EcoService Models Library (ESML)
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Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
Compare Criteria:
Show Criteria
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-92 | EM-99 | EM-123 | EM-126 |
EM-148 
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EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
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EM-630 | EM-655 |
EM-734
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EM-843 | EM-944 |
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EM Short Name
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Runoff potential of pesticides, Europe | Landscape importance for crops, Europe | Land-use change and wildlife products, Europe | Annual profit from agriculture, South Australia | InVEST - Water provision, Francoli River, Spain | Coral and land development, St.Croix, VI, USA | Coral taxa and land development, St.Croix, VI, USA | InVEST carbon storage and sequestration (v3.2.0) | Carbon sequestration, Guánica Bay, Puerto Rico | Value of finfish, St. Croix, USVI | Nutrient Tracking Tool (NTT), north central Texas, USA | WaterWorld v2, Santa Basin, Peru | Hunting recreation, Wisconsin, USA | WESP: Marsh and open water, ID, USA | Mourning dove abundance, Piedmont region, USA | COBRA v 4.1 |
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EM Full Name
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Runoff potential of pesticides, Europe | Landscape importance for crop-based production, Europe | Land-use change effects on wildlife products, Europe | Annual profit from agriculture, South Australia | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) v2.4.2 - Water provision, Francoli River, Spain | Coral colony density and land development, St.Croix, Virgin Islands, USA | Coral taxa richness and land development, St.Croix, Virgin Islands, USA | InVEST v3.2.0 Carbon storage and sequestration | Carbon sequestration, Guánica Bay, Puerto Rico, USA | Relative value of finfish (on reef), St. Croix, USVI | Nutrient Tracking Tool (NTT), Upper North Bosque River watershed, Texas, USA | WaterWorld v2, Santa Basin, Peru | Hunting recreation, Wisconsin, USA | WESP: Deepwater marsh and open Water waterfowl habitat, Idaho, USA | Mourning dove abundance, Piedmont ecoregion, USA | COBRA (CO–Benefits Risk Assessment) v 4.1 |
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EM Source or Collection
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None | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | InVEST | US EPA | US EPA | InVEST | US EPA | US EPA | None | None | None | None | None | US EPA |
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EM Source Document ID
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254 | 228 | 228 | 243 | 280 | 96 | 96 | 315 | 338 | 335 | 354 | 368 | 376 |
393 ?Comment:Additional data came from electronic appendix provided by author Chris Murphy. |
405 |
437 ?Comment:User's manual is provided at the webpage. |
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Document Author
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Schriever, C. A., and Liess, M. | Haines-Young, R., Potschin, M. and Kienast, F. | Haines-Young, R., Potschin, M. and Kienast, F. | Crossman, N. D., Bryan, B. A., and Summers, D. M. | Marques, M., Bangash, R.F., Kumar, V., Sharp, R., and Schuhmacher, M. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | Oliver, L. M., Lehrter, J. C. and Fisher, W. S. | The Natural Capital Project | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Saleh, A., O. Gallego, E. Osei, H. Lal, C. Gross, S. McKinney, and H. Cover | Van Soesbergen, A. and M. Mulligan | Qiu, J. and M. G. Turner | Murphy, C. and T. Weekley | Riffel, S., Scognamillo, D., and L. W. Burger | US EPA |
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Document Year
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2007 | 2012 | 2012 | 2011 | 2013 | 2011 | 2011 | 2015 | 2017 | 2014 | 2011 | 2018 | 2013 | 2012 | 2008 | 2021 |
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Document Title
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Mapping ecological risk of agricultural pesticide runoff | 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 | Carbon payments and low-cost conservation | The impact of climate change on water provision under a low flow regime: A case study of the ecosystems services in the Francoli river basin | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Relating landscape development intensity to coral reef condition in the watersheds of St. Croix, US Virgin Islands | Carbon storage and sequestration - InVEST (v3.2.0) | 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 | Nutrient Tracking Tool - a user-friendly tool for calculating nutrient reductions for water quality trading | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Spatial interactions among ecosystem services in an urbanizing agricultural watershed | Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context. | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | CO-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA) |
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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 |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Webpage |
Software and Access
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EM ID
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EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
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EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
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EM-630 | EM-655 |
EM-734
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EM-843 | EM-944 |
| Not applicable | Not applicable | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | Not applicable | http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | www.policysupport.org/waterworld | Not applicable | Not applicable | Not applicable | https://www.epa.gov/cobra | |
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Contact Name
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Carola Alexandra Schriever | Marion Potschin | Marion Potschin | Neville D. Crossman | Montse Marquès | Leah Oliver | Leah Oliver | The Natural Capital Project | Susan H. Yee | Susan H. Yee | Ali Saleh | Arnout van Soesbergen | Monica G. Turner | Chris Murphy | Sam Riffell | Emma Zinsmeister |
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Contact Address
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Helmholtz Centre for Environmental Research - UFZ, Department of System Ecotoxicology, Permoserstrasse 15, 04318 Leipzig, Germany | 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 | CSIRO Ecosystem Sciences, PMB 2, Glen Osmond, South Australia, 5064, Australia | Environmental Analysis and Management Group, Department d'Enginyeria Qimica, Universitat Rovira I Virgili, Tarragona, Catalonia, Spain | National Health and Environmental Research Effects Laboratory | National Health and Environmental Research Effects Laboratory | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | 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 | Texas Institute for Applied Environmental Research-Tarleton State University, Stephenville, TX 76401,USA | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | Not reported | Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | EPA’s Office of Atmospheric Programs’ Climate Protection Partnerships Division |
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Contact Email
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carola.schriever@ufz.de | marion.potschin@nottingham.ac.uk | marion.potschin@nottingham.ac.uk | neville.crossman@csiro.au | montserrat.marques@fundacio.urv.cat | leah.oliver@epa.gov | leah.oliver@epa.gov | invest@naturalcapitalproject.org | yee.susan@epa.gov | yee.susan@epa.gov | saleh@tiaer.tarleton.edu | arnout.van_soesbergen@kcl.ac.uk | turnermg@wisc.edu | chris.murphy@idfg.idaho.gov | sriffell@cfr.msstate.edu | zinsmeister.emma@epa.gov |
EM Description
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EM ID
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EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
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EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
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EM-630 | EM-655 |
EM-734
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EM-843 | EM-944 |
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Summary Description
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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." | 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 … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture." | 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…are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Wildlife products); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000." AUTHOR'S DESCRIPTION: "Wildlife products belongs to the service group Biotic Materials in the CICES system; it includes the provisioning of all non-edible raw material products that are gained through non-agricultural practices or which are produced as a by-product of commercial and non-commercial forests, primarily in non-intensively used land or semi-natural and natural areas….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." | ABSTRACT: "A price on carbon is expected to generate demand for carbon offset schemes. This demand could drive investment in tree-based monocultures that provide higher carbon yields than diverse plantings of native tree and shrub species, which sequester less carbon but provide greater variation in vegetation structure and composition. Economic instruments such as species conservation banking, the creation and trading of credits that represent biological-diversity values on private land, could close the financial gap between monocultures and more diverse plantings by providing payments to individuals who plant diverse species in locations that contribute to conservation and restoration goals. We studied a highly modified agricultural system in southern Australia that is typical of many temperate agriculture zones globally (i.e., has a high proportion of endangered species, high levels of habitat fragmentation, and presence of non-native species). We quantified the economic returns from agriculture and from carbon plantings." AUTHOR'S DESCRIPTION: "The economic returns of carbon plantings are highly variable and depend primarily on carbon yield and price and opportunity costs (Newell & Stavins 2000; Richards & Stokes 2004; Torres et al. 2010). In this context, opportunity cost is usually expressed as the profit from agricultural production…We based our calculations of agricultural profit on Bryan et al. (2009), who calculated profit at full equity (i.e., economic return to land, capital, and management, exclusive of financial debt). We calculated an annual profit at full equity (PFEc) layer for each commodity (c) in the set of agricultural commodities (C), where C is wheat, field peas, beef cattle, or sheep." | Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "InVEST 2.4.2 model runs as script tool in the ArcGIS 10 ArcTool-Box on a gridded map at an annual average time step, and its results can be reported in either biophysical or monetary terms, depending on the needs and the availability of information. It is most effectively used within a decision making process that starts with a series of stakeholder consultations to identify questions and services of interest to policy makers, communities, and various interest groups. These questions may concern current service delivery and how services may be affected by new programmes, policies, and conditions in the future. For questions regarding the future, stakeholders develop scenarios of management interventions or natural changes to explore the consequences of potential changes on natural resources [21]. This tool informs managers and policy makers about the impacts of alternative resource management choices on the economy, human well-being, and the environment, in an integrated way [22]. The spatial resolution of analyses is flexible, allowing users to address questions at the local, regional or global scales. | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | AUTHOR'S DESCRIPTION: "In this exploratory comparison, stony coral condition was related to watershed LULC and LDI values. We also compared the capacity of other potential human activity indicators to predict coral reef condition using multivariate analysis." (294) | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | 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…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(5) value of finfish," | ABSTRACT: "The Nutrient Tracking Tool (NTrT) is an enhanced version of the Nitrogen Trading Tool, a user-friendly Web-based computer program originally developed by the USDA. The NTrT estimates nutrient (nitrogen and phosphorus) and sediment losses from fields managed under a variety of cropping patterns and management practices through its user-friendly, Web-based linkage to the Agricultural Policy Environmental eXtender (APEX) model. It also accesses the USDA Natural Resources Conservation Service’s Web Soil Survey to utilize their geographic information system interface for field and operation identification and load soil information. The NTrT provides farmers, government officials, and other users with a fast and efficient method of estimating nitrogen and phosphorus credits for water quality trading, as well as other water quality, water quantity, and farm production impacts associated with conservation practices. The information obtained from the tool can help farmers determine the most cost-effective conservation practice alternatives for their individual operations and provide them with more advantageous options in a water quality credit trading program. An application of the NTrT to evaluate conservation practices on fields receiving dairy manure in a north central Texas watershed indicates that phosphorus-based application rates, filter strips, forest buffers, and complete manure export off the farm all result in reduced phosphorus losses from the fields on which those practices were implemented. When compared to a base¬line condition that entailed manure application at the nitrogen agronomic rate of receiving crops, the reductions in total phosphorus losses associated with these practices ranged from 15% (2P Rate scenario) to 76% (forest buffer scenario)." AUTHOR'S DESCRIPTION: "This paper provides a brief overview of the NTrT and presents results of verification and application of the tool on a selected field on a test field in the Upper North Bosque River (UNBR) watershed in Texas…simulations for the baseline and all five alternative scenarios were replicated for each of 90 specific soil types in Erath County, Texas…results reported and discussed in this report represent the averages of the output for all soil types." | 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 | AUTHOR'S DESCRIPTION (from Supporting Information): "The hunting recreation service was estimated as a function of the extent of wildlife areas open for hunting, the number of game species, proximity to population center, and accessibility. Similar assumptions were made for this assessment: larger areas and places with more game species would support more hunting, areas closer to large population centers would be used more than remote areas, and proximity to major roads would increase access and use of an area. We first obtained the boundary of public wild areas from Wisconsin DNR and calculated the amount of areas for each management unit. The number of game species (Spe) for each area was derived from Dane County Parks Division (70). We used the same population density (Pop) and road buffer layer (Road) described in the previous forest recreation section. The variables Spe, Pop, and Road were weighted to ranges of 0–40, 0–40, and 0–20, respectively, based on the relative importance of each in determining this service. We estimated overall hunting recreation service for each 30-m grid cell with the following equation: HRSi = Ai Σ(Spei + Popi +Roadi), where HRS is hunting recreation score, A is the area of public wild areas open for hunting/fishing, Spe represents the number of game species, Pop stands for the proximity to population centers, and Road is the distance to major roads. To simplify interpretation, we rescaled the original hunting recreation score (ranging from 0 to 28,000) to a range of 0–100, with 0 representing no hunting recreation service and 100 representing highest service. | A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level. | ABSTRACT:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds. " | Introduction: "COBRA is a screening tool that provides preliminary estimates of the impact of air pollution emission changes on ambient particulate matter (PM) air pollution concentrations, translates this into health effect impacts, and then monetizes these impacts, as illustrated below. The model does not require expertise in air quality modeling, health effects assessment, or economic valuation. Built into COBRA are emissions inventories, a simplified air quality model, health impact equations, and economic valuations ready for use, based on assumptions that EPA currently uses as reasonable best estimates. COBRA also enables advanced users to import their own datasets of emissions inventories, population, incidence, health impact functions, and valuation functions. Analyses can be performed at the state or county level and across the 14 major emissions categories (these categories are called “tiers”) included in the National Emissions Inventory. COBRA presents results in tabular as well as geographic form, and enables policy analysts to obtain a first-order approximation of the benefits of different mitigation scenarios under consideration. However, COBRA is only a screening tool. More sophisticated, albeit time- and resource-intensive, modeling approaches are currently available to obtain a more refined picture of the health and economic impacts of changes in emissions. EPA initially developed COBRA as a desktop application. In 2021, EPA released a web-based version of the tool, known as the COBRA Web Edition. Although the desktop version and web versions of COBRA both use the same methodology to calculate outdoor air quality and health impacts from changes in air pollution emissions, the desktop version offers additional advanced features that are not included in the more streamlined Web Edition. In particular, the desktop version is preloaded with input data on emissions, population, and baseline health incidence for 2016, 2023, and 2028; the Web Edition includes data only for 2023. Similarly, the desktop version allows users to import custom input datasets, while the Web Edition does not. The Web Edition, however, does not require the user to download or install additional software, and it runs more quickly than the desktop version. Users might choose to use the desktop version if they would like to use advanced features, such as custom input data and/or use the preloaded data for 2016 or 2028. Otherwise, users may choose to use the Web Edition for data analysis relevant to 2023. The process for entering emissions input data into COBRA is very similar for the desktop and web versions of the tool. The remainder of this User’s Manual focuses on the steps required to run the desktop version of the tool. The same general process can be used with the Web Edition." |
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Specific Policy or Decision Context Cited
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European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | None identified | None identified | None identified | None identified | Not applicable | Not applicable | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None reported | None identified |
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Biophysical Context
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Not applicable | No additional description provided | No additional description provided | Mix of remnant native vegetation and agricultural land. Remnant vegetation is in 20 large (>10,000 ha) contiguous fragments where rainfall is low. Acacia spp. and Eucalyptus spp. are the dominant tree species in the remnant vegetation, and major native vegetation types are open forests, woodlands, and open woodlands. Dominant agricultural uses are annual crops, annual legumes, and grazing of sheep and cows. The climate is Mediterranean with average annual rainfall ranging from 250 mm to 1000 mm. | Mediteranean coastal mountains | nearshore; <1.5 km offshore; <12 m depth | nearshore; <1.5 km offshore; <12 m depth | Not applicable | No additional description provided | No additional description provided | The UNBR watershed is comprised primarily of two main physiographic areas, the West Cross Timbers and the Grand Prairie Land Resource Areas. In the West Cross Timbers, soils are primarily fine sandy loams with sandy clay subsoils. Soils in the Grand Prairie area, on the other hand, are typically calcareous clays and clay loams (Ward et al. 1992). | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | No additional description provided | restored, enhanced and created wetlands | Conservation Reserve Program lands left to go fallow | No additional description provided |
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EM Scenario Drivers
em.detail.scenarioDriverHelp
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No scenarios presented | No scenarios presented | Recent historical land-use change from 1990-2000 | No scenarios presented | IPPC scenarios A2- severe changes in temperature and precipitation, B1 - more moderate variations in temperature and precipitation schemes from the present | Not applicable | Not applicable | Optional future scenarios for changed LULC and wood harvest | No scenarios presented | No scenarios presented | Conservation management strategies to reduce phosphorus losses | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | No scenarios presented | Sites, function or habitat focus | N/A | No scenarios presented |
EM Relationship to Other EMs or Applications
|
EM ID
em.detail.idHelp
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EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
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EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
|
EM-630 | EM-655 |
EM-734
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EM-843 | EM-944 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
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|
Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method Only | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
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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 | Application of existing model | Application of existing model | New or revised model | Application of existing model | New or revised model | WESP Deepwater Marsh | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
em.detail.relatedEmHelp
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EM ID
em.detail.idHelp
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|
EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
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EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
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EM-630 | EM-655 |
EM-734
|
EM-843 | EM-944 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
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Doc-255 | Doc-256 | Doc-257 | Doc-231 | Doc-228 | Doc-228 | Doc-238 | Doc-239 | Doc-240 | Doc-241 | Doc-242 | Doc-244 | Doc-307 | Doc-311 | Doc-338 | Doc-205 | None | None | Doc-309 | None | None | Doc-352 | None | None | Doc-390 | Doc-405 | None |
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EM ID for related EM
em.detail.relatedEmEmIdHelp
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None | EM-119 | EM-120 | EM-121 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-166 | EM-170 | EM-171 | EM-122 | EM-124 | EM-125 | EM-162 | EM-164 | EM-165 | EM-166 | EM-170 | EM-171 | EM-99 | EM-119 | EM-120 | EM-121 | None | EM-344 | EM-368 | EM-437 | EM-111 | None | None | EM-349 | None | None | EM-549 | None | None | EM-718 | EM-729 | EM-743 | EM-756 | EM-757 | EM-759 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-751 | EM-768 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-844 | EM-845 | EM-846 | EM-847 | None |
EM Modeling Approach
EM Relationship to Time
|
EM ID
em.detail.idHelp
?
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EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
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EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
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EM-630 | EM-655 |
EM-734
|
EM-843 | EM-944 |
|
EM Temporal Extent
em.detail.tempExtentHelp
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2000 | 2000 | 1990-2000 | 2002-2008 | 1971-2100 | 2006-2007 | 2006-2007 | Not applicable | 1978 - 2013 | 2006-2007, 2010 | 1960-2001 | 1950-2071 | 2000-2006 | 2010-2013 | 2008 | Not applicable |
|
EM Time Dependence
em.detail.timeDependencyHelp
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time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-dependent | time-stationary | Not applicable |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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future time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | both | Not applicable | past time | Not applicable | Not applicable |
|
EM Time Continuity
em.detail.continueDiscreteHelp
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|
discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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|
1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
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Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Year | Not applicable | Not applicable | Day | Month | Not applicable | Not applicable | Not applicable | Not applicable |
EM Spatial Extent
|
EM ID
em.detail.idHelp
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|
EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
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EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
|
EM-630 | EM-655 |
EM-734
|
EM-843 | EM-944 |
|
Bounding Type
em.detail.boundingTypeHelp
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Geopolitical | Geopolitical | Geopolitical | Physiographic or Ecological | Watershed/Catchment/HUC | Physiographic or Ecological | Physiographic or Ecological | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Geopolitical |
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Spatial Extent Name
em.detail.extentNameHelp
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EU-15 | The EU-25 plus Switzerland and Norway | The EU-25 plus Switzerland and Norway | Agricultural districts of the state of South Australia | Francoli River | St. Croix, U.S. Virgin Islands | St.Croix, U.S. Virgin Islands | Not applicable | Guanica Bay watershed | Coastal zone surrounding St. Croix | Upper North Bosque River watershed | Santa Basin | Yahara Watershed, Wisconsin | Wetlands in Idaho | Piedmont Ecoregion | Not applicable |
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Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
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>1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | 10-100 km^2 | 10-100 km^2 | Not applicable | 1000-10,000 km^2. | 100-1000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 100,000-1,000,000 km^2 | 100,000-1,000,000 km^2 | Not applicable |
Spatial Distribution of Computations
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EM ID
em.detail.idHelp
?
|
EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
|
EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
|
EM-630 | EM-655 |
EM-734
|
EM-843 | EM-944 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
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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 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 lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) |
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Spatial Grain Type
em.detail.spGrainTypeHelp
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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 | Not applicable | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | Not applicable | map scale, for cartographic feature |
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Spatial Grain Size
em.detail.spGrainSizeHelp
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10 km x 10 km | 1 km x 1 km | 1 km x 1 km | 1 ha | 30m x 30m | Not applicable | Not applicable | application specific | 30 m x 30 m | 10 m x 10 m | Not applicable | 1 km2 | 30m x 30m | Not applicable | Not applicable | user defined |
EM Structure and Computation Approach
|
EM ID
em.detail.idHelp
?
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EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
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EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
|
EM-630 | EM-655 |
EM-734
|
EM-843 | EM-944 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Logic- or rule-based | Logic- or rule-based | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | * | Analytic | Numeric | Analytic | Analytic |
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EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic |
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Statistical Estimation of EM
em.detail.statisticalEstimationHelp
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None |
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Model Checking Procedures Used
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EM ID
em.detail.idHelp
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EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
|
EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
|
EM-630 | EM-655 |
EM-734
|
EM-843 | EM-944 |
|
Model Calibration Reported?
em.detail.calibrationHelp
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No | No | No | No | No | Yes | Yes | Not applicable | No | Yes | Yes | No | No | No | Yes | Not applicable |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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No | No | No | No | No | Yes | Yes | Not applicable | No | No | No | No | No | No | No | Not applicable |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None |
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None | None | None | None | None | None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | Yes | No | No |
Yes ?Comment:Used Nash-Sutcliffe model efficiency index |
No | No | Not applicable | No | Yes | No | Yes | No | No | No | Not applicable |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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Yes | No | No | No | No | Yes | Yes | Not applicable | No | No | No | No | No | No | No | Not applicable |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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Yes | No | No | No | No | No | No | Not applicable | No | No | No | No | No | No | Yes | Not applicable |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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No | 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 | Unclear | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
?
| EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
|
EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
|
EM-630 | EM-655 |
EM-734
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EM-843 | EM-944 |
|
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None | None | None |
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None |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
?
| EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
|
EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
|
EM-630 | EM-655 |
EM-734
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EM-843 | EM-944 |
| None | None | None | None | None |
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None |
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None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
?
|
EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
|
EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
|
EM-630 | EM-655 |
EM-734
|
EM-843 | EM-944 |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
50.01 | 50.53 | 50.53 | -34.9 | 41.26 | 17.75 | 17.75 | -9999 | 17.96 | 17.73 | 32.09 | -9.05 | 43.1 | 44.06 | 36.23 | Not applicable |
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Centroid Longitude
em.detail.ddLongHelp
?
|
4.67 | 7.6 | 7.6 | 138.7 | 1.18 | -64.75 | -64.75 | -9999 | -67.02 | -64.77 | -98.12 | -77.81 | -89.4 | -114.69 | -81.9 | Not applicable |
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Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | NAD83 | NAD83 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Not applicable |
Environments and Scales Modeled
|
EM ID
em.detail.idHelp
?
|
EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
|
EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
|
EM-630 | EM-655 |
EM-734
|
EM-843 | EM-944 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
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) | Agroecosystems | Rivers and Streams | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Not applicable | Inland Wetlands | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Agroecosystems | None | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Forests | Agroecosystems | Created Greenspace | Grasslands | Inland Wetlands | Grasslands | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Arable lands in near-stream environments | Not applicable | Not applicable | Agricultural land for annual crops, annual legumes, and grazing of sheep and cows | Coastal mountains | stony coral reef | stony coral reef | Terrestrial environments, but not specified for methods | 13 LULC were used | Coral reefs | Rangeland and forage fields for dairy | tropical, coastal to montane | Mixed environment watershed of prairie converted to predominantly agriculture and urban landscape | created, restored and enhanced wetlands | grasslands | Not applicable |
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EM Ecological Scale
em.detail.ecoScaleHelp
?
|
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 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 corresponds to the Environmental Sub-class | Other or unclear (comment) | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
?
|
EM ID
em.detail.idHelp
?
|
EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
|
EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
|
EM-630 | EM-655 |
EM-734
|
EM-843 | EM-944 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Guild or Assemblage | Guild or Assemblage | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Not applicable | Species | Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
?
| EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
|
EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
|
EM-630 | EM-655 |
EM-734
|
EM-843 | EM-944 |
| None Available | None Available | None Available |
|
None Available |
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None Available | None Available |
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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.detail.cicesHelp
?
| EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
|
EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
|
EM-630 | EM-655 |
EM-734
|
EM-843 | EM-944 |
| 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>
fegs1Help
?
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
?
| EM-92 | EM-99 | EM-123 | EM-126 |
EM-148
|
EM-194 | EM-260 | EM-374 | EM-430 | EM-462 |
EM-584
|
EM-630 | EM-655 |
EM-734
|
EM-843 | EM-944 |
| None |
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None |
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None | None |
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None | None | None |
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None |