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-84 | EM-88 | EM-392 |
EM-397 
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EM-729
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EM-863
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EM Short Name
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ACRU, South Africa | Area and hotspots of carbon storage, South Africa | EPA H2O, Tampa Bay Region, FL,USA | Wetland shellfish production, Gulf of Mexico, USA | WESP: Urban Stormwater Treatment, ID, USA | SLAMM, Tampa Bay, FL, USA |
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EM Full Name
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ACRU (Agricultural Catchments Research Unit), South Africa | Area and hotspots of carbon storage, South Africa | EPA H2O, Tampa Bay Region, FL, USA | Wetland shellfish production, Gulf of Mexico, USA | WESP: Urban Stormwater Treament, ID, USA | SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA |
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EM Source or Collection
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None | None | US EPA |
US EPA ?Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science |
None | None |
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EM Source Document ID
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271 | 271 | 321 | 324 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
415 ?Comment:Secondary sources: Documents 412 and 413. |
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Document Author
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Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Ranade, P., Soter, G., Russell, M., Harvey, J., and K. Murphy | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Murphy, C. and T. Weekley | Sherwood, E. T. and H. S. Greening |
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Document Year
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2008 | 2008 | 2015 | 2012 | 2012 | 2014 |
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Document Title
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Mapping ecosystem services for planning and management | Mapping ecosystem services for planning and management | EPA H20 User Manual | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats |
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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 journal manuscript | Published journal manuscript | Published EPA report | Published journal manuscript | Published report | Published journal manuscript |
Software and Access
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EM ID
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EM-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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| Not applicable | Not applicable | http://www.epa.gov/ged/tbes/EPAH2O | Not applicable | Not applicable | http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html | |
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Contact Name
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Roland E Schulze | Benis Egoh | Marc J. Russell, Ph.D. | Stephen J. Jordan | Chris Murphy | Edward T. Sherwood |
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Contact Address
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School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | USEPA GED, One Sabine Island Dr., Gulf Breeze, FL 32561 | U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA |
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Contact Email
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schulzeR@nu.ac.za | Not reported | russell.marc@epa.gov | jordan.steve@epa.gov | chris.murphy@idfg.idaho.gov | esherwood@tbep.org |
EM Description
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EM ID
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EM-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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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." | 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…In this study, only carbon storage was mapped because of a lack of data on the other functions related to the regulation of global climate such as carbon sequestration and the effects of changes in albedo. Carbon is stored above or below the ground and South African studies have found higher levels of carbon storage in thicket than in savanna, grassland and renosterveld (Mills et al., 2005). This information was used by experts to classify vegetation types (Mucina and Rutherford, 2006), according to their carbon storage potential, into three categories: low to none (e.g. desert), medium (e.g. grassland), high (e.g. thicket, forest) (Rouget et al., 2004). All vegetation types with medium and high carbon storage potential were identified as the range of carbon storage. Areas of high carbon storage potential where it is essential to retain this store were mapped as the carbon storage hotspot." | AUTHORS DESCRIPTION: "EPA H2O is a GIS based demonstration tool for assessing ecosystem goods and services (EGS). It was developed as a preliminary assessment tool in support of research being conducted in the Tampa Bay watershed. It provides information, data, approaches and guidance that communities can use to examine alternative land use scenarios in the context of nature’s benefits to the human community. . . EPA H2O allows users for the Tampa Bay estuary and its watershed to: • Gain a greater understanding of the significance of EGS, • Explore the spatial distribution of EGS and other ecosystem features, • Obtain map and summary statistics of EGS production's potential value, • Analyze and compare potential impacts from predicted development scenarios or user specified changes in land use patterns on EGS production's potential value EPA H2O is designed for analyzing data at neighborhood to regional scales.. . The tool is transportable to other locations if the required data are available. . . . | ABSTRACT: "We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for … commercial blue crab Callinectes sapidus and penaeid shrimp fisheries in the Gulf of Mexico." | 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 Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…" |
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Specific Policy or Decision Context Cited
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None identified | None identified | None reported | None identified | None identified | None identified |
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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. | 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 | Estuarine environments and marsh-land interfaces | restored, enhanced and created wetlands | No additional description provided |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | Land Use, EGS algorithm values, | Shellfish type; Changes to submerged aquatic vegetation (SAV) | Sites, function or habitat focus | Varying sea level rise (baseline - 2m), and two habitat adaption strategies |
EM Relationship to Other EMs or Applications
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EM ID
em.detail.idHelp
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EM-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Ten runs; blue crab and penaeid shrimp, each combined with five different submerged aquatic vegetation habitat areas. |
Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs |
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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 | WESP - Urban Stormwater Treatment | Application of existing 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-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-272 ?Comment:Doc ID 272 was also used as a source document for this EM |
Doc-271 | None | None | Doc-390 | Doc-412 | Doc-413 |
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EM ID for related EM
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None | EM-85 | EM-86 | EM-87 | None | EM-604 | EM-603 | EM-718 | EM-734 | EM-857 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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EM Temporal Extent
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1950-1993 | Not reported | Not applicable | 1950 - 2050 | 2010-2011 | 2002-2100 |
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EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | future time | past time | Not applicable |
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EM Time Continuity
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discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable |
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EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | Varies by Run | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Day | Not applicable | Not applicable | Year | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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Bounding Type
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Geopolitical | Geopolitical |
Geopolitical ?Comment:Extent was Tampa Bay area in example, but boundary can be geopolitical or watershed derived. |
Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Watershed/Catchment/HUC |
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Spatial Extent Name
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South Africa | South Africa | Tampa Bay region | Gulf of Mexico (estuarine and coastal) | Wetlands in idaho | Tampa Bay estuary watershed |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. |
Spatial Distribution of Computations
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EM ID
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EM-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Computations at this pixel scale pertain to certain variables specific to Mobile Bay. |
spatially lumped (in all cases) | 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 | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
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Distributed by catchments with average size of 65,000 ha | Distributed across catchments with average size of 65,000 ha | 30m x 30m | 55.2 km^2 | Not applicable | 10 x 10 m |
EM Structure and Computation Approach
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EM ID
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EM-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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EM Computational Approach
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Numeric | Analytic | Analytic | Numeric | 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-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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Model Calibration Reported?
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No | No | No | 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 | No | No | No | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No | No | No | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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| None | 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-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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Centroid Latitude
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-30 | -30 | 28.05 | 30.44 | 44.06 | 27.76 |
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Centroid Longitude
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25 | 25 | -82.52 | -87.99 | -114.69 | -82.54 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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EM Environmental Sub-Class
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Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Inland Wetlands | Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Not reported | Not applicable | All terestrial landcover and waterbodies | Submerged aquatic vegetation in estuaries and coastal lagoons | created, restored and enhanced wetlands | Esturary and associated urban and terrestrial environment |
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EM Ecological Scale
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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 | 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-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Species | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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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-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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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-84 | EM-88 | EM-392 |
EM-397
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EM-729
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EM-863
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None |
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None | None |