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-91 | EM-137 |
EM-380 
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EM-392 |
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EM Short Name
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RHyME2, Upper Mississippi River basin, USA | i-Tree Hydro v4.0 | VELMA plant-soil, Oregon, USA | EPA H2O, Tampa Bay Region, FL,USA |
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EM Full Name
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RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | i-Tree Hydro v4.0 (default data option) | VELMA (Visualizing Ecosystems for Land Management Assessments) plant-soil, Oregon, USA | EPA H2O, Tampa Bay Region, FL, USA |
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EM Source or Collection
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US EPA | i-Tree | USDA Forest Service | US EPA | US EPA |
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EM Source Document ID
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123 | 198 | 317 | 321 |
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Document Author
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Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | USDA Forest Service | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Ranade, P., Soter, G., Russell, M., Harvey, J., and K. Murphy |
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Document Year
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2013 | Not Reported | 2013 | 2015 |
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Document Title
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Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | i-Tree Hydro User's Manual v. 4.0 | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | EPA H20 User Manual |
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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 |
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Comments on Status
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Published journal manuscript | Webpage | Published journal manuscript | Published EPA report |
Software and Access
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EM ID
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EM-91 | EM-137 |
EM-380
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EM-392 |
| Not applicable | http://www.itreetools.org | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | http://www.epa.gov/ged/tbes/EPAH2O | |
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Contact Name
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Liem Tran | Not applicable | Alex Abdelnour | Marc J. Russell, Ph.D. |
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Contact Address
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Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | Not applicable | Dept. of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | USEPA GED, One Sabine Island Dr., Gulf Breeze, FL 32561 |
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Contact Email
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ltran1@utk.edu | Not applicable | abdelnouralex@gmail.com | russell.marc@epa.gov |
EM Description
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EM ID
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EM-91 | EM-137 |
EM-380
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EM-392 |
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Summary Description
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ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | ABSTRACT: "i-Tree Hydro is the first urban hydrology model that is specifically designed to model vegetation effects and to be calibrated against measured stream flow data. It is designed to model the effects of changes in urban tree cover and impervious surfaces on hourly stream flows and water quality at the watershed level." AUTHOR'S DESCRIPTION: "The purpose of i-Tree Hydro is to simulate hourly changes in stream flow (and water quality) given changes in tree and impervious cover in the watershed. The following is an overview of the process: 1) Determine your watershed of analysis and stream gauge station. i-Tree Hydro works on a watershed basis with the watershed determined as the total drainage area upstream from a measured stream gauge. Stream gauge availability varies. 2) Download national digital elevation data. Once the area and location of the watershed are known, digital elevation data are downloaded from the USGS for an area that encompasses the entire watershed. ArcGIS software is then used to create a digital elevation map and to determine the exact boundary for the watershed upstream from the gauge station location. 3) Determine cover attributes of the watershed and gather other required data. i-Tree Canopy and other sources can be used to determine the tree cover, shrub cover, impervious surface and other cover types. Information about other aspects of the watershed such as proportion of evergreen trees and shrubs, leaf area index, and a variety of hydrologic parameters must be collected. 4) Get started with Hydro. Once these input data are ready, they are loaded into Hydro to begin analysis. 5) Calibrate the model. The Hydro model contains an auto-calibration routine that tries to find the best fit between the stream flow predicted by the model and the stream flow measured at the stream gauge station given the various inputs. The model can also be manually calibrated to improve the fit by changing the parameters as needed. 6) Model new scenarios: Once the model is properly calibrated, tree and impervious cover parameters can be changed to illustrate the impact on stream flow and water quality." | ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a plant-soil model (Figure (A3)) that simulates ecosystem carbon storage and the cycling of C and N between a plant biomass layer and the active soil pools. Specifically, the plant-soil model simulates the interaction among aboveground plant biomass, soil organic carbon (SOC), soil nitrogen including dissolved nitrate (NO3), ammonium (NH4), and organic nitrogen, as well as DOC (equations (A7)–(A12)). Daily atmospheric inputs of wet and dry nitrogen deposition are accounted for in the ammonium pool of the shallow soil layer (equation (A13)). Uptake of ammonium and nitrate by plants is modeled using a Type II Michaelis-Menten function (equation (A14)). Loss of plant biomass is simulated through a density-dependent mortality. The mortality rate and the nitrogen uptake rate mimic the exponential increase in biomass mortality and the accelerated growth rate, respectively, as plants go through succession and reach equilibrium (equations (A14)–(A18)). Vertical transport of nutrients from one layer to another in a soil column is a function of water drainage (equations (A19)–(A22)). Decomposition of SOC follows first-order kinetics controlled by soil temperature and moisture content as described in the terrestrial ecosystem model (TEM) of Raich et al. [1991] (equations (A23)–(A26)). Nitrification (equations (A27)–(A30)) and denitrification (equations (A31)–(A34)) were simulated using the equations from the generalized model of N2 and N2O production of Parton et al. [1996, 2001] and Del Grosso et al. [2000]. [12] The soil column model is placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other through the downslope lateral transport of water and nutrients (Figure (A1)). Surface and subsurface lateral flow are routed using a multiple flow direction method [Freeman, 1991; Quinn et al., 1991]. As with vertical drainage of soil water, lateral subsurface downslope flow i | 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. . . . |
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Specific Policy or Decision Context Cited
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Not reported | None identified | None identified | None reported |
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Biophysical Context
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No additional description provided | No additional description provided | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | Not applicable |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | Forest management (harvest/no harvest) | Land Use, EGS algorithm values, |
EM Relationship to Other EMs or Applications
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EM ID
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EM-91 | EM-137 |
EM-380
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EM-392 |
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Method Only, Application of Method or Model Run
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Method + Application | Method Only | Method + Application (multiple runs exist) View EM Runs | Method + Application |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised 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-91 | EM-137 |
EM-380
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EM-392 |
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Document ID for related EM
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Doc-123 | Doc-190 | Doc-223 | Doc-13 | Doc-317 | None |
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EM ID for related EM
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None | EM-109 | EM-142 | EM-51 | EM-375 | EM-379 | EM-884 | EM-883 | EM-887 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-91 | EM-137 |
EM-380
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EM-392 |
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EM Temporal Extent
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1987-1997 | Not applicable | 1969-2008 | Not applicable |
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EM Time Dependence
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time-stationary | time-dependent | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time | Not applicable |
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EM Time Continuity
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Not applicable | discrete | discrete | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | 1 | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Hour | Day | Not applicable |
EM Spatial Extent
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EM ID
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EM-91 | EM-137 |
EM-380
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EM-392 |
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Bounding Type
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Watershed/Catchment/HUC | Not applicable | Watershed/Catchment/HUC |
Geopolitical ?Comment:Extent was Tampa Bay area in example, but boundary can be geopolitical or watershed derived. |
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Spatial Extent Name
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Upper Mississippi River basin; St. Croix River Watershed | Not applicable | H. J. Andrews LTER WS10 | Tampa Bay region |
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Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 | Not applicable | 10-100 ha | 1000-10,000 km^2. |
Spatial Distribution of Computations
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EM ID
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EM-91 | EM-137 |
EM-380
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EM-392 |
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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) |
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Spatial Grain Type
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NHDplus v1 | area, for pixel or radial feature | 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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NHDplus v1 | 30 x 30 m | 30 m x 30 m surface pixel and 2-m depth soil column | 30m x 30m |
EM Structure and Computation Approach
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EM ID
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EM-91 | EM-137 |
EM-380
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EM-392 |
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EM Computational Approach
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Numeric | Numeric | Numeric | Analytic |
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EM Determinism
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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-91 | EM-137 |
EM-380
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EM-392 |
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Model Calibration Reported?
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Yes | Not applicable | Yes | No |
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Model Goodness of Fit Reported?
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Yes | Not applicable | No | No |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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Model Operational Validation Reported?
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No | Not applicable | No | No |
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Model Uncertainty Analysis Reported?
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No | Not applicable | No | No |
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Model Sensitivity Analysis Reported?
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No ?Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. |
Not applicable | Yes | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | No | 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-91 | EM-137 |
EM-380
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EM-392 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-91 | EM-137 |
EM-380
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EM-392 |
| None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-91 | EM-137 |
EM-380
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EM-392 |
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Centroid Latitude
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42.5 | -9999 | 44.25 | 28.05 |
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Centroid Longitude
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-90.63 | -9999 | -122.33 | -82.52 |
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Centroid Datum
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WGS84 | Not applicable | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Not applicable | Provided | Estimated |
Environments and Scales Modeled
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EM ID
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EM-91 | EM-137 |
EM-380
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EM-392 |
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EM Environmental Sub-Class
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Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Rivers and Streams | Ground Water | Created Greenspace | Rivers and Streams | Ground Water | Forests | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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None | Urban watersheds | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | All terestrial landcover and waterbodies |
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EM Ecological Scale
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Ecosystem | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to 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-91 | EM-137 |
EM-380
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EM-392 |
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EM Organismal Scale
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Not applicable | Community | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-91 | EM-137 |
EM-380
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EM-392 |
| None Available | 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.detail.cicesHelp
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| EM-91 | EM-137 |
EM-380
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EM-392 |
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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>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-91 | EM-137 |
EM-380
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EM-392 |
| None |
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None |
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