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-97 |
EM-122 ![]() |
EM-701 | EM-940 |
EM Short Name
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AnnAGNPS, Kaskaskia River watershed, IL, USA | Land-use change and crop-based production, Europe | Blue-winged Teal recruits, CREP wetlands, IA, USA | OpenNSPECT v. 1.1, California, U.S. |
EM Full Name
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AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | Land-use change effects on crop-based production, Europe | Blue-winged Teal duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | OpenNSPECT v. 1.1, California, U.S. |
EM Source or Collection
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US EPA | EU Biodiversity Action 5 | None | None |
EM Source Document ID
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137 | 228 |
372 ?Comment:Document 373 is a secondary source for this EM. |
433 ?Comment:Additional source for this EM: NOAA, 2012. National Oceanic and Atmospheric Administration. Technical Guide for OpenNSPECT, Version 1.1, p. 44. http://www.csc.noaa.gov/digitalcoast/tools/opennspect. |
Document Author
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Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Haines-Young, R., Potschin, M. and Kienast, F. | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Morrison, K. D. and C. A. Kolden |
Document Year
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2011 | 2012 | 2010 | 2015 |
Document Title
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AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Modeling the impacts of wildfire on runoff and pollutant transport from coastal watersheds to the nearshore environment |
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 report | Published journal manuscript |
EM ID
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EM-97 |
EM-122 ![]() |
EM-701 | EM-940 |
https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | Not applicable | Not applicable | https://coast.noaa.gov/digitalcoast/tools/opennspect.html | |
Contact Name
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Yongping Yuan | Marion Potschin | David Otis | Crystal A. Kolden |
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 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Not reported |
Contact Email
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yuan.yongping@epa.gov | marion.potschin@nottingham.ac.uk | dotis@iastate.edu | ckolden@uidaho. Edu |
EM ID
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EM-97 |
EM-122 ![]() |
EM-701 | EM-940 |
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" | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Crop-based production); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: "The analysis for “Crop-based production” maps all the areas that are important for food crops produced through commercial agriculture….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." | ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | ABSTRACT: "Wildfire is a common disturbance that can significantly alter vegetation in watersheds and affect the rate of sediment and nutrient transport to adjacent nearshore oceanic environments. Changes in runoff resulting from heterogeneous wildfire effects are not well-understood due to both limitations in the field measurement of runoff and temporally-limited spatial data available to parameterize runoff models. We apply replicable, scalable methods for modeling wildfire impacts on sediment and nonpoint source pollutant export into the nearshore environment, and assess relationships between wildfire severity and runoff. Nonpoint source pollutants were modeled using a GIS-based empirical deterministic model parameterized with multi-year land cover data to quantify fire-induced increases in transport to the nearshore environment. Results indicate post-fire concentration increases in phosphorus by 161 percent, sediments by 350 percent and total suspended solids (TSS) by 53 percent above pre-fire years. Higher wildfire severity was associated with the greater increase in exports of pollutants and sediment to the nearshore environment, primarily resulting from the conversion of forest and shrubland to grassland. This suggests that increasing wildfire severity with climate change will increase potential negative impacts to adjacent marine ecosystems. The approach used is replicable and can be utilized to assess the effects of other types of land cover change at landscape scales. It also provides a planning and prioritization framework for management activities associated with wildfire, including suppression, thinning, and post-fire rehabilitation, allowing for quantification of potential negative impacts to the nearshore environment in coastal basins." |
Specific Policy or Decision Context Cited
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Not reported | None identified | None identified | None identified |
Biophysical Context
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Upper Mississipi River basin, elevation 142-194m, | No additional description provided | Prairie Pothole Region of Iowa | Central California coast includes twelve adjacent watersheds covering 87,638 ha and rises steeply from sea level to just below 1800 m within a few km from the coast, and experiences a Mediterranean climate, with fire season typically lasting from June to November. Precipitation is dependent on elevation ranging from 65 cm near the coast to over 130 cm at ridge top. Three ecological zones occur within the study area. These zones are comprised of grasslands, coastal sage scrub, chaparral, oak forests, mixed broadleaf evergreen forest, and coniferous forests. |
EM Scenario Drivers
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Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use changes (2000-2030) | No scenarios presented | No scenarios presented |
EM ID
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EM-97 |
EM-122 ![]() |
EM-701 | EM-940 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised 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-97 |
EM-122 ![]() |
EM-701 | EM-940 |
Document ID for related EM
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Doc-142 | Doc-238 | Doc-239 | Doc-240 | Doc-241 | Doc-242 | Doc-228 | Doc-372 | Doc-373 | Doc-431 |
EM ID for related EM
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None | EM-123 | EM-124 | EM-125 | EM-162 | EM-164 | EM-165 | EM-166 | EM-170 | EM-171 | EM-99 | EM-119 | EM-120 | EM-121 | EM-705 | EM-704 | EM-703 | EM-702 | EM-700 | EM-632 | EM-938 |
EM Modeling Approach
EM ID
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EM-97 |
EM-122 ![]() |
EM-701 | EM-940 |
EM Temporal Extent
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1980-2006 | 1990-2030 | 1987-2007 | 2005-2008 |
EM Time Dependence
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time-stationary | time-dependent | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | future time | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | discrete | Not applicable | Not applicable |
EM Temporal Grain Size Value
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Not applicable | 6, 10, and 30 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Year | Not applicable | Not applicable |
EM ID
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EM-97 |
EM-122 ![]() |
EM-701 | EM-940 |
Bounding Type
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Watershed/Catchment/HUC | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Watershed/Catchment/HUC |
Spatial Extent Name
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East Fork Kaskaskia River watershed basin | The EU-25 plus Switzerland and Norway | CREP (Conservation Reserve Enhancement Program | Big Sur region, central California |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 | 100-1000 km^2 |
EM ID
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EM-97 |
EM-122 ![]() |
EM-701 | EM-940 |
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) |
Spatial Grain Type
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length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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1 km^2 | 1 km x 1 km | multiple, individual, irregular sites | irregular |
EM ID
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EM-97 |
EM-122 ![]() |
EM-701 | EM-940 |
EM Computational Approach
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Numeric | Logic- or rule-based | Analytic | Analytic |
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-97 |
EM-122 ![]() |
EM-701 | EM-940 |
Model Calibration Reported?
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No | No | Unclear | 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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Yes | No | No | No |
Model Uncertainty Analysis Reported?
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Yes | No | No | No |
Model Sensitivity Analysis Reported?
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Unclear | No | No | No |
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-122 ![]() |
EM-701 | EM-940 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-97 |
EM-122 ![]() |
EM-701 | EM-940 |
None | None | None |
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Centroid Lat/Long (Decimal Degree)
EM ID
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EM-97 |
EM-122 ![]() |
EM-701 | EM-940 |
Centroid Latitude
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38.69 | 50.53 | 42.62 | 35.96 |
Centroid Longitude
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-89.1 | 7.6 | -93.84 | -121.43 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated |
EM ID
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EM-97 |
EM-122 ![]() |
EM-701 | EM-940 |
EM Environmental Sub-Class
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Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Agroecosystems | Grasslands | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Row crop agriculture in Kaskaskia river basin | Not applicable | Wetlands buffered by grassland within agroecosystems | Coastal watersheds |
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 corresponds to 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-97 |
EM-122 ![]() |
EM-701 | EM-940 |
EM Organismal Scale
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Not applicable | Not applicable | Individual or population, within a species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-97 |
EM-122 ![]() |
EM-701 | EM-940 |
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-97 |
EM-122 ![]() |
EM-701 | EM-940 |
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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)
EM-97 |
EM-122 ![]() |
EM-701 | EM-940 |
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None |