Design, development, and application of LANDIS-II, a spatial landscape simulation model with flexible temporal and spatial resolution

Treesearch

Robert M. Scheller; James B. Domingo; Brian R. Sturtevant; Jeremy S. Williams; Arnold Rudy; Eric J. Gustafson; David J. Mladenoff

2007-01-01

We introduce LANDIS-II, a landscape model designed to simulate forest succession and disturbances. LANDIS-II builds upon and preserves the functionality of previous LANDIS forest landscape simulation models. LANDIS-II is distinguished by the inclusion of variable time steps for different ecological processes; our use of a rigorous development and testing process used...

Integrating satellite actual evapotranspiration patterns into distributed model parametrization and evaluation for a mesoscale catchment

NASA Astrophysics Data System (ADS)

Demirel, M. C.; Mai, J.; Stisen, S.; Mendiguren González, G.; Koch, J.; Samaniego, L. E.

2016-12-01

Distributed hydrologic models are traditionally calibrated and evaluated against observations of streamflow. Spatially distributed remote sensing observations offer a great opportunity to enhance spatial model calibration schemes. For that it is important to identify the model parameters that can change spatial patterns before the satellite based hydrologic model calibration. Our study is based on two main pillars: first we use spatial sensitivity analysis to identify the key parameters controlling the spatial distribution of actual evapotranspiration (AET). Second, we investigate the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mesoscale Hydrologic Model (mHM). This distributed model is selected as it allows for a change in the spatial distribution of key soil parameters through the calibration of pedo-transfer function parameters and includes options for using fully distributed daily Leaf Area Index (LAI) directly as input. In addition the simulated AET can be estimated at the spatial resolution suitable for comparison to the spatial patterns observed using MODIS data. We introduce a new dynamic scaling function employing remotely sensed vegetation to downscale coarse reference evapotranspiration. In total, 17 parameters of 47 mHM parameters are identified using both sequential screening and Latin hypercube one-at-a-time sampling methods. The spatial patterns are found to be sensitive to the vegetation parameters whereas streamflow dynamics are sensitive to the PTF parameters. The results of multi-objective model calibration show that calibration of mHM against observed streamflow does not reduce the spatial errors in AET while they improve only the streamflow simulations. We will further examine the results of model calibration using only multi spatial objective functions measuring the association between observed AET and simulated AET maps and another case including spatial and streamflow metrics together.

Methods used to parameterize the spatially-explicit components of a state-and-transition simulation model

USGS Publications Warehouse

Sleeter, Rachel; Acevedo, William; Soulard, Christopher E.; Sleeter, Benjamin M.

2015-01-01

Spatially-explicit state-and-transition simulation models of land use and land cover (LULC) increase our ability to assess regional landscape characteristics and associated carbon dynamics across multiple scenarios. By characterizing appropriate spatial attributes such as forest age and land-use distribution, a state-and-transition model can more effectively simulate the pattern and spread of LULC changes. This manuscript describes the methods and input parameters of the Land Use and Carbon Scenario Simulator (LUCAS), a customized state-and-transition simulation model utilized to assess the relative impacts of LULC on carbon stocks for the conterminous U.S. The methods and input parameters are spatially explicit and describe initial conditions (strata, state classes and forest age), spatial multipliers, and carbon stock density. Initial conditions were derived from harmonization of multi-temporal data characterizing changes in land use as well as land cover. Harmonization combines numerous national-level datasets through a cell-based data fusion process to generate maps of primary LULC categories. Forest age was parameterized using data from the North American Carbon Program and spatially-explicit maps showing the locations of past disturbances (i.e. wildfire and harvest). Spatial multipliers were developed to spatially constrain the location of future LULC transitions. Based on distance-decay theory, maps were generated to guide the placement of changes related to forest harvest, agricultural intensification/extensification, and urbanization. We analyze the spatially-explicit input parameters with a sensitivity analysis, by showing how LUCAS responds to variations in the model input. This manuscript uses Mediterranean California as a regional subset to highlight local to regional aspects of land change, which demonstrates the utility of LUCAS at many scales and applications.

Spatial heterogeneity of leaf area index across scales from simulation and remote sensing

NASA Astrophysics Data System (ADS)

Reichenau, Tim G.; Korres, Wolfgang; Montzka, Carsten; Schneider, Karl

2016-04-01

Leaf area index (LAI, single sided leaf area per ground area) influences mass and energy exchange of vegetated surfaces. Therefore LAI is an input variable for many land surface schemes of coupled large scale models, which do not simulate LAI. Since these models typically run on rather coarse resolution grids, LAI is often inferred from coarse resolution remote sensing. However, especially in agriculturally used areas, a grid cell of these products often covers more than a single land-use. In that case, the given LAI does not apply to any single land-use. Therefore, the overall spatial heterogeneity in these datasets differs from that on resolutions high enough to distinguish areas with differing land-use. Detailed process-based plant growth models simulate LAI for separate plant functional types or specific species. However, limited availability of observations causes reduced spatial heterogeneity of model input data (soil, weather, land-use). Since LAI is strongly heterogeneous in space and time and since processes depend on LAI in a nonlinear way, a correct representation of LAI spatial heterogeneity is also desirable on coarse resolutions. The current study assesses this issue by comparing the spatial heterogeneity of LAI from remote sensing (RapidEye) and process-based simulations (DANUBIA simulation system) across scales. Spatial heterogeneity is assessed by analyzing LAI frequency distributions (spatial variability) and semivariograms (spatial structure). Test case is the arable land in the fertile loess plain of the Rur catchment near the Germany-Netherlands border.

Model for Atmospheric Propagation of Spatially Combined Laser Beams

DTIC Science & Technology

2016-09-01

thesis modeling tools is discussed. In Chapter 6, the thesis validated the model with analytical computations and simulations result from...using propagation model . Based on both the analytical computation and WaveTrain results, the diraction e ects simulated in the propagation model are...NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS MODEL FOR ATMOSPHERIC PROPAGATION OF SPATIALLY COMBINED LASER BEAMS by Kum Leong Lee

Modeling structural change in spatial system dynamics: A Daisyworld example.

PubMed

Neuwirth, C; Peck, A; Simonović, S P

2015-03-01

System dynamics (SD) is an effective approach for helping reveal the temporal behavior of complex systems. Although there have been recent developments in expanding SD to include systems' spatial dependencies, most applications have been restricted to the simulation of diffusion processes; this is especially true for models on structural change (e.g. LULC modeling). To address this shortcoming, a Python program is proposed to tightly couple SD software to a Geographic Information System (GIS). The approach provides the required capacities for handling bidirectional and synchronized interactions of operations between SD and GIS. In order to illustrate the concept and the techniques proposed for simulating structural changes, a fictitious environment called Daisyworld has been recreated in a spatial system dynamics (SSD) environment. The comparison of spatial and non-spatial simulations emphasizes the importance of considering spatio-temporal feedbacks. Finally, practical applications of structural change models in agriculture and disaster management are proposed.

Effects of input uncertainty on cross-scale crop modeling

NASA Astrophysics Data System (ADS)

Waha, Katharina; Huth, Neil; Carberry, Peter

2014-05-01

The quality of data on climate, soils and agricultural management in the tropics is in general low or data is scarce leading to uncertainty in process-based modeling of cropping systems. Process-based crop models are common tools for simulating crop yields and crop production in climate change impact studies, studies on mitigation and adaptation options or food security studies. Crop modelers are concerned about input data accuracy as this, together with an adequate representation of plant physiology processes and choice of model parameters, are the key factors for a reliable simulation. For example, assuming an error in measurements of air temperature, radiation and precipitation of ± 0.2°C, ± 2 % and ± 3 % respectively, Fodor & Kovacs (2005) estimate that this translates into an uncertainty of 5-7 % in yield and biomass simulations. In our study we seek to answer the following questions: (1) are there important uncertainties in the spatial variability of simulated crop yields on the grid-cell level displayed on maps, (2) are there important uncertainties in the temporal variability of simulated crop yields on the aggregated, national level displayed in time-series, and (3) how does the accuracy of different soil, climate and management information influence the simulated crop yields in two crop models designed for use at different spatial scales? The study will help to determine whether more detailed information improves the simulations and to advise model users on the uncertainty related to input data. We analyse the performance of the point-scale crop model APSIM (Keating et al., 2003) and the global scale crop model LPJmL (Bondeau et al., 2007) with different climate information (monthly and daily) and soil conditions (global soil map and African soil map) under different agricultural management (uniform and variable sowing dates) for the low-input maize-growing areas in Burkina Faso/West Africa. We test the models' response to different levels of input data from very little to very detailed information, and compare the models' abilities to represent the spatial variability and temporal variability in crop yields. We display the uncertainty in crop yield simulations from different input data and crop models in Taylor diagrams which are a graphical summary of the similarity between simulations and observations (Taylor, 2001). The observed spatial variability can be represented well from both models (R=0.6-0.8) but APSIM predicts higher spatial variability than LPJmL due to its sensitivity to soil parameters. Simulations with the same crop model, climate and sowing dates have similar statistics and therefore similar skill to reproduce the observed spatial variability. Soil data is less important for the skill of a crop model to reproduce the observed spatial variability. However, the uncertainty in simulated spatial variability from the two crop models is larger than from input data settings and APSIM is more sensitive to input data then LPJmL. Even with a detailed, point-scale crop model and detailed input data it is difficult to capture the complexity and diversity in maize cropping systems.

Binomial tau-leap spatial stochastic simulation algorithm for applications in chemical kinetics.

PubMed

Marquez-Lago, Tatiana T; Burrage, Kevin

2007-09-14

In cell biology, cell signaling pathway problems are often tackled with deterministic temporal models, well mixed stochastic simulators, and/or hybrid methods. But, in fact, three dimensional stochastic spatial modeling of reactions happening inside the cell is needed in order to fully understand these cell signaling pathways. This is because noise effects, low molecular concentrations, and spatial heterogeneity can all affect the cellular dynamics. However, there are ways in which important effects can be accounted without going to the extent of using highly resolved spatial simulators (such as single-particle software), hence reducing the overall computation time significantly. We present a new coarse grained modified version of the next subvolume method that allows the user to consider both diffusion and reaction events in relatively long simulation time spans as compared with the original method and other commonly used fully stochastic computational methods. Benchmarking of the simulation algorithm was performed through comparison with the next subvolume method and well mixed models (MATLAB), as well as stochastic particle reaction and transport simulations (CHEMCELL, Sandia National Laboratories). Additionally, we construct a model based on a set of chemical reactions in the epidermal growth factor receptor pathway. For this particular application and a bistable chemical system example, we analyze and outline the advantages of our presented binomial tau-leap spatial stochastic simulation algorithm, in terms of efficiency and accuracy, in scenarios of both molecular homogeneity and heterogeneity.

Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

NASA Astrophysics Data System (ADS)

Demirel, Mehmet C.; Mai, Juliane; Mendiguren, Gorka; Koch, Julian; Samaniego, Luis; Stisen, Simon

2018-02-01

Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.

The study of combining Latin Hypercube Sampling method and LU decomposition method (LULHS method) for constructing spatial random field

NASA Astrophysics Data System (ADS)

WANG, P. T.

2015-12-01

Groundwater modeling requires to assign hydrogeological properties to every numerical grid. Due to the lack of detailed information and the inherent spatial heterogeneity, geological properties can be treated as random variables. Hydrogeological property is assumed to be a multivariate distribution with spatial correlations. By sampling random numbers from a given statistical distribution and assigning a value to each grid, a random field for modeling can be completed. Therefore, statistics sampling plays an important role in the efficiency of modeling procedure. Latin Hypercube Sampling (LHS) is a stratified random sampling procedure that provides an efficient way to sample variables from their multivariate distributions. This study combines the the stratified random procedure from LHS and the simulation by using LU decomposition to form LULHS. Both conditional and unconditional simulations of LULHS were develpoed. The simulation efficiency and spatial correlation of LULHS are compared to the other three different simulation methods. The results show that for the conditional simulation and unconditional simulation, LULHS method is more efficient in terms of computational effort. Less realizations are required to achieve the required statistical accuracy and spatial correlation.

Optimal Spatial Design of Capacity and Quantity of Rainwater Catchment Systems for Urban Flood Mitigation

NASA Astrophysics Data System (ADS)

Huang, C.; Hsu, N.

2013-12-01

This study imports Low-Impact Development (LID) technology of rainwater catchment systems into a Storm-Water runoff Management Model (SWMM) to design the spatial capacity and quantity of rain barrel for urban flood mitigation. This study proposes a simulation-optimization model for effectively searching the optimal design. In simulation method, we design a series of regular spatial distributions of capacity and quantity of rainwater catchment facilities, and thus the reduced flooding circumstances using a variety of design forms could be simulated by SWMM. Moreover, we further calculate the net benefit that is equal to subtract facility cost from decreasing inundation loss and the best solution of simulation method would be the initial searching solution of the optimization model. In optimizing method, first we apply the outcome of simulation method and Back-Propagation Neural Network (BPNN) for developing a water level simulation model of urban drainage system in order to replace SWMM which the operating is based on a graphical user interface and is hard to combine with optimization model and method. After that we embed the BPNN-based simulation model into the developed optimization model which the objective function is minimizing the negative net benefit. Finally, we establish a tabu search-based algorithm to optimize the planning solution. This study applies the developed method in Zhonghe Dist., Taiwan. Results showed that application of tabu search and BPNN-based simulation model into the optimization model not only can find better solutions than simulation method in 12.75%, but also can resolve the limitations of previous studies. Furthermore, the optimized spatial rain barrel design can reduce 72% of inundation loss according to historical flood events.

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

NASA Astrophysics Data System (ADS)

Mizyuk, Artem; Senderov, Maxim; Korotaev, Gennady

2016-04-01

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

Validation of the SimSET simulation package for modeling the Siemens Biograph mCT PET scanner

NASA Astrophysics Data System (ADS)

Poon, Jonathan K.; Dahlbom, Magnus L.; Casey, Michael E.; Qi, Jinyi; Cherry, Simon R.; Badawi, Ramsey D.

2015-02-01

Monte Carlo simulation provides a valuable tool in performance assessment and optimization of system design parameters for PET scanners. SimSET is a popular Monte Carlo simulation toolkit that features fast simulation time, as well as variance reduction tools to further enhance computational efficiency. However, SimSET has lacked the ability to simulate block detectors until its most recent release. Our goal is to validate new features of SimSET by developing a simulation model of the Siemens Biograph mCT PET scanner and comparing the results to a simulation model developed in the GATE simulation suite and to experimental results. We used the NEMA NU-2 2007 scatter fraction, count rates, and spatial resolution protocols to validate the SimSET simulation model and its new features. The SimSET model overestimated the experimental results of the count rate tests by 11-23% and the spatial resolution test by 13-28%, which is comparable to previous validation studies of other PET scanners in the literature. The difference between the SimSET and GATE simulation was approximately 4-8% for the count rate test and approximately 3-11% for the spatial resolution test. In terms of computational time, SimSET performed simulations approximately 11 times faster than GATE simulations. The new block detector model in SimSET offers a fast and reasonably accurate simulation toolkit for PET imaging applications.

High resolution simulations of aerosol microphysics in a global and regionally nested chemical transport model

NASA Astrophysics Data System (ADS)

Adams, P. J.; Marks, M.

2015-12-01

The aerosol indirect effect is the largest source of forcing uncertainty in current climate models. This effect arises from the influence of aerosols on the reflective properties and lifetimes of clouds, and its magnitude depends on how many particles can serve as cloud droplet formation sites. Assessing levels of this subset of particles (cloud condensation nuclei, or CCN) requires knowledge of aerosol levels and their global distribution, size distributions, and composition. A key tool necessary to advance our understanding of CCN is the use of global aerosol microphysical models, which simulate the processes that control aerosol size distributions: nucleation, condensation/evaporation, and coagulation. Previous studies have found important differences in CO (Chen, D. et al., 2009) and ozone (Jang, J., 1995) modeled at different spatial resolutions, and it is reasonable to believe that short-lived, spatially-variable aerosol species will be similarly - or more - susceptible to model resolution effects. The goal of this study is to determine how CCN levels and spatial distributions change as simulations are run at higher spatial resolution - specifically, to evaluate how sensitive the model is to grid size, and how this affects comparisons against observations. Higher resolution simulations are necessary supports for model/measurement synergy. Simulations were performed using the global chemical transport model GEOS-Chem (v9-02). The years 2008 and 2009 were simulated at 4ox5o and 2ox2.5o globally and at 0.5ox0.667o over Europe and North America. Results were evaluated against surface-based particle size distribution measurements from the European Supersites for Atmospheric Aerosol Research project. The fine-resolution model simulates more spatial and temporal variability in ultrafine levels, and better resolves topography. Results suggest that the coarse model predicts systematically lower ultrafine levels than does the fine-resolution model. Significant differences are also evident with respect to model-measurement comparisons, and will be discussed.

Modifying a dynamic global vegetation model for simulating large spatial scale land surface water balance

NASA Astrophysics Data System (ADS)

Tang, G.; Bartlein, P. J.

2012-01-01

Water balance models of simple structure are easier to grasp and more clearly connect cause and effect than models of complex structure. Such models are essential for studying large spatial scale land surface water balance in the context of climate and land cover change, both natural and anthropogenic. This study aims to (i) develop a large spatial scale water balance model by modifying a dynamic global vegetation model (DGVM), and (ii) test the model's performance in simulating actual evapotranspiration (ET), soil moisture and surface runoff for the coterminous United States (US). Toward these ends, we first introduced development of the "LPJ-Hydrology" (LH) model by incorporating satellite-based land covers into the Lund-Potsdam-Jena (LPJ) DGVM instead of dynamically simulating them. We then ran LH using historical (1982-2006) climate data and satellite-based land covers at 2.5 arc-min grid cells. The simulated ET, soil moisture and surface runoff were compared to existing sets of observed or simulated data for the US. The results indicated that LH captures well the variation of monthly actual ET (R2 = 0.61, p < 0.01) in the Everglades of Florida over the years 1996-2001. The modeled monthly soil moisture for Illinois of the US agrees well (R2 = 0.79, p < 0.01) with the observed over the years 1984-2001. The modeled monthly stream flow for most 12 major rivers in the US is consistent R2 > 0.46, p < 0.01; Nash-Sutcliffe Coefficients >0.52) with observed values over the years 1982-2006, respectively. The modeled spatial patterns of annual ET and surface runoff are in accordance with previously published data. Compared to its predecessor, LH simulates better monthly stream flow in winter and early spring by incorporating effects of solar radiation on snowmelt. Overall, this study proves the feasibility of incorporating satellite-based land-covers into a DGVM for simulating large spatial scale land surface water balance. LH developed in this study should be a useful tool for studying effects of climate and land cover change on land surface hydrology at large spatial scales.

Integration agent-based models and GIS as a virtual urban dynamic laboratory

NASA Astrophysics Data System (ADS)

Chen, Peng; Liu, Miaolong

2007-06-01

Based on the Agent-based Model and spatial data model, a tight-coupling integrating method of GIS and Agent-based Model (ABM) is to be discussed in this paper. The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena such as urban dynamic. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, the agent-based model and spatial data model are discussed, and then the relationships affecting spatial data model and agent-based process models interaction. After that, a realistic crowd flow simulation experiment is presented. Using some tools provided by general GIS systems and a few specific programming languages, a new software system integrating GIS and MAS as a virtual laboratory applicable for simulating pedestrian flows in a crowd activity centre has been developed successfully. Under the environment supported by the software system, as an applicable case, a dynamic evolution process of the pedestrian's flows (dispersed process for the spectators) in a crowds' activity center - The Shanghai Stadium has been simulated successfully. At the end of the paper, some new research problems have been pointed out for the future.

Model simulations and proxy-based reconstructions for the European region in the past millennium (Invited)

NASA Astrophysics Data System (ADS)

Zorita, E.

2009-12-01

One of the objectives when comparing simulations of past climates to proxy-based climate reconstructions is to asses the skill of climate models to simulate climate change. This comparison may accomplished at large spatial scales, for instance the evolution of simulated and reconstructed Northern Hemisphere annual temperature, or at regional or point scales. In both approaches a 'fair' comparison has to take into account different aspects that affect the inevitable uncertainties and biases in the simulations and in the reconstructions. These efforts face a trade-off: climate models are believed to be more skillful at large hemispheric scales, but climate reconstructions are these scales are burdened by the spatial distribution of available proxies and by methodological issues surrounding the statistical method used to translate the proxy information into large-spatial averages. Furthermore, the internal climatic noise at large hemispheric scales is low, so that the sampling uncertainty tends to be also low. On the other hand, the skill of climate models at regional scales is limited by the coarse spatial resolution, which hinders a faithful representation of aspects important for the regional climate. At small spatial scales, the reconstruction of past climate probably faces less methodological problems if information from different proxies is available. The internal climatic variability at regional scales is, however, high. In this contribution some examples of the different issues faced when comparing simulation and reconstructions at small spatial scales in the past millennium are discussed. These examples comprise reconstructions from dendrochronological data and from historical documentary data in Europe and climate simulations with global and regional models. These examples indicate that the centennial climate variations can offer a reasonable target to assess the skill of global climate models and of proxy-based reconstructions, even at small spatial scales. However, as the focus shifts towards higher frequency variability, decadal or multidecadal, the need for larger simulation ensembles becomes more evident. Nevertheless,the comparison at these time scales may expose some lines of research on the origin of multidecadal regional climate variability.

Spatial-temporal consistency between gross primary productivity and solar-induced chlorophyll fluorescence of vegetation in China during 2007-2014

NASA Astrophysics Data System (ADS)

Ma, J.; Xiao, X.; Zhang, Y.; Chen, B.; Zhao, B.

2017-12-01

Great significance exists in accurately estimating spatial-temporal patterns of gross primary production (GPP) because of its important role in global carbon cycle. Satellite-based light use efficiency (LUE) models are regarded as an efficient tool in simulating spatially time-sires GPP. However, the estimation of the accuracy of GPP simulations from LUE at both spatial and temporal scales is still a challenging work. In this study, we simulated GPP of vegetation in China during 2007-2014 using a LUE model (Vegetation Photosynthesis Model, VPM) based on MODIS (moderate-resolution imaging spectroradiometer) images of 8-day temporal and 500-m spatial resolutions and NCEP (National Center for Environmental Prediction) climate data. Global Ozone Monitoring Instrument 2 (GOME-2) solar-induced chlorophyll fluorescence (SIF) data were used to compare with VPM simulated GPP (GPPVPM) temporally and spatially using linear correlation analysis. Significant positive linear correlations exist between monthly GPPVPM and SIF data over both single year (2010) and multiple years (2007-2014) in China. Annual GPPVPM is significantly positive correlated with SIF (R2>0.43) spatially for all years during 2007-2014 and all seasons in 2010 (R2>0.37). GPP dynamic trends is high spatial-temporal heterogeneous in China during 2007-2014. The results of this study indicate that GPPVPM is temporally and spatially in line with SIF data, and space-borne SIF data have great potential in validating and parameterizing GPP estimation of LUE-based models.

MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME

PubMed Central

Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda; Hellander, Andreas

2017-01-01

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments. PMID:28190948

Calibrating a forest landscape model to simulate frequent fire in Mediterranean-type shrublands

USGS Publications Warehouse

Syphard, A.D.; Yang, J.; Franklin, J.; He, H.S.; Keeley, J.E.

2007-01-01

In Mediterranean-type ecosystems (MTEs), fire disturbance influences the distribution of most plant communities, and altered fire regimes may be more important than climate factors in shaping future MTE vegetation dynamics. Models that simulate the high-frequency fire and post-fire response strategies characteristic of these regions will be important tools for evaluating potential landscape change scenarios. However, few existing models have been designed to simulate these properties over long time frames and broad spatial scales. We refined a landscape disturbance and succession (LANDIS) model to operate on an annual time step and to simulate altered fire regimes in a southern California Mediterranean landscape. After developing a comprehensive set of spatial and non-spatial variables and parameters, we calibrated the model to simulate very high fire frequencies and evaluated the simulations under several parameter scenarios representing hypotheses about system dynamics. The goal was to ensure that observed model behavior would simulate the specified fire regime parameters, and that the predictions were reasonable based on current understanding of community dynamics in the region. After calibration, the two dominant plant functional types responded realistically to different fire regime scenarios. Therefore, this model offers a new alternative for simulating altered fire regimes in MTE landscapes. ?? 2007 Elsevier Ltd. All rights reserved.

Modeling space-time correlations of velocity fluctuations in wind farms

NASA Astrophysics Data System (ADS)

Lukassen, Laura J.; Stevens, Richard J. A. M.; Meneveau, Charles; Wilczek, Michael

2018-07-01

An analytical model for the streamwise velocity space-time correlations in turbulent flows is derived and applied to the special case of velocity fluctuations in large wind farms. The model is based on the Kraichnan-Tennekes random sweeping hypothesis, capturing the decorrelation in time while including a mean wind velocity in the streamwise direction. In the resulting model, the streamwise velocity space-time correlation is expressed as a convolution of the pure space correlation with an analytical temporal decorrelation kernel. Hence, the spatio-temporal structure of velocity fluctuations in wind farms can be derived from the spatial correlations only. We then explore the applicability of the model to predict spatio-temporal correlations in turbulent flows in wind farms. Comparisons of the model with data from a large eddy simulation of flow in a large, spatially periodic wind farm are performed, where needed model parameters such as spatial and temporal integral scales and spatial correlations are determined from the large eddy simulation. Good agreement is obtained between the model and large eddy simulation data showing that spatial data may be used to model the full temporal structure of fluctuations in wind farms.

Applying fire spread simulators in New Zealand and Australia: Results from an international seminar

Treesearch

Tonja Opperman; Jim Gould; Mark Finney; Cordy Tymstra

2006-01-01

There is currently no spatial wildfire spread and growth simulation model used commonly across New Zealand or Australia. Fire management decision-making would be enhanced through the use of spatial fire simulators. Various groups from around the world met in January 2006 to evaluate the applicability of different spatial fire spread applications for common use in both...

Modeling the Land Use/Cover Change in an Arid Region Oasis City Constrained by Water Resource and Environmental Policy Change using Cellular Automata Model

NASA Astrophysics Data System (ADS)

Hu, X.; Li, X.; Lu, L.

2017-12-01

Land use/cover change (LUCC) is an important subject in the research of global environmental change and sustainable development, while spatial simulation on land use/cover change is one of the key content of LUCC and is also difficult due to the complexity of the system. The cellular automata (CA) model had an irreplaceable role in simulating of land use/cover change process due to the powerful spatial computing power. However, the majority of current CA land use/cover models were binary-state model that could not provide more general information about the overall spatial pattern of land use/cover change. Here, a multi-state logistic-regression-based Markov cellular automata (MLRMCA) model and a multi-state artificial-neural-network-based Markov cellular automata (MANNMCA) model were developed and were used to simulate complex land use/cover evolutionary process in an arid region oasis city constrained by water resource and environmental policy change, the Zhangye city during the period of 1990-2010. The results indicated that the MANNMCA model was superior to MLRMCA model in simulated accuracy. These indicated that by combining the artificial neural network with CA could more effectively capture the complex relationships between the land use/cover change and a set of spatial variables. Although the MLRMCA model were also some advantages, the MANNMCA model was more appropriate for simulating complex land use/cover dynamics. The two proposed models were effective and reliable, and could reflect the spatial evolution of regional land use/cover changes. These have also potential implications for the impact assessment of water resources, ecological restoration, and the sustainable urban development in arid areas.

Swarm Intelligence for Urban Dynamics Modelling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghnemat, Rawan; Bertelle, Cyrille; Duchamp, Gerard H. E.

2009-04-16

In this paper, we propose swarm intelligence algorithms to deal with dynamical and spatial organization emergence. The goal is to model and simulate the developement of spatial centers using multi-criteria. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. We propose an extension of the ant nest building algorithm with multi-center and adaptive process. Typically, this model is suitable to analyse and simulate urban dynamics like gentrification or the dynamics of the cultural equipment in urban area.

Swarm Intelligence for Urban Dynamics Modelling

NASA Astrophysics Data System (ADS)

Ghnemat, Rawan; Bertelle, Cyrille; Duchamp, Gérard H. E.

2009-04-01

In this paper, we propose swarm intelligence algorithms to deal with dynamical and spatial organization emergence. The goal is to model and simulate the developement of spatial centers using multi-criteria. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. We propose an extension of the ant nest building algorithm with multi-center and adaptive process. Typically, this model is suitable to analyse and simulate urban dynamics like gentrification or the dynamics of the cultural equipment in urban area.

Effects of Topography-based Subgrid Structures on Land Surface Modeling

NASA Astrophysics Data System (ADS)

Tesfa, T. K.; Ruby, L.; Brunke, M.; Thornton, P. E.; Zeng, X.; Ghan, S. J.

2017-12-01

Topography has major control on land surface processes through its influence on atmospheric forcing, soil and vegetation properties, network topology and drainage area. Consequently, accurate climate and land surface simulations in mountainous regions cannot be achieved without considering the effects of topographic spatial heterogeneity. To test a computationally less expensive hyper-resolution land surface modeling approach, we developed topography-based landunits within a hierarchical subgrid spatial structure to improve representation of land surface processes in the ACME Land Model (ALM) with minimal increase in computational demand, while improving the ability to capture the spatial heterogeneity of atmospheric forcing and land cover influenced by topography. This study focuses on evaluation of the impacts of the new spatial structures on modeling land surface processes. As a first step, we compare ALM simulations with and without subgrid topography and driven by grid cell mean atmospheric forcing to isolate the impacts of the subgrid topography on the simulated land surface states and fluxes. Recognizing that subgrid topography also has important effects on atmospheric processes that control temperature, radiation, and precipitation, methods are being developed to downscale atmospheric forcings. Hence in the second step, the impacts of the subgrid topographic structure on land surface modeling will be evaluated by including spatial downscaling of the atmospheric forcings. Preliminary results on the atmospheric downscaling and the effects of the new spatial structures on the ALM simulations will be presented.

Testing the skill of numerical hydraulic modeling to simulate spatiotemporal flooding patterns in the Logone floodplain, Cameroon

NASA Astrophysics Data System (ADS)

Fernández, Alfonso; Najafi, Mohammad Reza; Durand, Michael; Mark, Bryan G.; Moritz, Mark; Jung, Hahn Chul; Neal, Jeffrey; Shastry, Apoorva; Laborde, Sarah; Phang, Sui Chian; Hamilton, Ian M.; Xiao, Ningchuan

2016-08-01

Recent innovations in hydraulic modeling have enabled global simulation of rivers, including simulation of their coupled wetlands and floodplains. Accurate simulations of floodplains using these approaches may imply tremendous advances in global hydrologic studies and in biogeochemical cycling. One such innovation is to explicitly treat sub-grid channels within two-dimensional models, given only remotely sensed data in areas with limited data availability. However, predicting inundated area in floodplains using a sub-grid model has not been rigorously validated. In this study, we applied the LISFLOOD-FP hydraulic model using a sub-grid channel parameterization to simulate inundation dynamics on the Logone River floodplain, in northern Cameroon, from 2001 to 2007. Our goal was to determine whether floodplain dynamics could be simulated with sufficient accuracy to understand human and natural contributions to current and future inundation patterns. Model inputs in this data-sparse region include in situ river discharge, satellite-derived rainfall, and the shuttle radar topography mission (SRTM) floodplain elevation. We found that the model accurately simulated total floodplain inundation, with a Pearson correlation coefficient greater than 0.9, and RMSE less than 700 km2, compared to peak inundation greater than 6000 km2. Predicted discharge downstream of the floodplain matched measurements (Nash-Sutcliffe efficiency of 0.81), and indicated that net flow from the channel to the floodplain was modeled accurately. However, the spatial pattern of inundation was not well simulated, apparently due to uncertainties in SRTM elevations. We evaluated model results at 250, 500 and 1000-m spatial resolutions, and found that results are insensitive to spatial resolution. We also compared the model output against results from a run of LISFLOOD-FP in which the sub-grid channel parameterization was disabled, finding that the sub-grid parameterization simulated more realistic dynamics. These results suggest that analysis of global inundation is feasible using a sub-grid model, but that spatial patterns at sub-kilometer resolutions still need to be adequately predicted.

Performance of a two-leaf light use efficiency model for mapping gross primary productivity against remotely sensed sun-induced chlorophyll fluorescence data.

PubMed

Zan, Mei; Zhou, Yanlian; Ju, Weimin; Zhang, Yongguang; Zhang, Leiming; Liu, Yibo

2018-02-01

Estimating terrestrial gross primary production is an important task when studying the carbon cycle. In this study, the ability of a two-leaf light use efficiency model to simulate regional gross primary production in China was validated using satellite Global Ozone Monitoring Instrument - 2 sun-induced chlorophyll fluorescence data. The two-leaf light use efficiency model was used to estimate daily gross primary production in China's terrestrial ecosystems with 500-m resolution for the period from 2007 to 2014. Gross primary production simulated with the two-leaf light use efficiency model was resampled to a spatial resolution of 0.5° and then compared with sun-induced chlorophyll fluorescence. During the study period, sun-induced chlorophyll fluorescence and gross primary production simulated by the two-leaf light use efficiency model exhibited similar spatial and temporal patterns in China. The correlation coefficient between sun-induced chlorophyll fluorescence and monthly gross primary production simulated by the two-leaf light use efficiency model was significant (p<0.05, n=96) in 88.9% of vegetated areas in China (average value 0.78) and varied among vegetation types. The interannual variations in monthly sun-induced chlorophyll fluorescence and gross primary production simulated by the two-leaf light use efficiency model were similar in spring and autumn in most vegetated regions, but dissimilar in winter and summer. The spatial variability of sun-induced chlorophyll fluorescence and gross primary production simulated by the two-leaf light use efficiency model was similar in spring, summer, and autumn. The proportion of spatial variations of sun-induced chlorophyll fluorescence and annual gross primary production simulated by the two-leaf light use efficiency model explained by ranged from 0.76 (2011) to 0.80 (2013) during the study period. Overall, the two-leaf light use efficiency model was capable of capturing spatial and temporal variations in gross primary production in China. However, the model needs further improvement to better simulate gross primary production in summer. Copyright © 2017 Elsevier B.V. All rights reserved.

Simulation Studies of the Effect of Forest Spatial Structure on InSAR Signature

NASA Technical Reports Server (NTRS)

Sun, Guoqing; Liu, Dawei; Ranson, K. Jon; Koetz, Benjamin

2007-01-01

The height of scattering phase retrieved from InSAR data is considered being correlated with the tree height and the spatial structure of the forest stand. Though some researchers have used simple backscattering models to estimate tree height from the height of scattering center, the effect of forest spatial structure on InSAR data is not well understood yet. A three-dimensional coherent radar backscattering model for forest canopies based on realistic three-dimensional scene was used to investigate the effect in this paper. The realistic spatial structure of forest canopies was established either by field measurements (stem map) or through use of forest growth model. Field measurements or a forest growth model parameterized using local environmental parameters provides information of forest species composition and tree sizes in certain growth phases. A fractal tree model (L-system) was used to simulate individual 3- D tree structure of different ages or heights. Trees were positioned in a stand in certain patterns resulting in a 3-D medium of discrete scatterers. The radar coherent backscatter model took the 3-D forest scene as input and simulates the coherent radar backscattering signature. Interferometric SAR images of 3D scenes were simulated and heights of scattering phase centers were estimated from the simulated InSAR data. The effects of tree height, crown cover, crown depth, and the spatial distribution patterns of trees on the scattering phase center were analyzed. The results will be presented in the paper.

Spatial-temporal consistency between gross primary productivity and solar-induced chlorophyll fluorescence of vegetation in China during 2007-2014.

PubMed

Ma, Jun; Xiao, Xiangming; Zhang, Yao; Doughty, Russell; Chen, Bangqian; Zhao, Bin

2018-10-15

Accurately estimating spatial-temporal patterns of gross primary production (GPP) is important for the global carbon cycle. Satellite-based light use efficiency (LUE) models are regarded as an efficient tool in simulating spatial-temporal dynamics of GPP. However, the accuracy assessment of GPP simulations from LUE models at both spatial and temporal scales remains a challenge. In this study, we simulated GPP of vegetation in China during 2007-2014 using a LUE model (Vegetation Photosynthesis Model, VPM) based on MODIS (moderate-resolution imaging spectroradiometer) images with 8-day temporal and 500-m spatial resolutions and NCEP (National Center for Environmental Prediction) climate data. Global Ozone Monitoring Instrument 2 (GOME-2) solar-induced chlorophyll fluorescence (SIF) data were used to compare with VPM simulated GPP (GPP VPM ) temporally and spatially using linear correlation analysis. Significant positive linear correlations exist between monthly GPP VPM and SIF data over a single year (2010) and multiple years (2007-2014) in most areas of China. GPP VPM is also significantly positive correlated with GOME-2 SIF (R 2  > 0.43) spatially for seasonal scales. However, poor consistency was detected between GPP VPM and SIF data at yearly scale. GPP dynamic trends have high spatial-temporal variation in China during 2007-2014. Temperature, leaf area index (LAI), and precipitation are the most important factors influence GPP VPM in the regions of East Qinghai-Tibet Plateau, Loss Plateau, and Southwestern China, respectively. The results of this study indicate that GPP VPM is temporally and spatially in line with GOME-2 SIF data, and space-borne SIF data have great potential for evaluating LUE-based GPP models. Copyright © 2018 Elsevier B.V. All rights reserved.

Spatial surplus production modeling of Atlantic tunas and billfish.

PubMed

Carruthers, Thomas R; McAllister, Murdoch K; Taylor, Nathan G

2011-10-01

We formulate and simulation-test a spatial surplus production model that provides a basis with which to undertake multispecies, multi-area, stock assessment. Movement between areas is parameterized using a simple gravity model that includes a "residency" parameter that determines the degree of stock mixing among areas. The model is deliberately simple in order to (1) accommodate nontarget species that typically have fewer available data and (2) minimize computational demand to enable simulation evaluation of spatial management strategies. Using this model, we demonstrate that careful consideration of spatial catch and effort data can provide the basis for simple yet reliable spatial stock assessments. If simple spatial dynamics can be assumed, tagging data are not required to reliably estimate spatial distribution and movement. When applied to eight stocks of Atlantic tuna and billfish, the model tracks regional catch data relatively well by approximating local depletions and exchange among high-abundance areas. We use these results to investigate and discuss the implications of using spatially aggregated stock assessment for fisheries in which the distribution of both the population and fishing vary over time.

Towards a 3d Spatial Urban Energy Modelling Approach

NASA Astrophysics Data System (ADS)

Bahu, J.-M.; Koch, A.; Kremers, E.; Murshed, S. M.

2013-09-01

Today's needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing demand patterns (e.g. buildings) corresponding to their specific context. In addition, future energy systems are expected to integrate a considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a spatial representation in order to reflect the local context and the boundary conditions. This paper describes two recent research approaches developed at EIFER in the fields of (a) geo-localised simulation of heat energy demand in cities based on 3D morphological data and (b) spatially explicit Agent-Based Models (ABM) for the simulation of smart grids. 3D city models were used to assess solar potential and heat energy demand of residential buildings which enable cities to target the building refurbishment potentials. Distributed energy systems require innovative modelling techniques where individual components are represented and can interact. With this approach, several smart grid demonstrators were simulated, where heterogeneous models are spatially represented. Coupling 3D geodata with energy system ABMs holds different advantages for both approaches. On one hand, energy system models can be enhanced with high resolution data from 3D city models and their semantic relations. Furthermore, they allow for spatial analysis and visualisation of the results, with emphasis on spatially and structurally correlations among the different layers (e.g. infrastructure, buildings, administrative zones) to provide an integrated approach. On the other hand, 3D models can benefit from more detailed system description of energy infrastructure, representing dynamic phenomena and high resolution models for energy use at component level. The proposed modelling strategies conceptually and practically integrate urban spatial and energy planning approaches. The combined modelling approach that will be developed based on the described sectorial models holds the potential to represent hybrid energy systems coupling distributed generation of electricity with thermal conversion systems.

Multilevel discretized random field models with 'spin' correlations for the simulation of environmental spatial data

NASA Astrophysics Data System (ADS)

Žukovič, Milan; Hristopulos, Dionissios T.

2009-02-01

A current problem of practical significance is how to analyze large, spatially distributed, environmental data sets. The problem is more challenging for variables that follow non-Gaussian distributions. We show by means of numerical simulations that the spatial correlations between variables can be captured by interactions between 'spins'. The spins represent multilevel discretizations of environmental variables with respect to a number of pre-defined thresholds. The spatial dependence between the 'spins' is imposed by means of short-range interactions. We present two approaches, inspired by the Ising and Potts models, that generate conditional simulations of spatially distributed variables from samples with missing data. Currently, the sampling and simulation points are assumed to be at the nodes of a regular grid. The conditional simulations of the 'spin system' are forced to respect locally the sample values and the system statistics globally. The second constraint is enforced by minimizing a cost function representing the deviation between normalized correlation energies of the simulated and the sample distributions. In the approach based on the Nc-state Potts model, each point is assigned to one of Nc classes. The interactions involve all the points simultaneously. In the Ising model approach, a sequential simulation scheme is used: the discretization at each simulation level is binomial (i.e., ± 1). Information propagates from lower to higher levels as the simulation proceeds. We compare the two approaches in terms of their ability to reproduce the target statistics (e.g., the histogram and the variogram of the sample distribution), to predict data at unsampled locations, as well as in terms of their computational complexity. The comparison is based on a non-Gaussian data set (derived from a digital elevation model of the Walker Lake area, Nevada, USA). We discuss the impact of relevant simulation parameters, such as the domain size, the number of discretization levels, and the initial conditions.

A nonparametric spatial scan statistic for continuous data.

PubMed

Jung, Inkyung; Cho, Ho Jin

2015-10-20

Spatial scan statistics are widely used for spatial cluster detection, and several parametric models exist. For continuous data, a normal-based scan statistic can be used. However, the performance of the model has not been fully evaluated for non-normal data. We propose a nonparametric spatial scan statistic based on the Wilcoxon rank-sum test statistic and compared the performance of the method with parametric models via a simulation study under various scenarios. The nonparametric method outperforms the normal-based scan statistic in terms of power and accuracy in almost all cases under consideration in the simulation study. The proposed nonparametric spatial scan statistic is therefore an excellent alternative to the normal model for continuous data and is especially useful for data following skewed or heavy-tailed distributions.

Reconstructing the 2003/2004 H3N2 influenza epidemic in Switzerland with a spatially explicit, individual-based model

PubMed Central

2011-01-01

Background Simulation models of influenza spread play an important role for pandemic preparedness. However, as the world has not faced a severe pandemic for decades, except the rather mild H1N1 one in 2009, pandemic influenza models are inherently hypothetical and validation is, thus, difficult. We aim at reconstructing a recent seasonal influenza epidemic that occurred in Switzerland and deem this to be a promising validation strategy for models of influenza spread. Methods We present a spatially explicit, individual-based simulation model of influenza spread. The simulation model bases upon (i) simulated human travel data, (ii) data on human contact patterns and (iii) empirical knowledge on the epidemiology of influenza. For model validation we compare the simulation outcomes with empirical knowledge regarding (i) the shape of the epidemic curve, overall infection rate and reproduction number, (ii) age-dependent infection rates and time of infection, (iii) spatial patterns. Results The simulation model is capable of reproducing the shape of the 2003/2004 H3N2 epidemic curve of Switzerland and generates an overall infection rate (14.9 percent) and reproduction numbers (between 1.2 and 1.3), which are realistic for seasonal influenza epidemics. Age and spatial patterns observed in empirical data are also reflected by the model: Highest infection rates are in children between 5 and 14 and the disease spreads along the main transport axes from west to east. Conclusions We show that finding evidence for the validity of simulation models of influenza spread by challenging them with seasonal influenza outbreak data is possible and promising. Simulation models for pandemic spread gain more credibility if they are able to reproduce seasonal influenza outbreaks. For more robust modelling of seasonal influenza, serological data complementing sentinel information would be beneficial. PMID:21554680

A Study on the Effects of Spatial Scale on Snow Process in Hyper-Resolution Hydrological Modelling over Mountainous Areas

NASA Astrophysics Data System (ADS)

Garousi Nejad, I.; He, S.; Tang, Q.; Ogden, F. L.; Steinke, R. C.; Frazier, N.; Tarboton, D. G.; Ohara, N.; Lin, H.

2017-12-01

Spatial scale is one of the main considerations in hydrological modeling of snowmelt in mountainous areas. The size of model elements controls the degree to which variability can be explicitly represented versus what needs to be parameterized using effective properties such as averages or other subgrid variability parameterizations that may degrade the quality of model simulations. For snowmelt modeling terrain parameters such as slope, aspect, vegetation and elevation play an important role in the timing and quantity of snowmelt that serves as an input to hydrologic runoff generation processes. In general, higher resolution enhances the accuracy of the simulation since fine meshes represent and preserve the spatial variability of atmospheric and surface characteristics better than coarse resolution. However, this increases computational cost and there may be a scale beyond which the model response does not improve due to diminishing sensitivity to variability and irreducible uncertainty associated with the spatial interpolation of inputs. This paper examines the influence of spatial resolution on the snowmelt process using simulations of and data from the Animas River watershed, an alpine mountainous area in Colorado, USA, using an unstructured distributed physically based hydrological model developed for a parallel computing environment, ADHydro. Five spatial resolutions (30 m, 100 m, 250 m, 500 m, and 1 km) were used to investigate the variations in hydrologic response. This study demonstrated the importance of choosing the appropriate spatial scale in the implementation of ADHydro to obtain a balance between representing spatial variability and the computational cost. According to the results, variation in the input variables and parameters due to using different spatial resolution resulted in changes in the obtained hydrological variables, especially snowmelt, both at the basin-scale and distributed across the model mesh.

Multidimensional extended spatial evolutionary games.

PubMed

Krześlak, Michał; Świerniak, Andrzej

2016-02-01

The goal of this paper is to study the classical hawk-dove model using mixed spatial evolutionary games (MSEG). In these games, played on a lattice, an additional spatial layer is introduced for dependence on more complex parameters and simulation of changes in the environment. Furthermore, diverse polymorphic equilibrium points dependent on cell reproduction, model parameters, and their simulation are discussed. Our analysis demonstrates the sensitivity properties of MSEGs and possibilities for further development. We discuss applications of MSEGs, particularly algorithms for modelling cell interactions during the development of tumours. Copyright © 2015 Elsevier Ltd. All rights reserved.

InterSpread Plus: a spatial and stochastic simulation model of disease in animal populations.

PubMed

Stevenson, M A; Sanson, R L; Stern, M W; O'Leary, B D; Sujau, M; Moles-Benfell, N; Morris, R S

2013-04-01

We describe the spatially explicit, stochastic simulation model of disease spread, InterSpread Plus, in terms of its epidemiological framework, operation, and mode of use. The input data required by the model, the method for simulating contact and infection spread, and methods for simulating disease control measures are described. Data and parameters that are essential for disease simulation modelling using InterSpread Plus are distinguished from those that are non-essential, and it is suggested that a rational approach to simulating disease epidemics using this tool is to start with core data and parameters, adding additional layers of complexity if and when the specific requirements of the simulation exercise require it. We recommend that simulation models of disease are best developed as part of epidemic contingency planning so decision makers are familiar with model outputs and assumptions and are well-positioned to evaluate their strengths and weaknesses to make informed decisions in times of crisis. Copyright © 2012 Elsevier B.V. All rights reserved.

High resolution modelling of soil moisture patterns with TerrSysMP: A comparison with sensor network data

NASA Astrophysics Data System (ADS)

Gebler, S.; Hendricks Franssen, H.-J.; Kollet, S. J.; Qu, W.; Vereecken, H.

2017-04-01

The prediction of the spatial and temporal variability of land surface states and fluxes with land surface models at high spatial resolution is still a challenge. This study compares simulation results using TerrSysMP including a 3D variably saturated groundwater flow model (ParFlow) coupled to the Community Land Model (CLM) of a 38 ha managed grassland head-water catchment in the Eifel (Germany), with soil water content (SWC) measurements from a wireless sensor network, actual evapotranspiration recorded by lysimeters and eddy covariance stations and discharge observations. TerrSysMP was discretized with a 10 × 10 m lateral resolution, variable vertical resolution (0.025-0.575 m), and the following parameterization strategies of the subsurface soil hydraulic parameters: (i) completely homogeneous, (ii) homogeneous parameters for different soil horizons, (iii) different parameters for each soil unit and soil horizon and (iv) heterogeneous stochastic realizations. Hydraulic conductivity and Mualem-Van Genuchten parameters in these simulations were sampled from probability density functions, constructed from either (i) soil texture measurements and Rosetta pedotransfer functions (ROS), or (ii) estimated soil hydraulic parameters by 1D inverse modelling using shuffle complex evolution (SCE). The results indicate that the spatial variability of SWC at the scale of a small headwater catchment is dominated by topography and spatially heterogeneous soil hydraulic parameters. The spatial variability of the soil water content thereby increases as a function of heterogeneity of soil hydraulic parameters. For lower levels of complexity, spatial variability of the SWC was underrepresented in particular for the ROS-simulations. Whereas all model simulations were able to reproduce the seasonal evapotranspiration variability, the poor discharge simulations with high model bias are likely related to short-term ET dynamics and the lack of information about bedrock characteristics and an on-site drainage system in the uncalibrated model. In general, simulation performance was better for the SCE setups. The SCE-simulations had a higher inverse air entry parameter resulting in SWC dynamics in better correspondence with data than the ROS simulations during dry periods. This illustrates that small scale measurements of soil hydraulic parameters cannot be transferred to the larger scale and that interpolated 1D inverse parameter estimates result in an acceptable performance for the catchment.

`spup' - An R Package for Analysis of Spatial Uncertainty Propagation and Application to Trace Gas Emission Simulations

NASA Astrophysics Data System (ADS)

Sawicka, K.; Breuer, L.; Houska, T.; Santabarbara Ruiz, I.; Heuvelink, G. B. M.

2016-12-01

Computer models have become a crucial tool in engineering and environmental sciences for simulating the behaviour of complex static and dynamic systems. However, while many models are deterministic, the uncertainty in their predictions needs to be estimated before they are used for decision support. Advances in uncertainty propagation analysis and assessment have been paralleled by a growing number of software tools for uncertainty analysis, but none has gained recognition for a universal applicability, including case studies with spatial models and spatial model inputs. Due to the growing popularity and applicability of the open source R programming language we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. In particular, the `spup' package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model predictions. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo techniques, as well as several uncertainty visualization functions. Here we will demonstrate that the 'spup' package is an effective and easy-to-use tool to be applied even in a very complex study case, and that it can be used in multi-disciplinary research and model-based decision support. As an example, we use the ecological LandscapeDNDC model to analyse propagation of uncertainties associated with spatial variability of the model driving forces such as rainfall, nitrogen deposition and fertilizer inputs. The uncertainty propagation is analysed for the prediction of emissions of N2O and CO2 for a German low mountainous, agriculturally developed catchment. The study tests the effect of spatial correlations on spatially aggregated model outputs, and could serve as an advice for developing best management practices and model improvement strategies.

Vestibular models for design and evaluation of flight simulator motion

NASA Technical Reports Server (NTRS)

Bussolari, S. R.; Sullivan, R. B.; Young, L. R.

1986-01-01

The use of spatial orientation models in the design and evaluation of control systems for motion-base flight simulators is investigated experimentally. The development of a high-fidelity motion drive controller using an optimal control approach based on human vestibular models is described. The formulation and implementation of the optimal washout system are discussed. The effectiveness of the motion washout system was evaluated by studying the response of six motion washout systems to the NASA/AMES Vertical Motion Simulator for a single dash-quick-stop maneuver. The effects of the motion washout system on pilot performance and simulator acceptability are examined. The data reveal that human spatial orientation models are useful for the design and evaluation of flight simulator motion fidelity.

Review of forest landscape models: types, methods, development and applications

Treesearch

Weimin Xi; Robert N. Coulson; Andrew G. Birt; Zong-Bo Shang; John D. Waldron; Charles W. Lafon; David M. Cairns; Maria D. Tchakerian; Kier D. Klepzig

2009-01-01

Forest landscape models simulate forest change through time using spatially referenced data across a broad spatial scale (i.e. landscape scale) generally larger than a single forest stand. Spatial interactions between forest stands are a key component of such models. These models can incorporate other spatio-temporal processes such as...

Emerging spatial curvature can resolve the tension between high-redshift CMB and low-redshift distance ladder measurements of the Hubble constant

NASA Astrophysics Data System (ADS)

Bolejko, Krzysztof

2018-05-01

The measurements of the Hubble constant reveal a tension between high-redshift (CMB) and low-redshift (distance ladder) constraints. So far neither observational systematics nor new physics has been successfully implemented to explain away this tension. This paper presents a new solution to the Hubble constant problem. The solution is based on the Simsilun simulation (relativistic simulation of the large scale structure of the Universe) with the ray-tracing algorithm implemented. The initial conditions for the Simsilun simulation were set up as perturbations around the Λ CDM model. However, unlike in the standard cosmological model (i.e., Λ CDM model +perturbations ), within the Simsilun simulation relativistic and nonlinear evolution of cosmic structures lead to the phenomenon of emerging spatial curvature, where the mean spatial curvature evolves from the spatial flatness of the early Universe towards the slightly curved present-day Universe. Consequently, the present-day expansion rate is slightly faster compared to the spatially flat Λ CDM model. The results of the ray-tracing analysis show that the Universe which starts with initial conditions consistent with the Planck constraints should have the Hubble constant H0=72.5 ±2.1 km s-1 Mpc-1 . When the Simsilun simulation was rerun with no inhomogeneities imposed, the Hubble constant inferred within such a homogeneous simulation was H0=68.1 ±2.0 km s-1 Mpc-1 . Thus, the inclusion of nonlinear relativistic evolution that leads to the emergence of the spatial curvature can explain why the low-redshift measurements favor higher values compared to the high-redshift constraints and alleviate the tension between the CMB and distance ladder measurements of the Hubble constant.

Trap configuration and spacing influences parameter estimates in spatial capture-recapture models

USGS Publications Warehouse

Sun, Catherine C.; Fuller, Angela K.; Royle, J. Andrew

2014-01-01

An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals that arise due to the spatial organization of individuals relative to sampling devices. We simulated black bear (Ursus americanus) populations and spatial capture-recapture data to evaluate the influence of trap configuration and trap spacing on estimates of population size and a spatial scale parameter, sigma, that relates to home range size. We varied detection probability and home range size, and considered three trap configurations common to large-mammal mark-recapture studies: regular spacing, clustered, and a temporal sequence of different cluster configurations (i.e., trap relocation). We explored trap spacing and number of traps per cluster by varying the number of traps. The clustered arrangement performed well when detection rates were low, and provides for easier field implementation than the sequential trap arrangement. However, performance differences between trap configurations diminished as home range size increased. Our simulations suggest it is important to consider trap spacing relative to home range sizes, with traps ideally spaced no more than twice the spatial scale parameter. While spatial capture-recapture models can accommodate different sampling designs and still estimate parameters with accuracy and precision, our simulations demonstrate that aspects of sampling design, namely trap configuration and spacing, must consider study area size, ranges of individual movement, and home range sizes in the study population.

SimAlba: A Spatial Microsimulation Approach to the Analysis of Health Inequalities

PubMed Central

Campbell, Malcolm; Ballas, Dimitris

2016-01-01

This paper presents applied geographical research based on a spatial microsimulation model, SimAlba, aimed at estimating geographically sensitive health variables in Scotland. SimAlba has been developed in order to answer a variety of “what-if” policy questions pertaining to health policy in Scotland. Using the SimAlba model, it is possible to simulate the distributions of previously unknown variables at the small area level such as smoking, alcohol consumption, mental well-being, and obesity. The SimAlba microdataset has been created by combining Scottish Health Survey and Census data using a deterministic reweighting spatial microsimulation algorithm developed for this purpose. The paper presents SimAlba outputs for Scotland’s largest city, Glasgow, and examines the spatial distribution of the simulated variables for small geographical areas in Glasgow as well as the effects on individuals of different policy scenario outcomes. In simulating previously unknown spatial data, a wealth of new perspectives can be examined and explored. This paper explores a small set of those potential avenues of research and shows the power of spatial microsimulation modeling in an urban context. PMID:27818989

Spatial and temporal agreement in climate model simulations of the Interdecadal Pacific Oscillation

DOE PAGES

Henley, Benjamin J.; Meehl, Gerald; Power, Scott B.; ...

2017-01-31

Accelerated warming and hiatus periods in the long-term rise of Global Mean Surface Temperature (GMST) have, in recent decades, been associated with the Interdecadal Pacific Oscillation (IPO). Critically, decadal climate prediction relies on the skill of state-of-the-art climate models to reliably represent these low-frequency climate variations. We undertake a systematic evaluation of the simulation of the IPO in the suite of Coupled Model Intercomparison Project 5 (CMIP5) models. We track the IPO in pre-industrial (control) and all-forcings (historical) experiments using the IPO tripole index (TPI). The TPI is explicitly aligned with the observed spatial pattern of the IPO, and circumventsmore » assumptions about the nature of global warming. We find that many models underestimate the ratio of decadal-to-total variance in sea surface temperatures (SSTs). However, the basin-wide spatial pattern of positive and negative phases of the IPO are simulated reasonably well, with spatial pattern correlation coefficients between observations and models spanning the range 0.4–0.8. Deficiencies are mainly in the extratropical Pacific. Models that better capture the spatial pattern of the IPO also tend to more realistically simulate the ratio of decadal to total variance. Of the 13% of model centuries that have a fractional bias in the decadal-to-total TPI variance of 0.2 or less, 84% also have a spatial pattern correlation coefficient with the observed pattern exceeding 0.5. This result is highly consistent across both IPO positive and negative phases. This is evidence that the IPO is related to one or more inherent dynamical mechanisms of the climate system.« less

Spatial and temporal agreement in climate model simulations of the Interdecadal Pacific Oscillation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Henley, Benjamin J.; Meehl, Gerald; Power, Scott B.

Accelerated warming and hiatus periods in the long-term rise of Global Mean Surface Temperature (GMST) have, in recent decades, been associated with the Interdecadal Pacific Oscillation (IPO). Critically, decadal climate prediction relies on the skill of state-of-the-art climate models to reliably represent these low-frequency climate variations. We undertake a systematic evaluation of the simulation of the IPO in the suite of Coupled Model Intercomparison Project 5 (CMIP5) models. We track the IPO in pre-industrial (control) and all-forcings (historical) experiments using the IPO tripole index (TPI). The TPI is explicitly aligned with the observed spatial pattern of the IPO, and circumventsmore » assumptions about the nature of global warming. We find that many models underestimate the ratio of decadal-to-total variance in sea surface temperatures (SSTs). However, the basin-wide spatial pattern of positive and negative phases of the IPO are simulated reasonably well, with spatial pattern correlation coefficients between observations and models spanning the range 0.4–0.8. Deficiencies are mainly in the extratropical Pacific. Models that better capture the spatial pattern of the IPO also tend to more realistically simulate the ratio of decadal to total variance. Of the 13% of model centuries that have a fractional bias in the decadal-to-total TPI variance of 0.2 or less, 84% also have a spatial pattern correlation coefficient with the observed pattern exceeding 0.5. This result is highly consistent across both IPO positive and negative phases. This is evidence that the IPO is related to one or more inherent dynamical mechanisms of the climate system.« less

Modeling Np and Pu transport with a surface complexation model and spatially variant sorption capacities: Implications for reactive transport modeling and performance assessments of nuclear waste disposal sites

USGS Publications Warehouse

Glynn, P.D.

2003-01-01

One-dimensional (1D) geochemical transport modeling is used to demonstrate the effects of speciation and sorption reactions on the ground-water transport of Np and Pu, two redox-sensitive elements. Earlier 1D simulations (Reardon, 1981) considered the kinetically limited dissolution of calcite and its effect on ion-exchange reactions (involving 90Sr, Ca, Na, Mg and K), and documented the spatial variation of a 90Sr partition coefficient under both transient and steady-state chemical conditions. In contrast, the simulations presented here assume local equilibrium for all reactions, and consider sorption on constant potential, rather than constant charge, surfaces. Reardon's (1981) seminal findings on the spatial and temporal variability of partitioning (of 90Sr) are reexamined and found partially caused by his assumption of a kinetically limited reaction. In the present work, sorption is assumed the predominant retardation process controlling Pu and Np transport, and is simulated using a diffuse-double-layer-surface-complexation (DDLSC) model. Transport simulations consider the infiltration of Np- and Pu-contaminated waters into an initially uncontaminated environment, followed by the cleanup of the resultant contamination with uncontaminated water. Simulations are conducted using different spatial distributions of sorption capacities (with the same total potential sorption capacity, but with different variances and spatial correlation structures). Results obtained differ markedly from those that would be obtained in transport simulations using constant Kd, Langmuir or Freundlich sorption models. When possible, simulation results (breakthrough curves) are fitted to a constant K d advection-dispersion transport model and compared. Functional differences often are great enough that they prevent a meaningful fit of the simulation results with a constant K d (or even a Langmuir or Freundlich) model, even in the case of Np, a weakly sorbed radionuclide under the simulation conditions. Functional behaviors that cannot be fit include concentration trend reversals and radionuclide desorption spikes. Other simulation results are fit successfully but the fitted parameters (Kd and dispersivity) vary significantly depending on simulation conditions (e.g. "infiltration" vs. "cleanup" conditions). Notably, an increase in the variance of the specified sorption capacities results in a marked increase in the dispersion of the radionuclides. The results presented have implications for the simulation of radionuclide migration in performance assessments of nuclear waste-disposal sites, for the future monitoring of those sites, and more generally for modeling contaminant transport in ground-water environments. ?? 2003 Published by Elsevier Science Ltd.

Modeling the spatial and temporal variability in climate and primary productivity across the Luquillo Mountains, Puerto Rico.

Treesearch

Hongqing Wanga; Charles A.S. Halla; Frederick N. Scatenab; Ned Fetcherc; Wei Wua

2003-01-01

There are few studies that have examined the spatial variability of forest productivity over an entire tropical forested landscape. In this study, we used a spatially-explicit forest productivity model, TOPOPROD, which is based on the FORESTBGC model, to simulate spatial patterns of gross primary productivity (GPP), net primary productivity (NPP), and respiration over...

Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area

NASA Astrophysics Data System (ADS)

Di, Zhenhua; Duan, Qingyun; Wang, Chen; Ye, Aizhong; Miao, Chiyuan; Gong, Wei

2018-03-01

Forecasting skills of the complex weather and climate models have been improved by tuning the sensitive parameters that exert the greatest impact on simulated results based on more effective optimization methods. However, whether the optimal parameter values are still work when the model simulation conditions vary, which is a scientific problem deserving of study. In this study, a highly-effective optimization method, adaptive surrogate model-based optimization (ASMO), was firstly used to tune nine sensitive parameters from four physical parameterization schemes of the Weather Research and Forecasting (WRF) model to obtain better summer precipitation forecasting over the Greater Beijing Area in China. Then, to assess the applicability of the optimal parameter values, simulation results from the WRF model with default and optimal parameter values were compared across precipitation events, boundary conditions, spatial scales, and physical processes in the Greater Beijing Area. The summer precipitation events from 6 years were used to calibrate and evaluate the optimal parameter values of WRF model. Three boundary data and two spatial resolutions were adopted to evaluate the superiority of the calibrated optimal parameters to default parameters under the WRF simulations with different boundary conditions and spatial resolutions, respectively. Physical interpretations of the optimal parameters indicating how to improve precipitation simulation results were also examined. All the results showed that the optimal parameters obtained by ASMO are superior to the default parameters for WRF simulations for predicting summer precipitation in the Greater Beijing Area because the optimal parameters are not constrained by specific precipitation events, boundary conditions, and spatial resolutions. The optimal values of the nine parameters were determined from 127 parameter samples using the ASMO method, which showed that the ASMO method is very highly-efficient for optimizing WRF model parameters.

Simulating spatial and temporally related fire weather

Treesearch

Isaac C. Grenfell; Mark Finney; Matt Jolly

2010-01-01

Use of fire behavior models has assumed an increasingly important role for managers of wildfire incidents to make strategic decisions. For fire risk assessments and danger rating at very large spatial scales, these models depend on fire weather variables or fire danger indices. Here, we describe a method to simulate fire weather at a national scale that captures the...

Integrating ecosystem sampling, gradient modeling, remote sensing, and ecosystem simulation to create spatially explicit landscape inventories

Treesearch

Robert E. Keane; Matthew G. Rollins; Cecilia H. McNicoll; Russell A. Parsons

2002-01-01

Presented is a prototype of the Landscape Ecosystem Inventory System (LEIS), a system for creating maps of important landscape characteristics for natural resource planning. This system uses gradient-based field inventories coupled with gradient modeling remote sensing, ecosystem simulation, and statistical analyses to derive spatial data layers required for ecosystem...

Skin fluorescence model based on the Monte Carlo technique

NASA Astrophysics Data System (ADS)

Churmakov, Dmitry Y.; Meglinski, Igor V.; Piletsky, Sergey A.; Greenhalgh, Douglas A.

2003-10-01

The novel Monte Carlo technique of simulation of spatial fluorescence distribution within the human skin is presented. The computational model of skin takes into account spatial distribution of fluorophores following the collagen fibers packing, whereas in epidermis and stratum corneum the distribution of fluorophores assumed to be homogeneous. The results of simulation suggest that distribution of auto-fluorescence is significantly suppressed in the NIR spectral region, while fluorescence of sensor layer embedded in epidermis is localized at the adjusted depth. The model is also able to simulate the skin fluorescence spectra.

Coupling a three-dimensional subsurface flow model with a land surface model to simulate stream-aquifer-land interactions

NASA Astrophysics Data System (ADS)

Huang, M.; Bisht, G.; Zhou, T.; Chen, X.; Dai, H.; Hammond, G. E.; Riley, W. J.; Downs, J.; Liu, Y.; Zachara, J. M.

2016-12-01

A fully coupled three-dimensional surface and subsurface land model is developed and applied to a site along the Columbia River to simulate three-way interactions among river water, groundwater, and land surface processes. The model features the coupling of the Community Land Model version 4.5 (CLM4.5) and a massively-parallel multi-physics reactive tranport model (PFLOTRAN). The coupled model (CLM-PFLOTRAN) is applied to a 400m×400m study domain instrumented with groundwater monitoring wells in the Hanford 300 Area along the Columbia River. CLM-PFLOTRAN simulations are performed at three different spatial resolutions over the period 2011-2015 to evaluate the impact of spatial resolution on simulated variables. To demonstrate the difference in model simulations with and without lateral subsurface flow, a vertical-only CLM-PFLOTRAN simulation is also conducted for comparison. Results show that the coupled model is skillful in simulating stream-aquifer interactions, and the land-surface energy partitioning can be strongly modulated by groundwater-river water interactions in high water years due to increased soil moisture availability caused by elevated groundwater table. In addition, spatial resolution does not seem to impact the land surface energy flux simulations, although it is a key factor for accurately estimating the mass exchange rates at the boundaries and associated biogeochemical reactions in the aquifer. The coupled model developed in this study establishes a solid foundation for understanding co-evolution of hydrology and biogeochemistry along the river corridors under historical and future hydro-climate changes.

Simulating land-use changes by incorporating spatial autocorrelation and self-organization in CLUE-S modeling: a case study in Zengcheng District, Guangzhou, China

NASA Astrophysics Data System (ADS)

Mei, Zhixiong; Wu, Hao; Li, Shiyun

2018-06-01

The Conversion of Land Use and its Effects at Small regional extent (CLUE-S), which is a widely used model for land-use simulation, utilizes logistic regression to estimate the relationships between land use and its drivers, and thus, predict land-use change probabilities. However, logistic regression disregards possible spatial autocorrelation and self-organization in land-use data. Autologistic regression can depict spatial autocorrelation but cannot address self-organization, while logistic regression by considering only self-organization (NElogistic regression) fails to capture spatial autocorrelation. Therefore, this study developed a regression (NE-autologistic regression) method, which incorporated both spatial autocorrelation and self-organization, to improve CLUE-S. The Zengcheng District of Guangzhou, China was selected as the study area. The land-use data of 2001, 2005, and 2009, as well as 10 typical driving factors, were used to validate the proposed regression method and the improved CLUE-S model. Then, three future land-use scenarios in 2020: the natural growth scenario, ecological protection scenario, and economic development scenario, were simulated using the improved model. Validation results showed that NE-autologistic regression performed better than logistic regression, autologistic regression, and NE-logistic regression in predicting land-use change probabilities. The spatial allocation accuracy and kappa values of NE-autologistic-CLUE-S were higher than those of logistic-CLUE-S, autologistic-CLUE-S, and NE-logistic-CLUE-S for the simulations of two periods, 2001-2009 and 2005-2009, which proved that the improved CLUE-S model achieved the best simulation and was thereby effective to a certain extent. The scenario simulation results indicated that under all three scenarios, traffic land and residential/industrial land would increase, whereas arable land and unused land would decrease during 2009-2020. Apparent differences also existed in the simulated change sizes and locations of each land-use type under different scenarios. The results not only demonstrate the validity of the improved model but also provide a valuable reference for relevant policy-makers.

Comparing Lagrangian and Eulerian models for CO2 transport - a step towards Bayesian inverse modeling using WRF/STILT-VPRM

NASA Astrophysics Data System (ADS)

Pillai, D.; Gerbig, C.; Kretschmer, R.; Beck, V.; Karstens, U.; Neininger, B.; Heimann, M.

2012-10-01

We present simulations of atmospheric CO2 concentrations provided by two modeling systems, run at high spatial resolution: the Eulerian-based Weather Research Forecasting (WRF) model and the Lagrangian-based Stochastic Time-Inverted Lagrangian Transport (STILT) model, both of which are coupled to a diagnostic biospheric model, the Vegetation Photosynthesis and Respiration Model (VPRM). The consistency of the simulations is assessed with special attention paid to the details of horizontal as well as vertical transport and mixing of CO2 concentrations in the atmosphere. The dependence of model mismatch (Eulerian vs. Lagrangian) on models' spatial resolution is further investigated. A case study using airborne measurements during which two models showed large deviations from each other is analyzed in detail as an extreme case. Using aircraft observations and pulse release simulations, we identified differences in the representation of details in the interaction between turbulent mixing and advection through wind shear as the main cause of discrepancies between WRF and STILT transport at a spatial resolution such as 2 and 6 km. Based on observations and inter-model comparisons of atmospheric CO2 concentrations, we show that a refinement of the parameterization of turbulent velocity variance and Lagrangian time-scale in STILT is needed to achieve a better match between the Eulerian and the Lagrangian transport at such a high spatial resolution (e.g. 2 and 6 km). Nevertheless, the inter-model differences in simulated CO2 time series for a tall tower observatory at Ochsenkopf in Germany are about a factor of two smaller than the model-data mismatch and about a factor of three smaller than the mismatch between the current global model simulations and the data.

Modeling evolution of spatially distributed bacterial communities: a simulation with the haploid evolutionary constructor

PubMed Central

2015-01-01

Background Multiscale approaches for integrating submodels of various levels of biological organization into a single model became the major tool of systems biology. In this paper, we have constructed and simulated a set of multiscale models of spatially distributed microbial communities and study an influence of unevenly distributed environmental factors on the genetic diversity and evolution of the community members. Results Haploid Evolutionary Constructor software http://evol-constructor.bionet.nsc.ru/ was expanded by adding the tool for the spatial modeling of a microbial community (1D, 2D and 3D versions). A set of the models of spatially distributed communities was built to demonstrate that the spatial distribution of cells affects both intensity of selection and evolution rate. Conclusion In spatially heterogeneous communities, the change in the direction of the environmental flow might be reflected in local irregular population dynamics, while the genetic structure of populations (frequencies of the alleles) remains stable. Furthermore, in spatially heterogeneous communities, the chemotaxis might dramatically affect the evolution of community members. PMID:25708911

Simulation of semi-arid hydrological processes at different spatial resolutions using the AgroEcoSystem-Watershed (AgES-W) model

NASA Astrophysics Data System (ADS)

Green, T. R.; Erksine, R. H.; David, O.; Ascough, J. C., II; Kipka, H.; Lloyd, W. J.; McMaster, G. S.

2015-12-01

Water movement and storage within a watershed may be simulated at different spatial resolutions of land areas or hydrological response units (HRUs). Here, effects of HRU size on simulated soil water and surface runoff are tested using the AgroEcoSystem-Watershed (AgES-W) model with three different resolutions of HRUs. We studied a 56-ha agricultural watershed in northern Colorado, USA farmed primarily under a wheat-fallow rotation. The delineation algorithm was based upon topography (surface flow paths), land use (crop management strips and native grass), and mapped soil units (three types), which produced HRUs that follow the land use and soil boundaries. AgES-W model parameters that control surface and subsurface hydrology were calibrated using simulated daily soil moisture at different landscape positions and depths where soil moisture was measured hourly and averaged up to daily values. Parameter sets were both uniform and spatially variable with depth and across the watershed (5 different calibration approaches). Although forward simulations were computationally efficient (less than 1 minute each), each calibration required thousands of model runs. Execution of such large jobs was facilitated by using the Object Modeling System with the Cloud Services Innovation Platform to manage four virtual machines on a commercial web service configured with a total of 64 computational cores and 120 GB of memory. Results show how spatially distributed and averaged soil moisture and runoff at the outlet vary with different HRU delineations. The results will help guide HRU delineation, spatial resolution and parameter estimation methods for improved hydrological simulations in this and other semi-arid agricultural watersheds.

Rule-based spatial modeling with diffusing, geometrically constrained molecules.

PubMed

Gruenert, Gerd; Ibrahim, Bashar; Lenser, Thorsten; Lohel, Maiko; Hinze, Thomas; Dittrich, Peter

2010-06-07

We suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction network can be defined. For our implementation (based on LAMMPS), we have chosen an already existing formalism (BioNetGen) for the implicit specification of the reaction network. This compatibility allows to import existing models easily, i.e., only additional geometry data files have to be provided. Our simulations show that the obtained dynamics can be fundamentally different from those simulations that use classical reaction-diffusion approaches like Partial Differential Equations or Gillespie-type spatial stochastic simulation. We show, for example, that the combination of combinatorial complexity and geometric effects leads to the emergence of complex self-assemblies and transportation phenomena happening faster than diffusion (using a model of molecular walkers on microtubules). When the mentioned classical simulation approaches are applied, these aspects of modeled systems cannot be observed without very special treatment. Further more, we show that the geometric information can even change the organizational structure of the reaction system. That is, a set of chemical species that can in principle form a stationary state in a Differential Equation formalism, is potentially unstable when geometry is considered, and vice versa. We conclude that our approach provides a new general framework filling a gap in between approaches with no or rigid spatial representation like Partial Differential Equations and specialized coarse-grained spatial simulation systems like those for DNA or virus capsid self-assembly.

Rule-based spatial modeling with diffusing, geometrically constrained molecules

PubMed Central

2010-01-01

Background We suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction network can be defined. For our implementation (based on LAMMPS), we have chosen an already existing formalism (BioNetGen) for the implicit specification of the reaction network. This compatibility allows to import existing models easily, i.e., only additional geometry data files have to be provided. Results Our simulations show that the obtained dynamics can be fundamentally different from those simulations that use classical reaction-diffusion approaches like Partial Differential Equations or Gillespie-type spatial stochastic simulation. We show, for example, that the combination of combinatorial complexity and geometric effects leads to the emergence of complex self-assemblies and transportation phenomena happening faster than diffusion (using a model of molecular walkers on microtubules). When the mentioned classical simulation approaches are applied, these aspects of modeled systems cannot be observed without very special treatment. Further more, we show that the geometric information can even change the organizational structure of the reaction system. That is, a set of chemical species that can in principle form a stationary state in a Differential Equation formalism, is potentially unstable when geometry is considered, and vice versa. Conclusions We conclude that our approach provides a new general framework filling a gap in between approaches with no or rigid spatial representation like Partial Differential Equations and specialized coarse-grained spatial simulation systems like those for DNA or virus capsid self-assembly. PMID:20529264

Intercomparison of terrestrial carbon fluxes and carbon use efficiency simulated by CMIP5 Earth System Models

NASA Astrophysics Data System (ADS)

Kim, Dongmin; Lee, Myong-In; Jeong, Su-Jong; Im, Jungho; Cha, Dong Hyun; Lee, Sanggyun

2017-12-01

This study compares historical simulations of the terrestrial carbon cycle produced by 10 Earth System Models (ESMs) that participated in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Using MODIS satellite estimates, this study validates the simulation of gross primary production (GPP), net primary production (NPP), and carbon use efficiency (CUE), which depend on plant function types (PFTs). The models show noticeable deficiencies compared to the MODIS data in the simulation of the spatial patterns of GPP and NPP and large differences among the simulations, although the multi-model ensemble (MME) mean provides a realistic global mean value and spatial distributions. The larger model spreads in GPP and NPP compared to those of surface temperature and precipitation suggest that the differences among simulations in terms of the terrestrial carbon cycle are largely due to uncertainties in the parameterization of terrestrial carbon fluxes by vegetation. The models also exhibit large spatial differences in their simulated CUE values and at locations where the dominant PFT changes, primarily due to differences in the parameterizations. While the MME-simulated CUE values show a strong dependence on surface temperatures, the observed CUE values from MODIS show greater complexity, as well as non-linear sensitivity. This leads to the overall underestimation of CUE using most of the PFTs incorporated into current ESMs. The results of this comparison suggest that more careful and extensive validation is needed to improve the terrestrial carbon cycle in terms of ecosystem-level processes.

Parallel STEPS: Large Scale Stochastic Spatial Reaction-Diffusion Simulation with High Performance Computers

PubMed Central

Chen, Weiliang; De Schutter, Erik

2017-01-01

Stochastic, spatial reaction-diffusion simulations have been widely used in systems biology and computational neuroscience. However, the increasing scale and complexity of models and morphologies have exceeded the capacity of any serial implementation. This led to the development of parallel solutions that benefit from the boost in performance of modern supercomputers. In this paper, we describe an MPI-based, parallel operator-splitting implementation for stochastic spatial reaction-diffusion simulations with irregular tetrahedral meshes. The performance of our implementation is first examined and analyzed with simulations of a simple model. We then demonstrate its application to real-world research by simulating the reaction-diffusion components of a published calcium burst model in both Purkinje neuron sub-branch and full dendrite morphologies. Simulation results indicate that our implementation is capable of achieving super-linear speedup for balanced loading simulations with reasonable molecule density and mesh quality. In the best scenario, a parallel simulation with 2,000 processes runs more than 3,600 times faster than its serial SSA counterpart, and achieves more than 20-fold speedup relative to parallel simulation with 100 processes. In a more realistic scenario with dynamic calcium influx and data recording, the parallel simulation with 1,000 processes and no load balancing is still 500 times faster than the conventional serial SSA simulation. PMID:28239346

Parallel STEPS: Large Scale Stochastic Spatial Reaction-Diffusion Simulation with High Performance Computers.

PubMed

Chen, Weiliang; De Schutter, Erik

2017-01-01

Stochastic, spatial reaction-diffusion simulations have been widely used in systems biology and computational neuroscience. However, the increasing scale and complexity of models and morphologies have exceeded the capacity of any serial implementation. This led to the development of parallel solutions that benefit from the boost in performance of modern supercomputers. In this paper, we describe an MPI-based, parallel operator-splitting implementation for stochastic spatial reaction-diffusion simulations with irregular tetrahedral meshes. The performance of our implementation is first examined and analyzed with simulations of a simple model. We then demonstrate its application to real-world research by simulating the reaction-diffusion components of a published calcium burst model in both Purkinje neuron sub-branch and full dendrite morphologies. Simulation results indicate that our implementation is capable of achieving super-linear speedup for balanced loading simulations with reasonable molecule density and mesh quality. In the best scenario, a parallel simulation with 2,000 processes runs more than 3,600 times faster than its serial SSA counterpart, and achieves more than 20-fold speedup relative to parallel simulation with 100 processes. In a more realistic scenario with dynamic calcium influx and data recording, the parallel simulation with 1,000 processes and no load balancing is still 500 times faster than the conventional serial SSA simulation.

Continental-scale temperature covariance in proxy reconstructions and climate models

NASA Astrophysics Data System (ADS)

Hartl-Meier, Claudia; Büntgen, Ulf; Smerdon, Jason; Zorita, Eduardo; Krusic, Paul; Ljungqvist, Fredrik; Schneider, Lea; Esper, Jan

2017-04-01

Inter-continental temperature variability over the past millennium has been reported to be more coherent in climate model simulations than in multi-proxy-based reconstructions, a finding that undermines the representation of spatial variability in either of these approaches. We assess the covariance of summer temperatures among Northern Hemisphere continents by comparing tree-ring based temperature reconstructions with state-of-the-art climate model simulations over the past millennium. We find inter-continental temperature covariance to be larger in tree-ring-only reconstructions compared to those derived from multi-proxy networks, thus enhancing the agreement between proxy- and model-based spatial representations. A detailed comparison of simulated temperatures, however, reveals substantial spread among the models. Over the past millennium, inter-continental temperature correlations are driven by the cooling after major volcanic eruptions in 1257, 1452, 1601, and 1815. The coherence of these synchronizing events appears to be elevated in several climate simulations relative to their own covariance baselines and the proxy reconstructions, suggesting these models overestimate the amplitude of cooling in response to volcanic forcing at large spatial scales.

CDPOP: A spatially explicit cost distance population genetics program

Treesearch

Erin L. Landguth; S. A. Cushman

2010-01-01

Spatially explicit simulation of gene flow in complex landscapes is essential to explain observed population responses and provide a foundation for landscape genetics. To address this need, we wrote a spatially explicit, individual-based population genetics model (CDPOP). The model implements individual-based population modelling with Mendelian inheritance and k-allele...

Measuring directional urban spatial interaction in China: A migration perspective

PubMed Central

Li, Fangzhou; Feng, Zhiming; Li, Peng; You, Zhen

2017-01-01

The study of urban spatial interaction is closely linked to that of economic geography, urban planning, regional development, and so on. Currently, this topic is generating a great deal of interest among researchers who are striving to find accurate ways to measure urban spatial interaction. Classical spatial interaction models lack theoretical guidance and require complicated parameter-adjusting processes. The radiation model, however, as proposed by Simini et al. with rigorous formula derivation, can simulate directional urban spatial interaction. We applied the radiation model in China to simulate the directional migration number among 337 nationwide research units, comprising 4 municipalities and 333 prefecture-level cities. We then analyzed the overall situation in Chinese cities, the interaction intensity hierarchy, and the prime urban agglomerations from the perspective of migration. This was done to ascertain China’s urban spatial interaction and regional development from 2000 to 2010 to reveal ground realities. PMID:28141853

Measuring directional urban spatial interaction in China: A migration perspective.

PubMed

Li, Fangzhou; Feng, Zhiming; Li, Peng; You, Zhen

2017-01-01

The study of urban spatial interaction is closely linked to that of economic geography, urban planning, regional development, and so on. Currently, this topic is generating a great deal of interest among researchers who are striving to find accurate ways to measure urban spatial interaction. Classical spatial interaction models lack theoretical guidance and require complicated parameter-adjusting processes. The radiation model, however, as proposed by Simini et al. with rigorous formula derivation, can simulate directional urban spatial interaction. We applied the radiation model in China to simulate the directional migration number among 337 nationwide research units, comprising 4 municipalities and 333 prefecture-level cities. We then analyzed the overall situation in Chinese cities, the interaction intensity hierarchy, and the prime urban agglomerations from the perspective of migration. This was done to ascertain China's urban spatial interaction and regional development from 2000 to 2010 to reveal ground realities.

Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology.

PubMed

Schaff, James C; Gao, Fei; Li, Ye; Novak, Igor L; Slepchenko, Boris M

2016-12-01

Hybrid deterministic-stochastic methods provide an efficient alternative to a fully stochastic treatment of models which include components with disparate levels of stochasticity. However, general-purpose hybrid solvers for spatially resolved simulations of reaction-diffusion systems are not widely available. Here we describe fundamentals of a general-purpose spatial hybrid method. The method generates realizations of a spatially inhomogeneous hybrid system by appropriately integrating capabilities of a deterministic partial differential equation solver with a popular particle-based stochastic simulator, Smoldyn. Rigorous validation of the algorithm is detailed, using a simple model of calcium 'sparks' as a testbed. The solver is then applied to a deterministic-stochastic model of spontaneous emergence of cell polarity. The approach is general enough to be implemented within biologist-friendly software frameworks such as Virtual Cell.

Surrogate model approach for improving the performance of reactive transport simulations

NASA Astrophysics Data System (ADS)

Jatnieks, Janis; De Lucia, Marco; Sips, Mike; Dransch, Doris

2016-04-01

Reactive transport models can serve a large number of important geoscientific applications involving underground resources in industry and scientific research. It is common for simulation of reactive transport to consist of at least two coupled simulation models. First is a hydrodynamics simulator that is responsible for simulating the flow of groundwaters and transport of solutes. Hydrodynamics simulators are well established technology and can be very efficient. When hydrodynamics simulations are performed without coupled geochemistry, their spatial geometries can span millions of elements even when running on desktop workstations. Second is a geochemical simulation model that is coupled to the hydrodynamics simulator. Geochemical simulation models are much more computationally costly. This is a problem that makes reactive transport simulations spanning millions of spatial elements very difficult to achieve. To address this problem we propose to replace the coupled geochemical simulation model with a surrogate model. A surrogate is a statistical model created to include only the necessary subset of simulator complexity for a particular scenario. To demonstrate the viability of such an approach we tested it on a popular reactive transport benchmark problem that involves 1D Calcite transport. This is a published benchmark problem (Kolditz, 2012) for simulation models and for this reason we use it to test the surrogate model approach. To do this we tried a number of statistical models available through the caret and DiceEval packages for R, to be used as surrogate models. These were trained on randomly sampled subset of the input-output data from the geochemical simulation model used in the original reactive transport simulation. For validation we use the surrogate model to predict the simulator output using the part of sampled input data that was not used for training the statistical model. For this scenario we find that the multivariate adaptive regression splines (MARS) method provides the best trade-off between speed and accuracy. This proof-of-concept forms an essential step towards building an interactive visual analytics system to enable user-driven systematic creation of geochemical surrogate models. Such a system shall enable reactive transport simulations with unprecedented spatial and temporal detail to become possible. References: Kolditz, O., Görke, U.J., Shao, H. and Wang, W., 2012. Thermo-hydro-mechanical-chemical processes in porous media: benchmarks and examples (Vol. 86). Springer Science & Business Media.

The effects of seed dispersal on the simulation of long-term forest landscape change

Treesearch

Hong S. He; David J. Mladenoff

1999-01-01

The study of forest landscape change requires an understanding of the complex interactions of both spatial and temporal factors. Traditionally, forest gap models have been used to simulate change on small and independent plots. While gap models are useful in examining forest ecological dynamics across temporal scales, large, spatial processes, such as seed dispersal,...

The validity of flow approximations when simulating catchment-integrated flash floods

NASA Astrophysics Data System (ADS)

Bout, B.; Jetten, V. G.

2018-01-01

Within hydrological models, flow approximations are commonly used to reduce computation time. The validity of these approximations is strongly determined by flow height, flow velocity and the spatial resolution of the model. In this presentation, the validity and performance of the kinematic, diffusive and dynamic flow approximations are investigated for use in a catchment-based flood model. Particularly, the validity during flood events and for varying spatial resolutions is investigated. The OpenLISEM hydrological model is extended to implement both these flow approximations and channel flooding based on dynamic flow. The flow approximations are used to recreate measured discharge in three catchments, among which is the hydrograph of the 2003 flood event in the Fella river basin. Furthermore, spatial resolutions are varied for the flood simulation in order to investigate the influence of spatial resolution on these flow approximations. Results show that the kinematic, diffusive and dynamic flow approximation provide least to highest accuracy, respectively, in recreating measured discharge. Kinematic flow, which is commonly used in hydrological modelling, substantially over-estimates hydrological connectivity in the simulations with a spatial resolution of below 30 m. Since spatial resolutions of models have strongly increased over the past decades, usage of routed kinematic flow should be reconsidered. The combination of diffusive or dynamic overland flow and dynamic channel flooding provides high accuracy in recreating the 2003 Fella river flood event. Finally, in the case of flood events, spatial modelling of kinematic flow substantially over-estimates hydrological connectivity and flow concentration since pressure forces are removed, leading to significant errors.

Rapid simulation of spatial epidemics: a spectral method.

PubMed

Brand, Samuel P C; Tildesley, Michael J; Keeling, Matthew J

2015-04-07

Spatial structure and hence the spatial position of host populations plays a vital role in the spread of infection. In the majority of situations, it is only possible to predict the spatial spread of infection using simulation models, which can be computationally demanding especially for large population sizes. Here we develop an approximation method that vastly reduces this computational burden. We assume that the transmission rates between individuals or sub-populations are determined by a spatial transmission kernel. This kernel is assumed to be isotropic, such that the transmission rate is simply a function of the distance between susceptible and infectious individuals; as such this provides the ideal mechanism for modelling localised transmission in a spatial environment. We show that the spatial force of infection acting on all susceptibles can be represented as a spatial convolution between the transmission kernel and a spatially extended 'image' of the infection state. This representation allows the rapid calculation of stochastic rates of infection using fast-Fourier transform (FFT) routines, which greatly improves the computational efficiency of spatial simulations. We demonstrate the efficiency and accuracy of this fast spectral rate recalculation (FSR) method with two examples: an idealised scenario simulating an SIR-type epidemic outbreak amongst N habitats distributed across a two-dimensional plane; the spread of infection between US cattle farms, illustrating that the FSR method makes continental-scale outbreak forecasting feasible with desktop processing power. The latter model demonstrates which areas of the US are at consistently high risk for cattle-infections, although predictions of epidemic size are highly dependent on assumptions about the tail of the transmission kernel. Copyright © 2015 Elsevier Ltd. All rights reserved.

A geostatistical extreme-value framework for fast simulation of natural hazard events

PubMed Central

Stephenson, David B.

2016-01-01

We develop a statistical framework for simulating natural hazard events that combines extreme value theory and geostatistics. Robust generalized additive model forms represent generalized Pareto marginal distribution parameters while a Student’s t-process captures spatial dependence and gives a continuous-space framework for natural hazard event simulations. Efficiency of the simulation method allows many years of data (typically over 10 000) to be obtained at relatively little computational cost. This makes the model viable for forming the hazard module of a catastrophe model. We illustrate the framework by simulating maximum wind gusts for European windstorms, which are found to have realistic marginal and spatial properties, and validate well against wind gust measurements. PMID:27279768

On the Fidelity of Semi-distributed Hydrologic Model Simulations for Large Scale Catchment Applications

NASA Astrophysics Data System (ADS)

Ajami, H.; Sharma, A.; Lakshmi, V.

2017-12-01

Application of semi-distributed hydrologic modeling frameworks is a viable alternative to fully distributed hyper-resolution hydrologic models due to computational efficiency and resolving fine-scale spatial structure of hydrologic fluxes and states. However, fidelity of semi-distributed model simulations is impacted by (1) formulation of hydrologic response units (HRUs), and (2) aggregation of catchment properties for formulating simulation elements. Here, we evaluate the performance of a recently developed Soil Moisture and Runoff simulation Toolkit (SMART) for large catchment scale simulations. In SMART, topologically connected HRUs are delineated using thresholds obtained from topographic and geomorphic analysis of a catchment, and simulation elements are equivalent cross sections (ECS) representative of a hillslope in first order sub-basins. Earlier investigations have shown that formulation of ECSs at the scale of a first order sub-basin reduces computational time significantly without compromising simulation accuracy. However, the implementation of this approach has not been fully explored for catchment scale simulations. To assess SMART performance, we set-up the model over the Little Washita watershed in Oklahoma. Model evaluations using in-situ soil moisture observations show satisfactory model performance. In addition, we evaluated the performance of a number of soil moisture disaggregation schemes recently developed to provide spatially explicit soil moisture outputs at fine scale resolution. Our results illustrate that the statistical disaggregation scheme performs significantly better than the methods based on topographic data. Future work is focused on assessing the performance of SMART using remotely sensed soil moisture observations using spatially based model evaluation metrics.

A physiologically-inspired model reproducing the speech intelligibility benefit in cochlear implant listeners with residual acoustic hearing.

PubMed

Zamaninezhad, Ladan; Hohmann, Volker; Büchner, Andreas; Schädler, Marc René; Jürgens, Tim

2017-02-01

This study introduces a speech intelligibility model for cochlear implant users with ipsilateral preserved acoustic hearing that aims at simulating the observed speech-in-noise intelligibility benefit when receiving simultaneous electric and acoustic stimulation (EA-benefit). The model simulates the auditory nerve spiking in response to electric and/or acoustic stimulation. The temporally and spatially integrated spiking patterns were used as the final internal representation of noisy speech. Speech reception thresholds (SRTs) in stationary noise were predicted for a sentence test using an automatic speech recognition framework. The model was employed to systematically investigate the effect of three physiologically relevant model factors on simulated SRTs: (1) the spatial spread of the electric field which co-varies with the number of electrically stimulated auditory nerves, (2) the "internal" noise simulating the deprivation of auditory system, and (3) the upper bound frequency limit of acoustic hearing. The model results show that the simulated SRTs increase monotonically with increasing spatial spread for fixed internal noise, and also increase with increasing the internal noise strength for a fixed spatial spread. The predicted EA-benefit does not follow such a systematic trend and depends on the specific combination of the model parameters. Beyond 300 Hz, the upper bound limit for preserved acoustic hearing is less influential on speech intelligibility of EA-listeners in stationary noise. The proposed model-predicted EA-benefits are within the range of EA-benefits shown by 18 out of 21 actual cochlear implant listeners with preserved acoustic hearing. Copyright © 2016 Elsevier B.V. All rights reserved.

Surrogate modelling for the prediction of spatial fields based on simultaneous dimensionality reduction of high-dimensional input/output spaces.

PubMed

Crevillén-García, D

2018-04-01

Time-consuming numerical simulators for solving groundwater flow and dissolution models of physico-chemical processes in deep aquifers normally require some of the model inputs to be defined in high-dimensional spaces in order to return realistic results. Sometimes, the outputs of interest are spatial fields leading to high-dimensional output spaces. Although Gaussian process emulation has been satisfactorily used for computing faithful and inexpensive approximations of complex simulators, these have been mostly applied to problems defined in low-dimensional input spaces. In this paper, we propose a method for simultaneously reducing the dimensionality of very high-dimensional input and output spaces in Gaussian process emulators for stochastic partial differential equation models while retaining the qualitative features of the original models. This allows us to build a surrogate model for the prediction of spatial fields in such time-consuming simulators. We apply the methodology to a model of convection and dissolution processes occurring during carbon capture and storage.

A 3D radiative transfer model based on lidar data and its application on hydrological and ecosystem modeling

NASA Astrophysics Data System (ADS)

Li, W.; Su, Y.; Harmon, T. C.; Guo, Q.

2013-12-01

Light Detection and Ranging (lidar) is an optical remote sensing technology that measures properties of scattered light to find range and/or other information of a distant object. Due to its ability to generate 3-dimensional data with high spatial resolution and accuracy, lidar technology is being increasingly used in ecology, geography, geology, geomorphology, seismology, remote sensing, and atmospheric physics. In this study we construct a 3-dimentional (3D) radiative transfer model (RTM) using lidar data to simulate the spatial distribution of solar radiation (direct and diffuse) on the surface of water and mountain forests. The model includes three sub-models: a light model simulating the light source, a sensor model simulating the camera, and a scene model simulating the landscape. We use ground-based and airborne lidar data to characterize the 3D structure of the study area, and generate a detailed 3D scene model. The interactions between light and object are simulated using the Monte Carlo Ray Tracing (MCRT) method. A large number of rays are generated from the light source. For each individual ray, the full traveling path is traced until it is absorbed or escapes from the scene boundary. By locating the sensor at different positions and directions, we can simulate the spatial distribution of solar energy at the ground, vegetation and water surfaces. These outputs can then be incorporated into meteorological drivers for hydrologic and energy balance models to improve our understanding of hydrologic processes and ecosystem functions.

Impact of microwave derived soil moisture on hydrologic simulations using a spatially distributed water balance model

NASA Technical Reports Server (NTRS)

Lin, D. S.; Wood, E. F.; Famiglietti, J. S.; Mancini, M.

1994-01-01

Spatial distributions of soil moisture over an agricultural watershed with a drainage area of 60 ha were derived from two NASA microwave remote sensors, and then used as a feedback to determine the initial condition for a distributed water balance model. Simulated hydrologic fluxes over a period of twelve days were compared with field observations and with model predictions based on a streamflow derived initial condition. The results indicated that even the low resolution remotely sensed data can improve the hydrologic model's performance in simulating the dynamics of unsaturated zone soil moisture. For the particular watershed under study, the simulated water budget was not sensitive to the resolutions of the microwave sensors.

A one-dimensional sectional model to simulate multicomponent aerosol dynamics in the marine boundary layer 3. Numerical methods and comparisons with exact solutions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gelbard, F.; Fitzgerald, J.W.; Hoppel, W.A.

1998-07-01

We present the theoretical framework and computational methods that were used by {ital Fitzgerald} {ital et al.} [this issue (a), (b)] describing a one-dimensional sectional model to simulate multicomponent aerosol dynamics in the marine boundary layer. The concepts and limitations of modeling spatially varying multicomponent aerosols are elucidated. New numerical sectional techniques are presented for simulating multicomponent aerosol growth, settling, and eddy transport, coupled to time-dependent and spatially varying condensing vapor concentrations. Comparisons are presented with new exact solutions for settling and particle growth by simultaneous dynamic condensation of one vapor and by instantaneous equilibration with a spatially varying secondmore » vapor. {copyright} 1998 American Geophysical Union« less

Hydroclimatic Controls on the Means and Variability of Vegetation Phenology and Carbon Uptake

NASA Technical Reports Server (NTRS)

Koster, Randal Dean; Walker, Gregory K.; Collatz, George J.; Thornton, Peter E.

2013-01-01

Long-term, global offline (land-only) simulations with a dynamic vegetation phenology model are used to examine the control of hydroclimate over vegetation-related quantities. First, with a control simulation, the model is shown to capture successfully (though with some bias) key observed relationships between hydroclimate and the spatial and temporal variations of phenological expression. In subsequent simulations, the model shows that: (i) the global spatial variation of seasonal phenological maxima is controlled mostly by hydroclimate, irrespective of distributions in vegetation type, (ii) the occurrence of high interannual moisture-related phenological variability in grassland areas is determined by hydroclimate rather than by the specific properties of grassland, and (iii) hydroclimatic means and variability have a corresponding impact on the spatial and temporal distributions of gross primary productivity (GPP).

Can we improve streamflow simulation by using higher resolution rainfall information?

NASA Astrophysics Data System (ADS)

Lobligeois, Florent; Andréassian, Vazken; Perrin, Charles

2013-04-01

The catchment response to rainfall is the interplay between space-time variability of precipitation, catchment characteristics and antecedent hydrological conditions. Precipitation dominates the high frequency hydrological response, and its simulation is thus dependent on the way rainfall is represented. One of the characteristics which distinguishes distributed from lumped models is their ability to represent explicitly the spatial variability of precipitation and catchment characteristics. The sensitivity of runoff hydrographs to the spatial variability of forcing data has been a major concern of researchers over the last three decades. However, although the literature on the relationship between spatial rainfall and runoff response is abundant, results are contrasted and sometimes contradictory. Several studies concluded that including information on rainfall spatial distribution improves discharge simulation (e.g. Ajami et al., 2004, among others) whereas other studies showed the lack of significant improvement in simulations with better information on rainfall spatial pattern (e.g. Andréassian et al., 2004, among others). The difficulties to reach a clear consensus is mainly due to the fact that each modeling study is implemented only on a few catchments whereas the impact of the spatial distribution of rainfall on runoff is known to be catchment and event characteristics-dependent. Many studies are virtual experiments and only compare flow simulations, which makes it difficult to reach conclusions transposable to real-life case studies. Moreover, the hydrological rainfall-runoff models differ between the studies and the parameterization strategies sometimes tend to advantage the distributed approach (or the lumped one). Recently, Météo-France developed a rainfall reanalysis over the whole French territory at the 1-kilometer resolution and the hourly time step over a 10-year period combining radar data and raingauge measurements: weather radar data were corrected and adjusted with both hourly and daily raingauge data. Based on this new high resolution product, we propose a framework to evaluate the improvements in streamflow simulation by using higher resolution rainfall information. Semi-distributed modelling is performed for different spatial resolution of precipitation forcing: from lumped to semi-distributed simulations. Here we do not work on synthetic (simulated) streamflow, but with actual measurements, on a large set of 181 French catchments representing a variety of size and climate. The rainfall-runoff model is re-calibrated for each resolution of rainfall spatial distribution over a 5-year sub-period and evaluated on the complementary sub-period in validation mode. The results are analysed by catchment classes based on catchment area and for various types of rainfall events based on the spatial variability of precipitation. References Ajami, N. K., Gupta, H. V, Wagener, T. & Sorooshian, S. (2004) Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. Journal of Hydrology 298(1-4), 112-135. Andréassian, V., Oddos, A., Michel, C., Anctil, F., Perrin, C. & Loumagne, C. (2004) Impact of spatial aggregation of inputs and parameters on the efficiency of rainfall-runoff models: A theoretical study using chimera watersheds. Water Resources Research 40(5), 1-9.

Developpement D'un Modele Climatique Regional: Fizr Simulation des Conditions de Janvier de la Cote Ouest Nord Americaine

NASA Astrophysics Data System (ADS)

Goyette, Stephane

1995-11-01

Le sujet de cette these concerne la modelisation numerique du climat regional. L'objectif principal de l'exercice est de developper un modele climatique regional ayant les capacites de simuler des phenomenes de meso-echelle spatiale. Notre domaine d'etude se situe sur la Cote Ouest nord americaine. Ce dernier a retenu notre attention a cause de la complexite du relief et de son controle sur le climat. Les raisons qui motivent cette etude sont multiples: d'une part, nous ne pouvons pas augmenter, en pratique, la faible resolution spatiale des modeles de la circulation generale de l'atmosphere (MCG) sans augmenter a outrance les couts d'integration et, d'autre part, la gestion de l'environnement exige de plus en plus de donnees climatiques regionales determinees avec une meilleure resolution spatiale. Jusqu'alors, les MCG constituaient les modeles les plus estimes pour leurs aptitudes a simuler le climat ainsi que les changements climatiques mondiaux. Toutefois, les phenomenes climatiques de fine echelle echappent encore aux MCG a cause de leur faible resolution spatiale. De plus, les repercussions socio-economiques des modifications possibles des climats sont etroitement liees a des phenomenes imperceptibles par les MCG actuels. Afin de circonvenir certains problemes inherents a la resolution, une approche pratique vise a prendre un domaine spatial limite d'un MCG et a y imbriquer un autre modele numerique possedant, lui, un maillage de haute resolution spatiale. Ce processus d'imbrication implique alors une nouvelle simulation numerique. Cette "retro-simulation" est guidee dans le domaine restreint a partir de pieces d'informations fournies par le MCG et forcee par des mecanismes pris en charge uniquement par le modele imbrique. Ainsi, afin de raffiner la precision spatiale des previsions climatiques de grande echelle, nous developpons ici un modele numerique appele FIZR, permettant d'obtenir de l'information climatique regionale valide a la fine echelle spatiale. Cette nouvelle gamme de modeles-interpolateurs imbriques qualifies d'"intelligents" fait partie de la famille des modeles dits "pilotes". L'hypothese directrice de notre etude est fondee sur la supposition que le climat de fine echelle est souvent gouverne par des forcages provenant de la surface plutot que par des transports atmospheriques de grande echelle spatiale. La technique que nous proposons vise donc a guider FIZR par la Dynamique echantillonnee d'un MCG et de la forcer par la Physique du MCG ainsi que par un forcage orographique de meso-echelle, en chacun des noeuds de la grille fine de calculs. Afin de valider la robustesse et la justesse de notre modele climatique regional, nous avons choisi la region de la Cote Ouest du continent nord americain. Elle est notamment caracterisee par une distribution geographique des precipitations et des temperatures fortement influencee par le relief sous-jacent. Les resultats d'une simulation d'un mois de janvier avec FIZR demontrent que nous pouvons simuler des champs de precipitations et de temperatures au niveau de l'abri beaucoup plus pres des observations climatiques comparativement a ceux simules a partir d'un MCG. Ces performances sont manifestement attribuees au forcage orographique de meso-echelle de meme qu'aux caracteristiques de surface determinees a fine echelle. Un modele similaire a FIZR peut, en principe, etre implante sur l'importe quel MCG, donc, tout organisme de recherche implique en modelisation numerique mondiale de grande echelle pourra se doter d'un el outil de regionalisation.

Large-scale derived flood frequency analysis based on continuous simulation

NASA Astrophysics Data System (ADS)

Dung Nguyen, Viet; Hundecha, Yeshewatesfa; Guse, Björn; Vorogushyn, Sergiy; Merz, Bruno

2016-04-01

There is an increasing need for spatially consistent flood risk assessments at the regional scale (several 100.000 km2), in particular in the insurance industry and for national risk reduction strategies. However, most large-scale flood risk assessments are composed of smaller-scale assessments and show spatial inconsistencies. To overcome this deficit, a large-scale flood model composed of a weather generator and catchments models was developed reflecting the spatially inherent heterogeneity. The weather generator is a multisite and multivariate stochastic model capable of generating synthetic meteorological fields (precipitation, temperature, etc.) at daily resolution for the regional scale. These fields respect the observed autocorrelation, spatial correlation and co-variance between the variables. They are used as input into catchment models. A long-term simulation of this combined system enables to derive very long discharge series at many catchment locations serving as a basic for spatially consistent flood risk estimates at the regional scale. This combined model was set up and validated for major river catchments in Germany. The weather generator was trained by 53-year observation data at 528 stations covering not only the complete Germany but also parts of France, Switzerland, Czech Republic and Australia with the aggregated spatial scale of 443,931 km2. 10.000 years of daily meteorological fields for the study area were generated. Likewise, rainfall-runoff simulations with SWIM were performed for the entire Elbe, Rhine, Weser, Donau and Ems catchments. The validation results illustrate a good performance of the combined system, as the simulated flood magnitudes and frequencies agree well with the observed flood data. Based on continuous simulation this model chain is then used to estimate flood quantiles for the whole Germany including upstream headwater catchments in neighbouring countries. This continuous large scale approach overcomes the several drawbacks reported in traditional approaches for the derived flood frequency analysis and therefore is recommended for large scale flood risk case studies.

Analysis of skin tissues spatial fluorescence distribution by the Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Y Churmakov, D.; Meglinski, I. V.; Piletsky, S. A.; Greenhalgh, D. A.

2003-07-01

A novel Monte Carlo technique of simulation of spatial fluorescence distribution within the human skin is presented. The computational model of skin takes into account the spatial distribution of fluorophores, which would arise due to the structure of collagen fibres, compared to the epidermis and stratum corneum where the distribution of fluorophores is assumed to be homogeneous. The results of simulation suggest that distribution of auto-fluorescence is significantly suppressed in the near-infrared spectral region, whereas the spatial distribution of fluorescence sources within a sensor layer embedded in the epidermis is localized at an `effective' depth.

Sensitivity of chemistry-transport model simulations to the duration of chemical and transport operators: a case study with GEOS-Chem v10-01

NASA Astrophysics Data System (ADS)

Philip, Sajeev; Martin, Randall V.; Keller, Christoph A.

2016-05-01

Chemistry-transport models involve considerable computational expense. Fine temporal resolution offers accuracy at the expense of computation time. Assessment is needed of the sensitivity of simulation accuracy to the duration of chemical and transport operators. We conduct a series of simulations with the GEOS-Chem chemistry-transport model at different temporal and spatial resolutions to examine the sensitivity of simulated atmospheric composition to operator duration. Subsequently, we compare the species simulated with operator durations from 10 to 60 min as typically used by global chemistry-transport models, and identify the operator durations that optimize both computational expense and simulation accuracy. We find that longer continuous transport operator duration increases concentrations of emitted species such as nitrogen oxides and carbon monoxide since a more homogeneous distribution reduces loss through chemical reactions and dry deposition. The increased concentrations of ozone precursors increase ozone production with longer transport operator duration. Longer chemical operator duration decreases sulfate and ammonium but increases nitrate due to feedbacks with in-cloud sulfur dioxide oxidation and aerosol thermodynamics. The simulation duration decreases by up to a factor of 5 from fine (5 min) to coarse (60 min) operator duration. We assess the change in simulation accuracy with resolution by comparing the root mean square difference in ground-level concentrations of nitrogen oxides, secondary inorganic aerosols, ozone and carbon monoxide with a finer temporal or spatial resolution taken as "truth". Relative simulation error for these species increases by more than a factor of 5 from the shortest (5 min) to longest (60 min) operator duration. Chemical operator duration twice that of the transport operator duration offers more simulation accuracy per unit computation. However, the relative simulation error from coarser spatial resolution generally exceeds that from longer operator duration; e.g., degrading from 2° × 2.5° to 4° × 5° increases error by an order of magnitude. We recommend prioritizing fine spatial resolution before considering different operator durations in offline chemistry-transport models. We encourage chemistry-transport model users to specify in publications the durations of operators due to their effects on simulation accuracy.

Sensitivity of chemical transport model simulations to the duration of chemical and transport operators: a case study with GEOS-Chem v10-01

NASA Astrophysics Data System (ADS)

Philip, S.; Martin, R. V.; Keller, C. A.

2015-11-01

Chemical transport models involve considerable computational expense. Fine temporal resolution offers accuracy at the expense of computation time. Assessment is needed of the sensitivity of simulation accuracy to the duration of chemical and transport operators. We conduct a series of simulations with the GEOS-Chem chemical transport model at different temporal and spatial resolutions to examine the sensitivity of simulated atmospheric composition to temporal resolution. Subsequently, we compare the tracers simulated with operator durations from 10 to 60 min as typically used by global chemical transport models, and identify the timesteps that optimize both computational expense and simulation accuracy. We found that longer transport timesteps increase concentrations of emitted species such as nitrogen oxides and carbon monoxide since a more homogeneous distribution reduces loss through chemical reactions and dry deposition. The increased concentrations of ozone precursors increase ozone production at longer transport timesteps. Longer chemical timesteps decrease sulfate and ammonium but increase nitrate due to feedbacks with in-cloud sulfur dioxide oxidation and aerosol thermodynamics. The simulation duration decreases by an order of magnitude from fine (5 min) to coarse (60 min) temporal resolution. We assess the change in simulation accuracy with resolution by comparing the root mean square difference in ground-level concentrations of nitrogen oxides, ozone, carbon monoxide and secondary inorganic aerosols with a finer temporal or spatial resolution taken as truth. Simulation error for these species increases by more than a factor of 5 from the shortest (5 min) to longest (60 min) temporal resolution. Chemical timesteps twice that of the transport timestep offer more simulation accuracy per unit computation. However, simulation error from coarser spatial resolution generally exceeds that from longer timesteps; e.g. degrading from 2° × 2.5° to 4° × 5° increases error by an order of magnitude. We recommend prioritizing fine spatial resolution before considering different temporal resolutions in offline chemical transport models. We encourage the chemical transport model users to specify in publications the durations of operators due to their effects on simulation accuracy.

Simulation of spatially evolving turbulence and the applicability of Taylor's hypothesis in compressible flow

NASA Technical Reports Server (NTRS)

Lee, Sangsan; Lele, Sanjiva K.; Moin, Parviz

1992-01-01

For the numerical simulation of inhomogeneous turbulent flows, a method is developed for generating stochastic inflow boundary conditions with a prescribed power spectrum. Turbulence statistics from spatial simulations using this method with a low fluctuation Mach number are in excellent agreement with the experimental data, which validates the procedure. Turbulence statistics from spatial simulations are also compared to those from temporal simulations using Taylor's hypothesis. Statistics such as turbulence intensity, vorticity, and velocity derivative skewness compare favorably with the temporal simulation. However, the statistics of dilatation show a significant departure from those obtained in the temporal simulation. To directly check the applicability of Taylor's hypothesis, space-time correlations of fluctuations in velocity, vorticity, and dilatation are investigated. Convection velocities based on vorticity and velocity fluctuations are computed as functions of the spatial and temporal separations. The profile of the space-time correlation of dilatation fluctuations is explained via a wave propagation model.

Topologically Consistent Models for Efficient Big Geo-Spatio Data Distribution

NASA Astrophysics Data System (ADS)

Jahn, M. W.; Bradley, P. E.; Doori, M. Al; Breunig, M.

2017-10-01

Geo-spatio-temporal topology models are likely to become a key concept to check the consistency of 3D (spatial space) and 4D (spatial + temporal space) models for emerging GIS applications such as subsurface reservoir modelling or the simulation of energy and water supply of mega or smart cities. Furthermore, the data management for complex models consisting of big geo-spatial data is a challenge for GIS and geo-database research. General challenges, concepts, and techniques of big geo-spatial data management are presented. In this paper we introduce a sound mathematical approach for a topologically consistent geo-spatio-temporal model based on the concept of the incidence graph. We redesign DB4GeO, our service-based geo-spatio-temporal database architecture, on the way to the parallel management of massive geo-spatial data. Approaches for a new geo-spatio-temporal and object model of DB4GeO meeting the requirements of big geo-spatial data are discussed in detail. Finally, a conclusion and outlook on our future research are given on the way to support the processing of geo-analytics and -simulations in a parallel and distributed system environment.

Projected Future Vegetation Changes for the Northwest United States and Southwest Canada at a Fine Spatial Resolution Using a Dynamic Global Vegetation Model.

PubMed

Shafer, Sarah L; Bartlein, Patrick J; Gray, Elizabeth M; Pelltier, Richard T

2015-01-01

Future climate change may significantly alter the distributions of many plant taxa. The effects of climate change may be particularly large in mountainous regions where climate can vary significantly with elevation. Understanding potential future vegetation changes in these regions requires methods that can resolve vegetation responses to climate change at fine spatial resolutions. We used LPJ, a dynamic global vegetation model, to assess potential future vegetation changes for a large topographically complex area of the northwest United States and southwest Canada (38.0-58.0°N latitude by 136.6-103.0°W longitude). LPJ is a process-based vegetation model that mechanistically simulates the effect of changing climate and atmospheric CO2 concentrations on vegetation. It was developed and has been mostly applied at spatial resolutions of 10-minutes or coarser. In this study, we used LPJ at a 30-second (~1-km) spatial resolution to simulate potential vegetation changes for 2070-2099. LPJ was run using downscaled future climate simulations from five coupled atmosphere-ocean general circulation models (CCSM3, CGCM3.1(T47), GISS-ER, MIROC3.2(medres), UKMO-HadCM3) produced using the A2 greenhouse gases emissions scenario. Under projected future climate and atmospheric CO2 concentrations, the simulated vegetation changes result in the contraction of alpine, shrub-steppe, and xeric shrub vegetation across the study area and the expansion of woodland and forest vegetation. Large areas of maritime cool forest and cold forest are simulated to persist under projected future conditions. The fine spatial-scale vegetation simulations resolve patterns of vegetation change that are not visible at coarser resolutions and these fine-scale patterns are particularly important for understanding potential future vegetation changes in topographically complex areas.

Temporal acceleration of spatially distributed kinetic Monte Carlo simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chatterjee, Abhijit; Vlachos, Dionisios G.

The computational intensity of kinetic Monte Carlo (KMC) simulation is a major impediment in simulating large length and time scales. In recent work, an approximate method for KMC simulation of spatially uniform systems, termed the binomial {tau}-leap method, was introduced [A. Chatterjee, D.G. Vlachos, M.A. Katsoulakis, Binomial distribution based {tau}-leap accelerated stochastic simulation, J. Chem. Phys. 122 (2005) 024112], where molecular bundles instead of individual processes are executed over coarse-grained time increments. This temporal coarse-graining can lead to significant computational savings but its generalization to spatially lattice KMC simulation has not been realized yet. Here we extend the binomial {tau}-leapmore » method to lattice KMC simulations by combining it with spatially adaptive coarse-graining. Absolute stability and computational speed-up analyses for spatial systems along with simulations provide insights into the conditions where accuracy and substantial acceleration of the new spatio-temporal coarse-graining method are ensured. Model systems demonstrate that the r-time increment criterion of Chatterjee et al. obeys the absolute stability limit for values of r up to near 1.« less

A Comparison of Grizzly Bear Demographic Parameters Estimated from Non-Spatial and Spatial Open Population Capture-Recapture Models.

PubMed

Whittington, Jesse; Sawaya, Michael A

2015-01-01

Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capture-recapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal's home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNA-based data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786-1.071) for females, 0.844 (0.703-0.975) for males, and 0.882 (0.779-0.981) for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758-1.024) for females, 0.825 (0.700-0.948) for males, and 0.863 (0.771-0.957) for both sexes. The combination of low densities, low reproductive rates, and predominantly negative population growth rates suggest that Banff National Park's population of grizzly bears requires continued conservation-oriented management actions.

Heteroskedasticity as a leading indicator of desertification in spatially explicit data.

PubMed

Seekell, David A; Dakos, Vasilis

2015-06-01

Regime shifts are abrupt transitions between alternate ecosystem states including desertification in arid regions due to drought or overgrazing. Regime shifts may be preceded by statistical anomalies such as increased autocorrelation, indicating declining resilience and warning of an impending shift. Tests for conditional heteroskedasticity, a type of clustered variance, have proven powerful leading indicators for regime shifts in time series data, but an analogous indicator for spatial data has not been evaluated. A spatial analog for conditional heteroskedasticity might be especially useful in arid environments where spatial interactions are critical in structuring ecosystem pattern and process. We tested the efficacy of a test for spatial heteroskedasticity as a leading indicator of regime shifts with simulated data from spatially extended vegetation models with regular and scale-free patterning. These models simulate shifts from extensive vegetative cover to bare, desert-like conditions. The magnitude of spatial heteroskedasticity increased consistently as the modeled systems approached a regime shift from vegetated to desert state. Relative spatial autocorrelation, spatial heteroskedasticity increased earlier and more consistently. We conclude that tests for spatial heteroskedasticity can contribute to the growing toolbox of early warning indicators for regime shifts analyzed with spatially explicit data.

A Spatial Perspective of Droughts and Pluvials in the Tropics and their Relationships to ENSO in CMIP5 Model Simulations

NASA Astrophysics Data System (ADS)

Perez Arango, J. D.; Lintner, B. R.; Lyon, B.

2016-12-01

Although many aspects of the tropical response to ENSO are well-known, the spatial characteristics of the rainfall response to ENSO remain relatively unexplored. Moreover, in current generation climate models, the spatial signatures of the ENSO tropical teleconnection are more uncertain than other aspects of ENSO variability, such as the amplitude of rainfall anomalies. Following the approach of Lyon (2004) and Lyon and Barnston (2005), we analyze here integrated measures of the spatial extent of drought and pluvial conditions in the tropics and their relationship to ENSO in observations as well as simulations of Phase 5 of the Coupled Model Intercomparison Project (CMIP5) with prescribed SST forcing. We compute diagnostics including the model ensemble-means and standard deviations of moderate, intermediate, and severe droughts and pluvials and the lagged correlations with respect to ENSO-based SST indices like NINO3. Overall, in a tropics-wide sense, the models generally capture the areal extent of observed droughts and pluvials and their phasing with respect to ENSO. However, at more local scales, e.g., tropical South America, the simulated metrics agree less strongly with observations, underscoring the role of errors in the spatial patterns of ENSO-induced rainfall anomalies.

Comparison of HSPF and PRMS model simulated flows using different temporal and spatial scales in the Black Hills, South Dakota

USGS Publications Warehouse

Chalise, D. R.; Haj, Adel E.; Fontaine, T.A.

2018-01-01

The hydrological simulation program Fortran (HSPF) [Hydrological Simulation Program Fortran version 12.2 (Computer software). USEPA, Washington, DC] and the precipitation runoff modeling system (PRMS) [Precipitation Runoff Modeling System version 4.0 (Computer software). USGS, Reston, VA] models are semidistributed, deterministic hydrological tools for simulating the impacts of precipitation, land use, and climate on basin hydrology and streamflow. Both models have been applied independently to many watersheds across the United States. This paper reports the statistical results assessing various temporal (daily, monthly, and annual) and spatial (small versus large watershed) scale biases in HSPF and PRMS simulations using two watersheds in the Black Hills, South Dakota. The Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (r">rr), and coefficient of determination (R2">R2R2) statistics for the daily, monthly, and annual flows were used to evaluate the models’ performance. Results from the HSPF models showed that the HSPF consistently simulated the annual flows for both large and small basins better than the monthly and daily flows, and the simulated flows for the small watershed better than flows for the large watershed. In comparison, the PRMS model results show that the PRMS simulated the monthly flows for both the large and small watersheds better than the daily and annual flows, and the range of statistical error in the PRMS models was greater than that in the HSPF models. Moreover, it can be concluded that the statistical error in the HSPF and the PRMSdaily, monthly, and annual flow estimates for watersheds in the Black Hills was influenced by both temporal and spatial scale variability.

A new spatial snow distribution in hydrological models parameterized from observed spatial variability of precipitation.

NASA Astrophysics Data System (ADS)

Skaugen, Thomas; Weltzien, Ingunn

2016-04-01

The traditional catchment hydrological model with its many free calibration parameters is not a well suited tool for prediction under conditions for which is has not been calibrated. Important tasks for hydrological modelling such as prediction in ungauged basins and assessing hydrological effects of climate change are hence not solved satisfactory. In order to reduce the number of calibration parameters in hydrological models we have introduced a new model which uses a dynamic gamma distribution as the spatial frequency distribution of snow water equivalent (SWE). The parameters are estimated from observed spatial variability of precipitation and the magnitude of accumulation and melting events and are hence not subject to calibration. The relationship between spatial mean and variance of precipitation is found to follow a pattern where decreasing temporal correlation with increasing accumulation or duration of the event leads to a levelling off or even a decrease of the spatial variance. The new model for snow distribution is implemented in the, already parameter parsimonious, DDD (Distance Distribution Dynamics) hydrological model and was tested for 71 Norwegian catchments. We compared the new snow distribution model with the current operational snow distribution model where a fixed, calibrated coefficient of variation parameterizes a log-normal model for snow distribution. Results show that the precision of runoff simulations is equal, but that the new snow distribution model better simulates snow covered area (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" is prevented and hence spurious trends in SWE.

Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology

PubMed Central

Gao, Fei; Li, Ye; Novak, Igor L.; Slepchenko, Boris M.

2016-01-01

Hybrid deterministic-stochastic methods provide an efficient alternative to a fully stochastic treatment of models which include components with disparate levels of stochasticity. However, general-purpose hybrid solvers for spatially resolved simulations of reaction-diffusion systems are not widely available. Here we describe fundamentals of a general-purpose spatial hybrid method. The method generates realizations of a spatially inhomogeneous hybrid system by appropriately integrating capabilities of a deterministic partial differential equation solver with a popular particle-based stochastic simulator, Smoldyn. Rigorous validation of the algorithm is detailed, using a simple model of calcium ‘sparks’ as a testbed. The solver is then applied to a deterministic-stochastic model of spontaneous emergence of cell polarity. The approach is general enough to be implemented within biologist-friendly software frameworks such as Virtual Cell. PMID:27959915

Modeling of non-uniform spatial arrangement of fibers in a ceramic matrix composite

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, S.; Tewari, A.; Gokhale, A.M.

In the unidirectional fiber reinforced composites, the spatial agreement of fibers is often non-uniform. These non-uniformities are linked to the processing conditions, and they affect the properties of the composite. In this contribution, a recently developed digital image analysis technique is used to quantify the non-uniform spatial arrangement of Nicalon fibers in a ceramic matrix composite (CMC). These quantitative data are utilized to develop a six parameter computer simulated microstructure model that is statistically equivalent to the non-uniform microstructure of the CMC. The simulated microstructure can be utilized as a RVE for the micro-mechanical modeling studies.

A Microfluidics and Agent-Based Modeling Framework for Investigating Spatial Organization in Bacterial Colonies: The Case of Pseudomonas Aeruginosa and H1-Type VI Secretion Interactions.

PubMed

Wilmoth, Jared L; Doak, Peter W; Timm, Andrea; Halsted, Michelle; Anderson, John D; Ginovart, Marta; Prats, Clara; Portell, Xavier; Retterer, Scott T; Fuentes-Cabrera, Miguel

2018-01-01

The factors leading to changes in the organization of microbial assemblages at fine spatial scales are not well characterized or understood. However, they are expected to guide the succession of community development and function toward specific outcomes that could impact human health and the environment. In this study, we put forward a combined experimental and agent-based modeling framework and use it to interpret unique spatial organization patterns of H1-Type VI secretion system (T6SS) mutants of P . aeruginosa under spatial confinement. We find that key parameters, such as T6SS-mediated cell contact and lysis, spatial localization, relative species abundance, cell density and local concentrations of growth substrates and metabolites are influenced by spatial confinement. The model, written in the accessible programming language NetLogo, can be adapted to a variety of biological systems of interest and used to simulate experiments across a broad parameter space. It was implemented and run in a high-throughput mode by deploying it across multiple CPUs, with each simulation representing an individual well within a high-throughput microwell array experimental platform. The microfluidics and agent-based modeling framework we present in this paper provides an effective means by which to connect experimental studies in microbiology to model development. The work demonstrates progress in coupling experimental results to simulation while also highlighting potential sources of discrepancies between real-world experiments and idealized models.

A Microfluidics and Agent-Based Modeling Framework for Investigating Spatial Organization in Bacterial Colonies: The Case of Pseudomonas Aeruginosa and H1-Type VI Secretion Interactions

PubMed Central

Wilmoth, Jared L.; Doak, Peter W.; Timm, Andrea; Halsted, Michelle; Anderson, John D.; Ginovart, Marta; Prats, Clara; Portell, Xavier; Retterer, Scott T.; Fuentes-Cabrera, Miguel

2018-01-01

The factors leading to changes in the organization of microbial assemblages at fine spatial scales are not well characterized or understood. However, they are expected to guide the succession of community development and function toward specific outcomes that could impact human health and the environment. In this study, we put forward a combined experimental and agent-based modeling framework and use it to interpret unique spatial organization patterns of H1-Type VI secretion system (T6SS) mutants of P. aeruginosa under spatial confinement. We find that key parameters, such as T6SS-mediated cell contact and lysis, spatial localization, relative species abundance, cell density and local concentrations of growth substrates and metabolites are influenced by spatial confinement. The model, written in the accessible programming language NetLogo, can be adapted to a variety of biological systems of interest and used to simulate experiments across a broad parameter space. It was implemented and run in a high-throughput mode by deploying it across multiple CPUs, with each simulation representing an individual well within a high-throughput microwell array experimental platform. The microfluidics and agent-based modeling framework we present in this paper provides an effective means by which to connect experimental studies in microbiology to model development. The work demonstrates progress in coupling experimental results to simulation while also highlighting potential sources of discrepancies between real-world experiments and idealized models. PMID:29467721

Research on the decision-making model of land-use spatial optimization

NASA Astrophysics Data System (ADS)

He, Jianhua; Yu, Yan; Liu, Yanfang; Liang, Fei; Cai, Yuqiu

2009-10-01

Using the optimization result of landscape pattern and land use structure optimization as constraints of CA simulation results, a decision-making model of land use spatial optimization is established coupled the landscape pattern model with cellular automata to realize the land use quantitative and spatial optimization simultaneously. And Huangpi district is taken as a case study to verify the rationality of the model.

[Prediction and spatial distribution of recruitment trees of natural secondary forest based on geographically weighted Poisson model].

PubMed

Zhang, Ling Yu; Liu, Zhao Gang

2017-12-01

Based on the data collected from 108 permanent plots of the forest resources survey in Maoershan Experimental Forest Farm during 2004-2016, this study investigated the spatial distribution of recruitment trees in natural secondary forest by global Poisson regression and geographically weighted Poisson regression (GWPR) with four bandwidths of 2.5, 5, 10 and 15 km. The simulation effects of the 5 regressions and the factors influencing the recruitment trees in stands were analyzed, a description was given to the spatial autocorrelation of the regression residuals on global and local levels using Moran's I. The results showed that the spatial distribution of the number of natural secondary forest recruitment was significantly influenced by stands and topographic factors, especially average DBH. The GWPR model with small scale (2.5 km) had high accuracy of model fitting, a large range of model parameter estimates was generated, and the localized spatial distribution effect of the model parameters was obtained. The GWPR model at small scale (2.5 and 5 km) had produced a small range of model residuals, and the stability of the model was improved. The global spatial auto-correlation of the GWPR model residual at the small scale (2.5 km) was the lowe-st, and the local spatial auto-correlation was significantly reduced, in which an ideal spatial distribution pattern of small clusters with different observations was formed. The local model at small scale (2.5 km) was much better than the global model in the simulation effect on the spatial distribution of recruitment tree number.

Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories

PubMed Central

Donovan, Rory M.; Tapia, Jose-Juan; Sullivan, Devin P.; Faeder, James R.; Murphy, Robert F.; Dittrich, Markus; Zuckerman, Daniel M.

2016-01-01

The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation—by orders of magnitude for some observables. PMID:26845334

Precipitation From a Multiyear Database of Convection-Allowing WRF Simulations

NASA Astrophysics Data System (ADS)

Goines, D. C.; Kennedy, A. D.

2018-03-01

Convection-allowing models (CAMs) have become frequently used for operational forecasting and, more recently, have been utilized for general circulation model downscaling. CAM forecasts have typically been analyzed for a few case studies or over short time periods, but this limits the ability to judge the overall skill of deterministic simulations. Analysis over long time periods can yield a better understanding of systematic model error. Four years of warm season (April-August, 2010-2013)-simulated precipitation has been accumulated from two Weather Research and Forecasting (WRF) models with 4 km grid spacing. The simulations were provided by the National Center for Environmental Prediction (NCEP) and the National Severe Storms Laboratory (NSSL), each with different dynamic cores and parameterization schemes. These simulations are evaluated against the NCEP Stage-IV precipitation data set with similar 4 km grid spacing. The spatial distribution and diurnal cycle of precipitation in the central United States are analyzed using Hovmöller diagrams, grid point correlations, and traditional verification skill scoring (i.e., ETS; Equitable Threat Score). Although NCEP-WRF had a high positive error in total precipitation, spatial characteristics were similar to observations. For example, the spatial distribution of NCEP-WRF precipitation correlated better than NSSL-WRF for the Northern Plains. Hovmöller results exposed a delay in initiation and decay of diurnal precipitation by NCEP-WRF while both models had difficulty in reproducing the timing and location of propagating precipitation. ETS was highest for NSSL-WRF in all domains at all times. ETS was also higher in areas of propagating precipitation compared to areas of unorganized diurnal scattered precipitation. Monthly analysis identified unique differences between the two models in their abilities to correctly simulate the spatial distribution and zonal motion of precipitation through the warm season.

Spatial Sampling of Weather Data for Regional Crop Yield Simulations

NASA Technical Reports Server (NTRS)

Van Bussel, Lenny G. J.; Ewert, Frank; Zhao, Gang; Hoffmann, Holger; Enders, Andreas; Wallach, Daniel; Asseng, Senthold; Baigorria, Guillermo A.; Basso, Bruno; Biernath, Christian;

Simulation of Long-Term Landscape-Level Fuel Treatment Effects on Large Wildfires

Treesearch

Mark A. Finney; Rob C. Seli; Charles W. McHugh; Alan A. Ager; Berni Bahro; James K. Agee

2006-01-01

A simulation system was developed to explore how fuel treatments placed in random and optimal spatial patterns affect the growth and behavior of large fires when implemented at different rates over the course of five decades. The system consists of a forest/fuel dynamics simulation module (FVS), logic for deriving fuel model dynamics from FVS output, a spatial fuel...

Hydrological model uncertainty due to spatial evapotranspiration estimation methods

NASA Astrophysics Data System (ADS)

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

2016-05-01

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

Simulating the IPOD, East Asian summer monsoon, and their relationships in CMIP5

NASA Astrophysics Data System (ADS)

Yu, Miao; Li, Jianping; Zheng, Fei; Wang, Xiaofan; Zheng, Jiayu

2018-03-01

This paper evaluates the simulation performance of the 37 coupled models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) with respect to the East Asian summer monsoon (EASM) and the Indo-Pacific warm pool and North Pacific Ocean dipole (IPOD) and also the interrelationships between them. The results show that the majority of the models are unable to accurately simulate the interannual variability and long-term trends of the EASM, and their simulations of the temporal and spatial variations of the IPOD are also limited. Further analysis showed that the correlation coefficients between the simulated and observed EASM index (EASMI) is proportional to those between the simulated and observed IPOD index (IPODI); that is, if the models have skills to simulate one of them then they will likely generate good simulations of another. Based on the above relationship, this paper proposes a conditional multi-model ensemble method (CMME) that eliminates those models without capability to simulate the IPOD and EASM when calculating the multi-model ensemble (MME). The analysis shows that, compared with the MME, this CMME method can significantly improve the simulations of the spatial and temporal variations of both the IPOD and EASM as well as their interrelationship, suggesting the potential for the CMME approach to be used in place of the MME method.

Modifying a dynamic global vegetation model for simulating large spatial scale land surface water balances

NASA Astrophysics Data System (ADS)

Tang, G.; Bartlein, P. J.

2012-08-01

Satellite-based data, such as vegetation type and fractional vegetation cover, are widely used in hydrologic models to prescribe the vegetation state in a study region. Dynamic global vegetation models (DGVM) simulate land surface hydrology. Incorporation of satellite-based data into a DGVM may enhance a model's ability to simulate land surface hydrology by reducing the task of model parameterization and providing distributed information on land characteristics. The objectives of this study are to (i) modify a DGVM for simulating land surface water balances; (ii) evaluate the modified model in simulating actual evapotranspiration (ET), soil moisture, and surface runoff at regional or watershed scales; and (iii) gain insight into the ability of both the original and modified model to simulate large spatial scale land surface hydrology. To achieve these objectives, we introduce the "LPJ-hydrology" (LH) model which incorporates satellite-based data into the Lund-Potsdam-Jena (LPJ) DGVM. To evaluate the model we ran LH using historical (1981-2006) climate data and satellite-based land covers at 2.5 arc-min grid cells for the conterminous US and for the entire world using coarser climate and land cover data. We evaluated the simulated ET, soil moisture, and surface runoff using a set of observed or simulated data at different spatial scales. Our results demonstrate that spatial patterns of LH-simulated annual ET and surface runoff are in accordance with previously published data for the US; LH-modeled monthly stream flow for 12 major rivers in the US was consistent with observed values respectively during the years 1981-2006 (R2 > 0.46, p < 0.01; Nash-Sutcliffe Coefficient > 0.52). The modeled mean annual discharges for 10 major rivers worldwide also agreed well (differences < 15%) with observed values for these rivers. Compared to a degree-day method for snowmelt computation, the addition of the solar radiation effect on snowmelt enabled LH to better simulate monthly stream flow in winter and early spring for rivers located at mid-to-high latitudes. In addition, LH-modeled monthly soil moisture for the state of Illinois (US) agreed well (R2 = 0.79, p < 0.01) with observed data for the years 1984-2001. Overall, this study justifies both the feasibility of incorporating satellite-based land covers into a DGVM and the reliability of LH to simulate land-surface water balances. To better estimate surface/river runoff at mid-to-high latitudes, we recommended that LPJ-DGVM considers the effects of solar radiation on snowmelt.

Space evolution model and empirical analysis of an urban public transport network

NASA Astrophysics Data System (ADS)

Sui, Yi; Shao, Feng-jing; Sun, Ren-cheng; Li, Shu-jing

2012-07-01

This study explores the space evolution of an urban public transport network, using empirical evidence and a simulation model validated on that data. Public transport patterns primarily depend on traffic spatial-distribution, demands of passengers and expected utility of investors. Evolution is an iterative process of satisfying the needs of passengers and investors based on a given traffic spatial-distribution. The temporal change of urban public transport network is evaluated both using topological measures and spatial ones. The simulation model is validated using empirical data from nine big cities in China. Statistical analyses on topological and spatial attributes suggest that an evolution network with traffic demands characterized by power-law numerical values which distribute in a mode of concentric circles tallies well with these nine cities.

Different modelling approaches to evaluate nitrogen transport and turnover at the watershed scale

NASA Astrophysics Data System (ADS)

Epelde, Ane Miren; Antiguedad, Iñaki; Brito, David; Jauch, Eduardo; Neves, Ramiro; Garneau, Cyril; Sauvage, Sabine; Sánchez-Pérez, José Miguel

2016-08-01

This study presents the simulation of hydrological processes and nutrient transport and turnover processes using two integrated numerical models: Soil and Water Assessment Tool (SWAT) (Arnold et al., 1998), an empirical and semi-distributed numerical model; and Modelo Hidrodinâmico (MOHID) (Neves, 1985), a physics-based and fully distributed numerical model. This work shows that both models reproduce satisfactorily water and nitrate exportation at the watershed scale at annual and daily basis, MOHID providing slightly better results. At the watershed scale, both SWAT and MOHID simulated similarly and satisfactorily the denitrification amount. However, as MOHID numerical model was the only one able to reproduce adequately the spatial variation of the soil hydrological conditions and water table level fluctuation, it proved to be the only model able of reproducing the spatial variation of the nutrient cycling processes that are dependent to the soil hydrological conditions such as the denitrification process. This evidences the strength of the fully distributed and physics-based models to simulate the spatial variability of nutrient cycling processes that are dependent to the hydrological conditions of the soils.

Comparing Lagrangian and Eulerian models for CO2 transport - a step towards Bayesian inverse modeling using WRF/STILT-VPRM

NASA Astrophysics Data System (ADS)

Pillai, D.; Gerbig, C.; Kretschmer, R.; Beck, V.; Karstens, U.; Neininger, B.; Heimann, M.

2012-01-01

We present simulations of atmospheric CO2 concentrations provided by two modeling systems, run at high spatial resolution: the Eulerian-based Weather Research Forecasting (WRF) model and the Lagrangian-based Stochastic Time-Inverted Lagrangian Transport (STILT) model, both of which are coupled to a diagnostic biospheric model, the Vegetation Photosynthesis and Respiration Model (VPRM). The consistency of the simulations is assessed with special attention paid to the details of horizontal as well as vertical transport and mixing of CO2 concentrations in the atmosphere. The dependence of model mismatch (Eulerian vs. Lagrangian) on models' spatial resolution is further investigated. A case study using airborne measurements during which both models showed large deviations from each other is analyzed in detail as an extreme case. Using aircraft observations and pulse release simulations, we identified differences in the representation of details in the interaction between turbulent mixing and advection through wind shear as the main cause of discrepancies between WRF and STILT transport at a spatial resolution such as 2 and 6 km. Based on observations and inter-model comparisons of atmospheric CO2 concentrations, we show that a refinement of the parameterization of turbulent velocity variance and Lagrangian time-scale in STILT is needed to achieve a better match between the Eulerian and the Lagrangian transport at such a high spatial resolution (e.g. 2 and 6 km). Nevertheless, the inter-model differences in simulated CO2 time series for a tall tower observatory at Ochsenkopf in Germany are about a factor of two smaller than the model-data mismatch and about a factor of three smaller than the mismatch between the current global model simulations and the data. Thus suggests that it is reasonable to use STILT as an adjoint model of WRF atmospheric transport.

Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods

NASA Astrophysics Data System (ADS)

Mateo, Cherry May R.; Yamazaki, Dai; Kim, Hyungjun; Champathong, Adisorn; Vaze, Jai; Oki, Taikan

2017-10-01

Global-scale river models (GRMs) are core tools for providing consistent estimates of global flood hazard, especially in data-scarce regions. Due to former limitations in computational power and input datasets, most GRMs have been developed to use simplified representations of flow physics and run at coarse spatial resolutions. With increasing computational power and improved datasets, the application of GRMs to finer resolutions is becoming a reality. To support development in this direction, the suitability of GRMs for application to finer resolutions needs to be assessed. This study investigates the impacts of spatial resolution and flow connectivity representation on the predictive capability of a GRM, CaMa-Flood, in simulating the 2011 extreme flood in Thailand. Analyses show that when single downstream connectivity (SDC) is assumed, simulation results deteriorate with finer spatial resolution; Nash-Sutcliffe efficiency coefficients decreased by more than 50 % between simulation results at 10 km resolution and 1 km resolution. When multiple downstream connectivity (MDC) is represented, simulation results slightly improve with finer spatial resolution. The SDC simulations result in excessive backflows on very flat floodplains due to the restrictive flow directions at finer resolutions. MDC channels attenuated these effects by maintaining flow connectivity and flow capacity between floodplains in varying spatial resolutions. While a regional-scale flood was chosen as a test case, these findings should be universal and may have significant impacts on large- to global-scale simulations, especially in regions where mega deltas exist.These results demonstrate that a GRM can be used for higher resolution simulations of large-scale floods, provided that MDC in rivers and floodplains is adequately represented in the model structure.

Spatial Release From Masking in Simulated Cochlear Implant Users With and Without Access to Low-Frequency Acoustic Hearing

PubMed Central

Dietz, Mathias; Hohmann, Volker; Jürgens, Tim

2015-01-01

For normal-hearing listeners, speech intelligibility improves if speech and noise are spatially separated. While this spatial release from masking has already been quantified in normal-hearing listeners in many studies, it is less clear how spatial release from masking changes in cochlear implant listeners with and without access to low-frequency acoustic hearing. Spatial release from masking depends on differences in access to speech cues due to hearing status and hearing device. To investigate the influence of these factors on speech intelligibility, the present study measured speech reception thresholds in spatially separated speech and noise for 10 different listener types. A vocoder was used to simulate cochlear implant processing and low-frequency filtering was used to simulate residual low-frequency hearing. These forms of processing were combined to simulate cochlear implant listening, listening based on low-frequency residual hearing, and combinations thereof. Simulated cochlear implant users with additional low-frequency acoustic hearing showed better speech intelligibility in noise than simulated cochlear implant users without acoustic hearing and had access to more spatial speech cues (e.g., higher binaural squelch). Cochlear implant listener types showed higher spatial release from masking with bilateral access to low-frequency acoustic hearing than without. A binaural speech intelligibility model with normal binaural processing showed overall good agreement with measured speech reception thresholds, spatial release from masking, and spatial speech cues. This indicates that differences in speech cues available to listener types are sufficient to explain the changes of spatial release from masking across these simulated listener types. PMID:26721918

Theoretical Models of Protostellar Binary and Multiple Systems with AMR Simulations

NASA Astrophysics Data System (ADS)

Matsumoto, Tomoaki; Tokuda, Kazuki; Onishi, Toshikazu; Inutsuka, Shu-ichiro; Saigo, Kazuya; Takakuwa, Shigehisa

2017-05-01

We present theoretical models for protostellar binary and multiple systems based on the high-resolution numerical simulation with an adaptive mesh refinement (AMR) code, SFUMATO. The recent ALMA observations have revealed early phases of the binary and multiple star formation with high spatial resolutions. These observations should be compared with theoretical models with high spatial resolutions. We present two theoretical models for (1) a high density molecular cloud core, MC27/L1521F, and (2) a protobinary system, L1551 NE. For the model for MC27, we performed numerical simulations for gravitational collapse of a turbulent cloud core. The cloud core exhibits fragmentation during the collapse, and dynamical interaction between the fragments produces an arc-like structure, which is one of the prominent structures observed by ALMA. For the model for L1551 NE, we performed numerical simulations of gas accretion onto protobinary. The simulations exhibit asymmetry of a circumbinary disk. Such asymmetry has been also observed by ALMA in the circumbinary disk of L1551 NE.

Sensitivities of simulated satellite views of clouds to subgrid-scale overlap and condensate heterogeneity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hillman, Benjamin R.; Marchand, Roger T.; Ackerman, Thomas P.

Satellite simulators are often used to account for limitations in satellite retrievals of cloud properties in comparisons between models and satellite observations. The purpose of the simulator framework is to enable more robust evaluation of model cloud properties, so that di erences between models and observations can more con dently be attributed to model errors. However, these simulators are subject to uncertainties themselves. A fundamental uncertainty exists in connecting the spatial scales at which cloud properties are retrieved with those at which clouds are simulated in global models. In this study, we create a series of sensitivity tests using 4more » km global model output from the Multiscale Modeling Framework to evaluate the sensitivity of simulated satellite retrievals when applied to climate models whose grid spacing is many tens to hundreds of kilometers. In particular, we examine the impact of cloud and precipitation overlap and of condensate spatial variability. We find the simulated retrievals are sensitive to these assumptions. Specifically, using maximum-random overlap with homogeneous cloud and precipitation condensate, which is often used in global climate models, leads to large errors in MISR and ISCCP-simulated cloud cover and in CloudSat-simulated radar reflectivity. To correct for these errors, an improved treatment of unresolved clouds and precipitation is implemented for use with the simulator framework and is shown to substantially reduce the identified errors.« less

Modelling and simulation techniques for membrane biology.

PubMed

Burrage, Kevin; Hancock, John; Leier, André; Nicolau, Dan V

2007-07-01

One of the most important aspects of Computational Cell Biology is the understanding of the complicated dynamical processes that take place on plasma membranes. These processes are often so complicated that purely temporal models cannot always adequately capture the dynamics. On the other hand, spatial models can have large computational overheads. In this article, we review some of these issues with respect to chemistry, membrane microdomains and anomalous diffusion and discuss how to select appropriate modelling and simulation paradigms based on some or all the following aspects: discrete, continuous, stochastic, delayed and complex spatial processes.

Remotely sensed soil moisture input to a hydrologic model

NASA Technical Reports Server (NTRS)

Engman, E. T.; Kustas, W. P.; Wang, J. R.

1989-01-01

The possibility of using detailed spatial soil moisture maps as input to a runoff model was investigated. The water balance of a small drainage basin was simulated using a simple storage model. Aircraft microwave measurements of soil moisture were used to construct two-dimensional maps of the spatial distribution of the soil moisture. Data from overflights on different dates provided the temporal changes resulting from soil drainage and evapotranspiration. The study site and data collection are described, and the soil measurement data are given. The model selection is discussed, and the simulation results are summarized. It is concluded that a time series of soil moisture is a valuable new type of data for verifying model performance and for updating and correcting simulated streamflow.

Graph-based analysis of connectivity in spatially-explicit population models: HexSim and the Connectivity Analysis Toolkit

EPA Science Inventory

Background / Question / Methods Planning for the recovery of threatened species is increasingly informed by spatially-explicit population models. However, using simulation model results to guide land management decisions can be difficult due to the volume and complexity of model...

Spatial Dependence and Heterogeneity in Bayesian Factor Analysis: A Cross-National Investigation of Schwartz Values

ERIC Educational Resources Information Center

Stakhovych, Stanislav; Bijmolt, Tammo H. A.; Wedel, Michel

2012-01-01

In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the model parameters and demonstrates the consequences…

Water quality modeling in the dead end sections of drinking water distribution networks.

PubMed

Abokifa, Ahmed A; Yang, Y Jeffrey; Lo, Cynthia S; Biswas, Pratim

2016-02-01

Dead-end sections of drinking water distribution networks are known to be problematic zones in terms of water quality degradation. Extended residence time due to water stagnation leads to rapid reduction of disinfectant residuals allowing the regrowth of microbial pathogens. Water quality models developed so far apply spatial aggregation and temporal averaging techniques for hydraulic parameters by assigning hourly averaged water demands to the main nodes of the network. Although this practice has generally resulted in minimal loss of accuracy for the predicted disinfectant concentrations in main water transmission lines, this is not the case for the peripheries of the distribution network. This study proposes a new approach for simulating disinfectant residuals in dead end pipes while accounting for both spatial and temporal variability in hydraulic and transport parameters. A stochastic demand generator was developed to represent residential water pulses based on a non-homogenous Poisson process. Dispersive solute transport was considered using highly dynamic dispersion rates. A genetic algorithm was used to calibrate the axial hydraulic profile of the dead-end pipe based on the different demand shares of the withdrawal nodes. A parametric sensitivity analysis was done to assess the model performance under variation of different simulation parameters. A group of Monte-Carlo ensembles was carried out to investigate the influence of spatial and temporal variations in flow demands on the simulation accuracy. A set of three correction factors were analytically derived to adjust residence time, dispersion rate and wall demand to overcome simulation error caused by spatial aggregation approximation. The current model results show better agreement with field-measured concentrations of conservative fluoride tracer and free chlorine disinfectant than the simulations of recent advection dispersion reaction models published in the literature. Accuracy of the simulated concentration profiles showed significant dependence on the spatial distribution of the flow demands compared to temporal variation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Scalability of grid- and subbasin-based land surface modeling approaches for hydrologic simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tesfa, Teklu K.; Ruby Leung, L.; Huang, Maoyi

2014-03-27

This paper investigates the relative merits of grid- and subbasin-based land surface modeling approaches for hydrologic simulations, with a focus on their scalability (i.e., abilities to perform consistently across a range of spatial resolutions) in simulating runoff generation. Simulations produced by the grid- and subbasin-based configurations of the Community Land Model (CLM) are compared at four spatial resolutions (0.125o, 0.25o, 0.5o and 1o) over the topographically diverse region of the U.S. Pacific Northwest. Using the 0.125o resolution simulation as the “reference”, statistical skill metrics are calculated and compared across simulations at 0.25o, 0.5o and 1o spatial resolutions of each modelingmore » approach at basin and topographic region levels. Results suggest significant scalability advantage for the subbasin-based approach compared to the grid-based approach for runoff generation. Basin level annual average relative errors of surface runoff at 0.25o, 0.5o, and 1o compared to 0.125o are 3%, 4%, and 6% for the subbasin-based configuration and 4%, 7%, and 11% for the grid-based configuration, respectively. The scalability advantages of the subbasin-based approach are more pronounced during winter/spring and over mountainous regions. The source of runoff scalability is found to be related to the scalability of major meteorological and land surface parameters of runoff generation. More specifically, the subbasin-based approach is more consistent across spatial scales than the grid-based approach in snowfall/rainfall partitioning, which is related to air temperature and surface elevation. Scalability of a topographic parameter used in the runoff parameterization also contributes to improved scalability of the rain driven saturated surface runoff component, particularly during winter. Hence this study demonstrates the importance of spatial structure for multi-scale modeling of hydrological processes, with implications to surface heat fluxes in coupled land-atmosphere modeling.« less

Performance assessment of geospatial simulation models of land-use change--a landscape metric-based approach.

PubMed

Sakieh, Yousef; Salmanmahiny, Abdolrassoul

2016-03-01

Performance evaluation is a critical step when developing land-use and cover change (LUCC) models. The present study proposes a spatially explicit model performance evaluation method, adopting a landscape metric-based approach. To quantify GEOMOD model performance, a set of composition- and configuration-based landscape metrics including number of patches, edge density, mean Euclidean nearest neighbor distance, largest patch index, class area, landscape shape index, and splitting index were employed. The model takes advantage of three decision rules including neighborhood effect, persistence of change direction, and urbanization suitability values. According to the results, while class area, largest patch index, and splitting indices demonstrated insignificant differences between spatial pattern of ground truth and simulated layers, there was a considerable inconsistency between simulation results and real dataset in terms of the remaining metrics. Specifically, simulation outputs were simplistic and the model tended to underestimate number of developed patches by producing a more compact landscape. Landscape-metric-based performance evaluation produces more detailed information (compared to conventional indices such as the Kappa index and overall accuracy) on the model's behavior in replicating spatial heterogeneity features of a landscape such as frequency, fragmentation, isolation, and density. Finally, as the main characteristic of the proposed method, landscape metrics employ the maximum potential of observed and simulated layers for a performance evaluation procedure, provide a basis for more robust interpretation of a calibration process, and also deepen modeler insight into the main strengths and pitfalls of a specific land-use change model when simulating a spatiotemporal phenomenon.

Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors.

PubMed

Dhingra, Radhika; Jimenez, Violeta; Chang, Howard H; Gambhir, Manoj; Fu, Joshua S; Liu, Yang; Remais, Justin V

2013-09-01

Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis , the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001-2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057-2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses-including altered phenology-of disease vectors to altered climate.

Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors

PubMed Central

Dhingra, Radhika; Jimenez, Violeta; Chang, Howard H.; Gambhir, Manoj; Fu, Joshua S.; Liu, Yang; Remais, Justin V.

2014-01-01

Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis, the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001–2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057–2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses—including altered phenology—of disease vectors to altered climate. PMID:24772388

Can Regional Climate Modeling Capture the Observed Changes in Spatial Organization of Extreme Storms at Higher Temperatures?

NASA Astrophysics Data System (ADS)

Li, J.; Wasko, C.; Johnson, F.; Evans, J. P.; Sharma, A.

2018-05-01

The spatial extent and organization of extreme storm events has important practical implications for flood forecasting. Recently, conflicting evidence has been found on the observed changes of storm spatial extent with increasing temperatures. To further investigate this question, a regional climate model assessment is presented for the Greater Sydney region, in Australia. Two regional climate models were considered: the first a convection-resolving simulation at 2-km resolution, the second a resolution of 10 km with three different convection parameterizations. Both the 2- and the 10-km resolutions that used the Betts-Miller-Janjic convective scheme simulate decreasing storm spatial extent with increasing temperatures for 1-hr duration precipitation events, consistent with the observation-based study in Australia. However, other observed relationships of extreme rainfall with increasing temperature were not well represented by the models. Improved methods for considering storm organization are required to better understand potential future changes.

Can APEX Represent In-Field Spatial Variability and Simulate Its Effects On Crop Yields?

USDA-ARS?s Scientific Manuscript database

Precision agriculture, from variable rate nitrogen application to precision irrigation, promises improved management of resources by considering the spatial variability of topography and soil properties. Hydrologic models need to simulate the effects of this variability if they are to inform about t...

SIMULATED IMPACTS OF SMALL-SCALE SPATIAL DISTRIBUTION OF IMPERVIOUS AREA ON RUNOFF RESPONSE OF FIELD-SCALE CATCHMENTS

EPA Science Inventory

Impervious surface is known to negatively affect catchment hydrology through both its extent and spatial distribution. In this study, we empirically quantify via model simulations the impacts of different configurations of impervious surface on watershed response to rainfall. An ...

Modelling and simulation of a heat exchanger

NASA Technical Reports Server (NTRS)

Xia, Lei; Deabreu-Garcia, J. Alex; Hartley, Tom T.

1991-01-01

Two models for two different control systems are developed for a parallel heat exchanger. First by spatially lumping a heat exchanger model, a good approximate model which has a high system order is produced. Model reduction techniques are applied to these to obtain low order models that are suitable for dynamic analysis and control design. The simulation method is discussed to ensure a valid simulation result.

Smoldyn on graphics processing units: massively parallel Brownian dynamics simulations.

PubMed

Dematté, Lorenzo

2012-01-01

Space is a very important aspect in the simulation of biochemical systems; recently, the need for simulation algorithms able to cope with space is becoming more and more compelling. Complex and detailed models of biochemical systems need to deal with the movement of single molecules and particles, taking into consideration localized fluctuations, transportation phenomena, and diffusion. A common drawback of spatial models lies in their complexity: models can become very large, and their simulation could be time consuming, especially if we want to capture the systems behavior in a reliable way using stochastic methods in conjunction with a high spatial resolution. In order to deliver the promise done by systems biology to be able to understand a system as whole, we need to scale up the size of models we are able to simulate, moving from sequential to parallel simulation algorithms. In this paper, we analyze Smoldyn, a widely diffused algorithm for stochastic simulation of chemical reactions with spatial resolution and single molecule detail, and we propose an alternative, innovative implementation that exploits the parallelism of Graphics Processing Units (GPUs). The implementation executes the most computational demanding steps (computation of diffusion, unimolecular, and bimolecular reaction, as well as the most common cases of molecule-surface interaction) on the GPU, computing them in parallel on each molecule of the system. The implementation offers good speed-ups and real time, high quality graphics output

Simple Process-Based Simulators for Generating Spatial Patterns of Habitat Loss and Fragmentation: A Review and Introduction to the G-RaFFe Model

PubMed Central

Pe'er, Guy; Zurita, Gustavo A.; Schober, Lucia; Bellocq, Maria I.; Strer, Maximilian; Müller, Michael; Pütz, Sandro

2013-01-01

Landscape simulators are widely applied in landscape ecology for generating landscape patterns. These models can be divided into two categories: pattern-based models that generate spatial patterns irrespective of the processes that shape them, and process-based models that attempt to generate patterns based on the processes that shape them. The latter often tend toward complexity in an attempt to obtain high predictive precision, but are rarely used for generic or theoretical purposes. Here we show that a simple process-based simulator can generate a variety of spatial patterns including realistic ones, typifying landscapes fragmented by anthropogenic activities. The model “G-RaFFe” generates roads and fields to reproduce the processes in which forests are converted into arable lands. For a selected level of habitat cover, three factors dominate its outcomes: the number of roads (accessibility), maximum field size (accounting for land ownership patterns), and maximum field disconnection (which enables field to be detached from roads). We compared the performance of G-RaFFe to three other models: Simmap (neutral model), Qrule (fractal-based) and Dinamica EGO (with 4 model versions differing in complexity). A PCA-based analysis indicated G-RaFFe and Dinamica version 4 (most complex) to perform best in matching realistic spatial patterns, but an alternative analysis which considers model variability identified G-RaFFe and Qrule as performing best. We also found model performance to be affected by habitat cover and the actual land-uses, the latter reflecting on land ownership patterns. We suggest that simple process-based generators such as G-RaFFe can be used to generate spatial patterns as templates for theoretical analyses, as well as for gaining better understanding of the relation between spatial processes and patterns. We suggest caution in applying neutral or fractal-based approaches, since spatial patterns that typify anthropogenic landscapes are often non-fractal in nature. PMID:23724108

Simple process-based simulators for generating spatial patterns of habitat loss and fragmentation: a review and introduction to the G-RaFFe model.

PubMed

Pe'er, Guy; Zurita, Gustavo A; Schober, Lucia; Bellocq, Maria I; Strer, Maximilian; Müller, Michael; Pütz, Sandro

2013-01-01

Landscape simulators are widely applied in landscape ecology for generating landscape patterns. These models can be divided into two categories: pattern-based models that generate spatial patterns irrespective of the processes that shape them, and process-based models that attempt to generate patterns based on the processes that shape them. The latter often tend toward complexity in an attempt to obtain high predictive precision, but are rarely used for generic or theoretical purposes. Here we show that a simple process-based simulator can generate a variety of spatial patterns including realistic ones, typifying landscapes fragmented by anthropogenic activities. The model "G-RaFFe" generates roads and fields to reproduce the processes in which forests are converted into arable lands. For a selected level of habitat cover, three factors dominate its outcomes: the number of roads (accessibility), maximum field size (accounting for land ownership patterns), and maximum field disconnection (which enables field to be detached from roads). We compared the performance of G-RaFFe to three other models: Simmap (neutral model), Qrule (fractal-based) and Dinamica EGO (with 4 model versions differing in complexity). A PCA-based analysis indicated G-RaFFe and Dinamica version 4 (most complex) to perform best in matching realistic spatial patterns, but an alternative analysis which considers model variability identified G-RaFFe and Qrule as performing best. We also found model performance to be affected by habitat cover and the actual land-uses, the latter reflecting on land ownership patterns. We suggest that simple process-based generators such as G-RaFFe can be used to generate spatial patterns as templates for theoretical analyses, as well as for gaining better understanding of the relation between spatial processes and patterns. We suggest caution in applying neutral or fractal-based approaches, since spatial patterns that typify anthropogenic landscapes are often non-fractal in nature.

Modeling Best Management Practices (BMPs) with HSPF

EPA Science Inventory

The Hydrological Simulation Program-Fortran (HSPF) is a semi-distributed watershed model, which simulates hydrology and water quality processes at user-specified spatial and temporal scales. Although HSPF is a comprehensive and highly flexible model, a number of investigators not...

Optical phase aberration generation using a Liquid Crystal Spatial Light Modulator

NASA Astrophysics Data System (ADS)

Wilcox, Christopher C.

In this dissertation, a Liquid Crystal Spatial Light Modulator is used to simulate optical aberrations in an optical system. Any optical aberration can be simulated through the use of software developed for this project. A new method of simulating atmospheric turbulence is also presented. The Earth's atmosphere is a large, non-linear, non-homogeneous medium that is constantly flowing in a random fashion that affects light as it propagates through it. The Kolmogorov model for atmospheric turbulence is a description of the nature of the wavefront perturbations introduced by the atmosphere and it is one of the most accepted models. It is supported by a variety of experimental measurements and research and is quite widely used in simulations for atmospheric imaging. This model provides a statistical description of how random fluctuations in humidity and temperature affect the refractive index of the atmosphere for imaging through atmospheric turbulence. These refractive index fluctuations in turn affect the propagation of light through the atmosphere. An adaptive optical system can be developed to correct these wavefront perturbations for an optical system. However, prior to deployment, an adaptive optical system requires calibration and full characterization in the laboratory. Creating realistic atmospheric simulations is often expensive and computationally intensive using common techniques. To combat some of these issues often the temporal properties in the simulation are neglected. This dissertation outlines a new method developed for generating atmospheric turbulence and a testbed that simulates its aberrations far more inexpensively and with greater fidelity using a Liquid Crystal Spatial Light Modulator. This system allows the simulation of atmospheric seeing conditions ranging from very poor to very good and different algorithms may be easily employed on the device for comparison. These simulations can be dynamically generated and modified very quickly and easily. Using a Liquid Crystal Spatial Light Modulator to induce aberrations in an imaging system is not limited to simulating atmospheric turbulence. Any turbulence model can be used either statically or dynamically for multiple applications.

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

NASA Astrophysics Data System (ADS)

Skaugen, Thomas; Weltzien, Ingunn H.

2016-09-01

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

Water Quality Assessment Simulation Program (WASP8): Upgrades to the Advanced Toxicant Module for Simulating Dissolved Chemicals, Nanomaterials, and Solids

EPA Science Inventory

The Water Quality Analysis Simulation Program (WASP) is a dynamic, spatially-resolved, differential mass balance fate and transport modeling framework. WASP is used to develop models to simulate concentrations of environmental contaminants in surface waters and sediments. As a mo...

State-and-transition simulation models: a framework for forecasting landscape change

USGS Publications Warehouse

Daniel, Colin; Frid, Leonardo; Sleeter, Benjamin M.; Fortin, Marie-Josée

2016-01-01

SummaryA wide range of spatially explicit simulation models have been developed to forecast landscape dynamics, including models for projecting changes in both vegetation and land use. While these models have generally been developed as separate applications, each with a separate purpose and audience, they share many common features.We present a general framework, called a state-and-transition simulation model (STSM), which captures a number of these common features, accompanied by a software product, called ST-Sim, to build and run such models. The STSM method divides a landscape into a set of discrete spatial units and simulates the discrete state of each cell forward as a discrete-time-inhomogeneous stochastic process. The method differs from a spatially interacting Markov chain in several important ways, including the ability to add discrete counters such as age and time-since-transition as state variables, to specify one-step transition rates as either probabilities or target areas, and to represent multiple types of transitions between pairs of states.We demonstrate the STSM method using a model of land-use/land-cover (LULC) change for the state of Hawai'i, USA. Processes represented in this example include expansion/contraction of agricultural lands, urbanization, wildfire, shrub encroachment into grassland and harvest of tree plantations; the model also projects shifts in moisture zones due to climate change. Key model output includes projections of the future spatial and temporal distribution of LULC classes and moisture zones across the landscape over the next 50 years.State-and-transition simulation models can be applied to a wide range of landscapes, including questions of both land-use change and vegetation dynamics. Because the method is inherently stochastic, it is well suited for characterizing uncertainty in model projections. When combined with the ST-Sim software, STSMs offer a simple yet powerful means for developing a wide range of models of landscape dynamics.

Spatial modeling of cell signaling networks.

PubMed

Cowan, Ann E; Moraru, Ion I; Schaff, James C; Slepchenko, Boris M; Loew, Leslie M

2012-01-01

The shape of a cell, the sizes of subcellular compartments, and the spatial distribution of molecules within the cytoplasm can all control how molecules interact to produce a cellular behavior. This chapter describes how these spatial features can be included in mechanistic mathematical models of cell signaling. The Virtual Cell computational modeling and simulation software is used to illustrate the considerations required to build a spatial model. An explanation of how to appropriately choose between physical formulations that implicitly or explicitly account for cell geometry and between deterministic versus stochastic formulations for molecular dynamics is provided, along with a discussion of their respective strengths and weaknesses. As a first step toward constructing a spatial model, the geometry needs to be specified and associated with the molecules, reactions, and membrane flux processes of the network. Initial conditions, diffusion coefficients, velocities, and boundary conditions complete the specifications required to define the mathematics of the model. The numerical methods used to solve reaction-diffusion problems both deterministically and stochastically are then described and some guidance is provided in how to set up and run simulations. A study of cAMP signaling in neurons ends the chapter, providing an example of the insights that can be gained in interpreting experimental results through the application of spatial modeling. Copyright © 2012 Elsevier Inc. All rights reserved.

Hybrid methods for simulating hydrodynamics and heat transfer in multiscale (1D-3D) models

NASA Astrophysics Data System (ADS)

Filimonov, S. A.; Mikhienkova, E. I.; Dekterev, A. A.; Boykov, D. V.

2017-09-01

The work is devoted to application of different-scale models in the simulation of hydrodynamics and heat transfer of large and/or complex systems, which can be considered as a combination of extended and “compact” elements. The model consisting of simultaneously existing three-dimensional and network (one-dimensional) elements is called multiscale. The paper examines the relevance of building such models and considers three main options for their implementation: the spatial and the network parts of the model are calculated separately; spatial and network parts are calculated simultaneously (hydraulically unified model); network elements “penetrate” the spatial part and are connected through the integral characteristics at the tube/channel walls (hydraulically disconnected model). Each proposed method is analyzed in terms of advantages and disadvantages. The paper presents a number of practical examples demonstrating the application of multiscale models.

A Microfluidics and Agent-Based Modeling Framework for Investigating Spatial Organization in Bacterial Colonies: The Case of Pseudomonas Aeruginosa and H1-Type VI Secretion Interactions

DOE PAGES

Wilmoth, Jared L.; Doak, Peter W.; Timm, Andrea; ...

2018-02-06

The factors leading to changes in the organization of microbial assemblages at fine spatial scales are not well characterized or understood. However, they are expected to guide the succession of community development and function toward specific outcomes that could impact human health and the environment. In this study, we put forward a combined experimental and agent-based modeling framework and use it to interpret unique spatial organization patterns of H1-Type VI secretion system (T6SS) mutants of P. aeruginosa under spatial confinement. We find that key parameters, such as T6SS-mediated cell contact and lysis, spatial localization, relative species abundance, cell density andmore » local concentrations of growth substrates and metabolites are influenced by spatial confinement. The model, written in the accessible programming language NetLogo, can be adapted to a variety of biological systems of interest and used to simulate experiments across a broad parameter space. It was implemented and run in a high-throughput mode by deploying it across multiple CPUs, with each simulation representing an individual well within a high-throughput microwell array experimental platform. The microfluidics and agent-based modeling framework we present in this paper provides an effective means by which to connect experimental studies in microbiology to model development. The work demonstrates progress in coupling experimental results to simulation while also highlighting potential sources of discrepancies between real-world experiments and idealized models.« less

A Microfluidics and Agent-Based Modeling Framework for Investigating Spatial Organization in Bacterial Colonies: The Case of Pseudomonas Aeruginosa and H1-Type VI Secretion Interactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wilmoth, Jared L.; Doak, Peter W.; Timm, Andrea

The factors leading to changes in the organization of microbial assemblages at fine spatial scales are not well characterized or understood. However, they are expected to guide the succession of community development and function toward specific outcomes that could impact human health and the environment. In this study, we put forward a combined experimental and agent-based modeling framework and use it to interpret unique spatial organization patterns of H1-Type VI secretion system (T6SS) mutants of P. aeruginosa under spatial confinement. We find that key parameters, such as T6SS-mediated cell contact and lysis, spatial localization, relative species abundance, cell density andmore » local concentrations of growth substrates and metabolites are influenced by spatial confinement. The model, written in the accessible programming language NetLogo, can be adapted to a variety of biological systems of interest and used to simulate experiments across a broad parameter space. It was implemented and run in a high-throughput mode by deploying it across multiple CPUs, with each simulation representing an individual well within a high-throughput microwell array experimental platform. The microfluidics and agent-based modeling framework we present in this paper provides an effective means by which to connect experimental studies in microbiology to model development. The work demonstrates progress in coupling experimental results to simulation while also highlighting potential sources of discrepancies between real-world experiments and idealized models.« less

Introducing Perception and Modelling of Spatial Randomness in Classroom

ERIC Educational Resources Information Center

De Nóbrega, José Renato

2017-01-01

A strategy to facilitate understanding of spatial randomness is described, using student activities developed in sequence: looking at spatial patterns, simulating approximate spatial randomness using a grid of equally-likely squares, using binomial probabilities for approximations and predictions and then comparing with given Poisson…

Uncertainty of future projections of species distributions in mountainous regions.

PubMed

Tang, Ying; Winkler, Julie A; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang

2018-01-01

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.

Uncertainty of future projections of species distributions in mountainous regions

PubMed Central

Tang, Ying; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang

2018-01-01

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution. PMID:29320501

Compartmental and Spatial Rule-Based Modeling with Virtual Cell.

PubMed

Blinov, Michael L; Schaff, James C; Vasilescu, Dan; Moraru, Ion I; Bloom, Judy E; Loew, Leslie M

2017-10-03

In rule-based modeling, molecular interactions are systematically specified in the form of reaction rules that serve as generators of reactions. This provides a way to account for all the potential molecular complexes and interactions among multivalent or multistate molecules. Recently, we introduced rule-based modeling into the Virtual Cell (VCell) modeling framework, permitting graphical specification of rules and merger of networks generated automatically (using the BioNetGen modeling engine) with hand-specified reaction networks. VCell provides a number of ordinary differential equation and stochastic numerical solvers for single-compartment simulations of the kinetic systems derived from these networks, and agent-based network-free simulation of the rules. In this work, compartmental and spatial modeling of rule-based models has been implemented within VCell. To enable rule-based deterministic and stochastic spatial simulations and network-free agent-based compartmental simulations, the BioNetGen and NFSim engines were each modified to support compartments. In the new rule-based formalism, every reactant and product pattern and every reaction rule are assigned locations. We also introduce the rule-based concept of molecular anchors. This assures that any species that has a molecule anchored to a predefined compartment will remain in this compartment. Importantly, in addition to formulation of compartmental models, this now permits VCell users to seamlessly connect reaction networks derived from rules to explicit geometries to automatically generate a system of reaction-diffusion equations. These may then be simulated using either the VCell partial differential equations deterministic solvers or the Smoldyn stochastic simulator. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

HexSim - A general purpose framework for spatially-explicit, individual-based modeling

EPA Science Inventory

HexSim is a framework for constructing spatially-explicit, individual-based computer models designed for simulating terrestrial wildlife population dynamics and interactions. HexSim is useful for a broad set of modeling applications. This talk will focus on a subset of those ap...

Accounting for system dynamics in reserve design.

PubMed

Leroux, Shawn J; Schmiegelow, Fiona K A; Cumming, Steve G; Lessard, Robert B; Nagy, John

2007-10-01

Systematic conservation plans have only recently considered the dynamic nature of ecosystems. Methods have been developed to incorporate climate change, population dynamics, and uncertainty in reserve design, but few studies have examined how to account for natural disturbance. Considering natural disturbance in reserve design may be especially important for the world's remaining intact areas, which still experience active natural disturbance regimes. We developed a spatially explicit, dynamic simulation model, CONSERV, which simulates patch dynamics and fire, and used it to evaluate the efficacy of hypothetical reserve networks in northern Canada. We designed six networks based on conventional reserve design methods, with different conservation targets for woodland caribou habitat, high-quality wetlands, vegetation, water bodies, and relative connectedness. We input the six reserve networks into CONSERV and tracked the ability of each to maintain initial conservation targets through time under an active natural disturbance regime. None of the reserve networks maintained all initial targets, and some over-represented certain features, suggesting that both effectiveness and efficiency of reserve design could be improved through use of spatially explicit dynamic simulation during the planning process. Spatial simulation models of landscape dynamics are commonly used in natural resource management, but we provide the first illustration of their potential use for reserve design. Spatial simulation models could be used iteratively to evaluate competing reserve designs and select targets that have a higher likelihood of being maintained through time. Such models could be combined with dynamic planning techniques to develop a general theory for reserve design in an uncertain world.

Impact of Land Cover Characterization and Properties on Snow Albedo in Climate Models

NASA Astrophysics Data System (ADS)

Wang, L.; Bartlett, P. A.; Chan, E.; Montesano, P.

2017-12-01

The simulation of winter albedo in boreal and northern environments has been a particular challenge for land surface modellers. Assessments of output from CMIP3 and CMIP5 climate models have revealed that many simulations are characterized by overestimation of albedo in the boreal forest. Recent studies suggest that inaccurate representation of vegetation distribution, improper simulation of leaf area index, and poor treatment of canopy-snow processes are the primary causes of albedo errors. While several land cover datasets are commonly used to derive plant functional types (PFT) for use in climate models, new land cover and vegetation datasets with higher spatial resolution have become available in recent years. In this study, we compare the spatial distribution of the dominant PFTs and canopy cover fractions based on different land cover datasets, and present results from offline simulations of the latest version Canadian Land Surface Scheme (CLASS) over the northern Hemisphere land. We discuss the impact of land cover representation and surface properties on winter albedo simulations in climate models.

Harnessing Big Data to Represent 30-meter Spatial Heterogeneity in Earth System Models

NASA Astrophysics Data System (ADS)

Chaney, N.; Shevliakova, E.; Malyshev, S.; Van Huijgevoort, M.; Milly, C.; Sulman, B. N.

2016-12-01

Terrestrial land surface processes play a critical role in the Earth system; they have a profound impact on the global climate, food and energy production, freshwater resources, and biodiversity. One of the most fascinating yet challenging aspects of characterizing terrestrial ecosystems is their field-scale (˜30 m) spatial heterogeneity. It has been observed repeatedly that the water, energy, and biogeochemical cycles at multiple temporal and spatial scales have deep ties to an ecosystem's spatial structure. Current Earth system models largely disregard this important relationship leading to an inadequate representation of ecosystem dynamics. In this presentation, we will show how existing global environmental datasets can be harnessed to explicitly represent field-scale spatial heterogeneity in Earth system models. For each macroscale grid cell, these environmental data are clustered according to their field-scale soil and topographic attributes to define unique sub-grid tiles. The state-of-the-art Geophysical Fluid Dynamics Laboratory (GFDL) land model is then used to simulate these tiles and their spatial interactions via the exchange of water, energy, and nutrients along explicit topographic gradients. Using historical simulations over the contiguous United States, we will show how a robust representation of field-scale spatial heterogeneity impacts modeled ecosystem dynamics including the water, energy, and biogeochemical cycles as well as vegetation composition and distribution.

Mars-solar wind interaction: LatHyS, an improved parallel 3-D multispecies hybrid model

NASA Astrophysics Data System (ADS)

Modolo, Ronan; Hess, Sebastien; Mancini, Marco; Leblanc, Francois; Chaufray, Jean-Yves; Brain, David; Leclercq, Ludivine; Esteban-Hernández, Rosa; Chanteur, Gerard; Weill, Philippe; González-Galindo, Francisco; Forget, Francois; Yagi, Manabu; Mazelle, Christian

2016-07-01

In order to better represent Mars-solar wind interaction, we present an unprecedented model achieving spatial resolution down to 50 km, a so far unexplored resolution for global kinetic models of the Martian ionized environment. Such resolution approaches the ionospheric plasma scale height. In practice, the model is derived from a first version described in Modolo et al. (2005). An important effort of parallelization has been conducted and is presented here. A better description of the ionosphere was also implemented including ionospheric chemistry, electrical conductivities, and a drag force modeling the ion-neutral collisions in the ionosphere. This new version of the code, named LatHyS (Latmos Hybrid Simulation), is here used to characterize the impact of various spatial resolutions on simulation results. In addition, and following a global model challenge effort, we present the results of simulation run for three cases which allow addressing the effect of the suprathermal corona and of the solar EUV activity on the magnetospheric plasma boundaries and on the global escape. Simulation results showed that global patterns are relatively similar for the different spatial resolution runs, but finest grid runs provide a better representation of the ionosphere and display more details of the planetary plasma dynamic. Simulation results suggest that a significant fraction of escaping O+ ions is originated from below 1200 km altitude.

Application of GIS Rapid Mapping Technology in Disaster Monitoring

NASA Astrophysics Data System (ADS)

Wang, Z.; Tu, J.; Liu, G.; Zhao, Q.

2018-04-01

With the rapid development of GIS and RS technology, especially in recent years, GIS technology and its software functions have been increasingly mature and enhanced. And with the rapid development of mathematical statistical tools for spatial modeling and simulation, has promoted the widespread application and popularization of quantization in the field of geology. Based on the investigation of field disaster and the construction of spatial database, this paper uses remote sensing image, DEM and GIS technology to obtain the data information of disaster vulnerability analysis, and makes use of the information model to carry out disaster risk assessment mapping.Using ArcGIS software and its spatial data modeling method, the basic data information of the disaster risk mapping process was acquired and processed, and the spatial data simulation tool was used to map the disaster rapidly.

Spatio-Temporal Modelling of Dust Transport over Surface Mining Areas and Neighbouring Residential Zones.

PubMed

Matejicek, Lubos; Janour, Zbynek; Benes, Ludek; Bodnar, Tomas; Gulikova, Eva

2008-06-06

Projects focusing on spatio-temporal modelling of the living environment need to manage a wide range of terrain measurements, existing spatial data, time series, results of spatial analysis and inputs/outputs from numerical simulations. Thus, GISs are often used to manage data from remote sensors, to provide advanced spatial analysis and to integrate numerical models. In order to demonstrate the integration of spatial data, time series and methods in the framework of the GIS, we present a case study focused on the modelling of dust transport over a surface coal mining area, exploring spatial data from 3D laser scanners, GPS measurements, aerial images, time series of meteorological observations, inputs/outputs form numerical models and existing geographic resources. To achieve this, digital terrain models, layers including GPS thematic mapping, and scenes with simulation of wind flows are created to visualize and interpret coal dust transport over the mine area and a neighbouring residential zone. A temporary coal storage and sorting site, located near the residential zone, is one of the dominant sources of emissions. Using numerical simulations, the possible effects of wind flows are observed over the surface, modified by natural objects and man-made obstacles. The coal dust drifts with the wind in the direction of the residential zone and is partially deposited in this area. The simultaneous display of the digital map layers together with the location of the dominant emission source, wind flows and protected areas enables a risk assessment of the dust deposition in the area of interest to be performed. In order to obtain a more accurate simulation of wind flows over the temporary storage and sorting site, 3D laser scanning and GPS thematic mapping are used to create a more detailed digital terrain model. Thus, visualization of wind flows over the area of interest combined with 3D map layers enables the exploration of the processes of coal dust deposition at a local scale. In general, this project could be used as a template for dust-transport modelling which couples spatial data focused on the construction of digital terrain models and thematic mapping with data generated by numerical simulations based on Reynolds averaged Navier-Stokes equations.

Spatio-Temporal Modelling of Dust Transport over Surface Mining Areas and Neighbouring Residential Zones

PubMed Central

Matejicek, Lubos; Janour, Zbynek; Benes, Ludek; Bodnar, Tomas; Gulikova, Eva

2008-01-01

Projects focusing on spatio-temporal modelling of the living environment need to manage a wide range of terrain measurements, existing spatial data, time series, results of spatial analysis and inputs/outputs from numerical simulations. Thus, GISs are often used to manage data from remote sensors, to provide advanced spatial analysis and to integrate numerical models. In order to demonstrate the integration of spatial data, time series and methods in the framework of the GIS, we present a case study focused on the modelling of dust transport over a surface coal mining area, exploring spatial data from 3D laser scanners, GPS measurements, aerial images, time series of meteorological observations, inputs/outputs form numerical models and existing geographic resources. To achieve this, digital terrain models, layers including GPS thematic mapping, and scenes with simulation of wind flows are created to visualize and interpret coal dust transport over the mine area and a neighbouring residential zone. A temporary coal storage and sorting site, located near the residential zone, is one of the dominant sources of emissions. Using numerical simulations, the possible effects of wind flows are observed over the surface, modified by natural objects and man-made obstacles. The coal dust drifts with the wind in the direction of the residential zone and is partially deposited in this area. The simultaneous display of the digital map layers together with the location of the dominant emission source, wind flows and protected areas enables a risk assessment of the dust deposition in the area of interest to be performed. In order to obtain a more accurate simulation of wind flows over the temporary storage and sorting site, 3D laser scanning and GPS thematic mapping are used to create a more detailed digital terrain model. Thus, visualization of wind flows over the area of interest combined with 3D map layers enables the exploration of the processes of coal dust deposition at a local scale. In general, this project could be used as a template for dust-transport modelling which couples spatial data focused on the construction of digital terrain models and thematic mapping with data generated by numerical simulations based on Reynolds averaged Navier-Stokes equations. PMID:27879911

Functional CAR models for large spatially correlated functional datasets.

PubMed

Zhang, Lin; Baladandayuthapani, Veerabhadran; Zhu, Hongxiao; Baggerly, Keith A; Majewski, Tadeusz; Czerniak, Bogdan A; Morris, Jeffrey S

2016-01-01

We develop a functional conditional autoregressive (CAR) model for spatially correlated data for which functions are collected on areal units of a lattice. Our model performs functional response regression while accounting for spatial correlations with potentially nonseparable and nonstationary covariance structure, in both the space and functional domains. We show theoretically that our construction leads to a CAR model at each functional location, with spatial covariance parameters varying and borrowing strength across the functional domain. Using basis transformation strategies, the nonseparable spatial-functional model is computationally scalable to enormous functional datasets, generalizable to different basis functions, and can be used on functions defined on higher dimensional domains such as images. Through simulation studies, we demonstrate that accounting for the spatial correlation in our modeling leads to improved functional regression performance. Applied to a high-throughput spatially correlated copy number dataset, the model identifies genetic markers not identified by comparable methods that ignore spatial correlations.

Internal Catchment Process Simulation in a Snow-Dominated Basin: Performance Evaluation with Spatiotemporally Variable Runoff Generation and Groundwater Dynamics

NASA Astrophysics Data System (ADS)

Kuras, P. K.; Weiler, M.; Alila, Y.; Spittlehouse, D.; Winkler, R.

2006-12-01

Hydrologic models have been increasingly used in forest hydrology to overcome the limitations of paired watershed experiments, where vegetative recovery and natural variability obscure the inferences and conclusions that can be drawn from such studies. Models, however, are also plagued by uncertainty stemming from a limited understanding of hydrological processes in forested catchments and parameter equifinality is a common concern. This has created the necessity to improve our understanding of how hydrological systems work, through the development of hydrological measures, analyses and models that address the question: are we getting the right answers for the right reasons? Hence, physically-based, spatially-distributed hydrologic models should be validated with high-quality experimental data describing multiple concurrent internal catchment processes under a range of hydrologic regimes. The distributed hydrology soil vegetation model (DHSVM) frequently used in forest management applications is an example of a process-based model used to address the aforementioned circumstances, and this study takes a novel approach at collectively examining the ability of a pre-calibrated model application to realistically simulate outlet flows along with the spatial-temporal variation of internal catchment processes including: continuous groundwater dynamics at 9 locations, stream and road network flow at 67 locations for six individual days throughout the freshet, and pre-melt season snow distribution. Model efficiency was improved over prior evaluations due to continuous efforts in improving the quality of meteorological data in the watershed. Road and stream network flows were very well simulated for a range of hydrological conditions, and the spatial distribution of the pre-melt season snowpack was in general agreement with observed values. The model was effective in simulating the spatial variability of subsurface flow generation, except at locations where strong stream-groundwater interactions existed, as the model is not capable of simulating such processes and subsurface flows always drain to the stream network. The model has proven overall to be quite capable in realistically simulating internal catchment processes in the watershed, which creates more confidence in future model applications exploring the effects of various forest management scenarios on the watershed's hydrological processes.

Spatially distributed modelling of pesticide leaching at European scale with the PyCatch modelling framework

NASA Astrophysics Data System (ADS)

Schmitz, Oliver; van der Perk, Marcel; Karssenberg, Derek; Häring, Tim; Jene, Bernhard

2017-04-01

The modelling of pesticide transport through the soil and estimating its leaching to groundwater is essential for an appropriate environmental risk assessment. Pesticide leaching models commonly used in regulatory processes often lack the capability of providing a comprehensive spatial view, as they are implemented as non-spatial point models or only use a few combinations of representative soils to simulate specific plots. Furthermore, their handling of spatial input and output data and interaction with available Geographical Information Systems tools is limited. Therefore, executing several scenarios simulating and assessing the potential leaching on national or continental scale at high resolution is rather inefficient and prohibits the straightforward identification of areas prone to leaching. We present a new pesticide leaching model component of the PyCatch framework developed in PCRaster Python, an environmental modelling framework tailored to the development of spatio-temporal models (http://www.pcraster.eu). To ensure a feasible computational runtime of large scale models, we implemented an elementary field capacity approach to model soil water. Currently implemented processes are evapotranspiration, advection, dispersion, sorption, degradation and metabolite transformation. Not yet implemented relevant additional processes such as surface runoff, snowmelt, erosion or other lateral flows can be integrated with components already implemented in PyCatch. A preliminary version of the model executes a 20-year simulation of soil water processes for Germany (20 soil layers, 1 km2 spatial resolution, and daily timestep) within half a day using a single CPU. A comparison of the soil moisture and outflow obtained from the PCRaster implementation and PELMO, a commonly used pesticide leaching model, resulted in an R2 of 0.98 for the FOCUS Hamburg scenario. We will further discuss the validation of the pesticide transport processes and show case studies applied to European countries.

Improving Genomic Prediction in Cassava Field Experiments Using Spatial Analysis.

PubMed

Elias, Ani A; Rabbi, Ismail; Kulakow, Peter; Jannink, Jean-Luc

2018-01-04

Cassava ( Manihot esculenta Crantz) is an important staple food in sub-Saharan Africa. Breeding experiments were conducted at the International Institute of Tropical Agriculture in cassava to select elite parents. Taking into account the heterogeneity in the field while evaluating these trials can increase the accuracy in estimation of breeding values. We used an exploratory approach using the parametric spatial kernels Power, Spherical, and Gaussian to determine the best kernel for a given scenario. The spatial kernel was fit simultaneously with a genomic kernel in a genomic selection model. Predictability of these models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error compared to that of the base model having no spatial kernel. Results from our real and simulated data studies indicated that predictability can be increased by accounting for spatial variation irrespective of the heritability of the trait. In real data scenarios we observed that the accuracy can be increased by a median value of 3.4%. Through simulations, we showed that a 21% increase in accuracy can be achieved. We also found that Range (row) directional spatial kernels, mostly Gaussian, explained the spatial variance in 71% of the scenarios when spatial correlation was significant. Copyright © 2018 Elias et al.

A Comparison of Grizzly Bear Demographic Parameters Estimated from Non-Spatial and Spatial Open Population Capture-Recapture Models

PubMed Central

Whittington, Jesse; Sawaya, Michael A.

2015-01-01

Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capture-recapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal’s home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNA-based data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786–1.071) for females, 0.844 (0.703–0.975) for males, and 0.882 (0.779–0.981) for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758–1.024) for females, 0.825 (0.700–0.948) for males, and 0.863 (0.771–0.957) for both sexes. The combination of low densities, low reproductive rates, and predominantly negative population growth rates suggest that Banff National Park’s population of grizzly bears requires continued conservation-oriented management actions. PMID:26230262

Simulating ungulate herbivory across forest landscapes: A browsing extension for LANDIS-II

USGS Publications Warehouse

DeJager, Nathan R.; Drohan, Patrick J.; Miranda, Brian M.; Sturtevant, Brian R.; Stout, Susan L.; Royo, Alejandro; Gustafson, Eric J.; Romanski, Mark C.

2017-01-01

Browsing ungulates alter forest productivity and vegetation succession through selective foraging on species that often dominate early succession. However, the long-term and large-scale effects of browsing on forest succession are not possible to project without the use of simulation models. To explore the effects of ungulates on succession in a spatially explicit manner, we developed a Browse Extension that simulates the effects of browsing ungulates on the growth and survival of plant species cohorts within the LANDIS-II spatially dynamic forest landscape simulation model framework. We demonstrate the capabilities of the new extension and explore the spatial effects of ungulates on forest composition and dynamics using two case studies. The first case study examined the long-term effects of persistently high white-tailed deer browsing rates in the northern hardwood forests of the Allegheny National Forest, USA. In the second case study, we incorporated a dynamic ungulate population model to simulate interactions between the moose population and boreal forest landscape of Isle Royale National Park, USA. In both model applications, browsing reduced total aboveground live biomass and caused shifts in forest composition. Simulations that included effects of browsing resulted in successional patterns that were more similar to those observed in the study regions compared to simulations that did not incorporate browsing effects. Further, model estimates of moose population density and available forage biomass were similar to previously published field estimates at Isle Royale and in other moose-boreal forest systems. Our simulations suggest that neglecting effects of browsing when modeling forest succession in ecosystems known to be influenced by ungulates may result in flawed predictions of aboveground biomass and tree species composition.

Spatial demographic models to inform conservation planning of golden eagles in renewable energy landscapes

USGS Publications Warehouse

Wiens, J. David; Schumaker, Nathan H.; Inman, Richard D.; Esque, Todd C.; Longshore, Kathleen M.; Nussear, Kenneth E

2017-01-01

Spatial demographic models can help guide monitoring and management activities targeting at-risk species, even in cases where baseline data are lacking. Here, we provide an example of how site-specific changes in land use and anthropogenic stressors can be incorporated into a spatial demographic model to investigate effects on population dynamics of Golden Eagles (Aquila chrysaetos). Our study focused on a population of Golden Eagles exposed to risks associated with rapid increases in renewable energy development in southern California, U.S.A. We developed a spatially explicit, individual-based simulation model that integrated empirical data on demography of Golden Eagles with spatial data on the arrangement of nesting habitats, prey resources, and planned renewable energy development sites. Our model permitted simulated eagles of different stage-classes to disperse, establish home ranges, acquire prey resources, prospect for breeding sites, and reproduce. The distribution of nesting habitats, prey resources, and threats within each individual's home range influenced movement, reproduction, and survival. We used our model to explore potential effects of alternative disturbance scenarios, and proposed conservation strategies, on the future distribution and abundance of Golden Eagles in the study region. Results from our simulations suggest that probable increases in mortality associated with renewable energy infrastructure (e.g., collisions with wind turbines and vehicles, electrocution on power poles) could have negative consequences for population trajectories, but that site-specific conservation actions could reduce the magnitude of negative effects. Our study demonstrates the use of a flexible and expandable modeling framework to incorporate spatially dependent processes when determining relative effects of proposed management options to Golden Eagles and their habitats.

Does spatial variation in environmental conditions affect recruitment? A study using a 3-D model of Peruvian anchovy

NASA Astrophysics Data System (ADS)

Xu, Yi; Rose, Kenneth A.; Chai, Fei; Chavez, Francisco P.; Ayón, Patricia

2015-11-01

We used a 3-dimensional individual-based model (3-D IBM) of Peruvian anchovy to examine how spatial variation in environmental conditions affects larval and juvenile growth and survival, and recruitment. Temperature, velocity, and phytoplankton and zooplankton concentrations generated from a coupled hydrodynamic Nutrients-Phytoplankton-Zooplankton-Detritus (NPZD) model, mapped to a three dimensional rectangular grid, were used to simulate anchovy populations. The IBM simulated individuals as they progressed from eggs to recruitment at 10 cm. Eggs and yolk-sac larvae were followed hourly through the processes of development, mortality, and movement (advection), and larvae and juveniles were followed daily through the processes of growth, mortality, and movement (advection plus behavior). A bioenergetics model was used to grow larvae and juveniles. The NPZD model provided prey fields which influence both food consumption rate as well as behavior mediated movement with individuals going to grids cells having optimal growth conditions. We compared predicted recruitment for monthly cohorts for 1990 through 2004 between the full 3-D IBM and a point (0-D) model that used spatially-averaged environmental conditions. The 3-D and 0-D versions generated similar interannual patterns in monthly recruitment for 1991-2004, with the 3-D results yielding consistently higher survivorship. Both versions successfully captured the very poor recruitment during the 1997-1998 El Niño event. Higher recruitment in the 3-D simulations was due to higher survival during the larval stage resulting from individuals searching for more favorable temperatures that lead to faster growth rates. The strong effect of temperature was because both model versions provided saturating food conditions for larval and juvenile anchovies. We conclude with a discussion of how explicit treatment of spatial variation affected simulated recruitment, other examples of fisheries modeling analyses that have used a similar approach to assess the influence of spatial variation, and areas for further model development.

Validation of the Monte Carlo simulator GATE for indium-111 imaging.

PubMed

Assié, K; Gardin, I; Véra, P; Buvat, I

2005-07-07

Monte Carlo simulations are useful for optimizing and assessing single photon emission computed tomography (SPECT) protocols, especially when aiming at measuring quantitative parameters from SPECT images. Before Monte Carlo simulated data can be trusted, the simulation model must be validated. The purpose of this work was to validate the use of GATE, a new Monte Carlo simulation platform based on GEANT4, for modelling indium-111 SPECT data, the quantification of which is of foremost importance for dosimetric studies. To that end, acquisitions of (111)In line sources in air and in water and of a cylindrical phantom were performed, together with the corresponding simulations. The simulation model included Monte Carlo modelling of the camera collimator and of a back-compartment accounting for photomultiplier tubes and associated electronics. Energy spectra, spatial resolution, sensitivity values, images and count profiles obtained for experimental and simulated data were compared. An excellent agreement was found between experimental and simulated energy spectra. For source-to-collimator distances varying from 0 to 20 cm, simulated and experimental spatial resolution differed by less than 2% in air, while the simulated sensitivity values were within 4% of the experimental values. The simulation of the cylindrical phantom closely reproduced the experimental data. These results suggest that GATE enables accurate simulation of (111)In SPECT acquisitions.

Sensitivity of Hydrologic Extremes to Spatial Resolution of Meteorological Forcings: A Case Study of the Conterminous United States

NASA Astrophysics Data System (ADS)

Kao, S. C.; Naz, B. S.; Gangrade, S.; Ashfaq, M.; Rastogi, D.

2016-12-01

The magnitude and frequency of hydroclimate extremes are projected to increase in the conterminous United States (CONUS) with significant implications for future water resource planning and flood risk management. Nevertheless, apart from the change of natural environment, the choice of model spatial resolution could also artificially influence the features of simulated extremes. To better understand how the spatial resolution of meteorological forcings may affect hydroclimate projections, we test the runoff sensitivity using the Variable Infiltration Capacity (VIC) model that was calibrated for each CONUS 8-digit hydrologic unit (HUC8) at 1/24° ( 4km) grid resolution. The 1980-2012 gridded Daymet and PRISM meteorological observations are used to conduct the 1/24° resolution control simulation. Comparative simulations are achieved by smoothing the 1/24° forcing into 1/12° and 1/8° resolutions which are then used to drive the VIC model for the CONUS. In addition, we also test how the simulated high and low runoff conditions would react to change in precipitation (±10%) and temperature (+1°C). The results are further analyzed for various types of hydroclimate extremes across different watersheds in the CONUS. This work helps us understand the sensitivity of simulated runoff to different spatial resolutions of climate forcings and also its sensitivity to different watershed sizes and characteristics of extreme events in the future climate conditions.

APPLICATION OF THE HSPF MODEL TO THE SOUTH FORK OF THE BROAD RIVER WATERSHED IN NORTHEASTERN GEORGIA

EPA Science Inventory

The Hydrological Simulation Program-Fortran (HSPF) is a comprehensive watershed model which simulates hydrology and water quality at user-specified temporal and spatial scales. Well-established model calibration and validation procedures are followed when adjusting model paramete...

Challenges and needs in fire management: A landscape simulation modeling perspective [chapter 4

Treesearch

Robert E. Keane; Geoffrey J. Cary; Mike D. Flannigan

2011-01-01

Fire management will face many challenges in the future from global climate change to protecting people, communities, and values at risk. Simulation modeling will be a vital tool for addressing these challenges but the next generation of simulation models must be spatially explicit to address critical landscape ecology relationships and they must use mechanistic...

CHARACTERIZING SPATIAL AND TEMPORAL DYNAMICS: DEVELOPMENT OF A GRID-BASED WATERSHED MERCURY LOADING MODEL

EPA Science Inventory

A distributed grid-based watershed mercury loading model has been developed to characterize spatial and temporal dynamics of mercury from both point and non-point sources. The model simulates flow, sediment transport, and mercury dynamics on a daily time step across a diverse lan...

Findings and Challenges in Fine-Resolution Large-Scale Hydrological Modeling

NASA Astrophysics Data System (ADS)

Her, Y. G.

2017-12-01

Fine-resolution large-scale (FL) modeling can provide the overall picture of the hydrological cycle and transport while taking into account unique local conditions in the simulation. It can also help develop water resources management plans consistent across spatial scales by describing the spatial consequences of decisions and hydrological events extensively. FL modeling is expected to be common in the near future as global-scale remotely sensed data are emerging, and computing resources have been advanced rapidly. There are several spatially distributed models available for hydrological analyses. Some of them rely on numerical methods such as finite difference/element methods (FDM/FEM), which require excessive computing resources (implicit scheme) to manipulate large matrices or small simulation time intervals (explicit scheme) to maintain the stability of the solution, to describe two-dimensional overland processes. Others make unrealistic assumptions such as constant overland flow velocity to reduce the computational loads of the simulation. Thus, simulation efficiency often comes at the expense of precision and reliability in FL modeling. Here, we introduce a new FL continuous hydrological model and its application to four watersheds in different landscapes and sizes from 3.5 km2 to 2,800 km2 at the spatial resolution of 30 m on an hourly basis. The model provided acceptable accuracy statistics in reproducing hydrological observations made in the watersheds. The modeling outputs including the maps of simulated travel time, runoff depth, soil water content, and groundwater recharge, were animated, visualizing the dynamics of hydrological processes occurring in the watersheds during and between storm events. Findings and challenges were discussed in the context of modeling efficiency, accuracy, and reproducibility, which we found can be improved by employing advanced computing techniques and hydrological understandings, by using remotely sensed hydrological observations such as soil moisture and radar rainfall depth and by sharing the model and its codes in public domain, respectively.

SST Variation Due to Interactive Convective-Radiative Processes

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Shie, C.-L.; Johnson, D.; Simpson, J.; Li, X.; Sui, C.-H.

2000-01-01

The recent linking of Cloud-Resolving Models (CRMs) to Ocean-Mixed Layer (OML) models has provided a powerful new means of quantifying the role of cloud systems in ocean-atmosphere coupling. This is due to the fact that the CRM can better resolve clouds and cloud systems and allow for explicit cloud-radiation interaction. For example, Anderson (1997) applied an atmospheric forcing associated with a CRM simulated squall line to a 3-D OML model (one way or passive interaction). His results suggested that the spatial variability resulting from the squall forcing can last at least 24 hours when forced with otherwise spatially uniform fluxes. In addition, the sea surface salinity (SSS) variability continuously decreased following the forcing, while some of the SST variability remained when a diurnal mixed layer capped off the surface structure. The forcing used in the OML model, however, focused on shorter time (8 h) and smaller spatial scales (100-120 km). In this study, the 3-D Goddard Cumulus Ensemble Model (GCE; 512 x 512 x 23 cu km, 2-km horizontal resolution) is used to simulate convective active episodes occurring in the Western Pacific warm pool and Eastern Atlantic regions. The model is integrated for seven days, and the simulated results are coupled to an OML model to better understand the impact of precipitation and changes in the planetary boundary layer upon SST variation. We will specifically examine and compare the results of linking the OML model with various spatially-averaged outputs from GCE simulations (i.e., 2 km vs. 10-50 km horizontal resolutions), in order to help understand the SST sensitivity to multi-scale influences. This will allow us to assess the importance of explicitly simulated deep and shallow clouds, as well as the subgrid-scale effects (in coarse-model runs) upon SST variation. Results using both 1-D and 2-D OML models will be evaluated to assess the effects of horizontal advection.

Comparisons of Anvil Cirrus Spatial Characteristics between Airborne Observations in DC3 Campaign and WRF Simulations

NASA Astrophysics Data System (ADS)

D'Alessandro, J.; Diao, M.; Chen, M.

2015-12-01

John D'Alessandro1, Minghui Diao1, Ming Chen2, George Bryan2, Hugh Morrison21. Department of Meteorology and Climate Science, San Jose State University2. Mesoscale & Microscale Meteorology Division, National Center for Atmospheric Research, Boulder, CO, 80301 Ice crystal formation requires the prerequisite condition of ice supersaturation, i.e., relative humidity with respect to ice (RHi) greater than 100%. The formation and evolution of ice supersaturated regions (ISSRs) has large impact on the subsequent formation of ice clouds. To examine the characteristics of simulated ice supersaturated regions at various model spatial resolutions, case studies between airborne in-situ measurements in the NSF Deep Convective, Clouds and Chemistry (DC3) campaign (May - June 2012) and WRF simulations are conducted in this work. Recent studies using ~200 m in-situ observations showed that ice supersaturated regions are mostly around 1 km in horizontal scale (Diao et al. 2014). Yet it is still unclear if such observed characteristics can be represented by WRF simulations at various spatial resolutions. In this work, we compare the WRF simulated anvil cirrus spatial characteristics with those observed in the DC3 campaign over the southern great plains in US. The WRF model is run at 1 km and 3 km horizontal grid spacing with a recent update of Thompson microphysics scheme. Our comparisons focus on the spatial characteristics of ISSRs and cirrus clouds, including the distributions of their horizontal scales, the maximum relative humidity with respect to ice (RHi) and the relationship between RHi and temperature. Our previous work on the NCAR CM1 cloud-resolving model shows that the higher resolution runs (i.e., 250m and 1km) generally have better agreement with observations than the coarser resolution (4km) runs. We will examine if similar trend exists for WRF simulations in deep convection cases. In addition, we will compare the simulation results between WRF and CM1, particularly for spatial correlations between ISSRs and cirrus and their evolution (based on the method of Diao et al. 2013). Overall, our work will help to assess the representation of ISSRs and cirrus in WRF simulation based on comparisons with in-situ observations.

Application of dynamic Monte Carlo technique in proton beam radiotherapy using Geant4 simulation toolkit

NASA Astrophysics Data System (ADS)

Guan, Fada

Monte Carlo method has been successfully applied in simulating the particles transport problems. Most of the Monte Carlo simulation tools are static and they can only be used to perform the static simulations for the problems with fixed physics and geometry settings. Proton therapy is a dynamic treatment technique in the clinical application. In this research, we developed a method to perform the dynamic Monte Carlo simulation of proton therapy using Geant4 simulation toolkit. A passive-scattering treatment nozzle equipped with a rotating range modulation wheel was modeled in this research. One important application of the Monte Carlo simulation is to predict the spatial dose distribution in the target geometry. For simplification, a mathematical model of a human body is usually used as the target, but only the average dose over the whole organ or tissue can be obtained rather than the accurate spatial dose distribution. In this research, we developed a method using MATLAB to convert the medical images of a patient from CT scanning into the patient voxel geometry. Hence, if the patient voxel geometry is used as the target in the Monte Carlo simulation, the accurate spatial dose distribution in the target can be obtained. A data analysis tool---root was used to score the simulation results during a Geant4 simulation and to analyze the data and plot results after simulation. Finally, we successfully obtained the accurate spatial dose distribution in part of a human body after treating a patient with prostate cancer using proton therapy.

Calibration of a distributed hydrologic model for six European catchments using remote sensing data

NASA Astrophysics Data System (ADS)

Stisen, S.; Demirel, M. C.; Mendiguren González, G.; Kumar, R.; Rakovec, O.; Samaniego, L. E.

2017-12-01

While observed streamflow has been the single reference for most conventional hydrologic model calibration exercises, the availability of spatially distributed remote sensing observations provide new possibilities for multi-variable calibration assessing both spatial and temporal variability of different hydrologic processes. In this study, we first identify the key transfer parameters of the mesoscale Hydrologic Model (mHM) controlling both the discharge and the spatial distribution of actual evapotranspiration (AET) across six central European catchments (Elbe, Main, Meuse, Moselle, Neckar and Vienne). These catchments are selected based on their limited topographical and climatic variability which enables to evaluate the effect of spatial parameterization on the simulated evapotranspiration patterns. We develop a European scale remote sensing based actual evapotranspiration dataset at a 1 km grid scale driven primarily by land surface temperature observations from MODIS using the TSEB approach. Using the observed AET maps we analyze the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mHM model. This model allows calibrating one-basin-at-a-time or all-basins-together using its unique structure and multi-parameter regionalization approach. Results will indicate any tradeoffs between spatial pattern and discharge simulation during model calibration and through validation against independent internal discharge locations. Moreover, added value on internal water balances will be analyzed.

Development and deployment of a water-crop-nutrient simulation model embedded in a web application

NASA Astrophysics Data System (ADS)

Langella, Giuliano; Basile, Angelo; Coppola, Antonio; Manna, Piero; Orefice, Nadia; Terribile, Fabio

2016-04-01

It is long time by now that scientific research on environmental and agricultural issues spent large effort in the development and application of models for prediction and simulation in spatial and temporal domains. This is fulfilled by studying and observing natural processes (e.g. rainfall, water and chemicals transport in soils, crop growth) whose spatiotemporal behavior can be reproduced for instance to predict irrigation and fertilizer requirements and yield quantities/qualities. In this work a mechanistic model to simulate water flow and solute transport in the soil-plant-atmosphere continuum is presented. This desktop computer program was written according to the specific requirement of developing web applications. The model is capable to solve the following issues all together: (a) water balance and (b) solute transport; (c) crop modelling; (d) GIS-interoperability; (e) embedability in web-based geospatial Decision Support Systems (DSS); (f) adaptability at different scales of application; and (g) ease of code modification. We maintained the desktop characteristic in order to further develop (e.g. integrate novel features) and run the key program modules for testing and validation purporses, but we also developed a middleware component to allow the model run the simulations directly over the web, without software to be installed. The GIS capabilities allows the web application to make simulations in a user-defined region of interest (delimited over a geographical map) without the need to specify the proper combination of model parameters. It is possible since the geospatial database collects information on pedology, climate, crop parameters and soil hydraulic characteristics. Pedological attributes include the spatial distribution of key soil data such as soil profile horizons and texture. Further, hydrological parameters are selected according to the knowledge about the spatial distribution of soils. The availability and definition in the geospatial domain of these attributes allow the simulation outputs at a different spatial scale. Two different applications were implemented using the same framework but with different configurations of the software pieces making the physically based modelling chain: an irrigation tool simulating water requirements and their dates and a fertilization tool for optimizing in particular mineral nitrogen adds.

A DYNAMIC MODEL OF AN ESTUARINE INVASION BY A NON-NATIVE SEAGRASS

EPA Science Inventory

Mathematical and simulation models provide an excellent tool for examining and predicting biological invasions in time and space; however, traditional models do not incorporate dynamic rates of population growth, which limits their realism. We developed a spatially explicit simul...

Assessing accuracy of point fire intervals across landscapes with simulation modelling

Treesearch

Russell A. Parsons; Emily K. Heyerdahl; Robert E. Keane; Brigitte Dorner; Joseph Fall

2007-01-01

We assessed accuracy in point fire intervals using a simulation model that sampled four spatially explicit simulated fire histories. These histories varied in fire frequency and size and were simulated on a flat landscape with two forest types (dry versus mesic). We used three sampling designs (random, systematic grids, and stratified). We assessed the sensitivity of...

Spatial patterns and biodiversity in off-lattice simulations of a cyclic three-species Lotka-Volterra model

NASA Astrophysics Data System (ADS)

Avelino, P. P.; Bazeia, D.; Losano, L.; Menezes, J.; de Oliveira, B. F.

2018-02-01

Stochastic simulations of cyclic three-species spatial predator-prey models are usually performed in square lattices with nearest-neighbour interactions starting from random initial conditions. In this letter we describe the results of off-lattice Lotka-Volterra stochastic simulations, showing that the emergence of spiral patterns does occur for sufficiently high values of the (conserved) total density of individuals. We also investigate the dynamics in our simulations, finding an empirical relation characterizing the dependence of the characteristic peak frequency and amplitude on the total density. Finally, we study the impact of the total density on the extinction probability, showing how a low population density may jeopardize biodiversity.

Time-Domain Filtering for Spatial Large-Eddy Simulation

NASA Technical Reports Server (NTRS)

Pruett, C. David

1997-01-01

An approach to large-eddy simulation (LES) is developed whose subgrid-scale model incorporates filtering in the time domain, in contrast to conventional approaches, which exploit spatial filtering. The method is demonstrated in the simulation of a heated, compressible, axisymmetric jet, and results are compared with those obtained from fully resolved direct numerical simulation. The present approach was, in fact, motivated by the jet-flow problem and the desire to manipulate the flow by localized (point) sources for the purposes of noise suppression. Time-domain filtering appears to be more consistent with the modeling of point sources; moreover, time-domain filtering may resolve some fundamental inconsistencies associated with conventional space-filtered LES approaches.

A spatial model of wind shear and turbulence for flight simulation. Ph.D. Thesis - Colorado State Univ.

NASA Technical Reports Server (NTRS)

Campbell, C. W.

1984-01-01

A three dimensional model which combines measurements of wind shear in the real atmosphere with three dimensional Monte Carlo simulated turbulence was developed. The wind field over the body of an aircraft can be simulated and all aerodynamic loads and moments calculated.

Modeling molecular mixing in a spatially inhomogeneous turbulent flow

NASA Astrophysics Data System (ADS)

Meyer, Daniel W.; Deb, Rajdeep

2012-02-01

Simulations of spatially inhomogeneous turbulent mixing in decaying grid turbulence with a joint velocity-concentration probability density function (PDF) method were conducted. The inert mixing scenario involves three streams with different compositions. The mixing model of Meyer ["A new particle interaction mixing model for turbulent dispersion and turbulent reactive flows," Phys. Fluids 22(3), 035103 (2010)], the interaction by exchange with the mean (IEM) model and its velocity-conditional variant, i.e., the IECM model, were applied. For reference, the direct numerical simulation data provided by Sawford and de Bruyn Kops ["Direct numerical simulation and lagrangian modeling of joint scalar statistics in ternary mixing," Phys. Fluids 20(9), 095106 (2008)] was used. It was found that velocity conditioning is essential to obtain accurate concentration PDF predictions. Moreover, the model of Meyer provides significantly better results compared to the IECM model at comparable computational expense.

Projected future vegetation changes for the northwest United States and southwest Canada at a fine spatial resolution using a dynamic global vegetation model.

USGS Publications Warehouse

Shafer, Sarah; Bartlein, Patrick J.; Gray, Elizabeth M.; Pelltier, Richard T.

2015-01-01

Future climate change may significantly alter the distributions of many plant taxa. The effects of climate change may be particularly large in mountainous regions where climate can vary significantly with elevation. Understanding potential future vegetation changes in these regions requires methods that can resolve vegetation responses to climate change at fine spatial resolutions. We used LPJ, a dynamic global vegetation model, to assess potential future vegetation changes for a large topographically complex area of the northwest United States and southwest Canada (38.0–58.0°N latitude by 136.6–103.0°W longitude). LPJ is a process-based vegetation model that mechanistically simulates the effect of changing climate and atmospheric CO2 concentrations on vegetation. It was developed and has been mostly applied at spatial resolutions of 10-minutes or coarser. In this study, we used LPJ at a 30-second (~1-km) spatial resolution to simulate potential vegetation changes for 2070–2099. LPJ was run using downscaled future climate simulations from five coupled atmosphere-ocean general circulation models (CCSM3, CGCM3.1(T47), GISS-ER, MIROC3.2(medres), UKMO-HadCM3) produced using the A2 greenhouse gases emissions scenario. Under projected future climate and atmospheric CO2 concentrations, the simulated vegetation changes result in the contraction of alpine, shrub-steppe, and xeric shrub vegetation across the study area and the expansion of woodland and forest vegetation. Large areas of maritime cool forest and cold forest are simulated to persist under projected future conditions. The fine spatial-scale vegetation simulations resolve patterns of vegetation change that are not visible at coarser resolutions and these fine-scale patterns are particularly important for understanding potential future vegetation changes in topographically complex areas.

Projected Future Vegetation Changes for the Northwest United States and Southwest Canada at a Fine Spatial Resolution Using a Dynamic Global Vegetation Model

PubMed Central

Shafer, Sarah L.; Bartlein, Patrick J.; Gray, Elizabeth M.; Pelltier, Richard T.

2015-01-01

Future climate change may significantly alter the distributions of many plant taxa. The effects of climate change may be particularly large in mountainous regions where climate can vary significantly with elevation. Understanding potential future vegetation changes in these regions requires methods that can resolve vegetation responses to climate change at fine spatial resolutions. We used LPJ, a dynamic global vegetation model, to assess potential future vegetation changes for a large topographically complex area of the northwest United States and southwest Canada (38.0–58.0°N latitude by 136.6–103.0°W longitude). LPJ is a process-based vegetation model that mechanistically simulates the effect of changing climate and atmospheric CO2 concentrations on vegetation. It was developed and has been mostly applied at spatial resolutions of 10-minutes or coarser. In this study, we used LPJ at a 30-second (~1-km) spatial resolution to simulate potential vegetation changes for 2070–2099. LPJ was run using downscaled future climate simulations from five coupled atmosphere-ocean general circulation models (CCSM3, CGCM3.1(T47), GISS-ER, MIROC3.2(medres), UKMO-HadCM3) produced using the A2 greenhouse gases emissions scenario. Under projected future climate and atmospheric CO2 concentrations, the simulated vegetation changes result in the contraction of alpine, shrub-steppe, and xeric shrub vegetation across the study area and the expansion of woodland and forest vegetation. Large areas of maritime cool forest and cold forest are simulated to persist under projected future conditions. The fine spatial-scale vegetation simulations resolve patterns of vegetation change that are not visible at coarser resolutions and these fine-scale patterns are particularly important for understanding potential future vegetation changes in topographically complex areas. PMID:26488750

Evaluation of modeling NO2 concentrations driven by satellite-derived and bottom-up emission inventories using in situ measurements over China

NASA Astrophysics Data System (ADS)

Liu, Fei; van der A, Ronald J.; Eskes, Henk; Ding, Jieying; Mijling, Bas

2018-03-01

Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modeling surface NO2 concentrations from the CHIMERE regional chemical transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modeled ratio of NO2 to NOz. The model accurately reproduces the spatial variability in NO2 from in situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slope = 0.74 and 0.64 for the daily mean and daytime only) and the MIX (slope = 1.3 and 1.1) inventories, respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modeled concentrations is reduced, with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban or rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10-40 % higher than the corresponding model grid cell mean. This reduces the estimate of the negative bias of the DECSO-based simulation to the range of -30 to 0 % on average and more firmly establishes that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle but shows more significant disagreement between simulations and measurements during nighttime, which likely produces a positive model bias of about 15 % in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

Evaluation of Modeling NO2 Concentrations Driven by Satellite-Derived and Bottom-Up Emission Inventories Using In-Situ Measurements Over China

NASA Technical Reports Server (NTRS)

Liu, Fei; van der A, Ronald J.; Eskes, Henk; Ding, Jieying; Mijling, Bas

2018-01-01

Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modeling surface NO2 concentrations from the CHIMERE regional chemical transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modeled ratio of NO2 to NOz. The model accurately reproduces the spatial variability in NO2 from in situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slopeD0.74 and 0.64 for the daily mean and daytime only) and the MIX (slopeD1.3 and 1.1) inventories, respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modeled concentrations is reduced, with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban or rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10-40% higher than the corresponding model grid cell mean. This reduces the estimate of the negative bias of the DECSO-based simulation to the range of -30 to 0% on average and more firmly establishes that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle but shows more significant disagreement between simulations and measurements during nighttime, which likely produces a positive model bias of about 15% in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

Reserch on Urban Spatial Expansion Model Based on Multi-Object Gray Decision-Making and Ca: a Case Study of Pidu District, Chengdu City

NASA Astrophysics Data System (ADS)

Liu, Z.; Li, Y.

2018-04-01

This paper from the perspective of the Neighbor cellular space, Proposed a new urban space expansion model based on a new multi-objective gray decision and CA. The model solved the traditional cellular automata conversion rules is difficult to meet the needs of the inner space-time analysis of urban changes and to overcome the problem of uncertainty in the combination of urban drivers and urban cellular automata. At the same time, the study takes Pidu District as a research area and carries out urban spatial simulation prediction and analysis, and draws the following conclusions: (1) The design idea of the urban spatial expansion model proposed in this paper is that the urban driving factor and the neighborhood function are tightly coupled by the multi-objective grey decision method based on geographical conditions. The simulation results show that the simulation error of urban spatial expansion is less than 5.27 %. The Kappa coefficient is 0.84. It shows that the model can better capture the inner transformation mechanism of the city. (2) We made a simulation prediction for Pidu District of Chengdu by discussing Pidu District of Chengdu as a system instance.In this way, we analyzed the urban growth tendency of this area.presenting a contiguous increasing mode, which is called "urban intensive development". This expansion mode accorded with sustainable development theory and the ecological urbanization design theory.

LPJ-GUESS Simulated North America Vegetation for 21-0 ka Using the TraCE-21ka Climate Simulation

NASA Astrophysics Data System (ADS)

Shafer, S. L.; Bartlein, P. J.

2016-12-01

Transient climate simulations that span multiple millennia (e.g., TraCE-21ka) have become more common as computing power has increased, allowing climate models to complete long simulations in relatively short periods of time (i.e., months). These climate simulations provide information on the potential rate, variability, and spatial expression of past climate changes. They also can be used as input data for other environmental models to simulate transient changes for different components of paleoenvironmental systems, such as vegetation. Long, transient paleovegetation simulations can provide information on a range of ecological processes, describe the spatial and temporal patterns of changes in species distributions, and identify the potential locations of past species refugia. Paleovegetation simulations also can be used to fill in spatial and temporal gaps in observed paleovegetation data (e.g., pollen records from lake sediments) and to test hypotheses of past vegetation change. We used the TraCE-21ka transient climate simulation for 21-0 ka from CCSM3, a coupled atmosphere-ocean general circulation model. The TraCE-21ka simulated temperature, precipitation, and cloud data were regridded onto a 10-minute grid of North America. These regridded climate data, along with soil data and atmospheric carbon dioxide concentrations, were used as input to LPJ-GUESS, a general ecosystem model, to simulate North America vegetation from 21-0 ka. LPJ-GUESS simulates many of the processes controlling the distribution of vegetation (e.g., competition), although some important processes (e.g., dispersal) are not simulated. We evaluate the LPJ-GUESS-simulated vegetation (in the form of plant functional types and biomes) for key time periods and compare the simulated vegetation with observed paleovegetation data, such as data archived in the Neotoma Paleoecology Database. In general, vegetation simulated by LPJ-GUESS reproduces the major North America vegetation patterns (e.g., forest, grassland) with regional areas of disagreement between simulated and observed vegetation. We describe the regions and time periods with the greatest data-model agreement and disagreement, and discuss some of the strengths and weaknesses of both the simulated climate and simulated vegetation data.

Simulation of spatial and temporal properties of aftershocks by means of the fiber bundle model

NASA Astrophysics Data System (ADS)

Monterrubio-Velasco, Marisol; Zúñiga, F. R.; Márquez-Ramírez, Victor Hugo; Figueroa-Soto, Angel

2017-11-01

The rupture processes of any heterogeneous material constitute a complex physical problem. Earthquake aftershocks show temporal and spatial behaviors which are consequence of the heterogeneous stress distribution and multiple rupturing following the main shock. This process is difficult to model deterministically due to the number of parameters and physical conditions, which are largely unknown. In order to shed light on the minimum requirements for the generation of aftershock clusters, in this study, we perform a simulation of the main features of such a complex process by means of a fiber bundle (FB) type model. The FB model has been widely used to analyze the fracture process in heterogeneous materials. It is a simple but powerful tool that allows modeling the main characteristics of a medium such as the brittle shallow crust of the earth. In this work, we incorporate spatial properties, such as the Coulomb stress change pattern, which help simulate observed characteristics of aftershock sequences. In particular, we introduce a parameter ( P) that controls the probability of spatial distribution of initial loads. Also, we use a "conservation" parameter ( π), which accounts for the load dissipation of the system, and demonstrate its influence on the simulated spatio-temporal patterns. Based on numerical results, we find that P has to be in the range 0.06 < P < 0.30, whilst π needs to be limited by a very narrow range ( 0.60 < π < 0.66) in order to reproduce aftershocks pattern characteristics which resemble those of observed sequences. This means that the system requires a small difference in the spatial distribution of initial stress, and a very particular fraction of load transfer in order to generate realistic aftershocks.

Modeling spatial-temporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Z.; Zimmermann, N. E.; Poulter, B.

2015-11-01

Simulations of the spatial-temporal dynamics of wetlands are key to understanding the role of wetland biogeochemistry under past and future climate variability. Hydrologic inundation models, such as TOPMODEL, are based on a fundamental parameter known as the compound topographic index (CTI) and provide a computationally cost-efficient approach to simulate wetland dynamics at global scales. However, there remains large discrepancy in the implementations of TOPMODEL in land-surface models (LSMs) and thus their performance against observations. This study describes new improvements to TOPMODEL implementation and estimates of global wetland dynamics using the LPJ-wsl dynamic global vegetation model (DGVM), and quantifies uncertainties by comparing three digital elevation model products (HYDRO1k, GMTED, and HydroSHEDS) at different spatial resolution and accuracy on simulated inundation dynamics. In addition, we found that calibrating TOPMODEL with a benchmark wetland dataset can help to successfully delineate the seasonal and interannual variations of wetlands, as well as improve the spatial distribution of wetlands to be consistent with inventories. The HydroSHEDS DEM, using a river-basin scheme for aggregating the CTI, shows best accuracy for capturing the spatio-temporal dynamics of wetlands among the three DEM products. The estimate of global wetland potential/maximum is ∼ 10.3 Mkm2 (106 km2), with a mean annual maximum of ∼ 5.17 Mkm2 for 1980-2010. This study demonstrates the feasibility to capture spatial heterogeneity of inundation and to estimate seasonal and interannual variations in wetland by coupling a hydrological module in LSMs with appropriate benchmark datasets. It additionally highlights the importance of an adequate investigation of topographic indices for simulating global wetlands and shows the opportunity to converge wetland estimates across LSMs by identifying the uncertainty associated with existing wetland products.

Spatial interpolation schemes of daily precipitation for hydrologic modeling

USGS Publications Warehouse

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

2012-01-01

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

Modeling spatial-temporal dynamics of global wetlands: Comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Z.; Zimmermann, N. E.; Poulter, B.

2015-12-01

Simulations of the spatial-temporal dynamics of wetlands is key to understanding the role of wetland biogeochemistry under past and future climate variability. Hydrologic inundation models, such as TOPMODEL, are based on a fundamental parameter known as the compound topographic index (CTI) and provide a computationally cost-efficient approach to simulate global wetland dynamics. However, there remains large discrepancy in the implementations of TOPMODEL in land-surface models (LSMs) and thus their performance against observations. This study describes new improvements to TOPMODEL implementation and estimates of global wetland dynamics using the LPJ-wsl DGVM, and quantifies uncertainties by comparing three digital elevation model products (HYDRO1k, GMTED, and HydroSHEDS) at different spatial resolution and accuracy on simulated inundation dynamics. We found that calibrating TOPMODEL with a benchmark dataset can help to successfully predict the seasonal and interannual variations of wetlands, as well as improve the spatial distribution of wetlands to be consistent with inventories. The HydroSHEDS DEM, using a river-basin scheme for aggregating the CTI, shows best accuracy for capturing the spatio-temporal dynamics of wetland among three DEM products. This study demonstrates the feasibility to capture spatial heterogeneity of inundation and to estimate seasonal and interannual variations in wetland by coupling a hydrological module in LSMs with appropriate benchmark datasets. It additionally highlight the importance of an adequate understanding of topographic indices for simulating global wetlands and show the opportunity to converge wetland estimations in LSMs by identifying the uncertainty associated with existing wetland products.

Spatial and temporal variability in the R-5 infiltration data set: Déjà vu and rainfall-runoff simulations

NASA Astrophysics Data System (ADS)

Loague, Keith; Kyriakidis, Phaedon C.

1997-12-01

This paper is a continuation of the event-based rainfall-runoff model evaluation study reported by Loague and Freeze [1985[. Here we reevaluate the performance of a quasi-physically based rainfall-runoff model for three large events from the well-known R-5 catchment. Five different statistical criteria are used to quantitatively judge model performance. Temporal variability in the large R-5 infiltration data set [Loague and Gander, 1990] is filtered by working in terms of permeability. The transformed data set is reanalyzed via geostatistical methods to model the spatial distribution of permeability across the R-5 catchment. We present new estimates of the spatial distribution of infiltration that are in turn used in our rainfall-runoff simulations with the Horton rainfall-runoff model. The new rainfall-runoff simulations, complicated by reinfiltration impacts at the smaller scales of characterization, indicate that the near-surface hydrologic response of the R-5 catchment is most probably dominated by a combination of the Horton and Dunne overland flow mechanisms.

On validating remote sensing simulations using coincident real data

NASA Astrophysics Data System (ADS)

Wang, Mingming; Yao, Wei; Brown, Scott; Goodenough, Adam; van Aardt, Jan

2016-05-01

The remote sensing community often requires data simulation, either via spectral/spatial downsampling or through virtual, physics-based models, to assess systems and algorithms. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is one such first-principles, physics-based model for simulating imagery for a range of modalities. Complex simulation of vegetation environments subsequently has become possible, as scene rendering technology and software advanced. This in turn has created questions related to the validity of such complex models, with potential multiple scattering, bidirectional distribution function (BRDF), etc. phenomena that could impact results in the case of complex vegetation scenes. We selected three sites, located in the Pacific Southwest domain (Fresno, CA) of the National Ecological Observatory Network (NEON). These sites represent oak savanna, hardwood forests, and conifer-manzanita-mixed forests. We constructed corresponding virtual scenes, using airborne LiDAR and imaging spectroscopy data from NEON, ground-based LiDAR data, and field-collected spectra to characterize the scenes. Imaging spectroscopy data for these virtual sites then were generated using the DIRSIG simulation environment. This simulated imagery was compared to real AVIRIS imagery (15m spatial resolution; 12 pixels/scene) and NEON Airborne Observation Platform (AOP) data (1m spatial resolution; 180 pixels/scene). These tests were performed using a distribution-comparison approach for select spectral statistics, e.g., established the spectra's shape, for each simulated versus real distribution pair. The initial comparison results of the spectral distributions indicated that the shapes of spectra between the virtual and real sites were closely matched.

Development of flood routing simulation system of digital Qingjiang based on integrated spatial information technology

NASA Astrophysics Data System (ADS)

Yuan, Yanbin; Zhou, You; Zhu, Yaqiong; Yuan, Xiaohui; Sælthun, N. R.

2007-11-01

Based on digital technology, flood routing simulation system development is an important component of "digital catchment". Taking QingJiang catchment as a pilot case, in-depth analysis on informatization of Qingjiang catchment management being the basis, aiming at catchment data's multi-source, - dimension, -element, -subject, -layer and -class feature, the study brings the design thought and method of "subject-point-source database" (SPSD) to design system structure in order to realize the unified management of catchments data in great quantity. Using the thought of integrated spatial information technology for reference, integrating hierarchical structure development model of digital catchment is established. The model is general framework of the flood routing simulation system analysis, design and realization. In order to satisfy the demands of flood routing three-dimensional simulation system, the object-oriented spatial data model are designed. We can analyze space-time self-adapting relation between flood routing and catchments topography, express grid data of terrain by using non-directed graph, apply breadth first search arithmetic, set up search method for the purpose of dynamically searching stream channel on the basis of simulated three-dimensional terrain. The system prototype is therefore realized. Simulation results have demonstrated that the proposed approach is feasible and effective in the application.

Evaluation of fine soil moisture data from the IFloodS (NASA GPM) Ground Validation campaign using a fully-distributed ecohydrological model

NASA Astrophysics Data System (ADS)

Bastola, S.; Dialynas, Y. G.; Arnone, E.; Bras, R. L.

2014-12-01

The spatial variability of soil, vegetation, topography, and precipitation controls hydrological processes, consequently resulting in high spatio-temporal variability of most of the hydrological variables, such as soil moisture. Limitation in existing measuring system to characterize this spatial variability, and its importance in various application have resulted in a need of reconciling spatially distributed soil moisture evolution model and corresponding measurements. Fully distributed ecohydrological model simulates soil moisture at high resolution soil moisture. This is relevant for range of environmental studies e.g., flood forecasting. They can also be used to evaluate the value of space born soil moisture data, by assimilating them into hydrological models. In this study, fine resolution soil moisture data simulated by a physically-based distributed hydrological model, tRIBS-VEGGIE, is compared with soil moisture data collected during the field campaign in Turkey river basin, Iowa. The soil moisture series at the 2 and 4 inch depth exhibited a more rapid response to rainfall as compared to bottom 8 and 20 inch ones. The spatial variability in two distinct land surfaces of Turkey River, IA, reflects the control of vegetation, topography and soil texture in the characterization of spatial variability. The comparison of observed and simulated soil moisture at various depth showed that model was able to capture the dynamics of soil moisture at a number of gauging stations. Discrepancies are large in some of the gauging stations, which are characterized by rugged terrain and represented, in the model, through large computational units.

MOAB: a spatially explicit, individual-based expert system for creating animal foraging models

USGS Publications Warehouse

Carter, J.; Finn, John T.

1999-01-01

We describe the development, structure, and corroboration process of a simulation model of animal behavior (MOAB). MOAB can create spatially explicit, individual-based animal foraging models. Users can create or replicate heterogeneous landscape patterns, and place resources and individual animals of a goven species on that landscape to simultaneously simulate the foraging behavior of multiple species. The heuristic rules for animal behavior are maintained in a user-modifiable expert system. MOAB can be used to explore hypotheses concerning the influence of landscape patttern on animal movement and foraging behavior. A red fox (Vulpes vulpes L.) foraging and nest predation model was created to test MOAB's capabilities. Foxes were simulated for 30-day periods using both expert system and random movement rules. Home range size, territory formation and other available simulation studies. A striped skunk (Mephitis mephitis L.) model also was developed. The expert system model proved superior to stochastic in respect to territory formation, general movement patterns and home range size.

A component-based, integrated spatially distributed hydrologic/water quality model: AgroEcoSystem-Watershed (AgES-W) overview and application

USDA-ARS?s Scientific Manuscript database

AgroEcoSystem-Watershed (AgES-W) is a modular, Java-based spatially distributed model which implements hydrologic/water quality simulation components. The AgES-W model was previously evaluated for streamflow and recently has been enhanced with the addition of nitrogen (N) and sediment modeling compo...

Advances in the spatially distributed ages-w model: parallel computation, java connection framework (JCF) integration, and streamflow/nitrogen dynamics assessment

USDA-ARS?s Scientific Manuscript database

AgroEcoSystem-Watershed (AgES-W) is a modular, Java-based spatially distributed model which implements hydrologic and water quality (H/WQ) simulation components under the Java Connection Framework (JCF) and the Object Modeling System (OMS) environmental modeling framework. AgES-W is implicitly scala...

The Spatially-Distributed Agroecosystem-Watershed (Ages-W) Hydrologic/Water Quality (H/WQ) model for assessment of conservation effects

USDA-ARS?s Scientific Manuscript database

AgroEcoSystem-Watershed (AgES-W) is a modular, Java-based spatially distributed model which implements hydrologic/water quality (H/WQ) simulation components under the Object Modeling System (OMS3) environmental modeling framework. AgES-W has recently been enhanced with the addition of nitrogen (N) a...

Synchronization of autonomous objects in discrete event simulation

NASA Technical Reports Server (NTRS)

Rogers, Ralph V.

1990-01-01

Autonomous objects in event-driven discrete event simulation offer the potential to combine the freedom of unrestricted movement and positional accuracy through Euclidean space of time-driven models with the computational efficiency of event-driven simulation. The principal challenge to autonomous object implementation is object synchronization. The concept of a spatial blackboard is offered as a potential methodology for synchronization. The issues facing implementation of a spatial blackboard are outlined and discussed.

The Effect of Rainfall Measurement Technique and Its Spatiotemporal Resolution on Discharge Predictions in the Netherlands

NASA Astrophysics Data System (ADS)

Uijlenhoet, R.; Brauer, C.; Overeem, A.; Sassi, M.; Rios Gaona, M. F.

2014-12-01

Several rainfall measurement techniques are available for hydrological applications, each with its own spatial and temporal resolution. We investigated the effect of these spatiotemporal resolutions on discharge simulations in lowland catchments by forcing a novel rainfall-runoff model (WALRUS) with rainfall data from gauges, radars and microwave links. The hydrological model used for this analysis is the recently developed Wageningen Lowland Runoff Simulator (WALRUS). WALRUS is a rainfall-runoff model accounting for hydrological processes relevant to areas with shallow groundwater (e.g. groundwater-surface water feedback). Here, we used WALRUS for case studies in a freely draining lowland catchment and a polder with controlled water levels. We used rain gauge networks with automatic (hourly resolution but low spatial density) and manual gauges (high spatial density but daily resolution). Operational (real-time) and climatological (gauge-adjusted) C-band radar products and country-wide rainfall maps derived from microwave link data from a cellular telecommunication network were also used. Discharges simulated with these different inputs were compared to observations. We also investigated the effect of spatiotemporal resolution with a high-resolution X-band radar data set for catchments with different sizes. Uncertainty in rainfall forcing is a major source of uncertainty in discharge predictions, both with lumped and with distributed models. For lumped rainfall-runoff models, the main source of input uncertainty is associated with the way in which (effective) catchment-average rainfall is estimated. When catchments are divided into sub-catchments, rainfall spatial variability can become more important, especially during convective rainfall events, leading to spatially varying catchment wetness and spatially varying contribution of quick flow routes. Improving rainfall measurements and their spatiotemporal resolution can improve the performance of rainfall-runoff models, indicating their potential for reducing flood damage through real-time control.

Simulation modeling of forest landscape disturbances: Where do we go from here?

Treesearch

Ajith H. Perera; Brian R. Sturtevant; Lisa J. Buse

2015-01-01

It was nearly a quarter-century ago when Turner and Gardner (1991) drew attention to methods of quantifying landscape patterns and processes, including simulation modeling. The many authors who contributed to that seminal text collectively signaled the emergence of a new field—spatially explicit simulation modeling of broad-scale ecosystem dynamics. Of particular note...

A Numerical Model to Assess Soil Fluxes from Meteoric 10Be Data

NASA Astrophysics Data System (ADS)

Campforts, B.; Govers, G.; Vanacker, V.; Vanderborght, J.; Smolders, E.; Baken, S.

2015-12-01

Meteoric 10Be may be mobile in the soil system. The latter hampers a direct translation of meteoric 10Be inventories into spatial variations in erosion and deposition rates. Here, we present a spatially explicit 2D model that allows us to simulate the behaviour of meteoric 10Be in the soil system. The Be2D model is then used to analyse the potential impact of human-accelerated soil fluxes on meteoric 10Be inventories. The model consists of two parts. A first component deals with advective and diffusive mobility of meteoric 10Be within the soil profile including particle migration, chemical leaching and bioturbation, whereas a second component describes lateral soil (and meteoric 10Be) fluxes over the hillslope. Soil depth is calculated dynamically, accounting for soil production through weathering and lateral soil fluxes from creep, water and tillage erosion. Model simulations show that meteoric 10Be inventories can indeed be related to erosion and deposition, across a wide range of geomorphological and pedological settings. However, quantification of the effects of vertical mobility is essential for a correct interpretation of the observed spatial patterns in 10Be data. Moreover, our simulations suggest that meteoric 10Be can be used as a tracer to unravel human impact on soil fluxes when soils have a high retention capacity for meteoric meteoric 10Be. Application of the Be2D model to existing data sets shows that model parameters can reliably be constrained, resulting in a good agreement between simulated and observed meteoric 10Be concentrations and inventories. This confirms the suitability of the Be2D model as a robust tool to underpin quantitative interpretations of spatial variability in meteoric 10Be data for eroding landscapes.

Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models

NASA Astrophysics Data System (ADS)

Candy, Adam S.; Pietrzak, Julie D.

2018-01-01

The approaches taken to describe and develop spatial discretisations of the domains required for geophysical simulation models are commonly ad hoc, model- or application-specific, and under-documented. This is particularly acute for simulation models that are flexible in their use of multi-scale, anisotropic, fully unstructured meshes where a relatively large number of heterogeneous parameters are required to constrain their full description. As a consequence, it can be difficult to reproduce simulations, to ensure a provenance in model data handling and initialisation, and a challenge to conduct model intercomparisons rigorously. This paper takes a novel approach to spatial discretisation, considering it much like a numerical simulation model problem of its own. It introduces a generalised, extensible, self-documenting approach to carefully describe, and necessarily fully, the constraints over the heterogeneous parameter space that determine how a domain is spatially discretised. This additionally provides a method to accurately record these constraints, using high-level natural language based abstractions that enable full accounts of provenance, sharing, and distribution. Together with this description, a generalised consistent approach to unstructured mesh generation for geophysical models is developed that is automated, robust and repeatable, quick-to-draft, rigorously verified, and consistent with the source data throughout. This interprets the description above to execute a self-consistent spatial discretisation process, which is automatically validated to expected discrete characteristics and metrics. Library code, verification tests, and examples available in the repository at https://github.com/shingleproject/Shingle. Further details of the project presented at http://shingleproject.org.

A simple stochastic rainstorm generator for simulating spatially and temporally varying rainfall

NASA Astrophysics Data System (ADS)

Singer, M. B.; Michaelides, K.; Nichols, M.; Nearing, M. A.

2016-12-01

In semi-arid to arid drainage basins, rainstorms often control both water supply and flood risk to marginal communities of people. They also govern the availability of water to vegetation and other ecological communities, as well as spatial patterns of sediment, nutrient, and contaminant transport and deposition on local to basin scales. All of these landscape responses are sensitive to changes in climate that are projected to occur throughout western North America. Thus, it is important to improve characterization of rainstorms in a manner that enables statistical assessment of rainfall at spatial scales below that of existing gauging networks and the prediction of plausible manifestations of climate change. Here we present a simple, stochastic rainstorm generator that was created using data from a rich and dense network of rain gauges at the Walnut Gulch Experimental Watershed (WGEW) in SE Arizona, but which is applicable anywhere. We describe our methods for assembling pdfs of relevant rainstorm characteristics including total annual rainfall, storm area, storm center location, and storm duration. We also generate five fitted intensity-duration curves and apply a spatial rainfall gradient to generate precipitation at spatial scales below gauge spacing. The model then runs by Monte Carlo simulation in which a total annual rainfall is selected before we generate rainstorms until the annual precipitation total is reached. The procedure continues for decadal simulations. Thus, we keep track of the hydrologic impact of individual storms and the integral of precipitation over multiple decades. We first test the model using ensemble predictions until we reach statistical similarity to the input data from WGEW. We then employ the model to assess decadal precipitation under simulations of climate change in which we separately vary the distribution of total annual rainfall (trend in moisture) and the intensity-duration curves used for simulation (trends in storminess). We demonstrate the model output through spatial maps of rainfall and through statistical comparisons of relevant parameters and distributions. Finally, discuss how the model can be used to understand basin-scale hydrology in terms of soil moisture, runoff, and erosion.

Understand the Air-Sea Coupling Processes in High Wind Conditions Using a Synthesized Data Analysis/modeling Approach

DTIC Science & Technology

2007-09-30

secondary gap outflow that appeared in COAMPS simulations ( Cherrett 2006). Figure 3d shows similar SST spatial variations as in Fig. 3c with slight... Cherrett , R. C. 2006: Observed and Simulated temporal and spatial variations of the gap outflow region, M.S. Thesis, Meteorology Department, Naval

Empirical methods for modeling landscape change, ecosystem services, and biodiversity

Treesearch

David Lewis; Ralph Alig

2009-01-01

The purpose of this paper is to synthesize recent economics research aimed at integrating discrete-choice econometric models of land-use change with spatially-explicit landscape simulations and quantitative ecology. This research explicitly models changes in the spatial pattern of landscapes in two steps: 1) econometric estimation of parcel-scale transition...

Linking Temporal-Optimization and Spatial-Simulation Models for Forest Planning

Treesearch

Larry A. Leefers; Eric J. Gustafson; Phillip Freeman

2003-01-01

Increasingly, resource management agencies and researchers have turned their analysis and modeling efforts towards spatial and temporal information. This is driven by the need to address wildlife concerns, landscape issues, and social/economic questions. Historically, the USDA Forest Service has used optimization models (i.e., FORPLAN and Spectrum) for timber harvest...

Increasingly, Data Availability Limits Model Predictive Capacity: the Western Lake Erie Basin, a Case Study

NASA Astrophysics Data System (ADS)

Behrman, K. D.; Johnson, M. V. V.; Atwood, J. D.; Norfleet, M. L.

2016-12-01

Recent algal blooms in Western Lake Erie Basin (WLEB) have renewed scientific community's interest in developing process based models to better understand and predict the drivers of eutrophic conditions in the lake. At the same time, in order to prevent future blooms, farmers, local communities and policy makers are interested in developing spatially explicit nutrient and sediment management plans at various scales, from field to watershed. These interests have fueled several modeling exercises intended to locate "hotspots" in the basin where targeted adoption of additional agricultural conservation practices could provide the most benefit to water quality. The models have also been used to simulate various scenarios representing potential agricultural solutions. The Soil and Water Assessment Tool (SWAT) and its sister model, the Agricultural Policy Environmental eXtender (APEX), have been used to simulate hydrology of interacting land uses in thousands of scientific studies around the world. High performance computing allows SWAT and APEX users to continue to improve and refine the model specificity to make predictions at small-spatial scales. Consequently, data inputs and calibration/validation data are now becoming the limiting factor to model performance. Water quality data for the tributaries and rivers that flow through WLEB is spatially and temporally limited. Land management data, including conservation practice and nutrient management data, are not publicly available at fine spatial and temporal scales. Here we show the data uncertainties associated with modeling WLEB croplands at a relatively large spatial scale (HUC-4) using site management data from over 1,000 farms collected by the Conservation Effects Assessment Project (CEAP). The error associated with downscaling this data to the HUC-8 and HUC-12 scale is shown. Simulations of spatially explicit dynamics can be very informative, but care must be taken when policy decisions are made based on models with unstated, but implicit assumptions. As we interpret modeling results, we must communicate the spatial and temporal scale for which the model was developed and at which the data is valid. When there is little to no data to enable appropriate validation and calibration, the results must be interpreted with appropriate skepticism.

Modeling winter wheat phenological responses to water deficits in the Unified Plant Growth Model (UPGM) component of the spatially distributed Agricultural Ecosystem Services (AgES) model

USDA-ARS?s Scientific Manuscript database

Accurately predicting phenology in crop simulation models is critical for correctly simulating crop production. While extensive work in modeling phenology has focused on the temperature response function (resulting in robust phenology models), limited work on quantifying the phenological responses t...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xiong, Wei; Balkovic, Juraj; van der Velde, M.

Crop models are increasingly used to assess impacts of climate change/variability and management practices on productivity and environmental performance of alternative cropping systems. Calibration is an important procedure to improve reliability of model simulations, especially for large area applications. However, global-scale crop model calibration has rarely been exercised due to limited data availability and expensive computing cost. Here we present a simple approach to calibrate Environmental Policy Integrated Climate (EPIC) model for a global implementation of rice. We identify four parameters (potential heat unit – PHU, planting density – PD, harvest index – HI, and biomass energy ratio – BER)more » and calibrate them regionally to capture the spatial pattern of reported rice yield in 2000. Model performance is assessed by comparing simulated outputs with independent FAO national data. The comparison demonstrates that the global calibration scheme performs satisfactorily in reproducing the spatial pattern of rice yield, particularly in main rice production areas. Spatial agreement increases substantially when more parameters are selected and calibrated, but with varying efficiencies. Among the parameters, PHU and HI exhibit the highest efficiencies in increasing the spatial agreement. Simulations with different calibration strategies generate a pronounced discrepancy of 5–35% in mean yields across latitude bands, and a small to moderate difference in estimated yield variability and yield changing trend for the period of 1981–2000. Present calibration has little effects in improving simulated yield variability and trends at both regional and global levels, suggesting further works are needed to reproduce temporal variability of reported yields. This study highlights the importance of crop models’ calibration, and presents the possibility of a transparent and consistent up scaling approach for global crop simulations given current availability of global databases of weather, soil, crop calendar, fertilizer and irrigation management information, and reported yield.« less

Investigating the dependence of SCM simulated precipitation and clouds on the spatial scale of large-scale forcing at SGP [Investigating the scale dependence of SCM simulated precipitation and cloud by using gridded forcing data at SGP

DOE PAGES

Tang, Shuaiqi; Zhang, Minghua; Xie, Shaocheng

2017-08-05

Large-scale forcing data, such as vertical velocity and advective tendencies, are required to drive single-column models (SCMs), cloud-resolving models, and large-eddy simulations. Previous studies suggest that some errors of these model simulations could be attributed to the lack of spatial variability in the specified domain-mean large-scale forcing. This study investigates the spatial variability of the forcing and explores its impact on SCM simulated precipitation and clouds. A gridded large-scale forcing data during the March 2000 Cloud Intensive Operational Period at the Atmospheric Radiation Measurement program's Southern Great Plains site is used for analysis and to drive the single-column version ofmore » the Community Atmospheric Model Version 5 (SCAM5). When the gridded forcing data show large spatial variability, such as during a frontal passage, SCAM5 with the domain-mean forcing is not able to capture the convective systems that are partly located in the domain or that only occupy part of the domain. This problem has been largely reduced by using the gridded forcing data, which allows running SCAM5 in each subcolumn and then averaging the results within the domain. This is because the subcolumns have a better chance to capture the timing of the frontal propagation and the small-scale systems. As a result, other potential uses of the gridded forcing data, such as understanding and testing scale-aware parameterizations, are also discussed.« less

SWMM Modeling Methods for Simulating Green Infrastructure at a Suburban Headwatershed: User’s Guide

EPA Science Inventory

Urban stormwater runoff quantity and quality are strongly dependent upon catchment properties. Models are used to simulate the runoff characteristics, but the output from a stormwater management model is dependent on how the catchment area is subdivided and represented as spatial...

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

The advances in computer science and the availability of new detailed data-sets have led to a growing number of distributed hydrological models applied to finer and finer grid resolutions for larger and larger catchment areas. It was argued, however, that this trend does not necessarily guarantee better understanding of the hydrological processes or it is even not necessary for specific modelling applications. In the present study, this topic is further discussed in relation to the soil spatial heterogeneity and its effect on simulated hydrological state and fluxes. To this end, three methods are developed and used for the characterization of the soil heterogeneity at different spatial scales. The methods are applied at the soil map of the upper Neckar catchment (Germany), as example. The different soil realizations are assessed regarding their impact on simulated state and fluxes using the distributed hydrological model mHM. The results are analysed by aggregating the model outputs at different spatial scales based on the Representative Elementary Scale concept (RES) proposed by Refsgaard et al. (2016). The analysis is further extended in the present study by aggregating the model output also at different temporal scales. The results show that small scale soil variabilities are not relevant when the integrated hydrological responses are considered e.g., simulated streamflow or average soil moisture over sub-catchments. On the contrary, these small scale soil variabilities strongly affect locally simulated states and fluxes i.e., soil moisture and evapotranspiration simulated at the grid resolution. A clear trade-off is also detected by aggregating the model output by spatial and temporal scales. Despite the scale at which the soil variabilities are (or are not) relevant is not universal, the RES concept provides a simple and effective framework to quantify the predictive capability of distributed models and to identify the need for further model improvements e.g., finer resolution input. For this reason, the integration in this analysis of all the relevant input factors (e.g., precipitation, vegetation, geology) could provide a strong support for the definition of the right scale for each specific model application. In this context, however, the main challenge for a proper model assessment will be the correct characterization of the spatio- temporal variability of each input factor. Refsgaard, J.C., Højberg, A.L., He, X., Hansen, A.L., Rasmussen, S.H., Stisen, S., 2016. Where are the limits of model predictive capabilities?: Representative Elementary Scale - RES. Hydrol. Process. doi:10.1002/hyp.11029

A Digital Sensor Simulator of the Pushbroom Offner Hyperspectral Imaging Spectrometer

PubMed Central

Tao, Dongxing; Jia, Guorui; Yuan, Yan; Zhao, Huijie

2014-01-01

Sensor simulators can be used in forecasting the imaging quality of a new hyperspectral imaging spectrometer, and generating simulated data for the development and validation of the data processing algorithms. This paper presents a novel digital sensor simulator for the pushbroom Offner hyperspectral imaging spectrometer, which is widely used in the hyperspectral remote sensing. Based on the imaging process, the sensor simulator consists of a spatial response module, a spectral response module, and a radiometric response module. In order to enhance the simulation accuracy, spatial interpolation-resampling, which is implemented before the spatial degradation, is developed to compromise the direction error and the extra aliasing effect. Instead of using the spectral response function (SRF), the dispersive imaging characteristics of the Offner convex grating optical system is accurately modeled by its configuration parameters. The non-uniformity characteristics, such as keystone and smile effects, are simulated in the corresponding modules. In this work, the spatial, spectral and radiometric calibration processes are simulated to provide the parameters of modulation transfer function (MTF), SRF and radiometric calibration parameters of the sensor simulator. Some uncertainty factors (the stability, band width of the monochromator for the spectral calibration, and the integrating sphere uncertainty for the radiometric calibration) are considered in the simulation of the calibration process. With the calibration parameters, several experiments were designed to validate the spatial, spectral and radiometric response of the sensor simulator, respectively. The experiment results indicate that the sensor simulator is valid. PMID:25615727

Complex Dynamics of Wetland Ecosystem with Nonlinear Harvesting: Application to Chilika Lake in Odisha, India

NASA Astrophysics Data System (ADS)

Upadhyay, Ranjit Kumar; Tiwari, S. K.; Roy, Parimita

2015-06-01

In this paper, an attempt has been made to study the spatial and temporal dynamical interactions among the species of wetland ecosystem through a mathematical model. The model represents the population dynamics of phytoplankton, zooplankton and fish species found in Chilika lake, Odisha, India. Nonlinear stability analysis of both the temporal and spatial models has been carried out. Maximum sustainable yield and optimal harvesting policy have been studied for a nonspatial model system. Numerical simulation has been performed to figure out the parameters responsible for the complex dynamics of the wetland system. Significant outcomes of our numerical findings and their interpretations from an ecological point of view are provided in this paper. Numerical simulation of spatial model exhibits some interesting and beautiful patterns. We have also pointed out the parameters that are responsible for the good health of wetland ecosystem.

[Spatial structure analysis and distribution simulation of Therioaphis trifolii population based on geostatistics and GIS].

PubMed

Zhang, Rong; Leng, Yun-fa; Zhu, Meng-meng; Wang, Fang

2007-11-01

Based on geographic information system and geostatistics, the spatial structure of Therioaphis trifolii population of different periods in Yuanzhou district of Guyuan City, the southern Ningxia Province, was analyzed. The spatial distribution of Therioaphis trifolii population was also simulated by ordinary Kriging interpretation. The results showed that Therioaphis trifolii population of different periods was correlated spatially in the study area. The semivariograms of Therioaphis trifolii could be described by exponential model, indicating an aggregated spatial arrangement. The spatial variance varied from 34.13%-48.77%, and the range varied from 8.751-12.049 km. The degree and direction of aggregation showed that the trend was increased gradually from southwest to northeast. The dynamic change of Therioaphis trifolii population in different periods could be analyzed intuitively on the simulated maps of the spatial distribution from the two aspects of time and space, The occurrence position and degree of Therioaphis trifolii to a state of certain time could be determined easily.

Permeability Sensitivity Functions and Rapid Simulation of Hydraulic-Testing Measurements Using Perturbation Theory

NASA Astrophysics Data System (ADS)

Escobar Gómez, J. D.; Torres-Verdín, C.

2018-03-01

Single-well pressure-diffusion simulators enable improved quantitative understanding of hydraulic-testing measurements in the presence of arbitrary spatial variations of rock properties. Simulators of this type implement robust numerical algorithms which are often computationally expensive, thereby making the solution of the forward modeling problem onerous and inefficient. We introduce a time-domain perturbation theory for anisotropic permeable media to efficiently and accurately approximate the transient pressure response of spatially complex aquifers. Although theoretically valid for any spatially dependent rock/fluid property, our single-phase flow study emphasizes arbitrary spatial variations of permeability and anisotropy, which constitute key objectives of hydraulic-testing operations. Contrary to time-honored techniques, the perturbation method invokes pressure-flow deconvolution to compute the background medium's permeability sensitivity function (PSF) with a single numerical simulation run. Subsequently, the first-order term of the perturbed solution is obtained by solving an integral equation that weighs the spatial variations of permeability with the spatial-dependent and time-dependent PSF. Finally, discrete convolution transforms the constant-flow approximation to arbitrary multirate conditions. Multidimensional numerical simulation studies for a wide range of single-well field conditions indicate that perturbed solutions can be computed in less than a few CPU seconds with relative errors in pressure of <5%, corresponding to perturbations in background permeability of up to two orders of magnitude. Our work confirms that the proposed joint perturbation-convolution (JPC) method is an efficient alternative to analytical and numerical solutions for accurate modeling of pressure-diffusion phenomena induced by Neumann or Dirichlet boundary conditions.

Collapse Causes Analysis and Numerical Simulation for a Rigid Frame Multiple Arch Bridge

NASA Astrophysics Data System (ADS)

Zuo, XinDai

2018-03-01

Following the collapse accident of Baihe Bridge, the author built a plane model of the whole bridge firstly and analyzed the carrying capacity of the structure for a 170-tons lorry load. Then the author built a spatial finite element model which can accurately simulate the bridge collapse course. The collapse course was simulated and the accident scene was reproduced. Spatial analysis showed rotational stiffness of the pier bottom had a large influence on the collapse from of the superstructures. The conclusion was that the170 tons lorry load and multiple arch bridge design were the important factors leading to collapse.

Laboratory simulation of atmospheric turbulence induced optical wavefront distortion

NASA Astrophysics Data System (ADS)

Taylor, Travis Shane

1999-11-01

Many creative approaches have been taken in the past for simulating the effect that atmospheric turbulence has on optical beams. Most of the experimental architectures have been complicated and consisted of many optical elements as well as moving components. These techniques have shown a modicum of success; however, they are not completely controllable or predictable. A benchtop technique for experimentally producing one important effect that atmospheric turbulence has on optical beams (phase distortion) is presented here. The system is completely controllable and predictable while accurately representing the statistical nature of the problem. Previous experimentation in optical processing through turbulent media has demonstrated that optical wavefront distortions can be produced via spatial light modulating (SLM) devices, and most turbulence models and experimental results indicate that turbulence can be represented as a phase fluctuation. The amplitude distributions in the resulting far field are primarily due to propagation of the phase. Operating a liquid crystal television (LCTV) in the ``phase- mostly'' mode, a phase fluctuation type model for turbulence is utilized in the present investigation, and a real-time experiment for demonstrating the effects was constructed. For an optical system to simulate optical wavefront distortions due to atmospheric turbulence, the following are required: (1)An optical element that modulates the phasefront of an optical beam (2)A model and a technique for generating spatially correlated turbulence simulating distributions (3)Hardware and software for displaying and manipulating the information addressing the optical phase modulation device The LCTV is ideal for this application. When operated in the ``phase-mostly'' mode some LCTVs can modulate the phasefront of an optical beam by as much as 2π and an algorithm for generating spatially correlated phase screens can be constructed via mathematical modeling software such as Mathcad[2]. The phase screens can then be manipulated and displayed on the LCTV using a computer with an appropriate framegrabber and software. The present system consists of an Epson liquid crystal television (which was optimized to modulate up to 2π of phase), a Macintosh IIci with a framegrabber card, a QuickTime movie consisting of multiple video frames of two dimensional arrays of spatially correlated grayscale images, and two polarizers. The movie is displayed on the television via the framegrabber, and the polarizers are used to operate the television in a mode that mostly modulates the spatial phase distribution of the optical wavefront. The frames of the movie are created using an accepted turbulence model for spatially correlated variations in index of refraction, and each subsequent frame of the movie is calculated following an accepted model for temporally varying turbulence. The model used for generating spatial functions or ``phase screens'' which simulate turbulence is the well known Kolmogorov model. These ``phase screens'' are then used, employing a Taylor's frozen flow model, to simulate temporally varying turbulence. A single ``phase screen'' is given a random velocity vector between 0 and.55 meters per second to simulate temporally varying turbulence. The system is used to distort optical beams as if the beams had propagated through a long pathlength of wavefront distorting medium, such as the atmosphere.

Simulating forest landscape disturbances as coupled human and natural systems

USGS Publications Warehouse

Wimberly, Michael; Sohl, Terry L.; Liu, Zhihua; Lamsal, Aashis

2015-01-01

Anthropogenic disturbances resulting from human land use affect forest landscapes over a range of spatial and temporal scales, with diverse influences on vegetation patterns and dynamics. These processes fall within the scope of the coupled human and natural systems (CHANS) concept, which has emerged as an important framework for understanding the reciprocal interactions and feedbacks that connect human activities and ecosystem responses. Spatial simulation modeling of forest landscape change is an important technique for exploring the dynamics of CHANS over large areas and long time periods. Landscape models for simulating interactions between human activities and forest landscape dynamics can be grouped into two main categories. Forest landscape models (FLMs) focus on landscapes where forests are the dominant land cover and simulate succession and natural disturbances along with forest management activities. In contrast, land change models (LCMs) simulate mosaics of different land cover and land use classes that include forests in addition to other land uses such as developed areas and agricultural lands. There are also several examples of coupled models that combine elements of FLMs and LCMs. These integrated models are particularly useful for simulating human–natural interactions in landscapes where human settlement and agriculture are expanding into forested areas. Despite important differences in spatial scale and disciplinary scope, FLMs and LCMs have many commonalities in conceptual design and technical implementation that can facilitate continued integration. The ultimate goal will be to implement forest landscape disturbance modeling in a CHANS framework that recognizes the contextual effects of regional land use and other human activities on the forest ecosystem while capturing the reciprocal influences of forests and their disturbances on the broader land use mosaic.

Land Use Change on Household Farms in the Ecuadorian Amazon: Design and Implementation of an Agent-Based Model.

PubMed

Mena, Carlos F; Walsh, Stephen J; Frizzelle, Brian G; Xiaozheng, Yao; Malanson, George P

2011-01-01

This paper describes the design and implementation of an Agent-Based Model (ABM) used to simulate land use change on household farms in the Northern Ecuadorian Amazon (NEA). The ABM simulates decision-making processes at the household level that is examined through a longitudinal, socio-economic and demographic survey that was conducted in 1990 and 1999. Geographic Information Systems (GIS) are used to establish spatial relationships between farms and their environment, while classified Landsat Thematic Mapper (TM) imagery is used to set initial land use/land cover conditions for the spatial simulation, assess from-to land use/land cover change patterns, and describe trajectories of land use change at the farm and landscape levels. Results from prior studies in the NEA provide insights into the key social and ecological variables, describe human behavioral functions, and examine population-environment interactions that are linked to deforestation and agricultural extensification, population migration, and demographic change. Within the architecture of the model, agents are classified as active or passive. The model comprises four modules, i.e., initialization, demography, agriculture, and migration that operate individually, but are linked through key household processes. The main outputs of the model include a spatially-explicit representation of the land use/land cover on survey and non-survey farms and at the landscape level for each annual time-step, as well as simulated socio-economic and demographic characteristics of households and communities. The work describes the design and implementation of the model and how population-environment interactions can be addressed in a frontier setting. The paper contributes to land change science by examining important pattern-process relations, advocating a spatial modeling approach that is capable of synthesizing fundamental relationships at the farm level, and links people and environment in complex ways.

Spatial resolution limitation of liquid crystal spatial light modulator

NASA Astrophysics Data System (ADS)

Wang, Xinghua; Wang, Bin; McManamon, Paul F., III; Pouch, John J.; Miranda, Felix A.; Anderson, James E.; Bos, Philip J.

2004-10-01

The effect of fringing electric fields in a liquid crystal (LC) Optical Phased Array (OPA), also referred to as a spatial light modulator (SLM), is a governing factor that determines the diffraction efficiency (DE) of the LC OPA for high resolution spatial phase modulation. In this article, the fringing field effect in a high resolution LC OPA is studied by accurate modeling the DE of the LC blazed gratings by LC director simulation and Finite Difference Time Domain (FDTD) simulation. Influence factors that contribute significantly to the DE are discussed. Such results provide fundamental understanding for high resolution LC devices.

Performance Evaluation of 18F Radioluminescence Microscopy Using Computational Simulation

PubMed Central

Wang, Qian; Sengupta, Debanti; Kim, Tae Jin; Pratx, Guillem

2017-01-01

Purpose Radioluminescence microscopy can visualize the distribution of beta-emitting radiotracers in live single cells with high resolution. Here, we perform a computational simulation of 18F positron imaging using this modality to better understand how radioluminescence signals are formed and to assist in optimizing the experimental setup and image processing. Methods First, the transport of charged particles through the cell and scintillator and the resulting scintillation is modeled using the GEANT4 Monte-Carlo simulation. Then, the propagation of the scintillation light through the microscope is modeled by a convolution with a depth-dependent point-spread function, which models the microscope response. Finally, the physical measurement of the scintillation light using an electron-multiplying charge-coupled device (EMCCD) camera is modeled using a stochastic numerical photosensor model, which accounts for various sources of noise. The simulated output of the EMCCD camera is further processed using our ORBIT image reconstruction methodology to evaluate the endpoint images. Results The EMCCD camera model was validated against experimentally acquired images and the simulated noise, as measured by the standard deviation of a blank image, was found to be accurate within 2% of the actual detection. Furthermore, point-source simulations found that a reconstructed spatial resolution of 18.5 μm can be achieved near the scintillator. As the source is moved away from the scintillator, spatial resolution degrades at a rate of 3.5 μm per μm distance. These results agree well with the experimentally measured spatial resolution of 30–40 μm (live cells). The simulation also shows that the system sensitivity is 26.5%, which is also consistent with our previous experiments. Finally, an image of a simulated sparse set of single cells is visually similar to the measured cell image. Conclusions Our simulation methodology agrees with experimental measurements taken with radioluminescence microscopy. This in silico approach can be used to guide further instrumentation developments and to provide a framework for improving image reconstruction. PMID:28273348

Effect of climate data on simulated carbon and nitrogen balances for Europe

NASA Astrophysics Data System (ADS)

Blanke, Jan Hendrik; Lindeskog, Mats; Lindström, Johan; Lehsten, Veiko

2016-05-01

In this study, we systematically assess the spatial variability in carbon and nitrogen balance simulations related to the choice of global circulation models (GCMs), representative concentration pathways (RCPs), spatial resolutions, and the downscaling methods used as calculated with LPJ-GUESS. We employed a complete factorial design and performed 24 simulations for Europe with different climate input data sets and different combinations of these four factors. Our results reveal that the variability in simulated output in Europe is moderate with 35.6%-93.5% of the total variability being common among all combinations of factors. The spatial resolution is the most important factor among the examined factors, explaining 1.5%-10.7% of the total variability followed by GCMs (0.3%-7.6%), RCPs (0%-6.3%), and downscaling methods (0.1%-4.6%). The higher-order interactions effect that captures nonlinear relations between the factors and random effects is pronounced and accounts for 1.6%-45.8% to the total variability. The most distinct hot spots of variability include the mountain ranges in North Scandinavia and the Alps, and the Iberian Peninsula. Based on our findings, we advise to conduct the application of models such as LPJ-GUESS at a reasonably high spatial resolution which is supported by the model structure. There is no notable gain in simulations of ecosystem carbon and nitrogen stocks and fluxes from using regionally downscaled climate in preference to bias-corrected, bilinearly interpolated CMIP5 projections.

Gulf Coast megaregion evacuation traffic simulation modeling and analysis.

DOT National Transportation Integrated Search

2015-12-01

This paper describes a project to develop a micro-level traffic simulation for a megaregion. To : accomplish this, a mass evacuation event was modeled using a traffic demand generation process that : created a spatial and temporal distribution of dep...

: “Developing Regional Modeling Techniques Applicable for Simulating Future Climate Conditions in the Carolinas”

EPA Science Inventory

Global climate models (GCMs) are currently used to obtain information about future changes in the large-scale climate. However, such simulations are typically done at coarse spatial resolutions, with model grid boxes on the order of 100 km on a horizontal side. Therefore, techniq...

AN INDIVIDUAL-BASED SIMULATION MODEL FOR MOTTLED SCULPIN (COTTUS BAIRDI) IN A SOUTHERN APPALACHIAN STREAM

EPA Science Inventory

We describe and analyze a spatially explicit, individual-based model for the local population dynamics of mottled sculpin (Cottus bairdi). The model simulated daily growth, mortality, movement and spawning of individuals within a reach of stream. Juvenile and adult growth was bas...

Estimating thermal regimes of bull trout and assessing the potential effects of climate warming on critical habitats

USGS Publications Warehouse

Jones, Leslie A.; Muhlfeld, Clint C.; Marshall, Lucy A.; McGlynn, Brian L.; Kershner, Jeffrey L.

2013-01-01

Understanding the vulnerability of aquatic species and habitats under climate change is critical for conservation and management of freshwater systems. Climate warming is predicted to increase water temperatures in freshwater ecosystems worldwide, yet few studies have developed spatially explicit modelling tools for understanding the potential impacts. We parameterized a nonspatial model, a spatial flow-routed model, and a spatial hierarchical model to predict August stream temperatures (22-m resolution) throughout the Flathead River Basin, USA and Canada. Model comparisons showed that the spatial models performed significantly better than the nonspatial model, explaining the spatial autocorrelation found between sites. The spatial hierarchical model explained 82% of the variation in summer mean (August) stream temperatures and was used to estimate thermal regimes for threatened bull trout (Salvelinus confluentus) habitats, one of the most thermally sensitive coldwater species in western North America. The model estimated summer thermal regimes of spawning and rearing habitats at <13 C° and foraging, migrating, and overwintering habitats at <14 C°. To illustrate the useful application of such a model, we simulated climate warming scenarios to quantify potential loss of critical habitats under forecasted climatic conditions. As air and water temperatures continue to increase, our model simulations show that lower portions of the Flathead River Basin drainage (foraging, migrating, and overwintering habitat) may become thermally unsuitable and headwater streams (spawning and rearing) may become isolated because of increasing thermal fragmentation during summer. Model results can be used to focus conservation and management efforts on populations of concern, by identifying critical habitats and assessing thermal changes at a local scale.

Surface Variability of Short-wavelength Radiation and Temperature on Exoplanets around M Dwarfs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Xin; Tian, Feng; Wang, Yuwei

2017-03-10

It is a common practice to use 3D General Circulation Models (GCM) with spatial resolution of a few hundred kilometers to simulate the climate of Earth-like exoplanets. The enhanced albedo effect of clouds is especially important for exoplanets in the habitable zones around M dwarfs that likely have fixed substellar regions and substantial cloud coverage. Here, we carry out mesoscale model simulations with 3 km spatial resolution driven by the initial and boundary conditions in a 3D GCM and find that it could significantly underestimate the spatial variability of both the incident short-wavelength radiation and the temperature at planet surface.more » Our findings suggest that mesoscale models with cloud-resolving capability be considered for future studies of exoplanet climate.« less

Neural network simulation of soil NO3 dynamic under potato crop system

NASA Astrophysics Data System (ADS)

Goulet-Fortin, Jérôme; Morais, Anne; Anctil, François; Parent, Léon-Étienne; Bolinder, Martin

2013-04-01

Nitrate leaching is a major issue in sandy soils intensively cropped to potato. Modelling could test and improve management practices, particularly as regard to the optimal N application rates. Lack of input data is an important barrier for the application of classical process-based models to predict soil NO3 content (SNOC) and NO3 leaching (NOL). Alternatively, data driven models such as neural networks (NN) could better take into account indicators of spatial soil heterogeneity and plant growth pattern such as the leaf area index (LAI), hence reducing the amount of soil information required. The first objective of this study was to evaluate NN and hybrid models to simulate SNOC in the 0-40 cm soil layer considering inter-annual variations, spatial soil heterogeneity and differential N application rates. The second objective was to evaluate the same methodology to simulate seasonal NOL dynamic at 1 m deep. To this aim, multilayer perceptrons with different combinations of driving meteorological variables, functions of the LAI and state variables of external deterministic models have been trained and evaluated. The state variables from external models were: drainage estimated by the CLASS model and the soil temperature estimated by an ICBM subroutine. Results of SNOC simulations were compared to field data collected between 2004 and 2011 at several experimental plots under potato cropping systems in Québec, Eastern Canada. Results of NOL simulation were compared to data obtained in 2012 from 11 suction lysimeters installed in 2 experimental plots under potato cropping systems in the same region. The most performing model for SNOC simulation was obtained using a 4-input hybrid model composed of 1) cumulative LAI, 2) cumulative drainage, 3) soil temperature and 4) day of year. The most performing model for NOL simulation was obtained using a 5-input NN model composed of 1) N fertilization rate at spring, 2) LAI, 3) cumulative rainfall, 4) the day of year and 5) the percentage of clay content. The MAE was 22% for SNOC simulation and 23% for NOL simulation. High sensitivity to LAI suggests that the model may take into account field and sub-field spatial variability and support N management. Further studies are needed to fully validate the method, particularly in the case of NOL simulation.

Stochastic simulation of spatially correlated geo-processes

USGS Publications Warehouse

Christakos, G.

1987-01-01

In this study, developments in the theory of stochastic simulation are discussed. The unifying element is the notion of Radon projection in Euclidean spaces. This notion provides a natural way of reconstructing the real process from a corresponding process observable on a reduced dimensionality space, where analysis is theoretically easier and computationally tractable. Within this framework, the concept of space transformation is defined and several of its properties, which are of significant importance within the context of spatially correlated processes, are explored. The turning bands operator is shown to follow from this. This strengthens considerably the theoretical background of the geostatistical method of simulation, and some new results are obtained in both the space and frequency domains. The inverse problem is solved generally and the applicability of the method is extended to anisotropic as well as integrated processes. Some ill-posed problems of the inverse operator are discussed. Effects of the measurement error and impulses at origin are examined. Important features of the simulated process as described by geomechanical laws, the morphology of the deposit, etc., may be incorporated in the analysis. The simulation may become a model-dependent procedure and this, in turn, may provide numerical solutions to spatial-temporal geologic models. Because the spatial simu??lation may be technically reduced to unidimensional simulations, various techniques of generating one-dimensional realizations are reviewed. To link theory and practice, an example is computed in detail. ?? 1987 International Association for Mathematical Geology.

Hybrid Optimal Design of the Eco-Hydrological Wireless Sensor Network in the Middle Reach of the Heihe River Basin, China

PubMed Central

Kang, Jian; Li, Xin; Jin, Rui; Ge, Yong; Wang, Jinfeng; Wang, Jianghao

2014-01-01

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables. PMID:25317762

Modeling and predicting urban growth pattern of the Tokyo metropolitan area based on cellular automata

NASA Astrophysics Data System (ADS)

Zhao, Yaolong; Zhao, Junsan; Murayama, Yuji

2008-10-01

The period of high economic growth in Japan which began in the latter half of the 1950s led to a massive migration of population from rural regions to the Tokyo metropolitan area. This phenomenon brought about rapid urban growth and urban structure changes in this area. Purpose of this study is to establish a constrained CA (Cellular Automata) model with GIS (Geographical Information Systems) to simulate urban growth pattern in the Tokyo metropolitan area towards predicting urban form and landscape for the near future. Urban land-use is classified into multi-categories for interpreting the effect of interaction among land-use categories in the spatial process of urban growth. Driving factors of urban growth pattern, such as land condition, railway network, land-use zoning, random perturbation, and neighborhood interaction and so forth, are explored and integrated into this model. These driving factors are calibrated based on exploratory spatial data analysis (ESDA), spatial statistics, logistic regression, and "trial and error" approach. The simulation is assessed at both macro and micro classification levels in three ways: visual approach; fractal dimension; and spatial metrics. Results indicate that this model provides an effective prototype to simulate and predict urban growth pattern of the Tokyo metropolitan area.

Hybrid optimal design of the eco-hydrological wireless sensor network in the middle reach of the Heihe River Basin, China.

PubMed

Kang, Jian; Li, Xin; Jin, Rui; Ge, Yong; Wang, Jinfeng; Wang, Jianghao

2014-10-14

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables.

Scaling Properties of Arctic Sea Ice Deformation in a High‐Resolution Viscous‐Plastic Sea Ice Model and in Satellite Observations

PubMed Central

Losch, Martin; Menemenlis, Dimitris

2018-01-01

Abstract Sea ice models with the traditional viscous‐plastic (VP) rheology and very small horizontal grid spacing can resolve leads and deformation rates localized along Linear Kinematic Features (LKF). In a 1 km pan‐Arctic sea ice‐ocean simulation, the small‐scale sea ice deformations are evaluated with a scaling analysis in relation to satellite observations of the Envisat Geophysical Processor System (EGPS) in the Central Arctic. A new coupled scaling analysis for data on Eulerian grids is used to determine the spatial and temporal scaling and the coupling between temporal and spatial scales. The spatial scaling of the modeled sea ice deformation implies multifractality. It is also coupled to temporal scales and varies realistically by region and season. The agreement of the spatial scaling with satellite observations challenges previous results with VP models at coarser resolution, which did not reproduce the observed scaling. The temporal scaling analysis shows that the VP model, as configured in this 1 km simulation, does not fully resolve the intermittency of sea ice deformation that is observed in satellite data. PMID:29576996

Scaling Properties of Arctic Sea Ice Deformation in a High-Resolution Viscous-Plastic Sea Ice Model and in Satellite Observations

NASA Astrophysics Data System (ADS)

Hutter, Nils; Losch, Martin; Menemenlis, Dimitris

2018-01-01

Sea ice models with the traditional viscous-plastic (VP) rheology and very small horizontal grid spacing can resolve leads and deformation rates localized along Linear Kinematic Features (LKF). In a 1 km pan-Arctic sea ice-ocean simulation, the small-scale sea ice deformations are evaluated with a scaling analysis in relation to satellite observations of the Envisat Geophysical Processor System (EGPS) in the Central Arctic. A new coupled scaling analysis for data on Eulerian grids is used to determine the spatial and temporal scaling and the coupling between temporal and spatial scales. The spatial scaling of the modeled sea ice deformation implies multifractality. It is also coupled to temporal scales and varies realistically by region and season. The agreement of the spatial scaling with satellite observations challenges previous results with VP models at coarser resolution, which did not reproduce the observed scaling. The temporal scaling analysis shows that the VP model, as configured in this 1 km simulation, does not fully resolve the intermittency of sea ice deformation that is observed in satellite data.

Scaling Properties of Arctic Sea Ice Deformation in a High-Resolution Viscous-Plastic Sea Ice Model and in Satellite Observations.

PubMed

Hutter, Nils; Losch, Martin; Menemenlis, Dimitris

2018-01-01

Sea ice models with the traditional viscous-plastic (VP) rheology and very small horizontal grid spacing can resolve leads and deformation rates localized along Linear Kinematic Features (LKF). In a 1 km pan-Arctic sea ice-ocean simulation, the small-scale sea ice deformations are evaluated with a scaling analysis in relation to satellite observations of the Envisat Geophysical Processor System (EGPS) in the Central Arctic. A new coupled scaling analysis for data on Eulerian grids is used to determine the spatial and temporal scaling and the coupling between temporal and spatial scales. The spatial scaling of the modeled sea ice deformation implies multifractality. It is also coupled to temporal scales and varies realistically by region and season. The agreement of the spatial scaling with satellite observations challenges previous results with VP models at coarser resolution, which did not reproduce the observed scaling. The temporal scaling analysis shows that the VP model, as configured in this 1 km simulation, does not fully resolve the intermittency of sea ice deformation that is observed in satellite data.

Multi-Algorithm Particle Simulations with Spatiocyte.

PubMed

Arjunan, Satya N V; Takahashi, Koichi

2017-01-01

As quantitative biologists get more measurements of spatially regulated systems such as cell division and polarization, simulation of reaction and diffusion of proteins using the data is becoming increasingly relevant to uncover the mechanisms underlying the systems. Spatiocyte is a lattice-based stochastic particle simulator for biochemical reaction and diffusion processes. Simulations can be performed at single molecule and compartment spatial scales simultaneously. Molecules can diffuse and react in 1D (filament), 2D (membrane), and 3D (cytosol) compartments. The implications of crowded regions in the cell can be investigated because each diffusing molecule has spatial dimensions. Spatiocyte adopts multi-algorithm and multi-timescale frameworks to simulate models that simultaneously employ deterministic, stochastic, and particle reaction-diffusion algorithms. Comparison of light microscopy images to simulation snapshots is supported by Spatiocyte microscopy visualization and molecule tagging features. Spatiocyte is open-source software and is freely available at http://spatiocyte.org .

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jager, Yetta; Efroymson, Rebecca Ann; Sublette, K.

Quantitative tools are needed to evaluate the ecological effects of increasing petroleum production. In this article, we describe two stochastic models for simulating the spatial distribution of brine spills on a landscape. One model uses general assumptions about the spatial arrangement of spills and their sizes; the second model distributes spills by siting rectangular well complexes and conditioning spill probabilities on the configuration of pipes. We present maps of landscapes with spills produced by the two methods and compare the ability of the models to reproduce a specified spill area. A strength of the models presented here is their abilitymore » to extrapolate from the existing landscape to simulate landscapes with a higher (or lower) density of oil wells.« less

Linking linear programming and spatial simulation models to predict landscape effects of forest management alternatives

Treesearch

Eric J. Gustafson; L. Jay Roberts; Larry A. Leefers

2006-01-01

Forest management planners require analytical tools to assess the effects of alternative strategies on the sometimes disparate benefits from forests such as timber production and wildlife habitat. We assessed the spatial patterns of alternative management strategies by linking two models that were developed for different purposes. We used a linear programming model (...

Model intra-comparison of transboundary sulfate loadings over springtime east Asia

NASA Astrophysics Data System (ADS)

Goto, D.; Ohara, T.; Nakajima, T.; Takemura, T.; Kajino, M.; Dai, T.; Matsui, H.; Takami, A.; Hatakeyama, S.; Aoki, K.; Sugimoto, N.; Shimizu, A.

2013-12-01

Over east Asia, a spatial gradient of sulfate aerosols from source to outflow regions has not fully evaluated by simulations. In the present study, we executed a global aerosol-transport model (SPRINTARS) during April 2006 to investigate the spatial gradient of sulfate aerosols using multiple measurements including surface mass concentration, aerosol optical thickness, and vertical profiles of extinction coefficients for spherical particles. We also performed sensitivity experiments to estimate possible uncertainties of sulfate mass loadings caused by macrophysical processes; emission inventory, dynamic core, and spatial resolution. Among the experiments, although a difference in the surface sulfate mass concentrations over east Asia was large, none of the simulations in the present study as well as regional models reproduced the spatial gradient of the surface sulfate from the source over China to the outflow regions in Japan. The sensitivity of different macrophysical factors to the surface sulfate differs from that to sulfate loadings in the column especially in the marine boundary layers (MBL). Therefore, to properly simulate the transboundary air pollution over east Asia is required to use multiple measurements in both the source and outflow regions especially in the MBL during the polluted days.

Regional-specific Stochastic Simulation of Spatially-distributed Ground-motion Time Histories using Wavelet Packet Analysis

NASA Astrophysics Data System (ADS)

Huang, D.; Wang, G.

2014-12-01

Stochastic simulation of spatially distributed ground-motion time histories is important for performance-based earthquake design of geographically distributed systems. In this study, we develop a novel technique to stochastically simulate regionalized ground-motion time histories using wavelet packet analysis. First, a transient acceleration time history is characterized by wavelet-packet parameters proposed by Yamamoto and Baker (2013). The wavelet-packet parameters fully characterize ground-motion time histories in terms of energy content, time- frequency-domain characteristics and time-frequency nonstationarity. This study further investigates the spatial cross-correlations of wavelet-packet parameters based on geostatistical analysis of 1500 regionalized ground motion data from eight well-recorded earthquakes in California, Mexico, Japan and Taiwan. The linear model of coregionalization (LMC) is used to develop a permissible spatial cross-correlation model for each parameter group. The geostatistical analysis of ground-motion data from different regions reveals significant dependence of the LMC structure on regional site conditions, which can be characterized by the correlation range of Vs30 in each region. In general, the spatial correlation and cross-correlation of wavelet-packet parameters are stronger if the site condition is more homogeneous. Using the regional-specific spatial cross-correlation model and cokriging technique, wavelet packet parameters at unmeasured locations can be best estimated, and regionalized ground-motion time histories can be synthesized. Case studies and blind tests demonstrated that the simulated ground motions generally agree well with the actual recorded data, if the influence of regional-site conditions is considered. The developed method has great potential to be used in computational-based seismic analysis and loss estimation in a regional scale.

Tangible Landscape: Cognitively Grasping the Flow of Water

NASA Astrophysics Data System (ADS)

Harmon, B. A.; Petrasova, A.; Petras, V.; Mitasova, H.; Meentemeyer, R. K.

2016-06-01

Complex spatial forms like topography can be challenging to understand, much less intentionally shape, given the heavy cognitive load of visualizing and manipulating 3D form. Spatiotemporal processes like the flow of water over a landscape are even more challenging to understand and intentionally direct as they are dependent upon their context and require the simulation of forces like gravity and momentum. This cognitive work can be offloaded onto computers through 3D geospatial modeling, analysis, and simulation. Interacting with computers, however, can also be challenging, often requiring training and highly abstract thinking. Tangible computing - an emerging paradigm of human-computer interaction in which data is physically manifested so that users can feel it and directly manipulate it - aims to offload this added cognitive work onto the body. We have designed Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process. Conceptually Tangible Landscape couples a malleable physical model with a digital model of a landscape through a continuous cycle of 3D scanning, geospatial modeling, and projection. We ran a flow modeling experiment to test whether tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto computers and our bodies. We used hydrological simulations and statistics to quantitatively assess spatial performance. We found that Tangible Landscape enhanced 3D spatial performance and helped users understand water flow.

Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning.

PubMed

Hellander, Stefan; Hellander, Andreas; Petzold, Linda

2017-12-21

The reaction-diffusion master equation (RDME) is a model that allows for efficient on-lattice simulation of spatially resolved stochastic chemical kinetics. Compared to off-lattice hard-sphere simulations with Brownian dynamics or Green's function reaction dynamics, the RDME can be orders of magnitude faster if the lattice spacing can be chosen coarse enough. However, strongly diffusion-controlled reactions mandate a very fine mesh resolution for acceptable accuracy. It is common that reactions in the same model differ in their degree of diffusion control and therefore require different degrees of mesh resolution. This renders mesoscopic simulation inefficient for systems with multiscale properties. Mesoscopic-microscopic hybrid methods address this problem by resolving the most challenging reactions with a microscale, off-lattice simulation. However, all methods to date require manual partitioning of a system, effectively limiting their usefulness as "black-box" simulation codes. In this paper, we propose a hybrid simulation algorithm with automatic system partitioning based on indirect a priori error estimates. We demonstrate the accuracy and efficiency of the method on models of diffusion-controlled networks in 3D.

Three-dimensional Model of Tissue and Heavy Ions Effects

NASA Technical Reports Server (NTRS)

Ponomarev, Artem L.; Sundaresan, Alamelu; Huff, Janice L.; Cucinotta, Francis A.

2007-01-01

A three-dimensional tissue model was incorporated into a new Monte Carlo algorithm that simulates passage of heavy ions in a tissue box . The tissue box was given as a realistic model of tissue based on confocal microscopy images. The action of heavy ions on the cellular matrix for 2- or 3-dimensional cases was simulated. Cells were modeled as a cell culture monolayer in one example, where the data were taken directly from microscopy (2-d cell matrix), and as a multi-layer obtained from confocal microscopy (3-d case). Image segmentation was used to identify cells with precise areas/volumes in an irradiated cell culture monolayer, and slices of tissue with many cell layers. The cells were then inserted into the model box of the simulated physical space pixel by pixel. In the case of modeled tissues (3-d), the tissue box had periodic boundary conditions imposed, which extrapolates the technique to macroscopic volumes of tissue. For the real tissue (3-d), specific spatial patterns for cell apoptosis and necrosis are expected. The cell patterns were modeled based on action cross sections for apoptosis and necrosis estimated from current experimental data. A spatial correlation function indicating a higher spatial concentration of damaged cells from heavy ions relative to the low-LET radiation cell damage pattern is presented. The spatial correlation effects among necrotic cells can help studying microlesions in organs, and probable effects of directionality of heavy ion radiation on epithelium and endothelium.

EVALUATING HYDROLOGICAL RESPONSE TO ...

EPA Pesticide Factsheets

Studies of future management and policy options based on different assumptions provide a mechanism to examine possible outcomes and especially their likely benefits or consequences. Planning and assessment in land and water resource management are evolving toward complex, spatially explicit regional assessments. These problems have to be addressed with distributed models that can compute runoff and erosion at different spatial and temporal scales. The extensive data requirements and the difficult task of building input parameter files, however, have long been an obstacle to the timely and cost-effective use of such complex models by resource managers. The U.S. EPA Landscape Ecology Branch in collaboration with the USDA-ARS Southwest Watershed Research Center has developed a geographic information system (GIS) tool to facilitate this process. A GIS provides the framework within which spatially distributed data are collected and used to prepare model input files, and model results are evaluated. The Automated Geospatial Watershed Assessment (AGWA) tool uses widely available standardized spatial datasets that can be obtained via the internet at no cost to the user. The data are used to develop input parameter files for KINEROS2 and SWAT, two watershed runoff and erosion simulation models that operate at different spatial and temporal scales. AGWA automates the process of transforming digital data into simulation model results and provides a visualization tool

Integrating continuous stocks and flows into state-and-transition simulation models of landscape change

USGS Publications Warehouse

Daniel, Colin J.; Sleeter, Benjamin M.; Frid, Leonardo; Fortin, Marie-Josée

2018-01-01

State-and-transition simulation models (STSMs) provide a general framework for forecasting landscape dynamics, including projections of both vegetation and land-use/land-cover (LULC) change. The STSM method divides a landscape into spatially-referenced cells and then simulates the state of each cell forward in time, as a discrete-time stochastic process using a Monte Carlo approach, in response to any number of possible transitions. A current limitation of the STSM method, however, is that all of the state variables must be discrete.Here we present a new approach for extending a STSM, in order to account for continuous state variables, called a state-and-transition simulation model with stocks and flows (STSM-SF). The STSM-SF method allows for any number of continuous stocks to be defined for every spatial cell in the STSM, along with a suite of continuous flows specifying the rates at which stock levels change over time. The change in the level of each stock is then simulated forward in time, for each spatial cell, as a discrete-time stochastic process. The method differs from the traditional systems dynamics approach to stock-flow modelling in that the stocks and flows can be spatially-explicit, and the flows can be expressed as a function of the STSM states and transitions.We demonstrate the STSM-SF method by integrating a spatially-explicit carbon (C) budget model with a STSM of LULC change for the state of Hawai'i, USA. In this example, continuous stocks are pools of terrestrial C, while the flows are the possible fluxes of C between these pools. Importantly, several of these C fluxes are triggered by corresponding LULC transitions in the STSM. Model outputs include changes in the spatial and temporal distribution of C pools and fluxes across the landscape in response to projected future changes in LULC over the next 50 years.The new STSM-SF method allows both discrete and continuous state variables to be integrated into a STSM, including interactions between them. With the addition of stocks and flows, STSMs provide a conceptually simple yet powerful approach for characterizing uncertainties in projections of a wide range of questions regarding landscape change.

Simulation of net infiltration and potential recharge using a distributed-parameter watershed model of the Death Valley region, Nevada and California

USGS Publications Warehouse

Hevesi, Joseph A.; Flint, Alan L.; Flint, Lorraine E.

2003-01-01

This report presents the development and application of the distributed-parameter watershed model, INFILv3, for estimating the temporal and spatial distribution of net infiltration and potential recharge in the Death Valley region, Nevada and California. The estimates of net infiltration quantify the downward drainage of water across the lower boundary of the root zone and are used to indicate potential recharge under variable climate conditions and drainage basin characteristics. Spatial variability in recharge in the Death Valley region likely is high owing to large differences in precipitation, potential evapotranspiration, bedrock permeability, soil thickness, vegetation characteristics, and contributions to recharge along active stream channels. The quantity and spatial distribution of recharge representing the effects of variable climatic conditions and drainage basin characteristics on recharge are needed to reduce uncertainty in modeling ground-water flow. The U.S. Geological Survey, in cooperation with the Department of Energy, developed a regional saturated-zone ground-water flow model of the Death Valley regional ground-water flow system to help evaluate the current hydrogeologic system and the potential effects of natural or human-induced changes. Although previous estimates of recharge have been made for most areas of the Death Valley region, including the area defined by the boundary of the Death Valley regional ground-water flow system, the uncertainty of these estimates is high, and the spatial and temporal variability of the recharge in these basins has not been quantified. To estimate the magnitude and distribution of potential recharge in response to variable climate and spatially varying drainage basin characteristics, the INFILv3 model uses a daily water-balance model of the root zone with a primarily deterministic representation of the processes controlling net infiltration and potential recharge. The daily water balance includes precipitation (as either rain or snow), snow accumulation, sublimation, snowmelt, infiltration into the root zone, evapotranspiration, drainage, water content change throughout the root-zone profile (represented as a 6-layered system), runoff (defined as excess rainfall and snowmelt) and surface water run-on (defined as runoff that is routed downstream), and net infiltration (simulated as drainage from the bottom root-zone layer). Potential evapotranspiration is simulated using an hourly solar radiation model to simulate daily net radiation, and daily evapotranspiration is simulated as an empirical function of root zone water content and potential evapotranspiration. The model uses daily climate records of precipitation and air temperature from a regionally distributed network of 132 climate stations and a spatially distributed representation of drainage basin characteristics defined by topography, geology, soils, and vegetation to simulate daily net infiltration at all locations, including stream channels with intermittent streamflow in response to runoff from rain and snowmelt. The temporal distribution of daily, monthly, and annual net infiltration can be used to evaluate the potential effect of future climatic conditions on potential recharge. The INFILv3 model inputs representing drainage basin characteristics were developed using a geographic information system (GIS) to define a set of spatially distributed input parameters uniquely assigned to each grid cell of the INFILv3 model grid. The model grid, which was defined by a digital elevation model (DEM) of the Death Valley region, consists of 1,252,418 model grid cells with a uniform grid cell dimension of 278.5 meters in the north-south and east-west directions. The elevation values from the DEM were used with monthly regression models developed from the daily climate data to estimate the spatial distribution of daily precipitation and air temperature. The elevation values were also used to simulate atmosp

Spatial uncertainty analysis: Propagation of interpolation errors in spatially distributed models

USGS Publications Warehouse

Phillips, D.L.; Marks, D.G.

1996-01-01

In simulation modelling, it is desirable to quantify model uncertainties and provide not only point estimates for output variables but confidence intervals as well. Spatially distributed physical and ecological process models are becoming widely used, with runs being made over a grid of points that represent the landscape. This requires input values at each grid point, which often have to be interpolated from irregularly scattered measurement sites, e.g., weather stations. Interpolation introduces spatially varying errors which propagate through the model We extended established uncertainty analysis methods to a spatial domain for quantifying spatial patterns of input variable interpolation errors and how they propagate through a model to affect the uncertainty of the model output. We applied this to a model of potential evapotranspiration (PET) as a demonstration. We modelled PET for three time periods in 1990 as a function of temperature, humidity, and wind on a 10-km grid across the U.S. portion of the Columbia River Basin. Temperature, humidity, and wind speed were interpolated using kriging from 700- 1000 supporting data points. Kriging standard deviations (SD) were used to quantify the spatially varying interpolation uncertainties. For each of 5693 grid points, 100 Monte Carlo simulations were done, using the kriged values of temperature, humidity, and wind, plus random error terms determined by the kriging SDs and the correlations of interpolation errors among the three variables. For the spring season example, kriging SDs averaged 2.6??C for temperature, 8.7% for relative humidity, and 0.38 m s-1 for wind. The resultant PET estimates had coefficients of variation (CVs) ranging from 14% to 27% for the 10-km grid cells. Maps of PET means and CVs showed the spatial patterns of PET with a measure of its uncertainty due to interpolation of the input variables. This methodology should be applicable to a variety of spatially distributed models using interpolated inputs.

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

PubMed

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

2015-04-01

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

Validation of a Monte Carlo simulation of the Inveon PET scanner using GATE

NASA Astrophysics Data System (ADS)

Lu, Lijun; Zhang, Houjin; Bian, Zhaoying; Ma, Jianhua; Feng, Qiangjin; Chen, Wufan

2016-08-01

The purpose of this study is to validate the application of GATE (Geant4 Application for Tomographic Emission) Monte Carlo simulation toolkit in order to model the performance characteristics of Siemens Inveon small animal PET system. The simulation results were validated against experimental/published data in accordance with the NEMA NU-4 2008 protocol for standardized evaluation of spatial resolution, sensitivity, scatter fraction (SF) and noise equivalent counting rate (NECR) of a preclinical PET system. An agreement of less than 18% was obtained between the radial, tangential and axial spatial resolutions of the simulated and experimental results. The simulated peak NECR of mouse-size phantom agreed with the experimental result, while for the rat-size phantom simulated value was higher than experimental result. The simulated and experimental SFs of mouse- and rat- size phantom both reached an agreement of less than 2%. It has been shown the feasibility of our GATE model to accurately simulate, within certain limits, all major performance characteristics of Inveon PET system.

Spatial Rule-Based Modeling: A Method and Its Application to the Human Mitotic Kinetochore

PubMed Central

Ibrahim, Bashar; Henze, Richard; Gruenert, Gerd; Egbert, Matthew; Huwald, Jan; Dittrich, Peter

2013-01-01

A common problem in the analysis of biological systems is the combinatorial explosion that emerges from the complexity of multi-protein assemblies. Conventional formalisms, like differential equations, Boolean networks and Bayesian networks, are unsuitable for dealing with the combinatorial explosion, because they are designed for a restricted state space with fixed dimensionality. To overcome this problem, the rule-based modeling language, BioNetGen, and the spatial extension, SRSim, have been developed. Here, we describe how to apply rule-based modeling to integrate experimental data from different sources into a single spatial simulation model and how to analyze the output of that model. The starting point for this approach can be a combination of molecular interaction data, reaction network data, proximities, binding and diffusion kinetics and molecular geometries at different levels of detail. We describe the technique and then use it to construct a model of the human mitotic inner and outer kinetochore, including the spindle assembly checkpoint signaling pathway. This allows us to demonstrate the utility of the procedure, show how a novel perspective for understanding such complex systems becomes accessible and elaborate on challenges that arise in the formulation, simulation and analysis of spatial rule-based models. PMID:24709796

Evaluation of a semi-distributed model through an assessment of the spatial coherence of Intercatchment Groundwater Flows

NASA Astrophysics Data System (ADS)

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

2016-04-01

Semi-distributed hydrological models aim to provide useful information to understand and manage the spatial distribution of water resources. However, their evaluation is often limited to independent and single evaluations at each sub-catchment within larger catchments. This enables to qualify model performance at different points, but does not provide a coherent assessment of the overall spatial consistency of the model. To cope with these methodological deficiencies, we propose a two-step strategy. First, we apply a sequential spatial calibration procedure to define spatially consistent model parameters. Secondly, we evaluate the hydrological simulations using variables that involve some dependency between sub-catchments to evaluate the overall coherence of model outputs. In this study, we particularly choose to look at the simulated Intercatchment Groundwater Flows (IGF). The idea is that the water that is lost in one place should be recovered somewhere else within the catchment to guarantee a spatially coherent water balance in time. The model used is a recently developed daily semi-distributed model, which is based on a spatial distribution of the lumped GR5J model. The model has five parameters for each sub-catchments and a streamflow velocity parameter for flow routing between them. It implements two reservoirs, one for production and one for routing, and estimates IGF according to the level of the second in a way that catchment can release water to IGF during high flows and receive water through IGF during low flows. The calibration of the model is performed from upstream to downstream, making an efficient use of spatially distributed streamflow measurements. To take model uncertainty into account, we implemented three variants of the original model structure, each one computing in a different way the IGF in each sub-catchment. The study is applied on over 1000 catchments in France. By exploring a wide area and a variability of hydrometeorological conditions, we aim to detect IGF even between catchments which can be quite distant from one another.

Spec2Harv: Converting Spectrum output to HARVEST input

Treesearch

Eric J. Gustafson; Luke V. Rasmussen; Larry A. Leefers

2003-01-01

Spec2Harv was developed to automate the conversion of harvest schedules generated by the Spectrum model into script files that can be used by the HARVEST simulation model to simulate the implementation of the Spectrum schedules in a spatially explicit way.

The Influence of Spatial Configuration of Residential Area and Vector Populations on Dengue Incidence Patterns in an Individual-Level Transmission Model.

PubMed

Kang, Jeon-Young; Aldstadt, Jared

2017-07-15

Dengue is a mosquito-borne infectious disease that is endemic in tropical and subtropical countries. Many individual-level simulation models have been developed to test hypotheses about dengue virus transmission. Often these efforts assume that human host and mosquito vector populations are randomly or uniformly distributed in the environment. Although, the movement of mosquitoes is affected by spatial configuration of buildings and mosquito populations are highly clustered in key buildings, little research has focused on the influence of the local built environment in dengue transmission models. We developed an agent-based model of dengue transmission in a village setting to test the importance of using realistic environments in individual-level models of dengue transmission. The results from one-way ANOVA analysis of simulations indicated that the differences between scenarios in terms of infection rates as well as serotype-specific dominance are statistically significant. Specifically, the infection rates in scenarios of a realistic environment are more variable than those of a synthetic spatial configuration. With respect to dengue serotype-specific cases, we found that a single dengue serotype is more often dominant in realistic environments than in synthetic environments. An agent-based approach allows a fine-scaled analysis of simulated dengue incidence patterns. The results provide a better understanding of the influence of spatial heterogeneity on dengue transmission at a local scale.

Restricted spatial regression in practice: Geostatistical models, confounding, and robustness under model misspecification

USGS Publications Warehouse

Hanks, Ephraim M.; Schliep, Erin M.; Hooten, Mevin B.; Hoeting, Jennifer A.

2015-01-01

In spatial generalized linear mixed models (SGLMMs), covariates that are spatially smooth are often collinear with spatially smooth random effects. This phenomenon is known as spatial confounding and has been studied primarily in the case where the spatial support of the process being studied is discrete (e.g., areal spatial data). In this case, the most common approach suggested is restricted spatial regression (RSR) in which the spatial random effects are constrained to be orthogonal to the fixed effects. We consider spatial confounding and RSR in the geostatistical (continuous spatial support) setting. We show that RSR provides computational benefits relative to the confounded SGLMM, but that Bayesian credible intervals under RSR can be inappropriately narrow under model misspecification. We propose a posterior predictive approach to alleviating this potential problem and discuss the appropriateness of RSR in a variety of situations. We illustrate RSR and SGLMM approaches through simulation studies and an analysis of malaria frequencies in The Gambia, Africa.

OpenMP parallelization of a gridded SWAT (SWATG)

NASA Astrophysics Data System (ADS)

Zhang, Ying; Hou, Jinliang; Cao, Yongpan; Gu, Juan; Huang, Chunlin

2017-12-01

Large-scale, long-term and high spatial resolution simulation is a common issue in environmental modeling. A Gridded Hydrologic Response Unit (HRU)-based Soil and Water Assessment Tool (SWATG) that integrates grid modeling scheme with different spatial representations also presents such problems. The time-consuming problem affects applications of very high resolution large-scale watershed modeling. The OpenMP (Open Multi-Processing) parallel application interface is integrated with SWATG (called SWATGP) to accelerate grid modeling based on the HRU level. Such parallel implementation takes better advantage of the computational power of a shared memory computer system. We conducted two experiments at multiple temporal and spatial scales of hydrological modeling using SWATG and SWATGP on a high-end server. At 500-m resolution, SWATGP was found to be up to nine times faster than SWATG in modeling over a roughly 2000 km2 watershed with 1 CPU and a 15 thread configuration. The study results demonstrate that parallel models save considerable time relative to traditional sequential simulation runs. Parallel computations of environmental models are beneficial for model applications, especially at large spatial and temporal scales and at high resolutions. The proposed SWATGP model is thus a promising tool for large-scale and high-resolution water resources research and management in addition to offering data fusion and model coupling ability.

Linking a one-dimensional pesticide fate model to a three-dimensional groundwater model to simulate pollution risks of shallow and deep groundwater underlying fractured till.

PubMed

Stenemo, Fredrik; Jørgensen, Peter R; Jarvis, Nicholas

2005-09-01

The one-dimensional pesticide fate model MACRO was loose-linked to the three-dimensional discrete fracture/matrix diffusion model FRAC3DVS to describe transport of the pesticide mecoprop in a fractured moraine till and local sand aquifer (5-5.5 m depth) overlying a regional limestone aquifer (16 m depth) at Havdrup, Denmark. Alternative approaches to describe the upper boundary in the groundwater model were examined. Field-scale simulations were run to compare a uniform upper boundary condition with a spatially variable upper boundary derived from Monte-Carlo simulations with MACRO. Plot-scale simulations were run to investigate the influence of the temporal resolution of the upper boundary conditions for fluxes in the groundwater model and the effects of different assumptions concerning the macropore/fracture connectivity between the two models. The influence of within-field variability of leaching on simulated mecoprop concentrations in the local aquifer was relatively small. A fully transient simulation with FRAC3DVS gave 20 times larger leaching to the regional aquifer compared to the case with steady-state water flow, assuming full connectivity with respect to macropores/fractures across the boundary between the two models. For fully transient simulations 'disconnecting' the macropores/fractures at the interface between the two models reduced leaching by a factor 24. A fully connected, transient simulation with FRAC3DVS, with spatially uniform upper boundary fluxes derived from a MACRO simulation with 'effective' parameters is therefore recommended for assessing leaching risks to the regional aquifer, at this, and similar sites.

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

NASA Astrophysics Data System (ADS)

Los, S. O.

2015-06-01

A model was developed to simulate spatial, seasonal and interannual variations in vegetation in response to temperature, precipitation and atmospheric CO2 concentrations; the model addresses shortcomings in current implementations. The model uses the minimum of 12 temperature and precipitation constraint functions to simulate NDVI. Functions vary based on the Köppen-Trewartha climate classification to take adaptations of vegetation to climate into account. The simulated NDVI, referred to as the climate constrained vegetation index (CCVI), captured the spatial variability (0.82 < r <0.87), seasonal variability (median r = 0.83) and interannual variability (median global r = 0.24) in NDVI. The CCVI simulated the effects of adverse climate on vegetation during the 1984 drought in the Sahel and during dust bowls of the 1930s and 1950s in the Great Plains in North America. A global CO2 fertilisation effect was found in NDVI data, similar in magnitude to that of earlier estimates (8 % for the 20th century). This effect increased linearly with simple ratio, a transformation of the NDVI. Three CCVI scenarios, based on climate simulations using the representative concentration pathway RCP4.5, showed a greater sensitivity of vegetation towards precipitation in Northern Hemisphere mid latitudes than is currently implemented in climate models. This higher sensitivity is of importance to assess the impact of climate variability on vegetation, in particular on agricultural productivity.

Geostatistics: a new tool for describing spatially-varied surface conditions from timber harvested and burned hillslopes

Treesearch

Peter R. Robichaud

1997-01-01

Geostatistics provides a method to describe the spatial continuity of many natural phenomena. Spatial models are based upon the concept of scaling, kriging and conditional simulation. These techniques were used to describe the spatially-varied surface conditions on timber harvest and burned hillslopes. Geostatistical techniques provided estimates of the ground cover (...

Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling.

PubMed

Stoy, Paul C; Quaife, Tristan

2015-01-01

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.

Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling

PubMed Central

Stoy, Paul C.; Quaife, Tristan

2015-01-01

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes. PMID:26067835

CDFISH: an individual-based, spatially-explicit, landscape genetics simulator for aquatic species in complex riverscapes

USGS Publications Warehouse

Erin L. Landguth,; Muhlfeld, Clint C.; Luikart, Gordon

2012-01-01

We introduce Cost Distance FISHeries (CDFISH), a simulator of population genetics and connectivity in complex riverscapes for a wide range of environmental scenarios of aquatic organisms. The spatially-explicit program implements individual-based genetic modeling with Mendelian inheritance and k-allele mutation on a riverscape with resistance to movement. The program simulates individuals in subpopulations through time employing user-defined functions of individual migration, reproduction, mortality, and dispersal through straying on a continuous resistance surface.

Mapping and spatial-temporal modeling of Bromus tectorum invasion in central Utah

NASA Astrophysics Data System (ADS)

Jin, Zhenyu

Cheatgrass, or Downy Brome, is an exotic winter annual weed native to the Mediterranean region. Since its introduction to the U.S., it has become a significant weed and aggressive invader of sagebrush, pinion-juniper, and other shrub communities, where it can completely out-compete native grasses and shrubs. In this research, remotely sensed data combined with field collected data are used to investigate the distribution of the cheatgrass in Central Utah, to characterize the trend of the NDVI time-series of cheatgrass, and to construct a spatially explicit population-based model to simulate the spatial-temporal dynamics of the cheatgrass. This research proposes a method for mapping the canopy closure of invasive species using remotely sensed data acquired at different dates. Different invasive species have their own distinguished phenologies and the satellite images in different dates could be used to capture the phenology. The results of cheatgrass abundance prediction have a good fit with the field data for both linear regression and regression tree models, although the regression tree model has better performance than the linear regression model. To characterize the trend of NDVI time-series of cheatgrass, a novel smoothing algorithm named RMMEH is presented in this research to overcome some drawbacks of many other algorithms. By comparing the performance of RMMEH in smoothing a 16-day composite of the MODIS NDVI time-series with that of two other methods, which are the 4253EH, twice and the MVI, we have found that RMMEH not only keeps the original valid NDVI points, but also effectively removes the spurious spikes. The reconstructed NDVI time-series of different land covers are of higher quality and have smoother temporal trend. To simulate the spatial-temporal dynamics of cheatgrass, a spatially explicit population-based model is built applying remotely sensed data. The comparison between the model output and the ground truth of cheatgrass closure demonstrates that the model could successfully simulate the spatial-temporal dynamics of cheatgrass in a simple cheatgrass-dominant environment. The simulation of the functional response of different prescribed fire rates also shows that this model is helpful to answer management questions like, "What are the effects of prescribed fire to invasive species?" It demonstrates that a medium fire rate of 10% can successfully prevent cheatgrass invasion.

Influence of mesh structure on 2D full shallow water equations and SCS Curve Number simulation of rainfall/runoff events

NASA Astrophysics Data System (ADS)

Caviedes-Voullième, Daniel; García-Navarro, Pilar; Murillo, Javier

2012-07-01

SummaryHydrological simulation of rain-runoff processes is often performed with lumped models which rely on calibration to generate storm hydrographs and study catchment response to rain. In this paper, a distributed, physically-based numerical model is used for runoff simulation in a mountain catchment. This approach offers two advantages. The first is that by using shallow-water equations for runoff flow, there is less freedom to calibrate routing parameters (as compared to, for example, synthetic hydrograph methods). The second, is that spatial distributions of water depth and velocity can be obtained. Furthermore, interactions among the various hydrological processes can be modeled in a physically-based approach which may depend on transient and spatially distributed factors. On the other hand, the undertaken numerical approach relies on accurate terrain representation and mesh selection, which also affects significantly the computational cost of the simulations. Hence, we investigate the response of a gauged catchment with this distributed approach. The methodology consists of analyzing the effects that the mesh has on the simulations by using a range of meshes. Next, friction is applied to the model and the response to variations and interaction with the mesh is studied. Finally, a first approach with the well-known SCS Curve Number method is studied to evaluate its behavior when coupled with a shallow-water model for runoff flow. The results show that mesh selection is of great importance, since it may affect the results in a magnitude as large as physical factors, such as friction. Furthermore, results proved to be less sensitive to roughness spatial distribution than to mesh properties. Finally, the results indicate that SCS-CN may not be suitable for simulating hydrological processes together with a shallow-water model.

Use of circulation types classifications to evaluate AR4 climate models over the Euro-Atlantic region

NASA Astrophysics Data System (ADS)

Pastor, M. A.; Casado, M. J.

2012-10-01

This paper presents an evaluation of the multi-model simulations for the 4th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) in terms of their ability to simulate the ERA40 circulation types over the Euro-Atlantic region in winter season. Two classification schemes, k-means and SANDRA, have been considered to test the sensitivity of the evaluation results to the classification procedure. The assessment allows establishing different rankings attending spatial and temporal features of the circulation types. Regarding temporal characteristics, in general, all AR4 models tend to underestimate the frequency of occurrence. The best model simulating spatial characteristics is the UKMO-HadGEM1 whereas CCSM3, UKMO-HadGEM1 and CGCM3.1(T63) are the best simulating the temporal features, for both classification schemes. This result agrees with the AR4 models ranking obtained when having analysed the ability of the same AR4 models to simulate Euro-Atlantic variability modes. This study has proved the utility of applying such a synoptic climatology approach as a diagnostic tool for models' assessment. The ability of the models to properly reproduce the position of ridges and troughs and the frequency of synoptic patterns, will therefore improve our confidence in the response of models to future climate changes.

Multi-model analysis of terrestrial carbon cycles in Japan: limitations and implications of model calibration using eddy flux observations

NASA Astrophysics Data System (ADS)

Ichii, K.; Suzuki, T.; Kato, T.; Ito, A.; Hajima, T.; Ueyama, M.; Sasai, T.; Hirata, R.; Saigusa, N.; Ohtani, Y.; Takagi, K.

2010-07-01

Terrestrial biosphere models show large differences when simulating carbon and water cycles, and reducing these differences is a priority for developing more accurate estimates of the condition of terrestrial ecosystems and future climate change. To reduce uncertainties and improve the understanding of their carbon budgets, we investigated the utility of the eddy flux datasets to improve model simulations and reduce variabilities among multi-model outputs of terrestrial biosphere models in Japan. Using 9 terrestrial biosphere models (Support Vector Machine - based regressions, TOPS, CASA, VISIT, Biome-BGC, DAYCENT, SEIB, LPJ, and TRIFFID), we conducted two simulations: (1) point simulations at four eddy flux sites in Japan and (2) spatial simulations for Japan with a default model (based on original settings) and a modified model (based on model parameter tuning using eddy flux data). Generally, models using default model settings showed large deviations in model outputs from observation with large model-by-model variability. However, after we calibrated the model parameters using eddy flux data (GPP, RE and NEP), most models successfully simulated seasonal variations in the carbon cycle, with less variability among models. We also found that interannual variations in the carbon cycle are mostly consistent among models and observations. Spatial analysis also showed a large reduction in the variability among model outputs. This study demonstrated that careful validation and calibration of models with available eddy flux data reduced model-by-model differences. Yet, site history, analysis of model structure changes, and more objective procedure of model calibration should be included in the further analysis.

Internal variability of a dynamically downscaled climate over North America

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jiali; Bessac, Julie; Kotamarthi, Rao

This study investigates the internal variability (IV) of a regional climate model, and considers the impacts of horizontal resolution and spectral nudging on the IV. A 16-member simulation ensemble was conducted using the Weather Research Forecasting model for three model configurations. Ensemble members included simulations at spatial resolutions of 50 km and 12 km without spectral nudging and simulations at a spatial resolution of 12 km with spectral nudging. All the simulations were generated over the same domain, which covered much of North America. The degree of IV was measured as the spread between the individual members of the ensemblemore » during the integration period. The IV of the 12 km simulation with spectral nudging was also compared with a future climate change simulation projected by the same model configuration. The variables investigated focus on precipitation and near-surface air temperature. While the IVs show a clear annual cycle with larger values in summer and smaller values in winter, the seasonal IV is smaller for a 50-km spatial resolution than for a 12-km resolution when nudging is not applied. Applying a nudging technique to the 12-km simulation reduces the IV by a factor of two, and produces smaller IV than the simulation at 50 km without nudging. Applying a nudging technique also changes the geographic distributions of IV in all examined variables. The IV is much smaller than the inter-annual variability at seasonal scales for regionally averaged temperature and precipitation. The IV is also smaller than the projected changes in air-temperature for the mid- and late 21st century. However, the IV is larger than the projected changes in precipitation for the mid- and late 21st century.« less

Internal variability of a dynamically downscaled climate over North America

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jiali; Bessac, Julie; Kotamarthi, Rao

This study investigates the internal variability (IV) of a regional climate model, and considers the impacts of horizontal resolution and spectral nudging on the IV. A 16-member simulation ensemble was conducted using the Weather Research Forecasting model for three model configurations. Ensemble members included simulations at spatial resolutions of 50 and 12 km without spectral nudging and simulations at a spatial resolution of 12 km with spectral nudging. All the simulations were generated over the same domain, which covered much of North America. The degree of IV was measured as the spread between the individual members of the ensemble duringmore » the integration period. The IV of the 12 km simulation with spectral nudging was also compared with a future climate change simulation projected by the same model configuration. The variables investigated focus on precipitation and near-surface air temperature. While the IVs show a clear annual cycle with larger values in summer and smaller values in winter, the seasonal IV is smaller for a 50-km spatial resolution than for a 12-km resolution when nudging is not applied. Applying a nudging technique to the 12-km simulation reduces the IV by a factor of two, and produces smaller IV than the simulation at 50 km without nudging. Applying a nudging technique also changes the geographic distributions of IV in all examined variables. The IV is much smaller than the inter-annual variability at seasonal scales for regionally averaged temperature and precipitation. The IV is also smaller than the projected changes in air-temperature for the mid- and late twenty-first century. However, the IV is larger than the projected changes in precipitation for the mid- and late twenty-first century.« less

Internal variability of a dynamically downscaled climate over North America

NASA Astrophysics Data System (ADS)

Wang, Jiali; Bessac, Julie; Kotamarthi, Rao; Constantinescu, Emil; Drewniak, Beth

2018-06-01

This study investigates the internal variability (IV) of a regional climate model, and considers the impacts of horizontal resolution and spectral nudging on the IV. A 16-member simulation ensemble was conducted using the Weather Research Forecasting model for three model configurations. Ensemble members included simulations at spatial resolutions of 50 and 12 km without spectral nudging and simulations at a spatial resolution of 12 km with spectral nudging. All the simulations were generated over the same domain, which covered much of North America. The degree of IV was measured as the spread between the individual members of the ensemble during the integration period. The IV of the 12 km simulation with spectral nudging was also compared with a future climate change simulation projected by the same model configuration. The variables investigated focus on precipitation and near-surface air temperature. While the IVs show a clear annual cycle with larger values in summer and smaller values in winter, the seasonal IV is smaller for a 50-km spatial resolution than for a 12-km resolution when nudging is not applied. Applying a nudging technique to the 12-km simulation reduces the IV by a factor of two, and produces smaller IV than the simulation at 50 km without nudging. Applying a nudging technique also changes the geographic distributions of IV in all examined variables. The IV is much smaller than the inter-annual variability at seasonal scales for regionally averaged temperature and precipitation. The IV is also smaller than the projected changes in air-temperature for the mid- and late twenty-first century. However, the IV is larger than the projected changes in precipitation for the mid- and late twenty-first century.

Internal variability of a dynamically downscaled climate over North America

NASA Astrophysics Data System (ADS)

Wang, Jiali; Bessac, Julie; Kotamarthi, Rao; Constantinescu, Emil; Drewniak, Beth

2017-09-01

This study investigates the internal variability (IV) of a regional climate model, and considers the impacts of horizontal resolution and spectral nudging on the IV. A 16-member simulation ensemble was conducted using the Weather Research Forecasting model for three model configurations. Ensemble members included simulations at spatial resolutions of 50 and 12 km without spectral nudging and simulations at a spatial resolution of 12 km with spectral nudging. All the simulations were generated over the same domain, which covered much of North America. The degree of IV was measured as the spread between the individual members of the ensemble during the integration period. The IV of the 12 km simulation with spectral nudging was also compared with a future climate change simulation projected by the same model configuration. The variables investigated focus on precipitation and near-surface air temperature. While the IVs show a clear annual cycle with larger values in summer and smaller values in winter, the seasonal IV is smaller for a 50-km spatial resolution than for a 12-km resolution when nudging is not applied. Applying a nudging technique to the 12-km simulation reduces the IV by a factor of two, and produces smaller IV than the simulation at 50 km without nudging. Applying a nudging technique also changes the geographic distributions of IV in all examined variables. The IV is much smaller than the inter-annual variability at seasonal scales for regionally averaged temperature and precipitation. The IV is also smaller than the projected changes in air-temperature for the mid- and late twenty-first century. However, the IV is larger than the projected changes in precipitation for the mid- and late twenty-first century.

Void Growth and Coalescence Simulations

DTIC Science & Technology

2013-08-01

distortion and damage, minimum time step, and appropriate material model parameters. Further, a temporal and spatial convergence study was used to...estimate errors, thus, this study helps to provide guidelines for modeling of materials with voids. Finally, we use a Gurson model with Johnson-Cook...spatial convergence study was used to estimate errors, thus, this study helps to provide guidelines for modeling of materials with voids. Finally, we

Disease spread across multiple scales in a spatial hierarchy: effect of host spatial structure and of inoculum quantity and distribution.

PubMed

Gosme, Marie; Lucas, Philippe

2009-07-01

Spatial patterns of both the host and the disease influence disease spread and crop losses. Therefore, the manipulation of these patterns might help improve control strategies. Considering disease spread across multiple scales in a spatial hierarchy allows one to capture important features of epidemics developing in space without using explicitly spatialized variables. Thus, if the system under study is composed of roots, plants, and planting hills, the effect of host spatial pattern can be studied by varying the number of plants per planting hill. A simulation model based on hierarchy theory was used to simulate the effects of large versus small planting hills, low versus high level of initial infections, and aggregated versus uniform distribution of initial infections. The results showed that aggregating the initially infected plants always resulted in slower epidemics than spreading out the initial infections uniformly. Simulation results also showed that, in most cases, disease epidemics were slower in the case of large host aggregates (100 plants/hill) than with smaller aggregates (25 plants/hill), except when the initially infected plants were both numerous and spread out uniformly. The optimal strategy for disease control depends on several factors, including initial conditions. More importantly, the model offers a framework to account for the interplay between the spatial characteristics of the system, rates of infection, and aggregation of the disease.

Coupling Cellular Automata Land Use Change with Distributed Hydrologic Models

NASA Astrophysics Data System (ADS)

Shu, L.; Duffy, C.

2017-12-01

There has been extensive research on LUC modeling with broad applications to simulating urban growth and changing demographic patterns across multiple scales. The importance of land conversion is a critical issue in watershed scale studies and is generally not treated in most watershed modeling approaches. In this study we apply spatially explicit hydrologic and landuse change models and the Conestoga Watershed in Lancaster County, Pennsylvania. The Penn State Integrated Hydrologic Model (PIHM) partitions the water balance in space and time over the urban catchment, the coupled Cellular Automata Land Use Change model (CALUC) dynamically simulates the evolution of land use classes based on physical measures associated with population change and land use demand factors. The CALUC model is based on iteratively applying discrete rules to each individual spatial cell. The essence the CA modeling involves calculation of the Transition Potential (TP) for conversion of a grid cell from one land use class to another. This potential includes five factors: random perturbation, suitability, accessibility, neighborhood effect, inertia effects and zonal factors. In spite of simplicity, this CALUC model has been shown to be very effective for simulating LUC leading to the emergence of complex spatial patterns. The components of TP are derived from present land use data for landuse reanalysis and for realistic future land use scenarios. For the CALUC we use early-settlement (circa 1790) initial land class values and final or present-day (2010) land classes to calibrate the model. CALUC- PIHM dynamically simulates the hydrologic response of conversion from pre-settlement to present landuse. The simulations highlight the capability and value of dynamic coupling of catchment hydrology with land use change over long time periods. Analysis of the simulation uses various metrics such as the distributed water balance, flow duration curves, etc. to show how deforestation, urbanization and agricultural land development interact for the period 1790- present.

Fast Magnetotail Reconnection: Challenge to Global MHD Modeling

NASA Astrophysics Data System (ADS)

Kuznetsova, M. M.; Hesse, M.; Rastaetter, L.; Toth, G.; de Zeeuw, D.; Gombosi, T.

2005-05-01

Representation of fast magnetotail reconnection rates during substorm onset is one of the major challenges to global MHD modeling. Our previous comparative study of collisionless magnetic reconnection in GEM Challenge geometry demonstrated that the reconnection rate is controlled by ion nongyrotropic behavior near the reconnection site and that it can be described in terms of nongyrotropic corrections to the magnetic induction equation. To further test the approach we performed MHD simulations with nongyrotropic corrections of forced reconnection for the Newton Challenge setup. As a next step we employ the global MHD code BATSRUS and test different methods to model fast magnetotail reconnection rates by introducing non-ideal corrections to the induction equation in terms of nongyrotropic corrections, spatially localized resistivity, or current dependent resistivity. The BATSRUS adaptive grid structure allows to perform global simulations with spatial resolution near the reconnection site comparable with spatial resolution of local MHD simulations for the Newton Challenge. We select solar wind conditions which drive the accumulation of magnetic field in the tail lobes and subsequent magnetic reconnection and energy release. Testing the ability of global MHD models to describe magnetotail evolution during substroms is one of the elements of science based validation efforts at the Community Coordinated Modeling Center.

Simulating CRN derived erosion rates in a transient Andean catchment using the TTLEM model

NASA Astrophysics Data System (ADS)

Campforts, Benjamin; Vanacker, Veerle; Herman, Frédéric; Schwanghart, Wolfgang; Tenrorio Poma, Gustavo; Govers, Gerard

2017-04-01

Assessing the impact of mountain building and erosion on the earth surface is key to reconstruct and predict terrestrial landscape evolution. Landscape evolution models (LEMs) are an essential tool in this research effort as they allow to integrate our growing understanding of physical processes governing erosion and transport of mass across the surface. The recent development of several LEMs opens up new areas of research in landscape evolution. Here, we want to seize this opportunity by answering a fundamental research question: does a model designed to simulate landscape evolution over geological timescales allows to simulate spatially varying erosion rates at a millennial timescale? We selected the highly transient Paute catchment in the Southeastern Ecuadorian Andes as a study area. We found that our model (TTLEM) is capable to better explain the spatial patterns of ca. 30 Cosmogenic Radio Nuclide (CRN) derived catchment wide erosion rates in comparison to a classical, statistical approach. Thus, the use of process-based landscape evolution models may not only be of great help to understand long-term landscape evolution but also in understanding spatial and temporal variations in sediment fluxes at the millennial time scale.

How to use the Stand-Damage Model: Version 2.0. (Computer program)

Treesearch

J.J. Colbert; George Racin

2001-01-01

The Stand-Damage Model simulates the growth of a forest stand, a spatially homogeneous collection of trees growing on a site. The model simulates growth from an initial inventory, user-prescribed management practices, and the effects of gypsy moth defoliation. Here we provide installation and operating instructions for Version 2.0.

CDMetaPOP: An individual-based, eco-evolutionary model for spatially explicit simulation of landscape demogenetics

USGS Publications Warehouse

Landguth, Erin L; Bearlin, Andrew; Day, Casey; Dunham, Jason B.

2016-01-01

1. Combining landscape demographic and genetics models offers powerful methods for addressing questions for eco-evolutionary applications.2. Using two illustrative examples, we present Cost–Distance Meta-POPulation, a program to simulate changes in neutral and/or selection-driven genotypes through time as a function of individual-based movement, complex spatial population dynamics, and multiple and changing landscape drivers.3. Cost–Distance Meta-POPulation provides a novel tool for questions in landscape genetics by incorporating population viability analysis, while linking directly to conservation applications.

The Atlanta Urban Heat Island Mitigation and Air Quality Modeling Project: How High-Resoution Remote Sensing Data Can Improve Air Quality Models

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William L.; Khan, Maudood N.

2006-01-01

The Atlanta Urban Heat Island and Air Quality Project had its genesis in Project ATLANTA (ATlanta Land use Analysis: Temperature and Air quality) that began in 1996. Project ATLANTA examined how high-spatial resolution thermal remote sensing data could be used to derive better measurements of the Urban Heat Island effect over Atlanta. We have explored how these thermal remote sensing, as well as other imaged datasets, can be used to better characterize the urban landscape for improved air quality modeling over the Atlanta area. For the air quality modeling project, the National Land Cover Dataset and the local scale Landpro99 dataset at 30m spatial resolutions have been used to derive land use/land cover characteristics for input into the MM5 mesoscale meteorological model that is one of the foundations for the Community Multiscale Air Quality (CMAQ) model to assess how these data can improve output from CMAQ. Additionally, land use changes to 2030 have been predicted using a Spatial Growth Model (SGM). SGM simulates growth around a region using population, employment and travel demand forecasts. Air quality modeling simulations were conducted using both current and future land cover. Meteorological modeling simulations indicate a 0.5 C increase in daily maximum air temperatures by 2030. Air quality modeling simulations show substantial differences in relative contributions of individual atmospheric pollutant constituents as a result of land cover change. Enhanced boundary layer mixing over the city tends to offset the increase in ozone concentration expected due to higher surface temperatures as a result of urbanization.

Addressing spatial scales and new mechanisms in climate impact ecosystem modeling

NASA Astrophysics Data System (ADS)

Poulter, B.; Joetzjer, E.; Renwick, K.; Ogunkoya, G.; Emmett, K.

2015-12-01

Climate change impacts on vegetation distributions are typically addressed using either an empirical approach, such as a species distribution model (SDM), or with process-based methods, for example, dynamic global vegetation models (DGVMs). Each approach has its own benefits and disadvantages. For example, an SDM is constrained by data and few parameters, but does not include adaptation or acclimation processes or other ecosystem feedbacks that may act to mitigate or enhance climate effects. Alternatively, a DGVM model includes many mechanisms relating plant growth and disturbance to climate, but simulations are costly to perform at high-spatial resolution and there remains large uncertainty on a variety of fundamental physical processes. To address these issues, here, we present two DGVM-based case studies where i) high-resolution (1 km) simulations are being performed for vegetation in the Greater Yellowstone Ecosystem using a biogeochemical, forest gap model, LPJ-GUESS, and ii) where new mechanisms for simulating tropical tree-mortality are being introduced. High-resolution DGVM model simulations require not only computing and reorganizing code but also a consideration of scaling issues on vegetation dynamics and stochasticity and also on disturbance and migration. New mechanisms for simulating forest mortality must consider hydraulic limitations and carbon reserves and their interactions on source-sink dynamics and in controlling water potentials. Improving DGVM approaches by addressing spatial scale challenges and integrating new approaches for estimating forest mortality will provide new insights more relevant for land management and possibly reduce uncertainty by physical processes more directly comparable to experimental and observational evidence.

Spatial Analysis of Biomass Supply: Economic and Environmental Impacts

USDA-ARS?s Scientific Manuscript database

The EPIC simulation model is used with SSURGO soils, field location information, and a transportation cost model to analyze potential biomass supply for a West Central MN bioenergy plant. The simulation shows the relationship between biomass price, locations of where biomass production is profitable...

Utility of computer simulations in landscape genetics

Treesearch

Bryan K. Epperson; Brad H. McRae; Kim Scribner; Samuel A. Cushman; Michael S. Rosenberg; Marie-Josee Fortin; Patrick M. A. James; Melanie Murphy; Stephanie Manel; Pierre Legendre; Mark R. T. Dale

2010-01-01

Population genetics theory is primarily based on mathematical models in which spatial complexity and temporal variability are largely ignored. In contrast, the field of landscape genetics expressly focuses on how population genetic processes are affected by complex spatial and temporal environmental heterogeneity. It is spatially explicit and relates patterns to...

Spatial Fluctuations of the Intergalactic Temperature-Density Relation After Hydrogen Reionization

NASA Astrophysics Data System (ADS)

Keating, Laura C.; Puchwein, Ewald; Haehnelt, Martin G.

2018-04-01

The thermal state of the post-reionization IGM is sensitive to the timing of reionization and the nature of the ionizing sources. We have modelled here the thermal state of the IGM in cosmological radiative transfer simulations of a realistic, extended, spatially inhomogeneous hydrogen reionization process, carefully calibrated with Lyα forest data. We compare these with cosmological simulations run using a spatially homogeneous ionizing background. The simulations with a realistic growth of ionized regions and a realistic spread in reionization redshifts show, as expected, significant spatial fluctuations in the temperature-density relation (TDR) of the post-reionization IGM. The most recently ionized regions are hottest and exhibit a flatter TDR. In simulations consistent with the average TDR inferred from Lyα forest data, these spatial fluctuations have a moderate but noticeable effect on the statistical properties of the Lyα opacity of the IGM at z ˜ 4 - 6. This should be taken into account in accurate measurements of the thermal properties of the IGM and the free-streaming of dark matter from Lyα forest data in this redshift range. The spatial variations of the TDR predicted by our simulations are, however, smaller by about a factor two than would be necessary to explain the observed large spatial opacity fluctuations on large (≥ 50 h-1 comoving Mpc) scales at z ≳ 5.5.

Spatial fluctuations of the intergalactic temperature-density relation after hydrogen reionization

NASA Astrophysics Data System (ADS)

Keating, Laura C.; Puchwein, Ewald; Haehnelt, Martin G.

2018-07-01

The thermal state of the post-reionization IGM is sensitive to the timing of reionization and the nature of the ionizing sources. We have modelled here the thermal state of the IGM in cosmological radiative transfer simulations of a realistic, extended, spatially inhomogeneous hydrogen reionization process, carefully calibrated with Ly α forest data. We compare these with cosmological simulations run using a spatially homogeneous ionizing background. The simulations with a realistic growth of ionized regions and a realistic spread in reionization redshifts show, as expected, significant spatial fluctuations in the temperature-density relation (TDR) of the post-reionization IGM. The most recently ionized regions are hottest and exhibit a flatter TDR. In simulations consistent with the average TDR inferred from Ly α forest data, these spatial fluctuations have a moderate but noticeable effect on the statistical properties of the Ly α opacity of the IGM at z ˜ 4-6. This should be taken into account in accurate measurements of the thermal properties of the IGM and the free-streaming of dark matter from Ly α forest data in this redshift range. The spatial variations of the TDR predicted by our simulations are, however, smaller by about a factor of 2 than would be necessary to explain the observed large spatial opacity fluctuations on large (≥50 h-1 comoving Mpc) scales atz ≳ 5.5.

Coarse-grained hydrodynamics from correlation functions

NASA Astrophysics Data System (ADS)

Palmer, Bruce

2018-02-01

This paper will describe a formalism for using correlation functions between different grid cells as the basis for determining coarse-grained hydrodynamic equations for modeling the behavior of mesoscopic fluid systems. Configurations from a molecular dynamics simulation or other atomistic simulation are projected onto basis functions representing grid cells in a continuum hydrodynamic simulation. Equilibrium correlation functions between different grid cells are evaluated from the molecular simulation and used to determine the evolution operator for the coarse-grained hydrodynamic system. The formalism is demonstrated on a discrete particle simulation of diffusion with a spatially dependent diffusion coefficient. Correlation functions are calculated from the particle simulation and the spatially varying diffusion coefficient is recovered using a fitting procedure.

LANDIS 4.0 users guide. LANDIS: a spatially explicit model of forest landscape disturbance, management, and succession

Treesearch

Hong S. He; Wei Li; Brian R. Sturtevant; Jian Yang; Bo Z. Shang; Eric J. Gustafson; David J. Mladenoff

2005-01-01

LANDIS 4.0 is new-generation software that simulates forest landscape change over large spatial and temporal scales. It is used to explore how disturbances, succession, and management interact to determine forest composition and pattern. Also describes software architecture, model assumptions and provides detailed instructions on the use of the model.

Modeling and simulating industrial land-use evolution in Shanghai, China

NASA Astrophysics Data System (ADS)

Qiu, Rongxu; Xu, Wei; Zhang, John; Staenz, Karl

2018-01-01

This study proposes a cellular automata-based Industrial and Residential Land Use Competition Model to simulate the dynamic spatial transformation of industrial land use in Shanghai, China. In the proposed model, land development activities in a city are delineated as competitions among different land-use types. The Hedonic Land Pricing Model is adopted to implement the competition framework. To improve simulation results, the Land Price Agglomeration Model was devised to simulate and adjust classic land price theory. A new evolutionary algorithm-based parameter estimation method was devised in place of traditional methods. Simulation results show that the proposed model closely resembles actual land transformation patterns and the model can not only simulate land development, but also redevelopment processes in metropolitan areas.

An Investigation on the Spatial Variability of Manning Roughness Coefficients in Continental-scale River Routing Simulations

NASA Astrophysics Data System (ADS)

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

2017-12-01

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.

Effects of a transient sea surface temperature anomaly on the energetics of the Mintz-Arakawa model atmosphere

NASA Technical Reports Server (NTRS)

Chow, S. H.

1974-01-01

The possible response of the atmosphere, as simulated by the two level Mintz-Arakawa global general circulation model, to a transient North Pacific sea surface temperature anomaly is investigated in terms of the energetics both in the spatial and wave number domains. Results indicate that the transient SST variations of reasonable magnitude in the North Pacific Ocean can induce a disturbing effect on the global energetics both in the spatial and wave number domains. The ability of the two level Mintz-Arakawa model to simulate the atmospheric energetics is also examined. Except in the tropics, the model exhibits a reasonable and realistic energy budget.

Simulation of blood flow in deformable vessels using subject-specific geometry and spatially varying wall properties

PubMed Central

Xiong, Guanglei; Figueroa, C. Alberto; Xiao, Nan; Taylor, Charles A.

2011-01-01

SUMMARY Simulation of blood flow using image-based models and computational fluid dynamics has found widespread application to quantifying hemodynamic factors relevant to the initiation and progression of cardiovascular diseases and for planning interventions. Methods for creating subject-specific geometric models from medical imaging data have improved substantially in the last decade but for many problems, still require significant user interaction. In addition, while fluid–structure interaction methods are being employed to model blood flow and vessel wall dynamics, tissue properties are often assumed to be uniform. In this paper, we propose a novel workflow for simulating blood flow using subject-specific geometry and spatially varying wall properties. The geometric model construction is based on 3D segmentation and geometric processing. Variable wall properties are assigned to the model based on combining centerline-based and surface-based methods. We finally demonstrate these new methods using an idealized cylindrical model and two subject-specific vascular models with thoracic and cerebral aneurysms. PMID:21765984

Spatial capture-recapture models allowing Markovian transience or dispersal

USGS Publications Warehouse

Royle, J. Andrew; Fuller, Angela K.; Sutherland, Chris

2016-01-01

Spatial capture–recapture (SCR) models are a relatively recent development in quantitative ecology, and they are becoming widely used to model density in studies of animal populations using camera traps, DNA sampling and other methods which produce spatially explicit individual encounter information. One of the core assumptions of SCR models is that individuals possess home ranges that are spatially stationary during the sampling period. For many species, this assumption is unlikely to be met and, even for species that are typically territorial, individuals may disperse or exhibit transience at some life stages. In this paper we first conduct a simulation study to evaluate the robustness of estimators of density under ordinary SCR models when dispersal or transience is present in the population. Then, using both simulated and real data, we demonstrate that such models can easily be described in the BUGS language providing a practical framework for their analysis, which allows us to evaluate movement dynamics of species using capture–recapture data. We find that while estimators of density are extremely robust, even to pathological levels of movement (e.g., complete transience), the estimator of the spatial scale parameter of the encounter probability model is confounded with the dispersal/transience scale parameter. Thus, use of ordinary SCR models to make inferences about density is feasible, but interpretation of SCR model parameters in relation to movement should be avoided. Instead, when movement dynamics are of interest, such dynamics should be parameterized explicitly in the model.

Dynamic Shade and Irradiance Simulation of Aquatic Landscapes and Watersheds

EPA Science Inventory

Penumbra is a landscape shade and irradiance simulation model that simulates how solar energy spatially and temporally interacts within dynamic ecosystems such as riparian zones, forests, and other terrain that cast topological shadows. Direct and indirect solar energy accumulate...

Addressing Spatial Dependence Bias in Climate Model Simulations—An Independent Component Analysis Approach

NASA Astrophysics Data System (ADS)

Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

2018-02-01

Conventional bias correction is usually applied on a grid-by-grid basis, meaning that the resulting corrections cannot address biases in the spatial distribution of climate variables. To solve this problem, a two-step bias correction method is proposed here to correct time series at multiple locations conjointly. The first step transforms the data to a set of statistically independent univariate time series, using a technique known as independent component analysis (ICA). The mutually independent signals can then be bias corrected as univariate time series and back-transformed to improve the representation of spatial dependence in the data. The spatially corrected data are then bias corrected at the grid scale in the second step. The method has been applied to two CMIP5 General Circulation Model simulations for six different climate regions of Australia for two climate variables—temperature and precipitation. The results demonstrate that the ICA-based technique leads to considerable improvements in temperature simulations with more modest improvements in precipitation. Overall, the method results in current climate simulations that have greater equivalency in space and time with observational data.

Multiple Point Statistics algorithm based on direct sampling and multi-resolution images

NASA Astrophysics Data System (ADS)

Julien, S.; Renard, P.; Chugunova, T.

2017-12-01

Multiple Point Statistics (MPS) has become popular for more than one decade in Earth Sciences, because these methods allow to generate random fields reproducing highly complex spatial features given in a conceptual model, the training image, while classical geostatistics techniques based on bi-point statistics (covariance or variogram) fail to generate realistic models. Among MPS methods, the direct sampling consists in borrowing patterns from the training image to populate a simulation grid. This latter is sequentially filled by visiting each of these nodes in a random order, and then the patterns, whose the number of nodes is fixed, become narrower during the simulation process, as the simulation grid is more densely informed. Hence, large scale structures are caught in the beginning of the simulation and small scale ones in the end. However, MPS may mix spatial characteristics distinguishable at different scales in the training image, and then loose the spatial arrangement of different structures. To overcome this limitation, we propose to perform MPS simulation using a decomposition of the training image in a set of images at multiple resolutions. Applying a Gaussian kernel onto the training image (convolution) results in a lower resolution image, and iterating this process, a pyramid of images depicting fewer details at each level is built, as it can be done in image processing for example to lighten the space storage of a photography. The direct sampling is then employed to simulate the lowest resolution level, and then to simulate each level, up to the finest resolution, conditioned to the level one rank coarser. This scheme helps reproduce the spatial structures at any scale of the training image and then generate more realistic models. We illustrate the method with aerial photographies (satellite images) and natural textures. Indeed, these kinds of images often display typical structures at different scales and are well-suited for MPS simulation techniques.

Improved Satellite-based Crop Yield Mapping by Spatially Explicit Parameterization of Crop Phenology

NASA Astrophysics Data System (ADS)

Jin, Z.; Azzari, G.; Lobell, D. B.

2016-12-01

Field-scale mapping of crop yields with satellite data often relies on the use of crop simulation models. However, these approaches can be hampered by inaccuracies in the simulation of crop phenology. Here we present and test an approach to use dense time series of Landsat 7 and 8 acquisitions data to calibrate various parameters related to crop phenology simulation, such as leaf number and leaf appearance rates. These parameters are then mapped across the Midwestern United States for maize and soybean, and for two different simulation models. We then implement our recently developed Scalable satellite-based Crop Yield Mapper (SCYM) with simulations reflecting the improved phenology parameterizations, and compare to prior estimates based on default phenology routines. Our preliminary results show that the proposed method can effectively alleviate the underestimation of early-season LAI by the default Agricultural Production Systems sIMulator (APSIM), and that spatially explicit parameterization for the phenology model substantially improves the SCYM performance in capturing the spatiotemporal variation in maize and soybean yield. The scheme presented in our study thus preserves the scalability of SCYM, while significantly reducing its uncertainty.

Smoldyn: particle-based simulation with rule-based modeling, improved molecular interaction and a library interface.

PubMed

Andrews, Steven S

2017-03-01

Smoldyn is a spatial and stochastic biochemical simulator. It treats each molecule of interest as an individual particle in continuous space, simulating molecular diffusion, molecule-membrane interactions and chemical reactions, all with good accuracy. This article presents several new features. Smoldyn now supports two types of rule-based modeling. These are a wildcard method, which is very convenient, and the BioNetGen package with extensions for spatial simulation, which is better for complicated models. Smoldyn also includes new algorithms for simulating the diffusion of surface-bound molecules and molecules with excluded volume. Both are exact in the limit of short time steps and reasonably good with longer steps. In addition, Smoldyn supports single-molecule tracking simulations. Finally, the Smoldyn source code can be accessed through a C/C ++ language library interface. Smoldyn software, documentation, code, and examples are at http://www.smoldyn.org . steven.s.andrews@gmail.com. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Classifying and comparing spatial models of fire dynamics

Treesearch

Geoffrey J. Cary; Robert E. Keane; Mike D. Flannigan

2007-01-01

Wildland fire is a significant disturbance in many ecosystems worldwide and the interaction of fire with climate and vegetation over long time spans has major effects on vegetation dynamics, ecosystem carbon budgets, and patterns of biodiversity. Landscape-Fire-Succession Models (LFSMs) that simulate the linked processes of fire and vegetation development in a spatial...

Spatially Distributed Assimilation of Remotely Sensed Leaf Area Index and Potential Evapotranspiration for Hydrologic Modeling in Wetland Landscapes

EPA Science Inventory

Evapotranspiration (ET), a highly dynamic flux in wetland landscapes, regulates the accuracy of surface/sub-surface runoff simulation in a hydrologic model. However, considerable uncertainty in simulating ET-related processes remains, including our limited ability to incorporate ...

Does model structure limit the use of satellite data as hydrologic forcing for distributed operational models?

NASA Astrophysics Data System (ADS)

Bowman, A. L.; Franz, K.; Hogue, T. S.

2015-12-01

We are investigating the implications for use of satellite data in operational streamflow prediction. Specifically, the consequence of potential hydrologic model structure deficiencies on the ability to achieve improved forecast accuracy through the use of satellite data. We want to understand why advanced data do not lead to improved streamflow simulations by exploring how various fluxes and states differ among models of increasing complexity. In a series of prior studies, we investigated the use of a daily satellite-derived potential evapotranspiration (PET) estimate as input to the National Weather Service (NWS) streamflow forecast models for watersheds in the Upper Mississippi and Red river basins. Although the spatial PET product appears to represent the day-to-day variability in PET more realistically than current climatological methods used by the NWS, the impact of the satellite data on streamflow simulations results in slightly poorer model efficiency overall. Analysis of the model states indicates the model progresses differently between simulations with baseline PET and the satellite-derived PET input, though variation in streamflow simulations overall is negligible. For instance, the upper zone states, responsible for the high flows of a hydrograph, show a profound difference, while simulation of the peak flows tend to show little variation in the timing and magnitude. Using the spatial PET input, the lower zone states show improvement with simulating the recession limb and baseflow portion of the hydrograph. We anticipate that through a better understanding of the relationship between model structure, model states, and simulated streamflow we will be able to diagnose why simulations of discharge from the forecast model have failed to improve when provided seemingly more representative input data. Identifying model limitations are critical to demonstrating the full benefit of a satellite data for operational use.

A MODELING AND SIMULATION LANGUAGE FOR BIOLOGICAL CELLS WITH COUPLED MECHANICAL AND CHEMICAL PROCESSES

PubMed Central

Somogyi, Endre; Glazier, James A.

2017-01-01

Biological cells are the prototypical example of active matter. Cells sense and respond to mechanical, chemical and electrical environmental stimuli with a range of behaviors, including dynamic changes in morphology and mechanical properties, chemical uptake and secretion, cell differentiation, proliferation, death, and migration. Modeling and simulation of such dynamic phenomena poses a number of computational challenges. A modeling language describing cellular dynamics must naturally represent complex intra and extra-cellular spatial structures and coupled mechanical, chemical and electrical processes. Domain experts will find a modeling language most useful when it is based on concepts, terms and principles native to the problem domain. A compiler must then be able to generate an executable model from this physically motivated description. Finally, an executable model must efficiently calculate the time evolution of such dynamic and inhomogeneous phenomena. We present a spatial hybrid systems modeling language, compiler and mesh-free Lagrangian based simulation engine which will enable domain experts to define models using natural, biologically motivated constructs and to simulate time evolution of coupled cellular, mechanical and chemical processes acting on a time varying number of cells and their environment. PMID:29303160

A MODELING AND SIMULATION LANGUAGE FOR BIOLOGICAL CELLS WITH COUPLED MECHANICAL AND CHEMICAL PROCESSES.

PubMed

Somogyi, Endre; Glazier, James A

2017-04-01

Biological cells are the prototypical example of active matter. Cells sense and respond to mechanical, chemical and electrical environmental stimuli with a range of behaviors, including dynamic changes in morphology and mechanical properties, chemical uptake and secretion, cell differentiation, proliferation, death, and migration. Modeling and simulation of such dynamic phenomena poses a number of computational challenges. A modeling language describing cellular dynamics must naturally represent complex intra and extra-cellular spatial structures and coupled mechanical, chemical and electrical processes. Domain experts will find a modeling language most useful when it is based on concepts, terms and principles native to the problem domain. A compiler must then be able to generate an executable model from this physically motivated description. Finally, an executable model must efficiently calculate the time evolution of such dynamic and inhomogeneous phenomena. We present a spatial hybrid systems modeling language, compiler and mesh-free Lagrangian based simulation engine which will enable domain experts to define models using natural, biologically motivated constructs and to simulate time evolution of coupled cellular, mechanical and chemical processes acting on a time varying number of cells and their environment.

Landscape modeling for Everglades ecosystem restoration

USGS Publications Warehouse

DeAngelis, D.L.; Gross, L.J.; Huston, M.A.; Wolff, W.F.; Fleming, D.M.; Comiskey, E.J.; Sylvester, S.M.

1998-01-01

A major environmental restoration effort is under way that will affect the Everglades and its neighboring ecosystems in southern Florida. Ecosystem and population-level modeling is being used to help in the planning and evaluation of this restoration. The specific objective of one of these modeling approaches, the Across Trophic Level System Simulation (ATLSS), is to predict the responses of a suite of higher trophic level species to several proposed alterations in Everglades hydrology. These include several species of wading birds, the snail kite, Cape Sable seaside sparrow, Florida panther, white-tailed deer, American alligator, and American crocodile. ATLSS is an ecosystem landscape-modeling approach and uses Geographic Information System (GIS) vegetation data and existing hydrology models for South Florida to provide the basic landscape for these species. A method of pseudotopography provides estimates of water depths through time at 28 ?? 28-m resolution across the landscape of southern Florida. Hydrologic model output drives models of habitat and prey availability for the higher trophic level species. Spatially explicit, individual-based computer models simulate these species. ATLSS simulations can compare the landscape dynamic spatial pattern of the species resulting from different proposed water management strategies. Here we compare the predicted effects of one possible change in water management in South Florida with the base case of no change. Preliminary model results predict substantial differences between these alternatives in some biotic spatial patterns. ?? 1998 Springer-Verlag.

Excellent approach to modeling urban expansion by fuzzy cellular automata: agent base model

NASA Astrophysics Data System (ADS)

Khajavigodellou, Yousef; Alesheikh, Ali A.; Mohammed, Abdulrazak A. S.; Chapi, Kamran

2014-09-01

Recently, the interaction between humans and their environment is the one of important challenges in the world. Landuse/ cover change (LUCC) is a complex process that includes actors and factors at different social and spatial levels. The complexity and dynamics of urban systems make the applicable practice of urban modeling very difficult. With the increased computational power and the greater availability of spatial data, micro-simulation such as the agent based and cellular automata simulation methods, has been developed by geographers, planners, and scholars, and it has shown great potential for representing and simulating the complexity of the dynamic processes involved in urban growth and land use change. This paper presents Fuzzy Cellular Automata in Geospatial Information System and remote Sensing to simulated and predicted urban expansion pattern. These FCA-based dynamic spatial urban models provide an improved ability to forecast and assess future urban growth and to create planning scenarios, allowing us to explore the potential impacts of simulations that correspond to urban planning and management policies. A fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on Land use change is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. The model integrates an ABM (agent-based model) and FCA (Fuzzy Cellular Automata) to investigate a complex decision-making process and future urban dynamic processes. Based on this model rapid development and green land protection under the influences of the behaviors and decision modes of regional authority agents, real estate developer agents, resident agents and non- resident agents and their interactions have been applied to predict the future development patterns of the Erbil metropolitan region.

A cellular automaton model for ship traffic flow in waterways

NASA Astrophysics Data System (ADS)

Qi, Le; Zheng, Zhongyi; Gang, Longhui

2017-04-01

With the development of marine traffic, waterways become congested and more complicated traffic phenomena in ship traffic flow are observed. It is important and necessary to build a ship traffic flow model based on cellular automata (CAs) to study the phenomena and improve marine transportation efficiency and safety. Spatial discretization rules for waterways and update rules for ship movement are two important issues that are very different from vehicle traffic. To solve these issues, a CA model for ship traffic flow, called a spatial-logical mapping (SLM) model, is presented. In this model, the spatial discretization rules are improved by adding a mapping rule. And the dynamic ship domain model is considered in the update rules to describe ships' interaction more exactly. Take the ship traffic flow in the Singapore Strait for example, some simulations were carried out and compared. The simulations show that the SLM model could avoid ship pseudo lane-change efficiently, which is caused by traditional spatial discretization rules. The ship velocity change in the SLM model is consistent with the measured data. At finally, from the fundamental diagram, the relationship between traffic ability and the lengths of ships is explored. The number of ships in the waterway declines when the proportion of large ships increases.

Non-stationary Return Levels of CMIP5 Multi-model Temperature Extremes

DOE PAGES

Cheng, L.; Phillips, T. J.; AghaKouchak, A.

2015-05-01

The objective of this study is to evaluate to what extent the CMIP5 climate model simulations of the climate of the twentieth century can represent observed warm monthly temperature extremes under a changing environment. The biases and spatial patterns of 2-, 10-, 25-, 50- and 100-year return levels of the annual maxima of monthly mean temperature (hereafter, annual temperature maxima) from CMIP5 simulations are compared with those of Climatic Research Unit (CRU) observational data considered under a non-stationary assumption. The results show that CMIP5 climate models collectively underestimate the mean annual maxima over arid and semi-arid regions that are mostmore » subject to severe heat waves and droughts. Furthermore, the results indicate that most climate models tend to underestimate the historical annual temperature maxima over the United States and Greenland, while generally disagreeing in their simulations over cold regions. Return level analysis shows that with respect to the spatial patterns of the annual temperature maxima, there are good agreements between the CRU observations and most CMIP5 simulations. However, the magnitudes of the simulated annual temperature maxima differ substantially across individual models. Discrepancies are generally larger over higher latitudes and cold regions.« less

A spatial scaling relationship for soil moisture in a semiarid landscape, using spatial scaling relationships for pedology

NASA Astrophysics Data System (ADS)

Willgoose, G. R.; Chen, M.; Cohen, S.; Saco, P. M.; Hancock, G. R.

2013-12-01

In humid areas it is generally considered that soil moisture scales spatially according to the wetness index of the landscape. This scaling arises from lateral flow downslope of ground water within the soil zone. However, in semi-arid and drier regions, this lateral flow is small and fluxes are dominated by vertical flows driven by infiltration and evapotranspiration. Thus, in the absence of runon processes, soil moisture at a location is more driven by local factors such as soil and vegetation properties at that location rather than upstream processes draining to that point. The 'apparent' spatial randomness of soil and vegetation properties generally suggests that soil moisture for semi-arid regions is spatially random. In this presentation a new analysis of neutron probe data during summer from the Tarrawarra site near Melbourne, Australia shows persistent spatial organisation of soil moisture over several years. This suggests a link between permanent features of the catchment (e.g. soil properties) and soil moisture distribution, even though the spatial pattern of soil moisture during the 4 summers monitored appears spatially random. This and other data establishes a prima facie case that soil variations drive spatial variation in soil moisture. Accordingly, we used a previously published spatial scaling relationship for soil properties derived using the mARM pedogenesis model to simulate the spatial variation of soil grading. This soil grading distribution was used in the Rosetta pedotransfer model to derive a spatial distribution of soil functional properties (e.g. saturated hydraulic conductivity, porosity). These functional properties were then input into the HYDRUS-1D soil moisture model and soil moisture simulated for 3 years at daily resolution. The HYDRUS model used had previously been calibrated to field observed soil moisture data at our SASMAS field site. The scaling behaviour of soil moisture derived from this modelling will be discussed and compared with observed data from our SASMAS field sites.

Modelling carbon responses of tundra ecosystems to historical and projected climate: Sensitivity of pan-Arctic carbon storage to temporal and spatial variation in climate

USGS Publications Warehouse

McGuire, A.D.; Clein, Joy S.; Melillo, J.M.; Kicklighter, D.W.; Meier, R.A.; Vorosmarty, C.J.; Serreze, Mark C.

2000-01-01

Historical and projected climate trends for high latitudes show substantial temporal and spatial variability. To identify uncertainties in simulating carbon (C) dynamics for pan-Arctic tundra, we compare the historical and projected responses of tundra C storage from 1921 to 2100 between simulations by the Terrestrial Ecosystem Model (TEM) for the pan-Arctic and the Kuparuk River Basin, which was the focus of an integrated study of C dynamics from 1994 to 1996. In the historical period from 1921 to 1994, the responses of net primary production (NPP) and heterotrophic respiration (RH) simulated for the Kuparuk River Basin and the pan-Arctic are correlated with the same factors; NPP is positively correlated with net nitrogen mineralization (NMIN) and RH is negatively correlated with mean annual soil moisture. In comparison to the historical period, the spatially aggregated responses of NPP and RH for the Kuparuk River Basin and the pan-Arctic in our simulations for the projected period have different sensitivities to temperature, soil moisture and NMIN. In addition to being sensitive to soil moisture during the projected period, RH is also sensitive to temperature and there is a significant correlation between RH and NMIN. We interpret the increases in NPP during the projected period as being driven primarily by increases in NMIN, and that the correlation between NPP and temperature in the projected period is a result primarily of the causal linkage between temperature, RH, and NMIN. Although similar factors appear to be controlling simulated regional-and biome-scale C dynamics, simulated C dynamics at the two scales differ in magnitude with higher increases in C storage simulated for the Kuparuk River Basin than for the pan-Arctic at the end of the historical period and throughout the projected period. Also, the results of the simulations indicate that responses of C storage show different climate sensitivities at regional and pan-Arctic spatial scales and that these sensitivities change across the temporal scope of the simulations. The results of the TEM simulations indicate that the scaling of C dynamics to a region of arctic tundra may not represent C dynamics of pan-Arctic tundra because of the limited spatial variation in climate and vegetation within a region relative to the pan-Arctic. For reducing uncertainties, our analyses highlight the importance of incorporating the understanding gained from process-level studies of C dynamics in a region of arctic tundra into process-based models that simulate C dynamics in a spatially explicit fashion across the spatial domain of pan-Arctic tundra. Also, efforts to improve gridded datasets of historical climate for the pan-Arctic would advance the ability to assess the responses of C dynamics for pan-Arctic tundra in a more realistic fashion. A major challenge will be to incorporate topographic controls over soil moisture in assessing the response of C storage for pan-Arctic tundra.

An exactly solvable, spatial model of mutation accumulation in cancer

NASA Astrophysics Data System (ADS)

Paterson, Chay; Nowak, Martin A.; Waclaw, Bartlomiej

2016-12-01

One of the hallmarks of cancer is the accumulation of driver mutations which increase the net reproductive rate of cancer cells and allow them to spread. This process has been studied in mathematical models of well mixed populations, and in computer simulations of three-dimensional spatial models. But the computational complexity of these more realistic, spatial models makes it difficult to simulate realistically large and clinically detectable solid tumours. Here we describe an exactly solvable mathematical model of a tumour featuring replication, mutation and local migration of cancer cells. The model predicts a quasi-exponential growth of large tumours, even if different fragments of the tumour grow sub-exponentially due to nutrient and space limitations. The model reproduces clinically observed tumour growth times using biologically plausible rates for cell birth, death, and migration rates. We also show that the expected number of accumulated driver mutations increases exponentially in time if the average fitness gain per driver is constant, and that it reaches a plateau if the gains decrease over time. We discuss the realism of the underlying assumptions and possible extensions of the model.

An alternative way to evaluate chemistry-transport model variability

NASA Astrophysics Data System (ADS)

Menut, Laurent; Mailler, Sylvain; Bessagnet, Bertrand; Siour, Guillaume; Colette, Augustin; Couvidat, Florian; Meleux, Frédérik

2017-03-01

A simple and complementary model evaluation technique for regional chemistry transport is discussed. The methodology is based on the concept that we can learn about model performance by comparing the simulation results with observational data available for time periods other than the period originally targeted. First, the statistical indicators selected in this study (spatial and temporal correlations) are computed for a given time period, using colocated observation and simulation data in time and space. Second, the same indicators are used to calculate scores for several other years while conserving the spatial locations and Julian days of the year. The difference between the results provides useful insights on the model capability to reproduce the observed day-to-day and spatial variability. In order to synthesize the large amount of results, a new indicator is proposed, designed to compare several error statistics between all the years of validation and to quantify whether the period and area being studied were well captured by the model for the correct reasons.

Assessment of the performance of CORDEX-SA experiments in simulating seasonal mean temperature over the Himalayan region for the present climate: Part I

NASA Astrophysics Data System (ADS)

Nengker, T.; Choudhary, A.; Dimri, A. P.

2018-04-01

The ability of an ensemble of five regional climate models (hereafter RCMs) under Coordinated Regional Climate Downscaling Experiments-South Asia (hereafter, CORDEX-SA) in simulating the key features of present day near surface mean air temperature (Tmean) climatology (1970-2005) over the Himalayan region is studied. The purpose of this paper is to understand the consistency in the performance of models across the ensemble, space and seasons. For this a number of statistical measures like trend, correlation, variance, probability distribution function etc. are applied to evaluate the performance of models against observation and simultaneously the underlying uncertainties between them for four different seasons. The most evident finding from the study is the presence of a large cold bias (-6 to -8 °C) which is systematically seen across all the models and across space and time over the Himalayan region. However, these RCMs with its fine resolution perform extremely well in capturing the spatial distribution of the temperature features as indicated by a consistently high spatial correlation (greater than 0.9) with the observation in all seasons. In spite of underestimation in simulated temperature and general intensification of cold bias with increasing elevation the models show a greater rate of warming than the observation throughout entire altitudinal stretch of study region. During winter, the simulated rate of warming gets even higher at high altitudes. Moreover, a seasonal response of model performance and its spatial variability to elevation is found.

A GRASS GIS module to obtain an estimation of glacier behavior under climate change: A pilot study on Italian glacier

NASA Astrophysics Data System (ADS)

Strigaro, Daniele; Moretti, Massimiliano; Mattavelli, Matteo; Frigerio, Ivan; Amicis, Mattia De; Maggi, Valter

2016-09-01

The aim of this work is to integrate the Minimal Glacier Model in a Geographic Information System Python module in order to obtain spatial simulations of glacier retreat and to assess the future scenarios with a spatial representation. The Minimal Glacier Models are a simple yet effective way of estimating glacier response to climate fluctuations. This module can be useful for the scientific and glaciological community in order to evaluate glacier behavior, driven by climate forcing. The module, called r.glacio.model, is developed in a GRASS GIS (GRASS Development Team, 2016) environment using Python programming language combined with different libraries as GDAL, OGR, CSV, math, etc. The module is applied and validated on the Rutor glacier, a glacier in the south-western region of the Italian Alps. This glacier is very large in size and features rather regular and lively dynamics. The simulation is calibrated by reconstructing the 3-dimensional dynamics flow line and analyzing the difference between the simulated flow line length variations and the observed glacier fronts coming from ortophotos and DEMs. These simulations are driven by the past mass balance record. Afterwards, the future assessment is estimated by using climatic drivers provided by a set of General Circulation Models participating in the Climate Model Inter-comparison Project 5 effort. The approach devised in r.glacio.model can be applied to most alpine glaciers to obtain a first-order spatial representation of glacier behavior under climate change.

Evaluation of UTLS Carbon Monoxide Simulations in GMI and GEOS-Chem Chemical Transport Models using Aura MLS Observations

NASA Technical Reports Server (NTRS)

Huang, Lei; Jiang, Jonathan H.; Murray, Lee T.; Damon, Megan R.; Su, Hui; Livesey, Nathaniel J.

2016-01-01

This study evaluates the distribution and variation of carbon monoxide (CO) in the upper troposphere and lower stratosphere (UTLS) during 2004-2012 as simulated by two chemical transport models, using the latest version of Aura Microwave Limb Sounder (MLS) observations. The simulated spatial distributions, temporal variations and vertical transport of CO in the UTLS region are compared with those observed by MLS. We also investigate the impact of surface emissions and deep convection on CO concentrations in the UTLS over different regions, using both model simulations and MLS observations. Global Modeling Initiative (GMI) and GEOS-Chem simulations of UTLS CO both show similar spatial distributions to observations. The global mean CO values simulated by both models agree with MLS observations at 215 and 147 hPa, but are significantly underestimated by more than 40% at 100 hPa. In addition, the models underestimate the peak CO values by up to 70% at 100 hPa, 60% at 147 hPa and 40% at 215 hPa, with GEOS-Chem generally simulating more CO at 100 hPa and less CO at 215 hPa than GMI. The seasonal distributions of CO simulated by both models are in better agreement with MLS in the Southern Hemisphere (SH) than in the Northern Hemisphere (NH), with disagreements between model and observations over enhanced CO regions such as southern Africa. The simulated vertical transport of CO shows better agreement with MLS in the tropics and the SH subtropics than the NH subtropics. We also examine regional variations in the relationships among surface CO emission, convection and UTLS CO concentrations. The two models exhibit emission-convection- CO relationships similar to those observed by MLS over the tropics and some regions with enhanced UTLS CO.

Light extraction in planar light-emitting diode with nonuniform current injection: model and simulation.

PubMed

Khmyrova, Irina; Watanabe, Norikazu; Kholopova, Julia; Kovalchuk, Anatoly; Shapoval, Sergei

2014-07-20

We develop an analytical and numerical model for performing simulation of light extraction through the planar output interface of the light-emitting diodes (LEDs) with nonuniform current injection. Spatial nonuniformity of injected current is a peculiar feature of the LEDs in which top metal electrode is patterned as a mesh in order to enhance the output power of light extracted through the top surface. Basic features of the model are the bi-plane computation domain, related to other areas of numerical grid (NG) cells in these two planes, representation of light-generating layer by an ensemble of point light sources, numerical "collection" of light photons from the area limited by acceptance circle and adjustment of NG-cell areas in the computation procedure by the angle-tuned aperture function. The developed model and procedure are used to simulate spatial distributions of the output optical power as well as the total output power at different mesh pitches. The proposed model and simulation strategy can be very efficient in evaluation of the output optical performance of LEDs with periodical or symmetrical configuration of the electrodes.

Spatial Heterodyne Observations of Water (SHOW) vapour in the upper troposphere and lower stratosphere from a high altitude aircraft: Modelling and sensitivity analysis

NASA Astrophysics Data System (ADS)

Langille, J. A.; Letros, D.; Zawada, D.; Bourassa, A.; Degenstein, D.; Solheim, B.

2018-04-01

A spatial heterodyne spectrometer (SHS) has been developed to measure the vertical distribution of water vapour in the upper troposphere and the lower stratosphere with a high vertical resolution (∼500 m). The Spatial Heterodyne Observations of Water (SHOW) instrument combines an imaging system with a monolithic field-widened SHS to observe limb scattered sunlight in a vibrational band of water (1363 nm-1366 nm). The instrument has been optimized for observations from NASA's ER-2 aircraft as a proof-of-concept for a future low earth orbit satellite deployment. A robust model has been developed to simulate SHOW ER-2 limb measurements and retrievals. This paper presents the simulation of the SHOW ER-2 limb measurements along a hypothetical flight track and examines the sensitivity of the measurement and retrieval approach. Water vapour fields from an Environment and Climate Change Canada forecast model are used to represent realistic spatial variability along the flight path. High spectral resolution limb scattered radiances are simulated using the SASKTRAN radiative transfer model. It is shown that the SHOW instrument onboard the ER-2 is capable of resolving the water vapour variability in the UTLS from approximately 12 km - 18 km with ±1 ppm accuracy. Vertical resolutions between 500 m and 1 km are feasible. The along track sampling capability of the instrument is also discussed.

Characteristics of the mixing volume model with the interactions among spatially distributed particles for Lagrangian simulations of turbulent mixing

NASA Astrophysics Data System (ADS)

Watanabe, Tomoaki; Nagata, Koji

2016-11-01

The mixing volume model (MVM), which is a mixing model for molecular diffusion in Lagrangian simulations of turbulent mixing problems, is proposed based on the interactions among spatially distributed particles in a finite volume. The mixing timescale in the MVM is derived by comparison between the model and the subgrid scale scalar variance equation. A-priori test of the MVM is conducted based on the direct numerical simulations of planar jets. The MVM is shown to predict well the mean effects of the molecular diffusion under various conditions. However, a predicted value of the molecular diffusion term is positively correlated to the exact value in the DNS only when the number of the mixing particles is larger than two. Furthermore, the MVM is tested in the hybrid implicit large-eddy-simulation/Lagrangian-particle-simulation (ILES/LPS). The ILES/LPS with the present mixing model predicts well the decay of the scalar variance in planar jets. This work was supported by JSPS KAKENHI Nos. 25289030 and 16K18013. The numerical simulations presented in this manuscript were carried out on the high performance computing system (NEC SX-ACE) in the Japan Agency for Marine-Earth Science and Technology.

An individual-based simulation model for mottled sculpin (Cottus bairdi) in a southern Appalachian stream

Treesearch

Brenda Rashleigh; Gary D. Grossman

2005-01-01

We describe and analyze a spatially explicit, individual-based model for the local population dynamics of mottled sculpin (Cottus bairdi). The model simulated daily growth, mortality, movement and spawning of individuals within a reach of stream. Juvenile and adult growth was based on consumption bioenergetics of benthic macroinvertebrate prey;...

Evaluating crown fire rate of spread predictions from physics-based models

Treesearch

C. M. Hoffman; J. Ziegler; J. Canfield; R. R. Linn; W. Mell; C. H. Sieg; F. Pimont

2015-01-01

Modeling the behavior of crown fires is challenging due to the complex set of coupled processes that drive the characteristics of a spreading wildfire and the large range of spatial and temporal scales over which these processes occur. Detailed physics-based modeling approaches such as FIRETEC and the Wildland Urban Interface Fire Dynamics Simulator (WFDS) simulate...

Getting the biggest birch for the bang: restoring and expanding upland birchwoods in the Scottish Highlands by managing red deer

PubMed Central

Tanentzap, Andrew J; Zou, James; Coomes, David A

2013-01-01

High deer populations threaten the conservation value of woodlands and grasslands, but predicting the success of deer culling, in terms of allowing vegetation to recover, is difficult. Numerical simulation modeling is one approach to gain insight into the outcomes of management scenarios. We develop a spatially explicit model to predict the responses of Betula spp. to red deer (Cervus elaphus) and land management in the Scottish Highlands. Our model integrates a Bayesian stochastic stage-based matrix model within the framework of a widely used individual-based forest simulation model, using data collected along spatial and temporal gradients in deer browsing. By initializing our model with the historical spatial locations of trees, we find that densities of juvenile trees (<3 m tall) predicted after 9–13 years closely match counts observed in the field. This is among the first tests of the accuracy of a dynamical simulation model for predicting the responses of tree regeneration to herbivores. We then test the relative importance of deer browsing, ground cover vegetation, and seed availability in facilitating landscape-level birch regeneration using simulations in which we varied these three variables. We find that deer primarily control transitions of birch to taller (>3 m) height tiers over 30 years, but regeneration also requires suitable ground cover for seedling establishment. Densities of adult seed sources did not influence regeneration, nor did an active management scenario where we altered the spatial configuration of adults by creating “woodland islets”. Our results show that managers interested in maximizing tree regeneration cannot simply reduce deer densities but must also improve ground cover for seedling establishment, and the model we develop now enables managers to quantify explicitly how much both these factors need to be altered. More broadly, our findings emphasize the need for land managers to consider the impacts of large herbivores rather than their densities. PMID:23919137

Multivariate Non-Symmetric Stochastic Models for Spatial Dependence Models

NASA Astrophysics Data System (ADS)

Haslauer, C. P.; Bárdossy, A.

2017-12-01

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

Simulating the Effects of Alternative Forest Management Strategies on Landscape Structure

Treesearch

Eric J. Gustafson; Thomas Crow

1996-01-01

Quantitative, spatial tools are needed to assess the long-term spatial consequences of alternative management strategies for land use planning and resource management. We constructed a timber harvest allocation model (HARVEST) that provides a visual and quantitative means to predict the spatial pattern of forest openings produced by alternative harvest strategies....

Improving the XAJ Model on the Basis of Mass-Energy Balance

NASA Astrophysics Data System (ADS)

Fang, Yuanhao; Corbari, Chiara; Zhang, Xingnan; Mancini, Marco

2014-11-01

Introduction: The Xin'anjiang(XAJ) model is a conceptual model developed by the group led by Prof. Ren-Jun Zhao, which takes the pan evaporation as one of its input and then computes the effective evapotranspiration (ET) of the catchment by mass balance. Such scheme can ensure a good performance of discharge simulation but has obvious defects, one of which is that the effective ET is spatially-constant over the computation unit, neglecting the spatial variation of variables that influence the effective ET and therefore the simulation of ET and SM by the XAJ model, comparing with discharge, is less reliable. In this study, The XAJ model was improved to employ both energy and mass balance to compute the ET following the energy-mass balance scheme of FEST-EWB. model.

Improving the XAJ Model on the Basis of Mass-Energy Balance

NASA Astrophysics Data System (ADS)

Fang, Yuanghao; Corbari, Chiara; Zhang, Xingnan; Mancini, Marco

2014-11-01

The Xin’anjiang(XAJ) model is a conceptual model developed by the group led by Prof. Ren-Jun Zhao, which takes the pan evaporation as one of its input and then computes the effective evapotranspiration (ET) of the catchment by mass balance. Such scheme can ensure a good performance of discharge simulation but has obvious defects, one of which is that the effective ET is spatially-constant over the computation unit, neglecting the spatial variation of variables that influence the effective ET and therefore the simulation of ET and SM by the XAJ model, comparing with discharge, is less reliable. In this study, The XAJ model was improved to employ both energy and mass balance to compute the ET following the energy-mass balance scheme of FEST-EWB. model.

Improved simulation of river water and groundwater exchange in an alluvial plain using the SWAT model

USDA-ARS?s Scientific Manuscript database

Hydrological interaction between surface and subsurface water systems has a significant impact on water quality, ecosystems and biogeochemistry cycling of both systems. Distributed models have been developed to simulate this function, but they require detailed spatial inputs and extensive computati...

The regional climate model RegCM3 performances over several regions and climate regimes

NASA Astrophysics Data System (ADS)

Coppola, E.; Rauscher, S.; Gao, X.; Giorgi, F.; Im, E. S.; Mariotti, L.; Seth, A.; Sylla, M. B.

2009-04-01

Regional Climate models are more and more needed to provide high resolution regional climate information in climate impact studies. Water availability in a future scenario is the main request of policy makers for adaptation and mitigation purposes. However precipitation changes are unlikely to be as spatially coherent as temperature changes and they are closely related to the regional model itself. In addition model skill varies regionally. An example of several ICTP regional climate model (RegCM3) simulations is reported over China, Korea, Africa, Central and Southern America, Europe and Australia. Over China, Australia, and Korea the regional model improves the simulation compared to the driving GCM when compared with CRU observations. In China, for example, the higher resolution of the regional model inhibits the penetration of the monsoon precipitation front from the southern slope of the Himalaya onto the Tibetan Plateau. In Korea the nested domain simulation (20 km) shows an encouraging performance with regard to capturing extreme precipitation episodes and the finer spatial distribution reflects the detailed geography of the Korean Peninsula. Over South America, RegCM captures the annual cycle of precipitation over Northeast Brazil and the South American Monsoon region, although the monsoon onset occurs too early in the model. Precipitation over the Amazon is not well captured, with too little precipitation associated with weak easterlies and reduced moisture transport into the interior of the continent. RegCM simulates the annual cycle of precipitation over Central America and the Caribbean fairly well; in particular, the complex spatial distribution of the Mid-Summer Drought, a decrease in precipitation that occurs during the middle of the rainy season in July and August, is better captured by RegCM than by the GCM. In addition, RegCM simulates the strength and position of the Caribbean low level jet, a mesoscale feature related to precipitation anomalies in the region. Over Africa our analysis shows that RegCM3 is able to reproduce fairly well the spatial variability of seasonal mean temperature, precipitation and the associated low-level circulation. However, monsoon flow is over predicted while African Easterly Jet (AEJ) core underestimated and shifted a bit northward. Finally, over Europe the regional model shows a cold bias for most part of the year and a wet bias in winter and spring. Rain frequency is too high especially over the mountainous regions. The spatial patter of the precipitation extreme is well represented in the model although a slight overestimation of the 95, 98 99 percentile is evident.

The impact of forest structure and spatial scale on the relationship between ground plot above ground biomass and GEDI lidar waveforms

NASA Astrophysics Data System (ADS)

Armston, J.; Marselis, S.; Hancock, S.; Duncanson, L.; Tang, H.; Kellner, J. R.; Calders, K.; Disney, M.; Dubayah, R.

2017-12-01

The NASA Global Ecosystem Dynamics Investigation (GEDI) will place a multi-beam waveform lidar instrument on the International Space Station (ISS) to provide measurements of forest vertical structure globally. These measurements of structure will underpin empirical modelling of above ground biomass density (AGBD) at the scale of individual GEDI lidar footprints (25m diameter). The GEDI pre-launch calibration strategy for footprint level models relies on linking AGBD estimates from ground plots with GEDI lidar waveforms simulated from coincident discrete return airborne laser scanning data. Currently available ground plot data have variable and often large uncertainty at the spatial resolution of GEDI footprints due to poor colocation, allometric model error, sample size and plot edge effects. The relative importance of these sources of uncertainty partly depends on the quality of ground measurements and region. It is usually difficult to know the magnitude of these uncertainties a priori so a common approach to mitigate their influence on model training is to aggregate ground plot and waveform lidar data to a coarser spatial scale (0.25-1ha). Here we examine the impacts of these principal sources of uncertainty using a 3D simulation approach. Sets of realistic tree models generated from terrestrial laser scanning (TLS) data or parametric modelling matched to tree inventory data were assembled from four contrasting forest plots across tropical rainforest, deciduous temperate forest, and sclerophyll eucalypt woodland sites. These tree models were used to simulate geometrically explicit 3D scenes with variable tree density, size class and spatial distribution. GEDI lidar waveforms are simulated over ground plots within these scenes using monte carlo ray tracing, allowing the impact of varying ground plot and waveform colocation error, forest structure and edge effects on the relationship between ground plot AGBD and GEDI lidar waveforms to be directly assessed. We quantify the sensitivity of calibration equations relating GEDI lidar structure measurements and AGBD to these factors at a range of spatial scales (0.0625-1ha) and discuss the implications for the expanding use of existing in situ ground plot data by GEDI.

Rupture Propagation for Stochastic Fault Models

NASA Astrophysics Data System (ADS)

Favreau, P.; Lavallee, D.; Archuleta, R.

2003-12-01

The inversion of strong motion data of large earhquakes give the spatial distribution of pre-stress on the ruptured faults and it can be partially reproduced by stochastic models, but a fundamental question remains: how rupture propagates, constrained by the presence of spatial heterogeneity? For this purpose we investigate how the underlying random variables, that control the pre-stress spatial variability, condition the propagation of the rupture. Two stochastic models of prestress distributions are considered, respectively based on Cauchy and Gaussian random variables. The parameters of the two stochastic models have values corresponding to the slip distribution of the 1979 Imperial Valley earthquake. We use a finite difference code to simulate the spontaneous propagation of shear rupture on a flat fault in a 3D continuum elastic body. The friction law is the slip dependent friction law. The simulations show that the propagation of the rupture front is more complex, incoherent or snake-like for a prestress distribution based on Cauchy random variables. This may be related to the presence of a higher number of asperities in this case. These simulations suggest that directivity is stronger in the Cauchy scenario, compared to the smoother rupture of the Gauss scenario.

Facing the scaling problem: A multi-methodical approach to simulate soil erosion at hillslope and catchment scale

NASA Astrophysics Data System (ADS)

Schmengler, A. C.; Vlek, P. L. G.

2012-04-01

Modelling soil erosion requires a holistic understanding of the sediment dynamics in a complex environment. As most erosion models are scale-dependent and their parameterization is spatially limited, their application often requires special care, particularly in data-scarce environments. This study presents a hierarchical approach to overcome the limitations of a single model by using various quantitative methods and soil erosion models to cope with the issues of scale. At hillslope scale, the physically-based Water Erosion Prediction Project (WEPP)-model is used to simulate soil loss and deposition processes. Model simulations of soil loss vary between 5 to 50 t ha-1 yr-1 dependent on the spatial location on the hillslope and have only limited correspondence with the results of the 137Cs technique. These differences in absolute soil loss values could be either due to internal shortcomings of each approach or to external scale-related uncertainties. Pedo-geomorphological soil investigations along a catena confirm that estimations by the 137Cs technique are more appropriate in reflecting both the spatial extent and magnitude of soil erosion at hillslope scale. In order to account for sediment dynamics at a larger scale, the spatially-distributed WaTEM/SEDEM model is used to simulate soil erosion at catchment scale and to predict sediment delivery rates into a small water reservoir. Predicted sediment yield rates are compared with results gained from a bathymetric survey and sediment core analysis. Results show that specific sediment rates of 0.6 t ha-1 yr-1 by the model are in close agreement with observed sediment yield calculated from stratigraphical changes and downcore variations in 137Cs concentrations. Sediment erosion rates averaged over the entire catchment of 1 to 2 t ha-1 yr-1 are significantly lower than results obtained at hillslope scale confirming an inverse correlation between the magnitude of erosion rates and the spatial scale of the model. The study has shown that the use of multiple methods facilitates the calibration and validation of models and might provide a more accurate measure for soil erosion rates in ungauged catchments. Moreover, the approach could be used to identify the most appropriate working and operational scales for soil erosion modelling.

Interstitial diffusion and the relationship between compartment modelling and multi-scale spatial-temporal modelling of (18)F-FLT tumour uptake dynamics.

PubMed

Liu, Dan; Chalkidou, Anastasia; Landau, David B; Marsden, Paul K; Fenwick, John D

2014-09-07

Tumour cell proliferation can be imaged via positron emission tomography of the radiotracer 3'-deoxy-3'-18F-fluorothymidine (18F-FLT). Conceptually, the number of proliferating cells might be expected to correlate more closely with the kinetics of 18F-FLT uptake than with uptake at a fixed time. Radiotracer uptake kinetics are standardly visualized using parametric maps of compartment model fits to time-activity-curves (TACs) of individual voxels. However the relationship between the underlying spatiotemporal accumulation of FLT and the kinetics described by compartment models has not yet been explored. In this work tumour tracer uptake is simulated using a mechanistic spatial-temporal model based on a convection-diffusion-reaction equation solved via the finite difference method. The model describes a chain of processes: the flow of FLT between the spatially heterogeneous tumour vasculature and interstitium; diffusion and convection of FLT within the interstitium; transport of FLT into cells; and intracellular phosphorylation. Using values of model parameters estimated from the biological literature, simulated FLT TACs are generated with shapes and magnitudes similar to those seen clinically. Results show that the kinetics of the spatial-temporal model can be recovered accurately by fitting a 3-tissue compartment model to FLT TACs simulated for those tumours or tumour sub-volumes that can be viewed as approximately closed, for which tracer diffusion throughout the interstitium makes only a small fractional change to the quantity of FLT they contain. For a single PET voxel of width 2.5-5 mm we show that this condition is roughly equivalent to requiring that the relative difference in tracer uptake between the voxel and its neighbours is much less than one.

Using a spatially-distributed hydrologic biogeochemistry model with a nitrogen transport module to study the spatial variation of carbon processes in a Critical Zone Observatory

DOE PAGES

Shi, Yuning; Eissenstat, David M.; He, Yuting; ...

2018-05-12

Terrestrial carbon processes are affected by soil moisture, soil temperature, nitrogen availability and solar radiation, among other factors. Most of the current ecosystem biogeochemistry models represent one point in space, and have limited characterization of hydrologic processes. Therefore these models can neither resolve the topographically driven spatial variability of water, energy, and nutrient, nor their effects on carbon processes. A spatially-distributed land surface hydrologic biogeochemistry model, Flux-PIHM-BGC, is developed by coupling the Biome-BGC model with a physically-based land surface hydrologic model, Flux-PIHM. In the coupled system, each Flux-PIHM model grid couples a 1-D Biome-BGC model. In addition, a topographic solarmore » radiation module and an advection-driven nitrogen transport module are added to represent the impact of topography on nutrient transport and solar energy distribution. Because Flux-PIHM is able to simulate lateral groundwater flow and represent the land surface heterogeneities caused by topography, Flux-PIHM-BGC is capable of simulating the complex interaction among water, energy, nutrient, and carbon in time and space. The Flux-PIHM-BGC model is tested at the Susquehanna/Shale Hills Critical Zone Observatory. Model results show that distributions of carbon and nitrogen stocks and fluxes are strongly affected by topography and landscape position, and tree growth is nitrogen limited. The predicted aboveground and soil carbon distributions generally agree with the macro patterns observed. Although the model underestimates the spatial variation, the predicted watershed average values are close to the observations. Lastly, the coupled Flux-PIHM-BGC model provides an important tool to study spatial variations in terrestrial carbon and nitrogen processes and their interactions with environmental factors, and to predict the spatial structure of the responses of ecosystems to climate change.« less

Using a spatially-distributed hydrologic biogeochemistry model with a nitrogen transport module to study the spatial variation of carbon processes in a Critical Zone Observatory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shi, Yuning; Eissenstat, David M.; He, Yuting

Terrestrial carbon processes are affected by soil moisture, soil temperature, nitrogen availability and solar radiation, among other factors. Most of the current ecosystem biogeochemistry models represent one point in space, and have limited characterization of hydrologic processes. Therefore these models can neither resolve the topographically driven spatial variability of water, energy, and nutrient, nor their effects on carbon processes. A spatially-distributed land surface hydrologic biogeochemistry model, Flux-PIHM-BGC, is developed by coupling the Biome-BGC model with a physically-based land surface hydrologic model, Flux-PIHM. In the coupled system, each Flux-PIHM model grid couples a 1-D Biome-BGC model. In addition, a topographic solarmore » radiation module and an advection-driven nitrogen transport module are added to represent the impact of topography on nutrient transport and solar energy distribution. Because Flux-PIHM is able to simulate lateral groundwater flow and represent the land surface heterogeneities caused by topography, Flux-PIHM-BGC is capable of simulating the complex interaction among water, energy, nutrient, and carbon in time and space. The Flux-PIHM-BGC model is tested at the Susquehanna/Shale Hills Critical Zone Observatory. Model results show that distributions of carbon and nitrogen stocks and fluxes are strongly affected by topography and landscape position, and tree growth is nitrogen limited. The predicted aboveground and soil carbon distributions generally agree with the macro patterns observed. Although the model underestimates the spatial variation, the predicted watershed average values are close to the observations. Lastly, the coupled Flux-PIHM-BGC model provides an important tool to study spatial variations in terrestrial carbon and nitrogen processes and their interactions with environmental factors, and to predict the spatial structure of the responses of ecosystems to climate change.« less

A Dasymetric-Based Monte Carlo Simulation Approach to the Probabilistic Analysis of Spatial Variables

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morton, April M; Piburn, Jesse O; McManamay, Ryan A

2017-01-01

Monte Carlo simulation is a popular numerical experimentation technique used in a range of scientific fields to obtain the statistics of unknown random output variables. Despite its widespread applicability, it can be difficult to infer required input probability distributions when they are related to population counts unknown at desired spatial resolutions. To overcome this challenge, we propose a framework that uses a dasymetric model to infer the probability distributions needed for a specific class of Monte Carlo simulations which depend on population counts.

A spatial model to aggregate point-source and nonpoint-source water-quality data for large areas

USGS Publications Warehouse

White, D.A.; Smith, R.A.; Price, C.V.; Alexander, R.B.; Robinson, K.W.

1992-01-01

More objective and consistent methods are needed to assess water quality for large areas. A spatial model, one that capitalizes on the topologic relationships among spatial entities, to aggregate pollution sources from upstream drainage areas is described that can be implemented on land surfaces having heterogeneous water-pollution effects. An infrastructure of stream networks and drainage basins, derived from 1:250,000-scale digital-elevation models, define the hydrologic system in this spatial model. The spatial relationships between point- and nonpoint pollution sources and measurement locations are referenced to the hydrologic infrastructure with the aid of a geographic information system. A maximum-branching algorithm has been developed to simulate the effects of distance from a pollutant source to an arbitrary downstream location, a function traditionally employed in deterministic water quality models. ?? 1992.

The influence of bed friction variability due to land cover on storm-driven barrier island morphodynamics

USGS Publications Warehouse

Passeri, Davina L.; Long, Joseph W.; Plant, Nathaniel G.; Bilskie, Matthew V.; Hagen, Scott C.

2018-01-01

Variations in bed friction due to land cover type have the potential to influence morphologic change during storm events; the importance of these variations can be studied through numerical simulation and experimentation at locations with sufficient observational data to initialize realistic scenarios, evaluate model accuracy and guide interpretations. Two-dimensional in the horizontal plane (2DH) morphodynamic (XBeach) simulations were conducted to assess morphodynamic sensitivity to spatially varying bed friction at Dauphin Island, AL using hurricanes Ivan (2004) and Katrina (2005) as experimental test cases. For each storm, three bed friction scenarios were simulated: (1) a constant Chezy coefficient across land and water, (2) a constant Chezy coefficient across land and depth-dependent Chezy coefficients across water, and (3) spatially varying Chezy coefficients across land based on land use/land cover (LULC) data and depth-dependent Chezy coefficients across water. Modeled post-storm bed elevations were compared qualitatively and quantitatively with post-storm lidar data. Results showed that implementing spatially varying bed friction influenced the ability of XBeach to accurately simulate morphologic change during both storms. Accounting for frictional effects due to large-scale variations in vegetation and development reduced cross-barrier sediment transport and captured overwash and breaching more accurately. Model output from the spatially varying friction scenarios was used to examine the need for an existing sediment transport limiter, the influence of pre-storm topography and the effects of water level gradients on storm-driven morphodynamics.

A Gaussian random field model for similarity-based smoothing in Bayesian disease mapping.

PubMed

Baptista, Helena; Mendes, Jorge M; MacNab, Ying C; Xavier, Miguel; Caldas-de-Almeida, José

2016-08-01

Conditionally specified Gaussian Markov random field (GMRF) models with adjacency-based neighbourhood weight matrix, commonly known as neighbourhood-based GMRF models, have been the mainstream approach to spatial smoothing in Bayesian disease mapping. In the present paper, we propose a conditionally specified Gaussian random field (GRF) model with a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping. The model, named similarity-based GRF, is motivated for modelling disease mapping data in situations where the underlying small area relative risks and the associated determinant factors do not vary systematically in space, and the similarity is defined by "similarity" with respect to the associated disease determinant factors. The neighbourhood-based GMRF and the similarity-based GRF are compared and accessed via a simulation study and by two case studies, using new data on alcohol abuse in Portugal collected by the World Mental Health Survey Initiative and the well-known lip cancer data in Scotland. In the presence of disease data with no evidence of positive spatial correlation, the simulation study showed a consistent gain in efficiency from the similarity-based GRF, compared with the adjacency-based GMRF with the determinant risk factors as covariate. This new approach broadens the scope of the existing conditional autocorrelation models. © The Author(s) 2016.

The effect of area size and predation on the time to extinction of prairie vole populations. simulation studies via SERDYCA: a Spatially-Explicit Individual-Based Model of Rodent Dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kostova, T; Carlsen, T

2003-11-21

We present a spatially-explicit individual-based computational model of rodent dynamics, customized for the prairie vole species, M. Ochrogaster. The model is based on trophic relationships and represents important features such as territorial competition, mating behavior, density-dependent predation and dispersal out of the modeled spatial region. Vegetation growth and vole fecundity are dependent on climatic components. The results of simulations show that the model correctly predicts the overall temporal dynamics of the population density. Time-series analysis shows a very good match between the periods corresponding to the peak population density frequencies predicted by the model and the ones reported in themore » literature. The model is used to study the relation between persistence, landscape area and predation. We introduce the notions of average time to extinction (ATE) and persistence frequency to quantify persistence. While the ATE decreases with decrease of area, it is a bell-shaped function of the predation level: increasing for 'small' and decreasing for 'large' predation levels.« less

Simulating Spatial Growth Patterns in Developing Countries: A Case of Shama in the Western Region of Ghana.

NASA Astrophysics Data System (ADS)

Inkoom, J. N.; Nyarko, B. K.

2014-12-01

The integration of geographic information systems (GIS) and agent-based modelling (ABM) can be an efficient tool to improve spatial planning practices. This paper utilizes GIS and ABM approaches to simulate spatial growth patterns of settlement structures in Shama. A preliminary household survey on residential location decision-making choice served as the behavioural rule for household agents in the model. Physical environment properties of the model were extracted from a 2005 image implemented in NetLogo. The resulting growth pattern model was compared with empirical growth patterns to ascertain the model's accuracy. The paper establishes that the development of unplanned structures and its evolving structural pattern are a function of land price, proximity to economic centres, household economic status and location decision-making patterns. The application of the proposed model underlines its potential for integration into urban planning policies and practices, and for understanding residential decision-making processes in emerging cities in developing countries. Key Words: GIS; Agent-based modelling; Growth patterns; NetLogo; Location decision making; Computational Intelligence.

Simulating historical variability in the amount of old forests in the Oregon Coast Range.

Treesearch

M.C. Wimberly; T.M. Spies; C.J. Long; C. Whitlock

2000-01-01

We developed the landscape age-class demographics simulator (LADS) to model historical variability in the amount of old-growth and late-successional forest in the Oregon Coast Range over the past 3,000 years. The model simulated temporal and spatial patterns of forest fires along with the resulting fluctuations in the distribution of forest age classes across the...

Mercury and methylmercury stream concentrations in a Coastal Plain watershed: A multi-scale simulation analysis

USGS Publications Warehouse

Knightes, Christopher D.; Golden, Heather E.; Journey, Celeste A.; Davis, Gary M.; Conrads, Paul; Marvin-DiPasquale, Mark; Brigham, Mark E.; Bradley, Paul M.

2014-01-01

Mercury is a ubiquitous global environmental toxicant responsible for most US fish advisories. Processes governing mercury concentrations in rivers and streams are not well understood, particularly at multiple spatial scales. We investigate how insights gained from reach-scale mercury data and model simulations can be applied at broader watershed scales using a spatially and temporally explicit watershed hydrology and biogeochemical cycling model, VELMA. We simulate fate and transport using reach-scale (0.1 km2) study data and evaluate applications to multiple watershed scales. Reach-scale VELMA parameterization was applied to two nested sub-watersheds (28 km2 and 25 km2) and the encompassing watershed (79 km2). Results demonstrate that simulated flow and total mercury concentrations compare reasonably to observations at different scales, but simulated methylmercury concentrations are out-of-phase with observations. These findings suggest that intricacies of methylmercury biogeochemical cycling and transport are under-represented in VELMA and underscore the complexity of simulating mercury fate and transport.

Effects of distribution density and cell dimension of 3D vegetation model on canopy NDVI simulation base on DART

NASA Astrophysics Data System (ADS)

Tao, Zhu; Shi, Runhe; Zeng, Yuyan; Gao, Wei

2017-09-01

The 3D model is an important part of simulated remote sensing for earth observation. Regarding the small-scale spatial extent of DART software, both the details of the model itself and the number of models of the distribution have an important impact on the scene canopy Normalized Difference Vegetation Index (NDVI).Taking the phragmitesaustralis in the Yangtze Estuary as an example, this paper studied the effect of the P.australias model on the canopy NDVI, based on the previous studies of the model precision, mainly from the cell dimension of the DART software and the density distribution of the P.australias model in the scene, As well as the choice of the density of the P.australiass model under the cost of computer running time in the actual simulation. The DART Cell dimensions and the density of the scene model were set by using the optimal precision model from the existing research results. The simulation results of NDVI with different model densities under different cell dimensions were analyzed by error analysis. By studying the relationship between relative error, absolute error and time costs, we have mastered the density selection method of P.australias model in the simulation of small-scale spatial scale scene. Experiments showed that the number of P.australias in the simulated scene need not be the same as those in the real environment due to the difference between the 3D model and the real scenarios. The best simulation results could be obtained by keeping the density ratio of about 40 trees per square meter, simultaneously, of the visual effects.

A polygon-based modeling approach to assess exposure of resources and assets to wildfire

Treesearch

Matthew P. Thompson; Joe Scott; Jeffrey D. Kaiden; Julie W. Gilbertson-Day

2013-01-01

Spatially explicit burn probability modeling is increasingly applied to assess wildfire risk and inform mitigation strategy development. Burn probabilities are typically expressed on a per-pixel basis, calculated as the number of times a pixel burns divided by the number of simulation iterations. Spatial intersection of highly valued resources and assets (HVRAs) with...

Landscape patterns from mathematical morphology on maps with contagion

Treesearch

Kurt Riitters; Peter Vogt; Pierre Soille; Christine Estreguil

2009-01-01

The perceived realism of simulated maps with contagion (spatial autocorrelation) has led to their use for comparing landscape pattern metrics and as habitat maps for modeling organism movement across landscapes. The objective of this study was to conduct a neutral model analysis of pattern metrics defined by morphological spatial pattern analysis (MSPA) on maps with...

DQE simulation of a-Se x-ray detectors using ARTEMIS

NASA Astrophysics Data System (ADS)

Fang, Yuan; Badano, Aldo

2016-03-01

Detective Quantum Efficiency (DQE) is one of the most important image quality metrics for evaluating the spatial resolution performance of flat-panel x-ray detectors. In this work, we simulate the DQE of amorphous selenium (a-Se) xray detectors with a detailed Monte Carlo transport code (ARTEMIS) for modeling semiconductor-based direct x-ray detectors. The transport of electron-hole pairs is achieved with a spatiotemporal model that accounts for recombination and trapping of carriers and Coulombic effects of space charge and external applied electric field. A range of x-ray energies has been simulated from 10 to 100 keV. The DQE results can be used to study the spatial resolution characteristics of detectors at different energies.

Evaluation of a numerical model's ability to predict bed load transport observed in braided river experiments

NASA Astrophysics Data System (ADS)

Javernick, Luke; Redolfi, Marco; Bertoldi, Walter

2018-05-01

New data collection techniques offer numerical modelers the ability to gather and utilize high quality data sets with high spatial and temporal resolution. Such data sets are currently needed for calibration, verification, and to fuel future model development, particularly morphological simulations. This study explores the use of high quality spatial and temporal data sets of observed bed load transport in braided river flume experiments to evaluate the ability of a two-dimensional model, Delft3D, to predict bed load transport. This study uses a fixed bed model configuration and examines the model's shear stress calculations, which are the foundation to predict the sediment fluxes necessary for morphological simulations. The evaluation is conducted for three flow rates, and model setup used highly accurate Structure-from-Motion (SfM) topography and discharge boundary conditions. The model was hydraulically calibrated using bed roughness, and performance was evaluated based on depth and inundation agreement. Model bed load performance was evaluated in terms of critical shear stress exceedance area compared to maps of observed bed mobility in a flume. Following the standard hydraulic calibration, bed load performance was tested for sensitivity to horizontal eddy viscosity parameterization and bed morphology updating. Simulations produced depth errors equal to the SfM inherent errors, inundation agreement of 77-85%, and critical shear stress exceedance in agreement with 49-68% of the observed active area. This study provides insight into the ability of physically based, two-dimensional simulations to accurately predict bed load as well as the effects of horizontal eddy viscosity and bed updating. Further, this study highlights how using high spatial and temporal data to capture the physical processes at work during flume experiments can help to improve morphological modeling.

Estimating riparian understory vegetation cover with beta regression and copula models

USGS Publications Warehouse

Eskelson, Bianca N.I.; Madsen, Lisa; Hagar, Joan C.; Temesgen, Hailemariam

2011-01-01

Understory vegetation communities are critical components of forest ecosystems. As a result, the importance of modeling understory vegetation characteristics in forested landscapes has become more apparent. Abundance measures such as shrub cover are bounded between 0 and 1, exhibit heteroscedastic error variance, and are often subject to spatial dependence. These distributional features tend to be ignored when shrub cover data are analyzed. The beta distribution has been used successfully to describe the frequency distribution of vegetation cover. Beta regression models ignoring spatial dependence (BR) and accounting for spatial dependence (BRdep) were used to estimate percent shrub cover as a function of topographic conditions and overstory vegetation structure in riparian zones in western Oregon. The BR models showed poor explanatory power (pseudo-R2 ≤ 0.34) but outperformed ordinary least-squares (OLS) and generalized least-squares (GLS) regression models with logit-transformed response in terms of mean square prediction error and absolute bias. We introduce a copula (COP) model that is based on the beta distribution and accounts for spatial dependence. A simulation study was designed to illustrate the effects of incorrectly assuming normality, equal variance, and spatial independence. It showed that BR, BRdep, and COP models provide unbiased parameter estimates, whereas OLS and GLS models result in slightly biased estimates for two of the three parameters. On the basis of the simulation study, 93–97% of the GLS, BRdep, and COP confidence intervals covered the true parameters, whereas OLS and BR only resulted in 84–88% coverage, which demonstrated the superiority of GLS, BRdep, and COP over OLS and BR models in providing standard errors for the parameter estimates in the presence of spatial dependence.

An assessment of global climate model-simulated climate for the western cordillera of Canada (1961-90)

NASA Astrophysics Data System (ADS)

Bonsal, Barrie R.; Prowse, Terry D.; Pietroniro, Alain

2003-12-01

Climate change is projected to significantly affect future hydrologic processes over many regions of the world. This is of particular importance for alpine systems that provide critical water supplies to lower-elevation regions. The western cordillera of Canada is a prime example where changes to temperature and precipitation could have profound hydro-climatic impacts not only for the cordillera itself, but also for downstream river systems and the drought-prone Canadian Prairies. At present, impact researchers primarily rely on global climate models (GCMs) for future climate projections. The main objective of this study is to assess several GCMs in their ability to simulate the magnitude and spatial variability of current (1961-90) temperature and precipitation over the western cordillera of Canada. In addition, several gridded data sets of observed climate for the study region are evaluated.Results reveal a close correspondence among the four gridded data sets of observed climate, particularly for temperature. There is, however, considerable variability regarding the various GCM simulations of this observed climate. The British, Canadian, German, Australian, and US GFDL models are superior at simulating the magnitude and spatial variability of mean temperature. The Japanese GCM is of intermediate ability, and the US NCAR model is least representative of temperature in this region. Nearly all the models substantially overestimate the magnitude of total precipitation, both annually and on a seasonal basis. An exception involves the British (Hadley) model, which best represents the observed magnitude and spatial variability of precipitation. This study improves our understanding regarding the accuracy of GCM climate simulations over the western cordillera of Canada. The findings may assist in producing more reliable future scenarios of hydro-climatic conditions over various regions of the country. Copyright

Errors and uncertainties in regional climate simulations of rainfall variability over Tunisia: a multi-model and multi-member approach

NASA Astrophysics Data System (ADS)

Fathalli, Bilel; Pohl, Benjamin; Castel, Thierry; Safi, Mohamed Jomâa

2018-02-01

Temporal and spatial variability of rainfall over Tunisia (at 12 km spatial resolution) is analyzed in a multi-year (1992-2011) ten-member ensemble simulation performed using the WRF model, and a sample of regional climate hindcast simulations from Euro-CORDEX. RCM errors and skills are evaluated against a dense network of local rain gauges. Uncertainties arising, on the one hand, from the different model configurations and, on the other hand, from internal variability are furthermore quantified and ranked at different timescales using simple spread metrics. Overall, the WRF simulation shows good skill for simulating spatial patterns of rainfall amounts over Tunisia, marked by strong altitudinal and latitudinal gradients, as well as the rainfall interannual variability, in spite of systematic errors. Mean rainfall biases are wet in both DJF and JJA seasons for the WRF ensemble, while they are dry in winter and wet in summer for most of the used Euro-CORDEX models. The sign of mean annual rainfall biases over Tunisia can also change from one member of the WRF ensemble to another. Skills in regionalizing precipitation over Tunisia are season dependent, with better correlations and weaker biases in winter. Larger inter-member spreads are observed in summer, likely because of (1) an attenuated large-scale control on Mediterranean and Tunisian climate, and (2) a larger contribution of local convective rainfall to the seasonal amounts. Inter-model uncertainties are globally stronger than those attributed to model's internal variability. However, inter-member spreads can be of the same magnitude in summer, emphasizing the important stochastic nature of the summertime rainfall variability over Tunisia.

A comparison of adaptive sampling designs and binary spatial models: A simulation study using a census of Bromus inermis

USGS Publications Warehouse

Irvine, Kathryn M.; Thornton, Jamie; Backus, Vickie M.; Hohmann, Matthew G.; Lehnhoff, Erik A.; Maxwell, Bruce D.; Michels, Kurt; Rew, Lisa

2013-01-01

Commonly in environmental and ecological studies, species distribution data are recorded as presence or absence throughout a spatial domain of interest. Field based studies typically collect observations by sampling a subset of the spatial domain. We consider the effects of six different adaptive and two non-adaptive sampling designs and choice of three binary models on both predictions to unsampled locations and parameter estimation of the regression coefficients (species–environment relationships). Our simulation study is unique compared to others to date in that we virtually sample a true known spatial distribution of a nonindigenous plant species, Bromus inermis. The census of B. inermis provides a good example of a species distribution that is both sparsely (1.9 % prevalence) and patchily distributed. We find that modeling the spatial correlation using a random effect with an intrinsic Gaussian conditionally autoregressive prior distribution was equivalent or superior to Bayesian autologistic regression in terms of predicting to un-sampled areas when strip adaptive cluster sampling was used to survey B. inermis. However, inferences about the relationships between B. inermis presence and environmental predictors differed between the two spatial binary models. The strip adaptive cluster designs we investigate provided a significant advantage in terms of Markov chain Monte Carlo chain convergence when trying to model a sparsely distributed species across a large area. In general, there was little difference in the choice of neighborhood, although the adaptive king was preferred when transects were randomly placed throughout the spatial domain.

Analytical approximation of a stochastic, spatial simulation model of fire and forest landscape dynamics

Treesearch

A.J. Tepley; E.A. Thomann

2012-01-01

Recent increases in computation power have prompted enormous growth in the use of simulation models in ecological research. These models are valued for their ability to account for much of the ecological complexity found in field studies, but this ability usually comes at the cost of losing transparency into how the models work. In order to foster greater understanding...

Bayesian spatial transformation models with applications in neuroimaging data

PubMed Central

Miranda, Michelle F.; Zhu, Hongtu; Ibrahim, Joseph G.

2013-01-01

Summary The aim of this paper is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) and a set of covariates. Our STMs include a varying Box-Cox transformation model for dealing with the issue of non-Gaussian distributed imaging data and a Gaussian Markov Random Field model for incorporating spatial smoothness of the imaging data. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations and real data analysis demonstrate that the STM significantly outperforms the voxel-wise linear model with Gaussian noise in recovering meaningful geometric patterns. Our STM is able to reveal important brain regions with morphological changes in children with attention deficit hyperactivity disorder. PMID:24128143

Wildlife tradeoffs based on landscape models of habitat preference

USGS Publications Warehouse

Loehle, C.; Mitchell, M.S.; White, M.

2000-01-01

Wildlife tradeoffs based on landscape models of habitat preference were presented. Multiscale logistic regression models were used and based on these models a spatial optimization technique was utilized to generate optimal maps. The tradeoffs were analyzed by gradually increasing the weighting on a single species in the objective function over a series of simulations. Results indicated that efficiency of habitat management for species diversity could be maximized for small landscapes by incorporating spatial context.

Dynamic Shade and Irradiance Simulation of Aquatic ...

EPA Pesticide Factsheets

Penumbra is a landscape shade and irradiance simulation model that simulates how solar energy spatially and temporally interacts within dynamic ecosystems such as riparian zones, forests, and other terrain that cast topological shadows. Direct and indirect solar energy accumulates across landscapes and is the main energy driver for increasing aquatic and landscape temperatures at both local and holistic scales. Landscape disturbances such as landuse change, clear cutting, and fire can cause significant variations in the resulting irradiance reaching particular locations. Penumbra can simulate solar angles and irradiance at definable temporal grains as low as one minute while simulating landscape shadowing up to an entire year. Landscapes can be represented at sub-meter resolutions with appropriate spatial data inputs, such as field data or elevation and surface object heights derived from light detection and ranging (LiDAR) data. This work describes Penumbra’s framework and methodology, external model integration capability, and appropriate model application for a variety of watershed restoration project types. First, an overview of Penumbra’s framework reveals what this model adds to the existing ecological modeling domain. Second, Penumbra’s stand-alone and integration modes are explained and demonstrated. Stand-alone modeling results are showcased within the 3-D visualization tool VISTAS (VISualizing Terrestrial-Aquatic Systems), which fluently summariz

Regional model simulations of New Zealand climate

NASA Astrophysics Data System (ADS)

Renwick, James A.; Katzfey, Jack J.; Nguyen, Kim C.; McGregor, John L.

1998-03-01

Simulation of New Zealand climate is examined through the use of a regional climate model nested within the output of the Commonwealth Scientific and Industrial Research Organisation nine-level general circulation model (GCM). R21 resolution GCM output is used to drive a regional model run at 125 km grid spacing over the Australasian region. The 125 km run is used in turn to drive a simulation at 50 km resolution over New Zealand. Simulations with a full seasonal cycle are performed for 10 model years. The focus is on the quality of the simulation of present-day climate, but results of a doubled-CO2 run are discussed briefly. Spatial patterns of mean simulated precipitation and surface temperatures improve markedly as horizontal resolution is increased, through the better resolution of the country's orography. However, increased horizontal resolution leads to a positive bias in precipitation. At 50 km resolution, simulated frequency distributions of daily maximum/minimum temperatures are statistically similar to those of observations at many stations, while frequency distributions of daily precipitation appear to be statistically different to those of observations at most stations. Modeled daily precipitation variability at 125 km resolution is considerably less than observed, but is comparable to, or exceeds, observed variability at 50 km resolution. The sensitivity of the simulated climate to changes in the specification of the land surface is discussed briefly. Spatial patterns of the frequency of extreme temperatures and precipitation are generally well modeled. Under a doubling of CO2, the frequency of precipitation extremes changes only slightly at most locations, while air frosts become virtually unknown except at high-elevation sites.

A novel explicit approach to model bromide and pesticide transport in soils containing macropores

NASA Astrophysics Data System (ADS)

Klaus, J.; Zehe, E.

2011-01-01

The present study tests whether an explicit treatment of worm burrows is feasible for simulating water flow, bromide and pesticide transport in structured heterogeneous soils. The essence is to represent worm burrows as morphologically connected paths of low flow resistance in the spatially highly resolved model domain. A recent Monte Carlo study (Klaus and Zehe, 2010) revealed that this approach allowed successful reproduction of tile drain event discharge recorded during an irrigation experiment at a tile drained field site. However, several "hillslope architectures" that were all consistent with the available extensive data base allowed a good reproduction of tile drain flow response. Our second objective was thus to find out whether this "equifinality" in spatial model setups may be reduced when including bromide tracer data in the model falsification process. We thus simulated transport of bromide and Isoproturon (IPU) for the 13 spatial model setups, which performed best with respect to reproduce tile drain event discharge, without any further calibration. All model setups allowed a very good prediction of the temporal dynamics of cumulated bromide leaching into the tile drain, while only four of them matched the accumulated water balance and accumulated bromide loss into the tile drain. The number of behavioural model architectures could thus be reduced to four. One of those setups was used for simulating transport of IPU, using different parameter combinations to characterise adsorption according to the Footprint data base. Simulations could, however, only reproduce the observed leaching behaviour, when we allowed for retardation coefficients that were very close to one.

Modelling spatial patterns of urban growth in Africa

PubMed Central

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

2013-01-01

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

Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies.

PubMed

Bhaskar, Anand; Javanmard, Adel; Courtade, Thomas A; Tse, David

2017-03-15

Genetic variation in human populations is influenced by geographic ancestry due to spatial locality in historical mating and migration patterns. Spatial population structure in genetic datasets has been traditionally analyzed using either model-free algorithms, such as principal components analysis (PCA) and multidimensional scaling, or using explicit spatial probabilistic models of allele frequency evolution. We develop a general probabilistic model and an associated inference algorithm that unify the model-based and data-driven approaches to visualizing and inferring population structure. Our spatial inference algorithm can also be effectively applied to the problem of population stratification in genome-wide association studies (GWAS), where hidden population structure can create fictitious associations when population ancestry is correlated with both the genotype and the trait. Our algorithm Geographic Ancestry Positioning (GAP) relates local genetic distances between samples to their spatial distances, and can be used for visually discerning population structure as well as accurately inferring the spatial origin of individuals on a two-dimensional continuum. On both simulated and several real datasets from diverse human populations, GAP exhibits substantially lower error in reconstructing spatial ancestry coordinates compared to PCA. We also develop an association test that uses the ancestry coordinates inferred by GAP to accurately account for ancestry-induced correlations in GWAS. Based on simulations and analysis of a dataset of 10 metabolic traits measured in a Northern Finland cohort, which is known to exhibit significant population structure, we find that our method has superior power to current approaches. Our software is available at https://github.com/anand-bhaskar/gap . abhaskar@stanford.edu or ajavanma@usc.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Land Use Change on Household Farms in the Ecuadorian Amazon: Design and Implementation of an Agent-Based Model

PubMed Central

Mena, Carlos F.; Walsh, Stephen J.; Frizzelle, Brian G.; Xiaozheng, Yao; Malanson, George P.

2010-01-01

This paper describes the design and implementation of an Agent-Based Model (ABM) used to simulate land use change on household farms in the Northern Ecuadorian Amazon (NEA). The ABM simulates decision-making processes at the household level that is examined through a longitudinal, socio-economic and demographic survey that was conducted in 1990 and 1999. Geographic Information Systems (GIS) are used to establish spatial relationships between farms and their environment, while classified Landsat Thematic Mapper (TM) imagery is used to set initial land use/land cover conditions for the spatial simulation, assess from-to land use/land cover change patterns, and describe trajectories of land use change at the farm and landscape levels. Results from prior studies in the NEA provide insights into the key social and ecological variables, describe human behavioral functions, and examine population-environment interactions that are linked to deforestation and agricultural extensification, population migration, and demographic change. Within the architecture of the model, agents are classified as active or passive. The model comprises four modules, i.e., initialization, demography, agriculture, and migration that operate individually, but are linked through key household processes. The main outputs of the model include a spatially-explicit representation of the land use/land cover on survey and non-survey farms and at the landscape level for each annual time-step, as well as simulated socio-economic and demographic characteristics of households and communities. The work describes the design and implementation of the model and how population-environment interactions can be addressed in a frontier setting. The paper contributes to land change science by examining important pattern-process relations, advocating a spatial modeling approach that is capable of synthesizing fundamental relationships at the farm level, and links people and environment in complex ways. PMID:24436501

On the use of a PM2.5 exposure simulator to explain birthweight

PubMed Central

Berrocal, Veronica J.; Gelfand, Alan E.; Holland, David M.; Burke, Janet; Miranda, Marie Lynn

2010-01-01

In relating pollution to birth outcomes, maternal exposure has usually been described using monitoring data. Such characterization provides a misrepresentation of exposure as it (i) does not take into account the spatial misalignment between an individual’s residence and monitoring sites, and (ii) it ignores the fact that individuals spend most of their time indoors and typically in more than one location. In this paper, we break with previous studies by using a stochastic simulator to describe personal exposure (to particulate matter) and then relate simulated exposures at the individual level to the health outcome (birthweight) rather than aggregating to a selected spatial unit. We propose a hierarchical model that, at the first stage, specifies a linear relationship between birthweight and personal exposure, adjusting for individual risk factors and introduces random spatial effects for the census tract of maternal residence. At the second stage, our hierarchical model specifies the distribution of each individual’s personal exposure using the empirical distribution yielded by the stochastic simulator as well as a model for the spatial random effects. We have applied our framework to analyze birthweight data from 14 counties in North Carolina in years 2001 and 2002. We investigate whether there are certain aspects and time windows of exposure that are more detrimental to birthweight by building different exposure metrics which we incorporate, one by one, in our hierarchical model. To assess the difference in relating ambient exposure to birthweight versus personal exposure to birthweight, we compare estimates of the effect of air pollution obtained from hierarchical models that linearly relate ambient exposure and birthweight versus those obtained from our modeling framework. Our analysis does not show a significant effect of PM2.5 on birthweight for reasons which we discuss. However, our modeling framework serves as a template for analyzing the relationship between personal exposure and longer term health endpoints. PMID:21691413

Numerical simulations of significant orographic precipitation in Madeira island

NASA Astrophysics Data System (ADS)

Couto, Flavio Tiago; Ducrocq, Véronique; Salgado, Rui; Costa, Maria João

2016-03-01

High-resolution simulations of high precipitation events with the MESO-NH model are presented, and also used to verify that increasing horizontal resolution in zones of complex orography, such as in Madeira island, improve the simulation of the spatial distribution and total precipitation. The simulations succeeded in reproducing the general structure of the cloudy systems over the ocean in the four periods considered of significant accumulated precipitation. The accumulated precipitation over the Madeira was better represented with the 0.5 km horizontal resolution and occurred under four distinct synoptic situations. Different spatial patterns of the rainfall distribution over the Madeira have been identified.

Implementing system simulation of C3 systems using autonomous objects

NASA Technical Reports Server (NTRS)

Rogers, Ralph V.

1987-01-01

The basis of all conflict recognition in simulation is a common frame of reference. Synchronous discrete-event simulation relies on the fixed points in time as the basic frame of reference. Asynchronous discrete-event simulation relies on fixed-points in the model space as the basic frame of reference. Neither approach provides sufficient support for autonomous objects. The use of a spatial template as a frame of reference is proposed to address these insufficiencies. The concept of a spatial template is defined and an implementation approach offered. Discussed are the uses of this approach to analyze the integration of sensor data associated with Command, Control, and Communication systems.

Landscape Level Carbon and Water Balances and Agricultural Production in Mountainous Terrain of the Haean Basin, South Korea

NASA Astrophysics Data System (ADS)

Lee, B.; Geyer, R.; Seo, B.; Lindner, S.; Walther, G.; Tenhunen, J. D.

2009-12-01

The process-based spatial simulation model PIXGRO was used to estimate gross primary production, ecosystem respiration, net ecosystem CO2 exchange and water use by forest and crop fields of Haean Basin, South Korea at landscape scale. Simulations are run for individual years from early spring to late fall, providing estimates for dry land crops and rice paddies with respect to carbon gain, biomass and leaf area development, allocation of photoproducts to the belowground ecosystem compartment, and harvest yields. In the case of deciduous oak forests, gas exchange is estimated, but spatial simulation of growth over the single annual cycles is not included. Spatial parameterization of the model is derived for forest LAI based on remote sensing, for forest and cropland fluxes via eddy covariance and chamber studies, for soil characteristics by generalization from spatial surveys, for climate drivers by generalizing observations at ca. 20 monitoring stations distributed throughout the basin and along the elevation gradient from 500 to 1000 m, and for incident radiation via modelling of the radiation components in complex terrain. Validation of the model is being carried out at point scale based on comparison of model output at selected locations with observations as well as with known trends in ecosystem response documented in the literature. The resulting modelling tool is useful for estimation of ecosystem services at landscape scale, first expressed as kg ha-1 crop yield, but via future cooperative studies also in terms of monetary gain to individual farms and farming cooperatives applying particular management strategies.

Sensitivity of modeled estuarine circulation to spatial and temporal resolution of input meteorological forcing of a cold frontal passage

NASA Astrophysics Data System (ADS)

Weaver, Robert J.; Taeb, Peyman; Lazarus, Steven; Splitt, Michael; Holman, Bryan P.; Colvin, Jeffrey

2016-12-01

In this study, a four member ensemble of meteorological forcing is generated using the Weather Research and Forecasting (WRF) model in order to simulate a frontal passage event that impacted the Indian River Lagoon (IRL) during March 2015. The WRF model is run to provide high and low, spatial (0.005° and 0.1°) and temporal (30 min and 6 h) input wind and pressure fields. The four member ensemble is used to force the Advanced Circulation model (ADCIRC) coupled with Simulating Waves Nearshore (SWAN) and compute the hydrodynamic and wave response. Results indicate that increasing the spatial resolution of the meteorological forcing has a greater impact on the results than increasing the temporal resolution in coastal systems like the IRL where the length scales are smaller than the resolution of the operational meteorological model being used to generate the forecast. Changes in predicted water elevations are due in part to the upwind and downwind behavior of the input wind forcing. The significant wave height is more sensitive to the meteorological forcing, exhibited by greater ensemble spread throughout the simulation. It is important that the land mask, seen by the meteorological model, is representative of the geography of the coastal estuary as resolved by the hydrodynamic model. As long as the temporal resolution of the wind field captures the bulk characteristics of the frontal passage, computational resources should be focused so as to ensure that the meteorological model resolves the spatial complexities, such as the land-water interface, that drive the land use responsible for dynamic downscaling of the winds.

A High-Resolution Spatially Explicit Monte-Carlo Simulation Approach to Commercial and Residential Electricity and Water Demand Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morton, April M; McManamay, Ryan A; Nagle, Nicholas N

Abstract As urban areas continue to grow and evolve in a world of increasing environmental awareness, the need for high resolution spatially explicit estimates for energy and water demand has become increasingly important. Though current modeling efforts mark significant progress in the effort to better understand the spatial distribution of energy and water consumption, many are provided at a course spatial resolution or rely on techniques which depend on detailed region-specific data sources that are not publicly available for many parts of the U.S. Furthermore, many existing methods do not account for errors in input data sources and may thereforemore » not accurately reflect inherent uncertainties in model outputs. We propose an alternative and more flexible Monte-Carlo simulation approach to high-resolution residential and commercial electricity and water consumption modeling that relies primarily on publicly available data sources. The method s flexible data requirement and statistical framework ensure that the model is both applicable to a wide range of regions and reflective of uncertainties in model results. Key words: Energy Modeling, Water Modeling, Monte-Carlo Simulation, Uncertainty Quantification Acknowledgment This manuscript has been authored by employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. Accordingly, the United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.« less

Towards anatomic scale agent-based modeling with a massively parallel spatially explicit general-purpose model of enteric tissue (SEGMEnT_HPC).

PubMed

Cockrell, Robert Chase; Christley, Scott; Chang, Eugene; An, Gary

2015-01-01

Perhaps the greatest challenge currently facing the biomedical research community is the ability to integrate highly detailed cellular and molecular mechanisms to represent clinical disease states as a pathway to engineer effective therapeutics. This is particularly evident in the representation of organ-level pathophysiology in terms of abnormal tissue structure, which, through histology, remains a mainstay in disease diagnosis and staging. As such, being able to generate anatomic scale simulations is a highly desirable goal. While computational limitations have previously constrained the size and scope of multi-scale computational models, advances in the capacity and availability of high-performance computing (HPC) resources have greatly expanded the ability of computational models of biological systems to achieve anatomic, clinically relevant scale. Diseases of the intestinal tract are exemplary examples of pathophysiological processes that manifest at multiple scales of spatial resolution, with structural abnormalities present at the microscopic, macroscopic and organ-levels. In this paper, we describe a novel, massively parallel computational model of the gut, the Spatially Explicitly General-purpose Model of Enteric Tissue_HPC (SEGMEnT_HPC), which extends an existing model of the gut epithelium, SEGMEnT, in order to create cell-for-cell anatomic scale simulations. We present an example implementation of SEGMEnT_HPC that simulates the pathogenesis of ileal pouchitis, and important clinical entity that affects patients following remedial surgery for ulcerative colitis.

Modeling individual movement decisions of brown hare (Lepus europaeus) as a key concept for realistic spatial behavior and exposure: A population model for landscape-level risk assessment.

PubMed

Kleinmann, Joachim U; Wang, Magnus

2017-09-01

Spatial behavior is of crucial importance for the risk assessment of pesticides and for the assessment of effects of agricultural practice or multiple stressors, because it determines field use, exposition, and recovery. Recently, population models have increasingly been used to understand the mechanisms driving risk and recovery or to conduct landscape-level risk assessments. To include spatial behavior appropriately in population models for use in risk assessments, a new method, "probabilistic walk," was developed, which simulates the detailed daily movement of individuals by taking into account food resources, vegetation cover, and the presence of conspecifics. At each movement step, animals decide where to move next based on probabilities being determined from this information. The model was parameterized to simulate populations of brown hares (Lepus europaeus). A detailed validation of the model demonstrated that it can realistically reproduce various natural patterns of brown hare ecology and behavior. Simulated proportions of time animals spent in fields (PT values) were also comparable to field observations. It is shown that these important parameters for the risk assessment may, however, vary in different landscapes. The results demonstrate the value of using population models to reduce uncertainties in risk assessment and to better understand which factors determine risk in a landscape context. Environ Toxicol Chem 2017;36:2299-2307. © 2017 SETAC. © 2017 SETAC.

Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: Insights into spatial variability using high-resolution satellite data

PubMed Central

Alexeeff, Stacey E.; Schwartz, Joel; Kloog, Itai; Chudnovsky, Alexandra; Koutrakis, Petros; Coull, Brent A.

2016-01-01

Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1km x 1km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R2 yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with greater than 0.9 out-of-sample R2 yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the standard errors. Land use regression models performed better in chronic effects simulations. These results can help researchers when interpreting health effect estimates in these types of studies. PMID:24896768

Measurements and simulation of forest leaf area index and net primary productivity in Northern China.

PubMed

Wang, P; Sun, R; Hu, J; Zhu, Q; Zhou, Y; Li, L; Chen, J M

2007-11-01

Large scale process-based modeling is a useful approach to estimate distributions of global net primary productivity (NPP). In this paper, in order to validate an existing NPP model with observed data at site level, field experiments were conducted at three sites in northern China. One site is located in Qilian Mountain in Gansu Province, and the other two sites are in Changbaishan Natural Reserve and Dunhua County in Jilin Province. Detailed field experiments are discussed and field data are used to validate the simulated NPP. Remotely sensed images including Landsat Enhanced Thematic Mapper plus (ETM+, 30 m spatial resolution in visible and near infrared bands) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, 15m spatial resolution in visible and near infrared bands) are used to derive maps of land cover, leaf area index, and biomass. Based on these maps, field measured data, soil texture and daily meteorological data, NPP of these sites are simulated for year 2001 with the boreal ecosystem productivity simulator (BEPS). The NPP in these sites ranges from 80 to 800 gCm(-2)a(-1). The observed NPP agrees well with the modeled NPP. This study suggests that BEPS can be used to estimate NPP in northern China if remotely sensed images of high spatial resolution are available.

MEG source localization of spatially extended generators of epileptic activity: comparing entropic and hierarchical bayesian approaches.

PubMed

Chowdhury, Rasheda Arman; Lina, Jean Marc; Kobayashi, Eliane; Grova, Christophe

2013-01-01

Localizing the generators of epileptic activity in the brain using Electro-EncephaloGraphy (EEG) or Magneto-EncephaloGraphy (MEG) signals is of particular interest during the pre-surgical investigation of epilepsy. Epileptic discharges can be detectable from background brain activity, provided they are associated with spatially extended generators. Using realistic simulations of epileptic activity, this study evaluates the ability of distributed source localization methods to accurately estimate the location of the generators and their sensitivity to the spatial extent of such generators when using MEG data. Source localization methods based on two types of realistic models have been investigated: (i) brain activity may be modeled using cortical parcels and (ii) brain activity is assumed to be locally smooth within each parcel. A Data Driven Parcellization (DDP) method was used to segment the cortical surface into non-overlapping parcels and diffusion-based spatial priors were used to model local spatial smoothness within parcels. These models were implemented within the Maximum Entropy on the Mean (MEM) and the Hierarchical Bayesian (HB) source localization frameworks. We proposed new methods in this context and compared them with other standard ones using Monte Carlo simulations of realistic MEG data involving sources of several spatial extents and depths. Detection accuracy of each method was quantified using Receiver Operating Characteristic (ROC) analysis and localization error metrics. Our results showed that methods implemented within the MEM framework were sensitive to all spatial extents of the sources ranging from 3 cm(2) to 30 cm(2), whatever were the number and size of the parcels defining the model. To reach a similar level of accuracy within the HB framework, a model using parcels larger than the size of the sources should be considered.

MEG Source Localization of Spatially Extended Generators of Epileptic Activity: Comparing Entropic and Hierarchical Bayesian Approaches

PubMed Central

Chowdhury, Rasheda Arman; Lina, Jean Marc; Kobayashi, Eliane; Grova, Christophe

2013-01-01

Localizing the generators of epileptic activity in the brain using Electro-EncephaloGraphy (EEG) or Magneto-EncephaloGraphy (MEG) signals is of particular interest during the pre-surgical investigation of epilepsy. Epileptic discharges can be detectable from background brain activity, provided they are associated with spatially extended generators. Using realistic simulations of epileptic activity, this study evaluates the ability of distributed source localization methods to accurately estimate the location of the generators and their sensitivity to the spatial extent of such generators when using MEG data. Source localization methods based on two types of realistic models have been investigated: (i) brain activity may be modeled using cortical parcels and (ii) brain activity is assumed to be locally smooth within each parcel. A Data Driven Parcellization (DDP) method was used to segment the cortical surface into non-overlapping parcels and diffusion-based spatial priors were used to model local spatial smoothness within parcels. These models were implemented within the Maximum Entropy on the Mean (MEM) and the Hierarchical Bayesian (HB) source localization frameworks. We proposed new methods in this context and compared them with other standard ones using Monte Carlo simulations of realistic MEG data involving sources of several spatial extents and depths. Detection accuracy of each method was quantified using Receiver Operating Characteristic (ROC) analysis and localization error metrics. Our results showed that methods implemented within the MEM framework were sensitive to all spatial extents of the sources ranging from 3 cm2 to 30 cm2, whatever were the number and size of the parcels defining the model. To reach a similar level of accuracy within the HB framework, a model using parcels larger than the size of the sources should be considered. PMID:23418485

Spatial-temporal-covariance-based modeling, analysis, and simulation of aero-optics wavefront aberrations.

PubMed

Vogel, Curtis R; Tyler, Glenn A; Wittich, Donald J

2014-07-01

We introduce a framework for modeling, analysis, and simulation of aero-optics wavefront aberrations that is based on spatial-temporal covariance matrices extracted from wavefront sensor measurements. Within this framework, we present a quasi-homogeneous structure function to analyze nonhomogeneous, mildly anisotropic spatial random processes, and we use this structure function to show that phase aberrations arising in aero-optics are, for an important range of operating parameters, locally Kolmogorov. This strongly suggests that the d5/3 power law for adaptive optics (AO) deformable mirror fitting error, where d denotes actuator separation, holds for certain important aero-optics scenarios. This framework also allows us to compute bounds on AO servo lag error and predictive control error. In addition, it provides us with the means to accurately simulate AO systems for the mitigation of aero-effects, and it may provide insight into underlying physical processes associated with turbulent flow. The techniques introduced here are demonstrated using data obtained from the Airborne Aero-Optics Laboratory.

Using a GIS-based spot growth model and visual simulator to evaluate the effects of silvicultural treatments on southern pine beetle-infested stands

Treesearch

Chiao-Ying Chou; Roy L. Hedden; Bo Song; Thomas M. Williams

2013-01-01

Many models are available for simulating the probability of southern pine beetle (Dendroctonus frontalis Zimmermann) (SPB) infestation and outbreak dynamics. However, only a few models focused on the potential spatial SPB growth. Although the integrated pest management systems are currently adopted, SPB management is still challenging because of...

Assessing modeled Greenland surface mass balance in the GISS Model E2 and its sensitivity to surface albedo

NASA Astrophysics Data System (ADS)

Alexander, Patrick; LeGrande, Allegra N.; Koenig, Lora S.; Tedesco, Marco; Moustafa, Samiah E.; Ivanoff, Alvaro; Fischer, Robert P.; Fettweis, Xavier

2016-04-01

The surface mass balance (SMB) of the Greenland Ice Sheet (GrIS) plays an important role in global sea level change. Regional Climate Models (RCMs) such as the Modèle Atmosphérique Régionale (MAR) have been employed at high spatial resolution with relatively complex physics to simulate ice sheet SMB. Global climate models (GCMs) incorporate less sophisticated physical schemes and provide outputs at a lower spatial resolution, but have the advantage of modeling the interaction between different components of the earth's oceans, climate, and land surface at a global scale. Improving the ability of GCMs to represent ice sheet SMB is important for making predictions of future changes in global sea level. With the ultimate goal of improving SMB simulated by the Goddard Institute for Space Studies (GISS) Model E2 GCM, we compare simulated GrIS SMB against the outputs of the MAR model and radar-derived estimates of snow accumulation. In order to reproduce present-day climate variability in the Model E2 simulation, winds are constrained to match the reanalysis datasets used to force MAR at the lateral boundaries. We conduct a preliminary assessment of the sensitivity of the simulated Model E2 SMB to surface albedo, a parameter that is known to strongly influence SMB. Model E2 albedo is set to a fixed value of 0.8 over the entire ice sheet in the initial configuration of the model (control case). We adjust this fixed value in an ensemble of simulations over a range of 0.4 to 0.8 (roughly the range of observed summer GrIS albedo values) to examine the sensitivity of ice-sheet-wide SMB to albedo. We prescribe albedo from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 v6 to examine the impact of a more realistic spatial and temporal variations in albedo. An age-dependent snow albedo parameterization is applied, and its impact on SMB relative to observations and the RCM is assessed.

Simulation of the spatial frequency-dependent sensitivities of Acoustic Emission sensors

NASA Astrophysics Data System (ADS)

Boulay, N.; Lhémery, A.; Zhang, F.

2018-05-01

Typical configurations of nondestructive testing by Acoustic Emission (NDT/AE) make use of multiple sensors positioned on the tested structure for detecting evolving flaws and possibly locating them by triangulation. Sensors positions must be optimized for ensuring global coverage sensitivity to AE events and minimizing their number. A simulator of NDT/AE is under development to provide help with designing testing configurations and with interpreting measurements. A global model performs sub-models simulating the various phenomena taking place at different spatial and temporal scales (crack growth, AE source and radiation, wave propagation in the structure, reception by sensors). In this context, accurate modelling of sensors behaviour must be developed. These sensors generally consist of a cylindrical piezoelectric element of radius approximately equal to its thickness, without damping and bonded to its case. Sensors themselves are bonded to the structure being tested. Here, a multiphysics finite element simulation tool is used to study the complex behaviour of AE sensor. The simulated behaviour is shown to accurately reproduce the high-amplitude measured contributions used in the AE practice.

Global Gridded Crop Model Evaluation: Benchmarking, Skills, Deficiencies and Implications.

NASA Technical Reports Server (NTRS)

Muller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe; Deryng, Delphine; Folberth, Christian; Glotter, Michael; Hoek, Steven;

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Runoff simulation sensitivity to remotely sensed initial soil water content

NASA Astrophysics Data System (ADS)

Goodrich, D. C.; Schmugge, T. J.; Jackson, T. J.; Unkrich, C. L.; Keefer, T. O.; Parry, R.; Bach, L. B.; Amer, S. A.

1994-05-01

A variety of aircraft remotely sensed and conventional ground-based measurements of volumetric soil water content (SW) were made over two subwatersheds (4.4 and 631 ha) of the U.S. Department of Agriculture's Agricultural Research Service Walnut Gulch experimental watershed during the 1990 monsoon season. Spatially distributed soil water contents estimated remotely from the NASA push broom microwave radiometer (PBMR), an Institute of Radioengineering and Electronics (IRE) multifrequency radiometer, and three ground-based point methods were used to define prestorm initial SW for a distributed rainfall-runoff model (KINEROS; Woolhiser et al., 1990) at a small catchment scale (4.4 ha). At a medium catchment scale (631 ha or 6.31 km2) spatially distributed PBMR SW data were aggregated via stream order reduction. The impacts of the various spatial averages of SW on runoff simulations are discussed and are compared to runoff simulations using SW estimates derived from a simple daily water balance model. It was found that at the small catchment scale the SW data obtained from any of the measurement methods could be used to obtain reasonable runoff predictions. At the medium catchment scale, a basin-wide remotely sensed average of initial water content was sufficient for runoff simulations. This has important implications for the possible use of satellite-based microwave soil moisture data to define prestorm SW because the low spatial resolutions of such sensors may not seriously impact runoff simulations under the conditions examined. However, at both the small and medium basin scale, adequate resources must be devoted to proper definition of the input rainfall to achieve reasonable runoff simulations.

Incorporating GIS data into an agent-based model to support planning policy making for the development of creative industries

NASA Astrophysics Data System (ADS)

Liu, Helin; Silva, Elisabete A.; Wang, Qian

2016-07-01

This paper presents an extension to the agent-based model "Creative Industries Development-Urban Spatial Structure Transformation" by incorporating GIS data. Three agent classes, creative firms, creative workers and urban government, are considered in the model, and the spatial environment represents a set of GIS data layers (i.e. road network, key housing areas, land use). With the goal to facilitate urban policy makers to draw up policies locally and optimise the land use assignment in order to support the development of creative industries, the improved model exhibited its capacity to assist the policy makers conducting experiments and simulating different policy scenarios to see the corresponding dynamics of the spatial distributions of creative firms and creative workers across time within a city/district. The spatiotemporal graphs and maps record the simulation results and can be used as a reference by the policy makers to adjust land use plans adaptively at different stages of the creative industries' development process.

Publically accessible decision support system of the spatially referenced regressions on watershed attributes (SPARROW) model and model enhancements in South Carolina

Treesearch

Celeste Journey; Anne B. Hoos; David E. Ladd; John W. brakebill; Richard A. Smith

2016-01-01

The U.S. Geological Survey (USGS) National Water Quality Assessment program has developed a web-based decision support system (DSS) to provide free public access to the steady-stateSPAtially Referenced Regressions On Watershed attributes (SPARROW) model simulation results on nutrient conditions in streams and rivers and to offer scenario testing capabilities for...

A perturbation analysis of a mechanical model for stable spatial patterning in embryology

NASA Astrophysics Data System (ADS)

Bentil, D. E.; Murray, J. D.

1992-12-01

We investigate a mechanical cell-traction mechanism that generates stationary spatial patterns. A linear analysis highlights the model's potential for these heterogeneous solutions. We use multiple-scale perturbation techniques to study the evolution of these solutions and compare our solutions with numerical simulations of the model system. We discuss some potential biological applications among which are the formation of ridge patterns, dermatoglyphs, and wound healing.

Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping.

PubMed

Lawson, A B; Carroll, R; Faes, C; Kirby, R S; Aregay, M; Watjou, K

2017-12-01

It is often the case that researchers wish to simultaneously explore the behavior of and estimate overall risk for multiple, related diseases with varying rarity while accounting for potential spatial and/or temporal correlation. In this paper, we propose a flexible class of multivariate spatio-temporal mixture models to fill this role. Further, these models offer flexibility with the potential for model selection as well as the ability to accommodate lifestyle, socio-economic, and physical environmental variables with spatial, temporal, or both structures. Here, we explore the capability of this approach via a large scale simulation study and examine a motivating data example involving three cancers in South Carolina. The results which are focused on four model variants suggest that all models possess the ability to recover simulation ground truth and display improved model fit over two baseline Knorr-Held spatio-temporal interaction model variants in a real data application.

Improved estimation of hydraulic conductivity by combining stochastically simulated hydrofacies with geophysical data.

PubMed

Zhu, Lin; Gong, Huili; Chen, Yun; Li, Xiaojuan; Chang, Xiang; Cui, Yijiao

2016-03-01

Hydraulic conductivity is a major parameter affecting the output accuracy of groundwater flow and transport models. The most commonly used semi-empirical formula for estimating conductivity is Kozeny-Carman equation. However, this method alone does not work well with heterogeneous strata. Two important parameters, grain size and porosity, often show spatial variations at different scales. This study proposes a method for estimating conductivity distributions by combining a stochastic hydrofacies model with geophysical methods. The Markov chain model with transition probability matrix was adopted to re-construct structures of hydrofacies for deriving spatial deposit information. The geophysical and hydro-chemical data were used to estimate the porosity distribution through the Archie's law. Results show that the stochastic simulated hydrofacies model reflects the sedimentary features with an average model accuracy of 78% in comparison with borehole log data in the Chaobai alluvial fan. The estimated conductivity is reasonable and of the same order of magnitude of the outcomes of the pumping tests. The conductivity distribution is consistent with the sedimentary distributions. This study provides more reliable spatial distributions of the hydraulic parameters for further numerical modeling.

Neural dynamics of motion processing and speed discrimination.

PubMed

Chey, J; Grossberg, S; Mingolla, E

1998-09-01

A neural network model of visual motion perception and speed discrimination is presented. The model shows how a distributed population code of speed tuning, that realizes a size-speed correlation, can be derived from the simplest mechanisms whereby activations of multiple spatially short-range filters of different size are transformed into speed-turned cell responses. These mechanisms use transient cell responses to moving stimuli, output thresholds that covary with filter size, and competition. These mechanisms are proposed to occur in the V1-->MT cortical processing stream. The model reproduces empirically derived speed discrimination curves and simulates data showing how visual speed perception and discrimination can be affected by stimulus contrast, duration, dot density and spatial frequency. Model motion mechanisms are analogous to mechanisms that have been used to model 3-D form and figure-ground perception. The model forms the front end of a larger motion processing system that has been used to simulate how global motion capture occurs, and how spatial attention is drawn to moving forms. It provides a computational foundation for an emerging neural theory of 3-D form and motion perception.

Spatial abstraction for autonomous robot navigation.

PubMed

Epstein, Susan L; Aroor, Anoop; Evanusa, Matthew; Sklar, Elizabeth I; Parsons, Simon

2015-09-01

Optimal navigation for a simulated robot relies on a detailed map and explicit path planning, an approach problematic for real-world robots that are subject to noise and error. This paper reports on autonomous robots that rely on local spatial perception, learning, and commonsense rationales instead. Despite realistic actuator error, learned spatial abstractions form a model that supports effective travel.

An advanced stochastic weather generator for simulating 2-D high-resolution climate variables

NASA Astrophysics Data System (ADS)

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

2017-07-01

A new stochastic weather generator, Advanced WEather GENerator for a two-dimensional grid (AWE-GEN-2d) is presented. The model combines physical and stochastic approaches to simulate key meteorological variables at high spatial and temporal resolution: 2 km × 2 km and 5 min for precipitation and cloud cover and 100 m × 100 m and 1 h for near-surface air temperature, solar radiation, vapor pressure, atmospheric pressure, and near-surface wind. The model requires spatially distributed data for the calibration process, which can nowadays be obtained by remote sensing devices (weather radar and satellites), reanalysis data sets and ground stations. AWE-GEN-2d is parsimonious in terms of computational demand and therefore is particularly suitable for studies where exploring internal climatic variability at multiple spatial and temporal scales is fundamental. Applications of the model include models of environmental systems, such as hydrological and geomorphological models, where high-resolution spatial and temporal meteorological forcing is crucial. The weather generator was calibrated and validated for the Engelberg region, an area with complex topography in the Swiss Alps. Model test shows that the climate variables are generated by AWE-GEN-2d with a level of accuracy that is sufficient for many practical applications.

Model-assisted analysis of spatial and temporal variations in fruit temperature and transpiration highlighting the role of fruit development.

PubMed

Nordey, Thibault; Léchaudel, Mathieu; Saudreau, Marc; Joas, Jacques; Génard, Michel

2014-01-01

Fruit physiology is strongly affected by both fruit temperature and water losses through transpiration. Fruit temperature and its transpiration vary with environmental factors and fruit characteristics. In line with previous studies, measurements of physical and thermal fruit properties were found to significantly vary between fruit tissues and maturity stages. To study the impact of these variations on fruit temperature and transpiration, a modelling approach was used. A physical model was developed to predict the spatial and temporal variations of fruit temperature and transpiration according to the spatial and temporal variations of environmental factors and thermal and physical fruit properties. Model predictions compared well to temperature measurements on mango fruits, making it possible to accurately simulate the daily temperature variations of the sunny and shaded sides of fruits. Model simulations indicated that fruit development induced an increase in both the temperature gradient within the fruit and fruit water losses, mainly due to fruit expansion. However, the evolution of fruit characteristics has only a very slight impact on the average temperature and the transpiration per surface unit. The importance of temperature and transpiration gradients highlighted in this study made it necessary to take spatial and temporal variations of environmental factors and fruit characteristics into account to model fruit physiology.

Modeling sediment yield in small catchments at event scale: Model comparison, development and evaluation

NASA Astrophysics Data System (ADS)

Tan, Z.; Leung, L. R.; Li, H. Y.; Tesfa, T. K.

2017-12-01

Sediment yield (SY) has significant impacts on river biogeochemistry and aquatic ecosystems but it is rarely represented in Earth System Models (ESMs). Existing SY models focus on estimating SY from large river basins or individual catchments so it is not clear how well they simulate SY in ESMs at larger spatial scales and globally. In this study, we compare the strengths and weaknesses of eight well-known SY models in simulating annual mean SY at about 400 small catchments ranging in size from 0.22 to 200 km2 in the US, Canada and Puerto Rico. In addition, we also investigate the performance of these models in simulating event-scale SY at six catchments in the US using high-quality hydrological inputs. The model comparison shows that none of the models can reproduce the SY at large spatial scales but the Morgan model performs the better than others despite its simplicity. In all model simulations, large underestimates occur in catchments with very high SY. A possible pathway to reduce the discrepancies is to incorporate sediment detachment by landsliding, which is currently not included in the models being evaluated. We propose a new SY model that is based on the Morgan model but including a landsliding soil detachment scheme that is being developed. Along with the results of the model comparison and evaluation, preliminary findings from the revised Morgan model will be presented.

How model and input uncertainty impact maize yield simulations in West Africa

NASA Astrophysics Data System (ADS)

Waha, Katharina; Huth, Neil; Carberry, Peter; Wang, Enli

2015-02-01

Crop models are common tools for simulating crop yields and crop production in studies on food security and global change. Various uncertainties however exist, not only in the model design and model parameters, but also and maybe even more important in soil, climate and management input data. We analyze the performance of the point-scale crop model APSIM and the global scale crop model LPJmL with different climate and soil conditions under different agricultural management in the low-input maize-growing areas of Burkina Faso, West Africa. We test the models’ response to different levels of input information from little to detailed information on soil, climate (1961-2000) and agricultural management and compare the models’ ability to represent the observed spatial (between locations) and temporal variability (between years) in crop yields. We found that the resolution of different soil, climate and management information influences the simulated crop yields in both models. However, the difference between models is larger than between input data and larger between simulations with different climate and management information than between simulations with different soil information. The observed spatial variability can be represented well from both models even with little information on soils and management but APSIM simulates a higher variation between single locations than LPJmL. The agreement of simulated and observed temporal variability is lower due to non-climatic factors e.g. investment in agricultural research and development between 1987 and 1991 in Burkina Faso which resulted in a doubling of maize yields. The findings of our study highlight the importance of scale and model choice and show that the most detailed input data does not necessarily improve model performance.

The significance of spatial variability of rainfall on streamflow: A synthetic analysis at the Upper Lee catchment, UK

NASA Astrophysics Data System (ADS)

Pechlivanidis, Ilias; McIntyre, Neil; Wheater, Howard

2017-04-01

Rainfall, one of the main inputs in hydrological modeling, is a highly heterogeneous process over a wide range of scales in space, and hence the ignorance of the spatial rainfall information could affect the simulated streamflow. Calibration of hydrological model parameters is rarely a straightforward task due to parameter equifinality and parameters' 'nature' to compensate for other uncertainties, i.e. structural and forcing input. In here, we analyse the significance of spatial variability of rainfall on streamflow as a function of catchment scale and type, and antecedent conditions using the continuous time, semi-distributed PDM hydrological model at the Upper Lee catchment, UK. The impact of catchment scale and type is assessed using 11 nested catchments ranging in scale from 25 to 1040 km2, and further assessed by artificially changing the catchment characteristics and translating these to model parameters with uncertainty using model regionalisation. Synthetic rainfall events are introduced to directly relate the change in simulated streamflow to the spatial variability of rainfall. Overall, we conclude that the antecedent catchment wetness and catchment type play an important role in controlling the significance of the spatial distribution of rainfall on streamflow. Results show a relationship between hydrograph characteristics (streamflow peak and volume) and the degree of spatial variability of rainfall for the impermeable catchments under dry antecedent conditions, although this decreases at larger scales; however this sensitivity is significantly undermined under wet antecedent conditions. Although there is indication that the impact of spatial rainfall on streamflow varies as a function of catchment scale, the variability of antecedent conditions between the synthetic catchments seems to mask this significance. Finally, hydrograph responses to different spatial patterns in rainfall depend on assumptions used for model parameter estimation and also the spatial variation in parameters indicating the need of an uncertainty framework in such investigation.

Contrasting spatial structures of Atlantic Multidecadal Oscillation between observations and slab ocean model simulations

NASA Astrophysics Data System (ADS)

Sun, Cheng; Li, Jianping; Kucharski, Fred; Xue, Jiaqing; Li, Xiang

2018-04-01

The spatial structure of Atlantic multidecadal oscillation (AMO) is analyzed and compared between the observations and simulations from slab ocean models (SOMs) and fully coupled models. The observed sea surface temperature (SST) pattern of AMO is characterized by a basin-wide monopole structure, and there is a significantly high degree of spatial coherence of decadal SST variations across the entire North Atlantic basin. The observed SST anomalies share a common decadal-scale signal, corresponding to the basin-wide average (i. e., the AMO). In contrast, the simulated AMO in SOMs (AMOs) exhibits a tripole-like structure, with the mid-latitude North Atlantic SST showing an inverse relationship with other parts of the basin, and the SOMs fail to reproduce the observed strong spatial coherence of decadal SST variations associated with the AMO. The observed spatial coherence of AMO SST anomalies is identified as a key feature that can be used to distinguish the AMO mechanism. The tripole-like SST pattern of AMOs in SOMs can be largely explained by the atmosphere-forced thermodynamics mechanism due to the surface heat flux changes associated with the North Atlantic Oscillation (NAO). The thermodynamic forcing of AMOs by the NAO gives rise to a simultaneous inverse NAO-AMOs relationship at both interannual and decadal timescales and a seasonal phase locking of the AMOs variability to the cold season. However, the NAO-forced thermodynamics mechanism cannot explain the observed NAO-AMO relationship and the seasonal phase locking of observed AMO variability to the warm season. At decadal timescales, a strong lagged relationship between NAO and AMO is observed, with the NAO leading by up to two decades, while the simultaneous correlation of NAO with AMO is weak. This lagged relationship and the spatial coherence of AMO can be well understood from the view point of ocean dynamics. A time-integrated NAO index, which reflects the variations in Atlantic meridional overturning circulation (AMOC) and northward ocean heat transport caused by the accumulated effect of NAO forcing, reasonably well captures the observed multidecadal fluctuations in the AMO. Further analysis using the fully coupled model simulations provides direct modeling evidence that the observed spatial coherence of decadal SST variations across North Atlantic basin can be reproduced only by including the AMOC-related ocean dynamics, and the AMOC acts as a common forcing signal that results in a spatially coherent variation of North Atlantic SST.

Estimating and interpreting migration of Amazonian forests using spatially implicit and semi-explicit neutral models.

PubMed

Pos, Edwin; Guevara Andino, Juan Ernesto; Sabatier, Daniel; Molino, Jean-François; Pitman, Nigel; Mogollón, Hugo; Neill, David; Cerón, Carlos; Rivas-Torres, Gonzalo; Di Fiore, Anthony; Thomas, Raquel; Tirado, Milton; Young, Kenneth R; Wang, Ophelia; Sierra, Rodrigo; García-Villacorta, Roosevelt; Zagt, Roderick; Palacios Cuenca, Walter; Aulestia, Milton; Ter Steege, Hans

2017-06-01

With many sophisticated methods available for estimating migration, ecologists face the difficult decision of choosing for their specific line of work. Here we test and compare several methods, performing sanity and robustness tests, applying to large-scale data and discussing the results and interpretation. Five methods were selected to compare for their ability to estimate migration from spatially implicit and semi-explicit simulations based on three large-scale field datasets from South America (Guyana, Suriname, French Guiana and Ecuador). Space was incorporated semi-explicitly by a discrete probability mass function for local recruitment, migration from adjacent plots or from a metacommunity. Most methods were able to accurately estimate migration from spatially implicit simulations. For spatially semi-explicit simulations, estimation was shown to be the additive effect of migration from adjacent plots and the metacommunity. It was only accurate when migration from the metacommunity outweighed that of adjacent plots, discrimination, however, proved to be impossible. We show that migration should be considered more an approximation of the resemblance between communities and the summed regional species pool. Application of migration estimates to simulate field datasets did show reasonably good fits and indicated consistent differences between sets in comparison with earlier studies. We conclude that estimates of migration using these methods are more an approximation of the homogenization among local communities over time rather than a direct measurement of migration and hence have a direct relationship with beta diversity. As betadiversity is the result of many (non)-neutral processes, we have to admit that migration as estimated in a spatial explicit world encompasses not only direct migration but is an ecological aggregate of these processes. The parameter m of neutral models then appears more as an emerging property revealed by neutral theory instead of being an effective mechanistic parameter and spatially implicit models should be rejected as an approximation of forest dynamics.

Analysis of spatial thermal field in a magnetic bearing

NASA Astrophysics Data System (ADS)

Wajnert, Dawid; Tomczuk, Bronisław

2018-03-01

This paper presents two mathematical models for temperature field analysis in a new hybrid magnetic bearing. Temperature distributions have been calculated using a three dimensional simulation and a two dimensional one. A physical model for temperature testing in the magnetic bearing has been developed. Some results obtained from computer simulations were compared with measurements.

High-resolution dynamic downscaling of CMIP5 output over the Tropical Andes

NASA Astrophysics Data System (ADS)

Reichler, Thomas; Andrade, Marcos; Ohara, Noriaki

2015-04-01

Our project is targeted towards making robust predictions of future changes in climate over the tropical part of the South American Andes. This goal is challenging, since tropical lowlands, steep mountains, and snow covered subarctic surfaces meet over relatively short distances, leading to distinct climate regimes within the same domain and pronounced spatial gradients in virtually every climate quantity. We use an innovative approach to solve this problem, including several quadruple nested versions of WRF, a systematic validation strategy to find the version of WRF that best fits our study region, spatial resolutions at the kilometer scale, 20-year-long simulation periods, and bias-corrected output from various CMIP5 simulations that also include the multi-model mean of all CMIP5 models. We show that the simulated changes in climate are consistent with the results from the global climate models and also consistent with two different versions of WRF. We also discuss the expected changes in snow and ice, derived from off-line coupling the regional simulations to a carefully calibrated snow and ice model.

[Temporal and spatial heterogeneity analysis of optimal value of sensitive parameters in ecological process model: The BIOME-BGC model as an example.

PubMed

Li, Yi Zhe; Zhang, Ting Long; Liu, Qiu Yu; Li, Ying

2018-01-01

The ecological process models are powerful tools for studying terrestrial ecosystem water and carbon cycle at present. However, there are many parameters for these models, and weather the reasonable values of these parameters were taken, have important impact on the models simulation results. In the past, the sensitivity and the optimization of model parameters were analyzed and discussed in many researches. But the temporal and spatial heterogeneity of the optimal parameters is less concerned. In this paper, the BIOME-BGC model was used as an example. In the evergreen broad-leaved forest, deciduous broad-leaved forest and C3 grassland, the sensitive parameters of the model were selected by constructing the sensitivity judgment index with two experimental sites selected under each vegetation type. The objective function was constructed by using the simulated annealing algorithm combined with the flux data to obtain the monthly optimal values of the sensitive parameters at each site. Then we constructed the temporal heterogeneity judgment index, the spatial heterogeneity judgment index and the temporal and spatial heterogeneity judgment index to quantitatively analyze the temporal and spatial heterogeneity of the optimal values of the model sensitive parameters. The results showed that the sensitivity of BIOME-BGC model parameters was different under different vegetation types, but the selected sensitive parameters were mostly consistent. The optimal values of the sensitive parameters of BIOME-BGC model mostly presented time-space heterogeneity to different degrees which varied with vegetation types. The sensitive parameters related to vegetation physiology and ecology had relatively little temporal and spatial heterogeneity while those related to environment and phenology had generally larger temporal and spatial heterogeneity. In addition, the temporal heterogeneity of the optimal values of the model sensitive parameters showed a significant linear correlation with the spatial heterogeneity under the three vegetation types. According to the temporal and spatial heterogeneity of the optimal values, the parameters of the BIOME-BGC model could be classified in order to adopt different parameter strategies in practical application. The conclusion could help to deeply understand the parameters and the optimal values of the ecological process models, and provide a way or reference for obtaining the reasonable values of parameters in models application.

Modelling Spatial Dependence Structures Between Climate Variables by Combining Mixture Models with Copula Models

NASA Astrophysics Data System (ADS)

Khan, F.; Pilz, J.; Spöck, G.

2017-12-01

Spatio-temporal dependence structures play a pivotal role in understanding the meteorological characteristics of a basin or sub-basin. This further affects the hydrological conditions and consequently will provide misleading results if these structures are not taken into account properly. In this study we modeled the spatial dependence structure between climate variables including maximum, minimum temperature and precipitation in the Monsoon dominated region of Pakistan. For temperature, six, and for precipitation four meteorological stations have been considered. For modelling the dependence structure between temperature and precipitation at multiple sites, we utilized C-Vine, D-Vine and Student t-copula models. For temperature, multivariate mixture normal distributions and for precipitation gamma distributions have been used as marginals under the copula models. A comparison was made between C-Vine, D-Vine and Student t-copula by observational and simulated spatial dependence structure to choose an appropriate model for the climate data. The results show that all copula models performed well, however, there are subtle differences in their performances. The copula models captured the patterns of spatial dependence structures between climate variables at multiple meteorological sites, however, the t-copula showed poor performance in reproducing the dependence structure with respect to magnitude. It was observed that important statistics of observed data have been closely approximated except of maximum values for temperature and minimum values for minimum temperature. Probability density functions of simulated data closely follow the probability density functions of observational data for all variables. C and D-Vines are better tools when it comes to modelling the dependence between variables, however, Student t-copulas compete closely for precipitation. Keywords: Copula model, C-Vine, D-Vine, Spatial dependence structure, Monsoon dominated region of Pakistan, Mixture models, EM algorithm.

Numerical simulation of double‐diffusive finger convection

USGS Publications Warehouse

Hughes, Joseph D.; Sanford, Ward E.; Vacher, H. Leonard

2005-01-01

A hybrid finite element, integrated finite difference numerical model is developed for the simulation of double‐diffusive and multicomponent flow in two and three dimensions. The model is based on a multidimensional, density‐dependent, saturated‐unsaturated transport model (SUTRA), which uses one governing equation for fluid flow and another for solute transport. The solute‐transport equation is applied sequentially to each simulated species. Density coupling of the flow and solute‐transport equations is accounted for and handled using a sequential implicit Picard iterative scheme. High‐resolution data from a double‐diffusive Hele‐Shaw experiment, initially in a density‐stable configuration, is used to verify the numerical model. The temporal and spatial evolution of simulated double‐diffusive convection is in good agreement with experimental results. Numerical results are very sensitive to discretization and correspond closest to experimental results when element sizes adequately define the spatial resolution of observed fingering. Numerical results also indicate that differences in the molecular diffusivity of sodium chloride and the dye used to visualize experimental sodium chloride concentrations are significant and cause inaccurate mapping of sodium chloride concentrations by the dye, especially at late times. As a result of reduced diffusion, simulated dye fingers are better defined than simulated sodium chloride fingers and exhibit more vertical mass transfer.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Accounting for Forest Harvest and Wildfire in a Spatially-distributed Carbon Cycle Process Model

NASA Astrophysics Data System (ADS)

Turner, D. P.; Ritts, W.; Kennedy, R. E.; Yang, Z.; Law, B. E.

2009-12-01

Forests are subject to natural disturbances in the form of wildfire, as well as management-related disturbances in the form of timber harvest. These disturbance events have strong impacts on local and regional carbon budgets, but quantifying the associated carbon fluxes remains challenging. The ORCA Project aims to quantify regional net ecosystem production (NEP) and net biome production (NBP) in Oregon, California, and Washington, and we have adopted an integrated approach based on Landsat imagery and ecosystem modeling. To account for stand-level carbon fluxes, the Biome-BGC model has been adapted to simulate multiple severities of fire and harvest. New variables include snags, direct fire emissions, and harvest removals. New parameters include fire-intensity-specific combustion factors for each carbon pool (based on field measurements) and proportional removal rates for harvest events. To quantify regional fluxes, the model is applied in a spatially-distributed mode over the domain of interest, with disturbance history derived from a time series of Landsat images. In stand-level simulations, the post disturbance transition from negative (source) to positive (sink) NEP is delayed approximately a decade in the case of high severity fire compared to harvest. Simulated direct pyrogenic emissions range from 11 to 25 % of total non-soil ecosystem carbon. In spatial mode application over Oregon and California, the sum of annual pyrogenic emissions and harvest removals was generally less that half of total NEP, resulting in significant carbon sequestration on the land base. Spatially and temporally explicit simulation of disturbance-related carbon fluxes will contribute to our ability to evaluate effects of management on regional carbon flux, and in our ability to assess potential biospheric feedbacks to climate change mediated by changing disturbance regimes.

Elevation-dependent warming in global climate model simulations at high spatial resolution

NASA Astrophysics Data System (ADS)

Palazzi, Elisa; Mortarini, Luca; Terzago, Silvia; von Hardenberg, Jost

2018-06-01

The enhancement of warming rates with elevation, so-called elevation-dependent warming (EDW), is one of the regional, still not completely understood, expressions of global warming. Sentinels of climate and environmental changes, mountains have experienced more rapid and intense warming trends in the recent decades, leading to serious impacts on mountain ecosystems and downstream. In this paper we use a state-of-the-art Global Climate Model (EC-Earth) to investigate the impact of model spatial resolution on the representation of this phenomenon and to highlight possible differences in EDW and its causes in different mountain regions of the Northern Hemisphere. To this end we use EC-Earth climate simulations at five different spatial resolutions, from ˜ 125 to ˜ 16 km, to explore the existence and the driving mechanisms of EDW in the Colorado Rocky Mountains, the Greater Alpine Region and the Tibetan Plateau-Himalayas. Our results show that the more frequent EDW drivers in all regions and seasons are the changes in albedo and in downward thermal radiation and this is reflected in both daytime and nighttime warming. In the Tibetan Plateau-Himalayas and in the Greater Alpine Region, an additional driver is the change in specific humidity. We also find that, while generally the model shows no clear resolution dependence in its ability to simulate the existence of EDW in the different regions, specific EDW characteristics such as its intensity and the relative role of different driving mechanisms may be different in simulations performed at different spatial resolutions. Moreover, we find that the role of internal climate variability can be significant in modulating the EDW signal, as suggested by the spread found in the multi-member ensemble of the EC-Earth experiments which we use.

Parallel Monte Carlo transport modeling in the context of a time-dependent, three-dimensional multi-physics code

DOE Office of Scientific and Technical Information (OSTI.GOV)

Procassini, R.J.

1997-12-31

The fine-scale, multi-space resolution that is envisioned for accurate simulations of complex weapons systems in three spatial dimensions implies flop-rate and memory-storage requirements that will only be obtained in the near future through the use of parallel computational techniques. Since the Monte Carlo transport models in these simulations usually stress both of these computational resources, they are prime candidates for parallelization. The MONACO Monte Carlo transport package, which is currently under development at LLNL, will utilize two types of parallelism within the context of a multi-physics design code: decomposition of the spatial domain across processors (spatial parallelism) and distribution ofmore » particles in a given spatial subdomain across additional processors (particle parallelism). This implementation of the package will utilize explicit data communication between domains (message passing). Such a parallel implementation of a Monte Carlo transport model will result in non-deterministic communication patterns. The communication of particles between subdomains during a Monte Carlo time step may require a significant level of effort to achieve a high parallel efficiency.« less

Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China.

PubMed

Cao, Chunxiang; Chen, Wei; Zheng, Sheng; Zhao, Jian; Wang, Jinfeng; Cao, Wuchun

2016-01-01

Severe acute respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases.

Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China

PubMed Central

Cao, Chunxiang; Zheng, Sheng; Zhao, Jian; Wang, Jinfeng; Cao, Wuchun

2016-01-01

Severe acute respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases. PMID:27597972

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

NASA Astrophysics Data System (ADS)

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt; Stuehn, Torsten

2017-11-01

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach, the theoretical modeling and scaling laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. These two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.

A case study of the Weather Research and Forecasting model applied to the Joint Urban 2003 tracer field experiment. Part 2: Gas tracer dispersion

DOE PAGES

Nelson, Matthew A.; Brown, Michael J.; Halverson, Scot A.; ...

2016-07-28

Here, the Quick Urban & Industrial Complex (QUIC) atmospheric transport, and dispersion modelling, system was evaluated against the Joint Urban 2003 tracer-gas measurements. This was done using the wind and turbulence fields computed by the Weather Research and Forecasting (WRF) model. We compare the simulated and observed plume transport when using WRF-model-simulated wind fields, and local on-site wind measurements. Degradation of the WRF-model-based plume simulations was cased by errors in the simulated wind direction, and limitations in reproducing the small-scale wind-field variability. We explore two methods for importing turbulence from the WRF model simulations into the QUIC system. The firstmore » method uses parametrized turbulence profiles computed from WRF-model-computed boundary-layer similarity parameters; and the second method directly imports turbulent kinetic energy from the WRF model. Using the WRF model’s Mellor-Yamada-Janjic boundary-layer scheme, the parametrized turbulence profiles and the direct import of turbulent kinetic energy were found to overpredict and underpredict the observed turbulence quantities, respectively. Near-source building effects were found to propagate several km downwind. These building effects and the temporal/spatial variations in the observed wind field were often found to have a stronger influence over the lateral and vertical plume spread than the intensity of turbulence. Correcting the WRF model wind directions using a single observational location improved the performance of the WRF-model-based simulations, but using the spatially-varying flow fields generated from multiple observation profiles generally provided the best performance.« less

A case study of the Weather Research and Forecasting model applied to the Joint Urban 2003 tracer field experiment. Part 2: Gas tracer dispersion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nelson, Matthew A.; Brown, Michael J.; Halverson, Scot A.

Here, the Quick Urban & Industrial Complex (QUIC) atmospheric transport, and dispersion modelling, system was evaluated against the Joint Urban 2003 tracer-gas measurements. This was done using the wind and turbulence fields computed by the Weather Research and Forecasting (WRF) model. We compare the simulated and observed plume transport when using WRF-model-simulated wind fields, and local on-site wind measurements. Degradation of the WRF-model-based plume simulations was cased by errors in the simulated wind direction, and limitations in reproducing the small-scale wind-field variability. We explore two methods for importing turbulence from the WRF model simulations into the QUIC system. The firstmore » method uses parametrized turbulence profiles computed from WRF-model-computed boundary-layer similarity parameters; and the second method directly imports turbulent kinetic energy from the WRF model. Using the WRF model’s Mellor-Yamada-Janjic boundary-layer scheme, the parametrized turbulence profiles and the direct import of turbulent kinetic energy were found to overpredict and underpredict the observed turbulence quantities, respectively. Near-source building effects were found to propagate several km downwind. These building effects and the temporal/spatial variations in the observed wind field were often found to have a stronger influence over the lateral and vertical plume spread than the intensity of turbulence. Correcting the WRF model wind directions using a single observational location improved the performance of the WRF-model-based simulations, but using the spatially-varying flow fields generated from multiple observation profiles generally provided the best performance.« less

Evaluation of uncertainty in capturing the spatial variability and magnitudes of extreme hydrological events for the uMngeni catchment, South Africa

NASA Astrophysics Data System (ADS)

Kusangaya, Samuel; Warburton Toucher, Michele L.; van Garderen, Emma Archer

2018-02-01

Downscaled General Circulation Models (GCMs) output are used to forecast climate change and provide information used as input for hydrological modelling. Given that our understanding of climate change points towards an increasing frequency, timing and intensity of extreme hydrological events, there is therefore the need to assess the ability of downscaled GCMs to capture these extreme hydrological events. Extreme hydrological events play a significant role in regulating the structure and function of rivers and associated ecosystems. In this study, the Indicators of Hydrologic Alteration (IHA) method was adapted to assess the ability of simulated streamflow (using downscaled GCMs (dGCMs)) in capturing extreme river dynamics (high and low flows), as compared to streamflow simulated using historical climate data from 1960 to 2000. The ACRU hydrological model was used for simulating streamflow for the 13 water management units of the uMngeni Catchment, South Africa. Statistically downscaled climate models obtained from the Climate System Analysis Group at the University of Cape Town were used as input for the ACRU Model. Results indicated that, high flows and extreme high flows (one in ten year high flows/large flood events) were poorly represented both in terms of timing, frequency and magnitude. Simulated streamflow using dGCMs data also captures more low flows and extreme low flows (one in ten year lowest flows) than that captured in streamflow simulated using historical climate data. The overall conclusion was that although dGCMs output can reasonably be used to simulate overall streamflow, it performs poorly when simulating extreme high and low flows. Streamflow simulation from dGCMs must thus be used with caution in hydrological applications, particularly for design hydrology, as extreme high and low flows are still poorly represented. This, arguably calls for the further improvement of downscaling techniques in order to generate climate data more relevant and useful for hydrological applications such as in design hydrology. Nevertheless, the availability of downscaled climatic output provide the potential of exploring climate model uncertainties in different hydro climatic regions at local scales where forcing data is often less accessible but more accurate at finer spatial scales and with adequate spatial detail.

Assessing the competing roles of model resolution and meteorological forcing fidelity in hyperresolution simulations of snowpack and streamflow in the southern Rocky Mountains

NASA Astrophysics Data System (ADS)

Gochis, D. J.; Dugger, A. L.; Karsten, L. R.; Barlage, M. J.; Sampson, K. M.; Yu, W.; Pan, L.; McCreight, J. L.; Howard, K.; Busto, J.; Deems, J. S.

2017-12-01

Hydrometeorological processes vary over comparatively short length scales in regions of complex terrain such as the southern Rocky Mountains. Changes in temperature, precipitation, wind and solar radiation can vary significantly across elevation gradients, terrain landform and land cover conditions throughout the region. Capturing such variability in hydrologic models can necessitate the utilization of so-called `hyper-resolution' spatial meshes with effective element spacings of less than 100m. However, it is often difficult to obtain meteorological forcings of high quality in such regions at those resolutions which can result in significant uncertainty in fundamental in hydrologic model inputs. In this study we examine the comparative influences of meteorological forcing data fidelity and spatial resolution on seasonal simulations of snowpack evolution, runoff and streamflow in a set of high mountain watersheds in southern Colorado. We utilize the operational, NOAA National Water Model configuration of the community WRF-Hydro system as a baseline and compare against it, additional model scenarios with differing specifications of meteorological forcing data, with and without topographic downscaling adjustments applied, with and without experimental high resolution radar derived precipitation estimates and with WRF-Hydro configurations of progressively finer spatial resolution. The results suggest significant influence from and importance of meteorological downscaling techniques in controlling spatial distributions of meltout and runoff timing. The use of radar derived precipitation exhibits clear sensitivity on hydrologic simulation skill compared with the use of coarser resolution, background precipitation analyses. Advantages and disadvantages of the utilization of progressively higher resolution model configurations both in terms of computational requirements and model fidelity are also discussed.

A validated agent-based model to study the spatial and temporal heterogeneities of malaria incidence in the rainforest environment.

PubMed

Pizzitutti, Francesco; Pan, William; Barbieri, Alisson; Miranda, J Jaime; Feingold, Beth; Guedes, Gilvan R; Alarcon-Valenzuela, Javiera; Mena, Carlos F

2015-12-22

The Amazon environment has been exposed in the last decades to radical changes that have been accompanied by a remarkable rise of both Plasmodium falciparum and Plasmodium vivax malaria. The malaria transmission process is highly influenced by factors such as spatial and temporal heterogeneities of the environment and individual-based characteristics of mosquitoes and humans populations. All these determinant factors can be simulated effectively trough agent-based models. This paper presents a validated agent-based model of local-scale malaria transmission. The model reproduces the environment of a typical riverine village in the northern Peruvian Amazon, where the malaria transmission is highly seasonal and apparently associated with flooding of large areas caused by the neighbouring river. Agents representing humans, mosquitoes and the two species of Plasmodium (P. falciparum and P. vivax) are simulated in a spatially explicit representation of the environment around the village. The model environment includes: climate, people houses positions and elevation. A representation of changes in the mosquito breeding areas extension caused by the river flooding is also included in the simulation environment. A calibration process was carried out to reproduce the variations of the malaria monthly incidence over a period of 3 years. The calibrated model is also able to reproduce the spatial heterogeneities of local scale malaria transmission. A "what if" eradication strategy scenario is proposed: if the mosquito breeding sites are eliminated through mosquito larva habitat management in a buffer area extended at least 200 m around the village, the malaria transmission is eradicated from the village. The use of agent-based models can reproduce effectively the spatiotemporal variations of the malaria transmission in a low endemicity environment dominated by river floodings like in the Amazon.

Application of spatial models to the stopover ecology of trans-Gulf migrants

Treesearch

Theodore R. Simons; Scott M. Pearson; Frank R. Moore

2000-01-01

Studies at migratory stopover sites along the northern coast of the Gulf of Mexico are providing an understanding of how weather, habitat, and energetic factors combine to shape the stopover ecology of trans-Gulf migrants. We are coupling this understanding with analyses of landscape-level patterns of habitat availability by using spatially explicit models to simulate...

Spatial simulation of forest succession and timber harvesting using LANDIS

Treesearch

Eric J. Gustafson; Stephen R. Shifley; David J. Mladenoff; Kevin K. Nimerfro; Hong S. He

2000-01-01

The LANDIS model simulates ecological dynamics, including forest succession, disturbance, seed dispersal and establishment, fire and wind disturbance, and their interactions. We describe the addition to LANDIS of capabilities to simulate forest vegetation management, including harvest. Stands (groups of cells) are prioritized for harvest using one of four ranking...

An analysis of simulated and observed storm characteristics

NASA Astrophysics Data System (ADS)

Benestad, R. E.

2010-09-01

A calculus-based cyclone identification (CCI) method has been applied to the most recent re-analysis (ERAINT) from the European Centre for Medium-range Weather Forecasts and results from regional climate model (RCM) simulations. The storm frequency for events with central pressure below a threshold value of 960-990hPa were examined, and the gradient wind from the simulated storm systems were compared with corresponding estimates from the re-analysis. The analysis also yielded estimates for the spatial extent of the storm systems, which was also included in the regional climate model cyclone evaluation. A comparison is presented between a number of RCMs and the ERAINT re-analysis in terms of their description of the gradient winds, number of cyclones, and spatial extent. Furthermore, a comparison between geostrophic wind estimated though triangules of interpolated or station measurements of SLP is presented. Wind still represents one of the more challenging variables to model realistically.

In pursuit of an accurate spatial and temporal model of biomolecules at the atomistic level: a perspective on computer simulation.

PubMed

Gray, Alan; Harlen, Oliver G; Harris, Sarah A; Khalid, Syma; Leung, Yuk Ming; Lonsdale, Richard; Mulholland, Adrian J; Pearson, Arwen R; Read, Daniel J; Richardson, Robin A

2015-01-01

Despite huge advances in the computational techniques available for simulating biomolecules at the quantum-mechanical, atomistic and coarse-grained levels, there is still a widespread perception amongst the experimental community that these calculations are highly specialist and are not generally applicable by researchers outside the theoretical community. In this article, the successes and limitations of biomolecular simulation and the further developments that are likely in the near future are discussed. A brief overview is also provided of the experimental biophysical methods that are commonly used to probe biomolecular structure and dynamics, and the accuracy of the information that can be obtained from each is compared with that from modelling. It is concluded that progress towards an accurate spatial and temporal model of biomacromolecules requires a combination of all of these biophysical techniques, both experimental and computational.

Application and theoretical analysis of the flamelet model for supersonic turbulent combustion flows in the scramjet engine

NASA Astrophysics Data System (ADS)

Gao, Zhenxun; Wang, Jingying; Jiang, Chongwen; Lee, Chunhian

2014-11-01

In the framework of Reynolds-averaged Navier-Stokes simulation, supersonic turbulent combustion flows at the German Aerospace Centre (DLR) combustor and Japan Aerospace Exploration Agency (JAXA) integrated scramjet engine are numerically simulated using the flamelet model. Based on the DLR combustor case, theoretical analysis and numerical experiments conclude that: the finite rate model only implicitly considers the large-scale turbulent effect and, due to the lack of the small-scale non-equilibrium effect, it would overshoot the peak temperature compared to the flamelet model in general. Furthermore, high-Mach-number compressibility affects the flamelet model mainly through two ways: the spatial pressure variation and the static enthalpy variation due to the kinetic energy. In the flamelet library, the mass fractions of the intermediate species, e.g. OH, are more sensible to the above two effects than the main species such as H2O. Additionally, in the combustion flowfield where the pressure is larger than the value adopted in the generation of the flamelet library or the conversion from the static enthalpy to the kinetic energy occurs, the temperature obtained by the flamelet model without taking compressibility effects into account would be undershot, and vice versa. The static enthalpy variation effect has only little influence on the temperature simulation of the flamelet model, while the effect of the spatial pressure variation may cause relatively large errors. From the JAXA case, it is found that the flamelet model cannot in general be used for an integrated scramjet engine. The existence of the inlet together with the transverse injection scheme could cause large spatial variations of pressure, so the pressure value adopted for the generation of a flamelet library should be fine-tuned according to a pre-simulation of pure mixing.

Large scale snow water status monitoring: comparison of different snow water products in the upper Colorado basins

USGS Publications Warehouse

Artan, G.A.; Verdin, J.P.; Lietzow, R.

2013-01-01

We illustrate the ability to monitor the status of snowpack over large areas by using a~spatially distributed snow accumulation and ablation model in the Upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) datasets; remaining meteorological model input data was from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a~region of the Western United States that covers parts of the Upper Colorado Basin. We also compared the SWE product estimated from the Special Sensor Microwave Imager (SSM/I) and Scanning Multichannel Microwave Radiometer (SMMR) to the SNODAS and SNOTEL SWE datasets. Agreement between the spatial distribution of the simulated SWE with both SNODAS and SNOTEL was high for the two model runs for the entire snow accumulation period. Model-simulated SWEs, both with MPE and TRMM, were significantly correlated spatially on average with the SNODAS (r = 0.81 and r = 0.54; d.f. = 543) and the SNOTEL SWE (r = 0.85 and r = 0.55; d.f. = 543), when monthly basinwide simulated average SWE the correlation was also highly significant (r = 0.95 and r = 0.73; d.f. = 12). The SWE estimated from the passive microwave imagery was not correlated either with the SNODAS SWE or (r = 0.14, d.f. = 7) SNOTEL-reported SWE values (r = 0.08, d.f. = 7). The agreement between modeled SWE and the SWE recorded by SNODAS and SNOTEL weakened during the snowmelt period due to an underestimation bias of the air temperature that was used as model input forcing.

Spatial and Temporal Uncertainty of Crop Yield Aggregations

NASA Technical Reports Server (NTRS)

Porwollik, Vera; Mueller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Iizumi, Toshichika; Ray, Deepak K.; Ruane, Alex C.; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe;

Integration of Multiple Data Sources to Simulate the Dynamics of Land Systems

PubMed Central

Deng, Xiangzheng; Su, Hongbo; Zhan, Jinyan

2008-01-01

In this paper we present and develop a new model, which we have called Dynamics of Land Systems (DLS). The DLS model is capable of integrating multiple data sources to simulate the dynamics of a land system. Three main modules are incorporated in DLS: a spatial regression module, to explore the relationship between land uses and influencing factors, a scenario analysis module of the land uses of a region during the simulation period and a spatial disaggregation module, to allocate land use changes from a regional level to disaggregated grid cells. A case study on Taips County in North China is incorporated in this paper to test the functionality of DLS. The simulation results under the baseline, economic priority and environmental scenarios help to understand the land system dynamics and project near future land-use trajectories of a region, in order to focus management decisions on land uses and land use planning. PMID:27879726

GIS and agent based spatial-temporal simulation modeling for assessing tourism social carrying capacity: a study on Mount Emei scenic area, China

NASA Astrophysics Data System (ADS)

Zhang, Renjun

2007-06-01

Each scenic area can sustain a specific level of acceptance of tourist development and use, beyond which further development can result in socio-cultural deterioration or a decline in the quality of the experience gained by visitors. This specific level is called carrying capacity. Social carrying capacity can be defined as the maximum level of use (in terms of numbers and activities) that can be absorbed by an area without an unacceptable decline in the quality of experience of visitors and without an unacceptable adverse impact on the society of the area. It is difficult to assess the carrying capacity, because the carrying capacity is determined by not only the number of visitors, but also the time, the type of the recreation, the characters of each individual and the physical environment. The objective of this study is to build a spatial-temporal simulation model to simulate the spatial-temporal distribution of tourists. This model is a tourist spatial behaviors simulator (TSBS). Based on TSBS, the changes of each visitor's travel patterns such as location, cost, and other states data are recoded in a state table. By analyzing this table, the intensity of the tourist use in any area can be calculated; the changes of the quality of tourism experience can be quantized and analyzed. So based on this micro simulation method the social carrying capacity can be assessed more accurately, can be monitored proactively and managed adaptively. In this paper, the carrying capacity of Mount Emei scenic area is analyzed as followed: The author selected the intensity of the crowd as the monitoring Indicators. it is regarded that longer waiting time means more crowded. TSBS was used to simulate the spatial-temporal distribution of tourists. the average of waiting time all the visitors is calculated. And then the author assessed the social carrying capacity of Mount Emei scenic area, found the key factors have impacted on social carrying capacity. The results show that the TSBS-aided method for assessing carrying capacity is dynamic, quantifiable and more accurate.

Evaluation of snowmelt simulation in the Weather Research and Forecasting model

NASA Astrophysics Data System (ADS)

Jin, Jiming; Wen, Lijuan

2012-05-01

The objective of this study is to better understand and improve snowmelt simulations in the advanced Weather Research and Forecasting (WRF) model by coupling it with the Community Land Model (CLM) Version 3.5. Both WRF and CLM are developed by the National Center for Atmospheric Research. The automated Snow Telemetry (SNOTEL) station data over the Columbia River Basin in the northwestern United States are used to evaluate snowmelt simulations generated with the coupled WRF-CLM model. These SNOTEL data include snow water equivalent (SWE), precipitation, and temperature. The simulations cover the period of March through June 2002 and focus mostly on the snowmelt season. Initial results show that when compared to observations, WRF-CLM significantly improves the simulations of SWE, which is underestimated when the release version of WRF is coupled with the Noah and Rapid Update Cycle (RUC) land surface schemes, in which snow physics is oversimplified. Further analysis shows that more realistic snow surface energy allocation in CLM is an important process that results in improved snowmelt simulations when compared to that in Noah and RUC. Additional simulations with WRF-CLM at different horizontal spatial resolutions indicate that accurate description of topography is also vital to SWE simulations. WRF-CLM at 10 km resolution produces the most realistic SWE simulations when compared to those produced with coarser spatial resolutions in which SWE is remarkably underestimated. The coupled WRF-CLM provides an important tool for research and forecasts in weather, climate, and water resources at regional scales.

A field-validated model for in situ transport of polymer-stabilized nZVI and implications for subsurface injection.

PubMed

Krol, Magdalena M; Oleniuk, Andrew J; Kocur, Chris M; Sleep, Brent E; Bennett, Peter; Xiong, Zhong; O'Carroll, Denis M

2013-07-02

Nanoscale zerovalent iron (nZVI) particles have significant potential to remediate contaminated source zones. However, the transport of these particles through porous media is not well understood, especially at the field scale. This paper describes the simulation of a field injection of carboxylmethyl cellulose (CMC) stabilized nZVI using a 3D compositional simulator, modified to include colloidal filtration theory (CFT). The model includes composition dependent viscosity and spatially and temporally variable velocity, appropriate for the simulation of push-pull tests (PPTs) with CMC stabilized nZVI. Using only attachment efficiency as a fitting parameter, model results were in good agreement with field observations when spatially variable viscosity effects on collision efficiency were included in the transport modeling. This implies that CFT-modified transport equations can be used to simulate stabilized nZVI field transport. Model results show that an increase in solution viscosity, resulting from injection of CMC stabilized nZVI suspension, affects nZVI mobility by decreasing attachment as well as changing the hydraulics of the system. This effect is especially noticeable with intermittent pumping during PPTs. Results from this study suggest that careful consideration of nZVI suspension formulation is important for optimal delivery of nZVI which can be facilitated with the use of a compositional simulator.

Spatial Moment Equations for a Groundwater Plume with Degradation and Rate-Limited Sorption

EPA Science Inventory

In this note, we analytically derive the solution for the spatial moments of groundwater solute concentration distributions simulated by a one-dimensional model that assumes advective-dispersive transport with first-order degradation and rate-limited sorption. Sorption kinetics...

Real-time dynamics simulation of the Cassini spacecraft using DARTS. Part 1: Functional capabilities and the spatial algebra algorithm

NASA Technical Reports Server (NTRS)

Jain, A.; Man, G. K.

1993-01-01

This paper describes the Dynamics Algorithms for Real-Time Simulation (DARTS) real-time hardware-in-the-loop dynamics simulator for the National Aeronautics and Space Administration's Cassini spacecraft. The spacecraft model consists of a central flexible body with a number of articulated rigid-body appendages. The demanding performance requirements from the spacecraft control system require the use of a high fidelity simulator for control system design and testing. The DARTS algorithm provides a new algorithmic and hardware approach to the solution of this hardware-in-the-loop simulation problem. It is based upon the efficient spatial algebra dynamics for flexible multibody systems. A parallel and vectorized version of this algorithm is implemented on a low-cost, multiprocessor computer to meet the simulation timing requirements.

Spatially explicit simulation of hydrologically controlled carbon and nitrogen cycles and associated feedback mechanisms in a boreal ecosystem

NASA Astrophysics Data System (ADS)

Govind, Ajit; Chen, Jing Ming; Ju, Weimin

2009-06-01

Ecosystem models that simulate biogeochemical processes usually ignore hydrological controls that govern them. It is quite possible that topographically driven water fluxes significantly influence the spatial distribution of C sources and sinks because of their large contribution to the local water balance. To investigate this, we simulated biogeochemical processes along with the associated feedback mechanisms in a boreal ecosystem using a spatially explicit hydroecological model, boreal ecosystem productivity simulator (BEPS)-TerrainLab V2.0, that has a tight coupling of ecophysiological, hydrological, and biogeochemical processes. First, the simulated dynamics of snowpack, soil temperature, net ecosystem productivity (NEP), and total ecosystem respiration (TER) were validated with high-frequency measurements for 2 years. The model was able to explain 80% of the variability in NEP and 84% of the variability in TER. Further, we investigated the influence of topographically driven subsurface base flow on soil C and N cycling and on the spatiotemporal patterns of C sources and sinks using three hydrological modeling scenarios that differed in hydrological conceptualizations. In general, the scenarios that had nonexplicit hydrological representation overestimated NEP, as opposed to the scenario that had an explicit (realistic) representation. The key processes controlling the NEP differences were attributed to the combined effects of variations in photosynthesis (due to changes in stomatal conductance and nitrogen (N) availability), heterotrophic respiration, and autotrophic respiration, all of which occur simultaneously affecting NEP. Feedback relationships were also found to exacerbate the differences. We identified six types of NEP differences (biases), of which the most commonly found was due to an underestimation of the existing C sources, highlighting the vulnerability of regional-scale ecosystem models that ignore hydrological processes.

An immersed boundary-lattice Boltzmann model for biofilm growth and its impact on the NAPL dissolution in porous media

NASA Astrophysics Data System (ADS)

Benioug, M.; Yang, X.

2017-12-01

The evolution of microbial phase within porous medium is a complex process that involves growth, mortality, and detachment of the biofilm or attachment of moving cells. A better understanding of the interactions among biofilm growth, flow and solute transport and a rigorous modeling of such processes are essential for a more accurate prediction of the fate of pollutants (e.g. NAPLs) in soils. However, very few works are focused on the study of such processes in multiphase conditions (oil/water/biofilm systems). Our proposed numerical model takes into account the mechanisms that control bacterial growth and its impact on the dissolution of NAPL. An Immersed Boundary - Lattice Boltzmann Model (IB-LBM) is developed for flow simulations along with non-boundary conforming finite volume methods (volume of fluid and reconstruction methods) used for reactive solute transport. A sophisticated cellular automaton model is also developed to describe the spatial distribution of bacteria. A series of numerical simulations have been performed on complex porous media. A quantitative diagram representing the transitions between the different biofilm growth patterns is proposed. The bioenhanced dissolution of NAPL in the presence of biofilms is simulated at the pore scale. A uniform dissolution approach has been adopted to describe the temporal evolution of trapped blobs. Our simulations focus on the dissolution of NAPL in abiotic and biotic conditions. In abiotic conditions, we analyze the effect of the spatial distribution of NAPL blobs on the dissolution rate under different assumptions (blobs size, Péclet number). In biotic conditions, different conditions are also considered (spatial distribution, reaction kinetics, toxicity) and analyzed. The simulated results are consistent with those obtained from the literature.

Simulations of Madden-Julian Oscillation in High Resolution Atmospheric General Circulation Model

NASA Astrophysics Data System (ADS)

Deng, Liping; Stenchikov, Georgiy; McCabe, Matthew; Bangalath, HamzaKunhu; Raj, Jerry; Osipov, Sergey

2014-05-01

The simulation of tropical signals, especially the Madden-Julian Oscillation (MJO), is one of the major deficiencies in current numerical models. The unrealistic features in the MJO simulations include the weak amplitude, more power at higher frequencies, displacement of the temporal and spatial distributions, eastward propagation speed being too fast, and a lack of coherent structure for the eastward propagation from the Indian Ocean to the Pacific (e.g., Slingo et al. 1996). While some improvement in simulating MJO variance and coherent eastward propagation has been attributed to model physics, model mean background state and air-sea interaction, studies have shown that the model resolution, especially for higher horizontal resolution, may play an important role in producing a more realistic simulation of MJO (e.g., Sperber et al. 2005). In this study, we employ unique high-resolution (25-km) simulations conducted using the Geophysical Fluid Dynamics Laboratory global High Resolution Atmospheric Model (HIRAM) to evaluate the MJO simulation against the European Center for Medium-range Weather Forecasts (ECMWF) Interim re-analysis (ERAI) dataset. We specifically focus on the ability of the model to represent the MJO related amplitude, spatial distribution, eastward propagation, and horizontal and vertical structures. Additionally, as the HIRAM output covers not only an historic period (1979-2012) but also future period (2012-2050), the impact of future climate change related to the MJO is illustrated. The possible changes in intensity and frequency of extreme weather and climate events (e.g., strong wind and heavy rainfall) in the western Pacific, the Indian Ocean and the Middle East North Africa (MENA) region are highlighted.

Role of spatial inhomogenity in GPCR dimerisation predicted by receptor association-diffusion models

NASA Astrophysics Data System (ADS)

Deshpande, Sneha A.; Pawar, Aiswarya B.; Dighe, Anish; Athale, Chaitanya A.; Sengupta, Durba

2017-06-01

G protein-coupled receptor (GPCR) association is an emerging paradigm with far reaching implications in the regulation of signalling pathways and therapeutic interventions. Recent super resolution microscopy studies have revealed that receptor dimer steady state exhibits sub-second dynamics. In particular the GPCRs, muscarinic acetylcholine receptor M1 (M1MR) and formyl peptide receptor (FPR), have been demonstrated to exhibit a fast association/dissociation kinetics, independent of ligand binding. In this work, we have developed a spatial kinetic Monte Carlo model to investigate receptor homo-dimerisation at a single receptor resolution. Experimentally measured association/dissociation kinetic parameters and diffusion coefficients were used as inputs to the model. To test the effect of membrane spatial heterogeneity on the simulated steady state, simulations were compared to experimental statistics of dimerisation. In the simplest case the receptors are assumed to be diffusing in a spatially homogeneous environment, while spatial heterogeneity is modelled to result from crowding, membrane micro-domains and cytoskeletal compartmentalisation or ‘corrals’. We show that a simple association-diffusion model is sufficient to reproduce M1MR association statistics, but fails to reproduce FPR statistics despite comparable kinetic constants. A parameter sensitivity analysis is required to reproduce the association statistics of FPR. The model reveals the complex interplay between cytoskeletal components and their influence on receptor association kinetics within the features of the membrane landscape. These results constitute an important step towards understanding the factors modulating GPCR organisation.

Bayesian spatial transformation models with applications in neuroimaging data.

PubMed

Miranda, Michelle F; Zhu, Hongtu; Ibrahim, Joseph G

2013-12-01

The aim of this article is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) and a set of covariates. The proposed STM include a varying Box-Cox transformation model for dealing with the issue of non-Gaussian distributed imaging data and a Gaussian Markov random field model for incorporating spatial smoothness of the imaging data. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations and real data analysis demonstrate that the STM significantly outperforms the voxel-wise linear model with Gaussian noise in recovering meaningful geometric patterns. Our STM is able to reveal important brain regions with morphological changes in children with attention deficit hyperactivity disorder. © 2013, The International Biometric Society.

A flexible object-oriented software framework for developing complex multimedia simulations.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sydelko, P. J.; Dolph, J. E.; Christiansen, J. H.

Decision makers involved in brownfields redevelopment and long-term stewardship must consider environmental conditions, future-use potential, site ownership, area infrastructure, funding resources, cost recovery, regulations, risk and liability management, community relations, and expected return on investment in a comprehensive and integrated fashion to achieve desired results. Successful brownfields redevelopment requires the ability to assess the impacts of redevelopment options on multiple interrelated aspects of the ecosystem, both natural and societal. Computer-based tools, such as simulation models, databases, and geographical information systems (GISs) can be used to address brownfields planning and project execution. The transparent integration of these tools into a comprehensivemore » and dynamic decision support system would greatly enhance the brownfields assessment process. Such a system needs to be able to adapt to shifting and expanding analytical requirements and contexts. The Dynamic Information Architecture System (DIAS) is a flexible, extensible, object-oriented framework for developing and maintaining complex multidisciplinary simulations of a wide variety of application domains. The modeling domain of a specific DIAS-based simulation is determined by (1) software objects that represent the real-world entities that comprise the problem space (atmosphere, watershed, human), and (2) simulation models and other data processing applications that express the dynamic behaviors of the domain entities. Models and applications used to express dynamic behaviors can be either internal or external to DIAS, including existing legacy models written in various languages (FORTRAN, C, etc.). The flexible design framework of DIAS makes the objects adjustable to the context of the problem without a great deal of recoding. The DIAS Spatial Data Set facility allows parameters to vary spatially depending on the simulation context according to any of a number of 1-D, 2-D, or 3-D topologies. DIAS is also capable of interacting with other GIS packages and can import many standard spatial data formats. DIAS simulation capabilities can also be extended by including societal process models. Models that implement societal behaviors of individuals and organizations within larger DIAS-based natural systems simulations allow for interaction and feedback among natural and societal processes. The ability to simulate the complex interplay of multimedia processes makes DIAS a promising tool for constructing applications for comprehensive community planning, including the assessment of multiple development and redevelopment scenarios.« less

A rank-based approach for correcting systematic biases in spatial disaggregation of coarse-scale climate simulations

NASA Astrophysics Data System (ADS)

Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

2017-07-01

Use of General Circulation Model (GCM) precipitation and evapotranspiration sequences for hydrologic modelling can result in unrealistic simulations due to the coarse scales at which GCMs operate and the systematic biases they contain. The Bias Correction Spatial Disaggregation (BCSD) method is a popular statistical downscaling and bias correction method developed to address this issue. The advantage of BCSD is its ability to reduce biases in the distribution of precipitation totals at the GCM scale and then introduce more realistic variability at finer scales than simpler spatial interpolation schemes. Although BCSD corrects biases at the GCM scale before disaggregation; at finer spatial scales biases are re-introduced by the assumptions made in the spatial disaggregation process. Our study focuses on this limitation of BCSD and proposes a rank-based approach that aims to reduce the spatial disaggregation bias especially for both low and high precipitation extremes. BCSD requires the specification of a multiplicative bias correction anomaly field that represents the ratio of the fine scale precipitation to the disaggregated precipitation. It is shown that there is significant temporal variation in the anomalies, which is masked when a mean anomaly field is used. This can be improved by modelling the anomalies in rank-space. Results from the application of the rank-BCSD procedure improve the match between the distributions of observed and downscaled precipitation at the fine scale compared to the original BCSD approach. Further improvements in the distribution are identified when a scaling correction to preserve mass in the disaggregation process is implemented. An assessment of the approach using a single GCM over Australia shows clear advantages especially in the simulation of particularly low and high downscaled precipitation amounts.

Leveraging Mechanism Simplicity and Strategic Averaging to Identify Signals from Highly Heterogeneous Spatial and Temporal Ozone Data

NASA Astrophysics Data System (ADS)

Brown-Steiner, B.; Selin, N. E.; Prinn, R. G.; Monier, E.; Garcia-Menendez, F.; Tilmes, S.; Emmons, L. K.; Lamarque, J. F.; Cameron-Smith, P. J.

2017-12-01

We summarize two methods to aid in the identification of ozone signals from underlying spatially and temporally heterogeneous data in order to help research communities avoid the sometimes burdensome computational costs of high-resolution high-complexity models. The first method utilizes simplified chemical mechanisms (a Reduced Hydrocarbon Mechanism and a Superfast Mechanism) alongside a more complex mechanism (MOZART-4) within CESM CAM-Chem to extend the number of simulated meteorological years (or add additional members to an ensemble) for a given modeling problem. The Reduced Hydrocarbon mechanism is twice as fast, and the Superfast mechanism is three times faster than the MOZART-4 mechanism. We show that simplified chemical mechanisms are largely capable of simulating surface ozone across the globe as well as the more complex chemical mechanisms, and where they are not capable, a simple standardized anomaly emulation approach can correct for their inadequacies. The second method uses strategic averaging over both temporal and spatial scales to filter out the highly heterogeneous noise that underlies ozone observations and simulations. This method allows for a selection of temporal and spatial averaging scales that match a particular signal strength (between 0.5 and 5 ppbv), and enables the identification of regions where an ozone signal can rise above the ozone noise over a given region and a given period of time. In conjunction, these two methods can be used to "scale down" chemical mechanism complexity and quantitatively determine spatial and temporal scales that could enable research communities to utilize simplified representations of atmospheric chemistry and thereby maximize their productivity and efficiency given computational constraints. While this framework is here applied to ozone data, it could also be applied to a broad range of geospatial data sets (observed or modeled) that have spatial and temporal coverage.

Gaussian Process Regression Model in Spatial Logistic Regression

NASA Astrophysics Data System (ADS)

Sofro, A.; Oktaviarina, A.

2018-01-01

Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.

Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model

NASA Astrophysics Data System (ADS)

Yang, B.; Qian, Y.; Lin, G.; Leung, R.; Zhang, Y.

2011-12-01

The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. While the latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic important-sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e., the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Uncertainty Quantification and Parameter Tuning: A Case Study of Convective Parameterization Scheme in the WRF Regional Climate Model

NASA Astrophysics Data System (ADS)

Qian, Y.; Yang, B.; Lin, G.; Leung, R.; Zhang, Y.

2012-04-01

The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. The latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic important-sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e., the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model

NASA Astrophysics Data System (ADS)

Yang, B.; Qian, Y.; Lin, G.; Leung, R.; Zhang, Y.

2012-03-01

The current tuning process of parameters in global climate models is often performed subjectively or treated as an optimization procedure to minimize model biases based on observations. While the latter approach may provide more plausible values for a set of tunable parameters to approximate the observed climate, the system could be forced to an unrealistic physical state or improper balance of budgets through compensating errors over different regions of the globe. In this study, the Weather Research and Forecasting (WRF) model was used to provide a more flexible framework to investigate a number of issues related uncertainty quantification (UQ) and parameter tuning. The WRF model was constrained by reanalysis of data over the Southern Great Plains (SGP), where abundant observational data from various sources was available for calibration of the input parameters and validation of the model results. Focusing on five key input parameters in the new Kain-Fritsch (KF) convective parameterization scheme used in WRF as an example, the purpose of this study was to explore the utility of high-resolution observations for improving simulations of regional patterns and evaluate the transferability of UQ and parameter tuning across physical processes, spatial scales, and climatic regimes, which have important implications to UQ and parameter tuning in global and regional models. A stochastic importance sampling algorithm, Multiple Very Fast Simulated Annealing (MVFSA) was employed to efficiently sample the input parameters in the KF scheme based on a skill score so that the algorithm progressively moved toward regions of the parameter space that minimize model errors. The results based on the WRF simulations with 25-km grid spacing over the SGP showed that the precipitation bias in the model could be significantly reduced when five optimal parameters identified by the MVFSA algorithm were used. The model performance was found to be sensitive to downdraft- and entrainment-related parameters and consumption time of Convective Available Potential Energy (CAPE). Simulated convective precipitation decreased as the ratio of downdraft to updraft flux increased. Larger CAPE consumption time resulted in less convective but more stratiform precipitation. The simulation using optimal parameters obtained by constraining only precipitation generated positive impact on the other output variables, such as temperature and wind. By using the optimal parameters obtained at 25-km simulation, both the magnitude and spatial pattern of simulated precipitation were improved at 12-km spatial resolution. The optimal parameters identified from the SGP region also improved the simulation of precipitation when the model domain was moved to another region with a different climate regime (i.e. the North America monsoon region). These results suggest that benefits of optimal parameters determined through vigorous mathematical procedures such as the MVFSA process are transferable across processes, spatial scales, and climatic regimes to some extent. This motivates future studies to further assess the strategies for UQ and parameter optimization at both global and regional scales.

Transition index maps for urban growth simulation: application of artificial neural networks, weight of evidence and fuzzy multi-criteria evaluation.

PubMed

Shafizadeh-Moghadam, Hossein; Tayyebi, Amin; Helbich, Marco

2017-06-01

Transition index maps (TIMs) are key products in urban growth simulation models. However, their operationalization is still conflicting. Our aim was to compare the prediction accuracy of three TIM-based spatially explicit land cover change (LCC) models in the mega city of Mumbai, India. These LCC models include two data-driven approaches, namely artificial neural networks (ANNs) and weight of evidence (WOE), and one knowledge-based approach which integrates an analytical hierarchical process with fuzzy membership functions (FAHP). Using the relative operating characteristics (ROC), the performance of these three LCC models were evaluated. The results showed 85%, 75%, and 73% accuracy for the ANN, FAHP, and WOE. The ANN was clearly superior compared to the other LCC models when simulating urban growth for the year 2010; hence, ANN was used to predict urban growth for 2020 and 2030. Projected urban growth maps were assessed using statistical measures, including figure of merit, average spatial distance deviation, producer accuracy, and overall accuracy. Based on our findings, we recomend ANNs as an and accurate method for simulating future patterns of urban growth.

A parallel implementation of an off-lattice individual-based model of multicellular populations

NASA Astrophysics Data System (ADS)

Harvey, Daniel G.; Fletcher, Alexander G.; Osborne, James M.; Pitt-Francis, Joe

2015-07-01

As computational models of multicellular populations include ever more detailed descriptions of biophysical and biochemical processes, the computational cost of simulating such models limits their ability to generate novel scientific hypotheses and testable predictions. While developments in microchip technology continue to increase the power of individual processors, parallel computing offers an immediate increase in available processing power. To make full use of parallel computing technology, it is necessary to develop specialised algorithms. To this end, we present a parallel algorithm for a class of off-lattice individual-based models of multicellular populations. The algorithm divides the spatial domain between computing processes and comprises communication routines that ensure the model is correctly simulated on multiple processors. The parallel algorithm is shown to accurately reproduce the results of a deterministic simulation performed using a pre-existing serial implementation. We test the scaling of computation time, memory use and load balancing as more processes are used to simulate a cell population of fixed size. We find approximate linear scaling of both speed-up and memory consumption on up to 32 processor cores. Dynamic load balancing is shown to provide speed-up for non-regular spatial distributions of cells in the case of a growing population.

Modeling of earthquake ground motion in the frequency domain

NASA Astrophysics Data System (ADS)

Thrainsson, Hjortur

In recent years, the utilization of time histories of earthquake ground motion has grown considerably in the design and analysis of civil structures. It is very unlikely, however, that recordings of earthquake ground motion will be available for all sites and conditions of interest. Hence, there is a need for efficient methods for the simulation and spatial interpolation of earthquake ground motion. In addition to providing estimates of the ground motion at a site using data from adjacent recording stations, spatially interpolated ground motions can also be used in design and analysis of long-span structures, such as bridges and pipelines, where differential movement is important. The objective of this research is to develop a methodology for rapid generation of horizontal earthquake ground motion at any site for a given region, based on readily available source, path and site characteristics, or (sparse) recordings. The research includes two main topics: (i) the simulation of earthquake ground motion at a given site, and (ii) the spatial interpolation of earthquake ground motion. In topic (i), models are developed to simulate acceleration time histories using the inverse discrete Fourier transform. The Fourier phase differences, defined as the difference in phase angle between adjacent frequency components, are simulated conditional on the Fourier amplitude. Uniformly processed recordings from recent California earthquakes are used to validate the simulation models, as well as to develop prediction formulas for the model parameters. The models developed in this research provide rapid simulation of earthquake ground motion over a wide range of magnitudes and distances, but they are not intended to replace more robust geophysical models. In topic (ii), a model is developed in which Fourier amplitudes and Fourier phase angles are interpolated separately. A simple dispersion relationship is included in the phase angle interpolation. The accuracy of the interpolation model is assessed using data from the SMART-1 array in Taiwan. The interpolation model provides an effective method to estimate ground motion at a site using recordings from stations located up to several kilometers away. Reliable estimates of differential ground motion are restricted to relatively limited ranges of frequencies and inter-station spacings.

[Application of spatially explicit landscape model in soil loss study in Huzhong area].

PubMed

Xu, Chonggang; Hu, Yuanman; Chang, Yu; Li, Xiuzhen; Bu, Renchang; He, Hongshi; Leng, Wenfang

2004-10-01

Universal Soil Loss Equation (USLE) has been widely used to estimate the average annual soil loss. In most of the previous work on soil loss evaluation on forestland, cover management factor was calculated from the static forest landscape. The advent of spatially explicit forest landscape model in the last decade, which explicitly simulates the forest succession dynamics under natural and anthropogenic disturbances (fire, wind, harvest and so on) on heterogeneous landscape, makes it possible to take into consideration the change of forest cover, and to dynamically simulate the soil loss in different year (e.g. 10 years and 20 years after current year). In this study, we linked a spatially explicit landscape model (LANDIS) with USLE to simulate the soil loss dynamics under two scenarios: fire and no harvest, fire and harvest. We also simulated the soil loss with no fire and no harvest as a control. The results showed that soil loss varied periodically with simulation year, and the amplitude of change was the lowest under the control scenario and the highest under the fire and no harvest scenario. The effect of harvest on soil loss could not be easily identified on the map; however, the cumulative effect of harvest on soil loss was larger than that of fire. Decreasing the harvest area and the percent of bare soil increased by harvest could significantly reduce soil loss, but had no significant effects on the dynamic of soil loss. Although harvest increased the annual soil loss, it tended to decrease the variability of soil loss between different simulation years.

Integrating flood modelling in a hydrological catchment model: flow approximations and spatial resolution.

NASA Astrophysics Data System (ADS)

van den Bout, Bastian; Jetten, Victor

2017-04-01

Within hydrological models, flow approximations are commonly used to reduce computation time. The validity of these approximations is strongly determined by flow height, flow velocity, the spatial resolution of the model, and by the manner in which flow routing is implemented. The assumptions of these approximations can furthermore limit emergent behavior, and influence flow behavior under space-time scaling. In this presentation, the validity and performance of the kinematic, diffusive and dynamic flow approximations are investigated for use in a catchment-based flood model. Particularly, the validity during flood events and for varying spatial resolutions is investigated. The OpenLISEM hydrological model is extended to implement these flow approximations and channel flooding based on dynamic flow. The kinematic routing uses a predefined converging flow network, the diffusive and dynamic routing uses a 2D flow solution over a DEM. The channel flow in all cases is a 1D kinematic wave approximation. The flow approximations are used to recreate measured discharge in three catchments of different size in China, Spain and Italy, among which is the hydrograph of the 2003 flood event in the Fella river basin (Italy). Furthermore, spatial resolutions are varied for the flood simulation in order to investigate the influence of spatial resolution on these flow approximations. Results show that the kinematic, diffusive and dynamic flow approximation provide least to highest accuracy, respectively, in recreating measured temporal variation of the discharge. Kinematic flow, which is commonly used in hydrological modelling, substantially over-estimates hydrological connectivity in the simulations with a spatial resolution of below 30 meters. Since spatial resolutions of models have strongly increased over the past decades, usage of routed kinematic flow should be reconsidered. In the case of flood events, spatial modelling of kinematic flow substantially over-estimates hydrological connectivity and flow concentration, leading to significant errors. The combination of diffusive or dynamic overland flow and dynamic channel flooding provides high accuracy in recreating the 2003 Fella river flood event. Finally, flow approximations substantially influenced the predictive potential of the (flash) flood model.

Modeling of Long-Term Evolution of Hydrophysical Fields of the Black Sea

NASA Astrophysics Data System (ADS)

Dorofeyev, V. L.; Sukhikh, L. I.

2017-11-01

The long-term evolution of the Black Sea dynamics (1980-2020) is reconstructed by numerical simulation. The model of the Black Sea circulation has 4.8 km horizontal spatial resolution and 40 levels in z-coordinates. The mixing processes in the upper layer are parameterized by Mellor-Yamada turbulent model. For the sea surface boundary conditions, atmospheric forcing functions were used, provided for the Black Sea region by the Euro mediterranean Center on Climate Change (CMCC) from the COSMO-CLM regional climate model. These data have a spatial resolution of 14 km and a daily temporal resolution. To evaluate the quality of the hydrodynamic fields derived from the simulation, they were compared with in-situ hydrological measurements and similar results from physical reanalysis of the Black Sea.

An approach for modelling snowcover ablation and snowmelt runoff in cold region environments

NASA Astrophysics Data System (ADS)

Dornes, Pablo Fernando

Reliable hydrological model simulations are the result of numerous complex interactions among hydrological inputs, landscape properties, and initial conditions. Determination of the effects of these factors is one of the main challenges in hydrological modelling. This situation becomes even more difficult in cold regions due to the ungauged nature of subarctic and arctic environments. This research work is an attempt to apply a new approach for modelling snowcover ablation and snowmelt runoff in complex subarctic environments with limited data while retaining integrity in the process representations. The modelling strategy is based on the incorporation of both detailed process understanding and inputs along with information gained from observations of basin-wide streamflow phenomenon; essentially a combination of deductive and inductive approaches. The study was conducted in the Wolf Creek Research Basin, Yukon Territory, using three models, a small-scale physically based hydrological model, a land surface scheme, and a land surface hydrological model. The spatial representation was based on previous research studies and observations, and was accomplished by incorporating landscape units, defined according to topography and vegetation, as the spatial model elements. Comparisons between distributed and aggregated modelling approaches showed that simulations incorporating distributed initial snowcover and corrected solar radiation were able to properly simulate snowcover ablation and snowmelt runoff whereas the aggregated modelling approaches were unable to represent the differential snowmelt rates and complex snowmelt runoff dynamics. Similarly, the inclusion of spatially distributed information in a land surface scheme clearly improved simulations of snowcover ablation. Application of the same modelling approach at a larger scale using the same landscape based parameterisation showed satisfactory results in simulating snowcover ablation and snowmelt runoff with minimal calibration. Verification of this approach in an arctic basin illustrated that landscape based parameters are a feasible regionalisation framework for distributed and physically based models. In summary, the proposed modelling philosophy, based on the combination of an inductive and deductive reasoning, is a suitable strategy for reliable predictions of snowcover ablation and snowmelt runoff in cold regions and complex environments.

The inclusion of ocean-current effects in a tidal-current model as forcing in the convection term and its application to the mesoscale fate of CO2 seeping from the seafloor

NASA Astrophysics Data System (ADS)

Sakaizawa, Ryosuke; Kawai, Takaya; Sato, Toru; Oyama, Hiroyuki; Tsumune, Daisuke; Tsubono, Takaki; Goto, Koichi

2018-03-01

The target seas of tidal-current models are usually semi-closed bays, minimally affected by ocean currents. For these models, tidal currents are simulated in computational domains with a spatial scale of a couple hundred kilometers or less, by setting tidal elevations at their open boundaries. However, when ocean currents cannot be ignored in the sea areas of interest, such as in open seas near coastlines, it is necessary to include ocean-current effects in these tidal-current models. In this study, we developed a numerical method to analyze tidal currents near coasts by incorporating pre-calculated ocean-current velocities. First, a large regional-scale simulation with a spatial scale of several thousand kilometers was conducted and temporal changes in the ocean-current velocity at each grid point were stored. Next, the spatially and temporally interpolated ocean-current velocity was incorporated as forcing into the cross terms of the convection term of a tidal-current model having computational domains with spatial scales of hundreds of kilometers or less. Then, we applied this method to the diffusion of dissolved CO2 in a sea area off Tomakomai, Japan, and compared the numerical results and measurements to validate the proposed method.

Simulated GOLD Observations of Atmospheric Waves

NASA Astrophysics Data System (ADS)

Correira, J.; Evans, J. S.; Lumpe, J. D.; Rusch, D. W.; Chandran, A.; Eastes, R.; Codrescu, M.

2016-12-01

The Global-scale Observations of the Limb and Disk (GOLD) mission will measure structures in the Earth's airglow layer due to dynamical forcing by vertically and horizontally propagating waves. These measurements focus on global-scale structures, including compositional and temperature responses resulting from dynamical forcing. Daytime observations of far-UV emissions by GOLD will be used to generate two-dimensional maps of the ratio of atomic oxygen and molecular nitrogen column densities (ΣO/N2 ) as well as neutral temperature that provide signatures of large-scale spatial structure. In this presentation, we use simulations to demonstrate GOLD's capability to deduce periodicities and spatial dimensions of large-scale waves from the spatial and temporal evolution observed in composition and temperature maps. Our simulations include sophisticated forward modeling of the upper atmospheric airglow that properly accounts for anisotropy in neutral and ion composition, temperature, and solar illumination. Neutral densities and temperatures used in the simulations are obtained from global circulation and climatology models that have been perturbed by propagating waves with a range of amplitudes, periods, and sources of excitation. Modeling of airglow emission and predictions of ΣO/N2 and neutral temperatures are performed with the Atmospheric Ultraviolet Radiance Integrated Code (AURIC) and associated derived product algorithms. Predicted structure in ΣO/N2 and neutral temperature due to dynamical forcing by propagating waves is compared to existing observations. Realistic GOLD Level 2 data products are generated from simulated airglow emission using algorithm code that will be implemented operationally at the GOLD Science Data Center.

Modeling a secular trend by Monte Carlo simulation of height biased migration in a spatial network.

PubMed

Groth, Detlef

2017-04-01

Background: In a recent Monte Carlo simulation, the clustering of body height of Swiss military conscripts within a spatial network with characteristic features of the natural Swiss geography was investigated. In this study I examined the effect of migration of tall individuals into network hubs on the dynamics of body height within the whole spatial network. The aim of this study was to simulate height trends. Material and methods: Three networks were used for modeling, a regular rectangular fishing net like network, a real world example based on the geographic map of Switzerland, and a random network. All networks contained between 144 and 148 districts and between 265-307 road connections. Around 100,000 agents were initially released with average height of 170 cm, and height standard deviation of 6.5 cm. The simulation was started with the a priori assumption that height variation within a district is limited and also depends on height of neighboring districts (community effect on height). In addition to a neighborhood influence factor, which simulates a community effect, body height dependent migration of conscripts between adjacent districts in each Monte Carlo simulation was used to re-calculate next generation body heights. In order to determine the direction of migration for taller individuals, various centrality measures for the evaluation of district importance within the spatial network were applied. Taller individuals were favored to migrate more into network hubs, backward migration using the same number of individuals was random, not biased towards body height. Network hubs were defined by the importance of a district within the spatial network. The importance of a district was evaluated by various centrality measures. In the null model there were no road connections, height information could not be delivered between the districts. Results: Due to the favored migration of tall individuals into network hubs, average body height of the hubs, and later, of the whole network increased by up to 0.1 cm per iteration depending on the network model. The general increase in height within the network depended on connectedness and on the amount of height information that was exchanged between neighboring districts. If higher amounts of neighborhood height information were exchanged, the general increase in height within the network was large (strong secular trend). The trend in the homogeneous fishnet like network was lowest, the trend in the random network was highest. Yet, some network properties, such as the heteroscedasticity and autocorrelations of the migration simulation models differed greatly from the natural features observed in Swiss military conscript networks. Autocorrelations of district heights for instance, were much higher in the migration models. Conclusion: This study confirmed that secular height trends can be modeled by preferred migration of tall individuals into network hubs. However, basic network properties of the migration simulation models differed greatly from the natural features observed in Swiss military conscripts. Similar network-based data from other countries should be explored to better investigate height trends with Monte Carlo migration approach.

Modelisation numerique de l'hydrologie pour l'aide a la gestion des bassins versants, par l'utilisation conjointe des systemes d'information geographique et de la methode des elements finis un nouvel outil pour le developpement durable SAGESS

NASA Astrophysics Data System (ADS)

Bel Hadj Kacem, Mohamed Salah

All hydrological processes are affected by the spatial variability of the physical parameters of the watershed, and also by human intervention on the landscape. The water outflow from a watershed strictly depends on the spatial and temporal variabilities of the physical parameters of the watershed. It is now apparent that the integration of mathematical models into GIS's can benefit both GIS and three-dimension environmental models: a true modeling capability can help the modeling community bridge the gap between planners, scientists, decision-makers and end-users. The main goal of this research is to design a practical tool to simulate run-off water surface using Geographic design a practical tool to simulate run-off water surface using Geographic Information Systems and the simulation of the hydrological behavior by the Finite Element Method.

A GIS-based modeling system for petroleum waste management. Geographical information system.

PubMed

Chen, Z; Huang, G H; Li, J B

2003-01-01

With an urgent need for effective management of petroleum-contaminated sites, a GIS-aided simulation (GISSIM) system is presented in this study. The GISSIM contains two components: an advanced 3D numerical model and a geographical information system (GIS), which are integrated within a general framework. The modeling component undertakes simulation for the fate of contaminants in subsurface unsaturated and saturated zones. The GIS component is used in three areas throughout the system development and implementation process: (i) managing spatial and non-spatial databases; (ii) linking inputs, model, and outputs; and (iii) providing an interface between the GISSIM and its users. The developed system is applied to a North American case study. Concentrations of benzene, toluene, and xylenes in groundwater under a petroleum-contaminated site are dynamically simulated. Reasonable outputs have been obtained and presented graphically. They provide quantitative and scientific bases for further assessment of site-contamination impacts and risks, as well as decisions on practical remediation actions.

The Shale Hills Critical Zone Observatory for Embedded Sensing and Simulation

NASA Astrophysics Data System (ADS)

Duffy, C.; Davis, K.; Kane, T.; Boyer, E.

2009-04-01

The future of environmental observing systems will utilize embedded sensor networks with continuous real-time measurement of hydrologic, atmospheric, biogeochemical, and ecological variables across diverse terrestrial environments. Embedded environmental sensors, benefitting from advances in information sciences, networking technology, materials science, computing capacity, and data synthesis methods, are undergoing revolutionary change. It is now possible to field spatially-distributed, multi-node sensor networks that provide density and spatial coverage previously accessible only via numerical simulation. At the same time, computational tools are advancing rapidly to the point where it is now possible to simulate the physical processes controlling individual parcels of water and solutes through the complete terrestrial water cycle. Our goal for the Penn State Critical Zone Observatory is to apply environmental sensor arrays, integrated hydrologic models deployed and coordinated at a testbed within the Penn State Experimental Forest. The NSF-funded CZO is designed to observe the detailed space and time complexities of the water and energy cycle for a watershed and ultimately the river basin for all physical states and fluxes (groundwater, soil moisture, temperature, streamflow, latent heat, snowmelt, chemistry, isotopes etc.). Presently fully-coupled physical models are being developed that link the atmosphere-land-vegetation-subsurface system into a fully-coupled distributed system. During the last 5 years the Penn State Integrated Hydrologic Modeling System has been under development as an open-source community modeling project funded by NSF EAR/GEO and NSF CBET/ENG. PIHM represents a strategy for the formulation and solution of fully-coupled process equations at the watershed and river basin scales, and includes a tightly coupled GIS tool for data handling, domain decomposition, optimal unstructured grid generation, and model parameterization. (PIHM; http://sourceforge.net/projects/pihmmodel/; http://sourceforge.net/projects/pihmgis/ ) The CZO sensor and simulation system is being developed to have the following elements: 1) extensive, spatially-distributed smart sensor networks to gather intensive soil, geologic, hydrologic, geochemical and isotopic data; 2) spatially-explicit multiphysics models/solutions of the land-subsurface-vegetation-atmosphere system; and 3) parallel/distributed, adaptive algorithms for rapidly simulating the states of the watershed at high resolution, and 4) signal processing tools for data mining and parameter estimation. The prototype proposed sensor array and simulation system proposed is demonstrated with preliminary results from our first year.

Use of spatially refined satellite remote sensing fire detection data to initialize and evaluate coupled weather-wildfire growth model simulations

NASA Astrophysics Data System (ADS)

Schroeder, W.; Coen, J.; Oliva, P.

2013-12-01

Availability of spatially refined satellite active fire detection data is gradually increasing. For example, the new 375 m Visible Infrared Imaging Radiometer Suite (VIIRS) data show improved active fire detection performance for both small and large size fires. The VIIRS data have proved superior to MODIS for mapping of wildfires events spanning several days to weeks of either continued or intermittent activity, delivering 12-h active fire data of improved spatial fidelity. The VIIRS active fire data are complemented by other satellite active fire data sets of similar or higher spatial resolution, including the new 30 m Landsat-8. Additional assets should include the upcoming 20 m Sentinel-2 Landsat-class satellite program by the European Space Agency to be launched in 2014-15. These improved active fire data sets are fostering new applications that rely on higher resolution input fire data. In this study, we describe the characteristics of the new VIIRS and Landsat-8 data and demonstrate one such new application of satellite active fire data in support of fire behavior modeling. We present results for a wildfire observed in June 2012 in New Mexico using an innovative approach to improving the simulation of large, long-duration wildfires, either for retrospective studies or forecasting in a number of geophysical applications. The approach uses (1) the Coupled Atmosphere-Wildland Fire Environment (CAWFE) Model, a numerical weather prediction model two-way coupled with a module representing the rate of spread of a wildfire's flaming front, its rate of consumption of different wildland fuels, and the feedback of this heat release upon the atmosphere - i.e. 'how a fire creates its own weather', combined with (2) spatially refined 375 m VIIRS active fire data, which is used for initialization of a wildfire already in progress in the model and evaluation of its simulated progression at the time of the next pass. Results show that initializing a fire that is 'in progress' with VIIRS data and a weather simulation based on more recent atmospheric analyses can overcome several issues and improve the simulation of late-developing fires and of later periods (particularly those with growth periods separated by lulls) in a long-lived fire.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Parallelization of a spatial random field characterization process using the Method of Anchored Distributions and the HTCondor high throughput computing system

NASA Astrophysics Data System (ADS)

Osorio-Murillo, C. A.; Over, M. W.; Frystacky, H.; Ames, D. P.; Rubin, Y.

2013-12-01

A new software application called MAD# has been coupled with the HTCondor high throughput computing system to aid scientists and educators with the characterization of spatial random fields and enable understanding the spatial distribution of parameters used in hydrogeologic and related modeling. MAD# is an open source desktop software application used to characterize spatial random fields using direct and indirect information through Bayesian inverse modeling technique called the Method of Anchored Distributions (MAD). MAD relates indirect information with a target spatial random field via a forward simulation model. MAD# executes inverse process running the forward model multiple times to transfer information from indirect information to the target variable. MAD# uses two parallelization profiles according to computational resources available: one computer with multiple cores and multiple computers - multiple cores through HTCondor. HTCondor is a system that manages a cluster of desktop computers for submits serial or parallel jobs using scheduling policies, resources monitoring, job queuing mechanism. This poster will show how MAD# reduces the time execution of the characterization of random fields using these two parallel approaches in different case studies. A test of the approach was conducted using 1D problem with 400 cells to characterize saturated conductivity, residual water content, and shape parameters of the Mualem-van Genuchten model in four materials via the HYDRUS model. The number of simulations evaluated in the inversion was 10 million. Using the one computer approach (eight cores) were evaluated 100,000 simulations in 12 hours (10 million - 1200 hours approximately). In the evaluation on HTCondor, 32 desktop computers (132 cores) were used, with a processing time of 60 hours non-continuous in five days. HTCondor reduced the processing time for uncertainty characterization by a factor of 20 (1200 hours reduced to 60 hours.)

Synergetic use of Sentinel-1 and 2 to improve agro-hydrological modeling. Results of groundwater pumping estimates in south-India and nitrogen excess in south-west of France

NASA Astrophysics Data System (ADS)

Ferrant, S.; Le Page, M.; Kerr, Y. H.; Selles, A.; Mermoz, S.; Al-Bitar, A.; Muddu, S.; Gascoin, S.; Marechal, J. C.; Durand, P.; Salmon-Monviola, J.; Ceschia, E.; Bustillo, V.

2016-12-01

Nitrogen transfers at agricultural catchment level are intricately linked to water transfers. Agro-hydrological modeling approaches aim at integrating spatial heterogeneity of catchment physical properties together with agricultural practices to spatially estimate the water and nitrogen cycles. As in hydrology, the calibration schemes are designed to optimize the performance of the temporal dynamics and biases in model simulations, while ignoring the simulated spatial pattern. Yet, crop uses, i.e. transpiration and nitrogen exported by harvest, are the main fluxes at the catchment scale, highly variable in space and time. Geo-information time-series of vegetation and water index with multi-spectral optical detection S2 together with surface roughness time series with C-band radar detection S1 are used to reset soil water holding capacity parameters (depth, porosity) and agricultural practices (sowing date, irrigated area extent) of a crop model coupled with a hydrological model. This study takes two agro-hydrological contexts as demonstrators: 1-spatial nitrogen excess estimation in south-west of France, and 2-groundwater extraction for rice irrigation in south-India. Spatio-temporal patterns are involved in respectively surface water contamination due to over-fertilization and local groundwater shortages due to over-pumping for above rice inundation. Optimized Leaf Area Index profiles are simulated at the satellite images pixel level using an agro-hydrological model to reproduce spatial and temporal crop growth dynamics in south-west of France, improving the in-stream nitrogen fluxes by 12%. Accurate detection of irrigated area extents are obtained with the thresholding method based on optical indices, with a kappa of 0.81 for the dry season 2016. The actual monsoon season is monitored and will be presented. These extents drive the groundwater pumping and are highly variable in time (from 2 to 8% of the total area).

Spatially distributed potential evapotranspiration modeling and climate projections.

PubMed

Gharbia, Salem S; Smullen, Trevor; Gill, Laurence; Johnston, Paul; Pilla, Francesco

2018-08-15

Evapotranspiration integrates energy and mass transfer between the Earth's surface and atmosphere and is the most active mechanism linking the atmosphere, hydrosphsophere, lithosphere and biosphere. This study focuses on the fine resolution modeling and projection of spatially distributed potential evapotranspiration on the large catchment scale as response to climate change. Six potential evapotranspiration designed algorithms, systematically selected based on a structured criteria and data availability, have been applied and then validated to long-term mean monthly data for the Shannon River catchment with a 50m 2 cell size. The best validated algorithm was therefore applied to evaluate the possible effect of future climate change on potential evapotranspiration rates. Spatially distributed potential evapotranspiration projections have been modeled based on climate change projections from multi-GCM ensembles for three future time intervals (2020, 2050 and 2080) using a range of different Representative Concentration Pathways producing four scenarios for each time interval. Finally, seasonal results have been compared to baseline results to evaluate the impact of climate change on the potential evapotranspiration and therefor on the catchment dynamical water balance. The results present evidence that the modeled climate change scenarios would have a significant impact on the future potential evapotranspiration rates. All the simulated scenarios predicted an increase in potential evapotranspiration for each modeled future time interval, which would significantly affect the dynamical catchment water balance. This study addresses the gap in the literature of using GIS-based algorithms to model fine-scale spatially distributed potential evapotranspiration on the large catchment systems based on climatological observations and simulations in different climatological zones. Providing fine-scale potential evapotranspiration data is very crucial to assess the dynamical catchment water balance to setup management scenarios for the water abstractions. This study illustrates a transferable systematic method to design GIS-based algorithms to simulate spatially distributed potential evapotranspiration on the large catchment systems. Copyright © 2018 Elsevier B.V. All rights reserved.

Influence of snowpack and melt energy heterogeneity on snow cover depletion and snowmelt runoff simulation in a cold mountain environment

NASA Astrophysics Data System (ADS)

DeBeer, Chris M.; Pomeroy, John W.

2017-10-01

The spatial heterogeneity of mountain snow cover and ablation is important in controlling patterns of snow cover depletion (SCD), meltwater production, and runoff, yet is not well-represented in most large-scale hydrological models and land surface schemes. Analyses were conducted in this study to examine the influence of various representations of snow cover and melt energy heterogeneity on both simulated SCD and stream discharge from a small alpine basin in the Canadian Rocky Mountains. Simulations were performed using the Cold Regions Hydrological Model (CRHM), where point-scale snowmelt computations were made using a snowpack energy balance formulation and applied to spatial frequency distributions of snow water equivalent (SWE) on individual slope-, aspect-, and landcover-based hydrological response units (HRUs) in the basin. Hydrological routines were added to represent the vertical and lateral transfers of water through the basin and channel system. From previous studies it is understood that the heterogeneity of late winter SWE is a primary control on patterns of SCD. The analyses here showed that spatial variation in applied melt energy, mainly due to differences in net radiation, has an important influence on SCD at multiple scales and basin discharge, and cannot be neglected without serious error in the prediction of these variables. A single basin SWE distribution using the basin-wide mean SWE (SWE ‾) and coefficient of variation (CV; standard deviation/mean) was found to represent the fine-scale spatial heterogeneity of SWE sufficiently well. Simulations that accounted for differences in (SWE ‾) among HRUs but neglected the sub-HRU heterogeneity of SWE were found to yield similar discharge results as simulations that included this heterogeneity, while SCD was poorly represented, even at the basin level. Finally, applying point-scale snowmelt computations based on a single SWE depth for each HRU (thereby neglecting spatial differences in internal snowpack energetics over the distributions) was found to yield similar SCD and discharge results as simulations that resolved internal energy differences. Spatial/internal snowpack melt energy effects are more pronounced at times earlier in spring before the main period of snowmelt and SCD, as shown in previously published work. The paper discusses the importance of these findings as they apply to the warranted complexity of snowmelt process simulation in cold mountain environments, and shows how the end-of-winter SWE distribution represents an effective means of resolving snow cover heterogeneity at multiple scales for modelling, even in steep and complex terrain.

Influence of spatiotemporally distributed irradiance data input on temperature evolution in parabolic trough solar field simulations

NASA Astrophysics Data System (ADS)

Bubolz, K.; Schenk, H.; Hirsch, T.

2016-05-01

Concentrating solar field operation is affected by shadowing through cloud movement. For line focusing systems the impact of varying irradiance has been studied before by several authors with simulations of relevant thermodynamics assuming spatially homogeneous irradiance or using artificial test signals. While today's simulation capabilities allow more and more a higher spatiotemporal resolution of plant processes there are only few studies on influence of spatially distributed irradiance due to lack of available data. Based on recent work on generating real irradiance maps with high spatial resolution this paper demonstrates their influence on solar field thermodynamics. For a case study an irradiance time series is chosen. One solar field section with several loops and collecting header is modeled for simulation purpose of parabolic trough collectors and oil as heat transfer medium. Assuming homogeneous mass flow distribution among all loops we observe spatially varying temperature characteristics. They are analysed without and with mass flow control and their impact on solar field control design is discussed. Finally, the potential of distributed irradiance data is outlined.

A fast recursive algorithm for molecular dynamics simulation

NASA Technical Reports Server (NTRS)

Jain, A.; Vaidehi, N.; Rodriguez, G.

1993-01-01

The present recursive algorithm for solving molecular systems' dynamical equations of motion employs internal variable models that reduce such simulations' computation time by an order of magnitude, relative to Cartesian models. Extensive use is made of spatial operator methods recently developed for analysis and simulation of the dynamics of multibody systems. A factor-of-450 speedup over the conventional O(N-cubed) algorithm is demonstrated for the case of a polypeptide molecule with 400 residues.

Burn severity mapping using simulation modeling and satellite imagery

Treesearch

Eva C. Karau; Robert E. Keane

2010-01-01

Although burn severity maps derived from satellite imagery provide a landscape view of fire impacts, fire effects simulation models can provide spatial fire severity estimates and add a biotic context in which to interpret severity. In this project, we evaluated two methods of mapping burn severity in the context of rapid post-fire assessment for four wildfires in...

Challenges of forest landscape modeling - simulating large landscapes and validating results

Treesearch

Hong S. He; Jian Yang; Stephen R. Shifley; Frank R. Thompson

2011-01-01

Over the last 20 years, we have seen a rapid development in the field of forest landscape modeling, fueled by both technological and theoretical advances. Two fundamental challenges have persisted since the inception of FLMs: (1) balancing realistic simulation of ecological processes at broad spatial and temporal scales with computing capacity, and (2) validating...

Self Diagnostic Adhesive for Bonded Joints in Aircraft Structures

DTIC Science & Technology

2016-10-04

validated under the fatigue/dynamic loading condition. 3) Both SEM (Spectral Element Modeling) and FEM ( Finite Element Modeling) simulation of the...Sensors ..................................................................... 22 Parametric Study of Sensor Performance via Finite Element Simulation...The frequency range that we are interested is around 800 kHz. Conventional linear finite element method (FEM) requires a very fine spatial

Development of a station based climate database for SWAT and APEX assessments in the U.S.

USDA-ARS?s Scientific Manuscript database

Water quality simulation models such as the Soil and Water Assessment Tool (SWAT) and Agricultural Policy EXtender (APEX) are widely used in the U.S. These models require large amounts of spatial and tabular data to simulate the natural world. Accurate and seamless daily climatic data are critical...

GAPPARD: a computationally efficient method of approximating gap-scale disturbance in vegetation models

NASA Astrophysics Data System (ADS)

Scherstjanoi, M.; Kaplan, J. O.; Thürig, E.; Lischke, H.

2013-02-01

Models of vegetation dynamics that are designed for application at spatial scales larger than individual forest gaps suffer from several limitations. Typically, either a population average approximation is used that results in unrealistic tree allometry and forest stand structure, or models have a high computational demand because they need to simulate both a series of age-based cohorts and a number of replicate patches to account for stochastic gap-scale disturbances. The detail required by the latter method increases the number of calculations by two to three orders of magnitude compared to the less realistic population average approach. In an effort to increase the efficiency of dynamic vegetation models without sacrificing realism, and to explore patterns of spatial scaling in forests, we developed a new method for simulating stand-replacing disturbances that is both accurate and 10-50x faster than approaches that use replicate patches. The GAPPARD (approximating GAP model results with a Probabilistic Approach to account for stand Replacing Disturbances) method works by postprocessing the output of deterministic, undisturbed simulations of a cohort-based vegetation model by deriving the distribution of patch ages at any point in time on the basis of a disturbance probability. With this distribution, the expected value of any output variable can be calculated from the output values of the deterministic undisturbed run at the time corresponding to the patch age. To account for temporal changes in model forcing, e.g., as a result of climate change, GAPPARD performs a series of deterministic simulations and interpolates between the results in the postprocessing step. We integrated the GAPPARD method in the forest models LPJ-GUESS and TreeM-LPJ, and evaluated these in a series of simulations along an altitudinal transect of an inner-alpine valley. With GAPPARD applied to LPJ-GUESS results were insignificantly different from the output of the original model LPJ-GUESS using 100 replicate patches, but simulation time was reduced by approximately the factor 10. Our new method is therefore highly suited rapidly approximating LPJ-GUESS results, and provides the opportunity for future studies over large spatial domains, allows easier parameterization of tree species, faster identification of areas of interesting simulation results, and comparisons with large-scale datasets and forest models.

Catchment hydro-biogeochemical response to forest harvest intensity and spatial pattern

EPA Science Inventory

We apply a new model, Visualizing Ecosystems for Land Management Assessment (VELMA), to Watershed 10 (WS10) in the H.J. Andrews Experimental Forest to simulate the effects of harvest intensity and spatial pattern on catchment hydrological and biogeochemical processes. Specificall...

Spatial variability of summer Florida precipitation and its impact on microwave radiometer rainfall-measurement systems

NASA Technical Reports Server (NTRS)

Turner, B. J.; Austin, G. L.

1993-01-01

Three-dimensional radar data for three summer Florida storms are used as input to a microwave radiative transfer model. The model simulates microwave brightness observations by a 19-GHz, nadir-pointing, satellite-borne microwave radiometer. The statistical distribution of rainfall rates for the storms studied, and therefore the optimal conversion between microwave brightness temperatures and rainfall rates, was found to be highly sensitive to the spatial resolution at which observations were made. The optimum relation between the two quantities was less sensitive to the details of the vertical profile of precipitation. Rainfall retrievals were made for a range of microwave sensor footprint sizes. From these simulations, spatial sampling-error estimates were made for microwave radiometers over a range of field-of-view sizes. The necessity of matching the spatial resolution of ground truth to radiometer footprint size is emphasized. A strategy for the combined use of raingages, ground-based radar, microwave, and visible-infrared (VIS-IR) satellite sensors is discussed.

Numerical Simulation of Abandoned Gob Methane Drainage through Surface Vertical Wells

PubMed Central

Hu, Guozhong

2015-01-01

The influence of the ventilation system on the abandoned gob weakens, so the gas seepage characteristics in the abandoned gob are significantly different from those in a normal mining gob. In connection with this, this study physically simulated the movement of overlying rock strata. A spatial distribution function for gob permeability was derived. A numerical model using FLUENT for abandoned gob methane drainage through surface wells was established, and the derived spatial distribution function for gob permeability was imported into the numerical model. The control range of surface wells, flow patterns and distribution rules for static pressure in the abandoned gob under different well locations were determined using the calculated results from the numerical model. PMID:25955438

Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature

NASA Astrophysics Data System (ADS)

Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker

2018-04-01

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as "field" or "global" significance. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ∘ grid resolution. Monthly temperature climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. In winter and in most regions in summer, the downscaled distributions are statistically indistinguishable from the observed ones. A systematic cold summer bias occurs in deep river valleys due to overestimated elevations, in coastal areas due probably to enhanced sea breeze circulation, and over large lakes due to the interpolation of water temperatures. Urban areas in concave topography forms have a warm summer bias due to the strong heat islands, not reflected in the observations. WRF-NOAH generates appropriate fine-scale features in the monthly temperature field over regions of complex topography, but over spatially homogeneous areas even small biases can lead to significant deteriorations relative to the driving reanalysis. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barnes, Hannah C.; Houze, Robert A.

To equitably compare the spatial pattern of ice microphysical processes produced by three microphysical parameterizations with each other, observations, and theory, simulations of tropical oceanic mesoscale convective systems (MCSs) in the Weather Research and Forecasting (WRF) model were forced to develop the same mesoscale circulations as observations by assimilating radial velocity data from a Doppler radar. The same general layering of microphysical processes was found in observations and simulations with deposition anywhere above the 0°C level, aggregation at and above the 0°C level, melting at and below the 0°C level, and riming near the 0°C level. Thus, this study ismore » consistent with the layered ice microphysical pattern portrayed in previous conceptual models and indicated by dual-polarization radar data. Spatial variability of riming in the simulations suggests that riming in the midlevel inflow is related to convective-scale vertical velocity perturbations. Finally, this study sheds light on limitations of current generally available bulk microphysical parameterizations. In each parameterization, the layers in which aggregation and riming took place were generally too thick and the frequency of riming was generally too high compared to the observations and theory. Additionally, none of the parameterizations produced similar details in every microphysical spatial pattern. Discrepancies in the patterns of microphysical processes between parameterizations likely factor into creating substantial differences in model reflectivity patterns. It is concluded that improved parameterizations of ice-phase microphysics will be essential to obtain reliable, consistent model simulations of tropical oceanic MCSs.« less

LAPSUS: soil erosion - landscape evolution model

NASA Astrophysics Data System (ADS)

van Gorp, Wouter; Temme, Arnaud; Schoorl, Jeroen

2015-04-01

LAPSUS is a soil erosion - landscape evolution model which is capable of simulating landscape evolution of a gridded DEM by using multiple water, mass movement and human driven processes on multiple temporal and spatial scales. It is able to deal with a variety of human landscape interventions such as landuse management and tillage and it can model their interactions with natural processes. The complex spatially explicit feedbacks the model simulates demonstrate the importance of spatial interaction of human activity and erosion deposition patterns. In addition LAPSUS can model shallow landsliding, slope collapse, creep, solifluction, biological and frost weathering, fluvial behaviour. Furthermore, an algorithm to deal with natural depressions has been added and event-based modelling with an improved infiltration description and dust deposition has been pursued. LAPSUS has been used for case studies in many parts of the world and is continuously developing and expanding. it is now available for third-party and educational use. It has a comprehensive user interface and it is accompanied by a manual and exercises. The LAPSUS model is highly suitable to quantify and understand catchment-scale erosion processes. More information and a download link is available on www.lapsusmodel.nl.

Compressive self-interference Fresnel digital holography with faithful reconstruction

NASA Astrophysics Data System (ADS)

Wan, Yuhong; Man, Tianlong; Han, Ying; Zhou, Hongqiang; Wang, Dayong

2017-05-01

We developed compressive self-interference digital holographic approach that allows retrieving three-dimensional information of the spatially incoherent objects from single-shot captured hologram. The Fresnel incoherent correlation holography is combined with parallel phase-shifting technique to instantaneously obtain spatial-multiplexed phase-shifting holograms. The recording scheme is regarded as compressive forward sensing model, thus the compressive-sensing-based reconstruction algorithm is implemented to reconstruct the original object from the under sampled demultiplexed sub-holograms. The concept was verified by simulations and experiments with simulating use of the polarizer array. The proposed technique has great potential to be applied in 3D tracking of spatially incoherent samples.

Perspective taking in language: integrating the spatial and action domains.

PubMed

Beveridge, Madeleine E L; Pickering, Martin J

2013-09-17

Language is an inherently social behavior. In this paper, we bring together two research areas that typically occupy distinct sections of the literature: perspective taking in spatial language (whether people represent a scene from their own or a different spatial perspective), and perspective taking in action language (the extent to which they simulate an action as though they were performing that action). First, we note that vocabulary is used inconsistently across the spatial and action domains, and propose a more transparent vocabulary that will allow researchers to integrate action- and spatial-perspective taking. Second, we note that embodied theories of language comprehension often make the narrow assumption that understanding action descriptions involves adopting the perspective of an agent carrying out that action. We argue that comprehenders can adopt embodied action-perspectives other than that of the agent, including those of the patient or an observer. Third, we review evidence showing that perspective taking in spatial language is a flexible process. We argue that the flexibility of spatial-perspective taking provides a means for conversation partners engaged in dialogue to maximize similarity between their situation models. These situation models can then be used as the basis for action language simulations, in which language users adopt a particular action-perspective.

Perspective taking in language: integrating the spatial and action domains

PubMed Central

Beveridge, Madeleine E. L.; Pickering, Martin J.

2013-01-01

Language is an inherently social behavior. In this paper, we bring together two research areas that typically occupy distinct sections of the literature: perspective taking in spatial language (whether people represent a scene from their own or a different spatial perspective), and perspective taking in action language (the extent to which they simulate an action as though they were performing that action). First, we note that vocabulary is used inconsistently across the spatial and action domains, and propose a more transparent vocabulary that will allow researchers to integrate action- and spatial-perspective taking. Second, we note that embodied theories of language comprehension often make the narrow assumption that understanding action descriptions involves adopting the perspective of an agent carrying out that action. We argue that comprehenders can adopt embodied action-perspectives other than that of the agent, including those of the patient or an observer. Third, we review evidence showing that perspective taking in spatial language is a flexible process. We argue that the flexibility of spatial-perspective taking provides a means for conversation partners engaged in dialogue to maximize similarity between their situation models. These situation models can then be used as the basis for action language simulations, in which language users adopt a particular action-perspective. PMID:24062676

Effects of spatially distributed sectoral water management on the redistribution of water resources in an integrated water model

NASA Astrophysics Data System (ADS)

Voisin, Nathalie; Hejazi, Mohamad I.; Leung, L. Ruby; Liu, Lu; Huang, Maoyi; Li, Hong-Yi; Tesfa, Teklu

2017-05-01

Realistic representations of sectoral water withdrawals and consumptive demands and their allocation to surface and groundwater sources are important for improving modeling of the integrated water cycle. To inform future model development, we enhance the representation of water management in a regional Earth system (ES) model with a spatially distributed allocation of sectoral water demands simulated by a regional integrated assessment (IA) model to surface and groundwater systems. The integrated modeling framework (IA-ES) is evaluated by analyzing the simulated regulated flow and sectoral supply deficit in major hydrologic regions of the conterminous U.S, which differ from ES studies looking at water storage variations. Decreases in historical supply deficit are used as metrics to evaluate IA-ES model improvement in representating the complex sectoral human activities for assessing future adaptation and mitigation strategies. We also assess the spatial changes in both regulated flow and unmet demands, for irrigation and nonirrigation sectors, resulting from the individual and combined additions of groundwater and return flow modules. Results show that groundwater use has a pronounced regional and sectoral effect by reducing water supply deficit. The effects of sectoral return flow exhibit a clear east-west contrast in the hydrologic patterns, so the return flow component combined with the IA sectoral demands is a major driver for spatial redistribution of water resources and water deficits in the US. Our analysis highlights the need for spatially distributed sectoral representation of water management to capture the regional differences in interbasin redistribution of water resources and deficits.

Configuration of the thermal landscape determines thermoregulatory performance of ectotherms

PubMed Central

Sears, Michael W.; Angilletta, Michael J.; Schuler, Matthew S.; Borchert, Jason; Dilliplane, Katherine F.; Stegman, Monica; Rusch, Travis W.; Mitchell, William A.

2016-01-01

Although most organisms thermoregulate behaviorally, biologists still cannot easily predict whether mobile animals will thermoregulate in natural environments. Current models fail because they ignore how the spatial distribution of thermal resources constrains thermoregulatory performance over space and time. To overcome this limitation, we modeled the spatially explicit movements of animals constrained by access to thermal resources. Our models predict that ectotherms thermoregulate more accurately when thermal resources are dispersed throughout space than when these resources are clumped. This prediction was supported by thermoregulatory behaviors of lizards in outdoor arenas with known distributions of environmental temperatures. Further, simulations showed how the spatial structure of the landscape qualitatively affects responses of animals to climate. Biologists will need spatially explicit models to predict impacts of climate change on local scales. PMID:27601639

Towards Anatomic Scale Agent-Based Modeling with a Massively Parallel Spatially Explicit General-Purpose Model of Enteric Tissue (SEGMEnT_HPC)

PubMed Central

Cockrell, Robert Chase; Christley, Scott; Chang, Eugene; An, Gary

2015-01-01

Perhaps the greatest challenge currently facing the biomedical research community is the ability to integrate highly detailed cellular and molecular mechanisms to represent clinical disease states as a pathway to engineer effective therapeutics. This is particularly evident in the representation of organ-level pathophysiology in terms of abnormal tissue structure, which, through histology, remains a mainstay in disease diagnosis and staging. As such, being able to generate anatomic scale simulations is a highly desirable goal. While computational limitations have previously constrained the size and scope of multi-scale computational models, advances in the capacity and availability of high-performance computing (HPC) resources have greatly expanded the ability of computational models of biological systems to achieve anatomic, clinically relevant scale. Diseases of the intestinal tract are exemplary examples of pathophysiological processes that manifest at multiple scales of spatial resolution, with structural abnormalities present at the microscopic, macroscopic and organ-levels. In this paper, we describe a novel, massively parallel computational model of the gut, the Spatially Explicitly General-purpose Model of Enteric Tissue_HPC (SEGMEnT_HPC), which extends an existing model of the gut epithelium, SEGMEnT, in order to create cell-for-cell anatomic scale simulations. We present an example implementation of SEGMEnT_HPC that simulates the pathogenesis of ileal pouchitis, and important clinical entity that affects patients following remedial surgery for ulcerative colitis. PMID:25806784

Comparison of Large eddy dynamo simulation using dynamic sub-grid scale (SGS) model with a fully resolved direct simulation in a rotating spherical shell

NASA Astrophysics Data System (ADS)

Matsui, H.; Buffett, B. A.

2017-12-01

The flow in the Earth's outer core is expected to have vast length scale from the geometry of the outer core to the thickness of the boundary layer. Because of the limitation of the spatial resolution in the numerical simulations, sub-grid scale (SGS) modeling is required to model the effects of the unresolved field on the large-scale fields. We model the effects of sub-grid scale flow and magnetic field using a dynamic scale similarity model. Four terms are introduced for the momentum flux, heat flux, Lorentz force and magnetic induction. The model was previously used in the convection-driven dynamo in a rotating plane layer and spherical shell using the Finite Element Methods. In the present study, we perform large eddy simulations (LES) using the dynamic scale similarity model. The scale similarity model is implement in Calypso, which is a numerical dynamo model using spherical harmonics expansion. To obtain the SGS terms, the spatial filtering in the horizontal directions is done by taking the convolution of a Gaussian filter expressed in terms of a spherical harmonic expansion, following Jekeli (1981). A Gaussian field is also applied in the radial direction. To verify the present model, we perform a fully resolved direct numerical simulation (DNS) with the truncation of the spherical harmonics L = 255 as a reference. And, we perform unresolved DNS and LES with SGS model on coarser resolution (L= 127, 84, and 63) using the same control parameter as the resolved DNS. We will discuss the verification results by comparison among these simulations and role of small scale fields to large scale fields through the role of the SGS terms in LES.

Mapping the spatial distribution of Aedes aegypti and Aedes albopictus.

PubMed

Ding, Fangyu; Fu, Jingying; Jiang, Dong; Hao, Mengmeng; Lin, Gang

2018-02-01

Mosquito-borne infectious diseases, such as Rift Valley fever, Dengue, Chikungunya and Zika, have caused mass human death with the transnational expansion fueled by economic globalization. Simulating the distribution of the disease vectors is of great importance in formulating public health planning and disease control strategies. In the present study, we simulated the global distribution of Aedes aegypti and Aedes albopictus at a 5×5km spatial resolution with high-dimensional multidisciplinary datasets and machine learning methods Three relatively popular and robust machine learning models, including support vector machine (SVM), gradient boosting machine (GBM) and random forest (RF), were used. During the fine-tuning process based on training datasets of A. aegypti and A. albopictus, RF models achieved the highest performance with an area under the curve (AUC) of 0.973 and 0.974, respectively, followed by GBM (AUC of 0.971 and 0.972, respectively) and SVM (AUC of 0.963 and 0.964, respectively) models. The simulation difference between RF and GBM models was not statistically significant (p>0.05) based on the validation datasets, whereas statistically significant differences (p<0.05) were observed for RF and GBM simulations compared with SVM simulations. From the simulated maps derived from RF models, we observed that the distribution of A. albopictus was wider than that of A. aegypti along a latitudinal gradient. The discriminatory power of each factor in simulating the global distribution of the two species was also analyzed. Our results provided fundamental information for further study on disease transmission simulation and risk assessment. Copyright © 2017 Elsevier B.V. All rights reserved.

EFFECTS OF LEAF AREA PROFILES AND CANOPY STRATIFICATION ON SIMULATED ENERGY FLUXES: THE PROBLEM OF VERTICAL SPATIAL SCALE. (R827676)

EPA Science Inventory

We investigated the effects of the shape of leaf area profiles and the number of canopy layers on simulated sensible and latent heat fluxes using a gradient diffusion-based biometeorological model. Three research questions were addressed through simulation experiments: (1) Given ...

Three-Dimensional Numerical Simulation of Water Quality and Sediment-Associated Processes with Application to a Mississippi Delta Lake

USDA-ARS?s Scientific Manuscript database

A three-dimensional water quality model was developed for simulating temporal and spatial variations of phytoplankton, nutrients, and dissolved oxygen in freshwater bodies. Effects of suspended and bed sediment on the water quality processes were simulated. A formula was generated from field measure...

Mercury and methylmercury stream concentrations in a Coastal Plain watershed: a multi-scale simulation analysis.

PubMed

Knightes, C D; Golden, H E; Journey, C A; Davis, G M; Conrads, P A; Marvin-DiPasquale, M; Brigham, M E; Bradley, P M

2014-04-01

Mercury is a ubiquitous global environmental toxicant responsible for most US fish advisories. Processes governing mercury concentrations in rivers and streams are not well understood, particularly at multiple spatial scales. We investigate how insights gained from reach-scale mercury data and model simulations can be applied at broader watershed scales using a spatially and temporally explicit watershed hydrology and biogeochemical cycling model, VELMA. We simulate fate and transport using reach-scale (0.1 km(2)) study data and evaluate applications to multiple watershed scales. Reach-scale VELMA parameterization was applied to two nested sub-watersheds (28 km(2) and 25 km(2)) and the encompassing watershed (79 km(2)). Results demonstrate that simulated flow and total mercury concentrations compare reasonably to observations at different scales, but simulated methylmercury concentrations are out-of-phase with observations. These findings suggest that intricacies of methylmercury biogeochemical cycling and transport are under-represented in VELMA and underscore the complexity of simulating mercury fate and transport. Published by Elsevier Ltd.

Modeling wildlife populations with HexSim

EPA Science Inventory

HexSim is a framework for constructing spatially-explicit, individual-based computer models designed for simulating terrestrial wildlife population dynamics and interactions. HexSim is useful for a broad set of modeling applications including population viability analysis for on...

An agent-based approach for modeling dynamics of contagious disease spread

PubMed Central

Perez, Liliana; Dragicevic, Suzana

2009-01-01

Background The propagation of communicable diseases through a population is an inherent spatial and temporal process of great importance for modern society. For this reason a spatially explicit epidemiologic model of infectious disease is proposed for a greater understanding of the disease's spatial diffusion through a network of human contacts. Objective The objective of this study is to develop an agent-based modelling approach the integrates geographic information systems (GIS) to simulate the spread of a communicable disease in an urban environment, as a result of individuals' interactions in a geospatial context. Methods The methodology for simulating spatiotemporal dynamics of communicable disease propagation is presented and the model is implemented using measles outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by agents associated to places where they interact with other agents. They are endowed with mobility, through a transportation network allowing them to move between places within the urban environment, in order to represent the spatial heterogeneity and the complexity involved in infectious diseases diffusion. The model is implemented on georeferenced land use dataset from Metro Vancouver and makes use of census data sets from Statistics Canada for the municipality of Burnaby, BC, Canada study site. Results The results provide insights into the application of the model to calculate ratios of susceptible/infected in specific time frames and urban environments, due to its ability to depict the disease progression based on individuals' interactions. It is demonstrated that the dynamic spatial interactions within the population lead to high numbers of exposed individuals who perform stationary activities in areas after they have finished commuting. As a result, the sick individuals are concentrated in geographical locations like schools and universities. Conclusion The GIS-agent based model designed for this study can be easily customized to study the disease spread dynamics of any other communicable disease by simply adjusting the modeled disease timeline and/or the infection model and modifying the transmission process. This type of simulations can help to improve comprehension of disease spread dynamics and to take better steps towards the prevention and control of an epidemic outbreak. PMID:19656403

State updating of a distributed hydrological model with Ensemble Kalman Filtering: Effects of updating frequency and observation network density on forecast accuracy

NASA Astrophysics Data System (ADS)

Rakovec, O.; Weerts, A.; Hazenberg, P.; Torfs, P.; Uijlenhoet, R.

2012-12-01

This paper presents a study on the optimal setup for discharge assimilation within a spatially distributed hydrological model (Rakovec et al., 2012a). The Ensemble Kalman filter (EnKF) is employed to update the grid-based distributed states of such an hourly spatially distributed version of the HBV-96 model. By using a physically based model for the routing, the time delay and attenuation are modelled more realistically. The discharge and states at a given time step are assumed to be dependent on the previous time step only (Markov property). Synthetic and real world experiments are carried out for the Upper Ourthe (1600 km2), a relatively quickly responding catchment in the Belgian Ardennes. The uncertain precipitation model forcings were obtained using a time-dependent multivariate spatial conditional simulation method (Rakovec et al., 2012b), which is further made conditional on preceding simulations. We assess the impact on the forecasted discharge of (1) various sets of the spatially distributed discharge gauges and (2) the filtering frequency. The results show that the hydrological forecast at the catchment outlet is improved by assimilating interior gauges. This augmentation of the observation vector improves the forecast more than increasing the updating frequency. In terms of the model states, the EnKF procedure is found to mainly change the pdfs of the two routing model storages, even when the uncertainty in the discharge simulations is smaller than the defined observation uncertainty. Rakovec, O., Weerts, A. H., Hazenberg, P., Torfs, P. J. J. F., and Uijlenhoet, R.: State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy, Hydrol. Earth Syst. Sci. Discuss., 9, 3961-3999, doi:10.5194/hessd-9-3961-2012, 2012a. Rakovec, O., Hazenberg, P., Torfs, P. J. J. F., Weerts, A. H., and Uijlenhoet, R.: Generating spatial precipitation ensembles: impact of temporal correlation structure, Hydrol. Earth Syst. Sci. Discuss., 9, 3087-3127, doi:10.5194/hessd-9-3087-2012, 2012b.

Sensitivity of a Bayesian atmospheric-transport inversion model to spatio-temporal sensor resolution applied to the 2006 North Korean nuclear test

NASA Astrophysics Data System (ADS)

Lundquist, K. A.; Jensen, D. D.; Lucas, D. D.

2017-12-01

Atmospheric source reconstruction allows for the probabilistic estimate of source characteristics of an atmospheric release using observations of the release. Performance of the inversion depends partially on the temporal frequency and spatial scale of the observations. The objective of this study is to quantify the sensitivity of the source reconstruction method to sparse spatial and temporal observations. To this end, simulations of atmospheric transport of noble gasses are created for the 2006 nuclear test at the Punggye-ri nuclear test site. Synthetic observations are collected from the simulation, and are taken as "ground truth". Data denial techniques are used to progressively coarsen the temporal and spatial resolution of the synthetic observations, while the source reconstruction model seeks to recover the true input parameters from the synthetic observations. Reconstructed parameters considered here are source location, source timing and source quantity. Reconstruction is achieved by running an ensemble of thousands of dispersion model runs that sample from a uniform distribution of the input parameters. Machine learning is used to train a computationally-efficient surrogate model from the ensemble simulations. Monte Carlo sampling and Bayesian inversion are then used in conjunction with the surrogate model to quantify the posterior probability density functions of source input parameters. This research seeks to inform decision makers of the tradeoffs between more expensive, high frequency observations and less expensive, low frequency observations.

A Neurobehavioral Model of Flexible Spatial Language Behaviors

PubMed Central

Lipinski, John; Schneegans, Sebastian; Sandamirskaya, Yulia; Spencer, John P.; Schöner, Gregor

2012-01-01

We propose a neural dynamic model that specifies how low-level visual processes can be integrated with higher level cognition to achieve flexible spatial language behaviors. This model uses real-word visual input that is linked to relational spatial descriptions through a neural mechanism for reference frame transformations. We demonstrate that the system can extract spatial relations from visual scenes, select items based on relational spatial descriptions, and perform reference object selection in a single unified architecture. We further show that the performance of the system is consistent with behavioral data in humans by simulating results from 2 independent empirical studies, 1 spatial term rating task and 1 study of reference object selection behavior. The architecture we present thereby achieves a high degree of task flexibility under realistic stimulus conditions. At the same time, it also provides a detailed neural grounding for complex behavioral and cognitive processes. PMID:21517224

Los Angeles megacity: a high-resolution land–atmosphere modelling system for urban CO 2 emissions

DOE PAGES

Feng, Sha; Lauvaux, Thomas; Newman, Sally; ...

2016-07-22

Megacities are major sources of anthropogenic fossil fuel CO 2 (FFCO 2) emissions. The spatial extents of these large urban systems cover areas of 10 000 km 2 or more with complex topography and changing landscapes. We present a high-resolution land–atmosphere modelling system for urban CO 2 emissions over the Los Angeles (LA) megacity area. The Weather Research and Forecasting (WRF)-Chem model was coupled to a very high-resolution FFCO 2 emission product, Hestia-LA, to simulate atmospheric CO 2 concentrations across the LA megacity at spatial resolutions as fine as ~1 km. We evaluated multiple WRF configurations, selecting one that minimizedmore » errors in wind speed, wind direction, and boundary layer height as evaluated by its performance against meteorological data collected during the CalNex-LA campaign (May–June 2010). Our results show no significant difference between moderate-resolution (4 km) and high-resolution (1.3 km) simulations when evaluated against surface meteorological data, but the high-resolution configurations better resolved planetary boundary layer heights and vertical gradients in the horizontal mean winds. We coupled our WRF configuration with the Vulcan 2.2 (10 km resolution) and Hestia-LA (1.3 km resolution) fossil fuel CO 2 emission products to evaluate the impact of the spatial resolution of the CO 2 emission products and the meteorological transport model on the representation of spatiotemporal variability in simulated atmospheric CO 2 concentrations. We find that high spatial resolution in the fossil fuel CO 2 emissions is more important than in the atmospheric model to capture CO 2 concentration variability across the LA megacity. Finally, we present a novel approach that employs simultaneous correlations of the simulated atmospheric CO 2 fields to qualitatively evaluate the greenhouse gas measurement network over the LA megacity. Spatial correlations in the atmospheric CO 2 fields reflect the coverage of individual measurement sites when a statistically significant number of sites observe emissions from a specific source or location. We conclude that elevated atmospheric CO 2 concentrations over the LA megacity are composed of multiple fine-scale plumes rather than a single homogenous urban dome. Furthermore, we conclude that FFCO 2 emissions monitoring in the LA megacity requires FFCO 2 emissions modelling with ~1 km resolution because coarser-resolution emissions modelling tends to overestimate the observational constraints on the emissions estimates.« less

Los Angeles megacity: a high-resolution land–atmosphere modelling system for urban CO 2 emissions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feng, Sha; Lauvaux, Thomas; Newman, Sally

Megacities are major sources of anthropogenic fossil fuel CO 2 (FFCO 2) emissions. The spatial extents of these large urban systems cover areas of 10 000 km 2 or more with complex topography and changing landscapes. We present a high-resolution land–atmosphere modelling system for urban CO 2 emissions over the Los Angeles (LA) megacity area. The Weather Research and Forecasting (WRF)-Chem model was coupled to a very high-resolution FFCO 2 emission product, Hestia-LA, to simulate atmospheric CO 2 concentrations across the LA megacity at spatial resolutions as fine as ~1 km. We evaluated multiple WRF configurations, selecting one that minimizedmore » errors in wind speed, wind direction, and boundary layer height as evaluated by its performance against meteorological data collected during the CalNex-LA campaign (May–June 2010). Our results show no significant difference between moderate-resolution (4 km) and high-resolution (1.3 km) simulations when evaluated against surface meteorological data, but the high-resolution configurations better resolved planetary boundary layer heights and vertical gradients in the horizontal mean winds. We coupled our WRF configuration with the Vulcan 2.2 (10 km resolution) and Hestia-LA (1.3 km resolution) fossil fuel CO 2 emission products to evaluate the impact of the spatial resolution of the CO 2 emission products and the meteorological transport model on the representation of spatiotemporal variability in simulated atmospheric CO 2 concentrations. We find that high spatial resolution in the fossil fuel CO 2 emissions is more important than in the atmospheric model to capture CO 2 concentration variability across the LA megacity. Finally, we present a novel approach that employs simultaneous correlations of the simulated atmospheric CO 2 fields to qualitatively evaluate the greenhouse gas measurement network over the LA megacity. Spatial correlations in the atmospheric CO 2 fields reflect the coverage of individual measurement sites when a statistically significant number of sites observe emissions from a specific source or location. We conclude that elevated atmospheric CO 2 concentrations over the LA megacity are composed of multiple fine-scale plumes rather than a single homogenous urban dome. Furthermore, we conclude that FFCO 2 emissions monitoring in the LA megacity requires FFCO 2 emissions modelling with ~1 km resolution because coarser-resolution emissions modelling tends to overestimate the observational constraints on the emissions estimates.« less

Continuous Sub-daily Rainfall Simulation for Regional Flood Risk Assessment - Modelling of Spatio-temporal Correlation Structure of Extreme Precipitation in the Austrian Alps

NASA Astrophysics Data System (ADS)

Salinas, J. L.; Nester, T.; Komma, J.; Bloeschl, G.

2017-12-01

Generation of realistic synthetic spatial rainfall is of pivotal importance for assessing regional hydroclimatic hazard as the input for long term rainfall-runoff simulations. The correct reproduction of observed rainfall characteristics, such as regional intensity-duration-frequency curves, and spatial and temporal correlations is necessary to adequately model the magnitude and frequency of the flood peaks, by reproducing antecedent soil moisture conditions before extreme rainfall events, and joint probability of flood waves at confluences. In this work, a modification of the model presented by Bardossy and Platte (1992), where precipitation is first modeled on a station basis as a multivariate autoregressive model (mAr) in a Normal space. The spatial and temporal correlation structures are imposed in the Normal space, allowing for a different temporal autocorrelation parameter for each station, and simultaneously ensuring the positive-definiteness of the correlation matrix of the mAr errors. The Normal rainfall is then transformed to a Gamma-distributed space, with parameters varying monthly according to a sinusoidal function, in order to adapt to the observed rainfall seasonality. One of the main differences with the original model is the simulation time-step, reduced from 24h to 6h. Due to a larger availability of daily rainfall data, as opposite to sub-daily (e.g. hourly), the parameters of the Gamma distributions are calibrated to reproduce simultaneously a series of daily rainfall characteristics (mean daily rainfall, standard deviations of daily rainfall, and 24h intensity-duration-frequency [IDF] curves), as well as other aggregated rainfall measures (mean annual rainfall, and monthly rainfall). The calibration of the spatial and temporal correlation parameters is performed in a way that the catchment-averaged IDF curves aggregated at different temporal scales fit the measured ones. The rainfall model is used to generate 10.000 years of synthetic precipitation, fed into a rainfall-runoff model to derive the flood frequency in the Tirolean Alps in Austria. Given the number of generated events, the simulation framework is able to generate a large variety of rainfall patterns, as well as reproduce the variograms of relevant extreme rainfall events in the region of interest.

Dispersal leads to spatial autocorrelation in species distributions: A simulation model

USGS Publications Warehouse

Bahn, V.; Krohn, W.B.; O'Connor, R.J.

2008-01-01

Compared to population growth regulated by local conditions, dispersal has been underappreciated as a central process shaping the spatial distribution of populations. This paper asks: (a) which conditions increase the importance of dispersers relative to local recruits in determining population sizes? and (b) how does dispersal influence the spatial distribution patterns of abundances among connected populations? We approached these questions with a simulation model of populations on a coupled lattice with cells of continuously varying habitat quality expressed as carrying capacities. Each cell contained a population with the basic dynamics of density-regulated growth, and was connected to other populations by immigration and emigration. The degree to which dispersal influenced the distribution of population sizes depended most strongly on the absolute amount of dispersal, and then on the potential population growth rate. Dispersal decaying in intensity with distance left close neighbours more alike in population size than distant populations, leading to an increase in spatial autocorrelation. The spatial distribution of species with low potential growth rates is more dependent on dispersal than that of species with high growth rates; therefore, distribution modelling for species with low growth rates requires particular attention to autocorrelation, and conservation management of these species requires attention to factors curtailing dispersal, such as fragmentation and dispersal barriers. ?? 2007 Elsevier B.V. All rights reserved.