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-69 | EM-194 |
EM-224 
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EM-627 |
EM-719
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EM-1018 |
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EM Short Name
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Soil carbon content, Central French Alps | Coral and land development, St.Croix, VI, USA | FORCLIM v2.9, West Cascades, OR, USA | N removal by wetland restoration, Midwest, USA | Seed mix for native plant establishment, IA, USA | WMOSTsustainable water Danvers-Middleton, MA |
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EM Full Name
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Soil carbon content, Central French Alps | Coral colony density and land development, St.Croix, Virgin Islands, USA | FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA | Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA | Cost-effective seed mix design for native plant establishment, Iowa, USA | WMOST sustainable water management initiative Danvers-Middleton, MA |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | US EPA | None | None | US EPA |
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EM Source Document ID
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260 | 96 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
370 ?Comment:Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006. |
394 | 477 |
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Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers | Meissen, J. | United States EPA |
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Document Year
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2011 | 2011 | 2007 | 2006 | 2018 | 2013 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins | Cost-effective seed mix design and first-year management | Watershed Management Optimization Support Tool (WMOST) v1 User manual |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Neither peer reviewed nor published (explain in Comment) | Peer reviewed and published | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published report | Published EPA report |
Software and Access
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EM ID
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EM-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
| Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NHEERL&dirEntryId=262280 | |
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Contact Name
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Sandra Lavorel | Leah Oliver | Richard T. Busing | William G. Crumpton | Justin Meissen | Naomi Detenbeck |
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Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | National Health and Environmental Research Effects Laboratory | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011 | Tallgrass Prairie Center, University of Northern Iowa | NHEERL, Atlantic Ecology Division Narragansett, RI 02882 |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | leah.oliver@epa.gov | rtbusing@aol.com | crumpton@iastate.edu | Not reported | detenbeck.naomi@epa.gov |
EM Description
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EM ID
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EM-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
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Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in soil carbon was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Soil carbon for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on soil carbon. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | 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) | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." AUTHOR'S DESCRIPTION: "An analysis of forest successional dynamics was performed on ecoregions 4a and 4b, which cover the south Santiam watershed area selected for intensive study. In each of these two ecoregions, a set of 20 simulated sites was compared to survey plot data summaries. Survey data were analysed by stand age class and simulations of corresponding ages. The statistical methods described…were applied in comparison of actual with simulated forest composition and total basal area by age class. Separate simulations were run with and without fire." | ABSTRACT: "The primary objective of this project was to estimate the nitrate reduction that could be achieved using restored wetlands as nitrogen sinks in tile-drained regions of the upper Mississippi River (UMR) and Ohio River basins. This report provides an assessment of nitrate concentrations and loads across the UMR and Ohio River basins and the mass reduction of nitrate loading that could be achieved using wetlands to intercept nonpoint source nitrate loads. Nitrate concentration and stream discharge data were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations and to develop a model of FWA nitrate concentration based on land use. Land use accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. Annual water yield for grid cells was estimated by interpolating over selected USGS monitoring station water yields across the UMR and Ohio River basins. For 1990 to 1999, mass nitrate export from each grid area was estimated as the product of the FWA nitrate concentration, water yield and grid area. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Nitrate reduction was estimated using a temperature dependent, area-based, first order model. Model inputs included local temperature from the National Climatic Data Center and water yield estimated from USGS stream flow data. Results were used to develop a nonlinear model for percent nitrate removal as a function of hydraulic loading rate (HLR) and temperature. Mass nitrate removal for potential wetland restorations distributed across the UMR and Ohio River basin was estimated based on the expected mass load and the predicted percent removal. Similar functions explained most of the variability in per cent and mass removal reported for field scale experimental wetlands in the UMR and Ohio River basins. Results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas." AUTHOR'S DESCRIPTION: "Percent nitrate removal was estimated based on HLR functions (Figure 19) spanning a 3 fold range in loss rate coefficient (Crumpton 2001) and encompassing the observed performance reported for wetlands in the UMR and Ohio River basins (Table 2, Figure 7). The nitrate load was multiplied by the expected percent nitrate removal to estimate the mass removal. This procedure was repeated for each restoration scenario each year in the simulation period (1990 to 1999)… for a scenario with a wetland/watershed area ratio of 2%. These results are based on the assumption that the FWA nitrate concentration versus percent row crop r | AUTHOR'S DESCRIPTION: "Restoring ecosystem services at scale requires executing conservation programs in a way that is resource and cost efficient as well as ecologically effective…Seed mix design is one of the largest determinants of project cost and ecological outcomes for prairie reconstructions. In particular, grass-to-forb seeding ratio affects cost since forb seed can be much more expensive relative to grass species (Prairie Moon Nursery 2012). Even for seed mixes with the same overall seeding rates, a mix with a low grass-to-forb seeding ratio is considerably more expensive than one with a high grass-to-forb ratio. Seeding rates for different plant functional groups that are too high or low may also adversely affect ecological outcomes…First-year management may also play a role in cost-effective prairie reconstruction. Post-agricultural sites where restoration typically occurs are often quickly dominated by fast-growing annual weeds by the time sown prairie seeds begin germinating (Smith et al. 2010)… Williams and others (2007) showed that prairie seedlings sown into established warm-season grasses were reliant on high light conditions created by frequently mowing tall vegetation in order to survive in subsequent years…Our objective was to compare native plant establishment and cost effectiveness with and without first-year mowing for three different seed mixes that differed in grass to forb ratio and soil type customization. With knowledge of plant establishment, cost effectiveness, and mowing management outcomes, conservation practitioners will be better equipped to restore prairie efficiently and successfully." | ABSTRACT: "The Watershed Management Optimization Support Tool (WMOST) is intended to be used as a screening tool as part of an integrated watershed management process such as that described in EPA’s watershed planning handbook (EPA 2008).1 The objective of WMOST is to serve as a public-domain, efficient, and user-friendly tool for local water resources managers and planners to screen a widerange of potential water resources management options across their watershed or jurisdiction for costeffectiveness as well as environmental and economic sustainability (Zoltay et al 2010). Examples of options that could be evaluated with the tool include projects related to stormwater, water supply, wastewater and water-related resources such as Low-Impact Development (LID) and land conservation. The tool is intended to aid in evaluating the environmental and economic costs, benefits, trade-offs and co-benefits of various management options. In addition, the tool is intended to facilitate the evaluation of low impact development (LID) and green infrastructure as alternative or complementary management options in projects proposed for State Revolving Funds (SRF). WMOST is a screening model that is spatially lumped with a daily or monthly time step. The model considers water flows but does not yet consider water quality. The optimization of management options is solved using linear programming. The target user group for WMOST consists of local water resources managers, including municipal water works superintendents and their consultants. This document includes a presentation of a case study appling WMOST to the Danvers-Middleton, MA sustainable water management initiative. |
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Specific Policy or Decision Context Cited
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None identified | Not applicable | None Identified | None identified | Seed mix design and management practices for native plant restoration | Not applicable |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | nearshore; <1.5 km offshore; <12 m depth | West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions | No additional description provided | The soils underlying the study site are primarily poorly drained Clyde clay loams, with a minor component of somewhat poorly drained Floyd loams in the northwest (NRCS 2016). Topographically, the study site is level, and slopes do not exceed 5% grade. Land use prior to this experiment was agricultural, with corn and soybeans consistently grown in rotation at the site. | None |
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EM Scenario Drivers
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No scenarios presented | Not applicable | Two scenarios modelled, forests with and without fire | More conservative, average and less conservative nitrate loss rate | No scenarios presented |
None ?Comment:Not presented with scenarios, but the model was run with multiple scenarios for costs related to varying instream minimum flows and provided the associated costs for each run. |
EM Relationship to Other EMs or Applications
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EM ID
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EM-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Method + Application (multiple runs exist) | 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 | Application of existing model | 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.detail.relatedEmHelp
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EM ID
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EM-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
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Document ID for related EM
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Doc-260 | Doc-269 | None | Doc-22 | Doc-23 | None | Doc-395 | Doc-477 |
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EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | EM-146 | EM-208 | EM-186 | None | EM-728 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
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EM Temporal Extent
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2007-2009 | 2006-2007 | >650 yrs | 1973-1999 | 2015-2017 | Not applicable |
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EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | past time | future time | Not applicable |
Not applicable ?Comment:method description |
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EM Time Continuity
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Not applicable | Not applicable | discrete | discrete | discrete | discrete |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | 1 | 1 | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year | Day | Year | Day |
EM Spatial Extent
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EM ID
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EM-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or ecological | Watershed/Catchment/HUC | Other | Watershed/Catchment/HUC |
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Spatial Extent Name
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Central French Alps | St. Croix, U.S. Virgin Islands | West Cascades, Oregon | Upper Mississippi River and Ohio River basins | Iowa State University Northeast Research and Demonstration Farm | Danvers-Middleton |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 100-1000 km^2 | >1,000,000 km^2 | <1 ha | 10-100 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
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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) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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20 m x 20 m | Not applicable | 0.08 ha | 1 km2 | 20 ft x 28 ft | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
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EM Computational Approach
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Analytic | Analytic | Numeric | Numeric | Analytic | Numeric |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | stochastic | 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-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
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Model Calibration Reported?
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No | Yes | No | No | Not applicable | Unclear |
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Model Goodness of Fit Reported?
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Yes | Yes | No | No | Not applicable | Unclear |
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Goodness of Fit (metric| value | unit)
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None | None | None | None |
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Model Operational Validation Reported?
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Yes | No | Yes |
No ?Comment:However, agreement of submodel and intermediate components; annual discharge (R2=0.79), and nitrate-N load (R2=0.74), based on GIS land use were determined in comparison with USGS NASQAN data. |
No | Not applicable |
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Model Uncertainty Analysis Reported?
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No | Yes | No | No | Not applicable | Not applicable |
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Model Sensitivity Analysis Reported?
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No | No | No | No | Not applicable | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 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-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
| None |
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None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
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Centroid Latitude
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45.05 | 17.75 | 44.24 | 40.6 | 42.93 | 42.58 |
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Centroid Longitude
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6.4 | -64.75 | -122.24 | -88.4 | -92.57 | -70.93 |
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Centroid Datum
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WGS84 | NAD83 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated | Provided | Estimated |
Environments and Scales Modeled
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EM ID
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EM-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Forests | Rivers and Streams | Inland Wetlands | Agroecosystems | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows. | stony coral reef | Primarily conifer forest | Agroecosystems and associated drainage and wetlands | Research farm in historic grassland | watershed |
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EM Ecological Scale
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Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to 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-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
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EM Organismal Scale
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Community | Guild or Assemblage | Species | Not applicable | Community | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
| None Available |
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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-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
| None |
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None |
<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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-69 | EM-194 |
EM-224
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EM-627 |
EM-719
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EM-1018 |
| None |
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None |
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None |