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
EM ID
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EM-92 | EM-97 | EM-465 | EM-493 |
EM Short Name
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Runoff potential of pesticides, Europe | AnnAGNPS, Kaskaskia River watershed, IL, USA | Pharmaceutical product potential, St. Croix, USVI | EnviroAtlas-Carbon sequestered by trees |
EM Full Name
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Runoff potential of pesticides, Europe | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | Relative pharmaceutical product potential (on reef), St. Croix, USVI | US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA |
EM Source or Collection
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None | US EPA | US EPA | US EPA | EnviroAtlas | i-Tree |
EM Source Document ID
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254 | 137 | 335 |
223 ?Comment:Additional source: I-tree Eco (doc# 345). |
Document Author
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Schriever, C. A., and Liess, M. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | US EPA Office of Research and Development - National Exposure Research Laboratory |
Document Year
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2007 | 2011 | 2014 | 2013 |
Document Title
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Mapping ecological risk of agricultural pesticide runoff | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | EnviroAtlas - Featured Community |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website |
EM ID
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EM-92 | EM-97 | EM-465 | EM-493 |
Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | Not applicable | https://www.epa.gov/enviroatlas | |
Contact Name
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Carola Alexandra Schriever | Yongping Yuan | Susan H. Yee | EnviroAtlas Team |
Contact Address
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Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Not reported |
Contact Email
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carola.schriever@ufz.de | yuan.yongping@epa.gov | yee.susan@epa.gov | enviroatlas@epa.gov |
EM ID
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EM-92 | EM-97 | EM-465 | EM-493 |
Summary Description
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ABSTRACT: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception." | 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" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…When data on sponge diversity is unavailable, benthic habitat coverages may be used to estimate relative magnitudes of sponge diversity and abundance as an indicator of potential pharmaceutical production (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to potential pharmaceutical production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Pharmaceutical product potential = ΣiciMi where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians), and Mi is the relative magnitude of sponge diversity associated with each habitat." | The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group." |
Specific Policy or Decision Context Cited
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European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | Not reported | None identified | None identified |
Biophysical Context
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Not applicable | Upper Mississipi River basin, elevation 142-194m, | No additional description provided | No additional description provided |
EM Scenario Drivers
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No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | No scenarios presented | No scenarios presented |
EM ID
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EM-92 | EM-97 | EM-465 | EM-493 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-92 | EM-97 | EM-465 | EM-493 |
Document ID for related EM
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Doc-255 | Doc-256 | Doc-257 | Doc-142 | None | Doc-345 |
EM ID for related EM
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None | None | None | None |
EM Modeling Approach
EM ID
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EM-92 | EM-97 | EM-465 | EM-493 |
EM Temporal Extent
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2000 | 1980-2006 | 2006-2007, 2010 | 2010-2013 |
EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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discrete | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Day | Not applicable | Not applicable | Not applicable |
EM ID
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EM-92 | EM-97 | EM-465 | EM-493 |
Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical |
Spatial Extent Name
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EU-15 | East Fork Kaskaskia River watershed basin | Coastal zone surrounding St. Croix | Durham NC and vicinity |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 |
EM ID
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EM-92 | EM-97 | EM-465 | EM-493 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Census block groups |
Spatial Grain Type
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area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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10 km x 10 km | 1 km^2 | 10 m x 10 m | irregular |
EM ID
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EM-92 | EM-97 | EM-465 | EM-493 |
EM Computational Approach
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Analytic | Numeric | Analytic | Numeric |
EM Determinism
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deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-92 | EM-97 | EM-465 | EM-493 |
Model Calibration Reported?
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No | No | Yes | No |
Model Goodness of Fit Reported?
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No | No | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None | None |
Model Operational Validation Reported?
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No | Yes | Yes | No |
Model Uncertainty Analysis Reported?
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Yes | Yes | No | No |
Model Sensitivity Analysis Reported?
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Yes | Unclear | No | No |
Model Sensitivity Analysis Include Interactions?
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No | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-92 | EM-97 | EM-465 | EM-493 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-92 | EM-97 | EM-465 | EM-493 |
None | None |
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None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-92 | EM-97 | EM-465 | EM-493 |
Centroid Latitude
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50.01 | 38.69 | 17.73 | 35.99 |
Centroid Longitude
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4.67 | -89.1 | -64.77 | -78.96 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | None provided |
Centroid Coordinates Status
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Estimated | Provided | Estimated | Estimated |
EM ID
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EM-92 | EM-97 | EM-465 | EM-493 |
EM Environmental Sub-Class
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Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Agroecosystems | Near Coastal Marine and Estuarine | Created Greenspace | Atmosphere |
Specific Environment Type
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Arable lands in near-stream environments | Row crop agriculture in Kaskaskia river basin | Coral reefs | Urban and vicinity |
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 is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-92 | EM-97 | EM-465 | EM-493 |
EM Organismal Scale
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Not applicable | Not applicable | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-92 | EM-97 | EM-465 | EM-493 |
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)
EM-92 | EM-97 | EM-465 | EM-493 |
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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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None |
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None |