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
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EM ID
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EM-97 | EM-598 |
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EM Short Name
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AnnAGNPS, Kaskaskia River watershed, IL, USA | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland |
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EM Full Name
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AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | DeNitrification-DeComposition simulation of N2O flux Ireland |
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EM Source or Collection
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US EPA | None |
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EM Source Document ID
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137 | 358 |
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Document Author
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Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. |
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Document Year
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2011 | 2010 |
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Document Title
em.detail.sourceIdHelp
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AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | 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 |
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Document Status
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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 |
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EM ID
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EM-97 | EM-598 |
| https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | http://www.dndc.sr.unh.edu | |
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Contact Name
em.detail.contactNameHelp
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Yongping Yuan | M. Abdalla |
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Contact Address
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U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland |
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Contact Email
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yuan.yongping@epa.gov | abdallm@tcd.ie |
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EM ID
em.detail.idHelp
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EM-97 | EM-598 |
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Summary Description
em.detail.summaryDescriptionHelp
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AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | 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. 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. |
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Specific Policy or Decision Context Cited
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Not reported | climate change |
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Biophysical Context
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Upper Mississipi River basin, elevation 142-194m, | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C |
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EM Scenario Drivers
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Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | fertilization |
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EM ID
em.detail.idHelp
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EM-97 | EM-598 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application |
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New or Pre-existing EM?
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New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
em.detail.idHelp
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EM-97 | EM-598 |
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-142 | None |
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EM ID for related EM
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None | EM-593 |
EM Modeling Approach
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EM ID
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EM-97 | EM-598 |
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EM Temporal Extent
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1980-2006 | 1961-1990 |
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EM Time Dependence
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time-stationary | time-dependent |
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EM Time Reference (Future/Past)
em.detail.futurePastHelp
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Not applicable | both |
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EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | discrete |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | 1 |
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EM Temporal Grain Size Unit
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Not applicable | Day |
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EM ID
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EM-97 | EM-598 |
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Bounding Type
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Watershed/Catchment/HUC | Point or points |
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Spatial Extent Name
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East Fork Kaskaskia River watershed basin | Oak Park Research centre |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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100-1000 km^2 | 1-10 ha |
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EM ID
em.detail.idHelp
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EM-97 | EM-598 |
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EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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length, for linear feature (e.g., stream mile) | Not applicable |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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1 km^2 | Not applicable |
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EM ID
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EM-97 | EM-598 |
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EM Computational Approach
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Numeric | Numeric |
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EM Determinism
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deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-97 | EM-598 |
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Model Calibration Reported?
em.detail.calibrationHelp
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No | Yes |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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Model Operational Validation Reported?
em.detail.validationHelp
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Yes | Yes |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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Yes | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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Unclear | No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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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-97 | EM-598 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-97 | EM-598 |
| None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-97 | EM-598 |
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Centroid Latitude
em.detail.ddLatHelp
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38.69 | 52.86 |
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Centroid Longitude
em.detail.ddLongHelp
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-89.1 | 6.54 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | None provided |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided |
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EM ID
em.detail.idHelp
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EM-97 | EM-598 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Agroecosystems |
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Specific Environment Type
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Row crop agriculture in Kaskaskia river basin | farm pasture |
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EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
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EM-97 | EM-598 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-97 | EM-598 |
| 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)
| EM-97 | EM-598 |
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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-97 | EM-598 |
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