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-51 ![]() |
EM-109 ![]() |
EM-417 |
EM Short Name
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EnviroAtlas-Nat. filtration-water | UFORE-Hydro, Baltimore, MD, USA | SWAT, Guanica Bay, Puerto Rico, USA |
EM Full Name
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US EPA EnviroAtlas - Natural filtration (of water by tree cover); Example is shown for Durham NC and vicinity, USA |
UFORE-Hydro (Urban Forest Effects - Hydrology) v1, Dead Run Catchment, Baltimore, MD ?Comment:UFORE-Hydro is now incorporated in the i-Tree suite of models as iTree-Hydro. |
SWAT (Soil and Water Assessment Tool) Guánica Bay, Puerto Rico, USA |
EM Source or Collection
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US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
i-Tree | USDA Forest Service | US EPA |
EM Source Document ID
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223 | 190 | 334 |
Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Wang, J., Endreny, T. A. and Nowak, D. J. | Hu, W. and Y. Yuan |
Document Year
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2013 | 2008 | 2013 |
Document Title
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EnviroAtlas - Featured Community | Mechanistic simulation of tree effects in an urban water balance model | Evaluation of Soil Erosion and Sediment Yield for the Ridge Watersheds in the Guanica Bay Watershed, Puerto Rico, Using the SWAT Model |
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 on US EPA EnviroAtlas website | Published journal manuscript | Published EPA report |
EM ID
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EM-51 ![]() |
EM-109 ![]() |
EM-417 |
https://www.epa.gov/enviroatlas | http://www.itreetools.org/ | Not applicable | |
Contact Name
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EnviroAtlas Team | Jun Wang | Yongping Yuan |
Contact Address
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Not reported | Environmental Resources and Forest Engineering, Colecge of Environmental Science and Forestry, State University of New York, Syracuse, New York 13210 | USEPA, ORD, NERL, Environmental sciences Division, Las Vegas, Nevada |
Contact Email
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enviroatlas@epa.gov | Not reported | Yuan.Yongping@epa.gov |
EM ID
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EM-51 ![]() |
EM-109 ![]() |
EM-417 |
Summary Description
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The Natural Filtration model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. METADATA ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina... runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA DESCRIPTION: "The i-Tree Hydro model estimates the effects of tree and impervious cover on hourly stream flow values for a watershed (Wang et al 2008). i-Tree Hydro also estimates changes in water quality using hourly runoff estimates and mean and median national event mean concentration (EMC) values. The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results… After calibration, the model was run a number of times under various conditions to see how the stream flow would respond given varying tree and impervious cover in the watershed… The term event mean concentration (EMC) is a statistical parameter used to represent the flow-proportional average concentration of a given parameter during a storm event. EMC data is used for estimating pollutant loading into watersheds. The response outputs were calculated as kg of pollutant per square meter of land area for pollutants. These per square meter values were multiplied by the square meters of land area in the block group to estimate the effects at the block group level." METADATA DESCRIPTION PARAPHRASED: Changes in water quality were estimated for the following pollutants (entered as separate runs); total suspended solids (TSS), total phosphorus, soluble phosphorus, nitrites and nitrates, total Kjeldahl nitrogen (TKN), biochemical oxygen demand (BOD5), chemical oxygen demand (COD5), and copper. "Reduction in annual runoff (census block group)" variable data was derived from the EnviroAtlas water recharge coverage which used the i-Tree Hydro model. | ABSTRACT: "A semidistributed, physical-based Urban Forest Effects – Hydrology (UFORE-Hydro) model was created to simulate and study tree effects on urban hydrology and guide management of urban runoff at the catchment scale. The model simulates hydrological processes of precipitation, interception, evaporation, infiltration, and runoff using data inputs of weather, elevation, and land cover along with nine channel, soil, and vegetation parameters. Weather data are pre-processed by UFORE using Penman-Monteith equations to provide potential evaporation terms for open water and vegetation. Canopy interception algorithms modified established routines to better account for variable density urban trees, short vegetation, and seasonal growth phenology. Actual evaporation algorithms allocate potential energy between leaf surface storage and transpiration from soil storage. Infiltration algorithms use a variable rain rate Green-Ampt formulation and handle both infiltration excess and saturation excess ponding and runoff. Stream discharge is the sum of surface runoff and TOPMODEL- based subsurface flow equations. Automated calibration routines that use observed discharge has been coupled to the model." FURTHER DESCRIPTION: UFORE-Hydro was tested in the urban Dead Run catchment of Baltimore, Maryland, USA. | AUTHOR'S DESCRIPTION: " SWAT is a physically-based continuous watershed simulation model that operates on a daily time step. It is designed for long-term simulations. The U.S. Department of Agriculture-Agriculture Research Station (USDA-ARS) Grassland, Soil and Water Research Laboratory in Temple, Texas created SWAT in the early 1990s. It has undergone continual review and expansion of capabilities since it was created (Arnold et al., 1998; Neitsch, et al., 2011a and b). This model has the ability to predict changes in water, sediment, nutrient and pesticide loads with respect to the different management conditions in watershed. Major components of the SWAT model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides and agricultural management practices (Neitsch et al., 2011b). SWAT subdivides a watershed into multiple sub-watersheds, and the subwatersheds are further divided into Hydrologic Response Units (HRUs) that consist of homogeneous land use, soils, slope, and management (Gassman et al., 2007; Neitsch, et al., 2011b; Williams et al., 2008). |
Specific Policy or Decision Context Cited
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None identified | None identified | None Identified |
Biophysical Context
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No additional description provided | No additional description provided | Need to fill in |
EM Scenario Drivers
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No scenarios presented |
Base case; increase pervious area tree cover to 40%; increase impervious area tree cover to 40%; double impervious area to 60%; halve pervious area tree cover to 6%; double pervious area tree cover to 24% and increase pervious area tree cover to 20%. ?Comment:Base case is existing conditions. |
Planting type, fertilizing rate, harvest rate |
EM ID
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EM-51 ![]() |
EM-109 ![]() |
EM-417 |
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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Application of existing 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-51 ![]() |
EM-109 ![]() |
EM-417 |
Document ID for related EM
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Doc-198 | Doc-198 | None |
EM ID for related EM
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EM-137 | EM-142 | EM-137 | None |
EM Modeling Approach
EM ID
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EM-51 ![]() |
EM-109 ![]() |
EM-417 |
EM Temporal Extent
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1999-2010 | 2000 | 1981-2004 |
EM Time Dependence
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time-stationary ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, operated on an hourly timestep. The final annual flow parameter however is time stationary. |
time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | both | future time |
EM Time Continuity
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Not applicable | discrete | discrete |
EM Temporal Grain Size Value
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Not applicable | 1 | 1 |
EM Temporal Grain Size Unit
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Not applicable | Hour | Day |
EM ID
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EM-51 ![]() |
EM-109 ![]() |
EM-417 |
Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC |
Spatial Extent Name
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Durham, NC and vicinity | Dead Run Catchement, Baltimore, MD | Guanica Bay, Puerto Rico watersheds |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | 10-100 km^2 | 100-1000 km^2 |
EM ID
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EM-51 ![]() |
EM-109 ![]() |
EM-417 |
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) |
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 |
Spatial Grain Size
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irregular | irregular topographically delineated similar units | 30m x 30m |
EM ID
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EM-51 ![]() |
EM-109 ![]() |
EM-417 |
EM Computational Approach
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Analytic ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, was numeric. The final parameter however did not require iteration. |
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-51 ![]() |
EM-109 ![]() |
EM-417 |
Model Calibration Reported?
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Unclear | Yes |
Yes ?Comment:Used 1981 and 1982 data to calibrate hydrology. |
Model Goodness of Fit Reported?
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No | Yes |
No ?Comment:Calibration for both the stream flow and Sediment concentration of the mode |
Goodness of Fit (metric| value | unit)
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None |
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Model Operational Validation Reported?
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Unclear | Yes |
Yes ?Comment:Validation with 1983-1984 data from USGS. Used streamflow and water quality data from two stations |
Model Uncertainty Analysis Reported?
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Unclear | Unclear | Unclear |
Model Sensitivity Analysis Reported?
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Unclear | No |
Yes ?Comment:Yes for both runoff and sediment |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | No |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-51 ![]() |
EM-109 ![]() |
EM-417 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-51 ![]() |
EM-109 ![]() |
EM-417 |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-51 ![]() |
EM-109 ![]() |
EM-417 |
Centroid Latitude
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35.99 | 39.31 | 18.19 |
Centroid Longitude
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-78.96 | -76.74 | -66.76 |
Centroid Datum
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None provided | WGS84 | WGS84 |
Centroid Coordinates Status
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Estimated | Provided | Estimated |
EM ID
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EM-51 ![]() |
EM-109 ![]() |
EM-417 |
EM Environmental Sub-Class
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Rivers and Streams | Created Greenspace | Rivers and Streams | Ground Water | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Urban areas including streams | Urban watershed | watershed |
EM Ecological Scale
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Not applicable | 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-51 ![]() |
EM-109 ![]() |
EM-417 |
EM Organismal Scale
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Not applicable | Community | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-51 ![]() |
EM-109 ![]() |
EM-417 |
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-51 ![]() |
EM-109 ![]() |
EM-417 |
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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-51 ![]() |
EM-109 ![]() |
EM-417 |
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None |