EcoService Models Library (ESML)
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Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-337 | EM-627 |
EM-660 
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EM-841 | EM-968 |
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EM Short Name
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Rate of Fire Spread | N removal by wetland restoration, Midwest, USA | RUM: Valuing fishing quality, Michigan, USA | Brown-headed cowbird abundance, Piedmont, USA | EPA Stormwater Manamgement Model |
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EM Full Name
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Rate of Fire Spread | Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Brown-headed cowbird abundance, Piedmont ecoregion, USA | Storm Water Management Model User's Manual Version 5.2 |
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EM Source or Collection
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None | None | None | None | US EPA |
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EM Source Document ID
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306 |
370 ?Comment:Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006. |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
405 | 452 |
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Document Author
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Rothermel, Richard C. | Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Riffel, S., Scognamillo, D., and L. W. Burger | Rossman, L. A., M., Simon |
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Document Year
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1972 | 2006 | 2014 | 2008 | 2022 |
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Document Title
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A Mathematical model for predicting fire spread in wildland fuels | Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins | Valuing recreational fishing quality at rivers and streams | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Storm Water Management Model User's Manual Version 5.2 |
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Document Status
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Documented, not peer reviewed | Neither peer reviewed nor published (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) |
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Comments on Status
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Published USDA Forest Service report | Published report | Published journal manuscript | Published journal manuscript | Published EPA report |
Software and Access
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EM ID
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EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
| http://firelab.org/project/farsite | Not applicable | Not applicable | Not applicable | https://www.epa.gov/water-research/storm-water-management-model-swmm | |
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Contact Name
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Charles McHugh | William G. Crumpton | Richard Melstrom | Sam Riffell | David Burden |
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Contact Address
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RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011 | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | U.S. EPA Research Center for Environmental Solutions and Emergency Response (CESER) Mail Drop: 314 P.O. Box #1198 Ada, OK 74821-1198 |
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Contact Email
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cmchugh@fs.fed.us | crumpton@iastate.edu | melstrom@okstate.edu | sriffell@cfr.msstate.edu | burden.david@epa.gov |
EM Description
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EM ID
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EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
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Summary Description
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ABSTRACT: "The development of a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models. The model was developed for and is now being used as a basis for appraising fire spread and intensity in the National Fire Danger Rating System. The initial work was done using fuel arrays composed of uniform size particles. Three fuel sizes were tested over a wide range of bulk densities. These were 0.026-inch-square cut excelsior, 114-inch sticks, and 112-inch sticks. The problem of mixed fuel sizes was then resolved by weighting the various particle sizes that compose actual fuel arrays by either surface area or loading, depending upon the feature of the fire being predicted. The model is complete in the sense that no prior knowledge of a fuel's burning characteristics is required. All that is necessary are inputs describing the physical and chemical makeup of the fuel and the environmental conditions in which it is expected to burn. Inputs include fuel loading, fuel depth, fuel particle surface-area-to-volume ratio, fuel particle heat content, fuel particle moisture and mineral content, and the moisture content at which extinction can be expected. Environmental inputs are mean wind velocity and slope of terrain. For heterogeneous mixtures, the fuel properties are entered for each particle size. The model as originally conceived was for dead fuels in a uniform stratum contiguous to the ground, such as litter or grass. It has been found to be useful, however, for fuels ranging from pine needle litter to heavy logging slash and for California brush fields." **FARSITE4 will no longer be supported or available for download or further supported. FlamMap6 now includes FARSITE.** | ABSTRACT: "The primary objective of this project was to estimate the nitrate reduction that could be achieved using restored wetlands as nitrogen sinks in tile-drained regions of the upper Mississippi River (UMR) and Ohio River basins. This report provides an assessment of nitrate concentrations and loads across the UMR and Ohio River basins and the mass reduction of nitrate loading that could be achieved using wetlands to intercept nonpoint source nitrate loads. Nitrate concentration and stream discharge data were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations and to develop a model of FWA nitrate concentration based on land use. Land use accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. Annual water yield for grid cells was estimated by interpolating over selected USGS monitoring station water yields across the UMR and Ohio River basins. For 1990 to 1999, mass nitrate export from each grid area was estimated as the product of the FWA nitrate concentration, water yield and grid area. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Nitrate reduction was estimated using a temperature dependent, area-based, first order model. Model inputs included local temperature from the National Climatic Data Center and water yield estimated from USGS stream flow data. Results were used to develop a nonlinear model for percent nitrate removal as a function of hydraulic loading rate (HLR) and temperature. Mass nitrate removal for potential wetland restorations distributed across the UMR and Ohio River basin was estimated based on the expected mass load and the predicted percent removal. Similar functions explained most of the variability in per cent and mass removal reported for field scale experimental wetlands in the UMR and Ohio River basins. Results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas." AUTHOR'S DESCRIPTION: "Percent nitrate removal was estimated based on HLR functions (Figure 19) spanning a 3 fold range in loss rate coefficient (Crumpton 2001) and encompassing the observed performance reported for wetlands in the UMR and Ohio River basins (Table 2, Figure 7). The nitrate load was multiplied by the expected percent nitrate removal to estimate the mass removal. This procedure was repeated for each restoration scenario each year in the simulation period (1990 to 1999)… for a scenario with a wetland/watershed area ratio of 2%. These results are based on the assumption that the FWA nitrate concentration versus percent row crop r | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " |
EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. Running under Windows, SWMM 5 provides an integrated environment for editing study area input data, running hydrologic, hydraulic and water quality simulations, and viewing the results in a variety of formats. These include color coded drainage area and conveyance system maps, time series graphs and tables, profile plots, and statistical frequency analyses. This user’s manual describes in detail how to run SWMM 5.2. It includes instructions on how to build a drainage system model, how to set various simulation options, and how to view results in a variety of formats. It also describes the different types of files used by SWMM and provides useful tables of parameter values. Detailed descriptions of the theory behind SWMM 5 and the numerical methods it employs can be found in a separate set of reference manuals. ?Comment:The variables used for this ESML entry were derived from the quick tutorial section of the SWMM manual. |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None reported | NA |
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Biophysical Context
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Not applicable | No additional description provided | stream and river reaches of Michigan | Conservation Reserve Program lands left to go fallow | NA |
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EM Scenario Drivers
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No scenarios presented | More conservative, average and less conservative nitrate loss rate | targeted sport fish biomass | N/A | NA |
EM Relationship to Other EMs or Applications
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EM ID
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EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
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Method Only, Application of Method or Model Run
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Method Only | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
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Document ID for related EM
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None | None | None | Doc-405 | None |
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EM ID for related EM
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None | None | None | EM-831 | EM-838 | EM-839 | EM-842 | EM-843 | EM-844 | EM-845 | EM-846 | EM-847 | EM-971 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
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EM Temporal Extent
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Not applicable | 1973-1999 | 2008-2010 | 2008 | Not applicable |
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EM Time Dependence
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Not applicable | time-dependent | time-stationary | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable | Not applicable | both |
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EM Time Continuity
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Not applicable | discrete | Not applicable | Not applicable | continuous |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Day | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
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Bounding Type
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Not applicable | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | No location (no locational reference given) |
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Spatial Extent Name
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Not applicable | Upper Mississippi River and Ohio River basins | HUCS in Michigan | Piedmont Ecoregion | Not applicable |
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Spatial Extent Area (Magnitude)
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Not applicable | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | Not applicable |
Spatial Distribution of Computations
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EM ID
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EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
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EM Spatial Distribution
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Not applicable | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
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Not applicable | 1 km2 | reach in HUC | Not applicable | mm |
EM Structure and Computation Approach
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EM ID
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EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
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EM Computational Approach
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Analytic | Numeric | Numeric | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
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Model Calibration Reported?
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Not applicable | No | No | Yes | Not applicable |
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Model Goodness of Fit Reported?
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Not applicable | No | Yes | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None |
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None | None |
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Model Operational Validation Reported?
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No |
No ?Comment:However, agreement of submodel and intermediate components; annual discharge (R2=0.79), and nitrate-N load (R2=0.74), based on GIS land use were determined in comparison with USGS NASQAN data. |
No | No | Not applicable |
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Model Uncertainty Analysis Reported?
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Not applicable | No | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
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Not applicable | No | No | Yes | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Unclear | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
| None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
| None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
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Centroid Latitude
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-9999 | 40.6 | 45.12 | 36.23 | Not applicable |
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Centroid Longitude
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-9999 | -88.4 | 85.18 | -81.9 | Not applicable |
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Centroid Datum
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Not applicable | WGS84 | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
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Not applicable | Estimated | Estimated | Estimated | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Agroecosystems | Rivers and Streams | Grasslands | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Not applicable | Agroecosystems and associated drainage and wetlands | stream reaches | grasslands | User-defined catchments |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Other or unclear (comment) |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
| None Available | None Available |
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None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
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| EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
| None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-337 | EM-627 |
EM-660
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EM-841 | EM-968 |
| None |
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