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-368 | EM-598 | EM-617 | EM-652 |
EM Short Name
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InVEST - Water Yield (v3.0) | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | RBI Spatial Analysis Method | Savannah Sparrow density, CREP, Iowa, USA |
EM Full Name
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InVEST v3.0 Reservoir Hydropower Projection, aka Water Yield | DeNitrification-DeComposition simulation of N2O flux Ireland | Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method | Savannah Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA |
EM Source or Collection
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InVEST | None | None | None |
EM Source Document ID
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311 | 358 | 367 | 372 |
Document Author
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Natural Capital Project | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Bousquin, J., Mazzotta M., and W. Berry | 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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2015 | 2010 | 2017 | 2010 |
Document Title
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Water Yield: Reservoir Hydropower Production- InVEST (v3.0) | 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. | 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 | Peer reviewed and published |
Comments on Status
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Web published | Published journal manuscript | Published EPA report | Published report |
EM ID
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EM-368 | EM-598 | EM-617 | EM-652 |
https://www.naturalcapitalproject.org/invest/ | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | |
Contact Name
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Natural Capital Project | M. Abdalla | Justin Bousquin | David Otis |
Contact Address
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371 Serra Mall, Stanford University, Stanford, Ca 94305 | 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 | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University |
Contact Email
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invest@naturalcapitalproject.org | abdallm@tcd.ie | bousquin.justin@epa.gov | dotis@iastate.edu |
EM ID
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EM-368 | EM-598 | EM-617 | EM-652 |
Summary Description
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Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. AUTHOR'S DESCRIPTION: "The InVEST Reservoir Hydropower model estimates the relative contributions of water from different parts of a landscape, offering insight into how changes in land use patterns affect annual surface water yield and hydropower production. Modeling the connections between landscape changes and hydrologic processes is not simple. Sophisticated models of these connections and associated processes (such as the WEAP model) are resource and data intensive and require substantial expertise. To accommodate more contexts, for which data are readily available, InVEST maps and models the annual average water yield from a landscape used for hydropower production, rather than directly addressing the affect of LULC changes on hydropower failure as this process is closely linked to variation in water inflow on a daily to monthly timescale. Instead, InVEST calculates the relative contribution of each land parcel to annual average hydropower production and the value of this contribution in terms of energy production. The net present value of hydropower production over the life of the reservoir also can be calculated by summing discounted annual revenues. The model runs on a gridded map. It estimates the quantity and value of water used for hydropower production from each subwatershed in the area of interest. It has three components, which run sequentially. First, it determines the amount of water running off each pixel as the precipitation less the fraction of the water that undergoes evapotranspiration. The model does not differentiate between surface, subsurface and baseflow, but assumes that all water yield from a pixel reaches the point of interest via one of these pathways. This model then sums and averages water yield to the subwatershed level. The pixel-scale calculations allow us to represent the heterogeneity of key driving factors in water yield such as soil type, precipitation, vegetation type, etc. However, the theory we are using as the foundation of this set of models was developed at the subwatershed to watershed scale. We are only confident in the interpretation of these models at the subwatershed scale, so all outputs are summed and/or averaged to the subwatershed scale. We do continue to provide pixel-scale representations of some outputs for calibration and model-checking purposes only. These pixel-scale maps are not to be interpreted for understanding of hydrological processes or to inform decision making of any kind. | 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: "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: "The migratory bird benefits of the 27 CREP sites were predicted for Savannah Sparrow (Passerculus sandwichensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SASP density = e^(-1.581362 + 0.0229603 *bbspath + 0.01024* grass3200 + 0.0255867 * hay3200) |
Specific Policy or Decision Context Cited
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None identified | climate change | None identified | None identified |
Biophysical Context
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None applicable | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | wetlands | Prairie pothole region of north-central Iowa |
EM Scenario Drivers
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N/A | fertilization | N/A | No scenarios presented |
EM ID
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EM-368 | EM-598 | EM-617 | EM-652 |
Method Only, Application of Method or Model Run
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Method Only | Method + Application | Method Only | Method + Application |
New or Pre-existing EM?
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New or revised model | Application of existing model | New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-368 | EM-598 | EM-617 | EM-652 |
Document ID for related EM
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Doc-307 | Doc-280 | Doc-338 | Doc-205 | None | None | Doc-372 |
EM ID for related EM
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EM-437 | EM-148 | EM-344 | EM-111 | EM-593 | None | EM-648 | EM-649 | EM-650 | EM-651 |
EM Modeling Approach
EM ID
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EM-368 | EM-598 | EM-617 | EM-652 |
EM Temporal Extent
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Not applicable | 1961-1990 | Not applicable | 1992-2007 |
EM Time Dependence
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time-dependent | time-dependent | time-stationary | time-stationary |
EM Time Reference (Future/Past)
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future time | both | Not applicable | Not applicable |
EM Time Continuity
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discrete | discrete | Not applicable | Not applicable |
EM Temporal Grain Size Value
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1 | 1 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Year | Day | Not applicable | Not applicable |
EM ID
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EM-368 | EM-598 | EM-617 | EM-652 |
Bounding Type
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Not applicable | Point or points | Not applicable | Multiple unrelated locations (e.g., meta-analysis) |
Spatial Extent Name
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Not applicable | Oak Park Research centre | Not applicable | CREP (Conservation Reserve Enhancement Program) wetland sites |
Spatial Extent Area (Magnitude)
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Not applicable | 1-10 ha | Not applicable | 1-10 km^2 |
EM ID
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EM-368 | EM-598 | EM-617 | EM-652 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:pixel is likely 30m x 30m |
spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
Spatial Grain Type
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area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) |
Spatial Grain Size
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Not specified | Not applicable | Not reported | multiple, individual, irregular shaped sites |
EM ID
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EM-368 | EM-598 | EM-617 | EM-652 |
EM Computational Approach
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Numeric | Numeric | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-368 | EM-598 | EM-617 | EM-652 |
Model Calibration Reported?
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Yes ?Comment:Annual Yield can be calibrated with actual yield based up 10 year average input data though this was an "optional" part of the model. Calibrate with total precipitation and potential evapotranspiration. Before the calibration process is commenced, the modelers suggest performing a sensitivity analysis with the observed runoff data to define the parameters that influence model outputs the most. The calibration can then focus on highly sensitive parameters followed by less sensitive ones. |
Yes | Not applicable | Unclear |
Model Goodness of Fit Reported?
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Not applicable |
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)
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None |
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None | None |
Model Operational Validation Reported?
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No | Yes | Not applicable | Unclear |
Model Uncertainty Analysis Reported?
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No | No | Not applicable | No |
Model Sensitivity Analysis Reported?
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Not applicable | No | Not applicable | No |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-368 | EM-598 | EM-617 | EM-652 |
None |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-368 | EM-598 | EM-617 | EM-652 |
None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-368 | EM-598 | EM-617 | EM-652 |
Centroid Latitude
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-9999 | 52.86 | Not applicable | 42.62 |
Centroid Longitude
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-9999 | 6.54 | Not applicable | -93.84 |
Centroid Datum
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Not applicable | None provided | Not applicable | WGS84 |
Centroid Coordinates Status
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Not applicable | Provided | Not applicable | Estimated |
EM ID
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EM-368 | EM-598 | EM-617 | EM-652 |
EM Environmental Sub-Class
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Rivers and Streams | Agroecosystems | Inland Wetlands | Inland Wetlands | Agroecosystems | Grasslands |
Specific Environment Type
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Watershed | farm pasture | Restored wetlands | Grassland buffering inland wetlands set in agricultural land |
EM Ecological Scale
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Not applicable | 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-368 | EM-598 | EM-617 | EM-652 |
EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
EM-368 | EM-598 | EM-617 | EM-652 |
None Available | None Available | None Available |
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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-368 | EM-598 | EM-617 | EM-652 |
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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)
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