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-964 | EM-968 |
EM Short Name
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EnviroAtlas-Nat. filtration-water | EcoSim II - method | EPA Stormwater Manamgement Model |
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 | EcoSim II - method | Storm Water Management Model User's Manual Version 5.2 |
EM Source or Collection
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US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
None | US EPA |
EM Source Document ID
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223 | 448 | 452 |
Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell | Rossman, L. A., M., Simon |
Document Year
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2013 | 2000 | 2022 |
Document Title
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EnviroAtlas - Featured Community | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II | Storm Water Management Model User's Manual Version 5.2 |
Document Status
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Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) |
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-964 | EM-968 |
https://www.epa.gov/enviroatlas | https://ecopath.org/downloads/ | https://www.epa.gov/water-research/storm-water-management-model-swmm | |
Contact Name
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EnviroAtlas Team | Carl Walters | David Burden |
Contact Address
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Not reported | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 | U.S. EPA Research Center for Environmental Solutions and Emergency Response (CESER) Mail Drop: 314 P.O. Box #1198 Ada, OK 74821-1198 |
Contact Email
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enviroatlas@epa.gov | c.walters@oceans.ubc.ca | burden.david@epa.gov |
EM ID
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EM-51 ![]() |
EM-964 | EM-968 |
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: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores " |
EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. Running under Windows, SWMM 5 provides an integrated environment for editing study area input data, running hydrologic, hydraulic and water quality simulations, and viewing the results in a variety of formats. These include color coded drainage area and conveyance system maps, time series graphs and tables, profile plots, and statistical frequency analyses. This user’s manual describes in detail how to run SWMM 5.2. It includes instructions on how to build a drainage system model, how to set various simulation options, and how to view results in a variety of formats. It also describes the different types of files used by SWMM and provides useful tables of parameter values. Detailed descriptions of the theory behind SWMM 5 and the numerical methods it employs can be found in a separate set of reference manuals. ?Comment:The variables used for this ESML entry were derived from the quick tutorial section of the SWMM manual. |
Specific Policy or Decision Context Cited
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None identified | None | NA |
Biophysical Context
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No additional description provided | None, Ocean ecosystems | NA |
EM Scenario Drivers
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No scenarios presented | N/A | NA |
EM ID
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EM-51 ![]() |
EM-964 | EM-968 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method Only | Method Only |
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-964 | EM-968 |
Document ID for related EM
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Doc-198 | None | None |
EM ID for related EM
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EM-137 | EM-142 | EM-1055 | EM-971 |
EM Modeling Approach
EM ID
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EM-51 ![]() |
EM-964 | EM-968 |
EM Temporal Extent
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1999-2010 | Not applicable | Not applicable |
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 | both |
EM Time Continuity
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Not applicable |
discrete ?Comment:Modeller dependent |
continuous |
EM Temporal Grain Size Value
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Not applicable | 1 | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Day | Not applicable |
EM ID
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EM-51 ![]() |
EM-964 | EM-968 |
Bounding Type
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Geopolitical | Other | No location (no locational reference given) |
Spatial Extent Name
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Durham, NC and vicinity | Not applicable | Not applicable |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | Not applicable | Not applicable |
EM ID
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EM-51 ![]() |
EM-964 | EM-968 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all 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) | Not applicable | area, for pixel or radial feature |
Spatial Grain Size
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irregular | Not applicable | mm |
EM ID
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EM-51 ![]() |
EM-964 | EM-968 |
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. |
Analytic | 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-964 | EM-968 |
Model Calibration Reported?
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Unclear | No | Not applicable |
Model Goodness of Fit Reported?
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No | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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Unclear | Not applicable | Not applicable |
Model Uncertainty Analysis Reported?
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Unclear | Not applicable | Not applicable |
Model Sensitivity Analysis Reported?
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Unclear | Not applicable | 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-51 ![]() |
EM-964 | EM-968 |
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-51 ![]() |
EM-964 | EM-968 |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-51 ![]() |
EM-964 | EM-968 |
Centroid Latitude
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35.99 | Not applicable | Not applicable |
Centroid Longitude
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-78.96 | Not applicable | Not applicable |
Centroid Datum
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None provided | Not applicable | Not applicable |
Centroid Coordinates Status
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Estimated | Not applicable | Not applicable |
EM ID
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EM-51 ![]() |
EM-964 | EM-968 |
EM Environmental Sub-Class
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Rivers and Streams | Created Greenspace | Open Ocean and Seas | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Urban areas including streams | Pelagic | User-defined catchments |
EM Ecological Scale
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Not applicable | Ecological scale corresponds to the Environmental Sub-class | Other or unclear (comment) |
Scale of differentiation of organisms modeled
EM ID
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EM-51 ![]() |
EM-964 | EM-968 |
EM Organismal Scale
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Not applicable |
Other (Comment) ?Comment:Varied levels of taxonomic order |
Not applicable |
Taxonomic level and name of organisms or groups identified
EM-51 ![]() |
EM-964 | EM-968 |
None Available |
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None Available |
EnviroAtlas URL
EM-51 ![]() |
EM-964 | EM-968 |
None Available | Big game hunting recreation demand | None Available |
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-964 | EM-968 |
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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-964 | EM-968 |
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