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-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
EM Short Name
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i-Tree Eco: Carbon storage & sequestration, USA | EnviroAtlas - Natural biological nitrogen fixation | Pollination ES, Central French Alps | Stream nitrogen removal, Mississippi R. basin, USA | KINEROS2, River Ravna watershed, Bulgaria | Salmon habitat values, west coast of Canada | Esocid spawning, St. Louis River, MN/WI, USA | SWAT, Guanica Bay, Puerto Rico, USA | CRPI, St. Croix, USVI | InVEST fisheries, lobster, South Africa | N removal by wetland restoration, Midwest, USA | SolVES, Bridger-Teton NF, WY | EcoAIM v.1.0 APG, MD | Sedge Wren density, CREP, Iowa, USA | ESII Tool, Michigan, USA | WESP: Urban Stormwater Treatment, ID, USA | Arthropod flower preference, CA, USA | NBS benefits explorer |
EM Full Name
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i-Tree Eco carbon storage and sequestration (trees), USA | US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Pollination ecosystem service estimated from plant functional traits, Central French Alps | Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | KINEROS (Kinematic runoff and erosion model) v2, River Ravna watershed,Bulgaria | Value of habitat quality changes for salmon populations, South Thompson watershed, west coast of Canada | Esocid spawning, St. Louis River estuary, MN & WI, USA | SWAT (Soil and Water Assessment Tool) Guánica Bay, Puerto Rico, USA | CRPI (Coral Reef Protection Index, St. Croix, USVI | Integrated Valuation of Ecosystem Services and Trade-offs Fisheries, rock lobster, South Africa | Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA | SolVES, Social Values for Ecosystem Services, Bridger-Teton National Forest, WY | EcoAIM v.1.0, Aberdeen Proving Ground, MD | Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | ESII (Ecosystem Services Identification and Inventory) Tool, Michigan, USA | WESP: Urban Stormwater Treament, ID, USA | Arthropod flower type preference, California, USA | Benefit Accounting of Nature-Based Solutions for Watersheds: Guide |
EM Source or Collection
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i-Tree | USDA Forest Service | US EPA | EnviroAtlas | EU Biodiversity Action 5 | US EPA | EU Biodiversity Action 5 | None | US EPA | US EPA | US EPA | InVEST | None | None | None | None | None | None | None | None |
EM Source Document ID
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195 |
262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
260 | 52 |
248 ?Comment:Document 277 is also a source document for this EM |
286 | 332 | 334 | 335 |
349 ?Comment:Supplemented with the InVEST Users Guide fisheries. |
370 ?Comment:Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006. |
369 | 374 | 372 |
392 ?Comment:Document 391 is an additional source for this EM. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
399 | 471 |
Document Author
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Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E. | US EPA Office of Research and Development - National Exposure Research Laboratory | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Hill, B. and Bolgrien, D. | Nedkov, S., Burkhard, B. | Knowler, D.J., MacGregor, B.W., Bradford, M.J., Peterman, R.M | Ted R. Angradi, David W. Bolgrien, Jonathon J. Launspach, Brent J. Bellinger, Matthew A. Starry, Joel C. Hoffman, Mike E. Sierszen, Anett S. Trebitz, and Tom P. Hollenhorst | Hu, W. and Y. Yuan | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Ward, Michelle, Hugh Possingham, Johathan R. Rhodes, Peter Mumby | Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | Booth, P., Law, S. , Ma, J. Turnley, J., and J.W. Boyd | 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 | Guertin, F., K. Halsey, T. Polzin, M. Rogers, and B. Witt | Murphy, C. and T. Weekley | Lundin, O., Ward, K.L., and N.M. Williams | Brill, G., T. Shiao, C. Kammeyer, S. Diringer, K. Vigerstol, N. Ofosu-Amaah, M. Matosich, C. Müller-Zantop, W. Larson and T. Dekker |
Document Year
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2013 | 2013 | 2011 | 2011 | 2012 | 2003 | 2016 | 2013 | 2014 | 2018 | 2006 | 2014 | 2014 | 2010 | 2019 | 2012 | 2018 | 2022 |
Document Title
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Carbon storage and sequestration by trees in urban and community areas of the United States | EnviroAtlas - National | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Nitrogen removal by streams and rivers of the Upper Mississippi River basin | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | Valuing freshwater salmon habitat on the west coast of Canada | Mapping ecosystem service indicators of a Great Lakes estuarine Area of Concern | Evaluation of Soil Erosion and Sediment Yield for the Ridge Watersheds in the Guanica Bay Watershed, Puerto Rico, Using the SWAT Model | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Food, money and lobsters: Valuing ecosystem services to align environmental management with Sustainable Development Goals | Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Implementation of EcoAIM - A Multi-Objective Decision Support Tool for Ecosystem Services at Department of Defense Installations | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | From ash pond to riverside wetlands: Making the business case for engineered natural technologies | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Indentifying native plants for coordinated hanbitat manegement of arthroppod pollinators, herbivores and natural enemies | Benefit Accounting of Nature-Based Solutions for Watersheds: Guide |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Neither peer reviewed nor published (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | 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 on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published report | Published report | Published journal manuscript | Published report | Published journal manuscript | Published report |
EM ID
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EM-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
Not applicable | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | http://www.tucson.ars.ag.gov/agwa/ | Not applicable | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | Not applicable | https://www.esiitool.com/ | Not applicable | Not applicable | https://nbsbenefitsexplorer.net/tool | |
Contact Name
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David J. Nowak |
EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Sandra Lavorel | Brian Hill | David C. Goodrich | Duncan Knowler | Ted R. Angradi | Yongping Yuan | Susan H. Yee | Michelle Ward | William G. Crumpton | Benson Sherrouse | Pieter Booth | David Otis | Not reported | Chris Murphy | Ola Lundin | Gregg Brill |
Contact Address
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USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA | Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA | USDA - ARS Southwest Watershed Research Center, 2000 E. Allen Rd., Tucson, AZ 85719 | School of Resource and Environmental Management, Simon Fraser University, Burnaby, Canada BC V5H 1S6 | United States Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboraty, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804 USA | USEPA, ORD, NERL, Environmental sciences Division, Las Vegas, Nevada | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | ARC Centre of Excellence for Environmental Decisions, The University of Queensland, Brisbane, QLD 4072, Australia | Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011 | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | Exponent Inc., Bellevue WA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Not reported | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Ecology, Swedish Univ. of Agricultural Sciences, Uppsala, Sweden | Not reported |
Contact Email
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dnowak@fs.fed.us | enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | hill.brian@epa.gov | agwa@tucson.ars.ag.gov | djk@sfu.ca | angradi.theodore@epa.gov | Yuan.Yongping@epa.gov | yee.susan@epa.gov | m.ward@uq.edu.au | crumpton@iastate.edu | bcsherrouse@usgs.gov | pbooth@ramboll.com | dotis@iastate.edu | Not reported | chris.murphy@idfg.idaho.gov | ola.lundin@slu.se | Not reported |
EM ID
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EM-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
Summary Description
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ABSTRACT: "Carbon storage and sequestration by urban trees in the United States was quantified to assess the magnitude and role of urban forests in relation to climate change. Urban tree field data from 28 cities and 6 states were used to determine the average carbon density per unit of tree cover. These data were applied to statewide urban tree cover measurements to determine total urban forest carbon storage and annual sequestration by state and nationally. Urban whole tree carbon storage densities average 7.69 kg C m^2 of tree cover and sequestration densities average 0.28 kg C m^2 of tree cover per year. Total tree carbon storage in U.S. urban areas (c. 2005) is estimated at 643 million tonnes ($50.5 billion value; 95% CI = 597 million and 690 million tonnes) and annual sequestration is estimated at 25.6 million tonnes ($2.0 billion value; 95% CI = 23.7 million to 27.4 million tonnes)." | DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The pollination ecosystem service map was a simple sums of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to pollination ecosystem services are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | ABSTRACT: "We used stream chemistry and hydrogeomorphology data from 549 stream and 447 river sites to estimate NO3–N removal in the Upper Mississippi, Missouri, and Ohio Rivers. We used two N removal models to predict NO3–N input and removal. NO3–N input ranged from 0.01 to 338 kg/km*d in the Upper Mississippi River to 0.01–54 kg/ km*d in the Missouri River. Cumulative river network NO3–N input was 98700–101676 Mg/year in the Ohio River, 85,961–89,288 Mg/year in the Upper Mississippi River, and 59,463–61,541 Mg/year in the Missouri River. NO3–N output was highest in the Upper Mississippi River (0.01–329 kg/km*d ), followed by the Ohio and Missouri Rivers (0.01–236 kg/km*d ) sub-basins. Cumulative river network NO3–N output was 97,499 Mg/year for the Ohio River, 84,361 Mg/year for the Upper Mississippi River, and 59,200 Mg/year for the Missouri River. Proportional NO3–N removal (PNR) based on the two models ranged from 0.01 to 0.28. NO3–N removal was inversely correlated with stream order, and ranged from 0.01 to 8.57 kg/km*d in the Upper Mississippi River to 0.001–1.43 kg/km*d in the Missouri River. Cumulative river network NO3–N removal predicted by the two models was: Upper Mississippi River 4152 and 4152 Mg/year, Ohio River 3743 and 378 Mg/year, and Missouri River 2,277 and 197 Mg/year. PNR removal was negatively correlated with both stream order (r = −0.80–0.87) and the percent of the catchment in agriculture (r = −0.38–0.76)." | ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. The use of the catchment based hydrologic model KINEROS and the GIS AGWA tool provided data about peak rivers’ flows and the capability of different land cover types to “capture” and regulate some parts of the water." AUTHOR'S DESCRIPTION: "KINEROS is a distributed, physically based, event model describing the processes of interception, dynamic infiltration, surface runoff and erosion from watersheds characterized by predominantly overland flow. The watershed is conceptualized as a cascade and the channels, over which the flow is routed in a top–down approach, are using a finite difference solution of the one-dimensional kinematic wave equations (Semmens et al., 2005). Rainfall excess, which leads to runoff, is defined as the difference between precipitation amount and interception and infiltration depth. The rate at which infiltration occurs is not constant but depends on the rainfall rate and the accumulated infiltration amount, or the available moisture condition of the soil. The AGWA tool is a multipurpose hydrologic analysis system addressed to: (1) provide a simple, direct and repeatable method for hydrologic modeling; (2) use basic, attainable GIS data; (3) be compatible with other geospatial basin-based environmental analysis software; and (4) be useful for scenario development and alternative future simulation work at multiple scales (Miller et al., 2002). AGWA provides the functionality to conduct the processes of modeling and assessment for…KINEROS." | ABSTRACT: "In this paper, we present a framework for valuing benefits for fisheries from protecting areas from degradation, using the example of the Strait of Georgia coho salmon fishery in southern British Columbia, Canada. Our study improves upon previous methods used to value fish habitat in two major respects. First, we use a bioeconomic model of the coho fishery to derive estimates of value that are consistent with economic theory. Second, we estimate the value of changing the quality of fish habitat by using empirical analyses to link fish population dynamics with indices of land use in surrounding watersheds." | ABSTRACT: "Estuaries provide multiple ecosystem services from which humans benefit…We described an approach, with examples, for assessing how local-scale actions affect the extent and distribution of coastal ecosystem services, using the St. Louis River estuary (SLRE) of western Lake Superior as a case study. We based our approach on simple models applied to spatially explicity biophysical data that allows us to map the providing area of ecosystem services at high resolution (10-m^2 pixel) across aquatic and riparian habitats…Aspects of our approach can be adapted by communities for use in support of local decision-making." AUTHOR'S DESCRIPTION: "We derived the decision criteria used to map the IEGS habitat proxy of esocid spawning from habitat suitability information for two species that have similar but not identical spawning habitat and behavior." | AUTHOR'S DESCRIPTION: " SWAT is a physically-based continuous watershed simulation model that operates on a daily time step. It is designed for long-term simulations. The U.S. Department of Agriculture-Agriculture Research Station (USDA-ARS) Grassland, Soil and Water Research Laboratory in Temple, Texas created SWAT in the early 1990s. It has undergone continual review and expansion of capabilities since it was created (Arnold et al., 1998; Neitsch, et al., 2011a and b). This model has the ability to predict changes in water, sediment, nutrient and pesticide loads with respect to the different management conditions in watershed. Major components of the SWAT model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides and agricultural management practices (Neitsch et al., 2011b). SWAT subdivides a watershed into multiple sub-watersheds, and the subwatersheds are further divided into Hydrologic Response Units (HRUs) that consist of homogeneous land use, soils, slope, and management (Gassman et al., 2007; Neitsch, et al., 2011b; Williams et al., 2008). | 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...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events (UNEP-WCMC (United Nations Environment Programme, World Conservation Monitoring Centre), 2006; WRI (World Resources Institute), 2009), but is often quantified as wave energy attenuation, an intermediate service that contributes to shoreline protection by reducing rates of erosion or coastal inundation (Principeet al., 2012)...An alternative index has been developed specifically for coral reefs, the Coral Reef Protection Index (CRPI), that accounts for the continuity of the reef and distance from shore in addition to reef habitat type (Burke et al., 2008): CRPI = ((Reef type + Reef distribution + Reef distance)/10) x 4 where the scaled magnitude of coastal protection due to each factor ranges from 0 (no protection) to 4 (very high protection; Table 2)." | AUTHOR'S DESCRIPTION: "Here we develop a method for assessing future scenarios of environmental management change that improve coastal ecosystem services and thereby, support the success of the SDGs. We illustrate application of the method using a case study of South Africa’s West Coast Rock Lobster fishery within the Table Mountain National Park (TMNP) Marine Protected Area...We calculated the retrospective and current value of the West Coast Rock Lobster fishery using published and unpublished data from various sources and combined the market worth of landed lobster from recreational fishers, small-scale fisheries (SSF), large-scale fisheries (LSF) and poachers. Then using the InVEST tool, we combined data to build scenarios that describe possible futures for the West Coast Rock Lobster fishery (see Table 1). The first scenario, entitled ‘Business as Usual’ (BAU), takes the current situation and most up-to-date data to model the future if harvest continues at the existing rate. The second scenario is entitled ‘Redirect the Poachers’ (RP), which attempts to model implementation of strict management, whereby poaching is minimised from the Marine Protected Area and other economic and nutritional sources are made available through government initiatives. The third scenario, entitled ‘Large Scale Cutbacks’ (LSC), excludes large-scale fisheries from harvesting West Coast Rock Lobster within the TMNP Marine Protected Area." | 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: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | [ABSTRACT: "This report describes the demonstration of the EcoAIM decision support framework and GIS-based tool. EcoAIM identifies and quantifies the ecosystem services provided by the natural resources at the Aberdeen Proving Ground (APG). A structured stakeholder process determined the mission and non-mission priorities at the site, elicited the natural resource management decision process, identified the stakeholders and their roles, and determine the ecosystem services of priority that impact missions and vice versa. The EcoAIM tool was customized to quantify in a geospatial context, five ecosystem services – vista aesthetics, landscape aesthetics, recreational opportunities, habitat provisioning for biodiversity and nutrient sequestration. The demonstration included a Baseline conditions quantification of ecosystem services and the effects of a land use change in the Enhanced Use Lease parcel in cantonment area (Scenario 1). Biodiversity results ranged widely and average scores decreased by 10% after Scenario 1. Landscape aesthetics scores increased by 10% after Scenario 1. Final scores did not change for recreation or nutrient sequestration because scores were outside the boundaries of the baseline condition. User feedback after the demonstration indicated positive reviews of EcoAIM as being useful and usable for land use decisions and particularly for use as a communication tool. " | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. 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... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Sedge Wren (Cistothorus platensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SEWR density = 1-1/1+e^(-0.8015652 + 0.08500569 * grass400) *e^(-0.7982511 + 0.0285891 * bbspath + 0.0105094 *grass400) | ABSTRACT: "The 2015 announcement of The Dow Chemical Company's (Dow) Valuing Nature Goal, which aims to identify $1 billion in business value from projects that are better for nature, gives nature a spot at the project design table. To support this goal, Dow and The Nature Conservancy have extended their long-standing collaboration and are now working to develop a defensible methodology to support the implementation of the goal. This paper reviews the nature valuation methodology framework developed by the Collaboration in support of the goal. The nature valuation methodology is a three-step process that engages Dow project managers at multiple stages in the project design and capital allocation processes. The three-step process identifies projects that may have a large impact on nature and then promotes the use of ecosystem service tools, such as the Ecosystem Services Identification and Inventory Tool, to enhance the project design so that it better supports ecosystem health. After reviewing the nature valuation methodology, we describe the results from a case study of redevelopment plans for a 23-acre site adjacent to Dow's Michigan Operations plant along the Tittabawassee River." AUTHOR'S DESCRIPTION: "The ESII Tool measures the environmental impact or proposed land changes through eight specific ecosystem services: (i) water provisioning, (ii) air quality control (nitrogen and particulate removal), (iii) climate regulation (carbon uptake and localized air temperature regulation), (iv) erosion regulation, (v) water quality control (nitrogen and filtration), (vi) water temperature regulation, (vii) water quantity control, and (viii) aesthetics (noise and visual). The ESII Tool allows for direct comparison of the performance of these eight ecosystem services both across project sites and across project design proposals within a site." "The team was also asked to use an iterative design process using the ESII Tool to create alternative restoration scenarios…The project team developed three alternative restoration designs: i) standard brownfield restoration (i.e., cap and plant grass) on the ash pond and 4-D property (referred to as SBR); ii) ecological restoration (i.e., excavate ash and associated soil for secured disposal in approved landfill and restore historic forest, prairie, wetland) of the ash pond only, with SBR on the 4-D property (referred to as ER); and iii) ecological restoration on the ash pond and 4- D property (referred to as ER+)." | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT: " Plant species differed in attractiveness for each arthropod functional group. Floral area of the focal plant species positively affected honeybee, predator, and parasitic wasp attractiveness. Later bloom period was associated with lower numbers of parasitic wasps. Flower type (actinomorphic, composite, or zygomorphic) predicted attractiveness for honeybees, which preferred actinomorphic over composite flowers and for parasitic wasps, which preferred composite flowers over actinomorphic flowers. 4. Across plant species, herbivore, predator, and parasitic wasp abundances were positively correlated, and honeybee abundance correlated negatively to herbivore abundance. 5. Synthesis and applications. We use data from our common garden experiment to inform evidence-based selection of plants that support pollinators and natural enemies without enhancing potential pests. We recommend selecting plant species with a high floral area per ground area unit, as this metric predicts the abundances of several groups of beneficial arthropods. Multiple correlations between functionally important arthropod groups across plant species stress the importance of a multifunctional approach to arthropod habitat management. " Changes in arthropod abundance were estimated for flower type (entered as separate runs); Actinomorphic, Composite, Zygomorphic. 43 plant species evaluated included Amsinckia intermedia, Calandrinia menziesii, Nemophila maculata, Nemophila menziesii, Phacelia ciliata, Achillea millefolium, Collinsia heterophylla, Fagopyrum esculentum, Lasthenia fremontii, Lasthenia glabrata, Limnanthes alba, Lupinus microcarpus densiflorus, Lupinus succelentus, Phacelia californica, Phacelia campanularia, Phacelia tanacetifolia, Salvia columbariae, Sphaeralcea ambigua, Trifolium fucatum, Trifolium gracilentum, Antirrhinum conutum, Clarkia purpurea, Clarkia unguiculata, Clarkia williamsonii, Eriophyllum lanatum, Eschscholzia californica, Monardella villosa, Scrophularia californica, Asclepia eriocarpa, Asclepia fascicularis, Camissoniopsis Cheiranthifolia, Eriogonum fasciculatum, Gilia capitata, Grindelia camporum, Helianthus annuus, Lupinus formosus, Malacothrix saxatilis, Oenothera elata, Helianthus bolanderi, Helianthus californicus, Madia elegans, Trichostema lanceolatum, Heterotheca grandiflora." | Watersheds around the world are in peril and risk further decline from climate change and human impacts, like pollution, degrading landscapes, and unsustainable water use. These impacts can inhibit the ability of ecosystems to regulate water flows, sequester carbon to reduce atmospheric greenhouse gas levels, maintain biodiversity and healthy waterways, promote social well-being, offer economic opportunities, and sustain agricultural productivity. Climate change is exacerbating these impacts by shifting weather and precipitation patterns, degrading habitats, and increasing the recurrence and severity of natural disasters. Urgent action is needed to address these impacts by implementing nature-based solutions (NBS). NBS protect, sustainably manage, and restore natural or modified watersheds, to address societal challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits (IUCN, 2016). Investment in NBS offers a mechanism to restore degraded watersheds and protect intact ones, leading to improved water quality and quantity, improved carbon sequestration and increased biodiversity, among many other social and economic benefits. NBS also support climate mitigation and adaptation efforts and reduce the impacts from other shocks, such as floods, droughts, and extreme weather events. Implementing NBS can also help advance progress toward achieving the United Nations Sustainable Development Goals (SDGs), particularly SDG 2 (zero hunger), SDG 6 (water), SDG 11 (sustainable cities and communities), SDG 13 (climate action), and SDG 15 (life on land). NBS therefore support social, economic and environmental objectives, and may be particularly important in supporting vulnerable communities. |
Specific Policy or Decision Context Cited
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Not reported | None Identified | None identified | Not applicable | None identified | None identified | Federal delisting of an area of concern (AOC) | None Identified | None identified | Future rock lobster fisheries management | None identified | None | None reported | None identified | Use ESII to answer the following business decision question: how can Dow close the ash pond while enhancing ecosystem services to Dow and the community and creating local habitat, for a lesser overall cost to Dow than the option currently defined? | None identified | None reported | None identified |
Biophysical Context
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Urban areas 3.0% of land in U.S. and Urban/community land (5.3%) in 2000. | No additional description provided | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Agricultural landuse , 1st-10th order streams | Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | No additional description provided | No additional description provided | Need to fill in | No additional description provided | No additional description provided | No additional description provided | Rocky mountain conifer forests | Chesapeake bay coastal plain, elev. 60ft. | Prairie pothole region of north-central Iowa | No additional description provided | restored, enhanced and created wetlands | Mediteranean climate | NA |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | Not applicable | No scenarios presented | Habitat quality | The effect of habitat restoration on esocid spawning area was simulated by varying biophysical changes. | Planting type, fertilizing rate, harvest rate | No scenarios presented | Fisheries exploitation; fishing vulnerability (of age classes) | More conservative, average and less conservative nitrate loss rate | N/A | N/A | No scenarios presented | Alternative restoration designs | Sites, function or habitat focus | Arthropod groups | No scenarios presented |
EM ID
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EM-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only |
New or Pre-existing EM?
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Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
Application of existing model | WESP - Urban Stormwater Treatment | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
Document ID for related EM
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None |
Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
Doc-260 | Doc-154 | Doc-155 |
Doc-277 | Doc-294 | Doc-249 | Doc-250 ?Comment:Document 277 is also a source document for this EM |
None | None | None | None | None | None | None | None | Doc-372 | Doc-391 | Doc-390 | None | None |
EM ID for related EM
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None | None | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-83 | None | EM-132 | EM-133 | EM-179 | EM-183 | EM-180 | EM-181 | None | None | None | None | None | EM-629 | EM-626 | None | EM-652 | EM-651 | EM-649 | EM-648 | EM-712 | EM-718 | EM-734 | None | None |
EM Modeling Approach
EM ID
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EM-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
EM Temporal Extent
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1989-2010 | 2006-2010 | Not reported | 2000-2008 | Not reported | 1989-1999 | 2013 | 1981-2004 | 2006-2007, 2010 | 1986-2115 | 1973-1999 | 2004-2008 | 2014 | 1992-2007 | Not reported | 2010-2011 | 2015-2016 | Not applicable |
EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | Not applicable | future time | future time | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
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discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | Not applicable | Not reported | Not applicable | Not applicable | 1 | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Not applicable | Not reported | Not applicable | Not applicable | Day | Not applicable | Year | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM ID
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EM-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
Bounding Type
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Geopolitical | Geopolitical | Physiographic or Ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Point or points | Not applicable |
Spatial Extent Name
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United States | counterminous United States | Central French Alps | Upper Mississippi, Ohio and Missouri River sub-basins | River Ravna watershed | South Thompson watershed | St. Louis River estuary | Guanica Bay, Puerto Rico watersheds | Coastal zone surrounding St. Croix | Table Mountain National Park Marine Protected Area | Upper Mississippi River and Ohio River basins | National Park | Aberdeen Proving Ground | CREP (Conservation Reserve Enhancement Program) wetland sites | Dow Midland Operations facility ash pond and Posey Riverside (4-D property) | Wetlands in idaho | Harry Laidlaw Jr. Honey Bee Research facility | Not applicable |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | >1,000,000 km^2 | 10-100 km^2 | >1,000,000 km^2 | 10-100 km^2 | 1000-10,000 km^2. | 10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 1-10 km^2 | 10-100 ha | 100,000-1,000,000 km^2 | <1 ha | Not applicable |
EM ID
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EM-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
EM Spatial Distribution
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spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Watersheds (12-digit HUCs). |
spatially distributed (in at least some cases) | 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) | 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) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:500m x 500m is also used for some computations. The evaluation does include some riparian buffers which are linear features along streams. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
Spatial Grain Type
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | 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) | Not applicable | Not applicable | Not applicable |
Spatial Grain Size
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1 m^2 | irregular | 20 m x 20 m | 1 km | 25 m x 25 m | Not applicable | 10 m x 10 m | 30m x 30m | 10 m x 10 m | Not applicable | 1 km2 | 30m2 | 100m x 100m | multiple, individual, irregular shaped sites | map unit | Not applicable | Not applicable | Not applicable |
EM ID
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EM-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
EM Computational Approach
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Numeric | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Numeric | Numeric | Numeric | Numeric | Analytic | Analytic | Numeric | Numeric | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
Model Calibration Reported?
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No | No | No | No | Yes | Yes | No |
Yes ?Comment:Used 1981 and 1982 data to calibrate hydrology. |
Yes | No | No | No |
No ?Comment:Nutrient sequestion submodel ( EPA's P8 model has been long used) |
Unclear | Unclear | No | Not applicable | Not applicable |
Model Goodness of Fit Reported?
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No | No | No | No | No | No | No |
No ?Comment:Calibration for both the stream flow and Sediment concentration of the mode |
No | No | No | Yes | Not applicable | No | No | No | Not applicable | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None | None | None | None | None |
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None | None | None |
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None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | No | No | No | No |
Yes ?Comment:Validation with 1983-1984 data from USGS. Used streamflow and water quality data from two stations |
Yes |
Yes ?Comment:A validation analysis was carried out running the model using data from 1880 to 2001, and then comparing the output for the adult population with the 2001 published data. |
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 | Unclear | Unclear | No | Not applicable | Unclear |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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Yes ?Comment:An error of sampling was reported, but not an error of estimation Estimation error was unknown and reported as likely larger than the error of sampling. |
No | No | Yes | No | No | No | Unclear | No | No | No | No | No | No | No | No | No | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | No | Unclear | No | Yes | No |
Yes ?Comment:Yes for both runoff and sediment |
No | No | No | No |
Unclear ?Comment:Just cannot tell, but no mention of sensitivity was made. |
No | No | No | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 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-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
Comment:EM presents carbon storage and sequestration rates for country and by individual state |
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None | None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
None | None | None | None | None |
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None | None |
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None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
Centroid Latitude
em.detail.ddLatHelp
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40.16 | 39.5 | 45.05 | 36.98 | 42.8 | 49.29 | 46.74 | 18.19 | 17.73 | -34.18 | 40.6 | 43.93 | 39.46 | 42.62 | 43.6 | 44.06 | 38.54 | Not applicable |
Centroid Longitude
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-99.79 | -98.35 | 6.4 | -89.13 | 24 | -123.8 | -92.14 | -66.76 | -64.77 | 18.35 | -88.4 | 110.24 | 76.12 | -93.84 | -84.24 | -114.69 | -121.79 | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Not applicable |
EM ID
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EM-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Forests | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Rivers and Streams | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Forests | Rivers and Streams | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Rivers and Streams | Inland Wetlands | Agroecosystems | Forests | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Created Greenspace | Grasslands | Scrubland/Shrubland | Inland Wetlands | Agroecosystems | Grasslands | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Urban forests | Terrestrial | Subalpine terraces, grasslands, and meadows. | Not applicable | Primarily forested watershed | Rivers and streams | freshwater estuary | watershed | Coral reefs | Rocky coast, mixed coast, sandy coast, rocky inshore, sandy inshore, rocky shelf and unconsolidated shelf | Agroecosystems and associated drainage and wetlands | Montain forest | Coastal Plain | Grassland buffering inland wetlands set in agricultural land | Ash pond and surrounding environment | created, restored and enhanced wetlands | Agricultural fields | None |
EM Ecological Scale
em.detail.ecoScaleHelp
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Zone within an ecosystem | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is coarser 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 | 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 | Ecological scale corresponds to 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 | Ecological scale corresponds to 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 | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
EM Organismal Scale
em.detail.orgScaleHelp
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Species ?Comment:Trees were identified to species for the differential growth and biomass estimates part of the analysis. |
Not applicable | Community | Not applicable | Not applicable |
Other (Comment) ?Comment:Coho salmon stock |
Not applicable | Not applicable | Community | Individual or population, within a species | Not applicable | Not applicable | Not applicable | Species | Not applicable | Not applicable | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
None Available | None Available | None Available | None Available | None Available |
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None Available | None Available |
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None Available | None Available | None Available |
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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-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
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None |
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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)
EM-24 | EM-63 | EM-82 | EM-93 | EM-130 |
EM-177 ![]() |
EM-415 | EM-417 | EM-446 |
EM-541 ![]() |
EM-627 | EM-628 | EM-647 | EM-650 |
EM-713 ![]() |
EM-729 ![]() |
EM-779 ![]() |
EM-1001 |
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None |
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None |
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None |
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None |
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None |