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-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
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EM Short Name
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EnviroAtlas-Air pollutant removal | Community flowering date, Central French Alps | Area and hotspots of soil retention, South Africa | Runoff potential of pesticides, Europe | Reduction in pesticide runoff risk, Europe | PATCH, western USA | Landscape importance for recreation, Europe | Land-use change and crop-based production, Europe | Land-use change and recreation, Europe | KINEROS2, River Ravna watershed, Bulgaria | Urban Temperature, Baltimore, MD, USA | Coastal protection, Europe | Erosion prevention by vegetation, Portel, Portugal | VELMA hydro, Oregon, USA | Water storage in Lago Lucchetti, Puerto Rico, USA | Carbon sequestration, Guánica Bay, Puerto Rico | Decrease in wave runup, St. Croix, USVI | Yasso07 - SOC, Loess Plateau, China | EnviroAtlas - Restorable wetlands | Coastal protection in Belize | DayCent N2O flux simulation, Ireland | WaterWorld v2, Santa Basin, Peru | WTP for a beach day, Massachusetts, USA | Northern Shoveler recruits, CREP wetlands, IA, USA | Total duck recruits, CREP wetlands, Iowa, USA | EPA Stormwater Manamgement Model |
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EM Full Name
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US EPA EnviroAtlas - Pollutants (air) removed annually by tree cover; Example is shown for Durham NC and vicinity, USA | Community weighted mean flowering date, Central French Alps | Area and hotspots of soil retention, South Africa | Runoff potential of pesticides, Europe | Reduction in pesticide runoff risk, Europe | PATCH (Program to Assist in Tracking Critical Habitat), western USA | Landscape importance for recreation, Europe | Land-use change effects on crop-based production, Europe | Land-use change effects on recreation, Europe | KINEROS (Kinematic runoff and erosion model) v2, River Ravna watershed,Bulgaria | Urban Air Temperature Change, Baltimore, MD, USA | Coastal protection, Europe | Soil erosion prevention provided by vegetation cover, Portel municipality, Portugal | VELMA (visualizing ecosystems for land management assessments) hydro, Oregon, USA | Water storage in Lago Lucchetti, Puerto Rico, USA | Carbon sequestration, Guánica Bay, Puerto Rico, USA | Decrease in wave runup (by reef), St. Croix, USVI | Yasso07 - Land Use Effects on Soil Organic Carbon Stocks in the Loess Plateau, China | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize: | DayCent simulation N2O flux and climate change, Ireland | WaterWorld v2, Santa Basin, Peru | Willingness to pay (WTP) for a beach day, Barnstable, Massachusetts, USA | Northern Shoveler duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Total duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Storm Water Management Model User's Manual Version 5.2 |
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EM Source or Collection
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US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Eco model. |
EU Biodiversity Action 5 | None | None | None | US EPA | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | i-Tree | USDA Forest Service | EU Biodiversity Action 5 | EU Biodiversity Action 5 | US EPA | US EPA | US EPA | US EPA | None | US EPA | EnviroAtlas | InVEST | None | None | US EPA | None | None | US EPA |
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EM Source Document ID
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223 | 260 | 271 | 254 | 255 | 2 | 228 | 228 | 228 |
248 ?Comment:Document 277 is also a source document for this EM |
217 | 296 | 281 | 13 | 336 | 338 | 335 | 344 | 262 | 350 | 358 | 368 | 386 |
372 ?Comment:Document 373 is a secondary source for this EM. |
372 ?Comment:Document 373 is a secondary source for this EM. |
452 |
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Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Schriever, C. A., and Liess, M. | Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, C. | Carroll, C, Phillips, M. K. , Lopez-Gonzales, C. A and Schumaker, N. H. | Haines-Young, R., Potschin, M. and Kienast, F. | Haines-Young, R., Potschin, M. and Kienast, F. | Haines-Young, R., Potschin, M. and Kienast, F. | Nedkov, S., Burkhard, B. | Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I. | Liquete, C., Zulian, G., Delgado, I., Stips, A., and Maes, J. | Guerra, C.A., Pinto-Correia, T., Metzger, M.J. | Abdelnour, A., Stieglitz, M., Pan, F. and McKane, R. B. | Bousquin, J., W.S. Fisher, J. Carriger, and E. Huertas | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | US EPA Office of Research and Development - National Exposure Research Laboratory | Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Van Soesbergen, A. and M. Mulligan | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | 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 | 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 | Rossman, L. A., M., Simon |
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Document Year
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2013 | 2011 | 2008 | 2007 | 2012 | 2006 | 2012 | 2012 | 2012 | 2012 | 2016 | 2013 | 2014 | 2011 | 2014 | 2017 | 2014 | 2015 | 2013 | 2016 | 2010 | 2018 | 2018 | 2010 | 2010 | 2022 |
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Document Title
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EnviroAtlas - Featured Community | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecosystem services for planning and management | Mapping ecological risk of agricultural pesticide runoff | Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction | Defining recovery goals and strategies for endangered species: The wolf as a case study | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD | Assessment of coastal protection as an ecosystem service in Europe | Mapping soil erosion prevention using an ecosystem service modeling framework for integrated land management and policy | Catchment hydrological responses to forest harvest amount and spatial pattern | A Bayesian belief network approach to explore alternative decisions for sediment control and water storage capacity at Lago Lucchetti, Puerto Rico | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | EnviroAtlas - National | The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Storm Water Management Model User's Manual Version 5.2 |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) |
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Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published EPA report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published report | Published EPA report |
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EM ID
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EM-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
| https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | http://www.tucson.ars.ag.gov/agwa/ | Not applicable | Not applicable | Not applicable | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | Not applicable | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | https://www.epa.gov/enviroatlas | Not identified in paper | Not applicable | www.policysupport.org/waterworld | Not applicable | Not applicable | Not applicable | https://www.epa.gov/water-research/storm-water-management-model-swmm | |
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Contact Name
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EnviroAtlas Team | Sandra Lavorel | Benis Egoh | Carola Alexandra Schriever | Sven Lautenbach | Carlos Carroll | Marion Potschin | Marion Potschin | Marion Potschin | David C. Goodrich | Gordon M. Heisler | Camino Liquete | Carlos A. Guerra | A. Abdelnour | Susan Yee | Susan H. Yee | Susan H. Yee | Xing Wu | EnviroAtlas Team | Greg Guannel | M. Abdalla | Arnout van Soesbergen | Kate K, Mulvaney | David Otis | David Otis | David Burden |
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Contact Address
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Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany | Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany | Klamath Center for Conservation Research, Orleans, CA 95556 | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | USDA - ARS Southwest Watershed Research Center, 2000 E. Allen Rd., Tucson, AZ 85719 | 5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210 | European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra, VA, Italy | Instituto de Ciências Agrárias e Ambientais Mediterrânicas, Universidade de Évora, Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal | Dept. of Civil and Environmental Engineering, Goergia Institute of Technology, Atlanta, GA 30332-0335, USA | NHEERL Gulf Ecology Division, Gulf Breeze FL 32561 | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Chinese Academy of Sciences, Beijing 100085, China | Not reported | The Nature Conservancy, Coral Gables, FL. USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | Not reported | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | U.S. EPA Research Center for Environmental Solutions and Emergency Response (CESER) Mail Drop: 314 P.O. Box #1198 Ada, OK 74821-1198 |
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Contact Email
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enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | Not reported | carola.schriever@ufz.de | sven.lautenbach@ufz.de | carlos@cklamathconservation.org | marion.potschin@nottingham.ac.uk | marion.potschin@nottingham.ac.uk | marion.potschin@nottingham.ac.uk | agwa@tucson.ars.ag.gov | gheisler@fs.fed.us | camino.liquete@gmail.com | cguerra@uevora.pt | abdelnouralex@gmail.com | yee.susan@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | xingwu@rceesac.cn | enviroatlas@epa.gov | greg.guannel@gmail.com | abdallm@tcd.ie | arnout.van_soesbergen@kcl.ac.uk | Mulvaney.Kate@EPA.gov | dotis@iastate.edu | dotis@iastate.edu | burden.david@epa.gov |
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EM ID
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EM-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
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Summary Description
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The Air Pollutant Removal model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina. ... pollution removal ... are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA: The maps, estimate and illustrate the variation in the amount of six airborne pollutants, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM10), and particulate matter (PM2.5), removed by trees. PM10 is for particulate matter greater than 2.5 microns and less than 10 microns. DATA FACT SHEET: "The data for this map are based on the land cover derived for each EnviroAtlas community and the pollution removal models in i-Tree, a toolkit developed by the USDA Forest Service. The land cover data were created from aerial photography through remote sensing methods; tree cover was then summarized as the percentage of each census block group. The i-Tree pollution removal module uses the tree cover data by block group, the closest hourly meteorological monitoring data for the community, and the closest pollution monitoring data... hourly estimates of pollution removal by trees were combined with atmospheric data to estimate hourly percent air quality improvement due to pollution removal for each pollutant." | ABSTRACT: "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." | AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover." | ABSTRACT: "The approach is based on the runoff potential (RP) of stream sites, by a spatially explicit calculation based on pesticide use, precipitation, topography, land use and soil characteristics in the near-stream environment. The underlying simplified model complies with the limited availability and resolution of data at larger scales." AUTHOR'S DESCRIPTION: "The RP is based on a mathematical model that describes runoff losses of a compound with generalized properties and which was developed from a proposal by the Organisation for Economic Co-operation and Development (OECD) for estimating dissolved runoff inputs of a pesticide into surface waters (OECD, 1998)...The runoff model underlying RP calculates the dissolved amount of a generic substance that was applied in the near environment of a stream site and that is expected to reach the stream site during one rainfall event. The dissolved amount results from a single application in the near-stream environment (i.e., a two-sided 100-m stream corridor extending for 1500 m upstream of the site) and is the amount of applied substance in the designated corridor reduced due to the influence of the site-specific key environmental factors precipitation, soil characteristics, topography, and plant interception." | AUTHOR'S DESCRIPTION: "We used a spatially explicit model to predict the potential exposure of small streams to insecticides (run-off potential – RP) as well as the resulting ecological risk (ER) for freshwater fauna on the European scale (Schriever and Liess 2007; Kattwinkel et al. 2011)...The recovery of community structure after exposure to insecticides is facilitated by the presence of undisturbed upstream stretches that can act as sources for recolonization (Niemi et al. 1990; Hatakeyama and Yokoyama 1997). In the absence of such sources for recolonization, the structure of the aquatic community at sites that are exposed to insecticides differs significantly from that of reference sites (Liess and von der Ohe 2005)...Hence, we calculated the ER depending on RP for insecticides and the amount of recolonization zones. ER gives the percentage of stream sites in each grid cell (10 × 10 km) in which the composition of the aquatic community deviated from that of good ecological status according to the WFD. In a second step, we estimated the service provided by the environment comparing the ER of a landscape lacking completely recolonization sources with that of the actual landscape configuration. Hence, the ES provided by non-arable areas (forests, pastures, natural grasslands, moors and heathlands) was calculated as the reduction of ER for sensitive species. The service can be thought of as a habitat provisioning/nursery service that leads to an improvement of ecological water quality." | **Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** AUTHORS' DESCRIPTION: "PATCH (program to assist in tracking critical habitat), the SEPM used here, is designed for studying territorial vertebrates. It links the survival and fecundity of individual animals to geographic information system (GIS) data on mortality risk and habitat productivity at the scale of an individual or pack territory. Territories are allocated by intersecting the GIS data with an array of hexagonal cells. The different habitat types in the GIS maps are assigned weights based on the relative levels of fecundity and survival expected in those habitat classes. Base survival and reproductive rates, derived from published field studies, are then supplied to the model as a population projection matrix. The model scales these base matrix values using the mean of the habitat weights within each hexagon, with lower means translating into lower survival rates or reproductive output. Each individual in the population is tracked through a yearly cycle of survival, fecundity, and dispersal events. Environmental stochasticity is incorporated by drawing each year’s base population matrix from a randomized set of matrices whose elements were drawn from a beta (survival) or normal (fecundity) distribution. Adult organisms are classified as either territorial or floaters. The movement of territorial individuals is governed by a parameter for site fidelity, but floaters must always search for available breeding sites. As pack size increases, pack members in the model have a greater tendency to disperse and search for new available breeding sites. Movement decisions use a directed random walk that combines varying proportions of randomness, correlation, and attraction to higher-quality habitat (Schumaker 1998)." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Recreation” ... The potential to deliver services is assumed to be influenced by land-use ... and bioclimatic and landscape properties such as mountainous terrain, adjacency to coastal and wetland ecosystems, as well as adjacency to landscape protection zones." AUTHOR'S DESCRIPTION: "Recreation… is broadly defined as all areas where landscape properties are favourable for active recreation purposes." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Crop-based production); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: "The analysis for “Crop-based production” maps all the areas that are important for food crops produced through commercial agriculture….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Recreation); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: " 'Recreation' is broadly defined as all areas where landscape properties are favourable for active recreation purposes….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." | 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." | 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." | ABSTRACT: "Mapping and assessment of ecosystem services is essential to provide scientific support to global and EU biodiversity policy. Coastal protection has been mostly analysed in the frame of coastal vulnerability studies or in local, habitat-specific assessments. This paper provides a conceptual and methodological approach to assess coastal protection as an ecosystem service at different spatial–temporal scales, and applies it to the entire EU coastal zone. The assessment of coastal protection incorporates 14 biophysical and socio-economic variables from both terrestrial and marine datasets. Those variables define three indicators: coastal protection capacity, coastal exposure and human demand for protection. A questionnaire filled by coastal researchers helped assign ranks to categorical parameters and weights to the individual variables. The three indicators are then framed into the ecosystem services cascade model to estimate how coastal ecosystems provide protection, in particular describing the service function, flow and benefit. The results are comparative and aim to support integrated land and marine spatial planning. The main drivers of change for the provision of coastal protection come from the widespread anthropogenic pressures in the European coastal zone, for which a short quantitative analysis is provided." | ABSTRACT: "We present an integrative conceptual framework to estimate the provision of soil erosion prevention (SEP) by combining the structural impact of soil erosion and the social–ecological processes that allow for its mitigation. The framework was tested and illustrated in the Portel municipality in Southern Portugal, a Mediterranean silvo-pastoral system that is prone to desertification and soil degradation. The results show a clear difference in the spatial and temporal distribution of the capacity for ecosystem service provision and the actual ecosystem service provision." AUTHOR'S DESCRIPTION: "To begin assessing the contribution of SEP we need to identify the structural impact of soil erosion, that is, the erosion that would occur when vegetation is absent and therefore no ES is provided. It determines the potential soil erosion in a given place and time and is related to rainfall erosivity (that is, the erosive potential of rainfall), soil erodibility (as a characteristic of the soil type) and local topography. Although external drivers can have an effect on these variables (for example, climate change), they are less prone to be changed directly by human action. The actual ES provision reduces the total amount of structural impact, and we define the remaining impact as the ES mitigated impact. We can then define the capacity for ES provision as a key component to determine the fraction of the structural impact that is mitigated…Following the conceptual outline, we will estimate the SEP provided by vegetation cover using an adaptation of the Universal Soil Loss Equation (USLE)." | AUTHOR'S DESCRIPTION: "VELMA uses a distributed soil column framework to simulate the movement of water and nutrients (NH4, NO3, DON, DOC) within the soil, between the soil and the vegetation, and between the soil surface and vegetation to the atmosphere. The soil column model consists of three coupled submodels: (1) a hydrological model that simulates vertical and lateral movement of water within soil, losses of water from soil and vegetation to the atmosphere, and the growth and ablation of the seasonal snowpack, (2) a soil temperature model that simulates daily soil layer temperatures from surface air temperature and snow depth, and (3) a plant-soil model that simulates C and N dynamics. (Note: for the purposes of this paper we describe only the hydrologic aspects of the model.) Each soil column consists of n soil layers. Soil water balance is solved for each layer (equations (A1)–(A6)). We employ a simple logistic function that is based on the degree of saturation to capture the breakthrough characteristics of soil water drainage (equations (A7)–(A9)). Evapotranspiration increases exponentially with increasing soil water storage and asymptotically approaches the potential evapotranspiration rate (PET) as water storage reaches saturation [Davies and Allen, 1973; Federer, 1979, 1982; Spittlehouse and Black, 1981] (equation (A12)). PET is estimated using a simple temperature-based method [Hamon, 1963] (equation (A13)). An evapotranspiration recovery function is used to account for the effects of changes in stand-level transpiration rates during succession, e.g., after fire or harvest (equation (B2)). Snowmelt is estimated using the degree-day approach [Rango and Martinec, 1995] and accounts for the effects of rain on snow [Harr, 1981] (equation (A10)). [15] The soil column model is placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other through the downslope lateral transport of water (Figures A1 and A2). Surface and subsurface lateral flow are routed using a multiple flow direction method [Freeman, 1991; Quinn et al., 1991]. As with vertical drainage of soil water, lateral subsurface downslope flow is modeled using a simple logistic function multiplied by a factor to account for the local topographic slope angle (equation (A16))… The model is forced with daily temperature and precipitation. Daily observed streamflow data is used to calibrate and validate simulated discharge." "Model calibration is needed to accurately capture the pre- and postharvest dynamics at WS10. This model calibration consists of two simulations: an old-growth simulation for the period 1969-1974 and a post-harvest simulation for the period 1975-2008." Two additional sets of VELMA simulations examining changes in streamflow are presented in the paper, but not included here. Twenty simulations were conducted varying the location across the watershed of a 20% har | AUTHORS DESCRIPTION: " A Bayesian belief network (BBN) was developed to characterize the effects of sediment accumulation on the water storage capacity of Lago Lucchetti (located in southwest Puerto Rico) and to forecast the life expectancy (usefulness) of the reservoir under different management scenarios. The conceptual approach included water and sediment delivery from two sources, from the Lucchetti watershed and from a tunnel linking Lago Lucchetti to three upstream reservoirs. Variables in the model included precipitation and erosion factors (soil type, landscape slope, and land use) applied to the Lucchetti watershed and to watersheds of the upstream reservoirs. The lack of available data for water and sediment flows in the watershed and through tunnels connecting the reservoirs led to several unique methods for network data acquisition. Status quo model runs demonstrated that sediment trapping has continuously declined in all four reservoirs since their construction and that every year a greater proportion of sediment is moving downstream through tunnels or spillways. The model compared favorably with incidental measured data in the region. Sensitivity analysis demonstrated that current sediment accumulation in Lago Lucchetti can be attributed in large part to sediment erosion from the Lucchetti watershed with only minor influence (~8%) from upstream reservoirs. Two decision scenarios were explored by including additional nodes for (1) partial and full conversion of sungrown land use to shadegrown coffee cultivation in the Lucchetti watershed and (2) partial or complete dredging the reservoir. Both management actions were examined singly and in combination for effects on reservoir life expectancy (beyond the year 2000) with varying water capacity targets. Using 50% water storage capacity as a target, model runs for status quo (no decision implemented) resulted in a probability range that averaged 5.57 years (or 50% water storage capacity in the year 2005). Partial and full conversion of land from sungrown to shadegrown coffee cultivation raised the life expectancy to 5.89 and 6.57 years, respectively. Partial and complete dredging of the reservoir resulted in a life expectancy of 37.3 and 40.7 years, and full implementation of both management options resulted in a life expectancy of 44.4 years." | AUTHOR'S DESCRIPTION: "In addition to affecting water quality, the ecosystem services of nitrogen retention, phosphorous retention, and sediment retention were also considered to contribute to stakeholder goals of maintaining the productivity of agricultural land and reducing soil loss. Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class, applying the mean of rates for natural sub-tropical ecosystems obtained from the literature." | 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...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events...Wave run-up, R, can be estimated as R = H(tan α/(√H/Ho) where H is the wave height nearshore, Ho is the deep water wave height, and α is the angle of the beach slope. R may be corrected by a multiplier depending on the porosity of the shoreline surface...The contribution of each grid cell to reduction in wave run-up would depend on its contribution to wave height attenuation (Eq. (S3))." | ABSTRACT: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau." | DATA FACT SHEET: "This EnviroAtlas national map depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | AUTHOR'S DESCRIPTION: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "We used existing studies in a meta-analysis to estimate appropriate benefit transfer values of consumer surplus per beach visit for Barnstable. The studies we include in the model are for beaches across the United States, allowing the metaregression model to be more broadly applicable to other beaches and for values to be adjusted based on appropriate site attributes...To identify relevant studies, we selected 25 studies of beach use and swimming from the Recreation Use Values Database (RUVD), where consumer surplus values are presented as value per day in 2016 dollars...We added beach length and history of closures to contextualize the model for our application by proxying water quality and site quality." Equation 1, page 11, provides the meta-regression. | ABSTRACT: "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…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | ABSTRACT: "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…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). |
EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. Running under Windows, SWMM 5 provides an integrated environment for editing study area input data, running hydrologic, hydraulic and water quality simulations, and viewing the results in a variety of formats. These include color coded drainage area and conveyance system maps, time series graphs and tables, profile plots, and statistical frequency analyses. This user’s manual describes in detail how to run SWMM 5.2. It includes instructions on how to build a drainage system model, how to set various simulation options, and how to view results in a variety of formats. It also describes the different types of files used by SWMM and provides useful tables of parameter values. Detailed descriptions of the theory behind SWMM 5 and the numerical methods it employs can be found in a separate set of reference manuals. ?Comment:The variables used for this ESML entry were derived from the quick tutorial section of the SWMM manual. |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | AUTHOR DESCRIPTION: "Comprehensive habitat and viability assessments. . . [more rigoursly defined] can clarify debate of goals for recovery of large carnivores"; Endangered Species Act and related litigation | None identified | None identified | None identified | None identified | None identified | Supports global and EU biodiversity policy | None identified | None identified | None reported | None identified | None identified | None | None Identified | Future rock lobster fisheries management | climate change | None identified | Economic value of protecting coastal beach water quality from contamination caused closures. | None identified | None identified | NA |
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Biophysical Context
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No additional description provided | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Not applicable | Not applicable | Great Plains to Pacific Coast, northern Rocky Mountains, Pacific Northwest | No additional description provided | No additional description provided | No 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. | 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 description provided | Open savannah-like forest of cork (Quercus suber) and holm (Quercus ilex) oaks, with trees of different ages randomly dispersed in changing densities, and pastures in the under cover. The pastures are mostly natural in a mosaic with patches of shrubs, which differ in size and the distribution depends mainly on the grazing intensity. Shallow, poor soils are prone to erosion, especially in areas with high grazing pressure. | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. Mean annual precipitation is 2300 mm and falls primarily as rain between October and April. Total rainfall during June– September averages 200 mm. Snow rarely persists longer than a couple of weeks and usually melts within 1 to 2 days. Average annual streamflow is 1600 mm, which is approximately 70% of annual precipitation. Soils are of the Frissel series, classified as Typic Dystrochrepts with fine loamy to loamy-skeletal texture that are generally deep and well drained. These soils quickly transmit subsurface water to the stream. Prior to the 1975 100% clearcut, WS10 was a 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). The dominant vegetation of WS10 today is a 35 year old mixed Douglasfir and western hemlock stand. | No additional reported | No additional description provided | No additional description provided | Agricultural plain, hills, gulleys, forest, grassland, Central China | No additional description provided | barrier reef and fringing reef in nearshore coastal marine system | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | Four separate beaches within the community of Barnstable | Prairie Pothole Region of Iowa | Prairie Pothole Region of Iowa | NA |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Population growth, road development (density) on public vs private land | No scenarios presented | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use changes (2000-2030) | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use change (2000-2030) | No scenarios presented | No scenarios presented | No scenarios presented | Different land management practices as represented by the comparison of different grazing intensities (i.e., livestock densities) in the whole study area and in three Civil Parishes within the study area | Stand age; old-growth (pre-harvest), and harvested (postharvest) | Management alternatives | No scenarios presented | No scenarios presented | Land use change | No scenarios presented | Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions | air temperature, precipitation, Atmospheric CO2 concentrations | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | No scenarios presented | No scenarios presented | No scenarios presented | NA |
|
EM ID
em.detail.idHelp
?
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EM-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) | Method + Application | Method + Application | Method + Application | Method Only |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
Application of existing 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 | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
|
EM ID
em.detail.idHelp
?
|
EM-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
2008-2010 | 2007-2008 | Not reported | 2000 | 2000 | 2000-2025 | 2000 | 1990-2030 | 1990-2030 | Not reported | May 5-Sept 30 2006 | 1992-2010 | January to December 2003 | 1969-2008 | 1962-2006 | 1978 - 2013 | 2006-2007, 2010 | 1969-2011 | 2006-2013 | 2005-2013 | 1961-1990 | 1950-2071 | July 1, 2011 to June 31, 2016 | 1987-2007 | 1987-2007 | Not applicable |
|
EM Time Dependence
em.detail.timeDependencyHelp
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time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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future time | Not applicable | Not applicable | future time | Not applicable | future time | Not applicable | future time | Not applicable | future time | future time | Not applicable | future time | future time | future time | Not applicable | Not applicable | past time | Not applicable | Not applicable | both | both | Not applicable | Not applicable | Not applicable | both |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
discrete | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | discrete | Not applicable | discrete | discrete | Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | discrete | Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | Not applicable | continuous |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
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1 | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | 6, 10, and 30 | Not applicable | Not reported | 1 | Not applicable | 1 | 1 | 1 | Not applicable | Not applicable | 1 | Not applicable | 1 | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
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Hour | Not applicable | Not applicable | Day | Not applicable | Year | Not applicable | Year | Not applicable | Not reported | Hour | Not applicable | Month | Day | Year | Not applicable | Not applicable | Year | Not applicable | Second | Day | Month | Not applicable | Not applicable | Not applicable | Not applicable |
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EM ID
em.detail.idHelp
?
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EM-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
|
Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Physiographic or Ecological | Geopolitical | Geopolitical | Geopolitical | Physiographic or ecological | Geopolitical | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Geopolitical | Geopolitical | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical | Geopolitical | Point or points | Watershed/Catchment/HUC | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | No location (no locational reference given) |
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Spatial Extent Name
em.detail.extentNameHelp
?
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Durham NC and vicinity | Central French Alps | South Africa | EU-15 | EU-27 | Western United States | The EU-25 plus Switzerland and Norway | The EU-25 plus Switzerland and Norway | The EU-25 plus Switzerland and Norway | River Ravna watershed | Baltimore, MD | Shoreline of the European Union-27 | Portel municipality | H. J. Andrews LTER WS10 | Guanica Bay watershed | Guanica Bay watershed | Coastal zone surrounding St. Croix | Yangjuangou catchment | conterminous United States | Coast of Belize | Oak Park Research centre | Santa Basin | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | CREP (Conservation Reserve Enhancement Program | CREP (Conservation Reserve Enhancement Program | Not applicable |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
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100-1000 km^2 | 10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 10-100 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 10-100 ha | 100-1000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 1-10 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 1-10 ha | 10,000-100,000 km^2 | 10-100 ha | 10,000-100,000 km^2 | 10,000-100,000 km^2 | Not applicable |
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EM ID
em.detail.idHelp
?
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EM-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) ?Comment:Spatial grain type is census block group. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | 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 | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | 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 | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | length, for linear feature (e.g., stream mile) | Not applicable | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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irregular | 20 m x 20 m | Distributed across catchments with average size of 65,000 ha | 10 km x 10 km | 10 km x 10 km | 504 km^2 | 1 km x 1 km | 1 km x 1 km | 1 km x 1 km | 25 m x 25 m | 10m x 10m | Irregular | 250 m x 250 m | 30 m x 30 m surface pixel and 2-m depth soil column | Not reported | 30 m x 30 m | 10 m x 10 m | 30m x 30m | irregular | 1 meter | Not applicable | 1 km2 | by beach site | multiple, individual, irregular sites | multiple, individual, irregular sites | mm |
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EM ID
em.detail.idHelp
?
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EM-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
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Numeric | Analytic | Analytic | Analytic | Analytic | Numeric | Logic- or rule-based | Logic- or rule-based | Logic- or rule-based | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Numeric | * | Analytic | Analytic | Analytic | Numeric |
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EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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None |
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EM ID
em.detail.idHelp
?
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EM-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
|
Model Calibration Reported?
em.detail.calibrationHelp
?
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Unclear | No | No | No | No | Unclear | No | No | No | Yes | Yes | No | No | Yes | Yes | No | Yes | Yes | No | No | No | No | Yes | Unclear | Unclear | Not applicable |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
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No | Yes | No | No | No | No | No | No | No | No | Yes | No | No | Yes | No | No | No |
Yes ?Comment:For the year 2006 and 2011 |
No | No |
Yes ?Comment:for N2O fluxes |
No | Yes | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None |
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None | None | None | None | None | None | None | None |
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None | None |
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None | None | None |
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None | None |
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None |
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None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
?
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No | No | No | No | Yes | No | Yes | No | No | No | No | No | No | No | No | No | Yes | No | No |
No ?Comment:Used the SWAN model (see below for referenece) with Generation 1 or 2 wind-wave formulations to validate the wave development portion of the model. Booij N, Ris RC, Holthuijsen LH. A third-generation wave model for coastal regions 1. Model description and validation. J Geophys Res. American Geophysical Union; 1999;104: 7649?7666. |
Yes | Yes | No | No | No | Not applicable |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
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No | No | No | Yes | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
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No | No | No | Yes | No |
Yes ?Comment:No results reported. Just a general statement was made about PATCH sensitivity and that demographic parameters are more sensitive that variation in other parameters such as dispersadistance . Reference made to another publication Carroll et al. 2003. Use of population viability analysis and reserve slelection algorithms in regional conservation plans. Ecol. App. 13:1773-1789. |
No | No | No | No | No | No | No | No | No | No | No | No | No | No | No | No |
Yes ?Comment:p-values of <0.05 and <0.01 provided for regression coefficient explanatory variables. |
No | No | Not applicable |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | No | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
|
EM-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
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None |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
|
EM-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
| None | None | None | None | None | None | None | None | None | None | None |
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None | None | None |
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None | None |
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None | None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
?
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EM-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
|
Centroid Latitude
em.detail.ddLatHelp
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35.99 | 45.05 | -30 | 50.01 | 50.53 | 39.88 | 50.53 | 50.53 | 50.53 | 42.8 | 39.28 | 48.2 | 38.3 | 44.15 | 18.09 | 17.96 | 17.73 | 36.7 | 39.5 | 18.63 | 52.86 | -9.05 | 41.64 | 42.62 | 42.62 | Not applicable |
|
Centroid Longitude
em.detail.ddLongHelp
?
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-78.96 | 6.4 | 25 | 4.67 | 7.6 | -113.81 | 7.6 | 7.6 | 7.6 | 24 | -76.62 | 16.35 | -7.7 | -122.2 | -66.86 | -67.02 | -64.77 | 109.52 | -98.35 | -88.22 | 6.54 | -77.81 | -70.29 | -93.84 | -93.84 | Not applicable |
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Centroid Datum
em.detail.datumHelp
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None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Not applicable |
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EM ID
em.detail.idHelp
?
|
EM-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
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Created Greenspace | Atmosphere | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Forests | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Atmosphere | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Scrubland/Shrubland | Rivers and Streams | Ground Water | Forests | Lakes and Ponds | Inland Wetlands | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Agroecosystems | Near Coastal Marine and Estuarine | Agroecosystems | None | Near Coastal Marine and Estuarine | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Urban and vicinity | Subalpine terraces, grasslands, and meadows. | Not reported | Arable lands in near-stream environments | Streams and near upstream environments | Not reported | Not applicable | Not applicable | Not applicable | Primarily forested watershed | Urban landscape and surrounding area | Coastal zones | Silvo-pastoral system | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Lago Luchetti | 13 LULC were used | Coral reefs | Loess plain | Terrestrial | coral reefs | farm pasture | tropical, coastal to montane | Saltwater beach | Wetlands buffered by grassland within agroecosystems | Wetlands buffered by grassland within agroecosystems | User-defined catchments |
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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 | Not applicable | 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 coarser than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale 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 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 | Other or unclear (comment) | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Other or unclear (comment) |
Scale of differentiation of organisms modeled
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EM ID
em.detail.idHelp
?
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EM-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Community | Not applicable | Not applicable | Not applicable | Species | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Guild or Assemblage | Not applicable |
Taxonomic level and name of organisms or groups identified
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EM-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
| None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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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-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
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None |
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None |
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None |
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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-59 |
EM-71 | EM-86 | EM-92 | EM-94 |
EM-98 |
EM-121 |
EM-122 |
EM-125 |
EM-130 | EM-306 | EM-320 |
EM-321 |
EM-375 |
EM-421 | EM-430 | EM-450 | EM-469 | EM-492 |
EM-542 |
EM-593 |
EM-630 | EM-682 | EM-702 | EM-705 | EM-968 |
|
None | None | None | None |
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