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-59 
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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EM Short Name
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EnviroAtlas-Air pollutant removal | RHyME2, Upper Mississippi River basin, USA | AnnAGNPS, Kaskaskia River watershed, IL, USA | Fish species habitat value, Tampa Bay, FL, USA | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | Alewife derived nutrients, Connecticut, USA |
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EM Full Name
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US EPA EnviroAtlas - Pollutants (air) removed annually by tree cover; Example is shown for Durham NC and vicinity, USA | 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 | Fish species habitat value, Tampa Bay, FL, USA | DeNitrification-DeComposition simulation of N2O flux Ireland | Alewife derived nutrients in stream food web, Connecticut, USA |
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EM Source or Collection
em.detail.emSourceOrCollectionHelp
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US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Eco model. |
US EPA | US EPA | US EPA | None | None |
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EM Source Document ID
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223 | 123 | 137 | 187 | 358 | 384 |
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Document Author
em.detail.documentAuthorHelp
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US EPA Office of Research and Development - National Exposure Research Laboratory | 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. | Fulford, R., Yoskowitz, D., Russell, M., Dantin, D., and Rogers, J. | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Walters, A. W., R. T. Barnes, and D. M. Post |
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Document Year
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2013 | 2013 | 2011 | 2016 | 2010 | 2009 |
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Document Title
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EnviroAtlas - Featured Community | 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 | Habitat and recreational fishing opportunity in Tampa Bay: Linking ecological and ecosystem services to human beneficiaries | 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 | Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs |
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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 | Peer reviewed and published |
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Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
em.detail.idHelp
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EM-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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| https://www.epa.gov/enviroatlas | Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | Not applicable | http://www.dndc.sr.unh.edu | Not applicable | |
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Contact Name
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EnviroAtlas Team | Liem Tran | Yongping Yuan | Richard Fulford | M. Abdalla | Annika W. Walters |
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Contact Address
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Not reported | 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 | USEPA Gulf Ecology Division, Gulf Breeze, FL 32561 | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Dept. of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USA |
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Contact Email
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enviroatlas@epa.gov | ltran1@utk.edu | yuan.yongping@epa.gov | Fulford.Richard@epa.gov | abdallm@tcd.ie | annika.walters@yale.edu |
EM Description
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EM ID
em.detail.idHelp
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EM-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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Summary Description
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The Air Pollutant Removal model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina. ... pollution removal ... are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA: The maps, estimate and illustrate the variation in the amount of six airborne pollutants, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM10), and particulate matter (PM2.5), removed by trees. PM10 is for particulate matter greater than 2.5 microns and less than 10 microns. DATA FACT SHEET: "The data for this map are based on the land cover derived for each EnviroAtlas community and the pollution removal models in i-Tree, a toolkit developed by the USDA Forest Service. The land cover data were created from aerial photography through remote sensing methods; tree cover was then summarized as the percentage of each census block group. The i-Tree pollution removal module uses the tree cover data by block group, the closest hourly meteorological monitoring data for the community, and the closest pollution monitoring data... hourly estimates of pollution removal by trees were combined with atmospheric data to estimate hourly percent air quality improvement due to pollution removal for each pollutant." | 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" | ABSTRACT: "Estimating value of estuarine habitat to human beneficiaries requires that we understand how habitat alteration impacts function through both production and delivery of ecosystem goods and services (EGS). Here we expand on the habitat valuation technique of Bell (1997) with an estimate of recreational angler willingness-to-pay combined with estimates of angler effort, fish population size, and fish and angler distribution. Results suggest species-specific fishery value is impacted by angler interest and stock status, as the most targeted fish (spotted seatrout) did not have the highest specific value (fish−1). Reduced population size and higher size at capture resulted in higher specific value for common snook. Habitat value estimated from recreational fishing value and fish-angler distributions supported an association between seagrass and habitat value, yet this relationship was also impacted by distance to access points. This analysis does not provide complete valuation of habitat as it considers only one service (fishing), but demonstrates a methodology to consider functional equivalency of all habitat features as a part of a habitat mosaic rather than in isolation, as well as how to consider both EGS production and delivery to humans (e.g., anglers) in any habitat valuation, which are critical for a transition to ecosystem management." | 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: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year... There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems." |
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Specific Policy or Decision Context Cited
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None identified | Not reported | Not reported | None identifed | climate change | None identified |
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Biophysical Context
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No additional description provided | No additional description provided | Upper Mississipi River basin, elevation 142-194m, | shallow bay (mean 3.7m), transition zone between warm temperate and tropical biogeographic provinces. Highly urbanized watershed | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Alewife spawning runs typically occur Mid March - May. |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | No scenarios presented | fertilization | No scenarios presented |
EM Relationship to Other EMs or Applications
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EM ID
em.detail.idHelp
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EM-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | 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 | 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
em.detail.idHelp
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EM-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-345 | Doc-123 | Doc-142 | None | None | Doc-383 |
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EM ID for related EM
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None | None | None | None | EM-593 | EM-661 | EM-665 | EM-666 | EM-672 | EM-674 | EM-673 |
EM Modeling Approach
EM Relationship to Time
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EM ID
em.detail.idHelp
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EM-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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EM Temporal Extent
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2008-2010 | 1987-1997 | 1980-2006 | 2006-2011 | 1961-1990 | 1979-2009 |
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EM Time Dependence
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time-dependent | time-stationary | 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 | Not applicable | both | Not applicable |
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EM Time Continuity
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discrete | Not applicable | 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 | Not applicable | 1 | Not applicable |
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EM Temporal Grain Size Unit
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Hour | Not applicable | Not applicable | Not applicable | Day | Not applicable |
EM Spatial Extent
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EM ID
em.detail.idHelp
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EM-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or Ecological | Point or points | Watershed/Catchment/HUC |
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Spatial Extent Name
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Durham NC and vicinity | Upper Mississippi River basin; St. Croix River Watershed | East Fork Kaskaskia River watershed basin | Tampa Bay | Oak Park Research centre | Bride Brook |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 1-10 ha | 1-10 ha |
Spatial Distribution of Computations
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EM ID
em.detail.idHelp
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EM-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Spatial grain type is census block group. |
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 lumped (in all cases) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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other (specify), for irregular (e.g., stream reach, lake basin) | NHDplus v1 | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | Not applicable | Not applicable |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | NHDplus v1 | 1 km^2 | 1 km^2 | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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EM Computational Approach
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Numeric | Numeric | Numeric | Analytic | Numeric | Analytic |
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EM Determinism
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deterministic | 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-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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Model Calibration Reported?
em.detail.calibrationHelp
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Unclear | Yes | No | No | Yes |
Yes ?Comment:The fish counter (for alewife numbers) was calibrated. |
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Model Goodness of Fit Reported?
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No | Yes | No | No |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
No |
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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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None |
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Model Operational Validation Reported?
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No | No | Yes | No | Yes | No |
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Model Uncertainty Analysis Reported?
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No | No | Yes | No | No | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No |
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 | No | No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | 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])
em.detail.relationToSpaceTerrestrialHelp
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EM-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
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EM-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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| None | None | None |
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None |
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Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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Centroid Latitude
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35.99 | 42.5 | 38.69 | 27.74 | 52.86 | 41.32 |
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Centroid Longitude
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-78.96 | -90.63 | -89.1 | -82.57 | 6.54 | -72.24 |
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Centroid Datum
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None provided | WGS84 | WGS84 | WGS84 | None provided | WGS84 |
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Centroid Coordinates Status
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Estimated | Estimated | Provided | Estimated | Provided | Provided |
Environments and Scales Modeled
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EM ID
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EM-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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EM Environmental Sub-Class
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Created Greenspace | Atmosphere | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Agroecosystems | Near Coastal Marine and Estuarine | Agroecosystems | Rivers and Streams |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Urban and vicinity | None | Row crop agriculture in Kaskaskia river basin | Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh | farm pasture | Coastal stream |
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EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer than that of the Environmental Sub-class | Ecosystem | Ecological scale corresponds to the Environmental Sub-class | Zone within an ecosystem | 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
em.detail.nameOfOrgsOrGroupsHelp
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EM ID
em.detail.idHelp
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EM-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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EM Organismal Scale
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Not applicable | Not applicable | Not applicable | Species | Not applicable | Individual or population, within a species |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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EM-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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| None Available | None Available | None Available |
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None Available |
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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.detail.cicesHelp
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EM-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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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-59
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EM-91 | EM-97 |
EM-102
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EM-598 |
EM-667
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None |
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