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-92 | EM-97 | EM-327 | EM-627 | EM-657 |
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EM Short Name
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Runoff potential of pesticides, Europe | AnnAGNPS, Kaskaskia River watershed, IL, USA | ARIES sediment regulation, Puget Sound Region, USA | N removal by wetland restoration, Midwest, USA | REQI (River Ecosystem Quality Index), Italy |
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EM Full Name
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Runoff potential of pesticides, Europe | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | ARIES (Artificial Intelligence for Ecosystem Services) Sediment Regulation for Reservoirs, Puget Sound Region, Washington, USA | Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA | REQI (River Ecosystem Quality Index), Marecchia River, Italy |
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EM Source or Collection
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None | US EPA | ARIES | None | None |
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EM Source Document ID
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254 | 137 | 302 |
370 ?Comment:Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006. |
378 |
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Document Author
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Schriever, C. A., and Liess, M. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers | Santolini, R, E. Morri, G. Pasini, G. Giovagnoli, C. Morolli, and G. Salmoiraghi |
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Document Year
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2007 | 2011 | 2014 | 2006 | 2014 |
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Document Title
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Mapping ecological risk of agricultural pesticide runoff | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins | Assessing the quality of riparian areas: the case of River Ecosystem Quality Index applied to the Marecchia river (Italy) |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Neither peer reviewed nor published (explain in Comment) | 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 report | Published journal manuscript |
Software and Access
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EM ID
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EM-92 | EM-97 | EM-327 | EM-627 | EM-657 |
| Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | http://aries.integratedmodelling.org/ | Not applicable | Not applicable | |
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Contact Name
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Carola Alexandra Schriever | Yongping Yuan | Ken Bagstad | William G. Crumpton | Elisa Morri |
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Contact Address
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Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Geosciences and Environmental Change Science Center, US Geological Survey | Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011 | Dept. of Earth, Life, and Environmental Sciences, Urbino university, via ca le suore, campus scientifico Enrico Mattei, Urbino 61029 Italy |
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Contact Email
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carola.schriever@ufz.de | yuan.yongping@epa.gov | kjbagstad@usgs.gov | crumpton@iastate.edu | elisa.morri@uniurb.it |
EM Description
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EM ID
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EM-92 | EM-97 | EM-327 | EM-627 | EM-657 |
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Summary Description
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ABSTRACT: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception." | 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" | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We mapped sediment regulation as the location of sediment sinks (depositional areas in floodplains), which can absorb sediment transported by hydrologic flows from upstream sources (erosionprone areas) prior to reaching users. In this case the benefit of avoided sedimentation is provided to 29 major reservoirs. Avoided sedimentation helps maintain the ability of reservoirs to provide benefits including hydroelectric power generation, flood control, recreation, and water supply to beneficiaries through the region. Avoided reservoir sedimentation likely helps to protect each of these benefits in different ways, i.e., increased turbidity or the loss of reservoir storage capacity may have a greater impact on some provision of some benefit types than others. For our purposes we ended the modeling and mapping exercise at the reservoirs. Reservoir sedimentation reduces their storage capacity, typically decreasing their ability to provide these benefits without costly dredging. We thus used a probabilistic Bayesian model of soil erosion incorporating vegetation, soils, and rainfall influences and calibrated using regional data from coarser scale and/or RUSLE derived erosion models (Bagstad et al. 2011). We probabilistically modeled sediment deposition in floodplains using data for floodplain vegetation, floodplain width, and stream gradient, which can influence rates of deposition. We calculated the ratio of actual to theoretical sediment regulation using the aggregated sink values upstream of reservoirs in the Puget Sound region, divided by aggregated theoretical sink values for the entire landscape." | ABSTRACT: "The primary objective of this project was to estimate the nitrate reduction that could be achieved using restored wetlands as nitrogen sinks in tile-drained regions of the upper Mississippi River (UMR) and Ohio River basins. This report provides an assessment of nitrate concentrations and loads across the UMR and Ohio River basins and the mass reduction of nitrate loading that could be achieved using wetlands to intercept nonpoint source nitrate loads. Nitrate concentration and stream discharge data were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations and to develop a model of FWA nitrate concentration based on land use. Land use accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. Annual water yield for grid cells was estimated by interpolating over selected USGS monitoring station water yields across the UMR and Ohio River basins. For 1990 to 1999, mass nitrate export from each grid area was estimated as the product of the FWA nitrate concentration, water yield and grid area. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Nitrate reduction was estimated using a temperature dependent, area-based, first order model. Model inputs included local temperature from the National Climatic Data Center and water yield estimated from USGS stream flow data. Results were used to develop a nonlinear model for percent nitrate removal as a function of hydraulic loading rate (HLR) and temperature. Mass nitrate removal for potential wetland restorations distributed across the UMR and Ohio River basin was estimated based on the expected mass load and the predicted percent removal. Similar functions explained most of the variability in per cent and mass removal reported for field scale experimental wetlands in the UMR and Ohio River basins. Results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas." AUTHOR'S DESCRIPTION: "Percent nitrate removal was estimated based on HLR functions (Figure 19) spanning a 3 fold range in loss rate coefficient (Crumpton 2001) and encompassing the observed performance reported for wetlands in the UMR and Ohio River basins (Table 2, Figure 7). The nitrate load was multiplied by the expected percent nitrate removal to estimate the mass removal. This procedure was repeated for each restoration scenario each year in the simulation period (1990 to 1999)… for a scenario with a wetland/watershed area ratio of 2%. These results are based on the assumption that the FWA nitrate concentration versus percent row crop r | ABSTRACT: "Riparian areas support a set of river functions and of ecosystem services (ESs). Their role is essential in reducing negative human impacts on river functionality. These aspects could be contained in the River Basin Management Plan, which is the tool for managing and planning freshwater ecosystems in a river basin. In this paper, a new index was developed, namely the River Ecosystem Quality Index (REQI). It is composed of five ecological indices, which assess the quality of riparian areas, and it was first applied to the Marecchia river (central Italy). The REQI was also compared with the Italian River Functionality Index (IFF) and the ESs measured as the capacity of land cover in providing human benefits. Data have shown a decrease in the quality of riparian areas, from the upper to lower part of river, with 53% of all subareas showing medium-quality values…" AUTHOR'S DESCRIPTION: "The evaluation of the quality of the riparian areas is based on the analysis of two fundamental elements of riparian areas: vegetation (characteristics and distribution) and wild birds, measured with standardized methodology and used as indicators of environmental quality and changes...To represent the REQI, each of the five indicators was initially scored with its own range (Figure 3(a)—(e)). Then, all results were redistributed in ranges from 1 to 5, where 5 is the best condition of all indices. Redistributed results were finally summed." |
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Specific Policy or Decision Context Cited
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European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | Not reported | None identified | None identified | None identified |
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Biophysical Context
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Not applicable | Upper Mississipi River basin, elevation 142-194m, | No additional description provided | No additional description provided | No additional description provided |
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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 | No scenarios presented | More conservative, average and less conservative nitrate loss rate | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
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EM-92 | EM-97 | EM-327 | EM-627 | EM-657 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) | Method + Application |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised 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-92 | EM-97 | EM-327 | EM-627 | EM-657 |
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Document ID for related EM
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Doc-255 | Doc-256 | Doc-257 | Doc-142 | Doc-303 | Doc-305 | None | None |
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EM ID for related EM
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None | None | None | None | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-92 | EM-97 | EM-327 | EM-627 | EM-657 |
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EM Temporal Extent
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2000 | 1980-2006 | 1971-2005 | 1973-1999 |
1996-2003 ?Comment:All the ecological analyses are based on the production of a 1:10,000 scale map of land cover with detailed classes for the vegetation obtained by overlapping the photogrammetric analysis (AIMA flight 1996) and the 2003 land-use map. |
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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 | future time | 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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Day | Not applicable | Not applicable | Day | Not applicable |
EM Spatial Extent
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EM ID
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EM-92 | EM-97 | EM-327 | EM-627 | EM-657 |
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Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC |
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Spatial Extent Name
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EU-15 | East Fork Kaskaskia River watershed basin | Puget Sound Region | Upper Mississippi River and Ohio River basins | Marecchia river catchment |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-92 | EM-97 | EM-327 | EM-627 | EM-657 |
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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 distributed (in at least some 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 | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature |
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Spatial Grain Size
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10 km x 10 km | 1 km^2 | 200m x 200m | 1 km2 | 500 m x 1000 m |
EM Structure and Computation Approach
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EM ID
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EM-92 | EM-97 | EM-327 | EM-627 | EM-657 |
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EM Computational Approach
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Analytic | Numeric | 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-92 | EM-97 | EM-327 | EM-627 | EM-657 |
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Model Calibration Reported?
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No | No | Yes | No | Not applicable |
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Model Goodness of Fit Reported?
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No | No | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None |
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Model Operational Validation Reported?
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No | Yes | No |
No ?Comment:However, agreement of submodel and intermediate components; annual discharge (R2=0.79), and nitrate-N load (R2=0.74), based on GIS land use were determined in comparison with USGS NASQAN data. |
Yes ?Comment:R2 values of the analysis between the REQI, the capacity of land cover to provide ESs, and the Italian River Functionality Quality Index ? IFF. |
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Model Uncertainty Analysis Reported?
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Yes | Yes | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
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Yes | Unclear | No | No | Not applicable |
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Model Sensitivity Analysis Include Interactions?
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No | 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-92 | EM-97 | EM-327 | EM-627 | EM-657 |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-92 | EM-97 | EM-327 | EM-627 | EM-657 |
| None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-92 | EM-97 | EM-327 | EM-627 | EM-657 |
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Centroid Latitude
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50.01 | 38.69 | 48 | 40.6 | 43.89 |
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Centroid Longitude
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4.67 | -89.1 | -123 | -88.4 | 12.3 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Provided | Estimated | Estimated | Estimated |
Environments and Scales Modeled
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EM ID
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EM-92 | EM-97 | EM-327 | EM-627 | EM-657 |
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EM Environmental Sub-Class
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Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Agroecosystems | Rivers and Streams | Lakes and Ponds | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Agroecosystems | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Arable lands in near-stream environments | Row crop agriculture in Kaskaskia river basin | Terrestrial environment surrounding a large estuary | Agroecosystems and associated drainage and wetlands | Riparian zone along major river |
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EM Ecological Scale
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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 | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of 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-92 | EM-97 | EM-327 | EM-627 | EM-657 |
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Not applicable |
Species ?Comment:Bird species for faunistic index of conservation. |
Taxonomic level and name of organisms or groups identified
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| EM-92 | EM-97 | EM-327 | EM-627 | EM-657 |
| 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-92 | EM-97 | EM-327 | EM-627 | EM-657 |
| 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-92 | EM-97 | EM-327 | EM-627 | EM-657 |
| None |
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None |
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None |