscape with a hierarchical spatial count model

    Science.gov (United States)

    Thogmartin, W.E.; Knutson, M.G.

    2007-01-01

    Much of what is known about avian species-habitat relations has been derived from studies of birds at local scales. It is entirely unclear whether the relations observed at these scales translate to the larger landscape in a predictable linear fashion. We derived habitat models and mapped predicted abundances for three forest bird species of eastern North America using bird counts, environmental variables, and hierarchical models applied at three spatial scales. Our purpose was to understand habitat associations at multiple spatial scales and create predictive abundance maps for purposes of conservation planning at a landscape scale given the constraint that the variables used in this exercise were derived from local-level studies. Our models indicated a substantial influence of landscape context for all species, many of which were counter to reported associations at finer spatial extents. We found land cover composition provided the greatest contribution to the relative explained variance in counts for all three species; spatial structure was second in importance. No single spatial scale dominated any model, indicating that these species are responding to factors at multiple spatial scales. For purposes of conservation planning, areas of predicted high abundance should be investigated to evaluate the conservation potential of the landscape in their general vicinity. In addition, the models and spatial patterns of abundance among species suggest locations where conservation actions may benefit more than one species. ?? 2006 Springer Science+Business Media B.V.

  3. Spatial data modelling and maximum entropy theory

    Czech Academy of Sciences Publication Activity Database

    Klimešová, Dana; Ocelíková, E.

    2005-01-01

    Roč. 51, č. 2 (2005), s. 80-83 ISSN 0139-570X Institutional research plan: CEZ:AV0Z10750506 Keywords : spatial data classification * distribution function * error distribution Subject RIV: BD - Theory of Information

  4. Spatial Field Variability Mapping of Rice Crop using Clustering Technique from Space Borne Hyperspectral Data

    Science.gov (United States)

    Moharana, S.; Dutta, S.

    2015-12-01

    Precision farming refers to field-specific management of an agricultural crop at a spatial scale with an aim to get the highest achievable yield and to achieve this spatial information on field variability is essential. The difficulty in mapping of spatial variability occurring within an agriculture field can be revealed by employing spectral techniques in hyperspectral imagery rather than multispectral imagery. However an advanced algorithm needs to be developed to fully make use of the rich information content in hyperspectral data. In the present study, potential of hyperspectral data acquired from space platform was examined to map the field variation of paddy crop and its species discrimination. This high dimensional data comprising 242 spectral narrow bands with 30m ground resolution Hyperion L1R product acquired for Assam, India (30th Sept and 3rd Oct, 2014) were allowed for necessary pre-processing steps followed by geometric correction using Hyperion L1GST product. Finally an atmospherically corrected and spatially deduced image consisting of 112 band was obtained. By employing an advanced clustering algorithm, 12 different clusters of spectral waveforms of the crop were generated from six paddy fields for each images. The findings showed that, some clusters were well discriminated representing specific rice genotypes and some clusters were mixed treating as a single rice genotype. As vegetation index (VI) is the best indicator of vegetation mapping, three ratio based VI maps were also generated and unsupervised classification was performed for it. The so obtained 12 clusters of paddy crop were mapped spatially to the derived VI maps. From these findings, the existence of heterogeneity was clearly captured in one of the 6 rice plots (rice plot no. 1) while heterogeneity was observed in rest of the 5 rice plots. The degree of heterogeneous was found more in rice plot no.6 as compared to other plots. Subsequently, spatial variability of paddy field was

  5. Not just another variable: untangling the spatialities of power in social-ecological systems

    Directory of Open Access Journals (Sweden)

    Micah L. Ingalls

    2017-09-01

    Full Text Available Increased attention has been paid to how the spatial dimensions of social-ecological systems are formative in shaping their ability to negotiate change and remain resilient. This paper moves this research further by exploring how diverse forms of power play a crucial role in shaping these spatial dimensions and the production of social-ecological outcomes. Grounding these explorations in a National Protected Area in Lao PDR, this paper explores how power relationships operate through the spatial and temporal domains of complex systems. Findings suggest (at least four important insights: (1 the exercise of power materializes in policies and programs and becomes written onto the spaces of social-ecological systems through boundary creation, zonation, and other social processes that (redefine spatial meanings; these meanings vary by social actor; (2 policies and programs map out unevenly across space and time as they interact with antecedent social-ecological conditions in ways that preclude linear causal relationships between policy and outcomes; (3 although local in their expression, spatialized disputes in social-ecological systems draw on cross-scalar discourses and networks of power to bolster, undermine, and (delegitimize competing environmental and social narratives; and (4 however powerful institutions and actor-networks may be, they are never fully hegemonic as they are attenuated by other discourses and operations of power, although these all play out across a highly uneven sociopolitical terrain. Paying greater attention to the spatial and temporal dynamics of power may be much more than a project of introducing yet another variable into the already complex admixture of analytic elements. Rather, by rendering these explicit as objects of analysis, common insights may change entirely or even be overturned.

  6. A soil-landscape framework for understanding spatial and temporal variability in biogeochemical processes in catchments

    Science.gov (United States)

    McGuire, K. J.; Bailey, S. W.; Ross, D. S.

    2017-12-01

    Heterogeneity in biophysical properties within catchments challenges how we quantify and characterize biogeochemical processes and interpret catchment outputs. Interactions between the spatiotemporal variability of hydrological states and fluxes and soil development can spatially structure catchments, leading to a framework for understanding patterns in biogeochemical processes. In an upland, glaciated landscape at the Hubbard Brook Experimental Forest (HBEF) in New Hampshire, USA, we are embracing the structure and organization of soils to understand the spatial relations between runoff production zones, distinct soil-biogeochemical environments, and solute retention and release. This presentation will use observations from the HBEF to demonstrate that a soil-landscape framework is essential in understanding the spatial and temporal variability of biogeochemical processes in this catchment. Specific examples will include how laterally developed soils reveal the location of active runoff production zones and lead to gradients in primary mineral dissolution and the distribution of weathering products along hillslopes. Soil development patterns also highlight potential carbon and nitrogen cycling hotspots, differentiate acidic conditions, and affect the regulation of surface water quality. Overall, this work demonstrates the importance of understanding the landscape-level structural organization of soils in characterizing the variation and extent of biogeochemical processes that occur in catchments.

  7. Spatial and Temporal Variabilities of Solar and Longwave Radiation Fluxes below a Coniferous Forest in the French Alps

    Science.gov (United States)

    Sicart, J. E.; Ramseyer, V.; Lejeune, Y.; Essery, R.; Webster, C.; Rutter, N.

    2017-12-01

    At high altitudes and latitudes, snow has a large influence on hydrological processes. Large fractions of these regions are covered by forests, which have a strong influence on snow accumulation and melting processes. Trees absorb a large part of the incoming shortwave radiation and this heat load is mostly dissipated as longwave radiation. Trees shelter the snow surface from wind, so sub-canopy snowmelt depends mainly on the radiative fluxes: vegetation attenuates the transmission of shortwave radiation but enhances longwave irradiance to the surface. An array of 13 pyranometers and 11 pyrgeometers was deployed on the snow surface below a coniferous forest at the CEN-MeteoFrance Col de Porte station in the French Alps (1325 m asl) during the 2017 winter in order to investigate spatial and temporal variabilities of solar and infrared irradiances in different meteorological conditions. Sky view factors measured with hemispherical photographs at each radiometer location were in a narrow range from 0.2 to 0.3. The temperature of the vegetation was measured with IR thermocouples and an IR camera. In clear sky conditions, the attenuation of solar radiation by the canopy reached 96% and its spatial variability exceeded 100 W m-2. Longwave irradiance varied by 30 W m-2 from dense canopy to gap areas. In overcast conditions, the spatial variabilities of solar and infrared irradiances were reduced and remained closely related to the sky view factor. A simple radiative model taking into account the penetration through the canopy of the direct and diffuse solar radiation, and isotropic infrared emission of the vegetation as a blackbody emitter, accurately reproduced the dynamics of the radiation fluxes at the snow surface. Model results show that solar transmissivity of the canopy in overcast conditions is an excellent proxy of the sky view factor and the emitting temperature of the vegetation remained close to the air temperature in this typically dense Alpine forest.

  8. Analysis of streamflow variability in Alpine catchments at multiple spatial and temporal scales

    Science.gov (United States)

    Pérez Ciria, T.; Chiogna, G.

    2017-12-01

    Alpine watersheds play a pivotal role in Europe for water provisioning and for hydropower production. In these catchments, temporal fluctuations of river discharge occur at multiple temporal scales due to natural as well as anthropogenic driving forces. In the last decades, modifications of the flow regime have been observed and their origin lies in the complex interplay between construction of dams for hydro power production, changes in water management policies and climatic changes. The alteration of the natural flow has negative impacts on the freshwater biodiversity and threatens the ecosystem integrity of the Alpine region. Therefore, understanding the temporal and spatial variability of river discharge has recently become a particular concern for environmental protection and represents a crucial contribution to achieve sustainable water resources management in the Alps. In this work, time series analysis is conducted for selected gauging stations in the Inn and the Adige catchments, which cover a large part of the central and eastern region of the Alps. We analyze the available time series using the continuous wavelet transform and change-point analyses for determining how and where changes have taken place. Although both catchments belong to different climatic zones of the Greater Alpine Region, streamflow properties share some similar characteristics. The comparison of the collected streamflow time series in the two catchments permits detecting gradients in the hydrological system dynamics that depend on station elevation, longitudinal location in the Alps and catchment area. This work evidences that human activities (e.g., water management practices and flood protection measures, changes in legislation and market regulation) have major impacts on streamflow and should be rigorously considered in hydrological models.

  9. Evaluation of spatial and spatiotemporal estimation methods in simulation of precipitation variability patterns

    Science.gov (United States)

    Bayat, Bardia; Zahraie, Banafsheh; Taghavi, Farahnaz; Nasseri, Mohsen

    2013-08-01

    Identification of spatial and spatiotemporal precipitation variations plays an important role in different hydrological applications such as missing data estimation. In this paper, the results of Bayesian maximum entropy (BME) and ordinary kriging (OK) are compared for modeling spatial and spatiotemporal variations of annual precipitation with and without incorporating elevation variations. The study area of this research is Namak Lake watershed located in the central part of Iran with an area of approximately 90,000 km2. The BME and OK methods have been used to model the spatial and spatiotemporal variations of precipitation in this watershed, and their performances have been evaluated using cross-validation statistics. The results of the case study have shown the superiority of BME over OK in both spatial and spatiotemporal modes. The results have shown that BME estimates are less biased and more accurate than OK. The improvements in the BME estimates are mostly related to incorporating hard and soft data in the estimation process, which resulted in more detailed and reliable results. Estimation error variance for BME results is less than OK estimations in the study area in both spatial and spatiotemporal modes.

  10. The spatial distribution of known predictors of autism spectrum disorders impacts geographic variability in prevalence in central North Carolina

    Directory of Open Access Journals (Sweden)

    Hoffman Kate

    2012-10-01

    Full Text Available Abstract Background The causes of autism spectrum disorders (ASD remain largely unknown and widely debated; however, evidence increasingly points to the importance of environmental exposures. A growing number of studies use geographic variability in ASD prevalence or exposure patterns to investigate the association between environmental factors and ASD. However, differences in the geographic distribution of established risk and predictive factors for ASD, such as maternal education or age, can interfere with investigations of ASD etiology. We evaluated geographic variability in the prevalence of ASD in central North Carolina and the impact of spatial confounding by known risk and predictive factors. Methods Children meeting a standardized case definition for ASD at 8 years of age were identified through records-based surveillance for 8 counties biennially from 2002 to 2008 (n=532. Vital records were used to identify the underlying cohort (15% random sample of children born in the same years as children with an ASD, n=11,034, and to obtain birth addresses. We used generalized additive models (GAMs to estimate the prevalence of ASD across the region by smoothing latitude and longitude. GAMs, unlike methods used in previous spatial analyses of ASD, allow for extensive adjustment of individual-level risk factors (e.g. maternal age and education when evaluating spatial variability of disease prevalence. Results Unadjusted maps revealed geographic variation in surveillance-recognized ASD. Children born in certain regions of the study area were up to 1.27 times as likely to be recognized as having ASD compared to children born in the study area as a whole (prevalence ratio (PR range across the study area 0.57-1.27; global P=0.003. However, geographic gradients of ASD prevalence were attenuated after adjusting for spatial confounders (adjusted PR range 0.72-1.12 across the study area; global P=0.052. Conclusions In these data, spatial variation of ASD

  11. Comparison of Three Plot Selection Methods for Estimating Change in Temporally Variable, Spatially Clustered Populations.

    Energy Technology Data Exchange (ETDEWEB)

    Thompson, William L. [Bonneville Power Administration, Portland, OR (US). Environment, Fish and Wildlife

    2001-07-01

    Monitoring population numbers is important for assessing trends and meeting various legislative mandates. However, sampling across time introduces a temporal aspect to survey design in addition to the spatial one. For instance, a sample that is initially representative may lose this attribute if there is a shift in numbers and/or spatial distribution in the underlying population that is not reflected in later sampled plots. Plot selection methods that account for this temporal variability will produce the best trend estimates. Consequently, I used simulation to compare bias and relative precision of estimates of population change among stratified and unstratified sampling designs based on permanent, temporary, and partial replacement plots under varying levels of spatial clustering, density, and temporal shifting of populations. Permanent plots produced more precise estimates of change than temporary plots across all factors. Further, permanent plots performed better than partial replacement plots except for high density (5 and 10 individuals per plot) and 25% - 50% shifts in the population. Stratified designs always produced less precise estimates of population change for all three plot selection methods, and often produced biased change estimates and greatly inflated variance estimates under sampling with partial replacement. Hence, stratification that remains fixed across time should be avoided when monitoring populations that are likely to exhibit large changes in numbers and/or spatial distribution during the study period. Key words: bias; change estimation; monitoring; permanent plots; relative precision; sampling with partial replacement; temporary plots.

  12. Spatial Inequalities in the Incidence of Colorectal Cancer and Associated Factors in the Neighborhoods of Tehran, Iran: Bayesian Spatial Models

    Directory of Open Access Journals (Sweden)

    Kamyar Mansori

    2018-01-01

    Full Text Available Objectives The aim of this study was to determine the factors associated with the spatial distribution of the incidence of colorectal cancer (CRC in the neighborhoods of Tehran, Iran using Bayesian spatial models. Methods This ecological study was implemented in Tehran on the neighborhood level. Socioeconomic variables, risk factors, and health costs were extracted from the Equity Assessment Study conducted in Tehran. The data on CRC incidence were extracted from the Iranian population-based cancer registry. The Besag-York-Mollié (BYM model was used to identify factors associated with the spatial distribution of CRC incidence. The software programs OpenBUGS version 3.2.3, ArcGIS 10.3, and GeoDa were used for the analysis. Results The Moran index was statistically significant for all the variables studied (p<0.05. The BYM model showed that having a women head of household (median standardized incidence ratio [SIR], 1.63; 95% confidence interval [CI], 1.06 to 2.53, living in a rental house (median SIR, 0.82; 95% CI, 0.71 to 0.96, not consuming milk daily (median SIR, 0.71; 95% CI, 0.55 to 0.94 and having greater household health expenditures (median SIR, 1.34; 95% CI, 1.06 to 1.68 were associated with a statistically significant elevation in the SIR of CRC. The median (interquartile range and mean (standard deviation values of the SIR of CRC, with the inclusion of all the variables studied in the model, were 0.57 (1.01 and 1.05 (1.31, respectively. Conclusions Inequality was found in the spatial distribution of CRC incidence in Tehran on the neighborhood level. Paying attention to this inequality and the factors associated with it may be useful for resource allocation and developing preventive strategies in atrisk areas.

  13. Spatial variability in denitrification rates in an Oregon tidal salt marsh

    Science.gov (United States)

    Modeling denitrification (DeN) is particularly challenging in tidal systems, which play a vital role in buffering adjacent coastal waters from nitrogen inputs. These systems are hydrologically and biogeochemically complex, varying on fine temporal and spatial scales. As part of a...

  14. A protocol for measuring spatial variables in soft-sediment tide pools

    Directory of Open Access Journals (Sweden)

    Marina R. Brenha-Nunes

    2016-01-01

    Full Text Available ABSTRACT We present a protocol for measuring spatial variables in large (>50 m2 soft-sediment tide pool. Secondarily, we present the fish capture efficiency of a sampling protocol that based on such spatial variables to calculate relative abundances. The area of the pool is estimated by summing areas of basic geometric forms; the depth, by taken representative measurements of the depth variability of each pool's sector, previously determined according to its perimeter; and the volume, by considering the pool as a prism. These procedures were a trade-off between the acquisition of reliable estimates and the minimization of both the cost of operating and the time spent in field. The fish sampling protocol is based on two con secutive stages: 1 two people search for fishes under structures (e.g., rocks and litters on the pool and capture them with hand seines; 2 these structures are removed and then a beach-seine is hauled over the whole pool. Our method is cheaper than others and fast to operate considering the time in low tides. The method to sample fish is quite efficient resulting in a capture efficiency of 89%.

  15. Impact of radionuclide spatial variability on groundwater quality downstream from a shallow waste burial in the Chernobyl Exclusion Zone

    Science.gov (United States)

    Nguyen, H. L.; de Fouquet, C.; Courbet, C.; Simonucci, C. A.

    2016-12-01

    The effects of spatial variability of hydraulic parameters and initial groundwater plume localization on the possible extent of groundwater pollution plumes have already been broadly studied. However, only a few studies, such as Kjeldsen et al. (1995), take into account the effect of source term spatial variability. We explore this question with the 90Sr migration modeling from a shallow waste burial located in the Chernobyl Exclusion Zone to the underlying sand aquifer. Our work is based upon groundwater sampled once or twice a year since 1995 until 2015 from about 60 piezometers and more than 3,000 137Cs soil activity measurements. These measurements were taken in 1999 from one of the trenches dug after the explosion of the Chernobyl nuclear power plant, the so-called "T22 Trench", where radioactive waste was buried in 1987. The geostatistical analysis of 137Cs activity data in soils from Bugai et al. (2005) is first reconsidered to delimit the trench borders using georadar data as a covariable and to perform geostatistical simulations in order to evaluate the uncertainties of this inventory. 90Sr activity in soils is derived from 137Cs/154Eu and 90Sr/154Eu activity ratios in Chernobyl hot fuel particles (Bugai et al., 2003). Meanwhile, a coupled 1D non saturated/3D saturated transient transport model is constructed under the MELODIE software (IRSN, 2009). The previous 90Sr transport model developed by Bugai et al. (2012) did not take into account the effect of water table fluctuations highlighted by Van Meir et al. (2007) which may cause some discrepancies between model predictions and field observations. They are thus reproduced on a 1D vertical non saturated model. The equiprobable radionuclide localization maps produced by the geostatistical simulations are selected to illustrate different heterogeneities in the radionuclide inventory and are implemented in the 1D model. The obtained activity fluxes from all the 1D vertical models are then injected in a 3D

  16. Industrial implementation of spatial variability control by real-time SPC

    Science.gov (United States)

    Roule, O.; Pasqualini, F.; Borde, M.

    2016-10-01

    Advanced technology nodes require more and more information to get the wafer process well setup. The critical dimension of components decreases following Moore's law. At the same time, the intra-wafer dispersion linked to the spatial non-uniformity of tool's processes is not capable to decrease in the same proportions. APC systems (Advanced Process Control) are being developed in waferfab to automatically adjust and tune wafer processing, based on a lot of process context information. It can generate and monitor complex intrawafer process profile corrections between different process steps. It leads us to put under control the spatial variability, in real time by our SPC system (Statistical Process Control). This paper will outline the architecture of an integrated process control system for shape monitoring in 3D, implemented in waferfab.

  17. Fine scale spatial variability of microbial pesticide degradation in soil: scales, controlling factors, and implications

    DEFF Research Database (Denmark)

    Dechesne, Arnaud; Badawi, N.; Aamand, Jens

    2014-01-01

    across pesticide classes: they include some soil characteristics (pH) and some agricultural management practices (pesticide application, tillage), while other potential controlling factors have more conflicting effects depending on the site or the pesticide. Evidence demonstrating the importance......Pesticide biodegradation is a soil microbial function of critical importance for modern agriculture and its environmental impact. While it was once assumed that this activity was homogeneously distributed at the field scale, mounting evidence indicates that this is rarely the case. Here, we...... critically examine the literature on spatial variability of pesticide biodegradation in agricultural soil. We discuss the motivations, methods, and main findings of the primary literature. We found significant diversity in the approaches used to describe and quantify spatial heterogeneity, which complicates...

  18. Analysis of the spatial variability of crop yield and soil properties in small agricultural plots

    Directory of Open Access Journals (Sweden)

    Vieira Sidney Rosa

    2003-01-01

    Full Text Available The objective of this study was to assess spatial variability of soil properties and crop yield under no tillage as a function of time, in two soil/climate conditions in São Paulo State, Brazil. The two sites measured approximately one hectare each and were cultivated with crop sequences which included corn, soybean, cotton, oats, black oats, wheat, rye, rice and green manure. Soil fertility, soil physical properties and crop yield were measured in a 10-m grid. The soils were a Dusky Red Latossol (Oxisol and a Red Yellow Latossol (Ultisol. Soil sampling was performed in each field every two years after harvesting of the summer crop. Crop yield was measured at the end of each crop cycle, in 2 x 2.5 m sub plots. Data were analysed using semivariogram analysis and kriging interpolation for contour map generation. Yield maps were constructed in order to visually compare the variability of yields, the variability of the yield components and related soil properties. The results show that the factors affecting the variability of crop yield varies from one crop to another. The changes in yield from one year to another suggest that the causes of variability may change with time. The changes with time for the cross semivariogram between phosphorus in leaves and soybean yield is another evidence of this result.

  19. Spatial variability of soil carbon stock in the Urucu river basin, Central Amazon-Brazil.

    Science.gov (United States)

    Ceddia, Marcos Bacis; Villela, André Luis Oliveira; Pinheiro, Érika Flávia Machado; Wendroth, Ole

    2015-09-01

    The Amazon Forest plays a major role in C sequestration and release. However, few regional estimates of soil organic carbon (SOC) stock in this ecoregion exist. One of the barriers to improve SOC estimates is the lack of recent soil data at high spatial resolution, which hampers the application of new methods for mapping SOC stock. The aims of this work were: (i) to quantify SOC stock under undisturbed vegetation for the 0-30 and the 0-100 cm under Amazon Forest; (ii) to correlate the SOC stock with soil mapping units and relief attributes and (iii) to evaluate three geostatistical techniques to generate maps of SOC stock (ordinary, isotopic and heterotopic cokriging). The study site is located in the Central region of Amazon State, Brazil. The soil survey covered the study site that has an area of 80 km(2) and resulted in a 1:10,000 soil map. It consisted of 315 field observations (96 complete soil profiles and 219 boreholes). SOC stock was calculated by summing C stocks by horizon, determined as a product of BD, SOC and the horizon thickness. For each one of the 315 soil observations, relief attributes were derived from a topographic map to understand SOC dynamics. The SOC stocks across 30 and 100 cm soil depth were 3.28 and 7.32 kg C m(-2), respectively, which is, 34 and 16%, lower than other studies. The SOC stock is higher in soils developed in relief forms exhibiting well-drained soils, which are covered by Upland Dense Tropical Rainforest. Only SOC stock in the upper 100 cm exhibited spatial dependence allowing the generation of spatial variability maps based on spatial (co)-regionalization. The CTI was inversely correlated with SOC stock and was the only auxiliary variable feasible to be used in cokriging interpolation. The heterotopic cokriging presented the best performance for mapping SOC stock. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. Recent developments in spatial analysis spatial statistics, behavioural modelling, and computational intelligence

    CERN Document Server

    Getis, Arthur

    1997-01-01

    In recent years, spatial analysis has become an increasingly active field, as evidenced by the establishment of educational and research programs at many universities. Its popularity is due mainly to new technologies and the development of spatial data infrastructures. This book illustrates some recent developments in spatial analysis, behavioural modelling, and computational intelligence. World renown spatial analysts explain and demonstrate their new and insightful models and methods. The applications are in areas of societal interest such as the spread of infectious diseases, migration behaviour, and retail and agricultural location strategies. In addition, there is emphasis on the uses of new technologoies for the analysis of spatial data through the application of neural network concepts.

  1. Groundwater Quality: Analysis of Its Temporal and Spatial Variability in a Karst Aquifer.

    Science.gov (United States)

    Pacheco Castro, Roger; Pacheco Ávila, Julia; Ye, Ming; Cabrera Sansores, Armando

    2018-01-01

    This study develops an approach based on hierarchical cluster analysis for investigating the spatial and temporal variation of water quality governing processes. The water quality data used in this study were collected in the karst aquifer of Yucatan, Mexico, the only source of drinking water for a population of nearly two million people. Hierarchical cluster analysis was applied to the quality data of all the sampling periods lumped together. This was motivated by the observation that, if water quality does not vary significantly in time, two samples from the same sampling site will belong to the same cluster. The resulting distribution maps of clusters and box-plots of the major chemical components reveal the spatial and temporal variability of groundwater quality. Principal component analysis was used to verify the results of cluster analysis and to derive the variables that explained most of the variation of the groundwater quality data. Results of this work increase the knowledge about how precipitation and human contamination impact groundwater quality in Yucatan. Spatial variability of groundwater quality in the study area is caused by: a) seawater intrusion and groundwater rich in sulfates at the west and in the coast, b) water rock interactions and the average annual precipitation at the middle and east zones respectively, and c) human contamination present in two localized zones. Changes in the amount and distribution of precipitation cause temporal variation by diluting groundwater in the aquifer. This approach allows to analyze the variation of groundwater quality controlling processes efficiently and simultaneously. © 2017, National Ground Water Association.

  2. Electrically tunable spatially variable switching in ferroelectric liquid crystal/water system

    Science.gov (United States)

    Choudhary, A.; Coondoo, I.; Prakash, J.; Sreenivas, K.; Biradar, A. M.

    2009-04-01

    An unusual switching phenomenon in the region outside conducting patterned area in ferroelectric liquid crystal (FLC) containing about 1-2 wt % of water has been observed. The presence of water in the studied heterogeneous system was confirmed by Fourier transform infrared spectroscopy. The observed optical studies have been emphasized on the "spatially variable switching" phenomenon of the molecules in the nonconducting region of the cell. The observed phenomenon is due to diffusion of water between the smectic layers of the FLC and the interaction of the curved electric field lines with the FLC molecules in the nonconducting region.

  3. Long-term Observations of Intense Precipitation Small-scale Spatial Variability in a Semi-arid Catchment

    Science.gov (United States)

    Cropp, E. L.; Hazenberg, P.; Castro, C. L.; Demaria, E. M.

    2017-12-01

    In the southwestern US, the summertime North American Monsoon (NAM) provides about 60% of the region's annual precipitation. Recent research using high-resolution atmospheric model simulations and retrospective predictions has shown that since the 1950's, and more specifically in the last few decades, the mean daily precipitation in the southwestern U.S. during the NAM has followed a decreasing trend. Furthermore, days with more extreme precipitation have intensified. The current work focuses the impact of these long-term changes on the observed small-scale spatial variability of intense precipitation. Since limited long-term high-resolution observational data exist to support such climatological-induced spatial changes in precipitation frequency and intensity, the current work utilizes observations from the USDA-ARS Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona. Within this 150 km^2 catchment over 90 rain gauges have been installed since the 1950s, measuring at sub-hourly resolution. We have applied geospatial analyses and the kriging interpolation technique to identify long-term changes in the spatial and temporal correlation and anisotropy of intense precipitation. The observed results will be compared with the previously model simulated results, as well as related to large-scale variations in climate patterns, such as the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO).

  4. Modeling spatial processes with unknown extremal dependence class

    KAUST Repository

    Huser, Raphaë l G.; Wadsworth, Jennifer L.

    2017-01-01

    Many environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes, cannot capture this, which can lead to a preference for models

  5. Estimation of the high-spatial-resolution variability in extreme wind speeds for forestry applications

    Directory of Open Access Journals (Sweden)

    A. Venäläinen

    2017-07-01

    Full Text Available The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In Finland, a forested country, over 50 % of the current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high-spatial-resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20 m spatial resolution CORINE land-use dataset and high-resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalyzed data. Using a geospatial re-mapping technique the data were downscaled to 26 meteorological station locations to represent very diverse environments. Applying a comparison, we find that the downscaled 10-year return levels represent 66 % of the observed variation among the stations examined. In addition, the spatial variation in wind-multiplier-downscaled 10-year return level wind was compared with the WAsP model-simulated wind. The heterogeneous test area was situated in northern Finland, and it was found that the major features of the spatial variation were similar, but in some locations, there were relatively large differences. The results indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations with the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount of wind damage for certain forest stand configurations.

  6. The contribution of hydroxylamine content to spatial variability of N2O formation in soil of a Norway spruce forest

    Science.gov (United States)

    Liu, Shurong; Herbst, Michael; Bol, Roland; Gottselig, Nina; Pütz, Thomas; Weymann, Daniel; Wiekenkamp, Inge; Vereecken, Harry; Brüggemann, Nicolas

    2016-04-01

    Hydroxylamine (NH2OH), a reactive intermediate of several microbial nitrogen turnover processes, is a potential precursor of nitrous oxide (N2O) formation in the soil. However, the contribution of soil NH2OH to soil N2O emission rates in natural ecosystems is unclear. Here, we determined the spatial variability of NH2OH content and potential N2O emission rates of organic (Oh) and mineral (Ah) soil layers of a Norway spruce forest, using a recently developed analytical method for the determination of soil NH2OH content, combined with a geostatistical Kriging approach. Potential soil N2O emission rates were determined by laboratory incubations under oxic conditions, followed by gas chromatographic analysis and complemented by ancillary measurements of soil characteristics. Stepwise multiple regressions demonstrated that the potential N2O emission rates, NH2OH and nitrate (NO3-) content were spatially highly correlated, with hotspots for all three parameters observed in the headwater of a small creek flowing through the sampling area. In contrast, soil ammonium (NH4+) was only weakly correlated with potential N2O emission rates, and was excluded from the multiple regression models. While soil NH2OH content explained the potential soil N2O emission rates best for both layers, also NO3- and Mn content turned out to be significant parameters explaining N2O formation in both soil layers. The Kriging approach was improved markedly by the addition of the co-variable information of soil NH2OH and NO3- content. The results indicate that determination of soil NH2OH content could provide crucial information for the prediction of the spatial variability of soil N2O emissions.

  7. Spatial autocorrelation method using AR model; Kukan jiko sokanho eno AR model no tekiyo

    Energy Technology Data Exchange (ETDEWEB)

    Yamamoto, H; Obuchi, T; Saito, T [Iwate University, Iwate (Japan). Faculty of Engineering

    1996-05-01

    Examination was made about the applicability of the AR model to the spatial autocorrelation (SAC) method, which analyzes the surface wave phase velocity in a microtremor, for the estimation of the underground structure. In this examination, microtremor data recorded in Morioka City, Iwate Prefecture, was used. In the SAC method, a spatial autocorrelation function with the frequency as a variable is determined from microtremor data observed by circular arrays. Then, the Bessel function is adapted to the spatial autocorrelation coefficient with the distance between seismographs as a variable for the determination of the phase velocity. The result of the AR model application in this study and the results of the conventional BPF and FFT method were compared. It was then found that the phase velocities obtained by the BPF and FFT methods were more dispersed than the same obtained by the AR model. The dispersion in the BPF method is attributed to the bandwidth used in the band-pass filter and, in the FFT method, to the impact of the bandwidth on the smoothing of the cross spectrum. 2 refs., 7 figs.

  8. Modeling the spatial reach of the LFP

    DEFF Research Database (Denmark)

    Lindén, Henrik; Tetzlaff, Tom; Potjans, Tobias C

    2011-01-01

    The local field potential (LFP) reflects activity of many neurons in the vicinity of the recording electrode and is therefore useful for studying local network dynamics. Much of the nature of the LFP is, however, still unknown. There are, for instance, contradicting reports on the spatial extent ...

  9. Spatial modeling of potential woody biomass flow

    Science.gov (United States)

    Woodam Chung; Nathaniel Anderson

    2012-01-01

    The flow of woody biomass to end users is determined by economic factors, especially the amount available across a landscape and delivery costs of bioenergy facilities. The objective of this study develop methodology to quantify landscape-level stocks and potential biomass flows using the currently available spatial database road network analysis tool. We applied this...

  10. Modeling fixation locations using spatial point processes.

    Science.gov (United States)

    Barthelmé, Simon; Trukenbrod, Hans; Engbert, Ralf; Wichmann, Felix

    2013-10-01

    Whenever eye movements are measured, a central part of the analysis has to do with where subjects fixate and why they fixated where they fixated. To a first approximation, a set of fixations can be viewed as a set of points in space; this implies that fixations are spatial data and that the analysis of fixation locations can be beneficially thought of as a spatial statistics problem. We argue that thinking of fixation locations as arising from point processes is a very fruitful framework for eye-movement data, helping turn qualitative questions into quantitative ones. We provide a tutorial introduction to some of the main ideas of the field of spatial statistics, focusing especially on spatial Poisson processes. We show how point processes help relate image properties to fixation locations. In particular we show how point processes naturally express the idea that image features' predictability for fixations may vary from one image to another. We review other methods of analysis used in the literature, show how they relate to point process theory, and argue that thinking in terms of point processes substantially extends the range of analyses that can be performed and clarify their interpretation.

  11. Inter-annual and spatial variability in hillslope runoff and mercury flux during spring snowmelt.

    Science.gov (United States)

    Haynes, Kristine M; Mitchell, Carl P J

    2012-08-01

    Spring snowmelt is an important period of mercury (Hg) export from watersheds. Limited research has investigated the potential effects of climate variability on hydrologic and Hg fluxes during spring snowmelt. The purpose of this research was to assess the potential impacts of inter-annual climate variability on Hg mobility in forested uplands, as well as spatial variability in hillslope hydrology and Hg fluxes. We compared hydrological flows, Hg and solute mobility from three adjacent hillslopes in the S7 watershed of the Marcell Experimental Forest, Minnesota during two very different spring snowmelt periods: one following a winter (2009-2010) with severely diminished snow accumulation (snow water equivalent (SWE) = 48 mm) with an early melt, and a second (2010-2011) with significantly greater winter snow accumulation (SWE = 98 mm) with average to late melt timing. Observed inter-annual differences in total Hg (THg) and dissolved organic carbon (DOC) yields were predominantly flow-driven, as the proportion by which solute yields increased was the same as the increase in runoff. Accounting for inter-annual differences in flow, there was no significant difference in THg and DOC export between the two snowmelt periods. The spring 2010 snowmelt highlighted the important contribution of melting soil frost in the timing of a considerable portion of THg exported from the hillslope, accounting for nearly 30% of the THg mobilized. Differences in slope morphology and soil depths to the confining till layer were important in controlling the large observed spatial variability in hydrological flowpaths, transmissivity feedback responses, and Hg flux trends across the adjacent hillslopes.

  12. Spatial and Temporal Variability of Potential Evaporation across North American Forests

    Directory of Open Access Journals (Sweden)

    Robbie A. Hember

    2017-01-01

    Full Text Available Given the widespread ecological implications that would accompany any significant change in evaporative demand of the atmosphere, this study investigated spatial and temporal variation in several accepted expressions of potential evaporation (PE. The study focussed on forest regions of North America, with 1 km-resolution spatial coverage and a monthly time step, from 1951–2014. We considered Penman’s model (EPen, the Priestley–Taylor model (EPT, ‘reference’ rates based on the Penman–Monteith model for grasslands (ERG, and reference rates for forests that are moderately coupled (ERFu and well coupled (ERFc to the atmosphere. To give context to the models, we also considered a statistical fit (EPanFit to measurements of pan evaporation (EPan. We documented how each model compared with EPan, differences in attribution of variance in PE to specific driving factors, mean spatial patterns, and time trends from 1951–2014. The models did not agree strongly on the sensitivity to underlying drivers, zonal variation of PE, or on the magnitude of trends from 1951–2014. Sensitivity to vapour pressure deficit (Da differed among models, being absent from EPT and strongest in ERFc. Time trends in reference rates derived from the Penman–Monteith equation were highly sensitive to how aerodynamic conductance was set. To the extent that EPanFit accurately reflects the sensitivity of PE to Da over land surfaces, future trends in PE based on the Priestley–Taylor model may underestimate increasing evaporative demand, while reference rates for forests, that assume strong canopy-atmosphere coupling in the Penman–Monteith model, may overestimate increasing evaporative demand. The resulting historical database, covering the spectrum of different models of PE applied in modern studies, can serve to further investigate biosphere-hydroclimate relationships across North America.

  13. Spatial scale effects in environmental risk-factor modelling for diseases

    Directory of Open Access Journals (Sweden)

    Ram K. Raghavan

    2013-05-01

    Full Text Available Studies attempting to identify environmental risk factors for diseases can be seen to extract candidate variables from remotely sensed datasets, using a single buffer-zone surrounding locations from where disease status are recorded. A retrospective case-control study using canine leptospirosis data was conducted to verify the effects of changing buffer-zones (spatial extents on the risk factors derived. The case-control study included 94 case dogs predominantly selected based on positive polymerase chain reaction (PCR test for leptospires in urine, and 185 control dogs based on negative PCR. Land cover features from National Land Cover Dataset (NLCD and Kansas Gap Analysis Program (KS GAP around geocoded addresses of cases/controls were extracted using multiple buffers at every 500 m up to 5,000 m, and multivariable logistic models were used to estimate the risk of different land cover variables to dogs. The types and statistical significance of risk factors identified changed with an increase in spatial extent in both datasets. Leptospirosis status in dogs was significantly associated with developed high-intensity areas in models that used variables extracted from spatial extents of 500-2000 m, developed medium-intensity areas beyond 2,000 m and up to 3,000 m, and evergreen forests beyond 3,500 m and up to 5,000 m in individual models in the NLCD. Significant associations were seen in urban areas in models that used variables extracted from spatial extents of 500-2,500 m and forest/woodland areas beyond 2,500 m and up to 5,000 m in individual models in Kansas gap analysis programme datasets. The use of ad hoc spatial extents can be misleading or wrong, and the determination of an appropriate spatial extent is critical when extracting environmental variables for studies. Potential work-arounds for this problem are discussed.

  14. Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV).

    Science.gov (United States)

    Poblete, Tomas; Ortega-Farías, Samuel; Moreno, Miguel Angel; Bardeen, Matthew

    2017-10-30

    Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψ stem ). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500-800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψ stem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R²) obtained between ANN outputs and ground-truth measurements of Ψ stem were between 0.56-0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψ stem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of -9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26-0.27 MPa, 0.32-0.34 MPa and -24.2-25.6%, respectively.

  15. Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV

    Directory of Open Access Journals (Sweden)

    Tomas Poblete

    2017-10-01

    Full Text Available Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem. However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI that use information between 500–800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2 obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56–0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψstem with a mean absolute error (MAE of 0.1 MPa, root mean square error (RMSE of 0.12 MPa, and relative error (RE of −9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26–0.27 MPa, 0.32–0.34 MPa and −24.2–25.6%, respectively.

  16. Latin hypercube sampling and geostatistical modeling of spatial uncertainty in a spatially explicit forest landscape model simulation

    Science.gov (United States)

    Chonggang Xu; Hong S. He; Yuanman Hu; Yu Chang; Xiuzhen Li; Rencang Bu

    2005-01-01

    Geostatistical stochastic simulation is always combined with Monte Carlo method to quantify the uncertainty in spatial model simulations. However, due to the relatively long running time of spatially explicit forest models as a result of their complexity, it is always infeasible to generate hundreds or thousands of Monte Carlo simulations. Thus, it is of great...

  17. The dynamic and indirect spatial effects of neighborhood conditions on land value, spatial panel dynamic econometrics model

    Science.gov (United States)

    Fitriani, Rahma; Sumarminingsih, Eni; Astutik, Suci

    2017-05-01

    Land value is the product of past decision of its use leading to its value, as well as the value of the surrounded land. It is also affected by the local characteristic and the spillover development demand of the previous time period. The effect of each factor on land value will have dynamic and spatial virtues. Thus, a spatial panel dynamic model is used to estimate the particular effects. The model will be useful for predicting the future land value or the effect of implemented policy on land value. The objective of this paper is to derive the dynamic and indirect spatial marginal effects of the land characteristic and the spillover development demand on land value. Each effect is the partial derivative of the expected land value based on the spatial dynamic model with respect to each variable, by considering different time period and different location. The results indicate that the instant change of local or neighborhood characteristics on land value affect the local and the immediate neighborhood land value. However, the longer the change take place, the effect will spread further, not only on the immediate neighborhood.

  18. Pair and triplet approximation of a spatial lattice population model with multiscale dispersal using Markov chains for estimating spatial autocorrelation.

    Science.gov (United States)

    Hiebeler, David E; Millett, Nicholas E

    2011-06-21

    We investigate a spatial lattice model of a population employing dispersal to nearest and second-nearest neighbors, as well as long-distance dispersal across the landscape. The model is studied via stochastic spatial simulations, ordinary pair approximation, and triplet approximation. The latter method, which uses the probabilities of state configurations of contiguous blocks of three sites as its state variables, is demonstrated to be greatly superior to pair approximations for estimating spatial correlation information at various scales. Correlations between pairs of sites separated by arbitrary distances are estimated by constructing spatial Markov processes using the information from both approximations. These correlations demonstrate why pair approximation misses basic qualitative features of the model, such as decreasing population density as a large proportion of offspring are dropped on second-nearest neighbors, and why triplet approximation is able to include them. Analytical and numerical results show that, excluding long-distance dispersal, the initial growth rate of an invading population is maximized and the equilibrium population density is also roughly maximized when the population spreads its offspring evenly over nearest and second-nearest neighboring sites. Copyright © 2011 Elsevier Ltd. All rights reserved.

  19. Spatial variability of soil carbon across Mexico and the United States

    Science.gov (United States)

    Vargas, R.; Guevara, M.; Cruz Gaistardo, C.; Paz, F.; de Jong, B.; Etchevers, J.

    2015-12-01

    Soil organic carbon (SOC) is directly linked to soil quality, food security, and land use/global environmental change. We use publicly available information on SOC and couple it with digital elevation models and derived terrain attributes using a machine learning approach. We found a strong spatial dependency of SOC across the United States, but less spatial dependency of SOC across Mexico. Using High Performance Computing (HPC) we derived a 1 km resolution map of SOC across Mexico and the United States. We tested different machine learning methods (e.g., kernel based, tree based and/or Geo-statistics approaches) for computational efficiency and statistical accuracy. Using random forest combined with geo-statistics we were able to explain >70% of SOC variance for Mexico and >40% in the case of the United States via cross validation. These results compare with other published estimates of SOC at 1km resolution that only explain <30% of SOC variance across the world. Topographic attributes derived from digital elevation models are freely available globally at fine spatial resolution (<100 m), and this information allowed us to make predictions of SOC at fine scales. We further tested this approach using SOC information from the International Soil Carbon Network to predict SOC in other regions of the world. We conclude that this approach (using public information and open source platforms for data analysis) could be implemented to predict detailed explicit information of SOC across different spatial scales.

  20. Small Scale Spatial Variability of Apparent Electrical Conductivity within a Paddy Field

    International Nuclear Information System (INIS)

    Aimrun, W.; Amin, M.S.M.; Ezrin, M.H.; Amin, M.S.M.

    2010-01-01

    Quick variability description is an important component for zone management practices. Precision farming requires topping up of only the nutrients that are lacking in the soil to attain the highest yield with the least input. The apparent soil electrical conductivity (ECa) sensor is a useful tool in mapping to identify areas of contrasting soil properties. In non saline soils, ECa is a substitute measurement for soil texture. It is directly related to both water holding capacity and Cation Exchange Capacity (CEC), which are key ingredients of productivity. This sensor measures the ECa across a field quickly and gives detailed soil features (one-second interval) with few operators. Hence, a dense sampling is possible and therefore a high-resolution ECa map can be produced. This study aims to characterize the variability of soil ECa within a Malaysian paddy field with respect to the spatial and seasonal variability. The study was conducted at Block C, Sawah Sempadan, Selangor, Malaysia, for three continuous seasons. Soil ECa was collected after harvesting period. The results showed that deep ECa visualized the pattern of the former river routes clearly as continuous lines (about 45 m width) at the northern and central regions of the study area. This exploration has shown different maps with higher contrast as compared to the existing soil series map for the study area. Seasonal variability test showed that the ECa that was acquired during rainy season (collected after harvest in December to January) has the highest value as compared to another season.

  1. Spatial and Temporal Variability and Trends in 2001-2016 Global Fire Activity

    Science.gov (United States)

    Earl, Nick; Simmonds, Ian

    2018-03-01

    Fire regimes across the globe have great spatial and temporal variability, and these are influence by many factors including anthropogenic management, climate, and vegetation types. Here we utilize the satellite-based "active fire" product, from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, to statistically analyze variability and trends in fire activity from the global to regional scales. We split up the regions by economic development, region/geographical land use, clusters of fire-abundant areas, or by religious/cultural influence. Weekly cycle tests are conducted to highlight and quantify part of the anthropogenic influence on fire regime across the world. We find that there is a strong statistically significant decline in 2001-2016 active fires globally linked to an increase in net primary productivity observed in northern Africa, along with global agricultural expansion and intensification, which generally reduces fire activity. There are high levels of variability, however. The large-scale regions exhibit either little change or decreasing in fire activity except for strong increasing trends in India and China, where rapid population increase is occurring, leading to agricultural intensification and increased crop residue burning. Variability in Canada has been linked to a warming global climate leading to a longer growing season and higher fuel loads. Areas with a strong weekly cycle give a good indication of where fire management is being applied most extensively, for example, the United States, where few areas retain a natural fire regime.

  2. Error-in-variables models in calibration

    Science.gov (United States)

    Lira, I.; Grientschnig, D.

    2017-12-01

    In many calibration operations, the stimuli applied to the measuring system or instrument under test are derived from measurement standards whose values may be considered to be perfectly known. In that case, it is assumed that calibration uncertainty arises solely from inexact measurement of the responses, from imperfect control of the calibration process and from the possible inaccuracy of the calibration model. However, the premise that the stimuli are completely known is never strictly fulfilled and in some instances it may be grossly inadequate. Then, error-in-variables (EIV) regression models have to be employed. In metrology, these models have been approached mostly from the frequentist perspective. In contrast, not much guidance is available on their Bayesian analysis. In this paper, we first present a brief summary of the conventional statistical techniques that have been developed to deal with EIV models in calibration. We then proceed to discuss the alternative Bayesian framework under some simplifying assumptions. Through a detailed example about the calibration of an instrument for measuring flow rates, we provide advice on how the user of the calibration function should employ the latter framework for inferring the stimulus acting on the calibrated device when, in use, a certain response is measured.

  3. Spatial Variability of Sources and Mixing State of Atmospheric Particles in a Metropolitan Area.

    Science.gov (United States)

    Ye, Qing; Gu, Peishi; Li, Hugh Z; Robinson, Ellis S; Lipsky, Eric; Kaltsonoudis, Christos; Lee, Alex K Y; Apte, Joshua S; Robinson, Allen L; Sullivan, Ryan C; Presto, Albert A; Donahue, Neil M

    2018-05-30

    Characterizing intracity variations of atmospheric particulate matter has mostly relied on fixed-site monitoring and quantifying variability in terms of different bulk aerosol species. In this study, we performed ground-based mobile measurements using a single-particle mass spectrometer to study spatial patterns of source-specific particles and the evolution of particle mixing state in 21 areas in the metropolitan area of Pittsburgh, PA. We selected sampling areas based on traffic density and restaurant density with each area ranging from 0.2 to 2 km 2 . Organics dominate particle composition in all of the areas we sampled while the sources of organics differ. The contribution of particles from traffic and restaurant cooking varies greatly on the neighborhood scale. We also investigate how primary and aged components in particles mix across the urban scale. Lastly we quantify and map the particle mixing state for all areas we sampled and discuss the overall pattern of mixing state evolution and its implications. We find that in the upwind and downwind of the urban areas, particles are more internally mixed while in the city center, particle mixing state shows large spatial heterogeneity that is mostly driven by emissions. This study is to our knowledge, the first study to perform fine spatial scale mapping of particle mixing state using ground-based mobile measurement and single-particle mass spectrometry.

  4. Spatially uniform but temporally variable bacterioplankton in a semi-enclosed coastal area.

    Science.gov (United States)

    Meziti, Alexandra; Kormas, Konstantinos A; Moustaka-Gouni, Maria; Karayanni, Hera

    2015-07-01

    Studies focusing on the temporal and spatial dynamics of bacterioplankton communities within littoral areas undergoing direct influences from the coast are quite limited. In addition, they are more complicated to resolve compared to communities in the open ocean. In order to elucidate the effects of spatial vs. temporal variability on bacterial communities in a highly land-influenced semi-enclosed gulf, surface bacterioplankton communities from five coastal sites in Igoumenitsa Gulf (Ionian Sea, Greece) were analyzed over a nine-month period using 16S rDNA 454-pyrosequencing. Temporal differences were more pronounced than spatial ones, with lower diversity indices observed during the summer months. During winter and early spring, bacterial communities were dominated by SAR11 representatives, while this pattern changed in May when they were abruptly replaced by members of Flavobacteriales, Pseudomonadales, and Alteromonadales. Additionally, correlation analysis showed high negative correlations between the presence of SAR11 OTUs in relation to temperature and sunlight that might have driven, directly or indirectly, the disappearance of these OTUs in the summer months. The dominance of SAR11 during the winter months further supported the global distribution of the clade, not only in the open-sea, but also in coastal systems. This study revealed that specific bacteria exhibited distinct succession patterns in an anthropogenic-impacted coastal system. The major bacterioplankton component was represented by commonly found marine bacteria exhibiting seasonal dynamics, while freshwater and terrestrial-related phylotypes were absent. Copyright © 2015 Elsevier GmbH. All rights reserved.

  5. Temporal and Spatial Variability of Droughts in Southwest China from 1961 to 2012

    Directory of Open Access Journals (Sweden)

    Yaohuan Huang

    2015-10-01

    Full Text Available Southwest China (SC has suffered a series of super extreme droughts in the last decade. This study analyzed the temporal and spatial variations of drought in SC from 1961 to 2012. Based on precipitation anomaly index (PAI that was derived from 1 km gridded precipitation data, three time scales (month, year and decade for the drought frequency (DF and drought area were applied to estimate the spatio-temporal structure of droughts. A time-series analysis showed that winter droughts and spring droughts occurred frequently for almost half of the year from November to March. Summer droughts occasionally occurred in severe drought decades: the 1960s, 1980s and 2000s. During the period of observation, the percent of drought area in SC increased from the 1960s (<5% to the 2000s (>25%. A total of 57% of the area was affected by drought in 2011, when the area experienced its most severe drought both in terms of area and severity. The spatial analysis, which benefitted from the gridded data, detailed that all of SC is at drought risk except for the central Sichuan Basin. The area at high risk for severe and extreme droughts was localized in the mountains of the junction of Sichuan and Yunnan. The temporal and spatial variability can be prerequisites for drought resistance planning and drought risk management of SC.

  6. Spatial Variability of Perchlorate along a Traverse Route from Zhongshan Station to Dome A, East Antarctica

    Science.gov (United States)

    Jiang, S.; Cole-Dai, J.; Li, Y.; An, C.

    2016-12-01

    Snow deposition and accumulation on the Antarctic ice sheet preserve records of climatic change, as well as those of chemical characteristics of the environment. Chemical composition of snow and ice cores can be used to track the sources of important substances including pollutants and to investigate relationships between atmospheric chemistry and climatic conditions. Recent development in analytical methodology has enabled the determination of ultra-trace levels of perchlorate in polar snow. We have measured perchlorate concentrations in surface snow samples collected along a traverse route from Zhongshan Station to Dome A in East Antarctica to determine the level of atmospheric perchlorate in East Antarctica and to assess the spatial variability of perchlorate along the traverse route. Results show that the perchlorate concentrations vary between 32 and 200 ng kg-1, with an average of 104.3 ng kg-1. And perchlorate concentration profile presents regional variation patterns along the traverse route. In the coastal region, perchlorate concentration displays an apparent decreasing relationship with increasing distance inland; it exhibits no apparent trend in the intermediate region from 200 to 1000 km. The inland region from 1000 to 1244 km presents a generally increasing trend of perchlorate concentration approaching the dome. Different rates of atmospheric production, dilution by snow accumulation and re-deposition of snow-emitted perchlorate (post-depositional change) are the three possible factors influencing the spatial variability of perchlorate over Antarctica.

  7. Spatial variability of N, P, and K in rice field in Sawah Sempadan, Malaysia

    Directory of Open Access Journals (Sweden)

    Saeed Mohamed Eltaib

    2002-04-01

    Full Text Available The variability of soil chemical properties such as total N, available P, and exchangeable K were examined on a 1.2 ha rice (Oryza sativa field. The soil (n = 72 samples were systematically taken from individual fields in Sawah Sempadan in thirty-six locations at two depths (0-20 and 20-30 cm. The Differential Global Positioning System (DGPS was used for locating the sample position. Geostatistical techniques were used to analyze the soil chemical properties variability of the samples that assist in site-specific management of the field. Results showed that areas of similarity were much greater for the soil chemical properties measured at the depth of (0-20 cm than that of the second lower (20- 30 cm. The ranges of the semivariogram for total N, available P, and exchangeable K were 12, and 13 m (0-20 cm, 12 and 38 m (20-30 cm, respectively. Point kriging calculated from the semivariogram was employed for spatial distribution map. The results suggested that soil chemical properties measured may be spatially dependent even within the small.

  8. Spatial variability of oceanic phycoerythrin spectral types derived from airborne laser-induced fluorescence emissions

    Science.gov (United States)

    Hoge, Frank E.; Wright, C. Wayne; Kana, Todd M.; Swift, Robert N.; Yungel, James K.

    1998-07-01

    We report spatial variability of oceanic phycoerythrin spectral types detected by means of a blue spectral shift in airborne laser-induced fluorescence emission. The blue shift of the phycoerythrobilin fluorescence is known from laboratory studies to be induced by phycourobilin chromophore substitution at phycoerythrobilin chromophore sites in some strains of phycoerythrin-containing marine cyanobacteria. The airborne 532-nm laser-induced phycoerythrin fluorescence of the upper oceanic volume showed distinct segregation of cyanobacterial chromophore types in a flight transect from coastal water to the Sargasso Sea in the western North Atlantic. High phycourobilin levels were restricted to the oceanic (oligotrophic) end of the flight transect, in agreement with historical ship findings. These remotely observed phycoerythrin spectral fluorescence shifts have the potential to permit rapid, wide-area studies of the spatial variability of spectrally distinct cyanobacteria, especially across interfacial regions of coastal and oceanic water masses. Airborne laser-induced phytoplankton spectral fluorescence observations also further the development of satellite algorithms for passive detection of phytoplankton pigments. Optical modifications to the NASA Airborne Oceanographic Lidar are briefly described that permitted observation of the fluorescence spectral shifts.

  9. Decadal changes of reference crop evapotranspiration attribution: Spatial and temporal variability over China 1960-2011

    Science.gov (United States)

    Fan, Ze-Xin; Thomas, Axel

    2018-05-01

    Atmospheric evaporative demand can be used as a measure of the hydrological cycle and the global energy balance. Its long-term variation and the role of driving climatic factors have received increasingly attention in climate change studies. FAO-Penman-Monteith reference crop evapotranspiration rates were estimated for 644 meteorological stations over China for the period 1960-2011 to analyze spatial and temporal attribution variability. Attribution of climatic variables to reference crop evapotranspiration rates was not stable over the study period. While for all of China the contribution of sunshine duration remained relatively stable, the importance of relative humidity increased considerably during the last two decades, particularly in winter. Spatially distributed attribution analysis shows that the position of the center of maximum contribution of sunshine duration has shifted from Southeast to Northeast China while in West China the contribution of wind speed has decreased dramatically. In contrast relative humidity has become an important factor in most parts of China. Changes in the Asian Monsoon circulation may be responsible for altered patterns of cloudiness and a general decrease of wind speeds over China. The continuously low importance of temperature confirms that global warming does not necessarily lead to rising atmospheric evaporative demand.

  10. Applicability of API ZYM to capture seasonal and spatial variabilities in lake and river sediments.

    Science.gov (United States)

    Patel, Drashti; Gismondi, Renee; Alsaffar, Ali; Tiquia-Arashiro, Sonia M

    2018-05-02

    Waters draining into a lake carry with them much of the suspended sediment that is transported by rivers and streams from the local drainage basin. The organic matter processing in the sediments is executed by heterotrophic microbial communities, whose activities may vary spatially and temporally. Thus, to capture and evaluate some of these variabilities in the sediments, we sampled six sites: three from the St. Clair River and three from Lake St. Clair in spring, summer, fall, and winter of 2016. At all sites and dates, we investigated the spatial and temporal variations in 19 extracellular enzyme activities using API ZYM. Our results indicated that a broad range of enzymes were found to be active in the sediments. Phosphatases, lipases, and esterases were synthesized most intensively by the sediment microbial communities. No consistent difference was found between the lake and sediment samples. Differences were more obvious between sites and seasons. Sites with the highest metabolic (enzyme) diversity reflected the capacity of the sediment microbial communities to breakdown a broader range of substrates and may be linked to differences in river and lake water quality. The seasonal variability of the enzymes activities was governed by the variations of environmental factors caused by anthropogenic and terrestrial inputs, and provides information for a better understanding of the dynamics of sediment organic matter of the river and lake ecosystems. The experimental results suggest that API ZYM is a simple and rapid enzyme assay procedure to evaluate natural processes in ecosystems and their changes.

  11. The Significance of the Spatial Variability of Rainfall on the Numerical Simulation of Urban Floods

    Directory of Open Access Journals (Sweden)

    Laurent Guillaume Courty

    2018-02-01

    Full Text Available The growth of urban population, combined with an increase of extreme events due to climate change call for a better understanding and representation of urban floods. The uncertainty in rainfall distribution is one of the most important factors that affects the watershed response to a given precipitation event. However, most of the investigations on this topic have considered theoretical scenarios, with little reference to case studies in the real world. This paper incorporates the use of spatially-variable precipitation data from a long-range radar in the simulation of the severe floods that impacted the city of Hull, U.K., in June 2007. This radar-based rainfall field is merged with rain gauge data using a Kriging with External Drift interpolation technique. The utility of this spatially-variable information is investigated through the comparison of computed flooded areas (uniform and radar against those registered by public authorities. Both results show similar skills at reproducing the real event, but differences in the total precipitated volumes, water depths and flooded areas are illustrated. It is envisaged that in urban areas and with the advent of higher resolution radars, these differences will be more important and call for further investigation.

  12. The spatial variable glacier mass loss over the southeast Tibet Plateau and the climate cause analyses

    Science.gov (United States)

    Ke, L.; Ding, X.; Song, C.; Sheng, Y.

    2016-12-01

    Temperate glaciers can be highly sensitive to global climate change due to relatively humid and warm local climate. Numerous temperate glaciers are distributed in the southeastern Tibet Plateau (SETP) and their changes are still poorly represented. Based on a latest glacier inventory and ICESat altimetry measurements, we examine the spatial heterogeneity of glacier change in the SETP (including the central and eastern Nyainqêntanglha ranges) and further analyze its relation with climate change by using station-based and gridded meteorological data. Our results show that SETP glaciers experienced drastic surface lowering at about -0.84±0.26 m a-1 on average over 2003-2008. Debris-covered ice thinned at an average rate of -1.13±0.32 m a-1, in comparison with -0.92±0.17 m a-1 over the debris-free ice areas. The thinning rate is the strongest in the southeastern sub-region (up to -1.24 m a-1 ) and moderate ( -0.45 m a-1 ) in the central and northwestern parts, which is in general agreement with the pattern of surface mass changes based on the GRACE gravimetry observation. Long-term climate data at weather stations show that, in comparison with the period of 1992-2002, mean temperature increased by 0.46 °C - 0.59 °C in the recent decade (2003-2013); while the change of summer precipitation exhibited remarkably spatial variability, following a southeast-northwest contrasting pattern (decreasing by over 10% in the southeast, to stable level in the central region, and increment up to 10% in the northwest). This spatially variable precipitation change is consistent with results from CN05 grid data and ERA re-analysis data, and agrees well with the spatial pattern of glacier surface elevation changes. The results suggest that overall negative glacier mass balances in SETP are governed by temperature rising, while the different precipitation change could contribute to inconsistent glacier thinning rates. The spatial pattern of precipitation decrease and mass loss might

  13. . Redundancy and blocking in the spatial domain: A connectionist model

    Directory of Open Access Journals (Sweden)

    I. P. L. Mc Laren

    2002-01-01

    Full Text Available How can the observations of spatial blocking (Rodrigo, Chamizo, McLaren & Mackintosh, 1997 and cue redundancy (O’Keefe and Conway, 1978 be reconciled within the framework provided by an error-correcting, connectionist account of spatial navigation? I show that an implementation of McLaren’s (1995 better beta model can serve this purpose, and examine some of the implications for spatial learning and memory.

  14. Assessing spatial and temporal variability of phytoplankton communities' composition in the Iroise Sea ecosystem (Brittany, France): A 3D modeling approach. Part 1: Biophysical control over plankton functional types succession and distribution

    Science.gov (United States)

    Cadier, Mathilde; Gorgues, Thomas; Sourisseau, Marc; Edwards, Christopher A.; Aumont, Olivier; Marié, Louis; Memery, Laurent

    2017-01-01

    Understanding the dynamic interplay between physical, biogeochemical and biological processes represents a key challenge in oceanography, particularly in shelf seas where complex hydrodynamics are likely to drive nutrient distribution and niche partitioning of phytoplankton communities. The Iroise Sea includes a tidal front called the 'Ushant Front' that undergoes a pronounced seasonal cycle, with a marked signal during the summer. These characteristics as well as relatively good observational sampling make it a region of choice to study processes impacting phytoplankton dynamics. This innovative modeling study employs a phytoplankton-diversity model, coupled to a regional circulation model to explore mechanisms that alter biogeography of phytoplankton in this highly dynamic environment. Phytoplankton assemblages are mainly influenced by the depth of the mixed layer on a seasonal time scale. Indeed, solar incident irradiance is a limiting resource for phototrophic growth and small phytoplankton cells are advantaged over larger cells. This phenomenon is particularly relevant when vertical mixing is intense, such as during winter and early spring. Relaxation of wind-induced mixing in April causes an improvement of irradiance experienced by cells across the whole study area. This leads, in late spring, to a competitive advantage of larger functional groups such as diatoms as long as the nutrient supply is sufficient. This dominance of large, fast-growing autotrophic cells is also maintained during summer in th