EcoService Models Library (ESML)
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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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
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EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | Plant species diversity, Central French Alps | InVEST crop pollination, Costa Rica | WaSSI, Conterminous USA | Natural amenities and population migration, USA | HWB indicator-ADI, Great Lakes, USA |
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EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Plant species diversity, Central French Alps | InVEST crop pollination, Costa Rica | Water Supply Stress Index, Conterminous USA | Natural amenities and rural population migration, USA | Human well being indicator- Area Deprivation Index (ADI) , Great Lakes waterfront, USA |
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EM Source or Collection
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US EPA | EnviroAtlas | EU Biodiversity Action 5 | InVEST |
USDA Forest Service ?Comment:While the user guide on which model entry is based has not been peer reviewed, several peer reviewed journal articles describing this USA HUC8 version of WaSSI have been published. |
USDA Forest Service | US EPA |
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EM Source Document ID
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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 | 279 | 341 | 375 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
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Document Author
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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. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Peter Caldwell, Ge Sun, Steve McNulty, Jennifer Moore Myers, Erika Cohen, Robert Herring, Erik Martinez | Cordell H. K., V. Heboyan, F. Santos, J. C. Bergstrom | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick |
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Document Year
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2013 | 2011 | 2009 | 2013 | 2011 | None |
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Document Title
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EnviroAtlas - National | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Modelling pollination services across agricultural landscapes | WaSSI Ecosystem Services Model | Natural amenities and rural population migration | Human well-being and natural capital indictors for Great Lakes waterfront revitalization |
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Document Status
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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 but unpublished (explain in Comment) |
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Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | While the user guide on which model entry is based has not been peer reviewed, several peer reviewed journal articles describing this USA HUC8 version of WaSSI have been published. | Published report | Journal manuscript submitted or in review |
Software and Access
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EM ID
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EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
| https://www.epa.gov/enviroatlas | Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html | http://www.wassiweb.sgcp.ncsu.edu/ | Not applicable | Not applicable | |
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Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Sandra Lavorel | Eric Lonsdorf | Ge Sun | Ken Cordell | Ted Angradi |
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Contact Address
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Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest Service, 920 Main Campus Dr. Venture II, Suite 300, Raleigh, NC 27606 | U.S. Department of Agriculture, Forest Service, Southern Research Station, Athens, GA 30602 | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 |
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Contact Email
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enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | ericlonsdorf@lpzoo.org | gesun@fs.fed.us | Not reported | tedangradi@gmail.com |
EM Description
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EM ID
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EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
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Summary Description
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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: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of abiotic variables." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery." AUTHOR'S DESCRIPTION: "…Lacking information on seasonality, a single flight season was assumed for all species..." | AUTHORS DESCRIPTION: "WaSSI simulates monthly water and carbon dynamics at the Hydrologic Unit Code 8 level in the US. Three modules are integrated within the WaSSI model framework. The water balance module computes ecosystem water use, evapotranspiration and the water yield from each watershed. Water yield is sometimes referred to as runoff and can be thought of as the amount of streamflow at the outlet of each watershed due to hydrologic processes in each watershed in isolation without any flow contribution from upstream watersheds. The ecosystem productivity module simulates carbon gains and losses in each watershed or grid cell as functions of evapotranspiration. The water supply and demand module routes and accumulates the water yield through the river network according to topological relationships between adjacent watersheds, subtracts consumptive water use by humans from river flows, and compares water supply to water demand to compute the water supply stress index, or WaSSI." | ABSTRACT: "Research suggests that significant relationships exist between rural population change and natural amenities. Thus, understanding and predicting domestic migration trends as a function of changes in natural amenities is important for effective regional growth and development policies and strategies. In this study, we first estimated an econometric model which showed the effects of natural amenities, such as climate and landscape variables, on rural population migration patterns in the United States between 1990 and 2007. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections. Results suggest that people prefer rural areas with mild winters and cooler summers; thus we can expect a direct impact of climate change on population migration when areas associated with these conditions change. Results also suggest preference for varied landscapes that feature a mix of forest land and open space (e g , pasture and range land). During the projection period from 2010 to 2060 in the United States, changes in natural amenities were predicted to have positive effects on rural population migration trends in most parts of the Intermountain and Pacific Northwest regions, and some parts of the Southeastern, South Central, and Northeastern U S regions (e g , Southern Appalachian Mountains, Ozark Mountains, northern New England). Changes in natural amenities were predicted to have negative effects on rural population migration trends during the projection period in Midwestern regions (e g , Great Plains and North Central regions)." AUTHOR'S DESCRIPTION: "This model was estimated for 2,014 rural counties in the continental United States using various national data bases and sources. The estimated model was then used to predict the effects of changes in these variables on rural county net migration and population growth to 2060 under alternative future climate and land use projections." | 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." |
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Specific Policy or Decision Context Cited
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None Identified | None identified | None identified | WaSSI can be used to project the regional effects of forest land cover change, climate change, and water withdrawals on river flows, water supply stress, and ecosystem productivity (i.e., carbon sequestration).WaSSI can be used to evaluate trade-offs among management strategies that influence multiple ecosystem services | None identified | None identified |
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Biophysical Context
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No additional description provided | Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | No additional description provided | Conterminous US | No additional description provided | Waterfront districts on south Lake Michigan and south lake Erie |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented |
No scenarios presented ?Comment:Model can be run from WaSSI website using a historic data set (1961 - 2010) or projections from various climate models representing different emissions scenarios and time periods from recent past to 2099. |
Climate projections based on the CGCM 3 1 general circulation model of moderate warming (IPCC). The A1B scenario assumes a growing world population that peaks in the mid-century and balanced technological growth. | N/A |
EM Relationship to Other EMs or Applications
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EM ID
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EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model |
Application of existing model ?Comment:. |
New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
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Document ID for related EM
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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-279 | None | None | Doc-422 |
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EM ID for related EM
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None | EM-65 | EM-66 | EM-68 | EM-69 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-338 | EM-339 | None | None | EM-886 | EM-888 | EM-889 | EM-890 | EM-891 | EM-894 | EM-895 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
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EM Temporal Extent
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2006-2010 | 2007-2009 | 2001-2002 | 1961-2009 | 1982-2060 | 2022 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | future time | future time | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Month | Year | Not applicable |
EM Spatial Extent
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EM ID
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EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
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Bounding Type
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Geopolitical | Physiographic or Ecological | Other | Watershed/Catchment/HUC | Geopolitical | Geopolitical |
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Spatial Extent Name
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counterminous United States | Central French Alps | Large coffee farm, Valle del General | All 8-digit hydrologic unit codes (HUC-8) in the conterminous USA | continental United States | Great Lakes waterfront |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. |
Spatial Distribution of Computations
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EM ID
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EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
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EM Spatial Distribution
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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) ?Comment:Spatial grain for computations is the HUC-8. A HUC-12 version is under development. Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | map scale, for cartographic feature | Not applicable |
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Spatial Grain Size
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irregular | 20 m x 20 m | 30 m x 30 m | Computations are at the 8-digit HUC scale. MostHUC-8 watersheds are within a range of 800-8000 km^2 (500-5000 mi^2) in size. | varies | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
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EM Computational Approach
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Analytic | Analytic | Analytic | Numeric | Numeric | Numeric |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
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Model Calibration Reported?
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No | No | Unclear | No | Yes | No |
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Model Goodness of Fit Reported?
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No | Yes | No | No | No | No |
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Goodness of Fit (metric| value | unit)
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None |
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None | None | None | None |
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Model Operational Validation Reported?
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No | No | Yes | No | No | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | Yes | No | No | Yes |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
| None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
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Centroid Latitude
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39.5 | 45.05 | 9.13 | 39.83 | 39.8 | 42.26 |
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Centroid Longitude
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-98.35 | 6.4 | -83.37 | -98.58 | -98.55 | -87.84 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Provided | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems |
Lakes and Ponds ?Comment:Watershed model represents all land areas, major streams and rivers. Since leaf area index, LAI, is an important variable, forests, created greenspaces (e.g., urban forests) and scrub/shrub subclasses are included. |
Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Terrestrial | Subalpine terraces, grasslands, and meadows | Cropland and surrounding landscape | Not applicable | Terrestrial environments including water bodies and coastlines | Lake Michigan & Lake Erie waterfront |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale is finer than that of the Environmental Sub-class |
Ecological scale is coarser than that of the Environmental Sub-class ?Comment:Terrestrial characteristics are aggregated at a broad (HUC-8) scale; different types of aquatic sub-classes are not differentiated. |
Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
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EM Organismal Scale
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Not applicable | Community | Species | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
| None Available | None Available |
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None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
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| EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-63 | EM-70 | EM-340 | EM-439 | EM-653 | EM-893 |
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None |
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None | None |