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-24 | EM-598 | EM-706 |
EM-948 ![]() |
EM Short Name
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i-Tree Eco: Carbon storage & sequestration, USA | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | WESP Method | Global forest stock, biomass and carbon downscaled |
EM Full Name
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i-Tree Eco carbon storage and sequestration (trees), USA | DeNitrification-DeComposition simulation of N2O flux Ireland | Method for the Wetland Ecosystem Services Protocol (WESP) | Global forest growing stock, biomass and carbon downscaled map |
EM Source or Collection
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i-Tree | USDA Forest Service | None | None | None |
EM Source Document ID
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195 | 358 | 390 | 442 |
Document Author
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Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E. | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Adamus, P. R. | Kindermann, G.E., I. McCallum, S. Fritz, and M. Obersteiner |
Document Year
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2013 | 2010 | 2016 | 2008 |
Document Title
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Carbon storage and sequestration by trees in urban and community areas of the United States | 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 | Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3. | A global forest growing stock, biomass and carbon map based on FAO statistics |
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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Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript |
EM ID
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EM-24 | EM-598 | EM-706 |
EM-948 ![]() |
Not applicable | http://www.dndc.sr.unh.edu |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
Not applicable | |
Contact Name
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David J. Nowak | M. Abdalla | Paul R. Adamus | Georg Kindermann |
Contact Address
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USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | 6028 NW Burgundy Dr. Corvallis, OR 97330 | International Institute for Applied Systems Analysis, Laxenburg, Austria |
Contact Email
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dnowak@fs.fed.us | abdallm@tcd.ie | adamus7@comcast.net | kinder(at)iiasa.ac.at |
EM ID
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EM-24 | EM-598 | EM-706 |
EM-948 ![]() |
Summary Description
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ABSTRACT: "Carbon storage and sequestration by urban trees in the United States was quantified to assess the magnitude and role of urban forests in relation to climate change. Urban tree field data from 28 cities and 6 states were used to determine the average carbon density per unit of tree cover. These data were applied to statewide urban tree cover measurements to determine total urban forest carbon storage and annual sequestration by state and nationally. Urban whole tree carbon storage densities average 7.69 kg C m^2 of tree cover and sequestration densities average 0.28 kg C m^2 of tree cover per year. Total tree carbon storage in U.S. urban areas (c. 2005) is estimated at 643 million tonnes ($50.5 billion value; 95% CI = 597 million and 690 million tonnes) and annual sequestration is estimated at 25.6 million tonnes ($2.0 billion value; 95% CI = 23.7 million to 27.4 million tonnes)." | 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 Wetland Ecosystem Services Protocol (WESP) is a standardized template for creating regionalized methods which then can be used to rapid assess ecosystem services (functions and values) of all wetland types throughout a focal region. To date, regionalized versions of WESP have been developed (or are ongoing) for government agencies or NGOs in Oregon, Alaska, Alberta, New Brunswick, and Nova Scotia. WESP also may be used directly in its current condition to assess these services at the scale of an individual wetland, but without providing a regional context for interpreting that information. Nonetheless, WESP takes into account many landscape factors, especially as they relate to the potential or actual benefits of a wetland’s functions. A WESP assessment requires completing a single three-part data form, taking about 1-3 hours. Responses to questions on that form are based on review of aerial imagery and observations during a single site visit; GIS is not required. After data are entered in an Excel spreadsheet, the spreadsheet uses science-based logic models to automatically generate scores intended to reflect a wetland’s ability to support the following functions: Water Storage and Delay, Stream Flow Support, Water Cooling, Sediment Retention and Stabilization, Phosphorus Retention, Nitrate Removal and Retention, Carbon Sequestration, Organic Nutrient Export, Aquatic Invertebrate Habitat, Anadromous Fish Habitat, Non-anadromous Fish Habitat, Amphibian & Reptile Habitat, Waterbird Feeding Habitat, Waterbird Nesting Habitat, Songbird, Raptor and Mammal Habitat, Pollinator Habitat, and Native Plant Habitat. For all but two of these functions, scores are given for both components of an ecosystem service: function and benefit. In addition, wetland Ecological Condition (Integrity), Public Use and Recognition, Wetland Sensitivity, and Stressors are scored. Scores generated by WESP may be used to (a) estimate a wetland’s relative ecological condition, stress, and sensitivity, (b) compare relative levels of ecosystem services among different wetland types, or (c) compare those in a single wetland before and after restoration, enhancement, or loss."] | ABSTRACT: "Currently, information on forest biomass is available from a mixture of sources, including in-situ measurements, national forest inventories, administrative-level statistics, model outputs and regional satellite products. These data tend to be regional or national, based on different methodologies and not easily accessible. One of the few maps available is the Global Forest Resources Assessment (FRA) produced by the Food and Agriculture Organization of the United Nations (FAO 2005) which contains aggregated country-level information about the growing stock, biomass and carbon stock in forests for 229 countries and territories. This paper presents a technique to downscale the aggregated results of the FRA2005 from the country level to a half degree global spatial dataset containing forest growing stock; above/belowground biomass, dead wood and total forest biomass; and above-ground, below-ground, dead wood, litter and soil carbon. In all cases, the number of countries providing data is incomplete. For those countries with missing data, values were estimated using regression equations based on a downscaling model. The downscaling method is derived using a relationship between net primary productivity (NPP) and biomass and the relationship between human impact and biomass assuming a decrease in biomass with an increased level of human activity. The results, presented here, represent one of the first attempts to produce a consistent global spatial database at half degree resolution containing forest growing stock, biomass and carbon stock values. All results from the methodology described in this paper are available online at www. iiasa.ac.at/Research/FOR/. " |
Specific Policy or Decision Context Cited
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Not reported | climate change | None identified | None identified |
Biophysical Context
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Urban areas 3.0% of land in U.S. and Urban/community land (5.3%) in 2000. | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | None | No additional description provided |
EM Scenario Drivers
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No scenarios presented | fertilization | N/A | No scenarios presented |
EM ID
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EM-24 | EM-598 | EM-706 |
EM-948 ![]() |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method Only | 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 | 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-24 | EM-598 | EM-706 |
EM-948 ![]() |
Document ID for related EM
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None | None | None | None |
EM ID for related EM
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None | EM-593 | EM-718 | None |
EM Modeling Approach
EM ID
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EM-24 | EM-598 | EM-706 |
EM-948 ![]() |
EM Temporal Extent
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1989-2010 | 1961-1990 | Not applicable | 1999-2005 |
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-24 | EM-598 | EM-706 |
EM-948 ![]() |
Bounding Type
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Geopolitical | Point or points | Not applicable | No location (no locational reference given) |
Spatial Extent Name
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United States | Oak Park Research centre | Not applicable | Global |
Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 1-10 ha | Not applicable | >1,000,000 km^2 |
EM ID
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EM-24 | EM-598 | EM-706 |
EM-948 ![]() |
EM Spatial Distribution
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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
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area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature |
Spatial Grain Size
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1 m^2 | Not applicable | not reported | 0.5 x 0.5 degrees |
EM ID
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EM-24 | EM-598 | EM-706 |
EM-948 ![]() |
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-24 | EM-598 | EM-706 |
EM-948 ![]() |
Model Calibration Reported?
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No | Yes | Not applicable | No |
Model Goodness of Fit Reported?
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No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable |
Yes ?Comment:For the 0.5 grid level equation where the country forest level is missing. |
Goodness of Fit (metric| value | unit)
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None |
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None |
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Model Operational Validation Reported?
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No | Yes | No | Yes |
Model Uncertainty Analysis Reported?
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Yes ?Comment:An error of sampling was reported, but not an error of estimation Estimation error was unknown and reported as likely larger than the error of sampling. |
No | Not applicable | No |
Model Sensitivity Analysis Reported?
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No | 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-24 | EM-598 | EM-706 |
EM-948 ![]() |
Comment:EM presents carbon storage and sequestration rates for country and by individual state |
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-24 | EM-598 | EM-706 |
EM-948 ![]() |
None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-24 | EM-598 | EM-706 |
EM-948 ![]() |
Centroid Latitude
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40.16 | 52.86 | Not applicable | 44.51 |
Centroid Longitude
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-99.79 | 6.54 | Not applicable | -123.51 |
Centroid Datum
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WGS84 | None provided | Not applicable | WGS84 |
Centroid Coordinates Status
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Estimated | Provided | Not applicable | Estimated |
EM ID
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EM-24 | EM-598 | EM-706 |
EM-948 ![]() |
EM Environmental Sub-Class
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Forests | Created Greenspace | Agroecosystems | Inland Wetlands | Forests |
Specific Environment Type
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Urban forests | farm pasture | Wetlands | Forests |
EM Ecological Scale
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Zone within an ecosystem | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to 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-24 | EM-598 | EM-706 |
EM-948 ![]() |
EM Organismal Scale
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Species ?Comment:Trees were identified to species for the differential growth and biomass estimates part of the analysis. |
Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-24 | EM-598 | EM-706 |
EM-948 ![]() |
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-24 | EM-598 | EM-706 |
EM-948 ![]() |
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None |
<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-24 | EM-598 | EM-706 |
EM-948 ![]() |
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None |