EcoService Models Library (ESML)
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Compare EMs
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.
EM Identity and Description
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EM ID
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EM-97 | EM-598 | EM-617 | EM-937 |
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EM Short Name
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AnnAGNPS, Kaskaskia River watershed, IL, USA | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | RBI Spatial Analysis Method | EPA national stormwater calculator tool |
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EM Full Name
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AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | DeNitrification-DeComposition simulation of N2O flux Ireland | Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method | Environmental Protection Agency National stormwater calculator tool |
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EM Source or Collection
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US EPA | None | None | US EPA |
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EM Source Document ID
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137 | 358 | 367 |
428 ?Comment:This is a tool available on the web for downloading to personal computers. A manual is also available for further documentation of the tool. |
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Document Author
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Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Bousquin, J., Mazzotta M., and W. Berry | Rossman, L.A., Bernagros, J.T., Barr, C.M., and M.A. Simon |
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Document Year
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2011 | 2010 | 2017 | 2022 |
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Document Title
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AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual. | EPA National Stormwater Calculator Web App users guide-Version 3.4.0. |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published EPA report | Published EPA report |
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EM ID
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EM-97 | EM-598 | EM-617 | EM-937 |
| https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | http://www.dndc.sr.unh.edu | Not applicable | https://www.epa.gov/water-research/national-stormwatercalculator | |
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Contact Name
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Yongping Yuan | M. Abdalla | Justin Bousquin | Lewis Rossman |
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Contact Address
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U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561 | Center for environmental solutions and emergency response, Cincinnati, Ohio |
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Contact Email
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yuan.yongping@epa.gov | abdallm@tcd.ie | bousquin.justin@epa.gov | n.a. |
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EM ID
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EM-97 | EM-598 | EM-617 | EM-937 |
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Summary Description
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AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. | AUTHOR DESCRIPTION: "The Rapid Benefits Indicators (RBI) approach consists of five steps and is outlined in Assessing the Benefits of Wetland Restoration – A Rapid Benefits Indicators Approach for Decision Makers, hereafter referred to as the “guide.” The guide presents the assessment approach, detailing each step of the indicator development process and providing an example application in the “Step in Action” pages. The spatial analysis toolset is intended to be used to analyze existing spatial information to produce metrics for many of the indicators developed in that guide. This spatial analysis toolset manual gives directions on the mechanics of the tool and its data requirements, but does not detail the reasoning behind the indicators and how to use results of the assessment; this information is found in the guide. " | "Abstract: EPA’s National Stormwater Calculator (SWC) is a software application tool that estimates the annual amount of rainwater and frequency of runoff from a specific site using green infrastructure as low impact development controls. The SWC is designed for use by anyone interested in reducing runoff from a property, including site developers, landscape architects, urban planners, and homeowners. This User’s guide contains information on the SWC web application. SWC Version 3.4 contains has updated historical meteorological data (from 1970 - 2006 to 1990 - 2019), updated Bureau of Labor Statistics Cost Data (from 2018 to 2020), and the 5.1.015 Stormwater Management Model (SWMM) engine (from 5.1.007). Evaporation was calculated by the Hargreaves method (EPA, 2015), based on historical or future daily temperature data." |
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Specific Policy or Decision Context Cited
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Not reported | climate change | None identified | None given |
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Biophysical Context
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Upper Mississipi River basin, elevation 142-194m, | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | wetlands | Sites up to 12 acres |
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EM Scenario Drivers
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Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | fertilization | N/A | Climate change scenarios |
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EM ID
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EM-97 | EM-598 | EM-617 | EM-937 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method Only | Method Only |
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New or Pre-existing EM?
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New or revised model | Application of existing 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-97 | EM-598 | EM-617 | EM-937 |
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Document ID for related EM
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Doc-142 | None | None | None |
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EM ID for related EM
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None | EM-593 | None | None |
EM Modeling Approach
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EM ID
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EM-97 | EM-598 | EM-617 | EM-937 |
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EM Temporal Extent
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1980-2006 | 1961-1990 | Not applicable | Not applicable |
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EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | both | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | discrete | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Day | Not applicable | Not applicable |
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EM ID
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EM-97 | EM-598 | EM-617 | EM-937 |
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Bounding Type
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Watershed/Catchment/HUC | Point or points | Not applicable | Not applicable |
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Spatial Extent Name
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East Fork Kaskaskia River watershed basin | Oak Park Research centre | Not applicable | Not applicable |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | 1-10 ha | Not applicable | Not applicable |
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EM ID
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EM-97 | EM-598 | EM-617 | EM-937 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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length, for linear feature (e.g., stream mile) | Not applicable | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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1 km^2 | Not applicable | Not reported | Not applicable |
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EM ID
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EM-97 | EM-598 | EM-617 | EM-937 |
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EM Computational Approach
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Numeric | Numeric | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-97 | EM-598 | EM-617 | EM-937 |
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Model Calibration Reported?
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No | Yes | Not applicable | Not applicable |
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Model Goodness of Fit Reported?
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No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable | Not applicable |
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Goodness of Fit (metric| value | unit)
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None |
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None | None |
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Model Operational Validation Reported?
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Yes | Yes | Not applicable | Not applicable |
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Model Uncertainty Analysis Reported?
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Yes | No | Not applicable | Not applicable |
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Model Sensitivity Analysis Reported?
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Unclear | No | Not applicable | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-97 | EM-598 | EM-617 | EM-937 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-97 | EM-598 | EM-617 | EM-937 |
| None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-97 | EM-598 | EM-617 | EM-937 |
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Centroid Latitude
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38.69 | 52.86 | Not applicable | Not applicable |
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Centroid Longitude
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-89.1 | 6.54 | Not applicable | Not applicable |
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Centroid Datum
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WGS84 | None provided | Not applicable | Not applicable |
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Centroid Coordinates Status
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Provided | Provided | Not applicable | Not applicable |
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EM ID
em.detail.idHelp
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EM-97 | EM-598 | EM-617 | EM-937 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Agroecosystems | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Row crop agriculture in Kaskaskia river basin | farm pasture | Restored wetlands | Terrrestrial landcover |
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EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
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EM-97 | EM-598 | EM-617 | EM-937 |
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-97 | EM-598 | EM-617 | EM-937 |
| None Available | None Available | None Available | 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)
| EM-97 | EM-598 | EM-617 | EM-937 |
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None |
<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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
| EM-97 | EM-598 | EM-617 | EM-937 |
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