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-122 ![]() |
EM-455 |
EM Short Name
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UFORE-Hydro, Baltimore, MD, USA | Land-use change and crop-based production, Europe | Value of a reef dive site, St. Croix, USVI |
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. |
Land-use change effects on crop-based production, Europe | Value of a dive site (reef), St. Croix, USVI |
EM Source or Collection
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i-Tree | USDA Forest Service | EU Biodiversity Action 5 | US EPA |
EM Source Document ID
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190 | 228 | 335 |
Document Author
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Wang, J., Endreny, T. A. and Nowak, D. J. | Haines-Young, R., Potschin, M. and Kienast, F. | Yee, S. H., Dittmar, J. A., and L. M. Oliver |
Document Year
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2008 | 2012 | 2014 |
Document Title
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Mechanistic simulation of tree effects in an urban water balance model | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI |
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 journal manuscript |
EM ID
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EM-109 ![]() |
EM-122 ![]() |
EM-455 |
http://www.itreetools.org/ | Not applicable | Not applicable | |
Contact Name
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Jun Wang | Marion Potschin | Susan H. Yee |
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 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA |
Contact Email
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Not reported | marion.potschin@nottingham.ac.uk | yee.susan@epa.gov |
EM ID
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EM-109 ![]() |
EM-122 ![]() |
EM-455 |
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. | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Crop-based production); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: "The analysis for “Crop-based production” maps all the areas that are important for food crops produced through commercial agriculture….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Another method to quantify recreational opportunities is to use survey data of tourists and recreational visitors to the reefs to generate statistical models to quantify the link between reef condition and production of recreation-related ecosystem services. Wielgus et al. (2003) used interviews with SCUBA divers in Israel to derive coefficients for a choice model in which willingness to pay for higher quality dive sites was determined in part by a weighted combination of factors identified with dive quality: Relative value of dive site = 0.1227(Scoral+Sfish+Acoral+Afish)+0.0565V where Scoral, Sfish are coral and fish richness, Acoral, Afish are abundances of fish and coral per square meter, and V is water visibility (meters)." |
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 | No additional description provided |
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. |
Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use changes (2000-2030) | No scenarios presented |
EM ID
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EM-109 ![]() |
EM-122 ![]() |
EM-455 |
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-109 ![]() |
EM-122 ![]() |
EM-455 |
Document ID for related EM
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Doc-198 | Doc-238 | Doc-239 | Doc-240 | Doc-241 | Doc-242 | Doc-228 | None |
EM ID for related EM
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EM-137 | EM-123 | EM-124 | EM-125 | EM-162 | EM-164 | EM-165 | EM-166 | EM-170 | EM-171 | EM-99 | EM-119 | EM-120 | EM-121 | None |
EM Modeling Approach
EM ID
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EM-109 ![]() |
EM-122 ![]() |
EM-455 |
EM Temporal Extent
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2000 | 1990-2030 | 2006-2007, 2010 |
EM Time Dependence
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time-dependent | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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both | future time | Not applicable |
EM Time Continuity
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discrete | discrete | Not applicable |
EM Temporal Grain Size Value
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1 | 6, 10, and 30 | Not applicable |
EM Temporal Grain Size Unit
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Hour | Year | Not applicable |
EM ID
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EM-109 ![]() |
EM-122 ![]() |
EM-455 |
Bounding Type
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Watershed/Catchment/HUC | Geopolitical | Physiographic or ecological |
Spatial Extent Name
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Dead Run Catchement, Baltimore, MD | The EU-25 plus Switzerland and Norway | Coastal zone surrounding St. Croix |
Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | 100-1000 km^2 |
EM ID
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EM-109 ![]() |
EM-122 ![]() |
EM-455 |
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) | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
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irregular topographically delineated similar units | 1 km x 1 km | 10 m x 10 m |
EM ID
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EM-109 ![]() |
EM-122 ![]() |
EM-455 |
EM Computational Approach
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Numeric | Logic- or rule-based | Analytic |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-109 ![]() |
EM-122 ![]() |
EM-455 |
Model Calibration Reported?
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Yes | No | Yes |
Model Goodness of Fit Reported?
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Yes | No | No |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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Yes | No | Yes |
Model Uncertainty Analysis Reported?
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Unclear | No | No |
Model Sensitivity Analysis Reported?
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No | No | No |
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-109 ![]() |
EM-122 ![]() |
EM-455 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-109 ![]() |
EM-122 ![]() |
EM-455 |
None | None |
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Centroid Lat/Long (Decimal Degree)
EM ID
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EM-109 ![]() |
EM-122 ![]() |
EM-455 |
Centroid Latitude
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39.31 | 50.53 | 17.73 |
Centroid Longitude
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-76.74 | 7.6 | -64.77 |
Centroid Datum
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WGS84 | WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Estimated | Estimated |
EM ID
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EM-109 ![]() |
EM-122 ![]() |
EM-455 |
EM Environmental Sub-Class
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Rivers and Streams | Ground Water | Created Greenspace | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine |
Specific Environment Type
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Urban watershed | Not applicable | Coral reefs |
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 | 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-122 ![]() |
EM-455 |
EM Organismal Scale
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Community | Not applicable | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
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EM-122 ![]() |
EM-455 |
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-109 ![]() |
EM-122 ![]() |
EM-455 |
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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-122 ![]() |
EM-455 |
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