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-87 |
EM-112 
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EM-177
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EM-493 |
EM-542
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EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | Area & hotspots of soil accumulation, South Africa | InVEST nutrient retention, Hood Canal, WA, USA | Salmon habitat values, west coast of Canada | EnviroAtlas-Carbon sequestered by trees | Coastal protection in Belize |
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EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Area and hotspots of soil accumulation, South Africa | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) nutrient retention, Hood Canal, WA, USA | Value of habitat quality changes for salmon populations, South Thompson watershed, west coast of Canada | US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA | Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize: |
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EM Source or Collection
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US EPA | EnviroAtlas | None | InVEST | None | US EPA | EnviroAtlas | i-Tree | InVEST |
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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. |
271 | 205 | 286 |
223 ?Comment:Additional source: I-tree Eco (doc# 345). |
350 |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M. | Knowler, D.J., MacGregor, B.W., Bradford, M.J., Peterman, R.M | US EPA Office of Research and Development - National Exposure Research Laboratory | Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes |
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Document Year
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2013 | 2008 | 2013 | 2003 | 2013 | 2016 |
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Document Title
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EnviroAtlas - National | Mapping ecosystem services for planning and management | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | Valuing freshwater salmon habitat on the west coast of Canada | EnviroAtlas - Featured Community | The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience |
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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 |
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Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript |
Software and Access
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EM ID
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EM-63 | EM-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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| https://www.epa.gov/enviroatlas | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | https://www.epa.gov/enviroatlas | Not identified in paper | |
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Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Benis Egoh | J.E. Toft | Duncan Knowler | EnviroAtlas Team | Greg Guannel |
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Contact Address
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Not reported | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA | School of Resource and Environmental Management, Simon Fraser University, Burnaby, Canada BC V5H 1S6 | Not reported | The Nature Conservancy, Coral Gables, FL. USA |
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Contact Email
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enviroatlas@epa.gov | Not reported | jetoft@stanford.edu | djk@sfu.ca | enviroatlas@epa.gov | greg.guannel@gmail.com |
EM Description
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EM ID
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EM-63 | EM-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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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." | AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil scientists often use soil depth to model soil production potential (soil formation) (Heimsath et al., 1997; Yuan et al., 2006). The accumulation of soil organic matter is an important process of soil formation which can be badly affected by habitat degradation and transformation (de Groot et al., 2002). Soil depth and leaf litter were used as proxies for soil accumulation. Soil depth is positively correlatedwith soil organic matter (Yuan et al., 2006); deep soils have the capacity to hold more nutrients. Litter cover was described above. Data on soil depth were obtained from the land capability map of South Africa and thresholds were based on the literature (Schoeman et al., 2002; Tekle, 2004). Areas with at least 0.4 m depth and 30% litter cover were mapped as important areas for soil accumulation, i.e. its geographic range. The hotspot was mapped as areas with at least 0.8 m depth and a 70% litter cover." | InVEST Nutrient Retention Model 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. AUTHOR'S DESCRIPTION: "We modelled discharge and total nitrogen for the 153 perennial sub-watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparameterized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)" (2) "We used the InVEST Nutrient Retention model to quantify the total nitrogen load for each subwatershed. Inputs to the Nutrient Retention model include water yield, land use and land cover, and nutrient loading and filtration rates (Table 1; Conte et al., 2011; Tallis et al., 2011). The nutrient model quantifies natural and anthropogenic sources of total nitrogen within each subwatershed, allowing managers to identify subwatersheds potentially at risk of contributing excessive nitrogen loads given the predicted development and climate future." ( P. 4) | ABSTRACT: "In this paper, we present a framework for valuing benefits for fisheries from protecting areas from degradation, using the example of the Strait of Georgia coho salmon fishery in southern British Columbia, Canada. Our study improves upon previous methods used to value fish habitat in two major respects. First, we use a bioeconomic model of the coho fishery to derive estimates of value that are consistent with economic theory. Second, we estimate the value of changing the quality of fish habitat by using empirical analyses to link fish population dynamics with indices of land use in surrounding watersheds." | The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group." | 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." |
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Specific Policy or Decision Context Cited
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None Identified | None identified | Land use change | None identified | None identified | Future rock lobster fisheries management |
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Biophysical Context
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No additional description provided | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | No additional description provided | No additional description provided | No additional description provided | barrier reef and fringing reef in nearshore coastal marine system |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | Future land use and land cover; climate change | Habitat quality | No scenarios presented | Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions |
EM Relationship to Other EMs or Applications
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EM ID
em.detail.idHelp
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EM-63 | EM-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing 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
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EM ID
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EM-63 | EM-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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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-271 | Doc-309 | Doc-338 | None | Doc-345 | None |
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EM ID for related EM
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None | EM-85 | EM-86 | EM-88 | EM-363 | EM-438 | EM-179 | EM-183 | EM-180 | EM-181 | None | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-63 | EM-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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EM Temporal Extent
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2006-2010 | Not reported | 2005-7; 2035-45 | 1989-1999 | 2010-2013 | 2005-2013 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Second |
EM Spatial Extent
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EM ID
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EM-63 | EM-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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Bounding Type
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Geopolitical | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Geopolitical |
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Spatial Extent Name
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counterminous United States | South Africa | Hood Canal | South Thompson watershed | Durham NC and vicinity | Coast of Belize |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 100-1000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-63 | EM-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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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 lumped (in all cases) |
spatially distributed (in at least some cases) ?Comment:Census block groups |
spatially distributed (in at least some cases) |
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Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) |
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Spatial Grain Size
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irregular | Distributed across catchments with average size of 65,000 ha | 30 m x 30 m | Not applicable | irregular | 1 meter |
EM Structure and Computation Approach
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EM ID
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EM-63 | EM-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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EM Computational Approach
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Analytic | Analytic | Other or unclear (comment) | Analytic | Numeric | Analytic |
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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-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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Model Calibration Reported?
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No | No | Yes | Yes | No | No |
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Model Goodness of Fit Reported?
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No | No | No | No | No | No |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None | None |
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Model Operational Validation Reported?
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No | No | Yes | No | 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. |
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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 | Yes | No | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | No | No | 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-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-63 | EM-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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| None | None |
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None |
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Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-63 | EM-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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Centroid Latitude
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39.5 | -30 | 47.8 | 49.29 | 35.99 | 18.63 |
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Centroid Longitude
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-98.35 | 25 | -122.7 | -123.8 | -78.96 | -88.22 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-63 | EM-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Rivers and Streams | Near Coastal Marine and Estuarine | Created Greenspace | Atmosphere | Near Coastal Marine and Estuarine |
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Specific Environment Type
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Terrestrial | Not applicable | glacier-carved saltwater fjord | Rivers and streams | Urban and vicinity | coral reefs |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-63 | EM-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable |
Other (Comment) ?Comment:Coho salmon stock |
Not applicable | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-63 | EM-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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| None Available | None Available | None Available |
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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)
em.detail.cicesHelp
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| EM-63 | EM-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-63 | EM-87 |
EM-112
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EM-177
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EM-493 |
EM-542
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None | None |
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None |
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