Root Zone Water Quality Model 2 (RZWQM2) is a whole-system model for studying crop production and environmental quality under current and changing climate conditions. It emphasizes the effects of agricultural management practices on physical, chemical and biological processes. RZWQM2 is a one-dimensional model with a pseudo 2-dimensional drainage flow. Crop simulation options include the generic plant growth model, DSSAT-CSM 4.0 and HERMES SUCROS models. It also can simulate surface energy balance with components from the SHAW model and water erosion from the GLEAMS model. An automated parameter estimation algorithm (PEST) was added to RZWQM2 for objective model calibration and uncertainty analysis.
Resource Title: RZWQM2 download page.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=412&modecode=30-12-30-25
Root Zone Water Quality Model 2 (RZWQM2) is a whole-system model for studying crop production and environmental quality under current and changing climate conditions. It emphasizes the effects of agricultural management practices on physical, chemical and biological processes. RZWQM2 is a one-dimensional model with a pseudo 2-dimensional drainage flow. Crop simulation options include the generic plant growth model, DSSAT-CSM 4.0 and HERMES SUCROS models. It also can simulate surface energy balance with components from the SHAW model and water erosion from the GLEAMS model. An automated parameter estimation algorithm (PEST) was added to RZWQM2 for objective model calibration and uncertainty analysis.
Resource Title: RZWQM2 download page.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=412&modecode=30-12-30-25
A mathematical model is constructed to quantify the loss of resilience in collapsing honey bee colonies due to the presence of a strong Allee effect. In the model, recruitment and mortality of adult bees have substantial social components, with recruitment enhanced and mortality reduced by additional adult bee numbers. The result is an Allee effect, a net per-individual rate of hive increase that increases as a function of adult bee numbers. The Allee effect creates a critical minimum size in adult bee numbers, below which mortality is greater than recruitment, with ensuing loss of viability of the hive. Under ordinary and favorable environmental circumstances, the critical size is low, and hives remain large, sending off viably-sized swarms (naturally or through beekeeping management) when hive numbers approach an upper stable equilibrium size (carrying capacity). However, both the lower critical size and the upper stable size depend on many parameters related to demographic rates and their enhancement by bee sociality. Any environmental factors that increase mortality, decrease recruitment, or interfere with the social moderation of these rates has the effect of exacerbating the Allee effect by increasing the lower critical size and substantially decreasing the upper stable size. As well, the basin of attraction to the upper stable size, defined by the model potential function, becomes narrower and shallower, indicating the loss of resilience as the hive becomes subjected to increased risk of falling below the critical size. Environmental effects of greater severity can cause the two equilibria to merge and the basin of attraction to the upper stable size to disappear, resulting in collapse of the hive from any initial size. The model suggests that multiple proximate causes, among them pesticides, mites, pathogens, and climate change, working singly or in combinations, could trigger hive collapse. This data supplement provides a text file containing 7 scripts written in the R programming language for reproducing Figures 1\u20137.
Resource Title: S1 R scripts for figures.
File Name: Web Page, url: https://doi.org/10.1371/journal.pone.0150055.s001
Text file containing 7 scripts written in the R programming language for reproducing Figs 1\u20137 of this article.
Resource Software Recommended: R programming language,url: https://www.r-project.org/
A mathematical model is constructed to quantify the loss of resilience in collapsing honey bee colonies due to the presence of a strong Allee effect. In the model, recruitment and mortality of adult bees have substantial social components, with recruitment enhanced and mortality reduced by additional adult bee numbers. The result is an Allee effect, a net per-individual rate of hive increase that increases as a function of adult bee numbers. The Allee effect creates a critical minimum size in adult bee numbers, below which mortality is greater than recruitment, with ensuing loss of viability of the hive. Under ordinary and favorable environmental circumstances, the critical size is low, and hives remain large, sending off viably-sized swarms (naturally or through beekeeping management) when hive numbers approach an upper stable equilibrium size (carrying capacity). However, both the lower critical size and the upper stable size depend on many parameters related to demographic rates and their enhancement by bee sociality. Any environmental factors that increase mortality, decrease recruitment, or interfere with the social moderation of these rates has the effect of exacerbating the Allee effect by increasing the lower critical size and substantially decreasing the upper stable size. As well, the basin of attraction to the upper stable size, defined by the model potential function, becomes narrower and shallower, indicating the loss of resilience as the hive becomes subjected to increased risk of falling below the critical size. Environmental effects of greater severity can cause the two equilibria to merge and the basin of attraction to the upper stable size to disappear, resulting in collapse of the hive from any initial size. The model suggests that multiple proximate causes, among them pesticides, mites, pathogens, and climate change, working singly or in combinations, could trigger hive collapse. This data supplement provides a text file containing 7 scripts written in the R programming language for reproducing Figures 1\u20137.
Resource Title: S1 R scripts for figures.
File Name: Web Page, url: https://doi.org/10.1371/journal.pone.0150055.s001
Text file containing 7 scripts written in the R programming language for reproducing Figs 1\u20137 of this article.
Resource Software Recommended: R programming language,url: https://www.r-project.org/
Spatial Modeling for Resources Framework (SMRF) was developed at the USDA Agricultural Research Service (ARS) in Boise, ID, and was designed to increase the flexibility of taking measured weather data and distributing the point measurements across a watershed. SMRF was developed to be used as an operational or research framework, where ease of use, efficiency, and ability to run in near real time are high priorities.
\nHighlights
\nFeatures\nSMRF was developed as a modular framework to enable new modules to be easily intigrated and utilized.
\nResource Title: SMRF GitHub repository.
File Name: Web Page, url: https://github.com/USDA-ARS-NWRC/smrf
SMRF was designed to increase the flexibility of taking measured weather data, or atmospheric models, and distributing the data across a watershed.
Spatial Modeling for Resources Framework (SMRF) was developed at the USDA Agricultural Research Service (ARS) in Boise, ID, and was designed to increase the flexibility of taking measured weather data and distributing the point measurements across a watershed. SMRF was developed to be used as an operational or research framework, where ease of use, efficiency, and ability to run in near real time are high priorities.
\nHighlights
\nFeatures\nSMRF was developed as a modular framework to enable new modules to be easily intigrated and utilized.
\nResource Title: SMRF GitHub repository.
File Name: Web Page, url: https://github.com/USDA-ARS-NWRC/smrf
SMRF was designed to increase the flexibility of taking measured weather data, or atmospheric models, and distributing the data across a watershed.
The\u00a0need\u00a0for\u00a0a\u00a0research\u00a0tool\u00a0that\u00a0integrates\u00a0the\u00a0many\u00a0physical\u00a0and\u00a0biological\u00a0processes\u00a0on\u00a0a farm has led to the development of the Integrated Farm System Model (IFSM). The model has been used to evaluate a wide variety of technologies and management strategies, and these analyses have been reported in the scientific and farm-trade literature. Systems research in dairy and beef production remains as the primary purpose of this tool, but the model also provides an effective teaching aid. With the model, students gain a better appreciation for the complexity of livestock forage systems. The learn how small changes affect many parts of the system, causing unanticipated results. They may also use the model to develop a more optimum food production system. When used in extension type teaching, producers can learn more about their farms and obtain information useful in strategic planning. By testing and comparing different options with the model, those offering the greatest economic benefit with acceptable environmental impact can be found.
\nInput information is supplied to the program through three parameter files. The farm parameter file contains data describing the farm such as crop areas, soil type, equipment and structures used, numbers of animals at various ages, harvest, tillage, and manure handling strategies, and prices for various farm inputs and outputs. The machinery file includes parameters for each machine available for use on a simulated farm.
\nSimulation output is available in four files, which contain summary tables, report tables, optional tables, and parameter tables. The summary tables provide average performance, environmental impact, costs, and returns for the years simulated. These values consist of crop yields, feeds produced, feeds bought and sold, manure produced, nutrient losses to the environment, production costs, income from products sold, and the net return or profitability of the farm. Values are provided for the average and standard deviation of each over all simulated years. The report tables provide extensive output information including all the data given in the summary tables. In these tables, values are given for each simulated year of weather as well as the mean and variance over all simulated years. Optional tables are available for a closer inspection of how the components of the full simulation are functioning. These tables include very detailed data, often on a daily basis. Parameter tables summarize the input parameters specified for a given simulation. These tables provide a convenient method of documenting the parameter settings used for a simulation.
\nResource Title: Projected Climate Data for IFSM.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00
Downscaled climate data (1950 to 2100) are available for 78 locations across the United States formatted for use in IFSM. Each location includes 18 climate files created using 9 general circulation models (GCM) and 2 projected emission scenarios. Emission scenarios include Representative Concentration Pathways (RCP) 4.5 and 8.5 where RCP 4.5 represents a somewhat optimistic outlook for reducing greenhouse gas emissions and 8.5 represents continuing the current trend for emissions.
The\u00a0need\u00a0for\u00a0a\u00a0research\u00a0tool\u00a0that\u00a0integrates\u00a0the\u00a0many\u00a0physical\u00a0and\u00a0biological\u00a0processes\u00a0on\u00a0a farm has led to the development of the Integrated Farm System Model (IFSM). The model has been used to evaluate a wide variety of technologies and management strategies, and these analyses have been reported in the scientific and farm-trade literature. Systems research in dairy and beef production remains as the primary purpose of this tool, but the model also provides an effective teaching aid. With the model, students gain a better appreciation for the complexity of livestock forage systems. The learn how small changes affect many parts of the system, causing unanticipated results. They may also use the model to develop a more optimum food production system. When used in extension type teaching, producers can learn more about their farms and obtain information useful in strategic planning. By testing and comparing different options with the model, those offering the greatest economic benefit with acceptable environmental impact can be found.
\nInput information is supplied to the program through three parameter files. The farm parameter file contains data describing the farm such as crop areas, soil type, equipment and structures used, numbers of animals at various ages, harvest, tillage, and manure handling strategies, and prices for various farm inputs and outputs. The machinery file includes parameters for each machine available for use on a simulated farm.
\nSimulation output is available in four files, which contain summary tables, report tables, optional tables, and parameter tables. The summary tables provide average performance, environmental impact, costs, and returns for the years simulated. These values consist of crop yields, feeds produced, feeds bought and sold, manure produced, nutrient losses to the environment, production costs, income from products sold, and the net return or profitability of the farm. Values are provided for the average and standard deviation of each over all simulated years. The report tables provide extensive output information including all the data given in the summary tables. In these tables, values are given for each simulated year of weather as well as the mean and variance over all simulated years. Optional tables are available for a closer inspection of how the components of the full simulation are functioning. These tables include very detailed data, often on a daily basis. Parameter tables summarize the input parameters specified for a given simulation. These tables provide a convenient method of documenting the parameter settings used for a simulation.
\nResource Title: Projected Climate Data for IFSM.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=497&modecode=80-70-05-00
Downscaled climate data (1950 to 2100) are available for 78 locations across the United States formatted for use in IFSM. Each location includes 18 climate files created using 9 general circulation models (GCM) and 2 projected emission scenarios. Emission scenarios include Representative Concentration Pathways (RCP) 4.5 and 8.5 where RCP 4.5 represents a somewhat optimistic outlook for reducing greenhouse gas emissions and 8.5 represents continuing the current trend for emissions.
The Dairy Gas Emissions Model (DairyGEM) uses process level simulation and process related emission factors to predict ammonia, hydrogen sulfide, VOC and greenhouse gas emissions along with the carbon, energy and water footprints of dairy production systems. A process-based simulation is used to predict ammonia, hydrogen sulfide, and VOC emissions as influenced by climate and farm management. Net carbon dioxide, methane, and nitrous oxide emissions are also estimated using process simulation or process related emission factors. Environmental footprints are determined that include both primary and secondary sources. Primary sources occur during the farm production process and secondary sources are those occurring during the production of resources used including machinery, fuel, electricity, fertilizer, pesticides, and plastic.
Resource Title: Dairy Gas Emissions Model - home.
File Name: Web Page, url: https://www.ars.usda.gov/northeast-area/up-pa/pswmru/docs/dairy-gas-emissions-model/
Provides description of the DairyGEM model with links to reference manual, download instructions, and training video module.
The Dairy Gas Emissions Model (DairyGEM) uses process level simulation and process related emission factors to predict ammonia, hydrogen sulfide, VOC and greenhouse gas emissions along with the carbon, energy and water footprints of dairy production systems. A process-based simulation is used to predict ammonia, hydrogen sulfide, and VOC emissions as influenced by climate and farm management. Net carbon dioxide, methane, and nitrous oxide emissions are also estimated using process simulation or process related emission factors. Environmental footprints are determined that include both primary and secondary sources. Primary sources occur during the farm production process and secondary sources are those occurring during the production of resources used including machinery, fuel, electricity, fertilizer, pesticides, and plastic.
Resource Title: Dairy Gas Emissions Model - home.
File Name: Web Page, url: https://www.ars.usda.gov/northeast-area/up-pa/pswmru/docs/dairy-gas-emissions-model/
Provides description of the DairyGEM model with links to reference manual, download instructions, and training video module.
Cligen is a stochastic weather generator which produces daily estimates of precipitation, temperature, dewpoint, wind, and solar radiation for a single geographic point, using monthly parameters (means, SD's, skewness, etc.) derived from the historic measurements. Unlike other climate generators, it produces individual storm parameter estimates, including time to peak, peak intensity, and storm duration, which are required to run the WEPP and the WEPS soil erosion models. Station parameter files to run Cligen for several thousand U. S. sites are available for download from this website: also data and software to build station files for international sites. With the exception of Tmin, Tmax, and Tdew temperatures (changed in January 2004), daily estimates for each parameter are generated independently of the others. With the current random number generator, subsequent runs on the same machine made with identical inputs will produce identical results.
\nUsers of daily simulation models should consider the impacts of Cligen's characteristics on their application. Individual parameter distributions may be expected to reproduce monthly historic distributions quite well. However, if the model in question is sensitive to the daily interactions of two or more of the parameters Cligen produces, Cligen may not be the most appropriate weather generator to use. This is because for a given day, it generates solar radiation, and maximum and minimum temperatures completely independently from precipitation. Experience and common sense tell us that these parameters are NOT independent. In practice this may not be a huge issue, since it is not uncommon for models to be sensitive to one weather parameter on a daily basis, and relatively insensitive to the others, as long as their monthly trends are preserved.
Resource Title: Cligen.
File Name: Web Page, url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/
Overview, source code downloads, data files, and publications.
Cligen is a stochastic weather generator which produces daily estimates of precipitation, temperature, dewpoint, wind, and solar radiation for a single geographic point, using monthly parameters (means, SD's, skewness, etc.) derived from the historic measurements. Unlike other climate generators, it produces individual storm parameter estimates, including time to peak, peak intensity, and storm duration, which are required to run the WEPP and the WEPS soil erosion models. Station parameter files to run Cligen for several thousand U. S. sites are available for download from this website: also data and software to build station files for international sites. With the exception of Tmin, Tmax, and Tdew temperatures (changed in January 2004), daily estimates for each parameter are generated independently of the others. With the current random number generator, subsequent runs on the same machine made with identical inputs will produce identical results.
\nUsers of daily simulation models should consider the impacts of Cligen's characteristics on their application. Individual parameter distributions may be expected to reproduce monthly historic distributions quite well. However, if the model in question is sensitive to the daily interactions of two or more of the parameters Cligen produces, Cligen may not be the most appropriate weather generator to use. This is because for a given day, it generates solar radiation, and maximum and minimum temperatures completely independently from precipitation. Experience and common sense tell us that these parameters are NOT independent. In practice this may not be a huge issue, since it is not uncommon for models to be sensitive to one weather parameter on a daily basis, and relatively insensitive to the others, as long as their monthly trends are preserved.
Resource Title: Cligen.
File Name: Web Page, url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/
Overview, source code downloads, data files, and publications.
Environmental Policy Integrated Climate (EPIC) model is a cropping systems model that was developed to estimate soil productivity as affected by erosion as part of the Soil and Water Resources Conservation Act analysis for 1980, which revealed a significant need for improving technology for evaluating the impacts of soil erosion on soil productivity. EPIC simulates approximately eighty crops with one crop growth model using unique parameter values for each crop. It can be configured for a wide range of crop rotations and other vegetative systems, tillage systems, and other management strategies. It predicts effects of management decisions on soil, water, nutrient and pesticide movements, and their combined impact on soil loss, water quality, and crop yields for areas with homogeneous soils and management.
\nEPIC functions on a daily time step and can simulate hundreds of years. Since the initial development, EPIC has been continually improving through the additions of algorithms to simulate water quality, nitrogen and carbon cycling, climate change, and the effects of atmospheric carbon dioxide. The processes simulated include leaf interception of solar radiation; conversion to biomass; division of biomass into roots, above ground mass, and economic yield; root growth; water use; and nutrient uptake. It can be configured for a wide range of crop rotations and other vegetative systems, tillage systems, and other management practices. The model can also assess the cost of erosion for determining optimal management strategies.
Resource Title: Environmental Policy Integrated Climate (EPIC) Model.
File Name: Web Page, url: https://epicapex.tamu.edu/epic/
Web site for the EPIC model: describes capabilities, examples of applications, and download links for executables, source code, and supporting tools.
Environmental Policy Integrated Climate (EPIC) model is a cropping systems model that was developed to estimate soil productivity as affected by erosion as part of the Soil and Water Resources Conservation Act analysis for 1980, which revealed a significant need for improving technology for evaluating the impacts of soil erosion on soil productivity. EPIC simulates approximately eighty crops with one crop growth model using unique parameter values for each crop. It can be configured for a wide range of crop rotations and other vegetative systems, tillage systems, and other management strategies. It predicts effects of management decisions on soil, water, nutrient and pesticide movements, and their combined impact on soil loss, water quality, and crop yields for areas with homogeneous soils and management.
\nEPIC functions on a daily time step and can simulate hundreds of years. Since the initial development, EPIC has been continually improving through the additions of algorithms to simulate water quality, nitrogen and carbon cycling, climate change, and the effects of atmospheric carbon dioxide. The processes simulated include leaf interception of solar radiation; conversion to biomass; division of biomass into roots, above ground mass, and economic yield; root growth; water use; and nutrient uptake. It can be configured for a wide range of crop rotations and other vegetative systems, tillage systems, and other management practices. The model can also assess the cost of erosion for determining optimal management strategies.
Resource Title: Environmental Policy Integrated Climate (EPIC) Model.
File Name: Web Page, url: https://epicapex.tamu.edu/epic/
Web site for the EPIC model: describes capabilities, examples of applications, and download links for executables, source code, and supporting tools.
Please note: This software is no longer being updated or maintained, and is out of date.
\nSPUR2 DOS ver. 2.2 is a general grassland ecosystem simulation model designed to determine beef cattle performance and production by simultaneously simulating production of up to 15 plant species on 36 heterogeneous grassland sites. SPUR2 simulates grassland hydrology, nitrogen cycling, and soil organic matter on grazed ecosystems as well as rangeland production under different climatic regimes, environmental conditions, and management alternatives.
Resource Title: SPUR2 download page.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=241&modecode=30-12-30-25
Please note: This software is no longer being updated or maintained, and is out of date.
\nSPUR2 DOS ver. 2.2 is a general grassland ecosystem simulation model designed to determine beef cattle performance and production by simultaneously simulating production of up to 15 plant species on 36 heterogeneous grassland sites. SPUR2 simulates grassland hydrology, nitrogen cycling, and soil organic matter on grazed ecosystems as well as rangeland production under different climatic regimes, environmental conditions, and management alternatives.
Resource Title: SPUR2 download page.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=241&modecode=30-12-30-25
iSnobal is a physically-based distributed snowmelt model. Snowmelt is the principal source for soil moisture, ground-water re-charge, and stream-flow in mountainous regions of the western US, Canada, and other similar regions of the world. Information on the timing, magnitude, and contributing area of melt under variable or changing climate conditions is required for successful water and resource management. A coupled energy and mass-balance model iSnobal is used to simulate the development and melting of the seasonal snowcover in several mountain basins in California, Idaho, and Utah. Simulations are done over basins varying from 1 to 2500 km2 , with simulation periods varying from a few days for the smallest basin, Emerald Lake watershed in California, to multiple snow seasons for the Park City area in Utah. The model is driven by topographically corrected estimates of radiation, temperature, humidity, wind, and precipitation. Simulation results in all basins closely match independently measured snow water equivalent, snow depth, or runoff during both the development and depletion of the snowcover. Spatially distributed estimates of snow deposition and melt allow us to better understand the interaction between topographic structure, climate, and moisture availability in mountain basins of the western US. Application of topographically distributed models such as this will lead to improved water resource and watershed management.
Resource Title: Image Processing Workbench (IPW).
File Name: Web Page, url: https://gitlab.com/ars-snow/ipw
GitHub repository where the model can be accessed and downloaded.
iSnobal is a physically-based distributed snowmelt model. Snowmelt is the principal source for soil moisture, ground-water re-charge, and stream-flow in mountainous regions of the western US, Canada, and other similar regions of the world. Information on the timing, magnitude, and contributing area of melt under variable or changing climate conditions is required for successful water and resource management. A coupled energy and mass-balance model iSnobal is used to simulate the development and melting of the seasonal snowcover in several mountain basins in California, Idaho, and Utah. Simulations are done over basins varying from 1 to 2500 km2 , with simulation periods varying from a few days for the smallest basin, Emerald Lake watershed in California, to multiple snow seasons for the Park City area in Utah. The model is driven by topographically corrected estimates of radiation, temperature, humidity, wind, and precipitation. Simulation results in all basins closely match independently measured snow water equivalent, snow depth, or runoff during both the development and depletion of the snowcover. Spatially distributed estimates of snow deposition and melt allow us to better understand the interaction between topographic structure, climate, and moisture availability in mountain basins of the western US. Application of topographically distributed models such as this will lead to improved water resource and watershed management.
Resource Title: Image Processing Workbench (IPW).
File Name: Web Page, url: https://gitlab.com/ars-snow/ipw
GitHub repository where the model can be accessed and downloaded.
Effectively managing salt affected irrigated lands and judicially using irrigation water of marginal salinity quality requires understanding the interactions among many inputs. They include soil salinity, crop salt tolerances, soil physical properties, irrigation water quality, irrigation management, water table depth and quality, climatic factors and crop yield. Visualizing the simultaneous interactions among this many factors over a cropping season extends beyond the capacity of the human mind.
\nAn interactive computer program was developed to simulate the interactions among the above factors. It shows how changing one factor impacts the outcome of the other factors for a single growing season. The user selects a climate, a crop, and soil characteristics from menu lists, and then sets the water table depth and quality, irrigation (river or well) water quality and then develops an irrigation schedule. On execution, the relative yield reductions due to over irrigation, under irrigation, and salinity, water table rise or fall and surface runoff are shown numerically for the growing season. Soil water content, soil salinity, water table depth changes and rain and irrigation events during the season are also shown graphically.
\nThis is an educational tool designed to teach the concepts of salinity and irrigation management and IS NOT an irrigation scheduling program nor a management tool. Two versions have been developed, one using metric units, southern hemisphere growing seasons and Australian terminology, and a second using northern hemisphere growing seasons, with units and terminology currently used in US irrigated agriculture. An option in the US version also allows use of metric units.
\nThe SWAGMAN acronym and the SWAGMAN-Whatif program copyrights are owned by Commonwealth Scientific and Industrial Research Organization (CSIRO) of Australia. The SWAGMAN acronym is not to be used for other programs.
\nResource Title: SWAGMAN.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=207
download page
Effectively managing salt affected irrigated lands and judicially using irrigation water of marginal salinity quality requires understanding the interactions among many inputs. They include soil salinity, crop salt tolerances, soil physical properties, irrigation water quality, irrigation management, water table depth and quality, climatic factors and crop yield. Visualizing the simultaneous interactions among this many factors over a cropping season extends beyond the capacity of the human mind.
\nAn interactive computer program was developed to simulate the interactions among the above factors. It shows how changing one factor impacts the outcome of the other factors for a single growing season. The user selects a climate, a crop, and soil characteristics from menu lists, and then sets the water table depth and quality, irrigation (river or well) water quality and then develops an irrigation schedule. On execution, the relative yield reductions due to over irrigation, under irrigation, and salinity, water table rise or fall and surface runoff are shown numerically for the growing season. Soil water content, soil salinity, water table depth changes and rain and irrigation events during the season are also shown graphically.
\nThis is an educational tool designed to teach the concepts of salinity and irrigation management and IS NOT an irrigation scheduling program nor a management tool. Two versions have been developed, one using metric units, southern hemisphere growing seasons and Australian terminology, and a second using northern hemisphere growing seasons, with units and terminology currently used in US irrigated agriculture. An option in the US version also allows use of metric units.
\nThe SWAGMAN acronym and the SWAGMAN-Whatif program copyrights are owned by Commonwealth Scientific and Industrial Research Organization (CSIRO) of Australia. The SWAGMAN acronym is not to be used for other programs.
\nResource Title: SWAGMAN.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=207
download page
Global warming is expected to lead to a more vigorous hydrological cycle, including more total rainfall and more frequent high intensity rainfall events. Rainfall amounts and intensities increased on average in the United States during the 20th century and, according to climate change models, they are expected to continue to increase during the 21st century. These rainfall changes, along with expected changes in temperature, solar radiation, and atmospheric CO2 concentrations, will have significant impacts on soil erosion rates. The processes involved in the impact of climate change on soil erosion by water are complex, involving changes in rainfall amounts and intensities, number of days of precipitation, ratio of rain to snow, plant biomass production, plant residue decomposition rates, soil microbial activity, evapo-transpiration rates, and shifts in land use necessary to accommodate a new climatic regime. WEPPCAT is a web-based erosion simulation tool that allows for the assessment of changes in erosion rates as a consequence of user-defined climate change scenarios. This tool is based on the USDA-ARS Water Erosion Prediction Project (WEPP) erosion model. It has the capability of taking into account all of the erosion-affecting processes listed above.
\nThis applications has been packaged into a virtual machine. Please fill out the form below to download the application.
Resource Title: WEPPCAT.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=WEPPCAT&modecode=20-22-10-00
This applications has been packaged into a virtual machine. Please fill out the form below to download the application.
Global warming is expected to lead to a more vigorous hydrological cycle, including more total rainfall and more frequent high intensity rainfall events. Rainfall amounts and intensities increased on average in the United States during the 20th century and, according to climate change models, they are expected to continue to increase during the 21st century. These rainfall changes, along with expected changes in temperature, solar radiation, and atmospheric CO2 concentrations, will have significant impacts on soil erosion rates. The processes involved in the impact of climate change on soil erosion by water are complex, involving changes in rainfall amounts and intensities, number of days of precipitation, ratio of rain to snow, plant biomass production, plant residue decomposition rates, soil microbial activity, evapo-transpiration rates, and shifts in land use necessary to accommodate a new climatic regime. WEPPCAT is a web-based erosion simulation tool that allows for the assessment of changes in erosion rates as a consequence of user-defined climate change scenarios. This tool is based on the USDA-ARS Water Erosion Prediction Project (WEPP) erosion model. It has the capability of taking into account all of the erosion-affecting processes listed above.
\nThis applications has been packaged into a virtual machine. Please fill out the form below to download the application.
Resource Title: WEPPCAT.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=WEPPCAT&modecode=20-22-10-00
This applications has been packaged into a virtual machine. Please fill out the form below to download the application.
West Texas Mesonet Agro-Climate Monitor shows the precipitation, temperature, weather forecast for mesonet stations in West Texas. Although dependent on rainfall and other climate factors to produce crops, West Texas crop consultants, extension agents, and agricultural producers have few tools that allow them to track the current growing season\u2019s climate conditions and determine how current conditions compare with those of past years. The West Texas Mesonet Agro-Climate Monitor (ACM), a JavaScript web application based on daily data from Texas Tech University\u2019s mesonet weather station network, was designed to meet this need. By displaying continuously updated information on variables such as soil temperature, cumulative growing degree days (GDD), cumulative precipitation, and first freeze dates, the ACM allows producers to monitor planting conditions, track crop development, and compare current conditions with those during the previous 10 yr\u2019s growing seasons. In illustrating how mesonet data might be used as an operational climate data resource, the ACM might also serve as a conceptual model for other high resolution climate tools that estimate measures of current climate using continuously updated daily data sets.
Resource Title: West Texas Mesonet Agro-Climate Monitor.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=484&modecode=30-96-05-00
download page
West Texas Mesonet Agro-Climate Monitor shows the precipitation, temperature, weather forecast for mesonet stations in West Texas. Although dependent on rainfall and other climate factors to produce crops, West Texas crop consultants, extension agents, and agricultural producers have few tools that allow them to track the current growing season\u2019s climate conditions and determine how current conditions compare with those of past years. The West Texas Mesonet Agro-Climate Monitor (ACM), a JavaScript web application based on daily data from Texas Tech University\u2019s mesonet weather station network, was designed to meet this need. By displaying continuously updated information on variables such as soil temperature, cumulative growing degree days (GDD), cumulative precipitation, and first freeze dates, the ACM allows producers to monitor planting conditions, track crop development, and compare current conditions with those during the previous 10 yr\u2019s growing seasons. In illustrating how mesonet data might be used as an operational climate data resource, the ACM might also serve as a conceptual model for other high resolution climate tools that estimate measures of current climate using continuously updated daily data sets.
Resource Title: West Texas Mesonet Agro-Climate Monitor.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=484&modecode=30-96-05-00
download page
The Snowmelt-Runoff Model (WinSRM) is designed to simulate and forecast daily streamflow in mountain basins where snowmelt is a major runoff factor. The Snowmelt Runoff Model (SRM) is a simple degree-day model that requires remote sensing input in the form of basin or zonal snow cover extent. The model has been tested successfully on over 60 basins worldwide in the simulation and forecast modes. Model variables are derived from actual observations of temperature, precipitation, and snow covered area. Model parameters can either be derived from measurements or estimated by hydrological judgement taking into account the basin characteristics, physical laws, and theoretical or empirical relationships. To facilitate use of SRM, a microcomputer version of the program has been developed for IBM compatible personal computers. The program itself features user-oriented input and multiple self-help screens which allow the user to select the kind of data input employed and the output products desired.
Resource Title: Snowmelt Runoff Model for Windows (WinSRM).
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=7&modecode=80-42-05-10
download page
The Snowmelt-Runoff Model (WinSRM) is designed to simulate and forecast daily streamflow in mountain basins where snowmelt is a major runoff factor. The Snowmelt Runoff Model (SRM) is a simple degree-day model that requires remote sensing input in the form of basin or zonal snow cover extent. The model has been tested successfully on over 60 basins worldwide in the simulation and forecast modes. Model variables are derived from actual observations of temperature, precipitation, and snow covered area. Model parameters can either be derived from measurements or estimated by hydrological judgement taking into account the basin characteristics, physical laws, and theoretical or empirical relationships. To facilitate use of SRM, a microcomputer version of the program has been developed for IBM compatible personal computers. The program itself features user-oriented input and multiple self-help screens which allow the user to select the kind of data input employed and the output products desired.
Resource Title: Snowmelt Runoff Model for Windows (WinSRM).
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=7&modecode=80-42-05-10
download page
The purpose of this tool is to estimate daily maximum and minimum air temperatures for a yearly cycle at any location on the globe. Global TempSIM predicts the daily average air temperature based upon 30-yr (1961 \u2022 1990) temperature records that were compiled and interpolated by Legates and Willmott (1990a and 1990b) with further improvements by Willmott and Matsuura (1995) (data located at http://climate.geog.udel.edu/~climate/).
Resource Title: Global TempSIM - Version 1.0.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=158&modecode=50-60-05-00
download page
The purpose of this tool is to estimate daily maximum and minimum air temperatures for a yearly cycle at any location on the globe. Global TempSIM predicts the daily average air temperature based upon 30-yr (1961 \u2022 1990) temperature records that were compiled and interpolated by Legates and Willmott (1990a and 1990b) with further improvements by Willmott and Matsuura (1995) (data located at http://climate.geog.udel.edu/~climate/).
Resource Title: Global TempSIM - Version 1.0.
File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=158&modecode=50-60-05-00
download page