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-321 ![]() |
EM-605 ![]() |
EM-1018 |
EM Short Name
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Erosion prevention by vegetation, Portel, Portugal | VELMA v2.0, Ohio, USA | WMOSTsustainable water Danvers-Middleton, MA |
EM Full Name
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Soil erosion prevention provided by vegetation cover, Portel municipality, Portugal | Visualizing Ecosystems for Land Management Assessments (VELMA) v2.0, Shayler Crossing watershed, Ohio, USA | WMOST sustainable water management initiative Danvers-Middleton, MA |
EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA |
EM Source Document ID
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281 |
359 ?Comment:Document #366 is a supporting document for this EM. McKane et al. 2014, VELMA Version 2.0 User Manual and Technical Documentation. |
477 |
Document Author
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Guerra, C.A., Pinto-Correia, T., Metzger, M.J. | Hoghooghi, N., H. E. Golden, B. P. Bledsoe, B. L. Barnhart, A. F. Brookes, K. S. Djang, J. J. Halama, R. B. McKane, C. T. Nietch, and P. P. Pettus | United States EPA |
Document Year
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2014 | 2018 | 2013 |
Document Title
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Mapping soil erosion prevention using an ecosystem service modeling framework for integrated land management and policy | Cumulative effects of low impact development on watershed hydrology in a mixed land-cover system | Watershed Management Optimization Support Tool (WMOST) v1 User manual |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published EPA report |
EM ID
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EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
Not applicable | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NHEERL&dirEntryId=262280 | |
Contact Name
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Carlos A. Guerra | Heather Golden | Naomi Detenbeck |
Contact Address
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Instituto de Ciências Agrárias e Ambientais Mediterrânicas, Universidade de Évora, Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal | National Exposure Research Laboratory, Office of Research and Development, US EPA, Cincinnati, OH 45268, USA | NHEERL, Atlantic Ecology Division Narragansett, RI 02882 |
Contact Email
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cguerra@uevora.pt | Golden.Heather@epa.gov | detenbeck.naomi@epa.gov |
EM ID
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EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
Summary Description
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ABSTRACT: "We present an integrative conceptual framework to estimate the provision of soil erosion prevention (SEP) by combining the structural impact of soil erosion and the social–ecological processes that allow for its mitigation. The framework was tested and illustrated in the Portel municipality in Southern Portugal, a Mediterranean silvo-pastoral system that is prone to desertification and soil degradation. The results show a clear difference in the spatial and temporal distribution of the capacity for ecosystem service provision and the actual ecosystem service provision." AUTHOR'S DESCRIPTION: "To begin assessing the contribution of SEP we need to identify the structural impact of soil erosion, that is, the erosion that would occur when vegetation is absent and therefore no ES is provided. It determines the potential soil erosion in a given place and time and is related to rainfall erosivity (that is, the erosive potential of rainfall), soil erodibility (as a characteristic of the soil type) and local topography. Although external drivers can have an effect on these variables (for example, climate change), they are less prone to be changed directly by human action. The actual ES provision reduces the total amount of structural impact, and we define the remaining impact as the ES mitigated impact. We can then define the capacity for ES provision as a key component to determine the fraction of the structural impact that is mitigated…Following the conceptual outline, we will estimate the SEP provided by vegetation cover using an adaptation of the Universal Soil Loss Equation (USLE)." | ABSTRACT: "Low Impact Development (LID) is an alternative to conventional urban stormwater management practices, which aims at mitigating the impacts of urbanization on water quantity and quality. Plot and local scale studies provide evidence of LID effectiveness; however, little is known about the overall watershed scale influence of LID practices. This is particularly true in watersheds with a land cover that is more diverse than that of urban or suburban classifications alone. We address this watershed-scale gap by assessing the effects of three common LID practices (rain gardens, permeable pavement, and riparian buffers) on the hydrology of a 0.94 km2 mixed land cover watershed. We used a spatially-explicit ecohydrological model, called Visualizing Ecosystems for Land Management Assessments (VELMA), to compare changes in watershed hydrologic responses before and after the implementation of LID practices. For the LID scenarios, we examined different spatial configurations, using 25%, 50%, 75% and 100% implementation extents, to convert sidewalks into rain gardens, and parking lots and driveways into permeable pavement. We further applied 20 m and 40 m riparian buffers along streams that were adjacent to agricultural land cover…" AUTHOR'S DESCRIPTION: "VELMA’s modeling domain is a three-dimensional matrix that includes information regarding surface topography, land use, and four soil layers. VELMA uses a distributed soil column framework to model the lateral and vertical movement of water and nutrients through the four soil layers. A soil water balance is solved for each layer. The soil column model is placed within a watershed framework to create a spatially distributed model applicable to watersheds (Figure 2, shown here with LID practices). Adjacent soil columns interact through down-gradient water transport. Water entering each pixel (via precipitation or flow from an adjacent pixel) can either first infiltrate into the implemented LID and the top soil layer, and then to the downslope pixel, or continue its downslope movement as the lateral surface flow. Surface and subsurface lateral flow are routed using a multiple flow direction method, as described in Abdelnour et al. [21]. A detailed description of the processes and equations can be found in McKane et al. [32], Abdelnour et al. [21], Abdelnour et al. [40]." | ABSTRACT: "The Watershed Management Optimization Support Tool (WMOST) is intended to be used as a screening tool as part of an integrated watershed management process such as that described in EPA’s watershed planning handbook (EPA 2008).1 The objective of WMOST is to serve as a public-domain, efficient, and user-friendly tool for local water resources managers and planners to screen a widerange of potential water resources management options across their watershed or jurisdiction for costeffectiveness as well as environmental and economic sustainability (Zoltay et al 2010). Examples of options that could be evaluated with the tool include projects related to stormwater, water supply, wastewater and water-related resources such as Low-Impact Development (LID) and land conservation. The tool is intended to aid in evaluating the environmental and economic costs, benefits, trade-offs and co-benefits of various management options. In addition, the tool is intended to facilitate the evaluation of low impact development (LID) and green infrastructure as alternative or complementary management options in projects proposed for State Revolving Funds (SRF). WMOST is a screening model that is spatially lumped with a daily or monthly time step. The model considers water flows but does not yet consider water quality. The optimization of management options is solved using linear programming. The target user group for WMOST consists of local water resources managers, including municipal water works superintendents and their consultants. This document includes a presentation of a case study appling WMOST to the Danvers-Middleton, MA sustainable water management initiative. |
Specific Policy or Decision Context Cited
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None identified | None identified | Not applicable |
Biophysical Context
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Open savannah-like forest of cork (Quercus suber) and holm (Quercus ilex) oaks, with trees of different ages randomly dispersed in changing densities, and pastures in the under cover. The pastures are mostly natural in a mosaic with patches of shrubs, which differ in size and the distribution depends mainly on the grazing intensity. Shallow, poor soils are prone to erosion, especially in areas with high grazing pressure. | The Shayler Crossing (SHC) watershed is a subwatershed of the East Fork Little Miami River Watershed in southwest Ohio, USA and falls within the Till Plains region of the Central Lowland physiographic province. The Till Plains region is a topographically young and extensive flat plain, with many areas remaining undissected by even the smallest stream. The bedrock is buried under a mantle of glacial drift 3–15 m thick. The Digital Elevation Model (DEM) has a maximum value of ~269 m (North American_1983 datum) within the watershed boundary (Figure 1). The soils are primarily the Avonburg and Rossmoyne series, with high silty clay loam content and poor to moderate infiltration. Average annual precipitation for the period, 1990 through 2011, was 1097.4 _ 173.5 mm. Average annual air temperature for the same period was 12 _C Mixed land cover suburban watershed. The primary land uses consist of 64.1% urban or developed area (including 37% lawn, 12% building, 6.5% street, 6.4% sidewalk, and 2.1% parking lot and driveway), 23% agriculture, and 13% deciduous forest. Total imperviousness covers approximately 27% of the watershed area. | None |
EM Scenario Drivers
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Different land management practices as represented by the comparison of different grazing intensities (i.e., livestock densities) in the whole study area and in three Civil Parishes within the study area | Three types of Low Impact Development (LID) practices (rain gardens, permeable pavements, forested riparian buffers) applied a different conversion levels. |
None ?Comment:Not presented with scenarios, but the model was run with multiple scenarios for costs related to varying instream minimum flows and provided the associated costs for each run. |
EM ID
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EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application |
New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
Document ID for related EM
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Doc-282 | Doc-283 | Doc-284 | Doc-285 | Doc-13 | Doc-366 | Doc-477 |
EM ID for related EM
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None | EM-375 | EM-377 | EM-378 | EM-884 | EM-883 | EM-887 | None |
EM Modeling Approach
EM ID
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EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
EM Temporal Extent
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January to December 2003 | Jan 1, 2009 to Dec 31, 2011 | Not applicable |
EM Time Dependence
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time-dependent | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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future time | past time |
Not applicable ?Comment:method description |
EM Time Continuity
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discrete | discrete | discrete |
EM Temporal Grain Size Value
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1 | 1 | 1 |
EM Temporal Grain Size Unit
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Month | Day | Day |
EM ID
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EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC |
Spatial Extent Name
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Portel municipality | Shayler Crossing watershed, a subwatershed of the East Fork Little Miami River Watershed | Danvers-Middleton |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | 10-100 ha | 10-100 km^2 |
EM ID
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EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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250 m x 250 m | 10m x 10m | Not applicable |
EM ID
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EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
EM Computational Approach
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Analytic | Numeric | Numeric |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
Model Calibration Reported?
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No | Yes | Unclear |
Model Goodness of Fit Reported?
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No |
Yes ?Comment:Goodness of fit for calibrated (2009-2010) and observed streamflow. |
Unclear |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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No | Yes | Not applicable |
Model Uncertainty Analysis Reported?
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No | No | Not applicable |
Model Sensitivity Analysis Reported?
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No | No | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
Centroid Latitude
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38.3 | 39.19 | 42.58 |
Centroid Longitude
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-7.7 | -84.29 | -70.93 |
Centroid Datum
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WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Provided | Estimated |
EM ID
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EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Scrubland/Shrubland | Rivers and Streams | Ground Water | Forests | Agroecosystems | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Silvo-pastoral system | Mixed land cover suburban watershed | watershed |
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 |
Scale of differentiation of organisms modeled
EM ID
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EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
EM Organismal Scale
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Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
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-321 ![]() |
EM-605 ![]() |
EM-1018 |
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None |
<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-321 ![]() |
EM-605 ![]() |
EM-1018 |
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None |