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-379 | EM-438 | EM-658 |
EM Short Name
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VELMA soil temperature, Oregon, USA | InVESTv3.0 Nutrient retention, Guánica Bay | Polyscape, Wales |
EM Full Name
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VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Nutrient retention, Guánica Bay, Puerto Rico, USA | Polyscape, Wales |
EM Source or Collection
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US EPA | US EPA | InVEST | None |
EM Source Document ID
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317 | 338 | 379 |
Document Author
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Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Jackson, B., T. Pagella, F. Sinclair, B. Orellana, A. Henshaw, B. Reynolds, N. Mcintyre, H. Wheater, and A. Eycott |
Document Year
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2013 | 2017 | 2013 |
Document Title
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Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Polyscape: A GIS mapping framework providing efficient and spatially explicit landscape-scale valuation of multple ecosystem services |
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-379 | EM-438 | EM-658 |
Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | http://www.naturalcapitalproject.org/invest/ |
https://www.lucitools.org/ ?Comment:The LUCI (Land Utilisation and Capability Indicator) model, is a second-generation extension and software implementation of the Polyscape framework. |
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Contact Name
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Alex Abdelnour | Susan H. Yee | Bethanna Jackson |
Contact Address
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Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | School of Geography, Environment and Earth Sciences, Victoria University of Wellington, PO Box 600, Wellington, New Zealand |
Contact Email
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abdelnouralex@gmail.com | yee.susan@epa.gov | bethanna.jackson@vuw.ac.nz |
EM ID
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EM-379 | EM-438 | EM-658 |
Summary Description
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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 soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation" | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "Nutrient retention was estimated by first calculating water yield and establishing the quantity of nitrogen or phosphorus retained by different land cover classes using a water purification model (InVEST 3.0.0; Tallis et al., 2013). Different land cover classes were assumed to have different capacities for retaining nutrients, depending on the efficiency of vegetation in removing either nitrogen or phosphorus and the rates of nitrogen or phosphorus loading." “Use of other models in conjunction with this model:Average runoff per pixel modeled here were derived from the InVEST Water Yield model" | ABSTRACT: "This paper introduces a GIS framework (Polyscape) designed to explore spatially explicit synergies and trade-offs amongst ecosystem services to support landscape management (from individual fields through to catchments of ca 10,000 km2 scale). Algorithms are described and results presented from a case study application within an upland Welsh catchment (Pontbren). Polyscape currently includes algorithms to explore the impacts of land cover change on flood risk, habitat connectivity, erosion and associated sediment delivery to receptors, carbon sequestration and agricultural productivity. Algorithms to trade these single-criteria landscape valuations against each other are also provided, identifying where multiple service synergies exist or could be established. Changes in land management can be input to the tool and “traffic light” coded impact maps produced, allowing visualisation of the impact of different decisions. Polyscape hence offers a means for prioritising existing feature preservation and identifying opportunities for landscape change. The basic algorithms can be applied using widely available national scale digital elevation, land use and soil data. Enhanced output is possible where higher resolution data are available..." AUTHOR'S DESCRIPTION: "The framework acts as a screening tool to identify areas where scientific investigation might be valuably directed and/or where a lack of information exists, and allows flexibility and quick visualisation of the impact of different rural land management decisions on a variety of sustainability criteria. Specifically, Polyscape is designed to facilitate: 1. spatially explicit policy implementation; 2. integration of policy implementation across sectors (e.g., water, biodiversity, agriculture and forestry); 3. participation (and learning) by many different stakeholder groups. Importantly, it is designed not as a prescriptive decision making tool, but as a negotiation tool. Algorithms allow identification of ideas of where change might be beneficial – for example where installation of “structures” such as ponds or buffer strips might be considered optimal at a farm scale – but also allows users to trial their own plans and build in their own knowledge/restrictions. The framework aims to highlight areas with maximum potential for improvement, not to place value judgements on which methods (e.g., tillage change, land use change, hard engineering approaches) might be appropriate to realise such potential. Furthermore, the toolbox aims to identify areas of existing high value – e.g., particularly productive cropland, wetlands..." "Our case study site is the 12.5 km2 catchment of the Pontbren in mid-Wales." NOTE: The LUCI (Land Utilisation and Capability Indicator) model, is a second-generation extension and software implementation of the Polyscape framework, as described in EM-659. https://esml.epa.gov/detail/em/659 |
Specific Policy or Decision Context Cited
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None identified | Improving water quality | Polyscape acts as a screening tool to allow flexibility and visualisation of the impact of different rural land management decisions. |
Biophysical Context
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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. | No additional description provided | Elevation ranges between 170 m and 425 m |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Initial habitat coverage (1990), and planting additional broadleaved woodland (2001-2007) |
EM ID
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EM-379 | EM-438 | EM-658 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application |
New or Pre-existing EM?
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Application of existing model | Application of existing 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-379 | EM-438 | EM-658 |
Document ID for related EM
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Doc-13 | Doc-317 | Doc-309 | Doc-205 | Doc-380 |
EM ID for related EM
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EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | EM-363 | EM-112 | EM-659 |
EM Modeling Approach
EM ID
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EM-379 | EM-438 | EM-658 |
EM Temporal Extent
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1969-2008 | 1980 - 2013 | 1990-2007 |
EM Time Dependence
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time-dependent | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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future time | other or unclear (comment) | Not applicable |
EM Time Continuity
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discrete | discrete | Not applicable |
EM Temporal Grain Size Value
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1 | 1 | Not applicable |
EM Temporal Grain Size Unit
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Day | Year | Not applicable |
EM ID
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EM-379 | EM-438 | EM-658 |
Bounding Type
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Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC |
Spatial Extent Name
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H. J. Andrews LTER WS10 | Guanica Bay Study Area | Pontbren catchment |
Spatial Extent Area (Magnitude)
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10-100 ha | 1000-10,000 km^2. | 10-100 km^2 |
EM ID
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EM-379 | EM-438 | EM-658 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:See below, grain includes vertical, subsurface dimension. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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volume, for 3-D feature | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
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30 m x 30 m surface pixel and 2-m depth soil column | 30 m x 30 m | Not reported |
EM ID
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EM-379 | EM-438 | EM-658 |
EM Computational Approach
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Numeric | Numeric | 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-379 | EM-438 | EM-658 |
Model Calibration Reported?
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No | No | No |
Model Goodness of Fit Reported?
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No | No | No |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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No | No | No |
Model Uncertainty Analysis Reported?
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No | 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-379 | EM-438 | EM-658 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-379 | EM-438 | EM-658 |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-379 | EM-438 | EM-658 |
Centroid Latitude
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44.25 | 17.97 | 52.61 |
Centroid Longitude
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-122.33 | -66.93 | -3.3 |
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-379 | EM-438 | EM-658 |
EM Environmental Sub-Class
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Forests | Aquatic Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Open Ocean and Seas | Forests | Agroecosystems | Created Greenspace | Scrubland/Shrubland | Barren | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Grasslands |
Specific Environment Type
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400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | 13 LULC were used | mainly of ‘improved’ pasture, semi-natural, unmanaged moorland, mature woodland, recent tree plantations, and small paved/roofed areas, root crops and open water |
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-379 | EM-438 | EM-658 |
EM Organismal Scale
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Not applicable | Not applicable | Unsure |
Taxonomic level and name of organisms or groups identified
EM-379 | EM-438 | EM-658 |
None Available | None Available | None Available |
EnviroAtlas URL
EM-379 | EM-438 | EM-658 |
Average Annual Precipitation | Total Annual Reduced Nitrogen Deposition, The Watershed Boundary Dataset (WBD) | The National Hydrography Dataset (NHD) |
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-379 | EM-438 | EM-658 |
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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-379 | EM-438 | EM-658 |
None | None |
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