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: SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA (EM-863)
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EM Identification (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
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EM Short Name
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Green biomass production, Central French Alps | Community flowering date, Central French Alps | Annual profit from agriculture, South Australia | Cultural ecosystem services, Bilbao, Spain | C Sequestration and De-N, Tampa Bay, FL, USA | FORCLIM v2.9, West Cascades, OR, USA | Coral taxa and land development, St.Croix, VI, USA | Urban Temperature, Baltimore, MD, USA | Retained rainwater, Guánica Bay, Puerto Rico | WaSSI, Conterminous USA | Sedge Wren density, CREP, Iowa, USA | P8 UCM | Estuary visitation, Cape Cod, MA | WESP: Irrigation water, ID, USA | SLAMM, Tampa Bay, FL, USA | NC HUC-12 conservation prioritization tool | Drainage water recycling, Midwest, USA | Eastern Meadowlark Abundance | EcoSim II - method | GI toolkit users guide | CommunityViz, Albany county, Wyoming | CommunityViz, Albany county, Wyoming |
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EM Full Name
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Green biomass production, Central French Alps | Community weighted mean flowering date, Central French Alps | Annual profit from agriculture, South Australia | Cultural ecosystem services, Bilbao, Spain | Value of Carbon Sequestration and Denitrification benefits, Tampa Bay, FL, USA | FORCLIM (FORests in a changing CLIMate) v2.9, West Cascades, OR, USA | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | Urban Air Temperature Change, Baltimore, MD, USA | Retained rainwater, Guánica Bay, Puerto Rico, USA | Water Supply Stress Index, Conterminous USA | Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | P8 Urban Catchment model method | Value of recreational use of an estuary, Cape Cod, Massachusetts | WESP: Irrigation return water treatment, Idaho, USA | SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA | NC HUC-12 conservation prioritization tool v. 1.0, North Carolina, USA | Drainage water recycling, Midwest, US | TEST: CRP Impacts on Eastern Meadowlark Abundance | EcoSim II - method | Green Infrastructure valuation toolkit users guide | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming. | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming. |
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EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | * |
* ?Comment:EU Mapping Studies |
US EPA | US EPA | US EPA | i-Tree | USDA Forest Service | US EPA |
USDA Forest Service ?Comment:While the user guide on which model entry is based has not been peer reviewed, several peer reviewed journal articles describing this USA HUC8 version of WaSSI have been published. |
* | * | US EPA | * | None | * | * |
* ?Comment:Could not find any information pertaining to a model collection. |
* | * | * | * |
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EM Source Document ID
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260 | 260 | 243 | 191 | 186 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
96 | 217 | 338 | 341 | 372 |
377 ?Comment:Published to the web. Previously versions prepared for EPA. |
387 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
415 ?Comment:Secondary sources: Documents 412 and 413. |
443 ?Comment:Doc 444 is an additional source for this EM |
446 | 405 | 448 | 474 |
479 ?Comment:Published as a report by the University of Wyoming, but no record of peer review. |
479 ?Comment:Published as a report by the University of Wyoming, but no record of peer review. |
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Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H. | Russell, M. and Greening, H. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Peter Caldwell, Ge Sun, Steve McNulty, Jennifer Moore Myers, Erika Cohen, Robert Herring, Erik Martinez | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Walker, W. Jr., and J.D. Walker | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta | Murphy, C. and T. Weekley | Sherwood, E. T. and H. S. Greening | Warnell, K., I. Golden, and C. Canfield | Reinhart, B.D., Frankenberger, J.R., Hay, C.H., and Helmers, J.M. | Riffel, S., Scognamillo, D., and L. W. Burger | Walters, C., Pauly, D., Christensen, V., and J.F. Kitchell | Genecon LLP. | Lieske, S. N., Mullen, S., Knapp, M., & Hamerlinck, J. D. | Lieske, S. N., Mullen, S., Knapp, M., & Hamerlinck, J. D. |
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Document Year
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2011 | 2011 | 2011 | 2013 | 2013 | 2007 | 2011 | 2016 | 2017 | 2013 | 2010 | 2015 | 2019 | 2012 | 2014 | 2023 | 2019 | 2008 | 2000 | 2010 | 2003 | 2003 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Carbon payments and low-cost conservation | Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning | Estimating benefits in a recovering estuary: Tampa Bay, Florida | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | WaSSI Ecosystem Services Model | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | P8 Urban Catchment Model Version 3.5 | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats | Conservation planning tools for NC's people & nature | Simulated water quality and irrigation benefits from drainage wter recycling at two tile-drained sites in the U.S. Midwest | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Representing density dependent consequences of life history strategies in aquatic ecostems: EcoSim II | Building natural value for sustainable economic development The green infrastructure valuation toolkit user guide | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming |
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Document Status
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* | * | * | * | * | * | * | * | * | Not peer reviewed but is published (explain in Comment) | * | Not peer reviewed but is published (explain in Comment) | Peer reviewed but unpublished (explain in Comment) | * | Peer reviewed and published | * | * | * | * | * | Not peer reviewed but is published (explain in Comment) | Not peer reviewed but is published (explain in Comment) |
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Comments on Status
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* | * | * | * | * | * | * | * | * | While the user guide on which model entry is based has not been peer reviewed, several peer reviewed journal articles describing this USA HUC8 version of WaSSI have been published. | Published report | Published report | Draft manuscript-work progressing | Published report | Published journal manuscript | Webpage | * | * | * | Published report | Published report | Published report |
Software and Access (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
| Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://www.wassiweb.sgcp.ncsu.edu/ | Not applicable | http://www.wwwalker.net/p8/v35/webhelp/splash.htm | Not applicable | Not applicable | http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html | https://prioritizationcobenefitstool.users.earthengine.app/view/nc-huc-12-conservation-prioritizer | Not applicable | Not applicable | https://ecopath.org/downloads/ | https://www.merseyforest.org.uk/services/gi-val/ | https://communityviz.com/ | https://communityviz.com/ | |
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Contact Name
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Sandra Lavorel | Sandra Lavorel | Neville D. Crossman | Izaskun Casado-Arzuaga | M. Russell | Richard T. Busing | Leah Oliver | Gordon M. Heisler | Susan H. Yee | Ge Sun | David Otis | William Walker Jr., PhD | Mulvaney, Kate | Chris Murphy | Edward T. Sherwood | Katie Warnell | Benjamin Reinhart |
L. Wes Burger ?Comment:Lead author, Sam Riffell, pass away. Using last author. |
Carl Walters | The Mercey Forest | Scott Lieske | Scott Lieske |
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Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain | US EPA, Gulf Ecology Division, 1 Sabine Island Dr, Gulf Breeze, FL 32563, USA | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | National Health and Environmental Research Effects Laboratory | 5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210 | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest Service, 920 Main Campus Dr. Venture II, Suite 300, Raleigh, NC 27606 | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | Concord, Massachusetts | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA | Not reported | Agricultural & Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA | Mississippi State University, Mississippi State, MS | Fisheries Centre, University of British Columbia, Vancouver, British Columbia, British Columbia, Canada, V6T 1Z4 | Moss Ln, Woolston, Warrington WA3 6QX, United Kingdom | Department of Agricultural & Applied Economics University of Wyoming, Laramie WY 82071 | Department of Agricultural & Applied Economics University of Wyoming, Laramie WY 82071 |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | neville.crossman@csiro.au | izaskun.casado@ehu.es | Russell.Marc@epamail.epa.gov | rtbusing@aol.com | leah.oliver@epa.gov | gheisler@fs.fed.us | yee.susan@epa.gov | gesun@fs.fed.us | dotis@iastate.edu | bill@wwwalker.net | None reported | chris.murphy@idfg.idaho.gov | esherwood@tbep.org | katie.warnell@duke.edu | breinhar@purdue.edu | w.burger@msstate.edu | c.walters@oceans.ubc.ca | mail@merseyforest.org.uk | lieske@uwyo.edu | lieske@uwyo.edu |
EM Description (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
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Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "Community-weighted mean date of flowering onset was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns from agriculture and from carbon plantings." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010). In this context, opportunity cost is usually expressed as the profit from agricultural production…We based our calculations of agricultural profit on Bryan et al. (2009), who calculated profit at full equity (i.e., economic return to land, capital, and management, exclusive of financial debt). We calculated an annual profit at full equity (PFEc) layer for each commodity (c) in the set of agricultural commodities (C), where C is wheat, field peas, beef cattle, or sheep." | ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management." | AUTHOR'S DESCRIPTION: "...we examine the change in the production of ecosystem goods produced as a result of restoration efforts and potential relative cost savings for the Tampa Bay community from seagrass expansion (more than 3,100 ha) and coastal marsh and mangrove restoration (∼600 ha), since 1990… The objectives of this article are to explore the roles that ecological processes and resulting ecosystem goods have in maintaining healthy estuarine systems by (1) quantifying the production of specific ecosystem goods in a subtropical estuarine system and (2) determining potential cost savings of improved water quality and increased habitat in a recovering estuary." (pp. 2) | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." AUTHOR'S DESCRIPTION: "An analysis of forest successional dynamics was performed on ecoregions 4a and 4b, which cover the south Santiam watershed area selected for intensive study. In each of these two ecoregions, a set of 20 simulated sites was compared to survey plot data summaries. Survey data were analysed by stand age class and simulations of corresponding ages. The statistical methods described…were applied in comparison of actual with simulated forest composition and total basal area by age class. Separate simulations were run with and without fire." | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation." | AUTHOR'S DESCRIPTION: "In total, 19 ecosystem services metrics were identified as relevant to stakeholder objectives in the Guánica Bay watershed identified during the 2013 Public Values Forum (Table 2)...Ecological production functions were applied to translate LULC measures of ecosystem condition to supply of ecosystem services…The volume of retained rainwater per unit area (in^3/in^2) includes both the maximum soil moisture retention and the initial abstraction of water before runoff due to infiltration, evaporation, or interception by vegetation…" | AUTHORS DESCRIPTION: "WaSSI simulates monthly water and carbon dynamics at the Hydrologic Unit Code 8 level in the US. Three modules are integrated within the WaSSI model framework. The water balance module computes ecosystem water use, evapotranspiration and the water yield from each watershed. Water yield is sometimes referred to as runoff and can be thought of as the amount of streamflow at the outlet of each watershed due to hydrologic processes in each watershed in isolation without any flow contribution from upstream watersheds. The ecosystem productivity module simulates carbon gains and losses in each watershed or grid cell as functions of evapotranspiration. The water supply and demand module routes and accumulates the water yield through the river network according to topological relationships between adjacent watersheds, subtracts consumptive water use by humans from river flows, and compares water supply to water demand to compute the water supply stress index, or WaSSI." | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Sedge Wren (Cistothorus platensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SEWR density = 1-1/1+e^(-0.8015652 + 0.08500569 * grass400) *e^(-0.7982511 + 0.0285891 * bbspath + 0.0105094 *grass400) | Author description: " P8 simulates the generation and transport of stormwater runoff pollutants in urban watersheds. Continuous water-balance and mass-balance calculations are performed on a user-defined drainage system consisting of the following elements: - Watersheds (<= 250 nonpoint source areas) - Devices (<=75 runoff storage/treatment areas or BMP's) - Particles (<= 5 fractions with different settling velocities) - Water Quality Components (<= 10 associated with particles) Simulations are driven by hourly precipitation and daily air temperature time series. Runoff contributions from snowmelt are also simulated. 'P8' abbreviates "Program for Predicting Polluting Particle Passage Thru Pits, Puddles, and Ponds", which more or less captures the basic features and functions of the model. It has been developed for use by engineers and planners in designing and evaluating runoff treatment schemes for existing or proposed urban developments. Design objectives are typically expressed in terms of percentage reduction in suspended solids or other water quality component. Despite its limitations, P8 has been used by state and local regulatory agencies as a consistent framework for evaluating proposed developments. Depending on applications, other models could be either too simple (easily used, but ignoring important factors) or too complex (requiring considerable site-specific data and/or user expertise). P8 attempts to strike a balance to between those extremes. Predicted water quality components include total suspended solids (sum of the individual particle fractions), total phosphorus, total Kjeldahl nitrogen, copper, lead, zinc, and total hydrocarbons. Simulated BMP types include detention ponds (wet, dry, extended), infiltration basins, swales, buffer strips, or other devices with user-specified stage/discharge curves and infiltration rates. A simple water budget algorithm can be used to estimate groundwater storage and stream base flow in watershed-scale applications. Initial calibrations were based upon runoff quality and particle settling velocity data collected under the EPA's Nationwide Urban Runoff Program (Athayede et al., 1983). Calibrations to impervious area runoff parameters for Wisconsin watersheds have been subsequently developed. Inputs are structured in terms which should be familiar to planners and engineers involved in hydrologic evaluation. Several tabular and graphic output formats are provided. " | [ABSTRACT: "Estimates of the types and number of recreational users visiting an estuary are critical data for quantifying the value of recreation and how that value might change with variations in water quality or other management decisions. However, estimates of recreational use are minimal and conventional intercept surveys methods are often infeasible for widespread application to estuaries. Therefore, a practical observational sampling approach was developed to quantify the recreational use of an estuary without the use of surveys. Designed to be simple and fast to allow for replication, the methods involved the use of periodic instantaneous car counts multiplied by extrapolation factors derived from all-day counts. This simple sampling approach can be used to estimate visitation to diverse types of access points on an estuary in a single day as well as across multiple days. Evaluation of this method showed that when periodic counts were taken within a preferred time window (from 11am-4:30pm), the estimates were within 44 percent of actual daily visitation. These methods were applied to the Three Bays estuary system on Cape Cod, USA. The estimated combined use across all its public access sites is similar to the use at a mid-sized coastal beach, demonstrating the value of estuarine systems. Further, this study is the first to quantify the variety and magnitude of recreational uses at several different types of access points throughout the estuary using observational methods. This work can be transferred to the many small coastal access points used for recreation across New England and beyond." ] | 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: "The Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…" | ABSTRACT: "Conservation organizations and land trusts in North Carolina are increasingly focused on how their work can contribute to both human and ecosystem resilience and adaptation to climate change, as well as directly mitigate climate change through carbon storage and sequestration. Recent state executive and legislative actions also underscore the importance of natural systems for climate adaptation and mitigation, and may provide additional funding for conservation and restoration for those purposes in the near term. To make it more efficient for conservation organizations working in North Carolina to consider a broad suite of conservation benefits in their work, the Conservation Trust for North Carolina and the Nicholas Institute for Energy, Environment & Sustainability at Duke University have developed two online tools for identifying priority areas for conservation action and estimating benefit metrics for specific properties. The conservation prioritization tool finds the sub-watersheds in North Carolina with the greatest potential to provide a set of user-selected conservation benefits. It allows users to identify priority areas for future conservation work within the entire state or a defined region. This high-level tool allows for quick and easy exploration without the need for spatial analysis expertise." | [Enter up to 65000 characters] | 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, comprehen- sive 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. AUTHOR'S DESCRIPTION: For each species, we developed multiple regression models for the entire study area and for each of the seven ecological regions separately. We included only those routes that met quality standards for both bird abundance and land use data, and this left a total of 636 useable routes. The number of routes within individual ecological regions ranged from a low of 55 (central hardwoods) to a high of 154 (Appalachian Mountains). Using our estimates of bird abundance as response variables and landscape variables as explanatory variables, we used a stepwise selection process (retaining only explanatory variables that satisfied α < 0.05) to build models for each of the seven ecological regions and for the study region as a whole. | ABSTRACT: " EcoSim II uses results from the Ecopath procedure for trophic mass-balance analysis to define biomass dynamics models for predicting temporal change in exploited ecosystems. Key populations can be repre- sented in further detail by using delay-difference models to account for both biomass and numbers dynamics. A major problem revealed by linking the population and biomass dynamics models is in representation of population responses to changes in food supply; simple proportional growth and reproductive responses lead to unrealistic predic- tions of changes in mean body size with changes in fishing mortality. EcoSim II allows users to specify life history mechanisms to avoid such unrealistic predictions: animals may translate changes in feed- ing rate into changes in reproductive rather than growth rates, or they may translate changes in food availability into changes in foraging time that in turn affects predation risk. These options, along with model relationships for limits on prey availabil- ity caused by predation avoidance tactics, tend to cause strong compensatory responses in modeled populations. It is likely that such compensatory responses are responsible for our inability to find obvious correlations between interacting trophic components in fisheries time-series data. But Eco- sim II does not just predict strong compensatory responses: it also suggests that large piscivores may be vulnerable to delayed recruitment collapses caused by increases in prey species that are in turn competitors/predators of juvenile piscivores " | [The toolkit provides a very helpful introduction to the evidence demonstrating the benefits of green infrastructure interventions. It offers a structured argument that speaks the language of regeneration and economic developments. The 11 economic benefits structure provides a relatively simple high level means of presenting and communicating the benefits of green infrastructure projects in economic contexts, although it also brings some risks of double-counting (see Limitations below). The toolkit provides a structured approach to value green infrastructure benefits in monetary, quantitative and qualitative terms, with equal weight being applied to each of these three ways to present existing evidence. It can add value to and inform the decision-making process, particularly when used at an early stage to get broad brush figures and weigh pros and cons.The toolkit relies on current state-of-the-art evidence and valuation techniques for green infrastructure benefits. However, the toolkit also highlights the need for considerable improvement and expansion of the evidence base to enable future iterations to provide improved valuations. The toolkit helps make green infrastructure benefits ‘visible’ to potential funders. The inclusion of environmental benefits in cost benefit analysis is currently very difficult, often requiring professional assistance. Such assistance is frequently beyond the means of many groups seeking project funding. The toolkit is aimed at filling this gap, providing a means of scoping out the indicative benefits of green infrastructure using tools and approaches accessible to many projects and groups. However, whilst the toolkit provides a means of undertaking a broad Value for Money assessment, it must but emphasised that this is only indicative and cannot replace more rigorous formal project appraisal techniques.] | The Wyoming Community VizTM Partnership was established in 2001 to promote the use of geographic information system-based planning support systems and related decision support technologies in community land-use planning and economic development activities in the State of Wyoming. Partnership members include several state agencies, local governments and several nongovernment organizations. Partnership coordination is provided by the Wyoming Rural Development Council. Research and technical support is coordinated by the Wyoming Geographic Information Science Center’s Spatial Decision Support System Research Program at the University of Wyoming. In June 2002, the Partnership initiated a three-phase plan to promote Community VizTM based planning support systems in Wyoming. Phase I of the Partnership plan was a “proof of concept” pilot project set in Albany County in southeastern Wyoming. The goal of the project was to demonstrate the application of Community VizTM to a Wyoming-specific issue (in this case, aquifer protection) and to determine potential challenges for broader adoption in terms of data requirements, computing infrastructure and technological expertise. The results of the Phase I pilot project are detailed in this report. Efforts are currently underway to secure funding for Phase II of the plan, which expands the use of Community VizTM into four additional Wyoming communities. Specific Phase II objectives are to expand the type and number of issues addressed by Community VizTM and increase the use of Community VizTM in the planning process. As a part of Phase II the Partnership will create a technical assistance network aimed at assisting communities with the technical challenges in applying the software to their planning issues. The third phase will expand the program to more communities in the state, maintain the technical assistance network, and monitor the impact of Community VizTM on the planning process. | The Wyoming Community VizTM Partnership was established in 2001 to promote the use of geographic information system-based planning support systems and related decision support technologies in community land-use planning and economic development activities in the State of Wyoming. Partnership members include several state agencies, local governments and several nongovernment organizations. Partnership coordination is provided by the Wyoming Rural Development Council. Research and technical support is coordinated by the Wyoming Geographic Information Science Center’s Spatial Decision Support System Research Program at the University of Wyoming. In June 2002, the Partnership initiated a three-phase plan to promote Community VizTM based planning support systems in Wyoming. Phase I of the Partnership plan was a “proof of concept” pilot project set in Albany County in southeastern Wyoming. The goal of the project was to demonstrate the application of Community VizTM to a Wyoming-specific issue (in this case, aquifer protection) and to determine potential challenges for broader adoption in terms of data requirements, computing infrastructure and technological expertise. The results of the Phase I pilot project are detailed in this report. Efforts are currently underway to secure funding for Phase II of the plan, which expands the use of Community VizTM into four additional Wyoming communities. Specific Phase II objectives are to expand the type and number of issues addressed by Community VizTM and increase the use of Community VizTM in the planning process. As a part of Phase II the Partnership will create a technical assistance network aimed at assisting communities with the technical challenges in applying the software to their planning issues. The third phase will expand the program to more communities in the state, maintain the technical assistance network, and monitor the impact of Community VizTM on the planning process. |
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Specific Policy or Decision Context Cited
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* | * | * | Land management, ecosystem management, response to EU 2020 Biodiversity Strategy | Restoration of seagrass | * | Not applicable | * | Meeting water demands for agriculture and domestic purposes. | WaSSI can be used to project the regional effects of forest land cover change, climate change, and water withdrawals on river flows, water supply stress, and ecosystem productivity (i.e., carbon sequestration).WaSSI can be used to evaluate trade-offs among management strategies that influence multiple ecosystem services | * | * | * | * | None identified | Allows users to prioritize HUCs within their area of interest based on their conservation goals. | None | Food Security Act of 1985 | None | None | None provided | None provided |
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Biophysical Context
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Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | Northern Spain; Bizkaia region | Recovering estuary; Seagrass; Coastal fringe; Saltwater marsh; Mangrove | West Cascade lowlands (4a), and west Cascade montane (4b) ecoregions | nearshore; <1.5 km offshore; <12 m depth | One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site. | No additional descriptions provided | Conterminous US | Prairie pothole region of north-central Iowa | Urban setting | None identified | restored, enhanced and created wetlands | No additional description provided | * | None | Bird Conservation Regions ranging from Central to eastern United States and from the Gulf of Mexico to the Great Lakes. | None, Ocean ecosystems | N/A | Groundwater recharge area, City of Laramie | Groundwater recharge area, City of Laramie |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Habitat loss or restoration in Tampa Bay Estuary | Two scenarios modelled, forests with and without fire | Not applicable | No scenarios presented | No scenarios presented |
No scenarios presented ?Comment:Model can be run from WaSSI website using a historic data set (1961 - 2010) or projections from various climate models representing different emissions scenarios and time periods from recent past to 2099. |
No scenarios presented | N/A | N/A | Sites, function or habitat focus | Varying sea level rise (baseline - 2m), and two habitat adaption strategies | No scenarios presented | None | Separate models created for each Bird Conservation Region, including different land use, agriculture, and CRP variable values. | N/A | N/A | Continuation of trends | Aquifer protection |
EM Relationship to Other EMs or Applications
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EM ID
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) | Method Only | None | Method + Application (multiple runs exist) | Method Only | Method Only | Model Run Associated with a Specific EM Application | Model Run Associated with a Specific EM Application |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model |
Application of existing model ?Comment:. |
Application of existing model ?Comment:Models developed by Quamen (2007). |
New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | None | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
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Document ID for related EM
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Doc-260 | Doc-260 | Doc-269 | Doc-244 | None | None | Doc-22 | Doc-23 | None | Doc-220 | Doc-219 | Doc-218 | None | None | Doc-372 | None | None | Doc-390 | Doc-412 | Doc-413 |
Doc-444 ?Comment:The secondary source, document 444, is the website for running the tool. |
None | None | None | None | Doc-473 | Doc-473 |
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EM ID for related EM
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EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | None | EM-146 | EM-208 | EM-186 | None | None | None | None | EM-652 | EM-651 | EM-649 | EM-648 | None | EM-682 | EM-684 | EM-685 | EM-718 | EM-734 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-768 | EM-857 | None | None | None | EM-1055 | None | None | None |
EM Modeling Approach
EM Relationship to Time (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
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EM Temporal Extent
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2007-2009 | 2007-2008 | 2002-2008 | 2000 - 2007 | 1982-2010 | >650 yrs | 2006-2007 | May 5-Sept 30 2006 | 2006 - 2012 | 1961-2009 | 1992-2007 | Not applicable | Summer 2017 | 2010-2012 | 2002-2100 | Not applicable | None | 1995-1999 | Not applicable | Not applicable | 2050 | 2000 |
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EM Time Dependence
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* | * | * | * | * | time-dependent | * | time-dependent | * | time-dependent | * | time-dependent | time-dependent | time-dependent | time-stationary | * | None | * | time-dependent | time-dependent | * | * |
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EM Time Reference (Future/Past)
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* | * | * | * | * | past time | * | future time | * | future time | * | * | past time | past time | Not applicable | * | None | * | both | * | * | * |
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EM Time Continuity
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* | * | * | * | * | discrete | * | discrete | * | discrete | * | discrete | discrete | * | Not applicable | * | None | * |
discrete ?Comment:Modeller dependent |
* | * | * |
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EM Temporal Grain Size Value
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* | * | * | * | * | 1 | * | 1 | * | 1 | * | 1 | 1 | * | Not applicable | * | None | * | 1 | * | * | * |
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EM Temporal Grain Size Unit
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* | * | * | * | * | Year | * | Hour | * | Month | * | Hour | Day | * | Not applicable | * | None | * | Day | * | * | * |
EM spatial extent (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
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Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Physiographic or Ecological | Physiographic or ecological | Physiographic or Ecological | Geopolitical | * | * | Multiple unrelated locations (e.g., meta-analysis) | Not applicable | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Watershed/Catchment/HUC | Not applicable | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Other | Not applicable | * | * |
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Spatial Extent Name
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Central French Alps | Central French Alps | Agricultural districts of the state of South Australia | Bilbao Metropolitan Greenbelt | Tampa Bay Estuary | West Cascades, Oregon | St.Croix, U.S. Virgin Islands | Baltimore, MD | Guanica Bay watershed | All 8-digit hydrologic unit codes (HUC-8) in the conterminous USA | CREP (Conservation Reserve Enhancement Program) wetland sites | Not applicable | Three Bays, Cape Cod | Wetlands in idaho | Tampa Bay estuary watershed | Not applicable | Western & Eastern Corn Belt Plains | Bird Conservation Regions comprising the northern bobwhite breeding range. | Not applicable | Not applicable | Laramie City's aquifer protection area | Laramie City's aquifer protection area |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | * | 100-1000 km^2 | 10-100 km^2 | 100-1000 km^2 | * | >1,000,000 km^2 | 1-10 km^2 | Not applicable | * | 100,000-1,000,000 km^2 | 1000-10,000 km^2. | Not applicable | 100,000-1,000,000 km^2 | >1,000,000 km^2 | Not applicable | Not applicable | 10-100 km^2 | 10-100 km^2 |
Spatial Distribution of Computations (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
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EM Spatial Distribution
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* | * | * | * | * | * | spatially lumped (in all cases) | * | * |
* ?Comment:Spatial grain for computations is the HUC-8. A HUC-12 version is under development. Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
* | spatially lumped (in all cases) | * | spatially lumped (in all cases) | spatially distributed (in at least some cases) | * | None | * | spatially lumped (in all cases) | * | spatially lumped (in all cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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* | * | * | * | * | * | Not applicable | * | * | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | length, for linear feature (e.g., stream mile) | Not applicable | area, for pixel or radial feature | map scale, for cartographic feature | None | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | * | Not applicable | Not applicable |
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Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | 1 ha | 2 m x 2 m | 1 ha | 0.08 ha | Not applicable | 10m x 10m | 30 m x 30 m | Computations are at the 8-digit HUC scale. MostHUC-8 watersheds are within a range of 800-8000 km^2 (500-5000 mi^2) in size. | multiple, individual, irregular shaped sites | Not applicable | beach length | Not applicable | 10 x 10 m | HUC 12 | None | 1962 km^2 | Not applicable | Not reported | Not applicable | Not applicable |
EM Structure and Computation Approach (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
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EM Computational Approach
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* | * | * | * | * | Numeric | * | * | * | Numeric | * | Numeric | Numeric | Numeric | Analytic | Other or unclear (comment) | * | * | * | Numeric | Numeric | Numeric |
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EM Determinism
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* | * | * | * | * | * | * | * | * | * | * | * | * | * | deterministic | * | None | * | * | * | * | * |
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Statistical Estimation of EM
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Model Checking Procedures Used (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
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Model Calibration Reported?
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* | * | * | * | Yes | * | Yes | Yes | * | * | Unclear | Yes | Yes | * | No | Not applicable | None |
Unclear ?Comment:Does accounting for autocorrelation count as validation? |
* | Not applicable | Unclear | Unclear |
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Model Goodness of Fit Reported?
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Yes | Yes | * | * | * | * | Yes | Yes | * | * | * | Not applicable | * | * | No | Not applicable | None | Not applicable | * | Not applicable | * | * |
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Goodness of Fit (metric| value | unit)
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* | * | * | * |
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* | * | * | * | * | * | None | * | * | * | * | * | * | * |
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Model Operational Validation Reported?
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Yes | * | * | Yes | * | Yes | * | * | * | * | Unclear | Not applicable | * | * | No | Not applicable | None | * | Not applicable | Not applicable | Unclear | Unclear |
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Model Uncertainty Analysis Reported?
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* | * | * | * | * | * | Yes | * | * | * | * | Not applicable | * | * | No | Not applicable | None | * | Not applicable | Not applicable | Unclear | Unclear |
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Model Sensitivity Analysis Reported?
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* | * | * | * | * | * | * | * | * | * | * | Not applicable | * | * | No | Not applicable | None | * | Not applicable | Not applicable | Unclear | Unclear |
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Model Sensitivity Analysis Include Interactions?
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* | * | * | * | * | * | * | * | * | * | * | * | * | * | Not applicable | * | None | * | * | * | * | * |
EM Locations, Environments, Ecology
Location of EM Application (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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| New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
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Centroid Lat/Long (Decimal Degree)
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EM ID
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
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Centroid Latitude
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45.05 | 45.05 | -34.9 | 43.25 | 27.95 | 44.24 | 17.75 | 39.28 | 17.96 | 39.83 | 42.62 | Not applicable | 41.62 | 44.06 | 27.76 | Not applicable | None | 36.53 | Not applicable | Not applicable | 41.31 | 41.31 |
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Centroid Longitude
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6.4 | 6.4 | 138.7 | -2.92 | -82.47 | -122.24 | -64.75 | -76.62 | -67.02 | -98.58 | -93.84 | Not applicable | -70.42 | -114.69 | -82.54 | Not applicable | None | -88.45 | Not applicable | Not applicable | -105.46 | -105.46 |
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Centroid Datum
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* | * | * | * | * | * | NAD83 | * | * | * | * | Not applicable | * | * | WGS84 | Not applicable | None | NAD83 | Not applicable | Not applicable | * | * |
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Centroid Coordinates Status
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Provided | Provided | * | Provided | * | * | * | * | * | * | * | Not applicable | * | * | Estimated | Not applicable | None | * | Not applicable | Not applicable | * | * |
Environments and Scales Modeled (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
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EM Environmental Sub-Class
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Forests | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Atmosphere | Inland Wetlands | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren |
Lakes and Ponds ?Comment:Watershed model represents all land areas, major streams and rivers. Since leaf area index, LAI, is an important variable, forests, created greenspaces (e.g., urban forests) and scrub/shrub subclasses are included. |
Inland Wetlands | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Inland Wetlands | Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Terrestrial Environment (sub-classes not fully specified) ?Comment:Is there a way to choose more than one? |
Open Ocean and Seas | Not applicable | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Ground Water | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | none | Subtropical Estuary | Primarily conifer forest | stony coral reef | Urban landscape and surrounding area | 13 LULC were used | Not applicable | Grassland buffering inland wetlands set in agricultural land | Urban catchments | Beaches | created, restored and enhanced wetlands | Esturary and associated urban and terrestrial environment | Terrestrial and freshwater aquatic | Plains | A mixture of developed and natural environments including cultivated and non-cultivated cropland, pastures, roads / railways, and urban areas as well as grasslands, forest, and freshwater habitats spanning the central to eastern United States. | Pelagic | Multiple | watershed | watershed |
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EM Ecological Scale
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Not applicable | Not applicable | * | * | * | * | * | Ecological scale corresponds to the Environmental Sub-class | * |
Ecological scale is coarser than that of the Environmental Sub-class ?Comment:Terrestrial characteristics are aggregated at a broad (HUC-8) scale; different types of aquatic sub-classes are not differentiated. |
Ecological scale corresponds to the Environmental Sub-class | * | * | * | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is coarser 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 corresponds to the Environmental Sub-class |
Organisms modeled (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
Scale of differentiation of organisms modeled
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EM ID
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New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
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EM Organismal Scale
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Community | Community | Guild or Assemblage | * | * | Species | Guild or Assemblage | * | * | * | Species | * | * | * | Not applicable | * | None | Species |
Other (Comment) ?Comment:Varied levels of taxonomic order |
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Taxonomic level and name of organisms or groups identified
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EnviroAtlas URL
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EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
* Note that run information is shown only where run data differ from the "parent" entry shown at left
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | EM-224 | 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 | EM-743 | EM-863 | New or revised model | New or revised model | New or revised model | New or revised model | Continuation of trends | Aquifer protection | |
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EM Variable Names (and Units)
* Note that for runs, variable name is displayed only where data for that variable differed by run AND those differences were reported in the source document. Where differences occurred but were not reported, the variable is not listed. Click on variable name to view details.
Predictor
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Intermediate
Response
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