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-417 | EM-598 | EM-617 | EM-655 |
EM Short Name
em.detail.shortNameHelp
?
|
SWAT, Guanica Bay, Puerto Rico, USA | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | RBI Spatial Analysis Method | Hunting recreation, Wisconsin, USA |
EM Full Name
em.detail.fullNameHelp
?
|
SWAT (Soil and Water Assessment Tool) Guánica Bay, Puerto Rico, USA | DeNitrification-DeComposition simulation of N2O flux Ireland | Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method | Hunting recreation, Wisconsin, USA |
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
US EPA | None | None | None |
EM Source Document ID
|
334 | 358 | 367 | 376 |
Document Author
em.detail.documentAuthorHelp
?
|
Hu, W. and Y. Yuan | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Bousquin, J., Mazzotta M., and W. Berry | Qiu, J. and M. G. Turner |
Document Year
em.detail.documentYearHelp
?
|
2013 | 2010 | 2017 | 2013 |
Document Title
em.detail.sourceIdHelp
?
|
Evaluation of Soil Erosion and Sediment Yield for the Ridge Watersheds in the Guanica Bay Watershed, Puerto Rico, Using the SWAT Model | 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. | Spatial interactions among ecosystem services in an urbanizing agricultural watershed |
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 EPA report | Published journal manuscript | Published EPA report | Published journal manuscript |
EM ID
em.detail.idHelp
?
|
EM-417 | EM-598 | EM-617 | EM-655 |
Not applicable | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | |
Contact Name
em.detail.contactNameHelp
?
|
Yongping Yuan | M. Abdalla | Justin Bousquin | Monica G. Turner |
Contact Address
|
USEPA, ORD, NERL, Environmental sciences Division, Las Vegas, Nevada | 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 | Not reported |
Contact Email
|
Yuan.Yongping@epa.gov | abdallm@tcd.ie | bousquin.justin@epa.gov | turnermg@wisc.edu |
EM ID
em.detail.idHelp
?
|
EM-417 | EM-598 | EM-617 | EM-655 |
Summary Description
em.detail.summaryDescriptionHelp
?
|
AUTHOR'S DESCRIPTION: " SWAT is a physically-based continuous watershed simulation model that operates on a daily time step. It is designed for long-term simulations. The U.S. Department of Agriculture-Agriculture Research Station (USDA-ARS) Grassland, Soil and Water Research Laboratory in Temple, Texas created SWAT in the early 1990s. It has undergone continual review and expansion of capabilities since it was created (Arnold et al., 1998; Neitsch, et al., 2011a and b). This model has the ability to predict changes in water, sediment, nutrient and pesticide loads with respect to the different management conditions in watershed. Major components of the SWAT model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides and agricultural management practices (Neitsch et al., 2011b). SWAT subdivides a watershed into multiple sub-watersheds, and the subwatersheds are further divided into Hydrologic Response Units (HRUs) that consist of homogeneous land use, soils, slope, and management (Gassman et al., 2007; Neitsch, et al., 2011b; Williams et al., 2008). | 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. " | AUTHOR'S DESCRIPTION (from Supporting Information): "The hunting recreation service was estimated as a function of the extent of wildlife areas open for hunting, the number of game species, proximity to population center, and accessibility. Similar assumptions were made for this assessment: larger areas and places with more game species would support more hunting, areas closer to large population centers would be used more than remote areas, and proximity to major roads would increase access and use of an area. We first obtained the boundary of public wild areas from Wisconsin DNR and calculated the amount of areas for each management unit. The number of game species (Spe) for each area was derived from Dane County Parks Division (70). We used the same population density (Pop) and road buffer layer (Road) described in the previous forest recreation section. The variables Spe, Pop, and Road were weighted to ranges of 0–40, 0–40, and 0–20, respectively, based on the relative importance of each in determining this service. We estimated overall hunting recreation service for each 30-m grid cell with the following equation: HRSi = Ai Σ(Spei + Popi +Roadi), where HRS is hunting recreation score, A is the area of public wild areas open for hunting/fishing, Spe represents the number of game species, Pop stands for the proximity to population centers, and Road is the distance to major roads. To simplify interpretation, we rescaled the original hunting recreation score (ranging from 0 to 28,000) to a range of 0–100, with 0 representing no hunting recreation service and 100 representing highest service. |
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None Identified | climate change | None identified | None identified |
Biophysical Context
|
Need to fill in | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | wetlands | No additional description provided |
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
Planting type, fertilizing rate, harvest rate | fertilization | N/A | No scenarios presented |
EM ID
em.detail.idHelp
?
|
EM-417 | EM-598 | EM-617 | EM-655 |
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-417 | EM-598 | EM-617 | EM-655 |
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
None | 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-417 | EM-598 | EM-617 | EM-655 |
EM Temporal Extent
em.detail.tempExtentHelp
?
|
1981-2004 | 1961-1990 | Not applicable | 2000-2006 |
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-dependent | time-dependent | time-stationary | time-stationary |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
future time | both | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
discrete | discrete | Not applicable | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
1 | 1 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Day | Day | Not applicable | Not applicable |
EM ID
em.detail.idHelp
?
|
EM-417 | EM-598 | EM-617 | EM-655 |
Bounding Type
em.detail.boundingTypeHelp
?
|
Watershed/Catchment/HUC | Point or points | Not applicable | Watershed/Catchment/HUC |
Spatial Extent Name
em.detail.extentNameHelp
?
|
Guanica Bay, Puerto Rico watersheds | Oak Park Research centre | Not applicable | Yahara Watershed, Wisconsin |
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-417 | EM-598 | EM-617 | EM-655 |
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
?
|
area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
30m x 30m | Not applicable | Not reported | 30m x 30m |
EM ID
em.detail.idHelp
?
|
EM-417 | EM-598 | EM-617 | EM-655 |
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Numeric | Numeric | Analytic | Analytic |
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-417 | EM-598 | EM-617 | EM-655 |
Model Calibration Reported?
em.detail.calibrationHelp
?
|
Yes ?Comment:Used 1981 and 1982 data to calibrate hydrology. |
Yes | Not applicable | No |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No ?Comment:Calibration for both the stream flow and Sediment concentration of the mode |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable | No |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
|
|
None | None |
Model Operational Validation Reported?
em.detail.validationHelp
?
|
Yes ?Comment:Validation with 1983-1984 data from USGS. Used streamflow and water quality data from two stations |
Yes | Not applicable | No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
Unclear | No | Not applicable | No |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
Yes ?Comment:Yes for both runoff and sediment |
No | Not applicable | No |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
No | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-417 | EM-598 | EM-617 | EM-655 |
|
|
None |
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-417 | EM-598 | EM-617 | EM-655 |
None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
?
|
EM-417 | EM-598 | EM-617 | EM-655 |
Centroid Latitude
em.detail.ddLatHelp
?
|
18.19 | 52.86 | Not applicable | 43.1 |
Centroid Longitude
em.detail.ddLongHelp
?
|
-66.76 | 6.54 | Not applicable | -89.4 |
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | None provided | Not applicable | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Provided | Not applicable | Provided |
EM ID
em.detail.idHelp
?
|
EM-417 | EM-598 | EM-617 | EM-655 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Inland Wetlands | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands |
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
watershed | farm pasture | Restored wetlands | Mixed environment watershed of prairie converted to predominantly agriculture and urban landscape |
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
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 | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
?
|
EM-417 | EM-598 | EM-617 | EM-655 |
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-417 | EM-598 | EM-617 | EM-655 |
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-417 | EM-598 | EM-617 | EM-655 |
|
|
|
|
<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-417 | EM-598 | EM-617 | EM-655 |
None |
|
|
None |