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
EM ID
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EM-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
EM Short Name
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Soil carbon content, Central French Alps | Agronomic ES and plant traits, Central French Alps | InVEST nutrient retention, Hood Canal, WA, USA | Mangrove development, Tampa Bay, FL, USA | Rate of Fire Spread | InVEST water yield, Xitiaoxi River basin, China | VELMA hydro, Oregon, USA | Wetland shellfish production, Gulf of Mexico, USA | WaterWorld v2, Santa Basin, Peru | Waterfowl pairs, CREP wetlands, Iowa, USA | Sedge Wren density, CREP, Iowa, USA | LUCI, New Zealand | Estuary visitation, Cape Cod, MA | Beach visitation, Barnstable, MA, USA | C sequestration in grassland restoration, England | National invertebrate community rank index | Neighborhood greenness and health, FL, USA | VELMA v. 2.1 contaminant modeling | IPaC, USFWS, USA | ComunityViz - land-sea planning submodel |
EM Full Name
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Soil carbon content, Central French Alps | Agronomic ecosystem service estimated from plant functional traits, Central French Alps | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) nutrient retention, Hood Canal, WA, USA | Mangrove wetland development, Tampa Bay, FL, USA | Rate of Fire Spread | InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) water yield, Xitiaoxi River basin, China | VELMA (visualizing ecosystems for land management assessments) hydro, Oregon, USA | Wetland shellfish production, Gulf of Mexico, USA | WaterWorld v2, Santa Basin, Peru | Waterfowl pairs, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | LUCI (Land Utilisation and Capability Indicator), New Zealand | Value of recreational use of an estuary, Cape Cod, Massachusetts | Beach visitation, Barnstable, Massachusetts, USA | Carbon sequestration in grassland diversity restoration, England | National invertebrate community ranking index (NICRI) | Neighborhood greenness and chronic health conditions in Medicare beneficiaries, Miami-Dade County, Florida, USA | VELMA (Visualizing Ecosystem Land Management Assessments) v. 2.1 contaminant modeling | Information for Planning and Conservation tool, USFWS, U.S. | A technical guide to the integrated land-sea planning toolkit |
EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | InVEST | US EPA | None | InVEST | US EPA |
US EPA ?Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science |
None | None | None | None | US EPA | US EPA | None | None | None | US EPA | None | None |
EM Source Document ID
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260 | 260 | 205 | 97 | 306 | 307 | 13 | 324 | 368 | 372 | 372 |
380 ?Comment:Document 381 is an additional source for this EM. |
387 | 386 | 396 | 407 | 417 |
423 ?Comment:Document #430 is an additional source for this EM. Document #423 has been imcorporated into the more recently published document #430. |
451 ?Comment:Assume peer reviewed at least internally by USFWS |
473 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M. | Osland, M. J., Spivak, A. C., Nestlerode, J. A., Lessmann, J. M., Almario, A. E., Heitmuller, P. T., Russell, M. J., Krauss, K. W., Alvarez, F., Dantin, D. D., Harvey, J. E., From, A. S., Cormier, N. and Stagg, C.L. | Rothermel, Richard C. | Zhang C., Li, W., Zhang, B., and Liu, M. | Abdelnour, A., Stieglitz, M., Pan, F. and McKane, R. B. | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Van Soesbergen, A. and M. Mulligan | 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 | Trodahl, M. I., B. M. Jackson, J. R. Deslippe, and A. K. Metherell | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta | Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta | De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett | Cuffney, Tom | Brown, S. C., J. Lombard, K. Wang, M. M. Byrne, M. Toro, E. Plater-Zyberk, D. J. Feaster, J. Kardys, M. I. Nardi, G. Perez-Gomez, H. M. Pantin, and J. Szapocznik | McKane | U.S. Fish and Wildlife Service | Crist, P., Madden, K., Varley, I., Eslinger, D., Walker, D., Anderson, A., Morehead, S. and Dunton, K., |
Document Year
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2011 | 2011 | 2013 | 2012 | 1972 | 2012 | 2011 | 2012 | 2018 | 2010 | 2010 | 2017 | 2019 | 2018 | 2011 | 2003 | 2016 | None | None | 2009 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence | A Mathematical model for predicting fire spread in wildland fuels | Water yield of Xitiaoxi River basin based on InVEST modeling | Catchment hydrological responses to forest harvest amount and spatial pattern | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | 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 | Investigating trade-offs between water quality and agricultural productivity using the Land Utilisation and Capability Indicator (LUCI)-A New Zealand application | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA | Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts | Additional carbon sequestration benefits of grassland diversity restoration | Invertebrate Status Index | Neighborhood greenness and chronic health conditions in Medicare beneficiaries | Tutorial A.1 – Contaminant Fate and Transport Modeling Concepts; VELMA 2.1 “How To” Documentation | Information for Planning and Consultation (IPaC | Integrated Land-Sea Planning: A Technical Guide to the Integrated Land-Sea Planning Toolkit. |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Documented, not peer reviewed | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published USDA Forest Service report | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published report | Published journal manuscript | Draft manuscript-work progressing | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published EPA report | Published report | Published report |
EM ID
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EM-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | http://firelab.org/project/farsite | https://www.naturalcapitalproject.org/invest/ | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | www.policysupport.org/waterworld | Not applicable | Not applicable |
info@lucitools.org ?Comment:To obtain LUCI, email us your enquiry at info@lucitools.org with information about: The problem you are seeking to solve or your research question. The country and region you wish to apply LUCI in. What data you have with as much detail as possible about the data sources. Your timeframe or deadlines. |
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=354355 | https://ipac.ecosphere.fws.gov/ | https://repositories.lib.utexas.edu/bitstreams/3dee92a8-9373-4bcc-be25-eda74e81fabf/download | |
Contact Name
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Sandra Lavorel | Sandra Lavorel | J.E. Toft | Michael Osland | Charles McHugh | Li Wenhua | A. Abdelnour | Stephen J. Jordan | Arnout van Soesbergen | David Otis | David Otis | Martha I. Trodahl | Mulvaney, Kate | Kate K, Mulvaney | Gerlinde B. De Deyn | Tom Cuffney | Scott C. Brown | Robert B. McKane | USFWS |
Patrick Crist ?Comment:No contact information provided |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561 | RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808 | Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China | Dept. of Civil and Environmental Engineering, Goergia Institute of Technology, Atlanta, GA 30332-0335, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | 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 | School of Geography, Environment & Earth Sciences, Victoria University of Wellington, New Zealand | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI | Not reported | Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands | 3916 Sunset Ridge Rd, Raleigh, NC 27607 | Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 NW 14th Street, Clinical Research Building (CRB), Room 1065, Miami FL 33136 | US EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ecology Division, Corvallis, Oregon 97333 | 911 NE 11th Avenue Portland, OR 97232 | None provided |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | jetoft@stanford.edu | mosland@usgs.gov | cmchugh@fs.fed.us | liwh@igsnrr.ac.cn | abdelnouralex@gmail.com | jordan.steve@epa.gov | arnout.van_soesbergen@kcl.ac.uk | dotis@iastate.edu | dotis@iastate.edu | Not reported | None reported | Mulvaney.Kate@EPA.gov | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com | tcuffney@usgs.gov | sbrown@med.miami.edu | mckane.bob@epa.gov | fwhq_ipac@fws.gov | patrick@planitfwd.com |
EM ID
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EM-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in soil carbon was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Soil carbon for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on soil carbon. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use." | 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: "The Agronomic ecosystem service map is a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to agronomic ecosystem services are based on stakeholders’ perceptions, given positive or negative contributions." | InVEST Nutrient Retention Model Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We modelled discharge and total nitrogen for the 153 perennial sub-watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparameterized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)" (2) "We used the InVEST Nutrient Retention model to quantify the total nitrogen load for each subwatershed. Inputs to the Nutrient Retention model include water yield, land use and land cover, and nutrient loading and filtration rates (Table 1; Conte et al., 2011; Tallis et al., 2011). The nutrient model quantifies natural and anthropogenic sources of total nitrogen within each subwatershed, allowing managers to identify subwatersheds potentially at risk of contributing excessive nitrogen loads given the predicted development and climate future." ( P. 4) | ABSTRACT: "Mangrove wetland restoration and creation effortsare increasingly proposed as mechanisms to compensate for mangrove wetland losses. However, ecosystem development and functional equivalence in restored and created mangrove wetlands are poorly understood. We compared a 20-year chronosequence of created tidal wetland sites in Tampa Bay, Florida (USA) to natural reference mangrove wetlands. Across the chronosequence, our sites represent the succession from salt marsh to mangrove forest communities. Our results identify important soil and plant structural differences between the created and natural reference wetland sites; however, they also depict a positive developmental trajectory for the created wetland sites that reflects tightly coupled plant-soil development. Because upland soils and/or dredge spoils were used to create the new mangrove habitats, the soils at younger created sites and at lower depths (10–30 cm) had higher bulk densities, higher sand content, lower soil organic matter (SOM), lower total carbon (TC), and lower total nitrogen (TN) than did natural reference wetland soils. However, in the upper soil layer (0–10 cm), SOM, TC, and TN increased with created wetland site age simultaneously with mangrove forest growth. The rate of created wetland soil C accumulation was comparable to literature values for natural mangrove wetlands. Notably, the time to equivalence for the upper soil layer of created mangrove wetlands appears to be faster than for many other wetland ecosystem types. Collectively, our findings characterize the rate and trajectory of above- and below-ground changes associated with ecosystem development in created mangrove wetlands; this is valuable information for environmental managers planning to sustain existing mangrove wetlands or mitigate for mangrove wetland losses." | ABSTRACT: "The development of a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models. The model was developed for and is now being used as a basis for appraising fire spread and intensity in the National Fire Danger Rating System. The initial work was done using fuel arrays composed of uniform size particles. Three fuel sizes were tested over a wide range of bulk densities. These were 0.026-inch-square cut excelsior, 114-inch sticks, and 112-inch sticks. The problem of mixed fuel sizes was then resolved by weighting the various particle sizes that compose actual fuel arrays by either surface area or loading, depending upon the feature of the fire being predicted. The model is complete in the sense that no prior knowledge of a fuel's burning characteristics is required. All that is necessary are inputs describing the physical and chemical makeup of the fuel and the environmental conditions in which it is expected to burn. Inputs include fuel loading, fuel depth, fuel particle surface-area-to-volume ratio, fuel particle heat content, fuel particle moisture and mineral content, and the moisture content at which extinction can be expected. Environmental inputs are mean wind velocity and slope of terrain. For heterogeneous mixtures, the fuel properties are entered for each particle size. The model as originally conceived was for dead fuels in a uniform stratum contiguous to the ground, such as litter or grass. It has been found to be useful, however, for fuels ranging from pine needle litter to heavy logging slash and for California brush fields." **FARSITE4 will no longer be supported or available for download or further supported. FlamMap6 now includes FARSITE.** | 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. ABSTRACT: "A water yield model based on InVEST was employed to estimate water runoff in the Xitiaoxi River basin…In order to test model accuracy the natural runoff of Xitiaoxi River was estimated based on linear regression relation of rainfall-runoff in a 'reference period'." AUTHOR'S DESCRIPTION: "The water yield model is based on the Budyko curve (1974) and annual precipitation…Water yield models require land use and land cover, precipitation, average annual potential evapotranspiration, soil depth, plant available water content, watersheds and sub-watersheds as well as a biophysical table reflecting the attributes of each land use and land cover." | 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 | ABSTRACT: "We present concepts and case studies linking the production functions (contributions to recruitment) of critical habitats to commercial and recreational fishery values by combining site specific research data with spatial analysis and population models. We present examples illustrating various spatial scales of analysis, with indicators of economic value, for … commercial blue crab Callinectes sapidus and penaeid shrimp fisheries in the Gulf of Mexico." | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "Number of duck pairs per site was estimated for 6 species of ducks: Mallard (Anas platyrhynchos), Blue-winged Teal (Anas discors), Northern Shoveler (Anas clypeata), Gadwall (Anas strepera), Northern Pintail (Anas acuta), and Wood Duck (Aix sponsa), using models developed by Cowardin et al. (1995). Pair abundance was based on wetland class (i.e., temporary, seasonal, semi-permanent, lake, or river), wetland size, and a set of species specific regression coefficients. All CREP wetlands were considered semi-permanent for this analysis; therefore only coefficients associated with the semipermanent wetland pair model were used in calculations. The general equation used to estimate the pairs per wetland was: Pairs = e (a + bx + α) * p where, e = mathematical constant ≈ 2.718, a = species specific regression coefficient a, b = species specific regression coefficient b, x = the natural log of wetland size, α = species specific alpha value, and p = proportion of the basin containing water (assumed to be 0.90 for this analysis)" | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Sedge Wren (Cistothorus platensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SEWR density = 1-1/1+e^(-0.8015652 + 0.08500569 * grass400) *e^(-0.7982511 + 0.0285891 * bbspath + 0.0105094 *grass400) | ABSTRACT: "...The Land Utilisation & Capability Indicator (LUCI) is a GIS framework that considers impacts of land use on multiple ecosystem services in a holistic and spatially explicit manner. Due to its fine spatial scale and focus on the rural environment, LUCI is well-placed to help both farm and catchment managers to explore and quantify spatially explicit solutions to improve water quality while also maintaining or enhancing other ecosystem service outcomes. LUCI water quality and agricultural productivity models were applied to a catchment in the Bay of Plenty, New Zealand. Nitrogen (N) and phosphorus (P) sources, sinks and pathways in the landscape were identified and trade-offs and synergies between water quality and agricultural productivity were investigated. Results indicate that interventions to improve water quality are likely to come at the expense of agriculturally productive land. Nonetheless, loss of agriculturally productive land can be minimised by using LUCI to identify, at a fine spatial scale, the most appropriate locations for nutrient intervention. Spatially targeted and strategic nutrient source management and pathway interception can improve water quality, while minimising negative financial impacts on farms. Our results provide spatially explicit solutions to optimize agricultural productivity and water quality, which will inform better farm, land and catchment management as well as national and international policy." AUTHOR'S DESCRIPTION (of OVERSEER submodel): "Water quality models within LUCI use an enhanced, spatially representative export co-efficient (EC) approach to model total nitrogen (TN) and total phosphorus (TP) exports to water… Here, ECs for pastoral land cover are calculated by LUCI using algorithms derived from a large ( > 14 000 samples), pastorally based national dataset. The dataset consists of detailed farm nutrient input and management variables that have been entered and run using OVERSEER® to generate nutrient loss predictions, which are also included in the dataset." NOTE: The LUCI model, is a second-generation extension and software implementation of the Polyscape framework, as described in EM-658. https://esml.epa.gov/detail/em/658 | [ABSTRACT: "Estimates of the types and number of recreational users visiting an estuary are critical data for quantifying the value of recreation and how that value might change with variations in water quality or other management decisions. However, estimates of recreational use are minimal and conventional intercept surveys methods are often infeasible for widespread application to estuaries. Therefore, a practical observational sampling approach was developed to quantify the recreational use of an estuary without the use of surveys. Designed to be simple and fast to allow for replication, the methods involved the use of periodic instantaneous car counts multiplied by extrapolation factors derived from all-day counts. This simple sampling approach can be used to estimate visitation to diverse types of access points on an estuary in a single day as well as across multiple days. Evaluation of this method showed that when periodic counts were taken within a preferred time window (from 11am-4:30pm), the estimates were within 44 percent of actual daily visitation. These methods were applied to the Three Bays estuary system on Cape Cod, USA. The estimated combined use across all its public access sites is similar to the use at a mid-sized coastal beach, demonstrating the value of estuarine systems. Further, this study is the first to quantify the variety and magnitude of recreational uses at several different types of access points throughout the estuary using observational methods. This work can be transferred to the many small coastal access points used for recreation across New England and beyond." ] | 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 needed beach visitation estimates to assess the number of people who would be impacted by beach closures. We modeled visits by combining daily parking counts with other factors that help explain variations in attendance, including weather, day of the week or point within a season, and physical differences in sites (Kreitler et al. 2013). We designed the resulting model to estimate visitation for uncounted days as well as for beaches without counts on a given day. When combined with estimates of value per day, the visitation model can be used to value a lost beach day while accounting for beach size, time of season, and other factors...Since our count data of visitation for all four beaches are relatively large numbers (mean = 490, SD = 440), we used a log-linear regression model as opposed to a count data model. We selected a random effects model to account for time invariant variables such as parking spaces, modeling differences across beaches based on this variable…" Equation 2, page 15, provides the econometric regression. | ABSTRACT: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…" | ABSTRACT: "The Invertebrate Status Index is a multimetric index that was derived for the NAWQA Program to provide a simple national characterization of benthic invertebrate communities. This index— referred to here as the National Invertebrate Community Ranking Index (NICRI)—provides a simple method of placing community conditions within the context of all sites sampled by the NAWQA Program. The multimetric index approach is the most commonly used method of characterizing biological conditions within the U.S. (Barbour and others, 1999). Using this approach, communities may be compared by considering how individual metrics vary among sites or by combining individual metrics into a single composite (i.e., multimetric) index and examining how this single index varies among sites. Combining metrics into a single multimetric index simplifies the presentation of results (Barbour and others, 1999) and minimizes weaknesses that may be associated with individual metrics (Ohio EPA, 1987a,b). The NICRI is a multimetric index that combines 11 metrics (RICH, EPTR, CG_R, PR_R, EPTRP, CHRP, V2DOMP, EPATOLR, EPATOLA, DIVSHAN, and EVEN; Table 1) into a single, nationally consistent, composite index. The NICRI was used to rank 140 sites of the FY94 group of study units, with median values used for sites where data were available for multiple reaches and(or) multiple years. Average metric scores were then rescaled using the PERCENTRANK function and multiplied by 100 to produce a final NICRI score that ranged from 0 (low ranking relative to other NAWQA Program sites and presumably diminished community conditions) to 100 (high ranking relative to other NAWQA Program sites and presumably excellent community conditions). " | ABSTRACT: "Introduction: Prior studies suggest that exposure to the natural environment may impact health. The present study examines the association between objective measures of block-level greenness (vegetative presence) and chronic medical conditions, including cardiometabolic conditions, in a large population-based sample of Medicare beneficiaries in Miami-Dade County, Florida. Methods: The sample included 249,405 Medicare beneficiaries aged >=65 years whose location (ZIP+4) within Miami-Dade County, Florida, did not change, from 2010 to 2011. Data were obtained in 2013 and multilevel analyses conducted in 2014 to examine relationships between greenness, measured by mean Normalized Difference Vegetation Index from satellite imagery at the Census block level, and chronic health conditions in 2011, adjusting for neighborhood median household income, individual age, gender, race, and ethnicity. Results: Higher greenness was significantly associated with better health, adjusting for covariates: An increase in mean block-level Normalized Difference Vegetation Index from 1 SD less to 1 SD more than the mean was associated with 49 fewer chronic conditions per 1,000 individuals, which is approximately similar to a reduction in age of the overall study population by 3 years. This same level of increase in mean Normalized Difference Vegetation Index was associated with a reduced risk of diabetes by 14%, hypertension by 13%, and hyperlipidemia by 10%. Planned post-hoc analyses revealed stronger and more consistently positive relationships between greenness and health in lower- than higher-income neighborhoods. Conclusions: Greenness or vegetative presence may be effective in promoting health in older populations, particularly in poor neighborhoods, possibly due to increased time outdoors, physical activity, or stress mitigation." | ABSTRACT: "This document describes the conceptual framework underpinning the use of VELMA 2.1 to model fate and transport of organic contaminants within watersheds. We review how VELMA 2.1 simulates contaminant fate and transport within soils and hillslopes as a function of two processes: (1) the partitioning of the total amount of a contaminant between sorbed (immobile) and aqueous (mobile) phases; and (2) the vertical and lateral transport of the contaminant’s aqueous phase within surface and subsurface waters." | IPaC is a project planning tool that streamlines the USFWS environmental review process. Explores species and habitat: See if any listed species, critical habitat, migratory birds or other natural resources may be impacted by your project. Using the map tool, explore other resources in your location, such as wetlands, wildlife refuges, GAP land cover, and other important biological resources. Conduct a regulatory review: Log in and define a project to get an official species list and evaluate potential impacts on resources managed by the U.S. Fish and Wildlife Service. Follow IPaC's Endangered Species Act (ESA) Review process—a streamlined, step-by-step consultation process available in select areas for certain project types, agencies, and species. Build a Consultation Package: Consultation Package Builder (CPB) replaces and improves on the original Impact Analysis by providing an interactive, step-by-step process to help you prepare a full consultation package leveraging U.S. Fish and Wildlife Service data and recommendations, including conservation measures designed to help you avoid or minimize effects to listed species. | CommunityViz® is an advanced yet easy-to-use GIS software extension that is designed to help people visualize, analyze, and communicate about important planning decisions. Widely adopted by land-use planners, it supports informed, collaborative decision-making by illustrating and analyzing alternative planning scenarios. It features flexible and interactive analysis tools, a rich set of presentation tools, and several options for 3D visualization of future places. In the land-sea toolkit, CommunityViz (sometimes referred to as “Cviz”) serves as the platform for creating land use scenarios. It models how urban growth could occur over time as the result of present-day decisions regarding land use and regulation. The resulting future growth conditions are passed to NatureServe Vista (Vista) and NSPECT for impact assessment, and those results can be returned to CommunityViz for display and for guidance in development of revisions to planning scenarios. Throughout the integration process, CommunityViz provides the ability to assess a variety of socio-economic indicators attached to the land-use scenarios. |
Specific Policy or Decision Context Cited
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None identified | None identified | Land use change | Not applicable | None identified | None identified | None identified | None identified | None identified | None identified | None identified | Land management trade off between agricultural productivity and water quality | None identified | To assess the number of people who would be impacted by beach closures. | None identified | None Identified | None identified | None identified | Determination of Effects on ESA listed taxa. | None provided |
Biophysical Context
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Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes | No additional description provided | mangrove forest,Salt marsh, estuary, sea level, | Not applicable | Mean elevation of 266 m, with southwestern mountainous area. Subtropical monsoon climate. Annual average temperature of 12.2-15.6 °C. Annual mean precipitation is 1500 mm, and over 70% of rainfall occurs in the flood season (Apr-Oct). | 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. | Estuarine environments and marsh-land interfaces | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | Prairie pothole region of north-central Iowa | Prairie pothole region of north-central Iowa | Groundwater dominated, volcanic caldera catchment, largely comprised of porous allophanic and pumice soils. | None identified | Four separate beaches within the community of Barnstable | Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility. | Streams and Rivers | No additional description provided | No additional description provided | N/A | Not applicable |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | Future land use and land cover; climate change | Not applicable | No scenarios presented | No scenarios presented | Stand age; old-growth (pre-harvest), and harvested (postharvest) | Shellfish type; Changes to submerged aquatic vegetation (SAV) | 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 | N/A | No scenarios presented | Additional benefits due to biodiversity restoration practices | N/A | No scenarios presented | No scenarios presented | N/A | No scenarios presented |
EM ID
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EM-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method Only | Method + Application | Method + Application (multiple runs exist) View EM Runs |
Method + Application (multiple runs exist) View EM Runs ?Comment:Ten runs; blue crab and penaeid shrimp, each combined with five different submerged aquatic vegetation habitat areas. |
Method + Application (multiple runs exist) | 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 | Method Only | Method Only | Method Only |
New or Pre-existing EM?
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New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | Application of existing model | New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
EM Temporal Extent
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2007-2009 | Not reported | 2005-7; 2035-45 | 1990-2010 | Not applicable | 2003-2007 | 1969-2008 | 1950 - 2050 | 1950-2071 | 2002-2007 | 1992-2007 | 1930-2013 | Summer 2017 | 2011 - 2016 | 1990-2007 | 1991-1994 | 2010-2011 | Not applicable | Not applicable | Not applicable |
EM Time Dependence
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time-stationary | time-stationary | time-stationary | time-dependent | Not applicable | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | future time | both | Not applicable | Not applicable | Not applicable | past time | past time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable | Not applicable | continuous | Not applicable | Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | other or unclear (comment) |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Varies by Run | 1 | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Day | Year | Month | Not applicable | Not applicable | Not applicable | Day | Day | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable |
EM ID
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EM-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
Bounding Type
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Physiographic or Ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Physiographic or Ecological | Not applicable | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Other | Other | Geopolitical | Not applicable | Not applicable | Not applicable |
Spatial Extent Name
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Central French Alps | Central French Alps | Hood Canal | Tampa Bay | Not applicable | Xitiaoxi River basin | H. J. Andrews LTER WS10 | Gulf of Mexico (estuarine and coastal) | Santa Basin | CREP (Conservation Reserve Enhancement Program) wetland sites | CREP (Conservation Reserve Enhancement Program) wetland sites | Lake Rotorua catchment | Three Bays, Cape Cod | Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach) | Colt Park meadows, Ingleborough National Nature Reserve, northern England | Not applicable | Miami-Dade County | Not applicable | Not applicable | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | Not applicable | 1000-10,000 km^2. | 10-100 ha | 10,000-100,000 km^2 | 10,000-100,000 km^2 | 1-10 km^2 | 1-10 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 10-100 ha | <1 ha | Not applicable | 1000-10,000 km^2. | Not applicable | Not applicable | Not applicable |
EM ID
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EM-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
EM Spatial Distribution
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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) | Not applicable | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Computations at this pixel scale pertain to certain variables specific to Mobile Bay. |
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) | other or unclear (comment) |
Spatial Grain Type
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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 | Not applicable | 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 | 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 | length, for linear feature (e.g., stream mile) | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | volume, for 3-D feature | Not applicable | Not applicable |
Spatial Grain Size
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20 m x 20 m | 20 m x 20 m | 30 m x 30 m | m^2 | Not applicable | Not reported | 30 m x 30 m surface pixel and 2-m depth soil column | 55.2 km^2 | 1 km2 | multiple, individual, irregular shaped sites | multiple, individual, irregular shaped sites | 5m x 5m | beach length | by beach site | 3 m x 3 m | stream reach | Census block | user defined | Not applicable | Not applicable |
EM ID
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EM-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
EM Computational Approach
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Analytic | Analytic | Other or unclear (comment) | Analytic | Analytic | Analytic | Numeric | Numeric | * | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Other or unclear (comment) | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | Not applicable | deterministic |
Statistical Estimation of EM
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None |
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EM ID
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EM-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
Model Calibration Reported?
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No | No | Yes | No | Not applicable | Yes | Yes | Yes | No | Unclear | Unclear | No | Yes | Yes | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | No | No | No | Not applicable | No | Yes | No | No | No | No | No | No | No | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None | None | None |
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None | None | None | None | None | None | None | None | None | None | None | None | None |
Model Operational Validation Reported?
em.detail.validationHelp
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Yes | No | Yes | No | No | No | No | No | Yes | Unclear | Unclear | No | No | No | No | No | No | Not applicable | Not applicable | Not applicable |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | Yes | Not applicable | No | No | No | No | No | No | No | No | No | No | Yes | No | Not applicable | Not applicable | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | Yes | Yes | Not applicable | Yes | No | No | No | No | No | No | No | Yes | No | Yes | No | Not applicable | Not applicable | Not applicable |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | No | No | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Yes | 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-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
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None |
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None |
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None |
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Comment:No specific location but developed in United States |
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None | None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
None | None |
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Comment:Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian |
None | None | None |
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None | None | None | None |
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None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 45.05 | 47.8 | 27.8 | -9999 | 30.55 | 44.15 | 30.44 | -9.05 | 42.62 | 42.62 | -38.14 | 41.62 | 41.64 | 54.2 | Not applicable | 25.64 | Not applicable | Not applicable | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | 6.4 | -122.7 | -82.4 | -9999 | 119.5 | -122.2 | -87.99 | -77.81 | -93.84 | -93.84 | 176.25 | -70.42 | -70.29 | -2.35 | Not applicable | -80.5 | Not applicable | Not applicable | Not applicable |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | Not applicable | Not applicable | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Provided | Estimated | Estimated | Not applicable | Provided | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Not applicable | Estimated | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Ground Water | Forests | Near Coastal Marine and Estuarine | None | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Ground Water | Forests | Agroecosystems | Scrubland/Shrubland | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Grasslands | Rivers and Streams | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Created Greenspace | Grasslands | Scrubland/Shrubland | Barren | Tundra | Ice and Snow | Atmosphere | Not applicable |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows. | Subalpine terraces, grasslands, and meadows. | glacier-carved saltwater fjord | Created Mangrove wetlands | Not applicable | Watershed | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Submerged aquatic vegetation in estuaries and coastal lagoons | tropical, coastal to montane | Wetlands buffered by grassland set in agricultural land | Grassland buffering inland wetlands set in agricultural land | Largely agricultural, commercial forestry, non-commercial forest and shrubland and urban | Beaches | Saltwater beach | fertilized grassland (historically hayed) | benthic habitat | urban neighborhood greenspace | Terrestrial environment | None | None |
EM Ecological Scale
em.detail.ecoScaleHelp
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Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Other or unclear (comment) | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Community | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Species | Not applicable | Species | Species | Not applicable | Not applicable | Not applicable | Community |
Other (Comment) ?Comment:Community metrics of tolerance, food groups, sensitivity, taxa richness, diversity |
Not applicable | Not applicable |
Other (Comment) ?Comment:ESA designations include species and Ecological Significan Units of species |
Community |
Taxonomic level and name of organisms or groups identified
EM-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
None Available | None Available | None Available |
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None Available | None Available | None Available |
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None Available |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
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-69 | EM-80 |
EM-112 ![]() |
EM-154 | EM-337 | EM-344 |
EM-375 ![]() |
EM-397 ![]() |
EM-630 |
EM-632 ![]() |
EM-650 | EM-659 | EM-683 | EM-684 |
EM-735 ![]() |
EM-848 | EM-876 | EM-892 | EM-967 | EM-1005 |
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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None | None |
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None |
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None |