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-119 | EM-491 |
EM-605 
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EM-728
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EM Short Name
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RHyME2, Upper Mississippi River basin, USA | Landscape importance for wildlife products, Europe | EnviroAtlas - Crops with no pollinator habitat | VELMA v2.0, Ohio, USA | Seed mix and mowing in prairie reconstruction, USA |
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EM Full Name
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RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | Landscape importance for wildlife products, Europe | US EPA EnviroAtlas - Acres of pollinated crops with no nearby pollinator habitat, USA | Visualizing Ecosystems for Land Management Assessments (VELMA) v2.0, Shayler Crossing watershed, Ohio, USA | Seed mix design and first year management in prairie reconstruction, IA, USA |
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EM Source or Collection
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US EPA | EU Biodiversity Action 5 | US EPA | EnviroAtlas | US EPA | None |
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EM Source Document ID
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123 | 228 | 262 |
359 ?Comment:Document #366 is a supporting document for this EM. McKane et al. 2014, VELMA Version 2.0 User Manual and Technical Documentation. |
395 |
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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. | Haines-Young, R., Potschin, M. and Kienast, F. | US EPA Office of Research and Development - National Exposure Research Laboratory | Hoghooghi, N., H. E. Golden, B. P. Bledsoe, B. L. Barnhart, A. F. Brookes, K. S. Djang, J. J. Halama, R. B. McKane, C. T. Nietch, and P. P. Pettus | Meissen, J. C., A. J. Glidden, M. E. Sherrard, K. J. Elgersma, and L. L. Jackson |
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Document Year
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2013 | 2012 | 2013 | 2018 | 2019 |
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Document Title
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Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | EnviroAtlas - National | Cumulative effects of low impact development on watershed hydrology in a mixed land-cover system | Seed mix design and first year management influence multifunctionality and cost-effectiveness in prairie reconstruction |
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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 | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-91 | EM-119 | EM-491 |
EM-605
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EM-728
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| Not applicable | Not applicable | https://www.epa.gov/enviroatlas | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | Not applicable | |
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Contact Name
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Liem Tran | Marion Potschin | EnviroAtlas Team | Heather Golden | Justin Meissen |
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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 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Not reported | National Exposure Research Laboratory, Office of Research and Development, US EPA, Cincinnati, OH 45268, USA | Tallgrass Prairie Center, 2412 West 27th Street, Cedar Falls, IA 50614-0294, USA |
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Contact Email
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ltran1@utk.edu | marion.potschin@nottingham.ac.uk | enviroatlas@epa.gov | Golden.Heather@epa.gov | justin.meissen@uni.edu |
EM Description
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EM ID
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EM-91 | EM-119 | EM-491 |
EM-605
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EM-728
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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: "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 methods are explored in relation to mapping and assessing … “Wildlife Products” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Wildlife Products…includes the provisioning of all non-edible raw material products that are gained through non-agriculutural practices or which are produced as a by-product of commercial and non-commercial forests, primarily in non-intensively used land or semi-natural and natural areas." | DATA FACT SHEET: "This EnviroAtlas national map estimates the total acres of agricultural crops within each 12-digit hydrologic unit (HUC) that have varying amounts of nearby forested pollinator habitat. The crop types selected from the U.S. Department of Agriculture Cropland Data Layer (CDL) require (or would benefit from) the presence of pollinators, but crops may have no nearby native pollinator habitat. This metric is based on the average flight distance of native bees, both social and solitary, that nest in woodland habitats and forage on native plants and cultivated crops." "The metric was generated using the ESRI ArcMap Neighborhood Distance tool in conjunction with a blended landcover grid, which included the 2006 National Land Cover Dataset (NLCD) and U.S. Department of Agriculture National Agricultural Statistics Service Cropland Data Layer (CDL). Pollinator habitat is defined as trees (fruit, nut, deciduous, and evergreen) for nesting and associated woodland for additional pollen sources. Crops that either require or benefit from pollination were selected and a distance measure of 2.8 kilometers (the average bee species’ foraging distance from the nest4) was used to assess presence or absence of nearby native pollinator habitat. The total area of crops without nearby pollinator habitat was summarized by 12-digit HUC boundaries taken from the NHDPlusV2 Watershed Boundary Dataset (WBD Snapshot)." | ABSTRACT: "Low Impact Development (LID) is an alternative to conventional urban stormwater management practices, which aims at mitigating the impacts of urbanization on water quantity and quality. Plot and local scale studies provide evidence of LID effectiveness; however, little is known about the overall watershed scale influence of LID practices. This is particularly true in watersheds with a land cover that is more diverse than that of urban or suburban classifications alone. We address this watershed-scale gap by assessing the effects of three common LID practices (rain gardens, permeable pavement, and riparian buffers) on the hydrology of a 0.94 km2 mixed land cover watershed. We used a spatially-explicit ecohydrological model, called Visualizing Ecosystems for Land Management Assessments (VELMA), to compare changes in watershed hydrologic responses before and after the implementation of LID practices. For the LID scenarios, we examined different spatial configurations, using 25%, 50%, 75% and 100% implementation extents, to convert sidewalks into rain gardens, and parking lots and driveways into permeable pavement. We further applied 20 m and 40 m riparian buffers along streams that were adjacent to agricultural land cover…" AUTHOR'S DESCRIPTION: "VELMA’s modeling domain is a three-dimensional matrix that includes information regarding surface topography, land use, and four soil layers. VELMA uses a distributed soil column framework to model the lateral and vertical movement of water and nutrients through the four soil layers. A soil water balance is solved for each layer. The soil column model is placed within a watershed framework to create a spatially distributed model applicable to watersheds (Figure 2, shown here with LID practices). Adjacent soil columns interact through down-gradient water transport. Water entering each pixel (via precipitation or flow from an adjacent pixel) can either first infiltrate into the implemented LID and the top soil layer, and then to the downslope pixel, or continue its downslope movement as the lateral surface flow. Surface and subsurface lateral flow are routed using a multiple flow direction method, as described in Abdelnour et al. [21]. A detailed description of the processes and equations can be found in McKane et al. [32], Abdelnour et al. [21], Abdelnour et al. [40]." | ABSTRACT: "Agricultural intensification continues to diminish many ecosystem services in the North American Corn Belt. Conservation programs may be able to combat these losses more efficiently by developing initiatives that attempt to balance multiple ecological benefits. In this study, we examine how seed mix design and first year management influence three ecosystem services commonly provided by tallgrass prairie reconstructions (erosion control, weed resistance, and pollinator resources). We established research plots with three seed mixes, with and without first year mowing. The grass-dominated “Economy” mix had 21 species and a 3:1 grass-to-forb seeding ratio. The forb-dominated “Pollinator”mix had 38 species and a 1:3 grass-to-forb seeding ratio. The grass:forb balanced “Diversity” mix, which was designed to resemble regional prairie remnants, had 71 species and a 1:1 grass-to-forb ratio. To assess ecosystem services, we measured native stem density, cover, inflorescence production, and floral richness from 2015 to 2018. The Economy mix had high native cover and stem density, but produced few inflorescences and had low floral richness. The Pollinator mix had high inflorescence production and floral richness, but also had high bare ground and weed cover. The Diversity mix had high inflorescence production and floral richness (comparable to the Pollinator mix) and high native cover and stem density (comparable to the Economy mix). First year mowing accelerated native plant establishment and inflorescence production, enhancing the provisioning of ecosystem services during the early stages of a reconstruction. Our results indicate that prairie reconstructions with thoughtfully designed seed mixes can effectively address multiple conservation challenges." |
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Specific Policy or Decision Context Cited
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Not reported | None identified | None Identified | None identified | None identified |
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Biophysical Context
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No additional description provided | No additional description provided | No additional description provided | The Shayler Crossing (SHC) watershed is a subwatershed of the East Fork Little Miami River Watershed in southwest Ohio, USA and falls within the Till Plains region of the Central Lowland physiographic province. The Till Plains region is a topographically young and extensive flat plain, with many areas remaining undissected by even the smallest stream. The bedrock is buried under a mantle of glacial drift 3–15 m thick. The Digital Elevation Model (DEM) has a maximum value of ~269 m (North American_1983 datum) within the watershed boundary (Figure 1). The soils are primarily the Avonburg and Rossmoyne series, with high silty clay loam content and poor to moderate infiltration. Average annual precipitation for the period, 1990 through 2011, was 1097.4 _ 173.5 mm. Average annual air temperature for the same period was 12 _C Mixed land cover suburban watershed. The primary land uses consist of 64.1% urban or developed area (including 37% lawn, 12% building, 6.5% street, 6.4% sidewalk, and 2.1% parking lot and driveway), 23% agriculture, and 13% deciduous forest. Total imperviousness covers approximately 27% of the watershed area. | The site, located at the Iowa State University Northeast Research and Demonstration Farm near Nashua, Iowa, is relatively level with slopes not exceeding a 5% grade. Soil composition is primarily poorly drained Clyde clay loams with a minor component of somewhat poorly drained Floyd loams. Sub-surface tile drains exist on site and are spaced approximately 18–24m apart. The land was used for corn and soybean production prior to site establishment in 2015. |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | Three types of Low Impact Development (LID) practices (rain gardens, permeable pavements, forested riparian buffers) applied a different conversion levels. | Seed mix design |
EM Relationship to Other EMs or Applications
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EM ID
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EM-91 | EM-119 | EM-491 |
EM-605
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EM-728
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs |
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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 | 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-119 | EM-491 |
EM-605
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EM-728
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Document ID for related EM
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Doc-123 | Doc-231 | Doc-228 | None | Doc-13 | Doc-366 | Doc-394 |
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EM ID for related EM
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None | EM-99 | EM-120 | EM-121 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-166 | EM-170 | EM-171 | None | EM-375 | EM-377 | EM-378 | EM-884 | EM-883 | EM-887 | EM-719 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-91 | EM-119 | EM-491 |
EM-605
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EM-728
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EM Temporal Extent
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1987-1997 | 2000 | 2001-2015 | Jan 1, 2009 to Dec 31, 2011 | 2015-2018 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | past time | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | Not applicable | discrete | discrete |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not applicable | 1 | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Day | Year |
EM Spatial Extent
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EM ID
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EM-91 | EM-119 | EM-491 |
EM-605
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EM-728
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Bounding Type
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Watershed/Catchment/HUC | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Other |
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Spatial Extent Name
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Upper Mississippi River basin; St. Croix River Watershed | The EU-25 plus Switzerland and Norway | conterminous United States | Shayler Crossing watershed, a subwatershed of the East Fork Little Miami River Watershed | Iowa State University Northeast Research and Demonstration Farm near Nashua, Iowa |
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Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 10-100 ha | <1 ha |
Spatial Distribution of Computations
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EM ID
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EM-91 | EM-119 | EM-491 |
EM-605
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EM-728
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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) | 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 | area, for pixel or radial feature |
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Spatial Grain Size
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NHDplus v1 | 1 km x 1 km | irregular | 10m x 10m | 6.1 m x 8.53 m |
EM Structure and Computation Approach
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EM ID
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EM-91 | EM-119 | EM-491 |
EM-605
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EM-728
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EM Computational Approach
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Numeric | Logic- or rule-based | Analytic | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | stochastic |
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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-119 | EM-491 |
EM-605
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EM-728
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Model Calibration Reported?
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Yes | No | No | Yes | Not applicable |
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Model Goodness of Fit Reported?
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Yes | No | No |
Yes ?Comment:Goodness of fit for calibrated (2009-2010) and observed streamflow. |
No |
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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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No | Yes | No | Yes | No |
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Model Uncertainty Analysis Reported?
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No | No | No | 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. |
No | No | No | No |
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Model Sensitivity Analysis Include Interactions?
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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-91 | EM-119 | EM-491 |
EM-605
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EM-728
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-91 | EM-119 | EM-491 |
EM-605
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EM-728
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| None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-91 | EM-119 | EM-491 |
EM-605
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EM-728
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Centroid Latitude
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42.5 | 50.53 | 39.5 | 39.19 | 42.93 |
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Centroid Longitude
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-90.63 | 7.6 | -98.35 | -84.29 | -92.57 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Provided | Provided |
Environments and Scales Modeled
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EM ID
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EM-91 | EM-119 | EM-491 |
EM-605
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EM-728
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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 | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Ground Water | Forests | Agroecosystems | Created Greenspace | Agroecosystems | Grasslands |
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Specific Environment Type
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None | Not applicable | Terrestrial | Mixed land cover suburban watershed | prairie/grassland reconstruction at demonstration farm site |
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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 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 |
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-119 | EM-491 |
EM-605
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EM-728
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EM Organismal Scale
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Not applicable | Not applicable | Guild or Assemblage | Not applicable | Community |
Taxonomic level and name of organisms or groups identified
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| EM-91 | EM-119 | EM-491 |
EM-605
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EM-728
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| None Available | None Available |
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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)
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| EM-91 | EM-119 | EM-491 |
EM-605
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EM-728
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-91 | EM-119 | EM-491 |
EM-605
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EM-728
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| None |
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None |