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-109 ![]() |
EM-260 |
EM Short Name
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UFORE-Hydro, Baltimore, MD, USA | Coral taxa and land development, St.Croix, VI, USA |
EM Full Name
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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. |
Coral taxa richness and land development, St.Croix, Virgin Islands, USA |
EM Source or Collection
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i-Tree | USDA Forest Service | US EPA |
EM Source Document ID
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190 | 96 |
Document Author
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Wang, J., Endreny, T. A. and Nowak, D. J. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. |
Document Year
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2008 | 2011 |
Document Title
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Mechanistic simulation of tree effects in an urban water balance model | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands |
Document Status
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Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript |
EM ID
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EM-109 ![]() |
EM-260 |
http://www.itreetools.org/ | Not applicable | |
Contact Name
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Jun Wang | Leah Oliver |
Contact Address
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Environmental Resources and Forest Engineering, Colecge of Environmental Science and Forestry, State University of New York, Syracuse, New York 13210 | National Health and Environmental Research Effects Laboratory |
Contact Email
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Not reported | leah.oliver@epa.gov |
EM ID
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EM-109 ![]() |
EM-260 |
Summary Description
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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: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) |
Specific Policy or Decision Context Cited
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None identified | Not applicable |
Biophysical Context
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No additional description provided | nearshore; <1.5 km offshore; <12 m depth |
EM Scenario Drivers
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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. |
Not applicable |
EM ID
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EM-109 ![]() |
EM-260 |
Method Only, Application of Method or Model Run
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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 |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-109 ![]() |
EM-260 |
Document ID for related EM
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Doc-198 | None |
EM ID for related EM
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EM-137 | None |
EM Modeling Approach
EM ID
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EM-109 ![]() |
EM-260 |
EM Temporal Extent
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2000 | 2006-2007 |
EM Time Dependence
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time-dependent | time-stationary |
EM Time Reference (Future/Past)
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both | Not applicable |
EM Time Continuity
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discrete | Not applicable |
EM Temporal Grain Size Value
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1 | Not applicable |
EM Temporal Grain Size Unit
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Hour | Not applicable |
EM ID
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EM-109 ![]() |
EM-260 |
Bounding Type
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Watershed/Catchment/HUC | Physiographic or Ecological |
Spatial Extent Name
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Dead Run Catchement, Baltimore, MD | St.Croix, U.S. Virgin Islands |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 |
EM ID
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EM-109 ![]() |
EM-260 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially lumped (in all cases) |
Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable |
Spatial Grain Size
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irregular topographically delineated similar units | Not applicable |
EM ID
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EM-109 ![]() |
EM-260 |
EM Computational Approach
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Numeric | Analytic |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-109 ![]() |
EM-260 |
Model Calibration Reported?
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Yes | Yes |
Model Goodness of Fit Reported?
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Yes | Yes |
Goodness of Fit (metric| value | unit)
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Model Operational Validation Reported?
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Yes | No |
Model Uncertainty Analysis Reported?
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Unclear | Yes |
Model Sensitivity Analysis Reported?
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No | No |
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-109 ![]() |
EM-260 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-109 ![]() |
EM-260 |
None |
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Centroid Lat/Long (Decimal Degree)
EM ID
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EM-109 ![]() |
EM-260 |
Centroid Latitude
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39.31 | 17.75 |
Centroid Longitude
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-76.74 | -64.75 |
Centroid Datum
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WGS84 | NAD83 |
Centroid Coordinates Status
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Provided | Estimated |
EM ID
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EM-109 ![]() |
EM-260 |
EM Environmental Sub-Class
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Rivers and Streams | Ground Water | Created Greenspace | Near Coastal Marine and Estuarine |
Specific Environment Type
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Urban watershed | stony coral reef |
EM Ecological Scale
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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-109 ![]() |
EM-260 |
EM Organismal Scale
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Community | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
EM-109 ![]() |
EM-260 |
None Available |
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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-109 ![]() |
EM-260 |
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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-109 ![]() |
EM-260 |
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