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
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EM ID
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EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
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EM Short Name
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RHyME2, Upper Mississippi River basin, USA | AnnAGNPS, Kaskaskia River watershed, IL, USA | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | Atlantis ecosystem harvest submodel | GI toolkit users guide |
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EM Full Name
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RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | DeNitrification-DeComposition simulation of N2O flux Ireland | Lessons in modelling and management of marine ecosystems: the Atlantis experience | Green Infrastructure valuation toolkit users guide |
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EM Source or Collection
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US EPA | US EPA | None | None | None |
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EM Source Document ID
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123 | 137 | 358 | 463 | 474 |
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Document Author
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Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | 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. | Genecon LLP. |
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Document Year
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2013 | 2011 | 2010 | 2011 | 2010 |
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Document Title
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Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | 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 | Lessons in modelling and management of marine ecosystems: the Atlantis experience | Building natural value for sustainable economic development The green infrastructure valuation toolkit user guide |
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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 journal manuscript | Published journal manuscript | Published report |
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EM ID
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EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
| Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | http://www.dndc.sr.unh.edu | https://research.csiro.au/atlantis/home/links/ | https://www.merseyforest.org.uk/services/gi-val/ | |
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Contact Name
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Liem Tran | Yongping Yuan | M. Abdalla | Elizabeth Fulton | The Mercey Forest |
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Contact Address
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Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. | Moss Ln, Woolston, Warrington WA3 6QX, United Kingdom |
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Contact Email
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ltran1@utk.edu | yuan.yongping@epa.gov | abdallm@tcd.ie | beth.fulton@csiro.au | mail@merseyforest.org.uk |
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EM ID
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EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
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Summary Description
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ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | 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. | 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. | [The toolkit provides a very helpful introduction to the evidence demonstrating the benefits of green infrastructure interventions. It offers a structured argument that speaks the language of regeneration and economic developments. The 11 economic benefits structure provides a relatively simple high level means of presenting and communicating the benefits of green infrastructure projects in economic contexts, although it also brings some risks of double-counting (see Limitations below). The toolkit provides a structured approach to value green infrastructure benefits in monetary, quantitative and qualitative terms, with equal weight being applied to each of these three ways to present existing evidence. It can add value to and inform the decision-making process, particularly when used at an early stage to get broad brush figures and weigh pros and cons.The toolkit relies on current state-of-the-art evidence and valuation techniques for green infrastructure benefits. However, the toolkit also highlights the need for considerable improvement and expansion of the evidence base to enable future iterations to provide improved valuations. The toolkit helps make green infrastructure benefits ‘visible’ to potential funders. The inclusion of environmental benefits in cost benefit analysis is currently very difficult, often requiring professional assistance. Such assistance is frequently beyond the means of many groups seeking project funding. The toolkit is aimed at filling this gap, providing a means of scoping out the indicative benefits of green infrastructure using tools and approaches accessible to many projects and groups. However, whilst the toolkit provides a means of undertaking a broad Value for Money assessment, it must but emphasised that this is only indicative and cannot replace more rigorous formal project appraisal techniques.] |
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Specific Policy or Decision Context Cited
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Not reported | Not reported | climate change | None identified | None |
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Biophysical Context
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No additional description provided | Upper Mississipi River basin, elevation 142-194m, | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | NA | N/A |
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EM Scenario Drivers
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No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | fertilization | No scenarios presented | N/A |
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EM ID
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EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
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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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New or revised model | New or revised model | Application of existing 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-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
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Document ID for related EM
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Doc-123 | Doc-142 | None | Doc-456 | Doc-459 | Doc-461 | Doc-463 | None |
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EM ID for related EM
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None | None | EM-593 | EM-978 | EM-981 | EM-983 | EM-985 | EM-990 | None |
EM Modeling Approach
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EM ID
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EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
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EM Temporal Extent
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1987-1997 | 1980-2006 | 1961-1990 | Not applicable | Not applicable |
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EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | both | Not applicable | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | discrete | continuous | Not applicable |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | Not applicable | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Day | Not applicable | Not applicable |
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EM ID
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EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
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Bounding Type
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Watershed/Catchment/HUC | Watershed/Catchment/HUC | Point or points | Not applicable | Not applicable |
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Spatial Extent Name
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Upper Mississippi River basin; St. Croix River Watershed | East Fork Kaskaskia River watershed basin | Oak Park Research centre | Not applicable | Not applicable |
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Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 | 100-1000 km^2 | 1-10 ha | Not applicable | Not applicable |
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EM ID
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EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
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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) | Not applicable | spatially distributed (in at least some cases) |
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Spatial Grain Type
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NHDplus v1 | length, for linear feature (e.g., stream mile) | Not applicable | Not applicable | area, for pixel or radial feature |
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Spatial Grain Size
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NHDplus v1 | 1 km^2 | Not applicable | Not applicable | Not reported |
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EM ID
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EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
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EM Computational Approach
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Numeric | Numeric | Numeric | Analytic | Numeric |
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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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EM ID
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EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
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Model Calibration Reported?
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Yes | No | Yes | Not applicable | Not applicable |
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Model Goodness of Fit Reported?
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Yes | 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 |
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None | None |
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Model Operational Validation Reported?
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No | Yes | Yes | Not applicable | Not applicable |
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Model Uncertainty Analysis Reported?
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No | Yes | No | Not applicable | Not applicable |
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Model Sensitivity Analysis Reported?
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No ?Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. |
Unclear | 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
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
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None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
| None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
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Centroid Latitude
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42.5 | 38.69 | 52.86 | Not applicable | Not applicable |
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Centroid Longitude
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-90.63 | -89.1 | 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 | Provided | Provided | Not applicable | Not applicable |
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EM ID
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EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
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EM Environmental Sub-Class
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Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Agroecosystems | Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas | Not applicable |
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Specific Environment Type
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None | Row crop agriculture in Kaskaskia river basin | farm pasture | Multiple | Multiple |
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EM Ecological Scale
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Ecosystem | Ecological scale corresponds to 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 is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
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EM ID
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EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
| 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)
| EM-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
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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-91 | EM-97 | EM-598 | EM-991 | EM-1004 |
| None |
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None | None |
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