EcoService Models Library (ESML)
loading
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
em.detail.idHelp
?
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
|
EM Short Name
em.detail.shortNameHelp
?
|
EnviroAtlas-Nat. filtration-water | EnviroAtlas - Natural biological nitrogen fixation | Fodder crude protein content, Central French Alps | Pollination ES, Central French Alps | ACRU, South Africa | Birds in estuary habitats, Yaquina Estuary, WA, USA | i-Tree Hydro v4.0 | Mangrove development, Tampa Bay, FL, USA | Urban Temperature, Baltimore, MD, USA | InVEST water yield, Xitiaoxi River basin, China | Wetland shellfish production, Gulf of Mexico, USA | InVESTv3.0 Nutrient retention, Guánica Bay | Coastal protection in Belize | WaterWorld v2, Santa Basin, Peru | Estuary recreational use, Cape Cod, MA | Floral resources on landfill sites, United Kingdom | Northern Shoveler recruits, CREP wetlands, IA, USA | Pollinators on landfill sites, United Kingdom | C sequestration in grassland restoration, England |
|
EM Full Name
em.detail.fullNameHelp
?
|
US EPA EnviroAtlas - Natural filtration (of water by tree cover); Example is shown for Durham NC and vicinity, USA | US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Fodder crude protein content, Central French Alps | Pollination ecosystem service estimated from plant functional traits, Central French Alps | ACRU (Agricultural Catchments Research Unit), South Africa | Bird use of estuarine habitats, Yaquina Estuary, WA, USA | i-Tree Hydro v4.0 (default data option) | Mangrove wetland development, Tampa Bay, FL, USA | Urban Air Temperature Change, Baltimore, MD, USA | InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) water yield, Xitiaoxi River basin, China | Wetland shellfish production, Gulf of Mexico, USA | InVEST (Integrated Valuation of Environmental Services and Tradeoffs)v3.0 Nutrient retention, Guánica Bay, Puerto Rico, USA | Coastal Protection provided by Coral, Seagrasses and Mangroves in Belize: | WaterWorld v2, Santa Basin, Peru | Estuary recreational use, Cape Cod, MA | Floral resources on landfill sites, East Midlands, United Kingdom | Northern Shoveler duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Pollinating insects on landfill sites, East Midlands, United Kingdon | Carbon sequestration in grassland diversity restoration, England |
|
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
US EPA | EnviroAtlas | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | US EPA | i-Tree | USDA Forest Service | US EPA | i-Tree | USDA Forest Service | InVEST |
US EPA ?Comment:Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science |
US EPA | InVEST | InVEST | None | US EPA | None | None | None | None |
|
EM Source Document ID
|
223 |
262 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
260 | 260 | 271 | 275 | 198 | 97 | 217 | 307 | 324 | 338 | 350 | 368 | 387 | 389 |
372 ?Comment:Document 373 is a secondary source for this EM. |
389 | 396 |
|
Document Author
em.detail.documentAuthorHelp
?
|
US EPA Office of Research and Development - National Exposure Research Laboratory | US EPA Office of Research and Development - National Exposure Research Laboratory | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Frazier, M. R., Lamberson, J. O. and Nelson, W. G. | USDA Forest Service | 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. | Heisler, G. M., Ellis, A., Nowak, D. and Yesilonis, I. | Zhang C., Li, W., Zhang, B., and Liu, M. | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Guannel, G., Arkema, K., Ruggiero, P., and G. Verutes | Van Soesbergen, A. and M. Mulligan | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | 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 | Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin | 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 |
|
Document Year
em.detail.documentYearHelp
?
|
2013 | 2013 | 2011 | 2011 | 2008 | 2014 | Not Reported | 2012 | 2016 | 2012 | 2012 | 2017 | 2016 | 2018 | 2019 | 2013 | 2010 | 2013 | 2011 |
|
Document Title
em.detail.sourceIdHelp
?
|
EnviroAtlas - Featured Community | EnviroAtlas - National | 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 | Mapping ecosystem services for planning and management | Intertidal habitat utilization patterns of birds in a Northeast Pacific estuary | i-Tree Hydro User's Manual v. 4.0 | Ecosystem development after mangrove wetland creation: plant–soil change across a 20-year chronosequence | Modeling and imaging land-cover influences on air-temperature in and near Baltimore, MD | Water yield of Xitiaoxi River basin based on InVEST modeling | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | The Power of Three: Coral Reefs, Seagrasses and Mangroves Protect Coastal Regions and Increase Their Resilience | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects | Additional carbon sequestration benefits of grassland diversity restoration |
|
Document Status
em.detail.statusCategoryHelp
?
|
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 but unpublished (explain in Comment) | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published |
|
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published on US EPA EnviroAtlas website | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Webpage | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Draft manuscript-work progressing | Published journal manuscript | Published report | Published journal manuscript | Published journal manuscript |
|
EM ID
em.detail.idHelp
?
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
| https://www.epa.gov/enviroatlas | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | http://www.itreetools.org | Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | Not applicable | http://www.naturalcapitalproject.org/invest/ | Not identified in paper | www.policysupport.org/waterworld | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | |
|
Contact Name
em.detail.contactNameHelp
?
|
EnviroAtlas Team |
EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Sandra Lavorel | Sandra Lavorel | Roland E Schulze |
M. R. Frazier ?Comment:Present address: M. R. Frazier National Center for Ecological Analysis and Synthesis, 735 State St. Suite 300, Santa Barbara, CA 93101, USA |
Not applicable | Michael Osland | Gordon M. Heisler | Li Wenhua | Stephen J. Jordan | Susan H. Yee | Greg Guannel | Arnout van Soesbergen | Mulvaney, Kate | Sam Tarrant | David Otis | Sam Tarrant | Gerlinde B. De Deyn |
|
Contact Address
|
Not reported | Not reported | 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 | School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa | Western Ecology Division, Office of Research and Development, U.S. Environmental Protection Agency, Pacific coastal Ecology Branch, 2111 SE marine Science Drive, Newport, OR 97365 | Not applicable | U.S. Environmental Protection Agency, Gulf Ecology Division, gulf Breeze, FL 32561 | 5 Moon Library, c/o SUNY-ESF, Syracuse, NY 13210 | Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China | U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL 32561, USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | The Nature Conservancy, Coral Gables, FL. USA | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K. | Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands |
|
Contact Email
|
enviroatlas@epa.gov | enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | schulzeR@nu.ac.za | frazier@nceas.ucsb.edu | Not applicable | mosland@usgs.gov | gheisler@fs.fed.us | liwh@igsnrr.ac.cn | jordan.steve@epa.gov | yee.susan@epa.gov | greg.guannel@gmail.com | arnout.van_soesbergen@kcl.ac.uk | Mulvaney.Kate@epa.gov | sam.tarrant@rspb.org.uk | dotis@iastate.edu | sam.tarrant@rspb.org.uk | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com |
|
EM ID
em.detail.idHelp
?
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
|
Summary Description
em.detail.summaryDescriptionHelp
?
|
The Natural Filtration model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. METADATA ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina... runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA DESCRIPTION: "The i-Tree Hydro model estimates the effects of tree and impervious cover on hourly stream flow values for a watershed (Wang et al 2008). i-Tree Hydro also estimates changes in water quality using hourly runoff estimates and mean and median national event mean concentration (EMC) values. The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results… After calibration, the model was run a number of times under various conditions to see how the stream flow would respond given varying tree and impervious cover in the watershed… The term event mean concentration (EMC) is a statistical parameter used to represent the flow-proportional average concentration of a given parameter during a storm event. EMC data is used for estimating pollutant loading into watersheds. The response outputs were calculated as kg of pollutant per square meter of land area for pollutants. These per square meter values were multiplied by the square meters of land area in the block group to estimate the effects at the block group level." METADATA DESCRIPTION PARAPHRASED: Changes in water quality were estimated for the following pollutants (entered as separate runs); total suspended solids (TSS), total phosphorus, soluble phosphorus, nitrites and nitrates, total Kjeldahl nitrogen (TKN), biochemical oxygen demand (BOD5), chemical oxygen demand (COD5), and copper. "Reduction in annual runoff (census block group)" variable data was derived from the EnviroAtlas water recharge coverage which used the i-Tree Hydro model. | DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., fodder crude protein content), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in fodder crude protein content 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…Fodder crude protein 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 fodder protein content. 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 pollination ecosystem service map was a simple sums 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 pollination ecosystem services are based on stakeholders’ perceptions, given positive (+1) or negative (-1) contributions." | AUTHOR'S DESCRIPTION (Doc ID 272): "ACRU is a daily timestep, physical conceptual and multipurpose model structured to simulate impacts of land cover/ use change. The model can output, inter alia, components of runoff, irrigation supply and demand, reservoir water budgets as well as sediment and crop yields." AUTHOR'S DESCRIPTION (Doc ID 271): "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…The total benefit to people of water supply is a function of both the quantity and quality with the ecosystem playing a key role in the latter. However, due to the lack of suitable national scale data on water quality for quantifying the service, runoff was used as an estimate of the benefit where runoff is the total water yield from a watershed including surface and subsurface flow. This assumes that runoff is positively correlated with quality, which is the case in South Africa (Allanson et al., 1990)…In South Africa, water resources are mapped in water management areas called catchments (vs. watersheds) where a catchment is defined as the area of land that is drained by a single river system, including its tributaries (DWAF, 2004). There are 1946 quaternary (4th order) catchments in South Africa, the smallest is 4800 ha and the average size is 65,000 ha. Schulze (1997) modelled annual runoff for each quaternary catchment. During modelling of runoff, he used rainfall data collected over a period of more than 30 years, as well as data on other climatic factors, soil characteristics and grassland as the land cover. In this study, median annual simulated runoff was used as a measure of surface water supply. The volume of runoff per quaternary catchment was calculated for surface water supply. The range (areas with runoff of 30 million m^3 or more) and hotspots (areas with runoff of 70 million m^3 or more) were defined using a combination of statistics and expert inputs due to a lack of published thresholds in the literature." | AUTHOR'S DESCRIPTION: "To describe bird utilization patterns of intertidal habitats within Yaquina estuary, Oregon, we conducted censuses to obtain bird species and abundance data for the five dominant estuarine intertidal habitats: Zostera marina (eelgrass), Upogebia (mud shrimp)/ mudflat, Neotrypaea (ghost shrimp)/sandflat, Zostera japonica (Japanese eelgrass), and low marsh. EPFs were developed for the following metrics of bird use: standardized species richness; Shannon diversity; and density for the following four groups: all birds, all birds excluding gulls, waterfowl (ducks and geese), and shorebirds." | ABSTRACT: "i-Tree Hydro is the first urban hydrology model that is specifically designed to model vegetation effects and to be calibrated against measured stream flow data. It is designed to model the effects of changes in urban tree cover and impervious surfaces on hourly stream flows and water quality at the watershed level." AUTHOR'S DESCRIPTION: "The purpose of i-Tree Hydro is to simulate hourly changes in stream flow (and water quality) given changes in tree and impervious cover in the watershed. The following is an overview of the process: 1) Determine your watershed of analysis and stream gauge station. i-Tree Hydro works on a watershed basis with the watershed determined as the total drainage area upstream from a measured stream gauge. Stream gauge availability varies. 2) Download national digital elevation data. Once the area and location of the watershed are known, digital elevation data are downloaded from the USGS for an area that encompasses the entire watershed. ArcGIS software is then used to create a digital elevation map and to determine the exact boundary for the watershed upstream from the gauge station location. 3) Determine cover attributes of the watershed and gather other required data. i-Tree Canopy and other sources can be used to determine the tree cover, shrub cover, impervious surface and other cover types. Information about other aspects of the watershed such as proportion of evergreen trees and shrubs, leaf area index, and a variety of hydrologic parameters must be collected. 4) Get started with Hydro. Once these input data are ready, they are loaded into Hydro to begin analysis. 5) Calibrate the model. The Hydro model contains an auto-calibration routine that tries to find the best fit between the stream flow predicted by the model and the stream flow measured at the stream gauge station given the various inputs. The model can also be manually calibrated to improve the fit by changing the parameters as needed. 6) Model new scenarios: Once the model is properly calibrated, tree and impervious cover parameters can be changed to illustrate the impact on stream flow and water quality." | 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." | An empirical model for predicting below-canopy air temperature differences is developed for evaluating urban structural and vegetation influences on air temperature in and near Baltimore, MD. AUTHOR'S DESCRIPTION: "The study . . . Developed an equation for predicting air temperature at the 1.5m height as temperature difference, T, between a reference weather station and other stations in a variety of land uses. Predictor variables were derived from differences in land cover and topography along with forcing atmospheric conditions. The model method was empirical multiple linear regression analysis.. . Independent variables included remotely sensed tree cover, impervious cover, water cover, descriptors of topography, an index of thermal stability, vapor pressure deficit, and antecedent precipitation." | 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." | 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." | 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: "Nutrient retention was estimated by first calculating water yield and establishing the quantity of nitrogen or phosphorus retained by different land cover classes using a water purification model (InVEST 3.0.0; Tallis et al., 2013). Different land cover classes were assumed to have different capacities for retaining nutrients, depending on the efficiency of vegetation in removing either nitrogen or phosphorus and the rates of nitrogen or phosphorus loading." “Use of other models in conjunction with this model:Average runoff per pixel modeled here were derived from the InVEST Water Yield model" | AUTHOR'S DESCRIPTION: "Natural habitats have the ability to protect coastal communities against the impacts of waves and storms, yet it is unclear how different habitats complement each other to reduce those impacts. Here, we investigate the individual and combined coastal protection services supplied by live corals on reefs, seagrass meadows, and mangrove forests during both non-storm and storm conditions, and under present and future sea-level conditions. Using idealized profiles of fringing and barrier reefs, we quantify the services supplied by these habitats using various metrics of inundation and erosion. We find that, together, live corals, seagrasses, and mangroves supply more protection services than any individual habitat or any combination of two habitats. Specifically, we find that, while mangroves are the most effective at protecting the coast under non-storm and storm conditions, live corals and seagrasses also moderate the impact of waves and storms, thereby further reducing the vulnerability of coastal regions. Also, in addition to structural differences, the amount of service supplied by habitats in our analysis is highly dependent on the geomorphic setting, habitat location and forcing conditions: live corals in the fringing reef profile supply more protection services than seagrasses; seagrasses in the barrier reef profile supply more protection services than live corals; and seagrasses, in our simulations, can even compensate for the long-term degradation of the barrier reef. Results of this study demonstrate the importance of taking integrated and place-based approaches when quantifying and managing for the coastal protection services supplied by ecosystems." | 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: "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 model focused on the various use by access point type (beaches, landings and way to water, boat use). This work can be transferred to the many small coastal access points used for recreation across New England and beyond." ] | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level… A “floral cover” method to represent available floral resources was used which combines floral abundance with inflorescence size. Mean area of the floral unit from above was measured for each flowering plant species and then multiplied by their frequencies." "Insect pollinated flowering plant species composition and floral abundance between sites by type were represented by non-metric multidimensional scaling (NMDS)...This method is sensitive to showing outliers and the distance between points shows the relative similarity (McCune & Grace 2002; Ollerton et al. 2009)." (This data is not entered into ESML) | ABSTRACT: "Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2*R*n where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = H*Z*B/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632). | ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each | 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…" |
|
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | None Identified | None identified | None identified | None identified | None identified | None identified | Not applicable | None identified | None identified | None identified | Improving water quality | Future rock lobster fisheries management | None identified | None identified | None identified | None identified | None identified | None identified |
|
Biophysical Context
|
No additional description provided | No additional description provided | Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Estuarine intertidal, eelgrass, mudflat, sandflat and low marsh | No additional description provided | mangrove forest,Salt marsh, estuary, sea level, | One airport site, one urban site, one site in deciduous leaf litter, and four sites in short grass ground cover. Measured sky view percentages ranged from 6% at the woods site, to 96% at the rural open site. | 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). | Estuarine environments and marsh-land interfaces | No additional description provided | barrier reef and fringing reef in nearshore coastal marine system | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | None identified | No additional description provided | Prairie Pothole Region of Iowa | No additional description provided | Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility. |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Not applicable | No scenarios presented | No scenarios presented | Shellfish type; Changes to submerged aquatic vegetation (SAV) | No scenarios presented | Reef type, Sea level increase, storm conditions, seagrass conditions, coral conditions, vegetation types and conditions | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | N/A | No scenarios presented | No scenarios presented | No scenarios presented | Additional benefits due to biodiversity restoration practices |
|
EM ID
em.detail.idHelp
?
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method + Application | Method + Application |
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 | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | Application of existing model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
|
EM ID
em.detail.idHelp
?
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-198 |
Doc-346 | Doc-347 ?Comment:EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM. |
Doc-260 | Doc-269 | Doc-260 |
Doc-272 ?Comment:Doc ID 272 was also used as a source document for this EM |
None | Doc-190 | Doc-223 | None | Doc-220 | Doc-219 | Doc-218 | Doc-280 | Doc-311 | Doc-338 | Doc-205 | None | Doc-309 | Doc-205 | None | None | None | None | Doc-372 | Doc-373 | Doc-389 | None |
|
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
EM-137 | EM-142 | None | EM-65 | EM-66 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-83 | None | None | EM-109 | EM-142 | EM-51 | None | None | EM-148 | EM-368 | EM-437 | EM-111 | EM-604 | EM-603 | EM-363 | EM-112 | None | None | EM-682 | EM-684 | EM-685 | EM-709 | EM-705 | EM-704 | EM-703 | EM-701 | EM-700 | EM-632 | EM-697 | None |
EM Modeling Approach
|
EM ID
em.detail.idHelp
?
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
1999-2010 | 2006-2010 | 2007-2009 | Not reported | 1950-1993 | December 2007 - November 2008 | Not applicable | 1990-2010 | May 5-Sept 30 2006 | 2003-2007 | 1950 - 2050 | 1980 - 2013 | 2005-2013 | 1950-2071 | Summer 2017 | 2007-2008 | 1987-2007 | 2007-2008 | 1990-2007 |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, operated on an hourly timestep. The final annual flow parameter however is time stationary. |
time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-dependent | time-dependent | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | future time | future time | Not applicable | future time | other or unclear (comment) | Not applicable | both | past time | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | continuous | discrete | Not applicable | discrete | discrete | discrete | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | 1 | Not applicable | Varies by Run | 1 | 1 | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Hour | Not applicable | Hour | Not applicable | Year | Year | Second | Month | Day | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM ID
em.detail.idHelp
?
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
|
Bounding Type
em.detail.boundingTypeHelp
?
|
Geopolitical | Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Physiographic or ecological | Not applicable | Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Other |
|
Spatial Extent Name
em.detail.extentNameHelp
?
|
Durham, NC and vicinity | counterminous United States | Central French Alps | Central French Alps | South Africa | Yaquina Estuary (intertidal), Oregon, USA | Not applicable | Tampa Bay | Baltimore, MD | Xitiaoxi River basin | Gulf of Mexico (estuarine and coastal) | Guanica Bay Study Area | Coast of Belize | Santa Basin | Three Bays, Cape Cod | East Midlands | CREP (Conservation Reserve Enhancement Program | East Midlands | Colt Park meadows, Ingleborough National Nature Reserve, northern England |
|
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
100-1000 km^2 | >1,000,000 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 1-10 km^2 | Not applicable | 100-1000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 10,000-100,000 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 10,000-100,000 km^2 | 1000-10,000 km^2. | 1000-10,000 km^2. | 10,000-100,000 km^2 | 1000-10,000 km^2. | <1 ha |
|
EM ID
em.detail.idHelp
?
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Watersheds (12-digit HUCs). |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | 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) ?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) |
|
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
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 | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (habitat type) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | length, for linear feature (e.g., stream mile) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
|
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
irregular | irregular | 20 m x 20 m | 20 m x 20 m | Distributed by catchments with average size of 65,000 ha | 0.87-104.29 ha | 30 x 30 m | m^2 | 10m x 10m | Not reported | 55.2 km^2 | 30 m x 30 m | 1 meter | 1 km2 | beach length | multiple unrelated locations | multiple, individual, irregular sites | multiple unrelated locations | 3 m x 3 m |
|
EM ID
em.detail.idHelp
?
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic ?Comment:The underlying i-Tree Hydro model, used to generate the annual flows for which EMCs were ultimately applied, was numeric. The final parameter however did not require iteration. |
Analytic | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | * | Numeric | Analytic | Analytic | Analytic | Analytic |
|
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic |
|
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
|
|
|
|
|
|
|
|
|
None |
|
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
|
Model Calibration Reported?
em.detail.calibrationHelp
?
|
Unclear | No | No | No | No | Unclear | Not applicable | No | Yes | Yes | Yes | No | No | No | Yes | Not applicable | Unclear | Not applicable | Not applicable |
|
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | No | Yes | No | No | No | Not applicable | No | Yes | No | No | No | No | No | No | Not applicable | No | Not applicable | Not applicable |
|
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None | None |
|
None | None | None | None | None |
|
None | None | None | None | None | None | None | None | None | None |
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
Unclear | No | Yes | No | No | No | Not applicable | No | No | No | No | No |
No ?Comment:Used the SWAN model (see below for referenece) with Generation 1 or 2 wind-wave formulations to validate the wave development portion of the model. Booij N, Ris RC, Holthuijsen LH. A third-generation wave model for coastal regions 1. Model description and validation. J Geophys Res. American Geophysical Union; 1999;104: 7649?7666. |
Yes | No | Not applicable | No | Not applicable | No |
|
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
Unclear | No | No | No | No | No | Not applicable | Yes | No | No | No | No | No | No | No | Not applicable | No | Not applicable | No |
|
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
Unclear | No | No | No | No | No | Not applicable | Yes | No | Yes | No | No | No | No | No | Not applicable | No | Not applicable | No |
|
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
|
|
|
|
|
|
None |
|
|
|
|
|
|
None | None |
|
|
|
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
| None | None | None | None | None |
|
None |
Comment:Realm: Tropical Atlantic Region: West Tropical Atlantic Province: Tropical Northwestern Atlantic Ecoregion: Floridian |
None | None |
|
None |
|
None |
|
None | None | None | None |
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
?
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
35.99 | 39.5 | 45.05 | 45.05 | -30 | 44.62 | -9999 | 27.8 | 39.28 | 30.55 | 30.44 | 17.97 | 18.63 | -9.05 | 41.62 | 52.22 | 42.62 | 52.22 | 54.2 |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
-78.96 | -98.35 | 6.4 | 6.4 | 25 | -124.06 | -9999 | -82.4 | -76.62 | 119.5 | -87.99 | -66.93 | -88.22 | -77.81 | -70.42 | -0.91 | -93.84 | -0.91 | -2.35 |
|
Centroid Datum
em.detail.datumHelp
?
|
None provided | WGS84 | WGS84 | WGS84 | WGS84 | None provided | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Estimated | Provided | Provided | Estimated | Provided | Not applicable | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided |
|
EM ID
em.detail.idHelp
?
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Rivers and Streams | Created Greenspace | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Created Greenspace | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Created Greenspace | Atmosphere | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Aquatic Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Open Ocean and Seas | Forests | Agroecosystems | Created Greenspace | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | None | Near Coastal Marine and Estuarine | Created Greenspace | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Created Greenspace | Grasslands | Agroecosystems | Grasslands |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Urban areas including streams | Terrestrial | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Not reported | Estuarine intertidal | Urban watersheds | Created Mangrove wetlands | Urban landscape and surrounding area | Watershed | Submerged aquatic vegetation in estuaries and coastal lagoons | 13 LULC were used | coral reefs | tropical, coastal to montane | Beaches | restored landfills and grasslands | Wetlands buffered by grassland within agroecosystems | restored landfills and grasslands | fertilized grassland (historically hayed) |
|
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale is coarser than that of 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 | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Other or unclear (comment) | Ecological scale 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 corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
|
EM ID
em.detail.idHelp
?
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Not applicable | Community | Community | Not applicable | Guild or Assemblage | Community | Not applicable | Not applicable | Not applicable | Species | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Individual or population, within a species | Individual or population, within a species | Individual or population, within a species | Community |
Taxonomic level and name of organisms or groups identified
|
EM-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
| None Available | None Available | None Available | None Available | None Available |
|
None Available |
|
None Available | None Available |
|
None Available | None Available | None Available | None Available |
|
|
|
None Available |
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-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
|
|
|
|
|
|
|
|
|
|
|
|
|
None |
|
|
|
|
|
<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-51 |
EM-63 | EM-68 | EM-82 | EM-84 | EM-103 | EM-137 | EM-154 | EM-306 | EM-344 |
EM-397 |
EM-438 |
EM-542 |
EM-630 |
EM-686 |
EM-697 |
EM-702 |
EM-709 |
EM-735 |
|
|
|
None |
|
|
|
|
|
|
|
None |
|
None |
|
None |
|
None | None |
Home
Search EMs
My
EMs
Learn about
ESML
Show Criteria
Hide Criteria