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-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
EM Short Name
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Green biomass production, Central French Alps | Community flowering date, Central French Alps | Stream nitrogen removal, Mississippi R. basin, USA | Cultural ecosystem services, Bilbao, Spain | Coral taxa and land development, St.Croix, VI, USA | SAV occurrence, St. Louis River, MN/WI, USA | EnviroAtlas - Restorable wetlands | Coastal protection in Belize | Chinook salmon value, Yaquina Bay, OR | N removal by wetland restoration, Midwest, USA | Mourning dove abundance, Piedmont region, USA | National invertebrate community rank index | HWB Blood pressure, Great Lakes waterfront, USA | VELMA v. 2.1 contaminant modeling | ESTIMAP - Pollination potential, Iran | Global forest stock, biomass and carbon downscaled | EcoSim II - method | EPA Stormwater Manamgement Model | Velma- 6PPD-Q concentrations, Seattle, WA | WMOST method |
EM Full Name
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Green biomass production, Central French Alps | Community weighted mean flowering date, Central French Alps | Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA | Cultural ecosystem services, Bilbao, Spain | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize: | Economic value of Chinook salmon by angler effort method, Yaquina Bay, OR | Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA | Mourning dove abundance, Piedmont ecoregion, USA | National invertebrate community ranking index (NICRI) | Human well being indicator- Blood pressure, Great Lakes waterfront, USA | VELMA (Visualizing Ecosystem Land Management Assessments) v. 2.1 contaminant modeling | ESTIMAP - Pollination potential, Iran | Global forest growing stock, biomass and carbon downscaled map | EcoSim II - method | Storm Water Management Model User's Manual Version 5.2 | VELMA: 6PPD-Quinone stormwater concentrations , Seattle, Washington | Watershed Management Optimization Support Tool (WMOST) v1 method |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA |
None ?Comment:EU Mapping Studies |
US EPA | US EPA | US EPA | EnviroAtlas | InVEST | US EPA | None | None | None | None | US EPA | None | None | None | US EPA | US EPA | US EPA |
EM Source Document ID
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260 | 260 | 52 | 191 | 96 | 330 | 262 | 350 | 324 |
370 ?Comment:Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006. |
405 | 407 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
423 ?Comment:Document #430 is an additional source for this EM. Document #423 has been imcorporated into the more recently published document #430. |
434 | 442 | 448 | 452 | 465 | 477 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Hill, B. and Bolgrien, D. | Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke | US EPA Office of Research and Development - National Exposure Research Laboratory | Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers | Riffel, S., Scognamillo, D., and L. W. Burger | Cuffney, Tom | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | McKane | Rahimi, E., Barghjelveh, S., and P. Dong | Kindermann, G.E., I. McCallum, S. Fritz, and M. Obersteiner | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell | Rossman, L. A., M., Simon | Halama JJ, McKane RB, Barnhart BL, Pettus PP, Brookes AF, Adams AK, Gockel CK, Djang KS, Phan V, Chokshi SM, Graham JJ, Tian Z, Peter KT and Kolodziej,EP | United States EPA |
Document Year
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2011 | 2011 | 2011 | 2013 | 2011 | 2013 | 2013 | 2016 | 2012 | 2006 | 2008 | 2003 | None | None | 2020 | 2008 | 2000 | 2022 | 2024 | 2013 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | 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 | Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary | EnviroAtlas - National | The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Invertebrate Status Index | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Tutorial A.1 – Contaminant Fate and Transport Modeling Concepts; VELMA 2.1 “How To” Documentation | Using the Lonsdorf and ESTIMAP models for large-scale pollination Using the Lonsdorf and ESTIMAP models for large-scale pollination mapping (Case study: Iran) | A global forest growing stock, biomass and carbon map based on FAO statistics | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II | Storm Water Management Model User's Manual Version 5.2 | Watershed analysis of urban stormwater contaminant 6PPD-Quinone hotspots and stream concentrations using a process-based ecohydrological model | Watershed Management Optimization Support Tool (WMOST) v1 User manual |
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 | Neither peer reviewed nor published (explain in Comment) | Peer reviewed and published | Other or unclear (explain in Comment) | Peer reviewed but unpublished (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) | 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 journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published report | Journal manuscript submitted or in review | Published EPA report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published journal manuscript | Published EPA report |
EM ID
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EM-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/enviroatlas | Not identified in paper | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=354355 | Not applicable | Not applicable | https://ecopath.org/downloads/ | https://www.epa.gov/water-research/storm-water-management-model-swmm | Not reported | https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NHEERL&dirEntryId=262280 | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | Brian Hill | Izaskun Casado-Arzuaga | Leah Oliver | Ted R. Angradi | EnviroAtlas Team | Greg Guannel | Stephen Jordan | William G. Crumpton | Sam Riffell | Tom Cuffney | Ted Angradi | Robert B. McKane | Ehsan Rahini | Georg Kindermann | Carl Walters | David Burden | Jonathan Halama | Naomi Detenbeck |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | 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 | Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain | National Health and Environmental Research Effects Laboratory | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | Not reported | The Nature Conservancy, Coral Gables, FL. USA | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011 | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | 3916 Sunset Ridge Rd, Raleigh, NC 27607 | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | US EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333 | Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran | International Institute for Applied Systems Analysis, Laxenburg, Austria | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 | U.S. EPA Research Center for Environmental Solutions and Emergency Response (CESER) Mail Drop: 314 P.O. Box #1198 Ada, OK 74821-1198 | U.S. Environmental Protection Agency, Corvallis, OR | NHEERL, Atlantic Ecology Division Narragansett, RI 02882 |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | hill.brian@epa.gov | izaskun.casado@ehu.es | leah.oliver@epa.gov | angradi.theodore@epa.gov | enviroatlas@epa.gov | greg.guannel@gmail.com | jordan.steve@epa.gov | crumpton@iastate.edu | sriffell@cfr.msstate.edu | tcuffney@usgs.gov | tedangradi@gmail.com | mckane.bob@epa.gov | ehsanrahimi666@gmail.com | kinder(at)iiasa.ac.at | c.walters@oceans.ubc.ca | burden.david@epa.gov | Halama.Jonathan@epa.gov | detenbeck.naomi@epa.gov |
EM ID
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EM-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
Summary Description
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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. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)." | 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: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | 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 "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management." | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake." | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | AUTHOR'S DESCRIPTION: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems." | ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. | 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:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " | ABSTRACT: "The Invertebrate Status Index is a multimetric index that was derived for the NAWQA Program to provide a simple national characterization of benthic invertebrate communities. This index— referred to here as the National Invertebrate Community Ranking Index (NICRI)—provides a simple method of placing community conditions within the context of all sites sampled by the NAWQA Program. The multimetric index approach is the most commonly used method of characterizing biological conditions within the U.S. (Barbour and others, 1999). Using this approach, communities may be compared by considering how individual metrics vary among sites or by combining individual metrics into a single composite (i.e., multimetric) index and examining how this single index varies among sites. Combining metrics into a single multimetric index simplifies the presentation of results (Barbour and others, 1999) and minimizes weaknesses that may be associated with individual metrics (Ohio EPA, 1987a,b). The NICRI is a multimetric index that combines 11 metrics (RICH, EPTR, CG_R, PR_R, EPTRP, CHRP, V2DOMP, EPATOLR, EPATOLA, DIVSHAN, and EVEN; Table 1) into a single, nationally consistent, composite index. The NICRI was used to rank 140 sites of the FY94 group of study units, with median values used for sites where data were available for multiple reaches and(or) multiple years. Average metric scores were then rescaled using the PERCENTRANK function and multiplied by 100 to produce a final NICRI score that ranged from 0 (low ranking relative to other NAWQA Program sites and presumably diminished community conditions) to 100 (high ranking relative to other NAWQA Program sites and presumably excellent community conditions). " | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context." | ABSTRACT: "This document describes the conceptual framework underpinning the use of VELMA 2.1 to model fate and transport of organic contaminants within watersheds. We review how VELMA 2.1 simulates contaminant fate and transport within soils and hillslopes as a function of two processes: (1) the partitioning of the total amount of a contaminant between sorbed (immobile) and aqueous (mobile) phases; and (2) the vertical and lateral transport of the contaminant’s aqueous phase within surface and subsurface waters." | Abstract: ". ..we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors." | ABSTRACT: "Currently, information on forest biomass is available from a mixture of sources, including in-situ measurements, national forest inventories, administrative-level statistics, model outputs and regional satellite products. These data tend to be regional or national, based on different methodologies and not easily accessible. One of the few maps available is the Global Forest Resources Assessment (FRA) produced by the Food and Agriculture Organization of the United Nations (FAO 2005) which contains aggregated country-level information about the growing stock, biomass and carbon stock in forests for 229 countries and territories. This paper presents a technique to downscale the aggregated results of the FRA2005 from the country level to a half degree global spatial dataset containing forest growing stock; above/belowground biomass, dead wood and total forest biomass; and above-ground, below-ground, dead wood, litter and soil carbon. In all cases, the number of countries providing data is incomplete. For those countries with missing data, values were estimated using regression equations based on a downscaling model. The downscaling method is derived using a relationship between net primary productivity (NPP) and biomass and the relationship between human impact and biomass assuming a decrease in biomass with an increased level of human activity. The results, presented here, represent one of the first attempts to produce a consistent global spatial database at half degree resolution containing forest growing stock, biomass and carbon stock values. All results from the methodology described in this paper are available online at www. iiasa.ac.at/Research/FOR/. " | ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores " |
EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. Running under Windows, SWMM 5 provides an integrated environment for editing study area input data, running hydrologic, hydraulic and water quality simulations, and viewing the results in a variety of formats. These include color coded drainage area and conveyance system maps, time series graphs and tables, profile plots, and statistical frequency analyses. This user’s manual describes in detail how to run SWMM 5.2. It includes instructions on how to build a drainage system model, how to set various simulation options, and how to view results in a variety of formats. It also describes the different types of files used by SWMM and provides useful tables of parameter values. Detailed descriptions of the theory behind SWMM 5 and the numerical methods it employs can be found in a separate set of reference manuals. ?Comment:The variables used for this ESML entry were derived from the quick tutorial section of the SWMM manual. |
ABSTRACT: "Coho salmon (Oncorhynchus kisutch) are highly sensitive to 6PPD-Quinone (6PPD-Q). Details of the hydrological and biogeochemical processes controlling spatial and temporal dynamics of 6PPD-Q fate and transport from points of deposition to receiving waters (e.g., streams, estuaries) are poorly understood. To understand the fate and transport of 6PPD and mechanisms leading to salmon mortality Visualizing Ecosystem Land Management Assessments (VELMA), an ecohydrological model developed by US Environmental Protection Agency (EPA), was enhanced to better understand and inform stormwater management planning by municipal, state, and federal partners seeking to reduce stormwater contaminant loads in urban streams draining to the Puget Sound National Estuary. This work focuses on the 5.5 km2 Longfellow Creek upper watershed (Seattle, Washington, United States), which has long exhibited high rates of acute urban runoff mortality syndrome in coho salmon. We present VELMA model results to elucidate these processes for the Longfellow Creek watershed across multiple scales–from 5-m grid cells to the entire watershed. Our results highlight hydrological and biogeochemical controls on 6PPD-Q flow paths, and hotspots within the watershed and its stormwater infrastructure, that ultimately impact contaminant transport to Longfellow Creek and Puget Sound. Simulated daily average 6PPD-Q and available observed 6PPD-Q peak in-stream grab sample concentrations (ng/L) corresponds within plus or minus 10 ng/L. Most importantly, VELMA’s high-resolution spatial and temporal analysis of 6PPD-Q hotspots provides a tool for prioritizing the locations, amounts, and types of green infrastructure that can most effectively reduce 6PPD-Q stream concentrations to levels protective of coho salmon and other aquatic species. " | ABSTRACT: "The Watershed Management Optimization Support Tool (WMOST) is intended to be used as a screening tool as part of an integrated watershed management process such as that described in EPA’s watershed planning handbook (EPA 2008).1 The objective of WMOST is to serve as a public-domain, efficient, and user-friendly tool for local water resources managers and planners to screen a widerange of potential water resources management options across their watershed or jurisdiction for costeffectiveness as well as environmental and economic sustainability (Zoltay et al 2010). Examples of options that could be evaluated with the tool include projects related to stormwater, water supply, wastewater and water-related resources such as Low-Impact Development (LID) and land conservation. The tool is intended to aid in evaluating the environmental and economic costs, benefits, trade-offs and co-benefits of various management options. In addition, the tool is intended to facilitate the evaluation of low impact development (LID) and green infrastructure as alternative or complementary management options in projects proposed for State Revolving Funds (SRF). WMOST is a screening model that is spatially lumped with a daily or monthly time step. The model considers water flows but does not yet consider water quality. The optimization of management options is solved using linear programming. The target user group for WMOST consists of local water resources managers, including municipal water works superintendents and their consultants. This document includes a user guide and presentation of two case studies as examples of how to apply WMOST. Theoretical documentation is provided in a separate report (EPA/600/R-13/151). " |
Specific Policy or Decision Context Cited
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None identified | None identified | Not applicable | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | Not applicable | None identified | None Identified | Future rock lobster fisheries management | None reported | None identified | None reported | None Identified | None identified | None identified | None reported | None identified | None | NA | Not reported | Not applicable |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Agricultural landuse , 1st-10th order streams | Northern Spain; Bizkaia region | nearshore; <1.5 km offshore; <12 m depth | submerged aquatic vegetation | No additional description provided | barrier reef and fringing reef in nearshore coastal marine system | Yaquina Bay estuary | No additional description provided | Conservation Reserve Program lands left to go fallow | Streams and Rivers | Waterfront districts on south Lake Michigan and south lake Erie | No additional description provided | None additional | No additional description provided | None, Ocean ecosystems | NA | 6PPD deposition from vehicle tire wear particles. | None |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Not applicable | No scenarios presented | Not applicable | No scenarios presented | No scenarios presented | Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions | N/A | More conservative, average and less conservative nitrate loss rate | N/A | N/A | N/A | No scenarios presented | N/A | No scenarios presented | N/A | NA | N/A | None |
EM ID
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EM-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) | Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method Only | Method Only | Method + Application | Method Only |
New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised 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 | 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-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
Document ID for related EM
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Doc-260 | Doc-260 | Doc-269 | Doc-154 | Doc-155 | None | None | None | None | None | None | None | Doc-405 | None | Doc-422 | Doc-430 | Doc-432 | None | None | None | Doc-366 | Doc-423 | Doc-430 | None |
EM ID for related EM
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EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | None | None | None | None | EM-604 | EM-397 | None | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-844 | EM-845 | EM-846 | EM-847 | EM-850 | EM-886 | EM-888 | EM-889 | EM-891 | EM-893 | EM-894 | EM-895 | EM-883 | EM-884 | EM-887 | EM-939 | None | None | EM-971 | None | None |
EM Modeling Approach
EM ID
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EM-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
EM Temporal Extent
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2007-2009 | 2007-2008 | 2000-2008 | 2000 - 2007 | 2006-2007 | 2010 - 2012 | 2006-2013 | 2005-2013 | 2003-2008 | 1973-1999 | 2008 | 1991-1994 | 2022 | Not applicable | 2020 | 1999-2005 | Not applicable | Not applicable | 9/2020-6/2021 | Not applicable |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | both | both | past time |
Not applicable ?Comment:method description |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable |
discrete ?Comment:Modeller dependent |
continuous | discrete | discrete |
EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | Not applicable | 1 | 1 |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Second | Not applicable | Day | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Day | Not applicable | Day | Month |
EM ID
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EM-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Geopolitical | Physiographic or Ecological | Physiographic or ecological | Geopolitical | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Other | Geopolitical | Not applicable | Geopolitical | No location (no locational reference given) | Other | No location (no locational reference given) | Watershed/Catchment/HUC | Not applicable |
Spatial Extent Name
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Central French Alps | Central French Alps | Upper Mississippi, Ohio and Missouri River sub-basins | Bilbao Metropolitan Greenbelt | St.Croix, U.S. Virgin Islands | St. Louis River Estuary | conterminous United States | Coast of Belize | Pacific Northwest | Upper Mississippi River and Ohio River basins | Piedmont Ecoregion | Not applicable | Great Lakes waterfront | Not applicable | Iran | Global | Not applicable | Not applicable | Longfellow creek | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | Not applicable | 1000-10,000 km^2. | Not applicable | >1,000,000 km^2 | >1,000,000 km^2 | Not applicable | Not applicable | 1-10 km^2 | Not applicable |
EM ID
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EM-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
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) | spatially lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:BH: Each individual transect?s data was parceled into location reports, and that each report?s ?quadrat? area was dependent upon the angle of the hydroacoustic sampling beam. The spatial grain is 0.07 m^2, 0.20 m^2 and 0.70 m^2 for depths of 1 meter, 2 meters and 3 meters, respectively. |
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 lumped (in all 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) ?Comment:Varies by inputs, but results are for areas of country |
spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
Spatial Grain Type
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area, for pixel or radial feature | 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 | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | Not applicable | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | volume, for 3-D feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | Not applicable | Not applicable |
Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | 1 km | 2 m x 2 m | Not applicable | 0.07 m^2 to 0.70 m^2 | irregular | 1 meter | Not applicable | 1 km2 | Not applicable | stream reach | Not applicable | user defined | ha^2 | 0.5 x 0.5 degrees | Not applicable | mm | Not applicable | Not applicable |
EM ID
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EM-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
EM Computational Approach
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Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Numeric |
EM Determinism
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deterministic | deterministic | 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-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
Model Calibration Reported?
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No | No | No | No | Yes | Yes | No | No | No | No | Yes | Not applicable | No | Not applicable | No | No | No | Not applicable | Yes | Not applicable |
Model Goodness of Fit Reported?
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Yes | Yes | No | No | Yes | Yes | No | No | No | No | No | Not applicable | No | Not applicable | No |
Yes ?Comment:For the 0.5 grid level equation where the country forest level is missing. |
No | Not applicable | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None |
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None | None | None | None | None | None | None | None | None |
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None | None | None | None |
Model Operational Validation Reported?
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Yes | No | No | Yes | No | Yes | No |
No ?Comment:Used the SWAN model (see below for referenece) with Generation 1 or 2 wind-wave formulations to validate the wave development portion of the model. Booij N, Ris RC, Holthuijsen LH. A third-generation wave model for coastal regions 1. Model description and validation. J Geophys Res. American Geophysical Union; 1999;104: 7649?7666. |
Yes ?Comment:Compared to a second methodological approach |
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 | No | Not applicable | No | Yes | Not applicable | Not applicable | Yes | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | Yes | No | Yes | No | No | No | No | No | No | Yes | No | Not applicable | No | No | Not applicable | Not applicable | Unclear | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | Unclear | No | No | No | No | No | No | No | Yes | Yes | Yes | Not applicable | No | No | Not applicable | Not applicable | Unclear | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Yes | 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-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
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None |
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Comment:No specific location but developed in United States |
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None |
Comment:Model for Iran - no form preset id for country |
None | None | None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
None | None | None | None |
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None | None |
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None | None | None | None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
Centroid Latitude
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45.05 | 45.05 | 36.98 | 43.25 | 17.75 | 46.72 | 39.5 | 18.63 | 44.62 | 40.6 | 36.23 | Not applicable | 42.26 | Not applicable | 32.29 | 44.51 | Not applicable | Not applicable | 47.55 | Not applicable |
Centroid Longitude
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6.4 | 6.4 | -89.13 | -2.92 | -64.75 | -96.13 | -98.35 | -88.22 | -124.02 | -88.4 | -81.9 | Not applicable | -87.84 | Not applicable | 53.68 | -123.51 | Not applicable | Not applicable | 122.37 | Not applicable |
Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | Not applicable | WGS84 | WGS84 | Not applicable | Not applicable | None provided | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Not applicable | Estimated | Estimated | Not applicable | Not applicable | Provided | Not applicable |
EM ID
em.detail.idHelp
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EM-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Rivers and Streams | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Agroecosystems | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Rivers and Streams | Inland Wetlands | Agroecosystems | Grasslands | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Forests | Open Ocean and Seas | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Not applicable | none | stony coral reef | Freshwater estuarine system | Terrestrial | coral reefs | Yaquina Bay | Agroecosystems and associated drainage and wetlands | grasslands | benthic habitat | Lake Michigan & Lake Erie waterfront | Terrestrial environment | terrestrial land types | Forests | Pelagic | User-defined catchments | small stream | watershed |
EM Ecological Scale
em.detail.ecoScaleHelp
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Not applicable | Not applicable | 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 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 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 | Other or unclear (comment) | 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
em.detail.idHelp
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EM-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Community | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Guild or Assemblage | Individual or population, within a species | Not applicable | Species |
Other (Comment) ?Comment:Community metrics of tolerance, food groups, sensitivity, taxa richness, diversity |
Not applicable | Not applicable | Not applicable | Not applicable |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
None Available | None Available | None Available | None Available |
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None Available | None Available | None Available |
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None Available |
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None Available | None Available | None Available |
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None Available |
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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-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
None | None |
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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 |
<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-65 | EM-71 | EM-93 | EM-193 | EM-260 | EM-414 | EM-492 |
EM-542 ![]() |
EM-603 | EM-627 | EM-843 | EM-848 | EM-890 | EM-892 | EM-941 |
EM-948 ![]() |
EM-964 | EM-968 | EM-993 | EM-1011 |
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None |
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None | None |
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None |
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None | None | None |
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None |
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Comment:Model identifies toxicant concentrations relative to the known LC50 for coho juveniles which is 95ng/L (Spromber and Scholz, 2011; |
None |