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-492 | EM-598 | EM-706 | EM-991 |
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EM Short Name
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i-Tree Eco: Carbon storage & sequestration, USA | EnviroAtlas - Restorable wetlands | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | WESP Method | Atlantis ecosystem harvest submodel |
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EM Full Name
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i-Tree Eco carbon storage and sequestration (trees), USA | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | DeNitrification-DeComposition simulation of N2O flux Ireland | Method for the Wetland Ecosystem Services Protocol (WESP) | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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EM Source or Collection
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i-Tree | USDA Forest Service | US EPA | EnviroAtlas | None | None | None |
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EM Source Document ID
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195 | 262 | 358 | 390 | 463 |
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Document Author
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Nowak, D. J., Greenfield, E. J., Hoehn, R. E. and Lapoint, E. | US EPA Office of Research and Development - National Exposure Research Laboratory | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Adamus, P. R. | Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C. |
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Document Year
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2013 | 2013 | 2010 | 2016 | 2011 |
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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 | 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 | Manual for the Wetland Ecosystem Services Protocol (WESP) v. 1.3. | Lessons in modelling and management of marine ecosystems: the Atlantis experience |
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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 on US EPA EnviroAtlas website | Published journal manuscript | Published report | Published journal manuscript |
Software and Access
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EM ID
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EM-24 | EM-492 | EM-598 | EM-706 | EM-991 |
| Not applicable | https://www.epa.gov/enviroatlas | http://www.dndc.sr.unh.edu |
http://people.oregonstate.edu/~adamusp/WESP/ ?Comment:This is an Excel spreadsheet calculator |
https://research.csiro.au/atlantis/home/links/ | |
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Contact Name
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David J. Nowak | EnviroAtlas Team | M. Abdalla | Paul R. Adamus | Elizabeth Fulton |
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Contact Address
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USDA Forest Service, Northern Research Station, Syracuse, NY 13210, USA | Not reported | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | 6028 NW Burgundy Dr. Corvallis, OR 97330 | Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. |
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Contact Email
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dnowak@fs.fed.us | enviroatlas@epa.gov | abdallm@tcd.ie | adamus7@comcast.net | beth.fulton@csiro.au |
EM Description
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EM ID
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EM-24 | EM-492 | EM-598 | EM-706 | EM-991 |
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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)." | 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. | 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."] | Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions. |
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Specific Policy or Decision Context Cited
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Not reported | None Identified | climate change | None identified | 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. | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | None | NA |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | fertilization | N/A | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-24 | EM-492 | EM-598 | EM-706 | EM-991 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method Only | Method Only |
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New or Pre-existing EM?
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Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing 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-492 | EM-598 | EM-706 | EM-991 |
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Document ID for related EM
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None | None | None | None | Doc-456 | Doc-459 | Doc-461 | Doc-463 |
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EM ID for related EM
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None | None | EM-593 | EM-718 | EM-978 | EM-981 | EM-983 | EM-985 | EM-990 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-24 | EM-492 | EM-598 | EM-706 | EM-991 |
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EM Temporal Extent
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1989-2010 | 2006-2013 | 1961-1990 | Not applicable | Not applicable |
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EM Time Dependence
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time-dependent | time-stationary | time-dependent | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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future time | Not applicable | both | Not applicable | Not applicable |
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EM Time Continuity
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discrete | Not applicable | discrete | Not applicable | continuous |
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EM Temporal Grain Size Value
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1 | Not applicable | 1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Year | Not applicable | Day | Not applicable | Not applicable |
EM Spatial Extent
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EM ID
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EM-24 | EM-492 | EM-598 | EM-706 | EM-991 |
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Bounding Type
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Geopolitical | Geopolitical | Point or points | Not applicable | Not applicable |
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Spatial Extent Name
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United States | conterminous United States | Oak Park Research centre | Not applicable | Not applicable |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | >1,000,000 km^2 | 1-10 ha | Not applicable | Not applicable |
Spatial Distribution of Computations
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EM ID
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EM-24 | EM-492 | EM-598 | EM-706 | EM-991 |
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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 lumped (in all cases) | spatially distributed (in at least some cases) | Not applicable |
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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) | Not applicable | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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1 m^2 | irregular | Not applicable | not reported | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-24 | EM-492 | EM-598 | EM-706 | EM-991 |
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EM Computational Approach
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Numeric | Analytic | Numeric | Analytic | 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-492 | EM-598 | EM-706 | EM-991 |
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Model Calibration Reported?
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No | No | Yes | Not applicable | Not applicable |
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Model Goodness of Fit Reported?
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No | No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
Not applicable | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None |
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None | None |
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Model Operational Validation Reported?
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No | No | Yes | No | Not applicable |
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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 | Not applicable | Not applicable |
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Model Sensitivity Analysis Reported?
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No | No | No | Not applicable | Not applicable |
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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-492 | EM-598 | EM-706 | EM-991 |
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)
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| EM-24 | EM-492 | EM-598 | EM-706 | EM-991 |
| None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-24 | EM-492 | EM-598 | EM-706 | EM-991 |
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Centroid Latitude
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40.16 | 39.5 | 52.86 | Not applicable | Not applicable |
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Centroid Longitude
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-99.79 | -98.35 | 6.54 | Not applicable | Not applicable |
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Centroid Datum
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WGS84 | WGS84 | None provided | Not applicable | Not applicable |
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Centroid Coordinates Status
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Estimated | Estimated | Provided | Not applicable | Not applicable |
Environments and Scales Modeled
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EM ID
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EM-24 | EM-492 | EM-598 | EM-706 | EM-991 |
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EM Environmental Sub-Class
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Forests | Created Greenspace | Agroecosystems | Agroecosystems | Inland Wetlands | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas |
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Specific Environment Type
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Urban forests | Terrestrial | farm pasture | Wetlands | Multiple |
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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 corresponds to 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-492 | EM-598 | EM-706 | EM-991 |
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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-492 | EM-598 | EM-706 | EM-991 |
| 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-492 | EM-598 | EM-706 | EM-991 |
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None |
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| EM-24 | EM-492 | EM-598 | EM-706 | EM-991 |
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None |
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None |