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
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EM ID
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EM-492 | EM-858 |
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EM Short Name
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EnviroAtlas - Restorable wetlands | ARIES Flood Reg, Santa Fe, NM |
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EM Full Name
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US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | ARIES Flood regulation, Santa Fe, New Mexico |
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EM Source or Collection
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US EPA | EnviroAtlas | None |
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EM Source Document ID
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262 | 411 |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. |
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Document Year
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2013 | 2018 |
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Document Title
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EnviroAtlas - National | Towards globally customizable ecosystem service models |
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Document Status
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Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript |
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EM ID
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EM-492 | EM-858 |
| https://www.epa.gov/enviroatlas |
https://integratedmodelling.org/hub/#/register ?Comment:Need to set up an account first and then can access the main integrated modelling hub page: |
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Contact Name
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EnviroAtlas Team | Javier Martinez-Lopez |
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Contact Address
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Not reported | BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain |
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Contact Email
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enviroatlas@epa.gov | javier.martinez@bc3research.org |
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EM ID
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EM-492 | EM-858 |
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Summary Description
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DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | ABSTRACT: "Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed. " |
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Specific Policy or Decision Context Cited
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None Identified | None identified |
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Biophysical Context
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No additional description provided | Watersheds surrounding Santa Fe and Albuquerque, New Mexico |
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EM Scenario Drivers
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No scenarios presented | N/A |
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EM ID
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EM-492 | EM-858 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application |
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New or Pre-existing EM?
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New or revised model | 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-492 | EM-858 |
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Document ID for related EM
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None | None |
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EM ID for related EM
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None | EM-859 |
EM Modeling Approach
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EM ID
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EM-492 | EM-858 |
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EM Temporal Extent
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2006-2013 | 1981-2015 |
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EM Time Dependence
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time-stationary | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable |
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EM ID
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EM-492 | EM-858 |
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Bounding Type
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Geopolitical | Watershed/Catchment/HUC |
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Spatial Extent Name
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conterminous United States | Santa Fe Fireshed |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 |
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EM ID
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EM-492 | EM-858 |
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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) |
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Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
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Spatial Grain Size
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irregular | 30 m |
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EM ID
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EM-492 | EM-858 |
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EM Computational Approach
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Analytic | Analytic |
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EM Determinism
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deterministic | deterministic |
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Statistical Estimation of EM
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EM ID
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EM-492 | EM-858 |
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Model Calibration Reported?
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No | Unclear |
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Model Goodness of Fit Reported?
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No | No |
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Goodness of Fit (metric| value | unit)
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None | None |
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Model Operational Validation Reported?
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No | No |
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Model Uncertainty Analysis Reported?
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No | No |
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Model Sensitivity Analysis Reported?
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No | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-492 | EM-858 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-492 | EM-858 |
| None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-492 | EM-858 |
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Centroid Latitude
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39.5 | 35.86 |
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Centroid Longitude
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-98.35 | -105.76 |
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Centroid Datum
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WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated |
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EM ID
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EM-492 | EM-858 |
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EM Environmental Sub-Class
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Agroecosystems | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Terrestrial | watersheds |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
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EM-492 | EM-858 |
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EM Organismal Scale
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Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-492 | EM-858 |
| None Available | None Available |
EnviroAtlas URL
| EM-492 | EM-858 |
| GAP Ecological Systems, The Watershed Boundary Dataset (WBD) | Dasymetric Allocation of Population, Average Annual Precipitation, Ecosystem Markets: Imperiled Species and Habitats |
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-492 | EM-858 |
| 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-492 | EM-858 |
| None | None |
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