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-605 
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EM-812
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EM-944 |
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EM Short Name
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VELMA v2.0, Ohio, USA | Wildflower mix supporting bees, CA, USA | COBRA v 4.1 |
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EM Full Name
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Visualizing Ecosystems for Land Management Assessments (VELMA) v2.0, Shayler Crossing watershed, Ohio, USA | Wildflower planting mix supporting bees in agricultural landscapes, CA, USA | COBRA (CO–Benefits Risk Assessment) v 4.1 |
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EM Source or Collection
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US EPA | None | US EPA |
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EM Source Document ID
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359 ?Comment:Document #366 is a supporting document for this EM. McKane et al. 2014, VELMA Version 2.0 User Manual and Technical Documentation. |
400 |
437 ?Comment:User's manual is provided at the webpage. |
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Document Author
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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 | Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters | US EPA |
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Document Year
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2018 | 2015 | 2021 |
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Document Title
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Cumulative effects of low impact development on watershed hydrology in a mixed land-cover system | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | CO-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA) |
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Document Status
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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 | Webpage |
Software and Access
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EM ID
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EM-605
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EM-812
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EM-944 |
| https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | Not applicable | https://www.epa.gov/cobra | |
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Contact Name
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Heather Golden | Neal Williams | Emma Zinsmeister |
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Contact Address
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National Exposure Research Laboratory, Office of Research and Development, US EPA, Cincinnati, OH 45268, USA | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | EPA’s Office of Atmospheric Programs’ Climate Protection Partnerships Division |
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Contact Email
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Golden.Heather@epa.gov | nmwilliams@ucdavis.edu | zinsmeister.emma@epa.gov |
EM Description
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EM ID
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EM-605
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EM-812
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EM-944 |
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Summary Description
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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: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm | Introduction: "COBRA is a screening tool that provides preliminary estimates of the impact of air pollution emission changes on ambient particulate matter (PM) air pollution concentrations, translates this into health effect impacts, and then monetizes these impacts, as illustrated below. The model does not require expertise in air quality modeling, health effects assessment, or economic valuation. Built into COBRA are emissions inventories, a simplified air quality model, health impact equations, and economic valuations ready for use, based on assumptions that EPA currently uses as reasonable best estimates. COBRA also enables advanced users to import their own datasets of emissions inventories, population, incidence, health impact functions, and valuation functions. Analyses can be performed at the state or county level and across the 14 major emissions categories (these categories are called “tiers”) included in the National Emissions Inventory. COBRA presents results in tabular as well as geographic form, and enables policy analysts to obtain a first-order approximation of the benefits of different mitigation scenarios under consideration. However, COBRA is only a screening tool. More sophisticated, albeit time- and resource-intensive, modeling approaches are currently available to obtain a more refined picture of the health and economic impacts of changes in emissions. EPA initially developed COBRA as a desktop application. In 2021, EPA released a web-based version of the tool, known as the COBRA Web Edition. Although the desktop version and web versions of COBRA both use the same methodology to calculate outdoor air quality and health impacts from changes in air pollution emissions, the desktop version offers additional advanced features that are not included in the more streamlined Web Edition. In particular, the desktop version is preloaded with input data on emissions, population, and baseline health incidence for 2016, 2023, and 2028; the Web Edition includes data only for 2023. Similarly, the desktop version allows users to import custom input datasets, while the Web Edition does not. The Web Edition, however, does not require the user to download or install additional software, and it runs more quickly than the desktop version. Users might choose to use the desktop version if they would like to use advanced features, such as custom input data and/or use the preloaded data for 2016 or 2028. Otherwise, users may choose to use the Web Edition for data analysis relevant to 2023. The process for entering emissions input data into COBRA is very similar for the desktop and web versions of the tool. The remainder of this User’s Manual focuses on the steps required to run the desktop version of the tool. The same general process can be used with the Web Edition." |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified |
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Biophysical Context
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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. | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca | No additional description provided |
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EM Scenario Drivers
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Three types of Low Impact Development (LID) practices (rain gardens, permeable pavements, forested riparian buffers) applied a different conversion levels. | Varied wildflower planting mixes of annuals and perennials | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-605
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EM-812
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EM-944 |
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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 Only |
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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 |
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-605
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EM-812
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EM-944 |
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Document ID for related EM
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Doc-13 | Doc-366 | Doc-400 | None |
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EM ID for related EM
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EM-375 | EM-377 | EM-378 | EM-884 | EM-883 | EM-887 | EM-784 | EM-793 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-605
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EM-812
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EM-944 |
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EM Temporal Extent
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Jan 1, 2009 to Dec 31, 2011 | 2011-2012 | Not applicable |
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EM Time Dependence
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time-dependent | time-dependent | Not applicable |
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EM Time Reference (Future/Past)
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past time | past time | Not applicable |
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EM Time Continuity
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discrete | discrete | Not applicable |
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EM Temporal Grain Size Value
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1 | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Day | Year | Not applicable |
EM Spatial Extent
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EM ID
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EM-605
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EM-812
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EM-944 |
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Bounding Type
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Watershed/Catchment/HUC |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
Geopolitical |
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Spatial Extent Name
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Shayler Crossing watershed, a subwatershed of the East Fork Little Miami River Watershed | Agricultural plots | Not applicable |
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Spatial Extent Area (Magnitude)
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10-100 ha | 10-100 km^2 | Not applicable |
Spatial Distribution of Computations
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EM ID
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EM-605
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EM-812
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EM-944 |
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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) |
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Spatial Grain Type
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area, for pixel or radial feature | Not applicable | map scale, for cartographic feature |
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Spatial Grain Size
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10m x 10m | Not applicable | user defined |
EM Structure and Computation Approach
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EM ID
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EM-605
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EM-812
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EM-944 |
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EM Computational Approach
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Numeric | Numeric | Analytic |
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EM Determinism
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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-605
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EM-812
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EM-944 |
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Model Calibration Reported?
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Yes | No | Not applicable |
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Model Goodness of Fit Reported?
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Yes ?Comment:Goodness of fit for calibrated (2009-2010) and observed streamflow. |
No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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Model Operational Validation Reported?
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Yes | No | Not applicable |
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Model Uncertainty Analysis Reported?
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No | No | Not applicable |
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Model Sensitivity Analysis Reported?
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No | No | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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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-605
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EM-812
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EM-944 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-605
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EM-812
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EM-944 |
| None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-605
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EM-812
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EM-944 |
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Centroid Latitude
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39.19 | 29.4 | Not applicable |
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Centroid Longitude
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-84.29 | -82.18 | Not applicable |
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Centroid Datum
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WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
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Provided | Provided | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-605
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EM-812
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EM-944 |
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EM Environmental Sub-Class
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Rivers and Streams | Ground Water | Forests | Agroecosystems | Created Greenspace | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Mixed land cover suburban watershed | Agricultural landscape | Not applicable |
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EM Ecological Scale
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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 |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-605
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EM-812
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EM-944 |
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EM Organismal Scale
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Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
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EM-605
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EM-812
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EM-944 |
| None Available |
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None Available |
EnviroAtlas URL
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EM-605
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EM-812
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EM-944 |
| Average Annual Precipitation, Average Annual Daily Potential Wind Energy | None Available | Total Annual Nitrogen Deposition |
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-605
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EM-812
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EM-944 |
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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-605
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EM-812
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EM-944 |
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None |