EcoService Models Library (ESML)
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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.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-24 | EM-88 | EM-492 | EM-598 |
EM-948 
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EM Short Name
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i-Tree Eco: Carbon storage & sequestration, USA | Area and hotspots of carbon storage, South Africa | EnviroAtlas - Restorable wetlands | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | Global forest stock, biomass and carbon downscaled |
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EM Full Name
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i-Tree Eco carbon storage and sequestration (trees), USA | Area and hotspots of carbon storage, South Africa | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | DeNitrification-DeComposition simulation of N2O flux Ireland | Global forest growing stock, biomass and carbon downscaled map |
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EM Source or Collection
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i-Tree | USDA Forest Service | None | US EPA | EnviroAtlas | None | None |
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EM Source Document ID
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195 | 271 | 262 | 358 | 442 |
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Document Author
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Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | US EPA Office of Research and Development - National Exposure Research Laboratory | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Kindermann, G.E., I. McCallum, S. Fritz, and M. Obersteiner |
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Document Year
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2013 | 2008 | 2013 | 2010 | 2008 |
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Document Title
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Carbon storage and sequestration by trees in urban and community areas of the United States | Mapping ecosystem services for planning and management | EnviroAtlas - National | 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 | A global forest growing stock, biomass and carbon map based on FAO statistics |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | 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 | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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| Not applicable | Not applicable | https://www.epa.gov/enviroatlas | http://www.dndc.sr.unh.edu | Not applicable | |
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Contact Name
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David J. Nowak | Benis Egoh | EnviroAtlas Team | M. Abdalla | Georg Kindermann |
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Contact Address
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USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Not reported | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | International Institute for Applied Systems Analysis, Laxenburg, Austria |
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Contact Email
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dnowak@fs.fed.us | Not reported | enviroatlas@epa.gov | abdallm@tcd.ie | kinder(at)iiasa.ac.at |
EM Description
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EM ID
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EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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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)." | AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…In this study, only carbon storage was mapped because of a lack of data on the other functions related to the regulation of global climate such as carbon sequestration and the effects of changes in albedo. Carbon is stored above or below the ground and South African studies have found higher levels of carbon storage in thicket than in savanna, grassland and renosterveld (Mills et al., 2005). This information was used by experts to classify vegetation types (Mucina and Rutherford, 2006), according to their carbon storage potential, into three categories: low to none (e.g. desert), medium (e.g. grassland), high (e.g. thicket, forest) (Rouget et al., 2004). All vegetation types with medium and high carbon storage potential were identified as the range of carbon storage. Areas of high carbon storage potential where it is essential to retain this store were mapped as the carbon storage hotspot." | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | 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. | 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/. " |
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Specific Policy or Decision Context Cited
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Not reported | None identified | None Identified | climate change | None identified |
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Biophysical Context
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Urban areas 3.0% of land in U.S. and Urban/community land (5.3%) in 2000. | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | No additional description provided |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | fertilization | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
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Application of existing model | New or revised model | New or revised model | Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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Document ID for related EM
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None | Doc-271 | None | None | None |
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EM ID for related EM
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None | EM-85 | EM-86 | EM-87 | None | EM-593 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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EM Temporal Extent
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1989-2010 | Not reported | 2006-2013 | 1961-1990 | 1999-2005 |
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EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | both | Not applicable |
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EM Time Continuity
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discrete | Not applicable | Not applicable | discrete | Not applicable |
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EM Temporal Grain Size Value
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1 | Not applicable | Not applicable | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Day | Not applicable |
EM Spatial Extent
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EM ID
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EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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Bounding Type
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Geopolitical | Geopolitical | Geopolitical | Point or points | No location (no locational reference given) |
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Spatial Extent Name
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United States | South Africa | conterminous United States | Oak Park Research centre | Global |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 1-10 ha | >1,000,000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
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area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
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1 m^2 | Distributed across catchments with average size of 65,000 ha | irregular | Not applicable | 0.5 x 0.5 degrees |
EM Structure and Computation Approach
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EM ID
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EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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EM Computational Approach
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Numeric | Analytic | Analytic | Numeric | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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Model Calibration Reported?
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No | No | No | Yes | No |
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Model Goodness of Fit Reported?
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No | No | No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Yes ?Comment:For the 0.5 grid level equation where the country forest level is missing. |
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Goodness of Fit (metric| value | unit)
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None | None | None |
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Model Operational Validation Reported?
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No | No | No | Yes | Yes |
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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 | No | No | No |
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Model Sensitivity Analysis Reported?
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No | No | No | No | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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Comment:EM presents carbon storage and sequestration rates for country and by individual state |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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| None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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Centroid Latitude
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40.16 | -30 | 39.5 | 52.86 | 44.51 |
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Centroid Longitude
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-99.79 | 25 | -98.35 | 6.54 | -123.51 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | None provided | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Estimated | Provided | Estimated |
Environments and Scales Modeled
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EM ID
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EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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EM Environmental Sub-Class
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Forests | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Agroecosystems | Forests |
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Specific Environment Type
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Urban forests | Not applicable | Terrestrial | farm pasture | Forests |
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EM Ecological Scale
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Zone within an ecosystem | 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 and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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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 | Not applicable |
Taxonomic level and name of organisms or groups identified
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| EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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| None Available | 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)
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| EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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None |
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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>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-24 | EM-88 | EM-492 | EM-598 |
EM-948
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None | None |
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None |