EcoService Models Library (ESML)
loading
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
em.detail.idHelp
?
|
EM-97 | EM-598 | EM-617 | EM-943 |
|
EM Short Name
em.detail.shortNameHelp
?
|
AnnAGNPS, Kaskaskia River watershed, IL, USA | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | RBI Spatial Analysis Method | Visitation to natural areas, New England, USA |
|
EM Full Name
em.detail.fullNameHelp
?
|
AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | DeNitrification-DeComposition simulation of N2O flux Ireland | Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method | Estimating natural area use with cell phone data, Narragansett Beach, New England, USA |
|
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
US EPA | None | None | US EPA |
|
EM Source Document ID
|
137 | 358 | 367 | 436 |
|
Document Author
em.detail.documentAuthorHelp
?
|
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. | Bousquin, J., Mazzotta M., and W. Berry | Merrill, N.H., Atkinson, S.F., Mulvaney, K.K., Mazzotta, K.K., and J. Bousquin |
|
Document Year
em.detail.documentYearHelp
?
|
2011 | 2010 | 2017 | 2020 |
|
Document Title
em.detail.sourceIdHelp
?
|
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 | Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual. | Using data derived from cellular phone locations to estimate visitation to natural areas: An application to water recreation in New England, USA |
|
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
|
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | Published journal manuscript | Published EPA report | Published journal manuscript |
|
EM ID
em.detail.idHelp
?
|
EM-97 | EM-598 | EM-617 | EM-943 |
| 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 | Not applicable | https://github.com/USEPA/Recreation_Benefits.git | |
|
Contact Name
em.detail.contactNameHelp
?
|
Yongping Yuan | M. Abdalla | Justin Bousquin | Nathaniel Merrill |
|
Contact Address
|
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 | US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561 | Atlantic Coastal Environmental Sciences Division, U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Narragansett, Rhode Island, United States of America, |
|
Contact Email
|
yuan.yongping@epa.gov | abdallm@tcd.ie | bousquin.justin@epa.gov | merrill.nathaniel@epa.gov |
|
EM ID
em.detail.idHelp
?
|
EM-97 | EM-598 | EM-617 | EM-943 |
|
Summary Description
em.detail.summaryDescriptionHelp
?
|
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. | AUTHOR DESCRIPTION: "The Rapid Benefits Indicators (RBI) approach consists of five steps and is outlined in Assessing the Benefits of Wetland Restoration – A Rapid Benefits Indicators Approach for Decision Makers, hereafter referred to as the “guide.” The guide presents the assessment approach, detailing each step of the indicator development process and providing an example application in the “Step in Action” pages. The spatial analysis toolset is intended to be used to analyze existing spatial information to produce metrics for many of the indicators developed in that guide. This spatial analysis toolset manual gives directions on the mechanics of the tool and its data requirements, but does not detail the reasoning behind the indicators and how to use results of the assessment; this information is found in the guide. " | ABSTRACT: "We introduce and validate the use of commercially available human mobility datasets based on cell phone locations to estimate visitation to natural areas. By combining this data with on-the-ground observations of visitation to water recreation areas in New England, we fit a model to estimate daily visitation for four months to more than 500 sites. The results show the potential for this new big data source of human mobility to overcome limitations in traditional methods of estimating visitation and to provide consistent information at policy-relevant scales. However, the data providers’ opaque and rapidly developing methods for processing locational information required a calibration and validation against data collected by traditional means to confidently reproduce the desired estimates of visitation. We found that with this calibration, the high-resolution information in both space and time provided by cell phone location-derived data creates opportunities for developing next-generation models of human interactions with the natural environment. " |
|
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
Not reported | climate change | None identified | None identified |
|
Biophysical Context
|
Upper Mississipi River basin, elevation 142-194m, | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | wetlands | Natural area water bodies |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | fertilization | N/A | N/A |
|
EM ID
em.detail.idHelp
?
|
EM-97 | EM-598 | EM-617 | EM-943 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application | Method Only | Method + Application |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
New or revised model | Application of existing model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
|
EM ID
em.detail.idHelp
?
|
EM-97 | EM-598 | EM-617 | EM-943 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-142 | None | None | None |
|
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
None | EM-593 | None | None |
EM Modeling Approach
|
EM ID
em.detail.idHelp
?
|
EM-97 | EM-598 | EM-617 | EM-943 |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
1980-2006 | 1961-1990 | Not applicable | 2017 |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-dependent | time-stationary | time-dependent |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | both | Not applicable | past time |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | discrete | Not applicable | discrete |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | 1 | Not applicable | 1 |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Day | Not applicable | Day |
|
EM ID
em.detail.idHelp
?
|
EM-97 | EM-598 | EM-617 | EM-943 |
|
Bounding Type
em.detail.boundingTypeHelp
?
|
Watershed/Catchment/HUC | Point or points | Not applicable | Point or points |
|
Spatial Extent Name
em.detail.extentNameHelp
?
|
East Fork Kaskaskia River watershed basin | Oak Park Research centre | Not applicable | Cape Cod |
|
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
100-1000 km^2 | 1-10 ha | Not applicable | 1000-10,000 km^2. |
|
EM ID
em.detail.idHelp
?
|
EM-97 | EM-598 | EM-617 | EM-943 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
|
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
length, for linear feature (e.g., stream mile) | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) |
|
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
1 km^2 | Not applicable | Not reported | water feature edge (beach) |
|
EM ID
em.detail.idHelp
?
|
EM-97 | EM-598 | EM-617 | EM-943 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Numeric | Numeric | Analytic | Numeric |
|
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic | deterministic |
|
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-97 | EM-598 | EM-617 | EM-943 |
|
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | Yes | Not applicable | Yes |
|
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable |
Yes ?Comment:Random forest model performance statistics |
|
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None |
|
None |
|
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
Yes | Yes | Not applicable | Yes |
|
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
Yes | No | Not applicable | Unclear |
|
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
Unclear | No | Not applicable | Yes |
|
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | Not applicable | Unclear |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-97 | EM-598 | EM-617 | EM-943 |
|
|
None |
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-97 | EM-598 | EM-617 | EM-943 |
| None | None | None | None |
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
?
|
EM-97 | EM-598 | EM-617 | EM-943 |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
38.69 | 52.86 | Not applicable | 41.72 |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
-89.1 | 6.54 | Not applicable | -70.29 |
|
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | None provided | Not applicable | WGS84 |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Provided | Provided | Not applicable | Estimated |
|
EM ID
em.detail.idHelp
?
|
EM-97 | EM-598 | EM-617 | EM-943 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Agroecosystems | Agroecosystems | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Row crop agriculture in Kaskaskia river basin | farm pasture | Restored wetlands | beaches |
|
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | 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
em.detail.idHelp
?
|
EM-97 | EM-598 | EM-617 | EM-943 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-97 | EM-598 | EM-617 | EM-943 |
| None Available | None Available | 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 | EM-617 | EM-943 |
|
|
|
|
<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 | EM-617 | EM-943 |
|
|
|
|
Home
Search EMs
My
EMs
Learn about
ESML
Show Criteria
Hide Criteria