EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-379 | EM-944 |
EM Short Name
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VELMA soil temperature, Oregon, USA | COBRA v 4.1 |
EM Full Name
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VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | COBRA (CO–Benefits Risk Assessment) v 4.1 |
EM Source or Collection
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US EPA | US EPA |
EM Source Document ID
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317 |
437 ?Comment:User's manual is provided at the webpage. |
Document Author
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Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | US EPA |
Document Year
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2013 | 2021 |
Document Title
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Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | CO-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA) |
Document Status
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Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Webpage |
EM ID
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EM-379 | EM-944 |
Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | https://www.epa.gov/cobra | |
Contact Name
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Alex Abdelnour | Emma Zinsmeister |
Contact Address
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Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | EPA’s Office of Atmospheric Programs’ Climate Protection Partnerships Division |
Contact Email
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abdelnouralex@gmail.com | zinsmeister.emma@epa.gov |
EM ID
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EM-379 | EM-944 |
Summary Description
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ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation" | 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." |
Specific Policy or Decision Context Cited
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None identified | None identified |
Biophysical Context
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Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. | No additional description provided |
EM Scenario Drivers
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No scenarios presented | No scenarios presented |
EM ID
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EM-379 | EM-944 |
Method Only, Application of Method or Model Run
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Method + Application | Method Only |
New or Pre-existing EM?
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Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-379 | EM-944 |
Document ID for related EM
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Doc-13 | Doc-317 | None |
EM ID for related EM
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EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | None |
EM Modeling Approach
EM ID
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EM-379 | EM-944 |
EM Temporal Extent
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1969-2008 | Not applicable |
EM Time Dependence
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time-dependent | Not applicable |
EM Time Reference (Future/Past)
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future time | Not applicable |
EM Time Continuity
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discrete | Not applicable |
EM Temporal Grain Size Value
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1 | Not applicable |
EM Temporal Grain Size Unit
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Day | Not applicable |
EM ID
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EM-379 | EM-944 |
Bounding Type
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Watershed/Catchment/HUC | Geopolitical |
Spatial Extent Name
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H. J. Andrews LTER WS10 | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 ha | Not applicable |
EM ID
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EM-379 | EM-944 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:See below, grain includes vertical, subsurface dimension. |
spatially distributed (in at least some cases) |
Spatial Grain Type
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volume, for 3-D feature | map scale, for cartographic feature |
Spatial Grain Size
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30 m x 30 m surface pixel and 2-m depth soil column | user defined |
EM ID
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EM-379 | EM-944 |
EM Computational Approach
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Numeric | Analytic |
EM Determinism
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deterministic | stochastic |
Statistical Estimation of EM
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EM ID
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EM-379 | EM-944 |
Model Calibration Reported?
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No | Not applicable |
Model Goodness of Fit Reported?
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No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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No | Not applicable |
Model Uncertainty Analysis Reported?
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No | Not applicable |
Model Sensitivity Analysis Reported?
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No | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-379 | EM-944 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-379 | EM-944 |
None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-379 | EM-944 |
Centroid Latitude
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44.25 | Not applicable |
Centroid Longitude
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-122.33 | Not applicable |
Centroid Datum
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WGS84 | Not applicable |
Centroid Coordinates Status
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Provided | Not applicable |
EM ID
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EM-379 | EM-944 |
EM Environmental Sub-Class
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Forests | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Not applicable |
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 |
Scale of differentiation of organisms modeled
EM ID
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EM-379 | EM-944 |
EM Organismal Scale
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Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-379 | EM-944 |
None Available | None Available |
EnviroAtlas URL
EM-379 | EM-944 |
Average Annual Precipitation | 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)
EM-379 | EM-944 |
None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-379 | EM-944 |
None | None |