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-593 ![]() |
EM-632 ![]() |
EM Short Name
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VELMA soil temperature, Oregon, USA | DayCent N2O flux simulation, Ireland | Waterfowl pairs, CREP wetlands, Iowa, USA |
EM Full Name
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VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | DayCent simulation N2O flux and climate change, Ireland | Waterfowl pairs, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA |
EM Source or Collection
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US EPA | None | None |
EM Source Document ID
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317 | 358 | 372 |
Document Author
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Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever |
Document Year
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2013 | 2010 | 2010 |
Document Title
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Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published report |
EM ID
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EM-379 |
EM-593 ![]() |
EM-632 ![]() |
Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | Not applicable | |
Contact Name
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Alex Abdelnour | M. Abdalla | David Otis |
Contact Address
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Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University |
Contact Email
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abdelnouralex@gmail.com | abdallm@tcd.ie | dotis@iastate.edu |
EM ID
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EM-379 |
EM-593 ![]() |
EM-632 ![]() |
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" | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "Number of duck pairs per site was estimated for 6 species of ducks: Mallard (Anas platyrhynchos), Blue-winged Teal (Anas discors), Northern Shoveler (Anas clypeata), Gadwall (Anas strepera), Northern Pintail (Anas acuta), and Wood Duck (Aix sponsa), using models developed by Cowardin et al. (1995). Pair abundance was based on wetland class (i.e., temporary, seasonal, semi-permanent, lake, or river), wetland size, and a set of species specific regression coefficients. All CREP wetlands were considered semi-permanent for this analysis; therefore only coefficients associated with the semipermanent wetland pair model were used in calculations. The general equation used to estimate the pairs per wetland was: Pairs = e (a + bx + α) * p where, e = mathematical constant ≈ 2.718, a = species specific regression coefficient a, b = species specific regression coefficient b, x = the natural log of wetland size, α = species specific alpha value, and p = proportion of the basin containing water (assumed to be 0.90 for this analysis)" |
Specific Policy or Decision Context Cited
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None identified | climate change | 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. | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Prairie pothole region of north-central Iowa |
EM Scenario Drivers
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No scenarios presented | air temperature, precipitation, Atmospheric CO2 concentrations | No scenarios presented |
EM ID
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EM-379 |
EM-593 ![]() |
EM-632 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs |
New or Pre-existing EM?
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Application of existing model | 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-593 ![]() |
EM-632 ![]() |
Document ID for related EM
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Doc-13 | Doc-317 | None | None |
EM ID for related EM
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EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | EM-598 | EM-705 | EM-703 | EM-702 | EM-701 | EM-700 |
EM Modeling Approach
EM ID
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EM-379 |
EM-593 ![]() |
EM-632 ![]() |
EM Temporal Extent
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1969-2008 | 1961-1990 | 2002-2007 |
EM Time Dependence
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time-dependent | time-dependent | time-stationary |
EM Time Reference (Future/Past)
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future time | both | Not applicable |
EM Time Continuity
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discrete | discrete | Not applicable |
EM Temporal Grain Size Value
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1 | 1 | Not applicable |
EM Temporal Grain Size Unit
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Day | Day | Not applicable |
EM ID
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EM-379 |
EM-593 ![]() |
EM-632 ![]() |
Bounding Type
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Watershed/Catchment/HUC | Point or points | Multiple unrelated locations (e.g., meta-analysis) |
Spatial Extent Name
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H. J. Andrews LTER WS10 | Oak Park Research centre | CREP (Conservation Reserve Enhancement Program) wetland sites |
Spatial Extent Area (Magnitude)
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10-100 ha | 1-10 ha | 1-10 km^2 |
EM ID
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EM-379 |
EM-593 ![]() |
EM-632 ![]() |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:See below, grain includes vertical, subsurface dimension. |
spatially lumped (in all cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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volume, for 3-D feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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30 m x 30 m surface pixel and 2-m depth soil column | Not applicable | multiple, individual, irregular shaped sites |
EM ID
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EM-379 |
EM-593 ![]() |
EM-632 ![]() |
EM Computational Approach
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Numeric | Numeric | Analytic |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-379 |
EM-593 ![]() |
EM-632 ![]() |
Model Calibration Reported?
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No | No | Unclear |
Model Goodness of Fit Reported?
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No |
Yes ?Comment:for N2O fluxes |
No |
Goodness of Fit (metric| value | unit)
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None |
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None |
Model Operational Validation Reported?
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No | Yes | Unclear |
Model Uncertainty Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Reported?
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No | No | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-379 |
EM-593 ![]() |
EM-632 ![]() |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-379 |
EM-593 ![]() |
EM-632 ![]() |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-379 |
EM-593 ![]() |
EM-632 ![]() |
Centroid Latitude
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44.25 | 52.86 | 42.62 |
Centroid Longitude
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-122.33 | 6.54 | -93.84 |
Centroid Datum
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WGS84 | None provided | WGS84 |
Centroid Coordinates Status
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Provided | Provided | Estimated |
EM ID
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EM-379 |
EM-593 ![]() |
EM-632 ![]() |
EM Environmental Sub-Class
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Forests | Agroecosystems | Inland Wetlands | Agroecosystems | Grasslands |
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). | farm pasture | Wetlands buffered by grassland set in agricultural land |
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 | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-379 |
EM-593 ![]() |
EM-632 ![]() |
EM Organismal Scale
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Not applicable | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
EM-379 |
EM-593 ![]() |
EM-632 ![]() |
None Available | None Available |
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EnviroAtlas URL
EM-379 |
EM-593 ![]() |
EM-632 ![]() |
Average Annual Precipitation | GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day) | None Available |
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-593 ![]() |
EM-632 ![]() |
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-593 ![]() |
EM-632 ![]() |
None |
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