Sample records for model spatial variability

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

    KAUST Repository

    Irincheeva, Irina


    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. Spatial variability and parametric uncertainty in performance assessment models

    International Nuclear Information System (INIS)

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


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

  3. Analytical model of reactive transport processes with spatially variable coefficients. (United States)

    Simpson, Matthew J; Morrow, Liam C


    Analytical solutions of partial differential equation (PDE) models describing reactive transport phenomena in saturated porous media are often used as screening tools to provide insight into contaminant fate and transport processes. While many practical modelling scenarios involve spatially variable coefficients, such as spatially variable flow velocity, v(x), or spatially variable decay rate, k(x), most analytical models deal with constant coefficients. Here we present a framework for constructing exact solutions of PDE models of reactive transport. Our approach is relevant for advection-dominant problems, and is based on a regular perturbation technique. We present a description of the solution technique for a range of one-dimensional scenarios involving constant and variable coefficients, and we show that the solutions compare well with numerical approximations. Our general approach applies to a range of initial conditions and various forms of v(x) and k(x). Instead of simply documenting specific solutions for particular cases, we present a symbolic worksheet, as supplementary material, which enables the solution to be evaluated for different choices of the initial condition, v(x) and k(x). We also discuss how the technique generalizes to apply to models of coupled multispecies reactive transport as well as higher dimensional problems.

  4. Sparse modeling of spatial environmental variables associated with asthma. (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


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

  5. Modeling temporal and spatial variability of crop yield (United States)

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


    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 (United States)

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


    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.


    Directory of Open Access Journals (Sweden)

    Igor Bogunović


    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. Sparse modeling of spatial environmental variables associated with asthma


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


    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 5–50 years over a three-year period. Each patient’s ...

  9. From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions (United States)

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


    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

  10. Influence of rainfall spatial variability on rainfall-runoff modelling: Benefit of a simulation approach? (United States)

    Emmanuel, I.; Andrieu, H.; Leblois, E.; Janey, N.; Payrastre, O.


    No consensus has yet been reached regarding the influence of rainfall spatial variability on runoff modelling at catchment outlets. To eliminate modelling and measurement errors, in addition to controlling rainfall variability and both the characteristics and hydrological behaviour of catchments, we propose to proceed by simulation. We have developed a simulation chain that combines a stream network model, a rainfall simulator and a distributed hydrological model (with four production functions and a distributed transfer function). Our objective here is to use this simulation chain as a simplified test bed in order to better understand the impact of the spatial variability of rainfall forcing. We applied the chain to contrasted situations involving catchments ranging from a few tens to several hundreds of square km2, thus corresponding to urban and peri-urban catchments for which surface runoff constitutes the dominant process. The results obtained confirm that the proposed simulation approach is helpful to better understand the influence of rainfall spatial variability on the catchment response. We have shown that significant dispersion exists not only between the various simulation scenarios (defined by a rainfall configuration and a catchment configuration), but also within each simulation scenario. These results show that the organisation of rainfall during the study event over the study catchment plays an important role, leading us to examine rainfall variability indexes capable of summarising the influence of rainfall spatial organisation on the catchment response. Thanks to the simulation chain, we have tested the variability indexes of Zoccatelli et al. (2010) and improved them by proposing two other indexes.

  11. Variability in results from negative binomial models for Lyme disease measured at different spatial scales. (United States)

    Tran, Phoebe; Waller, Lance


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

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

    Directory of Open Access Journals (Sweden)

    T. Skaugen


    Full Text Available 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 SD_G 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 SD_G 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.

  13. Spatial variability in compartmental fate modelling : Linking fugacity models and GIS. (United States)

    Wania, F


    A new approach is presented which is designed to address the spatial heterogeneity of the environment in compartmental mass balance models of chemical fate in the environment. It rests on the assumption of chemical equilibration within one phase despite prevailing environmental heterogeneity. Composite D- and Z-values are derived from sub-unit specific environmental parameters and are used to solve mass balance equations which can be adopted essentially unchanged from existing compartmental fugacity models. With the resulting common fugacity value for each compartment, sub-unit specific concentrations and process rates can be calculated. The approach is illustrated using the QWASI lake model to calculate the fate of hexachlorobenzene in a hypothetical lake sub-divided in four distinct sub-units. The approach allows the subdivision of each compartment in a large number of sub-units with distinct environmental characteristics without substantially increasing model complexity. This is a necessary condition for linking fugacity models to geographical information systems.

  14. A modeling approach to address spatial variability within the Culebra Dolomite transmissivity field

    International Nuclear Information System (INIS)

    LaVenue, A.M.; RamaRao, B.S.


    Spatial estimates of transmissivity, which are essential input to a groundwater flow model, are usually developed from a limited number of transmissivity measurements and therefore associated with an uncertainty. In an attempt to assess the spatial variability of the unmeasured transmissivities within the Culebra Dolomite near the Waste Isolation Pilot Plant (WIPP), a multiple realization approach is employed. An innovative aspect of the methodology is the generation of an ensemble of conditional simulations of the transmissivity field, which preserves the statistical moments and spatial correlation structure of the measured transmissivity field and horrors the measured values at their locations. Each simulation is then calibrated, using an iterative procedure, to match an exhaustive set of steady-state and transient pressure data. A completely automated inverse algorithm using pilot points as parameters of calibration was employed. The methodology was applied to the transmissivity fields for the Culebra Dolomite aquifer, and 70 conditional simulations were produced and calibrated. Based on an analysis of the calibrated transmissivity fields, additional data in a region east and north of the H-3 borehole would help to more accurately characterize the transmissivity of the region and reduce the uncertainty in calculating groundwater travel times. Progress in these areas would, in, turn, reduce the uncertainty in the prediction of concentrations at the accessible environment boundary

  15. Spatial aggregation for crop modelling at regional scales: the effects of soil variability (United States)

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


    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

  16. Modelling climate change effects on spatial variability in subcatchment flows in a mountain basin, New Zealand. (United States)

    Khadka, D.; Caruso, B. S.; Zammit, C.


    Climate change impacts on water resources can have significant spatial variability in heterogeneous mountain catchments. This study used the TopNet hydrological model to simulate existing and future streamflows under potential climate change in the Upper Waitaki River Basin, South Island, New Zealand. The basin includes unimpaired high-elevation catchments (Ahuriri), regulated glacier-fed catchments used for hydropower (Pukaki), and drier catchments in the Lower Waitaki (Hakataramea). Precipitation and temperature data for model input were based on the A2 emissions scenario and the average of 12 Global Circulation Models downscaled to the Virtual Climate Station Network (VCSN) database for the baseline (1980-1999), 2040s (2030-2049) and 2090s (2080-2099) periods. The percentage differences between 2040s and baseline median annual runoff range from 0-34%, 4-13% and 0-94%, and differences between 2090s and baseline are 0-70%, 10-30%, and 2-111% for the Pukaki, Ahuriri, and Hakataramea catchments, respectively. The spearman's rank correlation coefficient showed correlations between median flows and elevation in the Pukaki (0.71) and Ahuriri (0.43) catchments (α = 0.05). However, correlation between median flows with slope and elevation were -0.37 and -0.68, respectively, in the Hakataramea catchment. There was also correlation between median flows with ice (0.84) and bedrock (0.51) in Pukaki subcatchments, and with ice (0.41) and bedrock (0.54) in Ahuriri subcatchments (α = 0.05). This study suggested greater spatial variability of climate change impacts on runoff in drier, lower-elevation catchments (Hakataramea) compared to wetter catchments at higher elevations (Pukaki and Ahuriri).

  17. Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba. (United States)

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


    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

  18. Modeling the Spatial and Temporal Variability of Precipitation in Northwest Iran

    Directory of Open Access Journals (Sweden)

    Mohammad Arab Amiri


    Full Text Available Spatial and temporal variability analysis of precipitation is an important task in water resources planning and management. This study aims to analyze the spatial and temporal variability of precipitation in the northeastern corner of Iran using data from 24 well-distributed weather stations between 1991 and 2015. The mean annual rainfall, precipitation concentration index (PCI, and their coefficients of variation were mapped to examine the spatial variability of rainfall. An artificial neural network (ANN in association with the inverse distance weighted (IDW method was proposed as a hybrid interpolation method to map the spatial distribution of the detected trends of mean annual rainfall and PCI over the study region. In addition, principal component analysis (PCA was applied to annual precipitation time series in order to verify the results of the analysis using the mean annual rainfall and PCI data sets. Results show high variation in inter-annual precipitation in the west, and a moderate to high intra-annual variability over the whole region. Irregular year-to-year precipitation concentration is also observed in the northeastern and northwestern parts. All in all, the highest variations in inter-annual and intra-annual precipitation occurred over the western and northern parts, while the lowest variability was observed in the eastern part (i.e., the coastal region.

  19. The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland (United States)

    Szymanowski, Mariusz; Kryza, Maciej


    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

  20. Spatial Variability of Rainfall

    DEFF Research Database (Denmark)

    Jensen, N.E.; Pedersen, Lisbeth


    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...

  1. Modelling the temporal and spatial distribution of ecological variables in Beibu Gulf (United States)

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


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

  2. A simple model for the spatially-variable coastal response to hurricanes (United States)

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


    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

  3. Objective estimation of spatially variable parameters of epidemic type aftershock sequence model: Application to California (United States)

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


    The Epidemic Type Aftershock Sequence (ETAS) model is widely employed to model the spatiotemporal distribution of earthquakes, generally using spatially invariant parameters. We propose an efficient method for the estimation of spatially varying parameters, using the expectation maximization (EM) algorithm and spatial Voronoi tessellation ensembles. We use the Bayesian information criterion (BIC) to rank inverted models given their likelihood and complexity and select the best models to finally compute an ensemble 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 included in the Advanced National Seismic System catalog that occurred within the time period 1981-2015 in a spatial polygon around California. The results indicate significant spatial variation of the ETAS parameters. We find that the efficiency of earthquakes to trigger future ones (quantified by the branching ratio) positively correlates with surface heat flow. In contrast, the rate of earthquakes triggered by far-field tectonic loading or background seismicity rate shows no such correlation, suggesting the relevance of triggering possibly through fluid-induced activation. Furthermore, the branching ratio and background seismicity rate are found to be uncorrelated with hypocentral depths, indicating that the seismic coupling remains invariant of hypocentral depths in the study region. Additionally, triggering seems to be mostly dominated by small earthquakes. Consequently, the static stress change studies should not only focus on the Coulomb stress changes caused by specific moderate to large earthquakes but also account for the secondary static stress changes caused by smaller earthquakes.

  4. 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


    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

  5. Anomalous transport in disordered fracture networks: Spatial Markov model for dispersion with variable injection modes (United States)

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


    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.

  6. Variability in modeled cloud feedback tied to differences in the climatological spatial pattern of clouds (United States)

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


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

  7. Impact of Urbanization on Spatial Variability of Rainfall-A case study of Mumbai city with WRF Model (United States)

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


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

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

    Boluwade, Alaba; Madramootoo, Chandra


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

  9. 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

  10. Spatial Scaling of Environmental Variables Improves Species-Habitat Models of Fishes in a Small, Sand-Bed Lowland River. (United States)

    Radinger, Johannes; Wolter, Christian; Kail, Jochem


    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

  11. Spatial Scaling of Environmental Variables Improves Species-Habitat Models of Fishes in a Small, Sand-Bed Lowland River (United States)

    Radinger, Johannes; Wolter, Christian; Kail, Jochem


    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

  12. Spatial variability of the effect of air pollution on term birth weight: evaluating influential factors using Bayesian hierarchical models. (United States)

    Li, Lianfa; Laurent, Olivier; Wu, Jun


    Epidemiological studies suggest that air pollution is adversely associated with pregnancy outcomes. Such associations may be modified by spatially-varying factors including socio-demographic characteristics, land-use patterns and unaccounted exposures. Yet, few studies have systematically investigated the impact of these factors on spatial variability of the air pollution's effects. This study aimed to examine spatial variability of the effects of air pollution on term birth weight across Census tracts and the influence of tract-level factors on such variability. We obtained over 900,000 birth records from 2001 to 2008 in Los Angeles County, California, USA. Air pollution exposure was modeled at individual level for nitrogen dioxide (NO2) and nitrogen oxides (NOx) using spatiotemporal models. Two-stage Bayesian hierarchical non-linear models were developed to (1) quantify the associations between air pollution exposure and term birth weight within each tract; and (2) examine the socio-demographic, land-use, and exposure-related factors contributing to the between-tract variability of the associations between air pollution and term birth weight. Higher air pollution exposure was associated with lower term birth weight (average posterior effects: -14.7 (95 % CI: -19.8, -9.7) g per 10 ppb increment in NO2 and -6.9 (95 % CI: -12.9, -0.9) g per 10 ppb increment in NOx). The variation of the association across Census tracts was significantly influenced by the tract-level socio-demographic, exposure-related and land-use factors. Our models captured the complex non-linear relationship between these factors and the associations between air pollution and term birth weight: we observed the thresholds from which the influence of the tract-level factors was markedly exacerbated or attenuated. Exacerbating factors might reflect additional exposure to environmental insults or lower socio-economic status with higher vulnerability, whereas attenuating factors might indicate reduced

  13. 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


    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

  14. Spatial modelling of the variability of the soil moisture regime at the landscape scale in the southern Qilian Mountains, China (United States)

    Zhao, C.-Y.; Qi, P.-C.; Feng, Z.-D.


    The spatial and temporal variability of the soil moisture status gives an important base for the assessment of ecological (for restoration) and economic (for agriculture) conditions at micro- and meso-scales. It is also an essential input into many hydrological processes models. However, there has been a lack of effective methods for its estimation in the study area. The aim of this study was to determine the relationship between the soil moisture status and precipitation and topographic factors. First, this study compared a linear regression model with interpolating models for estimating monthly mean precipitation and selected the linear regression model to simulate the temporal-spatial variability of precipitation in the southern Qilian Mountainous areas of the Heihe River Basin. Combining topographic index with the distribution of precipitation, we calculated the soil moisture regime in the Pailugou catchment, one representative comprehensive research catchment. The modeled results were tested by the observed soil water content for different times. The correlation coefficient between the modeled soil moisture status and the observed soil water content is quite high (e.g. R2=0.76 in June), assuring our confidence in the spatially-modeled results of the soil moisture status. The method was applied to the southern Qilian Mountainous regions. The results showed that the modelled distribution of the soil moisture status reflected the interplay of the local and landscape climate processes. The driest sites occur on some ridges in northern part and western part of the study area, which are very small catchment areas and of low precipitation rates; the wettest are registered in the low river valley of the Heihe River and its major tributaries are in the eastern part due to large accumulating flow areas and higher precipitation rates. Temporally, the bigger variation of the soil moisture status in the study occurs in July and smaller difference appears in May.

  15. Modelled estimates of spatial variability of iron stress in the Atlantic sector of the Southern Ocean (United States)

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


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

  16. A new heat flux model for the Antarctic Peninsula incorporating spatially variable upper crustal radiogenic heat production (United States)

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


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

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

    Directory of Open Access Journals (Sweden)

    David M. Makori


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

  18. Modelling spatial and temporal variability of hydrologic impacts under climate changes over the Nenjiang River Basin, China (United States)

    Chen, Hao; Zhang, Wanchang


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

  19. Statistical modeling of the spatial variability of environmental noise levels in Montreal, Canada, using noise measurements and land use characteristics. (United States)

    Ragettli, Martina S; Goudreau, Sophie; Plante, Céline; Fournier, Michel; Hatzopoulou, Marianne; Perron, Stéphane; Smargiassi, Audrey


    The availability of noise maps to assess exposure to noise is often limited, especially in North American cities. We developed land use regression (LUR) models for LA eq24h , L night , and L den to assess the long-term spatial variability of environmental noise levels in Montreal, Canada, considering various transportation noise sources (road, rail, and air). To explore the effects of sampling duration, we compared our LA eq24h levels that were computed over at least five complete contiguous days of measurements to shorter sampling periods (20 min and 24 h). LUR models were built with General Additive Models using continuous 2-min noise measurements from 204 sites. Model performance (adjusted R 2 ) was 0.68, 0.59, and 0.69 for LA eq24h , L night , and L den , respectively. Main predictors of measured noise levels were road-traffic and vegetation variables. Twenty-minute non-rush hour measurements corresponded well with LA eq24h levels computed over 5 days at road-traffic sites (bias: -0.7 dB(A)), but not at rail (-2.1 dB(A)) nor at air (-2.2 dB(A)) sites. Our study provides important insights into the spatial variation of environmental noise levels in a Canadian city. To assess long-term noise levels, sampling strategies should be stratified by noise sources and preferably should include 1 week of measurements at locations exposed to rail and aircraft noise.

  20. Using stochastic models to incorporate spatial and temporal variability [Exercise 14 (United States)

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


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

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

    Directory of Open Access Journals (Sweden)

    Edith Gallagher


    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.

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

    Directory of Open Access Journals (Sweden)

    H. M. Horowitz


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

  3. Hydraulic modelling of the spatial and temporal variability in Atlantic salmon parr habitat availability in an upland stream. (United States)

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


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

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

    International Nuclear Information System (INIS)

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


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

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

    International Nuclear Information System (INIS)

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


    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

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

    International Nuclear Information System (INIS)

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


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

  7. 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


    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.

  8. Evaluation of 7Be fallout spatial variability

    International Nuclear Information System (INIS)

    Pinto, Victor Meriguetti


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

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

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


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

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

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


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

  11. Spatial variability in nutrient transport by HUC8, state, and subbasin based on Mississippi/Atchafalaya River Basin SPARROW models (United States)

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


    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. Geostatistical modeling of the spatial variability and risk areas of southern root-knot nematodes in relation to soil properties. (United States)

    Ortiz, B V; Perry, C; Goovaerts, P; Vellidis, G; Sullivan, D


    Identifying the spatial variability and risk areas for southern root-knot nematode [Meloidogyne incognita (Kofoid & White) Chitwood] (RKN) is key for site-specific management (SSM) of cotton (Gossypium hirsutum L.) fields. The objectives of this study were to: (i) determine the soil properties that influence RKN occurrence at different scales; and (ii) delineate risk areas of RKN by indicator kriging. The study site was a cotton field located in the southeastern coastal plain region of the USA. Nested semivariograms indicated that RKN samples, collected from a 50×50 m grid, exhibited a local and regional scale of variation describing small and large clusters of RKN population density. Factorial kriging decomposed RKN and soil properties variability into different spatial components. Scale dependent correlations between RKN data showed that the areas with high RKN population remained stable though the growing season. RKN data were strongly correlated with slope (SL) at local scale and with apparent soil electrical conductivity deep (EC(a-d)) at both local and regional scales, which illustrate the potential of these soil physical properties as surrogate data for RKN population. The correlation between RKN data and soil chemical properties was soil texture mediated. Indicator kriging (IK) maps developed using either RKN, the relation between RKN and soil electrical conductivity or a combination of both, depicted the probability for RKN population to exceed the threshold of 100 second stage juveniles/100 cm(3) of soil. Incorporating EC(a-d) as soft data improved predictions favoring the reduction of the number of RKN observations required to map areas at risk for high RKN population.

  13. Spatial soil variability as a guiding principle in nitrogen management

    NARCIS (Netherlands)

    Verhagen, J.


    This thesis focuses on the optimisation of N fertiliser application, taking into account spatially variable soil conditions. Spatial soil variability effects both cropproduction and nitrate leaching. Site specific management tries to address spatially variable conditions. Research on site

  14. 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


    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......The construction of detailed geological models for heterogeneous settings such as clay till is important to describe transport processes, particularly with regard to potential contamination pathways. In low-permeability clay matrices transport is controlled by diffusion, but fractures and sand......-lenses facilitate local advective flow. In glacial settings these geological features occur at diverse extent, geometry, degree of deformation, and spatial distribution. The high level of heterogeneity requires extensive data collection, respectively detailed geological mapping. However, when characterising...

  15. Mesoscale spatial variability in seawater cavitation thresholds (United States)

    Mel'nikov, N. P.; Elistratov, V. P.


    The paper presents the spatial variability of cavitation thresholds and some hydrological and hydrochemical parameters of seawater in the interfrontal zone of the Pacific Subarctic Front, in the Drake Passage, and in the equatorial part of the Pacific Ocean, measured in the near-surface layer to a depth of 70 m.

  16. Spatial cluster modelling

    CERN Document Server

    Lawson, Andrew B


    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. Generalized instrumental variable models


    Andrew Chesher; Adam Rosen


    This paper develops characterizations of identified sets of structures and structural features for complete and incomplete models involving continuous or discrete variables. Multiple values of unobserved variables can be associated with particular combinations of observed variables. This can arise when there are multiple sources of heterogeneity, censored or discrete endogenous variables, or inequality restrictions on functions of observed and unobserved variables. The models g...

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

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


    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.

  19. Classifying variability modeling techniques

    NARCIS (Netherlands)

    Sinnema, Marco; Deelstra, Sybren

    Variability modeling is important for managing variability in software product families, especially during product derivation. In the past few years, several variability modeling techniques have been developed, each using its own concepts to model the variability provided by a product family. The

  20. 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

  1. Estimating greenhouse gas emissions of European cities--modeling emissions with only one spatial and one socioeconomic variable. (United States)

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


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

  2. Effect of Variable Spatial Scales on USLE-GIS Computations (United States)

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


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

  3. Estimates of recharge in two arid basin aquifers: a model of spatially variable net infiltration and its implications (Red Light Draw and Eagle Flats, Texas, USA) (United States)

    Robertson, Wendy Marie; Sharp, John M.


    Methods of estimating recharge in arid basin aquifers (such as the 1 % rule, Maxey-Eakin method, storm-runoff infiltration and others) overlook the potential contribution of direct recharge on the basin floors. In the Trans-Pecos region of west Texas, USA, this has resulted in potential recharge and solute flux to basin aquifers being ignored. Observed trends in groundwater nitrate (NO3 -) concentrations and the presence of young (floors. A spatially variable net infiltration model (INFIL 3.0.1) was used to estimate the volume and spatial distribution of potential recharge to two basins: Red Light Draw and Eagle Flats. The INFIL model provides insight into the mechanisms by which recharge and solute flux occurs in arid basin systems. This method demonstrated that recharge is widespread; it is not limited to the mountainous areas and mountain-front recharge mechanisms, and up to 15 % of total potential recharge in these basins occurs across widespread areas of the basin floors. Models such as this should improve scientific understanding and sustainable management of arid basin aquifers in Texas and elsewhere.


    Directory of Open Access Journals (Sweden)

    Dennis Rödder


    Full Text Available Abstract. - Species distribution models (SDMs are increasingly used in many scientific fields, with most studies requiring the application of the SDM to predict the likelihood of occurrence and/or environmental suitability in locations and time periods outside the range of the data set used to fit the model. Uncertainty in the quality of SDM predictions caused by errors of interpolation and extrapolation has been acknowledged for a long time, but the explicit consideration of the magnitude of such errors is, as yet, uncommon. Among other issues, the spatial variation in the colinearity of the environmental predictor variables used in the development of SDMs may cause misleading predictions when applying SDMs to novel locations and time periods. In this paper, we provide a framework for the spatially explicit identification of areas prone to errors caused by changes in the inter-correlation structure (i.e. their colinearity of environmental predictors used for SDM development. The proposed method is compatible with all SDM algorithms currently employed, and expands the available toolbox for assessing the uncertainties raising from SDM predictions. We provide an implementation of the analysis as a script for the R statistical platform in an online appendix.

  5. Assessing the Importance of Incorporating Spatial and Temporal Variability of Soil and Plant Parameters into Local Water Balance Models for Precision Agriculture: Investigations within a California Vineyard (United States)

    Hubbard, S.; Pierce, L.; Grote, K.; Rubin, Y.


    Due Due to the high cash crop nature of premium winegrapes, recent research has focused on developing a better understanding of the factors that influence winegrape spatial and temporal variability. Precision grapevine irrigation schemes require consideration of the factors that regulate vineyard water use such as (1) plant parameters, (2) climatic conditions, and (3) water availability in the soil as a function of soil texture. The inability to sample soil and plant parameters accurately, at a dense enough resolution, and over large enough areas has limited previous investigations focused on understanding the influences of soil water and vegetation on water balance at the local field scale. We have acquired several novel field data sets to describe the small scale (decimeters to a hundred meters) spatial variability of soil and plant parameters within a 4 acre field study site at the Robert Mondavi Winery in Napa County, California. At this site, we investigated the potential of ground penetrating radar data (GPR) for providing estimates of near surface water content. Calibration of grids of 900 MHz GPR groundwave data with conventional soil moisture measurements revealed that the GPR volumetric water content estimation approach was valid to within 1 percent accuracy, and that the data grids provided unparalleled density of soil water content over the field site as a function of season. High-resolution airborne multispectral remote sensing data was also collected at the study site, which was converted to normalized difference vegetation index (NDVI) and correlated to leaf area index (LAI) using plant-based measurements within a parallel study. Meteorological information was available from a weather station of the California Irrigation management Information System, located less than a mile from our study area. The measurements were used within a 2-D Vineyard Soil Irrigation Model (VSIM), which can incorporate the spatially variable, high-resolution soil and plant

  6. Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability. (United States)

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


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

  7. Modeling Urinary Dysfunction After External Beam Radiation Therapy of the Prostate Using Bladder Dose-Surface Maps: Evidence of Spatially Variable Response of the Bladder Surface. (United States)

    Yahya, Noorazrul; Ebert, Martin A; House, Michael J; Kennedy, Angel; Matthews, John; Joseph, David J; Denham, James W


    We assessed the association of the spatial distribution of dose to the bladder surface, described using dose-surface maps, with the risk of urinary dysfunction. The bladder dose-surface maps of 754 participants from the TROG 03.04-RADAR trial were generated from the volumetric data by virtually cutting the bladder at the sagittal slice, intersecting the bladder center-of-mass through to the bladder posterior and projecting the dose information on a 2-dimensional plane. Pixelwise dose comparisons were performed between patients with and without symptoms (dysuria, hematuria, incontinence, and an International Prostate Symptom Score increase of ≥10 [ΔIPSS10]). The results with and without permutation-based multiple-comparison adjustments are reported. The pixelwise multivariate analysis findings (peak-event model for dysuria, hematuria, and ΔIPSS10; event-count model for incontinence), with adjustments for clinical factors, are also reported. The associations of the spatially specific dose measures to urinary dysfunction were dependent on the presence of specific symptoms. The doses received by the anteroinferior and, to lesser extent, posterosuperior surface of the bladder had the strongest relationship with the incidence of dysuria, hematuria, and ΔIPSS10, both with and without adjustment for clinical factors. For the doses to the posteroinferior region corresponding to the area of the trigone, the only symptom with significance was incontinence. A spatially variable response of the bladder surface to the dose was found for symptoms of urinary dysfunction. Limiting the dose extending anteriorly might help reduce the risk of urinary dysfunction. Copyright © 2016. Published by Elsevier Inc.

  8. Spatial Variability of Wet Troposphere Delays Over Inland Water Bodies (United States)

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


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

  9. Impact of rainfall spatial variability on Flash Flood Forecasting (United States)

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


    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

  10. Farmer decision making and spatial variables in northern Thailand (United States)

    Fox, Jefferson; Kanter, Rebekah; Yarnasarn, Sanay; Ekasingh, Methi; Jones, Royce


    This research has two interrelated objectives. The first is to determine the extent to which a relationship exists between farmer characteristics and farming practices in three villages in northern Thailand. The second is to use standard statistical methods for incorporating spatial variables into the analysis and to assess the effects of these variables on farmer decision making. The data base includes information on the location and size of villages, roads, streams, and fields; a digital elevation model with information on elevation, slope, and aspect; and information keyed to individual fields on crops and cropping methods and the ethnicity, income, and religion of farmers. The map data (517 plots) were entered into a computerized geographic information systems (GIS). Results suggest several hypotheses about the relationships between land use and owner characteristics. More significantly, the study concludes that spatial analysis appears to be most useful when the dependent variable is either continuous or ordinal. The outlook is not quite as optimistic when the dependent variable is a nonordinal categorical variable. Before spatial analysis can be applied regularly to social science data, better computational tools need to be developed.

  11. Spatial Variability in Column CO2 Inferred from High Resolution GEOS-5 Global Model Simulations: Implications for Remote Sensing and Inversions (United States)

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


    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

  12. Modeling Spatial and Temporal Variability of Soil Moisture in Shallow Depths of the Vadose Zone: A Comparison of two and Three Dimensional Simulations to Capture Relevant Physical Processes (United States)

    Smits, K. M.; Frippiat, C.; Sakaki, T.; Illangasekare, T. H.


    The distribution of water saturation of soils near the ground surface is of interest in various applications involving soil moisture variations due to land-atmospheric interaction, evaporation from soils and land mine detection. Natural soil heterogeneity in combination with water flux conditions at the soil surface creates complex spatial and temporal distributions of soil moisture in the near-surface vadose zone. Validation of numerical models that are designed to capture these processes is difficult due to the inherent complexities of the problem and the scarcity of laboratory data with accurately known hydraulic parameters. A few 3-D experimental studies have been performed in attempts to generate such data. However, these experiments are tedious to setup and many challenges exist in getting accurate spatially and temporally varying measurements of water saturation and pressure. As a result, most of the experimental studies simulating multiphase flow processes in the heterogeneous vadose zone are carried out in 1-D or 2-D test systems. The issue is then to determine whether results obtained in such simplified conditions capture the relevant physical processes occurring in real 3-D heterogeneous situations. A numerical study was conducted to compare the spatial and temporal variability of soil moisture in a 3-D heterogeneous synthetic aquifer with the predictions of simplified 2-D models of vertical slices of the aquifer. The heterogeneous medium is composed of five different sandy materials, with air entry pressures ranging from 9.7 to 81.8 cm and saturated hydraulic conductivities ranging from 0.597 to 0.0067 cm/s. The numerical experiment designed around a synthetic 3-D aquifer consists of (1) simulating the drainage of the synthetic aquifer, starting from a fully saturated situation, and (2) inducing evaporation at the surface after liquid drainage has ceased. We compare results from 3-D and 2-D numerical simulations at several point locations, representing

  13. Variable Spatial Springs for Robot Control

    NARCIS (Netherlands)

    Stramigioli, Stefano; Duindam, V.


    This article presents a passive way to implement varying spatial springs. These are springs with controlling ports which can be used to modify their spatial rest length or spatial properties. These controlling ports have a dual structure which allows one to supervise the potential energy injected

  14. 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...

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

    Directory of Open Access Journals (Sweden)

    Hone-Jay Chu


    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.

  16. Deciphering factors controlling groundwater arsenic spatial variability in Bangladesh (United States)

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


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

  17. Variable importance in latent variable regression models

    NARCIS (Netherlands)

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


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


    African Journals Online (AJOL)

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

  19. Crime Modeling using Spatial Regression Approach (United States)

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


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

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

    Energy Technology Data Exchange (ETDEWEB)

    Robert Pincus


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

  1. Spatial and temporal variability of column-integrated CO2: identifying drivers and variations from high-resolution model simulations and OCO-2 observations (United States)

    Chatterjee, A.; Ott, L.; Wennberg, P. O.; Kawa, S. R.; O'Dell, C.; Osterman, G. B.; Wunch, D.


    Isolating the drivers and variations in column-averaged dry air mole fraction of carbon dioxide (XCO2) is essential for mining information from space-based remote-sensing observations, such as those available from the Orbiting Carbon Observatory-2 (OCO-2). Contrary to the large number of studies analyzing the variability of surface CO2 concentrations, studies analyzing the spatiotemporal variability of XCO2 are relatively limited. More importantly, these results are either based on a sparse network of ground-based total column observations (i.e., from the Total Column Carbon Observing Network - TCCON) or derived from low-resolution model simulations. In this study, using the high-resolution (~7 km) GEOS-5 model simulated fields and the high-density observations from OCO-2, we investigate how variability in surface fluxes and/or meteorological drivers impact the observed XCO2 variability across a range of scales. The study focuses on ~13:30 LT and is designed to highlight the significant contributors to local and regional scale XCO2 variability from daily to seasonal timescales. In collaboration with the OCO-2 Validation team, the variability information is also being used to identify small geographical areas (<1° or ~100km) where the XCO2 is expected to be relatively constant. These small areas then serve as target regions for examining the potential of external variables (for e.g., surface reflectance, aerosol) to generate biases (variability) in the XCO2 retrievals in those regions. We will also show comparison results of the model-based variability analyses with the variability statistics derived from actual OCO-2 retrievals. This comparison serves as an important consistency check for the simulated fields from the GEOS-5 model. Finally, we will review these results in terms of assessing and quantifying representation errors as well as developing and implementing data thinning/'superobbing' algorithms for OCO-2 retrievals.

  2. Spatial and temporal variability in a butterfly population. (United States)

    Thomas, C D


    The dynamics of a butterfly (Plebejus argus) population were analysed at two levels, (i) the population as a whole and (ii) sections within the population. Some sections of the population fluctuated out of synchrony with others, such that the variability [SD Log(Density+1)] shown by the population as a whole was less than the variability shown by each part of the population - overall temporal variability was dampened by spatial asynchrony. Since observed population variability depends on the spatial scale that is sampled, comparisons of population variability among taxa should be carried out only with caution. Implications for island biogeography and conservation biology are discussed.

  3. Spatially Resolved Images and Solar Irradiance Variability

    Indian Academy of Sciences (India)


    Jan 27, 2016 ... 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 ...


    Directory of Open Access Journals (Sweden)

    James Ribeiro de Azevedo


    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.

  5. Spatial variability of extreme rainfall at radar subpixel scale (United States)

    Peleg, Nadav; Marra, Francesco; Fatichi, Simone; Paschalis, Athanasios; Molnar, Peter; Burlando, Paolo


    Extreme rainfall is quantified in engineering practice using Intensity-Duration-Frequency curves (IDF) that are traditionally derived from rain-gauges and more recently also from remote sensing instruments, such as weather radars. These instruments measure rainfall at different spatial scales: rain-gauge samples rainfall at the point scale while weather radar averages precipitation on a relatively large area, generally around 1 km2. As such, a radar derived IDF curve is representative of the mean areal rainfall over a given radar pixel and neglects the within-pixel rainfall variability. In this study, we quantify subpixel variability of extreme rainfall by using a novel space-time rainfall generator (STREAP model) that downscales in space the rainfall within a given radar pixel. The study was conducted using a unique radar data record (23 years) and a very dense rain-gauge network in the Eastern Mediterranean area (northern Israel). Radar-IDF curves, together with an ensemble of point-based IDF curves representing the radar subpixel extreme rainfall variability, were developed fitting Generalized Extreme Value (GEV) distributions to annual rainfall maxima. It was found that the mean areal extreme rainfall derived from the radar underestimate most of the extreme values computed for point locations within the radar pixel (on average, ∼70%). The subpixel variability of rainfall extreme was found to increase with longer return periods and shorter durations (e.g. from a maximum variability of 10% for a return period of 2 years and a duration of 4 h to 30% for 50 years return period and 20 min duration). For the longer return periods, a considerable enhancement of extreme rainfall variability was found when stochastic (natural) climate variability was taken into account. Bounding the range of the subpixel extreme rainfall derived from radar-IDF can be of major importance for different applications that require very local estimates of rainfall extremes.

  6. Spatially Resolved Images and Solar Irradiance Variability R ...

    Indian Academy of Sciences (India)

    ability is an equally important issue in solar physics. The UV irradiance variability has profound ... We derived the full disk indices (spatial K index and Full Width at Half. Maximum, FWHM, of the histogram taken ... been established that the spatial K index, the FWHM, the intensity of different chro- mospheric features are well ...

  7. Temporal and spatial variability in North Carolina piedmont stream temperature (United States)

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


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

  8. Disturbance History,Spatial Variability, and Patterns of Biodiversity (United States)

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


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

  9. Modeling for spatial multilevel structural data (United States)

    Min, Suqin; He, Xiaoqun


    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.

  10. Deglaciation-Induced Spatially Variable Sea Level Change: A Simple-Model Case Study for the Greenland and Antarctic Ice Sheets

    Directory of Open Access Journals (Sweden)

    M. Kuhn


    Full Text Available Some studies on deglaciation-induced sea level change provide only a global average change, thus neglecting the fact that sea level change is spatially variable. This is due mainly to the gravitational and visco-elastic feedback effects of the changing surface mass loads. In order to address this apparent misconception and raise further awareness, we provide a conceptual example based on a simulated total melt of the Greenland and Antarctic ice sheets. This would give a global average sea level change of about 64 m. However, due to the changed distribution of gravitating masses, the sea-level change depends on location, with a range of about −27 m to +79 m (i.e., sea-level will even fall in some places. This spatial dependency has several implications, such as >10% biases in global average sea-level change estimates based only on tide-gauge records, flooding of almost 10% of current land areas, an increase of the length of day by almost a half a second and a northward move of the centre of mass (geocentre by about 20 m.

  11. Modeling Shared Variables in VHDL

    DEFF Research Database (Denmark)

    Madsen, Jan; Brage, Jens P.


    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...

  12. Spatial scales of pollution from variable resolution satellite imaging

    International Nuclear Information System (INIS)

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


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

  13. Assessment of spatial rainfall variability in Lake Victoria Basin (United States)

    Kizza, M.; Westerberg, I.; Rodhe, A.; Ntale, H. K.


    A gridded monthly rainfall dataset having a spatial resolution of 2 km and covering the period 1960-2004 was derived for the Lake Victoria basin. Such a dataset is useful for hydrological modelling aimed at resource utilisation and for estimation of catchment inflow to Lake Victoria. The lake and its basin support more than 30 million people and also contribute substantially to the River Nile flow. The major challenge in analysing the lake water balance is the estimation of the rainfall over the lake which is complicated by the varying quality and spatial coverage of rain-gauge data in the basin. In this study we addressed these problems by using satellite-derived precipitation data from two products and rain-gauge data for 362 stations around the basin to derive a monthly precipitation dataset for the entire basin, including the lake. First, the rain-gauge data were quality controlled; resulting in a rejection of 13% of the stations while 12% needed corrective actions. These results emphasise the importance of a systematic quality control of rain-guage data in this region. Thereafter we filled short gaps in the daily data series which resulted in 9,429 additional months of data. Two interpolation methods were then assessed for spatial interpolation and the universal kriging method performed slightly better than the inverse distance weighting method. The rainfall patterns in the interpolated dataset were shown to be consistent with the spatial and temporal patterns expected at the large scale as a result of the climate variability in the basin. The key problem of how to account for the enhancement of rainfall over the lake surface because of the lake-land thermal contrasts was addressed by estimating a relationship between rain-gauge and satellite data. Two satellite rainfall products, TRMM 3B43 and PERSIANN were compared to the interpolated monthly rain-gauge data for the land part of the basin. The bias in the TRMM 3B43 rainfall estimates was higher than the bias

  14. Coastal upwelling south of Madagascar: Temporal and spatial variability (United States)

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


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

  15. Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes. (United States)

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


    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.

  16. 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.

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

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


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

  18. Using Spatial Gradients to Model Localization Phenomena

    Energy Technology Data Exchange (ETDEWEB)

    D.J.Bammann; D.Mosher; D.A.Hughes; N.R.Moody; P.R.Dawson


    We present the final report on a Laboratory-Directed Research and Development project, Using Spatial Gradients to Model Localization Phenomena, performed during the fiscal years 1996 through 1998. The project focused on including spatial gradients in the temporal evolution equations of the state variables that describe hardening in metal plasticity models. The motivation was to investigate the numerical aspects associated with post-bifurcation mesh dependent finite element solutions in problems involving damage or crack propagation as well as problems in which strain Localizations occur. The addition of the spatial gradients introduces a mathematical length scale that eliminates the mesh dependency of the solution. In addition, new experimental techniques were developed to identify the physical mechanism associated with the numerical length scale.

  19. Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring. (United States)

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


    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

  20. Spatial and temporal variability of Mediterranean drought events (United States)

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


    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

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

    DEFF Research Database (Denmark)

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


    Soil properties show signifficant spatial variability at local, regional and continental scales. This is a challenge for life cycle impact assessment (LCIA) of metals, because fate, bioavailability and effect factors are controlled by environmental chemistry and can vary orders of magnitude...... 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...

  2. Soil fertility assessment and mapping of spatial variability at ...

    African Journals Online (AJOL)

    Information on soil fertility assessment and mapping of arable land helps to design appropriate soil fertility management practices. Experiment was conducted at Amaregenda-Abajarso sub-watershed to assess the fertility status and mapping the spatial variability of selected soil fertility parameters. Based on land use type, ...

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

    African Journals Online (AJOL)

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

  4. Probabilistic and spatially variable niches inferred from demography (United States)

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


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

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

    NARCIS (Netherlands)

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


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

  6. Variability of Soil Temperature: A Spatial and Temporal Analysis. (United States)

    Walsh, Stephen J.; And Others


    Discusses an analysis of the relationship of soil temperatures at 3 depths to various climatic variables along a 200-kilometer transect in west-central Oklahoma. Reports that temperature readings increased from east to west. Concludes that temperature variations were explained by a combination of spatial, temporal, and biophysical factors. (SG)

  7. Quantitative analysis of spatial variability of geotechnical parameters (United States)

    Fang, Xing


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

  8. Temporal and spatial variability of the Denmark Strait Overflow (United States)

    Moritz, Martin; Nunes, Nuno; Jochumsen, Kerstin; Quadfasel, Detlef


    The Denmark Strait Overflow (DSO) represents about half of the export of dense waters formed in the Nordic Seas to the deep circulation in the North Atlantic. The passage connecting the two is wider than the Rossby radius of deformation, and highly variable meso-scale current fluctuations are observed in the overflow. In the summer of 2014, the mooring array used for monitoring the Denmark Strait Overflow was expanded from two to five moorings in order to better resolve its spatial variability. Continuous measurements of the velocity field were made using four acoustic profilers (ADCP) and one point current meter (RCM). The instruments were deployed along the sill between the deepest point and 33 km westward of it, towards the Greenland shelf. A descriptive analysis of the structure of the velocity field at the Denmark Strait sill is presented, along with its spatial and temporal variability. The fluctuations are dominated by passing meso-scale vortices, pulsating changes in the strength of the overflow and shifts in the location of the Polar Front. These changes and their respective contribution to the variability of the flow field are discussed with relation to the different source water masses for the DSO. The relationship between spatial coherence and temporal variability on daily to monthly time scales is explored, and the influence of meso-scale eddies on daily to weekly transport estimates is quantified. The results of the analysis are used to develop a measurement strategy for unbiased DSO transport estimates.

  9. Spatial scales of pollution from variable resolution satellite imaging. (United States)

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


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

  10. Ionospheric total electron content: Spatial patterns of variability (United States)

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


    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.

  11. Uncertainties in spatially aggregated predictions from a logistic regression model

    NARCIS (Netherlands)

    Horssen, P.W. van; Pebesma, E.J.; Schot, P.P.


    This paper presents a method to assess the uncertainty of an ecological spatial prediction model which is based on logistic regression models, using data from the interpolation of explanatory predictor variables. The spatial predictions are presented as approximate 95% prediction intervals. The

  12. Spatial variability of chemical properties of soil under pasture

    Directory of Open Access Journals (Sweden)

    Samuel Ferreira da Silva


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

  13. Spatial variability of correlated color temperature of lightning channels

    Directory of Open Access Journals (Sweden)

    Nobuaki Shimoji

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

  14. Spatial Variability of Soil Properties and its Impact on Simulated Surface Soil Moisture Patterns (United States)

    Korres, W.; Bothe, T.; Reichenau, T. G.; Schneider, K.


    The spatial variability of soil properties (particle size distribution, PSD, and bulk density, BD) has large effects on the spatial variability of soil moisture and therefore on plant growth and surface exchange processes. In model studies, soil properties from soil maps are considered homogeneous over mapping units, which neglects the small scale variability of soil properties and leads to underestimated small scale variability of simulated soil moisture. This study focuses on the validation of spatial variability of simulated surface soil moisture (SSM) in a winter wheat field in Western Germany using the eco-hydrological simulation system DANUBIA. SSM measurements were conducted at 20 different sampling points and nine different dates in 2008. Frequency distributions of BD and PSD were derived from an independent dataset (n = 486) of soil physical properties from Germany and the USA. In the simulations, BD and PSD were parameterized according to these frequency distributions. Mean values, coefficients of variation and frequency distributions of simulated SSM were compared to the field measurements. Using the heterogeneous model parameterization, up to 76 % of the frequency distribution of the measured SSM can be explained. Furthermore, the results show that BD has a larger impact on the variability of SSM than PSD. The introduced approach can be used for simulating mean SSM and SSM variability more accurately and can form the basis for a spatially heterogeneous parameterization of soil properties in mesoscale models.

  15. Directional semivariogram analysis to identify and rank controls on the spatial variability of fracture networks (United States)

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


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


    Directory of Open Access Journals (Sweden)

    Ashish Agarwal


    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.

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

    DEFF Research Database (Denmark)

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


    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...

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

    International Nuclear Information System (INIS)

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


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

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

    African Journals Online (AJOL)

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

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

    African Journals Online (AJOL)

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

  1. 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.


    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

  2. 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.


    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

  3. 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


    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

  4. FUEL3-D: A Spatially Explicit Fractal Fuel Distribution Model (United States)

    Russell A. Parsons


    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...

  5. Temporal Changes in the Spatial Variability of Soil Nutrients

    Energy Technology Data Exchange (ETDEWEB)

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


    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.

  6. Empirical spatial econometric modelling of small scale neighbourhood (United States)

    Gerkman, Linda


    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.

  7. Effects of soil hydraulic properties on the spatial variability of soil water content: Evidence from sensor network data and inverse modeling (United States)

    Improved understanding of the temporal variability and stability of soil water content (SWC) and its relation to local and nonlocal controls is a major challenge in modern hydrology. The objective of this study was to assess the effect of soil hydraulic parameters on temporal stability of SWC with...

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

    Directory of Open Access Journals (Sweden)

    Dohnal Michal


    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.

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

    Directory of Open Access Journals (Sweden)

    D. S. Martins


    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.

  10. Spatial variability in nutritional status of arabic coffee based on dris index

    Directory of Open Access Journals (Sweden)

    Samuel de Assis Silva


    Full Text Available The combined use of precision agriculture and the Diagnosis and Recommendation Integrated System (DRIS allows the spatial monitoring of coffee nutrient balance to provide more balanced and cost-effective fertilizer recommendations. The objective of this work was to evaluate the spatial variability in the nutritional status of two coffee varieties using the Mean Nutritional Balance Index (NBIm and its relationship with their respective yields. The experiment was conducted in eastern Minas Gerais in two areas, one planted with variety Catucaí and another with variety Catuaí. The NBIm of the two varieties and their yields were analyzed through geostatistics and, based on the models and parameters of the variograms, were interpolated to obtain their spatial distribution in the studied areas. Variety Catucai, with grater spatial variability, was more nutritional unbalanced than variety Catuai, and consequently produced lower yields. Excess of Fe and Mn makes these elements limiting yield factors.

  11. Impact of spatial-temporal variations of climatic variables onsummer maize yield in North China Plain

    NARCIS (Netherlands)

    Wu, D.; Yu, Q.; Wang, E.; Hengsdijk, H.


    Summer maize (Zea mays L.) is one of the dominant crops in the North China Plain (NCP). Itsgrowth is greatly influenced by the spatial-temporal variation of climatic variables, especially solar radiation, temperature and rainfall. The WOFOST (version 7.1) model was applied to evaluate the impact of

  12. Spatial variability of soil moisture retrieved by SMOS satellite (United States)

    Lukowski, Mateusz; Marczewski, Wojciech; Usowicz, Boguslaw; Rojek, Edyta; Slominski, Jan; Lipiec, Jerzy


    Standard statistical methods assume that the analysed variables are independent. Since the majority of the processes observed in the nature are continuous in space and time, this assumption introduces a significant limitation for understanding the examined phenomena. In classical approach, valuable information about the locations of examined observations is completely lost. However, there is a branch of statistics, called geostatistics, which is the study of random variables, but taking into account the space where they occur. A common example of so-called "regionalized variable" is soil moisture. Using in situ methods it is difficult to estimate soil moisture distribution because it is often significantly diversified. Thanks to the geostatistical methods, by employing semivariance analysis, it is possible to get the information about the nature of spatial dependences and their lengths. Since the Soil Moisture and Ocean Salinity mission launch in 2009, the estimation of soil moisture spatial distribution for regional up to continental scale started to be much easier. In this study, the SMOS L2 data for Central and Eastern Europe were examined. The statistical and geostatistical features of moisture distributions of this area were studied for selected natural soil phenomena for 2010-2014 including: freezing, thawing, rainfalls (wetting), drying and drought. Those soil water "states" were recognized employing ground data from the agro-meteorological network of ground-based stations SWEX and SMUDP2 data from SMOS. After pixel regularization, without any upscaling, the geostatistical methods were applied directly on Discrete Global Grid (15-km resolution) in ISEA 4H9 projection, on which SMOS observations are reported. Analysis of spatial distribution of SMOS soil moisture, carried out for each data set, in most cases did not show significant trends. It was therefore assumed that each of the examined distributions of soil moisture in the adopted scale satisfies

  13. 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...

  14. Spatial variability and trends of the rain intensity over Greece (United States)

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


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

  15. Modeling spatial variation in avian survival and residency probabilities (United States)

    Saracco, James F.; Royle, J. Andrew; DeSante, David F.; Gardner, Beth


    The importance of understanding spatial variation in processes driving animal population dynamics is widely recognized. Yet little attention has been paid to spatial modeling of vital rates. Here we describe a hierarchical spatial autoregressive model to provide spatially explicit year-specific estimates of apparent survival (phi) and residency (pi) probabilities from capture-recapture data. We apply the model to data collected on a declining bird species, Wood Thrush (Hylocichla mustelina), as part of a broad-scale bird-banding network, the Monitoring Avian Productivity and Survivorship (MAPS) program. The Wood Thrush analysis showed variability in both phi and pi among years and across space. Spatial heterogeneity in residency probability was particularly striking, suggesting the importance of understanding the role of transients in local populations. We found broad-scale spatial patterning in Wood Thrush phi and pi that lend insight into population trends and can direct conservation and research. The spatial model developed here represents a significant advance over approaches to investigating spatial pattern in vital rates that aggregate data at coarse spatial scales and do not explicitly incorporate spatial information in the model. Further development and application of hierarchical capture-recapture models offers the opportunity to more fully investigate spatiotemporal variation in the processes that drive population changes.

  16. Groundwater Variability Across Temporal and Spatial Scales in the Central and Northeastern U.S. (United States)

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


    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, Drivers, and Scale-Mismatch of Tundra Greenup Phenology at a Landscape Extent (United States)

    Kerby, J.


    Spatial variability of plant phenology has widespread implications for landscape-level processes like herbivore foraging and the carbon cycle, but has traditionally only been quantified on small plots by human observers or at broader scales using coarse satellite imagery. Many ecological patterns vary with their scale of measurement, yet scale-dependence in vegetation emergence is poorly understood, particularly in Arctic environments. To investigate the effect of spatial grain choice on quantifying variability in tundra emergence phenology, we extracted greenness profiles from a network of 50 near-surface time-lapse cameras (Phenocams) across 40 km2 of West Greenland tundra in two years with contrasting abiotic conditions. Using this landscape-extent dataset paired with satellite-based MODIS multispectral time-series, we examined the influence of spatial grain choice on the observed timing, spatial variability, and landscape correlates of tundra green-up phenology. We matched the spatial grain of emergence time-series with three levels of ecological organization: vegetation functional-type patches (ecological `level' were analyzed simultaneously using a hierarchical Bayesian mixed modeling framework. Despite the contrasting abiotic conditions in each study year, the annual spatial variability in emergence across the broader landscape measured at fine grains was of a much greater magnitude than between year differences measured from any data-source. Coarser-grained MODIS derived metrics of vegetation greenup were much less variable between years, and were also significantly correlated with different landscape-level features than the finer grained Phenocam datasets. This first report of fine-grained vegetation emergence phenology across a broad tundra landscape extent (40 km2) reveals clear scale-dependent dynamics in the timing, variability, and environmental drivers of greenup, and offers empirical insights into how fine-grained processes may contribute to broader

  18. Predictor variable resolution governs modeled soil types (United States)

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

  19. The active liquid Earth - importance of temporal and spatial variability (United States)

    Arheimer, Berit


    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

  20. Modeling the Variable Heliopause Location (United States)

    Hensley, Kerry


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

  1. Temporal and Spatial Variability in Landslide Susceptibility Analyses (United States)

    Trizzino, Rosamaria; Pagliarulo, Rossella


    The geomorphic processes in landscape evolution are commonly assumed deterministic, although their high variability in rates and time. As the stability analyses of slopes are concerned, the classical methods consider threshold values of the different elements (slope angle, friction angle, climatic conditions, hydrogeological conditions, seismicity) that condition the safety factors, but often widespread landscape instabilities occur when the threshold values are not exceeded. To analyze these phenomena we studied a model for defining an "average" pattern of landscape evolution starting from the single deterministic process. Many previous studies demonstrated the driving role of weathering and erosion processes in landslide evolution. Among these, the "instability principle of geomorphic equilibrium" (Scheidegger, 1983) stated the relevancy of exogenic processes (weathering, erosion, etc.) particularly in those places where preexisting micro topographic irregularities or lithological variations are recognizable. The present paper gives an example of the unstable growth of small perturbations from the initial conditions up to the landslide initiation, even if there were no measurable variations in external controls. In this analysis the geo- materials are considered as a weathering system mathematically depicted as an n-components nonlinear dynamical system. A hierarchical multiscale model of instability is applied. The model treats four spatial scales: 1) local regolith scale (weathering processes, in situ breakdown of geo-materials), 2) hill slope scale (allocation of weathered products: soil removal in solid form, via erosion and mass wasting, or in dissolved form via surface water flow), 3) landscape units (relationships between weathering and denudation), 4) broadest landscape scale (topographic and isostatic response to weathering-limited denudation, unloading or depositional loading). The landslide susceptibility analysis for the present study is located in

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

    International Nuclear Information System (INIS)

    Youngs, E.G.


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

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

    Directory of Open Access Journals (Sweden)

    Sandeep Pulla

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

  4. Spatial and temporal variability of chorus and hiss (United States)

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


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

  5. Continuous Spatial Process Models for Spatial Extreme Values

    KAUST Repository

    Sang, Huiyan


    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.

  6. Appropriatie spatial scales to achieve model output uncertainty goals

    NARCIS (Netherlands)

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


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

  7. A latent parameter node-centric model for spatial networks.

    Directory of Open Access Journals (Sweden)

    Nicholas D Larusso

    Full Text Available Spatial networks, in which nodes and edges are embedded in space, play a vital role in the study of complex systems. For example, many social networks attach geo-location information to each user, allowing the study of not only topological interactions between users, but spatial interactions as well. The defining property of spatial networks is that edge distances are associated with a cost, which may subtly influence the topology of the network. However, the cost function over distance is rarely known, thus developing a model of connections in spatial networks is a difficult task. In this paper, we introduce a novel model for capturing the interaction between spatial effects and network structure. Our approach represents a unique combination of ideas from latent variable statistical models and spatial network modeling. In contrast to previous work, we view the ability to form long/short-distance connections to be dependent on the individual nodes involved. For example, a node's specific surroundings (e.g. network structure and node density may make it more likely to form a long distance link than other nodes with the same degree. To capture this information, we attach a latent variable to each node which represents a node's spatial reach. These variables are inferred from the network structure using a Markov Chain Monte Carlo algorithm. We experimentally evaluate our proposed model on 4 different types of real-world spatial networks (e.g. transportation, biological, infrastructure, and social. We apply our model to the task of link prediction and achieve up to a 35% improvement over previous approaches in terms of the area under the ROC curve. Additionally, we show that our model is particularly helpful for predicting links between nodes with low degrees. In these cases, we see much larger improvements over previous models.

  8. Spatial variability of POPs in European background air

    Directory of Open Access Journals (Sweden)

    A. K. Halse


    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.

  9. Models and Inference for Multivariate Spatial Extremes

    KAUST Repository

    Vettori, Sabrina


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

  10. Using geomorphological variables to predict the spatial distribution of plant species in agricultural drainage networks. (United States)

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


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

  11. 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

  12. Temporal and spatial variability of global water balance (United States)

    McCabe, Gregory J.; Wolock, David M.


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

  13. Spatial Uncertainty Analysis of Ecological Models

    Energy Technology Data Exchange (ETDEWEB)

    Jager, H.I.; Ashwood, T.L.; Jackson, B.L.; King, A.W.


    The authors evaluated the sensitivity of a habitat model and a source-sink population model to spatial uncertainty in landscapes with different statistical properties and for hypothetical species with different habitat requirements. Sequential indicator simulation generated alternative landscapes from a source map. Their results showed that spatial uncertainty was highest for landscapes in which suitable habitat was rare and spatially uncorrelated. Although, they were able to exert some control over the degree of spatial uncertainty by varying the sampling density drawn from the source map, intrinsic spatial properties (i.e., average frequency and degree of spatial autocorrelation) played a dominant role in determining variation among realized maps. To evaluate the ecological significance of landscape variation, they compared the variation in predictions from a simple habitat model to variation among landscapes for three species types. Spatial uncertainty in predictions of the amount of source habitat depended on both the spatial life history characteristics of the species and the statistical attributes of the synthetic landscapes. Species differences were greatest when the landscape contained a high proportion of suitable habitat. The predicted amount of source habitat was greater for edge-dependent (interior) species in landscapes with spatially uncorrelated(correlated) suitable habitat. A source-sink model demonstrated that, although variation among landscapes resulted in relatively little variation in overall population growth rate, this spatial uncertainty was sufficient in some situations, to produce qualitatively different predictions about population viability (i.e., population decline vs. increase).



    Ayoubi, S.A; M. H. Alizadeh


    Soil erodibility is one of the key factors on some sediment and soil erosion models such as USLE, MUSLE, RUSLE, AUSLE (USLE modified in LS factor) and MMF and represents like K factor and is function of particle distribution, organic mater, soil structure and ermeability. Traditional methods do not take spatial variability and estimate precision of variables in to consideration and amount of them are constant across the whole of soil series .This study was performed to assess spatial variabil...

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

    Directory of Open Access Journals (Sweden)

    Daniel E Impoinvil

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

  16. Spatial variability of leaf wetness duration in different crop canopies (United States)

    Sentelhas, Paulo C.; Gillespie, Terry J.; Batzer, Jean C.; Gleason, Mark L.; Monteiro, José Eduardo B. A.; Pezzopane, José Ricardo M.; Pedro, Mário J.


    The spatial variability of leaf wetness duration (LWD) was evaluated in four different height-structure crop canopies: apple, coffee, maize, and grape. LWD measurements were made using painted flat plate, printed-circuit wetness sensors deployed in different positions above and inside the crops, with inclination angles ranging from 30 to 45°. For apple trees, the sensors were installed in 12 east-west positions: 4 at each of the top (3.3 m), middle (2.1 m), and bottom (1.1 m) levels. For young coffee plants (80 cm tall), four sensors were installed close to the leaves at heights of 20, 40, 60, and 80 cm. For the maize and grape crops, LWD sensors were installed in two positions, one just below the canopy top and another inside the canopy. Adjacent to each experiment, LWD was measured above nearby mowed turfgrass with the same kind of flat plate sensor, deployed at 30 cm and between 30 and 45°. We found average LWD varied by canopy position for apple and maize (Pcoffee plants, average LWD did not differ between the top and inside the canopy. The comparison by geometric mean regression analysis between crop and turfgrass LWD measurements showed that sensors at 30 cm over turfgrass provided quite accurate estimates of LWD at the top of the crops, despite large differences in crop height and structure, but poorer estimates for wetness within leaf canopies.

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

    Directory of Open Access Journals (Sweden)

    Thomas Panagopoulos


    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.

  18. Controls of Soil Spatial Variability in a Dry Tropical Forest. (United States)

    Pulla, Sandeep; Riotte, Jean; Suresh, H S; Dattaraja, H S; Sukumar, Raman


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

  19. Spatial and temporal variability in seasonal snow density

    KAUST Repository

    Bormann, Kathryn J.


    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.

  20. Spatial and temporal variability in forest growth in the Olympic Mountains, Washington : sensitivity to climatic variability

    Energy Technology Data Exchange (ETDEWEB)

    Holman, M.L. [Washington Univ., Seattle, WA (United States). College of Forest Resources; Peterson, D.L. [United States Dept. of Agriculture Forest Service, Seattle, WA (United States). Pacific Northwest Research Station


    Global climatic change may alter tree growth rates in some areas of the Pacific Northwest, which in turn could have a substantial effect on global carbon budgets. Annual basal area increment (BAI) was compared at different spatial scales among size classes and species at various locations in the western and northeastern Olympic Mountains. The aim of the study was to quantify variations in tree growth over the last 54 years and assess the sensitivity of Olympic forests to climate variability and change. Growth patterns for trees spanning a wide range of biophysical environments were examined at multiple spatial scales to determine the scale at which the trees had similar growth responses and the scale at which growth-limiting factors asserted their strongest influence. Mean interseries correlations were used to assess the degree of similarity among individual BAI time series per plot. Weak growth correlations at small spatial scales suggested that trees responded to local growth conditions. However, significant positive growth correlations between geographically adjacent forest types and watersheds indicated that there is a common overarching growth-limiting factor that affected tree growth over large areas. It was noted that the Sitka spruce forest type was the most sensitive to environmental change with the highest mean sensitivity, the highest potential for annual growth change, and the highest growth variability. In addition, this forest type was more likely to exhibit extreme positive growth responses. It was concluded that low elevation coniferous forests are relatively sensitive to changes in growth-limiting factors and may play an important role in storing carbon in a warmer climate. However, the study was limited by an inability to account for the effects of climate change on disturbances and biotic factors. 45 refs., 3 tabs., 5 figs.

  1. Synchronization Model for Pulsating Variables (United States)

    Takahashi, S.; Morikawa, M.


    A simple model is proposed, which describes the variety of stellar pulsations. In this model, a star is described as an integration of independent elements which interact with each other. This interaction, which may be gravitational or hydrodynamic, promotes the synchronization of elements to yield a coherent mean field pulsation provided some conditions are satisfied. In the case of opacity driven pulsations, the whole star is described as a coupling of many heat engines. In the case of stochastic oscillation, the whole star is described as a coupling of convection cells, interacting through their flow patterns. Convection cells are described by the Lorentz model. In both models, interactions of elements lead to various pulsations, from irregular to regular. The coupled Lorenz model also describes a light curve which shows a semi-regular variability and also shows a low-frequency enhancement proportional to 1/f in its power spectrum. This is in agreement with observations (Kiss et al. 2006). This new modeling method of ‘coupled elements’ may provide a powerful description for a variety of stellar pulsations.

  2. Factor analysis of soil spatial variability in gully erosion area of ...

    African Journals Online (AJOL)

    The effect of soil characteristics on gully development and distribution has made it desirable to determine the spatial variability of its physical and chemical properties. This paper examines the spatial variability of soil properties and factors contributing to the general pattern of variability in Agulu- Nanka- Oko gully complex, ...

  3. Quantifying watershed sensitivity to spatially variable N loading from mountain resort development (United States)

    McGlynn, B.; Gardner, K.; Marshall, L.


    Effectively managing watershed nitrogen (N) requires understanding of the sources and fate of anthropogenic N in terrestrial and aquatic ecosystems and their variation across space and time. Headwater streams in mountain environments may be particularly susceptible to N enrichment from residential and resort development. We examined watershed sensitivity to spatially variable N loading from mountain resort development in the 220 km2 West Fork of the Gallatin River, Big Sky, Montana, USA. We combined analyses of spatial and seasonal streamwater N and carbon (C) concentration data, watershed N mass balance calculations, three-component mixing models of N sources using nitrate (NO3-) isotopes, spatial and multiple regression approaches, and numerical modeling to examine the effects of anthropogenic N loading on the timing, magnitude, and speciation of watershed N retention and export. Our analyses indicate that biological uptake of N during the growing season masked N enrichment in the summer months. However, other results indicate considerable anthropogenic impacts to streamwater N export and speciation throughout the year and on an annual basis. Our new Big Sky nutrient export model (BiSN) incorporated spatial stream water chemistry, data from instream tracer additions and geologic weathering experiments, and terrain and land use analysis to quantify the spatial variability of watershed sensitivity to N loading and the relative importance of upland, riparian, and instream N retention (storage, removal, or transformation) across land use/land cover (LULC) and landscape positions. Modeling results revealed that small amounts of wastewater loading occurring in watershed areas with short travel times to the stream had disproportionately large impacts on watershed nitrate export compared to spatially distributed N loading or localized N loading in watershed areas with longer travel times. During summer base flow conditions, 98%-99% of watershed N retention occurred in

  4. Location Aggregation of Spatial Population CTMC Models

    Directory of Open Access Journals (Sweden)

    Luca Bortolussi


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

  5. Hierarchical modeling and analysis for spatial data

    CERN Document Server

    Banerjee, Sudipto; Gelfand, Alan E


    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

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

    Directory of Open Access Journals (Sweden)

    Liu Xiaoting


    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.

  7. Use of precision agriculture technology to investigate spatial variability in nitrogen yields in cut grassland. (United States)

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


    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.

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

    International Nuclear Information System (INIS)

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


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

  9. 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


    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.

  10. Spatial variability of soil nitrogen in a hilly valley: Multiscale patterns and affecting factors. (United States)

    Zhang, Shirong; Xia, Chunlan; Li, Ting; Wu, Chungui; Deng, Ouping; Zhong, Qinmei; Xu, Xiaoxun; Li, Yun; Jia, Yongxia


    Estimating the spatial distribution of soil nitrogen at different scales is crucial for improving soil nitrogen use efficiency and controlling nitrogen pollution. We evaluated the spatial variability of soil total nitrogen (TN) and available nitrogen (AN) in the Fujiang River Valley, a typical hilly region composed of low, medium and high hills in the central Sichuan Basin, China. We considered the two N forms at single hill, landscape and valley scales using a combined method of classical statistics, geostatistics and a geographic information system. The spatial patterns and grading areas of soil TN and AN were different among hill types and different scales. The percentages of higher grades of the two nitrogen forms decreased from low, medium to high hills. Hill type was a major factor determining the spatial variability of the two nitrogen forms across multiple scales in the valley. The main effects of general linear models indicated that the key affecting factors of soil TN and AN were hill type and fertilization at the single hill scale, hill type and soil type at the landscape scale, and hill type, slope position, parent material, soil type, land use and fertilization at the valley scale. Thus, the effects of these key factors on the two soil nitrogen forms became more significant with upscaling. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. Spatial Allocator for air quality modeling (United States)

    The Spatial Allocator is a set of tools that helps users manipulate and generate data files related to emissions and air quality modeling without requiring the use of a commercial Geographic Information System.

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

    Directory of Open Access Journals (Sweden)

    Arnaud eDechesne


    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.

  13. Spatial variability and stocks of soil organic carbon in the Gobi desert of Northwestern China.

    Directory of Open Access Journals (Sweden)

    Pingping Zhang

    Full Text Available Soil organic carbon (SOC plays an important role in improving soil properties and the C global cycle. Limited attention, though, has been given to assessing the spatial patterns and stocks of SOC in desert ecosystems. In this study, we quantitatively evaluated the spatial variability of SOC and its influencing factors and estimated SOC storage in a region (40 km2 of the Gobi desert. SOC exhibited a log-normal depth distribution with means of 1.6, 1.5, 1.4, and 1.4 g kg(-1 for the 0-10, 10-20, 20-30, and 30-40 cm layers, respectively, and was moderately variable according to the coefficients of variation (37-42%. Variability of SOC increased as the sampling area expanded and could be well parameterized as a power function of the sampling area. Significant correlations were detected between SOC and soil physical properties, i.e. stone, sand, silt, and clay contents and soil bulk density. The relatively coarse fractions, i.e. sand, silt, and stone contents, had the largest effects on SOC variability. Experimental semivariograms of SOC were best fitted by exponential models. Nugget-to-sill ratios indicated a strong spatial dependence for SOC concentrations at all depths in the study area. The surface layer (0-10 cm had the largest spatial dependency compared with the other layers. The mapping revealed a decreasing trend of SOC concentrations from south to north across this region of the Gobi desert, with higher levels close to an oasis and lower levels surrounded by mountains and near the desert. SOC density to depths of 20 and 40 cm for this 40 km2 area was estimated at 0.42 and 0.68 kg C m(-2, respectively. This study provides an important contribution to understanding the role of the Gobi desert in the global carbon cycle.

  14. From spatial ecology to spatial epidemiology: modeling spatial distributions of different cancer types with principal coordinates of neighbor matrices. (United States)

    Voutilainen, Ari; Tolppanen, Anna-Maija; Vehviläinen-Julkunen, Katri; Sherwood, Paula R


    Epidemiology and ecology share many fundamental research questions. Here we describe how principal coordinates of neighbor matrices (PCNM), a method from spatial ecology, can be applied to spatial epidemiology. PCNM is based on geographical distances among sites and can be applied to any set of sites providing a good coverage of a study area. In the present study, PCNM eigenvectors corresponding to positive autocorrelation were used as explanatory variables in linear regressions to model incidences of eight most common cancer types in Finnish municipalities (n = 320). The dataset was provided by the Finnish Cancer Registry and it included altogether 615,839 cases between 1953 and 2010. PCNM resulted in 165 vectors with a positive eigenvalue. The first PCNM vector corresponded to the wavelength of hundreds of kilometers as it contrasted two main subareas so that municipalities located in southwestern Finland had the highest positive site scores and those located in midwestern Finland had the highest negative scores in that vector. Correspondingly, the 165(th) PCNM vector indicated variation mainly between the two small municipalities located in South Finland. The vectors explained 13 - 58% of the spatial variation in cancer incidences. The number of outliers having standardized residual > |3| was very low, one to six per model, and even lower, zero to two per model, according to Chauvenet's criterion. The spatial variation of prostate cancer was best captured (adjusted r (2) = 0.579). PCNM can act as a complementary method to causal modeling to achieve a better understanding of the spatial structure of both the response and explanatory variables, and to assess the spatial importance of unmeasured explanatory factors. PCNM vectors can be used as proxies for demographics and causative agents to deal with autocorrelation, multicollinearity, and confounding variables. PCNM may help to extend spatial epidemiology to areas with limited availability of

  15. Variable Selection in Model-based Clustering: A General Variable Role Modeling


    Maugis, Cathy; Celeux, Gilles; Martin-Magniette, Marie-Laure


    The currently available variable selection procedures in model-based clustering assume that the irrelevant clustering variables are all independent or are all linked with the relevant clustering variables. We propose a more versatile variable selection model which describes three possible roles for each variable: The relevant clustering variables, the irrelevant clustering variables dependent on a part of the relevant clustering variables and the irrelevant clustering variables totally indepe...

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

    Directory of Open Access Journals (Sweden)

    Simone Becker Lopes


    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.

  17. A method for estimating spatially variable seepage and hydrualic conductivity in channels with very mild slopes (United States)

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


    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.

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

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


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

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

    KAUST Repository

    Zhang, L.


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

  20. Spatial and temporal variability of groundwater recharge in Geba basin, Northern Ethiopia (United States)

    Yenehun, Alemu; Walraevens, Kristine; Batelaan, Okke


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

  1. Interannual and spatial variability of maple syrup yield as related to climatic factors. (United States)

    Duchesne, Louis; Houle, Daniel


    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.

  2. Detecting high spatial variability of ice shelf basal mass balance, Roi Baudouin Ice Shelf, Antarctica (United States)

    Berger, Sophie; Drews, Reinhard; Helm, Veit; Sun, Sainan; Pattyn, Frank


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

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

    Directory of Open Access Journals (Sweden)

    S. Berger


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

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

    African Journals Online (AJOL)

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

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

    Directory of Open Access Journals (Sweden)

    D. Cromwell


    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

  6. Spatial variability of attributes and soil loss in the definition of management zones

    Directory of Open Access Journals (Sweden)

    Daniela Popim Miqueloni


    Full Text Available Management zones are the result of proper planning of soil use and occupation, which reduce the human impact on the environment. This study aimed to characterize chemical and physical soil traits and check possible management zones through the spatial variability of soil loss estimates and limiting attributes to the development of crops, determining the factors that most affect the erosion process. A total of 258 soil georeferenced points were sampled and had their chemical and physical characteristics determined. The soil losses were estimated by using the USLE model; erosion natural potential, risk and expectation; and anthropic factor of soil loss. The spatial variability of these characteristics was analyzed by descriptive statistics and geostatistics. The results indicated high soil loss, low erosion natural potential and moderate erosion risk for most of the area, with major losses in the convex landform. The anthropic factor and the expected erosion indicate inadequate use and occupation, particularly for the management of soil pH for citrus crop. The anthropic factors were important for the spatial analysis of erosion expectation, suggesting specific management zones.

  7. Spatial variability of heating profiles in windrowed poultry litter (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 ...

  8. Temporal and spatial variability in soil food web structure.

    NARCIS (Netherlands)

    Berg, M.P.; Bengtsson, J.


    Heterogeneity is a prominent feature of most ecosystems. As a result of environmental heterogeneity the distribution of many soil organisms shows a temporal as well as horizontal and vertical spatial patterning. In spite of this, food webs are usually portrayed as static networks with highly

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

    African Journals Online (AJOL)

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

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

    Indian Academy of Sciences (India)

    The spatial variation of CO2 flux above the canopy was mainly explained by the canopy density and photosynthetic efficiency of the mangrove species. The CO2 sink strength of the mangrove cover in different stations varied in the same way with the CO2 uptake potential of the species diversity in the respective sites.

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

    African Journals Online (AJOL)

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

  12. Determining spatial and temporal variability in quantity and quality of ...

    African Journals Online (AJOL)

    This study assessed the importance of spatial and temporal variation in plant quality and quantity for determining sustainable stocking rates in game, commercial and communal ranches in semi-arid savanna ... Habitat type had greater effects on plant quality, plant biomass and species composition than management type.

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

    African Journals Online (AJOL)

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

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

    Indian Academy of Sciences (India)

    such a complex and heterogeneous gigantic man- grove ecosystem. The structure and functioning of mangrove forests are influenced by several physico- chemical and bio-geographical factors like soil type, availability of water table, vapour pressure deficit, photosynthetic photon flux which again vary over different spatial ...

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

    African Journals Online (AJOL)


    According to UNEP. (1997) AI can be used to quantify precipitation availability over atmospheric water demand. The aridity representing the annual average over the 1950 to 2000 period was used. From Figure 3, the spatial distribution of AI is similar to that of surface water bodies. With the exception of southeastern part of ...

  16. Second-Phase Sampling Designs for Non-Stationary Spatial Variables. (United States)

    Delmelle, Eric M; Goovaerts, Pierre


    In spatial sampling, once initial samples of the primary variable have been collected, it is possible to take additional measurements, an approach known as second-phase sampling. Additional samples are usually collected away from observation locations, or where the kriging variance is maximum. However, the kriging variance (also known as prediction error variance) is independent of data values and computed under the assumption of stationary spatial process, which is often violated in practice. In this paper, we weight the kriging variance with another criterion, giving greater sampling importance to locations exhibiting significant spatial roughness that is computed by a spatial moving average window. Additional samples are allocated using a simulated annealing procedure since the weighted objective function is non-linear. A case study using an exhaustive remote sensing image illustrates the procedure. Combinations of first-phase systematic and nested sampling designs (or patterns) of varying densities are generated, while the location of additional observations is guided in a way which optimizes the proposed objective function. The true pixel value at the new points is extracted, the semivariogram model updated, and the image reconstructed. Second-phase sampling patterns optimizing the proposed criterion lead to predictions closer to the true image than when using the kriging variance as the main criterion. This improvement is stronger when there is a low density of first-phase samples, and decreases however as the initial density increases.

  17. 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


    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.

  18. Landscape-scale geomorphic change detection: Quantifying spatially variable uncertainty and circumventing legacy data issues (United States)

    Schaffrath, Keelin R.; Belmont, Patrick; Wheaton, Joseph M.


    Repeat surveys of high-resolution topographic data enable analysis of geomorphic change through digital elevation model (DEM) differencing. Such analyses are becoming increasingly common. However, techniques for developing robust estimates of spatially variable uncertainty in DEM differencing estimates have been slow to develop and are underutilized. Further, issues often arise when comparing recent to older data sets, because of differences in data quality. Airborne lidar data were collected in 2005 and 2012 in Blue Earth County, Minnesota (1980 km2) and the occurrence of an extreme flood in 2010 produced geomorphic change clearly observed in the field, providing an opportunity to estimate landscape-scale geomorphic change. Initial assessments of the lidar-derived digital elevation models (DEMs) indicated both a vertical bias attributed to different geoid models and localized offset strips in the DEM of difference from poor coregistration of the flightlines. We applied corrections for both issues and describe the methods we used to discern those issues and correct them. We then compare different threshold models to quantify uncertainty. Poor quantification of uncertainty can erroneously over- or underestimate real change. We show that application of a uniform threshold, often called a minimum level of detection, overestimates change in areas where change would not be expected, such as stable hillslopes, and underestimates change in areas where it is expected and has been observed, such as channel banks. We describe a spatially variable DEM error model that combines the influence of slope, point density, and vegetation in a fuzzy inference system. Vegetation is represented with a metric referred to as the cloud point density ratio that assesses the complete point cloud to describe the density of above ground features that may hinder bare-earth returns. We compare the significance of spatially variable versus spatially uniform DEM errors on change detection by

  19. A Core Language for Separate Variability Modeling

    DEFF Research Database (Denmark)

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


    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...... models. Our language, Featherweight VML, has several distinctive features. Its architecture and operations are inspired by the recently proposed Common Variability Language (CVL). Its semantics is considerably simpler than that of CVL, while remaining confluent (unlike CVL). We simplify complex......, which makes it suitable to serve as a specification for implementations of trustworthy variant derivation. Featherweight VML offers insights in the execution of other variability modeling languages such as the Orthogonal Variability Model and Delta Modeling. To the best of our knowledge...

  20. Effects of attentional and cognitive variables on unilateral spatial neglect. (United States)

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


    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. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. A Statistical Toolbox For Mining And Modeling Spatial Data

    Directory of Open Access Journals (Sweden)

    D’Aubigny Gérard


    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.

  2. A spatial and temporal continuous surface-subsurface hydrologic model (United States)

    Xiao, Qing-Fu; Ustin, Susan L.; Wallender, Wesley W.


    A hydrologic model integrating surface-subsurface processes was developed based on spatial and temporal continuity theory. The raster-based mass balance hydrologic model consists of several submodels which determine spatial and temporal patterns in precipitation, surface flow, infiltration, subsurface flow, and the linkages between these submodels. Model parameters and variables are derived directly or indirectly from satellite remote sensing data, topographic maps, soil maps, literature, and weather station data and are stored in a Geographic Information System (GIS) database used for visualization. Surface resolution of cells in the model is 20 m by 20 m (pixel resolution of the Systeme Probatoire d'Observation de la Terre (SPOT) satellite image) over a 2511 km2 study area around the Crazy Mountains, Alaska, a watershed on the Arctic Circle draining into the Yukon River. The outputs from this model illustrate the interaction of physical and biologic factors on the partitioning of hydrologic components in a complex landscape.

  3. Accounting for rainfall spatial variability in the prediction of flash floods (United States)

    Saharia, Manabendra; Kirstetter, Pierre-Emmanuel; Gourley, Jonathan J.; Hong, Yang; Vergara, Humberto; Flamig, Zachary L.


    Flash floods are a particularly damaging natural hazard worldwide in terms of both fatalities and property damage. In the United States, the lack of a comprehensive database that catalogues information related to flash flood timing, location, causative rainfall, and basin geomorphology has hindered broad characterization studies. First a representative and long archive of more than 15,000 flooding events during 2002-2011 is used to analyze the spatial and temporal variability of flash floods. We also derive large number of spatially distributed geomorphological and climatological parameters such as basin area, mean annual precipitation, basin slope etc. to identify static basin characteristics that influence flood response. For the same period, the National Severe Storms Laboratory (NSSL) has produced a decadal archive of Multi-Radar/Multi-Sensor (MRMS) radar-only precipitation rates at 1-km spatial resolution with 5-min temporal resolution. This provides an unprecedented opportunity to analyze the impact of event-level precipitation variability on flooding using a big data approach. To analyze the impact of sub-basin scale rainfall spatial variability on flooding, certain indices such as the first and second scaled moment of rainfall, horizontal gap, vertical gap etc. are computed from the MRMS dataset. Finally, flooding characteristics such as rise time, lag time, and peak discharge are linked to derived geomorphologic, climatologic, and rainfall indices to identify basin characteristics that drive flash floods. The database has been subjected to rigorous quality control by accounting for radar beam height and percentage snow in basins. So far studies involving rainfall variability indices have only been performed on a case study basis, and a large scale approach is expected to provide a deeper insight into how sub-basin scale precipitation variability affects flooding. Finally, these findings are validated using the National Weather Service storm reports and a

  4. Landscape Modelling and Simulation Using Spatial Data

    Directory of Open Access Journals (Sweden)

    Amjed Naser Mohsin AL-Hameedawi


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

  5. Spatial and interannual variability in Baltic sprat batch fecundity

    DEFF Research Database (Denmark)

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


    and ambient temperature explained 70% of variability in absolute batch fecundity. Oxygen content and fish condition were not related to sprat batch fecundity. Additionally, a negative effect of stock size on sprat batch fecundity in the Bornholm Basin was revealed. The obtained data and results are important...... 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...

  6. Spatial variability of soil penetration resistance influenced by season of sampling

    Directory of Open Access Journals (Sweden)

    Juan José Bonnin


    Full Text Available The aim of this work was to analyze the spatial distribution of soil compaction and the influence of soil water content on the resistance to penetration. The latter variable was described by the cone index. The soil at the study site was a Nitisol and the cone index data were obtained using a penetrometer. Soil resistance was assessed at 5 different depths, i.e. 0-10 cm, 10-20 cm, 20-30 cm, 30-40 cm and deeper than 40 cm, whereas soil water content was measured at 0-20 cm and 20-40 cm. Soil water conditions varied during the different samplings. Coefficients of variation for cone index ranged from 16.5% to 45.8% while those for soil water content varied from 8.96% to 21.38%. Results suggested a high correlation between soil resistance, as assessed by the cone index, and soil depth. However, the expected relation with soil water content was not observed. Spatial dependence was observed in 31 out of 35 data series, both cone index and soil water content. This structure was fitted to exponential models with nugget effect varying from 0 to 90% of the sill value. Four of the data series showed a random behaviour. Inverse distance technique was used in order to map the distribution of the variables when no spatial structure was observed. Ordinary kriging showed a smoothing of the maps compared to those from inverse distance weighing. Indicator kriging was used to map the cone index spatial distribution for recommendation of further soil management.

  7. Hydrological and environmental variables outperform spatial factors in structuring species, trait composition, and beta diversity of pelagic algae. (United States)

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


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

  8. Spatial Temporal Modelling of Particulate Matter for Health Effects Studies (United States)

    Hamm, N. A. S.


    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.

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

    African Journals Online (AJOL)

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

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

    African Journals Online (AJOL)


    The available moisture of soil was very low thus water holding capacity (WHC) and wilting point (WP) of ... cover crops, planting legumes, mulching ..... can thrive well if the field capacity can supply the required plant available water to the planted crops. 142. Variability of Soil Morphorlogical and Physico-Chemical Properties ...

  11. Dissecting the multi-scale spatial relationship of earthworm assemblages with soil environmental variability. (United States)

    Jiménez, Juan J; Decaëns, Thibaud; Lavelle, Patrick; Rossi, Jean-Pierre


    Studying the drivers and determinants of species, population and community spatial patterns is central to ecology. The observed structure of community assemblages is the result of deterministic abiotic (environmental constraints) and biotic factors (positive and negative species interactions), as well as stochastic colonization events (historical contingency). We analyzed the role of multi-scale spatial component of soil environmental variability in structuring earthworm assemblages in a gallery forest from the Colombian "Llanos". We aimed to disentangle the spatial scales at which species assemblages are structured and determine whether these scales matched those expressed by soil environmental variables. We also tested the hypothesis of the "single tree effect" by exploring the spatial relationships between root-related variables and soil nutrient and physical variables in structuring earthworm assemblages. Multivariate ordination techniques and spatially explicit tools were used, namely cross-correlograms, Principal Coordinates of Neighbor Matrices (PCNM) and variation partitioning analyses. The relationship between the spatial organization of earthworm assemblages and soil environmental parameters revealed explicitly multi-scale responses. The soil environmental variables that explained nested population structures across the multi-spatial scale gradient differed for earthworms and assemblages at the very-fine- (30 m), fine (10-20 m) and very fine scales (<10 m). Variation partitioning analysis revealed that the soil environmental variability explained from less than 1% to as much as 48% of the observed earthworm spatial variation. A large proportion of the spatial variation did not depend on the soil environmental variability for certain species. This finding could indicate the influence of contagious biotic interactions, stochastic factors, or unmeasured relevant soil environmental variables.

  12. Assimilation of temperature and hydraulic gradients for quantifying the spatial variability of streambed hydraulics (United States)

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


    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 fish spawning and other wildlife incubation, regional flow and hyporheic solute transport models in the Heihe River Basin, as well as in other similar hydrologic settings.

  13. Simulating maize yield and bomass with spatial variability of soil field capacity (United States)

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


    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.

  14. Spatial Modeling for Resources Framework (SMRF) (United States)

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

  15. Testing spatial heterogeneity with stock assessment models

    DEFF Research Database (Denmark)

    Jardim, Ernesto; Eero, Margit; Silva, Alexandra


    This paper describes a methodology that combines meta-population theory and stock assessment models to gain insights about spatial heterogeneity of the meta-population in an operational time frame. The methodology was tested with stochastic simulations for different degrees of connectivity betwee...

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

    CERN Document Server

    Burkholder, Earl F


    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...

  17. Cardinality-dependent Variability in Orthogonal Variability Models

    DEFF Research Database (Denmark)

    Mærsk-Møller, Hans Martin; Jørgensen, Bo Nørregaard


    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...

  18. Handbook of latent variable and related models

    CERN Document Server

    Lee, Sik-Yum


    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.

  19. 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...... 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...

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

    CERN Document Server

    Skrondal, Anders; Rabe-Hesketh, Sophia


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

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

    Abatzoglou, John T.; Ficklin, Darren L.


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

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

    National Research Council Canada - National Science Library

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


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

  3. 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


    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)

  4. Spatial capture-recapture models for search-encounter data (United States)

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


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

  5. Spatially explicit modeling in ecology: A review (United States)

    DeAngelis, Donald L.; Yurek, Simeon


    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.

  6. Temporal and spatial variabilities of Antarctic ice mass changes inferred by GRACE in a Bayesian framework (United States)

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


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

  7. Predicting Spatial Variability of Soil Organic Carbon in Delmarva Bays


    Blumenthal, Kinsey Megan


    Agricultural productivity, ecosystem health, and wetland restoration rely on soil organic carbon (SOC) as vital for microbial activity and plant health. This study assessed: (1) accuracy of topographic-based non-linear models for predicting SOC; and (2) the effect of analytic strategies and soil condition on performance of spectral-based models for predicting SOC. SOC data came from 28 agriculturally converted Delmarva Bays sampled down to 1 meter. R2 was used as an indicator of model perform...

  8. Spatial variability of E. coli in an urban salt-wedge estuary. (United States)

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


    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.

  9. Measuring spatial variability of land use associated with ...

    African Journals Online (AJOL)


    Jun 7, 2011 ... based on Information theory. Ecol. Model. 197 (1–2) 1–12. ZHOU F, XU YP, CHEN Y, XU CY, GAO YQ and DU JK (2013). Hydrological response to urbanisation at different spatio-temporal scales simulated by coupling of CLUE-S and the SWAT model in the Yangtze River Delta region. J. Hydrol.

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

    Directory of Open Access Journals (Sweden)

    J. Eeckman


    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.

  11. Spatial variability and sources of ammonia in three European cities (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


    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

  12. The Importance of Freshwater to Spatial Variability of Aragonite Saturation State in the Gulf of Alaska (United States)

    Siedlecki, Samantha A.; Pilcher, Darren J.; Hermann, Albert J.; Coyle, Ken; Mathis, Jeremy


    High-latitude and subpolar regions like the Gulf of Alaska (GOA) are more vulnerable than equatorial regions to rising carbon dioxide (CO2) levels, in part due to local processes that amplify the global signal. Recent field observations have shown that the shelf of the GOA is currently experiencing seasonal corrosive events (carbonate mineral saturation states Ω, Ω ocean acidification as well as local processes like increased low-alkalinity glacial meltwater discharge. While the glacial discharge mainly influences the inner shelf, on the outer shelf, upwelling brings corrosive waters from the deep GOA. In this work, we develop a high-resolution model for carbon dynamics in the GOA, identify regions of high variability of Ω, and test the sensitivity of those regions to changes in the chemistry of glacial meltwater discharge. Results indicate the importance of this climatically sensitive and relatively unconstrained regional freshwater forcing for Ω variability in the nearshore. The increase was nearly linear at 0.002 Ω per 100 µmol/kg increase in alkalinity in the freshwater runoff. We find that the local winds, biological processes, and freshwater forcing all contribute to the spatial distribution of Ω and identify which of these three is highly correlated to the variability in Ω. Given that the timing and magnitude of these processes will likely change during the next few decades, it is critical to elucidate the effect of local processes on the background ocean acidification signal using robust models, such as the one described here.

  13. Linking spatial and dynamic models for traffic maneuvers

    DEFF Research Database (Denmark)

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


    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...

  14. An Investigation on the Spatial Variability of Manning Roughness Coefficients in Continental-scale River Routing Simulations (United States)

    Luo, X.; Hong, Y.; Lei, X.; Leung, L. R.; Li, H. Y.; Getirana, A.


    As one essential component of the Earth system modeling, the continental-scale river routing computation plays an important role in applications of Earth system models, such as evaluating the impacts of the global change on water resources and flood hazards. The streamflow timing, which depends on the modeled flow velocities, can be an important aspect of the model results. River flow velocities have been estimated by using the Manning's equation where the Manning roughness coefficient is a key and sensitive parameter. In some early continental-scale studies, the Manning coefficient was determined with simplified methods, such as using a constant value for the entire basin. However, large spatial variability is expected in the Manning coefficients for the numerous channels composing the river network in distributed continental-scale hydrologic modeling. In the application of a continental-scale river routing model in the Amazon Basin, we use spatially varying Manning coefficients dependent on channel sizes and attempt to represent the dominant spatial variability of Manning coefficients. Based on the comparisons of simulation results with in situ streamflow records and remotely sensed river stages, we investigate the comparatively optimal Manning coefficients and explicitly demonstrate the advantages of using spatially varying Manning coefficients. The understanding obtained in this study could be helpful in the modeling of surface hydrology at regional to continental scales.

  15. Factor Copula Models for Replicated Spatial Data

    KAUST Repository

    Krupskii, Pavel


    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.

  16. Distributed multi-criteria model evaluation and spatial association analysis (United States)

    Scherer, Laura; Pfister, Stephan


    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. Chlorophyll dynamic accounts for spatial and temporal variabilities in terrestrial carbon uptake and evapotranspiration (United States)

    Croft, H.; Luo, X.; Chen, J. M.


    Terrestrial carbon and water fluxes are driven by a range of abiotic and biotic factors. State-of-art terrestrial biosphere models (TBMs) use numerical representations of these factors in conjunction with concise descriptions of biogeochemical processes to estimate terrestrial fluxes (i.e. gross primary productivity (GPP) and evapotranspiration(ET)). Leaf maximum carboxylation rate (Vcmax25) is a key biotic factor prescribed in TBM to determine CO2 assimilation rates and leaf stomatal conductivity for water transport, but the paucity of its measurements has long plagued the simulation of fluxes. This study uses leaf chlorophyll content (LCC) derived from remotely sensed data to account for spatial and temporal variations in Vcmax25 within a TBM framework. Results from the TBM with and without LCC were validated against measurements from 124 eddy-covariance towers (554 site-years) of FLUXNET. TBM using LCC reduced the biases of estimated GPP in 61% of the site-years and 59% for ET, with especially large improvements for biomes with strong seasonal cycles (e.g. deciduous forest, croplands and grasslands). In addition to the Vcmax25 adjustment imposed by LCC seasonal patterns, the spatial variability of LCC acts as an equally important part in reducing the errors of estimated fluxes by capturing the spatial variations of Vcmax25, especially during the summer. This study presents the first case of integrating satellite-derived LCC into a TBM at the global scale. Our results demonstrate the critical role of LCC in describing the variabilities in the terrestrial carbon uptake and ET and the necessity of including LCC in future TBMs.

  18. The spatial impact of neighbouring on the exports activities of COMESA countries by using spatial panel models (United States)

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


    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.

  19. Predicting road system speeds using spatial structure variables and network characteristics (United States)

    Hackney, Jeremy K.; Bernard, Michael; Bindra, Sumit; Axhausen, Kay W.


    Spatial regression is applied to GPS floating car measurements to build a predictive model of road system speed as a function of link type, time period, and spatial structure. The models correct for correlated spatial errors and autocorrelation of speeds. Correlation neighborhoods are based on either Euclidean or network distance. Econometric and statistical methods are used to choose the best model form and statistical neighborhood. Models of different types have different coefficient estimates and fit quality, which might affect inferences. Speed predictions are validated against a holdout sample to illustrate the usefulness of spatial regression in road system speed monitoring.

  20. Towards Quantitative Spatial Models of Seabed Sediment Composition.

    Directory of Open Access Journals (Sweden)

    David Stephens

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

  1. High-speed limnology: using advanced sensors to investigate spatial variability in biogeochemistry and hydrology. (United States)

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


    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.

  2. Spatial variability in forest growth—climate relationships in the Olympic Mountains, Washington. (United States)

    Jill M. Nakawatase; David L. Peterson


    For many Pacific Northwest forests, little is known about the spatial and temporal variability in tree growth - climate relationships, yet it is this information that is needed to predict how forests will respond to future climatic change. We studied the effects of climatic variability on forest growth at 74 plots in the western and northeastern Olympic Mountains....

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

    DEFF Research Database (Denmark)

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


    The spatial variability of rainfall within a single Local Area Weather Radar (LAWR) pixel of 500 x 500 m is quantified based on data from two locations. The work was motivated by the need to quantify the variability on this scale in order to provide an estimate of the uncertainty of using a single...

  4. Multi-technique assessment of spatial and temporal variability of methane fluxes in a peat meadow

    NARCIS (Netherlands)

    Hendriks, D.M.D.; van Huissteden, J.; Dolman, A.J.


    Methane fluxes measured in a eutrophic peat meadow in The Netherlands dominated by vascular plants showed high spatial and temporal variability. To elucidate this variability as well as the underlying processes, various measurement techniques were used: soil gradients of methane concentrations, the

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

    International Nuclear Information System (INIS)

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


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

  6. A Spatially Extended Model for Residential Segregation

    Directory of Open Access Journals (Sweden)

    Antonio Aguilera


    Full Text Available We analyze urban spatial segregation phenomenon in terms of the income distribution over a population, and an inflationary parameter weighting the evolution of housing prices. For this, we develop a discrete spatially extended model based on a multiagent approach. In our model, the mobility of socioeconomic agents is driven only by the housing prices. Agents exchange location in order to fit their status to the cost of their housing. On the other hand, the price of a particular house depends on the status of its tenant, and on the neighborhood mean lodging cost weighted by a control parameter. The agent's dynamics converges to a spatially organized configuration, whose regularity is measured by using an entropy-like indicator. This simple model provides a dynamical process organizing the virtual city, in a way that the population inequality and the inflationary parameter determine the degree of residential segregation in the final stage of the process, in agreement with the segregation-inequality thesis put forward by Douglas Massey.

  7. Evolution of dispersal in spatially and temporally variable environments: The importance of life cycles. (United States)

    Massol, François; Débarre, Florence


    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.

  8. Modeling the spatial structure of hog production in Denmark

    DEFF Research Database (Denmark)

    Larue, Solène; Abildtrup, Jens; Schmitt, Bertrand

    , the interaction between the location of hog production and slaughterhouses. It is the assumption that the location of slaughterhouses is influenced by the location of the primary producers, implying that this variable is endogenous, whereas the location of primary producers is independent of the location...... of slaughterhouses. This is due to the fact that transportation costs of pigs are paid by the cooperatives owning the slaughterhouses. This assumption is tested applying a spatial econometric model. The model is estimated for 1989, 1999 and 2004. In the latter period, it is the hypothesis that the demand for export...

  9. 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.

  10. Tannat grape composition responses to spatial variability of temperature in an Uruguay's coastal wine region (United States)

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


    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.

  11. Spatial succession modeling of biological communities: a multi-model approach. (United States)

    Zhang, WenJun; Wei, Wu


    Strong spatial correlation may exist in the spatial succession of biological communities, and the spatial succession can be mathematically described. It was confirmed by our study on spatial succession of both plant and arthropod communities along a linear transect of natural grassland. Both auto-correlation and cross-correlation analyses revealed that the succession of plant and arthropod communities exhibited a significant spatial correlation, and the spatial correlation for plant community succession was stronger than arthropod community succession. Theoretically it should be reasonable to infer a site's community composition from the last site in the linear transect. An artificial neural network for state space modeling (ANNSSM) was developed in present study. An algorithm (i.e., Importance Detection Method (IDM)) for determining the relative importance of input variables was proposed. The relative importance for plant families Gramineae, Compositae and Leguminosae, and arthropod orders Homoptera, Diptera and Orthoptera, were detected and analyzed using IDM. ANNSSM performed better than multivariate linear regression and ordinary differential equation, while ordinary differential equation exhibited the worst performance in the simulation and prediction of spatial succession of biological communities. A state transition probability model (STPM) was proposed to simulate the state transition process of biological communities. STPM performed better than multinomial logistic regression in the state transition modeling. We suggested a novel multi-model framework, i.e., the joint use of ANNSSM and STPM, to predict the spatial succession of biological communities. In this framework, ANNSSM and STPM can be separately used to simulate the continuous and discrete dynamics.

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

    Directory of Open Access Journals (Sweden)

    M. W. Shephard


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

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

    Directory of Open Access Journals (Sweden)

    U. Mishra


    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. Indoorgml - a Standard for Indoor Spatial Modeling (United States)

    Li, Ki-Joune


    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. Spatially explicit non-Mendelian diploid model


    Lanchier, N.; Neuhauser, C.


    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 comp...

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

    International Nuclear Information System (INIS)

    Rautman, C.A.; Flint, A.L.; Chornack, M.P.; Istok, J.D.


    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

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

    Hardy, T.; Wu, W.


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

  18. The quantitative modelling of human spatial habitability (United States)

    Wise, J. A.


    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).

  19. Modeling mental spatial reasoning about cardinal directions. (United States)

    Schultheis, Holger; Bertel, Sven; Barkowsky, Thomas


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

  20. Temporal and spatial variability of rainfall over Greece (United States)

    Markonis, Y.; Batelis, S. C.; Dimakos, Y.; Moschou, E.; Koutsoyiannis, D.


    Recent studies have showed that there is a significant decrease in rainfall over Greece during the last half of the pervious century, following an overall decrease of the precipitation at the eastern Mediterranean. However, during the last decade an increase in rainfall was observed in most regions of the country, contrary to the general circulation climate models forecasts. An updated high-resolution dataset of monthly sums and annual daily maxima records derived from 136 stations during the period 1940-2012 allowed us to present some new evidence for the observed change and its statistical significance. The statistical framework used to determine the significance of the slopes in annual rain was not limited to the time independency assumption (Mann-Kendall test), but we also investigated the effect of short- and long-term persistence through Monte Carlo simulation. Our findings show that (a) change occurs in different scales; most regions show a decline since 1950, an increase since 1980 and remain stable during the last 15 years; (b) the significance of the observed decline is highly dependent to the statistical assumptions used; there are indications that the Mann-Kendall test may be the least suitable method; and (c) change in time is strongly linked with the change in space; for scales below 40 years, relatively close regions may develop even opposite trends, while in larger scales change is more uniform.

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

    Energy Technology Data Exchange (ETDEWEB)

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


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

  2. Spatial Database Modeling for Indoor Navigation Systems (United States)

    Gotlib, Dariusz; Gnat, Miłosz


    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.

  3. Spatial Models and Networks of Living Systems

    DEFF Research Database (Denmark)

    Juul, Jeppe Søgaard

    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......When studying the dynamics of living systems, insight can often be gained by developing a mathematical model that can predict future behaviour of the system or help classify system characteristics. However, in living cells, organisms, and especially groups of interacting individuals, a large number...... of different factors influence the time development of the system. This often makes it challenging to construct a mathematical model from which to draw conclusions. One traditional way of capturing the dynamics in a mathematical model is to formulate a set of coupled differential equations for the essential...

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

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


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

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

    Directory of Open Access Journals (Sweden)

    Sirak Zenebe Gebreab


    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.

  6. Albedo Spatial Variability and Causes on the Western Greenland Ice Sheet Percolation Zone (United States)

    Lewis, G.; Osterberg, E. C.; Hawley, R. L.; Koffman, B. G.; Marshall, H. P.; Birkel, S. D.; Dibb, J. E.


    Many recent studies have concluded that Greenland Ice Sheet (GIS) mass loss has been accelerating over recent decades, but spatial and temporal variations in GIS mass balance remain poorly understood due to a complex relationship among precipitation and temperature changes, increasing melt and runoff, ice discharge, and surface albedo. Satellite measurements from MODerate resolution Imaging Spectroradiometer (MODIS) indicate that albedo has been declining over the past decade, but the cause and extent of GIS albedo change remains poorly constrained by field data. As fresh snow (albedo > 0.85) warms and melts, its albedo decreases due to snow grain growth, promoting solar absorption, higher snowpack temperatures and further melt. However, dark impurities like soot and dust can also significantly reduce snow albedo, even in the dry snow zone. While many regional climate models (e.g. the Regional Atmospheric Climate MOdel - RACMO2) calculate albedo spatial resolutions on the order of 10-30 km, and MODIS averages albedo over 500 m, surface features like sastrugi can affect albedo on much smaller scales. Here we assess the relative importance of grain size and shape vs. impurity concentrations on albedo in the western GIS percolation zone. We collected broadband albedo measurements (300-2500 nm at 3-8 nm resolution) at 35 locations using an ASD FieldSpec4 spectroradiometer to simultaneously quantify radiative fluxes and spectral reflectance. Measurements were collected on 10 x 10 m, 1 x 1 km, 5 x 5 km, and 10 x 10 km grids to determine the spatial variability of albedo as part of the 850-km Greenland Traverse for Accumulation and Climate Studies (GreenTrACS) traverse from Raven/Dye 2 to Summit. Additionally, we collected shallow (0-50 cm) snow pit samples every 5 cm at ASD measurement sites to quantify black carbon and mineral dust concentrations and size distributions using a Single Particle Soot Photometer and Coulter Counter, respectively. Preliminary results

  7. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo


    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.

  8. A Computational Model of Spatial Development (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.

  9. Spherical Process Models for Global Spatial Statistics

    KAUST Repository

    Jeong, Jaehong


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

  10. Latent spatial models and sampling design for landscape genetics (United States)

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


    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.

  11. Modelling spatial patterns of urban growth in Africa (United States)

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


    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

  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. Spatial and Temporal Variation in the Effects of Climatic Variables on Dugong Calf Production. (United States)

    Fuentes, Mariana M P B; Delean, Steven; Grayson, Jillian; Lavender, Sally; Logan, Murray; Marsh, Helene


    Knowledge of the relationships between environmental forcing and demographic parameters is important for predicting responses from climatic changes and to manage populations effectively. We explore the relationships between the proportion of sea cows (Dugong dugon) classified as calves and four climatic drivers (rainfall anomaly, Southern Oscillation El Niño Index [SOI], NINO 3.4 sea surface temperature index, and number of tropical cyclones) at a range of spatially distinct locations in Queensland, Australia, a region with relatively high dugong density. Dugong and calf data were obtained from standardized aerial surveys conducted along the study region. A range of lagged versions of each of the focal climatic drivers (1 to 4 years) were included in a global model containing the proportion of calves in each population crossed with each of the lagged versions of the climatic drivers to explore relationships. The relative influence of each predictor was estimated via Gibbs variable selection. The relationships between the proportion of dependent calves and the climatic drivers varied spatially and temporally, with climatic drivers influencing calf counts at sub-regional scales. Thus we recommend that the assessment of and management response to indirect climatic threats on dugongs should also occur at sub-regional scales.

  14. Spatial and temporal variability of soil temperature, moisture and surface soil properties (United States)

    Hajek, B. F.; Dane, J. H.


    The overall objectives of this research were to: (l) Relate in-situ measured soil-water content and temperature profiles to remotely sensed surface soil-water and temperature conditions; to model simultaneous heat and water movement for spatially and temporally changing soil conditions; (2) Determine the spatial and temporal variability of surface soil properties affecting emissivity, reflectance, and material and energy flux across the soil surface. This will include physical, chemical, and mineralogical characteristics of primary soil components and aggregate systems; and (3) Develop surface soil classes of naturally occurring and distributed soil property assemblages and group classes to be tested with respect to water content, emissivity and reflectivity. This document is a report of studies conducted during the period funded by NASA grants. The project was designed to be conducted over a five year period. Since funding was discontinued after three years, some of the research started was not completed. Additional publications are planned whenever funding can be obtained to finalize data analysis for both the arid and humid locations.

  15. Accounting for spatial effects in land use regression for urban air pollution modeling. (United States)

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


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


    Directory of Open Access Journals (Sweden)

    Ayoubi, S.A


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

  17. Spatial interpolation schemes of daily precipitation for hydrologic modeling (United States)

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


    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.

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


    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

  19. Single-grain cosmogenic Ne-21 concentrations in fluvial sediment reveal spatially variable erosion rates


    Alexandru T. Codilean; P. Bishop; F. M. Stuart; T. B. Hoey; D. Fabel; S. P. H. T. Freeman;  


    We evaluated the hypothesis that the spatial variation in erosion in a catchment is refl ected in the distribution of the cosmogenic nuclide concentrations in sediments leaving the catchment. Using published data and four new 10Be measurements in fl uvial sediment collected from the outlets of small river catchments, we constrained the spatial variability of erosion rates in the Gaub River catchment in Namibia. We combined these catchment-averaged erosion rates, and the mean slope values with...

  20. Increased Spatial Variability and Intensification of Extreme Monsoon Rainfall due to Urbanization. (United States)

    Paul, Supantha; Ghosh, Subimal; Mathew, Micky; Devanand, Anjana; Karmakar, Subhankar; Niyogi, Dev


    While satellite data provides a strong robust signature of urban feedback on extreme precipitation; urbanization signal is often not so prominent with station level data. To investigate this, we select the case study of Mumbai, India and perform a high resolution (1 km) numerical study with Weather Research and Forecasting (WRF) model for eight extreme rainfall days during 2014-2015. The WRF model is coupled with two different urban schemes, the Single Layer Urban Canopy Model (WRF-SUCM), Multi-Layer Urban Canopy Model (WRF-MUCM). The differences between the WRF-MUCM and WRF-SUCM indicate the importance of the structure and characteristics of urban canopy on modifications in precipitation. The WRF-MUCM simulations resemble the observed distributed rainfall. WRF-MUCM also produces intensified rainfall as compared to the WRF-SUCM and WRF-NoUCM (without UCM). The intensification in rainfall is however prominent at few pockets of urban regions, that is seen in increased spatial variability. We find that the correlation of precipitation across stations within the city falls below statistical significance at a distance greater than 10 km. Urban signature on extreme precipitation will be reflected on station rainfall only when the stations are located inside the urban pockets having intensified precipitation, which needs to be considered in future analysis.

  1. Evolution of learning strategies in temporally and spatially variable environments: a review of theory. (United States)

    Aoki, Kenichi; Feldman, Marcus W


    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.

  2. Evolution of learning strategies in temporally and spatially variable environments: A review of theory (United States)

    Aoki, Kenichi; Feldman, Marcus W.


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

  3. A novel spatial performance metric for robust pattern optimization of distributed hydrological models (United States)

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


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

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

    Energy Technology Data Exchange (ETDEWEB)

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


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

  5. Spatial Variability in Basal Mass Balance of the Roi Baudouin Ice Shelf, East Antarctica (United States)

    Berger, Sophie; Drews, Reinhard; Helm, Veit; Sun, Sainan; Pattyn, Frank


    Ice-shelf buttressing is an important component controlling the dynamic mass loss of ice sheets. The basal mass balance (BMB, i.e. the sum of melting/refreezing beneath ice shelves), and spatio-temporal variations thereof, critically impact the ice-shelf buttressing strength. Therefore, it is important to pinpoint BMB area-wide from space which is challenging because many input parameters are typically not well resolved. Here, we present the BMB field of the Roi Baudouin Ice Shelf, Dronning Maud Land, East Antarctica at 10 m gridding, based on mass conservation in a Lagrangian framework using interferometric elevations and surface velocities along with atmospheric modelling. We apply the total variation differentiation to account for noisy input data, which circumnavigates spatial averaging with corresponding loss of spatial resolution. At the core of our analysis is a high-resolution surface elevation model from the TandDEM-X satellites (consisting out of 43 scenes), from which we derive the hydrostatic ice thickness in 2013 and 2014. This dataset clearly resolves small-scale features such as ice-shelf channels, resulting in a yearly-averaged BMB field revealing much detail. Our satellite-based BMB field shows good agreement with on-site measurements from phase-sensitive radar over a two-week time period, and we compare the hydrostatic thickness with measurements from ground-penetrating radar highlighting unresolved spatial variations of firn density. Our BMB field ranges from -14.8 to 8.6 m/yr, with an average of -0.8 m/yr. Highest melting is found close to the grounding line, where ice thickness changes are most prominent. As an example for the small-scale variability in the BMB field, we investigate a previously identified englacial lake at 30 m depth extending over an area of 0.7 by 1.3 km. Using the TanDEM-X DEMs and kinematic GNSS we find localized surface lowering of 5 to 10 m/yr which we tentatively attribute to a transient adaptation to hydrostatic

  6. Integrating models that depend on variable data (United States)

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


    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

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

    Directory of Open Access Journals (Sweden)

    Ali Akbar Moosavi


    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

  8. Hierarchical spatial capture-recapture models: Modeling population density from stratified populations (United States)

    Royle, J. Andrew; Converse, Sarah J.


    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.

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

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


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

  10. modelling relationship between rainfall variability and yields

    African Journals Online (AJOL)

    yield models should be used for planning and forecasting the yield of millet and sorghum in the study area. Key words: modelling, rainfall, yields, millet, sorghum. INTRODUCTION. Meteorological variables, such as rainfall parameters, temperature, sunshine hours, relative humidity, and wind velocity and soil moisture are.

  11. Variability in shell models of GRBs (United States)

    Sumner, M. C.; Fenimore, E. E.


    Many cosmological models of gamma-ray bursts (GRBs) assume that a single relativistic shell carries kinetic energy away from the source and later converts it into gamma rays, perhaps by interactions with the interstellar medium or by internal shocks within the shell. Although such models are able to reproduce general trends in GRB time histories, it is difficult to reproduce the high degree of variability often seen in GRBs. The authors investigate methods of achieving this variability using a simplified external shock model. Since the model emphasizes geometric and statistical considerations, rather than the detailed physics of the shell, it is applicable to any theory that relies on relativistic shells. They find that the variability in GRBs gives strong clues to the efficiency with which the shell converts its kinetic energy into gamma rays.

  12. Spatial Stochastic Point Models for Reservoir Characterization

    Energy Technology Data Exchange (ETDEWEB)

    Syversveen, Anne Randi


    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.

  13. Theoretical aspects of spatial-temporal modeling

    CERN Document Server

    Matsui, Tomoko


    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...

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

    International Nuclear Information System (INIS)

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


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

  15. Spatial Variability and Uncertainty of Water Use Impacts from U.S. Feed and Milk Production. (United States)

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


    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.

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


    Li, B; Rodell, M; Famiglietti, JS


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

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

    Czech Academy of Sciences Publication Activity Database

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


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

  18. Spatial variability in grape yield and quality influenced by soil and crop nutrition characteristics


    Arnó Satorra, Jaume; Rosell Polo, Joan Ramon; Blanco Ortiz, Ricardo; Ramos Martín, Ma. C. (Ma. Concepción); Martínez Casasnovas, José Antonio


    Knowledge of spatial variability of soil fertility and plant nutrition is critical for planning and implementing site-specific vineyard management. To better understand the key drivers behind vineyard variability, yield mapping from 2002 to 2005 and 2007 (the monitor broke down in 2006) was used to identify zones of different productive potential in a Pinot Noir field located in Raimat (Lleida, Spain). Simultaneously, the vineyard field was sampled in 2002, 2003 and 2007, applying three diffe...

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

    Czech Academy of Sciences Publication Activity Database

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


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

  20. Bed Surface Responses to Spatially Variable Flow in Low Relative Submergence Conditions (United States)

    Monsalve Sepulveda, A.; Yager, E.


    Flow hydraulics and sediment fluxes in mountainous rivers are partly controlled by large relatively immobile grains and sediment patches. Generally, in these rivers the flow depth is similar to the size of these large grains (low relative submergence), and is characterized by 3D heterogeneity and plunging flow that can cause spatial distributions of bed surface elevations, textures, and sedimentation rates. Sediment patches, on the other hand, consist of distinct areas of the bed with relatively narrow grain size distributions (GSD) and greater sorting compared to that of the reach, can cause spatial distributions of flow properties, and therefore, a continuous feedback between them and flow hydraulics exists and partially controls the evolution of a river. Although sediment-water interactions are affected by sediment patches, they are rarely explicitly included in bedload transport calculations, in part because their formation and evolution are controlled by highly temporal and spatially variable mechanisms, such as shear stress fields, flow discharges, turbulence, and local GSD. To explore how the bed surface evolves and sediment patches are formed, we conducted a set of experiments in which we varied the relative submergence (RS) of staggered simulated boulders between runs. All experiments had the same average sediment transport capacity, upstream sediment supply, and initial gravel bed thickness and GSD. Different RS between experiments were achieved by simultaneously adjusting flow discharge and bed slope (2.15 - 3.7 %). To obtain a detailed flow field we combined our laboratory measurements with a 3D flow model. Around the boulders, the shear stress field was highly variable and controlled the sediment flux rates and its direction. The divergence in shear stress caused by the boulders promoted size-selective bedload deposition, which in some cases resulted in the formation of a coarse sediment patch upstream of the boulders but, for the higher slopes, a bar

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

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


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

  2. Climatic trends in hail precipitation in France: spatial, altitudinal, and temporal variability. (United States)

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


    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.

  3. Spatially explicit modelling of cholera epidemics (United States)

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


    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.

  4. Modeling strategic investment decisions in spatial markets

    International Nuclear Information System (INIS)

    Lorenczik, Stefan; Malischek, Raimund


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

  5. The Role of Visuo-Spatial Abilities in Recall of Spatial Descriptions: A Mediation Model (United States)

    Meneghetti, Chiara; De Beni, Rossana; Pazzaglia, Francesca; Gyselinck, Valerie


    This research investigates how visuo-spatial abilities (such as mental rotation--MR--and visuo-spatial working memory--VSWM--) work together to influence the recall of environmental descriptions. We tested a mediation model in which VSWM was assumed to mediate the relationship between MR and spatial text recall. First, 120 participants were…

  6. Characterization factors for terrestrial acidification at the global scale: a systematic analysis of spatial variability and uncertainty. (United States)

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


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

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

    Directory of Open Access Journals (Sweden)

    Indah Resti Ayuni Suri


    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.

  8. Gait variability: methods, modeling and meaning

    Directory of Open Access Journals (Sweden)

    Hausdorff Jeffrey M


    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.

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

    DEFF Research Database (Denmark)

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


    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...

  10. 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.


    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

  11. 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.


    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

  12. Spatial variability of wildland fuel characteristics in northern Rocky Mountain ecosystems (United States)

    Robert E. Keane; Kathy Gray; Valentina Bacciu


    We investigated the spatial variability of a number of wildland fuel characteristics for the major fuel components found in six common northern Rocky Mountain ecosystems. Surface fuel characteristics of loading, particle density, bulk density, and mineral content were measured for eight fuel components - four downed dead woody fuel size classes (1, 10, 100, 1000 hr),...

  13. Cost-effective design of long spatially variable soil slopes using conditional simulation

    NARCIS (Netherlands)

    Li, Y.; Hicks, M.A.; Vardon, P.J.


    The three dimensional nature of soil spatial variability implies the need for 3D analysis of geotechnical structures. This paper presents the probabilistic analysis of long slopes such as levees and highway embankments, which are usually analysed unrealistically in plane strain, thereby ignoring the

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

    NARCIS (Netherlands)

    Li, Y.


    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

  15. A descriptive analysis of temporal and spatial patterns of variability in Puget Sound oceanographic properties (United States)

    Stephanie Moore; Nathan J. Mantua; Jan A. Newton; Mitsuhiro Kawase; Mark J. Warner; Jonathan P. Kellogg


    Temporal and spatial patterns of variability in Puget Sound's oceanographic properties are determined using continuous vertical profile data from two long-term monitoring programs; monthly observations at 16 stations from 1993 to 2002, and biannual observations at 40 stations from 1998 to 2003. Climatological monthly means of temperature, salinity, and density...

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

    NARCIS (Netherlands)

    Kanning, W.


    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

  17. 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 Springer International Publishing Switzerland. Sorption is commonly agreed to be the major process underlying the transport and fate of polycyclic aromatic hydrocarbons (PAHs) in soils. However, there is still a scarcity of studies focusing on spatial variability at the field scale in part...

  18. Spatial and temporal variability in the larval fish assemblage of a ...

    African Journals Online (AJOL)

    Spatial and temporal variability in the larval fish assemblage of a warm temperate South African estuary, with notes on the effects of artificial channelling. ... Larval fishes were sampled within the estuary and marina for a period of two years. Samples were collected seasonally at 14 different sampling stations along the main ...

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

    International Nuclear Information System (INIS)

    Sahraoui, Yacine; Chateauneuf, Alaa


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

  20. Spatial pattern evaluation of a calibrated national hydrological model - a remote-sensing-based diagnostic approach (United States)

    Mendiguren, Gorka; Koch, Julian; Stisen, Simon


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

  1. Searching for the right scale in catchment hydrology: the effect of soil spatial variability in simulated states and fluxes (United States)

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


    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

  2. Monitoring meteorological spatial variability in viticulture using a low-cost Wireless Sensor Network (United States)

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


    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

  3. 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.

  4. Spatial Situation Models and Text Comprehension. (United States)

    Haenggi, Dieter; And Others


    Reports findings from three experiments designed to show how readers inferred spatial information relevant to a story character's movements through a previously memorized layout of a fictional building. Examines how inference measures are related to spatial imagery. (HB)

  5. A physically based analytical spatial air temperature and humidity model (United States)

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


    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...

  6. Quantifying watershed sensitivity to spatially variable N loading and the relative importance of watershed N retention mechanisms (United States)

    Gardner, Kristin K.; McGlynn, Brian L.; Marshall, Lucy A.


    The link between watershed nitrogen (N) loading and watershed nitrate (NO3-) export is poorly understood yet critical to addressing the growing global problem of watershed N enrichment. We introduce the Big Sky nutrient export model (BiSN) which incorporates spatial stream water chemistry, data from instream tracer additions and geologic weathering experiments, and terrain and land use analysis to quantify the spatial variability of watershed sensitivity to N loading and the relative importance of upland, riparian, and instream N retention (storage, removal, or transformation) across land use/land cover (LULC) and landscape positions. Bayesian Markov chain Monte Carlo (MCMC) methods were used for model specification and were helpful in assessing model and parameter uncertainty and advancing understanding of the primary processes governing watershed NO3- export. Modeling results revealed that small amounts of wastewater loading occurring in watershed areas with short travel times to the stream had disproportionately large impacts on watershed nitrate (NO3-) export compared to spatially distributed N loading or localized N loading in watershed areas with longer travel times. In contrast, spatially distributed N inputs of greater magnitude (terrestrial storage release and septic systems) had little influence on NO3- export. During summer base flow conditions, 98%-99% of watershed N retention occurred in the uplands, most likely from biological assimilation or lack of hydrologic transport. The relative role of instream N retention increased with N loading downstream through the stream network. This work demonstrates the importance of characterizing the spatial variability of watershed N loading, export and retention mechanisms, and considering landscape position of N sources to effectively manage watershed N.

  7. Spatial Variability of Grapevine Bud Burst Percentage and Its Association with Soil Properties at Field Scale. (United States)

    Li, Tao; Hao, Xinmei; Kang, Shaozhong


    There is a growing interest in precision viticulture with the development of global positioning system and geographical information system technologies. Limited information is available on spatial variation of bud behavior and its possible association with soil properties. The objective of this study was to investigate spatial variability of bud burst percentage and its association with soil properties based on 2-year experiments at a vineyard of arid northwest China. Geostatistical approach was used to describe the spatial variation in bud burst percentage within the vineyard. Partial least square regressions (PLSRs) of bud burst percentage with soil properties were used to evaluate the contribution of soil properties to overall spatial variability in bud burst percentage for the high, medium and low bud burst percentage groups. Within the vineyard, the coefficient of variation (CV) of bud burst percentage was 20% and 15% for 2012 and 2013 respectively. Bud burst percentage within the vineyard showed moderate spatial variability, and the overall spatial pattern of bud burst percentage was similar between the two years. Soil properties alone explained 31% and 37% of the total spatial variation respectively for the low group of 2012 and 2013, and 16% and 24% for the high group of 2012 and 2013 respectively. For the low group, the fraction of variations explained by soil properties was found similar between the two years, while there was substantial difference for the high group. The findings are expected to lay a good foundation for developing remedy measures in the areas with low bud burst percentage, thus in turn improving the overall grape yield and quality.

  8. Spectral Modelling for Spatial Network Analysis

    NARCIS (Netherlands)

    Nourian, P.; Rezvani, S.; Sariyildiz, I.S.; van der Hoeven, F.D.; Attar, Ramtin; Chronis, Angelos; Hanna, Sean; Turrin, Michela


    Spatial Networks represent the connectivity structure between units of space as a weighted graph whose links are weighted as to the strength of connections. In case of urban spatial networks, the units of space correspond closely to streets and in architectural spatial networks the units correspond

  9. Spatial and temporal variability of atmospheric sulfur-containing gases and particles during the Albatross campaign (United States)

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


    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.

  10. Spatial and temporal variability of Eh and pH over a rice field as related to lime addition

    Directory of Open Access Journals (Sweden)

    Luis Alberto Morales


    Full Text Available The aim of this study was to describe the effect of lime additions on the spatial variability of pH and Eh in a typic Plintacualf cultivated with rice, in Corrientes, Argentina. The 5.1 ha field was divided in three sub plots at which dolomitic lime additions were made at the rates zero, 625 kg ha-1 and 1250 kg ha-1. The soil was sampled at three stages: before sowing thus in aerobic conditions, and then two more times in anaerobiosis. Ninety-six samples per sub plot were taken on each of the three sampling stages on a grid of 11.9 x 20 m. Soil pH and Eh were measured by routine methods. The pH values increased, whereas Eh values decreased, following flooding. The coefficients of variation for pH was rather low during all the three studied periods. Conversely, the CV values for Eh were initially low but with a sharp increased in the second sampling date. The spatial variability of the studied soil properties was assessed using semivariogram analysis and examination of the maps constructed with values interpolated with kriging. Soil pH exhibited a rather strong spatial dependence, whereas soil Eh had a strong to moderate spatial dependence all over the three studied periods and for the three lime rates. Spherical models reaching a stable sill with low to moderate nugget effect were fitted to the experimental semivariograms for the 18 data sets (3 subplots, 3 liming rates and 2 properties studied. Spatial variability of pH and Eh on rice fields was far from negligible both on aerobic and on anaerobic conditions. In general pH exhibited a stronger spatial dependence than Eh and also showed a tendency to present smaller ranges of spatial dependence. Contour maps clearly showed the presence of small scale variability for pH and Eh within each liming treatment and during each of the three sampling dates. Neither pH or Eh had temporal stability of the pattern of spatial distribution on field studied.

  11. Modelling of the education quality of a high schools in Sumenep Regency using spatial structural equation modelling (United States)

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


    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.

  12. Estimation of the Scale of Fluctuation for Spatial Variables of RC Structures

    Directory of Open Access Journals (Sweden)

    Hilyati S.


    Full Text Available Dimensional and structural properties of RC structures are nonhomogenous due to the quality of workmanship, environmental and material variability. One of the required statistical information for spatial variability analysis of RC structures includes the scale of fluctuation, θ. This paper discusses the estimation of θ for two spatial variables; concrete compressive strength and concrete cover. Methods used to estimate the θ are the Curve fitting method and the Kriging Method. Kriging is an optimal interpolation method which uses the concept of randomness that allows the uncertainty of the predicted values to be calculated. Data measurements for concrete compressive strength and concrete cover were obtained from Peterson (1964 and Public Work Department of Malaysia respectively. The most reliable value for θ of fcu was determined and the value obtained for θ of c was found unreliable due to the insufficient of data points from the available data.

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

    DEFF Research Database (Denmark)

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


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

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

    Directory of Open Access Journals (Sweden)

    Hongqiang Liu


    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.

  15. Spatial Data Web Services Pricing Model Infrastructure (United States)

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


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

    Directory of Open Access Journals (Sweden)

    Kostas Alexandridis


    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.

  17. Reducing Spatial Data Complexity for Classification Models

    International Nuclear Information System (INIS)

    Ruta, Dymitr; Gabrys, Bogdan


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

  18. Spatial Statistical and Modeling Strategy for Inventorying and Monitoring Ecosystem Resources at Multiple Scales and Resolution Levels (United States)

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


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

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

    DEFF Research Database (Denmark)

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


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

  20. Spatial prediction of N2O emissions in pasture: a Bayesian model averaging analysis.

    Directory of Open Access Journals (Sweden)

    Xiaodong Huang

    Full Text Available Nitrous oxide (N2O is one of the greenhouse gases that can contribute to global warming. Spatial variability of N2O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N2O - environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, distance-based and neighbourhood based on spatial prediction of N2O emissions. This study aimed to assess the impact of three spatial correlation structures on spatial predictions and calibrate the spatial prediction using Bayesian model averaging (BMA based on replicated, irregular point-referenced data. The data were measured in 17 chambers randomly placed across a 271 m(2 field between October 2007 and September 2008 in the southeast of Australia. We used a Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR model to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N2O emissions across the study site. We compared these with a Bayesian regression model with independent errors. The three approaches resulted in different derived maps of spatial prediction of N2O emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of N2O emission from soil, but also better quantified uncertainties of soil parameters in the study. The hybrid model structure obtained by BMA improved the accuracy of spatial prediction of N2O emissions across this study region.

  1. Variable impact of chronic stress on spatial learning and memory in BXD mice. (United States)

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


    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. Published by Elsevier Inc.

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

    Directory of Open Access Journals (Sweden)

    Caique C. Medauar

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

  3. Spatial regression-based model specifications for exogenous and endogenous spatial interaction


    Manfred M Fischer; James P. LeSage


    Spatial interaction models represent a class of models that are used for modelling origin-destination flow data. The focus of this paper is on the log-normal version of the model. In this context, we consider spatial econometric specifications that can be used to accommodate two types of dependence scenarios, one involving endogenous interaction and the other exogenous interaction. These model specifications replace the conventional assumption of independence between origin-destination flows ...

  4. Confounding of three binary-variables counterfactual model


    Liu, Jingwei; Hu, Shuang


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


    Energy Technology Data Exchange (ETDEWEB)

    Huppenkothen, Daniela; Elenbaas, Chris; Watts, Anna L.; Horst, Alexander J. van der [Anton Pannekoek Institute for Astronomy, University of Amsterdam, Postbus 94249, 1090 GE Amsterdam (Netherlands); Brewer, Brendon J. [Department of Statistics, The University of Auckland, Private Bag 92019, Auckland 1142 (New Zealand); Hogg, David W. [Center for Data Science, New York University, 726 Broadway, 7th Floor, New York, NY 10003 (United States); Murray, Iain [School of Informatics, University of Edinburgh, Edinburgh EH8 9AB (United Kingdom); Frean, Marcus [School of Engineering and Computer Science, Victoria University of Wellington (New Zealand); Levin, Yuri [Monash Center for Astrophysics and School of Physics, Monash University, Clayton, Victoria 3800 (Australia); Kouveliotou, Chryssa, E-mail: [Astrophysics Office, ZP 12, NASA/Marshall Space Flight Center, Huntsville, AL 35812 (United States)


    Neutron stars are a prime laboratory for testing physical processes under conditions of strong gravity, high density, and extreme magnetic fields. Among the zoo of neutron star phenomena, magnetars stand out for their bursting behavior, ranging from extremely bright, rare giant flares to numerous, less energetic recurrent bursts. The exact trigger and emission mechanisms for these bursts are not known; favored models involve either a crust fracture and subsequent energy release into the magnetosphere, or explosive reconnection of magnetic field lines. In the absence of a predictive model, understanding the physical processes responsible for magnetar burst variability is difficult. Here, we develop an empirical model that decomposes magnetar bursts into a superposition of small spike-like features with a simple functional form, where the number of model components is itself part of the inference problem. The cascades of spikes that we model might be formed by avalanches of reconnection, or crust rupture aftershocks. Using Markov Chain Monte Carlo sampling augmented with reversible jumps between models with different numbers of parameters, we characterize the posterior distributions of the model parameters and the number of components per burst. We relate these model parameters to physical quantities in the system, and show for the first time that the variability within a burst does not conform to predictions from ideas of self-organized criticality. We also examine how well the properties of the spikes fit the predictions of simplified cascade models for the different trigger mechanisms.

  6. Spatial and temporal variability in nutrients and carbon uptake during 2004 and 2005 in the eastern equatorial Pacific Ocean

    DEFF Research Database (Denmark)

    Palacz, A. P.; Chai, F.


    to the Tropical Instability Waves. The aim of this study is to examine patterns of spatial and temporal variability in the biological uptake of NO3, Si(OH)4 and carbon in this region, and to evaluate the role of biological and physical interactions controlling these processes over seasonal...... and intra-seasonal time scales. Here, high resolution Pacific ROMS-CoSiNE model results are combined with in situ and remote sensing data. The results of model-data comparison reveal an excellent agreement in domain-average hydrographic and biological rate estimates, and patterns of spatio...

  7. Panchromatic SED modelling of spatially resolved galaxies (United States)

    Smith, Daniel J. B.; Hayward, Christopher C.


    We test the efficacy of the energy-balance spectral energy distribution (SED) fitting code MAGPHYS for recovering the spatially resolved properties of a simulated isolated disc galaxy, for which it was not designed. We perform 226 950 MAGPHYS SED fits to regions between 0.2 and 25 kpc in size across the galaxy's disc, viewed from three different sight-lines, to probe how well MAGPHYS can recover key galaxy properties based on 21 bands of UV-far-infrared model photometry. MAGPHYS yields statistically acceptable fits to >99 per cent of the pixels within the r-band effective radius and between 59 and 77 percent of pixels within 20 kpc of the nucleus. MAGPHYS is able to recover the distribution of stellar mass, star formation rate (SFR), specific SFR, dust luminosity, dust mass, and V-band attenuation reasonably well, especially when the pixel size is ≳ 1 kpc, whereas non-standard outputs (stellar metallicity and mass-weighted age) are recovered less well. Accurate recovery is more challenging in the smallest sub-regions of the disc (pixel scale ≲ 1 kpc), where the energy balance criterion becomes increasingly incorrect. Estimating integrated galaxy properties by summing the recovered pixel values, the true integrated values of all parameters considered except metallicity and age are well recovered at all spatial resolutions, ranging from 0.2 kpc to integrating across the disc, albeit with some evidence for resolution-dependent biases. These results must be considered when attempting to analyse the structure of real galaxies with actual observational data, for which the `ground truth' is unknown.

  8. Spatial scale, means and gradients of hydrographic variables define pelagic seascapes of bluefin and bullet tuna spawning distribution.

    Directory of Open Access Journals (Sweden)

    Diego Alvarez-Berastegui

    Full Text Available Seascape ecology is an emerging discipline focused on understanding how features of the marine habitat influence the spatial distribution of marine species. However, there is still a gap in the development of concepts and techniques for its application in the marine pelagic realm, where there are no clear boundaries delimitating habitats. Here we demonstrate that pelagic seascape metrics defined as a combination of hydrographic variables and their spatial gradients calculated at an appropriate spatial scale, improve our ability to model pelagic fish distribution. We apply the analysis to study the spawning locations of two tuna species: Atlantic bluefin and bullet tuna. These two species represent a gradient in life history strategies. Bluefin tuna has a large body size and is a long-distant migrant, while bullet tuna has a small body size and lives year-round in coastal waters within the Mediterranean Sea. The results show that the models performance incorporating the proposed seascape metrics increases significantly when compared with models that do not consider these metrics. This improvement is more important for Atlantic bluefin, whose spawning ecology is dependent on the local oceanographic scenario, than it is for bullet tuna, which is less influenced by the hydrographic conditions. Our study advances our understanding of how species perceive their habitat and confirms that the spatial scale at which the seascape metrics provide information is related to the spawning ecology and life history strategy of each species.

  9. [Spatial pattern of soil fertility in Bashan tea garden: a prediction based on environmental auxiliary variables]. (United States)

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


    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.

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

    Directory of Open Access Journals (Sweden)



    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.

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

    Directory of Open Access Journals (Sweden)

    Jeffrey L. Smith


    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.

  12. Spatial variability of detrended soil plow layer penetrometer resistance transect in a sugarcane field (United States)

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


    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

  13. Characterizing spatial variability of air pollution from vehicle traffic around the Houston Ship Channel area (United States)

    Zhang, Xueying; Craft, Elena; Zhang, Kai


    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.

  14. Natural climate variability in a coupled model

    International Nuclear Information System (INIS)

    Zebiak, S.E.; Cane, M.A.


    Multi-century simulations with a simplified coupled ocean-atmosphere model are described. These simulations reveal an impressive range of variability on decadal and longer time scales, in addition to the dominant interannual el Nino/Southern Oscillation signal that the model originally was designed to simulate. Based on a very large sample of century-long simulations, it is nonetheless possible to identify distinct model parameter sensitivities that are described here in terms of selected indices. Preliminary experiments motivated by general circulation model results for increasing greenhouse gases suggest a definite sensitivity to model global warming. While these results are not definitive, they strongly suggest that coupled air-sea dynamics figure prominently in global change and must be included in models for reliable predictions

  15. Spatial Econometric data analysis: moving beyond traditional models

    NARCIS (Netherlands)

    Florax, R.J.G.M.; Vlist, van der A.J.


    This article appraises recent advances in the spatial econometric literature. It serves as the introduction too collection of new papers on spatial econometric data analysis brought together in this special issue, dealing specifically with new extensions to the spatial econometric modeling

  16. Spatial variability of soil pH based on GIS combined with geostatistics in Panzhihua tobacco area

    International Nuclear Information System (INIS)

    Du Wei; Wang Changquan; Li Bing; Li Qiquan; Du Qian; Hu Jianxin; Liu Chaoke


    GIS and geostatistics were utilized to study the spatial variability of soil pH in Panzhihua tobacco area. Results showed that pH values in this area ranged from 4.5 to 8.3, especially 5.5 to 6.5, and in few areas were lower than 5.0 or higher than 7.0 which can meet the need of high-quality tobacco production. The best fitting model of variogram was exponential model with the nugget/sill of soil pH in 13.61% indicating strong spatial correlation. The change process was 5.40 km and the coefficient of determination was 0.491. The spatial variability of soil pH was mainly caused by structural factors such as cane, topography and soil type. The soil pH in Panzhihua tobacco area also showed a increasing trend of northwest to southeast trend. The pH of some areas in Caochang, Gonghe and Yumen were lower, and in Dalongtan were slightly higher. (authors)

  17. Mapping Spatial Variability of Soil Salinity in a Coastal Paddy Field Based on Electromagnetic Sensors (United States)

    Guo, Yan; Huang, Jingyi; Shi, Zhou; Li, Hongyi


    In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9) allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v) as well as other EMI instruments (e.g. DUALEM-421) can be incorporated to conduct Quasi-3D inversions for deeper soil profiles. PMID:26020969

  18. Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors. (United States)

    Guo, Yan; Huang, Jingyi; Shi, Zhou; Li, Hongyi


    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.

  19. Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors.

    Directory of Open Access Journals (Sweden)

    Yan Guo

    Full Text Available In coastal China, there is an urgent need to increase land area for agricultural production and urban development, where there is a rapid growing population. One solution is land reclamation from coastal tidelands, but soil salinization is problematic. As such, it is very important to characterize and map the within-field variability of soil salinity in space and time. Conventional methods are often time-consuming, expensive, labor-intensive, and unpractical. Fortunately, proximal sensing has become an important technology in characterizing within-field spatial variability. In this study, we employed the EM38 to study spatial variability of soil salinity in a coastal paddy field. Significant correlation relationship between ECa and EC1:5 (i.e. r >0.9 allowed us to use EM38 data to characterize the spatial variability of soil salinity. Geostatistical methods were used to determine the horizontal spatio-temporal variability of soil salinity over three consecutive years. The study found that the distribution of salinity was heterogeneous and the leaching of salts was more significant in the edges of the study field. By inverting the EM38 data using a Quasi-3D inversion algorithm, the vertical spatio-temporal variability of soil salinity was determined and the leaching of salts over time was easily identified. The methodology of this study can be used as guidance for researchers interested in understanding soil salinity development as well as land managers aiming for effective soil salinity monitoring and management practices. In order to better characterize the variations in soil salinity to a deeper soil profile, the deeper mode of EM38 (i.e., EM38v as well as other EMI instruments (e.g. DUALEM-421 can be incorporated to conduct Quasi-3D inversions for deeper soil profiles.

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

    Directory of Open Access Journals (Sweden)

    Sidney Rosa Vieira


    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

  1. Temporal and spatial variability of wind resources in the United States as derived from the Climate Forecast System Reanalysis (United States)

    Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman


    This study examines the spatial and temporal variability of wind speed at 80m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the...

  2. Spatial variability of surface fuels in treated and untreated ponderosa pine forests of the southern Rocky Mountains (United States)

    Emma Vakili; Chad M. Hoffman; Robert E. Keane; Wade T. Tinkham; Yvette Dickinson


    There is growing consensus that spatial variability in fuel loading at scales down to 0.5 m may govern fire behaviour and effects. However, there remains a lack of understanding of how fuels vary through space in wildland settings. This study quantifies surface fuel loading and its spatial variability in ponderosa pine sites before and after fuels treatment in the...

  3. Assessing effects of variation in global climate data sets on spatial predictions from climate envelope models (United States)

    Romañach, Stephanie; Watling, James I.; Fletcher, Robert J.; Speroterra, Carolina; Bucklin, David N.; Brandt, Laura A.; Pearlstine, Leonard G.; Escribano, Yesenia; Mazzotti, Frank J.


    Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

  4. Spatially explicit fate modelling of nanomaterials in natural waters

    NARCIS (Netherlands)

    Quik, J.T.K.; Klein, de J.J.M.; Koelmans, A.A.


    Site specific exposure assessments for engineered nanoparticles (ENPs) require spatially explicit fate models, which however are not yet available. Here we present an ENP fate model (NanoDUFLOW) that links ENP specific process descriptions to a spatially explicit hydrological model. The link enables

  5. Distribution patterns of epilithic diatoms along climatic, spatial and physicochemical variables in the Baltic Sea


    Virta, Leena; Soininen, Janne


    Abstract The species richness and community composition of the diatom communities were studied in the Baltic Sea, Northern Europe, to enhance knowledge about the diversity of these organisms in a brackish water ecosystem. Many organisms in the Baltic Sea have been studied extensively, but studies investigating littoral diatoms are scarce. The goal of this study was to examine the importance of climatic, spatial and water physicochemical variables as drivers of epilithic diato...

  6. Spatial and temporal CH4 flux variability in a shallow tropical floodplain lake, Pantanal, South America (United States)

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


    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.

  7. Spatial-temporal modelling of fMRI data through spatially regularized mixture of hidden process models. (United States)

    Shen, Yuan; Mayhew, Stephen D; Kourtzi, Zoe; Tiňo, Peter


    Previous work investigated a range of spatio-temporal constraints for fMRI data analysis to provide robust detection of neural activation. We present a mixture-based method for the spatio-temporal modelling of fMRI data. This approach assumes that fMRI time series are generated by a probabilistic superposition of a small set of spatio-temporal prototypes (mixture components). Each prototype comprises a temporal model that explains fMRI signals on a single voxel and the model's "region of influence" through a spatial prior over the voxel space. As the key ingredient of our temporal model, the Hidden Process Model (HPM) framework proposed in Hutchinson et al. (2009) is adopted to infer the overlapping cognitive processes triggered by stimuli. Unlike the original HPM framework, we use a parametric model of Haemodynamic Response Function (HRF) so that biological constraints are naturally incorporated in the HRF estimation. The spatial priors are defined in terms of a parameterised distribution. Thus, the total number of parameters in the model does not depend on the number of voxels. The resulting model provides a conceptually principled and computationally efficient approach to identify spatio-temporal patterns of neural activation from fMRI data, in contrast to most conventional approaches in the literature focusing on the detection of spatial patterns. We first verify the proposed model in a controlled experimental setting using synthetic data. The model is further validated on real fMRI data obtained from a rapid event-related visual recognition experiment (Mayhew et al., 2012). Our model enables us to evaluate in a principled manner the variability of neural activations within individual regions of interest (ROIs). The results strongly suggest that, compared with occipitotemporal regions, the frontal ones are less homogeneous, requiring two HPM prototypes per region. Despite the rapid event-related experimental design, the model is capable of disentangling the

  8. Spatial variability of the properties of marsh soils and their impact on vegetation (United States)

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


    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. Spatial variability of soil magnetic susceptibility in an agricultural field located in Eastern Ukraine (United States)

    Menshov, Oleksandr; Pereira, Paulo; Kruglov, Oleksandr


    .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 pJournal 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, 2285-2293. Jeleńska, M., Hasso-Agopsowicz, A., Kopcewicz, B., Sukhorada, A., Tyamina, K., Kądziałko-Hofmokl, M., Matviishina, Z. (2004) Magnetic properties of the profiles of polluted and non-polluted soils. A case study from Ukraine. Geophys. J. Int., 159, 104-116. Morton-Bernea, O., Hernandez, E., Martinez-Pichardo, E., Soler-Arechalde, A.M., Santa Cruz, R.L., Gonzalez-Hernandez, G., Beramendi-Orosco, L., Urritia-Fucugaushi, J. (2009) Mexico city topsoils: Heavy metals vs. magnetic susceptibility. Geoderma, 151, 121-125. Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J. Arcenegui, V., Zavala, L. Modelling the impacts of wildfire on ash thickness in a short-term period, Land Degradation and Development, (In Press), DOI: 10.1002/ldr.2195 Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J., Jordan, A. Burguet, M. (2013) Spatial models for monitoring the spatio-temporal evolution of ashes after fire - a case study of a burnt grassland in Lithuania, Solid Earth, 4, 153-165.

  10. Accounting for and predicting the influence of spatial autocorrelation in water quality modeling (United States)

    Miralha, L.; Kim, D.


    Although many studies have attempted to investigate the spatial trends of water quality, more attention is yet to be paid to the consequences of considering and ignoring the spatial autocorrelation (SAC) that exists in water quality parameters. Several studies have mentioned the importance of accounting for SAC in water quality modeling, as well as the differences in outcomes between models that account for and ignore SAC. However, the capacity to predict the magnitude of such differences is still ambiguous. In this study, we hypothesized that SAC inherently possessed by a response variable (i.e., water quality parameter) influences the outcomes of spatial modeling. We evaluated whether the level of inherent SAC is associated with changes in R-Squared, Akaike Information Criterion (AIC), and residual SAC (rSAC), after accounting for SAC during modeling procedure. The main objective was to analyze if water quality parameters with higher Moran's I values (inherent SAC measure) undergo a greater increase in R² and a greater reduction in both AIC and rSAC. We compared a non-spatial model (OLS) to two spatial regression approaches (spatial lag and error models). Predictor variables were the principal components of topographic (elevation and slope), land cover, and hydrological soil group variables. We acquired these data from federal online sources (e.g. USGS). Ten watersheds were selected, each in a different state of the USA. Results revealed that water quality parameters with higher inherent SAC showed substantial increase in R² and decrease in rSAC after performing spatial regressions. However, AIC values did not show significant changes. Overall, the higher the level of inherent SAC in water quality variables, the greater improvement of model performance. This indicates a linear and direct relationship between the spatial model outcomes (R² and rSAC) and the degree of SAC in each water quality variable. Therefore, our study suggests that the inherent level of

  11. Theoretical investigations of the new Cokriging method for variable-fidelity surrogate modeling

    DEFF Research Database (Denmark)

    Zimmermann, Ralf; Bertram, Anna


    Cokriging is a variable-fidelity surrogate modeling technique which emulates a target process based on the spatial correlation of sampled data of different levels of fidelity. In this work, we address two theoretical questions associated with the so-called new Cokriging method for variable fidelity...

  12. Fine-scale spatial and interannual cadmium isotope variability in the subarctic northeast Pacific (United States)

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


    We present dissolved cadmium (Cd) concentrations, [Cd], and stable isotope compositions, ε 112 / 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 -3Cd: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 ε 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 ε 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 ε 112 / 110Cd ≥ 15. A global compilation of low [Cd] surface water shows similar differences in maximum ε 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 ε 112 / 110Cd

  13. Spatial variability of vegetation index and soil properties in an integrated crop-livestock system

    Directory of Open Access Journals (Sweden)

    Alberto C. de C. Bernardi

    Full Text Available ABSTRACT The knowledge of soil property spatial variability is useful for determining the rational use of inputs, such as the site-specific application of lime and fertilizer. The objective of this study was to evaluate the vegetation index and spatial variability of physical and chemical soil properties in an integrated crop-livestock system (ICLS. Soil samples were taken from a 6.9 ha area in a regular hexagon grid at 0-0.20 m depths. Soil P, K, Ca, Mg, and cation exchange capacity - CEC; base saturation; clay and sand were analyzed. Soil electrical conductivity (ECa was measured with a contact sensor. The site was evaluated at the end of the corn season (April and during forage production (October using Landsat 5 images, remote sensing techniques and a geographic information system (GIS. Results showed that the normalized difference vegetation index (NDVI was associated with ECa and soil parameters, indicating crop and pasture variations in the ICLS. Geostatistics and GIS were effective tools for collecting data regarding the spatial variability of soil and crop indicators, identifying variation trends in the data, and assisting data interpretation to determine adequate management strategies.

  14. Characterization of spatial variability of the relative chlorophyll index in wheat crop

    Directory of Open Access Journals (Sweden)

    Osmar Henrique de Castro Pias


    Full Text Available Site-specific nitrogen application, based on relative chlorophyll index from leaves, may provide many economic and environmental benefits, however, the knowledge on sampling methodologies is still incipient. Thus, this study aimed to evaluate the use of different sampling grids to characterize the spatial variability of relative chlorophyll index of leaves from wheat crop and elaborate thematic maps for site-specific nitrogen application. For determining the relative chlorophyll index, a CFL 1030 chlorophyll meter was used on a regular sampling grid of 10 m x 10 m with 472 sampling points. Based on the initial sampling grid, by using the point elimination method, the simulation was performed in the following sampling grids: 10 m x 20 m; 20 m x 20 m; 20 m x 30 m; 30 m x 30 m; 30 m x 40 m; and 40 m x 40 m. The increase of the sampling grid reduced the diagnostic accuracy of relative chlorophyll index in wheat leaves. As the sampling grid increased, the maps became more general and information on the spatial variability of the relative chlorophyll index were lost. Sampling grids smaller or equal to 20 m x 20 m were effective to detect the spatial variability of the relative chlorophyll index in wheat leaves and enable the elaboration of thematic maps for site-specific nitrogen application.

  15. On joint deterministic grid modeling and sub-grid variability conceptual framework for model evaluation (United States)

    Ching, Jason; Herwehe, Jerold; Swall, Jenise

    The general situation (but exemplified in urban areas), where a significant degree of sub-grid variability (SGV) exists in grid models poses problems when comparing grid-based air-quality modeling results with observations. Typically, grid models ignore or parameterize processes and features that are at their sub-grid scale. Also, observations may be obtained in an area where significant spatial variability in the concentration fields exists. Consequently, model results and observations cannot be expected to be equal. To address this issue, we suggest a framework that can provide for qualitative judgments on model performance based on comparing observations to the grid predictions and its SGV distribution. Further, we (a) explore some characteristics of SGV, (b) comment on the contributions to SGV and (c) examine the implications to the modeling results at coarse grid resolution using examples from fine scale grid modeling of the Community Multi-scale Air Quality (CMAQ) modeling system.

  16. Spatial modelling and mapping of female genital mutilation in Kenya (United States)


    Background Female genital mutilation/cutting (FGM/C) is still prevalent in several communities in Kenya and other areas in Africa, as well as being practiced by some migrants from African countries living in other parts of the world. This study aimed at detecting clustering of FGM/C in Kenya, and identifying those areas within the country where women still intend to continue the practice. A broader goal of the study was to identify geographical areas where the practice continues unabated and where broad intervention strategies need to be introduced. Methods The prevalence of FGM/C was investigated using the 2008 Kenya Demographic and Health Survey (KDHS) data. The 2008 KDHS used a multistage stratified random sampling plan to select women of reproductive age (15–49 years) and asked questions concerning their FGM/C status and their support for the continuation of FGM/C. A spatial scan statistical analysis was carried out using SaTScan™ to test for statistically significant clustering of the practice of FGM/C in the country. The risk of FGM/C was also modelled and mapped using a hierarchical spatial model under the Integrated Nested Laplace approximation approach using the INLA library in R. Results The prevalence of FGM/C stood at 28.2% and an estimated 10.3% of the women interviewed indicated that they supported the continuation of FGM. On the basis of the Deviance Information Criterion (DIC), hierarchical spatial models with spatially structured random effects were found to best fit the data for both response variables considered. Age, region, rural–urban classification, education, marital status, religion, socioeconomic status and media exposure were found to be significantly associated with FGM/C. The current FGM/C status of a woman was also a significant predictor of support for the continuation of FGM/C. Spatial scan statistics confirm FGM clusters in the North-Eastern and South-Western regions of Kenya (p < 0.001). Conclusion This suggests that the

  17. Detecting the spatial and temporal variability of chlorophylla concentration and total suspended solids in Apalachicola Bay, Florida using MODIS imagery (United States)

    Wang, Hongfang; Hladik, C.M.; Huang, W.; Milla, K.; Edmiston, L.; Harwell, M.A.; Schalles, J.F.


    Apalachicola Bay, Florida, accounts for 90% of Florida's and 10% of the nation's eastern oyster (Crassostrea virginica) harvesting. Chlorophyll-a concentration and total suspended solids (TSS) are two important water quality variables, among other environmental factors such as salinity, for eastern oyster production in Apalachicola Bay. In this research, we developed regression models of the relationships between the reflectance of the Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra 250 m data and the two water quality variables based on the Bay-wide field data collected during 14-17 October 2002, a relatively dry period, and 3-5 April 2006, a relatively wet period, respectively. Then we selected the best regression models (highest coefficient of determination, R2) to derive Bay-wide maps of chlorophylla concentration and TSS for the two periods. The MODIS-derived maps revealed large spatial and temporal variations in chlorophylla concentration and TSS across the entire Apalachicola Bay. ?? 2010 Taylor & Francis.


    Directory of Open Access Journals (Sweden)

    H.-C. Chen


    Full Text Available How to effectively describe ecological patterns in nature over broader spatial scales and build a modeling ecological framework has become an important issue in ecological research. We test four modeling methods (MAXENT, DOMAIN, GLM and ANN to predict the potential habitat of Schima superba (Chinese guger tree, CGT with different spatial scale in the Huisun study area in Taiwan. Then we created three sampling design (from small to large scales for model development and validation by different combinations of CGT samples from aforementioned three sites (Tong-Feng watershed, Yo-Shan Mountain, and Kuan-Dau watershed. These models combine points of known occurrence and topographic variables to infer CGT potential spatial distribution. Our assessment revealed that the method performance from highest to lowest was: MAXENT, DOMAIN, GLM and ANN on small spatial scale. The MAXENT and DOMAIN two models were the most capable for predicting the tree's potential habitat. However, the outcome clearly indicated that the models merely based on topographic variables performed poorly on large spatial extrapolation from Tong-Feng to Kuan-Dau because the humidity and sun illumination of the two watersheds are affected by their microterrains and are quite different from each other. Thus, the models developed from topographic variables can only be applied within a limited geographical extent without a significant error. Future studies will attempt to use variables involving spectral information associated with species extracted from high spatial, spectral resolution remotely sensed data, especially hyperspectral image data, for building a model so that it can be applied on a large spatial scale.

  19. Graffiti for science - erosion painting reveals spatially variable erosivity of sediment-laden flows (United States)

    Beer, Alexander R.; Kirchner, James W.; Turowski, Jens M.


    Spatially distributed detection of bedrock erosion is a long-standing challenge. Here we show how the spatial distribution of surface erosion can be visualized and analysed by observing the erosion of paint from natural bedrock surfaces. If the paint is evenly applied, it creates a surface with relatively uniform erodibility, such that spatial variability in the erosion of the paint reflects variations in the erosivity of the flow and its entrained sediment. In a proof-of-concept study, this approach provided direct visual verification that sediment impacts were focused on upstream-facing surfaces in a natural bedrock gorge. Further, erosion painting demonstrated strong cross-stream variations in bedrock erosion, even in the relatively narrow (5 m wide) gorge that we studied. The left side of the gorge experienced high sediment throughput with abundant lateral erosion on the painted wall up to 80 cm above the bed, but the right side of the gorge only showed a narrow erosion band 15-40 cm above the bed, likely due to deposited sediment shielding the lower part of the wall. This erosion pattern therefore reveals spatial stream bed aggradation that occurs during flood events in this channel. The erosion painting method provides a simple technique for mapping sediment impact intensities and qualitatively observing spatially distributed erosion in bedrock stream reaches. It can potentially find wide application in both laboratory and field studies.

  20. Graffiti for science – erosion painting reveals spatially variable erosivity of sediment-laden flows

    Directory of Open Access Journals (Sweden)

    A. R. Beer


    Full Text Available Spatially distributed detection of bedrock erosion is a long-standing challenge. Here we show how the spatial distribution of surface erosion can be visualized and analysed by observing the erosion of paint from natural bedrock surfaces. If the paint is evenly applied, it creates a surface with relatively uniform erodibility, such that spatial variability in the erosion of the paint reflects variations in the erosivity of the flow and its entrained sediment. In a proof-of-concept study, this approach provided direct visual verification that sediment impacts were focused on upstream-facing surfaces in a natural bedrock gorge. Further, erosion painting demonstrated strong cross-stream variations in bedrock erosion, even in the relatively narrow (5 m wide gorge that we studied. The left side of the gorge experienced high sediment throughput with abundant lateral erosion on the painted wall up to 80 cm above the bed, but the right side of the gorge only showed a narrow erosion band 15–40 cm above the bed, likely due to deposited sediment shielding the lower part of the wall. This erosion pattern therefore reveals spatial stream bed aggradation that occurs during flood events in this channel. The erosion painting method provides a simple technique for mapping sediment impact intensities and qualitatively observing spatially distributed erosion in bedrock stream reaches. It can potentially find wide application in both laboratory and field studies.

  1. Capturing temporal and spatial variability in the chemistry of shallow permafrost ponds (United States)

    Morison, Matthew Q.; Macrae, Merrin L.; Petrone, Richard M.; Fishback, LeeAnn


    Across the circumpolar north, the fate of small freshwater ponds and lakes (exchange carbon and energy with the atmosphere, and their potential to inform researchers about past climates through sediment records. A changing climate has implications for the capacity of ponds and lakes to support organisms and store carbon, which in turn has important feedbacks to climate change. Thus, an improved understanding of pond biogeochemistry is needed. To characterize spatial and temporal patterns in water column chemistry, a suite of tundra ponds were examined to answer the following research questions: (1) does temporal variability exceed spatial variability? (2) If temporal variability exists, do all ponds (or groups of ponds) behave in a similar temporal pattern, linked to seasonal hydrologic drivers or precipitation events? Six shallow ponds located in the Hudson Bay Lowlands region were monitored between May and October 2015 (inclusive, spanning the entire open-water period). The ponds span a range of biophysical conditions including pond area, perimeter, depth, and shoreline development. Water samples were collected regularly, both bimonthly over the ice-free season and intensively during and following a large summer storm event. Samples were analysed for nitrogen speciation (NO3-, NH4+, dissolved organic nitrogen) and major ions (Cl-, SO42-, K+, Ca2+, Mg2+, Na+). Across all ponds, temporal variability (across the season and within a single rain event) exceeded spatial variability (variation among ponds) in concentrations of several major species (Cl-, SO42-, K+, Ca2+, Na+). Evapoconcentration and dilution of pond water with precipitation and runoff inputs were the dominant processes influencing a set of chemical species which are hydrologically driven (Cl-, Na+, K+, Mg2+, dissolved organic nitrogen), whereas the dissolved inorganic nitrogen species were likely mediated by processes within ponds. This work demonstrates the importance of understanding hydrologically

  2. A Biophysical Neural Model To Describe Spatial Visual Attention

    International Nuclear Information System (INIS)

    Hugues, Etienne; Jose, Jorge V.


    Visual scenes have enormous spatial and temporal information that are transduced into neural spike trains. Psychophysical experiments indicate that only a small portion of a spatial image is consciously accessible. Electrophysiological experiments in behaving monkeys have revealed a number of modulations of the neural activity in special visual area known as V4, when the animal is paying attention directly towards a particular stimulus location. The nature of the attentional input to V4, however, remains unknown as well as to the mechanisms responsible for these modulations. We use a biophysical neural network model of V4 to address these issues. We first constrain our model to reproduce the experimental results obtained for different external stimulus configurations and without paying attention. To reproduce the known neuronal response variability, we found that the neurons should receive about equal, or balanced, levels of excitatory and inhibitory inputs and whose levels are high as they are in in vivo conditions. Next we consider attentional inputs that can induce and reproduce the observed spiking modulations. We also elucidate the role played by the neural network to generate these modulations

  3. Spatial variability of climate change impacts on yield of rice and wheat in the Indian Ganga Basin. (United States)

    Mishra, Ashok; Singh, R; Raghuwanshi, N S; Chatterjee, C; Froebrich, Jochen


    Indian Ganga Basin (IGB), one of the most densely populated areas in the world, is facing a significant threat to food grain production, besides increased yield gap between actual and potential production, due to climate change. We have analyzed the spatial variability of climate change impacts on rice and wheat yields at three different locations representing the upper, middle and lower IGB. The DSSAT model is used to simulate the effects of climate variability and climate change on rice and wheat yields by analyzing: (i) spatial crop yield response to current climate, and (ii) impact of a changing climate as projected by two regional climate models, REMO and HadRM3, based on SRES A1B emission scenarios for the period 2011-2040. Results for current climate demonstrate a significant gap between actual and potential yield for upper, middle and lower IGB stations. The analysis based on RCM projections shows that during 2011-2040, the largest reduction in rice and wheat yields will occur in the upper IGB (reduction of potential rice and wheat yield respectively by 43.2% and 20.9% by REMO, and 24.8% and 17.2% by HadRM3). In the lower IGB, however, contrasting results are obtained, with HadRM3 based projections showing an increase in the potential rice and wheat yields, whereas, REMO based projections show decreased potential yields. We discuss the influence of agro-climatic factors; variation in temperature, length of maturity period and leaf area index which are responsible for modeled spatial variability in crop yield response within the IGB. Copyright © 2013 Elsevier B.V. All rights reserved.

  4. Characterization of meter-scale spatial variability of riverbed hydraulic conductivity in a lowland river (Aa River, Belgium) (United States)

    Ghysels, Gert; Benoit, Sien; Awol, Henock; Jensen, Evan Patrick; Debele Tolche, Abebe; Anibas, Christian; Huysmans, Marijke


    An improved general understanding of riverbed heterogeneity is of importance for all groundwater modeling studies that include river-aquifer interaction processes. Riverbed hydraulic conductivity (K) is one of the main factors controlling river-aquifer exchange fluxes. However, the meter-scale spatial variability of riverbed K has not been adequately mapped as of yet. This study aims to fill this void by combining an extensive field measurement campaign focusing on both horizontal and vertical riverbed K with a detailed geostatistical analysis of the meter-scale spatial variability of riverbed K . In total, 220 slug tests and 45 standpipe tests were performed at two test sites along the Belgian Aa River. Omnidirectional and directional variograms (along and across the river) were calculated. Both horizontal and vertical riverbed K vary over several orders of magnitude and show significant meter-scale spatial variation. Horizontal K shows a bimodal distribution. Elongated zones of high horizontal K along the river course are observed at both sections, indicating a link between riverbed structures, depositional environment and flow regime. Vertical K is lognormally distributed and its spatial variability is mainly governed by the presence and thickness of a low permeable organic layer at the top of the riverbed. The absence of this layer in the center of the river leads to high vertical K and is related to scouring of the riverbed by high discharge events. Variograms of both horizontal and vertical K show a clear directional anisotropy with ranges along the river being twice as large as those across the river.

  5. Spatial data modelling and maximum entropy theory

    Czech Academy of Sciences Publication Activity Database

    Klimešová, Dana; Ocelíková, E.


    Roč. 51, č. 2 (2005), s. 80-83 ISSN 0139-570X Institutional research plan: CEZ:AV0Z10750506 Keywords : spatial data classification * distribution function * error distribution Subject RIV: BD - Theory of Information

  6. Multimodal Similarity Gaussian Process Latent Variable Model. (United States)

    Song, Guoli; Wang, Shuhui; Huang, Qingming; Tian, Qi


    Data from real applications involve multiple modalities representing content with the same semantics from complementary aspects. However, relations among heterogeneous modalities are simply treated as observation-to-fit by existing work, and the parameterized modality specific mapping functions lack flexibility in directly adapting to the content divergence and semantic complicacy in multimodal data. In this paper, we build our work based on the Gaussian process latent variable model (GPLVM) to learn the non-parametric mapping functions and transform heterogeneous modalities into a shared latent space. We propose multimodal Similarity Gaussian Process latent variable model (m-SimGP), which learns the mapping functions between the intra-modal similarities and latent representation. We further propose multimodal distance-preserved similarity GPLVM (m-DSimGP) to preserve the intra-modal global similarity structure, and multimodal regularized similarity GPLVM (m-RSimGP) by encouraging similar/dissimilar points to be similar/dissimilar in the latent space. We propose m-DRSimGP, which combines the distance preservation in m-DSimGP and semantic preservation in m-RSimGP to learn the latent representation. The overall objective functions of the four models are solved by simple and scalable gradient decent techniques. They can be applied to various tasks to discover the nonlinear correlations and to obtain the comparable low-dimensional representation for heterogeneous modalities. On five widely used real-world data sets, our approaches outperform existing models on cross-modal content retrieval and multimodal classification.

  7. Robust Exponential Synchronization for a Class of Master-Slave Distributed Parameter Systems with Spatially Variable Coefficients and Nonlinear Perturbation

    Directory of Open Access Journals (Sweden)

    Chengdong Yang


    Full Text Available This paper addresses the exponential synchronization problem of a class of master-slave distributed parameter systems (DPSs with spatially variable coefficients and spatiotemporally variable nonlinear perturbation, modeled by a couple of semilinear parabolic partial differential equations (PDEs. With a locally Lipschitz constraint, the perturbation is a continuous function of time, space, and system state. Firstly, a sufficient condition for the robust exponential synchronization of the unforced semilinear master-slave PDE systems is investigated for all admissible nonlinear perturbations. Secondly, a robust distributed proportional-spatial derivative (P-sD state feedback controller is desired such that the closed-loop master-slave PDE systems achieve exponential synchronization. Using Lyapunov’s direct method and the technique of integration by parts, the main results of this paper are presented in terms of spatial differential linear matrix inequalities (SDLMIs. Finally, two numerical examples are provided to show the effectiveness of the proposed methods applied to the robust exponential synchronization problem of master-slave PDE systems with nonlinear perturbation.

  8. Adaptive Gaussian Predictive Process Models for Large Spatial Datasets (United States)

    Guhaniyogi, Rajarshi; Finley, Andrew O.; Banerjee, Sudipto; Gelfand, Alan E.


    Large point referenced datasets occur frequently in the environmental and natural sciences. Use of Bayesian hierarchical spatial models for analyzing these datasets is undermined by onerous computational burdens associated with parameter estimation. Low-rank spatial process models attempt to resolve this problem by projecting spatial effects to a lower-dimensional subspace. This subspace is determined by a judicious choice of “knots” or locations that are fixed a priori. One such representation yields a class of predictive process models (e.g., Banerjee et al., 2008) for spatial and spatial-temporal data. Our contribution here expands upon predictive process models with fixed knots to models that accommodate stochastic modeling of the knots. We view the knots as emerging from a point pattern and investigate how such adaptive specifications can yield more flexible hierarchical frameworks that lead to automated knot selection and substantial computational benefits. PMID:22298952

  9. Topological models and frameworks for 3D spatial objects (United States)

    Zlatanova, Siyka; Rahman, Alias Abdul; Shi, Wenzhong


    Topology is one of the mechanisms to describe relationships between spatial objects. Thus, it is the basis for many spatial operations. Models utilizing the topological properties of spatial objects are usually called topological models, and are considered by many researchers as the best suited for complex spatial analysis (i.e., the shortest path search). A number of topological models for two-dimensional and 2.5D spatial objects have been implemented (or are under consideration) by GIS and DBMS vendors. However, when we move to one more dimension (i.e., three-dimensions), the complexity of the relationships increases, and this requires new approaches, rules and representations. This paper aims to give an overview of the 3D topological models presented in the literature, and to discuss generic issues related to 3D modeling. The paper also considers models in object-oriented (OO) environments. Finally, future trends for research and development in this area are highlighted.

  10. Spatial Inequalities in the Incidence of Colorectal Cancer and Associated Factors in the Neighborhoods of Tehran, Iran: Bayesian Spatial Models. (United States)

    Mansori, Kamyar; Solaymani-Dodaran, Masoud; Mosavi-Jarrahi, Alireza; Motlagh, Ali Ganbary; Salehi, Masoud; Delavari, Alireza; Asadi-Lari, Mohsen


    The aim of this study was to determine the factors associated with the spatial distribution of the incidence of colorectal cancer (CRC) in the neighborhoods of Tehran, Iran using Bayesian spatial models. This ecological study was implemented in Tehran on the neighborhood level. Socioeconomic variables, risk factors, and health costs were extracted from the Equity Assessment Study conducted in Tehran. The data on CRC incidence were extracted from the Iranian population-based cancer registry. The Besag-York-Mollié (BYM) model was used to identify factors associated with the spatial distribution of CRC incidence. The software programs OpenBUGS version 3.2.3, ArcGIS 10.3, and GeoDa were used for the analysis. The Moran index was statistically significant for all the variables studied (p<0.05). The BYM model showed that having a women head of household (median standardized incidence ratio [SIR], 1.63; 95% confidence interval [CI], 1.06 to 2.53), living in a rental house (median SIR, 0.82; 95% CI, 0.71 to 0.96), not consuming milk daily (median SIR, 0.71; 95% CI, 0.55 to 0.94) and having greater household health expenditures (median SIR, 1.34; 95% CI, 1.06 to 1.68) were associated with a statistically significant elevation in the SIR of CRC. The median (interquartile range) and mean (standard deviation) values of the SIR of CRC, with the inclusion of all the variables studied in the model, were 0.57 (1.01) and 1.05 (1.31), respectively. Inequality was found in the spatial distribution of CRC incidence in Tehran on the neighborhood level. Paying attention to this inequality and the factors associated with it may be useful for resource allocation and developing preventive strategies in atrisk areas.

  11. Spatial Inequalities in the Incidence of Colorectal Cancer and Associated Factors in the Neighborhoods of Tehran, Iran: Bayesian Spatial Models

    Directory of Open Access Journals (Sweden)

    Kamyar Mansori


    Full Text Available Objectives The aim of this study was to determine the factors associated with the spatial distribution of the incidence of colorectal cancer (CRC in the neighborhoods of Tehran, Iran using Bayesian spatial models. Methods This ecological study was implemented in Tehran on the neighborhood level. Socioeconomic variables, risk factors, and health costs were extracted from the Equity Assessment Study conducted in Tehran. The data on CRC incidence were extracted from the Iranian population-based cancer registry. The Besag-York-Mollié (BYM model was used to identify factors associated with the spatial distribution of CRC incidence. The software programs OpenBUGS version 3.2.3, ArcGIS 10.3, and GeoDa were used for the analysis. Results The Moran index was statistically significant for all the variables studied (p<0.05. The BYM model showed that having a women head of household (median standardized incidence ratio [SIR], 1.63; 95% confidence interval [CI], 1.06 to 2.53, living in a rental house (median SIR, 0.82; 95% CI, 0.71 to 0.96, not consuming milk daily (median SIR, 0.71; 95% CI, 0.55 to 0.94 and having greater household health expenditures (median SIR, 1.34; 95% CI, 1.06 to 1.68 were associated with a statistically significant elevation in the SIR of CRC. The median (interquartile range and mean (standard deviation values of the SIR of CRC, with the inclusion of all the variables studied in the model, were 0.57 (1.01 and 1.05 (1.31, respectively. Conclusions Inequality was found in the spatial distribution of CRC incidence in Tehran on the neighborhood level. Paying attention to this inequality and the factors associated with it may be useful for resource allocation and developing preventive strategies in atrisk areas.

  12. Spatial variability of NDVI at different seasons in the Community of Madrid (Spain) (United States)

    Sotoca, Juan J. Martin; Saa-Requejo, Antonio; Borondo, Javier; Tarquis, Ana M.


    Agricultural drought quantification is one of the most important tasks in the characterization process of this natural hazard and its implications in crop insurance. Recently, several vegetation indexes based on remote-sensing data (VI) has been applied to quantify it (Dalezios et al, 2012). VIs are obtained combining several frequency bands that represent the relationship between photosynthesis and absorbed/reflected radiation. The most widely used VI is the Normalized Difference Vegetation Index (NDVI). It is based on the principle that healthy vegetation mainly absorbs visible light and reflects the near-infrared frequency band. Drought can be highly localized, and several authors have recognized the critical role of soil moisture and its spatial variability in agricultural losses (Anderson et al., 2011). Therefore, it is important to delimit locations within a homogeneous area that will share main NDVI statistics and in which the same threshold value can be applied to define drought event. In order to do so, we have applied for the first time in this context the method of singularity maps (Cheng and Agterberg, 1996) commonly used in localization of mineral deposits. The NDVI singularity maps calculated in each season through 2011/2012 are showed and discussed (Martín-Sotoca, 2014). References Anderson, M:C:, C. R. Hain, B. Wardlow, J. R. Mecikalski and W. P. Kustas (2011) Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States. J. Climate, 24, 2025-2044. Dalezios, N.R., A. Blanta, N.V. Spyropoulos and A.M. Tarquis (2012) Risk identification of agricultural drought for sustainable Agroecosystems. Nat. Hazards Earth Syst. Sci., 14, 2435-2448. Cheng, Q. and F.P. Agterberg (1996) Multifractal modeling and spatial statistics. Math. Geol., 28, 1-16. Martín-Sotoca, J.J. (2014) Estructura Espacial de la Sequía en Pastos y sus Aplicaciones en el Seguro Agrario. Master Thesis, UPM (In Spanish

  13. Potential for tree rings to reveal spatial patterns of past drought variability across western Australia (United States)

    O'Donnell, Alison J.; Cook, Edward R.; Palmer, Jonathan G.; Turney, Chris S. M.; Grierson, Pauline F.


    Proxy records have provided major insights into the variability of past climates over long timescales. However, for much of the Southern Hemisphere, the ability to identify spatial patterns of past climatic variability is constrained by the sparse distribution of proxy records. This is particularly true for mainland Australia, where relatively few proxy records are located. Here, we (1) assess the potential to use existing proxy records in the Australasian region—starting with the only two multi-century tree-ring proxies from mainland Australia—to reveal spatial patterns of past hydroclimatic variability across the western third of the continent, and (2) identify strategic locations to target for the development of new proxy records. We show that the two existing tree-ring records allow robust reconstructions of past hydroclimatic variability over spatially broad areas (i.e. > 3° × 3°) in inland north- and south-western Australia. Our results reveal synchronous periods of drought and wet conditions between the inland northern and southern regions of western Australia as well as a generally anti-phase relationship with hydroclimate in eastern Australia over the last two centuries. The inclusion of 174 tree-ring proxy records from Tasmania, New Zealand and Indonesia and a coral record from Queensland did not improve the reconstruction potential over western Australia. However, our findings suggest that the addition of relatively few new proxy records from key locations in western Australia that currently have low reconstruction skill will enable the development of a comprehensive drought atlas for the region, and provide a critical link to the drought atlases of monsoonal Asia and eastern Australia and New Zealand.

  14. Spatial and Temporal Variabilities of Solar and Longwave Radiation Fluxes below a Coniferous Forest in the French Alps (United States)

    Sicart, J. E.; Ramseyer, V.; Lejeune, Y.; Essery, R.; Webster, C.; Rutter, N.


    At high altitudes and latitudes, snow has a large influence on hydrological processes. Large fractions of these regions are covered by forests, which have a strong influence on snow accumulation and melting processes. Trees absorb a large part of the incoming shortwave radiation and this heat load is mostly dissipated as longwave radiation. Trees shelter the snow surface from wind, so sub-canopy snowmelt depends mainly on the radiative fluxes: vegetation attenuates the transmission of shortwave radiation but enhances longwave irradiance to the surface. An array of 13 pyranometers and 11 pyrgeometers was deployed on the snow surface below a coniferous forest at the CEN-MeteoFrance Col de Porte station in the French Alps (1325 m asl) during the 2017 winter in order to investigate spatial and temporal variabilities of solar and infrared irradiances in different meteorological conditions. Sky view factors measured with hemispherical photographs at each radiometer location were in a narrow range from 0.2 to 0.3. The temperature of the vegetation was measured with IR thermocouples and an IR camera. In clear sky conditions, the attenuation of solar radiation by the canopy reached 96% and its spatial variability exceeded 100 W m-2. Longwave irradiance varied by 30 W m-2 from dense canopy to gap areas. In overcast conditions, the spatial variabilities of solar and infrared irradiances were reduced and remained closely related to the sky view factor. A simple radiative model taking into account the penetration through the canopy of the direct and diffuse solar radiation, and isotropic infrared emission of the vegetation as a blackbody emitter, accurately reproduced the dynamics of the radiation fluxes at the snow surface. Model results show that solar transmissivity of the canopy in overcast conditions is an excellent proxy of the sky view factor and the emitting temperature of the vegetation remained close to the air temperature in this typically dense Alpine forest.

  15. Recent developments in spatial analysis spatial statistics, behavioural modelling, and computational intelligence

    CERN Document Server

    Getis, Arthur


    In recent years, spatial analysis has become an increasingly active field, as evidenced by the establishment of educational and research programs at many universities. Its popularity is due mainly to new technologies and the development of spatial data infrastructures. This book illustrates some recent developments in spatial analysis, behavioural modelling, and computational intelligence. World renown spatial analysts explain and demonstrate their new and insightful models and methods. The applications are in areas of societal interest such as the spread of infectious diseases, migration behaviour, and retail and agricultural location strategies. In addition, there is emphasis on the uses of new technologoies for the analysis of spatial data through the application of neural network concepts.

  16. Analysis of streamflow variability in Alpine catchments at multiple spatial and temporal scales (United States)

    Pérez Ciria, T.; Chiogna, G.


    Alpine watersheds play a pivotal role in Europe for water provisioning and for hydropower production. In these catchments, temporal fluctuations of river discharge occur at multiple temporal scales due to natural as well as anthropogenic driving forces. In the last decades, modifications of the flow regime have been observed and their origin lies in the complex interplay between construction of dams for hydro power production, changes in water management policies and climatic changes. The alteration of the natural flow has negative impacts on the freshwater biodiversity and threatens the ecosystem integrity of the Alpine region. Therefore, understanding the temporal and spatial variability of river discharge has recently become a particular concern for environmental protection and represents a crucial contribution to achieve sustainable water resources management in the Alps. In this work, time series analysis is conducted for selected gauging stations in the Inn and the Adige catchments, which cover a large part of the central and eastern region of the Alps. We analyze the available time series using the continuous wavelet transform and change-point analyses for determining how and where changes have taken place. Although both catchments belong to different climatic zones of the Greater Alpine Region, streamflow properties share some similar characteristics. The comparison of the collected streamflow time series in the two catchments permits detecting gradients in the hydrological system dynamics that depend on station elevation, longitudinal location in the Alps and catchment area. This work evidences that human activities (e.g., water management practices and flood protection measures, changes in legislation and market regulation) have major impacts on streamflow and should be rigorously considered in hydrological models.

  17. [Soil nutrients spatial variability and soil fertility suitability in Qujing tobacco-planting area]. (United States)

    Li, Qiang; Zhou, Ji-heng; Yang, Rong-sheng; Zhang, Zheng-yan; Xie, Yan; Zhang, Yi-yang; Huang, Kua-ke; Li, Wei


    By adopting GPS technique, 2088 sampling sites were installed in the tobacco-planting area of Qujing City, Yunnan Province, with 0-20 cm soil samples collected to determine their main nutrients contents. The overall characteristics and spatial variability of the tobacco soil nutrients were analyzed by classic statistics and geo-statistics, and the soil fertility suitability in planting tobacco was evaluated by the methods of fuzzy mathematics. In the study area, soil pH and soil organic matter, available S, and water-soluble Cl contents were appropriate, soil total N and alkalihydrolyzable N contents were too high, soil available K, Ca, Mg, Cu, Fe, Zn, Mo, and Mn contents were abundant, soil available P content was at medium level, while soil total P and K and available B contents were insufficient. All the nutrient indices presented anisotropic distribution, among which, the spatial variability of soil available P and B was mainly caused by random factors, and that of other nutrients was caused by the co-effects of structural and random factors. The spatial distribution map of soil fertility suitability index (SFI) showed that there was no the excellent grade region for tobacco-planting, good grade region accounted for 8.0%, general grade region accounted for 51.6%, moderate grade region accounted for 39.0%, and low grade region accounted for 1.4%.

  18. Spatial variability of the chemical properties of the soil in the coffee yield and quality

    Directory of Open Access Journals (Sweden)

    Felipe Andrés Rodríguez Garay


    Full Text Available Given the environmental and economic importance of the rational use of inputs for a competitive and sustainable agriculture, a greater understanding of the different variables involved in agricultural production is required. Therefore, the aim of this study was to establish the spatial behavior of the chemical properties of the soil and their relationship with coffee yield and quality on Typic Hapludands. Sampling was done randomly in 64 georefe-renced points to a depth of -0.20. Data were analyzed using descriptive and geostatistics statistics, linear correlations and multivariate methods cluster and principal components (PCA; additionally, the interpolation of data was conducted using the kriging method. The descriptive analysis showed high variability for chemical attributes, in terms of geostatistics, the results showed spatial dependence for all attributes except for the content of B in the soil. There was a 35.88 % correlation between the soil attributes (SOC content and the attributes of the crop (yield. Besides, an inverse relationship of 40.98 % between the reduction in threshing (decrease and the Ca content in soil was observed. Both principal analysis (PCA components and cluster analysis showed less relevance to the analysis on the attributes Na, P, B and yield. From cluster analysis and spatial distribution, management of coffee growing is proposed.

  19. The spatial distribution of known predictors of autism spectrum disorders impacts geographic variability in prevalence in central North Carolina

    Directory of Open Access Journals (Sweden)

    Hoffman Kate


    Full Text Available Abstract Background The causes of autism spectrum disorders (ASD remain largely unknown and widely debated; however, evidence increasingly points to the importance of environmental exposures. A growing number of studies use geographic variability in ASD prevalence or exposure patterns to investigate the association between environmental factors and ASD. However, differences in the geographic distribution of established risk and predictive factors for ASD, such as maternal education or age, can interfere with investigations of ASD etiology. We evaluated geographic variability in the prevalence of ASD in central North Carolina and the impact of spatial confounding by known risk and predictive factors. Methods Children meeting a standardized case definition for ASD at 8 years of age were identified through records-based surveillance for 8 counties biennially from 2002 to 2008 (n=532. Vital records were used to identify the underlying cohort (15% random sample of children born in the same years as children with an ASD, n=11,034, and to obtain birth addresses. We used generalized additive models (GAMs to estimate the prevalence of ASD across the region by smoothing latitude and longitude. GAMs, unlike methods used in previous spatial analyses of ASD, allow for extensive adjustment of individual-level risk factors (e.g. maternal age and education when evaluating spatial variability of disease prevalence. Results Unadjusted maps revealed geographic variation in surveillance-recognized ASD. Children born in certain regions of the study area were up to 1.27 times as likely to be recognized as having ASD compared to children born in the study area as a whole (prevalence ratio (PR range across the study area 0.57-1.27; global P=0.003. However, geographic gradients of ASD prevalence were attenuated after adjusting for spatial confounders (adjusted PR range 0.72-1.12 across the study area; global P=0.052. Conclusions In these data, spatial variation of ASD

  20. Practical likelihood analysis for spatial generalized linear mixed models

    DEFF Research Database (Denmark)

    Bonat, W. H.; Ribeiro, Paulo Justiniano


    , respectively, examples of binomial and count datasets modeled by spatial generalized linear mixed models. Our results show that the Laplace approximation provides similar estimates to Markov Chain Monte Carlo likelihood, Monte Carlo expectation maximization, and modified Laplace approximation. Some advantages...

  1. Spatial variability in denitrification rates in an Oregon tidal salt marsh (United States)

    Modeling denitrification (DeN) is particularly challenging in tidal systems, which play a vital role in buffering adjacent coastal waters from nitrogen inputs. These systems are hydrologically and biogeochemically complex, varying on fine temporal and spatial scales. As part of a...

  2. An empirical model of decadal ENSO variability

    Energy Technology Data Exchange (ETDEWEB)

    Kravtsov, S. [University of Wisconsin-Milwaukee, Department of Mathematical Sciences, Atmospheric Sciences Group, P. O. Box 413, Milwaukee, WI (United States)


    This paper assesses potential predictability of decadal variations in the El Nino/Southern Oscillation (ENSO) characteristics by constructing and performing simulations using an empirical nonlinear stochastic model of an ENSO index. The model employs decomposition of global sea-surface temperature (SST) anomalies into the modes that maximize the ratio of interdecadal-to-subdecadal SST variance to define low-frequency predictors called the canonical variates (CVs). When the whole available SST time series is so processed, the leading canonical variate (CV-1) is found to be well correlated with the area-averaged SST time series which exhibits a non-uniform warming trend, while the next two (CV-2 and CV-3) describe secular variability arguably associated with a combination of Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO) signals. The corresponding ENSO model that uses either all three (CVs 1-3) or only AMO/PDO-related (CVs 2 and 3) predictors captures well the observed autocorrelation function, probability density function, seasonal dependence of ENSO, and, most importantly, the observed interdecadal modulation of ENSO variance. The latter modulation, and its dependence on CVs, is shown to be inconsistent with the null hypothesis of random decadal ENSO variations simulated by multivariate linear inverse models. Cross-validated hindcasts of ENSO variance suggest a potential useful skill at decadal lead times. These findings thus argue that decadal modulations of ENSO variability may be predictable subject to our ability to forecast AMO/PDO-type climate modes; the latter forecasts may need to be based on simulations of dynamical models, rather than on a purely statistical scheme as in the present paper. (orig.)

  3. Impact of radionuclide spatial variability on groundwater quality downstream from a shallow waste burial in the Chernobyl Exclusion Zone (United States)

    Nguyen, H. L.; de Fouquet, C.; Courbet, C.; Simonucci, C. A.


    The effects of spatial variability of hydraulic parameters and initial groundwater plume localization on the possible extent of groundwater pollution plumes have already been broadly studied. However, only a few studies, such as Kjeldsen et al. (1995), take into account the effect of source term spatial variability. We explore this question with the 90Sr migration modeling from a shallow waste burial located in the Chernobyl Exclusion Zone to the underlying sand aquifer. Our work is based upon groundwater sampled once or twice a year since 1995 until 2015 from about 60 piezometers and more than 3,000 137Cs soil activity measurements. These measurements were taken in 1999 from one of the trenches dug after the explosion of the Chernobyl nuclear power plant, the so-called "T22 Trench", where radioactive waste was buried in 1987. The geostatistical analysis of 137Cs activity data in soils from Bugai et al. (2005) is first reconsidered to delimit the trench borders using georadar data as a covariable and to perform geostatistical simulations in order to evaluate the uncertainties of this inventory. 90Sr activity in soils is derived from 137Cs/154Eu and 90Sr/154Eu activity ratios in Chernobyl hot fuel particles (Bugai et al., 2003). Meanwhile, a coupled 1D non saturated/3D saturated transient transport model is constructed under the MELODIE software (IRSN, 2009). The previous 90Sr transport model developed by Bugai et al. (2012) did not take into account the effect of water table fluctuations highlighted by Van Meir et al. (2007) which may cause some discrepancies between model predictions and field observations. They are thus reproduced on a 1D vertical non saturated model. The equiprobable radionuclide localization maps produced by the geostatistical simulations are selected to illustrate different heterogeneities in the radionuclide inventory and are implemented in the 1D model. The obtained activity fluxes from all the 1D vertical models are then injected in a 3D

  4. Comparison of Three Plot Selection Methods for Estimating Change in Temporally Variable, Spatially Clustered Populations.

    Energy Technology Data Exchange (ETDEWEB)

    Thompson, William L. [Bonneville Power Administration, Portland, OR (US). Environment, Fish and Wildlife


    Monitoring population numbers is important for assessing trends and meeting various legislative mandates. However, sampling across time introduces a temporal aspect to survey design in addition to the spatial one. For instance, a sample that is initially representative may lose this attribute if there is a shift in numbers and/or spatial distribution in the underlying population that is not reflected in later sampled plots. Plot selection methods that account for this temporal variability will produce the best trend estimates. Consequently, I used simulation to compare bias and relative precision of estimates of population change among stratified and unstratified sampling designs based on permanent, temporary, and partial replacement plots under varying levels of spatial clustering, density, and temporal shifting of populations. Permanent plots produced more precise estimates of change than temporary plots across all factors. Further, permanent plots performed better than partial replacement plots except for high density (5 and 10 individuals per plot) and 25% - 50% shifts in the population. Stratified designs always produced less precise estimates of population change for all three plot selection methods, and often produced biased change estimates and greatly inflated variance estimates under sampling with partial replacement. Hence, stratification that remains fixed across time should be avoided when monitoring populations that are likely to exhibit large changes in numbers and/or spatial distribution during the study period. Key words: bias; change estimation; monitoring; permanent plots; relative precision; sampling with partial replacement; temporary plots.

  5. a Variable Resolution Global Spectral Model. (United States)

    Hardiker, Vivek Manohar

    A conformal transformation suggested by F. Schimdt is followed to implement a global spectral model with variable horizontal resolution. A conformal mapping is defined between the real physical sphere (Earth) to a transformed (Computational) sphere. The model equations are discretized on the computational sphere and the conventional spectral technique is applied to solve the model equations. There are two types of transformations used in the present study, namely, the Stretching transformation and the Rotation of the horizontal grid points. Application of the stretching transformation results in finer resolution along the meridional direction. The stretching is controlled by a parameter C. The rotation transformation can be used to relocate the North Pole of the model to any point on the geographic sphere. The idea is now to rotate the pole to the area of interest and refine the resolution around the new pole by applying the stretching transformation. The stretching transformation can be applied alone without the rotation. A T-42 Spectral Shallow-Water model is transformed by applying the stretching transformation alone as well as the two transformations together. A T-42 conventional Spectral Shallow-Water model is run as the control experiment and a conventional T-85 Spectral Shallow-Water model run is treated as the benchmark (Truth) solution. RMS error analysis for the geopotential field as well as the wind field is performed to evaluate the forecast made by the transformed model. It is observed that the RMS error of the transformed model is lower than that of the control run in a latitude band, for the case of stretching transformation alone, while for the total transformation (rotation followed by stretching), similar results are obtained for a rectangular domain. A multi-level global spectral model is designed from the current FSU global spectral model in order to implement the conformal transformation. The transformed T-85 model is used to study Hurricane

  6. Anisotropia no estudo da variabilidade espacial de algumas variáveis químicas do solo Anisotropy to analyze spatial variability of some spatially referenced soil chemical variables

    Directory of Open Access Journals (Sweden)

    Luciana Pagliosa Carvalho Guedes


    Full Text Available No estudo do mapeamento da fertilidade do solo, pelas técnicas de geoestatística, algumas características estruturais da variabilidade espacial devem ser consideradas, tais como continuidade espacial e ausência de anisotropia. Neste contexto, o presente trabalho apresenta uma análise da anisotropia no estudo da variabilidade espacial das variáveis químicas do solo: ferro (Fe, acidez potencial (H + Al, matéria orgânica (MO e Mn, de um conjunto de dados de 128 parcelas sem manejo químico localizado, espacialmente referenciados, estudados entre 1998 e 2002, em um Latossolo Vermelho distroférrico, em Cascavel-PR. A identificação da anisotropia foi realizada por meio da construção de semivariogramas direcionais com modelos ajustados, e a correção da anisotropia realizou-se por meio de transformações lineares e de modelos combinados. Em seguida, utilizou-se um modelo ajustado ao semivariograma omnidirecional para construção de mapas temáticos de variabilidade das variáveis estudadas. Observou-se a existência de anisotropia geométrica para a variável H + Al. Já as variáveis MO, Mn e Fe mostraram a presença de anisotropia combinada, sendo corrigida inicialmente a anisotropia geométrica e, posteriormente, a anisotropia zonal. Por meio do estudo da anisotropia, foi possível eliminar as direções privilegiadas, melhorando assim o ajuste dos semivariogramas e produzindo mapas temáticos das variáveis químicas estudadas com maior acurácia.Studies on soil fertility mapping based on geostatistics should consider some spatial variability characteristics such as spatial continuity and absence of anisotropy. The present study reports an anisotropic analysis of the spatial variability of the following soil chemical variables: iron (Fe, potential acidity (H + Al, organic matter (OM and manganese (Mn of a data set of 128 plots without local chemical management, spatially referenced, studied between 1998 and 2002 in a dystrophic Red

  7. Spatial autocorrelation method using AR model; Kukan jiko sokanho eno AR model no tekiyo

    Energy Technology Data Exchange (ETDEWEB)

    Yamamoto, H.; Obuchi, T.; Saito, T. [Iwate University, Iwate (Japan). Faculty of Engineering


    Examination was made about the applicability of the AR model to the spatial autocorrelation (SAC) method, which analyzes the surface wave phase velocity in a microtremor, for the estimation of the underground structure. In this examination, microtremor data recorded in Morioka City, Iwate Prefecture, was used. In the SAC method, a spatial autocorrelation function with the frequency as a variable is determined from microtremor data observed by circular arrays. Then, the Bessel function is adapted to the spatial autocorrelation coefficient with the distance between seismographs as a variable for the determination of the phase velocity. The result of the AR model application in this study and the results of the conventional BPF and FFT method were compared. It was then found that the phase velocities obtained by the BPF and FFT methods were more dispersed than the same obtained by the AR model. The dispersion in the BPF method is attributed to the bandwidth used in the band-pass filter and, in the FFT method, to the impact of the bandwidth on the smoothing of the cross spectrum. 2 refs., 7 figs.

  8. Spatial and Temporal Low-Dimensional Models for Fluid Flow (United States)

    Kalb, Virginia


    A document discusses work that obtains a low-dimensional model that captures both temporal and spatial flow by constructing spatial and temporal four-mode models for two classic flow problems. The models are based on the proper orthogonal decomposition at two reference Reynolds numbers. Model predictions are made at an intermediate Reynolds number and compared with direct numerical simulation results at the new Reynolds number.

  9. A protocol for measuring spatial variables in soft-sediment tide pools

    Directory of Open Access Journals (Sweden)

    Marina R. Brenha-Nunes


    Full Text Available ABSTRACT We present a protocol for measuring spatial variables in large (>50 m2 soft-sediment tide pool. Secondarily, we present the fish capture efficiency of a sampling protocol that based on such spatial variables to calculate relative abundances. The area of the pool is estimated by summing areas of basic geometric forms; the depth, by taken representative measurements of the depth variability of each pool's sector, previously determined according to its perimeter; and the volume, by considering the pool as a prism. These procedures were a trade-off between the acquisition of reliable estimates and the minimization of both the cost of operating and the time spent in field. The fish sampling protocol is based on two con secutive stages: 1 two people search for fishes under structures (e.g., rocks and litters on the pool and capture them with hand seines; 2 these structures are removed and then a beach-seine is hauled over the whole pool. Our method is cheaper than others and fast to operate considering the time in low tides. The method to sample fish is quite efficient resulting in a capture efficiency of 89%.

  10. Spatial modeling of potential woody biomass flow (United States)

    Woodam Chung; Nathaniel Anderson


    The flow of woody biomass to end users is determined by economic factors, especially the amount available across a landscape and delivery costs of bioenergy facilities. The objective of this study develop methodology to quantify landscape-level stocks and potential biomass flows using the currently available spatial database road network analysis tool. We applied this...

  11. Variability of fMRI-response patterns at different spatial observation scales. (United States)

    Ball, Tonio; Breckel, Thomas P K; Mutschler, Isabella; Aertsen, Ad; Schulze-Bonhage, Andreas; Hennig, Jürgen; Speck, Oliver


    Functional organization units of the cerebral cortex exist over a wide range of spatial scales, from local circuits to entire cortical areas. In the last decades, scale-space representations of neuroimaging data suited to probe the multi-scale nature of cortical functional organization have been introduced and methodologically elaborated. For this purpose, responses are statistically detected over a range of spatial scales using a family of Gaussian filters, with small filters being related to fine and large filters-to coarse spatial scales. The goal of the present study was to investigate the degree of variability of fMRI-response patterns over a broad range of observation scales. To this aim, the same fMRI data set obtained from 18 subjects during a visuomotor task was analyzed with a range of filters from 4- to 16-mm full width at half-maximum (FWHM). We found substantial observation-scale-related variability. For example, using filter widths of 6- to 8-mm FWHM, in the group-level results, significant responses in the right secondary visual but not in the primary visual cortex were detected. However, when larger filters were used, the responses in the right primary visual cortex reached significance. Often, responses in probabilistically defined areas were significant when both small and large filters, but not intermediate filter widths were applied. This suggests that brain responses can be organized in local clusters of multiple distinct activation foci. Our findings illustrate the potential of multi-scale fMRI analysis to reveal novel features in the spatial organization of human brain responses. Copyright © 2011 Wiley-Liss, Inc.

  12. Analysis of the spatial variability of crop yield and soil properties in small agricultural plots

    Directory of Open Access Journals (Sweden)

    Vieira Sidney Rosa


    Full Text Available The objective of this study was to assess spatial variability of soil properties and crop yield under no tillage as a function of time, in two soil/climate conditions in São Paulo State, Brazil. The two sites measured approximately one hectare each and were cultivated with crop sequences which included corn, soybean, cotton, oats, black oats, wheat, rye, rice and green manure. Soil fertility, soil physical properties and crop yield were measured in a 10-m grid. The soils were a Dusky Red Latossol (Oxisol and a Red Yellow Latossol (Ultisol. Soil sampling was performed in each field every two years after harvesting of the summer crop. Crop yield was measured at the end of each crop cycle, in 2 x 2.5 m sub plots. Data were analysed using semivariogram analysis and kriging interpolation for contour map generation. Yield maps were constructed in order to visually compare the variability of yields, the variability of the yield components and related soil properties. The results show that the factors affecting the variability of crop yield varies from one crop to another. The changes in yield from one year to another suggest that the causes of variability may change with time. The changes with time for the cross semivariogram between phosphorus in leaves and soybean yield is another evidence of this result.

  13. Free-streaming radiation in cosmological models with spatial curvature (United States)

    Wilson, M. L.


    The effects of spatial curvature on radiation anisotropy are examined for the standard Friedmann-Robertson-Walker model universes. The effect of curvature is found to be very important when considering fluctuations with wavelengths comparable to the horizon. It is concluded that the behavior of radiation fluctuations in models with spatial curvature is quite different from that in spatially flat models, and that models with negative curvature are most strikingly different. It is therefore necessary to take the curvature into account in careful studies of the anisotropy of the microwave background.

  14. The dynamic and indirect spatial effects of neighborhood conditions on land value, spatial panel dynamic econometrics model (United States)

    Fitriani, Rahma; Sumarminingsih, Eni; Astutik, Suci


    Land value is the product of past decision of its use leading to its value, as well as the value of the surrounded land. It is also affected by the local characteristic and the spillover development demand of the previous time period. The effect of each factor on land value will have dynamic and spatial virtues. Thus, a spatial panel dynamic model is used to estimate the particular effects. The model will be useful for predicting the future land value or the effect of implemented policy on land value. The objective of this paper is to derive the dynamic and indirect spatial marginal effects of the land characteristic and the spillover development demand on land value. Each effect is the partial derivative of the expected land value based on the spatial dynamic model with respect to each variable, by considering different time period and different location. The results indicate that the instant change of local or neighborhood characteristics on land value affect the local and the immediate neighborhood land value. However, the longer the change take place, the effect will spread further, not only on the immediate neighborhood.

  15. Spatial and temporal variability of past rainfall in western Australia inferred from tree rings (United States)

    O'Donnell, Alison; Cook, Edward; Turney, Chris; Palmer, Jonathan; Skrzypek, Grzegorz; Grierson, Pauline


    For much of the Southern Hemisphere, the ability to identify spatial and temporal patterns of past climatic variability is constrained by the short length of instrumental records and the sparse spatial distribution of proxy records. This is particularly true for continental Australia, where instrumental records are generally Australia. These chronologies are currently the only multi-century tree-ring records for mainland Australia. Both chronologies are strongly correlated with hydroclimate and allow robust reconstructions of past hydroclimatic variability over spatially broad areas (i.e., > 3° x 3°) of inland western Australia. These reconstructions represent significant extensions of the instrumental rainfall records and reveal inter-annual to multidecadal-scale variation in past hydroclimate over the last two centuries for northwest Australia and four centuries for southwest Australia. In both the northwest and southwest regions, periods of prolonged drought (typically extending between one and three decades) have been interspersed with shorter periods of above-average rainfall (typically less than a decade). Of particular note our northwest record reveals that the last two decades (1995-2012) have been unusually wet in inland northwest Australia compared to the previous two centuries. This period of unusually high rainfall coincided with both an anomalously high frequency and intensity of tropical cyclones in northwest Australia and the dominance of the positive phase of the Southern Annular Mode, both of which are major mechanisms of rainfall delivery to inland northwest Australia. Our tree-ring records also reveal the occurrence of several prolonged drought periods as well as extreme wet events in the last two centuries that were synchronous between northwest and southwest Australia, suggesting possible teleconnections between the two regions. In addition, there appears to have been a generally anti-phase relationship between the hydroclimate of inland

  16. Tracking animal movement by comparing trace element signatures in claws to spatial variability of elements in soils. (United States)

    Ethier, Danielle M; Kyle, Christopher J; Nocera, Joseph J


    Biogeochemical markers in ecology have provided a useful means for indicating geographic origin and movement patterns of species on various temporal and spatial scales. We used trace element analysis to resolve spatial and habitat-specific environmental gradients in elemental distributions that could be used to infer geographic origin and habitat association in a model terrestrial carnivore: American badger (Taxidea taxus jacksoni). To accomplish this, we generated element base-maps using spatial principal component analysis, and assessed habitat-specific signatures using multivariate statistics from soil element concentrations in southwestern Ontario, Canada. Using canonical correlation analysis (CCA) we also test whether element variability in the claw keratin of a terrestrial carnivore could be explained by the chemical variability in the soils of the local environment. Results demonstrated that trace element signatures in soils vary locally with land use practices and soil texture type and broadly with the underlying geology. CCA results suggest that chemical profiles in claws can be linked to the surrounding chemical environment, providing evidence that geographic patterns in mammalian movement can be discerned on the basis of claw chemistry. From this, we conclude that geographic assignment of individuals based on element profiles in their tissues and referenced against soil elemental distributions would be coarse (at a spatial scale of 100-1000 km, depending on the chemical heterogeneity of the landscape), but could be used to assess origin of highly mobile animals or habitat association of individuals. Compared to stable isotope analysis, the assessment of trace elements can provide a much greater level of detail in backcasting animal movement pathways. © 2013. Published by Elsevier B.V. All rights reserved.

  17. Understanding the Temporal and Spatial Variability of New Generation Gridded TMYs

    Energy Technology Data Exchange (ETDEWEB)

    Lopez, Anthony


    Presentation at ASHRAE about the spatial and temporal variability of gridded TMYs, discussing advanced GIS and Web services that allow for direct access to data, surface-based observations for thousands of stations, climate reanalysis data, and products derived from satellite data; new developments in NREL's solar databases based on both observed data and satellite-derived gridded data, status of TMY3 weather files, and NREL's plans for the next-generation TMY weather files; and also covers what is new and different in the Climatic Design Conditions Table in the 2013 ASHRAE Handbook of Fundamentals.

  18. Estimation of the high-spatial-resolution variability in extreme wind speeds for forestry applications (United States)

    Venäläinen, Ari; Laapas, Mikko; Pirinen, Pentti; Horttanainen, Matti; Hyvönen, Reijo; Lehtonen, Ilari; Junila, Päivi; Hou, Meiting; Peltola, Heli M.


    The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In Finland, a forested country, over 50 % of the current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high-spatial-resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20 m spatial resolution CORINE land-use dataset and high-resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalyzed data. Using a geospatial re-mapping technique the data were downscaled to 26 meteorological station locations to represent very diverse environments. Applying a comparison, we find that the downscaled 10-year return levels represent 66 % of the observed variation among the stations examined. In addition, the spatial variation in wind-multiplier-downscaled 10-year return level wind was compared with the WAsP model-simulated wind. The heterogeneous test area was situated in northern Finland, and it was found that the major features of the spatial variation were similar, but in some locations, there were relatively large differences. The results indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations with the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount) of wind damage for certain forest stand configurations.

  19. Spatial-Temporal Correlation Properties of the 3GPP Spatial Channel Model and the Kronecker MIMO Channel Model

    Directory of Open Access Journals (Sweden)

    Cheng-Xiang Wang


    Full Text Available The performance of multiple-input multiple-output (MIMO systems is greatly influenced by the spatial-temporal correlation properties of the underlying MIMO channels. This paper investigates the spatial-temporal correlation characteristics of the spatial channel model (SCM in the Third Generation Partnership Project (3GPP and the Kronecker-based stochastic model (KBSM at three levels, namely, the cluster level, link level, and system level. The KBSM has both the spatial separability and spatial-temporal separability at all the three levels. The spatial-temporal separability is observed for the SCM only at the system level, but not at the cluster and link levels. The SCM shows the spatial separability at the link and system levels, but not at the cluster level since its spatial correlation is related to the joint distribution of the angle of arrival (AoA and angle of departure (AoD. The KBSM with the Gaussian-shaped power azimuth spectrum (PAS is found to fit best the 3GPP SCM in terms of the spatial correlations. Despite its simplicity and analytical tractability, the KBSM is restricted to model only the average spatial-temporal behavior of MIMO channels. The SCM provides more insights of the variations of different MIMO channel realizations, but the implementation complexity is relatively high.

  20. Spatial Uncertainty Model for Visual Features Using a Kinect™ Sensor

    Directory of Open Access Journals (Sweden)

    Jae-Han Park


    Full Text Available This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.

  1. Spatial uncertainty model for visual features using a Kinect™ sensor. (United States)

    Park, Jae-Han; Shin, Yong-Deuk; Bae, Ji-Hun; Baeg, Moon-Hong


    This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.

  2. Spatial and Temporal Variability of Potential Evaporation across North American Forests

    Directory of Open Access Journals (Sweden)

    Robbie A. Hember


    Full Text Available Given the widespread ecological implications that would accompany any significant change in evaporative demand of the atmosphere, this study investigated spatial and temporal variation in several accepted expressions of potential evaporation (PE. The study focussed on forest regions of North America, with 1 km-resolution spatial coverage and a monthly time step, from 1951–2014. We considered Penman’s model (EPen, the Priestley–Taylor model (EPT, ‘reference’ rates based on the Penman–Monteith model for grasslands (ERG, and reference rates for forests that are moderately coupled (ERFu and well coupled (ERFc to the atmosphere. To give context to the models, we also considered a statistical fit (EPanFit to measurements of pan evaporation (EPan. We documented how each model compared with EPan, differences in attribution of variance in PE to specific driving factors, mean spatial patterns, and time trends from 1951–2014. The models did not agree strongly on the sensitivity to underlying drivers, zonal variation of PE, or on the magnitude of trends from 1951–2014. Sensitivity to vapour pressure deficit (Da differed among models, being absent from EPT and strongest in ERFc. Time trends in reference rates derived from the Penman–Monteith equation were highly sensitive to how aerodynamic conductance was set. To the extent that EPanFit accurately reflects the sensitivity of PE to Da over land surfaces, future trends in PE based on the Priestley–Taylor model may underestimate increasing evaporative demand, while reference rates for forests, that assume strong canopy-atmosphere coupling in the Penman–Monteith model, may overestimate increasing evaporative demand. The resulting historical database, covering the spectrum of different models of PE applied in modern studies, can serve to further investigate biosphere-hydroclimate relationships across North America.

  3. Bayesian spatial modeling of HIV mortality via zero-inflated Poisson models. (United States)

    Musal, Muzaffer; Aktekin, Tevfik


    In this paper, we investigate the effects of poverty and inequality on the number of HIV-related deaths in 62 New York counties via Bayesian zero-inflated Poisson models that exhibit spatial dependence. We quantify inequality via the Theil index and poverty via the ratios of two Census 2000 variables, the number of people under the poverty line and the number of people for whom poverty status is determined, in each Zip Code Tabulation Area. The purpose of this study was to investigate the effects of inequality and poverty in addition to spatial dependence between neighboring regions on HIV mortality rate, which can lead to improved health resource allocation decisions. In modeling county-specific HIV counts, we propose Bayesian zero-inflated Poisson models whose rates are functions of both covariate and spatial/random effects. To show how the proposed models work, we used three different publicly available data sets: TIGER Shapefiles, Census 2000, and mortality index files. In addition, we introduce parameter estimation issues of Bayesian zero-inflated Poisson models and discuss MCMC method implications. Copyright © 2012 John Wiley & Sons, Ltd.

  4. Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV). (United States)

    Poblete, Tomas; Ortega-Farías, Samuel; Moreno, Miguel Angel; Bardeen, Matthew


    Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψ stem ). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500-800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψ stem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R²) obtained between ANN outputs and ground-truth measurements of Ψ stem were between 0.56-0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψ stem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of -9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26-0.27 MPa, 0.32-0.34 MPa and -24.2-25.6%, respectively.

  5. Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV

    Directory of Open Access Journals (Sweden)

    Tomas Poblete


    Full Text Available Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem. However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI that use information between 500–800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2 obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56–0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψstem with a mean absolute error (MAE of 0.1 MPa, root mean square error (RMSE of 0.12 MPa, and relative error (RE of −9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26–0.27 MPa, 0.32–0.34 MPa and −24.2–25.6%, respectively.

  6. Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV) (United States)

    Bardeen, Matthew


    Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500–800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2) obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56–0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψstem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of −9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26–0.27 MPa, 0.32–0.34 MPa and −24.2–25.6%, respectively. PMID:29084169

  7. Environmental controls on the spatial variability of soil water dynamics in a small watershed (United States)

    Hu, Wei; Chau, Henry Wai; Qiu, Weiwen; Si, Bingcheng


    Soil water content (SWC) in the root zone is controlled by a suite of environmental variables. Complication arises from the cross-correlation between these environmental variables. Therefore, there is still a poor understanding on the controls of root zone SWC dynamics due, in part, to a lack of an appropriate method to untangle the controls. The objective of this study was to reveal the dominant controls of root zone soil water dynamics in a small watershed using an appropriate method based on empirical orthogonal function (EOF). For this purpose, SWC of 0-0.8 m layer in a small watershed on the Chinese Loess Plateau was used. The space-variant temporal anomaly (Rtn) of SWC, which is responsible for the spatial variability of soil water dynamics, was decomposed using the EOF. Results indicated that 86% of the total variations of Rtn were explained by three significant spatial structures (EOFs). Sand content and grass yield dominated the EOF1 of Rtn and elevation and aspect dominated EOF2 and EOF3 of Rtn , respectively. Moreover, their effects on soil water dynamics were time-dependent. The EOF analysis showed that three independent groups of factors (i.e., soil and vegetation dominated earth surface condition, elevation related near surface air humidity, and aspect regulated energy input) may drive the variability in soil water dynamics. Traditional correlation analysis, however, indicated that SWC was greater at higher elevation and sun-facing slopes, which distorted the soil water dynamics controls. Although original SWC-based partial correlation basically supported our findings, the results highly depended on the controlling factors selected. This study implied that Rtn rather than original SWC should be preferred for understanding soil water dynamics controls.

  8. Investigating Spatial Interdependence in E-Bike Choice Using Spatially Autoregressive Model

    Directory of Open Access Journals (Sweden)

    Chengcheng Xu


    Full Text Available Increased attention has been given to promoting e-bike usage in recent years. However, the research gap still exists in understanding the effects of spatial interdependence on e-bike choice. This study investigated how spatial interdependence affected the e-bike choice. The Moran’s I statistic test showed that spatial interdependence exists in e-bike choice at aggregated level. Bayesian spatial autoregressive logistic analyses were then used to investigate the spatial interdependence at individual level. Separate models were developed for commuting and non-commuting trips. The factors affecting e-bike choice are different between commuting and non-commuting trips. Spatial interdependence exists at both origin and destination sides of commuting and non-commuting trips. Travellers are more likely to choose e-bikes if their neighbours at the trip origin and destination also travel by e-bikes. And the magnitude of this spatial interdependence is different across various traffic analysis zones. The results suggest that, without considering spatial interdependence, the traditional methods may have biased estimation results and make systematic forecasting errors.

  9. Spatial variability of soil carbon across Mexico and the United States (United States)

    Vargas, R.; Guevara, M.; Cruz Gaistardo, C.; Paz, F.; de Jong, B.; Etchevers, J.


    Soil organic carbon (SOC) is directly linked to soil quality, food security, and land use/global environmental change. We use publicly available information on SOC and couple it with digital elevation models and derived terrain attributes using a machine learning approach. We found a strong spatial dependency of SOC across the United States, but less spatial dependency of SOC across Mexico. Using High Performance Computing (HPC) we derived a 1 km resolution map of SOC across Mexico and the United States. We tested different machine learning methods (e.g., kernel based, tree based and/or Geo-statistics approaches) for computational efficiency and statistical accuracy. Using random forest combined with geo-statistics we were able to explain >70% of SOC variance for Mexico and >40% in the case of the United States via cross validation. These results compare with other published estimates of SOC at 1km resolution that only explain <30% of SOC variance across the world. Topographic attributes derived from digital elevation models are freely available globally at fine spatial resolution (<100 m), and this information allowed us to make predictions of SOC at fine scales. We further tested this approach using SOC information from the International Soil Carbon Network to predict SOC in other regions of the world. We conclude that this approach (using public information and open source platforms for data analysis) could be implemented to predict detailed explicit information of SOC across different spatial scales.

  10. Spatial uncertainty modeling of fuzzy information in images for pattern classification.

    Directory of Open Access Journals (Sweden)

    Tuan D Pham

    Full Text Available The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction.

  11. Verification of Spatial Forecasts of Continuous Meteorological Variables Using Categorical and Object-Based Methods (United States)


    or missions. This report presents methods to verify forecast fields of meteorological variables that have been filtered by the application of a...forecast-evaluation technique has great potential in assessing forecasts of continuous variables that have been filtered by the application of a threshold...Distribution List 30 Approved for public release; distribution is unlimited. iv List of Figures Fig. 1 Triple- nested model domains: domain center

  12. Assessing spatial and temporal variability of phytoplankton communities' composition in the Iroise Sea ecosystem (Brittany, France): A 3D modeling approach. Part 1: Biophysical control over plankton functional types succession and distribution (United States)

    Cadier, Mathilde; Gorgues, Thomas; Sourisseau, Marc; Edwards, Christopher A.; Aumont, Olivier; Marié, Louis; Memery, Laurent


    Understanding the dynamic interplay between physical, biogeochemical and biological processes represents a key challenge in oceanography, particularly in shelf seas where complex hydrodynamics are likely to drive nutrient distribution and niche partitioning of phytoplankton communities. The Iroise Sea includes a tidal front called the 'Ushant Front' that undergoes a pronounced seasonal cycle, with a marked signal during the summer. These characteristics as well as relatively good observational sampling make it a region of choice to study processes impacting phytoplankton dynamics. This innovative modeling study employs a phytoplankton-diversity model, coupled to a regional circulation model to explore mechanisms that alter biogeography of phytoplankton in this highly dynamic environment. Phytoplankton assemblages are mainly influenced by the depth of the mixed layer on a seasonal time scale. Indeed, solar incident irradiance is a limiting resource for phototrophic growth and small phytoplankton cells are advantaged over larger cells. This phenomenon is particularly relevant when vertical mixing is intense, such as during winter and early spring. Relaxation of wind-induced mixing in April causes an improvement of irradiance experienced by cells across the whole study area. This leads, in late spring, to a competitive advantage of larger functional groups such as diatoms as long as the nutrient supply is sufficient. This dominance of large, fast-growing autotrophic cells is also maintained during summer in the productive tidally-mixed shelf waters. In the oligotrophic surface layer of the western part of the Iroise Sea, small cells coexist in a greater proportion with large, nutrient limited cells. The productive Ushant tidal front's region (1800 mgC·m- 2·d- 1 between August and September) is also characterized by a high degree of coexistence between three functional groups (diatoms, micro/nano-flagellates and small eukaryotes/cyanobacteria). Consistent with

  13. Spatial and temporal variability in outdoor, indoor, and personal PM 2.5 exposure (United States)

    Adgate, J. L.; Ramachandran, G.; Pratt, G. C.; Waller, L. A.; Sexton, K.

    Outdoor, indoor and personal PM 2.5 measurements were made in a population of nonsmoking adults from three communities in the Minneapolis-St. Paul metropolitan area between April and November 1999. Thirty-two healthy adult subjects (23 females, 9 males; mean age 42±10, range: 24-64 yr) were monitored for 2-15 days during the spring, summer, and fall monitoring seasons. Twenty-four hour average gravimetric PM 2.5 samples were collected using a federal reference monitor (Anderson RAAS2.5-300) located at outdoor (O) central sites in the Battle Creek (BCK), East St. Paul (ESP) and Phillips (PHI) communities. Concurrent 24-h average indoor (I) and personal (P), and a limited number of outdoor-at-home (O@H) samples were collected using inertial impactors (PEM™ Model 200, MSP, Inc). The O (geometric mean {GM}=8.6; n=271; range: 1.0-41 μg/m 3) were lower than I concentrations (GM=10.7; n=294; range 1.3-131 μg/m 3), which were lower than P concentrations (GM=19.0; n=332; range 2.2-298 μg/m 3). Correlation coefficients between O concentrations in the three communities were high and measured GM O levels in BCK were significantly lower than ESP, most likely because of local sources, but GM concentrations in PHI were not significantly different from BCK or ESP. On days with paired samples ( n=29), O concentrations were significantly lower (mean difference 2.9 μg/m 3; p=0.026) than O@H measurements (GM=11.3; range: 3.5-33.8 μg/m 3), likely due to local sources in communities. Observed I and P concentrations were more variable, probably because of residential central air conditioning and hours of household ventilation for I and P, and occupational and environmental tobacco smoke exposures outside the residence for P. Across all individuals and days the median PM 2.5 "personal cloud" was 5.7 μg/m 3, but the mean of the average for each participant was 15.7 μg/m 3, with very low values in participants who did not work outside the home and much higher values in subjects

  14. Stochastic Spatial Models in Ecology: A Statistical Physics Approach (United States)

    Pigolotti, Simone; Cencini, Massimo; Molina, Daniel; Muñoz, Miguel A.


    Ecosystems display a complex spatial organization. Ecologists have long tried to characterize them by looking at how different measures of biodiversity change across spatial scales. Ecological neutral theory has provided simple predictions accounting for general empirical patterns in communities of competing species. However, while neutral theory in well-mixed ecosystems is mathematically well understood, spatial models still present several open problems, limiting the quantitative understanding of spatial biodiversity. In this review, we discuss the state of the art in spatial neutral theory. We emphasize the connection between spatial ecological models and the physics of non-equilibrium phase transitions and how concepts developed in statistical physics translate in population dynamics, and vice versa. We focus on non-trivial scaling laws arising at the critical dimension D = 2 of spatial neutral models, and their relevance for biological populations inhabiting two-dimensional environments. We conclude by discussing models incorporating non-neutral effects in the form of spatial and temporal disorder, and analyze how their predictions deviate from those of purely neutral theories.

  15. Spatial and Temporal Variability and Trends in 2001-2016 Global Fire Activity (United States)

    Earl, Nick; Simmonds, Ian


    Fire regimes across the globe have great spatial and temporal variability, and these are influence by many factors including anthropogenic management, climate, and vegetation types. Here we utilize the satellite-based "active fire" product, from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, to statistically analyze variability and trends in fire activity from the global to regional scales. We split up the regions by economic development, region/geographical land use, clusters of fire-abundant areas, or by religious/cultural influence. Weekly cycle tests are conducted to highlight and quantify part of the anthropogenic influence on fire regime across the world. We find that there is a strong statistically significant decline in 2001-2016 active fires globally linked to an increase in net primary productivity observed in northern Africa, along with global agricultural expansion and intensification, which generally reduces fire activity. There are high levels of variability, however. The large-scale regions exhibit either little change or decreasing in fire activity except for strong increasing trends in India and China, where rapid population increase is occurring, leading to agricultural intensification and increased crop residue burning. Variability in Canada has been linked to a warming global climate leading to a longer growing season and higher fuel loads. Areas with a strong weekly cycle give a good indication of where fire management is being applied most extensively, for example, the United States, where few areas retain a natural fire regime.

  16. Modeling variability in porescale multiphase flow experiments

    Energy Technology Data Exchange (ETDEWEB)

    Ling, Bowen; Bao, Jie; Oostrom, Mart; Battiato, Ilenia; Tartakovsky, Alexandre M.


    Microfluidic devices and porescale numerical models are commonly used to study multiphase flow in biological, geological, and engineered porous materials. In this work, we perform a set of drainage and imbibition experiments in six identical microfluidic cells to study the reproducibility of multiphase flow experiments. We observe significant variations in the experimental results, which are smaller during the drainage stage and larger during the imbibition stage. We demonstrate that these variations are due to sub-porescale geometry differences in microcells (because of manufacturing defects) and variations in the boundary condition (i.e.,fluctuations in the injection rate inherent to syringe pumps). Computational simulations are conducted using commercial software STAR-CCM+, both with constant and randomly varying injection rate. Stochastic simulations are able to capture variability in the experiments associated with the varying pump injection rate.

  17. Modeling variability in porescale multiphase flow experiments (United States)

    Ling, Bowen; Bao, Jie; Oostrom, Mart; Battiato, Ilenia; Tartakovsky, Alexandre M.


    Microfluidic devices and porescale numerical models are commonly used to study multiphase flow in biological, geological, and engineered porous materials. In this work, we perform a set of drainage and imbibition experiments in six identical microfluidic cells to study the reproducibility of multiphase flow experiments. We observe significant variations in the experimental results, which are smaller during the drainage stage and larger during the imbibition stage. We demonstrate that these variations are due to sub-porescale geometry differences in microcells (because of manufacturing defects) and variations in the boundary condition (i.e., fluctuations in the injection rate inherent to syringe pumps). Computational simulations are conducted using commercial software STAR-CCM+, both with constant and randomly varying injection rates. Stochastic simulations are able to capture variability in the experiments associated with the varying pump injection rate.

  18. Updates to the Demographic and Spatial Allocation Models to ... (United States)

    EPA announced the availability of the draft report, Updates to the Demographic and Spatial Allocation Models to Produce Integrated Climate and Land Use Scenarios (ICLUS) for a 30-day public comment period. The ICLUS version 2 (v2) modeling tool furthered land change modeling by providing nationwide housing development scenarios up to 2100. ICLUS V2 includes updated population and land use data sets and addressing limitations identified in ICLUS v1 in both the migration and spatial allocation models. The companion user guide describes the development of ICLUS v2 and the updates that were made to the original data sets and the demographic and spatial allocation models. [2017 UPDATE] Get the latest version of ICLUS and stay up-to-date by signing up to the ICLUS mailing list. The GIS tool enables users to run SERGoM with the population projections developed for the ICLUS project and allows users to modify the spatial allocation housing density across the landscape.

  19. Spatially uniform but temporally variable bacterioplankton in a semi-enclosed coastal area. (United States)

    Meziti, Alexandra; Kormas, Konstantinos A; Moustaka-Gouni, Maria; Karayanni, Hera


    Studies focusing on the temporal and spatial dynamics of bacterioplankton communities within littoral areas undergoing direct influences from the coast are quite limited. In addition, they are more complicated to resolve compared to communities in the open ocean. In order to elucidate the effects of spatial vs. temporal variability on bacterial communities in a highly land-influenced semi-enclosed gulf, surface bacterioplankton communities from five coastal sites in Igoumenitsa Gulf (Ionian Sea, Greece) were analyzed over a nine-month period using 16S rDNA 454-pyrosequencing. Temporal differences were more pronounced than spatial ones, with lower diversity indices observed during the summer months. During winter and early spring, bacterial communities were dominated by SAR11 representatives, while this pattern changed in May when they were abruptly replaced by members of Flavobacteriales, Pseudomonadales, and Alteromonadales. Additionally, correlation analysis showed high negative correlations between the presence of SAR11 OTUs in relation to temperature and sunlight that might have driven, directly or indirectly, the disappearance of these OTUs in the summer months. The dominance of SAR11 during the winter months further supported the global distribution of the clade, not only in the open-sea, but also in coastal systems. This study revealed that specific bacteria exhibited distinct succession patterns in an anthropogenic-impacted coastal system. The major bacterioplankton component was represented by commonly found marine bacteria exhibiting seasonal dynamics, while freshwater and terrestrial-related phylotypes were absent. Copyright © 2015 Elsevier GmbH. All rights reserved.

  20. Temporal and Spatial Variability of Droughts in Southwest China from 1961 to 2012

    Directory of Open Access Journals (Sweden)

    Yaohuan Huang


    Full Text Available Southwest China (SC has suffered a series of super extreme droughts in the last decade. This study analyzed the temporal and spatial variations of drought in SC from 1961 to 2012. Based on precipitation anomaly index (PAI that was derived from 1 km gridded precipitation data, three time scales (month, year and decade for the drought frequency (DF and drought area were applied to estimate the spatio-temporal structure of droughts. A time-series analysis showed that winter droughts and spring droughts occurred frequently for almost half of the year from November to March. Summer droughts occasionally occurred in severe drought decades: the 1960s, 1980s and 2000s. During the period of observation, the percent of drought area in SC increased from the 1960s (<5% to the 2000s (>25%. A total of 57% of the area was affected by drought in 2011, when the area experienced its most severe drought both in terms of area and severity. The spatial analysis, which benefitted from the gridded data, detailed that all of SC is at drought risk except for the central Sichuan Basin. The area at high risk for severe and extreme droughts was localized in the mountains of the junction of Sichuan and Yunnan. The temporal and spatial variability can be prerequisites for drought resistance planning and drought risk management of SC.

  1. Physically-based modeling of topographic effects on spatial evapotranspiration and soil moisture patterns through radiation and wind

    Directory of Open Access Journals (Sweden)

    M. Liu


    Full Text Available In this paper, simulations with the Soil Water Atmosphere Plant (SWAP model are performed to quantify the spatial variability of both potential and actual evapotranspiration (ET, and soil moisture content (SMC caused by topography-induced spatial wind and radiation differences. To obtain the spatially distributed ET/SMC patterns, the field scale SWAP model is applied in a distributed way for both pointwise and catchment wide simulations. An adapted radiation model from r.sun and the physically-based meso-scale wind model METRAS PC are applied to obtain the spatial radiation and wind patterns respectively, which show significant spatial variation and correlation with aspect and elevation respectively. Such topographic dependences and spatial variations further propagate to ET/SMC. A strong spatial, seasonal-dependent, scale-relevant intra-catchment variability in daily/annual ET and less variability in SMC can be observed from the numerical experiments. The study concludes that topography has a significant effect on ET/SMC in the humid region where ET is a energy limited rather than water availability limited process. It affects the spatial runoff generation through spatial radiation and wind, therefore should be applied to inform hydrological model development. In addition, the methodology used in the study can serve as a general method for physically-based ET estimation for data sparse regions.

  2. [Nutrient spatial variability of tobacco soil restoration area and fertility suitability level evaluation]. (United States)

    Xu, Da-Bing; Deng, Jian-Qiang; Liu, Dong-Bi; Si, Guo-Han; Peng, Cheng-Lin; Yuan, Jia-Fu; Zhao, Shu-Jun; Wang, Rui


    By using geographic information system technology (GIS) and geostatistics methods, this paper studied the spatial variability of soil properties and available nutrients in the new regulation area units located in Qingjiangyuan modern tobacco agriculture science and technology park (Enshi, Hubei), suburb of Enshi City and the Baiyang base of Lichuan City, and further evaluation of the soil fertility suitability index (SFI) was carried out by use fuzzy mathematics. The results indicated that the effects of land restoration on the soil available phosphorus content variability and spatial distribution were very obvious, possibly due to the landform characteristics and restoration extent. The effect of land restoration on soil pH was small, however, serious soil acidification was detected in the soil sampled from Baiyang (pH soils taken from the suburb, Baiyang and Qingjiangyuan, respectively. In conclusion, attentions should be paid on soil acidification in Baiyang, soil fertility and equalization in the suburb, and soil fertility in the region of Qingjiangyuan with low SFI.

  3. The Significance of the Spatial Variability of Rainfall on the Numerical Simulation of Urban Floods

    Directory of Open Access Journals (Sweden)

    Laurent Guillaume Courty


    Full Text Available The growth of urban population, combined with an increase of extreme events due to climate change call for a better understanding and representation of urban floods. The uncertainty in rainfall distribution is one of the most important factors that affects the watershed response to a given precipitation event. However, most of the investigations on this topic have considered theoretical scenarios, with little reference to case studies in the real world. This paper incorporates the use of spatially-variable precipitation data from a long-range radar in the simulation of the severe floods that impacted the city of Hull, U.K., in June 2007. This radar-based rainfall field is merged with rain gauge data using a Kriging with External Drift interpolation technique. The utility of this spatially-variable information is investigated through the comparison of computed flooded areas (uniform and radar against those registered by public authorities. Both results show similar skills at reproducing the real event, but differences in the total precipitated volumes, water depths and flooded areas are illustrated. It is envisaged that in urban areas and with the advent of higher resolution radars, these differences will be more important and call for further investigation.

  4. Spatial variability of enthalpy in broiler house during the heating phase

    Directory of Open Access Journals (Sweden)

    Patrícia F. P. Ferraz


    Full Text Available ABSTRACT The thermal environment inside a broiler house has a great influence on animal welfare and productivity during the production phase. Enthalpy is a thermodynamic property that has been proposed to evaluate the internal broiler house environment, for being an indicator of the amount of energy contained in a mixture of water vapor and dry air. Therefore, this study aimed to characterize the spatial variability of enthalpy in a broiler house during the heating phase using geostatistics. The experiment was conducted in the spring season, in a commercial broiler house with heating system consisting of two furnaces that heat the air indirectly, in the first 14 days of the birds' life. It was possible to characterize enthalpy variability using geostatistical techniques, which allowed observing the spatial dependence through kriging maps. The analyses of the maps allowed observing problems in the heating system in regions inside the broiler house, which may cause a thermal discomfort to the animals besides productive and economic losses.

  5. Spatial variability of N, P, and K in rice field in Sawah Sempadan, Malaysia

    Directory of Open Access Journals (Sweden)

    Saeed Mohamed Eltaib


    Full Text Available The variability of soil chemical properties such as total N, available P, and exchangeable K were examined on a 1.2 ha rice (Oryza sativa field. The soil (n = 72 samples were systematically taken from individual fields in Sawah Sempadan in thirty-six locations at two depths (0-20 and 20-30 cm. The Differential Global Positioning System (DGPS was used for locating the sample position. Geostatistical techniques were used to analyze the soil chemical properties variability of the samples that assist in site-specific management of the field. Results showed that areas of similarity were much greater for the soil chemical properties measured at the depth of (0-20 cm than that of the second lower (20- 30 cm. The ranges of the semivariogram for total N, available P, and exchangeable K were 12, and 13 m (0-20 cm, 12 and 38 m (20-30 cm, respectively. Point kriging calculated from the semivariogram was employed for spatial distribution map. The results suggested that soil chemical properties measured may be spatially dependent even within the small.

  6. How spatial and temporal rainfall variability affect runoff across basin scales: insights from field observations in the (semi-)urbanised Charlotte watershed (United States)

    Ten Veldhuis, M. C.; Smith, J. A.; Zhou, Z.


    Impacts of rainfall variability on runoff response are highly scale-dependent. Sensitivity analyses based on hydrological model simulations have shown that impacts are likely to depend on combinations of storm type, basin versus storm scale, temporal versus spatial rainfall variability. So far, few of these conclusions have been confirmed on observational grounds, since high quality datasets of spatially variable rainfall and runoff over prolonged periods are rare. Here we investigate relationships between rainfall variability and runoff response based on 30 years of radar-rainfall datasets and flow measurements for 16 hydrological basins ranging from 7 to 111 km2. Basins vary not only in scale, but also in their degree of urbanisation. We investigated temporal and spatial variability characteristics of rainfall fields across a range of spatial and temporal scales to identify main drivers for variability in runoff response. We identified 3 ranges of basin size with different temporal versus spatial rainfall variability characteristics. Total rainfall volume proved to be the dominant agent determining runoff response at all basin scales, independent of their degree of urbanisation. Peak rainfall intensity and storm core volume are of secondary importance. This applies to all runoff parameters, including runoff volume, runoff peak, volume-to-peak and lag time. Position and movement of the storm with respect to the basin have a negligible influence on runoff response, with the exception of lag times in some of the larger basins. This highlights the importance of accuracy in rainfall estimation: getting the position right but the volume wrong will inevitably lead to large errors in runoff prediction. Our study helps to identify conditions where rainfall variability matters for correct estimation of the rainfall volume as well as the associated runoff response.

  7. Variability of Relative Sea Level Rise: Spatial and Temporal Correlations in Northwest Gulf of Mexico (United States)

    Tissot, P.; Reisinger, A. S.; Besonen, M. R.


    While our understanding of global sea level rise and its budget has made great progress over the past decade, the spatial and temporal variability of relative sea level rise along the coasts still needs to be better understood and quantified. We developed a technique to reduce the confidence intervals associated with relative sea level rise (RSLR) estimates for 15 tide gauges located along the Texas coast for the period 1993-2016. Seasonally detrended monthly mean water levels are highly correlated after removal of station-specific RSLR trends, which allows for the quantification of a common, low frequency oceanic signal. RSLR confidence intervals are reduced from over 1.9 mm/yr, on average 2.3mm, to less than 1.1 mm/yr, on average 0.7 mm/yr after removing this common signal. The resulting RSLR rates range from 3.0 to 8.4 mm/yr. The range is wider than the longer-term rates of 5.3, 3.8 and 1.9 mm/yr measured from north to south by the three National Water Level Observation Network (NWLON) stations covering the study area (over different and longer time spans). The results emphasize the importance of the spatial variability of the vertical land motion component of RSLR. The temporal variability of the coherent oceanic signal is not significantly correlated to the ENSO signal for the study period and is only weakly correlated to the AMO and PDO climate indices. The coherence of the signal is further investigated by comparison with other locations along the Gulf of Mexico and along the Northeast Atlantic coast. The results are discussed while considering strong local processes along the Northwest Gulf of Mexico, such as wind forcing and intermittent eddies and the spatially broader influence of the Gulf Stream. The local significance of the RSLR spatial and temporal differences are discussed in terms of the differences in inundation frequency for nuisance type flooding including comparing the time span to reach a probability of at least one nuisance flood event per

  8. Ozone Concentration Prediction via Spatiotemporal Autoregressive Model With Exogenous Variables (United States)

    Kamoun, W.; Senoussi, R.


    Forecast of environmental variables are nowadays of main concern for public health or agricultural management. In this context a large literature is devoted to spatio-temporal modelling of these variables using different statistical approaches. However, most of studies ignored the potential contribution of local (e.g. meteorological and/or geographical) covariables as well as the dynamical characteristics of observations. In this study, we present a spatiotemporal short term forecasting model for ozone concentration based on regularly observed covariables in predefined geographical sites. Our driving system simply combines a multidimensional second order autoregressive structured process with a linear regression model over influent exogenous factors and reads as follows: ‘2 ‘q j Z (t) = A (Î&,cedil;D )Ã- [ αiZ(t- i)]+ B (Î&,cedil;D )Ã- [ βjX (t)]+ ɛ(t) i=1 j=1 Z(t)=(Z1(t),…,Zn(t)) represents the vector of ozone concentration at time t of the n geographical sites, whereas Xj(t)=(X1j(t),…,Xnj(t)) denotes the jth exogenous variable observed over these sites. The nxn matrix functions A and B account for the spatial relationships between sites through the inter site distance matrix D and a vector parameter Î&.cedil; Multidimensional white noise ɛ is assumed to be Gaussian and spatially correlated but temporally independent. A covariance structure of Z that takes account of noise spatial dependences is deduced under a stationary hypothesis and then included in the likelihood function. Statistical model and estimation procedure: Contrarily to the widely used choice of a {0,1}-valued neighbour matrix A, we put forward two more natural choices of exponential or power decay. Moreover, the model revealed enough stable to readily accommodate the crude observations without the usual tedious and somewhat arbitrarily variable transformations. Data set and preliminary analysis: In our case, ozone variable represents here the daily maximum ozone

  9. Impacts of ozone air pollution and temperature extremes on crop yields: Spatial variability, adaptation and implications for future food security (United States)

    Tai, Amos P. K.; Val Martin, Maria


    Ozone air pollution and climate change pose major threats to global crop production, with ramifications for future food security. Previous studies of ozone and warming impacts on crops typically do not account for the strong ozone-temperature correlation when interpreting crop-ozone or crop-temperature relationships, or the spatial variability of crop-to-ozone sensitivity arising from varietal and environmental differences, leading to potential biases in their estimated crop losses. Here we develop an empirical model, called the partial derivative-linear regression (PDLR) model, to estimate the spatial variations in the sensitivities of wheat, maize and soybean yields to ozone exposures and temperature extremes in the US and Europe using a composite of multidecadal datasets, fully correcting for ozone-temperature covariation. We find generally larger and more spatially varying sensitivities of all three crops to ozone exposures than are implied by experimentally derived concentration-response functions used in most previous studies. Stronger ozone tolerance is found in regions with high ozone levels and high consumptive crop water use, reflecting the existence of spatial adaptation and effect of water constraints. The spatially varying sensitivities to temperature extremes also indicate stronger heat tolerance in crops grown in warmer regions. The spatial adaptation of crops to ozone and temperature we find can serve as a surrogate for future adaptation. Using the PDLR-derived sensitivities and 2000-2050 ozone and temperature projections by the Community Earth System Model, we estimate that future warming and unmitigated ozone pollution can combine to cause an average decline in US wheat, maize and soybean production by 13%, 43% and 28%, respectively, and a smaller decline for European crops. Aggressive ozone regulation is shown to offset such decline to various extents, especially for wheat. Our findings demonstrate the importance of considering ozone regulation

  10. Interdecadal variability in a global coupled model

    International Nuclear Information System (INIS)

    Storch, J.S. von.


    Interdecadal variations are studied in a 325-year simulation performed by a coupled atmosphere - ocean general circulation model. The patterns obtained in this study may be considered as characteristic patterns for interdecadal variations. 1. The atmosphere: Interdecadal variations have no preferred time scales, but reveal well-organized spatial structures. They appear as two modes, one is related with variations of the tropical easterlies and the other with the Southern Hemisphere westerlies. Both have red spectra. The amplitude of the associated wind anomalies is largest in the upper troposphere. The associated temperature anomalies are in thermal-wind balance with the zonal winds and are out-of-phase between the troposphere and the lower stratosphere. 2. The Pacific Ocean: The dominant mode in the Pacific appears to be wind-driven in the midlatitudes and is related to air-sea interaction processes during one stage of the oscillation in the tropics. Anomalies of this mode propagate westward in the tropics and the northward (southwestward) in the North (South) Pacific on a time scale of about 10 to 20 years. (orig.)

  11. A multivariate conditional model for streamflow prediction and spatial precipitation refinement (United States)

    Liu, Zhiyong; Zhou, Ping; Chen, Xiuzhi; Guan, Yinghui


    The effective prediction and estimation of hydrometeorological variables are important for water resources planning and management. In this study, we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates. This model consists of high dimensional vine copulas, conditional bivariate copula simulations, and a quantile-copula function. The vine copula is employed because of its flexibility in modeling the high dimensional joint distribution of multivariate data by building a hierarchy of conditional bivariate copulas. We investigate two cases to evaluate the performance and applicability of the proposed approach. In the first case, we generate one month ahead streamflow forecasts that incorporate multiple predictors including antecedent precipitation and streamflow records in a basin located in South China. The prediction accuracy of the vine-based model is compared with that of traditional data-driven models such as the support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS). The results indicate that the proposed model produces more skillful forecasts than SVR and ANFIS. Moreover, this probabilistic model yields additional information concerning the predictive uncertainty. The second case involves refining spatial precipitation estimates derived from the tropical rainfall measuring mission precipitationproduct for the Yangtze River basin by incorporating remotely sensed soil moisture data and the observed precipitation from meteorological gauges over the basin. The validation results indicate that the proposed model successfully refines the spatial precipitation estimates. Although this model is tested for specific cases, it can be extended to other hydrometeorological variables for predictions and spatial estimations.

  12. An API for Integrating Spatial Context Models with Spatial Reasoning Algorithms

    DEFF Research Database (Denmark)

    Kjærgaard, Mikkel Baun


    The integration of context-aware applications with spatial context models is often done using a common query language. However, algorithms that estimate and reason about spatial context information can benefit from a tighter integration. An object-oriented API makes such integration possible...... and can help reduce the complexity of algorithms making them easier to maintain and develop. This paper propose an object-oriented API for context models of the physical environment and extensions to a location modeling approach called geometric space trees for it to provide adequate support for location...... modeling. The utility of the API is evaluated in several real-world cases from an indoor location system, and spans several types of spatial reasoning algorithms....

  13. Passive sampling to capture the spatial variability of coarse particles by composition in Cleveland, OH (United States)

    Sawvel, Eric J.; Willis, Robert; West, Roger R.; Casuccio, Gary S.; Norris, Gary; Kumar, Naresh; Hammond, Davyda; Peters, Thomas M.


    Passive samplers deployed at 25 sites for three, week-long intervals were used to characterize spatial variability in the mass and composition of coarse particulate matter (PM10-2.5) in Cleveland, OH in summer 2008. The size and composition of individual particles determined using computer-controlled scanning electron microscopy with energy-dispersive X-ray spectroscopy (CCSEM-EDS) was then used to estimate PM10-2.5 concentrations (μg m-3) and its components in 13 particle classes. The highest PM10-2.5 mean mass concentrations were observed at three central industrial sites (35 μg m-3, 43 μg m-3, and 48 μg m-3), whereas substantially lower mean concentrations were observed to the west and east of this area at suburban background sites (13 μg m-3 and 15 μg m-3). PM10-2.5 mass and components associated with steel and cement production (Fe-oxide and Ca-rich) exhibited substantial heterogeneity with elevated concentrations observed in the river valley, stretching from Lake Erie south through the central industrial area and in the case of Fe-oxide to a suburban valley site. Other components (e.g., Si/Al-rich typical of crustal material) were considerably less heterogeneous. This work shows that some species of coarse particles are considerably more spatially heterogeneous than others in an urban area with a strong industrial core. It also demonstrates that passive sampling coupled with analysis by CCSEM-EDS is a useful tool to assess the spatial variability of particulate pollutants by composition.

  14. Spatial variability of shortwave radiative fluxes in the context of snowmelt (United States)

    Pinker, Rachel T.; Ma, Yingtao; Hinkelman, Laura; Lundquist, Jessica


    Snow-covered mountain ranges are a major source of water supply for run-off and groundwater recharge. Snowmelt supplies as much as 75% of surface water in basins of the western United States. Factors that affect the rate of snow melt include incoming shortwave and longwave radiation, surface albedo, snow emissivity, snow surface temperature, sensible and latent heat fluxes, ground heat flux, and energy transferred to the snowpack from deposited snow or rain. The net radiation generally makes up about 80% of the energy balance and is dominated by the shortwave radiation. Complex terrain poses a great challenge for obtaining the needed information on radiative fluxes from satellites due to elevation issues, spatially-variable cloud cover, rapidly changing surface conditions during snow fall and snow melt, lack of high quality ground truth for evaluation of the satellite based estimates, as well as scale issues between the ground observations and the satellite footprint. In this study we utilize observations of high spatial resolution (5-km) as available from the Moderate Resolution Imaging Spectro-radiometer (MODIS) to derive surface shortwave radiative fluxes in complex terrain, with attention to the impact of slopes on the amount of radiation received. The methodology developed has been applied to several water years (January to July during 2003, 2004, 2005 and 2009) over the western part of the United States, and the available information was used to derive metrics on spatial and temporal variability in the shortwave fluxes. It is planned to apply the findings from this study for testing improvements in Snow Water Equivalent (SWE) estimates.

  15. Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology. (United States)

    Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H


    Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in

  16. Selecting one among many referents in spatial situation models

    NARCIS (Netherlands)

    Bower, G.H.; Rinck, M.


    Five experiments related to anaphor resolution to a classic memory variable, namely, interference created by multiple uses of a given object-concept, and by spatial distance of the referent from the reader's focus of attention. Participants memorized a diagram of a building with rooms containing

  17. Prediction of water temperature metrics using spatial modelling in ...

    African Journals Online (AJOL)

    Water temperature regime dynamics should be viewed regionally, where regional divisions have an inherent underpinning by an understanding of natural thermal variability. The aim of this research was to link key water temperature metrics to readily-mapped environmental surrogates, and to produce spatial images of ...

  18. Pattern formation through spatial interactions in a modified Daisyworld model (United States)

    Alberti, Tommaso; Primavera, Leonardo; Lepreti, Fabio; Vecchio, Antonio; Carbone, Vincenzo


    The Daisyworld model is based on a hypothetical planet, like the Earth, which receives the radiant energy coming from a Sun-like star, and populated by two kinds of identical plants differing by their colour: white daisies reflecting light and black daisies absorbing light. The interactions and feedbacks between the collective biota of the planet and the incoming radiation form a self-regulating system where the conditions for life are maintained. We investigate a modified version of the Daisyworld model where a spatial dependency on latitude is introduced, and both a variable heat diffusivity along latitude and a simple greenhouse model are included. We show that the spatial interactions between the variables of the system can generate some equilibrium patterns which can locally stabilize the coexistence of the two vegetation types. The feedback on albedo is able to generate new equilibrium solutions which can efficiently self-regulate the planet climate, even for values of the solar luminosity relatively far from the current Earth conditions. The extension to spatial Daisyworld gives room to the possibility of inhomogeneous solar forcing in a curved planet, with explicit differences between poles and equator and the direct use of the heat diffusion equation. As a first approach, to describe a spherical planet, we consider the temperature T(θ,t) and the surface coverage as depending only on time and on latitude θ (-90° ≤ θ ≤ 90°). A second step is the introduction of the greenhouse effect in the model, the process by which outgoing infrared radiation is partly screened by greenhouse gases. This effect can be described by relaxing the black-body radiation hypothesis and by introducing a grayness function g(T) in the heat equation. As a third step, we consider a latitude dependence of the Earth's conductivity, χ = χ(θ). Considering these terms, using spherical coordinates and symmetry with respect to θ, the modified Daisyworld equations reduce to the

  19. Quantifying the influence of land-use and surface characteristics on spatial variability in the urban heat island (United States)

    Hart, Melissa A.; Sailor, David J.


    The urban thermal environment varies not only from its rural surroundings but also within the urban area due to intra-urban differences in land-use and surface characteristics. Understanding the causes of this intra-urban variability is a first step in improving urban planning and development. Toward this end, a method for quantifying causes of spatial variability in the urban heat island has been developed. This paper presents the method as applied to a specific test case of Portland, Oregon. Vehicle temperature traverses were used to determine spatial differences in summertime ~2 m air temperature across the metropolitan area in the afternoon. A tree-structured regression model was used to quantify the land-use and surface characteristics that have the greatest influence on daytime UHI intensity. The most important urban characteristic separating warmer from cooler regions of the Portland metropolitan area was canopy cover. Roadway area density was also an important determinant of local UHI magnitudes. Specifically, the air above major arterial roads was found to be warmer on weekdays than weekends, possibly due to increased anthropogenic activity from the vehicle sector on weekdays. In general, warmer regions of the city were associated with industrial and commercial land-use. The downtown core, whilst warmer than the rural surroundings, was not the warmest part of the Portland metropolitan area. This is thought to be due in large part to local shading effects in the urban canyons.

  20. Applications of spatial statistical network models to stream data (United States)

    Isaak, Daniel J.; Peterson, Erin E.; Ver Hoef, Jay M.; Wenger, Seth J.; Falke, Jeffrey A.; Torgersen, Christian E.; Sowder, Colin; Steel, E. Ashley; Fortin, Marie-Josée; Jordan, Chris E.; Ruesch, Aaron S.; Som, Nicholas; Monestiez, Pascal


    Streams and rivers host a significant portion of Earth's biodiversity and provide important ecosystem services for human populations. Accurate information regarding the status and trends of stream resources is vital for their effective conservation and management. Most statistical techniques applied to data measured on stream networks were developed for terrestrial applications and are not optimized for streams. A new class of spatial statistical model, based on valid covariance structures for stream networks, can be used with many common types of stream data (e.g., water quality attributes, habitat conditions, biological surveys) through application of appropriate distributions (e.g., Gaussian, binomial, Poisson). The spatial statistical network models account for spatial autocorrelation (i.e., nonindependence) among measurements, which allows their application to databases with clustered measurement locations. Large amounts of stream data exist in many areas where spatial statistical analyses could be used to develop novel insights, improve predictions at unsampled sites, and aid in the design of efficient monitoring strategies at relatively low cost. We review the topic of spatial autocorrelation and its effects on statistical inference, demonstrate the use of spatial statistics with stream datasets relevant to common research and management questions, and discuss additional applications and development potential for spatial statistics on stream networks. Free software for implementing the spatial statistical network models has been developed that enables custom applications with many stream databases.

  1. Spatial Modeling of Risk in Natural Resource Management

    Directory of Open Access Journals (Sweden)

    Peter Jones


    Full Text Available Making decisions in natural resource management involves an understanding of the risk and uncertainty of the outcomes, such as crop failure or cattle starvation, and of the normal spread of the expected production. Hedging against poor outcomes often means lack of investment and slow adoption of new methods. At the household level, production instability can have serious effects on income and food security. At the national level, it can have social and economic impacts that may affect all sectors of society. Crop models such as CERES-Maize are excellent tools for assessing weather-related production variability. WATBAL is a water balance model that can provide robust estimates of the potential growing days for a pasture. These models require large quantities of daily weather data that are rarely available. MarkSim is an application for generating synthetic daily weather files by estimating the third-order Markov model parameters from interpolated climate surfaces. The models can then be run for each distinct point on the map. This paper examines the growth of maize and pasture in dryland agriculture in southern Africa. Weather simulators produce independent estimates for each point on the map; however, we know that a spatial coherence of weather exists. We investigated a method of incorporating spatial coherence into MarkSim and show that it increases the variance of production. This means that all of the farmers in a coherent area share poor yields, with important consequences for food security, markets, transport, and shared grazing lands. The long-term aspects of risk are associated with global climate change. We used the results of a Global Circulation Model to extrapolate to the year 2055. We found that low maize yields would become more likely in the marginal areas, whereas they may actually increase in some areas. The same trend was found with pasture growth. We outline areas where further work is required before these tools and methods

  2. Mineralogy of the clay fraction of Alfisols in two slope curvatures: III - spatial variability

    Directory of Open Access Journals (Sweden)

    Livia Arantes Camargo


    Full Text Available A good knowledge of the spatial distribution of clay minerals in the landscape facilitates the understanding of the influence of relief on the content and crystallographic attributes of soil minerals such as goethite, hematite, kaolinite and gibbsite. This study aimed at describing the relationships between the mineral properties of the clay fraction and landscape shapes by determining the mineral properties of goethite, hematite, kaolinite and gibbsite, and assessing their dependence and spatial variability, in two slope curvatures. To this end, two 100 × 100 m grids were used to establish a total of 121 regularly spaced georeferenced sampling nodes 10 m apart. Samples were collected from the layer 0.0-0.2 m and analysed for iron oxides, and kaolinite and gibbsite in the clay fraction. Minerals in the clay fraction were characterized from their X-ray diffraction (XRD patterns, which were interpreted and used to calculate the width at half height (WHH and mean crystallite dimension (MCD of iron oxides, kaolinite, and gibbsite, as well as aluminium substitution and specific surface area (SSA in hematite and goethite. Additional calculations included the goethite and hematite contents, and the goethite/(goethite+hematite [Gt/(Gt+Hm] and kaolinite/(kaolinite+gibbsite [Kt/(Kt+Gb] ratios. Mineral properties were established by statistical analysis of the XRD data, and spatial dependence was assessed geostatistically. Mineralogical properties differed significantly between the convex area and concave area. The geostatistical analysis showed a greater number of mineralogical properties with spatial dependence and a higher range in the convex than in the concave area.

  3. The variable stellar wind of Rigel probed at high spatial and spectral resolution (United States)

    Chesneau, O.; Kaufer, A.; Stahl, O.; Colvinter, C.; Spang, A.; Dessart, L.; Prinja, R.; Chini, R.


    Context. Luminous BA-type supergiants are the brightest stars in the visible that can be observed in distant galaxies and are pot