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-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
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EM Short Name
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EnviroAtlas - Natural biological nitrogen fixation | InVEST water yield, Hood Canal, WA, USA | KINEROS2, River Ravna watershed, Bulgaria | ROS (Recreation Opportunity Spectrum), Europe | Rate of Fire Spread | Air pollutant removal, Guánica Bay, Puerto Rico | Land capability classification | Pharmaceutical product potential, St. Croix, USVI | WaterWorld v2, Santa Basin, Peru | Pollinators on landfill sites, United Kingdom | WESP: Marsh and open water, ID, USA | WESP: Marsh & wet meadow, ID, USA | Wild bees over 26 yrs of restored prairie, IL, USA | Mourning dove abundance, Piedmont region, USA | Indigo bunting abund, Piedmont region, USA | InVEST Coastal Vulnerability | Recreational fishery index, USA | HWB indicator-College degree, Great Lakes, USA | Pollutant dispersion by vegetation barriers | Drainage water recycling, Midwest, USA | Predicting ecosystem service values, Bangladesh | CAESAR landscape evolution model |
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EM Full Name
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US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), 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 | ROS (Recreation Opportunity Spectrum), Europe | Rate of Fire Spread | Air pollutant removal, Guánica Bay, Puerto Rico, USA | Land capability classification | Relative pharmaceutical product potential (on reef), St. Croix, USVI | WaterWorld v2, Santa Basin, Peru | Pollinating insects on landfill sites, East Midlands, United Kingdon | WESP: Deepwater marsh and open Water waterfowl habitat, Idaho, USA | WESP: Seasonally flooded marsh & wet meadow, Idaho, USA | Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA | Mourning dove abundance, Piedmont ecoregion, USA | Indigo bunting abundance, Piedmont ecoregion, USA | InVEST Coastal Vulnerability | Recreational fishery index for streams and rivers, USA | Human well being indicator-College degree, Great Lakes waterfront, USA | Pollutant dispersion by vegetation barriers | Drainage water recycling, Midwest, US | Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh | Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model |
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EM Source or Collection
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US EPA | EnviroAtlas | InVEST | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | US EPA | None | US EPA | None | None | None | None | None | None | None | InVEST | US EPA | US EPA | US EPA | 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. |
205 |
248 ?Comment:Document 277 is also a source document for this EM |
293 | 306 |
338 ?Comment:Manuscript in revision, should be published by end of 2016. |
340 | 335 | 368 | 389 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
401 | 405 | 405 | 408 | 414 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
435 | 446 | 457 | 468 |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | 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. | Paracchini, M.L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J.P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P.A., and Bidoglio, G. | Rothermel, Richard C. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | United States Department of Agriculture - Natural Resources Conservation Service | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Van Soesbergen, A. and M. Mulligan | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | Murphy, C. and T. Weekley | Murphy, C. and T. Weekley | Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree | Riffel, S., Scognamillo, D., and L. W. Burger | Riffel, S., Scognamillo, D., and L. W. Burger | The Natural Capital Project.org | Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Hashad, K. B. Yang, J. T. Steffens, R. W. Baldauf, P. Deshmukh, K. M. Zhang | Reinhart, B.D., Frankenberger, J.R., Hay, C.H., and Helmers, J.M. | Morshed, S. R., Fattah, M. A., Haque, M. N., & Morshed, S. Y. | Van De Wiel, M. J., Coulthard, T. J., Macklin, M. G., & Lewin, J. |
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Document Year
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2013 | 2013 | 2012 | 2014 | 1972 | 2017 | 2013 | 2014 | 2018 | 2013 | 2012 | 2012 | 2017 | 2008 | 2008 | None | 2021 | None | 2021 | 2019 | 2022 | 2007 |
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Document Title
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EnviroAtlas - National | 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 | Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU | A Mathematical model for predicting fire spread in wildland fuels | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | 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 | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Wild bee community change over a 26-year chronosequence of restored tallgrass prairie | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | InVEST Coastal Vulnerability | Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers. | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | Parameterizing pollutant dispersion downwind of roadside vegetation barriers | Simulated water quality and irrigation benefits from drainage wter recycling at two tile-drained sites in the U.S. Midwest | Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh | Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model |
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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 | Documented, not peer reviewed | 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) | Peer reviewed but unpublished (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 USDA Forest Service report | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Website users guide | Published journal manuscript | Journal manuscript submitted or in review | Journal manuscript submitted or in review | Published journal manuscript | Published journal manuscript | Published journal manuscript |
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EM ID
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EM-63 |
EM-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
| https://www.epa.gov/enviroatlas | https://www.naturalcapitalproject.org/invest/ | http://www.tucson.ars.ag.gov/agwa/ | Not applicable | http://firelab.org/project/farsite | Not applicable | Not applicable | Not applicable | www.policysupport.org/waterworld | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://naturalcapitalproject.stanford.edu/software/invest | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://www.coulthard.org.uk/ | |
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Contact Name
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EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
J.E. Toft | David C. Goodrich | Maria Luisa Paracchini | Charles McHugh | Susan H. Yee | United States Department of Agriculture | Susan H. Yee | Arnout van Soesbergen | Sam Tarrant | Chris Murphy | Chris Murphy | Sean R. Griffin | Sam Riffell | Sam Riffell | Not applicable | Gregg Lomnicky | Ted Angradi | K. Max Zhang | Benjamin Reinhart | Syed Riad Morshed | Marco J. Van De Wiel |
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Contact Address
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Not reported | 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 | Joint Research Centre, Institute for Environment and Sustainability, Via E.Fermi, 2749, I-21027 Ispra (VA), Italy | RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Not reported | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A. | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Not applicable | 200 SW 35th St., Corvallis, OR, 97333 | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA | Agricultural & Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA | Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, Bangladesh | Department of Geography, University of Western Ontario, London, Ontario, Canada |
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Contact Email
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enviroatlas@epa.gov | jetoft@stanford.edu | agwa@tucson.ars.ag.gov | luisa.paracchini@jrc.ec.europa.eu | cmchugh@fs.fed.us | yee.susan@epa.gov | http://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/contactus/ | yee.susan@epa.gov | arnout.van_soesbergen@kcl.ac.uk | sam.tarrant@rspb.org.uk | chris.murphy@idfg.idaho.gov | chris.murphy@idfg.idaho.gov | srgriffin108@gmail.com | sriffell@cfr.msstate.edu | sriffell@cfr.msstate.edu | Not applicable | lomnicky.gregg@epa.gov | tedangradi@gmail.com | kz33@cornell.edu | breinhar@purdue.edu | riad.kuet.urp16@gmail.com | mvandew3@uwo.ca |
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EM ID
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EM-63 |
EM-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
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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." | 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." | ABSTRACT: "Research on ecosystem services mapping and valuing has increased significantly in recent years. However, compared to provisioning and regulating services, cultural ecosystem services have not yet beenfully integrated into operational frameworks. One reason for this is that transdisciplinarity is required toaddress the issue, since by definition cultural services (encompassing physical, intellectual, spiritual inter-actions with biota) need to be analysed from multiple perspectives (i.e. ecological, social, behavioural).A second reason is the lack of data for large-scale assessments, as detailed surveys are a main sourceof information. Among cultural ecosystem services, assessment of outdoor recreation can be based ona large pool of literature developed mostly in social and medical science, and landscape and ecologystudies. This paper presents a methodology to include recreation in the conceptual framework for EUwide ecosystem assessments (Maes et al., 2013), which couples existing approaches for recreation man-agement at country level with behavioural data derived from surveys and population distribution data.The proposed framework is based on three components: the ecosystem function (recreation potential),the adaptation of the Recreation Opportunity Spectrum framework to characterise the ecosystem serviceand the distribution of potential demand in the EU." | ABSTRACT: "The development of a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models. The model was developed for and is now being used as a basis for appraising fire spread and intensity in the National Fire Danger Rating System. The initial work was done using fuel arrays composed of uniform size particles. Three fuel sizes were tested over a wide range of bulk densities. These were 0.026-inch-square cut excelsior, 114-inch sticks, and 112-inch sticks. The problem of mixed fuel sizes was then resolved by weighting the various particle sizes that compose actual fuel arrays by either surface area or loading, depending upon the feature of the fire being predicted. The model is complete in the sense that no prior knowledge of a fuel's burning characteristics is required. All that is necessary are inputs describing the physical and chemical makeup of the fuel and the environmental conditions in which it is expected to burn. Inputs include fuel loading, fuel depth, fuel particle surface-area-to-volume ratio, fuel particle heat content, fuel particle moisture and mineral content, and the moisture content at which extinction can be expected. Environmental inputs are mean wind velocity and slope of terrain. For heterogeneous mixtures, the fuel properties are entered for each particle size. The model as originally conceived was for dead fuels in a uniform stratum contiguous to the ground, such as litter or grass. It has been found to be useful, however, for fuels ranging from pine needle litter to heavy logging slash and for California brush fields." **FARSITE4 will no longer be supported or available for download or further supported. FlamMap6 now includes FARSITE.** | AUTHOR'S DESCRIPTION: "Air pollutant removal, particularly of large dust particles relevant to asthma, was identified as an ecosystem service contributing to the stakeholder objective to improve air quality…Rates of air pollutant removal depend on the downward flux of particles intercepted by the tree canopy…Because atmospheric pollutant concentration can vary widely across space and time, we standardized across watersheds by calculating the removal rate per unit concentration of pollutant, assuming a pollutant concentration of 1 g m^-3. Specifically, the removal rate was calculated per unit concentration of particulate matter greater than…PM<sub>10, applying a typical deposition velocity of 1.25 cm s^-1…" | 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." | 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: "...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 | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | 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." | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." | 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: [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: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. " | 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. | [Enter up to 65000 characters] | Land Use/Land Cover (LULC) provides provisional, supporting, cultural, and regulating ecosystem services that contribute to ecological environments, enhance human health and living, have economic advantages for sustaining living organisms. LULC transformation due to enormous urban expansion diminishing Ecosystem Services Values (ESVs) and discouraging sustainability. Though unplanned LULC transformation practice became more prevalent in developing countries, comprehensive assessment of LULC changes and their influences in ESVs are rarely attempted. This study aimed to illustrate and forecast the LULC changes and their influences on ESVs change in Jashore using remote sensing technologies. ESVs estimation and change analysis were conducted by utilizing -derived LULC data of the year 2000, 2010, and 2020 with the corresponding global value coefficients of each LULC type which are previously published. For simulating future LULC and ESVs, Land Change Modeler of TerrSet Geospatial Monitoring and Modeling Software was used in Multi-Layer Perceptron-Markov Chain and Artificial Neural Network method. The decline of agricultural land by 13.13% and waterbody by 5.79% has resulted in the reduction of total ESVs US$0.23 million (24.47%) during 2000–2020. The forecasted result shows that the built-up area will be dominant LULC in the future, and ESVs of provisioning and cultural services will be diminished by $0.107 million, $63400.3 by 2050 with the declination of agricultural, waterbody, vegetation, and vacant land covers. The study signifies the importance of a strategic rational land-use plan to strictly monitor and control the encroachment of built-up areas into vegetation, waterbodies, and agricultural land in addition to scientific mitigative policies for ensuring ecological sustainability. | 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. |
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Specific Policy or Decision Context Cited
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None Identified | Land use change | None identified | None identified | None identified | None identified | None provided | None identified | None identified | None identified | None identified | None identified | None identified | None reported | None reported | None identified | None identified | None identified | None identified | None | N/A | None identified |
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Biophysical Context
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No additional description provided | 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 | Not applicable | No additional description provided | No additional description provided | No additional description provided | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | No additional description provided | restored, enhanced and created wetlands | restored, enhanced and created wetlands | 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. | Conservation Reserve Program lands left to go fallow | Conservation Reserve Program lands left to go fallow | Not applicable | None | Waterfront districts on south Lake Michigan and south lake Erie | Communities living and working in near-road environments | None | Jashore city, Bangladesh | River Teifi, Lampeter, Wales |
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EM Scenario Drivers
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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 | No scenarios presented | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | No scenarios presented | Sites, function or habitat focus | Sites, function or habitat focus | No scenarios presented | N/A | N/A | Options for future sea level change and population change | N/A | N/A | None scenarios presented | None | No scenarios presented | Varying flow velocities and durations |
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EM ID
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EM-63 |
EM-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only | Method + Application | Method Only | 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 + Application | Method + Application | Method Only | Method + Application | Method + Application | Method Only | None | Method + Application | Method Only |
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New or Pre-existing EM?
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New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | Application of existing model | New or revised model | WESP Deepwater Marsh | 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 | New or revised model | None | 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-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
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Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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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-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 |
Doc-290 | Doc-291 | Doc-289 | None | None | None | None | None | Doc-389 | Doc-390 | Doc-390 | None | Doc-405 | Doc-405 | Doc-410 | None | Doc-422 | None | None | None | Doc-467 |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | EM-148 | EM-344 | EM-368 | EM-437 | EM-132 | EM-133 | None | None | None | None | None | None | EM-697 | EM-718 | EM-729 | EM-743 | EM-756 | EM-757 | EM-759 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-751 | EM-768 | EM-718 | EM-734 | EM-743 | None | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-844 | EM-845 | EM-846 | EM-847 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-843 | EM-844 | EM-845 | EM-847 | EM-851 | None | EM-886 | EM-888 | EM-889 | EM-890 | EM-891 | EM-893 | EM-894 | None | None | None | EM-997 |
EM Modeling Approach
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EM ID
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EM-63 |
EM-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
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EM Temporal Extent
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2006-2010 | 2005-7; 2035-45 | Not reported | Not reported | Not applicable | 2013 | Not applicable | 2006-2007, 2010 | 1950-2071 | 2007-2008 | 2010-2013 | 2010-2012 | 1988-2014 | 2008 | 2008 | Not applicable | 2013-2014 | 2022 | Not applicable | None | 2000-2050 | Not applicable |
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EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-stationary | Not applicable | time-stationary | Not applicable | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | Not applicable | None | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | both | Not applicable | past time | past time | Not applicable | Not applicable | Not applicable | Not applicable | past time | Not applicable | Not applicable | None | both | Not applicable |
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EM Time Continuity
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Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | None | discrete | continuous |
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EM Temporal Grain Size Value
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Not applicable | Not applicable | Not reported | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | None | 10 | Not applicable |
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EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not reported | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Month | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | None | Year | Not applicable |
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EM ID
em.detail.idHelp
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EM-63 |
EM-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
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Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Not applicable | Watershed/Catchment/HUC | Not applicable | Physiographic or ecological | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Physiographic or ecological | Physiographic or ecological | Not applicable | Geopolitical | Geopolitical | Not applicable | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | Watershed/Catchment/HUC |
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Spatial Extent Name
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counterminous United States | Hood Canal | River Ravna watershed | European Union countries | Not applicable | Guanica Bay watershed | Not applicable | Coastal zone surrounding St. Croix | Santa Basin | East Midlands | Wetlands in Idaho | Wetlands in idaho | Nachusa Grasslands | Piedmont Ecoregion | Piedmont Ecoregion | Not applicable | United States | Great Lakes waterfront | Not applicable | Western & Eastern Corn Belt Plains | Jashore city, Bangladesh | River Teifi |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | >1,000,000 km^2 | Not applicable | 1000-10,000 km^2. | Not applicable | 100-1000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | Not applicable | >1,000,000 km^2 | 1000-10,000 km^2. | Not applicable | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. |
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EM ID
em.detail.idHelp
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EM-63 |
EM-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
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EM Spatial Distribution
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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) | Not applicable |
spatially distributed (in at least some cases) ?Comment:pp. 14 - "Most ecosystem services were mapped at the same resolution as the LULC data (30 x 30 m^2)." I assumed that, unless otherwise specified, calculations were carried out on a 30 x 30 m^2 pixel. |
Not applicable | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all 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) | None | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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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 | Not applicable | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | Not applicable | length, for linear feature (e.g., stream mile) | None | map scale, for cartographic feature | Not applicable |
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Spatial Grain Size
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irregular | 30 m x 30 m | 25 m x 25 m | 100 m x 100 m | Not applicable | 30 m x 30 m | Not applicable | 10 m x 10 m | 1 km2 | multiple unrelated locations | Not applicable | Not applicable | Area varies by site | Not applicable | Not applicable | user defined | stream reach (site) | Not applicable | user defined | None | 30m | Not applicable |
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EM ID
em.detail.idHelp
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EM-63 |
EM-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
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EM Computational Approach
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Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Not applicable | Analytic | Numeric | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | * | Analytic | Analytic |
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EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | None | 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-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
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Model Calibration Reported?
em.detail.calibrationHelp
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No | Yes | Yes | No | Not applicable | Yes | Not applicable | Yes | No | Not applicable | No | No | No | Yes | Yes | Not applicable | No | No | Yes | None | Yes | Not applicable |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | No | Not applicable | No | Not applicable | No | No | Not applicable | No | No | No | No | No | Not applicable | No | No | Not applicable | None | Yes | Not applicable |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None | None |
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None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | Yes | No | No | No | No | No | Yes | Yes | Not applicable | No | No | No | No | No | Not applicable | No | No | Not applicable | None | Yes | Not applicable |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | Not applicable | No | Not applicable | No | No | Not applicable | No | No | No | No | No | Not applicable | No | No | Not applicable | None | Unclear | Not applicable |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | Yes | No | No | Not applicable | No | Not applicable | No | No | Not applicable | No | No | No | Yes | Yes | Not applicable | No | Yes | Not applicable | None | Unclear | Not applicable |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | None | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-63 |
EM-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
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None |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-63 |
EM-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
| None |
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None | None | None |
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None |
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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-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
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Centroid Latitude
em.detail.ddLatHelp
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39.5 | 47.8 | 42.8 | 48.2 | -9999 | 17.96 | Not applicable | 17.73 | -9.05 | 52.22 | 44.06 | 44.06 | 41.89 | 36.23 | 36.23 | Not applicable | 36.21 | 42.26 | Not applicable | None | 23.95 | 52.04 |
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Centroid Longitude
em.detail.ddLongHelp
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-98.35 | -122.7 | 24 | 16.35 | -9999 | -67.04 | Not applicable | -64.77 | -77.81 | -0.91 | -114.69 | -114.69 | -89.34 | -81.9 | -81.9 | Not applicable | -113.76 | -87.84 | Not applicable | None | 89.12 | -4.39 |
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Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | Not applicable | None | other | WGS84 |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Not applicable | Estimated | Estimated | Not applicable | None | Provided | Estimated |
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EM ID
em.detail.idHelp
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EM-63 |
EM-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Forests | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Open Ocean and Seas | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Grasslands | Inland Wetlands | Inland Wetlands | Agroecosystems | Grasslands | Grasslands | Grasslands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Tundra | Ice and Snow | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Terrestrial | glacier-carved saltwater fjord | Primarily forested watershed | Not applicable | Not applicable | Multiple environmental types present | None identified | Coral reefs | tropical, coastal to montane | restored landfills and grasslands | created, restored and enhanced wetlands | created, restored and enhanced wetlands | Restored prairie, prairie remnants, and cropland | grasslands | grasslands | Coastal environments | reach | Lake Michigan & Lake Erie waterfront | Communities living and working in near-road environments | Plains | Urban city | River |
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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 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 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 |
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 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 corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | 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 corresponds to 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-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
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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 | Guild or Assemblage | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Species | Species | Species | Not applicable | Guild or Assemblage | Not applicable | Not applicable | None | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-63 |
EM-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
| None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available |
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None Available | None Available |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
| EM-63 |
EM-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
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None |
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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-111 |
EM-130 | EM-184 | EM-337 | EM-423 | EM-434 | EM-465 |
EM-618 |
EM-709 |
EM-734 |
EM-760 |
EM-788 |
EM-843 | EM-846 | EM-849 | EM-862 | EM-895 | EM-942 | EM-961 | EM-979 | EM-998 |
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None |
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None | None |
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None | None | None | None |
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None |
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None | None | None |
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