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-982 |
EM Short Name
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VELMA soil temperature, Oregon, USA | Specific conductivity, USA |
EM Full Name
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VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | Specific Conductivity, USA |
EM Source or Collection
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US EPA | US EPA |
EM Source Document ID
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317 | 460 |
Document Author
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Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Olson, J.R., and S.M. Cormier |
Document Year
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2013 | 2019 |
Document Title
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Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Modeling Spatial and Temporal Variation in Natural Background Specific Conductivity |
Document Status
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Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript |
EM ID
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EM-379 | EM-982 |
Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | (https://edg.epa.gov/ metadata/catalog/main/home.page) | |
Contact Name
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Alex Abdelnour | John Olson |
Contact Address
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Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | California State Univ. Monterey Bay, 100 Campus Center, Seaside CA 93955 |
Contact Email
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abdelnouralex@gmail.com | joolson@csumb.edu |
EM ID
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EM-379 | EM-982 |
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" | We developed a random forest model that predicts natural background specific conductivity (SC), a measure of total dissolved ions, for all stream segments in the contiguous United States at monthly time steps between the years 2001 to 2015. Models were trained using 11 796 observations made at 1785 minimally impaired stream segments and validated with observations from an additional 92 segments. Static predictors of SC included geology, soils, and vegetation parameters. Temporal predictors were related to climate and enabled the model to make predictions for different dates. The model explained 95% of the variation in SC among validation observations (mean absolute error = 29 μS/cm, Nash-Sutcliffe efficiency = 0.85). The model performed well across the period of interest but exhibited bias in Coastal Plain and Xeric regions (26 and 30%, respectively). National model predictions showed large spatial variation with the greatest SC predicted to occur in the desert southwest and plains. Model predictions also reflected changes at individual streams during drought. |
Specific Policy or Decision Context Cited
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None identified | N/A |
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. | Stream segment taken from StreamCat database |
EM Scenario Drivers
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No scenarios presented | N/A |
EM ID
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EM-379 | EM-982 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application |
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-982 |
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-982 |
EM Temporal Extent
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1969-2008 | 2001-2015 |
EM Time Dependence
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time-dependent | time-dependent |
EM Time Reference (Future/Past)
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future time | past time |
EM Time Continuity
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discrete | discrete |
EM Temporal Grain Size Value
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1 | 3 |
EM Temporal Grain Size Unit
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Day | Month |
EM ID
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EM-379 | EM-982 |
Bounding Type
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Watershed/Catchment/HUC | Geopolitical |
Spatial Extent Name
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H. J. Andrews LTER WS10 | Contiguous United States |
Spatial Extent Area (Magnitude)
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10-100 ha | >1,000,000 km^2 |
EM ID
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EM-379 | EM-982 |
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 | area, for pixel or radial feature |
Spatial Grain Size
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30 m x 30 m surface pixel and 2-m depth soil column | 3.1 km2 |
EM ID
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EM-379 | EM-982 |
EM Computational Approach
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Numeric | Analytic |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-379 | EM-982 |
Model Calibration Reported?
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No | Yes |
Model Goodness of Fit Reported?
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No | Yes |
Goodness of Fit (metric| value | unit)
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None |
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Model Operational Validation Reported?
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No | Yes |
Model Uncertainty Analysis Reported?
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No | No |
Model Sensitivity Analysis Reported?
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No | Yes |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Yes |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-379 | EM-982 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-379 | EM-982 |
None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-379 | EM-982 |
Centroid Latitude
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44.25 | 39.83 |
Centroid Longitude
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-122.33 | 98.58 |
Centroid Datum
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WGS84 | WGS84 |
Centroid Coordinates Status
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Provided | Estimated |
EM ID
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EM-379 | EM-982 |
EM Environmental Sub-Class
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Forests | Rivers and Streams |
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). | Stream segment |
EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-379 | EM-982 |
EM Organismal Scale
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Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-379 | EM-982 |
None Available | None Available |
EnviroAtlas URL
EM-379 | EM-982 |
Average Annual Precipitation | GAP Ecological Systems, Average Annual Precipitation, Average Annual Daily Potential Wind Energy |
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-982 |
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-982 |
None |
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