WorldWideScience

Sample records for modeling spatial variability

  1. 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.

  2. 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

  3. Random spatial processes and geostatistical models for soil variables

    Science.gov (United States)

    Lark, R. M.

    2009-04-01

    Geostatistical models of soil variation have been used to considerable effect to facilitate efficient and powerful prediction of soil properties at unsampled sites or over partially sampled regions. Geostatistical models can also be used to investigate the scaling behaviour of soil process models, to design sampling strategies and to account for spatial dependence in the random effects of linear mixed models for spatial variables. However, most geostatistical models (variograms) are selected for reasons of mathematical convenience (in particular, to ensure positive definiteness of the corresponding variables). They assume some underlying spatial mathematical operator which may give a good description of observed variation of the soil, but which may not relate in any clear way to the processes that we know give rise to that observed variation in the real world. In this paper I shall argue that soil scientists should pay closer attention to the underlying operators in geostatistical models, with a view to identifying, where ever possible, operators that reflect our knowledge of processes in the soil. I shall illustrate how this can be done in the case of two problems. The first exemplar problem is the definition of operators to represent statistically processes in which the soil landscape is divided into discrete domains. This may occur at disparate scales from the landscape (outcrops, catchments, fields with different landuse) to the soil core (aggregates, rhizospheres). The operators that underly standard geostatistical models of soil variation typically describe continuous variation, and so do not offer any way to incorporate information on processes which occur in discrete domains. I shall present the Poisson Voronoi Tessellation as an alternative spatial operator, examine its corresponding variogram, and apply these to some real data. The second exemplar problem arises from different operators that are equifinal with respect to the variograms of the

  4. 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.

  5. Modeling temporal and spatial variability of crop yield

    Science.gov (United States)

    Bonetti, S.; Manoli, G.; Scudiero, E.; Morari, F.; Putti, M.; Teatini, P.

    2014-12-01

    In a world of increasing food insecurity the development of modeling tools capable of supporting on-farm decision making processes is highly needed to formulate sustainable irrigation practices in order to preserve water resources while maintaining adequate crop yield. The design of these practices starts from the accurate modeling of soil-plant-atmosphere interaction. We present an innovative 3D Soil-Plant model that couples 3D hydrological soil dynamics with a mechanistic description of plant transpiration and photosynthesis, including a crop growth module. Because of its intrinsically three dimensional nature, the model is able to capture spatial and temporal patterns of crop yield over large scales and under various climate and environmental factors. The model is applied to a 25 ha corn field in the Venice coastland, Italy, that has been continuously monitored over the years 2010 and 2012 in terms of both hydrological dynamics and yield mapping. The model results satisfactorily reproduce the large variability observed in maize yield (from 2 to 15 ton/ha). This variability is shown to be connected to the spatial heterogeneities of the farmland, which is characterized by several sandy paleo-channels crossing organic-rich silty soils. Salt contamination of soils and groundwater in a large portion of the area strongly affects the crop yield, especially outside the paleo-channels, where measured salt concentrations are lower than the surroundings. The developed model includes a simplified description of the effects of salt concentration in soil water on transpiration. The results seem to capture accurately the effects of salt concentration and the variability of the climatic conditions occurred during the three years of measurements. This innovative modeling framework paves the way to future large scale simulations of farmland dynamics.

  6. Mathematical Modeling of spatial disease variables by Spatial Fuzzy Logic for Spatial Decision Support Systems

    Science.gov (United States)

    Platz, M.; Rapp, J.; Groessler, M.; Niehaus, E.; Babu, A.; Soman, B.

    2014-11-01

    A Spatial Decision Support System (SDSS) provides support for decision makers and should not be viewed as replacing human intelligence with machines. Therefore it is reasonable that decision makers are able to use a feature to analyze the provided spatial decision support in detail to crosscheck the digital support of the SDSS with their own expertise. Spatial decision support is based on risk and resource maps in a Geographic Information System (GIS) with relevant layers e.g. environmental, health and socio-economic data. Spatial fuzzy logic allows the representation of spatial properties with a value of truth in the range between 0 and 1. Decision makers can refer to the visualization of the spatial truth of single risk variables of a disease. Spatial fuzzy logic rules that support the allocation of limited resources according to risk can be evaluated with measure theory on topological spaces, which allows to visualize the applicability of this rules as well in a map. Our paper is based on the concept of a spatial fuzzy logic on topological spaces that contributes to the development of an adaptive Early Warning And Response System (EWARS) providing decision support for the current or future spatial distribution of a disease. It supports the decision maker in testing interventions based on available resources and apply risk mitigation strategies and provide guidance tailored to the geo-location of the user via mobile devices. The software component of the system would be based on open source software and the software developed during this project will also be in the open source domain, so that an open community can build on the results and tailor further work to regional or international requirements and constraints. A freely available EWARS Spatial Fuzzy Logic Demo was developed wich enables a user to visualize risk and resource maps based on individual data in several data formats.

  7. 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.

  8. Modeling inter-subject variability in fMRI activation location: A Bayesian hierarchical spatial model

    Science.gov (United States)

    Xu, Lei; Johnson, Timothy D.; Nichols, Thomas E.; Nee, Derek E.

    2010-01-01

    Summary The aim of this work is to develop a spatial model for multi-subject fMRI data. There has been extensive work on univariate modeling of each voxel for single and multi-subject data, some work on spatial modeling of single-subject data, and some recent work on spatial modeling of multi-subject data. However, there has been no work on spatial models that explicitly account for inter-subject variability in activation locations. In this work, we use the idea of activation centers and model the inter-subject variability in activation locations directly. Our model is specified in a Bayesian hierarchical frame work which allows us to draw inferences at all levels: the population level, the individual level and the voxel level. We use Gaussian mixtures for the probability that an individual has a particular activation. This helps answer an important question which is not addressed by any of the previous methods: What proportion of subjects had a significant activity in a given region. Our approach incorporates the unknown number of mixture components into the model as a parameter whose posterior distribution is estimated by reversible jump Markov Chain Monte Carlo. We demonstrate our method with a fMRI study of resolving proactive interference and show dramatically better precision of localization with our method relative to the standard mass-univariate method. Although we are motivated by fMRI data, this model could easily be modified to handle other types of imaging data. PMID:19210732

  9. Identifying Spatially Variable Sensitivity of Model Predictions and Calibrations

    Science.gov (United States)

    McKenna, S. A.; Hart, D. B.

    2005-12-01

    Stochastic inverse modeling provides an ensemble of stochastic property fields, each calibrated to measured steady-state and transient head data. These calibrated fields are used as input for predictions of other processes (e.g., contaminant transport, advective travel time). Use of the entire ensemble of fields transfers spatial uncertainty in hydraulic properties to uncertainty in the predicted performance measures. A sampling-based sensitivity coefficient is proposed to determine the sensitivity of the performance measures to the uncertain values of hydraulic properties at every cell in the model domain. The basis of this sensitivity coefficient is the Spearman rank correlation coefficient. Sampling-based sensitivity coefficients are demonstrated using a recent set of transmissivity (T) fields created through a stochastic inverse calibration process for the Culebra dolomite in the vicinity of the WIPP site in southeastern New Mexico. The stochastic inverse models were created using a unique approach to condition a geologically-based conceptual model of T to measured T values via a multiGaussian residual field. This field is calibrated to both steady-state and transient head data collected over an 11 year period. Maps of these sensitivity coefficients provide a means of identifying the locations in the study area to which both the value of the model calibration objective function and the predicted travel times to a regulatory boundary are most sensitive to the T and head values. These locations can be targeted for deployment of additional long-term monitoring resources. Comparison of areas where the calibration objective function and the travel time have high sensitivity shows that these are not necessarily coincident with regions of high uncertainty. The sampling-based sensitivity coefficients are compared to analytically derived sensitivity coefficients at the 99 pilot point locations. Results of the sensitivity mapping exercise are being used in combination

  10. Testing in a Random Effects Panel Data Model with Spatially Correlated Error Components and Spatially Lagged Dependent Variables

    Directory of Open Access Journals (Sweden)

    Ming He

    2015-11-01

    Full Text Available We propose a random effects panel data model with both spatially correlated error components and spatially lagged dependent variables. We focus on diagnostic testing procedures and derive Lagrange multiplier (LM test statistics for a variety of hypotheses within this model. We first construct the joint LM test for both the individual random effects and the two spatial effects (spatial error correlation and spatial lag dependence. We then provide LM tests for the individual random effects and for the two spatial effects separately. In addition, in order to guard against local model misspecification, we derive locally adjusted (robust LM tests based on the Bera and Yoon principle (Bera and Yoon, 1993. We conduct a small Monte Carlo simulation to show the good finite sample performances of these LM test statistics and revisit the cigarette demand example in Baltagi and Levin (1992 to illustrate our testing procedures.

  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. Spatial variability and its scale dependency of observed and modeled soil moisture under different climate conditions

    Directory of Open Access Journals (Sweden)

    B. Li

    2012-09-01

    Full Text Available Past studies on soil moisture spatial variability have been mainly conducted in catchment scales where soil moisture is often sampled over a short time period. Because of limited climate and weather conditions, the observed soil moisture often exhibited smaller dynamic ranges which prevented the complete revelation of soil moisture spatial variability as a function of mean soil moisture. In this study, spatial statistics (mean, spatial variability and skewness of in situ soil moisture measurements (from a continuously monitored network across the US, modeled and satellite retrieved soil moisture obtained in a warm season (198 days were examined at large extent scales (>100 km over three different climate regions. The investigation on in situ measurements revealed that their spatial moments strongly depend on climates, with distinct mean, spatial variability and skewness observed in each climate zone. In addition, an upward convex shape, which was revealed in several smaller scale studies, was observed for the relationship between spatial variability of in situ soil moisture and its spatial mean across dry, intermediate, and wet climates. These climate specific features were vaguely or partially observable in modeled and satellite retrieved soil moisture estimates, which is attributed to the fact that these two data sets do not have climate specific and seasonal sensitive mean soil moisture values, in addition to lack of dynamic ranges. From the point measurements to satellite retrievals, soil moisture spatial variability decreased in each climate region. The three data sources all followed the power law in the scale dependency of spatial variability, with coarser resolution data showing stronger scale dependency than finer ones. The main findings from this study are: (1 the statistical distribution of soil moisture depends on spatial mean soil moisture values and thus need to be derived locally within any given area; (2 the boundedness of soil

  13. Effects of various representations of temporally and spatially variable agricultural processes in air quality dispersion modeling

    Science.gov (United States)

    Agricultural activities that are both temporally and spatially variable, such as tillage and harvesting, can be challenging to represent as sources in air quality dispersion modeling. Existing models were mainly developed to predict concentrations resulting from a stationary and continuous source wi...

  14. 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

  15. Spatial variability of the Black Sea surface temperature from high resolution modeling and satellite measurements

    Science.gov (United States)

    Mizyuk, Artem; Senderov, Maxim; Korotaev, Gennady

    2016-04-01

    Large number of numerical ocean models were implemented for the Black Sea basin during last two decades. They reproduce rather similar structure of synoptical variability of the circulation. Since 00-s numerical studies of the mesoscale structure are carried out using high performance computing (HPC). With the growing capacity of computing resources it is now possible to reconstruct the Black Sea currents with spatial resolution of several hundreds meters. However, how realistic these results can be? In the proposed study an attempt is made to understand which spatial scales are reproduced by ocean model in the Black Sea. Simulations are made using parallel version of NEMO (Nucleus for European Modelling of the Ocean). A two regional configurations with spatial resolutions 5 km and 2.5 km are described. Comparison of the SST from simulations with two spatial resolutions shows rather qualitative difference of the spatial structures. Results of high resolution simulation are compared also with satellite observations and observation-based products from Copernicus using spatial correlation and spectral analysis. Spatial scales of correlations functions for simulated and observed SST are rather close and differs much from satellite SST reanalysis. Evolution of spectral density for modelled SST and reanalysis showed agreed time periods of small scales intensification. Using of the spectral analysis for satellite measurements is complicated due to gaps. The research leading to this results has received funding from Russian Science Foundation (project № 15-17-20020)

  16. A model for the spatial distribution of snow water equivalent parameterized from the spatial variability of precipitation

    Science.gov (United States)

    Skaugen, Thomas; Weltzien, Ingunn H.

    2016-09-01

    Snow is an important and complicated element in hydrological modelling. The traditional catchment hydrological model with its many free calibration parameters, also in snow sub-models, is not a well-suited tool for predicting conditions for which it has not been calibrated. Such conditions include prediction in ungauged basins and assessing hydrological effects of climate change. In this study, a new model for the spatial distribution of snow water equivalent (SWE), parameterized solely from observed spatial variability of precipitation, is compared with the current snow distribution model used in the operational flood forecasting models in Norway. The former model uses a dynamic gamma distribution and is called Snow Distribution_Gamma, (SD_G), whereas the latter model has a fixed, calibrated coefficient of variation, which parameterizes a log-normal model for snow distribution and is called Snow Distribution_Log-Normal (SD_LN). The two models are implemented in the parameter parsimonious rainfall-runoff model Distance Distribution Dynamics (DDD), and their capability for predicting runoff, SWE and snow-covered area (SCA) is tested and compared for 71 Norwegian catchments. The calibration period is 1985-2000 and validation period is 2000-2014. Results show that SDG better simulates SCA when compared with MODIS satellite-derived snow cover. In addition, SWE is simulated more realistically in that seasonal snow is melted out and the building up of "snow towers" and giving spurious positive trends in SWE, typical for SD_LN, is prevented. The precision of runoff simulations using SDG is slightly inferior, with a reduction in Nash-Sutcliffe and Kling-Gupta efficiency criterion of 0.01, but it is shown that the high precision in runoff prediction using SD_LN is accompanied with erroneous simulations of SWE.

  17. May We Identify The Spatial Variability of Soil Hydraulic Properties Based On Measurements With "spatial Tdr"? A) Model Study

    Science.gov (United States)

    Zehe, E.; Becker, R.; Schädel, W.

    A dynamic system left without external disturbances, will always tend to a stable equilibrium state that is consistent with the internal physics. For natural soils such an equilibrium state is reached when the gradients of the total hydraulic potential tend to zero. This statement is still valid for heterogeneous soils, because the hydraulic po- tential is an intensive state variable and therefore continuous at discontinuities of the pore space. In contrary the soil water content is as an extensive property discontinu- ous at discontinuities of the pore space. Hence, a small scale soil moisture pattern that persists if the soil state tends to hydraulic equilibrium, reflects the lateral small scale variability of the pore space. The objectives of our study are to show a) whether and how we could use TDR observations to identify the small scale variability of the pore space. For that purpose we analyse artificial TDR measurements, taken from physi- cally based simulations of soil water dynamics in heterogeneous media. b) We want to introduce a new TDR technology which we call "Spatial TDR", that is suitable for that purposes. To produce the artificial TDR-datasets we generate random fields of soil porosity and saturated hydraulic conductivity with different statistical properties based on field data in a Luvisol and simulate artificial water dynamics in this model soil based on Richards-equation. Within this model soil we define several hypothetical "Spatial TDR" clusters, that differ in the lateral spacing and the number of the probes, in the temporal resolution of the hypothetical measurements and in the assumed mea- surement accuracy. If the model soil approaches hydraulic equilibrium, the remaining soil moisture pattern will be dominated by the statistical properties of the porosity. In contrary the variability of the hydraulic conductivity will dominate the soil moisture patterns during infiltration events. The hypothetical Spatial TDR measurements within the

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

    Directory of Open Access Journals (Sweden)

    A. Cabezas

    2010-02-01

    Full Text Available Sediment, Total Organic Carbon (TOC and total nitrogen (TN accumulation during one overbank flood (1.15 y 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 on 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 another factors as vegetation morphology 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 solid concentration.

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

    Science.gov (United States)

    Cabezas, A.; Angulo-Martínez, M.; Gonzalez-Sanchís, M.; Jimenez, J. J.; Comín, F. A.

    2010-08-01

    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.

  20. 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

  1. Spatial variability and prediction modeling of groundwater arsenic distributions in the shallowest alluvial aquifers in Bangladesh

    OpenAIRE

    Shamsudduha, M.

    2007-01-01

    Elevated arsenic in groundwater is the greatest environmental problem in Bangladesh. Spatial variability of arsenic in groundwater has been examined by semivariogram analysis that revealed high degree of small-scale spatial variability in alluvial aquifers. Small-scale variability of arsenic concentrations, indicated by high "nugget" values in semivariograms, is associated with heterogeneity in local-scale geology and geochemical processes. In unsampled locations, arsenic concentrations have ...

  2. Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

    Science.gov (United States)

    Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

    2013-01-01

    In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

  3. Spatial aggregation for crop modelling at regional scales: the effects of soil variability

    Science.gov (United States)

    Coucheney, Elsa; Villa, Ana; Eckersten, Henrik; Hoffmann, Holger; Jansson, Per-Erik; Gaiser, Thomas; Ewert, Franck; Lewan, Elisabet

    2017-04-01

    Modelling agriculture production and adaptation to the environment at regional or global scale receives much interest in the context of climate change. Process-based soil-crop models describe the flows of mass (i.e. water, carbon and nitrogen) and energy in the soil-plant-atmosphere system. As such, they represent valuable tools for predicting agricultural production in diverse agro-environmental contexts as well as for assessing impacts on the environment; e.g. leaching of nitrates, changes in soil carbon content and GHGs emissions. However, their application at regional and global scales for climate change impact studies raises new challenges related to model input data, calibration and evaluation. One major concern is to take into account the spatial variability of the environmental conditions (e.g. climate, soils, management practices) used as model input and because the impacts of climate change on cropping systems depend strongly on the site conditions and properties (1). For example climate change effects on yield can be either negative or positive depending on the soil type (2). Additionally, the use of different methods of upscaling and downscaling adds new sources of modelling uncertainties (3). In the present study, the effect of aggregating soil input data by area majority of soil mapping units was explored for spatially gridded simulations with the soil-vegetation model CoupModel for a region in Germany (North Rhine-Westphalia, NRW). The data aggregation effect (DAE) was analysed for wheat yield, water drainage, soil carbon mineralisation and nitrogen leaching below the root zone. DAE was higher for soil C and N variables than for yield and drainage and were strongly related to the spatial coverage of specific soils within the study region. These 'key soils' were identified by a model sensitivity analysis to soils present in the NRW region. The spatial aggregation of the key soils additionally influenced the DAE. Our results suggest that a spatial

  4. Modelling spatial variability and uncertainty of cadmium leaching to groundwater in an urban region

    Science.gov (United States)

    Beyer, Christof; Altfelder, Sven; Duijnisveld, Wilhelmus H. M.; Streck, Thilo

    2009-05-01

    SummaryOver the last century, soils in the region of Nordenham in northern Germany received high loads of heavy-metals by air-borne immissions from a close-by metal smelter. Based on measured soil properties and cadmium contamination data the leaching of Cd to groundwater was predicted for Nordenham using a numerical transport model based on the convection-dispersion equation. The main objective in this study was to account for the spatial variability and uncertainty of Cd sorption controlling soil properties ( pH, organic carbon content) and to analyze their propagation into the variance of area-related model outputs, i.e. Cd breakthrough concentrations at the groundwater surface. For this purpose a nested Monte-Carlo method was combined with deterministic numerical 1D simulations of Cd leaching. The transport model was parameterized without any parameter fitting involved. The validity of the model was verified by retrospective simulations from the initial operation of the smelter until the year of soil sampling. Forecast simulations were run for a period of 200 years. Predicted local scale Cd breakthrough concentrations at the groundwater surface were evaluated by spatial aggregation for single blocks at the field scale, yielding area-related concentrations with associated uncertainties from imprecise knowledge on local soil properties. Significant exceedance of the limit of the German drinking water ordinance of 5 μg L -1 is observed on approximately 90% of the study area with the average point in time of limit value exceedance being the year 2066 and a 90% prediction interval of 2049-2092. At the end of the simulation period, Cd concentrations at the groundwater surface still increase on large parts of the study area. The spatially averaged Cd concentration is 19.89 μg L -1 with a 90% prediction interval of 15.28-24.69 μg L -1. Locally, however, concentrations larger than 60 μg L -1 may be reached. Prediction uncertainty is only moderate and does not

  5. 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

  6. Modelling infiltration and geostatistical analysis of spatial variability of sorptivity and transmissivity in a flood spreading area

    Energy Technology Data Exchange (ETDEWEB)

    Haghighi-Fashi, F.; Sharifi, F.; Kamali, K.

    2014-06-01

    Knowledge of infiltration characteristics is useful in hydrological studies of agricultural soils. Soil hydraulic parameters such as steady infiltration rate, sorptivity, and transmissivity can exhibit appreciable spatial variability. The main objectives of this study were to examine several mathematical models of infiltration and to analyze the spatial variability of observed final infiltration rate, estimated sorptivity and estimated transmissivity in flood spreading and control areas in Ilam province, Iran. The suitability of geostatistics to describe such spatial variability was assessed using data from 30 infiltration measurements sampled along three lines. The Horton model provided the most accurate simulation of infiltration considering all measurements and the Philips two-term model provided less accurate simulation. A comparison of the measured values and the estimated final infiltration rates showed that the Kostiakov- Lewis, Kostiakov, and SCS models could not estimate the final infiltration rate as well as Horton model. Estimated sorptivity and transmissivity parameters of the Philips two-term model and final infiltration rate had spatial structure, and were considered to be structural variables over the transect pattern. The Gaussian model provided the best-fit theoretical variogram for these three parameters. Variogram values ranged from 99 and 88 m for sorptivity and final infiltration rate to 686 (spherical) and 384 m (Gaussian) for transmissivity. Sorptivity, transmissivity and final infiltration attributes showed a high degree of spatial dependence, being 0.99, 0.81 and 1, respectively. Results showed that kriging could be used to predict the studied parameters in the study area. (Author)

  7. Modeling the spatial variability of snow instability with the snow cover model SNOWPACK

    Science.gov (United States)

    Richter, Bettina; Reuter, Benjamin; Gaume, Johan; Fierz, Charles; Bavay, Mathias; van Herwijnen, Alec; Schweizer, Jürg

    2016-04-01

    Snow stratigraphy - key information for avalanche forecasting - can be obtained using numerical snow cover models driven by meteorological data. Simulations are typically performed for the locations of automatic weather station or for virtual slopes of varying aspect. However, it is unclear to which extent these simulations can represent the snowpack properties in the surrounding terrain, in particular snow instability, which is known to vary in space. To address this issue, we implemented two newly developed snow instability criteria in SNOWPACK relating to failure initiation and crack propagation, two fundamental processes for dry-snow slab avalanche release. Snow cover simulations were performed for the Steintälli field site above Davos (Eastern Swiss Alps), where snowpack data from several field campaigns are available. In each campaign, about 150 vertical snow penetration resistance profiles were sampled with the snow micro-penetrometer (SMP). For each profile, SMP and SNOWPACK- based instability criteria were compared. In addition, we carried out SNOWPACK simulations for multiple aspects and slope angles, allowing to obtain statistical distributions of the snow instability at the basin scale. Comparing the modeled to the observed distributions of snow instability suggests that it is feasible to obtain an adequate spatial representation of snow instability without high resolution distributed modeling. Hence, for the purpose of regional avalanche forecasting, simulations for a selection of virtual slopes seems sufficient to assess the influence of basic terrain features such as aspect and elevation.

  8. 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

  9. 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

  10. Spatial Variability of Rainfall

    DEFF Research Database (Denmark)

    Jensen, N.E.; Pedersen, Lisbeth

    2005-01-01

    As a part of a Local Area Weather Radar (LAWR) calibration exercise 15 km south of Århus, Denmark, the variability in accumulated rainfall within a single radar pixel (500 by 500 m) was measured using nine high-resolution rain gauges. The measured values indicate up to a 100% variation between...

  11. A simple model for the spatially-variable coastal response to hurricanes

    Science.gov (United States)

    Stockdon, H.F.; Sallenger, A.H.; Holman, R.A.; Howd, P.A.

    2007-01-01

    The vulnerability of a beach to extreme coastal change during a hurricane can be estimated by comparing the relative elevations of storm-induced water levels to those of the dune or berm. A simple model that defines the coastal response based on these elevations was used to hindcast the potential impact regime along a 50-km stretch of the North Carolina coast to the landfalls of Hurricane Bonnie on August 27, 1998, and Hurricane Floyd on September 16, 1999. Maximum total water levels at the shoreline were calculated as the sum of modeled storm surge, astronomical tide, and wave runup, estimated from offshore wave conditions and the local beach slope using an empirical parameterization. Storm surge and wave runup each accounted for ∼ 48% of the signal (the remaining 4% is attributed to astronomical tides), indicating that wave-driven process are a significant contributor to hurricane-induced water levels. Expected water levels and lidar-derived measures of pre-storm dune and berm elevation were used to predict the spatially-varying storm-impact regime: swash, collision, or overwash. Predictions were compared to the observed response quantified using a lidar topography survey collected following hurricane landfall. The storm-averaged mean accuracy of the model in predicting the observed impact regime was 55.4%, a significant improvement over the 33.3% accuracy associated with random chance. Model sensitivity varied between regimes and was highest within the overwash regime where the accuracies were 84.2% and 89.7% for Hurricanes Bonnie and Floyd, respectively. The model not only allows for prediction of the general coastal response to storms, but also provides a framework for examining the longshore-variable magnitudes of observed coastal change. For Hurricane Bonnie, shoreline and beach volume changes within locations that experienced overwash or dune erosion were two times greater than locations where wave runup was confined to the foreshore (swash regime

  12. 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

  13. 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

  14. Spatial variability of the parameters of a semi-distributed hydrological model

    Science.gov (United States)

    de Lavenne, Alban; Thirel, Guillaume; Andréassian, Vazken; Perrin, Charles; Ramos, Maria-Helena

    2016-05-01

    Ideally, semi-distributed hydrologic models should provide better streamflow simulations than lumped models, along with spatially-relevant water resources management solutions. However, the spatial distribution of model parameters raises issues related to the calibration strategy and to the identifiability of the parameters. To analyse these issues, we propose to base the evaluation of a semi-distributed model not only on its performance at streamflow gauging stations, but also on the spatial and temporal pattern of the optimised value of its parameters. We implemented calibration over 21 rolling periods and 64 catchments, and we analysed how well each parameter is identified in time and space. Performance and parameter identifiability are analysed comparatively to the calibration of the lumped version of the same model. We show that the semi-distributed model faces more difficulties to identify stable optimal parameter sets. The main difficulty lies in the identification of the parameters responsible for the closure of the water balance (i.e. for the particular model investigated, the intercatchment groundwater flow parameter).

  15. How does spatial variability of climate affect catchment streamflow predictions?

    Science.gov (United States)

    Spatial variability of climate can negatively affect catchment streamflow predictions if it is not explicitly accounted for in hydrologic models. In this paper, we examine the changes in streamflow predictability when a hydrologic model is run with spatially variable (distribute...

  16. 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.

  17. Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models.

    Science.gov (United States)

    Šiljić Tomić, Aleksandra N; Antanasijević, Davor Z; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A; Pocajt, Viktor V

    2016-05-01

    This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passing through Serbia were used as input variables. The optimization of the model was performed in three consecutive steps: firstly, the spatial influence of a monitoring station was examined; secondly, the monitoring period necessary to reach satisfactory performance was determined; and lastly, correlation analysis was applied to evaluate the relationship among water quality parameters. Root-mean-square error (RMSE) was used to evaluate model performance in the first two steps, whereas in the last step, multiple statistical indicators of performance were utilized. As a result, two optimized models were developed, a general regression neural network model (labeled GRNN-1) that covers the monitoring stations from the Danube inflow to the city of Novi Sad and a GRNN model (labeled GRNN-2) that covers the stations from the city of Novi Sad to the border with Romania. Both models demonstrated good agreement between the predicted and actually observed BOD values.

  18. Spatial Heterogeneity and Variability of a Large-Scale Vegetation Community Using a Power-Law Model

    Institute of Scientific and Technical Information of China (English)

    SONG Zhiyuan; HUANG Daming; SHIYOMI Masae; WANG Yusheng; TAKAHASHI Shigeo; YOSHIMICHI Hori; YAMAMURU Yasuo; CHEN Jun

    2005-01-01

    Spatial heterogeneity and stability are fundamental indices for describing vegetation communities. The spatial distribution characteristics of the vegetation in Nenjiang region of northeastern China were evaluated using a variance power-law model. The data fits the model well with estimates given for the levels of heterogeneity for not only single species but also the community as a whole. The linear regression indicates that the species in the community exhibit a consistently organized spatial pattern, as is often discovered in field surveys but rarely seen in artificial systems. The species deviations from the regression line, which exhibit a leptokurtic distribution, may reflect the variability of the community. Thus, the model provides a general tool for management and regulation of ecosystems, especially where there is human disturbances.

  19. Unitary evolution for anisotropic quantum cosmologies: models with variable spatial curvature

    CERN Document Server

    Pandey, Sachin

    2016-01-01

    Contrary to the general belief, there has recently been quite a few examples of unitary evolution of quantum cosmological models. The present work gives more examples, namely Bianchi type VI and type II. These examples are important as they involve varying spatial curvature unlike the most talked about homogeneous but anisotropic cosmological models like Bianchi I, V and IX. We exhibit either explicit example of the unitary solutions of the Wheeler-DeWitt equation, or at least show that a self-adjoint extension is possible.

  20. Unitary evolution for anisotropic quantum cosmologies: models with variable spatial curvature

    Science.gov (United States)

    Pandey, Sachin; Banerjee, Narayan

    2016-11-01

    Contrary to the general belief, there has recently been quite a few examples of unitary evolution of quantum cosmological models. The present work gives more examples, namely Bianchi type VI and type II. These examples are important as they involve varying spatial curvature unlike the most talked about homogeneous but anisotropic cosmological models like Bianchi I, V and IX. We exhibit either an explicit example of the unitary solutions of the Wheeler-DeWitt equation, or at least show that a self-adjoint extension is possible.

  1. 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

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

    Science.gov (United States)

    Radinger, Johannes; Wolter, Christian; Kail, Jochem

    2015-01-01

    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

  3. 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

  4. Effects of spatial variability of precipitation for process-orientated hydrological modelling: results from two nested catchments

    Directory of Open Access Journals (Sweden)

    D. Tetzlaff

    2005-01-01

    Full Text Available The importance of considering the spatial distribution of rainfall for process-oriented hydrological modelling is well-known. However, the application of rainfall radar data to provide such detailed spatial resolution is still under debate. In this study the process-oriented TACD (Tracer Aided Catchment model, Distributed model had been used to investigate the effects of different spatially distributed rainfall input on simulated discharge and runoff components on an event base. TACD is fully distributed (50x50 m2 raster cells and was applied on an hourly base. As model input rainfall data from up to 11 ground stations and high resolution rainfall radar data from an operational C-band radar were used. For seven rainfall events the discharge simulations were investigated in further detail for the mountainous Brugga catchment (40 km2 and the St. Wilhelmer Talbach (15.2 km2 sub-basin, which are located in the Southern Black Forest Mountains, south-west Germany. The significance of spatial variable precipitation data was clearly demonstrated. Dependent on event characteristics, localized rain cells were occasionally poorly captured even by a dense ground station network, and this resulted in insufficient model results. For such events, radar data can provide better input data. However, an extensive data adjustment using ground station data is required. Therefore, a new method was developed that considers the rainfall intensity distribution. The use of the distributed catchment model allowed further insights into spatially variable impacts of different rainfall estimates. Impacts for discharge predictions are the largest in areas that are dominated by the production of fast runoff components. To conclude, the improvements for distributed runoff simulation using high resolution rainfall radar input data are strongly dependent on the investigated scale, the event characteristics, the existing

  5. Greenland Ice Sheet seasonal and spatial mass variability from model simulations and GRACE (2003–2012

    Directory of Open Access Journals (Sweden)

    P. M. Alexander

    2015-11-01

    Full Text Available 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. Here, we examine spatiotemporal variations in mass over the GrIS derived from the Gravity Recovery and Climate Experiment (GRACE satellites for the 2003–2012 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 −179 and −240 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, there are significant differences in the timing of peaks in the annual cycle of mass change. At these scales, model biases, or unaccounted-for processes related to ice dynamics or hydrology may lead to the observed differences. This highlights the need for further evaluation of modelled processes at regional and seasonal scales, and further study of ice sheet

  6. Representation of spatial and temporal variability in large-domain hydrological models: case study for a mesoscale pre-Alpine basin

    NARCIS (Netherlands)

    Melsen, Lieke; Teuling, Adriaan; Torfs, Paul; Zappa, Massimiliano; Mizukami, Naoki; Clark, Martyn; Uijlenhoet, Remko

    2016-01-01

    The transfer of parameter sets over different temporal and spatial resolutions is common practice in many large-domain hydrological modelling studies. The degree to which parameters are transferable across temporal and spatial resolutions is an indicator of how well spatial and temporal variability

  7. Modelling spatial, altitudinal and temporal variability of annual precipitation in mountainous regions: The case of the Middle Zagros, Iran

    Science.gov (United States)

    Saeidabadi, Rashid; Najafi, Mohammed S.; Roshan, GholamReza; Fitchett, Jennifer M.; Abkharabat, Shoaieb

    2016-11-01

    Relationships between precipitation and elevation are difficult to model for mountainous regions, due to complexities in topography and moisture sources. Attempts to model these relationships need to be tested against long-term location specific meteorological data, and hence require a case-study approach. This study uses artificial neural networks to model these relationships for the Middle of Zagros region, in semi-arid western Iran. Precipitation data for the region were collected for 1995-2007. Annual precipitation was designated as the target variable for the network, which additionally included variables significantly related to precipitation for the region, including longitude, latitude, elevation, slope, distance from the ridge, and relative distance from moisture. Long-term changes in annual precipitation for the region are investigated for 1961-2010. The artificial neural network (ANN) model explains 76% of the spatial variability of precipitation in the Middle Zagros. Precipitation predominantly increases with elevation on the windward slope, to a maximum height of 2500 m.asl, and thereafter either remains constant or decreases slowly to the ridge. Precipitation in the region has decreased significantly over the study period, with fluctuations driven by AO, NAO, ENSO and variability in the strength of pressure centers. Spectral analysis reveals significant oscillations of 2-4 and 5 yr periods, which correspond temporally with cycles in macro-scale circulation, ENSO and the Mediterranean Low pressure.

  8. 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

    the geology of e.g. a contaminated site, it is not always possible to gather enough information to build a representative geological model. Mapping in analogue geological settings and applying geostatistical tools to simulate spatial variability of heterogeneities can improve ordinary geological models...... that are predicated only on vertical borehole information. This study documents methods to map geological heterogeneity in clay till and ways to calibrate geostatistical models with field observations. A well-exposed cross-section in an excavation pit was used to measure and illustrate the occurrence and distribution...... 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...

  9. Spatially aggregated data and variables in empirical analysis and model building for economics

    Directory of Open Access Journals (Sweden)

    Tamás Dusek

    2004-09-01

    Full Text Available Les problèmes posés par l’agrégation spatiale sont évoqués, d’un point de vue général et plus particulièrement en sociologie et en économie. On propose ensuite une typologie des données et des analyses spatiales en fonction de l’agrégation spatiale. La question des maillages modifiables est traitée sous deux aspects : celui de l’échelle et celui des limites, à partir de données hongroises. L’agrégation spatiale des données implique une analyse précise des relations entre théorie et données, ainsi qu’entre échelles micro et macro.

  10. Modeling circulation patterns induced by spatial cross-shore wind variability in a small-size coastal embayment

    Science.gov (United States)

    Cerralbo, Pablo; Espino, Manuel; Grifoll, Manel

    2016-08-01

    This contribution shows the importance of the cross-shore spatial wind variability in the water circulation in a small-sized micro-tidal bay. The hydrodynamic wind response at Alfacs Bay (Ebro River delta, NW Mediterranean Sea) is investigated with a numerical model (ROMS) supported by in situ observations. The wind variability observed in meteorological measurements is characterized with meteorological model (WRF) outputs. From the hydrodynamic simulations of the bay, the water circulation response is affected by the cross-shore wind variability, leading to water current structures not observed in the homogeneous-wind case. If the wind heterogeneity response is considered, the water exchange in the longitudinal direction increases significantly, reducing the water exchange time by around 20%. Wind resolutions half the size of the bay (in our case around 9 km) inhibit cross-shore wind variability, which significantly affects the resultant circulation pattern. The characteristic response is also investigated using idealized test cases. These results show how the wind curl contributes to the hydrodynamic response in shallow areas and promotes the exchange between the bay and the open sea. Negative wind curl is related to the formation of an anti-cyclonic gyre at the bay's mouth. Our results highlight the importance of considering appropriate wind resolution even in small-scale domains (such as bays or harbors) to characterize the hydrodynamics, with relevant implications in the water exchange time and the consequent water quality and ecological parameters.

  11. Comparison between theoretical footprint models and experimental measurements of intra-field spatial variability scalar fluxes over different sites

    Science.gov (United States)

    Masseroni, D.; Corbari, C.; Ceppi, A.; Milleo, G.; Mancini, M.

    2012-04-01

    Not many experimental data about intra-field spatial variability of scalar flux densities are presented in literature. In this work theoretical footprint models and experimental intra-field turbulent fluxes of latent, sensible heat and CO2 were compared. The experimental data were obtained using a mobile eddy covariance station moving it from a discontinuity point, represented by the field edge, to the centre of the field where a fixed eddy covariance station was placed. The experimental fields were in Landriano (PV) in the Po Valley, Italy and Barrax (Albacete) in Spain. Simple analytical footprint models that describe the representative source area for turbulent fluxes were compared with the experimental data. Mathematical relationship between footprint models and gamma function was explained. Energy balance closure was calculated starting from fixed tower measurements. Aerodynamic roughness and gamma distribution parameters were estimated for these specific fields.

  12. Spatial Modeling Techniques for Characterizing Geomaterials: Deterministic vs. Stochastic Modeling for Single-Variable and Multivariate Analyses%Spatial Modeling Techniques for Characterizing Geomaterials:Deterministic vs. Stochastic Modeling for Single-Variable and Multivariate Analyses

    Institute of Scientific and Technical Information of China (English)

    Katsuaki Koike

    2011-01-01

    Sample data in the Earth and environmental sciences are limited in quantity and sampling location and therefore, sophisticated spatial modeling techniques are indispensable for accurate imaging of complicated structures and properties of geomaterials. This paper presents several effective methods that are grouped into two categories depending on the nature of regionalized data used. Type I data originate from plural populations and type II data satisfy the prerequisite of stationarity and have distinct spatial correlations. For the type I data, three methods are shown to be effective and demonstrated to produce plausible results: (1) a spline-based method, (2) a combination of a spline-based method with a stochastic simulation, and (3) a neural network method. Geostatistics proves to be a powerful tool for type II data. Three new approaches of geostatistics are presented with case studies: an application to directional data such as fracture, multi-scale modeling that incorporates a scaling law,and space-time joint analysis for multivariate data. Methods for improving the contribution of such spatial modeling to Earth and environmental sciences are also discussed and future important problems to be solved are summarized.

  13. Sub-Hour Solar Data for Power System Modeling From Static Spatial Variability Analysis: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Hummon, M.; Ibanez, E.; Brinkman, G.; Lew, D.

    2012-12-01

    High penetration renewable integration studies need high quality solar power data with spatial-temporal correlations that are representative of a real system. This paper 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.

  14. Two models to compute an adjusted Green Vegetation Fraction taking into account the spatial variability of soil NDVI

    Science.gov (United States)

    Montandon, L. M.; Small, E.

    2008-12-01

    The green vegetation fraction (Fg) is an important climate and hydrologic model parameter. The commonly- used Fg model is a simple linear mixing of two NDVI end-members: bare soil NDVI (NDVIo) and full vegetation NDVI (NDVI∞). NDVI∞ is generally set as a percentile of the historical maximum NDVI for each land cover. This approach works well for areas where Fg reaches full cover (100%). Because many biomes do not reach Fg=0, however, NDVIo is often determined as a single invariant value for all land cover types. In general, it is selected among the lowest NDVI observed over bare or desert areas, yielding NDVIo close to zero. There are two issues with this approach: large-scale variability of soil NDVI is ignored and observations on a wide range of soils show that soil NDVI is often larger. Here we introduce and test two new approaches to compute Fg that takes into account the spatial variability of soil NDVI. The first approach uses a global soil NDVI database and time series of MODIS NDVI data over the conterminous United States to constrain possible soil NDVI values over each pixel. Fg is computed using a subset of the soils database that respects the linear mixing model condition NDVIo≤NDVIh, where NDVIh is the pixel historical minimum. The second approach uses an empirical soil NDVI model that combines information of soil organic matter content and texture to infer soil NDVI. The U.S. General Soil Map (STATSGO2) database is used as input for spatial soil properties. Using in situ measurements of soil NDVI from sites that span a range of land cover types, we test both models and compare their performance to the standard Fg model. We show that our models adjust the temporal Fg estimates by 40-90% depending on the land cover type and amplitude of the seasonal NDVI signal. Using MODIS NDVI and soil maps over the conterminous U.S., we also study the spatial distribution of Fg adjustments in February and June 2008. We show that the standard Fg method

  15. Modelling spatial and temporal variability of surface water-groundwater fluxes and heat exchange along a lowland river reach

    Science.gov (United States)

    Munz, Matthias; Schmidt, Christian; Fleckenstein, Jan; Oswald, Sascha

    2013-04-01

    In this study we used the deterministic, fully-integrated surface-subsurface flow and heat transport model (HydroGeoSphere) to investigate the spatial and temporal variability of surface water-groundwater (SFW-GW) interaction along a lowland river reach. The model incorporates the hydrological as well as the heat transport processes including (1) radiative fluxes warming and cooling the surface water; (2) seasonal groundwater temperature changes; (3) occasionally occurring heat inputs due to precipitation and (4) highly variable SFW-GW water advective heat exchange driven by the general relation between SFW and GW hydraulic heads and geomorphological structure of the riverbed. The study area is a 100 m long lowland river reach of the Selke river, at the boundary of the Harz mountains characterized by distinctive gravel bars. Continuous time series of hydraulic heads and temperatures at different depth in the river bank, the hyporheic zone and within the river are used to define the boundary conditions, to calibrate and to validate the numerical model. The 3D modelling results show that the water and heat exchange at the SFW-GW interface is highly variable in space with zones of daily temperature oscillations penetrating deep into the sediment and spots of daily constant temperature following the average GW temperature. To increase the understanding of evolving pattern, the observed temperature variations in space and time will be linked to dominant stream flow conditions, streambed morphology, advective and conductive heat exchange between SFW and GW and subsurface solute residence times. This study allows to analyse and quantify water and heat fluxes at the SFW-GW interface, to trace subsurface flow paths within the streambed sediments and thus improves the understanding of hyporheic zone exchange mechanisms. It is a sound basis for investigating quantitatively variations of sediment properties, boundary conditions and streambed morphology and also for subsequent

  16. 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.

  17. Influence of the spatial variability of rainfall on hydrograph modelling at catchment outlet: a case study in the Cevennes region, France

    Directory of Open Access Journals (Sweden)

    Emmanuel Isabelle

    2016-01-01

    Full Text Available The influence of rainfall spatial variability on hydrographs modelling at catchment outlet remains an open scientific debate. [1] have proposed rainfall variability indexes aiming at summarising the influence of rainfall spatial organisation on hydrographs features. This preliminary work was based on a large simulated database. The present article shows how the proposed indexes may be used in a real case study to discriminate rainfall events for which information on spatial rainfall organization is crucial for hydrograph modelling, and therefore to better illustrate the added value of high resolution rainfall information as input of hydrological models. The presented case study is located in the Cevennes Region in south-eastern France. The tested flow events are split into two subsets according to the values of the rainfall variability indexes. The comparison between modelled and measured hydrographs is then performed separately for each subset. The results obtained suggest that, on average, modelling results taking into account high resolution rainfall data are significantly improved for the subset for which the influence of rainfall variability is expected to be significant according to the indexes values. Although limited to a relatively small number of hydrographs, this case study can be viewed as a first confirmation of the pertinence of the rainfall variability indexes proposed in [1] to investigate the influence of rainfall spatial variability on the shape of hydrographs at catchment outlet.

  18. Spatial ascariasis risk estimation using socioeconomic variables.

    Science.gov (United States)

    Valencia, Luis Iván Ortiz; Fortes, Bruno de Paula Menezes Drumond; Medronho, Roberto de Andrade

    2005-12-01

    Frequently, disease incidence is mapped as area data, for example, census tracts, districts or states. Spatial disease incidence can be highly heterogeneous inside these areas. Ascariasis is a highly prevalent disease, which is associated with poor sanitation and hygiene. Geostatistics was applied to model spatial distribution of Ascariasis risk and socioeconomic risk events in a poor community in Rio de Janeiro, Brazil. Data were gathered from a coproparasitologic and a domiciliary survey in 1550 children aged 1-9. Ascariasis risk and socioeconomic risk events were spatially estimated using Indicator Kriging. Cokriging models with a Linear Model of Coregionalization incorporating one socioeconomic variable were implemented. If a housewife attended school for less than four years, the non-use of a home water filter, a household density greater than one, and a household income lower than one Brazilian minimum wage increased the risk of Ascariasis. Cokriging improved spatial estimation of Ascariasis risk areas when compared to Indicator Kriging and detected more Ascariasis very-high risk areas than the GIS Overlay method.

  19. Effects of model spatial resolution on ecohydrologic predictions and their sensitivity to inter-annual climate variability

    Science.gov (United States)

    Kyongho Son; Christina Tague; Carolyn Hunsaker

    2016-01-01

    The effect of fine-scale topographic variability on model estimates of ecohydrologic responses to climate variability in California’s Sierra Nevada watersheds has not been adequately quantified and may be important for supporting reliable climate-impact assessments. This study tested the effect of digital elevation model (DEM) resolution on model accuracy and estimates...

  20. Evaluation of spatial variability of soil arsenic adjacent to a disused cattle-dip site, using model-based geostatistics.

    Science.gov (United States)

    Niazi, Nabeel K; Bishop, Thomas F A; Singh, Balwant

    2011-12-15

    This study investigated the spatial variability of total and phosphate-extractable arsenic (As) concentrations in soil adjacent to a cattle-dip site, employing a linear mixed model-based geostatistical approach. The soil samples in the study area (n = 102 in 8.1 m(2)) were taken at the nodes of a 0.30 × 0.35 m grid. The results showed that total As concentration (0-0.2 m depth) and phosphate-extractable As concentration (at depths of 0-0.2, 0.2-0.4, and 0.4-0.6 m) in soil adjacent to the dip varied greatly. Both total and phosphate-extractable soil As concentrations significantly (p = 0.004-0.048) increased toward the cattle-dip. Using the linear mixed model, we suggest that 5 samples are sufficient to assess a dip site for soil (As) contamination (95% confidence interval of ±475.9 mg kg(-1)), but 15 samples (95% confidence interval of ±212.3 mg kg(-1)) is desirable baseline when the ultimate goal is to evaluate the effects of phytoremediation. Such guidelines on sampling requirements are crucial for the assessment of As contamination levels at other cattle-dip sites, and to determine the effect of phytoremediation on soil As.

  1. 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

  2. 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.

  3. The spatial and temporal variability of the surface mass balance in Antarctica: results from a regional climate model

    NARCIS (Netherlands)

    Lipzig, N.P.M. van; Meijgaard, E. van; Oerlemans, J.

    2002-01-01

    A 14 year integration with a regional atmospheric model (RACMO) is used to obtain detailed information on the Antarctic surface mass balance and to understand the mechanisms that are responsible for the spatial and temporal distribution of the surface mass balance. The model (Δx = 55 km) uses the pa

  4. 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.

  5. First measurement of the small-scale spatial variability of the rain drop size distribution: Results from a crucial experiment and maximum entropy modeling

    CERN Document Server

    Checa-Garcia, Ramiro

    2013-01-01

    The main challenges of measuring precipitation are related to the spatio-temporal variability of the drop-size distribution, to the uncertainties that condition the modeling of that distribution, and to the instrumental errors present in the in situ estimations. This PhD dissertation proposes advances in all these questions. The relevance of the spatial variability of the drop-size distribution for remote sensing measurements and hydro-meteorology field studies is asserted by analyzing the measurement of a set of disdrometers deployed on a network of 5 squared kilometers. This study comprises the spatial variability of integral rainfall parameters, the ZR relationships, and the variations within the one moment scaling method. The modeling of the drop-size distribution is analyzed by applying the MaxEnt method and comparing it with the methods of moments and the maximum likelihood. The instrumental errors are analyzed with a compressive comparison of sampling and binning uncertainties that affect actual device...

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

    Science.gov (United States)

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

    2015-10-01

    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.

  7. Zooplankton mortality in 3D ecosystem modelling considering variable spatial-temporal fish consumptions in the North Sea

    Science.gov (United States)

    Maar, Marie; Rindorf, Anna; Møller, Eva Friis; Christensen, Asbjørn; Madsen, Kristine S.; van Deurs, Mikael

    2014-05-01

    We tested the feasibility of imposing mesozooplankton mortality into a 3D model based on estimated consumption rates of the dominant planktivorous fish in the North Sea-Kattegat area. The spatial biomass distribution of Atlantic herring (Clupea harengus), horse mackerel (Trachurus trachurus), Atlantic mackerel (Scomber scombrus), sandeel (Ammodytidae) and European sprat (Sprattus sprattus) was derived from quarterly scientific trawl surveys and Danish commercial catches. Spatio-temporal indices of mortality were created based on the estimated biomasses and ingestion rates from the literature. The fish larvae grazing pressure was obtained from a spatial, size-based larval community model. In this model, larvae, herring and sandeel were the most important fish predators on mesozooplankton, but these groups had different spatial and temporal (seasonal) distributions. Fish larvae were particularly dominant in the eastern and southern areas in early summer. Herring and sandeel had the highest consumption in the central and north-western areas and were more important in late summer. The fish index changed the perceived annual, seasonal and spatial patterns in modelled mesozooplankton biomass, production and mortality. In the present study, the index was kept relatively simple and can be further developed with respect to the description of fish as well carnivorous zooplankton ingestion rates. The data input required to create the fish index is (i) planktivorous fish stock biomasses and (ii) relative fish spawning distribution information and (iii) physics (ocean currents and temperatures) for the region and situation of interest. The fish index seems promising as a realistic mortality term for lower trophic levels in 3D ecosystem models in areas with available data on fish stocks to improve management of marine resources.

  8. Significance of spatial variability in precipitation for process-oriented modelling: results from two nested catchments using radar and ground station data

    Directory of Open Access Journals (Sweden)

    D. Tetzlaff

    2005-01-01

    Full Text Available The importance of considering the spatial distribution of rainfall for process-oriented hydrological modelling is well-known. However, the application of rainfall radar data to provide such detailed spatial resolution is still under debate. In this study the process-oriented TACD (Tracer Aided Catchment model, Distributed model had been used to investigate the effects of different spatially distributed rainfall input on simulated discharge and runoff components on an event base. TACD is fully distributed (50x50m2 raster cells and was applied on an hourly base. As model input rainfall data from up to 7 ground stations and high resolution rainfall radar data from operational C-band radar were used. For seven rainfall events the discharge simulations were investigated in further detail for the mountainous Brugga catchment (40km2 and the St. Wilhelmer Talbach (15.2km2 sub-basin, which are located in the Southern Black Forest Mountains, south-west Germany. The significance of spatial variable precipitation data was clearly demonstrated. Dependent on event characteristics, localized rain cells were occasionally poorly captured even by a dense ground station network, and this resulted in inadequate model results. For such events, radar data can provide better input data. However, an extensive data adjustment using ground station data is required. For this purpose a method was developed that considers the temporal variability in rainfall intensity in high temporal resolution in combination with the total rainfall amount of both data sets. The use of the distributed catchment model allowed further insights into spatially variable impacts of different rainfall estimates. Impacts for discharge predictions are the largest in areas that are dominated by the production of fast runoff components. The improvements for distributed runoff simulation using high resolution rainfall radar input data are strongly dependent on the investigated scale, the event

  9. Spatial distribution analysis on climatic variables in northeast China

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Information ecology is a new research area of modern ecology.Here describes spatial distribution analysis methods of four sorts of climatic variables, i.e. temperature, precipitation, relative humidity and sunshine fraction on Northeast China. First,Digital terrain models was built with large-scale maps and vector data. Then trend surface analysis and interpolation method were used to analyze the spatial distribution of these four kinds of climatic variables at three temporal scale: (1) monthly data; (2)mean monthly data of thirty years, and (3) mean annual data of thirty years. Ecological information system were used for graphics analysis on the spatial distribution of these climate variables.

  10. Climate variability effects on spatial soil moisture dynamics

    OpenAIRE

    A. J. Teuling; Hupet, F.; R. Uijlenhoet; P. A. Troch

    2007-01-01

    We investigate the role of interannual climate variability on spatial soil moisture variability dynamics for a field site in Louvain-la-Neuve, Belgium. Observations were made during 3 years under intermediate (1999), wet (2000), and extremely dry conditions (2003). Soil moisture variability dynamics are simulated with a comprehensive model for the period 1989-2003. The results show that climate variability induces non-uniqueness and two distinct hysteresis modes in the yearly relation between...

  11. 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.

  12. Modelling large-scale spatial variability of soil properties with sequential stochastic simulation conditioned by universal kriging in a Hungarian study site

    Science.gov (United States)

    Szatmári, Gábor; Barta, Károly; Pásztor, László

    2015-04-01

    Modelling of large-scale spatial variability of soil properties is a promising subject in soil science, as well as in general environmental research, since the resulted model(s) can be applied to solve various problems. In addition to "purely" map an environmental element, the spatial uncertainty of the map product can deduced, specific areas could be identified and/or delineated (contaminated or endangered regions, plots for fertilization, etc.). Geostatistics, which can be regarded as a subset of statistics specialized in analysis and interpretation of geographically referenced data, offer a huge amount of tools to solve these tasks. Numerous spatial modeling methods have been developed in the past decades based on the regionalized variable theory. One of these techniques is sequential stochastic simulation, which can be conditioned with universal kriging (also referred to as regression kriging). As opposed to universal kriging (UK), sequential simulation conditioned with universal kriging (SSUK) provides not just one but several alternative and equally probable "maps", i.e. realizations. The realizations reproduce the global statistics (e.g. sample histogram, variogram), i.e. they reflect/model the reality in a certain global (and not local!) sense. In this paper we present and test SSUK developed in R-code and its utilizations in a water erosion affected study area. Furthermore, we compare the results from UK and SSUK. For this purpose, two soil variables were selected: soil organic matter (SOM) content and rooting depth (RD). SSUK approach is illustrated with a legacy soil dataset from a study area endangered by water erosion in Central Hungary. Legacy soil data was collected in the end of the 1980s in the framework of the National Land Evaluation Programme. Spatially exhaustive covariates were derived from a digital elevation model and from the land-use-map of the study area. SSUK was built upon a UK prediction system for both variables and 200 realizations

  13. Modelling spatial and temporal vegetation variability with the Climate Constrained Vegetation Index: evidence of CO2 fertilisation and of water stress in continental interiors

    Directory of Open Access Journals (Sweden)

    S. O. Los

    2015-06-01

    Full Text Available A model was developed to simulate spatial, seasonal and interannual variations in vegetation in response to temperature, precipitation and atmospheric CO2 concentrations; the model addresses shortcomings in current implementations. The model uses the minimum of 12 temperature and precipitation constraint functions to simulate NDVI. Functions vary based on the Köppen–Trewartha climate classification to take adaptations of vegetation to climate into account. The simulated NDVI, referred to as the climate constrained vegetation index (CCVI, captured the spatial variability (0.82 r r = 0.83 and interannual variability (median global r = 0.24 in NDVI. The CCVI simulated the effects of adverse climate on vegetation during the 1984 drought in the Sahel and during dust bowls of the 1930s and 1950s in the Great Plains in North America. A global CO2 fertilisation effect was found in NDVI data, similar in magnitude to that of earlier estimates (8 % for the 20th century. This effect increased linearly with simple ratio, a transformation of the NDVI. Three CCVI scenarios, based on climate simulations using the representative concentration pathway RCP4.5, showed a greater sensitivity of vegetation towards precipitation in Northern Hemisphere mid latitudes than is currently implemented in climate models. This higher sensitivity is of importance to assess the impact of climate variability on vegetation, in particular on agricultural productivity.

  14. 土工离心模型的空间变异性研究%Spatial Variability of Soil Properties in Geotechnical Centrifuge Models

    Institute of Scientific and Technical Information of China (English)

    何晔; 张璐璐; 王建华; 张敏

    2011-01-01

    土工离心模型的试验结果主要由模型箱内空间平均的土性指标影响.基于随机场的原理,研究了土工离心模型中土性指标的空间变异性.根据实测土密度和孔隙率数据,采用相关函数法计算相关距离,并讨论了模型与原型的空间变异性的相似关系.结果表明,离心模型中土性指标的点变异系数相比原位测试值偏小,相关距离则远小于原位土的相关距离.即使点变异系数和相关距离一致,不同比尺的模型对应原型的空间均值变异系数也不相等.给出了空间均值变异系数云图,可用来调整离心模型以满足原型空间均值变异系数的要求.%Performance of a geotechnical centrifuge model is controlled by the spatially averaged soil properties in the model. In this paper, the spatial variability of soil properties in geotechnical centrifuge models is investigated based on the theory of random field. The scale of fluctuation of soil density and porosity of two centrifuge models are estimated using the autocorrelation function method. The similarity of spatial variability between a model and its prototype is also studied. It is found that the point coefficients of variation in centrifuge models are slightly smaller than those in field. The scale of fluctuation in centrifuge models is much smaller than that in field. It is also found that the spatially averaged coefficients of variation are different for the centrifuge models with different size, which are designed to simulate the same prototype. In this paper, a contour plot of the spatially averaged coefficients of variation is provided. One may control the point coefficient of variation and the scale of fluctuation of the soil in a centrifuge model in order to achieve a required spatial averaged coefficient of variation for the corresponding prototype.

  15. Spatial and Temporal Variability of Trace Gas Columns Derived from WRF/Chem Regional Model Output: Planning for Geostationary Observations of Atmospheric Composition

    Science.gov (United States)

    Follette-Cook, M. B.; Pickering, K.; Crawford, J.; Duncan, B.; Loughner, C.; Diskin, G.; Fried, A.; Weinheimer, A.

    2015-01-01

    We quantify both the spatial and temporal variability of column integrated O3, NO2, CO, SO2, and HCHO over the Baltimore / Washington, DC area using output from the Weather Research and Forecasting model with on-line chemistry (WRF/Chem) for the entire month of July 2011, coinciding with the first deployment of the NASA Earth Venture program mission Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ). Using structure function analyses, we find that the model reproduces the spatial variability observed during the campaign reasonably well, especially for O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument will be the first NASA mission to make atmospheric composition observations from geostationary orbit and partially fulfills the goals of the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission. We relate the simulated variability to the precision requirements defined by the science traceability matrices of these space-borne missions. Results for O3 from 0- 2 km altitude indicate that the TEMPO instrument would be able to observe O3 air quality events over the Mid-Atlantic area, even on days when the violations of the air quality standard are not widespread. The results further indicated that horizontal gradients in CO from 0-2 km would be observable over moderate distances (= 20 km). The spatial and temporal results for tropospheric column NO2 indicate that TEMPO would be able to observe not only the large urban plumes at times of peak production, but also the weaker gradients between rush hours. This suggests that the proposed spatial and temporal resolutions for these satellites as well as their prospective precision requirements are sufficient to answer the science questions they are tasked to address.

  16. 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 ...

  17. 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

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

    African Journals Online (AJOL)

    Spatial Variability of Soil Morphorlogical and Physico-Chemical Properties in ... the spatial variability of soil morphological, physical and chemical properties in the ... organic matter (g/kg), and available phosphorus were extremely variable soil ...

  19. 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.

  20. 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.

  1. Spatial and temporal variability of chlorophyll in Bay of Bengal.

    Science.gov (United States)

    Jutla, A.; Akanda, S.; Islam, S.

    2009-04-01

    The Bay of Bengal (BoB) receives approximately 628 km3/ year of freshwater discharge from the Ganges and Brahmaputra rivers. Freshwater discharge from rivers increases the nutrient load and thereby enhances phytoplankton production in the BoB. Cholera, an infectious water-borne disease caused by bacterium Vibrio cholerae, remains endemic in the BoB region. Phytoplankton provides favorable environment for survival of cholera bacteria. Therefore, for development of any predictive model for cholera, it is important to quantify the spatial and temporal variability of phytoplankton in the BoB. Satellite remote sensing is the most effective way to quantify this variability over a range of space and time scales. Using ten years (1998-2007) of daily, weekly and monthly SeaWiFs chlorophyll, a surrogate variable for measuring phytoplankton, imagery we explore the spatial pattern and dominant temporal variability of chlorophyll over the BoB region. We find that chlorophyll in the coastal waters has more variability, both in temporal and spatial scales, than the offshore waters. Mechanism of production and space-time variability of coastal chlorophyll is different from those of offshore chlorophyll. While coastal chlorophyll is dominated by influx of terrestrial nutrients through river discharge, chlorophyll in the offshore region is primarily controlled by oceanic processes. We will also explore issues related to dominant space and time scales of chlorophyll variations in the entire bay.

  2. Spatially Resolved Images and Solar Irradiance Variability

    Indian Academy of Sciences (India)

    R. Kariyappa

    2008-03-01

    The Sun is the primary source of energy that governs both the terrestrial climate and near-earth space environment. Variations in UV irradiances seen at earth are the sum of global (solar dynamo) to regional (active region, plage, network, bright points and background) solar magnetic activities that can be identified through spatially resolved photospheric, chromospheric and coronal features. In this research, the images of CaII K-line (NSO/Sac Peak) have been analysed to segregate the various chromospheric features.We derived the different indices and estimated their contribution from the time series data to total CaII K emission flux and UV irradiance variability. A part of the important results from this research is discussed in this paper.

  3. 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 PM2.5 using data from Taipei metropolis, which exhibits typical Asian city characteristics. Annual average PM2.5 concentrations from 2006 to 2012 of 17 air quality monitoring stations established by Environmental Protection Administration of Taiwan were used for model development. PM2.5 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 PM2.5 while temples (r: 0.52 to 0.66) and Chinese restaurants (r: 0.31 to 0.44) were positively correlated to PM2.5 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 PM2.5 variation and was the dominant variable in the developed model. We suggest future studies consider

  4. Impact of rainfall spatial variability on Flash Flood Forecasting

    Science.gov (United States)

    Douinot, Audrey; Roux, Hélène; Garambois, Pierre-André; Larnier, Kevin

    2014-05-01

    According to the United States National Hazard Statistics database, flooding and flash flooding have caused the largest number of deaths of any weather-related phenomenon over the last 30 years (Flash Flood Guidance Improvement Team, 2003). Like the storms that cause them, flash floods are very variable and non-linear phenomena in time and space, with the result that understanding and anticipating flash flood genesis is far from straightforward. In the U.S., the Flash Flood Guidance (FFG) estimates the average number of inches of rainfall for given durations required to produce flash flooding in the indicated county. In Europe, flash flood often occurred on small catchments (approximately 100 km2) and it has been shown that the spatial variability of rainfall has a great impact on the catchment response (Le Lay and Saulnier, 2007). Therefore, in this study, based on the Flash flood Guidance method, rainfall spatial variability information is introduced in the threshold estimation. As for FFG, the threshold is the number of millimeters of rainfall required to produce a discharge higher than the discharge corresponding to the first level (yellow) warning of the French flood warning service (SCHAPI: Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations). The indexes δ1 and δ2 of Zoccatelli et al. (2010), based on the spatial moments of catchment rainfall, are used to characterize the rainfall spatial distribution. Rainfall spatial variability impacts on warning threshold and on hydrological processes are then studied. The spatially distributed hydrological model MARINE (Roux et al., 2011), dedicated to flash flood prediction is forced with synthetic rainfall patterns of different spatial distributions. This allows the determination of a warning threshold diagram: knowing the spatial distribution of the rainfall forecast and therefore the 2 indexes δ1 and δ2, the threshold value is read on the diagram. A warning threshold diagram is

  5. Modelling the local distribution of cold-water corals in relation to bathymetric variables: Adding spatial context to deep-sea video data

    Science.gov (United States)

    Dolan, Margaret F. J.; Grehan, Anthony J.; Guinan, Janine C.; Brown, Colin

    2008-11-01

    Video data and high-resolution multibeam bathymetry were acquired using a Remotely Operated Vehicle (ROV) on the flank of a carbonate mound (˜850 m depth) in the Porcupine Seabight, SW Ireland. The ROV-mounted multibeam system revealed details of bathymetry that were not resolved by ship-borne multibeam survey, but appear to be important in structuring the distribution of the cold-water corals Lophelia pertusa and Madrepora oculata. Quantitative measures of slope, orientation, roughness and curvature were calculated from the ROV multibeam bathymetry data across a range of spatial scales. These parameters were analysed for their ecological relevance to the distribution of the corals and used in an Ecological Niche Factor Analysis (ENFA) to identify the most suitable areas for coral colonisation within the extent of our ROV multibeam data. The suitability map covers an area nine times the size of the area imaged directly by video. Cross-validation of the results with video data indicates that the predictions are reliable. This combined survey and modelling approach offers a comprehensive method for ground-truthing discrete seabed features such as mounds. It provides spatial context to high-resolution deep-water video observations and highlights the importance of bathymetric variables in influencing coral distribution.

  6. Mixing in the Black Sea detected from the temporal and spatial variability of oxygen and sulfide - Argo float observations and numerical modelling

    Science.gov (United States)

    Stanev, E. V.; He, Y.; Staneva, J.; Yakushev, E.

    2014-10-01

    The temporal and spatial variability of the upper ocean hydrochemistry in the Black Sea is analysed using data originating from profiling floats with oxygen sensors and carried out with a coupled three-dimensional circulation-biogeochemical model including 24 biochemical state variables. Major focus is on the dynamics of suboxic zone which is the interface separating oxygenated and anoxic waters. The scatter of oxygen data seen when plotted in density coordinates is larger than those for temperature, salinity and passive tracers. This scatter is indicative of vigorous biogeochemical reactions in the suboxic zone, which acts as a boundary layer or internal sink for oxygen. This internal sink affects the mixing patterns of oxygen compared to the ones of conservative tracers. Two different regimes of ventilation of pycnocline were clearly identified: a gyre-dominated (cyclonic) regime in winter and a coastal boundary layer (anticyclonic eddy)-dominated regime in summer. These contrasting states are characterized by very different pathways of oxygen intrusions along the isopycnals and vertical oxygen conveyor belt organized in multiple-layered cells formed in each gyre. The contribution of the three-dimensional modelling to the understanding of the Black Sea hydro-chemistry, and in particular the coast-to-open-sea mixing, is also demonstrated. Evidence is given that the formation of oxic waters and of cold intermediate waters, although triggered by the same physical process, each follow a different evolution. The difference in the depths of the temperature minimum and the oxygen maximum indicates that the variability of oxygen is not only just a response to physical forcing and changes in the surface conditions, but undergoes its own evolution.

  7. 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

  8. 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, W. M.; Pawson, S.; Collatz, G. J.; Gregg, W. W.

    2012-12-01

    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 downscaled 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

  9. Evaluating spatial patterns in hydrological modelling

    DEFF Research Database (Denmark)

    Koch, Julian

    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...... 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...

  10. Minimizing Spatial Variability of Healthcare Spatial Accessibility—The Case of a Dengue Fever Outbreak

    Science.gov (United States)

    Chu, Hone-Jay; Lin, Bo-Cheng; Yu, Ming-Run; Chan, Ta-Chien

    2016-01-01

    Outbreaks of infectious diseases or multi-casualty incidents have the potential to generate a large number of patients. It is a challenge for the healthcare system when demand for care suddenly surges. Traditionally, valuation of heath care spatial accessibility was based on static supply and demand information. In this study, we proposed an optimal model with the three-step floating catchment area (3SFCA) to account for the supply to minimize variability in spatial accessibility. We used empirical dengue fever outbreak data in Tainan City, Taiwan in 2015 to demonstrate the dynamic change in spatial accessibility based on the epidemic trend. The x and y coordinates of dengue-infected patients with precision loss were provided publicly by the Tainan City government, and were used as our model’s demand. The spatial accessibility of heath care during the dengue outbreak from August to October 2015 was analyzed spatially and temporally by producing accessibility maps, and conducting capacity change analysis. This study also utilized the particle swarm optimization (PSO) model to decrease the spatial variation in accessibility and shortage areas of healthcare resources as the epidemic went on. The proposed method in this study can help decision makers reallocate healthcare resources spatially when the ratios of demand and supply surge too quickly and form clusters in some locations. PMID:27983611

  11. Minimizing Spatial Variability of Healthcare Spatial Accessibility—The Case of a Dengue Fever Outbreak

    Directory of Open Access Journals (Sweden)

    Hone-Jay Chu

    2016-12-01

    Full Text Available Outbreaks of infectious diseases or multi-casualty incidents have the potential to generate a large number of patients. It is a challenge for the healthcare system when demand for care suddenly surges. Traditionally, valuation of heath care spatial accessibility was based on static supply and demand information. In this study, we proposed an optimal model with the three-step floating catchment area (3SFCA to account for the supply to minimize variability in spatial accessibility. We used empirical dengue fever outbreak data in Tainan City, Taiwan in 2015 to demonstrate the dynamic change in spatial accessibility based on the epidemic trend. The x and y coordinates of dengue-infected patients with precision loss were provided publicly by the Tainan City government, and were used as our model’s demand. The spatial accessibility of heath care during the dengue outbreak from August to October 2015 was analyzed spatially and temporally by producing accessibility maps, and conducting capacity change analysis. This study also utilized the particle swarm optimization (PSO model to decrease the spatial variation in accessibility and shortage areas of healthcare resources as the epidemic went on. The proposed method in this study can help decision makers reallocate healthcare resources spatially when the ratios of demand and supply surge too quickly and form clusters in some locations.

  12. Climate variability effects on spatial soil moisture dynamics

    NARCIS (Netherlands)

    Teuling, A.J.; Hupet, F.; Uijlenhoet, R.; Troch, P.A.

    2007-01-01

    We investigate the role of interannual climate variability on spatial soil moisture variability dynamics for a field site in Louvain-la-Neuve, Belgium. Observations were made during 3 years under intermediate (1999), wet (2000), and extremely dry conditions (2003). Soil moisture variability dynamics

  13. Building dynamic spatial environmental models

    NARCIS (Netherlands)

    Karssenberg, D.J.

    2003-01-01

    An environmental model is a representation or imitation of complex natural phenomena that can be discerned by human cognitive processes. This thesis deals with the type of environmental models referred to as dynamic spatial environmental models. The word ‘spatial’ refers to the geographic domain whi

  14. Assessment of spatial structure of groundwater quality variables based on the entropy theory

    Directory of Open Access Journals (Sweden)

    Y. Mogheir

    2003-01-01

    Full Text Available Fundamental to the spatial sampling design of a groundwater quality monitoring network is the spatial structure of groundwater quality variables. The entropy theory presents an alternative approach to describe this variability. A case study is presented which used groundwater quality observations (13 years; 1987-2000 from groundwater domestic wells in the Gaza Strip, Palestine. The analyses of the spatial structure used the following variables: Electrical Conductivity (EC, Total Dissolved Solids (TDS, Calcium (Ca, Magnesium (Mg, Sodium (Na, Potassium (K, Chloride (Cl, Nitrate (NO3, Sulphate (SO4, alkalinity and hardness. For all these variables the spatial structure is described by means of Transinformation as a function of distance between wells (Transinformation Model and correlation also as a function of distance (Correlation Model. The parameters of the Transinformation Model analysed were: (1 the initial value of the Transinformation; (2 the rate of information decay; (3 the minimum constant value; and (4 the distance at which the Transinformation Model reaches its minimum value. Exponential decay curves were fitted to both models. The Transinformation Model was found to be superior to the Correlation Model in representing the spatial variability (structure. The parameters of the Transinformation Model were different for some variables and similar for others. That leads to a reduction of the variables to be monitored and consequently reduces the cost of monitoring. Keywords: transinformation, correlation, spatial structure, municipal wells, groundwater monitoring, entropy

  15. SPATIAL VARIABILITY OF SOIL PROPERTIES IN AN AGRARIAN REFORM SETTLEMENT

    Directory of Open Access Journals (Sweden)

    James Ribeiro de Azevedo

    2015-12-01

    Full Text Available ABSTRACT The study of soil chemical and physical properties variability is important for suitable management practices. The aim of this study was to evaluate the spatial variability of soil properties in the Malhada do Meio settlement to subsidize soil use planning. The settlement is located in Chapadinha, MA, Brazil, and has an area of 630.86 ha. The vegetation is seasonal submontane deciduous forest and steppe savanna. The geology is formed of sandstones and siltstones of theItapecuru Formation and by colluvial and alluvial deposits. The relief consists of hills with rounded and flat tops with an average altitude of 67 m, and frequently covered over by ferruginous duricrusts. A total of 183 georeferenced soil samples were collected at the depth of 0.00-0.20 m inPlintossolos, Neossolo andGleissolo. The following chemical variables were analyzed: pH(CaCl2, H+Al, Al, SB, V, CEC, P, K, OM, Ca, Mg, SiO2, Al2O3, and Fe2O3; along with particle size variables: clay, silt, and sand. Descriptive statistical and geostatistical analyses were carried out. The coefficient of variation (CV was high for most of the variables, with the exception of pH with a low CV, and of sand with a medium CV. The models fitted to the experimental semivariograms of these variables were the exponential and the spherical. The range values were from 999 m to 3,690 m. For the variables pH(CaCl2, SB, and clay, there are three specific areas for land use planning. The central part of the area (zone III, where thePlintossolos Pétricos and Neossolos Flúvicos occur, is the most suitable for crops due to higher macronutrient content, organic matter and pH. Zones I and II are indicated for environmental preservation.

  16. Spatial variability of chloride in concrete within homogeneously exposed areas

    NARCIS (Netherlands)

    Angst, U.M.; Polder, R.B.

    2014-01-01

    The concept of variability is increasingly considered in service life predictions. This paper reports experimental data on the spatial distribution of chloride in uncracked concrete subjected to homogeneous exposure. Chloride concentrations were measured with potentiometric sensors embedded in concr

  17. Modeling Pacific Decadal Variability

    Science.gov (United States)

    Schneider, N.

    2002-05-01

    Hypotheses for decadal variability rely on the large thermal inertia of the ocean to sequester heat and provide the long memory of the climate system. Understanding decadal variability requires the study of the generation of ocean anomalies at decadal frequencies, the evolution of oceanic signals, and the response of the atmosphere to oceanic perturbations. A sample of studies relevant for Pacific decadal variability will be reviewed in this presentation. The ocean integrates air-sea flux anomalies that result from internal atmospheric variability or broad-band coupled processes such as ENSO, or are an intrinsic part of the decadal feedback loop. Anomalies of Ekman pumping lead to deflections of the ocean thermocline and accompanying changes of the ocean circulation; perturbations of surface layer heat and fresh water budgets cause anomalies of T/S characteristics of water masses. The former process leads to decadal variability due to the dynamical adjustment of the mid latitude gyres or thermocline circulation; the latter accounts for the low frequency climate variations by the slow propagation of anomalies in the thermocline from the mid-latitude outcrops to the equatorial upwelling regions. Coupled modeling studies and ocean model hindcasts suggest that the adjustment of the North Pacific gyres to variation of Ekman pumping causes low frequency variations of surface temperature in the Kuroshio-Oyashio extension region. These changes appear predictable a few years in advance, and affect the local upper ocean heat budget and precipitation. The majority of low frequency variance is explained by the ocean's response to stochastic atmospheric forcing, the additional variance explained by mid-latitude ocean to atmosphere feedbacks appears to be small. The coupling of subtropical and tropical regions by the equator-ward motion in the thermocline can support decadal anomalies by changes of its speed and path, or by transporting water mass anomalies to the equatorial

  18. Managing Temporal and Spatial Variability in Vapor Intrusion Data

    Science.gov (United States)

    2012-03-28

    Managing Temporal and Spatial Variability in Vapor Intrusion Data Todd McAlary, M.Sc., P.Eng., P.G. Geosyntec Consultants, Inc...TITLE AND SUBTITLE Managing Temporal and Spatial Variability in Vapor Intrusion Data 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER...Koc (mL/g) OSWER indoor conc. at 10-6 risk (ppb) Vapour pressure (atm) Water solubility (g/l) 1,1,1-Trichloroethane 110 400

  19. Local models for spatial analysis

    CERN Document Server

    Lloyd, Christopher D

    2010-01-01

    Focusing on solutions, this second edition provides guidance to a wide variety of real-world problems. The text presents a complete introduction to key concepts and a clear mapping of the methods discussed. It also explores connections between methods. New chapters address spatial patterning in single variables and spatial relations. In addition, every chapter now includes links to key related studies. The author clearly distinguishes between local and global methods and provides more detailed coverage of geographical weighting, image texture measures, local spatial autocorrelation, and multic

  20. 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.

  1. Temporal variability in aboveground plant biomass decreases as spatial variability increases.

    Science.gov (United States)

    McGranahan, Devan Allen; Hovick, Torre J; Elmore, R Dwayne; Engle, David M; Fuhlendorf, Samuel D; Winter, Stephen L; Miller, James R; Debinski, Diane M

    2016-03-01

    Ecological theory predicts that diversity decreases variability in ecosystem function. We predict that, at the landscape scale, spatial variability created by a mosaic of contrasting patches that differ in time since disturbance will decrease temporal variability in aboveground plant biomass. Using data from a multi-year study of seven grazed tallgrass prairie landscapes, each experimentally managed for one to eight patches, we show that increased spatial variability driven by spatially patchy fire and herbivory reduces temporal variability in aboveground plant biomass. This pattern is associated with statistical evidence for the portfolio effect and a positive relationship between temporal variability and functional group synchrony as predicted by metacommunity variability theory. As disturbance from fire and grazing interact to create a shifting mosaic of spatially heterogeneous patches within a landscape, temporal variability in aboveground plant biomass can be dampened. These results suggest that spatially heterogeneous disturbance regimes contribute to a portfolio of ecosystem functions provided by biodiversity, including wildlife habitat, fuel, and forage. We discuss how spatial patterns of disturbance drive variability within and among patches.

  2. [Factors influencing the spatial variability in soil respiration under different land use regimes].

    Science.gov (United States)

    Chen, Shu-Tao; Liu, Qiao-Hui; Hu, Zheng-Hua; Liu, Yan; Ren, Jing-Quan; Xie, Wei

    2013-03-01

    In order to investigate the factors influencing the spatial variability in soil respiration under different land use regimes, field experiments were performed. Soil respiration and relevant environment, vegetation and soil factors were measured. The spatial variability in soil respiration and the relationship between soil respiration and these measured factors were investigated. Results indicated that land use regimes had significant effects on soil respiration. Soil respiration varied significantly (P DBH) of trees can be explained by a natural logarithmic function. A model composed of soil organic carbon (C, %), available phosphorous (AP, g x kg(-1)) and diameter at breast height (DBH, cm) explained 92.8% spatial variability in soil respiration for forest ecosystems.

  3. Spatial patterns of recent Antarctic surface temperature trends and the importance of natural variability: lessons from multiple reconstructions and the CMIP5 models

    Science.gov (United States)

    Sahai, A. K.; Borah, N.; Chattopadhyay, R.; Joseph, S.; Abhilash, S.

    2016-06-01

    The recent annually averaged warming of the Antarctic Peninsula, and of West Antarctica, stands in stark contrast to very small trends over East Antarctica. This asymmetry arises primarily from a highly significant warming of West Antarctica in austral spring and a cooling of East Antarctica in austral autumn. Here we examine whether this East-West asymmetry is a response to anthropogenic climate forcings or a manifestation of natural climate variability. We compare the observed Antarctic surface air temperature trends over two distinct time periods (1960-2005 and 1979-2005), and with those simulated by 40 models participating in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). We find that the observed East-West asymmetry differs substantially between the two periods and, furthermore, that it is completely absent from the forced response seen in the CMIP5 multi-model mean, from which all natural variability is eliminated by the averaging. We also examine the relationship between the Southern Annular mode (SAM) and Antarctic temperature trends, in both models and reanalyses, and again conclude that there is little evidence of anthropogenic SAM-induced driving of the recent temperature trends. These results offer new, compelling evidence pointing to natural climate variability as a key contributor to the recent warming of West Antarctica and of the Peninsula.

  4. Modeling for spatial multilevel structural data

    Science.gov (United States)

    Min, Suqin; He, Xiaoqun

    2013-03-01

    The traditional multilevel model assumed independence between groups. However, the datasets grouped by geographical units often has spatial dependence. The individual is influenced not only by its region but also by the adjacent regions, and level-2 residual distribution assumption of traditional multilevel model is violated. In order to deal with such spatial multilevel data, we introduce spatial statistics and spatial econometric models into multilevel model, and apply spatial parameters and adjacency matrix in traditional level-2 model to reflect the spatial autocorrelation. Spatial lag model express spatial effects. We build spatial multilevel model which consider both multilevel thinking and spatial correlation.

  5. Modeling Shared Variables in VHDL

    DEFF Research Database (Denmark)

    Madsen, Jan; Brage, Jens P.

    1994-01-01

    A set of concurrent processes communicating through shared variables is an often used model for hardware systems. This paper presents three modeling techniques for representing such shared variables in VHDL, depending on the acceptable constraints on accesses to the variables. Also a set of guide......A set of concurrent processes communicating through shared variables is an often used model for hardware systems. This paper presents three modeling techniques for representing such shared variables in VHDL, depending on the acceptable constraints on accesses to the variables. Also a set...

  6. Spatial interactions in agent-based modeling

    CERN Document Server

    Ausloos, Marcel; Merlone, Ugo

    2014-01-01

    Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed. It is stressed that agents can interact either indirectly through a shared environment and/or directly with each other. In such an approach, higher-order variables such as commodity prices, population dynamics or even institutions, are not exogenously specified but instead are seen as the results of interactions. It is highlighted in the chapter that the understanding of patterns emerging from such spatial interaction between agents is a key problem as much as their description through analytical or simulation means. The chapter reviews different approaches for modeling agents' behavior, taking into account either explicit spatial (lattice based) structures or networks. Some emphasis is placed on recent ABM as applied to the description of the dynamics of the geographical distribution o...

  7. Spatial variability of Chinook salmon spawning distribution and habitat preferences

    Science.gov (United States)

    Cram, Jeremy M.; Torgersen, Christian; Klett, Ryan S.; Pess, George R.; May, Darran; Pearsons, Todd N.; Dittman, Andrew H.

    2017-01-01

    We investigated physical habitat conditions associated with the spawning sites of Chinook Salmon Oncorhynchus tshawytscha and the interannual consistency of spawning distribution across multiple spatial scales using a combination of spatially continuous and discrete sampling methods. We conducted a census of aquatic habitat in 76 km of the upper main-stem Yakima River in Washington and evaluated spawning site distribution using redd survey data from 2004 to 2008. Interannual reoccupation of spawning areas was high, ranging from an average Pearson’s correlation of 0.62 to 0.98 in channel subunits and 10-km reaches, respectively. Annual variance in the interannual correlation of spawning distribution was highest in channel units and subunits, but it was low at reach scales. In 13 of 15 models developed for individual years (2004–2008) and reach lengths (800 m, 3 km, 6 km), stream power and depth were the primary predictors of redd abundance. Multiple channels and overhead cover were patchy but were important secondary and tertiary predictors of reach-scale spawning site selection. Within channel units and subunits, pool tails and thermal variability, which may be associated with hyporheic exchange, were important predictors of spawning. We identified spawning habitat preferences within reaches and channel units that are relevant for salmonid habitat restoration planning. We also identified a threshold (i.e., 2-km reaches) beyond which interannual spawning distribution was markedly consistent, which may be informative for prioritizing habitat restoration or conservation. Management actions may be improved through enhanced understanding of spawning habitat preferences and the consistency with which Chinook Salmon reoccupy spawning areas at different spatial scales.

  8. Rainfall variability modelling in Rwanda

    Science.gov (United States)

    Nduwayezu, E.; Kanevski, M.; Jaboyedoff, M.

    2012-04-01

    Support to climate change adaptation is a priority in many International Organisations meetings. But is the international approach for adaptation appropriate with field reality in developing countries? In Rwanda, the main problems will be heavy rain and/or long dry season. Four rainfall seasons have been identified, corresponding to the four thermal Earth ones in the south hemisphere: the normal season (summer), the rainy season (autumn), the dry season (winter) and the normo-rainy season (spring). The spatial rainfall decreasing from West to East, especially in October (spring) and February (summer) suggests an «Atlantic monsoon influence» while the homogeneous spatial rainfall distribution suggests an «Inter-tropical front » mechanism. The torrential rainfall that occurs every year in Rwanda disturbs the circulation for many days, damages the houses and, more seriously, causes heavy losses of people. All districts are affected by bad weather (heavy rain) but the costs of such events are the highest in mountains districts. The objective of the current research is to proceed to an evaluation of the potential rainfall risk by applying advanced geospatial modelling tools in Rwanda: geostatistical predictions and simulations, machine learning algorithm (different types of neural networks) and GIS. The research will include rainfalls variability mapping and probabilistic analyses of extreme events.

  9. Sensitivity of Average Annual Runoff to Spatial Variability and Temporal Correlation of Rainfall.

    Science.gov (United States)

    Babin, Steven M.

    1995-08-01

    This paper examines the sensitivity of annual area mean runoff calculations to the effects of spatial variability and temporal correlation of rainfall. The model used is based upon the hypothesis that the annual water balance is determined only by rainfall, potential evapotranspiration, and soil water storage. A simple bucket hydrology model with a seasonally varying potential evapotranspiration is used with rainfall data measured at several sites on the Delmarva Peninsula. Annual area mean runoffs are calculated for three cases: 1) actual spatial variability among the rain gauge sites and temporal correlation between consecutive 1-min rainfall amounts are maintained (the actual case); 2) actual spatial variability among the sites is maintained but temporal correlation between the consecutive 1-min rainfall amounts is minimized (the site-shuffled case); and 3) both spatial variability and temporal correlation are ignored (the area-averaged case). The actual case represents the baseline for comparison with the other two cases. The annual a' mean runoffs show little sensitivity to spatial variability and temporal correlation for this model. Therefore, if finite soil permeability effects are ignored in favor of simple water storage capacity, then spatial variability and temporal correlation of rainfall appear to have little impact on the annual area mean runoff for the data considered in this study.

  10. 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.

  11. 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.

  12. Temporal and Spatial Variability along the Deep Western Boundary Current

    Science.gov (United States)

    Schmidtko, Sunke; Fischer, Jürgen

    2017-04-01

    The North Atlantic Deep Western Boundary Current (DWBC) connects the polar and subpolar regions, where the ocean is ventilated to greater depth, with the tropical oceans and beyond. It is part of the global ocean circulation as the deep branch of the Atlantic meridional overturning circulation (AMOC). It has a core depth between 1500-4500m with water mass properties varying by origin and decade. We analyze all publically available CTD data from Porcupine Abyssal Plain along Denmark Straight, Labrador Sea, Cape Cod, Cape Hatteras and Bahamas to the equator. The spatial and temporal development is analyzed for the past five decades. Waters originating from the overflow regions between Greenland and Scotland and from the Labrador Sea merge along the pathway but show distinct temporal variability and trends. We distinguish between local and large-scale variability and relate our results with the atmospheric forcing of the North Atlantic. This gives insight into new key aspects to be validated with state of the art ocean circulation models.

  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

    for different soils. Here, variography is employed to analyse spatial variability of bioavailability factors (BFs) of metals at the global scale. First, published empirical regressions are employed to calculate BFs of metals for 7180 topsoil profiles. Next, geostatistical interpretation of calculated BFs...... 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...... at lags smaller than those in the data set. Geostatistical analyses indicate however, that BFs exhibit no signifficant spatial correlation at a range beyond 3200 km. Because BF is spatially correlated, its values at unsampled locations can be predicted, as demonstrated using ordinary kriggin method...

  14. 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

  15. Spatial Models and Networks of Living Systems

    DEFF Research Database (Denmark)

    Juul, Jeppe Søgaard

    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....... Such systems are known to be stabilized by spatial structure. Finally, I analyse data from a large mobile phone network and show that people who are topologically close in the network have similar communication patterns. This main part of the thesis is based on six different articles, which I have co...

  16. FUNDAMENTALS OF TRANSMISSION FLUCTUATION SPECTROMETRY WITH VARIABLE SPATIAL AVERAGING

    Institute of Scientific and Technical Information of China (English)

    Jianqi Shen; Ulrich Riebel; Marcus Breitenstein; Udo Kr(a)uter

    2003-01-01

    Transmission signal of radiation in suspension of particles performed with a high spatial and temporal resolution shows significant fluctuations, which are related to the physical properties of the particles and the process of spatial and temporal averaging. Exploiting this connection, it is possible to calculate the parti cie size distribution (PSD)and particle concentration. This paper provides an approach of transmission fluctuation spectrometry (TFS) with variable spatial averaging. The transmission fluctuations are expressed in terms of the expectancy of transmission square (ETS)and are obtained as a spectrum, which is a function of the variable beam diameter. The reversal point and the depth of the spectrum contain the information of particle size and particle concentration, respectively.

  17. 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

  18. Spatial Models and Networks of Living Systems

    DEFF Research Database (Denmark)

    Juul, Jeppe Søgaard

    . Such systems are known to be stabilized by spatial structure. Finally, I analyse data from a large mobile phone network and show that people who are topologically close in the network have similar communication patterns. This main part of the thesis is based on six different articles, which I have co...... 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...

  19. 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

    Streambed hydraulic conductivity is one of the main factors controlling variability in surface water-groundwater interactions, but only few studies aim at quantifying its spatial and temporal variability in different stream morphologies. Streambed horizontal hydraulic conductivities (Kh) were...... therefore determined from in-stream slug tests, vertical hydraulic conductivities (Kv) were calculated with in-stream permeameter tests and hydraulic heads were measured to obtain vertical head gradients at eight transects, each comprising five test locations, in a groundwater-dominated stream. Seasonal...... 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...

  20. 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).

  1. Ionospheric total electron content: Spatial patterns of variability

    Science.gov (United States)

    Lean, J. L.; Meier, R. R.; Picone, J. M.; Sassi, F.; Emmert, J. T.; Richards, P. G.

    2016-10-01

    The distinctive spatial patterns of the ionosphere's total electron content (TEC) response to solar, seasonal, diurnal, and geomagnetic influences are determined across the globe using a new statistical model constructed from 2-hourly TEC observations from 1998 to 2015. The model combines representations of the physical solar EUV photon and geomagnetic activity drivers with solar-modulated sinusoidal parameterizations of four seasonal cycles and solar-modulated and seasonally modulated parameterizations of three diurnal cycles. The average absolute residual of the data-model differences is 2.1 total electron content unit, 1 TECU = 1016 el m-2 (TECU) (9%) and the root-mean-square error is 3.5 TECU (15%). Solar and geomagnetic variability, the semiannual oscillation and the diurnal and semidiurnal oscillations all impact TEC most at low magnetic latitudes where TEC itself maximizes, with differing degrees of longitudinal inhomogeneity. In contrast, the annual oscillation manifests primarily in the Southern Hemisphere with maximum amplitude over midlatitude South America, extending to higher southern latitudes in the vicinity of the Weddell Sea. Nighttime TEC levels in the vicinity of the Weddell Sea exceed daytime levels every year in Southern Hemisphere summer as a consequence of the modulation of the diurnal oscillations by the seasonal oscillations. The anomaly, which is present at all phases of the solar cycle, commences sooner and ends later under solar minimum conditions. The model minus data residuals maximize at tropical magnetic latitudes in four geographical regions similar to the ionosphere pattern generated by lower atmospheric meteorology. Enhanced residuals at northern midlatitudes during winter are consistent with an influence of atmospheric gravity waves.

  2. Climatic variability and spatial distribution of herbaceous fodders in the Sudanian zone of Benin (West Africa.

    Directory of Open Access Journals (Sweden)

    Myrèse C. Ahoudji

    2016-01-01

    Full Text Available This study focused on future spatial distributions of Andropogon gayanus, Loxodera ledermanii and Alysicarpus ovalifolius regarding bioclimatic variables in the Sudanian zone of Benin, particularly in the W Biosphere Reserve (WBR. These species were selected according to their importance for animals feed and the intensification of exploitation pressure induced change in their natural spatial distribution. Twenty (20 bioclimatic variables were tested and variables with high auto-correlation values were eliminated. Then, we retained seven climatic variables for the model. A MaxEnt (Maximum Entropy method was used to identify all climatic factors which determined the spatial distribution of the three species. Spatial distribution showed for Andropogon gayanus, a regression of high area distribution in detriment of low and moderate areas. The same trend was observed for Loxodera ledermannii spatial distribution. For Alysicarpus ovalifolius, currently area with moderate and low distribution were the most represented but map showed in 2050 that area with high distribution increased. We can deduce that without bioclimatic variables, others factors such as: biotic interactions, dispersion constraints, anthropic pressure, human activities and another historic factor determined spatial distribution of species. Modeling techniques that require only presence data are therefore extremely valuable.

  3. 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 poss

  4. 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 poss

  5. 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

  6. 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

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

    Directory of Open Access Journals (Sweden)

    E. Zakharova

    2012-01-01

    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 twenty 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. For 83% of ISBA-A-gs grid cells (8 km × 8 km, sampled every 5 m by CAROLS observations at a spatial resolution of about 2 km, 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 ISBA-A-gs grid cells

  8. 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

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

    Energy Technology Data Exchange (ETDEWEB)

    Hevesi, J.A.; Flint, A.L. [Geological Survey, Mercury, NV (United States); Ambos, D.S. [Foothill Engineering Consultants, Mercury, NV (United States)

    1994-12-31

    Isohyetal maps of precipitation and numerical models for simulating precipitation are needed to characterize natural infiltration at Yucca Mountain, Nevada. The objective of this study was to characterize the spatial variability of precipitation within the domain of the natural catchments overlying the potential repository, and to define preliminary geostatistical models based on differences in storm type for the numerical simulation of precipitation.

  10. Spatial Variability of Soil Chemical Properties in the Reclaiming Marine Foreland to Yellow Sea of China

    Institute of Scientific and Technical Information of China (English)

    WEI Yi-chang; BAI You-lu; JIN Ji-yun; ZHANG Fang; ZHANG Li-ping; LIU Xiao-qiang

    2009-01-01

    Precise information about the spatial variability of soil properties is essential in developing site-specific soil management,such as variable rate application of fertilizers.In this study the sampling grid of 100 m×100 m was established to collect 1703 soil samples at the depth of 0-20 cm,and examine spatial patterns including 13 soil chemical properties (pH,OM,NH4+,PK,Ca,Mg,S,B,Cu,Fe,Mn,and Zn) in a 1760 ha rice field in Haifeng farm,China,from 6th to 22nd of April,2006,before fertilizer application and planting.Soil analysis was performed by ASI (Agro Services International) and data were analyzed both statistically and geostatistically.Results showed that the contents of soil OM,NH4+,and Zn in Haifeng farm were very low for rice production and those of others were enough to meet the need for rice cultivation.The spatial distribution model and spatial dependence level for 13 soil chemical properties varied in the field.Soil Mg and B showed strong spatial variability on both descriptive statistics and geostatistics,and other properties showed moderate spatial variability.Themaximum ranges for K,Ca,Mg,S,Cu and Mn were all~3990.6m and the minimum ranges for soil pH,OM,NH4+,P,Fe,and Zn ranged from 516.7 to 1166.2 m.Clearpatchy distribution of N,P,K,Mg,S,B,Mn,and Zn were found from their spatial distribution maps.This proved that sampling strategy for estimating variability should be adapted to the different soil chemical properties and field management.Therefore,the spatial variability of soil chemical properties with strong spatial dependence could be readily managed and a site-specific fertilization scheme for precision farming could be easily developed.

  11. MODELING SUPPLY CHAIN PERFORMANCE VARIABLES

    Directory of Open Access Journals (Sweden)

    Ashish Agarwal

    2005-01-01

    Full Text Available In order to understand the dynamic behavior of the variables that can play a major role in the performance improvement in a supply chain, a System Dynamics-based model is proposed. The model provides an effective framework for analyzing different variables affecting supply chain performance. Among different variables, a causal relationship among different variables has been identified. Variables emanating from performance measures such as gaps in customer satisfaction, cost minimization, lead-time reduction, service level improvement and quality improvement have been identified as goal-seeking loops. The proposed System Dynamics-based model analyzes the affect of dynamic behavior of variables for a period of 10 years on performance of case supply chain in auto business.

  12. Spatial Allometric Scaling of Cities Based on Variable Urban Boundaries

    CERN Document Server

    Chen, Yanguang; Li, Xijing

    2015-01-01

    It has been demonstrated that urban growth and hierarchies of cities follow the allometric scaling law. However, there is no study on the allometric relations of the internal spatial structure within a city. This paper is devoted to explore the allometric scaling of intraurban patterns by means of variable boundaries of cities. Based on a digital map, an urban boundary can be determined by given searching radius. Changing the searching radius, we have different urban boundaries for the same city. A set of urban boundaries based on different searching radius is similar to a set of isograms. Three typical measurements can be obtained, including urban area, total length of streets, and number of street nodes. These measurements represent three basic spatial elements of geographical systems, i.e., area, lines, and points. A finding is that the numerical relationships between urban area, street length, and node number follow allometric scaling laws. In practice, the spatial allometry can be used to estimate the fr...

  13. Spatial variability of fine particles in Parisian streets

    OpenAIRE

    Duché, Sarah; Beltrando, Gérard

    2012-01-01

    International audience; To study the spatial variability of airborne particles and to evaluate the personal and tourist exposure to fine particles in Paris, measurements of fine particles (PM2.5) concentrations have been made in Parisian streets in different mode of transport (bus, bike and walking), using a portable sensor. We use also meteorological parameters sensor (temperature,humidity and wind speed), a camera to view traffic and a GPS to compare with particles levels. PM2.5 levels are ...

  14. Understanding Mountain Range Spatial Variability of Surface Hoar

    Science.gov (United States)

    Hendrikx, J.

    2014-12-01

    Surface hoar, once buried often produce a persistent weak layer that is a common instability problem in the snow pack in SW Montana and many other areas around the world. Surface hoar is a common weak layer type in avalanche accidents in a continental and intermountain snowpack. It is however relatively well understood that aspect plays an important role in the spatial location of the growth, and survival of these grain forms, due to the unequal distribution of incoming radiation. However this factor alone does not explain the complex and often confusing spatial pattern of these grains forms throughout the landscape at larger, mountain range spatial scales. In this paper we present a unique data set including over one hundred days of manual observations of surface hoar at sixteen locations on Pioneer Mountain at the Yellowstone Club in southwestern Montana. Using this wealth of observational data located on different aspects, elevations and exposures, coupled with detailed meteorological observations, detailed site scale observations (e.g. Sky view plots) we examine the spatial variability of surface hoar at this scale, and examine the factors that control its spatial distribution. Our results further supports our preliminary work, which shows that small-scale meteorological differences, site scale differences, and local scale lapse rates can greatly influence the spatial variability of surface hoar, over and above that which aspect alone can explain. These results highlight our incomplete understanding of the processes at this large, mountain range scale, and are likely to have implications for both regional and local scale avalanche forecasting in environments where surface hoar cause ongoing instabilities.

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

    Energy Technology Data Exchange (ETDEWEB)

    Andrello, A.C.; Appoloni, C.R. [Universidade Estadual de Londrina, PR (Brazil). Dept. de Fisica

    2004-09-15

    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{sup -2} and for Mata UEL was 320 Bq m{sup -2}. (author)

  16. Role of spatial variability of rainfall intensity: improve- ment of Eagleson's classical model to explain the rela- tionship between the coefficient of variation of annual maximum discharge and catchment size

    Science.gov (United States)

    Kuzuha, Yasuhisa; Sivapalan, Murugesu; Tomosugi, Kunio; Kishii, Tokuo; Komatsu, Yosuke

    2006-04-01

    Eagleson's classical regional flood frequency model is investigated. Our intention was not to improve the model, but to reveal previously unidentified important and dominant hydrological processes in it. The change of the coefficient of variation (CV) of annual maximum discharge with catchment area can be viewed as representing the spatial variance of floods in a homogeneous region. Several researchers have reported that the CV decreases as the catchment area increases, at least for large areas. On the other hand, Eagleson's classical studies have been known as pioneer efforts that combine the concept of similarity analysis (scaling) with the derived flood frequency approach. As we have shown, the classical model can reproduce the empirical relationship between the mean annual maximum discharge and catchment area, but it cannot reproduce the empirical decreasing CV-catchment area curve. Therefore, we postulate that previously unidentified hydrological processes would be revealed if the classical model were improved to reproduce the decreasing of CV with catchment area. First, we attempted to improve the classical model by introducing a channel network, but this was ineffective. However, the classical model was improved by introducing a two-parameter gamma distribution for rainfall intensity. What is important is not the gamma distribution itself, but those characteristics of spatial variability of rainfall intensity whose CV decreases with increasing catchment area. Introducing the variability of rainfall intensity into the hydrological simulations explains how the CV of rainfall intensity decreases with increasing catchment area. It is difficult to reflect the rainfall-runoff processes in the model while neglecting the characteristics of rainfall intensity from the viewpoint of annual flood discharge variances.

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

    Science.gov (United States)

    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. PMID:26177286

  18. Exploring the spatial variability of soil properties in an Alfisol Catena

    Energy Technology Data Exchange (ETDEWEB)

    Rosemary, F.; Vitharana, U. W. A.; Indraratne, S. P.; Weerasooriya, R.; Mishra, U.

    2016-11-10

    Detailed digital soil maps showing the spatial heterogeneity of soil properties consistent with the landscape are required for site-specific management of plant nutrients, land use planning and process-based environmental modeling. We characterized the short-scale spatial heterogeneity of soil properties in an Alfisol catena in a tropical landscape of Sri Lanka. The impact of different land-uses (paddy, vegetable and un-cultivated) was examined to assess the impact of anthropogenic activities on the variability of soil properties at the catenary level. Conditioned Latin hypercube sampling was used to collect 58 geo-referenced topsoil samples (0–30 cm) from the study area. Soil samples were analyzed for pH, electrical conductivity (EC), organic carbon (OC), cation exchange capacity (CEC) and texture. The spatial correlation between soil properties was analyzed by computing crossvariograms and subsequent fitting of theoretical model. Spatial distribution maps were developed using ordinary kriging. The range of soil properties, pH: 4.3–7.9; EC: 0.01–0.18 dS m–1 ; OC: 0.1–1.37%; CEC: 0.44– 11.51 cmol (+) kg–1 ; clay: 1.5–25% and sand: 59.1–84.4% and their coefficient of variations indicated a large variability in the study area. Electrical conductivity and pH showed a strong spatial correlation which was reflected by the cross-variogram close to the hull of the perfect correlation. Moreover, cross-variograms calculated for EC and Clay, CEC and OC, CEC and clay and CEC and pH indicated weak positive spatial correlation between these properties. Relative nugget effect (RNE) calculated from variograms showed strongly structured spatial variability for pH, EC and sand content (RNE < 25%) while CEC, organic carbon and clay content showed moderately structured spatial variability (25% < RNE < 75%). Spatial dependencies for examined soil properties ranged from 48 to 984 m. The mixed effects model fitting followed by Tukey's post

  19. 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

  20. Modelling avalanche danger and understanding snow depth variability

    OpenAIRE

    2010-01-01

    This thesis addresses the causes of avalanche danger at a regional scale. Modelled snow stratigraphy variables were linked to [1] forecasted avalanche danger and [2] observed snowpack stability. Spatial variability of snowpack parameters in a region is an additional important factor that influences the avalanche danger. Snow depth and its change during individual snow fall periods are snowpack parameters which can be measured at a high spatial resolution. Hence, the spatial distribution of sn...

  1. 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

  2. Modeling Spatial and Temporal Variability of Residential Air Exchange Rates for the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS)

    Science.gov (United States)

    Air pollution health studies often use outdoor concentrations as exposure surrogates. Failure to account for variability of residential infiltration of outdoor pollutants can induce exposure errors and lead to bias and incorrect confidence intervals in health effect estimates. Th...

  3. Temporal Changes in the Spatial Variability of Soil Nutrients

    Energy Technology Data Exchange (ETDEWEB)

    R. L. Hoskinson; J. R. Hess; R. S. Alessi

    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.

  4. Hybrid Unifying Variable Supernetwork Model

    Institute of Scientific and Technical Information of China (English)

    LIU; Qiang; FANG; Jin-qing; LI; Yong

    2015-01-01

    In order to compare new phenomenon of topology change,evolution,hybrid ratio and network characteristics of unified hybrid network theoretical model with unified hybrid supernetwork model,this paper constructed unified hybrid variable supernetwork model(HUVSM).The first layer introduces a hybrid ratio dr,the

  5. 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

  6. 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.

  7. 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.

  8. Spatial variability of CO2 uptake in polygonal tundra

    DEFF Research Database (Denmark)

    Pirk, Norbert; Sievers, Jakob; Mertes, Jordan

    2017-01-01

    with an unmanned aerial vehicle (UAV) that mapped ice-wedge morphology to complement eddy covariance (EC) flux measurements of CO2. The analysis of spectral distributions showed that conventional EC methods do not accurately capture the turbulent CO2 exchange with a spatially heterogeneous surface that typically......The large spatial variability in Arctic tundra complicates the representative assessment of CO2 budgets. Accurate measurements of these heterogeneous landscapes are, however, essential to understanding their vulnerability to climate change. We surveyed a polygonal tundra lowland on Svalbard...... features small flux magnitudes. Nonlocal (low-frequency) flux contributions were especially pronounced during snow melt and introduced a large bias of -46 gC m(-2) to the annual CO2 budget in conventional methods (the minus sign indicates a higher uptake by the ecosystem). Our improved flux calculations...

  9. FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model

    Science.gov (United States)

    Russell A. Parsons

    2006-01-01

    Efforts to quantitatively evaluate the effectiveness of fuels treatments are hampered by inconsistencies between the spatial scale at which fuel treatments are implemented and the spatial scale, and detail, with which we model fire and fuel interactions. Central to this scale inconsistency is the resolution at which variability within the fuel bed is considered. Crown...

  10. Multi-spatial variability modes of the Atlantic Meridional Overturning Circulation

    Institute of Scientific and Technical Information of China (English)

    ZHOU Tianjun

    2003-01-01

    The multi-spatial variability modes of the Atlantic Meridional Overturning Circulation (MOC) are identified in the natural coupled simulation of two climate models, the MOC either oscillates at decadal scales with strong cross- equatorial flow or fluctuates locally at interannual scales with weaker cross-equatorial flow. Former studies mainly emphasize the paleo-environmental and paleo-climatic impacts of the meridional overturning states transition; this analysis indicates the existence of the multi-spatial variability modes of the MOC at interannual to decadal scales. Further analysis indicates that the conventionally used MOC index, which is defined as the maximum zonal mean meridional stream-function of the North Atlantic, cannot properly describe the multi-spatial variability characteristics of the MOC.

  11. 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.

  12. Verification of Spatial Forecasts of Continuous Meteorological Variables Using Categorical and Object-Based Methods

    Science.gov (United States)

    2016-08-01

    SUPPLEMENTARY NOTES 14. ABSTRACT Spatial forecasts from Numerical Weather Prediction (NWP) models of meteorological variables to support Army operations...contingency- table scores and statistics that can be calculated and, when analyzed together, may reveal more information about model performance and provide a... Model evaluation tools version 4.1 (METv4.1), user’s guide 4.1. Boulder (CO); 2013 May. [NOAA] Meteorological assimilation data ingest system (MADIS

  13. [Prediction of spatial distribution of forest carbon storage in Heilongjiang Province using spatial error model].

    Science.gov (United States)

    Liu, Chang; Li, Feng-Ri; Zhen, Zhen

    2014-10-01

    Abstract: Based on the data from Chinese National Forest Inventory (CNFI) and Key Ecological Benefit Forest Monitoring plots (5075 in total) in Heilongjiang Province in 2010 and concurrent meteorological data coming from 59 meteorological stations located in Heilongjiang, Jilin and Inner Mongolia, this paper established a spatial error model (SEM) by GeoDA using carbon storage as dependent variable and several independent variables, including diameter of living trees (DBH), number of trees per hectare (TPH), elevation (Elev), slope (Slope), and product of precipitation and temperature (Rain_Temp). Global Moran's I was computed for describing overall spatial autocorrelations of model results at different spatial scales. Local Moran's I was calculated at the optimal bandwidth (25 km) to present spatial distribution residuals. Intra-block spatial variances were computed to explain spatial heterogeneity of residuals. Finally, a spatial distribution map of carbon storage in Heilongjiang was visualized based on predictions. The results showed that the distribution of forest carbon storage in Heilongjiang had spatial effect and was significantly influenced by stand, topographic and meteorological factors, especially average DBH. SEM could solve the spatial autocorrelation and heterogeneity well. There were significant spatial differences in distribution of forest carbon storage. The carbon storage was mainly distributed in Zhangguangcai Mountain, Xiao Xing'an Mountain and Da Xing'an Mountain where dense, forests existed, rarely distributed in Songnen Plains, while Wanda Mountain had moderate-level carbon storage.

  14. Estimating Spatially Variable Parameters of the Epidemic Type Aftershock Sequence (ETAS) in California

    Science.gov (United States)

    Nandan, Shyam; Ouillon, Guy; Sornette, Didier; Wiemer, Stefan

    2016-04-01

    The ETAS model is widely employed to model the spatio-temporal distribution of earthquakes, generally using spatially invariant parameters, which is most likely a gross simplification considering the extremely heterogeneous structure of the Earth's crust. We propose an efficient method for the estimation of spatially varying parameters, using an expectation maximization (EM) algorithm and spatial Voronoi tessellations. We assume that each Voronoi cell is characterized by a set of eight constant ETAS parameters. For a given number of randomly distributed cells, Vi=1 to N, we jointly invert the ETAS parameters within each cell using an EM algorithm. This process is progressively repeated several times for a given N (which controls the complexity), which is itself increased incrementally. We use the Bayesian Information Criterion (BIC) to rank all the inverted models given their likelihood and complexity and select the top 1% models to compute the average model at any location. Using a synthetic catalog, we also check that the proposed method correctly inverts the known parameters. We apply the proposed method to earthquakes (M>=3) included in the ANSS catalog that occurred within the time period 1981-2016 in the spatial polygon defined by RELM/CSEP around California. The results indicate significant spatial variation of the ETAS parameters. Using these spatially variable estimates of ETAS parameters, we are better equipped to answer some important questions: (1) What is the seismic hazard (both long- and short-term) in a given region? (2) What kind of earthquakes dominate triggering? (3) are there regions where earthquakes are most likely preceded by foreshocks? Last but not the least, a possible correlation of the spatially varying ETAS parameters with spatially variable geophysical properties can lead to an improved understanding of the physics of earthquake triggering beside providing physical meaning to the parameters of the purely statistical ETAS model.

  15. Emergence of Strange Spatial Pattern in a Spatial Epidemic Model

    Institute of Scientific and Technical Information of China (English)

    SUN Gui-Quan; JIN Zhen; LIU Quan-Xing; LI Li

    2008-01-01

    Pattern formation of a spatial epidemic model with nonlinear incidence rate kI2 S/ (1 + αI2) is investigated. Our results show that strange spatial dynamics, i.e., filament-like pattern, can be obtained by both mathematical analysis and numerical simulation, which are different from the previous results in the spatial epidemic model such as stripe-like or spotted or coexistence of both pattern and so on. The obtained results well extend the finding of pattern formation in the epidemic model and may well explain the distribution of the infected of some epidemic.

  16. 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.

  17. Spatial variability of soils in a seasonally dry tropical forest

    Science.gov (United States)

    Pulla, Sandeep; Riotte, Jean; Suresh, Hebbalalu; Dattaraja, Handanakere; Sukumar, Raman

    2016-04-01

    Soil structures communities of plants and soil organisms in tropical forests. Understanding the controls of soil spatial variability can therefore potentially inform efforts towards forest restoration. We studied the relationship between soils and lithology, topography, vegetation and fire in a seasonally dry tropical forest in southern India. We extensively sampled soil (available nutrients, Al, pH, and moisture), rocks, relief, woody vegetation, and spatial variation in fire burn frequency in a permanent 50-ha plot. Lower elevation soils tended to be less moist and were depleted in several nutrients and clay. The availability of several nutrients was, in turn, linked to whole-rock chemical composition differences since some lithologies were associated with higher elevations, while the others tended to dominate lower elevations. We suggest that local-scale topography in this region has been shaped by the spatial distribution of lithologies, which differ in their susceptibility to weathering. Nitrogen availability was uncorrelated with the presence of trees belonging to Fabaceae, a family associated with N-fixing species. No effect of burning on soil parameters could be discerned at this scale.

  18. Spatial impacts of urban structures on micrometeorological variables

    Science.gov (United States)

    Koelbing, Merle; Schuetz, Tobias; Weiler, Markus

    2016-04-01

    The heterogeneity of urban surfaces including buildings and the urban vegetation causes high variability of micrometeorological variables on small spatial scales which makes it hard to observe or even predict climate conditions and in particular evapotranspiration with high resolution on the scale of entire cities. Regarding future climate changes and their impacts on urban climate and hydrology the predictability of these small scale variations becomes more and more relevant i.e. for city planners to improve the development of appropriate mitigation strategies. Therefore, new transfer functions for meteorological variables are needed, which consider the structural variability in urban areas and its impacts on the energy balance (shading effects, ventilation, lateral longwave energy fluxes). We approach this goal by testing a mobile meteorological station (the station is mounted on a bicycle trailer and transported by an E-Bike) as a means to derive empirical spatial transfer functions for specific urban structures. We observe air temperature and relative air humidity at 2 different heights, wind direction and speed, incoming and outgoing shortwave radiation as well as infrared temperature from above and below and the four directions. First measurements have been performed in December 2015 at 22 locations in four clusters, which represent manifold different characteristics of urban areas within the city of Freiburg. Every location has been monitored two to six times. Overall, nearly 200 measurements of each variable have been taken. Each measurement takes five minutes. Values are logged every 15 seconds. These measurements were analyzed with regard to a climate station mounted on a rooftop in the proximity of all clusters. Results show a systematic pattern in the differences between the values taken with the fixed and those taken with the mobile climate station, depending on the measurement locations. For example, lower air temperature and higher relative air

  19. 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.

  20. Spatial variability of throughfall and raindrops under a single canopy with different canopy structure

    Science.gov (United States)

    Nanko, Kazuki; Onda, Yuichi; Ito, Akane; Moriwaki, Hiromu

    2013-04-01

    To evaluate the spatial variability of throughfall amount, raindrops, and erosivity under a single canopy during calm meteorological conditions, indoor experiments were conducted using a 9.8-m-tall transplanted Japanese cypress (Chamaecyparis obtusa) and a large-scale rainfall simulator. Drop size distribution, drop velocity, and kinetic energy of throughfall varied spatially under a single canopy as did throughfall amount and rain rate. Compared with throughfall rain rate, the variability was similar in drop size distribution, lower in drop velocity, and higher in kinetic energy. The results suggest that the spatial distribution of throughfall amount was dominated by the canopy shape and position of branches inside the canopy, and thus the spatial distribution was correlated with the radial distance from the trunk. Throughfall amount and rate were lower at the midway point between the trunk and the canopy edge. Throughfall drop size indices (drop size distribution, drop velocity, and unit kinetic energy) varied spatially while did not differ significantly. On the other hand, time-specific throughfall kinetic energy was correlated with the radial distance from the trunk. The dependence the throughfall kinetic energy on the radial distance from the trunk was dominated by the spatial distribution of throughfall amount. The trend in the spatial distribution of throughfall revealed in this study will aid in modelling canopy water processes and in predicting soil erosion on the bare forest floor. The part of this study is published in Nanko et al. (2011, Agric. Forest. Meteorol. 151, 1173-1182).

  1. 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...

  2. Assessing temporal and spatial variability of hypoxia over the inner Louisiana-upper Texas shelf: Application of an unstructured-grid three-dimensional coupled hydrodynamic-water quality model

    Science.gov (United States)

    Justić, Dubravko; Wang, Lixia

    2014-01-01

    Patterns of temporal and spatial variability in hypoxia (hypoxia originates in bottom waters on the mid-continental shelf, where isolated pockets of hypoxic water develop during early spring and later join into a larger continuous hypoxic zone. The model accurately described the seasonal cycle of hypoxia at station C6, including the episodes of intermittent hypoxia during May and June, persistent hypoxia during July and August, and dissipation of hypoxia during September. The onset of hypoxia coincided with high stability of the water column (i.e., Richardson number values>1) and the initial transition from normoxia (i.e., 6 mg O2 l-1) to hypoxia lasted about three weeks. The model results point to a significant short-term variability in the extent of hypoxic bottom waters, indicating that the size of the mid-summer hypoxic zone cannot be adequately captured by a single shelfwide cruise. The dynamics of bottom-water hypoxia is clearly influenced by the bathymetric features of the LaTex shelf, namely the presence of three shallow shoals (hypoxia on the LaTex shelf is strongly modulated by the frequency and intensity of cold fronts and tropical storms. High winds associated with these events disturb stratification, causing partial or complete breakdown of hypoxia. However, cold fronts and tropical storms also cause significant sediment resuspension that fuels respiration in the lower water column, and in this manner promote redevelopment of hypoxia.

  3. What drives the spatial variability of primary productivity and matter fluxes in the north-west African upwelling system? A modelling approach

    Science.gov (United States)

    Auger, Pierre-Amaël; Gorgues, Thomas; Machu, Eric; Aumont, Olivier; Brehmer, Patrice

    2016-11-01

    A comparative box analysis based on a multi-decadal physical-biogeochemical hindcast simulation (1980-2009) was conducted to characterize the drivers of the spatial distribution of phytoplankton biomass and production in the north-west (NW) African upwelling system. Alongshore geostrophic flow related to large-scale circulation patterns associated with the influence of coastal topography is suggested to modulate the coastal divergence, and then the response of nutrient upwelling to wind forcing. In our simulation, this translates into a coastal upwelling of nitrate being significant in all regions but the Cape Blanc (CB) area. However, upwelling is found to be the dominant supplier of nitrate only in the northern Saharan Bank (NSB) and the Senegalo-Mauritanian (SM) regions. Elsewhere, nitrate supply is dominated by meridional advection, especially off Cape Blanc. Phytoplankton displays a similar behaviour with a supply by lateral advection which equals the net coastal phytoplankton growth in all coastal regions except the Senegalo-Mauritanian area. Noticeably, in the Cape Blanc area, the net coastal phytoplankton growth is mostly sustained by high levels of regenerated production exceeding new production by more than twofold, which is in agreement with the locally weak input of nitrate by coastal upwelling. Further offshore, the distribution of nutrients and phytoplankton is explained by the coastal circulation. Indeed, in the northern part of our domain (i.e. Saharan Bank), the coastal circulation is mainly alongshore, resulting in low offshore lateral advection of nutrients and phytoplankton. Conversely, lateral advection transports coastal nutrients and phytoplankton towards offshore areas in the latitudinal band off the Senegalo-Mauritanian region. Moreover, this latter offshore region benefits from transient southern intrusions of nutrient-rich waters from the Guinean upwelling.

  4. 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

  5. A new approach to model the variability of karstic recharge

    Directory of Open Access Journals (Sweden)

    A. Hartmann

    2012-02-01

    Full Text Available In karst systems, surface near dissolution carbonate rock results in a high spatial and temporal variability of groundwater recharge. To adequately represent the dominating recharge processes in hydrological models is still a challenge, especially in data scare regions. In this study, we developed a recharge model that is based on a perceptual model of the epikarst. It represents epikarst heterogeneity as a set of system property distributions to produce not only a single recharge time series, but a variety of time series representing the spatial recharge variability. We tested the new model with a unique set of spatially distributed flow and tracer observations in a karstic cave at Mt. Carmel, Israel. We transformed the spatial variability into statistical variables and apply an iterative calibration strategy in which more and more data was added to the calibration. Thereby, we could show that the model is only able to produce realistic results when the information about the spatial variability of the observations was included into the model calibration. We could also show that tracer information improves the model performance if data about the variability is not included.

  6. Consequences of spatial autocorrelation for niche-based models

    DEFF Research Database (Denmark)

    Segurado, P.; Araújo, Miguel B.; Kunin, W. E.

    2006-01-01

    variables, as measured by Moran's I, was analysed and compared between models. The effects of systematic subsampling of the data set and the inclusion of a contagion term to deal with spatial autocorrelation in models were assessed with projections made with GLM, as it was with this method that estimates...... were vulnerable to the effects of spatial autocorrelation. 5.  The procedures utilized to reduce the effects of spatial autocorrelation had varying degrees of success. Subsampling was partially effective in avoiding the inflation effect, whereas the inclusion of a contagion term fully eliminated......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...

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

    Energy Technology Data Exchange (ETDEWEB)

    Hevesi, J.A.; Flint, A.L. [Geological Survey, Mercury, NV (United States); Ambos, D.S. [Foothill Eng. Consultants, Mercury, NV (United States)

    1994-12-31

    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.

  8. Spatial variability in bank resistance to erosion on a large meandering, mixed bedrock-alluvial river

    Science.gov (United States)

    Konsoer, Kory M.; Rhoads, Bruce L.; Langendoen, Eddy J.; Best, James L.; Ursic, Mick E.; Abad, Jorge D.; Garcia, Marcelo H.

    2016-01-01

    Spatial heterogeneity in the erosion-resistance properties of the channel banks and floodplains associated with sediment characteristics, vegetation, or bedrock can have a substantial influence on the morphodynamics of meandering rivers, resulting in highly variable rates of bank erosion and complex patterns of planform evolution. Although past studies have examined the spatial variability in bank erodibility within small rivers, this aspect of the erosion-resistance properties for large rivers remains poorly understood. Furthermore, with the exception of recent numerical modeling that incorporates stochastic variability of floodplain erosional resistance, most models of meandering river dynamics have assumed uniform erodibility of the bank and floodplain materials. The present paper investigates the lateral and vertical heterogeneity in bank material properties and riparian vegetation within two elongate meander loops on a large mixed bedrock-alluvial river using several geotechnical field and laboratory methods. Additionally, the bank stability and toe-erosion numerical model (BSTEM) and repeat terrestrial LiDAR surveys are used to evaluate the capacity of the bank material properties to modify the rates and mechanisms of bank retreat. Results show that the textural properties of the bank materials, soil cohesion, and critical shear stress necessary for sediment entrainment differ substantially between the two bends and are also highly variable within each bend - laterally and vertically. Trees growing along the banks increase the resistance to erosion by contributing to the shear strength of the bank materials and are capable of increasing bank stability along a large river. Locally outcropping bedrock also influences bank erodibility in both bends. The results of this study demonstrate that spatial variability in the erosion-resistance properties of the channel banks is an important factor contributing to spatial variability in the rates and mechanisms of bank

  9. 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 important because the lapel has always been the 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.

  10. Spatial and temporal variability of the refractivity over Tahiti from a coarse network of GPS stations

    Science.gov (United States)

    Serafini, J.; Fadil, A.; Sichoix, L.; Barriot, J.

    2010-12-01

    Slant wet delays (SWD) caused by the presence of water vapor in the atmosphere are routinely obtained from GPS measurements. Powerful tomography techniques have been developed to derive from them the refractivity of the atmosphere, which could be subject to strong spatial and temporal variations, especially over tropical zones. In this poster we model the spatial and temporal variability of the refractivity over the Tahiti Island. In a first study, we model the spatial part of the variability. For this purpose, GPS data spanning a four months period (June-Sept 2010) from a coarse network of nine stations are analyzed using the GAMIT software package. In particular, we found that the SWD variability is more important at the South East of the Island. In a second study we reconstruct the temporal part of the variability. For this purpose, ten years of GPS data from the IGS station THTI, located on the Punaauia suburb of Papeete are processed with respect to the precise point positioning (PPP) mode of the GIPSY-OASIS II software package. The derived SWD allow us to reconstruct, through a regularized inverse process, time series of the ZWD and North / East gradients of the refractivity. We show that the main components of the SWD are relative to semi-diurnal and seasonal terms. Finally, this spatio-temporal model of the refractivity permits us to build a robust estimate of the covariance matrix of the underlying stochastic process.

  11. Patterns of variability in early-life traits of fishes depend on spatial scale of analysis.

    Science.gov (United States)

    Di Franco, Antonio; Guidetti, Paolo

    2011-06-23

    Estimates of early-life traits of fishes (e.g. pelagic larval duration (PLD) and spawning date) are essential for investigating and assessing patterns of population connectivity. Such estimates are available for a large number of both tropical and temperate fish species, but few studies have assessed their variability in space, especially across multiple scales. The present study, where a Mediterranean fish (i.e. the white seabream Diplodus sargus sargus) was used as a model, shows that spawning date and PLD are spatially more variable at a scale of kilometres than at a scale of tens to hundreds of kilometres. This study indicates the importance of considering spatial variability of early-life traits of fishes in order to properly delineate connectivity patterns at larval stages (e.g. by means of Lagrangian simulations), thus providing strategically useful information on connectivity and relevant management goals (e.g. the creation of networks of marine reserves).

  12. Spatial variable selection methods for investigating acute health effects of fine particulate matter components.

    Science.gov (United States)

    Boehm Vock, Laura F; Reich, Brian J; Fuentes, Montserrat; Dominici, Francesca

    2015-03-01

    Multi-site time series studies have reported evidence of an association between short term exposure to particulate matter (PM) and adverse health effects, but the effect size varies across the United States. Variability in the effect may partially be due to differing community level exposure and health characteristics, but also due to the chemical composition of PM which is known to vary greatly by location and time. The objective of this article is to identify particularly harmful components of this chemical mixture. Because of the large number of highly-correlated components, we must incorporate some regularization into a statistical model. We assume that, at each spatial location, the regression coefficients come from a mixture model with the flavor of stochastic search variable selection, but utilize a copula to share information about variable inclusion and effect magnitude across locations. The model differs from current spatial variable selection techniques by accommodating both local and global variable selection. The model is used to study the association between fine PM (PM <2.5μm) components, measured at 115 counties nationally over the period 2000-2008, and cardiovascular emergency room admissions among Medicare patients.

  13. A spatial interaction model with spatially structured origin and destination effects

    Science.gov (United States)

    LeSage, James P.; Llano, Carlos

    2013-07-01

    We introduce a Bayesian hierarchical regression model that extends the traditional least-squares regression model used to estimate gravity or spatial interaction relations involving origin-destination flows. Spatial interaction models attempt to explain variation in flows from n origin regions to n destination regions resulting in a sample of N = n 2 observations that reflect an n by n flow matrix converted to a vector. Explanatory variables typically include origin and destination characteristics as well as distance between each region and all other regions. Our extension introduces latent spatial effects parameters structured to follow a spatial autoregressive process. Individual effects parameters are included in the model to reflect latent or unobservable influences at work that are unique to each region treated as an origin and destination. That is, we estimate 2 n individual effects parameters using the sample of N = n 2 observations. We illustrate the method using a sample of commodity flows between 18 Spanish regions during the 2002 period.

  14. Spatial and temporal variability of rainfall in the Nile Basin

    Directory of Open Access Journals (Sweden)

    C. Onyutha

    2014-10-01

    Full Text Available Spatio-temporal variability in annual and seasonal rainfall totals were assessed at 37 locations of the Nile Basin in Africa using quantile perturbation method. To get insight into the spatial difference in rainfall statistics, the stations were grouped based on the pattern of the long-term mean of monthly rainfall and that of temporal variability. To find the origin of the driving forces for the temporal variability in rainfall, correlation analyses were carried out using global monthly sea level pressure and surface temperature. Further investigations to support the obtained correlations were made using a total of 10 climate indices. It was possible to obtain 3 groups of stations; those within the equatorial region (A, Sudan and Ethiopia (B, and Egypt (C. For group A, annual rainfall was found to be below (above the reference during the late 1940s to 1950s (1960s to mid 1980s. Conversely for groups B and C, the period 1930s to late 1950s (1960s to 1980s was characterized by anomalies being above (below the reference. For group A, significant linkages were found to Niño 3, Niño 3.4 and the North Atlantic and Indian Ocean drivers. Correlations of annual rainfall of group A with Pacific Ocean-related climate indices were inconclusive. With respect to the main wet seasons, the June to September rainfall of group B has strong connection to the influence from the Indian Ocean. For the March to May (October to February rainfall of group A (C, possible links to the Atlantic and Indian Oceans were found.

  15. Spatial variability overwhelms seasonal patterns in bacterioplankton communities across a river to ocean gradient

    Science.gov (United States)

    Fortunato, Caroline S; Herfort, Lydie; Zuber, Peter; Baptista, Antonio M; Crump, Byron C

    2012-01-01

    Few studies of microbial biogeography address variability across both multiple habitats and multiple seasons. Here we examine the spatial and temporal variability of bacterioplankton community composition of the Columbia River coastal margin using 16S amplicon pyrosequencing of 300 water samples collected in 2007 and 2008. Communities separated into seven groups (ANOSIM, P850 m). The ordination of these samples was correlated with salinity (ρ=−0.83) and depth (ρ=−0.62). Temporal patterns were obscured by spatial variability among the coastal environments, and could only be detected within individual groups. Thus, structuring environmental factors (for example, salinity, depth) dominate over seasonal changes in determining community composition. Seasonal variability was detected across an annual cycle in the river, estuary and plume where communities separated into two groups, early year (April–July) and late year (August–Nov), demonstrating annual reassembly of communities over time. Determining both the spatial and temporal variability of bacterioplankton communities provides a framework for modeling these communities across environmental gradients from river to deep ocean. PMID:22011718

  16. Spatial variability of anaerobic processes and wastewater pH in force mains

    DEFF Research Database (Denmark)

    Rudelle, Elise Alice; Nielsen, Asbjørn Haaning; Hvitved-Jacobsen, Thorkild;

    2016-01-01

    The present study focuses on anaerobic organic matter transformation processes in force mains for the purpose of improving existing sewer process models. Wastewater samples were obtained at 100 m intervals from a 1 km long pilot scale force main and measured for several wastewater parameters. Tra....... A significant spatial variablilty was observed for the average transformation rates of all parameters........ Transformation rates for selected parameters were calculated and their spatial variability analyzed. In terms of electron transfer, fermentation was the most significant process, resulting in a net volatile fatty acid formation of 0.83 mmol/L. Sulfate reduction resulted in a production of 0.73 mmol...

  17. Integrated spatial sampling modeling of geospatial data

    Institute of Scientific and Technical Information of China (English)

    LI Lianfa; WANG Jinfeng

    2004-01-01

    Spatial sampling is a necessary and important method for extracting geospatial data and its methodology directly affects the geo-analysis results. Counter to the deficiency of separate models of spatial sampling, this article analyzes three crucial elements of spatial sampling (frame, correlation and decision diagram) and induces its general integrated model. The program of Spatial Sampling Integration (SSI) has been developed with Component Object Model (COM) to realize the general integrated model. In two practical applications, i.e. design of the monitoring network of natural disasters and sampling survey of the areas of non-cultivated land, SSI has produced accurate results at less cost, better realizing the cost-effective goal of sampling toward the geo-objects with spatial correlation. The two cases exemplify expanded application and convenient implementation of the general integrated model with inset components in an integrated environment, which can also be extended to other modeling of spatial analysis.

  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. Spatial Variability of Nutrient Properties in Black Soil of Northeast China

    Institute of Scientific and Technical Information of China (English)

    ZHANG Xing-Yi; SUI Yue-Yu; ZHANG Xu-Dong; MENG Kai; S.J.HERBERT

    2007-01-01

    A total of 1400 soil samples from the plow layer (0-20 cm) at an approximate interval of 5 km were collected in the autumn of 2002 over the entire black soil arable crops region to determine the spatial variability of seven variables, such as total organic matter content (OMC), total N, total P, total K, alkali-dissolvable N (AN), available P (AP) and available K (AK), with classical statistics and geostatistical analysis across the entire black soil area in Northeast China. In nonsampled areas ordinary kriging was utilized for interpolation of estimated nutrient determinations. Classical statistics revealed highly significant (P ≤ 0.01) correlations with all seven of the soil properties, except for OMC with AP and total K with AK. In addition, using coefficients of variation, all soil properties, except for total K, were moderately variable. A geostatistical analysis indicated that structural factors, such as parent material, terrain, and water table, were the main causes of the spatial correlations. Strong spatial correlations were noted with OMC, total N, total P, AN, and AP, while they were moderate for total K and AK. The effective spatial autocorrelation of OMC, total N, total P, and AN ranged from 1 037 to 1 353 km, whereas the ranges of total K, AP, and AK were only from 6 to 138 km. The fit of the experimental semi-variograms to the theoretical models indicated that except for AN, kriging could successfully interpolate other six variables. Thus, the geostatistical method used on a large scale could accurately evaluate the spatial variability of most black soil nutrient properties in Northeast China.

  20. 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.

  1. Concomitant variables in finite mixture models

    NARCIS (Netherlands)

    Wedel, M

    The standard mixture model, the concomitant variable mixture model, the mixture regression model and the concomitant variable mixture regression model all enable simultaneous identification and description of groups of observations. This study reviews the different ways in which dependencies among

  2. Observed small spatial scale and seasonal variability of the CO2-system in the Southern Ocean

    Directory of Open Access Journals (Sweden)

    L. Resplandy

    2013-08-01

    Full Text Available The considerable uncertainties in the carbon budget of the Southern Ocean are largely attributed to unresolved variability, in particular at seasonal time scale and small spatial scale (~ 100 km. In this study, the variability of surface pCO2 and DIC at seasonal and small-spatial scales is examined using a dataset of surface drifters including ~ 80 000 measurements at high spatio-temporal resolution. On spatial scales of 100 km, we find gradients ranging from 5 to 50 μ atm for pCO2 and 2 to 30 μ mol kg−1 for DIC, with highest values in energetic and frontal regions. This result is supported by a second estimate obtained with SST satellite images and local DIC/SST relationships derived from drifters observations. We find that dynamical processes drives the variability of DIC at small spatial scale in most regions of the Southern Ocean, the cascade of large-scale gradients down to small spatial scales leading to gradients up to 15 μ mol kg−1 over 100 km. Although the role of biological activity is more localized, it enhances the variability up to 30 μ mol kg−1 over 100 km. The seasonal cycle of surface DIC is reconstructed following Mahadevan et al. (2011, using an annual climatology of DIC and a monthly climatology of mixed layer depth. This method is evaluated using drifters observations and proves to be a reasonable first-order estimate of the seasonality in the Southern Ocean, which could be used to validate models simulations. We find that small spatial scales structures are a non negligible source of variability for DIC, with amplitudes of about a third of the variations associated with the seasonality and up to 10 times the magnitude of large-scale gradients. The amplitude of small-scale variability reported here should be kept in mind when inferring temporal changes (seasonality, inter-annual variability, decadal trends of the carbon budget from low resolution observations and models.

  3. Nitrogen and phosphorus concentrations from agricultural catchments—influence of spatial and temporal variables

    Science.gov (United States)

    Arheimer, B.; Lidén, R.

    2000-01-01

    The eutrophication problem has drawn attention to nutrient leaching from arable land in southern Sweden, and further understanding of spatial and temporal variability is needed in order to develop decision-making tools. Thus, the influence of spatial and temporal variables was analysed statistically using empirical time series of different nutrient species from 35 well-documented catchments (2-35 km 2), which have been monitored for an average of 5 years. In the spatial analysis several significant correlations between winter median concentrations and catchment characteristics were found. The strongest correlation was found between inorganic nitrogen and land use, while concentrations of different phosphorus species were highly correlated to soil texture. Multiple linear regression models gave satisfactory results for prediction of median winter concentrations in unmeasured catchments, especially for inorganic nitrogen and phosphate. In the analysis of temporal variability within catchments, internal variables from a dynamic hydrological model (HBV) were linked to concentration fluxes. It was found that phosphorus and inorganic nitrogen concentrations were elevated during flow increase at low-flow conditions, while they were diluted as the wetness in the catchment increased. During unmonitored periods regression models were successful in predicting temporal variability of total phosphorus, phosphate and inorganic nitrogen, while organic nitrogen and particulate phosphorus could not be predicted with this approach. Dividing the data into different flow categories did not improve the prediction of nutrient concentration dynamics. The results and literature review presented, confirm parts of the present HBV-N model approach and will be useful for further development of nutrient routines linked to dynamic hydrological models.

  4. Modelling evolution in a spatial continuum

    Science.gov (United States)

    Barton, N. H.; Etheridge, A. M.; Véber, A.

    2013-01-01

    We survey a class of models for spatially structured populations which we have called spatial Λ-Fleming-Viot processes. They arise from a flexible framework for modelling in which the key innovation is that random genetic drift is driven by a Poisson point process of spatial 'events'. We demonstrate how this overcomes some of the obstructions to modelling populations which evolve in two-(and higher-) dimensional spatial continua, how its predictions match phenomena observed in data and how it fits with classical models. Finally we outline some directions for future research.

  5. 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 BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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

  6. Geostatistical independent simulation of spatially correlated soil variables

    Science.gov (United States)

    Boluwade, Alaba; Madramootoo, Chandra A.

    2015-12-01

    The selection of best management practices to reduce soil and water pollution often requires estimation of soil properties. It is important to find an efficient and robust technique to simulate spatially correlated soils parameters. Co-kriging and co-simulation are techniques that can be used. These methods are limited in terms of computer simulation due to the problem of solving large co-kriging systems and difficulties in fitting a valid model of coregionalization. The order of complexity increases as the number of covariables increases. This paper presents a technique for the conditional simulation of a non-Gaussian vector random field on point support scale. The technique is termed Independent Component Analysis (ICA). The basic principle underlining ICA is the determination of a linear representation of non-Gaussian data so that the components are considered statistically independent. With such representation, it would be easy and more computationally efficient to develop direct variograms for the components. The process is presented in two stages. The first stage involves the ICA decomposition. The second stage involves sequential Gaussian simulation of the generated components (which are derived from the first stage). This technique was applied for spatially correlated extractable cations such as magnesium (Mg) and iron (Fe) in a Canadian watershed. This paper has a strong application in stochastic quantification of uncertainties of soil attributes in soil remediation and soil rehabilitation.

  7. KING GEORGE ISLAND SPATIAL DATA MODEL

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Distribution,interoperability,interactivity,component are four main features of distributed GIS.Based on the principle of hypermap,hypermedia and distributed database,the paper comes up with a kind of distributed spatial data model which is in accordance with those features of distributed GIS.The model takes catalog service as the outline of spatial information globalization,and defines data structure of hypermap node in different level.Based on the model,it is feasible to manage and process distributed spatial information,and integrate multi_source,heterogeneous spatial data into a framework.Traditionally,to retrieve and access spatial data via Internet is only by theme or map name.With the concept of the model,it is possible to retrieve,load,and link spatial data by vector_based graphics on the Internet.

  8. Tools for Optimizing Management of a Spatially Variable Organic Field

    Directory of Open Access Journals (Sweden)

    Thomas Panagopoulos

    2015-03-01

    Full Text Available Geostatistical tools were used to estimate spatial relations between wheat yield and soil parameters under organic farming field conditions. Thematic maps of each factor were created as raster images in R software using kriging. The Geographic Resources Analysis Support System (GRASS calculated the principal component analysis raster images for soil parameters and yield. The correlation between the raster arising from the PC1 of soil and yield parameters showed high linear correlation (r = 0.75 and explained 48.50% of the data variance. The data show that durum wheat yield is strongly affected by soil parameter variability, and thus, the average production can be substantially lower than its potential. Soil water content was the limiting factor to grain yield and not nitrate as in other similar studies. The use of precision agriculture tools helped reduce the level of complexity between the measured parameters by the grouping of several parameters and demonstrating that precision agriculture tools can be applied in small organic fields, reducing costs and increasing wheat yield. Consequently, site-specific applications could be expected to improve the yield without increasing excessively the cost for farmers and enhance environmental and economic benefits.

  9. Spatial-Temporal variability of seismic hazard in Peninsular India

    Indian Academy of Sciences (India)

    Kishor Jaiswal; Ravi Sinha

    2008-11-01

    This paper examines the variability of seismic activity observed in the case of different geological zones of peninsular India (10°N–26°N; 68°E–90°E) based on earthquake catalog between the period 1842 and 2002 and estimates earthquake hazard for the region. With compilation of earthquake catalog in terms of moment magnitude and establishing broad completeness criteria, we derive the seismicity parameters for each geologic zone of peninsular India using maximum likelihood procedure. The estimated parameters provide the basis for understanding the historical seismicity associated with different geological zones of peninsular India and also provide important inputs for future seismic hazard estimation studies in the region. Based on present investigation, it is clear that earthquake recurrence activity in various geologic zones of peninsular India is distinct and varies considerably between its cratonic and rifting zones. The study identifies the likely hazards due to the possibility of moderate to large earthquakes in peninsular India and also presents the influence of spatial rate variation in the seismic activity of this region. This paper presents the influence of source zone characterization and recurrence rate variation pattern on the maximum earthquake magnitude estimation. The results presented in the paper provide a useful basis for probabilistic seismic hazard studies and microzonation studies in peninsular India.

  10. 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.

  11. 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.

  12. Multivariate spatially-structured variability of ovine helminth infections

    Directory of Open Access Journals (Sweden)

    Annibale Biggeri

    2007-11-01

    Full Text Available A cross-sectional survey was carried out on 2004-2005 in the Campania region, southern Italy, to study the multivariate geographical distribution of four different sheep helminths, i.e. Fasciola hepatica (liver fluke, Calicophoron (Paramphistomum daubneyi (rumen fluke, Dicrocoelium dendriticum (lancet fluke, and the gastrointestinal strongyle Haemonchus contortus. A series of multivariate Bayesian hierarchical models based on square root transformation of faecal egg counts were performed. The results were consistent with theoretical knowledge of the biology and epidemiology of the four studied helminths. In particular, the impact of common intermediate hosts (F. hepatica and C. daubneyi share the same intermediate host species was quantified and evidence of previously unknown ecological components was given. D. dendriticum was correlated to F. hepatica and H. contortus was found not to be spatially associated with the previously mentioned helminths.

  13. Continuous-Time Modeling with Spatial Dependence

    NARCIS (Netherlands)

    Oud, J.H.L.; Folmer, H.; Patuelli, R.; Nijkamp, P.

    2012-01-01

    (Spatial) panel data are routinely modeled in discrete time (DT). However, compelling arguments exist for continuous-time (CT) modeling of (spatial) panel data. Particularly, most social processes evolve in CT, so that statistical analysis in DT is an oversimplification, gives an incomplete

  14. Continuous-Time Modeling with Spatial Dependence

    NARCIS (Netherlands)

    Oud, J.; Folmer, H.; Patuelli, R.; Nijkamp, P.

    (Spatial) panel data are routinely modeled in discrete time (DT). However, compelling arguments exist for continuous-time (CT) modeling of (spatial) panel data. Particularly, most social processes evolve in CT, so that statistical analysis in DT is an oversimplification, gives an incomplete

  15. Spatially variable risk factors for malaria in a geographically heterogeneous landscape, western Kenya: an explorative study.

    Science.gov (United States)

    Homan, Tobias; Maire, Nicolas; Hiscox, Alexandra; Di Pasquale, Aurelio; Kiche, Ibrahim; Onoka, Kelvin; Mweresa, Collins; Mukabana, Wolfgang R; Ross, Amanda; Smith, Thomas A; Takken, Willem

    2016-01-04

    Large reductions in malaria transmission and mortality have been achieved over the last decade, and this has mainly been attributed to the scale-up of long-lasting insecticidal bed nets and indoor residual spraying with insecticides. Despite these gains considerable residual, spatially heterogeneous, transmission remains. To reduce transmission in these foci, researchers need to consider the local demographical, environmental and social context, and design an appropriate set of interventions. Exploring spatially variable risk factors for malaria can give insight into which human and environmental characteristics play important roles in sustaining malaria transmission. On Rusinga Island, western Kenya, malaria infection was tested by rapid diagnostic tests during two cross-sectional surveys conducted 3 months apart in 3632 individuals from 790 households. For all households demographic data were collected by means of questionnaires. Environmental variables were derived using Quickbird satellite images. Analyses were performed on 81 project clusters constructed by a traveling salesman algorithm, each containing 50-51 households. A standard linear regression model was fitted containing multiple variables to determine how much of the spatial variation in malaria prevalence could be explained by the demographic and environmental data. Subsequently, a geographically-weighted regression (GWR) was performed assuming non-stationarity of risk factors. Special attention was taken to investigate the effect of residual spatial autocorrelation and local multicollinearity. Combining the data from both surveys, overall malaria prevalence was 24%. Scan statistics revealed two clusters which had significantly elevated numbers of malaria cases compared to the background prevalence across the rest of the study area. A multivariable linear model including environmental and household factors revealed that higher socioeconomic status, outdoor occupation and population density were

  16. Bayesian Spatial Modelling with R-INLA

    OpenAIRE

    Finn Lindgren; Håvard Rue

    2015-01-01

    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...

  17. Spatial Variability Some Physical and Chemical Prpperties Soil surface In Dasht-e-Tabriz Different Landforms

    Science.gov (United States)

    Foroughifar, Hamed; Asghar Jafarzadeh, Ali; Torabi, Hosien; Aliasgharzad, Naser; Toomanian, Norair

    2010-05-01

    Spatial distribution of soil properties at the field and watershed scale(region scale) affect yield potential, hydrologic responses , and transport of herbicides and No3 to surface or groundwater.The present study aim was to evaluate some physical and chemical properties spatial variability and frequency distribution within and between landforms of Dash-e-Tabriz in the northwest of Iran.For this evaluation 98 samples from soils surface of layer according to grid sampling design and with 500-1000 meters distance based on soils variability were selected and analysed.Landforms were hill, piedmont plain, plain, river alluvial plain and lowland.The study of soil variables frequency distribution showed that Bd, CEC, Caco3, pH,clay and silt follow normal distribution ,which to study their variation one can use parametric statistical method.Variables such as MWD, N(total), SAR, EC, P(available) and sand showed log-normal distribution,that for their variation study,should first be transformed to a logarithmic scale.The variables frequency distribution increase within landforms,which in lowland, hill, and river alluvial plain they showed normal distribution and only EC in piedmont plain and sand, OC and N(total) in plain had log-normal distributions.The results indicate significantly differences of soil properties distribution among landforms,which clay ,pH, EC ,SAR and MWD, CEC, Bd, N(total), OC, P(available), sand, silt were strongly and moderately spatial dependent respectively and Caco3 had no spatial dependence and it is following nugget model.These results indicate that strong spatial dependence due to the effects of intrinsic factors such as parent material, relief and soil types. Also soil properties variations result from variation in depositional environments and or differences in pedogenic or hydrologic processes for different landform positions,and so it can be affected by the flood irrigation,fertilizeir addition,high watertable level or agriculture practices

  18. Spatial and temporal variability of rainfall erosivity factor for Switzerland

    Directory of Open Access Journals (Sweden)

    A. Steel

    2011-09-01

    Full Text Available Rainfall erosivity, considering rainfall amount and intensity, is an important parameter for soil erosion risk assessment under future land use and climate change. Despite its importance, rainfall erosivity is usually implemented in models with a low spatial and temporal resolution. The purpose of this study is to assess the temporal- and spatial distribution of rainfall erosivity (R-factor in Switzerland. Time series of 22 yr for rainfall (10 min resolution and temperature (1 h resolution data were analysed for 71 automatic gauging stations distributed throughout Switzerland. Multiple regression was used to interpolate the erosivity values of single stations and to generate a map for Switzerland. Latitude, longitude, average annual precipitation, biogeographic units (Jura, Midland, etc., aspect and elevation were used as covariates, of which average annual precipitation, elevation and the biographic unit (Western Alps were significant predictors. The mean value of long-term rainfall erosivity is 1323 MJ mm ha−1 h−1 yr−1 with a range of lowest values of 124 MJ mm ha−1 h−1 yr−1 at an elevated station in Grisons to highest values of 5611 MJ mm ha−1 h−1 yr−1 in Ticino. All stations have highest erosivity values from July to August and lowest values in the winter month. Swiss-wide the month May to October show significantly increasing trends of erosivity (p<0.005. Only in February a significantly decreasing trend of rainfall erosivity is found (p<0.01. The increasing trends of erosivity in May, September and October when vegetation cover is susceptible are likely to enhance soil erosion risk for certain agricultural crops and alpine grasslands in Switzerland.

  19. Use of precision agriculture technology to investigate spatial variability in nitrogen yields in cut grassland.

    Science.gov (United States)

    Bailey, J S; Wang, K; Jordan, C; Higgins, A

    2001-01-01

    Spatial variability in N uptake and utilisation by swards within uniformly managed field units could be responsible for a significant proportion of the NH3, N2O, NO3- and NOx (NO and NO2) 'pollutants' generated by agriculture and released to the environment. An investigation was commenced, therefore, to quantify, map and explain the spatial variability in sward N yield in a 'large' silage field and to assess the potential for managing this variability using some of the latest precision agriculture technology. Sward dry matter (DM) and N yields were predicted from the results of plant tissue analyses using mathematical models. Sward N yields were found to vary greatly across the field seemingly because of differences in net soil N mineralisation, but the pattern of variability appeared to remain constant with time. Conventional soil analysis of a range of soil chemical and physical properties, however, failed to explain this variability. It was concluded that the N-yield distribution map might be used in place of soil analysis as the basis for varying the rates of N application to different parts of the field with the twin objectives of maximising fertiliser use efficiency and minimising N emissions to air and water.

  20. Spatially correlated disturbances in a locally dispersing population model.

    Science.gov (United States)

    Hiebeler, David

    2005-01-01

    The basic contact process in continuous time is studied, where instead of single occupied sites becoming empty independently, larger-scale disturbance events simultaneously remove the population from contiguous blocks of sites. Stochastic spatial simulations and pair approximations were used to investigate the model. Increasing the spatial scale of disturbance events increases spatial clustering of the population and variability in growth rates within localized regions, reduces the effective overall population density, and increases the critical reproductive rate necessary for the population to persist. Pair approximations yield a closed-form analytic expression for equilibrium population density and the critical value necessary for persistence.

  1. Evaluating spatial patterns in hydrological modelling

    DEFF Research Database (Denmark)

    Koch, Julian

    of spatial information in a holistic assessment. Opposed, statistical measures typically only address a limited amount of spatial information. A web-based survey and a citizen science project are employed to quantify the collective perceptive skills of humans aiming at benchmarking spatial metrics...... of environmental science, such as meteorology, geostatistics or geography. In total, seven metrics are evaluated with respect to their capability to quantitatively compare spatial patterns. The human visual perception is often considered superior to computer based measures, because it integrates various dimensions...... with respect to their capability to mimic human evaluations. This PhD thesis aims at expanding the standard toolbox of spatial model evaluation with innovative metrics that adequately compare spatial patterns. Driven by the rise of more complex model structures and the increase of suitable remote sensing...

  2. 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.

  3. Temporal and spatial variability of the sea surface salinity in the Nordic Seas

    Science.gov (United States)

    Furevik, Tore; Bentsen, Mats; Drange, Helge; Johannessen, Johnny A.; Korablev, Alexander

    2002-12-01

    In this paper, the temporal and spatial variability of the sea surface salinity (SSS) in the Nordic Seas is investigated. The data include a Russian hydrographical database for the Nordic Seas and daily to weekly observations of salinity at Ocean Weather Station Mike (OWSM) (located at 66°N, 2°E in the Norwegian Sea). In addition, output from a medium-resolution version of the Miami Isopycnic Coordinate Ocean Model (MICOM), forced with daily National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data, is used to complement the analysis of the temporal and spatial fields constructed from the observational data sets. The Nordic Seas show a strong seasonal variability in the vertical density stratification and the mixed layer (ML) depth, with a weak stratification and a several hundred meters deep ML during winter and a well-defined shallow ML confined to the upper few tens of meters during summer. The seasonal variability strongly influences the strength of the high-frequency variability and to what extent subsurface anomalies are isolated from the surface. High-frequency variability has been investigated in terms of standard deviation of daily SSS, calculated for the different months of the year. From observations at OWSM, typical winter values range from 0.03 to 0.04 psu and summer values range from 0.06 to 0.07 psu. Results from the model simulation show that highest variability is found in frontal areas and in areas with strong stratification and lowest variability in the less stratified areas in the central Norwegian Sea and south of Iceland. Investigation of the interannual variability over the last 50 years shows a marked freshening of the Atlantic Water in the Norwegian and Greenland Seas. Moreover, the strength of the southern sector of the Polar front, as defined by the 34.8-35.0 psu isohalines along the western boundary of the inflowing Atlantic Water, undergoes significant interannual variability

  4. 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.

  5. A neuromorphic model of spatial lookahead planning.

    Science.gov (United States)

    Ivey, Richard; Bullock, Daniel; Grossberg, Stephen

    2011-04-01

    In order to create spatial plans in a complex and changing world, organisms need to rapidly adapt to novel configurations of obstacles that impede simple routes to goal acquisition. Some animals can mentally create successful multistep spatial plans in new visuo-spatial layouts that preclude direct, one-segment routes to goal acquisition. Lookahead multistep plans can, moreover, be fully developed before an animal executes any step in the plan. What neural computations suffice to yield preparatory multistep lookahead plans during spatial cognition of an obstructed two-dimensional scene? To address this question, we introduce a novel neuromorphic system for spatial lookahead planning in which a feasible sequence of actions is prepared before movement begins. The proposed system combines neurobiologically plausible mechanisms of recurrent shunting competitive networks, visuo-spatial diffusion, and inhibition-of-return. These processes iteratively prepare a multistep trajectory to the desired goal state in the presence of obstacles. The planned trajectory can be stored using a primacy gradient in a sequential working memory and enacted by a competitive queuing process. The proposed planning system is compared with prior planning models. Simulation results demonstrate system robustness to environmental variations. Notably, the model copes with many configurations of obstacles that lead other visuo-spatial planning models into selecting undesirable or infeasible routes. Our proposal is inspired by mechanisms of spatial attention and planning in primates. Accordingly, our simulation results are compared with neurophysiological and behavioral findings from relevant studies of spatial lookahead behavior.

  6. Quantification of the effect of spatially varying environmental contaminants into a cost model for soil remediation

    NARCIS (Netherlands)

    Broos, J.M.; Aarts, L.; Tooren, C.F.; Stein, A.

    1999-01-01

    In this study we investigated the effects of spatial variability of soil contaminants on cost calculations for soil remediation. Most cost models only provide a single figure, whereas spatial variability is one of the sources to contribute to the uncertainty. A cost model is applied to a study site

  7. Stochastic analyses of field-scale pesticide leaching risk as influenced by spatial variability in physical and biochemical parameters

    Science.gov (United States)

    Loll, Per; Moldrup, Per

    2000-04-01

    Field-scale pesticide leaching risk assessments were performed by incorporating a numerical, one-dimensional, water and pesticide transport and fate model into the two-step stochastic modeling approach by Loll and Moldrup [1998]. The numerical model included first-order pesticide degradation, linear equilibrium adsorption, and plant uptake of water and pesticide. Simazine was used as a model pesticide, and leaching risk was expressed as the cumulative mass fraction of applied pesticide leached below 100 cm after 1 year. Spatial variability in soil physical and biochemical data, as well as measured meteorological data from an average and a relatively wet year, was considered for two Danish field sites: (1) a coarse sandy soil, with relatively small variability in hydraulic properties, and (2) a sandy loam, with large variability in hydraulic properties. The two-step stochastic modeling approach was used to investigate the relative impact of spatial variability in saturated hydraulic conductivity Ks, soil-water retention through the Campbell [974] soil-water retention parameter b, and pesticide sorption through the organic carbon content (OC). For the coarse sandy soil, field-scale spatial variability in OC was the single most important parameter influencing leaching risk, whereas for the sandy loam, Ks was found more important than OC. The relative impact of field-scale spatial variability in these parameters was found independent of the meteorological conditions, whereas the absolute level of leaching risk was highly dependent on the meteorological conditions. Assuming a linear dependency between pesticide half-life and OC, a unified approach to modeling simultaneous field-scale variability in biodegradation and adsorption was proposed. Leaching risk assessments based on this approach showed that the parts of the field with both low biological activity and low adsorption capacity contributed with a dramatic increase in leaching risk, and suggested that field

  8. Performance analysis of improved methodology for incorporation of spatial/spectral variability in synthetic hyperspectral imagery

    Science.gov (United States)

    Scanlan, Neil W.; Schott, John R.; Brown, Scott D.

    2004-01-01

    Synthetic imagery has traditionally been used to support sensor design by enabling design engineers to pre-evaluate image products during the design and development stages. Increasingly exploitation analysts are looking to synthetic imagery as a way to develop and test exploitation algorithms before image data are available from new sensors. Even when sensors are available, synthetic imagery can significantly aid in algorithm development by providing a wide range of "ground truthed" images with varying illumination, atmospheric, viewing and scene conditions. One limitation of synthetic data is that the background variability is often too bland. It does not exhibit the spatial and spectral variability present in real data. In this work, four fundamentally different texture modeling algorithms will first be implemented as necessary into the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model environment. Two of the models to be tested are variants of a statistical Z-Score selection model, while the remaining two involve a texture synthesis and a spectral end-member fractional abundance map approach, respectively. A detailed comparative performance analysis of each model will then be carried out on several texturally significant regions of the resultant synthetic hyperspectral imagery. The quantitative assessment of each model will utilize a set of three peformance metrics that have been derived from spatial Gray Level Co-Occurrence Matrix (GLCM) analysis, hyperspectral Signal-to-Clutter Ratio (SCR) measures, and a new concept termed the Spectral Co-Occurrence Matrix (SCM) metric which permits the simultaneous measurement of spatial and spectral texture. Previous research efforts on the validation and performance analysis of texture characterization models have been largely qualitative in nature based on conducting visual inspections of synthetic textures in order to judge the degree of similarity to the original sample texture imagery. The quantitative

  9. 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.

  10. 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

  11. Map Analysis and Spatial Statistic: Assessment of Spatial Variability of Agriculture Land Conversion at Urban Fringe Area of Yogyakarta

    Science.gov (United States)

    Susilo, Bowo

    2016-11-01

    Urban development has brought various effects, one of which was the marginalization of the agricultural sector. Agricultural land is gradually converted to other type of land uses which considered more profitable. Conversion of agricultural land cannot be avoided but it should be controlled. Early identification on spatial distribution and intensity of agricultural land conversion as well as its related factor is necessary. Objective of the research were (1) to assess the spatial variability of agricultural land conversion, (2) to identify factors that affecting the spatial variability of agricultural land conversion. Research was conducted at urban fringe area of Yogyakarta. Spatial variability of agricultural land conversion was analysed using an index called Relative Conversion Index (RCI). Combined of map analysis and spatial statistical were used to determine the center of agricultural land conversion. Simple regression analysis was used to determine the factors associated with the conversion of agricultural land. The result shows that intensity of agricultural land conversion in the study area varies spatially as well as temporally. Intensity of agricultural land conversion in the period 1993-2000, involves three categories which are high, moderate and low. In the period of 2000-2007, the intensity of agricultural land conversion involves two categories which are high and low. Spatial variability of agricultural land conversion in the study area has a significant correlation with three factors: population growth, fragmentation of agricultural land and distance of agricultural land to the city

  12. Spatial and temporal variability of rainfall erosivity factor for Switzerland

    Directory of Open Access Journals (Sweden)

    K. Meusburger

    2012-01-01

    Full Text Available Rainfall erosivity, considering rainfall amount and intensity, is an important parameter for soil erosion risk assessment under future land use and climate change. Despite its importance, rainfall erosivity is usually implemented in models with a low spatial and temporal resolution. The purpose of this study is to assess the temporal- and spatial distribution of rainfall erosivity in form of the (Revised Universal Soil Loss Equation R-factor for Switzerland. Time series of 22 yr for rainfall (10 min resolution and temperature (1 h resolution data were analysed for 71 automatic gauging stations distributed throughout Switzerland. Regression-kriging was used to interpolate the rainfall erosivity values of single stations and to generate a map for Switzerland. Latitude, longitude, average annual precipitation, biogeographic units (Jura, Midland, etc., aspect and elevation were used as covariates, of which average annual precipitation, elevation and the biographic unit (Western Central Alps were significant (p<0.01 predictors. The mean value of long-term rainfall erosivity is 1330 MJ mm ha−1 h−1 yr−1 with a range of lowest values of 124 MJ mm ha−1 h−1 yr−1 at an elevated station in Grisons to highest values of 5611 MJ mm ha−1 h−1 yr−1 in Ticino. All stations have highest erosivity values from July to August and lowest values in the winter months. Swiss-wide the month May to October show significantly increasing trends of rainfall erosivity for the observed period (p<0.005. Only in February a significantly decreasing trend of rainfall erosivity is found (p<0.01. The increasing trends of rainfall erosivity in May, September and October when vegetation cover is scarce are likely to enhance soil erosion risk for certain agricultural crops and alpine grasslands in Switzerland.

  13. 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.

  14. Spatial variability of soil structure and its impact on transport processes and some associated land qualities.

    NARCIS (Netherlands)

    Finke, P.A.

    1992-01-01

    This thesis treats the impact of soil spatial variability on spatial variability of simulated land qualities. A sequence of procedures that were done to determine this impact is described in chapters 2 and 3. The subchapters correspond to seven manuscripts that either have appeared in or have been s

  15. Determinants of spatial variability of methane emissions from wet grasslands on peat soil

    NARCIS (Netherlands)

    Pol-Van Dasselaar, van den A.; Beusichem, van M.L.; Oenema, O.

    1999-01-01

    Methane (CH4) emissions from soils, representing the consequence of CH4 production, CH4 consumption and CH4 transport, are poorly characterised and show a large spatial variability. This study aimed to assess the determinants of field-scale spatial variability of CH4 emissions from wet grasslands on

  16. Spatial-temporal variability in GHG fluxes and their functional interpretation in RusFluxNet

    Science.gov (United States)

    Vasenev, Ivan; Meshalkina, Julia; Sarzhanov, Dmitriy; Mazirov, Ilia; Yaroslavtsev, Alex; Komarova, Tatiana; Tikhonova, Maria

    2016-04-01

    different meso- or micro-relief forms, natural or man-made succession studies, topsoil texture or organic matter state, subsoil or perched groundwater features. Zonal, seasonal and functional subdividing the monitoring data allows essentially increase the regression links between GHG fluxes and air or soil temperature and moisture (to 0.75-0.87) that is very important for their modeling and prediction. In taiga and mix-forest zones usually there is stronger effect on GHG fluxes by air temperature than soil one due to comparatively thin (from 3 till 10 cm) layer of principal soil organic and/or humus-accumulative horizons with maximum biological activity that usually determines the total rate of GHG soil fluxes. Unfavorable seasonal conditions (dry season or low temperature) determine essential (in 1.5-2 times) decreasing not only in soil GHG fluxes but in level of their spatial variability, intraseasonal and daily dynamics too. These trends are most obvious in case of more open and sensitive to the external factors ecosystems, for example in case of industrial area lawns or at the first stages of the windthrow or fallow-forest successions. Understanding the principal regional and land-use-determined regularities of spatial and temporal changes in ecosystem and soil GHG fluxes help better modeling them in the process of spatial intra- and extrapolations, seasonal and interseasonal predictions, taking into attention basic and current principal ecological factors limiting GHG fluxes and balances. Their introduction in the ecological or agroecological models and land-use decision support systems allows improve the quality of environmental/agroecological monitoring and control not only for GHG emission but also for soil organic matter conservation, manure and nitrogen fertilizer application that is often crucially important for sustainable rural development and profitable farming.

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

    African Journals Online (AJOL)

    ... in the temporal and spatial dynamics of mussel populations (Griffiths 1981; Crawford & ..... recruitment intensity (ANOYA, F ~ 9.357, df58, P < 0.01). (a) Regional ...... rocky shores: the role of geographic variation and wave action. J. Blogeogr.

  18. On spatially explicit models of cholera epidemics

    National Research Council Canada - National Science Library

    Bertuzzo, E; Casagrandi, R; Gatto, M; Rodriguez-Iturbe, I; Rinaldo, A

    2010-01-01

    We generalize a recently proposed model for cholera epidemics that accounts for local communities of susceptibles and infectives in a spatially explicit arrangement of nodes linked by networks having...

  19. SIMULATION MODELING SLOW SPATIALLY HETER- OGENEOUS COAGULATION

    Directory of Open Access Journals (Sweden)

    P. A. Zdorovtsev

    2013-01-01

    Full Text Available A new model of spatially inhomogeneous coagulation, i.e. formation of larger clusters by joint interaction of smaller ones, is under study. The results of simulation are compared with known analytical and numerical solutions.

  20. Spatial averaging infiltration model for layered soil

    Institute of Scientific and Technical Information of China (English)

    HU HePing; YANG ZhiYong; TIAN FuQiang

    2009-01-01

    To quantify the influences of soil heterogeneity on infiltration, a spatial averaging infiltration model for layered soil (SAI model) is developed by coupling the spatial averaging approach proposed by Chen et al. and the Generalized Green-Ampt model proposed by Jia et al. In the SAI model, the spatial heterogeneity along the horizontal direction is described by a probability distribution function, while that along the vertical direction is represented by the layered soils. The SAI model is tested on a typical soil using Monte Carlo simulations as the base model. The results show that the SAI model can directly incorporate the influence of spatial heterogeneity on infiltration on the macro scale. It is also found that the homogeneous assumption of soil hydraulic conductivity along the horizontal direction will overestimate the infiltration rate, while that along the vertical direction will underestimate the infiltration rate significantly during rainstorm periods. The SAI model is adopted in the spatial averaging hydrological model developed by the authors, and the results prove that it can be applied in the macro-scale hydrological and land surface process modeling in a promising way.

  1. Spatial averaging infiltration model for layered soil

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    To quantify the influences of soil heterogeneity on infiltration, a spatial averaging infiltration model for layered soil (SAI model) is developed by coupling the spatial averaging approach proposed by Chen et al. and the Generalized Green-Ampt model proposed by Jia et al. In the SAI model, the spatial hetero- geneity along the horizontal direction is described by a probability distribution function, while that along the vertical direction is represented by the layered soils. The SAI model is tested on a typical soil using Monte Carlo simulations as the base model. The results show that the SAI model can directly incorporate the influence of spatial heterogeneity on infiltration on the macro scale. It is also found that the homogeneous assumption of soil hydraulic conductivity along the horizontal direction will overes- timate the infiltration rate, while that along the vertical direction will underestimate the infiltration rate significantly during rainstorm periods. The SAI model is adopted in the spatial averaging hydrological model developed by the authors, and the results prove that it can be applied in the macro-scale hy- drological and land surface process modeling in a promising way.

  2. 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.

  3. 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...

  4. Spatial variability of selected physicochemical parameters within peat deposits in small valley mire: a geostatistical approach

    Directory of Open Access Journals (Sweden)

    Pawłowski Dominik

    2014-12-01

    Full Text Available Geostatistical methods for 2D and 3D modelling spatial variability of selected physicochemical properties of biogenic sediments were applied to a small valley mire in order to identify the processes that lead to the formation of various types of peat. A sequential Gaussian simulation was performed to reproduce the statistical distribution of the input data (pH and organic matter and their semivariances, as well as to honouring of data values, yielding more ‘realistic’ models that show microscale spatial variability, despite the fact that the input sample cores were sparsely distributed in the X-Y space of the study area. The stratigraphy of peat deposits in the Ldzań mire shows a record of long-term evolution of water conditions, which is associated with the variability in water supply over time. Ldzań is a fen (a rheotrophic mire with a through-flow of groundwater. Additionally, the vicinity of the Grabia River is marked by seasonal inundations of the southwest part of the mire and increased participation of mineral matter in the peat. In turn, the upper peat layers of some of the central part of Ldzań mire are rather spongy, and these peat-forming phytocoenoses probably formed during permanent waterlogging.

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

    Directory of Open Access Journals (Sweden)

    Louis Duchesne

    2014-06-01

    Full Text Available 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.

  6. GIS-Based Analytical Tools for Transport Planning: Spatial Regression Models for Transportation Demand Forecast

    Directory of Open Access Journals (Sweden)

    Simone Becker Lopes

    2014-04-01

    Full Text Available Considering the importance of spatial issues in transport planning, the main objective of this study was to analyze the results obtained from different approaches of spatial regression models. In the case of spatial autocorrelation, spatial dependence patterns should be incorporated in the models, since that dependence may affect the predictive power of these models. The results obtained with the spatial regression models were also compared with the results of a multiple linear regression model that is typically used in trips generation estimations. The findings support the hypothesis that the inclusion of spatial effects in regression models is important, since the best results were obtained with alternative models (spatial regression models or the ones with spatial variables included. This was observed in a case study carried out in the city of Porto Alegre, in the state of Rio Grande do Sul, Brazil, in the stages of specification and calibration of the models, with two distinct datasets.

  7. Temporal and spatial characteristics of sea surface height variability in the North Atlantic Ocean

    Directory of Open Access Journals (Sweden)

    D. Cromwell

    2006-01-01

    Full Text Available We investigate the spatial and temporal variability of sea surface height (SSH in the North Atlantic basin using satellite altimeter data from October 1992–January 2004. Our primary aim is to provide a detailed description of such variability, including that associated with propagating signals. We also investigate possible correlations between SSH variability and atmospheric pressure changes as represented by climate indices. We first investigate interannual SSH variations by deriving the complex empirical orthogonal functions (CEOFs of altimeter data lowpass-filtered at 18 months. We determine the spatial structure of the leading four modes (both in amplitude and phase and also the associated principal component (PC time series. Using wavelet analysis we derive the time-varying spectral density of the PCs, revealing when particular modes were strongest between 1992–2004. The spatial pattern of the leading CEOF, comprising 30% of the total variability, displays a 5-year periodicity in phase; signal propagation is particularly marked in the Labrador Sea. The second mode, with a dominant 3-year signal, has strong variability in the eastern basin. Secondly, we focus on the Azores subtropical frontal zone. The leading mode (35% is strong in the south and east of this region with strong variations at 3- and 5-year periods. The second mode (21% has a near-zonal band of low variance between  22°–27° N, sandwiched between two regions of high variance. Thirdly, we lowpass filter the altimeter data at a cutoff of 30 days, instead of 18 months, in order to retain signals associated with propagating baroclinic Rossby waves and/or eddies. The leading mode is the annual steric signal, around 46% of the SSH variability. The third and fourth CEOFs,  11% of the remaining variability, are associated with westward propagation which is particularly dominant in a "waveband" between 32°–36° N. For all three cases considered above, no significant cross

  8. Spatial variability of the topsoil organic carbon in the Moso bamboo forests of southern China in association with soil properties.

    Science.gov (United States)

    Zhang, Houxi; Zhuang, Shunyao; Qian, Haiyan; Wang, Feng; Ji, Haibao

    2015-01-01

    Understanding the spatial variability of soil organic carbon (SOC) must be enhanced to improve sampling design and to develop soil management strategies in terrestrial ecosystems. Moso bamboo (Phyllostachys pubescens Mazel ex Houz.) forests have a high SOC storage potential; however, they also vary significantly spatially. This study investigated the spatial variability of SOC (0-20 cm) in association with other soil properties and with spatial variables in the Moso bamboo forests of Jian'ou City, which is a typical bamboo hometown in China. 209 soil samples were collected from Moso bamboo stands and then analyzed for SOC, bulk density (BD), pH, cation exchange capacity (CEC), and gravel content (GC) based on spatial distribution. The spatial variability of SOC was then examined using geostatistics. A Kriging map was produced through ordinary interpolation and required sample numbers were calculated by classical and Kriging methods. An aggregated boosted tree (ABT) analysis was also conducted. A semivariogram analysis indicated that ln(SOC) was best fitted with an exponential model and that it exhibited moderate spatial dependence, with a nugget/sill ratio of 0.462. SOC was significantly and linearly correlated with BD (r = -0.373**), pH (r = -0.429**), GC (r = -0.163*), CEC (r = 0.263**), and elevation (r = 0.192**). Moreover, the Kriging method requires fewer samples than the classical method given an expected standard error level as per a variance analysis. ABT analysis indicated that the physicochemical variables of soil affected SOC variation more significantly than spatial variables did, thus suggesting that the SOC in Moso bamboo forests can be strongly influenced by management practices. Thus, this study provides valuable information in relation to sampling strategy and insight into the potential of adjustments in agronomic measure, such as in fertilization for Moso bamboo production.

  9. 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.

  10. Spatial variability of the trends in climatic variables across China during 1961-2010

    Science.gov (United States)

    Yang, Hanbo; Yang, Dawen; Hu, Qingfang; Lv, Huafang

    2015-05-01

    Distribution of meteorological stations is not uniform in many regions of the world, especially in developing countries like China. To eliminate the effect of uneven stations, this study produced a data set of areal average precipitation, air temperature, solar radiation, and wind speed from 736 meteorological station observations during 1961-2010 using an inverse-distance weighted technique. Based on the data set, this study detected the trends in climatic variables. Precipitation has a slight but no significant ( p = 0.78) trend for the whole of China and has a significant increase trend in northwest China. Surface air temperature has a significant ( p < 0.001) accelerating warming trend of 0.032 °C/a for the whole of China, and spatially larger in northern China than that in southern China. Solar radiation has a significant ( p < 0.001) dimming trend of -0.14 W/(m2 · a) for the whole of China, and the largest dimming trend appears in eastern China, the possible cause for which is a high-aerosol concentration. Surface wind speed has a significant ( p < 0.001) stilling trend of -0.012 m/(s·a) for the whole of China, the causes for which were speculated the changes in atmospheric circulation and surface roughness, as well as increases in aerosol concentration and the decrease in the south-north temperature gradient in China. In addition, three large-scale instrument replacements increase uncertainties of the trend analysis.

  11. Spatial and Temporal Variability of Satellite-Derived Cloud and Surface Characteristics During FIRE-ACE

    Science.gov (United States)

    Maslanik, J. A.; Key, J.; Fowler, C. W.; Nguyen, T.; Wang, X.a

    2000-01-01

    Advanced very high resolution radiometer (AVHRR) products calculated for the western Arctic for April-July 1998 are used to investigate spatial, temporal, and regional patterns and variability in energy budget parameters associated with ocean- ice-atmosphere interactions over the Arctic Ocean during the Surface Heat Budget of the Arctic Ocean (SHEBA) project and the First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment - Arctic Cloud Experiment (FIRE-ACE). The AVHRR-derived parameters include cloud fraction, clear-sky and all-sky skin temperature and broadband albedo, upwelling and downwelling shortwave and longwave radiation, cloud top pressure and temperature, and cloud optical depth. The remotely sensed products generally agree well with field observations at the SHEBA site, which in turn is shown to be representative of a surrounding region comparable in size to a climate-model grid cell. Time series of products for other locations in the western Arctic illustrate the magnitude of spatial variability during the study period and provide spatial and temporal detail useful for studying regional processes. The data illustrate the progression of reduction in cloud cover, albedo decrease, and the considerable heating of the open ocean associated with the anomalous decrease in sea ice cover in the eastern Beaufort Sea that began in late spring. Above-freezing temperatures are also recorded within the ice pack, suggesting warming of the open water areas within the ice cover.

  12. Performance of Information Criteria for Spatial Models.

    Science.gov (United States)

    Lee, Hyeyoung; Ghosh, Sujit K

    2009-01-01

    Model choice is one of the most crucial aspect in any statistical data analysis. It is well known that most models are just an approximation to the true data generating process but among such model approximations it is our goal to select the "best" one. Researchers typically consider a finite number of plausible models in statistical applications and the related statistical inference depends on the chosen model. Hence model comparison is required to identify the "best" model among several such candidate models. This article considers the problem of model selection for spatial data. The issue of model selection for spatial models has been addressed in the literature by the use of traditional information criteria based methods, even though such criteria have been developed based on the assumption of independent observations. We evaluate the performance of some of the popular model selection critera via Monte Carlo simulation experiments using small to moderate samples. In particular, we compare the performance of some of the most popular information criteria such as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Corrected AIC (AICc) in selecting the true model. The ability of these criteria to select the correct model is evaluated under several scenarios. This comparison is made using various spatial covariance models ranging from stationary isotropic to nonstationary models.

  13. 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.

  14. 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

  15. 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 ...

  16. Limited dependent variable models for panel data

    NARCIS (Netherlands)

    Charlier, E.

    1997-01-01

    Many economic phenomena require limited variable models for an appropriate treatment. In addition, panel data models allow the inclusion of unobserved individual-specific effects. These models are combined in this thesis. Distributional assumptions in the limited dependent variable models are

  17. BEYOND SEM: GENERAL LATENT VARIABLE MODELING

    National Research Council Canada - National Science Library

    Muthén, Bengt O

    2002-01-01

    This article gives an overview of statistical analysis with latent variables. Using traditional structural equation modeling as a starting point, it shows how the idea of latent variables captures a wide variety of statistical concepts...

  18. Spatial Variability of Soil Organic Carbon in a Watershed on the Loess Plateau

    Institute of Scientific and Technical Information of China (English)

    WANG Yun-Qiang; ZHANG Xing-Chang; ZHANG Jing-Li; LI Shun-Ji

    2009-01-01

    Soil organic carbon (SOC) has great impacts on global warming,land degradation and food security.Classic statistical and geostatistical methods were used to characterize and compare the spatial heterogeneity of SOC and related factors,such as topography,soil type and land use,in the Liudaogou watershed on the Loess Plateau of North China.SOC concentrations followed a log-normal distribution with an arithmetic and geometric means of 23.4 and 21.3 g kg-1,respectively,were moderately variable (CV=75.9%),and demonstrated a moderate spatial dependence according to the nugget ratio (34.7%).The experimental variogram of SOC was best-fitted by a spherical model,after the spatial outliers had been detected and subsequently eliminated.Lower SOC concentrations were associated with higher elevations.Warp soils and farmland had the highest SOC concentrations,while aeolian sand soil and shrublands had the lowest SOC values.The geostatistical characteristics of SOC for the different soil and land use types were different.These patterns were closely related to the spatial structure of topography,and soil and land use types.

  19. Assessing spatial variability of soil petroleum contamination using visible near-infrared diffuse reflectance spectroscopy.

    Science.gov (United States)

    Chakraborty, Somsubhra; Weindorf, David C; Zhu, Yuanda; Li, Bin; Morgan, Cristine L S; Ge, Yufeng; Galbraith, John

    2012-11-01

    Visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) is a rapid, non-destructive method for sensing the presence and amount of total petroleum hydrocarbon (TPH) contamination in soil. This study demonstrates the feasibility of VisNIR DRS to be used in the field to proximally sense and then map the areal extent of TPH contamination in soil. More specifically, we evaluated whether a combination of two methods, penalized spline regression and geostatistics could provide an efficient approach to assess spatial variability of soil TPH using VisNIR DRS data from soils collected from an 80 ha crude oil spill in central Louisiana, USA. Initially, a penalized spline model was calibrated to predict TPH contamination in soil by combining lab TPH values of 46 contaminated and uncontaminated soil samples and the first-derivative of VisNIR reflectance spectra of these samples. The r(2), RMSE, and bias of the calibrated penalized spline model were 0.81, 0.289 log(10) mg kg(-1), and 0.010 log(10) mg kg(-1), respectively. Subsequently, the penalized spline model was used to predict soil TPH content for 128 soil samples collected over the 80 ha study site. When assessed with a randomly chosen validation subset (n = 10) from the 128 samples, the penalized spline model performed satisfactorily (r(2) = 0.70; residual prediction deviation = 2.0). The same validation subset was used to assess point kriging interpolation after the remaining 118 predictions were used to produce an experimental semivariogram and map. The experimental semivariogram was fitted with an exponential model which revealed strong spatial dependence among soil TPH [r(2) = 0.76, nugget = 0.001 (log(10) mg kg(-1))(2), and sill 1.044 (log(10) mg kg(-1))(2)]. Kriging interpolation adequately interpolated TPH with r(2) and RMSE values of 0.88 and 0.312 log(10) mg kg(-1), respectively. Furthermore, in the kriged map, TPH distribution matched with the expected TPH variability of the study site. Since the

  20. Consistent patterns of spatial variability between NE Atlantic and Mediterraneanrocky shores

    NARCIS (Netherlands)

    dal Bello, M.; Leclerc, J.-C.; Benedetti-Cecchi, L.; Hummel, H.

    2016-01-01

    Examining how variability in population abundance and distribution is allotted among different spatial scales can inform of processes that are likely to generate that variability. Results of studies dealing with scale issues in marine benthic communities suggest that variability is concentrated at s

  1. Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors

    NARCIS (Netherlands)

    Jacobs, J.P.A.M.; Ligthart, J.E.; Vrijburg, H.

    2009-01-01

    We extend the three-step generalized methods of moments (GMM) approach of Kapoor, Kelejian, and Prucha (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory variables.

  2. Uncertainty in spatially explicit animal dispersal models

    Science.gov (United States)

    Mooij, Wolf M.; DeAngelis, Donald L.

    2003-01-01

    Uncertainty in estimates of survival of dispersing animals is a vexing difficulty in conservation biology. The current notion is that this uncertainty decreases the usefulness of spatially explicit population models in particular. We examined this problem by comparing dispersal models of three levels of complexity: (1) an event-based binomial model that considers only the occurrence of mortality or arrival, (2) a temporally explicit exponential model that employs mortality and arrival rates, and (3) a spatially explicit grid-walk model that simulates the movement of animals through an artificial landscape. Each model was fitted to the same set of field data. A first objective of the paper is to illustrate how the maximum-likelihood method can be used in all three cases to estimate the means and confidence limits for the relevant model parameters, given a particular set of data on dispersal survival. Using this framework we show that the structure of the uncertainty for all three models is strikingly similar. In fact, the results of our unified approach imply that spatially explicit dispersal models, which take advantage of information on landscape details, suffer less from uncertainly than do simpler models. Moreover, we show that the proposed strategy of model development safeguards one from error propagation in these more complex models. Finally, our approach shows that all models related to animal dispersal, ranging from simple to complex, can be related in a hierarchical fashion, so that the various approaches to modeling such dispersal can be viewed from a unified perspective.

  3. Validating a spatially distributed hydrological model with soil morphology data

    Directory of Open Access Journals (Sweden)

    T. Doppler

    2013-10-01

    Full Text Available Spatially distributed hydrological models are popular tools in hydrology and they are claimed to be useful to support management decisions. Despite the high spatial resolution of the computed variables, calibration and validation is often carried out only on discharge time-series at specific locations due to the lack of spatially distributed reference data. Because of this restriction, the predictive power of these models, with regard to predicted spatial patterns, can usually not be judged. An example of spatial predictions in hydrology is the prediction of saturated areas in agricultural catchments. These areas can be important source areas for the transport of agrochemicals to the stream. We set up a spatially distributed model to predict saturated areas in a 1.2 km2 catchment in Switzerland with moderate topography. Around 40% of the catchment area are artificially drained. We measured weather data, discharge and groundwater levels in 11 piezometers for 1.5 yr. For broadening the spatially distributed data sets that can be used for model calibration and validation, we translated soil morphological data available from soil maps into an estimate of the duration of soil saturation in the soil horizons. We used redox-morphology signs for these estimates. This resulted in a data set with high spatial coverage on which the model predictions were validated. In general, these saturation estimates corresponded well to the measured groundwater levels. We worked with a model that would be applicable for management decisions because of its fast calculation speed and rather low data requirements. We simultaneously calibrated the model to the groundwater levels in the piezometers and discharge. The model was able to reproduce the general hydrological behavior of the catchment in terms of discharge and absolute groundwater levels. However, the accuracy of the groundwater level predictions was not high enough to be used for the prediction of saturated areas

  4. Spatially-variable carbonation reactions in polycrystalline olivine

    Science.gov (United States)

    Wells, Rachel K.; Xiong, Wei; Sesti, Erika; Cui, Jinlei; Giammar, Daniel; Skemer, Philip; Hayes, Sophia E.; Conradi, Mark S.

    2017-05-01

    Carbon dioxide (CO2) injection into olivine-rich mafic and ultramafic rocks is expected to result in the precipitation of divalent metal carbonate minerals, permanently storing the CO2 underground. Previous experiments that used unconsolidated forsterite (Mg2SiO4) particles in experimental investigations of reactions with water and carbon dioxide have been useful for determining the identity, rates of formation, and spatial location of carbonate mineral reaction products. However there remains a need for information regarding the influence of the internal pore structure and grain boundary surfaces on the extent and locations of these reactions in dense aggregates. We conducted several experiments at 100 °C and 100 bar CO2 using sintered San Carlos olivine (Fo90) and pure forsterite (Fo100) cylinders, and we documented the type and spatial distribution of the reaction products. Timing of carbonation was measured using in-situ 13C NMR spectroscopy without removing the sample from the reactor. Ex-situ solid-state NMR spectroscopy, Raman spectroscopy, and electron microscopy were used to examine reacted samples and precipitates. Within 15 days, magnesite is observed only on the surface of Fo90. After 53 and 102 days of reaction, magnesite and amorphous silica are observed as a crust around the entire Fo100 cylinder and as isolated layers within the sample. The spatial transition from an amorphous silica layer to the host Fo100 indicates that the development of amorphous silica did not impede further forsterite dissolution. While earlier studies documented localized reactions at the grain scale, the development of distinct zones of magnesite and amorphous silica suggest that divalent metal cations are mobile during carbonation of olivine. Grain boundaries, pore structure, and geochemical gradients strongly influence the locations of silicate mineral dissolution and carbonate mineral precipitation even in the absence of advective transport or confinement. The clear

  5. Effects of attentional and cognitive variables on unilateral spatial neglect.

    Science.gov (United States)

    Ricci, Raffaella; Salatino, Adriana; Garbarini, Francesca; Ronga, Irene; Genero, Rosanna; Berti, Anna; Neppi-Mòdona, Marco

    2016-11-01

    Patients with visuospatial neglect when asked to cancel targets partially or totally omit to cancel contralesional stimuli. It has been shown that increasing the attentional demands of the cancellation task aggravates neglect contralesionally. However, some preliminary evidence also suggests that neglect might be worsened by engaging the patient in a demanding, non-spatial, cognitive activity (i.e. a mathematical task). We studied cancellation performance of 16 patients with right-hemisphere lesions, 8 with neglect, 8 without neglect, and 8 age-matched healthy control participants by means of five cancellation tasks which varied for the degree of attentional and/or high level cognitive demands (preattentive and attentive search of a visual target, searching for numbers containing the digit 3, even numbers, and multiples of 3). Results showed that attentive search of visual targets, relative to the preattentive search condition, aggravated neglect patients' performance. Moreover, searching for multiples not only worsened spatial neglect contralesionally, but also slowed down performance of patients with right-hemisphere lesions without neglect. Our findings further demonstrate the presence of specific deficits of attention in neglect. In addition, the worse performance of patients without neglect in the 'multiples of 3' task is consistent with the evidence that right-hemisphere lesions per se impair the ability to maintain attention (i.e. sustained attention). This suggests that the exacerbation of neglect during execution of a demanding, non-spatial, cognitive task might be explained by a deficit of sustained attention in addition to a selective deficit of spatial attention.

  6. Variable spatial magnetic field influences peripheral nerves regeneration in rats.

    Science.gov (United States)

    Suszyński, Krzysztof; Marcol, Wiesław; Szajkowski, Sebastian; Pietrucha-Dutczak, Marita; Cieślar, Grzegorz; Sieroń, Aleksander; Lewin-Kowalik, Joanna

    2014-09-01

    Generator of spatial magnetic field is one of most recent achievements among the magnetostimulators. This apparatus allows to obtain the rotating magnetic field. This new method may be more effective than other widely used techniques of magnetostimulation and magnetotherapy. We investigated the influence of alternating, spatial magnetic field on the regeneration of the crushed rat sciatic nerves. Functional and morphological evaluations were used. After crush injury of the right sciatic nerve, Wistar C rats (n = 80) were randomly divided into four groups (control and three experimental). The experimental groups (A, B, C) were exposed (20 min/day, 5 d/week, 4 weeks) to alternating spatial magnetic field of three different intensities. Sciatic Functional Index (SFI) and tensometric assessments were performed every week after nerve crush. Forty-eight hours before the sacrificing of animals, DiI (1,1'-di-octadecyl-3,3,3',3'-tetramethyloindocarbocyanine perchlorate) was applied 5 mm distally to the crush site. Collected nerves and dorsal root ganglia (DRG) were subjected to histological and immunohistochemical staining. The survival rate of DRG neurons was estimated. Regrowth and myelination of the nerves was examined. The results of SFI and tensometric assessment showed improvement in all experimental groups as compared to control, with best outcome observed in group C, exposed to the strongest magnetic field. In addition, DRG survival rate and nerve regeneration intensity were significantly higher in the C group. Above results indicate that strong spatial alternating magnetic field exerts positive effect on peripheral nerve regeneration and its application could be taken under consideration in the therapy of injured peripheral nerves.

  7. Regulation mechanisms in spatial stochastic development models

    CERN Document Server

    Finkelshtein, Dmitri

    2008-01-01

    The aim of this paper is to analyze different regulation mechanisms in spatial continuous stochastic development models. We describe the density behavior for models with global mortality and local establishment rates. We prove that the local self-regulation via a competition mechanism (density dependent mortality) may suppress a unbounded growth of the averaged density if the competition kernel is superstable.

  8. Uncertainty in spatially explicit animal dispersal models

    NARCIS (Netherlands)

    Mooij, W.M.; DeAngelis, D.L.

    2003-01-01

    Uncertainty in estimates of survival of dispersing animals is a vexing difficulty in conservation biology. The current notion is that this uncertainty decreases the usefulness of spatially explicit population models in particular. We examined this problem by comparing dispersal models of three level

  9. Linking the spatial variability of glacier mass loss to fjord geometry

    Science.gov (United States)

    Porter, D. F.; Tinto, K. J.; Boghosian, A.; Cochran, J. R.; Csatho, B. M.; Bell, R. E.

    2015-12-01

    There is compelling evidence of increasing mass loss of the ice sheets using a diverse set of observations, including increased thinning rates measured from both airborne and satellite altimeters, elevated mass fluxes resulting from the acceleration of outlet glaciers, and mass changes measured directly from satellite gravimetry. A dominant characteristic of observed change in Greenland outlet glaciers is that it is locally random. Numerous studies have revealed a high degree of spatial and temporal variability of outlet glacier mass change. Modeling studies suggest that increased ocean temperatures may be responsible for the observed glacial retreat in Greenland through increased basal melting, leading to increased calving rates, terminus retreat, glacier speedup, and eventually thinning of inland ice. Knowledge of fjord geometry is crucial for ice-ocean interaction because the availability of ocean heat to the ice will be restricted by narrow sills and shallow grounding lines. We investigate whether the variability in observed changes among Greenland glaciers can be partially explained by variation in fjord geometry. Using statistical techniques commonly employed to detect patterns in complex spatial data, we objectively show that mass change in Greenland tidewater glaciers between 2003 and 2009 is indeed mostly spatially incoherent. Except for a few clusters of similar change in the NW and Scoresby Sund regions, there is significant glacier-scale variability in mass loss rates. To understand the drivers of this local variability, we compare fjord bathymetries from all regions of Greenland, modeled using airborne gravimetry measurements from NASA Operation IceBridge flights, to estimates of glaciological change. Specifically, we investigate the correlation between water depths at the grounding line and the dynamic mass loss of tidewater glaciers. In theory, a deep grounding line will allow greater interaction with the warm Atlantic Water observed in most fjords

  10. 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.

  11. Cardinality-dependent Variability in Orthogonal Variability Models

    DEFF Research Database (Denmark)

    Mærsk-Møller, Hans Martin; Jørgensen, Bo Nørregaard

    2012-01-01

    During our work on developing and running a software product line for eco-sustainable greenhouse-production software tools, which currently have three products members we have identified a need for extending the notation of the Orthogonal Variability Model (OVM) to support what we refer to as car......During our work on developing and running a software product line for eco-sustainable greenhouse-production software tools, which currently have three products members we have identified a need for extending the notation of the Orthogonal Variability Model (OVM) to support what we refer...

  12. Comparing spatial and temporal transferability of hydrological model parameters

    Science.gov (United States)

    Patil, Sopan D.; Stieglitz, Marc

    2015-06-01

    Operational use of hydrological models requires the transfer of calibrated parameters either in time (for streamflow forecasting) or space (for prediction at ungauged catchments) or both. Although the effects of spatial and temporal parameter transfer on catchment streamflow predictions have been well studied individually, a direct comparison of these approaches is much less documented. Here, we compare three different schemes of parameter transfer, viz., temporal, spatial, and spatiotemporal, using a spatially lumped hydrological model called EXP-HYDRO at 294 catchments across the continental United States. Results show that the temporal parameter transfer scheme performs best, with lowest decline in prediction performance (median decline of 4.2%) as measured using the Kling-Gupta efficiency metric. More interestingly, negligible difference in prediction performance is observed between the spatial and spatiotemporal parameter transfer schemes (median decline of 12.4% and 13.9% respectively). We further demonstrate that the superiority of temporal parameter transfer scheme is preserved even when: (1) spatial distance between donor and receiver catchments is reduced, or (2) temporal lag between calibration and validation periods is increased. Nonetheless, increase in the temporal lag between calibration and validation periods reduces the overall performance gap between the three parameter transfer schemes. Results suggest that spatiotemporal transfer of hydrological model parameters has the potential to be a viable option for climate change related hydrological studies, as envisioned in the "trading space for time" framework. However, further research is still needed to explore the relationship between spatial and temporal aspects of catchment hydrological variability.

  13. Integrated statistical modelling of spatial landslide probability

    Science.gov (United States)

    Mergili, M.; Chu, H.-J.

    2015-09-01

    Statistical methods are commonly employed to estimate spatial probabilities of landslide release at the catchment or regional scale. Travel distances and impact areas are often computed by means of conceptual mass point models. The present work introduces a fully automated procedure extending and combining both concepts to compute an integrated spatial landslide probability: (i) the landslide inventory is subset into release and deposition zones. (ii) We employ a simple statistical approach to estimate the pixel-based landslide release probability. (iii) We use the cumulative probability density function of the angle of reach of the observed landslide pixels to assign an impact probability to each pixel. (iv) We introduce the zonal probability i.e. the spatial probability that at least one landslide pixel occurs within a zone of defined size. We quantify this relationship by a set of empirical curves. (v) The integrated spatial landslide probability is defined as the maximum of the release probability and the product of the impact probability and the zonal release probability relevant for each pixel. We demonstrate the approach with a 637 km2 study area in southern Taiwan, using an inventory of 1399 landslides triggered by the typhoon Morakot in 2009. We observe that (i) the average integrated spatial landslide probability over the entire study area corresponds reasonably well to the fraction of the observed landside area; (ii) the model performs moderately well in predicting the observed spatial landslide distribution; (iii) the size of the release zone (or any other zone of spatial aggregation) influences the integrated spatial landslide probability to a much higher degree than the pixel-based release probability; (iv) removing the largest landslides from the analysis leads to an enhanced model performance.

  14. Modelling the spatial distribution of ammonia emissions in the UK

    Energy Technology Data Exchange (ETDEWEB)

    Hellsten, S. [Centre for Ecology and Hydrology Edinburgh, Bush Estate, Penicuik, Midlothian EH26 0QB (United Kingdom); Institute of Geography, School of Geosciences, University of Edinburgh, Drummond Street, Edinburgh EH8 9XP (United Kingdom); IVL Swedish Environmental Research Institute Ltd, P.O. Box 5302, SE-400 14 Gothenburg (Sweden)], E-mail: sofie.hellsten@ivl.se; Dragosits, U. [Centre for Ecology and Hydrology Edinburgh, Bush Estate, Penicuik, Midlothian EH26 0QB (United Kingdom); Place, C.J. [Institute of Geography, School of Geosciences, University of Edinburgh, Drummond Street, Edinburgh EH8 9XP (United Kingdom); Vieno, M. [Centre for Ecology and Hydrology Edinburgh, Bush Estate, Penicuik, Midlothian EH26 0QB (United Kingdom); Institute of Atmospheric and Environmental Science, School of GeoSciences, University of Edinburgh, Crew Building, The King' s buildings, West Mains Road, Edinburgh EH9 3JN (United Kingdom); Dore, A.J. [Centre for Ecology and Hydrology Edinburgh, Bush Estate, Penicuik, Midlothian EH26 0QB (United Kingdom); Misselbrook, T.H. [Institute of Grassland and Environmental Research, North Wyke, Okehampton, Exeter EX 2SB (United Kingdom); Tang, Y.S.; Sutton, M.A. [Centre for Ecology and Hydrology Edinburgh, Bush Estate, Penicuik, Midlothian EH26 0QB (United Kingdom)

    2008-08-15

    Ammonia emissions (NH{sub 3}) are characterised by a high spatial variability at a local scale. When modelling the spatial distribution of NH{sub 3} emissions, it is important to provide robust emission estimates, since the model output is used to assess potential environmental impacts, e.g. exceedance of critical loads. The aim of this study was to provide a new, updated spatial NH{sub 3} emission inventory for the UK for the year 2000, based on an improved modelling approach and the use of updated input datasets. The AENEID model distributes NH{sub 3} emissions from a range of agricultural activities, such as grazing and housing of livestock, storage and spreading of manures, and fertilizer application, at a 1-km grid resolution over the most suitable landcover types. The results of the emission calculation for the year 2000 are analysed and the methodology is compared with a previous spatial emission inventory for 1996. - It is important to provide robust estimates of the spatial distribution of ammonia emissions, since the model output is used to assess potential environmental impacts, e.g. through the exceedance of critical loads.

  15. Spatial variability of the Rotterdam urban heat island as influenced by urban land use

    Science.gov (United States)

    Heusinkveld, Bert G.; Steeneveld, G. J.; Hove, L. W. A.; Jacobs, C. M. J.; Holtslag, A. A. M.

    2014-01-01

    Novel bicycle traverse meteorological measurements were made in Rotterdam to assess the spatial variation of temperature during a tropical day. Nocturnal spatial urban temperature differences of 7 K were found to be related to city morphology. During midday measurements, the downtown was up to 1.2 K warmer than the surrounding rural area while a city park was 4.0 K cooler than downtown. A regression analysis showed that the nocturnal measured urban heat island (UHI) can be linked to land use, namely vegetation, built-up area, and water and is most significant for vegetation. From the traverse observation data, a multiple linear regression model was constructed and independently validated with 3 year summertime UHI statistics derived from four urban fixed meteorological stations and two fixed rural stations. Wind rose analysis shows that UHI is strongest from easterly directions and that the temperature signal of the WMO station is influenced from urban directions. A regression model reproduced the nighttime spatial variability of the UHI within a fractional bias of 4.3% and was used to derive an UHI map of Rotterdam and surroundings. This map shows that high-density urban configurations lacking greenery or close to large water bodies are vulnerable to high nocturnal temperatures during heat waves. The UHI map can be used as a valuable planning tool for mitigating nocturnal urban heat stress or identifying neighborhoods at risk during heat waves.

  16. Spatial and temporal variability of soil electrical conductivity related to soil moisture

    Directory of Open Access Journals (Sweden)

    José Paulo Molin

    2013-02-01

    Full Text Available Soil electrical conductivity (ECa is a soil quality indicator associated to attributes interesting to site-specific soil management such as soil moisture and texture. Soil ECa provides information that helps guide soil management decisions, so we performed spatial evaluation of soil moisture in two experimental fields in two consecutive years and modeled its influence on soil ECa. Soil ECa, moisture and clay content were evaluated by statistical, geostatistical and regression analyses. Semivariogram models, adjusted for soil moisture, had strong spatial dependence, but the relationship between soil moisture and soil ECa was obtained only in one of the experimental fields, where soil moisture and clay content range was higher. In this same field, coefficients of determinations between soil moisture and clay content were above 0.70. In the second field, the low soil moisture and clay content range explain the absence of a relationship between soil ECa and soil moisture. Data repetition over the years, suggested that ECa is a qualitative indicator in areas with high spatial variability in soil texture.

  17. 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...

  18. 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.

  19. Explaining Spatial Variability in Wellbore Impairment Risk for Pennsylvania Oil and Gas Wells, 2000-2014

    Science.gov (United States)

    Santoro, R.; Ingraffea, A. R.

    2015-12-01

    Previous modeling (ingraffea et al. PNAS, 2014) indicated roughly two-times higher cumulative risk for wellbore impairment in unconventional wells, relative to conventional wells, and large spatial variation in risk for oil and gas wells drilled in the state of Pennsylvania. Impairment risk for wells in the northeast portion of the state were found to be 8.5-times greater than that of wells drilled in the rest of the state. Here, we set out to explain this apparent regional variability through Boosted Regression Tree (BRT) analysis of geographic, developmental, and general well attributes. We find that regional variability is largely driven by the nature of the development, i.e. whether conventional or unconventional development is dominant. Oil and natural gas market prices and total well depths present as major influences in wellbore impairment, with moderate influences from well densities and geologic factors. The figure depicts influence paths for predictors of impairments for the state (top left), SW region (top right), unconventional/NE region (bottom left) and conventional/NW region (bottom right) models. Influences are scaled to reflect percent contributions in explaining variability in the model.

  20. Variable Fidelity Aeroelastic Toolkit - Structural Model Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed innovation is a methodology to incorporate variable fidelity structural models into steady and unsteady aeroelastic and aeroservoelastic analyses in...

  1. Spatial variability of soybean yields under two systems cropping in savannah

    Directory of Open Access Journals (Sweden)

    Lúcio Bastos Madeiros

    2009-08-01

    Full Text Available The geostatistics is an important tool in analysis and description of soil variability properties. The maps of productivity are considered an excellent tool for analysis of performance at the level of agricultural property. Objective was to analyze the spatial yield variability of soybean in no-tillage e conventional-tillage. The experiment was carried out in two plots in sampling points defined accordingly to grid with dimension of 40 x 55 m, totaling 44 points spaced 5m. Statistical and geostatistical analysis were performed to monitor the range of spatial variability and spatial dependence. Soybean yield didn't present spatial dependence in none systems. The management in conventional-tillage was decisive in increase the yield of soybean in relation to no-tillage.Key-words: geostatistic; spatial dependence; management soil.

  2. 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.

  3. Do existing classification systems capture mountain snowpack heterogeneity? Accounting for spatial variability in a changing environment

    Science.gov (United States)

    Tennant, C.; Godsey, S.; Harpold, A. A.; Link, T. E.; Rajagopal, S.; Larsen, L.

    2016-12-01

    Spatial patterns of snow accumulation and melt control water and nutrient fluxes from mountain landscapes and determine the dynamics of resource availability for nearby human and ecological communities. Because seasonal snowpack is sensitive to changes in regional climate, there is a growing need for a snowpack classification system that (1) recognizes salient processes, (2) captures the variance of the system, (3) recognizes temporal and/or spatial change, and (4) has application to predicting snowmelt runoff regimes. Previous classification systems have focused on textural and stratigraphic snow characteristics or climatological observations to map broad geographic classes (e.g. maritime, continental, ephemeral, etc.). While these approaches have revealed general patterns, they may not capture the spatial heterogeneity of snowpack characteristics that are common across high relief terrain. Here, we use 1 km resolution gridded outputs from a physically based, spatially-distributed energy- and mass-balance snow model (SNODAS) to produce a snow classification system for the western U.S. and Great Plains. To meet the outlined criterion, we initially explored the ability of a large number of metrics (13) to characterize the amount, timing, and melt-rate of snowpack. Principal components analysis and pairwise correlations were used to identify a subset of metrics (6) that captured the variance of the system but also contributed unique information. K-means was used to delineate 12 process-based groups that reveal both climatic and orographic influences on snowpack accumulation, timing, and melt rate. The important effects of elevation-mediated processes in our classification system suggest a greater spatial diversity in snowpack patterns than suggested by previous characterizations (e.g. maritime-to-continental). Application of the system from the early 2000's to present reveals that interannual temporal and spatial variability have been greatest in the Columbia Plateau

  4. Snowpack spatial variability: Towards understanding its effect on remote sensing measurements and snow slope stability

    Science.gov (United States)

    Marshall, Hans-Peter

    The distribution of water in the snow-covered areas of the world is an important climate change indicator, and it is a vital component of the water cycle. At local and regional scales, the snow water equivalent (SWE), the amount of liquid water a given area of the snowpack represents, is very important for water resource management, flood forecasting, and prediction of available hydropower energy. Measurements from only a few automatic weather stations, such as the SNOTEL network, or sparse manual snowpack measurements are typically extrapolated for estimating SWE over an entire basin. Widespread spatial variability in the distribution of SWE and snowpack stratigraphy at local scales causes large errors in these basin estimates. Remote sensing measurements offer a promising alternative, due to their large spatial coverage and high temporal resolution. Although snow cover extent can currently be estimated from remote sensing data, accurately quantifying SWE from remote sensing measurements has remained difficult, due to a high sensitivity to variations in grain size and stratigraphy. In alpine snowpacks, the large degree of spatial variability of snowpack properties and geometry, caused by topographic, vegetative, and microclimatic effects, also makes prediction of snow avalanches very difficult. Ground-based radar and penetrometer measurements can quickly and accurately characterize snowpack properties and SWE in the field. A portable lightweight radar was developed, and allows a real-time estimate of SWE to within 10%, as well as measurements of depths of all major density transitions within the snowpack. New analysis techniques developed in this thesis allow accurate estimates of mechanical properties and an index of grain size to be retrieved from the SnowMicroPenetrometer. These two tools together allow rapid characterization of the snowpack's geometry, mechanical properties, and SWE, and are used to guide a finite element model to study the stress distribution

  5. Spatial and temporal variability of late Holocene sea-level changes in the North Atlantic (Invited)

    Science.gov (United States)

    Kemp, A.; Kopp, R. E.; Horton, B.; Cahill, N.

    2013-12-01

    Proxy sea-level reconstructions spanning the last ~2000 years capture multiple phases of climate and sea level behavior for model calibration, provide a pre-anthropogenic background against which to compare recent trends, and characterize patterns of natural spatial and temporal variability. In the western North Atlantic basin, salt-marsh sediment is an archive for reconstructing sea level with the decimeter and multi-decadal resolution necessary to characterize subtle changes. New and existing salt-marsh reconstructions from northern Florida, North Carolina, New Jersey, Connecticut, and Massachusetts provide a dataset for investigating spatial and temporal sea-level variability during the late Holocene. The reconstructions were developed using foraminifera, plants, and bulk sediment δ13C values as sea-level proxies. The age of sediment deposition was estimated from composite chronologies of radiocarbon and chronohorizons of regional pollution and land-use change that were combined in age depth models. We used a spatio-temporal Gaussian process model to identify and characterize persistent phases of sea level behavior during the late Holocene in the western North Atlantic Ocean. The results indicate an acceleration in global mean sea level from the early 19th century through the early 20th century. The rate of sea-level rise increased significantly in the late 19th century. The timing and magnitude of this rise varied among sites even after accounting for differences in glacio-isostatic adjustment. Sea level in North Carolina rose faster than in New Jersey sea-level during the Medieval Climate Optimum, while sea level in New Jersey rose faster during the Little Ice Age. Spatially variable sea-level rise on the Atlantic coast of North America can be caused by dynamic oceanographic processes and/or melting of the Greenland Ice Sheet. Our analysis suggests that plausible levels of meltwater input from Greenland would be inadequate to explain the reconstructed pattern

  6. Hydrological response to changing climate conditions: Spatial streamflow variability in the boreal region

    Science.gov (United States)

    Teutschbein, Claudia; Grabs, Thomas; Karlsen, Reinert H.; Laudon, Hjalmar; Bishop, Kevin

    2016-04-01

    It has long been recognized that streamflow-generating processes are not only dependent on climatic conditions, but also affected by physical catchment properties such as topography, geology, soils and land cover. We hypothesize that these landscape characteristics do not only lead to highly variable hydrologic behavior of rather similar catchments under the same stationary climate conditions (Karlsen et al., 2014), but that they also play a fundamental role for the sensitivity of a catchment to a changing climate (Teutschbein et al., 2015). A multi-model ensemble based on 15 regional climate models was combined with a multi-catchment approach to explore the hydrologic sensitivity of 14 partially nested and rather similar catchments in Northern Sweden to changing climate conditions and the importance of small-scale spatial variability. Current (1981-2010) and future (2061-2090) streamflow was simulated with the HBV model. As expected, projected increases in temperature and precipitation resulted in increased total available streamflow, with lower spring and summer flows, but substantially higher winter streamflow. Furthermore, significant changes in flow durations with lower chances of both high and low flows can be expected in boreal Sweden in the future. This overall trend in projected streamflow pattern changes was comparable among the analyzed catchments while the magnitude of change differed considerably. This suggests that catchments belonging to the same region can show distinctly different degrees of hydrological responses to the same external climate change signal. We reason that differences in spatially distributed physical catchment properties at smaller scales are not only of great importance for current streamflow behavior, but also play a major role as first-order control for the sensitivity of catchments to changing climate conditions. References Karlsen, R.H., T. Grabs, K. Bishop, H. Laudon, and J. Seibert (2014). Landscape controls on

  7. 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.

  8. Diurnal spatial variability of soil respiration estimated by ordinary kriging and sequential Gaussian simulation

    Science.gov (United States)

    de Bortoli Teixeira, Daniel; Rodrigo Panosso, Alan; Tadeu Pereira, Gener; Pelegrino Cerri, Carlos Eduardo; La Scala, Newton, Jr.

    2010-05-01

    The role of greenhouse gases in the climate change is well know, however, the balance of greenhouse gases due to land use and management is still lacking. Hence it is important to characterize the main aspects of soil respiration (or soil CO2 emission) in agricultural areas, including its spatial variability, as quantitatively as possible. The objective of this work was to study the diurnal spatial variability of the soil respiration including their estimations by different methods: ordinary kriging and sequential Gaussian simulation. Evaluations were conducted in a regular grid having 64 points installed over a bare Eutrustox clay texture during the morning and afternoon periods. Measurements were conducted from 7:30 - 10:30 am (morning) and 13:30 - 16:30 pm (afternoon) using a portable soil respiration system (LI-8100), Lincoln, NE, USA. In order to estimate the best interpolation method it was applied the so-called external validation, where the respiration values of 5 points in grid were removed from interpolation process and after were estimated in the same points by kriging or sequential Gaussian simulation methods. This evaluation was also based on the sum of the square of residues, comparing observed with predicted respiration values in each of the 5 points selected for external validation. The highest CO2 emission was observed in the afternoon period, with mean value of 6.24 µmol m-2 s-1, when compared to the morning (4.54 µmol m-2 s-1). Our results indicate that the measurement period (morning or afternoon) did not interfere into the definition of emission spatial variability structure, as coefficient of variation, spatial variability models and their parameters were quite similar in morning and afternoon. However, despite the high correlation between kriging and sequential Gaussian simulation respiration maps (R2 =0.99) sequential Gaussian simulation showed to be more efficient into the estimations of non-sampled emissions in both periods, mornings and

  9. Investigating soil controls on soil moisture spatial variability: Numerical simulations and field observations

    Science.gov (United States)

    Wang, Tiejun; Franz, Trenton E.; Zlotnik, Vitaly A.; You, Jinsheng; Shulski, Martha D.

    2015-05-01

    Due to its complex interactions with various processes and factors, soil moisture exhibits significant spatial variability across different spatial scales. In this study, a modeling approach and field observations were used to examine the soil control on the relationship between mean (θ bar) and standard deviation (σθ) of soil moisture content. For the numerical experiments, a 1-D vadose zone model along with van Genuchten parameters generated by pedotransfer functions was used for simulating soil moisture dynamics under different climate and surface conditions. To force the model, hydrometeorological and physiological data that spanned over three years from five research sites within the continental US were used. The modeling results showed that under bare surface conditions, different forms of the θ bar -σθ relationship as observed in experimental studies were produced. For finer soils, a positive θ bar -σθ relationship gradually changed to an upward convex and a negative one from arid to humid conditions; whereas, a positive relationship existed for coarser soils, regardless of climatic conditions. The maximum σθ for finer soils was larger under semiarid conditions than under arid and humid conditions, while the maximum σθ for coarser soils increased with increasing precipitation. Moreover, vegetation tended to reduce θ bar and σθ, and thus affected the θ bar -σθ relationship. A sensitivity analysis was also conducted to examine the controls of different van Genuchten parameters on the θ bar -σθ relationship under bare surface conditions. It was found that the residual soil moisture content mainly affected σθ under dry conditions, while the saturated soil moisture content and the saturated hydraulic conductivity largely controlled σθ under wet conditions. Importantly, the upward convex θ bar -σθ relationship was mostly caused by the shape factor n that accounts for pore size distribution. Finally, measured soil moisture data from a

  10. 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.

  11. Spatial variability of E. coli in an urban salt-wedge estuary.

    Science.gov (United States)

    Jovanovic, Dusan; Coleman, Rhys; Deletic, Ana; McCarthy, David

    2017-01-15

    This study investigated the spatial variability of a common faecal indicator organism, Escherichia coli, in an urban salt-wedge estuary in Melbourne, Australia. Data were collected through comprehensive depth profiling in the water column at four sites and included measurements of temperature, salinity, pH, dissolved oxygen, turbidity, and E. coli concentrations. Vertical variability of E. coli was closely related to the salt-wedge dynamics; in the presence of a salt-wedge, there was a significant decrease in E. coli concentrations with depth. Transverse variability was low and was most likely dwarfed by the analytical uncertainties of E. coli measurements. Longitudinal variability was also low, potentially reflecting minimal die-off, settling, and additional inputs entering along the estuary. These results were supported by a simple mixing model that predicted E. coli concentrations based on salinity measurements. Additionally, an assessment of a sentinel monitoring station suggested routine monitoring locations may produce conservative estimates of E. coli concentrations in stratified estuaries. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Geomorphometric tool associated with soil types and properties spatial variability at watersheds under tropical conditions

    Directory of Open Access Journals (Sweden)

    Sérgio Henrique Godinho Silva

    2016-08-01

    Full Text Available ABSTRACT The application of quantitative methods to digital soil and geomorphological mapping is becoming an increasing trend. One of these methods, Geomorphons, was developed to identify the ten most common landforms based on digital elevation models. This study aimed to make a quantitative assessment of the relationships between Geomorphons units, determined at three spatial resolutions and nine radii, and soil types and properties of two watersheds with different soil-landscape relationships in Brazil to help soil surveying and mapping under tropical conditions. The study was conducted at Lavrinha Creek (LCW and Marcela Creek (MCW watersheds, located in the state of Minas Gerais, Brazil. Spatial resolutions of 10, 20 and 30 m were the basis for generating Geomorphons at 9 radii of calculation for the watersheds. They were overlapped to detailed soil maps of the watersheds and a chi-square test was carried out to assess their relationship with soil types. Observation points were compared with the most highly correlated Geomorphons to also assess relationships with soil properties. Geomorphons with resolution of 30-m and radii of 20 and 50 cells, respectively for LCW and MCW, were more highly correlated with the variability of soil types, in accordance with the terrain features of these watersheds. The majority of observation points for each soil type was located in the same Geomorphon unit that was dominant when analyzing soil maps. There was less variability in soil properties between Geomorphon units, which was probably due to the highly weathered-leached stage of soils. Geomorphons can help to improve soil maps in tropical conditions when assessing soil variability due to its high correlation with tropical soil types variability.

  13. Soil internal drainage: temporal stability and spatial variability in succession bean-black oat

    Science.gov (United States)

    Salvador, M. M. S.; Libardi, P. L.; Moreira, N. B.; Sousa, H. H. F.; Neiverth, C. A.

    2012-04-01

    . During the period when the water flow in soil is higher, there is strong temporal stability in the depth of 0.40 m, which is the opposite for the periods of drying. The lowest relative difference and standard deviation for the internal drainage obtained during the cultivation of beans and depth of 0.40 m confirm the hypothesis that the research carried out during periods of soil water recharge have less variability than those in the drying period. Temporal stability was due to the topographic position of selected points, since the points chosen for the depth of 0.40 m in both growing seasons, are located on the lower portion of the relief, and the nominees for the depth of 0,80 m, the highest portion. There were differences in the spatial pattern of water flow in the soil along the crop succession, i.e. the seasonal demand for water by plants and evaporation from the soil at the time of drying, changed their distribution model with internal drainage phases and stages capillary rise.

  14. Stochastic spatial models of plant diseases

    CERN Document Server

    Brown, D H

    2001-01-01

    I present three models of plant--pathogen interactions. The models are stochastic and spatially explicit at the scale of individual plants. For each model, I use a version of pair approximation or moment closure along with a separation of timescales argument to determine the effects of spatial clustering on threshold structure. By computing the spatial structure early in an invasion, I find explicit corrections to mean field theory. In the first chapter, I present a lattice model of a disease that is not directly lethal to its host, but affects its ability to compete with neighbors. I use a type of pair approximation to determine conditions for invasions and coexistence. In the second chapter, I study a basic SIR epidemic point process in continuous space. I implement a multiplicative moment closure scheme to compute the threshold transmission rate as a function of spatial parameters. In the final chapter, I model the evolution of pathogen resistance when two plant species share a pathogen. Evolution may lead...

  15. 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)

  16. Spatial Variability of Salt Ions in Soils in Oasis of Delta of Weigan River-Kuqa River in Different Seasons

    Institute of Scientific and Technical Information of China (English)

    Yuanyuan XU; Tashpolat TIYIP; Fei ZHANG; Jun XIA; Guihong HAN; Mamat SAWUT

    2012-01-01

    Abstract [Objective] The aim was to research the spatial variability of salts' ions in soils of oases of Weigan River-Kuqa River to explore spatial correlation among soil ions and effects of seasonal changes on distribution of eight ions in soils. [Method] Based on soil salinizaiton, temporal and spatial distributions of eight ions in surface soils were researched and distribution maps were drawn with the help of classical statistics and geostatistics. [Result] All contents of salt ions were proved in consistent with normal distribution by K-S test, and variability of the ions changed with season, for example, variability of ions in soils in October was higher than that in April, which might be caused by little rainfall and high evaporation in October. It can be concluded from semivariance analysis that salt ions were all of high spatial autocorrelation. In addition, it can be concluded from spatial distribution that spatial structures of most ions are reasonable, except Ca2+ and Mg2' in soils collected in April of 2010 and Mg2+ in October of 2010, which were more suitable for linear model, and chloride-sulfate dominated in salinized soils in the researching area. [Conclusion] The research laid foundation for partition, improvement and use of salin- ized soils in delta of Weigan River-Kuqa River.

  17. Spatial and temporal variability of water repellency in a sandy soil contaminated with tar oil and heavy metals.

    Science.gov (United States)

    Buczko, Uwe; Bens, Oliver; Durner, Wolfgang

    2006-12-15

    Water repellency can induce preferential flow and thus affect water flow and contaminant transport at hazardous waste sites. Since the spatial patterns of water repellency are mostly unknown, it is problematic to use numerical transport models to predict leachate composition. In this study, the spatial variability of soil water repellency was studied at an industrial site contaminated with tar oil, chromium, copper and arsenic. The persistence of water repellency was assessed by the water drop penetration time (WDPT), and the degree of water repellency was quantified by the ethanol percentage (EP) test. Measurements were made at the soil surface along 3.5-12.1 m long transects at different times between March and October 2002. The spatial variability of WDPT, EP, water content, and organic matter content was quantified by variogram analyses. Both the persistence and the degree of water repellency varied seasonally, with the highest water repellency during the summer months. The correlation lengths of WDPT values ranged between 16 and 406 cm, whereas EP values showed no spatial correlation. For field-moist samples, a critical soil water threshold, below which water repellency prevails, was estimated to be 2.5-4%. For oven dry samples, the WDPT values were dependent on the water content prior to drying. The wide range of correlation lengths and the temporal dynamics of spatial repellency patterns suggest that simulations of solute leaching must consider the spatial and temporal variability of soil hydrophobic properties.

  18. Drivers for spatial variability in agricultural soil organic carbon stocks in Germany

    Science.gov (United States)

    Vos, Cora; Don, Axel; Hobley, Eleanor; Prietz, Roland; Heidkamp, Arne; Freibauer, Annette

    2017-04-01

    Soil organic carbon is one of the largest components of the global carbon cycle. It has recently gained importance in global efforts to mitigate climate change through carbon sequestration. In order to find locations suitable for carbon sequestration, and estimate the sequestration potential, however, it is necessary to understand the factors influencing the high spatial variability of soil organic carbon stocks. Due to numerous interacting factors that influence its dynamics, soil organic carbon stocks are difficult to predict. In the course of the German Agricultural Soil Inventory over 2500 agricultural sites were sampled and their soil organic carbon stocks determined. Data relating to more than 200 potential drivers of SOC stocks were compiled from laboratory measurements, farmer questionnaires and climate stations. The aims of this study were to 1) give an overview of soil organic carbon stocks in Germany's agricultural soils, 2) to quantify and explain the influence of explanatory variables on soil organic carbon stocks. Two different machine learning algorithms were used to identify the most important variables and multiple regression models were used to explore the influence of those variables. Models for predicting carbon stocks in different depth increments between 0-100 cm were developed, explaining up to 62% (validation, 98% calibration) of total variance. Land-use, land-use history, clay content and electrical conductivity were main predictors in the topsoil, while bedrock material, relief and electrical conductivity governed the variability of subsoil carbon stocks. We found 32% of all soils to be deeply anthropogenically transformed. The influence of climate related variables was surprisingly small (≤5% of explained variance), while site variables explained a large share of soil carbon variability (46-100% of explained variance), in particular in the subsoil. Thus, the understanding of SOC dynamics at regional scale requires a thorough description

  19. Spatially explicit non-Mendelian diploid model

    CERN Document Server

    Lanchier, N; 10.1214/09-AAP598

    2009-01-01

    We introduce a spatially explicit model for the competition between type $a$ and type $b$ alleles. Each vertex of the $d$-dimensional integer lattice is occupied by a diploid individual, which is in one of three possible states or genotypes: $aa$, $ab$ or $bb$. We are interested in the long-term behavior of the gene frequencies when Mendel's law of segregation does not hold. This results in a voter type model depending on four parameters; each of these parameters measures the strength of competition between genes during meiosis. We prove that with or without a spatial structure, type $a$ and type $b$ alleles coexist at equilibrium when homozygotes are poor competitors. The inclusion of a spatial structure, however, reduces the parameter region where coexistence occurs.

  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 Winter Accumulation on Taku Glacier, Southeast Alaska, between 2012 and 2015

    Science.gov (United States)

    Smith, B.; Campbell, S. W.; Hollander, J.; Slavin, B. V.; Wolf, J.; Wilner, J.; Moore, T.

    2015-12-01

    Glacier mass balance is an integral part of understanding a glacier's health and dynamics. A key component of determining mass balance is winter accumulation which is traditionally estimated by digging and measuring snow densities from within snow pits. However, this method represents a labor-intensive point measurement which may not fully capture spatial variability of accumulation. To more efficiently estimate spatial variability of winter accumulation across Taku Glacier and its main tributaries in southeastern Alaska in 2015, we used a 400 MHz Ground Penetrating Radar (GPR) Common Offset (CO) surveys along centerline transects which were also collected during a 2012 study. We used common midpoint (CMP) surveys, migration, snow pits, and probing to improve depth estimates and provide ground truth of winter accumulation depth measurements from CO surveys. We determined that the winter accumulation was significantly lower in 2015 than in 2012. However, gradients in accumulation versus elevation were consistent from year to year along centerline transects. We suggest that this low accumulation may be influencing the recent two year stall of Taku Glacier which has exhibited an advancing terminus for nearly a century. We recommend that further studies be conducted to extend the reach of this dataset beyond 2 years. This data would be invaluable to future models and mass balance studies on the Icefield and may capture key components that suggest a tipping point from advance to retreat of Taku Glacier.

  2. 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.

  3. 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...

  4. 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...

  5. Spatial variability and sources of ammonia in three European cities

    Science.gov (United States)

    Prevot, Andre S. H.; Elser, Miriam; El Haddad, Imad; Maasikmets, Marek; Bozzetti, Carlo; Robert, Wolf; Richter, Rene; Slowik, Jay; Teinemaa, Erik; Hueglin, Christoph; Baltensperger, Urs

    2017-04-01

    For the assessment of ammonia (NH3) effects on ecosystems and climate, one would ideally know the emission sources and also the spatial distributions. Agriculture is the largest global source of NH3. However traffic, especially gasoline vehicles, biomass burning or waste management can be significant in urban areas. Ambient NH3 measurements using cavity ring-down spectroscopy were performed online at high time resolution on a moving vehicle in three cities: Zurich (Switzerland), Tartu (Estonia) and Tallinn (Estonia). Initial tests showed that a regular inlet cannot be used. A heated line including an auxiliary flow was finally deployed to minimize NH3 adsorption onto the inlet walls. We will present the characterization of the response and recovery times of the measurement system which was used to deconvolve the true NH3 signal from the remaining adsorption-induced hysteresis. Parallel measurements with an Aerodyne aerosol mass spectrometer were used to correct the observed NH3 for the contribution of ammonium nitrate (NH4NO3) which completely evaporated to NH3 and nitric acid (HNO3) in the heated line at the chosen temperature, in contrast to ammonium sulfate. Finally, quantitative measurements of ambient NH3 are possible with sufficient time resolution to enable measurement of NH3 point or line sources with a mobile sampling platform. The NH3 analyzer and the aerosol mass spectrometer were complemented by an aethalometer to measure black carbon and various gas-phase analyzers to enable a complete characterization of the sources of air pollution, including the spatial distributions and the regional background concentrations and urban increments of all measured components. Although at all three locations similar urban increment levels of organic aerosols were attributed to biomass burning and traffic, traffic emissions clearly dominated the city enhancements of NH3, equivalent black carbon (eBC) and carbon dioxide (CO2). Concentration gradients in areas strongly

  6. Experimental falsification of Leggett's nonlocal variable model.

    Science.gov (United States)

    Branciard, Cyril; Ling, Alexander; Gisin, Nicolas; Kurtsiefer, Christian; Lamas-Linares, Antia; Scarani, Valerio

    2007-11-23

    Bell's theorem guarantees that no model based on local variables can reproduce quantum correlations. Also, some models based on nonlocal variables, if subject to apparently "reasonable" constraints, may fail to reproduce quantum physics. In this Letter, we introduce a family of inequalities, which use a finite number of measurement settings, and which therefore allow testing Leggett's nonlocal model versus quantum physics. Our experimental data falsify Leggett's model and are in agreement with quantum predictions.

  7. Eddies spatial variability at Makassar Strait – Flores Sea

    Science.gov (United States)

    Nuzula, F.; Syamsudin, M. L.; Yuliadi, L. P. S.; Purba, N. P.; Martono

    2017-01-01

    This study was aimed to get the distribution of eddies spatially and temporally from Makassar Waters (MW) to Flores Sea (FS), as well as its relations with the upwelling, the downwelling, and chlorophyll-a concentration. The study area extends from 115º–125º E to 2.5º–8º S. The datasets were consisted of monthly geostrophic currents, sea surface heights, sea surface temperatures, and chlorophyll-a from 2008 – 2012. The results showed that eddies which found at Makassar Strait (MS) has the highest diameter and speed of 255.3 km and 21.4 cm/s respectively, while at the southern MW has 266.4 km and 15.6 cm/s, and at FS has 182.04 km and 11.4 cm/s. From a total of 51 eddies found, the majority of eddies type was anticyclonic. At MS and FS, eddies formed along the year, whereas at southern MW were found missing in West Season. Moreover, the chlorophyll-a concentration was consistently higher at the eddies area. Even though, the correlation among eddies and the upwelling downwelling phenomena was not significantly as shown by sea surface temperatures value.

  8. Spatial monsoon variability with respect to NAO and SO

    Indian Academy of Sciences (India)

    S B Kakade; S S Dugam

    2006-10-01

    In this paper, the simultaneous effect of North Atlantic Oscillation (NAO) and Southern Oscillation (SO) on monsoon rainfall over different homogeneous regions/subdivisions of India is studied. The simultaneous effect of both NAO and SO on Indian summer monsoon rainfall (ISMR) is more important than their individual impact because both the oscillations exist simultaneously throughout the year. To represent the simultaneous impact of NAO and SO, an index called effective strength index (ESI) has been defined on the basis of monthly NAO and SO indices. The variation in the tendency of ESI from January through April has been analyzed and reveals that when this tendency is decreasing, then the ESI value throughout the monsoon season (June-September) of the year remains negative and vice versa. This study further suggests that during the negative phase of ESI tendency, almost all subdivisions of India show above-normal rainfall and vice versa. The correlation analysis indicates that the ESI-tendency is showing an inverse and statistically significant relationship with rainfall over 14 subdivisions of India. Area wise, about 50% of the total area of India shows statistically significant association. Moreover, the ESI-tendency shows a significant relationship with rainfall over north west India, west central India, central north east India, peninsular India and India as a whole. Thus, ESI-tendency can be used as a precursor for the prediction of Indian summer monsoon rainfall on a smaller spatial scale.

  9. Modeling Spatially Unrestricted Pedestrian Traffic on Footbridges

    DEFF Research Database (Denmark)

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

    2010-01-01

    The research into modelling walking-induced dynamic loading and its effects on footbridge structures and people using them has been intensified in the last decade after some high profile vibration serviceability failures. In particular, the crowd induced loading, characterised by spatially...... 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...

  10. Modelling spatial density using continuous wavelet transforms

    Indian Academy of Sciences (India)

    D Sudheer Reddy; N Gopal Reddy; A K Anilkumar

    2013-02-01

    Due to increase in the satelite launch activities from many countries around the world the orbital debris issue has become a major concern for the space agencies to plan a collision-free orbit design. The risk of collisions is calculated using the in situ measurements and available models. Spatial density models are useful in understanding the long-term likelihood of a collision in a particular region of space and also helpful in pre-launch orbit planning. In this paper, we present a method of estimating model parameters such as number of peaks and peak locations of spatial density model using continuous wavelets. The proposed methodology was experimented with two line element data and the results are presented.

  11. Environmental versus demographic variability in stochastic predator-prey models

    Science.gov (United States)

    Dobramysl, U.; Täuber, U. C.

    2013-10-01

    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.

  12. 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.

  13. Decision variables analysis for structured modeling

    Institute of Scientific and Technical Information of China (English)

    潘启树; 赫东波; 张洁; 胡运权

    2002-01-01

    Structured modeling is the most commonly used modeling method, but it is not quite addaptive to significant changes in environmental conditions. Therefore, Decision Variables Analysis(DVA), a new modelling method is proposed to deal with linear programming modeling and changing environments. In variant linear programming , the most complicated relationships are those among decision variables. DVA classifies the decision variables into different levels using different index sets, and divides a model into different elements so that any change can only have its effect on part of the whole model. DVA takes into consideration the complicated relationships among decision variables at different levels, and can therefore sucessfully solve any modeling problem in dramatically changing environments.

  14. 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

  15. Circulation weather types and spatial variability of daily precipitation in the Iberian Peninsula %K circulation weather types, daily gridded precipitation, Iberian Peninsula, spatial variability, seasonal variability

    Science.gov (United States)

    Ramos, Alexandre; Cortesi, Nicola; Trigo, Ricardo

    2014-10-01

    The relationships between atmospheric circulation patterns and daily Iberian rainfall are here explored at high spatial resolution (0.2°) using the Jenkinson and Collison automated classification scheme with 26 Weather Types (WTs). The WTs were computed by means of the daily EMULATE Mean Sea Level Pressure dataset (EMSLP) while the high resolution precipitation database corresponds to the recent Iberia02 daily gridded precipitation dataset over the 1950-2003 period. Six monthly indexes relating the WTs and precipitation were analyzed: their Frequency, the Mean Precipitation, the Percentage Contribution, the Area of Influence, the Precipitation Intensity and Efficiency. Except for the Frequency of the WTs, all other indexes were evaluated studying their spatial distribution over the Iberian Peninsula, focusing on a WT and a month at time. A small number of WTs (7) was found to capture a high percentage (~70%) of monthly Iberian precipitation. The Westerly WT is the most influent one, followed by the Cyclonic, the Northwesterly and the Southwesterly WTs. Westerly flows, however, do not affect the Mediterranean fringe or the Cantabrian coast, which are dominated by the Easterly and Northerly WTs, respectively. Rainfall along the Mediterranean coastline and the Ebro basin depends on a variety of WTs, but their effects are confined to narrow areas and short temporal intervals, suggesting that local factors such as convective processes, orography and the proximity to a warm water body could play a major role in precipitation processes. We show that the use of daily gridded precipitation dataset holds the advantage of measuring the daily rainfall amount due to each WT directly instead to relying on the predicted values of the regression model as done in previous works.

  16. Spatial variability, structure and composition of crustose algal communities in Diadema africanum barrens

    Science.gov (United States)

    Sangil, Carlos; Sansón, Marta; Díaz-Villa, Tania; Hernández, José Carlos; Clemente, Sabrina; Afonso-Carrillo, Julio

    2014-12-01

    Crustose algal communities were studied in Diadema africanum urchin barrens around Tenerife (Canary Islands, NE Atlantic). A hierarchical nested sampling design was used to study patterns of community variability at different spatial scales (sectors, three sides of the island; sites within each sector, 5-10 km apart; stations within each site, 50-100 m apart). Although noncrustose species contributed the most to community richness, cover was dominated by crustose forms, like the coralline algae Hydrolithon farinosum, H. samoënse, H. onkodes, Neogoniolithon orotavicum and N. hirtum, and the phaeophycean Pseudolithoderma adriaticum. The structure of these communities showed high spatial variability, and we found differences in the structure of urchin barrens when compared across different spatial scales. Multivariate analysis showed that variability in community structure was related to the five environmental variables studied (wave exposure, urchin density, substrate roughness, productivity and depth). Wave exposure was the variable that contributed most to community variability, followed by urchin density and substrate roughness. Productivity and depth had limited influence. The effects of these variables differed depending on the spatial scale; wave exposure and productivity were the main variables influencing community changes at the largest scale (between different sectors of the island), while D. africanum density, roughness and depth were the most influential at medium and small scales.

  17. A methodology to support the decision to invest in spatially variable nitrogen fertilisation

    NARCIS (Netherlands)

    Smit, A.B.; Stoorvogel, J.J.; Wossink, G.A.A.

    2000-01-01

    This paper reports a methodology to define and select basic activities for spatially variable N-management, referred to as management tracks. Their main purpose is to support decision making whether or not to apply variable nitrogen fertilisation. The methodology is based on biophysical simulation o

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

    CERN Document Server

    Skrondal, Anders

    2004-01-01

    METHODOLOGY THE OMNI-PRESENCE OF LATENT VARIABLES Introduction 'True' variable measured with error Hypothetical constructs Unobserved heterogeneity Missing values and counterfactuals Latent responses Generating flexible distributions Combining information Summary MODELING DIFFERENT RESPONSE PROCESSES Introduction Generalized linear models Extensions of generalized linear models Latent response formulation Modeling durations or survival Summary and further reading CLASSICAL LATENT VARIABLE MODELS Introduction Multilevel regression models Factor models and item respons

  19. Classification of missing values in spatial data using spin models

    CERN Document Server

    Žukovič, Milan; 10.1103/PhysRevE.80.011116

    2013-01-01

    A problem of current interest is the estimation of spatially distributed processes at locations where measurements are missing. Linear interpolation methods rely on the Gaussian assumption, which is often unrealistic in practice, or normalizing transformations, which are successful only for mild deviations from the Gaussian behavior. We propose to address the problem of missing values estimation on two-dimensional grids by means of spatial classification methods based on spin (Ising, Potts, clock) models. The "spin" variables provide an interval discretization of the process values, and the spatial correlations are captured in terms of interactions between the spins. The spins at the unmeasured locations are classified by means of the "energy matching" principle: the correlation energy of the entire grid (including prediction sites) is estimated from the sample-based correlations. We investigate the performance of the spin classifiers in terms of computational speed, misclassification rate, class histogram an...

  20. 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.

  1. Hot spots of mercury methylation in northern peatlands : spatial and seasonal variability

    Energy Technology Data Exchange (ETDEWEB)

    Mitchell, C.P.J.; Branfireun, B.A. [Toronto Univ., ON (Canada). Dept. of Geography; Heyes, A. [Maryland Univ., Solomons, MD (United States). Center for Environmental Science, Chesapeake Biological Lab; Kolka, R.K. [USDA Forest Service, North Central Research Station, Grand Rapids, MN (United States)

    2006-07-01

    Studies have shown that peatlands are an important sources of methylmercury (MeHg) to downstream aquatic ecosystems at the watershed scale. Hot spots of overly high MeHg concentration have been found to occur periodically in zones of groundwater upwelling. However, the spatiotemporal distribution, size, and importance of these MeHg hot spots to peatland MeHg export are not currently understood. For that reason, peat pore waters were sampled extensively throughout 4 small, northern peatlands in order to assess the spatial patterns of total mercury (HgT) and MeHg. The transferability of findings among peatlands was also assessed. Sampling took place during the spring, mid-summer and fall of 2005 at 2 peatlands in north central Minnesota and 2 in northwestern Ontario. In addition to this spatial survey, 4 high-resolution sampling grids were also established at the Minnesota sites in order to investigate the size of hot spots and the effects of upland runoff. In all cases, spatial variability in pore water MeHg concentration was much higher than the variability in HgT concentration, with standard deviations typically exceeding mean values. The spatial pattern of pore water MeHg concentration was characterized into the following 2 distinct zones: (1) the upland-peatland interface, where MeHg concentrations higher than 2 ng/L occur consistently, (2) the peatland interior, where concentrations rarely exceed 0.5 ng/L. In addition, extremely high concentrations of up to 12 ng/L were only found at the upland-peatland interface. These values corresponded to MeHg:HgT ratios greater than 65 per cent, while these ratios rarely exceeded 5 to 10 per cent elsewhere. It was concluded that these hot spots can be attributed to the limited reactants in zones where the in-situ biogeochemical milieu is conducive to mercury methylation. The uniform patterns noted across the peatlands indicate that spatial patterns of HgT and MeHg are important for mechanistic modelling and landscape

  2. Variability in a Community-Structured SIS Epidemiological Model.

    Science.gov (United States)

    Hiebeler, David E; Rier, Rachel M; Audibert, Josh; LeClair, Phillip J; Webber, Anna

    2015-04-01

    We study an SIS epidemiological model of a population partitioned into groups referred to as communities, households, or patches. The system is studied using stochastic spatial simulations, as well as a system of ordinary differential equations describing moments of the distribution of infectious individuals. The ODE model explicitly includes the population size, as well as the variability in infection levels among communities and the variability among stochastic realizations of the process. Results are compared with an earlier moment-based model which assumed infinite population size and no variance among realizations of the process. We find that although the amount of localized (as opposed to global) contact in the model has little effect on the equilibrium infection level, it does affect both the timing and magnitude of both types of variability in infection level.

  3. Evaluating stream health based environmental justice model performance at different spatial scales

    Science.gov (United States)

    Daneshvar, Fariborz; Nejadhashemi, A. Pouyan; Zhang, Zhen; Herman, Matthew R.; Shortridge, Ashton; Marquart-Pyatt, Sandra

    2016-07-01

    This study evaluated the effects of spatial resolution on environmental justice analysis concerning stream health. The Saginaw River Basin in Michigan was selected since it is an area of concern in the Great Lakes basin. Three Bayesian Conditional Autoregressive (CAR) models (ordinary regression, weighted regression and spatial) were developed for each stream health measure based on 17 socioeconomic and physiographical variables at three census levels. For all stream health measures, spatial models had better performance compared to the two non-spatial ones at the census tract and block group levels. Meanwhile no spatial dependency was found at the county level. Multilevel Bayesian CAR models were also developed to understand the spatial dependency at the three levels. Results showed that considering level interactions improved models' prediction. Residual plots also showed that models developed at the block group and census tract (in contrary to county level models) are able to capture spatial variations.

  4. 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.

  5. Spatial variability change of MgO content in Jelsava magnesite deposit (Slovakia)

    OpenAIRE

    J. Kondela; Jacko, S.; L. Vizi

    2017-01-01

    The presented paper deals with the study of the spatial variability changes of MgO content within the Jelšava magnesite deposit in Slovakia. The geostatistical structural analysis was used to study the spatial variability changes of the MgO content within three mining sectors A, B and C, localised in different parts of the deposit. The results show some important connections between the variability of MgO content and the structure setting of the deposit with utilization for the magnesite proc...

  6. Management model application at nested spatial levels in Mediterranean Basins

    Science.gov (United States)

    Lo Porto, Antonio; De Girolamo, Anna Maria; Froebrich, Jochen

    2014-05-01

    In the EU Water Framework Directive (WFD) implementation processes, hydrological and water quality models can be powerful tools that allow to design and test alternative management strategies, as well as judging their general feasibility and acceptance. Although in recent decades several models have been developed, their use in Mediterranean basins, where rivers have a temporary character, is quite complex and there is limited information in literature which can facilitate model applications and result evaluations in this region. The high spatial variability which characterizes rainfall events, soil hydrological properties and land uses of Mediterranean basin makes more difficult to simulate hydrological and water quality in this region than in other Countries. This variability also has several implications in modeling simulations results especially when simulations at different spatial scale are needed for watershed management purpose. It is well known that environmental processes operating at different spatial scale determine diverse impacts on water quality status (hydrological, chemical, ecological). Hence, the development of management strategies have to include both large scale (watershed) and local spatial scales approaches (e.g. stream reach). This paper presents the results of a study which analyzes how the spatial scale affects the results of hydrologic process and water quality of model simulations in a Mediterranean watershed. Several aspects involved in modeling hydrological and water quality processes at different spatial scale for river basin management are investigated including model data requirements, data availability, model results and uncertainty. A hydrologic and water quality model (SWAT) was used to simulate hydrologic processes and water quality at different spatial scales in the Candelaro river basin (Puglia, S-E Italy) and to design management strategies to reach as possible WFD goals. When studying a basin to assess its current status

  7. 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

    2017-05-01

    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.

  8. Spatial relationships between polychaete assemblages and environmental variables over broad geographical scales.

    Directory of Open Access Journals (Sweden)

    Lisandro Benedetti-Cecchi

    Full Text Available This study examined spatial relationships between rocky shore polychaete assemblages and environmental variables over broad geographical scales, using a database compiled within the Census of Marine Life NaGISA (Natural Geography In Shore Areas research program. The database consisted of abundance measures of polychaetes classified at the genus and family levels for 74 and 93 sites, respectively, from nine geographic regions. We tested the general hypothesis that the set of environmental variables emerging as potentially important drivers of variation in polychaete assemblages depend on the spatial scale considered. Through Moran's eigenvector maps we indentified three submodels reflecting spatial relationships among sampling sites at intercontinental (>10,000 km, continental (1000-5000 km and regional (20-500 km scales. Using redundancy analysis we found that most environmental variables contributed to explain a large and significant proportion of variation of the intercontinental submodel both for genera and families (54% and 53%, respectively. A subset of these variables, organic pollution, inorganic pollution, primary productivity and nutrient contamination was also significantly related to spatial variation at the continental scale, explaining 25% and 32% of the variance at the genus and family levels, respectively. These variables should therefore be preferably considered when forecasting large-scale spatial patterns of polychaete assemblages in relation to ongoing or predicted changes in environmental conditions. None of the variables considered in this study were significantly related to the regional submodel.

  9. 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.

  10. 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.

  11. Evaluation of the spatial variability of soil water content at the spatial resolution of SMAP data products : case studies in Italy and Morocco

    Science.gov (United States)

    Menenti, Massimo; Akdim, Nadia; Alfieri, Silvia Maria; Labbassi, Kamal; De Lorenzi, Francesca; Bonfante, Antonello; Basile, Angelo

    2014-05-01

    Frequent and contiguous observations of soil water content such as the ones to be provided by SMAP are potentially useful to improve distributed models of soil water balance. This requires matching of observations and model estimates provided both sample spatial patterns consistently. The spatial resolution of SMAP soil water content data products ranges from 3 km X 3 km to 40 km X 40 km. Even the highest spatial resolution may not be sufficient to capture the spatial variability due to terrain, soil properties and precipitation. We have evaluated the SMAP spatial resolution against spatial variability of soil water content in two Mediterranean landscapes: a hilly area dominated by vineyards and olive orchards in Central Italy and a large irrigation schemes (Doukkala) in Morocco. The "Valle Telesina" is a 20,000 ha complex landscape located in South Italy in the Campania region, which has a complex geology and geomorphology and it is characterised by an E-W elongated graben where the Calore river flows. The main crops are grapevine (6,448 ha) and olive (3,390 ha). Soil information was mainly derived from an existing soil map at 1:50 000 scale (Terribile et al., 1996). The area includes 47 SMUs (Soil Mapping Units) and about 60 soil typological units (STUs). (Bonfante et al., 2011). In Doukkala, the soil water retention and unsaturated capillary conductivity were estimated from grain size distribution of a number of samples (22 pilot points, each one sampled in 3 horizons of 20cm), and combined with a soil map. The land use classification was carried out using a NDVI time series at high spatial resolution (Landsat TM and SPOT HRV). We have calculated soil water content for each soil unit in each area in response to several climate cases generating daily maps of soil water content at different depths. To reproduce spatial sampling by SMAP we have filtered these spatial patterns by calculating box averages with grid sizes of 1 km X 1 km and 5 km X 5 km. We have

  12. [Spatial variability and management zone of soil major nutrients in tobacco fields in Qiannan mountainous region].

    Science.gov (United States)

    Wu, De-Chuan; Luo, Hong-Xiang; Song, Ze-Min; Guo, Guang-Dong; Chen, Yong-An; Li, Yu-Xiang; Jiang, Yu-Ping; Li, Zhang-Hai

    2014-06-01

    Spatial variability and management zone of soil major nutrients in tobacco fields in Qian-nan mountainous region were analyzed using geostatistics and fuzzy c-mean algorithm. Results indicated that the level of soil organic matter (OM) was moderate, and alkalytic nitrogen (AN), available phosphorus (AP) and available potassium (AK) were rich according to tobacco soil nutrient classification standards. Coefficients of variation (CV) of OM, AN, AP and AK were moderate. Contents of OM, AN, AP and AK fitted log-normal distributions. Correlation analysis showed moderate correlations between OM and AN, AP and AK. OM and AN were best described by Gaussian semivariogram models, while AP and AK were described by exponential models. The four nutrients displayed moderate spatial autocorrelation. There were significant differences among lag distances of four soil nutrients. OM, AN, AP and AK in the majority of studied regions varied at moderate to very rich levels, and deficiencies of OM, AN, AP and AK only accounted for 0.93%, 0.53%, 0.24% and 7.91% of the total studied region, respectively. Based on the results, the studied region was divided into two management zones (MZ), namely MZ1 and MZ2, accounting for 69. 8% and 30. 2% of the studied region respectively. The soil levels of OM, AN, AP and AK in MZ1 were significantly lower than those in MZ2 (P < 0.01).

  13. 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

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

    Directory of Open Access Journals (Sweden)

    U. Mishra

    2012-05-01

    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: 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 potential

  15. 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...

  16. Distributed multi-criteria model evaluation and spatial association analysis

    Science.gov (United States)

    Scherer, Laura; Pfister, Stephan

    2015-04-01

    high spatial association with the aridity index (ratio of mean annual precipitation to mean annual potential evapotranspiration). This association was still significant when controlling for slopes which manifested the second highest spatial association. In line with these findings, overall model efficiency of the entire Mississippi watershed appeared better when weighted with mean observed river discharge. Furthermore, the model received the highest rating with regards to PBIAS and was judged worst when considering NSE as the most comprehensive indicator. No universal performance indicator exists that considers all aspects of a hydrograph. Therefore, sound model evaluation must take into account multiple criteria. Since model efficiency varies in space which is masked by aggregated ratings spatially explicit model goodness should be communicated as standard praxis - at least as a measure of spatial variability of indicators. Furthermore, transparent documentation of the evaluation procedure also with regards to weighting of aggregated model performance is crucial but often lacking in published research. Finally, the high spatial association between model performance and aridity highlights the need to improve modelling schemes for arid conditions as priority over other aspects that might weaken model goodness.

  17. 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.

  18. 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...... length of the hydraulic conductivity has been determined for each of the three hydrogeological layers and is found to be small (1–2.5 m). The asymptotic longitudinal dispersivity of the aquifer has been estimated from the variance in hydraulic conductivity and the horizontal correlation length...

  19. 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...

  20. 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.

  1. Influence of deterministic geologic trends on spatial variability of hydrologic properties in volcanic tuff

    Energy Technology Data Exchange (ETDEWEB)

    Rautman, C.A. [Sandia National Labs., Albuquerque, NM (United States); Flint, A.L. [Geological Survey, Mercury, NV (United States); Chornack, M.P. [Geological Survey, Denver, CO (United States); Istok, J.D. [Oregon State Univ., Corvallis, OR (United States). Dept. of Civil Engineering; Fling, L.E. [Raytheon Services Nevada, Mercury, NV (United States)

    1992-12-31

    Hydrologic properties have been measured on outcrop samples taken from a detailed, two-dimension grid covering a 1.4 km outcrop exposure of the 10-m thick non-welded-to-welded, shardy base microstratigraphic unit of the Tiva Canyon Member of the Miocene Paintbrush Tuff at Yucca Mountain, Nevada. These data allow quantification of spatial trends in rock matrix properties that exist in this important hydrologic unit. Geologic investigation, combined with statistical and geostatistical analyses of the numerical data, indicates that spatial variability of matrix properties is related to deterministic geologic processes that operated throughout the region. Linear vertical trends in hydrologic properties are strongly developed in the shardy base microstratigraphic unit, and they are more accurately modeled using the concept of a thickness-normalized stratigraphic elevation within the unit, rather than absolute elevation. Hydrologic properties appear to be correlated over distances of 0.25 to 0.3 of the unit thickness after removing the deterministic vertical trend. The use of stratigraphic elevation allows scaling of identified trends by unit thickness which may be of particular importance in a basal, topography-blanketing unit such as this one. Horizontal changes in hydrologic properties do not appear to form obvious trends within the limited lateral geographic extent of the ash-flow environment that was examined. Matrix properties appear to be correlated horizontally over distances between 100 and 400 m. The existence and quantitative description of these trends and patterns of vertical spatial continuity should increase confidence in models of hydrologic properties and groundwater flow in this area that may be constructed to support the design of a potential high-level nuclear waste repository at Yucca Mountain.

  2. Vegetation and Variable Snow Cover: Spatial Patterns of Shrubland, and Grassland Snow

    Science.gov (United States)

    Liston, G. E.; Hiemstra, C. A.; Strack, J. E.

    2003-12-01

    Regions that experience long winters with snowfall and high winds frequently exhibit heterogeneous snow distribution patterns that arise from interactions among snow, wind, topography, and vegetation. Variable snow cover and resultant heterogeneities in albedo and growing season length can affect local weather patterns and energy budgets, and produce spatially co-variable ecosystem properties. While snow influences local atmospheric processes and ecosystems, an important and underappreciated feedback exists between vegetation and snow cover. Plant size, canopy density, and rigidity determine how much snow accumulates on the lee side of individual plants (e.g., shrubland vs. grassland). In addition, the canopy can also influence how much energy reaches the snowpack, thereby hindering or accelerating snowmelt. An overhanging canopy reduces incoming solar radiation while providing a source of turbulent sensible and longwave radiative energy. Historically, most snow vegetation interaction studies have been limited to areas that experience an abundance of snow (e.g., mountainous areas) where trees have a large influence on seasonal snow-cover. In contrast, snow cover patterns associated with shrublands and grasslands have received little attention, despite covering vast expanses (53%) of the seasonally snow-covered globe. In this study, snow depths were measured every two weeks from December through March in a small, 0.25 km2 study area located in North Park, Colorado. The study area possesses little topographic relief and consists of shrub patches, dominated by greasewood (Sarcobatus vermiculatus) and sagebrush (Artemisia tridentata), embedded in a matrix of graminoids (sedges, rushes, and grasses). Snow cover patterns and spatial statistics were dramatically different in graminoid-dominated cover compared with shrub cover. The graminoid snow cover was thinner, less variable, and more ephemeral than the shrub snow pack. Snow was readily eroded by wind from graminoid

  3. Research of ERP model system of spatial data warehouse

    Institute of Scientific and Technical Information of China (English)

    CHEN Xue-long; WANG Yan-zhang

    2004-01-01

    The broad sharing of spatial information is demanded in the infrastructure construction of spatial data in our country. And the spatial data warehouse realizes the effective management and sharing of spatial information serving as an efficient tool. This article proposes ERP model system that of general-decision-oriented for constructing spatial data warehouse from the aspect of decision application. In the end of article, the construction process of spatial data warehouse based on ERP model system is discussed.

  4. Spatial Aggregation: Data Model and Implementation

    CERN Document Server

    Gomez, Leticia; Kuijpers, Bart; Vaisman, Alejandro

    2007-01-01

    Data aggregation in Geographic Information Systems (GIS) is only marginally present in commercial systems nowadays, mostly through ad-hoc solutions. In this paper, we first present a formal model for representing spatial data. This model integrates geographic data and information contained in data warehouses external to the GIS. We define the notion of geometric aggregation, a general framework for aggregate queries in a GIS setting. We also identify the class of summable queries, which can be efficiently evaluated by precomputing the overlay of two or more of the thematic layers involved in the query. We also sketch a language, denoted GISOLAP-QL, for expressing queries that involve GIS and OLAP features. In addition, we introduce Piet, an implementation of our proposal, that makes use of overlay precomputation for answering spatial queries (aggregate or not). Our experimental evaluation showed that for a certain class of geometric queries with or without aggregation, overlay precomputation outperforms R-tre...

  5. Temporal and spatial variability of soil biological activity at European scale

    Science.gov (United States)

    Mallast, Janine; Rühlmann, Jörg

    2015-04-01

    The CATCH-C project aims to identify and improve the farm-compatibility of Soil Management Practices including to promote productivity, climate change mitigation and soil quality. The focus of this work concentrates on turnover conditions for soil organic matter (SOM). SOM is fundamental for the maintenance of quality and functions of soils while SOM storage is attributed a great importance in terms of climate change mitigation. The turnover conditions depend on soil biological activity characterized by climate and soil properties. Soil biological activity was investigated using two model concepts: a) Re_clim parameter within the ICBM (Introductory Carbon Balance Model) (Andrén & Kätterer 1997) states a climatic factor summarizing soil water storage and soil temperature and its influence on soil biological activity. b) BAT (biological active time) approach derived from model CANDY (CArbon and Nitrogen Dynamic) (Franko & Oelschlägel 1995) expresses the variation of soil moisture, soil temperature and soil aeration as a time scale and an indicator of biological activity for soil organic matter (SOM) turnover. During an earlier stage both model concepts, Re_clim and BAT, were applied based on a monthly data to assess spatial variability of turnover conditions across Europe. This hampers the investigation of temporal variability (e.g. intra-annual). The improved stage integrates daily data of more than 350 weather stations across Europe presented by Klein Tank et al. (2002). All time series data (temperature, precipitation and potential evapotranspiration and soil texture derived from the European Soil Database (JRC 2006)), are used to calculate soil biological activity in the arable layer. The resulting BAT and Re_clim values were spatio-temporal investigated. While "temporal" refers to a long-term trend analysis, "spatial" includes the investigation of soil biological activity variability per environmental zone (ENZ, Metzger et al. 2005 representing similar

  6. Spatial variability of soil moisture regimes at different scales: implications in the context of precision agriculture.

    Science.gov (United States)

    Voltz, M

    1997-01-01

    Precision agriculture is based on the concept of soil-specific management, which aims to adapt management within a field according to specific site conditions in order to maximize production and minimize environmental damage. This paper examines how the nature and sources of variation in soil moisture regimes affect our ability to simulate soil water behaviour within a field with adequate precision in order to advise optimal soil-specific management. Field examples of variation in soil moisture regimes are described to illustrate the difficulties involved. A discussion identifies three main points. First, it is recognized that the current modelling approaches to soil moisture regimes do not sufficiently account for local heterogeneities in soil and crop characteristics such as soil morphology and rooting patterns. Second, the estimation of within-field variation of soil hydraulic properties is difficult because of large short-range variation of the properties and general lack of observed data; one way to overcome this problem is to seek new measurement techniques or to find easy-to-measure auxiliary variables spatially correlated to the variables of interest. Last, as pollution impacts often become noticeable to society at scales larger than the scale of agricultural management, hydrological modelling can serve for linking both scales and advising agricultural practices that minimize undesirable pollution effects.

  7. 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.

  8. Spatial uncertainty assessment in modelling reference evapotranspiration at regional scale

    Directory of Open Access Journals (Sweden)

    G. Buttafuoco

    2010-07-01

    Full Text Available Evapotranspiration is one of the major components of the water balance and has been identified as a key factor in hydrological modelling. For this reason, several methods have been developed to calculate the reference evapotranspiration (ET0. In modelling reference evapotranspiration it is inevitable that both model and data input will present some uncertainty. Whatever model is used, the errors in the input will propagate to the output of the calculated ET0. Neglecting information about estimation uncertainty, however, may lead to improper decision-making and water resources management. One geostatistical approach to spatial analysis is stochastic simulation, which draws alternative and equally probable, realizations of a regionalized variable. Differences between the realizations provide a measure of spatial uncertainty and allow to carry out an error propagation analysis. Among the evapotranspiration models, the Hargreaves-Samani model was used.

    The aim of this paper was to assess spatial uncertainty of a monthly reference evapotranspiration model resulting from the uncertainties in the input attributes (mainly temperature at regional scale. A case study was presented for Calabria region (southern Italy. Temperature data were jointly simulated by conditional turning bands simulation with elevation as external drift and 500 realizations were generated.

    The ET0 was then estimated for each set of the 500 realizations of the input variables, and the ensemble of the model outputs was used to infer the reference evapotranspiration probability distribution function. This approach allowed to delineate the areas characterized by greater uncertainty, to improve supplementary sampling strategies and ET0 value predictions.

  9. Random Effect and Latent Variable Model Selection

    CERN Document Server

    Dunson, David B

    2008-01-01

    Presents various methods for accommodating model uncertainty in random effects and latent variable models. This book focuses on frequentist likelihood ratio and score tests for zero variance components. It also focuses on Bayesian methods for random effects selection in linear mixed effects and generalized linear mixed models

  10. Sampling Weights in Latent Variable Modeling

    Science.gov (United States)

    Asparouhov, Tihomir

    2005-01-01

    This article reviews several basic statistical tools needed for modeling data with sampling weights that are implemented in Mplus Version 3. These tools are illustrated in simulation studies for several latent variable models including factor analysis with continuous and categorical indicators, latent class analysis, and growth models. The…

  11. A Latent Variable Bayesian Approach to Spatial Clustering with Background Noise

    NARCIS (Netherlands)

    Kayabol, K.

    2011-01-01

    We propose a finite mixture model for clustering of the spatial data patterns. The model is based on the spatial distances between the data locations in such a way that both the distances of the points to the cluster centers and the distances of a given point to its neighbors within a defined window

  12. Spatial variability of available water and micro-sprinkler irrigation in cambisol

    Directory of Open Access Journals (Sweden)

    Larissa Luana Nicodemos Ferreira

    Full Text Available ABSTRACT The technology of irrigation is vital for agricultural production. Thus, description of spatial patterns of both water application and available water capacity in the soil, as well as their interactions, is essential to maximize efficiency of water use in irrigated areas. The objective of this study was to analyze spatial variability of available water capacity in the soil and water application via irrigation using geostatistics. The experiment was conducted in a commercial mango orchard in Cambisol irrigated by micro sprinkler system, in the municipality of Alto do Rodrigues, RN. Analyses of descriptive statistics and geostatistics were performed using the programs GeoR and GS+. Geostatistics was found suitable for describing the structure of spatial dependence of available water capacity in the soil and the flow rate distributed in the area by sprinklers. Moreover, even with good results for Christiansen Uniformity Coefficient (CU and Distribution Uniformity Coefficient (DU, the area showed spatial variability of flow rate.

  13. Comparing spatial and temporal transferability of hydrological model parameters

    Science.gov (United States)

    Patil, Sopan; Stieglitz, Marc

    2015-04-01

    Operational use of hydrological models requires the transfer of calibrated parameters either in time (for streamflow forecasting) or space (for prediction at ungauged catchments) or both. Although the effects of spatial and temporal parameter transfer on catchment streamflow predictions have been well studied individually, a direct comparison of these approaches is much less documented. In our view, such comparison is especially pertinent in the context of increasing appeal and popularity of the "trading space for time" approaches that are proposed for assessing the hydrological implications of anthropogenic climate change. Here, we compare three different schemes of parameter transfer, viz., temporal, spatial, and spatiotemporal, using a spatially lumped hydrological model called EXP-HYDRO at 294 catchments across the continental United States. Results show that the temporal parameter transfer scheme performs best, with lowest decline in prediction performance (median decline of 4.2%) as measured using the Kling-Gupta efficiency metric. More interestingly, negligible difference in prediction performance is observed between the spatial and spatiotemporal parameter transfer schemes (median decline of 12.4% and 13.9% respectively). We further demonstrate that the superiority of temporal parameter transfer scheme is preserved even when: (1) spatial distance between donor and receiver catchments is reduced, or (2) temporal lag between calibration and validation periods is increased. Nonetheless, increase in the temporal lag between calibration and validation periods reduces the overall performance gap between the three parameter transfer schemes. Results suggest that spatiotemporal transfer of hydrological model parameters has the potential to be a viable option for climate change related hydrological studies, as envisioned in the "trading space for time" framework. However, further research is still needed to explore the relationship between spatial and temporal

  14. Indoorgml - a Standard for Indoor Spatial Modeling

    Science.gov (United States)

    Li, Ki-Joune

    2016-06-01

    With recent progress of mobile devices and indoor positioning technologies, it becomes possible to provide location-based services in indoor space as well as outdoor space. It is in a seamless way between indoor and outdoor spaces or in an independent way only for indoor space. However, we cannot simply apply spatial models developed for outdoor space to indoor space due to their differences. For example, coordinate reference systems are employed to indicate a specific position in outdoor space, while the location in indoor space is rather specified by cell number such as room number. Unlike outdoor space, the distance between two points in indoor space is not determined by the length of the straight line but the constraints given by indoor components such as walls, stairs, and doors. For this reason, we need to establish a new framework for indoor space from fundamental theoretical basis, indoor spatial data models, and information systems to store, manage, and analyse indoor spatial data. In order to provide this framework, an international standard, called IndoorGML has been developed and published by OGC (Open Geospatial Consortium). This standard is based on a cellular notion of space, which considers an indoor space as a set of non-overlapping cells. It consists of two types of modules; core module and extension module. While core module consists of four basic conceptual and implementation modeling components (geometric model for cell, topology between cells, semantic model of cell, and multi-layered space model), extension modules may be defined on the top of the core module to support an application area. As the first version of the standard, we provide an extension for indoor navigation.

  15. Riparian zone processes and soil water total organic carbon (TOC: implications for spatial variability, upscaling and carbon exports

    Directory of Open Access Journals (Sweden)

    T. Grabs

    2012-03-01

    Full Text Available Groundwater flowing from hillslopes through riparian (near stream soils often undergoes chemical transformations that can substantially influence stream water chemistry. We used landscape analysis to predict total organic carbon (TOC concentrations profiles and groundwater levels measured in the riparian zone (RZ of a 67 km2 catchment in Sweden. TOC exported from 13 riparian soil profiles was then estimated based on the riparian flow-concentration integration model (RIM. Much of the observed spatial variability of riparian TOC concentrations in this system could be predicted from groundwater levels and the topographic wetness index (TWI. Organic riparian peat soils in forested areas emerged as hotspots exporting large amounts of TOC. Exports were subject to considerable temporal variations caused by a combination of variable flow conditions and changing soil water TOC concentrations. From more mineral riparian gley soils, on the other hand, only small amounts with relatively time-invariant concentrations were exported. Organic and mineral soils in RZs constitute a heterogeneous landscape mosaic that controls much of the spatial variability of stream water TOC. We developed an empirical regression-model based on the TWI to move beyond the plot scale to predict spatially variable riparian TOC concentration profiles for RZs underlain by glacial till.

  16. Spatial and temporal variability of mobile macro-invertebrate assemblages associated to coralligenous habitat

    Directory of Open Access Journals (Sweden)

    R. BEDINI

    2014-03-01

    Full Text Available The study aimed to investigate patterns of spatial and temporal variability of mobile macroinvertebrate assemblages associated to coralligenous habitat. A multi-factorial sampling design was used to test the hypotheses that the structure of assemblages and their spatial and temporal variability changed in relation to substrate inclination. Moreover, macroalgae and sessile macro-invertebrates were also investigated in order to detect eventual relationship between sessile and mobile assemblages. A total of 236 mobile macro-invertebrate taxa were identified, among them 2 Platyhelminthes, 4 Sipuncula, 6 Nemertea, 27 Mollusca, 86 Annelida, 103 Arthropoda, 8 Echinodermata. Results of the study showed that mobile macro-invertebrate assemblages of coralligenous habitat were little influenced by the inclination of substrate and by the morphology of sessile organisms, as patterns of variation were different between the two assemblages. Mobile macro-invertebrate assemblages changed among sampling dates within one year period and they showed high variability at the spatial scale examined.

  17. Isard's contributions to spatial interaction modeling

    Science.gov (United States)

    O'Kelly, M. E.

    . This short review, surveys Isard's role in promoting what has become known as spatial interaction modeling. Some contextual information on the milieu from which his work emerged is given, together with a selected number of works that are judged to have been influenced (directly and indirectly) by his work. It is suggested that this burgeoning field owes a lot to the foundations laid in the gravity model chapter of ``Methods''. The review is supplemented by a rather extensive bibliography of additional works that are indicative of the breadth of the impact of this field.

  18. On the spatial and temporal variability of ENSO precipitation and drought teleconnection in mainland Southeast Asia

    OpenAIRE

    T. A. Räsänen; Lindgren, V; Guillaume, J H A; Buckley, B.M.; Kummu, M.

    2015-01-01

    The variability in the hydroclimate over mainland Southeast Asia is strongly influenced by the El Niño–Southern Oscillation (ENSO) phenomenon, which has been linked to severe drought and floods that profoundly influence human societies and ecosystems alike. However, the spatial characteristics and long-term stationarity of ENSO's influence in the region are not well understood. We thus aim to analyse seasonal evolution and spatial variations in the effect of ENSO on precipit...

  19. Spatial Variability of Soil Cation Exchange Capacity in Hilly Tea Plantation Soils Under Different Sampling Scales

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Studies on the spatial variability of the soil cation exchange capacity (CEC) were made to provide a theoretical basis for an ecological tea plantation and management of soil fertilizer in the tea plantation. Geostatistics were used to analyze the spatial variability of soil CEC in the tea plantation site on Mengding Mountain in Sichuan Province of China on two sampling scales. It was found that, (1) on the small scale, the soil CEC was intensively spatially correlative, the rate of nugget to sill was 18.84% and the spatially dependent range was 1 818 m, and structural factors were the main factors that affected the spatial variability of the soil CEC; (2) on the microscale, the soil CEC was also consumingly spatially dependent,and the rate of nugget to sill was 16.52%, the spatially dependent range was 311 m, and the main factors affecting the spatial variability were just the same as mentioned earlier. On the small scale, soil CEC had a stronger anisotropic structure on the slope aspect, and a weaker one on the lateral side. According to the ordinary Kriging method, the equivalence of soil CEC distributed along the lateral aspect of the slope from northeast to outhwest, and the soil CEC reduced as the elevation went down. On the microscale, the anisotropic structure was different from that measured on the small scale. It had a stronger anisotropic structure on the aspect that was near the aspect of the slope, and a weaker one near the lateral aspect of the slope. The soil CEC distributed along the lateral aspect of the slope and some distributed in the form of plots.From the top to the bottom of the slope, the soil CEC increased initially, and then reduced, and finally increased.

  20. Temporal Trends and Spatial Variability of Vegetation Phenology over the Northern Hemisphere during 1982-2012

    OpenAIRE

    Siyuan Wang; Bojuan Yang; Qichun Yang; Linlin Lu; Xiaoyue Wang; Yaoyao Peng

    2016-01-01

    Satellite-derived vegetation phenology has been recognized as a key indicator for detecting changes in the terrestrial biosphere in response to global climate change. However, multi-decadal changes and spatial variation of vegetation phenology over the Northern Hemisphere and their relationship to climate change have not yet been fully investigated. In this article, we investigated the spatial variability and temporal trends of vegetation phenology over the Northern Hemisphere by calibrating ...

  1. 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.

  2. Estimation of Spatial Dynamic Nonparametric Durbin Models with Fixed Effects

    Science.gov (United States)

    Qian, Minghui; Hu, Ridong; Chen, Jianwei

    2016-01-01

    Spatial panel data models have been widely studied and applied in both scientific and social science disciplines, especially in the analysis of spatial influence. In this paper, we consider the spatial dynamic nonparametric Durbin model (SDNDM) with fixed effects, which takes the nonlinear factors into account base on the spatial dynamic panel…

  3. Spatial air pollution modelling for a West-African town

    Directory of Open Access Journals (Sweden)

    Sirak Zenebe Gebreab

    2015-11-01

    Full Text Available Land use regression (LUR modelling is a common approach used in European and Northern American epidemiological studies to assess urban and traffic related air pollution exposures. Studies applying LUR in Africa are lacking. A need exists to understand if this approach holds for an African setting, where urban features, pollutant exposures and data availability differ considerably from other continents. We developed a parsimonious regression model based on 48-hour nitrogen dioxide (NO2 concentrations measured at 40 sites in Kaédi, a medium sized West-African town, and variables generated in a geographic information system (GIS. Road variables and settlement land use characteristics were found to be important predictors of 48-hour NO2 concentration in the model. About 68% of concentration variability in the town was explained by the model. The model was internally validated by leave-one-out cross-validation and it was found to perform moderately well. Furthermore, its parameters were robust to sampling variation. We applied the model at 100 m pixels to create a map describing the broad spatial pattern of NO2 across Kaédi. In this research, we demonstrated the potential for LUR as a valid, cost-effective approach for air pollution modelling and mapping in an African town. If the methodology were to be adopted by environmental and public health authorities in these regions, it could provide a quick assessment of the local air pollution burden and potentially support air pollution policies and guidelines.

  4. Spatial probabilistic pulsatility model for enhancing photoplethysmographic imaging systems

    Science.gov (United States)

    Amelard, Robert; Clausi, David A.; Wong, Alexander

    2016-11-01

    Photoplethysmographic imaging (PPGI) is a widefield noncontact biophotonic technology able to remotely monitor cardiovascular function over anatomical areas. Although spatial context can provide insight into physiologically relevant sampling locations, existing PPGI systems rely on coarse spatial averaging with no anatomical priors for assessing arterial pulsatility. Here, we developed a continuous probabilistic pulsatility model for importance-weighted blood pulse waveform extraction. Using a data-driven approach, the model was constructed using a 23 participant sample with a large demographic variability (11/12 female/male, age 11 to 60 years, BMI 16.4 to 35.1 kg·m-2). Using time-synchronized ground-truth blood pulse waveforms, spatial correlation priors were computed and projected into a coaligned importance-weighted Cartesian space. A modified Parzen-Rosenblatt kernel density estimation method was used to compute the continuous resolution-agnostic probabilistic pulsatility model. The model identified locations that consistently exhibited pulsatility across the sample. Blood pulse waveform signals extracted with the model exhibited significantly stronger temporal correlation (W=35,pbpm].

  5. 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 poten......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...

  6. Quantifying the spatial variability in critical zone architecture through surface mapping and near-surface geophysics

    Science.gov (United States)

    DiBiase, R.; Del Vecchio, J.; Mount, G.; Hayes, J. L.; Comas, X.; Guo, L.; Lin, H.; Zarif, F.; Forsythe, B.; Brantley, S. L.

    2016-12-01

    The composition and structure of Earth's surface and shallow subsurface control the flux of water, solutes, and sediment from hillslopes into rivers. Additionally, bedrock weathering profiles and the stratigraphy of soil and colluvium preserve a record of past surface processes. However, landscapes often exhibit heterogeneity in critical zone architecture that is difficult to capture with remote sensing and costly to characterize through direct measurement in soil pits or drill cores. Here we present results from a multifaceted approach to quantifying spatial variability in critical zone architecture using airborne lidar topography, surface mapping, and a suite of geophysical surveys. We focus on Garner Run, a first order sandstone catchment in the Susquehanna Shale Hills Critical Zone Observatory situated in the valley and ridge province of central Pennsylvania, 80 km southwest of the last glacial maximum ice limit. Results from lidar topographic analysis and detailed mapping of surface cover (e.g., soil versus boulder-mantled) reveal a pattern of relict periglacial landforms and deposits that vary depending on slope position and aspect. Additionally, a drill core taken from an unchanneled valley at the head of Garner Run indicates at least 9 meters of alternating sand- and boulder-rich colluvial fill sourced from adjacent hillslopes, indicating the potential preservation of multiple cycles of periglacial climate conditions. Through the use of shallow geophysical techniques, including cross-valley transects of seismic refraction, multiple frequency ground-penetrating radar (GPR), and electrical resistivity tomography (ERT), we image spatial patterns in subsurface architecture at a range of scales (10-1,000 m), and high spatial resolution (cm). Notably, despite challenging environmental conditions, there is agreement among diverse subsurface methods in highlighting aspect-dependent controls on weathering zone thickness that furthermore can be directly connected to

  7. Spatial variability and landscape controls of near-surface permafrost within the Alaskan Yukon River Basin

    Science.gov (United States)

    Pastick, Neal J.; Jorgenson, M. Torre; Wylie, Bruce K.; Rose, Joshua R.; Rigge, Matthew; Walvoord, Michelle A.

    2014-01-01

    The distribution of permafrost is important to understand because of permafrost's influence on high-latitude ecosystem structure and functions. Moreover, near-surface (defined here as within 1 m of the Earth's surface) permafrost is particularly susceptible to a warming climate and is generally poorly mapped at regional scales. Subsequently, our objectives were to (1) develop the first-known binary and probabilistic maps of near-surface permafrost distributions at a 30 m resolution in the Alaskan Yukon River Basin by employing decision tree models, field measurements, and remotely sensed and mapped biophysical data; (2) evaluate the relative contribution of 39 biophysical variables used in the models; and (3) assess the landscape-scale factors controlling spatial variations in permafrost extent. Areas estimated to be present and absent of near-surface permafrost occupy approximately 46% and 45% of the Alaskan Yukon River Basin, respectively; masked areas (e.g., water and developed) account for the remaining 9% of the landscape. Strong predictors of near-surface permafrost include climatic indices, land cover, topography, and Landsat 7 Enhanced Thematic Mapper Plus spectral information. Our quantitative modeling approach enabled us to generate regional near-surface permafrost maps and provide essential information for resource managers and modelers to better understand near-surface permafrost distribution and how it relates to environmental factors and conditions.

  8. 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.

  9. Spatial and temporal variability in denudation across the Bolivian Andes from multiple geochronometers

    Science.gov (United States)

    Insel, Nadja; Ehlers, Todd A.; Schaller, Mirjam; Barnes, Jason B.; Tawackoli, Sohrab; Poulsen, Christopher J.

    2010-10-01

    We quantify spatial and temporal variations in denudation rates across the central Andean fold-thrust belt in Bolivia with particular focus on the Holocene. Measured and predicted 10Be cosmogenic radionuclide (CRN) concentrations in river sediments are used to (1) calculate catchment-averaged denudation rates from 17 basins across two transects at different latitudes, and (2) evaluate the sensitivity of Holocene climate change on the denudation history recorded by the CRN data. Estimated denudation rates vary by two orders of magnitude from 0.04 to 1.93 mm yr - 1 with mean values of 0.40 ± 0.29 mm yr - 1 in northern Bolivia and 0.51 ± 0.50 mm yr - 1 in the south. Results demonstrate no statistically significant correlation between denudation rates and morphological parameters such as relief, slope or drainage basin size. In addition, the CRN-derived denudation rates do not reflect present-day latitudinal variations in precipitation. Comparison to ˜ 130 previously published denudation rates calculated over long (thermochronology-derived; > 10 6 yrs), medium (CRN-derived; 10 2-10 4 yrs), and short timescales (sediment flux-derived; 10 1 yrs) indicate temporal variations in denudation rates that increase between 0 and 200% over the last ˜ 5 ka. CRN modeling results suggest that the CRN-derived denudation rates may not be fully adjusted to wetter climate conditions recorded in the central Andes since the mid-Holocene. We conclude that large spatial variability in CRN denudation may be due to local variations in tectonics (e.g. faulting), while large temporal variability in denudation may be due to temporal variations in climate.

  10. Healthcare environments and spatial variability of healthcare associated infection risk: cross-sectional surveys.

    Directory of Open Access Journals (Sweden)

    Jean Gaudart

    Full Text Available Prevalence of healthcare associated infections remains high in patients in intensive care units (ICU, estimated at 23.4% in 2011. It is important to reduce the overall risk while minimizing the cost and disruption to service provision by targeted infection control interventions. The aim of this study was to develop a monitoring tool to analyze the spatial variability of bacteriological contamination within the healthcare environment to assist in the planning of interventions. Within three cross-sectional surveys, in two ICU wards, air and surface samples from different heights and locations were analyzed. Surface sampling was carried out with tryptic Soy Agar contact plates and Total Viable Counts (TVC were calculated at 48 hrs (incubation at 37 °C. TVCs were analyzed using Poisson Generalized Additive Mixed Model for surface type analysis, and for spatial analysis. Through three cross-sectional survey, 370 samples were collected. Contamination varied from place-to-place, height-to-height, and by surface type. Hard-to-reach surfaces, such as bed wheels and floor area under beds, were generally more contaminated, but the height level at which maximal TVCs were found changed between cross-sectional surveys. Bedside locations and bed occupation were risk factors for contamination. Air sampling identified clusters of contamination around the nursing station and surface sampling identified contamination clusters at numerous bed locations. By investigating dynamic hospital wards, the methodology employed in this study will be useful to monitor contamination variability within the healthcare environment and should help to assist in the planning of interventions.

  11. Spatial variability and uncertainty in ecological risk assessment: a case study on the potential risk of cadmium for the little owl in a Dutch river flood plain.

    Science.gov (United States)

    Kooistra, Lammert; Huijbregts, Mark A J; Ragas, Ad M J; Wehrens, Ron; Leuven, Rob S E W

    2005-04-01

    This paper outlines a procedure that quantifies the impact of different sources of spatial variability and uncertainty on ecological risk estimates. The procedure is illustrated in a case study that estimates the risks of cadmium for a little owl (Athene noctua vidalli) living in a Dutch river flood plain along the river Rhine. A geographical information system (GIS) was used to quantify spatial variability in contaminant concentrations and habitats. It was combined with an exposure and effect model that uses Monte Carlo simulation to quantify parameter uncertainty. Spatial model uncertainty was assessed by the application of two different spatial interpolation methods (classification and kriging) and foraging ranges. The results of the case study show that parameter uncertainty is the main type of uncertainty influencing the risk estimate, and to a lesser extent spatial variability, while spatial model uncertainty was of minor importance. Compared to the deterministically calculated hazard index for the little owl (0.9), inclusion of spatial variability resulted in a median hazard index that can vary between 0.8 and 1.4. It is concluded that a single estimator for a whole flood plain may over- or underestimate risks for specific parts within the flood plain. Further research that expands the procedure presented in this paper is necessary to improve the incorporation of spatial factors in ecological risk assessment.

  12. 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

  13. 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).

  14. Modeling the spatial reach of the LFP.

    Science.gov (United States)

    Lindén, Henrik; Tetzlaff, Tom; Potjans, Tobias C; Pettersen, Klas H; Grün, Sonja; Diesmann, Markus; Einevoll, Gaute T

    2011-12-08

    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 of the region generating the LFP. Here, we use a detailed biophysical modeling approach to investigate the size of the contributing region by simulating the LFP from a large number of neurons around the electrode. We find that the size of the generating region depends on the neuron morphology, the synapse distribution, and the correlation in synaptic activity. For uncorrelated activity, the LFP represents cells in a small region (within a radius of a few hundred micrometers). If the LFP contributions from different cells are correlated, the size of the generating region is determined by the spatial extent of the correlated activity.

  15. Spatial variability of soil attributes and sugarcane yield in relation to topographic location

    OpenAIRE

    de Souza, Zigomar M.; Domingos G. P. Cerri; Paulo S. G. Magalhães; Siqueira, Diego S.

    2010-01-01

    Soils submitted to the same management system in places with little variation of landscape, manifest differentiated spatial variability of their attributes and crop yield. The aim of this work was to investigate the correlation between spatial variability of the soil attributes and sugarcane yield as a result of soil topography. To achieve this objective, a test area of 42 ha located at the São João Sugar Mill, in Araras, in the State of São Paulo, Brazil, was selected. Sugarcane yield was me...

  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. Spatial Database Modeling for Indoor Navigation Systems

    Science.gov (United States)

    Gotlib, Dariusz; Gnat, Miłosz

    2013-12-01

    For many years, cartographers are involved in designing GIS and navigation systems. Most GIS applications use the outdoor data. Increasingly, similar applications are used inside buildings. Therefore it is important to find the proper model of indoor spatial database. The development of indoor navigation systems should utilize advanced teleinformation, geoinformatics, geodetic and cartographical knowledge. The authors present the fundamental requirements for the indoor data model for navigation purposes. Presenting some of the solutions adopted in the world they emphasize that navigation applications require specific data to present the navigation routes in the right way. There is presented original solution for indoor data model created by authors on the basis of BISDM model. Its purpose is to expand the opportunities for use in indoor navigation.

  18. Modelling spatial patterns of urban growth in Africa.

    Science.gov (United States)

    Linard, Catherine; Tatem, Andrew J; Gilbert, Marius

    2013-10-01

    The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5-10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers.

  19. Modelling spatial patterns of urban growth in Africa

    Science.gov (United States)

    Linard, Catherine; Tatem, Andrew J.; Gilbert, Marius

    2013-01-01

    The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5–10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers. PMID:25152552

  20. 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

  1. 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.

  2. 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.

  3. Spatial interpolation schemes of daily precipitation for hydrologic modeling

    Science.gov (United States)

    Hwang, Y.; Clark, M.; 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.

  4. 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.

  5. Spatial variability of some soil properties varies in oil palm (Elaeis guineensis Jacq.) plantations of west coastal area of India

    Science.gov (United States)

    Behera, Sanjib Kumar; Suresh, Kancherla; Narsimha Rao, Bezawada; Mathur, Ravi Kumar; Shukla, Arvind Kumar; Manorama, Kamireddy; Ramachandrudu, Kummari; Harinarayana, Parasa; Prakash, Chandra

    2016-06-01

    Mapping spatial variability of soil properties is the key to efficient soil resource management for sustainable crop yield. Therefore, the present study was conducted to assess the spatial variability of soil properties such as acidity (pH), salinity (electrical conductivity (EC)), organic carbon, available K, available P, exchangeable Ca2+, exchangeable Mg2+, available S and hot water soluble B in surface (0-20 cm) and subsurface (20-40 cm) soil layers of oil palm plantations in south Goa district of Goa located in west coastal area of India. A total of 128 soil samples were collected from 64 oil palm plantations of Goa located at an approximate interval of 1-2 km and analyzed. Soil was acidic to neutral in reaction. Other soil properties varied widely in both the soil layers. Correlations between soil pH and exchangeable Ca2+, between soil EC and available K, between available P and available S and between exchangeable Ca2+ and exchangeable Mg2+ in both the soil layers were found to be positive and significant (P < 0.01). Geostatistical analysis revealed a varied spatial distribution pattern for the measured soil properties. Best-fit models for measured soil properties were exponential, Gaussian, stable, K-Bessel and spherical with moderate to strong spatial dependency. The results revealed that site-specific fertilizer management options needed to be adopted in the oil palm plantations of the study area owing to variability in soil properties.

  6. 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.

  7. Integrating models that depend on variable data

    Science.gov (United States)

    Banks, A. T.; Hill, M. C.

    2016-12-01

    Models of human-Earth systems are often developed with the goal of predicting the behavior of one or more dependent variables from multiple independent variables, processes, and parameters. Often dependent variable values range over many orders of magnitude, which complicates evaluation of the fit of the dependent variable values to observations. Many metrics and optimization methods have been proposed to address dependent variable variability, with little consensus being achieved. In this work, we evaluate two such methods: log transformation (based on the dependent variable being log-normally distributed with a constant variance) and error-based weighting (based on a multi-normal distribution with variances that tend to increase as the dependent variable value increases). Error-based weighting has the advantage of encouraging model users to carefully consider data errors, such as measurement and epistemic errors, while log-transformations can be a black box for typical users. Placing the log-transformation into the statistical perspective of error-based weighting has not formerly been considered, to the best of our knowledge. To make the evaluation as clear and reproducible as possible, we use multiple linear regression (MLR). Simulations are conducted with MatLab. The example represents stream transport of nitrogen with up to eight independent variables. The single dependent variable in our example has values that range over 4 orders of magnitude. Results are applicable to any problem for which individual or multiple data types produce a large range of dependent variable values. For this problem, the log transformation produced good model fit, while some formulations of error-based weighting worked poorly. Results support previous suggestions fthat error-based weighting derived from a constant coefficient of variation overemphasizes low values and degrades model fit to high values. Applying larger weights to the high values is inconsistent with the log

  8. 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

  9. Assessment of Temperature and Elevation Controls on Spatial Variability of Rainfall in Iran

    OpenAIRE

    Majid Javari

    2017-01-01

    With rainfall changes, hydrological process variability increases. This study predicts the potential effects of temperature and topography characteristics on rainfall spatial variability. Temperature and topography were considered as two effective factors that may influence monthly rainfall. This study uses rainfall and temperature data from 174 synoptic and climatic stations and 39,055 rain, elevation and temperature points extracted by ArcGIS10.3 over the 40 years (1975–2014). In this study...

  10. Basal respiration - a proxy to understand spatial variability of soil CO2 emissions in urban regions

    Science.gov (United States)

    Vasenev, Viacheslav; Stoorvogel, Jetse; Ananyeva, Nadezhda; Ivashchenko, Kristina; Vizirskaya, Marya; Valentini, Riccardo

    2015-04-01

    Soil respiration (Rs) is an important terrestrial CO2 efflux and received significant attention at different scale levels. However, the sampling density is limited and global Rs databases are biased towards natural ecosystems and towards north America and Europe. This limits our understanding of the spatial variability of Rs. The methodological constraints of direct Rs measurements in the field limit the number of observations. As an alternative approach to approximate the spatial variability of Rs, we used basal respiration (BR) as an indirect measurement. First, the direct Rs and indirect BR measurements were compared at a 10 km2 test area in Moscow city, which included adjacent forests, croplands and urban lawn plots. Rs was monitored by in situ chamber approach with an IR Li-820 gas analyzer at 50 points during the growing season (June-October 2013, 9 time repetitions per point). In the same area, 32 locations were sampled and BR was measured under controlled conditions. Rs was affected by anthropogenic disturbance with the highest values in urban lawns. BR was mainly controlled by soil organic carbon (SOC) with maximum rates in the forested area. Total variability reported by direct observations was 10% higher, than one for BR, although the spatial variability captured by both approaches was similar confirmed by significant correlation between variance coefficients (CV) of the values. This shows that BR is a relevant proxy to analyze the spatial variability of Rs. Subsequently, the sampling area was expanded to the Moscow region for which respiration was mapped using digital soil mapping techniques and BR as a proxy for Rs. Although the absolute levels of respiration remained uncertain, the spatial patterns of BR are likely to correspond well with Rs patterns. Land use largely determined the spatial heterogeneity of soil respiration. Most variation occurred in the urban areas. BR is a relevant and straightforward proxy to understand patterns of Rs especially

  11. Spatial Variability and Uncertainty of Water Use Impacts from U.S. Feed and Milk Production.

    Science.gov (United States)

    Henderson, Andrew D; Asselin-Balençon, Anne C; Heller, Martin; Lessard, Lindsay; Vionnet, Samuel; Jolliet, Olivier

    2017-02-21

    This paper addresses water use impacts of agriculture, developing a spatially explicit approach tracing the location of water use and water scarcity related to feed production, transport, and livestock, tracking uncertainties and illustrating the approach with a case study on dairy production in the United States. This approach was developed as a step to bring spatially variable production and impacts into a process-based life cycle assessment (LCA) context. As water resources and demands are spatially variable, it is critical to take into account the location of activities to properly understand the impacts of water use, accounting for each of the main feeds for milk production. At the crop production level, the example of corn grain shows that 59% of water stress associated with corn grain production in the United States is located in Nebraska, a state with moderate water stress and moderate corn production (11%). At the level of milk production, four watersheds account for 78% of the national water stress impact, as these areas have high milk production and relatively high water stress; it is the production of local silage and hay crops that drives water consumption in these areas. By considering uncertainty in both inventory data and impact characterization factors, we demonstrate that spatial variability may be larger than uncertainty, and that not systematically accounting for the two can lead to artificially high uncertainty. Using a nonspatial approach in a spatially variable setting can result in a significant underestimation or overestimation of water impacts. The approach demonstrated here could be applied to other spatially variable processes.

  12. Climatic trends in hail precipitation in France: spatial, altitudinal, and temporal variability.

    Science.gov (United States)

    Hermida, Lucía; Sánchez, José Luis; López, Laura; Berthet, Claude; Dessens, Jean; García-Ortega, Eduardo; Merino, Andrés

    2013-01-01

    Hail precipitation is characterized by enhanced spatial and temporal variability. Association Nationale d'Etude et de Lutte contre les Fléaux Atmosphériques (ANELFA) installed hailpad networks in the Atlantic and Midi-Pyrénées regions of France. Historical data of hail variables from 1990 to 2010 were used to characterize variability. A total of 443 stations with continuous records were chosen to obtain a first approximation of areas most affected by hail. The Cressman method was selected for this purpose. It was possible to find relationships between spatial distributions of the variables, which are supported by obtained Pearson correlations. Monthly and annual trends were examined using the Mann-Kendall test for each of the total affected hailpads. There were 154 pads with a positive trend; most were located between Tarbes and Saint-Gaudens. We found 177 pads with a negative trend, which were largely south of a pine forest in Landes. The remainder of the study area showed an elevated spatial variability with no pattern, even between relatively close hailpads. A similar pattern was found in Lérida (Spain) and Southeast France. In the entire area, monthly trends were predominantly negative in June, July, and August, whereas May had a positive trend; again, however, there was no spatial pattern. There was a high concentration of hailpads with positive trend near the Pyrenees, probably owing to orographic effects, and if we apply cluster analysis with the Mann-Kendall values, the spatial variability is accentuated for stations at higher altitude.

  13. Temporal and spatial variability response of groundwater level to land use/land cover change in oases of arid areas

    Institute of Scientific and Technical Information of China (English)

    YAN Jinfeng; CHEN Xi; LUO Geping; GUO Quanjun

    2006-01-01

    This paper conducts a case study on the impacts of land use/cover change (LUCC) on the temporal and spatial variability of the groundwater level in an arid oasis in the Sangong River Watershed by using the geographical information system (GIS),remote sensing (RS) and geostatistical methods. The temporal and spatial variability of the groundwater level in the watershed in 1978, 1987 and 1998 is regressed by using thesemivariogram model and Kriging interpolation. The LUCC classification maps derived from the aerial images in 1978, Landsat TM image in 1987 and Landsat ETM image in 1998 are used to superpose and analyze the conversion relationship of LUCC types in the regions with different isograms of the groundwater depth. The results show that the change of groundwater recharge was not so significant in the whole oasis, but the temporal and spatial LUCC was significant either in the normal flow periods or in the high flow periods during the 20-year period from 1978 to 1998, and there was a close correlation between them. There is generally a moderate spatial correlation of groundwater level (33.4%),and the spatial autocorrelation distance is 17.78 km.The regions where the groundwater level is sharply changed are also the regions where the land resources are increasingly exploited, which include mainly the exploitation of farmlands, woodlands, and building, industrial and mining lands. The study reveals that the LUCC affects strongly the temporal and spatial variability of the groundwater level in the arid oasis. The study results are of direct and practical significance for rationally utilizing shallow groundwater resources and maintaining the stability of the arid oasis.

  14. 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

  15. Addressing Spatial Variability of Surface-Layer Wind with Long-Range WindScanners

    DEFF Research Database (Denmark)

    Berg, Jacob; Vasiljevic, Nikola; Kelly, Mark C.;

    2015-01-01

    This paper presents an analysis of mean wind measurements from a coordinated system of long-range WindScanners. From individual scan patterns the mean wind field was reconstructed over a large area, and hence it highlights the spatial variability. From comparison with sonic anemometers, the quality...

  16. 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 pi

  17. 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

  18. Soil salinity and acidity: Spatial variability and effects on rice production in West Africa's mangrove zone.

    NARCIS (Netherlands)

    Sylla, M.

    1994-01-01

    In the mangrove environment of West Africa, high spatial and temporal variability of soil constraints (salinity and acidity) to rice production is a problem for the transfer and adoption of new agronomic techniques, for land use planning, and for soil and water management. Recently, several National

  19. 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

  20. 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 s

  1. Temporal and spatial variability of ammonia in urban and agricultural regions of northern Colorado, United States

    Science.gov (United States)

    Li, Yi; Thompson, Tammy M.; Van Damme, Martin; Chen, Xi; Benedict, Katherine B.; Shao, Yixing; Day, Derek; Boris, Alexandra; Sullivan, Amy P.; Ham, Jay; Whitburn, Simon; Clarisse, Lieven; Coheur, Pierre-François; Collett, Jeffrey L., Jr.

    2017-05-01

    Concentrated agricultural activities and animal feeding operations in the northeastern plains of Colorado represent an important source of atmospheric ammonia (NH3). The NH3 from these sources contributes to regional fine particle formation and to nitrogen deposition to sensitive ecosystems in Rocky Mountain National Park (RMNP), located ˜ 80 km to the west. In order to better understand temporal and spatial differences in NH3 concentrations in this source region, weekly concentrations of NH3 were measured at 14 locations during the summers of 2010 to 2015 using Radiello passive NH3 samplers. Weekly (biweekly in 2015) average NH3 concentrations ranged from 2.66 to 42.7 µg m-3, with the highest concentrations near large concentrated animal feeding operations (CAFOs). The annual summertime mean NH3 concentrations were stable in this region from 2010 to 2015, providing a baseline against which concentration changes associated with future changes in regional NH3 emissions can be assessed. Vertical profiles of NH3 were also measured on the 300 m Boulder Atmospheric Observatory (BAO) tower throughout 2012. The highest NH3 concentration along the vertical profile was always observed at the 10 m height (annual average concentration of 4.63 µg m-3), decreasing toward the surface (4.35 µg m-3) and toward higher altitudes (1.93 µg m-3). The NH3 spatial distributions measured using the passive samplers are compared with NH3 columns retrieved by the Infrared Atmospheric Sounding Interferometer (IASI) satellite and concentrations simulated by the Comprehensive Air Quality Model with Extensions (CAMx). The satellite comparison adds to a growing body of evidence that IASI column retrievals of NH3 provide very useful insight into regional variability in atmospheric NH3, in this case even in a region with strong local sources and sharp spatial gradients. The CAMx comparison indicates that the model does a reasonable job simulating NH3 concentrations near sources but tends to

  2. On the Spatial Variability of Arsenic Contamination in the Groundwater of Bangladesh

    Science.gov (United States)

    Karthik, B.; Islam, S.; Harvey, C. F.

    2001-05-01

    The widespread arsenic contamination of groundwater in Bangladesh has been recognized as posing a serious health problem to millions of people in the region. We have performed a detailed spatial analysis of arsenic data from groundwater in an attempt to identify dominant controls on the spatial distribution of arsenic. The variogram analysis suggests that large-scale geological and physical features control a significant fraction of the spatial variability in shallow wells (55 %) as well as in the deeper wells (88 %). We propose that the prevalence of higher arsenic concentrations of arsenic in shallow wells is because of the `small-scale' processes (less than 6 km. approx.) exerting a greater degree of control at shallower depths in the sediments. A comparison of the correlated spatial variability for high and low arsenic concentrations indicates that the `large scale' processes also control the distribution of higher arsenic concentrations to a significant extent. Through an indicator variogram analysis we demonstrate that the correlation structure of the arsenic magnitudes is primarily due to the spatial distribution of their locations, around an approximate concentration cut-off limit of 0.07 mg/L. Our results suggest that the complex spatial distribution of high-level arsenic concentrations is a consequence of interactions among multiscale geologic and geochemical processes.

  3. Hierarchical spatial capture-recapture models: Modeling population density from stratified populations

    Science.gov (United States)

    Royle, J. Andrew; Converse, Sarah J.

    2014-01-01

    Capture–recapture studies are often conducted on populations that are stratified by space, time or other factors. In this paper, we develop a Bayesian spatial capture–recapture (SCR) modelling framework for stratified populations – when sampling occurs within multiple distinct spatial and temporal strata.We describe a hierarchical model that integrates distinct models for both the spatial encounter history data from capture–recapture sampling, and also for modelling variation in density among strata. We use an implementation of data augmentation to parameterize the model in terms of a latent categorical stratum or group membership variable, which provides a convenient implementation in popular BUGS software packages.We provide an example application to an experimental study involving small-mammal sampling on multiple trapping grids over multiple years, where the main interest is in modelling a treatment effect on population density among the trapping grids.Many capture–recapture studies involve some aspect of spatial or temporal replication that requires some attention to modelling variation among groups or strata. We propose a hierarchical model that allows explicit modelling of group or strata effects. Because the model is formulated for individual encounter histories and is easily implemented in the BUGS language and other free software, it also provides a general framework for modelling individual effects, such as are present in SCR models.

  4. Spatial variability of soil fertility and its relation with cocoa yield

    Directory of Open Access Journals (Sweden)

    Railton O. dos Santos

    Full Text Available ABSTRACT The knowledge on the spatial variability of soil properties and crops is important for decision-making on agricultural management. The objective of this study was to evaluate the spatial variability of soil fertility and its relation with cocoa yield. The study was conducted over 14 months in an area cultivated with cocoa. A sampling grid was created to study soil chemical properties and cocoa yield (stratified in season, off-season and annual. The data were analyzed using descriptive and exploratory statistics, and geostatistics. The chemical attributes were classified using fuzzy logic to generate a soil fertility map, which was correlated with maps of crop yield. The soil of the area, except for the western region, showed possibilities ranging from medium to high for cocoa cultivation. Soil fertility showed positive spatial correlation with cocoa yield, and its effect was predominant only for the off-season and annual cocoa.

  5. 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

  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. Searching for the right scale in catchment hydrology: the effect of soil spatial variability in simulated states and fluxes

    Science.gov (United States)

    Baroni, Gabriele; Zink, Matthias; Kumar, Rohini; Samaniego, Luis; Attinger, Sabine

    2017-04-01

    The advances in computer science and the availability of new detailed data-sets have led to a growing number of distributed hydrological models applied to finer and finer grid resolutions for larger and larger catchment areas. It was argued, however, that this trend does not necessarily guarantee better understanding of the hydrological processes or it is even not necessary for specific modelling applications. In the present study, this topic is further discussed in relation to the soil spatial heterogeneity and its effect on simulated hydrological state and fluxes. To this end, three methods are developed and used for the characterization of the soil heterogeneity at different spatial scales. The methods are applied at the soil map of the upper Neckar catchment (Germany), as example. The different soil realizations are assessed regarding their impact on simulated state and fluxes using the distributed hydrological model mHM. The results are analysed by aggregating the model outputs at different spatial scales based on the Representative Elementary Scale concept (RES) proposed by Refsgaard et al. (2016). The analysis is further extended in the present study by aggregating the model output also at different temporal scales. The results show that small scale soil variabilities are not relevant when the integrated hydrological responses are considered e.g., simulated streamflow or average soil moisture over sub-catchments. On the contrary, these small scale soil variabilities strongly affect locally simulated states and fluxes i.e., soil moisture and evapotranspiration simulated at the grid resolution. A clear trade-off is also detected by aggregating the model output by spatial and temporal scales. Despite the scale at which the soil variabilities are (or are not) relevant is not universal, the RES concept provides a simple and effective framework to quantify the predictive capability of distributed models and to identify the need for further model improvements e

  8. Spatial Modeling of Iron Transformations Within Artificial Soil Aggregates

    Science.gov (United States)

    Kausch, M.; Meile, C.; Pallud, C.

    2008-12-01

    Structured soils exhibit significant variations in transport characteristics at the aggregate scale. Preferential flow occurs through macropores while predominantly diffusive exchange takes place in intra-aggregate micropores. Such environments characterized by mass transfer limitations are conducive to the formation of small-scale chemical gradients and promote strong spatial variation in processes controlling the fate of redox-sensitive elements such as Fe. In this study, we present a reactive transport model used to spatially resolve iron bioreductive processes occurring within a spherical aggregate at the interface between advective and diffusive domains. The model is derived from current conceptual models of iron(hydr)oxide (HFO) transformations and constrained by literature and experimental data. Data were obtained from flow-through experiments on artificial soil aggregates inoculated with Shewanella putrefaciens strain CN32, and include the temporal evolution of the bulk solution composition, as well as spatial information on the final solid phase distribution within aggregates. With all iron initially in the form of ferrihydrite, spatially heterogeneous formation of goethite/lepidocrocite, magnetite and siderite was observed during the course of the experiments. These transformations were reproduced by the model, which ascribes a central role to divalent iron as a driver of HFO transformations and master variable in the rate laws of the considered reaction network. The predicted dissolved iron breakthrough curves also match the experimental ones closely. Thus, the computed chemical concentration fields help identify factors governing the observed trends in the solid phase distribution patterns inside the aggregate. Building on a mechanistic description of transformation reactions, fluid flow and solute transport, the model was able to describe the observations and hence illustrates the importance of small-scale gradients and dynamics of bioreductive

  9. A mobile system for quantifying the spatial variability of the surface energy balance: design and application

    Science.gov (United States)

    Wohlfahrt, Georg; Tasser, Erich

    2015-05-01

    We present a mobile device for the quantification of the small-scale (a few square meters) spatial variability in the surface energy balance components and several auxiliary variables of short-statured (ecological research questions. The potential of the new device is demonstrated through four selected case studies, which cover the issues of net radiation heterogeneity within the footprint of eddy covariance flux measurements due to (1) land use and (2) slope and aspect of the underlying surface, (3) controls on landscape-scale variability in soil temperature and albedo and (4) the estimation of evapotranspiration based exclusively on measurements with the mobile device.

  10. Spatial Durbin Model (SDM For Identified Influence Dengue Hemorrhagic Fever Factors in Kabupaten Malang

    Directory of Open Access Journals (Sweden)

    Indah Resti Ayuni Suri

    2012-05-01

    Full Text Available Dengue Hemorrhagic Fever or usually populer call DBD (Demam Berdarah Degue is the cronic desease that caused by virus infection who carry by Aedes Aegypti mousquito. The observation act by DBD descriptioning and some factors territorial view that influence them, also DBD’s modeling use Spatial Durbin Model (SDM. SDM is the particullary case from Spatial Autoregresive Model (SAR, it means modeling with spatial lag at dependen variable and independen variable. This observation use ratio DBD invectors amount with population amount of citizenry at Kabupaten Malang in 2009. Some variable was used, those are the precentation of existention free number embrio, ratio of civil amount between family, procentation of healthy clinic between invectors and procentase of the invectors who taking care by medical help with amount of invectors. The fourth variables are independen variable to ratio of DBD invector amount with population of citizenry amount, as dependen variable trough spatial SDM modelling. The result of SDM parameter modelling, the significant influence variable in session % is the procentation of free amount embrio existention from their own district, the procentation of healthy clinic amount with the DBD invector amount from their own district, the ratio of the population of citizenry with the family from their neighborhood district, and the procentation of healthy clinic amount with the DBD invector amount from their neighborhood district.

  11. Spatial and Temporal Patterns of Vegetation in Water-Limited Ecosystems: The Role of Interannual Rainfall Variability.

    Science.gov (United States)

    Fernandez-Illescas, C. P.; Rodriguez-Iturbe, I.

    2001-12-01

    In water-limited ecosystems, soil water availability lies at the center of a complex coupling between vegetation and climate. Attempts to understand the temporal and spatial vegetation structure of such systems require the formulation of an spatially explicit model of evolutionary dynamics which accounts for temporal fluctuations in soil moisture amounts. Previous work has linked the analytical treatment of the vegetation-soil-climate system described by Laio et al. (2001) and Porporato et al. (2001) characterizing the impact of intraseasonal soil moisture variability on vegetation overall condition with the hierarchical competition-colonization model of Tilman (1994). In this way, the impact of interannual rainfall fluctuations is incorporated into a model of species competition. Such a hydrologically driven hierarchical competition-colonization model is here modified to differentiate between local and global seed dispersal abilities. Simulations at the La Copita savanna site in Texas where the herbaceous C4 Paspaleum setaceum and the woody Prosopis glandulosa (honey mesquite) coexist, suggest that interannual rainfall variability enhances the impact of local dispersion on the temporal evolution of species abundances as well as on the spatial structure of vegetation. Various descriptors of vegetation patterns, (e.g. cluster size distributions, fractal dimensions) and their sensitivity to interannual rainfall fluctuations will be discussed.

  12. Factors controlling spatial variability of DOC concentrations in soil solution at European level

    Science.gov (United States)

    Camino Serrano, Marta; Janssens, Ivan; Luyssaert, Sebastiaan; Gielen, Bert; Guenet, Bertrand; De Vos, Bruno; Ciais, Philippe

    2013-04-01

    The lateral transport of dissolved organic carbon (DOC) is an important and not well-understood process linking terrestrial and aquatic ecosystems. Up to day very few Earth System Models (ESMs) represent explicitly this process despite its crucial role in the global carbon cycle. However, to be able to integrate DOC leaching in ESMs, more accurate information is needed in order to better understand and predict DOC dynamics. DOC concentrations mainly vary by geographical location, soil and vegetation types, topography, season and climate. Within this framework, a database was designed to compile data on DOC in soil solution at different depths in different ecosystems around the world, with special focus on European sites. The database contains information on 349 sites, with 304 being forest, gathered from published literature and datasets accessible on the internet. A substantial dataset was provided by International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests). The database also includes other meta-data related to the sites, such as land cover, soil properties, climate, annual water balance and other soil solution parameters. The analysis of the database has been focused on: 1) the study of the environmental and physical factors that are acting as drivers of DOC concentrations changes in soil solution across sites at European level , and 2) the DOC distribution through the soil profile and how this varies with different vegetation types and soil properties. The preliminary results show that variables related to biological processes (Dry weight of the organic layer, for example) are the most important in explaining the spatial distribution of the DOC concentration in soil solution at the European scale. However, the interactions between variables are complex and we will need further analysis in order to draw more robust conclusions. With regards to the vertical profile of DOC, we found that there is a

  13. Determining the spatial variability of crop yields of two different climatic regions in Southwest Germany

    Science.gov (United States)

    Eshonkulov, Ravshan; Poyda, Arne; Ingwersen, Joachim; Streck, Thilo

    2017-04-01

    Assessing the spatial variability of soil physical properties is crucial for agricultural land management. We determined the spatial variability within two agricultural fields in the regions of Kraichgau and Swabian Jura in Southwest Germany. We determined soil physical properties and recorded the temporal development of soil mineral nitrogen (N) and water content as well as that of plant variables (phenology, biomass, leaf area index (LAI), N content, green vegetation fraction (GVF). The work was conducted during the vegetation periods of 2015 and 2016 in winter wheat, and winter rapeseed in Kraichgau and winter barley and silage maize on Swabian Jura. Measurements were taken in three-weekly intervals. On each field, we identified three plots with reduced plant development using high-resolution (RapidEye) satellite images ("cold spots"). Measurements taken on these cold spots were compared to those from five established (long-term) reference plots representing the average field variability. The software EXPERT-N was used to simulate the soil crop system at both cold spots and reference plots. Sensitivity analyses were conducted to identify the most important parameters for the determination of spatial variability in crop growth dynamics.

  14. Gaussian mixture model of heart rate variability.

    Directory of Open Access Journals (Sweden)

    Tommaso Costa

    Full Text Available 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.

  15. Bayesian variable selection for latent class models.

    Science.gov (United States)

    Ghosh, Joyee; Herring, Amy H; Siega-Riz, Anna Maria

    2011-09-01

    In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.

  16. 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.

  17. Entropy Theory of Polymer Glass-Formation in Variable Spatial Dimension

    Science.gov (United States)

    Xu, Wen-Sheng; Douglas, Jack; Freed, Karl

    The importance of packing frustration is broadly appreciated to be an important aspect of glass-formation. Recently, great interest has focused on using spatial dimensionality () as a theoretical tool for exploring this and other aspects of glass-forming liquids. We explore glass-formation in variable based on the generalized entropy theory, a synthesis of the Adam-Gibbs model with direct computation of the configurational entropy of polymer fluids using an established analytical statistical thermodynamic model. We find that structural relaxation in the fluid state asymptotically becomes Arrhenius in the limit and that the fluid transforms upon sufficient cooling above a critical dimension near into a dense amorphous state with a finite positive residual configurational entropy. The GET also predicts the variation with of measures of fragility and of the characteristic temperatures of glass-formation demarking the onset , middle , and end , of the broad glass transition. Direct computations of the isothermal compressibility and thermal expansion coefficient, which are physical measures of packing frustration, demonstrate that these fluid properties strongly correlate with the fragility of glass-formation. Back to three dimensions, we deduce apparently universal relationships between , a measure of the breadth of the glass-formation and both the isothermal compressibility and thermal expansion coefficient of polymer melts at .

  18. Spatial and temporal variability of atmospheric sulfur-containing gases and particles during the Albatross campaign

    Science.gov (United States)

    Sciare, J.; Baboukas, E.; Kanakidou, M.; Krischke, U.; Belviso, S.; Bardouki, H.; Mihalopoulos, N.

    2000-06-01

    To investigate the oxidation chemistry of dimethylsulfide (DMS) in the marine atmosphere, atmospheric DMS, SO2, as well as several DMS oxidation products in aerosol phase such as non-sea-salt sulfate (nss-SO4), methanesulfonate (MSA), and dimethylsulfoxide (DMSOp) have been measured during the Albatross campaign in the Atlantic Ocean from October 9 to November 2, 1996. Long-range transport, local sea-to-air flux of DMS (FDMS), marine boundary layer (MBL) height variation, and photochemistry were found to be the major factors controlling atmospheric DMS concentration which ranged from 29 to 396 parts per trillion by volume (pptv) (mean of 120±68 pptv) over the cruise. The spatial variability of MSA and DMSOp follows the latitudinal variations of FDMS. A 2-day period of intensive photochemistry associated with quite stable atmospheric conditions south of the equator allowed the observation of anticorrelated diurnal variations between DMS and its main oxidation products. A chemical box model describing sulfur chemistry in the marine atmosphere was used to reproduce these variations and investigate coherence of experimentally calculated fluxes FDMS with observed DMS atmospheric concentrations. The model results reveal that the measured OH levels are not sufficient to explain the observed DMS daytime variation. Oxidizing species other than OH, probably BrO, must be involved in the oxidation of DMS to reproduce the observed data.

  19. Neural Network Ensemble Residual Kriging Application for Spatial Variability of Soil Properties

    Institute of Scientific and Technical Information of China (English)

    SHEN Zhang-Quan; SHI Jie-Bin; WANG Ke; KONG Fan-Sheng; J. S. BAILEY

    2004-01-01

    High quality, agricultural nutrient distribution maps are necessary for precision management, but depend on initial soil sample analyses and interpolation techniques. To examine the methodologies for and explore the capability of interpolating soil properties based on neural network ensemble residual kriging, a silage field at Hayes, Northern Ireland, UK, was selected for this study with all samples being split into independent training and validation data sets. The training data set, comprised of five soil properties: soil pH, soil available P, soil available K, soil available Mg and soil available S,was modeled for spatial variability using 1) neural network ensemble residual kriging, 2) neural network ensemble and 3) kriging with their accuracies being estimated by means of the validation data sets. Ordinary kriging of the residuals provided accurate local estimates, while final estimates were produced as a sum of the artificial neural network (ANN)ensemble estimates and the ordinary kriging estimates of the residuals. Compared to kriging and neural network ensemble,the neural network ensemble residual kriging achieved better or similar accuracy for predicting and estimating contour maps. Thus, the results demonstrated that ANN ensemble residual kriging was an efficient alternative to the conventional geo-statistical models that were usually used for interpolation of a data set in the soil science area.

  20. A hierarchical model for spatial capture-recapture data

    Science.gov (United States)

    Royle, J. Andrew; Young, K.V.

    2008-01-01

    Estimating density is a fundamental objective of many animal population studies. Application of methods for estimating population size from ostensibly closed populations is widespread, but ineffective for estimating absolute density because most populations are subject to short-term movements or so-called temporary emigration. This phenomenon invalidates the resulting estimates because the effective sample area is unknown. A number of methods involving the adjustment of estimates based on heuristic considerations are in widespread use. In this paper, a hierarchical model of spatially indexed capture recapture data is proposed for sampling based on area searches of spatial sample units subject to uniform sampling intensity. The hierarchical model contains explicit models for the distribution of individuals and their movements, in addition to an observation model that is conditional on the location of individuals during sampling. Bayesian analysis of the hierarchical model is achieved by the use of data augmentation, which allows for a straightforward implementation in the freely available software WinBUGS. We present results of a simulation study that was carried out to evaluate the operating characteristics of the Bayesian estimator under variable densities and movement patterns of individuals. An application of the model is presented for survey data on the flat-tailed horned lizard (Phrynosoma mcallii) in Arizona, USA.

  1. 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...

  2. Spatial-temporal assessment of climate model drifts

    Science.gov (United States)

    Zanchettin, Davide; Woldeyes Arisido, Maeregu; Gaetan, Carlo; Rubino, Angelo

    2016-04-01

    Decadal climate forecasts with full-field initialized coupled climate models are affected by a growing error signal that develops due to the adjustment of the simulations from the assimilated state consistent with observations to the state consistent with the biased model's climatology. Sea-surface temperature (SST) drifts and biases are a major concern due to the central role of SST properties for the dynamical coupling between the atmosphere and the ocean, and for the associated variability. Therefore, strong SST drifts complicate the initialization and assessment of decadal climate prediction experiments, and can be detrimental for their overall quality. We propose a dynamic linear model based on a state-space approach and developed within a Bayesian hierarchical framework for probabilistic assessment of spatial and temporal characteristics of SST drifts in ensemble climate simulations. The state-space approach uses unobservable state variables to directly model the processes generating the observed variability. The statistical model is based on a sequential definition of the process having a conditional dependency only on the previous time step, which therefore corresponds to the Kalman filter formulas. In our formulation, the statistical model distinguishes between seasonal and longer-term drift components, and between large-scale and local drifts. We apply the Bayesian method to make inferences on the variance components of the Gaussian errors in both the observation and system equations of the state-space model. To this purpose, we draw samples from their posterior distributions using a Monte Carlo Markov Chain simulation technique with a Gibbs sampler. In this contribution we illustrate a first application of the model using the MiKlip prototype system for decadal climate predictions. We focus on the tropical Atlantic Ocean - a region where climate models are typically affected by a severe warm SST bias - to demonstrate how our approach allows for a more

  3. 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

  4. 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.

  5. Modelling variability in hospital bed occupancy.

    Science.gov (United States)

    Harrison, Gary W; Shafer, Andrea; Mackay, Mark

    2005-11-01

    A stochastic version of the Harrison-Millard multistage model of the flow of patients through a hospital division is developed in order to model correctly not only the average but also the variability in occupancy levels, since it is the variability that makes planning difficult and high percent occupancy levels increase the risk of frequent overflows. The model is fit to one year of data from the medical division of an acute care hospital in Adelaide, Australia. Admissions can be modeled as a Poisson process with rates varying by day of the week and by season. Methods are developed to use the entire annual occupancy profile to estimate transition rate parameters when admission rates are not constant and to estimate rate parameters that vary by day of the week and by season, which are necessary for the model variability to be as large as in the data. The final model matches well the mean, standard deviation and autocorrelation function of the occupancy data and also six months of data not used to estimate the parameters. Repeated simulations are used to construct percentiles of the daily occupancy distributions and thus identify ranges of normal fluctuations and those that are substantive deviations from the past, and also to investigate the trade-offs between frequency of overflows and the percent occupancy for both fixed and flexible bed allocations. Larger divisions can achieve more efficient occupancy levels than smaller ones with the same frequency of overflows. Seasonal variations are more significant than day-of-the-week variations and variable discharge rates are more significant than variable admission rates in contributing to overflows.

  6. Combining microsimulation and spatial interaction models for retail location analysis

    Science.gov (United States)

    Nakaya, Tomoki; Fotheringham, A. Stewart; Hanaoka, Kazumasa; Clarke, Graham; Ballas, Dimitris; Yano, Keiji

    2007-12-01

    Although the disaggregation of consumers is crucial in understanding the fragmented markets that are dominant in many developed countries, it is not always straightforward to carry out such disaggregation within conventional retail modelling frameworks due to the limitations of data. In particular, consumer grouping based on sampled data is not assured to link with the other statistics that are vital in estimating sampling biases and missing variables in the sampling survey. To overcome this difficulty, we propose a useful combination of spatial interaction modelling and microsimulation approaches for the reliable estimation of retail interactions based on a sample survey of consumer behaviour being linked with other areal statistics. We demonstrate this approach by building an operational retail interaction model to estimate expenditure flows from households to retail stores in a local city in Japan, Kusatsu City.

  7. Variable impact of chronic stress on spatial learning and memory in BXD mice.

    Science.gov (United States)

    Shea, Chloe J A; Carhuatanta, Kimberly A K; Wagner, Jessica; Bechmann, Naomi; Moore, Raquel; Herman, James P; Jankord, Ryan

    2015-10-15

    The effects of chronic stress on learning are highly variable across individuals. This variability stems from gene-environment interactions. However, the mechanisms by which stress affects genetic predictors of learning are unclear. Thus, we aim to determine whether the genetic pathways that predict spatial memory performance are altered by previous exposure to chronic stress. Sixty-two BXD recombinant inbred strains of mice, as well as parent strains C57BL/6J and DBA/2J, were randomly assigned as behavioral control or to a chronic variable stress paradigm and then underwent behavioral testing to assess spatial memory and learning performance using the Morris water maze. Quantitative trait loci (QTL) mapping was completed for average escape latency times for both control and stress animals. Loci on chromosomes 5 and 10 were found in both control and stress environmental populations; eight additional loci were found to be unique to either the control or stress environment. In sum, results indicate that certain genetic loci predict spatial memory performance regardless of prior stress exposure, while exposure to stress also reveals unique genetic predictors of training during the memory task. Thus, we find that genetic predictors contributing to spatial learning and memory are susceptible to the presence of chronic stress.

  8. 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 psoil 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 System Science, 11, 2285

  9. Interpolation of climate variables and temperature modeling

    Science.gov (United States)

    Samanta, Sailesh; Pal, Dilip Kumar; Lohar, Debasish; Pal, Babita

    2012-01-01

    Geographic Information Systems (GIS) and modeling are becoming powerful tools in agricultural research and natural resource management. This study proposes an empirical methodology for modeling and mapping of the monthly and annual air temperature using remote sensing and GIS techniques. The study area is Gangetic West Bengal and its neighborhood in the eastern India, where a number of weather systems occur throughout the year. Gangetic West Bengal is a region of strong heterogeneous surface with several weather disturbances. This paper also examines statistical approaches for interpolating climatic data over large regions, providing different interpolation techniques for climate variables' use in agricultural research. Three interpolation approaches, like inverse distance weighted averaging, thin-plate smoothing splines, and co-kriging are evaluated for 4° × 4° area, covering the eastern part of India. The land use/land cover, soil texture, and digital elevation model are used as the independent variables for temperature modeling. Multiple regression analysis with standard method is used to add dependent variables into regression equation. Prediction of mean temperature for monsoon season is better than winter season. Finally standard deviation errors are evaluated after comparing the predicted temperature and observed temperature of the area. For better improvement, distance from the coastline and seasonal wind pattern are stressed to be included as independent variables.

  10. Modeling Variability in Immunocompetence and Immunoresponsiveness

    NARCIS (Netherlands)

    Ask, B.; Waaij, van der E.H.; Bishop, S.C.

    2008-01-01

    The purposes of this paper were to 1) develop a stochastic model that would reflect observed variation between animals and across ages in immunocompetence and responsiveness; and 2) illustrate consequences of this variability for the statistical power of genotype comparisons and selection. A stochas

  11. Temporal stability of soil moisture spatial variability at two scales and its implication for optimal field monitoring

    Directory of Open Access Journals (Sweden)

    X. Zhou

    2007-05-01

    Full Text Available Soil moisture spatial distribution is a key component in characterizing and modeling water movement at multiple scales. The temporal stability of soil moisture spatial distribution at multiple depths was investigated at the 7.9-ha Shale Hills Catchment in central Pennsylvania with a year-round monitoring of 77 sites distributed across the catchment. For this catchment with heterogeneous soils and landforms, integration of soils information into the temporal stability assessment provided a more accurate location of representative monitoring sites for capturing mean soil moisture. The temporal stability pattern of soil moisture at the swale scale was similar to that at the catchment scale, suggesting that the swale could be used as a representative unit in the catchment study in terms of mean soil moisture dynamics. The temporal stability of soil moisture variability in this catchment varied over space and seasons. Temporally stable sites were found in the northwestern and southeastern parts of the catchment, while the areas near the stream and some swale areas had lower temporal stability. The spatial distribution of soil moisture was more stable over time during wet seasons, but less stable during transitional periods (i.e. drying or recharging periods. The temporal stability concept helps the optimal design of field monitoring sites and sampling strategies. On the other hand, the temporally unstable sites provide insights regarding the hydrological processes behind the spatial variability of soil moisture.

  12. Spatial variability of soil nutrient in paddy plantation: Sites FELCRA Seberang Perak

    Science.gov (United States)

    Kamarudin, H.; Adnan, N. A.; Mispan, M. R.; Athirah. A, A.

    2016-06-01

    The conventional methods currently used for rice cultivation in Malaysia are unable to give maximum yield although the yield production of paddy is increasing. This is due to the conversional method being unable to include soil properties as one of their parameters in agriculture management. Soil properties vary spatially in farm scale due to differences in topography, parent material, vegetation or land management and soil characteristics; also plantation productivity varies significantly over small spatial scales. Knowledge of spatial variability in soil fertility is important for site specific nutrient management. Analysis of spatial variability of soil nutrient of nitrogen (N), phosphorus (P) and potassium (K) were conducted in this study with the aid of GIS (i.e ArcGIS) and statistical softwares. In this study different temporal and depths of soil nutrient were extracted on the field and further analysis of N,P,K content were analysed in the chemical laboratory and using spatially technique in GIS sofware. The result indicated that for the Seberang Perak site of 58 hactares area, N and K are met minimum requirements nutrient content as outlines by the MARDI for paddy cultivation. However, P indicated poor condition in the study area; therefore the soil needs further attention and treatment.

  13. Identifying Spatially Inhomogeneous Relationships Between Drainage Density and Its Controlling Variables

    Science.gov (United States)

    Stepinski, T.; Ranatunga, T.; Jasiewicz, J.

    2011-12-01

    Spatial variation of the value of drainage density (D) is observed on variety of scales. It is attributed to a nonuniform distribution of variables that exert control over D. Comprehensive understanding of the dependence of D on its controlling factors is lacking because of complex, nonlinear character of such dependence. This study presents the use of the regression tree technique to identify different relationships between D and its controlling variables across the conterminous United States. Local drainage density (response variable) is calculated on a 4 km-size regular grid from high resolution stream network data from the National Hydrographic Dataset. Explanatory variables pertaining to geology, soil, terrain, climate, land cover, and vegetation density are also calculated on the same grid. The resulting grids are fed to a GUIDE algorithm to build a regression tree. The algorithm performs "regression by parts" - it hierarchically partitions the dataset so as to increase the accuracy of linear regression in each partition. Each final partition (a terminal node of the tree) contains entries in the dataset (cells in a grid) for which a good-fit linear relation between D and its controlling variables can be established. Ranges of explanatory variables in each node are determined by the path in the tree, and spatial extent (footprint of relationship) of the node is mapped. Collection of all such relations and their footprints provides comprehensive understanding of dependence of D on its controlling factors.

  14. Spatial and seasonal variabilities of the stable carbon isotope composition of soil CO2 concentration and flux in complex terrain

    Science.gov (United States)

    Liang, Liyin L.; Riveros-Iregui, Diego A.; Risk, David A.

    2016-09-01

    Biogeochemical processes driving the spatial variability of soil CO2 production and flux are well studied, but little is known about the variability in the spatial distribution of the stable carbon isotopes that make up soil CO2, particularly in complex terrain. Spatial differences in stable isotopes of soil CO2 could indicate fundamental differences in isotopic fractionation at the landscape level and may be useful to inform modeling of carbon cycling over large areas. We measured the spatial and seasonal variabilities of the δ13C of soil CO2 (δS) and the δ13C of soil CO2 flux (δP) in a subalpine forest ecosystem located in the Rocky Mountains of Montana. We found consistently more isotopically depleted values of δS and δP in low and wet areas of the landscape relative to steep and dry areas. Our results suggest that the spatial patterns of δS and δP are strongly mediated by soil water and soil respiration rate. More interestingly, our analysis revealed different temporal trends in δP across the landscape; in high landscape positions δP became more positive, whereas in low landscape positions δP became more negative with time. These trends might be the result of differential dynamics in the seasonality of soil moisture and its effects on soil CO2 production and flux. Our results suggest concomitant yet independent effects of water on physical (soil gas diffusivity) and biological (photosynthetic discrimination) processes that mediate δS and δP and are important when evaluating the δ13C of CO2 exchanged between soils and the atmosphere in complex terrain.

  15. 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...

  16. Modelling of the education quality of a high schools in Sumenep Regency using spatial structural equation modelling

    Science.gov (United States)

    Anekawati, Anik; Widjanarko Otok, Bambang; Purhadi; Sutikno

    2017-09-01

    In some cases, education research often involves the latent variables that have a causal relationship as well as a spatial effect. Therefore, it requires a statistical analysis technique called spatial structural equation modelling (spatial SEM). In this research, a spatial SEM was developed to model the quality of education in high schools in Sumenep Regency. This model was improved after the evaluation of an outer and inner model of the model scheme centroid, factor and path since some indicators were not valid. The path scheme model showed better results compared to the other schemes since all of its indicators were valid and its value of R-square increased. Furthermore, only the model of path scheme was tested for spatial effects. The result of the identification test of spatial effects on the inner model using a robust Lagrange multiplier test (using queen contiguity) showed that the education quality model leads to a spatial autoregressive model (SAR in SEM) with a significance level α of 5%, while the model of school infrastructure has no significant spatial effects. The improved model of SAR in SEM, the R2 value obtained was 47.33%, so that it is clear that data variation can be explained by the model of SAR in SEM for the quality of education in high schools.

  17. 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.

  18. 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.

  19. [Spatial pattern of soil fertility in Bashan tea garden: a prediction based on environmental auxiliary variables].

    Science.gov (United States)

    Qin, Le-feng; Yang, Chao; Lin, Fen-fang; Yang, Ning; Zheng, Xin-yu; Xu, Hong-wei; Wang, Ke

    2010-12-01

    Taking topographic factors and NDVI as auxiliary variables, and by using regression-kriging method, the spatial variation pattern of soil fertility in Bashan tea garden in the hilly area of Fuyang City was explored. The spatial variability of the soil fertility was mainly attributed to the structural factors such as relative elevation and flat/vertical curvature. The lower the relative elevation, the worse the soil fertility was. The overall soil fertility level was relatively high, and the area with lower soil fertility only accounted for 5% of the total. By using regression-kriging method with relative elevation as auxiliary variable, the prediction accuracy of soil fertility was obviously higher than that by using ordinary kriging method, with the mean error and root mean square error being 0. 028 and 0. 108, respectively. It was suggested that the prediction method used in this paper could fully reflect the effects of environmental variables on soil fertility , improve the prediction accuracy about the spatial pattern of soil fertility, and provide scientific basis for the precise management of tea garden.

  20. Spatial and temporal variability of snow depth and SWE in a small mountain catchment

    Directory of Open Access Journals (Sweden)

    T. Grünewald

    2010-01-01

    Full Text Available The spatio-temporal variability of the mountain snow cover determines the avalanche danger, snow water storage, permafrost distribution and the local distribution of fauna and flora. Using a new type of terrestrial laser scanner (TLS, which is particularly suited for measurements of snow covered surfaces, snow depth, snow water equivalent (SWE and melt rates have been monitored in a high alpine catchment during an ablation period. This allowed for the first time to get a high resolution (2.5 m cell size picture of spatial variability and its temporal development. A very high variability in which maximum snow depths between 0–9 m at the end of the accumulation season was found. This variability decreased during the ablation phase, although the dominant snow deposition features remained intact. The spatial patterns of calculated SWE were found to be similar to snow depth. Average daily melt rate was between 15 mm/d at the beginning of the ablation period and 30 mm/d at the end. The spatial variation of melt rates increased during the ablation rate and could not be explained in a simple manner by geographical or meteorological parameters, which suggests significant lateral energy fluxes contributing to observed melt. It could be qualitatively shown that the effect of the lateral energy transport must increase as the fraction of snow free surfaces increases during the ablation period.

  1. 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

  2. Spatial and temporal variability of spring ecosystems in Cuatro Ciénegas, MX

    Science.gov (United States)

    Corman, J. R.; Ramos, J.; Childers, D. L.; Elser, J. J.

    2013-12-01

    Springs in desert ecosystems provide vital water resources and are often hotspots of biodiversity. Indeed, the Cuatro Ciénegas (CC) Valley, México, which hosts >300 springs, lakes, streams, and ponds, has the highest rate of endemism in North America. This region of the Chihuahuan Desert and its aquatic ecosystems are thought to be supported by both precipitation events and local and regional aquifers, however, the hydrologic influence and connectivity of the springs are not well understood. We have been monitoring the physicochemical characteristics of this system since 1998 and yearly since 2011. Our basin-wide study of 15 different aquatic features provides an opportunity to (1) characterize the physicochemical and nutrient landscape of the aquatic ecosystems and (2) test the assumptions of hypothesized hydrologic dynamics. The aquatic ecosystems of CC have an impressive spatial diversity in their physicochemical properties and support a locally-connected hydrologic model of the valley. Aqueous specific conductivity spanned 1.1 - 9.6 mS/cm2, with the highest values found in the eastern lobe and lowest values in the southeastern and northern regions. Dissolved organic carbon concentrations ranged over two orders of magnitude (max: 5.3 mM), with a similar spatial variability as specific conductivity. Nutrient data also showed geographic trends, however patterns differed for nitrogen (N) and phosphorus (P). While total dissolved N and P were highest in the eastern lobe, the highest values of each were not found at the same sites. Rancho las Pozas had the highest N (>500 uM N), while Los Hundidos had the highest P concentrations (as high as 10.8 uM P). Atomic N:P ratios ranged from 7 - 997 across CC, with a mean of 139. Both the highest (>500) and lowest (27 cm of rain in some regions. A comparison of our longest record from Río Mesquites, a spring-fed stream, and Los Hundidos, a collection of spring-fed and evaporitic ponds, shows the spatial dissimilarity of CC

  3. 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.

  4. 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 (zone. Of particular interest is the potential occurrence of "hot 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

  5. Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

    Directory of Open Access Journals (Sweden)

    Hongqiang Liu

    2017-01-01

    Full Text Available A multivariate Bayesian spatial modeling approach was used to jointly model the counts of two types of crime, i.e., burglary and non-motor vehicle theft, and explore the geographic pattern of crime risks and relevant risk factors. In contrast to the univariate model, which assumes independence across outcomes, the multivariate approach takes into account potential correlations between crimes. Six independent variables are included in the model as potential risk factors. In order to fully present this method, both the multivariate model and its univariate counterpart are examined. We fitted the two models to the data and assessed them using the deviance information criterion. A comparison of the results from the two models indicates that the multivariate model was superior to the univariate model. Our results show that population density and bar density are clearly associated with both burglary and non-motor vehicle theft risks and indicate a close relationship between these two types of crime. The posterior means and 2.5% percentile of type-specific crime risks estimated by the multivariate model were mapped to uncover the geographic patterns. The implications, limitations and future work of the study are discussed in the concluding section.

  6. Evaluating the Value of High Spatial Resolution in National Capacity Expansion Models using ReEDS

    Energy Technology Data Exchange (ETDEWEB)

    Krishnan, Venkat; Cole, Wesley

    2016-11-14

    Power sector capacity expansion models (CEMs) have a broad range of spatial resolutions. This paper uses the Regional Energy Deployment System (ReEDS) model, a long-term national scale electric sector CEM, to evaluate the value of high spatial resolution for CEMs. ReEDS models the United States with 134 load balancing areas (BAs) and captures the variability in existing generation parameters, future technology costs, performance, and resource availability using very high spatial resolution data, especially for wind and solar modeled at 356 resource regions. In this paper we perform planning studies at three different spatial resolutions--native resolution (134 BAs), state-level, and NERC region level--and evaluate how results change under different levels of spatial aggregation in terms of renewable capacity deployment and location, associated transmission builds, and system costs. The results are used to ascertain the value of high geographically resolved models in terms of their impact on relative competitiveness among renewable energy resources.

  7. Abiotic factors influencing the spatial and temporal variability of juvenile fish in Pamlico Sound, North Carolina

    Energy Technology Data Exchange (ETDEWEB)

    Pietrafesa, L.J.; Janowitz, G.S.; Miller, J.M.; Noble, E.B.; Ross, S.W.; Epperly, S.P.

    1985-07-01

    A 3-D, time dependent model of the circulation in Pamlico Sound, NC, is used to relate the direction and magnitude of winds to the number of juvenile fish sampled at specified estuarine nursery locations. NC marine sport fishes are known to be spawned in NC continental waters, and then make transit to an through barrier island inlets, into Pamlico Sound. The juveniles then move 40-70 kilometers across the Sound to the nurseries. It is hypothesized that wind driven, pressure gradient induced and topographically steered currents, all abiotic factors, provide the transport mechanisms, during the recruitment period February-April, necessary for the transect. Moreover, the inherent variability in the atmospherically derived physical factors and the influence of topographic irregularities such as a large shoal which laterally bisects the Sound and bifurcates the bottom currents are seen as sources of the temporal and spatial variation observed in the distribution of juvenile fish, while the influence of biological processes is viewed as providing fine-tuned structuring.

  8. Spatial analysis of Tuberculosis in Rio de Janeiro in the period from 2005 to 2008 and associated socioeconomic factors using micro data and global spatial regression models.

    Science.gov (United States)

    Magalhães, Monica de Avelar Figueiredo Mafra; Medronho, Roberto de Andrade

    2017-03-01

    The present study analyses the spatial pattern of tuberculosis (TB) from 2005 to 2008 by identifying relevant socioeconomic variables for the occurrence of the disease through spatial statistical models. This ecological study was performed in Rio de Janeiro using new cases. The census sector was used as the unit of analysis. Incidence rates were calculated, and the Local Empirical Bayesian method was used. The spatial autocorrelation was verified with Moran's Index and local indicators of spatial association (LISA). Using Spearman's test, variables with significant correlation at 5% were used in the models. In the classic multivariate regression model, the variables that fitted better to the model were proportion of head of family with an income between 1 and 2 minimum wages, proportion of illiterate people, proportion of households with people living alone and mean income of the head of family. These variables were inserted in the Spatial Lag and Spatial Error models, and the results were compared. The former exhibited the best parameters: R2 = 0.3215, Log-Likelihood = -9228, Akaike Information Criterion (AIC) = 18,468 and Schwarz Bayesian Criterion (SBC) = 18,512. The statistical methods were effective in the identification of spatial patterns and in the definition of determinants of the disease providing a view of the heterogeneity in space, allowing actions aimed more at specific populations.

  9. 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

  10. 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.

  11. 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.

  12. A Model of Colonic Crypts using SBML Spatial

    Directory of Open Access Journals (Sweden)

    Carlo Maj

    2013-09-01

    Full Text Available The Spatial Processes package enables an explicit definition of a spatial environment on top of the normal dynamic modeling SBML capabilities. The possibility of an explicit representation of spatial dynamics increases the representation power of SBML. In this work we used those new SBML features to define an extensive model of colonic crypts composed of the main cellular types (from stem cells to fully differentiated cells, alongside their spatial dynamics.

  13. 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

  14. 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.

  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. Transient,spatially-varied recharge for groundwater modeling

    Science.gov (United States)

    Assefa, Kibreab; Woodbury, Allan

    2013-04-01

    This study is aimed at producing spatially and temporally varying groundwater recharge for transient groundwater modeling in a pilot watershed in the North Okanagan, Canada. The recharge modeling is undertaken by using a Richard's equation based finite element code (HYDRUS-1D) [Simunek et al., 2002], ArcGISTM [ESRI, 2011], ROSETTA [Schaap et al., 2001], in situ observations of soil temperature and soil moisture and a long term gridded climate data [Nielsen et al., 2010]. The public version of HYDUS-1D [Simunek et al., 2002] and another beta version with a detailed freezing and thawing module [Hansson et al., 2004] are first used to simulate soil temperature, snow pack and soil moisture over a one year experimental period. Statistical analysis of the results show both versions of HYDRUS-1D reproduce observed variables to the same degree. Correlation coefficients for soil temperature simulation were estimated at 0.9 and 0.8, at depths of 10 cm and 50 cm respectively; and for soil moisture, 0.8 and 0.6 at 10 cm and 50 cm respectively. This and other standard measures of model performance (root mean square error and average error) showed a promising performance of the HYDRUS-1D code in our pilot watershed. After evaluating model performance using field data and ROSETTA derived soil hydraulic parameters, the HYDRUS-1D code is coupled with ArcGISTM to produce spatially and temporally varying recharge maps throughout the Deep Creek watershed. Temporal and spatial analysis of 25 years daily recharge results at various representative points across the study watershed reveal significant temporal and spatial variations; average recharge estimated at 77.8 ± 50.8mm /year. This significant variation over the years, caused by antecedent soil moisture condition and climatic condition, illustrates the common flaw of assigning a constant percentage of precipitation throughout the simulation period. Groundwater recharge modeling has previously been attempted in the Okanagan Basin

  17. Spatial variability studies in São Paulo, Brazil along the last twenty five years

    Directory of Open Access Journals (Sweden)

    Sidney Rosa Vieira

    2010-01-01

    Full Text Available Soil properties vary in space due to many causes. For this reason it is wise to know the magnitude and behaviour of the variability for adequate data analysis and decision making. Our work on spatial variability of soil properties in São Paulo, Brazil began in 1982 with a very simple soil sampling in a small field. Much progress has been made since then on sampling designs, field equipment and methods, and mostly on computation equipment and softwares. This paper reports the results corresponding to some aspects of this progress, as far as the field, analysis and computation work are concerned. The objective of this study was to illustrate the use of geostatistics in data analysis for three sampling conditions on long term no-tillage system. The analysis is done on a wide range of field scales, variables, sampling schemes as well as repeating sampling scheme for the same variable in different years. Semivariograms are compared for the same variables in different scales and sampling dates and depths as to provide a guide for sampling spacing and number of samples. Normalized crop yield parameters for many years are used in the discussion of time variability and on the use of yield maps to locate management zones. The time of the year in which measurements of soil physical properties are made affected the results both in terms of descriptive statistical and spatial dependence parameters. Crop yields changed (soybean decrease and maize increase with time of no-tillage but the real cause was not identified. The length of time with no-tillage affected the range of dependence for the main crops (increased for soybean, maize and oats and therefore increased the size of the homogeneous management zones. The evolution of the sampling grid from 20 m with 63 sampling points to 10 m with 302 sampling points allowed for a much better knowledge of the spatial variability of crop yields but it had the reverse effect on the spatial variability of soil physical

  18. 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

  19. Spatial variability of soil organic carbon in the forestlands of northeast China

    Institute of Scientific and Technical Information of China (English)

    Ling Liu; Haiyan Wang; Wei Dai; Xiangdong Lei; Xiaojuan Yang; Xu Li

    2014-01-01

    Soil organic carbon (SOC) is an effective indicator of soil fertility and productivity, and it varies spatially and temporally in relation to other soil properties. Spatial variability of SOC in the forestlands of northeast China was characterized using geostatistics. Soil samples at the depths of 0-20 cm, 20-40 cm and 40-60 cm were collected from six-ty-three temporary plots to evaluate SOC concentration and density (SOCD) and other soil properties. We analyzed correlations between SOC and soil properties. Soil organic carbon concentrations were high. The total amount of C stored in soil (0-60 cm) was 16.23 kg·m-2 with the highest SOCD of 7.98 kg×m-2 in topsoil. Soil properties in most cases differed by horizon, suggesting different processes and effects in each horizon. Soil organic carbon had positive relationships with total N, P and K as well as readily available K, but did not show a significant posi-tive correlation with available P. Spatial factors including elevation, slope and aspect affected SOC distribution. Soil organic carbon at 0-60 cm had strong spatial autocorrelation with nugget/sill ratio of 5.7%, and moderate structured dependence was found at 0-20 cm, which indicated the existence of a highly developed spatial structure. Spatial distributions of SOC concentration and SOCD were estimated using regres-sion-kriging, with higher prediction accuracy than ordinary kriging. The fractal dimension of SOC indicated the preferential pattern of SOC dis-tribution, with the greatest spatial heterogeneity and strongest spatial dependence in the northeast-southwest direction.

  20. Spatial Variability of Trace Gases During DISCOVER-AQ: Planning for Geostationary Observations of Atmospheric Composition

    Science.gov (United States)

    Follette-Cook, Melanie B.; Pickering, K.; Crawford, J.; Appel, W.; Diskin, G.; Fried, A.; Loughner, C.; Pfister, G.; Weinheimer, A.

    2015-01-01

    Results from an in-depth analysis of trace gas variability in MD indicated that the variability in this region was large enough to be observable by a TEMPO-like instrument. The variability observed in MD is relatively similar to the other three campaigns with a few exceptions: CO variability in CA was much higher than in the other regions; HCHO variability in CA and CO was much lower; MD showed the lowest variability in NO2All model simulations do a reasonable job simulating O3 variability. For CO, the CACO simulations largely under over estimate the variability in the observations. The variability in HCHO is underestimated for every campaign. NO2 variability is slightly overestimated in MD, more so in CO. The TX simulation underestimates the variability in each trace gas. This is most likely due to missing emissions sources (C. Loughner, manuscript in preparation).Future Work: Where reasonable, we will use these model outputs to further explore the resolvability from space of these key trace gases using analyses of tropospheric column amounts relative to satellite precision requirements, similar to Follette-Cook et al. (2015).

  1. Spatial Modelling of Solar energy Potential in Kenya

    Directory of Open Access Journals (Sweden)

    Francis Omondi Oloo

    2015-06-01

    Full Text Available Solar energy is one of the readily available renewable energy resources in the developing countries within the tropical region. Kenya is one of the countries which receive an average of approximately 6.5 sunshine hours in a single day throughout the year. However, there is slow adoption of solar energy resources in the country due to limited information on the spatial variability solar energy potential. This study aims at assessing the potential of photovoltaic solar energy in Kenya. The factors that influence incident solar radiation which were considered in this task included atmospheric transmissivity and topography. The influence of atmospheric transmissivity was factored in by modelling monthly transmissivity factors from a combination of cloud cover, diffuse ratios and the effect of altitude. The contribution of topography was included by applying hemispherical viewshed analysis to determine the amount of incident global radiation on the surface based on the orientation of the terrain. GIS concepts were used to integrate the spatial datasets from different themes. The results showed that, about 70% of the land area in Kenya has the potential of receiving approximately 5kWh/m2/day throughout the year. In outline, this work successfully assessed the spatio-temporal variability in the characteristics of solar energy potential in Kenya and can be used as a basis for policy support in the country.

  2. Modelling Spatial Patterns of Vegetation in Desert Sand Dunes

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    A stochastic numerical approach was developed to model the actual standing biomass in the sand dunes of the northwestern Negev (Israel) and probable boundary conditions that may be responsible for the vegetation patterns investigated in detail. Our results for several variables characteristic for the prevailing climate, geomorphology, hydrology and biologicy at four measurement stations along a transect from northwest to southeast allowed for the development of a stochastic model for biomass distribution over the entire sand dune field (mesoscale) and at Nizzana experimental station (microscale). With this equation it was possible to compute andinterpolate a biomass index value for each grid point on the mesoscale and micro scale. The spatial distribution of biomass is negatively linked to distance from the sea, to rainfall and relief energy.

  3. Spatial and temporal CH4 flux variability in a shallow tropical floodplain lake, Pantanal, South America

    Science.gov (United States)

    Peixoto, R.; Enrich Prast, A.; Silva, E. C.; Pontual, L.; Marotta, H.; Pinho, L.; Bastviken, D.

    2012-04-01

    Spatial and temporal CH4 flux variability in a shallow tropical floodplain lake, Pantanal, South America Peixoto, R, Enrich-Prast, A., Silva, E. C., Pontual, L., Marotta, H., Pinho, L. Q. and Bastviken, D. Methane (CH4) is an important greenhouse gas produced during anaerobic decomposition of organic matter (OM). It can play a significant role in carbon emissions from tropical aquatic ecosystems to the atmosphere and have a substantial participation in greenhouse gas balances. However, most studies report low numbers of short-term (≤ 24h) measurements in each system and the spatial and temporal variability is poorly understood. In this study we analyzed the temporal and spatial variability of CH4 emissions from a shallow Pantanal lake. Pantanal is the world's largest savanna tropical floodplain with a significant input of organic matter from the drainage area around and an annual inundation pulse. Methane fluxes were measured in September 2008 with floating chambers over 24 hour periods for five consecutive days. We used > 20 chambers along transects from the marginal vegetated regions of the lake to the central parts of the lake. Methane fluxes were determined as described by Bastviken et al. 2010 (doi: 10.1021/es1005048). There was no significant difference of methane fluxes among sampling days. Methane fluxes at the vegetated area and the margin were significantly higher than at central parts of the lake showing clearly the importance of different compartments within lakes. This study indicates that a) 24 hour measurements may be representative for time perspectives of a week given similar weather conditions, while b) spatial variability within lakes must be considered to correctly evaluate CH4 emissions from aquatic systems.

  4. 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 at th......). Other factors, such as the soil structure, available water content and the soil mineral fraction, might greatly influence the transport phenomenon, thus acting for important environmental repercussions....

  5. Hybrid modeling of spatial continuity for application to numerical inverse problems

    Science.gov (United States)

    Friedel, Michael J.; Iwashita, Fabio

    2013-01-01

    A novel two-step modeling approach is presented to obtain optimal starting values and geostatistical constraints for numerical inverse problems otherwise characterized by spatially-limited field data. First, a type of unsupervised neural network, called the self-organizing map (SOM), is trained to recognize nonlinear relations among environmental variables (covariates) occurring at various scales. The values of these variables are then estimated at random locations across the model domain by iterative minimization of SOM topographic error vectors. Cross-validation is used to ensure unbiasedness and compute prediction uncertainty for select subsets of the data. Second, analytical functions are fit to experimental variograms derived from original plus resampled SOM estimates producing model variograms. Sequential Gaussian simulation is used to evaluate spatial uncertainty associated with the analytical functions and probable range for constraining variables. The hybrid modeling of spatial continuity is demonstrated using spatially-limited hydrologic measurements at different scales in Brazil: (1) physical soil properties (sand, silt, clay, hydraulic conductivity) in the 42 km2 Vargem de Caldas basin; (2) well yield and electrical conductivity of groundwater in the 132 km2 fractured crystalline aquifer; and (3) specific capacity, hydraulic head, and major ions in a 100,000 km2 transboundary fractured-basalt aquifer. These results illustrate the benefits of exploiting nonlinear relations among sparse and disparate data sets for modeling spatial continuity, but the actual application of these spatial data to improve numerical inverse modeling requires testing.

  6. Summer Temperature and Spatial Variability of all-Cause Mortality in Surat City, India.

    Science.gov (United States)

    Rathi, S K; Desai, V K; Jariwala, P; Desai, H; Naik, A; Joseph, A

    2017-01-01

    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. 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. 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. 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. 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.

  7. 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.

  8. Spatial variability of the properties of marsh soils and their impact on vegetation

    Science.gov (United States)

    Sidorova, V. A.; Svyatova, E. N.; Tseits, M. A.

    2015-03-01

    Spatial variability of the properties of soils and the character of vegetation was studied on seacoasts of the Velikii Island in the Kandalaksha Bay of the White Sea. It was found that the chemical and physicochemical properties of marsh soils (Tidalic Fluvisols) are largely dictated by the distance from the sea and elevation of the sampling point above sea level. The spatial distribution of the soil properties is described by a quadratic trend surface. With an increase in the distance from the sea, the concentration of ions in the soil solution decreases, and the organic carbon content and soil acidity become higher. The spatial dependence of the degree of variability in the soil properties is moderate. Regular changes in the soil properties along the sea-land gradient are accompanied by the presence of specific spatial patterns related to the system of temporary water streams, huge boulders, and beached heaps of sea algae and wood debris. The cluster analysis made it possible to distinguish between five soil classes corresponding to the following plant communities: barren surface (no permanent vegetation), clayey-sandy littoral with sparse halophytes, marsh with large rhizomatous grasses, and grass-forb-bunchberry vegetation of forest margins. The subdivision into classes is especially distinct with respect to the concentration of chloride ions. The following groups of factors affect the distribution of vegetation: the composition of the soil solution, the height above sea level, the pH of water suspensions, and the humus content.

  9. Nonlinear Dynamical Modeling and Forecast of ENSO Variability

    Science.gov (United States)

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

    2017-04-01

    New methodology of empirical modeling and forecast of nonlinear dynamical system variability [1] is applied to study of ENSO climate system. The methodology is based on two approaches: (i) nonlinear decomposition of data [2], that provides low-dimensional embedding for further modeling, and (ii) construction of empirical model in the form of low dimensional random dynamical ("stochastic") system [3]. Three monthly data sets are used for ENSO modeling and forecast: global sea surface temperature anomalies, troposphere zonal wind speed, and thermocline depth; all data sets are limited by 30 S, 30 N and have horizontal resolution 10x10 . We compare results of optimal data decomposition as well as prognostic skill of the constructed models for different combinations of involved data sets. We also present comparative analysis of ENSO indices forecasts fulfilled by our models and by IRI/CPC ENSO Predictions Plume. [1] A. Gavrilov, D. Mukhin, E. Loskutov, A. Feigin, 2016: Construction of Optimally Reduced Empirical Model by Spatially Distributed Climate Data. 2016 AGU Fall Meeting, Abstract NG31A-1824. [2] D. Mukhin, A. Gavrilov, E. Loskutov , A.Feigin, J.Kurths, 2015: Principal nonlinear dynamical modes of climate variability, Scientific Reports, rep. 5, 15510; doi: 10.1038/srep15510. [3] Ya. Molkov, D. Mukhin, E. Loskutov, A. Feigin, 2012: Random dynamical models from time series. Phys. Rev. E, Vol. 85, n.3.

  10. Fine-scale spatial and interannual cadmium isotope variability in the subarctic northeast Pacific

    Science.gov (United States)

    Janssen, D. J.; Abouchami, W.; Galer, S. J. G.; Cullen, J. T.

    2017-08-01

    We present dissolved cadmium (Cd) concentrations, [Cd], and stable isotope compositions, ɛ112/110Cd, in high-resolution depth profiles from five stations along the Line P transect in the subarctic northeast Pacific Ocean. In addition to profiles collected in 2012, subsurface isopycnal samples and surface samples were collected in 2013 and 2014 respectively, providing both temporal and spatial coverage. Surface waters are characterized by Cd depletion relative to phosphate (4 3 -PO) compared to deepwater 4 3 -Cd:PO, and high inferred remineralization ratios in the nutricline (0.45nmolμmol-1) are observed, consistent with Cd enrichment relative to phosphorus (P) in surface-derived biogenic particles. The correlation between Cd and 4 3 -PO weakens at depths where oxygen is highly depleted as shown by local minima in dissolved [Cd] and the tracer Cd*. The decoupling, which is driven by a deficit of Cd relative to 4 3 -PO, appears consistent with the recent hypothesis of dissolved Cd removal in oxygen-depleted regions by insoluble metal sulfide formation. Dissolved ɛ 112 / 110Cd indicates a biologically driven fractionation in surface waters with more positive (heavy) values in the upper water column and lower (light) values in deeper waters. The highest ɛ 112 / 110Cd observed in our sample set (5.19 ± 0.23) is comparable to observations from the Southern Ocean but is significantly lighter than maximum reported surface values from the subtropical North Pacific of ɛ 112 / 110Cd ≥ 15. A global compilation of low [Cd] surface water shows similar differences in maximum ɛ 112 / 110Cd. A surface water intercalibration should be prioritized to help determine if these differences at low [Cd] reflect true physical or biological variability or are due to analytical artefacts. Surface samples from the 2012 sampling campaign fit a closed-system Rayleigh fractionation model; however, surface waters sampled in 2014 had much lower [Cd] with relatively constant ɛ 112 / 110Cd

  11. Gamma-ray attenuation method as an efficient tool to investigate soil bulk density spatial variability

    Energy Technology Data Exchange (ETDEWEB)

    Pires, L.F., E-mail: lfpires@uepg.b [Laboratory of Soil Physics and Environmental Sciences, State University of Ponta Grossa, UEPG, C.E.P. 84.030-900 Ponta Grossa, PR (Brazil); Rosa, J.A. [Laboratory of Soil Physics, Agricultural Research Institute of Parana, IAPAR, C.E.P. 84.001-970 Ponta Grossa, PR (Brazil); Pereira, A.B. [Laboratory of Agrometeorology, State University of Ponta Grossa, UEPG, C.E.P. 84.030-900 Ponta Grossa, PR (Brazil); Arthur, R.C.J.; Bacchi, O.O.S. [Laboratory of Soil Physics, Center for Nuclear Energy in Agriculture, USP/C