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-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
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EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | AnnAGNPS, Kaskaskia River watershed, IL, USA | InVEST water yield, Hood Canal, WA, USA | KINEROS2, River Ravna watershed, Bulgaria | InVEST marine water quality, Hood Canal, WA, USA | N removal by wetlands, Contiguous USA | ARIES open Space, Puget Sound Region, USA | Land capability classification | Pharmaceutical product potential, St. Croix, USVI | EnviroAtlas-Carbon sequestered by trees | DayCent N2O flux simulation, Ireland | WaterWorld v2, Santa Basin, Peru | RUM: Valuing fishing quality, Michigan, USA | Pollinators on landfill sites, United Kingdom | Wild bees over 26 yrs of restored prairie, IL, USA | InVEST Coastal Vulnerability | ARIES Outdoor recreation, Santa Fe, NM | ARIES Sediment regulation, Santa Fe, NM | Recreational fishery index, USA | VELMA v. 2.0 LSR | Pollutant dispersion by vegetation barriers | Atlantis ecosystem physics submodel | CAESAR landscape evolution model | WASP method | MesoHABSIM, river habitat assessment, Poland |
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EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) water yield, Hood Canal, WA, USA | KINEROS (Kinematic runoff and erosion model) v2, River Ravna watershed,Bulgaria | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) marine water quality, Hood Canal, WA, USA | Nitrogen removal by wetlands as a function of loading, Contiguous USA | ARIES (Artificial Intelligence for Ecosystem Services) Open Space Proximity for Homeowners, Puget Sound Region, Washington, USA | Land capability classification | Relative pharmaceutical product potential (on reef), St. Croix, USVI | US EPA EnviroAtlas - Total carbon sequestered by tree cover; Example is shown for Durham NC and vicinity, USA | DayCent simulation N2O flux and climate change, Ireland | WaterWorld v2, Santa Basin, Peru | Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA | Pollinating insects on landfill sites, East Midlands, United Kingdon | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | InVEST Coastal Vulnerability | Artificial intelligence for Ecosystem Services (ARIES): Outdoor recreation, Santa Fe, New Mexico | Artificial Intelligence for Ecosystem Services (ARIES); Sediment regulation, Santa Fe, New Mexico | Recreational fishery index for streams and rivers, USA | VELMA (Visualizing Ecosystems for Land Management Assessments) Version 2.0 Leaf Stem Root (LSR) | Pollutant dispersion by vegetation barriers | Atlantis user's guide part I: general overview, physics & ecology | Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model | Water Quality Analysis Simulation Program Model method | Application of the Mesohabitat Simulation System (MesoHABSIM) for Assessing Impact of River Maintenance and Restoration Measures |
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EM Source or Collection
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US EPA | EnviroAtlas | US EPA | InVEST | EU Biodiversity Action 5 | InVEST | US EPA | ARIES | None | US EPA | US EPA | EnviroAtlas | i-Tree | None | None | None | None | None | InVEST | None | None | US EPA | US EPA | US EPA | None | None | None | None |
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EM Source Document ID
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262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
137 | 205 |
248 ?Comment:Document 277 is also a source document for this EM |
205 | 63 | 302 | 340 | 335 |
223 ?Comment:Additional source: I-tree Eco (doc# 345). |
358 | 368 |
382 ?Comment:Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010. |
389 | 401 | 408 | 411 | 411 | 414 | 366 | 435 | 461 | 468 | 472 | 495 |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M. | Nedkov, S., Burkhard, B. | Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M. | Jordan, S., Stoffer, J. and Nestlerode, J. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | United States Department of Agriculture - Natural Resources Conservation Service | Yee, S. H., Dittmar, J. A., and L. M. Oliver | US EPA Office of Research and Development - National Exposure Research Laboratory | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Van Soesbergen, A. and M. Mulligan | Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | The Natural Capital Project.org | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold | McKane, R. B., A. Brookes, K. Djang, M. Stieglitz, A. G. Abdelnour, F. Pan, J. J. Halama, P. B. Pettus and D. L. Phillips | Hashad, K. B. Yang, J. T. Steffens, R. W. Baldauf, P. Deshmukh, K. M. Zhang | Audzijonyte, A., Gorton, R., Kaplan, I., & Fulton, E. A. | Van De Wiel, M. J., Coulthard, T. J., Macklin, M. G., & Lewin, J. | Environmental Protection Agency | Suska, K. and Parasiewicz, P. |
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Document Year
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2013 | 2011 | 2013 | 2012 | 2013 | 2011 | 2014 | 2013 | 2014 | 2013 | 2010 | 2018 | 2014 | 2013 | 2017 | None | 2018 | 2018 | 2021 | 2014 | 2021 | 2017 | 2007 | 2024 | 2020 |
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Document Title
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EnviroAtlas - National | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | Wetlands as sinks for reactive nitrogen at continental and global scales: A meta-analysis | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | National Soil Survey Handbook - Part 622 - Interpretative Groups | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | EnviroAtlas - Featured Community | 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 | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Valuing recreational fishing quality at rivers and streams | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | InVEST Coastal Vulnerability | Towards globally customizable ecosystem service models | Towards globally customizable ecosystem service models | Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers. | VELMA Version 2.0 User Manual and Technical Documentation | Parameterizing pollutant dispersion downwind of roadside vegetation barriers | Atlantis user’s guide part I: general overview, physics & ecology | Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model | Water Quality Assessment Simulation Program | Application of the mesohabitat simulation system (mesohabsim) for assessing impact of river maintenance and restoration measures |
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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 | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | Not peer reviewed but is published (explain in Comment) | 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 | Published journal manuscript | Published report | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Website users guide | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Journal manuscript submitted or in review | Published report | Published journal manuscript | Published EPA report | Published journal manuscript |
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EM ID
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EM-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
| https://www.epa.gov/enviroatlas | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | https://www.naturalcapitalproject.org/invest/ | http://www.tucson.ars.ag.gov/agwa/ | https://www.naturalcapitalproject.org/invest/ | Not applicable | http://aries.integratedmodelling.org/ | Not applicable | Not applicable | https://www.epa.gov/enviroatlas | Not applicable | www.policysupport.org/waterworld | Not applicable | Not applicable | Not applicable | https://naturalcapitalproject.stanford.edu/software/invest |
https://integratedmodelling.org/hub/#/register ?Comment:Need to set up an account first and then can access the main integrated modelling hub page: |
https://integratedmodelling.org/hub/#/register ?Comment:Need to set up an account first and then can access the main integrated modelling hub page: |
Not applicable | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | Not applicable | https://research.csiro.au/atlantis/home/links/ | http://www.coulthard.org.uk/ | https://www.epa.gov/hydrowq/wasp8-download | https://mesohabsim.org/ | |
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Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Yongping Yuan | J.E. Toft | David C. Goodrich | J.E. Toft | Steve Jordan | Ken Bagstad | United States Department of Agriculture | Susan H. Yee | EnviroAtlas Team | M. Abdalla | Arnout van Soesbergen | Richard Melstrom | Sam Tarrant | Sean R. Griffin | Not applicable | Javier Martinez-Lopez | Javier Martinez-Lopez | Gregg Lomnicky | Robert B. McKane | K. Max Zhang | Asta Audzijonyte | Marco J. Van De Wiel | Environmental Protection Agency | k.suska@infish.com.pl |
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Contact Address
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Not reported | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA | USDA - ARS Southwest Watershed Research Center, 2000 E. Allen Rd., Tucson, AZ 85719 | Not reported | Gulf Ecology Division U.S. Environmental Protection Agency, 1 Sabine Island Drive, Gulf Breeze, Florida 32561 | Geosciences and Environmental Change Science Center, US Geological Survey | Not reported | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Not reported | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A. | Not applicable | BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | 200 SW 35th St., Corvallis, OR, 97333 | USEPA Office of Research and Development National Health and Environmental Effects Research Laboratory Western Ecology Division Corvallis, Oregon 97333 | Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA | University of Tasmania (Australia); Nature Research Centre (Lithuania) | Department of Geography, University of Western Ontario, London, Ontario, Canada | 1200 Pennsylvania Avenue, NW Washington, DC 20460 | Inland Fisheries Institute, Oczapowskiego Street 10, 10-719 Olsztyn, Poland |
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Contact Email
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enviroatlas@epa.gov | yuan.yongping@epa.gov | jetoft@stanford.edu | agwa@tucson.ars.ag.gov | jetoft@stanford.edu | steve.jordan@epa.gov | kjbagstad@usgs.gov | http://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/contactus/ | yee.susan@epa.gov | enviroatlas@epa.gov | abdallm@tcd.ie | arnout.van_soesbergen@kcl.ac.uk | melstrom@okstate.edu | sam.tarrant@rspb.org.uk | srgriffin108@gmail.com | Not applicable | javier.martinez@bc3research.org | javier.martinez@bc3research.org | lomnicky.gregg@epa.gov | mckane.bob@epa.gov | kz33@cornell.edu | Asta.Audzijonyte@utas.edu.au | mvandew3@uwo.ca | Google email group | k.suska@infish.com.pl |
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EM ID
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EM-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
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Summary Description
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DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | 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" | InVEST Water Yield and Scarcity Model Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We modelled discharge and total nitrogen for the 153 perennial sub- watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparame-terized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)… We modelled discharge using the InVESTWater Yield and Scarcity model. The model estimates discharge for user-defined subwatersheds based on the average annual precipitation, annual reference evapotranspiration, and a correction factor for vegetation type, soil depth, plant available water content, land use and land cover, root depth, elevation, saturated hydraulic conductivity, and consumptive water use" (2) | ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. The use of the catchment based hydrologic model KINEROS and the GIS AGWA tool provided data about peak rivers’ flows and the capability of different land cover types to “capture” and regulate some parts of the water." AUTHOR'S DESCRIPTION: "KINEROS is a distributed, physically based, event model describing the processes of interception, dynamic infiltration, surface runoff and erosion from watersheds characterized by predominantly overland flow. The watershed is conceptualized as a cascade and the channels, over which the flow is routed in a top–down approach, are using a finite difference solution of the one-dimensional kinematic wave equations (Semmens et al., 2005). Rainfall excess, which leads to runoff, is defined as the difference between precipitation amount and interception and infiltration depth. The rate at which infiltration occurs is not constant but depends on the rainfall rate and the accumulated infiltration amount, or the available moisture condition of the soil. The AGWA tool is a multipurpose hydrologic analysis system addressed to: (1) provide a simple, direct and repeatable method for hydrologic modeling; (2) use basic, attainable GIS data; (3) be compatible with other geospatial basin-based environmental analysis software; and (4) be useful for scenario development and alternative future simulation work at multiple scales (Miller et al., 2002). AGWA provides the functionality to conduct the processes of modeling and assessment for…KINEROS." | Marine Water Quality Model. Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We used outputs from the freshwater models as inputs to the marine water quality model.We adapted a box model that has been successfully applied in Puget Sound (Babson et al., 2006; Sutherland et al., 2011) to simulate seasonal and interannual variations in salinity, water temperature, and nitrates in the Canal." (p. 4) | ABSTRACT: "We compiled published data from wetland studies worldwide to estimate total Nr removal and to evaluate factors that influence removal rates. Over several orders of magnitude in wetland area and Nr loading rates, there is a positive, near-linear relationship between Nr removal and Nr loading. The linear model (null hypothesis) explains the data better than either a model of declining Nr removal efficiency with increasing Nr loading, or a Michaelis–Menten (saturation) model." | 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: "For open space proximity, we mapped the relative value of open space, highways that impede walking access or reduce visual and soundscape quality, and housing locations, connected by a flow model simulating physical access to desirable spaces. We used reviews of the hedonic valuation literature (Bourassa et al. 2004, McConnell and Walls 2005) to inform model development, ranking the influence of different open space characteristics on property values to parameterize the source and sink models. The model includes a distance decay function that accounts for changes with distance in the value of open space. We then computed the ratio of actual to theoretical provision of open space to compare the values accruing to homeowners relative to those for the entire landscape." | AUTHOR'S DESCRIPTION: "Definition. Land capability classification is a system of grouping soils primarily on the basis of their capability to produce common cultivated crops and pasture plants without deteriorating over a long period of time." "Class I (1) soils have slight limitations that restrict their use. Class II (2) soils have moderate limitations that reduce the choice of plants or require moderate conservation practices. Class III (3) soils have severe limitations that reduce the choice of plants or require special conservation practices, or both. Class IV (4) soils have very severe limitations that restrict the choice of plants or require very careful management, or both. Class V (5) soils have little or no hazard of erosion but have other limitations, impractical to remove, that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VI (6) soils have severe limitations that make them generally unsuited to cultivation and that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VII (7) soils have very severe limitations that make them unsuited to cultivation and that restrict their use mainly to rangeland, forestland, or wildlife habitat. Class VIII (8) soils and miscellaneous areas have limitations that preclude their use for commercial plant production and limit their use mainly to recreation, wildlife habitat, water supply, or esthetic purposes." [More information can be found at: http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054226#ex2] | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…When data on sponge diversity is unavailable, benthic habitat coverages may be used to estimate relative magnitudes of sponge diversity and abundance as an indicator of potential pharmaceutical production (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to potential pharmaceutical production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Pharmaceutical product potential = ΣiciMi where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians), and Mi is the relative magnitude of sponge diversity associated with each habitat." | The Total carbon sequestered by tree cover model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. DATA FACT SHEET: "This EnviroAtlas community map estimates the total metric tons (mt) of carbon that are removed annually from the atmosphere and sequestered in the above-ground biomass of trees in each census block group. The data for this map were derived from a high-resolution tree cover map developed by EPA. Within each census block group derived from U.S. Census data, the total amount of tree cover (m2) was determined using this remotely-sensed land cover data. The USDA Forest Service i-Tree model was used to estimate the annual carbon sequestration rate from state-based rates of kgC/m2 of tree cover/year. The state rates vary based on length of growing season and range from 0.168 kgC/m2 of tree cover/year (Alaska) to 0.581 kgC/m2 of tree cover/year (Hawaii). The national average rate is 0.306 kgC/m2 of tree cover/year. These national and state values are based on field data collected and analyzed in several cities by the U.S. Forest Service. These values were converted to metric tons of carbon removed and sequestered per year by census block group." | 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. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. 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. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. " | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each | ABSTRACT: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities." | Faced with an intensification of human activities and a changing climate, coastal communities need to better understand how modifications of the biological and physical environment (i.e. direct and indirect removal of natural habitats for coastal development) can affect their exposure to storm-induced erosion and flooding (inundation). The InVEST Coastal Vulnerability model produces a qualitative estimate of such exposure in terms of a vulnerability index, which differentiates areas with relatively high or low exposure to erosion and inundation during storms. By coupling these results with global population information, the model can show areas along a given coastline where humans are most vulnerable to storm waves and surge. The model does not take into account coastal processes that are unique to a region, nor does it predict long- or short-term changes in shoreline position or configuration. Model inputs, which serve as proxies for various complex shoreline processes that influence exposure to erosion and inundation, include: a polyline with attributes about local coastal geomorphology along the shoreline, polygons representing the location of natural habitats (e.g., seagrass, kelp, wetlands, etc.), rates of (observed) net sea-level change, a depth contour that can be used as an indicator for surge level (the default contour is the edge of the continental shelf), a digital elevation model (DEM) representing the topography of the coastal area, a point shapefile containing values of observed storm wind speed and wave power, and a raster representing population distribution. Outputs can be used to better understand the relative contributions of these different model variables to coastal exposure and highlight the protective services offered by natural habitats to coastal populations. This information can help coastal managers, planners, landowners and other stakeholders identify regions of greater risk to coastal hazards, which can in turn better inform development strategies and permitting. The results provide a qualitative representation of coastal hazard risks rather than quantifying shoreline retreat or inundation limits. | ABSTRACT: "Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed. " | ABSTRACT: "Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed. " | ABSTRACT: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.] | ABSTRACT: "VELMA – Visualizing Ecosystems for Land Management Assessments – is a spatially distributed, eco-hydrological model that links a land surface hydrology model with a terrestrial biogeochemistry model for simulating the integrated responses of vegetation, soil, and water resources to interacting stressors. For example, VELMA can simulate how changes in climate and land use interact to affect soil water storage, surface and subsurface runoff, vertical drainage, evapotranspiration, vegetation and soil carbon and nitrogen dynamics, and transport of nitrate, ammonium, and dissolved organic carbon and nitrogen to water bodies. VELMA differs from other existing eco-hydrology models in its simplicity, flexibility, and theoretical foundation. The model has a user-friendly Graphics User Interface (GUI) for easy input of model parameter values. In addition, advanced visualization of simulation results can enhance understanding of results and underlying concepts. VELMA’s visualization and interactivity features are packaged in an open-source, open-platform programming environment (Java / Eclipse). The development team for VELMA version 2.0 includes Dr. Bob McKane and coworkers at the U.S. Environmental Protection Agency’s Western Ecology Division, Dr. Marc Stieglitz and coworkers at the Georgia Institute of Technology, and Dr. Feifei Pan at the University of North Texas." | ABSTRACT: "Communities living and working in near-road environments are exposed to elevated levels of traffic-related air pollution (TRAP), causing adverse health effects. Roadside vegetation may help reduce TRAP through enhanced deposition and mixing….there are no studies that developed a dispersion model to characterize pollutant concentrations downwind of vegetation barriers. To account for the physical mechanisms, by which the vegetation barrier deposits and disperses pollutants, we propose a multi-region approach that describes the parameters of the standard Gaussian equations in each region. The four regions include the vegetation, a downwind wake, a transition, and a recovery zone. For each region, we fit the relevant Gaussian plume equation parameters as a function of the vegetation properties and the local wind speed. Furthermore, the model captures particle deposition which is a major factor in pollutant reduction by vegetation barriers. We generated data from 75 (CFD)-based simulations, using the Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) model, to parameterize the Gaussian-based equations. The simulations used reflected a wide range of vegetation barriers, with heights from 2-10 m, and various densities, represented by leaf area index values from 4-11, and evaluated under different urban conditions, represented by wind speeds from 1-5 m/s. The CTAG model has been evaluated against two field measurements to ensure that it can properly represent the vegetation barrier’s pollutant deposition and dispersion. The proposed multi-region Gaussian-based model was evaluated across 9 particle sizes and a tracer gas to assess its capability of capturing deposition. The multi-region model’s normalized mean error (NME) ranged between 0.18-0.3, the fractional bias (FB) ranged between -0.12-0.09, and R2 value ranged from 0.47-0.75 across all particle sizes and the tracer gas for ground level concentrations, which are within acceptable range. Even though the multi-region model is parameterized for coniferous trees, our sensitivity study indicates that the parameterized Gaussian-based model can provide useful predictions for hedge/bushes vegetative barriers as well." ADDITIONAL DESCRIPTION: Detailed variable relationships are described in the source document. The VRD associated with the ESML entry provides variables in a simplified form. | Before delving into Atlantis we would like to provide a little bit of background on the modelling framework and this manual. Atlantis is just one of many marine ecosystem models, originally known as BM2 (BoxModel 2) it was christened Atlantis by Villy Christensen in South Africa in 2001. Marine ecosystem models have existed for more than 50 years, though they have only grown in popular use since the advent of (fast) modern computing power. They have grown from a biophysical focus to include more and more of the human dimensions. This is reflected in the structure of this manual, which sequentially works through the physical then biological before getting into the human dimensions. Atlantis was originally developed with an eye to temperate marine ecosystems and fisheries, though it has grown through time. | We introduce a new computational model designed to simulate and investigate reach-scale alluvial dynamics within a landscape evolution model. The model is based on the cellular automaton concept, whereby the continued iteration of a series of local process ‘rules’ governs the behaviour of the entire system. The model is a modified version of the CAESAR landscape evolution model, which applies a suite of physically based rules to simulate the entrainment, transport and deposition of sediments. The CAESAR model has been altered to improve the representation of hydraulic and geomorphic processes in an alluvial environment. In-channel and overbank flow, sediment entrainment and deposition, suspended load and bed load transport, lateral erosion and bank failure have all been represented as local cellular automaton rules. Although these rules are relatively simple and straightforward, their combined and repeatedly iterated effect is such that complex, non-linear geomorphological response can be simulated within the model. Examples of such larger-scale, emergent responses include channel incision and aggradation, terrace formation, channel migration and river meandering, formation of meander cutoffs, and transitions between braided and single-thread channel patterns. In the current study, the model is illustrated on a reach of the River Teifi, near Lampeter, Wales, UK. | Web description: " The Water Quality Analysis Simulation Program (WASP) is an enhancement of the original WASP (Di Toro et al., 1983; Connolly and Winfield, 1984; Ambrose, R.B. et al., 1988). This model helps users interpret and predict water quality responses to natural phenomena and manmade pollution for various pollution management decisions. WASP is a dynamic compartment-modeling program for aquatic systems, including both the water column and the underlying benthos. WASP allows the user to investigate 1, 2, and 3 dimensional systems, and a variety of pollutant types. The state variables for the given modules are given in the table below. The time varying processes of advection, dispersion, point and diffuse mass loading and boundary exchange are represented in the model. WASP also can be linked with hydrodynamic and sediment transport models that can provide flows, depths velocities, temperature, salinity and sediment fluxes. This release of WASP contains the inclusion of the sediment diagenesis model linked to the Advanced Eutrophication sub model, which predicted sediment oxygen demand and nutrient fluxes from the underlying sediments " | Maintenance and restoration activities alter the river morphology and hydrology, and in consequence, alter fish habitats. The aim of this research was to investigate the change of habitat availability for fish guilds after carrying out maintenance works, commonly used river restoration measures and a restoration derived from fish habitat requirements. The selected study site is located at a close to natural condition section of Swider River in central Poland. The MesoHABSIM model was used to assess the area of suitable habitats in this site and predict habitat distribution at all planning scenarios. The affinity index which is a measure of similarity of two distributions showed that the likely distribution of habitats for fish resulting from simulated maintenance is 76.5% similar to that under measured conditions. The distribution of habitats caused by river restoration is also similar to that of the baseline in 73.2%. The resemblance between the restoration scenario focusing on fish habitat requirements and the reference conditions is 93.1%. It is beneficial to define the river restoration measures based on habitat availability for fish community. Modelling is a useful tool to simulate the changes and predict which guilds there is abundance of suitable habitats, and for which there are too few. It allows for more effective use of resources according to quantitative target states. |
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Specific Policy or Decision Context Cited
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None Identified | Not reported | Land use change | None identified | Land use change | None identified | None identified | None provided | None identified | None identified | climate change | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | Not applicable | None provided |
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Biophysical Context
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No additional description provided | Upper Mississipi River basin, elevation 142-194m, | Not additional description provided | Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | No additional description provided | Estuarine Emergent; Agricultural; Salt Marsh; Palustrine Emergent; Palustrine Forested | No additional description provided | No additional description provided | No additional description provided | No additional description provided | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | stream and river reaches of Michigan | No additional description provided | The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season. | Not applicable | Watersheds surrounding Santa Fe and Albuquerque, New Mexico | Watersheds surrounding Santa Fe and Albuquerque, New Mexico | None | No additional description provided | Communities living and working in near-road environments | Marine and coastal ecosystems | River Teifi, Lampeter, Wales | segments of streams modeled | Swider River, central Poland |
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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 | Future land use and land cover; climate change | No scenarios presented | future land use and land cover; Climate change | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | air temperature, precipitation, Atmospheric CO2 concentrations | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | targeted sport fish biomass | No scenarios presented | No scenarios presented | Options for future sea level change and population change | N/A | N/A | N/A | No scenarios presented | None scenarios presented | No scenarios presented | Varying flow velocities and durations | n/a | 1. Reference conditions |
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EM ID
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EM-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) | Method + Application | Method + Application | Method Only | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | Method + Application | Method + Application | Method Only | Method Only | Method Only | Method Only | Method Only | Method + Application (multiple runs exist) View EM Runs |
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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 | Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | 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-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
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Document ID for related EM
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Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
Doc-142 | Doc-280 | Doc-307 | Doc-311 | Doc-338 |
Doc-277 | Doc-294 | Doc-249 | Doc-250 ?Comment:Document 277 is also a source document for this EM |
None | None | Doc-303 | Doc-305 | None | None | Doc-345 | None | None | None | Doc-389 | None | Doc-410 | Doc-411 | Doc-411 | None | Doc-13 | Doc-317 | Doc-366 | Doc-359 | None | Doc-456 | Doc-459 | Doc-467 | None | None |
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EM ID for related EM
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None | None | EM-148 | EM-344 | EM-368 | EM-437 | EM-132 | EM-133 | None | None | None | None | None | None | EM-598 | None | None | EM-697 | None | EM-851 | EM-855 | EM-856 | EM-858 | None | None | EM-375 | EM-379 | EM-380 | EM-884 | EM-605 | EM-887 | EM-892 | None | EM-981 | EM-978 | EM-985 | EM-990 | EM-991 | EM-997 | None | None |
EM Modeling Approach
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EM ID
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EM-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
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EM Temporal Extent
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2006-2010 | 1980-2006 | 2005-7; 2035-45 | Not reported | varies by run, see runs for values | 2004 | 2000-2011 | Not applicable | 2006-2007, 2010 | 2010-2013 | 1961-1990 | 1950-2071 | 2008-2010 | 2007-2008 | 1988-2014 | Not applicable | 1981-2015 | 2011 | 2013-2014 |
Not applicable ?Comment:User defined model duration. |
Not applicable | Not applicable | Not applicable | Not applicable | 2014 |
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EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | Not applicable | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | Not applicable | time-dependent | time-dependent | time-dependent | time-stationary |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | both | both | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable |
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EM Time Continuity
em.detail.continueDiscreteHelp
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Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | continuous | continuous |
discrete ?Comment:Time frame is modeler dependent |
Not applicable |
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EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | Not reported | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable |
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EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Not applicable | Not applicable | Not applicable | Not reported | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Day | Month | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Day | Not applicable | Not applicable | Not applicable | Day | Not applicable |
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EM ID
em.detail.idHelp
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EM-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
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Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Not applicable | Physiographic or ecological | Geopolitical | Point or points | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Not applicable | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Not applicable | Not applicable | Not applicable | Watershed/Catchment/HUC | Not applicable | Watershed/Catchment/HUC |
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Spatial Extent Name
em.detail.extentNameHelp
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counterminous United States | East Fork Kaskaskia River watershed basin | Hood Canal | River Ravna watershed | Hood Canal | Contiguous U.S. | Puget Sound Region | Not applicable | Coastal zone surrounding St. Croix | Durham NC and vicinity | Oak Park Research centre | Santa Basin | HUCS in Michigan | East Midlands | Nachusa Grasslands | Not applicable | Santa Fe Fireshed | Santa Fe Fireshed | United States | Not applicable | Not applicable | Not applicable | River Teifi | Not applicable | Swider River |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | 100-1000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | 100-1000 km^2 | 100,000-1,000,000 km^2 | 10,000-100,000 km^2 | Not applicable | 100-1000 km^2 | 100-1000 km^2 | 1-10 ha | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 10-100 km^2 | Not applicable | 100-1000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | Not applicable | Not applicable | Not applicable | 1000-10,000 km^2. | Not applicable | 1000-10,000 km^2. |
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EM ID
em.detail.idHelp
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EM-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
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EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) ?Comment:Watersheds (12-digit HUCs). |
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 lumped (in all cases) | spatially distributed (in at least some cases) | Not applicable | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Census block groups |
spatially lumped (in all 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) | 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) ?Comment:User defined scale, from plot to basin size. |
spatially distributed (in at least some cases) | Not applicable | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some 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) | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | 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 | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | Not applicable | Not applicable | length, for linear feature (e.g., stream mile) | length, for linear feature (e.g., stream mile) |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | 1 km^2 | 30 m x 30 m | 25 m x 25 m | Not reported | Not applicable | 200m x 200m | Not applicable | 10 m x 10 m | irregular | Not applicable | 1 km2 | reach in HUC | multiple unrelated locations | Area varies by site | user defined | 30 m | 30 m | stream reach (site) | user defined | user defined | Not applicable | Not applicable | stream segment | Irregular |
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EM ID
em.detail.idHelp
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EM-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
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EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Not applicable | Analytic | Numeric | Numeric | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Analytic |
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EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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EM ID
em.detail.idHelp
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EM-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
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Model Calibration Reported?
em.detail.calibrationHelp
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No | No | Yes | Yes | No | Yes | No | Not applicable | Yes | No | No | No | No | Not applicable | No | Not applicable | Unclear | Unclear | No | Not applicable | Yes | Not applicable | Not applicable | Unclear | Unclear |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | No | No | Yes | No | Not applicable | No | No |
Yes ?Comment:for N2O fluxes |
No | Yes | Not applicable | No | Not applicable | No | No | No | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None |
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None | None | None | None |
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None |
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None | None | None | None | None | None | None | None | None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | Yes | Yes | No | No | No | No | No | Yes | No | Yes | Yes | No | Not applicable | No | Not applicable | No | No | No | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | Yes | No | No | No | Yes | No | Not applicable | No | No | No | No | No | Not applicable | No | Not applicable | No | No | No | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | No |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | Unclear | Yes | No | No | Yes | No | Not applicable | No | No | No | No | No | Not applicable | No | Not applicable | No | No | No | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | No |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | No | Not applicable | Not applicable | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 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-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
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None |
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None | None |
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None |
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None | None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
| None | None |
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None |
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None |
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None |
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None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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EM-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
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Centroid Latitude
em.detail.ddLatHelp
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39.5 | 38.69 | 47.8 | 42.8 | 47.8 | -9999 | 48 | Not applicable | 17.73 | 35.99 | 52.86 | -9.05 | 45.12 | 52.22 | 41.89 | Not applicable | 35.86 | 35.86 | 36.21 | Not applicable | Not applicable | Not applicable | 52.04 | Not applicable | 52.23 |
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Centroid Longitude
em.detail.ddLongHelp
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-98.35 | -89.1 | -122.7 | 24 | -122.7 | -9999 | -123 | Not applicable | -64.77 | -78.96 | 6.54 | -77.81 | 85.18 | -0.91 | -89.34 | Not applicable | -105.76 | -105.76 | -113.76 | Not applicable | Not applicable | Not applicable | -4.39 | Not applicable | 21.01 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | None provided | WGS84 | Not applicable | WGS84 | None provided | None provided | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | Not applicable | Not applicable | Not applicable | WGS84 | Not applicable | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Estimated | Estimated | Estimated | Not applicable | Estimated | Not applicable | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Provided | Not applicable | Estimated | Estimated | Estimated | Not applicable | Not applicable | Not applicable | Estimated | Not applicable | Estimated |
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EM ID
em.detail.idHelp
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EM-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Near Coastal Marine and Estuarine | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Forests | Near Coastal Marine and Estuarine | Inland Wetlands | Near Coastal Marine and Estuarine | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Created Greenspace | Atmosphere | Agroecosystems | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Created Greenspace | Grasslands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas | Rivers and Streams | Rivers and Streams | Rivers and Streams |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Terrestrial | Row crop agriculture in Kaskaskia river basin | glacier-carved saltwater fjord | Primarily forested watershed | glacier-carver saltwater fjord | Wetlands (multiple types) | Terrestrial environment surrounding a large estuary | None identified | Coral reefs | Urban and vicinity | farm pasture | tropical, coastal to montane | stream reaches | restored landfills and grasslands | Restored prairie, prairie remnants, and cropland | Coastal environments | watersheds | watersheds | reach | Terrestrial | Communities living and working in near-road environments | Multiple | River | Stream segment | temperate river system |
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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 | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Other or unclear (comment) ?Comment:Variable data was derived from multiple climate data stations distrubuted across the study area. The location and distribution of the data stations was not provided. |
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 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 is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | 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 |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
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EM-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
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EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Species | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
| EM-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
| None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | 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-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
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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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
| EM-63 | EM-97 |
EM-111 |
EM-130 | EM-131 | EM-196 | EM-315 | EM-434 | EM-465 | EM-493 |
EM-593 |
EM-618 |
EM-660 |
EM-709 |
EM-788 |
EM-849 | EM-859 | EM-860 | EM-862 | EM-883 | EM-942 | EM-983 | EM-998 | EM-1002 |
EM-1043 |
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None |
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None |
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None | None | None | None | None |
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None | None |
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