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-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
EM Short Name
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ACRU, South Africa | Runoff potential of pesticides, Europe | Landscape importance for crops, Europe | Birds in estuary habitats, Yaquina Estuary, WA, USA | Annual profit from agriculture, South Australia | InVEST - Water Yield (v3.0) | InVEST carbon storage and sequestration (v3.2.0) | Esocid spawning, St. Louis River, MN/WI, USA | Wave height attenuation, St. Croix, USVI | Reef density of S. gigas, St. Croix, USVI | Value of finfish, St. Croix, USVI | Nutrient Tracking Tool (NTT), north central Texas, USA | WaterWorld v2, Santa Basin, Peru | Northern Shoveler recruits, CREP wetlands, IA, USA | WESP: Urban Stormwater Treatment, ID, USA | Mourning dove abundance, Piedmont region, USA | HWB-home value, Great Lakes, USA | COBRA v 4.1 | Wetland biodiversity, Netherlands |
EM Full Name
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ACRU (Agricultural Catchments Research Unit), South Africa | Runoff potential of pesticides, Europe | Landscape importance for crop-based production, Europe | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | Annual profit from agriculture, South Australia | InVEST v3.0 Reservoir Hydropower Projection, aka Water Yield | InVEST v3.2.0 Carbon storage and sequestration | Esocid spawning, St. Louis River estuary, MN & WI, USA | Wave height attenuation (by reef), St. Croix, USVI | Relative density of Strombus gigas (on reef), St. Croix, USVI | Relative value of finfish (on reef), St. Croix, USVI | Nutrient Tracking Tool (NTT), Upper North Bosque River watershed, Texas, USA | WaterWorld v2, Santa Basin, Peru | Northern Shoveler duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | WESP: Urban Stormwater Treament, ID, USA | Mourning dove abundance, Piedmont ecoregion, USA | Human well being indicator-home value, Great Lakes waterfront, USA | COBRA (CO–Benefits Risk Assessment) v 4.1 | Wetland biodiversity response, Netherlands |
EM Source or Collection
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None | None | EU Biodiversity Action 5 | US EPA | None | InVEST | InVEST | US EPA | US EPA | US EPA | US EPA | None | None | None | None | None | None | US EPA | None |
EM Source Document ID
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271 | 254 | 228 | 275 | 243 | 311 | 315 | 332 | 335 | 335 | 335 | 354 | 368 |
372 ?Comment:Document 373 is a secondary source for this EM. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
405 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
437 ?Comment:User's manual is provided at the webpage. |
453 |
Document Author
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Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Schriever, C. A., and Liess, M. | Haines-Young, R., Potschin, M. and Kienast, F. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | Natural Capital Project | The Natural Capital Project | Ted R. Angradi, David W. Bolgrien, Jonathon J. Launspach, Brent J. Bellinger, Matthew A. Starry, Joel C. Hoffman, Mike E. Sierszen, Anett S. Trebitz, and Tom P. Hollenhorst | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Saleh, A., O. Gallego, E. Osei, H. Lal, C. Gross, S. McKinney, and H. Cover | Van Soesbergen, A. and M. Mulligan | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Murphy, C. and T. Weekley | Riffel, S., Scognamillo, D., and L. W. Burger | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | US EPA | Eppink, F.V., van den Bergh, J.C.J.M, and P. Rietveld |
Document Year
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2008 | 2007 | 2012 | 2014 | 2011 | 2015 | 2015 | 2016 | 2014 | 2014 | 2014 | 2011 | 2018 | 2010 | 2012 | 2008 | None | 2021 | 2004 |
Document Title
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Mapping ecosystem services for planning and management | Mapping ecological risk of agricultural pesticide runoff | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | Carbon payments and low-cost conservation | Water Yield: Reservoir Hydropower Production- InVEST (v3.0) | Carbon storage and sequestration - InVEST (v3.2.0) | Mapping ecosystem service indicators of a Great Lakes estuarine Area of Concern | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Nutrient Tracking Tool - a user-friendly tool for calculating nutrient reductions for water quality trading | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | CO-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA) | Modelling biodiversity and land use: urban growth, agriculture and nature in a wetland area |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | 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 but unpublished (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 | Web published | Website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published report | Published journal manuscript | Journal manuscript submitted or in review | Webpage | Published journal manuscript |
EM ID
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EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | Not applicable | Not applicable | http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | www.policysupport.org/waterworld | Not applicable | Not applicable | Not applicable | Not applicable | https://www.epa.gov/cobra | Not applicable | |
Contact Name
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Roland E Schulze | Carola Alexandra Schriever | Marion Potschin |
M. R. Frazier ?Comment:Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA |
Neville D. Crossman | Natural Capital Project | The Natural Capital Project | Ted R. Angradi | Susan H. Yee | Susan H. Yee | Susan H. Yee | Ali Saleh | Arnout van Soesbergen | David Otis | Chris Murphy | Sam Riffell | Ted Angradi | Emma Zinsmeister | Florian Eppink |
Contact Address
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School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa | Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365 | CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | 371 Serra Mall, Stanford University, Stanford, Ca 94305 | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | United States Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboraty, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804 USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Texas Institute for Applied Environmental Research-Tarleton State University, Stephenville, TX 76401,USA | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | EPA’s Office of Atmospheric Programs’ Climate Protection Partnerships Division | Institued for Environmental Studies, Free University, De Boelelaan 1087, 1081 HV Amsterdam, Netherlands |
Contact Email
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schulzeR@nu.ac.za | carola.schriever@ufz.de | marion.potschin@nottingham.ac.uk | frazier@nceas.ucsb.edu | neville.crossman@csiro.au | invest@naturalcapitalproject.org | invest@naturalcapitalproject.org | angradi.theodore@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | saleh@tiaer.tarleton.edu | arnout.van_soesbergen@kcl.ac.uk | dotis@iastate.edu | chris.murphy@idfg.idaho.gov | sriffell@cfr.msstate.edu | tedangradi@gmail.com | zinsmeister.emma@epa.gov | Florian.eppink@ivm.falw.vu.nl |
EM ID
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EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
Summary Description
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AUTHOR'S DESCRIPTION (Doc ID 272): "ACRU is a daily timestep, physical conceptual and multipurpose model structured to simulate impacts of land cover/ use change. The model can output, inter alia, components of runoff, irrigation supply and demand, reservoir water budgets as well as sediment and crop yields." AUTHOR'S DESCRIPTION (Doc ID 271): "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…The total benefit to people of water supply is a function of both the quantity and quality with the ecosystem playing a key role in the latter. However, due to the lack of suitable national scale data on water quality for quantifying the service, runoff was used as an estimate of the benefit where runoff is the total water yield from a watershed including surface and subsurface flow. This assumes that runoff is positively correlated with quality, which is the case in South Africa (Allanson et al., 1990)…In South Africa, water resources are mapped in water management areas called catchments (vs. watersheds) where a catchment is defined as the area of land that is drained by a single river system, including its tributaries (DWAF, 2004). There are 1946 quaternary (4th order) catchments in South Africa, the smallest is 4800 ha and the average size is 65,000 ha. Schulze (1997) modelled annual runoff for each quaternary catchment. During modelling of runoff, he used rainfall data collected over a period of more than 30 years, as well as data on other climatic factors, soil characteristics and grassland as the land cover. In this study, median annual simulated runoff was used as a measure of surface water supply. The volume of runoff per quaternary catchment was calculated for surface water supply. The range (areas with runoff of 30 million m^3 or more) and hotspots (areas with runoff of 70 million m^3 or more) were defined using a combination of statistics and expert inputs due to a lack of published thresholds in the literature." | ABSTRACT: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture." | AUTHOR'S DESCRIPTION: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds." | ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns from agriculture and from carbon plantings." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010). In this context, opportunity cost is usually expressed as the profit from agricultural production…We based our calculations of agricultural profit on Bryan et al. (2009), who calculated profit at full equity (i.e., economic return to land, capital, and management, exclusive of financial debt). We calculated an annual profit at full equity (PFEc) layer for each commodity (c) in the set of agricultural commodities (C), where C is wheat, field peas, beef cattle, or sheep." | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. AUTHOR'S DESCRIPTION: "The InVEST Reservoir Hydropower model estimates the relative contributions of water from different parts of a landscape, offering insight into how changes in land use patterns affect annual surface water yield and hydropower production. Modeling the connections between landscape changes and hydrologic processes is not simple. Sophisticated models of these connections and associated processes (such as the WEAP model) are resource and data intensive and require substantial expertise. To accommodate more contexts, for which data are readily available, InVEST maps and models the annual average water yield from a landscape used for hydropower production, rather than directly addressing the affect of LULC changes on hydropower failure as this process is closely linked to variation in water inflow on a daily to monthly timescale. Instead, InVEST calculates the relative contribution of each land parcel to annual average hydropower production and the value of this contribution in terms of energy production. The net present value of hydropower production over the life of the reservoir also can be calculated by summing discounted annual revenues. The model runs on a gridded map. It estimates the quantity and value of water used for hydropower production from each subwatershed in the area of interest. It has three components, which run sequentially. First, it determines the amount of water running off each pixel as the precipitation less the fraction of the water that undergoes evapotranspiration. The model does not differentiate between surface, subsurface and baseflow, but assumes that all water yield from a pixel reaches the point of interest via one of these pathways. This model then sums and averages water yield to the subwatershed level. The pixel-scale calculations allow us to represent the heterogeneity of key driving factors in water yield such as soil type, precipitation, vegetation type, etc. However, the theory we are using as the foundation of this set of models was developed at the subwatershed to watershed scale. We are only confident in the interpretation of these models at the subwatershed scale, so all outputs are summed and/or averaged to the subwatershed scale. We do continue to provide pixel-scale representations of some outputs for calibration and model-checking purposes only. These pixel-scale maps are not to be interpreted for understanding of hydrological processes or to inform decision making of any kind. | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | ABSTRACT: "Estuaries provide multiple ecosystem services from which humans benefit…We described an approach, with examples, for assessing how local-scale actions affect the extent and distribution of coastal ecosystem services, using the St. Louis River estuary (SLRE) of western Lake Superior as a case study. We based our approach on simple models applied to spatially explicity biophysical data that allows us to map the providing area of ecosystem services at high resolution (10-m^2 pixel) across aquatic and riparian habitats…Aspects of our approach can be adapted by communities for use in support of local decision-making." AUTHOR'S DESCRIPTION: "We derived the decision criteria used to map the IEGS habitat proxy of esocid spawning from habitat suitability information for two species that have similar but not identical spawning habitat and behavior." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events (UNEP-WCMC (United Nations Environment Programme, World Conservation Monitoring Centre), 2006; WRI (World Resources Institute), 2009), but is often quantified as wave energy attenuation, an intermediate service that contributes to shoreline protection by reducing rates of erosion or coastal inundation (Principeet al., 2012). From earlier models Gourlay (1996, 1997), Sheppard et al. (2005) developed a tool that can be used to estimate the percent attenuation in wave energy or wave height due to the presence of the reef. The decay in wave heights over the reef is described by dHrs/dx = -(fw/3π) (Hrs2/ (ηr+hr+ηw)2 where Hrs is significant wave height, fw is friction over the reef top, hr is water depth over the reef flat, and ηw is meteorological surge. Wave set-up on the reef-flat, ηr…" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(2) density of the queen conch Strombus gigas" | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(5) value of finfish," | ABSTRACT: "The Nutrient Tracking Tool (NTrT) is an enhanced version of the Nitrogen Trading Tool, a user-friendly Web-based computer program originally developed by the USDA. The NTrT estimates nutrient (nitrogen and phosphorus) and sediment losses from fields managed under a variety of cropping patterns and management practices through its user-friendly, Web-based linkage to the Agricultural Policy Environmental eXtender (APEX) model. It also accesses the USDA Natural Resources Conservation Service’s Web Soil Survey to utilize their geographic information system interface for field and operation identification and load soil information. The NTrT provides farmers, government officials, and other users with a fast and efficient method of estimating nitrogen and phosphorus credits for water quality trading, as well as other water quality, water quantity, and farm production impacts associated with conservation practices. The information obtained from the tool can help farmers determine the most cost-effective conservation practice alternatives for their individual operations and provide them with more advantageous options in a water quality credit trading program. An application of the NTrT to evaluate conservation practices on fields receiving dairy manure in a north central Texas watershed indicates that phosphorus-based application rates, filter strips, forest buffers, and complete manure export off the farm all result in reduced phosphorus losses from the fields on which those practices were implemented. When compared to a base¬line condition that entailed manure application at the nitrogen agronomic rate of receiving crops, the reductions in total phosphorus losses associated with these practices ranged from 15% (2P Rate scenario) to 76% (forest buffer scenario)." AUTHOR'S DESCRIPTION: "This paper provides a brief overview of the NTrT and presents results of verification and application of the tool on a selected field on a test field in the Upper North Bosque River (UNBR) watershed in Texas…simulations for the baseline and all five alternative scenarios were replicated for each of 90 specific soil types in Erath County, Texas…results reported and discussed in this report represent the averages of the output for all soil types." | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT:"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: "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." | Introduction: "COBRA is a screening tool that provides preliminary estimates of the impact of air pollution emission changes on ambient particulate matter (PM) air pollution concentrations, translates this into health effect impacts, and then monetizes these impacts, as illustrated below. The model does not require expertise in air quality modeling, health effects assessment, or economic valuation. Built into COBRA are emissions inventories, a simplified air quality model, health impact equations, and economic valuations ready for use, based on assumptions that EPA currently uses as reasonable best estimates. COBRA also enables advanced users to import their own datasets of emissions inventories, population, incidence, health impact functions, and valuation functions. Analyses can be performed at the state or county level and across the 14 major emissions categories (these categories are called “tiers”) included in the National Emissions Inventory. COBRA presents results in tabular as well as geographic form, and enables policy analysts to obtain a first-order approximation of the benefits of different mitigation scenarios under consideration. However, COBRA is only a screening tool. More sophisticated, albeit time- and resource-intensive, modeling approaches are currently available to obtain a more refined picture of the health and economic impacts of changes in emissions. EPA initially developed COBRA as a desktop application. In 2021, EPA released a web-based version of the tool, known as the COBRA Web Edition. Although the desktop version and web versions of COBRA both use the same methodology to calculate outdoor air quality and health impacts from changes in air pollution emissions, the desktop version offers additional advanced features that are not included in the more streamlined Web Edition. In particular, the desktop version is preloaded with input data on emissions, population, and baseline health incidence for 2016, 2023, and 2028; the Web Edition includes data only for 2023. Similarly, the desktop version allows users to import custom input datasets, while the Web Edition does not. The Web Edition, however, does not require the user to download or install additional software, and it runs more quickly than the desktop version. Users might choose to use the desktop version if they would like to use advanced features, such as custom input data and/or use the preloaded data for 2016 or 2028. Otherwise, users may choose to use the Web Edition for data analysis relevant to 2023. The process for entering emissions input data into COBRA is very similar for the desktop and web versions of the tool. The remainder of this User’s Manual focuses on the steps required to run the desktop version of the tool. The same general process can be used with the Web Edition." | ABSTRACT: "Wherever human land use is located near sensitive natural areas, such as wetlands, it has significant impacts on biodiversity in those areas. Both species richness and species composition are affected. As biodiversity is lost, conservation efforts increase and act as a constraint on land use options. Given these links, land use is a central factor in an ecological–economic analysis of biodiversity. This paper presents a general, dynamic simulation model of the interaction between wetland biodiversity and land use. Results for a set of scenarios suggest that urban growth is unsustainable and that there may be a conflict between conservation of distinct aspects of biodiversity. " |
Specific Policy or Decision Context Cited
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None identified | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | None identified | None identified | None identified | None identified | None identified | Federal delisting of an area of concern (AOC) | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None reported | None identified | None identified | None reported |
Biophysical Context
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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. | Not applicable | No additional description provided | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | None applicable | Not applicable | No additional description provided | No additional description provided | No additional description provided | No additional description provided | The UNBR watershed is comprised primarily of two main physiographic areas, the West Cross Timbers and the Grand Prairie Land Resource Areas. In the West Cross Timbers, soils are primarily fine sandy loams with sandy clay subsoils. Soils in the Grand Prairie area, on the other hand, are typically calcareous clays and clay loams (Ward et al. 1992). | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | Prairie Pothole Region of Iowa | restored, enhanced and created wetlands | Conservation Reserve Program lands left to go fallow | Waterfront districts on south Lake Michigan and south lake Erie | No additional description provided | Wetlands, Urbanization |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | N/A | Optional future scenarios for changed LULC and wood harvest | The effect of habitat restoration on esocid spawning area was simulated by varying biophysical changes. | No scenarios presented | No scenarios presented | No scenarios presented | Conservation management strategies to reduce phosphorus losses | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | No scenarios presented | Sites, function or habitat focus | N/A | N/A | No scenarios presented | land use, policy alternatives |
EM ID
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EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method Only | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing model | New or revised model | WESP - Urban Stormwater Treatment | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
Document ID for related EM
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Doc-272 ?Comment:Doc ID 272 was also used as a source document for this EM |
Doc-255 | Doc-256 | Doc-257 | Doc-231 | Doc-228 | None | Doc-244 | Doc-307 | Doc-280 | Doc-338 | Doc-205 | Doc-309 | None | None | None | None | Doc-352 | None | Doc-372 | Doc-373 | Doc-390 | Doc-405 | Doc-422 | None | None |
EM ID for related EM
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None | None | EM-119 | EM-120 | EM-121 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-166 | EM-170 | EM-171 | None | None | EM-437 | EM-148 | EM-344 | EM-111 | EM-349 | None | EM-448 | EM-449 | EM-450 | None | None | EM-549 | None | EM-705 | EM-704 | EM-703 | EM-701 | EM-700 | EM-632 | EM-718 | EM-734 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-844 | EM-845 | EM-846 | EM-847 | EM-886 | EM-889 | EM-890 | EM-891 | EM-893 | EM-894 | EM-895 | None | None |
EM Modeling Approach
EM ID
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EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
EM Temporal Extent
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1950-1993 | 2000 | 2000 | December 2007 - November 2008 | 2002-2008 | Not applicable | Not applicable | 2013 | 2006-2007, 2010 | 2006-2007, 2010 | 2006-2007, 2010 | 1960-2001 | 1950-2071 | 1987-2007 | 2010-2011 | 2008 | 2022 | Not applicable | Model run time |
EM Time Dependence
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time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | Not applicable | time-dependent |
EM Time Reference (Future/Past)
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future time | future time | Not applicable | Not applicable | Not applicable | future time | future time | Not applicable | Not applicable | Not applicable | Not applicable | future time | both | Not applicable | past time | Not applicable | Not applicable | Not applicable | future time |
EM Time Continuity
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discrete | discrete | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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1 | 1 | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 |
EM Temporal Grain Size Unit
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Day | Day | Not applicable | Not applicable | Not applicable | Year | Year | Not applicable | Not applicable | Not applicable | Not applicable | Day | Month | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year |
EM ID
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EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
Bounding Type
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Geopolitical | Geopolitical | Geopolitical | Physiographic or ecological | Physiographic or Ecological | Not applicable | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Geopolitical | Geopolitical | No location (no locational reference given) |
Spatial Extent Name
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South Africa | EU-15 | The EU-25 plus Switzerland and Norway | Yaquina Estuary (intertidal), Oregon, USA | Agricultural districts of the state of South Australia | Not applicable | Not applicable | St. Louis River estuary | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Upper North Bosque River watershed | Santa Basin | CREP (Conservation Reserve Enhancement Program | Wetlands in idaho | Piedmont Ecoregion | Great Lakes waterfront | Not applicable | Urban area |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 1-10 km^2 | 100,000-1,000,000 km^2 | Not applicable | Not applicable | 10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | Not applicable | 10-100 km^2 |
EM ID
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EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
EM Spatial Distribution
em.detail.distributeLumpHelp
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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 distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:pixel is likely 30m x 30m |
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 distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
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 (habitat type) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | Not applicable | map scale, for cartographic feature | area, for pixel or radial feature |
Spatial Grain Size
em.detail.spGrainSizeHelp
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Distributed by catchments with average size of 65,000 ha | 10 km x 10 km | 1 km x 1 km | 0.87-104.29 ha | 1 ha | Not specified | application specific | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | 10 m x 10 m | Not applicable | 1 km2 | multiple, individual, irregular sites | Not applicable | Not applicable | Not applicable | user defined | Not reported |
EM ID
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EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
EM Computational Approach
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Numeric | Analytic | Logic- or rule-based | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | * | Analytic | Numeric | Analytic | Numeric | 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 | stochastic | deterministic |
Statistical Estimation of EM
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None |
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EM ID
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EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | Unclear | No |
Yes ?Comment:Annual Yield can be calibrated with actual yield based up 10 year average input data though this was an "optional" part of the model. Calibrate with total precipitation and potential evapotranspiration. Before the calibration process is commenced, the modelers suggest performing a sensitivity analysis with the observed runoff data to define the parameters that influence model outputs the most. The calibration can then focus on highly sensitive parameters followed by less sensitive ones. |
Not applicable | No | Yes | Yes | Yes | Yes | No | Unclear | No | Yes | No | Not applicable | Yes |
Model Goodness of Fit Reported?
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No | No | No | No | No | Not applicable | Not applicable | No | No | No | No | No | No | No | No | No | No | Not applicable | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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No | No | Yes | No | No | No | Not applicable | No | Yes | Yes | Yes | No | Yes | No | No | No | No | Not applicable | No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | Yes | No | No | No | No | Not applicable | No | No | No | No | No | No | No | No | No | No | Not applicable | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | Yes | No | No | No | Not applicable | Not applicable | No | No | No | No | No | No | No | No | Yes | Yes | Not applicable | Yes |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Yes |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
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None | None |
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None | None | None |
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None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
None | None | None |
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None | None | None | None |
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None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
Centroid Latitude
em.detail.ddLatHelp
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-30 | 50.01 | 50.53 | 44.62 | -34.9 | -9999 | -9999 | 46.74 | 17.73 | 17.73 | 17.73 | 32.09 | -9.05 | 42.62 | 44.06 | 36.23 | 42.26 | Not applicable | -9999 |
Centroid Longitude
em.detail.ddLongHelp
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25 | 4.67 | 7.6 | -124.06 | 138.7 | -9999 | -9999 | -92.14 | -64.77 | -64.77 | -64.77 | -98.12 | -77.81 | -93.84 | -114.69 | -81.9 | -87.84 | Not applicable | -180 |
Centroid Datum
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WGS84 | WGS84 | WGS84 | None provided | WGS84 | Not applicable | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | None provided |
Centroid Coordinates Status
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Estimated | Estimated | Estimated | Provided | Estimated | Not applicable | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Agroecosystems | Rivers and Streams | Not applicable | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | None | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Not reported | Arable lands in near-stream environments | Not applicable | Estuarine intertidal | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Watershed | Terrestrial environments, but not specified for methods | freshwater estuary | Coral reefs | Coral reefs | Coral reefs | Rangeland and forage fields for dairy | tropical, coastal to montane | Wetlands buffered by grassland within agroecosystems | created, restored and enhanced wetlands | grasslands | Lake Michigan waterfront | Not applicable | Urban wetlands |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is coarser 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 is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Not applicable | 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 is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Other or unclear (comment) | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable | Guild or Assemblage | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Community | Species | Guild or Assemblage | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Species | Not applicable | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
None Available | None Available | None Available |
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None Available | None Available |
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None Available |
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None Available | None Available |
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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)
EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
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None |
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None | None |
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None |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-84 | EM-92 | EM-99 | EM-103 | EM-126 | EM-368 | EM-374 | EM-415 | EM-447 | EM-459 | EM-462 |
EM-584 ![]() |
EM-630 | EM-702 |
EM-729 ![]() |
EM-843 | EM-888 | EM-944 |
EM-969 ![]() |
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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 |
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None |
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None | None |
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