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-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
|
EM Short Name
em.detail.shortNameHelp
?
|
EnviroAtlas - Natural biological nitrogen fixation | Litter biomass production, Central French Alps | Fodder crude protein content, Central French Alps | Soil carbon content, Central French Alps | ACRU, South Africa | RHyME2, Upper Mississippi River basin, USA | AnnAGNPS, Kaskaskia River watershed, IL, USA | Landscape importance for crops, Europe | InVEST water yield, Hood Canal, WA, USA | KINEROS2, River Ravna watershed, Bulgaria | EnviroAtlas - Water recharge | Sed. denitrification, St. Louis River, MN/WI, USA | Wave energy attenuation, St. Croix, USVI | Reef dive site favorability, St. Croix, USVI | Nutrient Tracking Tool (NTT), north central Texas, USA | SolVES, Bridger-Teton NF, WY | Sedge Wren density, CREP, Iowa, USA | Estuary recreational use, Cape Cod, MA | Seed mix for native plant establishment, IA, USA |
|
EM Full Name
em.detail.fullNameHelp
?
|
US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA | Litter biomass production, Central French Alps | Fodder crude protein content, Central French Alps | Soil carbon content, Central French Alps | ACRU (Agricultural Catchments Research Unit), South Africa | RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA | AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA | Landscape importance for crop-based production, Europe | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) water yield, Hood Canal, WA, USA | KINEROS (Kinematic runoff and erosion model) v2, River Ravna watershed,Bulgaria | US EPA EnviroAtlas - Annual water recharge by tree cover; Example is shown for Durham NC and vicinity, USA | Sediment denitrification, St. Louis River estuary, Lake Superior, MN & WI, USA | Wave energy attenuation (by reef), St. Croix, USVI | Dive site favorability (reef), St. Croix, USVI | Nutrient Tracking Tool (NTT), Upper North Bosque River watershed, Texas, USA | SolVES, Social Values for Ecosystem Services, Bridger-Teton National Forest, WY | Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Estuary recreational use, Cape Cod, MA | Cost-effective seed mix design for native plant establishment, Iowa, USA |
|
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
US EPA | EnviroAtlas | EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | None | US EPA | US EPA | EU Biodiversity Action 5 | InVEST | EU Biodiversity Action 5 |
US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
US EPA | US EPA | US EPA | None | None | None | US EPA | None |
|
EM Source Document ID
|
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 | 260 | 271 | 123 | 137 | 228 | 205 |
248 ?Comment:Document 277 is also a source document for this EM |
223 ?Comment:Parameter default values used in the i-Tree Hydro model were obtained from the i-Tree website (Document ID 198, EM 137). |
333 | 335 | 335 | 354 | 369 | 372 | 387 | 394 |
|
Document Author
em.detail.documentAuthorHelp
?
|
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. | 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. | Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C. | Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M. | Haines-Young, R., Potschin, M. and Kienast, F. | 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. | Nedkov, S., Burkhard, B. | US EPA Office of Research and Development - National Exposure Research Laboratory | Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Saleh, A., O. Gallego, E. Osei, H. Lal, C. Gross, S. McKinney, and H. Cover | Sherrouse, B.C., Semmens, D.J., and J.M. Clement | 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 | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta | Meissen, J. |
|
Document Year
em.detail.documentYearHelp
?
|
2013 | 2011 | 2011 | 2011 | 2008 | 2013 | 2011 | 2012 | 2013 | 2012 | 2013 | 2014 | 2014 | 2014 | 2011 | 2014 | 2010 | 2019 | 2018 |
|
Document Title
em.detail.sourceIdHelp
?
|
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 | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping ecosystem services for planning and management | Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads | AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | EnviroAtlas - Featured Community | Sediment nitrification and denitrification in a Lake Superior estuary | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Nutrient Tracking Tool - a user-friendly tool for calculating nutrient reductions for water quality trading | An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA | Cost-effective seed mix design and first-year management |
|
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 and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) | Peer reviewed and published |
|
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Draft manuscript-work progressing | Published report |
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
| https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/ | Not applicable | https://www.naturalcapitalproject.org/invest/ | http://www.tucson.ars.ag.gov/agwa/ | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | Not applicable | http://ntt.tiaer.tarleton.edu/welcomes/new?locale=en | Not applicable | Not applicable | Not applicable | Not applicable | |
|
Contact Name
em.detail.contactNameHelp
?
|
EnviroAtlas Team ?Comment:Additional contact: Jana Compton, EPA |
Sandra Lavorel | Sandra Lavorel | Sandra Lavorel | Roland E Schulze | Liem Tran | Yongping Yuan | Marion Potschin | J.E. Toft | David C. Goodrich | EnviroAtlas Team | Brent J. Bellinger | Susan H. Yee | Susan H. Yee | Ali Saleh | Benson Sherrouse | David Otis | Mulvaney, Kate | Justin Meissen |
|
Contact Address
|
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 | 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 | Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA | U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA | USDA - ARS Southwest Watershed Research Center, 2000 E. Allen Rd., Tucson, AZ 85719 | Not reported | U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Texas Institute for Applied Environmental Research-Tarleton State University, Stephenville, TX 76401,USA | USGS, 5522 Research Park Dr., Baltimore, MD 21228, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI | Tallgrass Prairie Center, University of Northern Iowa |
|
Contact Email
|
enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | schulzeR@nu.ac.za | ltran1@utk.edu | yuan.yongping@epa.gov | marion.potschin@nottingham.ac.uk | jetoft@stanford.edu | agwa@tucson.ars.ag.gov | enviroatlas@epa.gov | bellinger.brent@epa.ogv | yee.susan@epa.gov | yee.susan@epa.gov | saleh@tiaer.tarleton.edu | bcsherrouse@usgs.gov | dotis@iastate.edu | Mulvaney.Kate@epa.gov | Not reported |
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
|
Summary Description
em.detail.summaryDescriptionHelp
?
|
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., litter biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in litter biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Litter biomass production 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 litter mass. 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. 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. 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." | 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." | ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin) | AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198" | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The methods are explored in relation to mapping and assessing … “Crop-based production” . . . The potential to deliver services is assumed to be influenced by (a) land-use, (b) net primary production, and (c) bioclimatic and landscape properties such as mountainous terrain." AUTHOR'S DESCRIPTION: "The analysis for "Crop-based production" maps all the areas that are important for food crops produced through commercial agriculture." | InVEST Water Yield and Scarcity 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 reparame-terized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)… We modelled discharge using the InVESTWater Yield and Scarcity model. The model estimates discharge for user-defined subwatersheds based on the average annual precipitation, annual reference evapotranspiration, and a correction factor for vegetation type, soil depth, plant available water content, land use and land cover, root depth, elevation, saturated hydraulic conductivity, and consumptive water use" (2) | ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. The use of the catchment based hydrologic model KINEROS and the GIS AGWA tool provided data about peak rivers’ flows and the capability of different land cover types to “capture” and regulate some parts of the water." AUTHOR'S DESCRIPTION: "KINEROS is a distributed, physically based, event model describing the processes of interception, dynamic infiltration, surface runoff and erosion from watersheds characterized by predominantly overland flow. The watershed is conceptualized as a cascade and the channels, over which the flow is routed in a top–down approach, are using a finite difference solution of the one-dimensional kinematic wave equations (Semmens et al., 2005). Rainfall excess, which leads to runoff, is defined as the difference between precipitation amount and interception and infiltration depth. The rate at which infiltration occurs is not constant but depends on the rainfall rate and the accumulated infiltration amount, or the available moisture condition of the soil. The AGWA tool is a multipurpose hydrologic analysis system addressed to: (1) provide a simple, direct and repeatable method for hydrologic modeling; (2) use basic, attainable GIS data; (3) be compatible with other geospatial basin-based environmental analysis software; and (4) be useful for scenario development and alternative future simulation work at multiple scales (Miller et al., 2002). AGWA provides the functionality to conduct the processes of modeling and assessment for…KINEROS." | The Water Recharge 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). The model was calibrated using hourly stream flow data to yield the best fit between model and measured stream flow results. Calibration coefficients (0-1 with 1.0 = perfect fit) were calculated for peak flow, base flow, and balance flow (peak and base). To estimate the effect of trees at the block group level for Durham, the Hydro model was run for: Gauging Station Name: SANDY CREEK AT CORNWALLIS RD NEAR DURHAM, NC, Gauging Station Location: 35°58'59.6",-78°57'24.5", Gauging Station Number: 0209722970. 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. To estimate block group effects, the block group was assumed to act similarly to the watershed in terms of hydrologic effects. To estimate the block group effect, the outputs of the watershed were determined for each possible combination of tree cover (0-100%) and impervious cover (0-100%). Thus, there were a total of 10,201 possible responses (101 x 101). For each block group, the percent tree cover and percent impervious cover combination (e.g., 30% tree / 20% impervious) was matched to the appropriate watershed hydrologic response output for that combination. The hydrologic response outputs were calculated as either percent change or absolute change in units of cubic meters of water per square meter of land area for water flow or 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. |
ABSTRACT: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature.We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones. In vegetated habitats, NIT and DeNIT rateswere highest in deep (ca. 2 m) water (249 and 2111 mg N m−2 d−1, respectively) and in the upper and lower reaches of the SLRE (N126 and 274 mg N m−2 d−1, respectively). Rates of DEA were similar among zones. In 2012, NIT, DeNIT, and DEA rateswere highest in July, May, and June, respectively. System-wide, we observed highest NIT (223 and 287 mgNm−2 d−1) and DeNIT (77 and 64 mgNm−2 d−1) rates in the harbor and from deep water, respectively. Amendment with NO3 − enhanced DeNIT rates more than carbon amendment; however, DeNIT and NIT rates were inversely related, suggesting the two processes are decoupled in sediments. Average proportion of N2O released during DEA (23–54%) was greater than from DeNIT (0–41%). Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates. A large flood occurred in 2012 which temporarily altered water chemistry and sediment nitrogen cycling." ?Comment:BH: I pasted the entire abstract because there is not specific mention of the combined sediment nitrification model. |
ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion, storm damage, or coastal inundation during extreme events (UNEP-WCMC (United Nations Environment Programme, World Conservation Monitoring Centre), 2006; WRI (World Resources Institute), 2009), but is often quantified as wave energy attenuation, an intermediate service that contributes to shoreline protection by reducing rates of erosion or coastal inundation (Principeet al., 2012)...The energy (attenuation) in a moving wave (E) can then be calculated by E = 1/8ρgH^2 where ρ is the density of seawater (1025 kg m^-3) and H is wave height (attenuation)." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…In lieu of surveys of diver opinion, recreational opportunities can also be estimated by actual field data of coral condition at preferred dive sites. A few studies have directly examined links between coral condition and production of recreational opportunities through field monitoring in an attempt to validate perceptions of recreational quality (Pendleton, 1994; Williams and Polunin, 2002; Leeworthy et al., 2004; Leujakand Ormond, 2007; Uyarraetal., 2009). Uyarraetal. (2009) used surveys to determine reef attributes related to diver perceptions of most and least favorite dive sites. Field data was used to narrow down the suite of potential preferred attributes to those that reflected actual site condition. We combined these attributes to form an index of dive site favorability: Dive site favorability = ΣipiRi where pi is the proportion of respondents indicating each attribute i that affected dive enjoyment positively. Ri is the mean relative magnitude of measured variables used to quantify each descriptive attribute i, including ‘fish abundance’ (pi=0.803), quantified by number of fish schools and fish species richness, and | ABSTRACT: "The Nutrient Tracking Tool (NTrT) is an enhanced version of the Nitrogen Trading Tool, a user-friendly Web-based computer program originally developed by the USDA. The NTrT estimates nutrient (nitrogen and phosphorus) and sediment losses from fields managed under a variety of cropping patterns and management practices through its user-friendly, Web-based linkage to the Agricultural Policy Environmental eXtender (APEX) model. It also accesses the USDA Natural Resources Conservation Service’s Web Soil Survey to utilize their geographic information system interface for field and operation identification and load soil information. The NTrT provides farmers, government officials, and other users with a fast and efficient method of estimating nitrogen and phosphorus credits for water quality trading, as well as other water quality, water quantity, and farm production impacts associated with conservation practices. The information obtained from the tool can help farmers determine the most cost-effective conservation practice alternatives for their individual operations and provide them with more advantageous options in a water quality credit trading program. An application of the NTrT to evaluate conservation practices on fields receiving dairy manure in a north central Texas watershed indicates that phosphorus-based application rates, filter strips, forest buffers, and complete manure export off the farm all result in reduced phosphorus losses from the fields on which those practices were implemented. When compared to a base¬line condition that entailed manure application at the nitrogen agronomic rate of receiving crops, the reductions in total phosphorus losses associated with these practices ranged from 15% (2P Rate scenario) to 76% (forest buffer scenario)." AUTHOR'S DESCRIPTION: "This paper provides a brief overview of the NTrT and presents results of verification and application of the tool on a selected field on a test field in the Upper North Bosque River (UNBR) watershed in Texas…simulations for the baseline and all five alternative scenarios were replicated for each of 90 specific soil types in Erath County, Texas…results reported and discussed in this report represent the averages of the output for all soil types." | [ABSTRACT: " "Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders.With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, socialvalue information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems. | 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: "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." ] | AUTHOR'S DESCRIPTION: "Restoring ecosystem services at scale requires executing conservation programs in a way that is resource and cost efficient as well as ecologically effective…Seed mix design is one of the largest determinants of project cost and ecological outcomes for prairie reconstructions. In particular, grass-to-forb seeding ratio affects cost since forb seed can be much more expensive relative to grass species (Prairie Moon Nursery 2012). Even for seed mixes with the same overall seeding rates, a mix with a low grass-to-forb seeding ratio is considerably more expensive than one with a high grass-to-forb ratio. Seeding rates for different plant functional groups that are too high or low may also adversely affect ecological outcomes…First-year management may also play a role in cost-effective prairie reconstruction. Post-agricultural sites where restoration typically occurs are often quickly dominated by fast-growing annual weeds by the time sown prairie seeds begin germinating (Smith et al. 2010)… Williams and others (2007) showed that prairie seedlings sown into established warm-season grasses were reliant on high light conditions created by frequently mowing tall vegetation in order to survive in subsequent years…Our objective was to compare native plant establishment and cost effectiveness with and without first-year mowing for three different seed mixes that differed in grass to forb ratio and soil type customization. With knowledge of plant establishment, cost effectiveness, and mowing management outcomes, conservation practitioners will be better equipped to restore prairie efficiently and successfully." |
|
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None Identified | None identified | None identified | None identified | None identified | Not reported | Not reported | None identified | Land use change | None identified | None identified | None identified | None identified | None identified | None identified | None | None identified | None identified | Seed mix design and management practices for native plant restoration |
|
Biophysical Context
|
No additional description provided | Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes | Elevation ranges from 1552 to 2442 m, predominantly on 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. | No additional description provided | Upper Mississipi River basin, elevation 142-194m, | No additional description provided | Not additional description provided | Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | Range of tree and impervious covers in urban setting | Estuarine system | No additional description provided | No additional description provided | The UNBR watershed is comprised primarily of two main physiographic areas, the West Cross Timbers and the Grand Prairie Land Resource Areas. In the West Cross Timbers, soils are primarily fine sandy loams with sandy clay subsoils. Soils in the Grand Prairie area, on the other hand, are typically calcareous clays and clay loams (Ward et al. 1992). | Rocky mountain conifer forests | Prairie pothole region of north-central Iowa | None identified | The soils underlying the study site are primarily poorly drained Clyde clay loams, with a minor component of somewhat poorly drained Floyd loams in the northwest (NRCS 2016). Topographically, the study site is level, and slopes do not exceed 5% grade. Land use prior to this experiment was agricultural, with corn and soybeans consistently grown in rotation at the site. |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target | No scenarios presented | Future land use and land cover; climate change | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | Conservation management strategies to reduce phosphorus losses | N/A | No scenarios presented | N/A | No scenarios presented |
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | 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
?
|
New or revised model | New or revised model | New or revised model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model |
Application of existing model ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model |
Application of existing model ?Comment:Models developed by Quamen (2007). |
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-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
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-260 | Doc-269 | Doc-260 | Doc-269 |
Doc-272 ?Comment:Doc ID 272 was also used as a source document for this EM |
Doc-123 | Doc-142 | Doc-231 | Doc-228 | Doc-280 | Doc-307 | Doc-311 | Doc-338 |
Doc-277 | Doc-294 | Doc-249 | Doc-250 ?Comment:Document 277 is also a source document for this EM |
Doc-198 ?Comment:Parameter default values used in the i-Tree Hydro model were obtained from the i-Tree website (Document ID 198, EM 137). |
None | Doc-335 | None | Doc-352 | None | Doc-372 | None | Doc-395 |
|
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
None | EM-65 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | 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-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | None | EM-119 | EM-120 | EM-121 | EM-162 | EM-164 | EM-165 | EM-122 | EM-123 | EM-124 | EM-125 | EM-166 | EM-170 | EM-171 | EM-148 | EM-344 | EM-368 | EM-437 | EM-132 | EM-133 | EM-137 | EM-51 | None | EM-447 | EM-449 | None | EM-549 | EM-629 | EM-626 | EM-652 | EM-651 | EM-649 | EM-648 | EM-682 | EM-684 | EM-685 | EM-728 |
EM Modeling Approach
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
2006-2010 | Not reported | 2007-2009 | 2007-2009 | 1950-1993 | 1987-1997 | 1980-2006 | 2000 | 2005-7; 2035-45 | Not reported | 2008-2010 | 2011 - 2012 | 2006-2007, 2010 | 2006-2007, 2010 | 1960-2001 | 2004-2008 | 1992-2007 | Summer 2017 | 2015-2017 |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | Not applicable | Not applicable | future time | Not applicable | Not applicable | past time | Not applicable |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | discrete |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not reported | Not applicable | Not applicable | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | 1 |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not reported | Not applicable | Not applicable | Not applicable | Not applicable | Day | Not applicable | Not applicable | Day | Year |
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
|
Bounding Type
em.detail.boundingTypeHelp
?
|
Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Physiographic or Ecological | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical | Multiple unrelated locations (e.g., meta-analysis) | Physiographic or ecological | Other |
|
Spatial Extent Name
em.detail.extentNameHelp
?
|
counterminous United States | Central French Alps | Central French Alps | Central French Alps | South Africa | Upper Mississippi River basin; St. Croix River Watershed | East Fork Kaskaskia River watershed basin | The EU-25 plus Switzerland and Norway | Hood Canal | River Ravna watershed | Durham, NC and vicinity | St. Louis River estuary | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Upper North Bosque River watershed | National Park | CREP (Conservation Reserve Enhancement Program) wetland sites | Three Bays, Cape Cod | Iowa State University Northeast Research and Demonstration Farm |
|
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
>1,000,000 km^2 | 10-100 km^2 | 10-100 km^2 | 10-100 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 100-1000 km^2 | >1,000,000 km^2 | 100,000-1,000,000 km^2 | 10-100 km^2 | 100-1000 km^2 | 10-100 km^2 | 100-1000 km^2 | 100-1000 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | 1-10 km^2 | 1000-10,000 km^2. | <1 ha |
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
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) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially 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) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | NHDplus v1 | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | 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) | length, for linear feature (e.g., stream mile) | area, for pixel or radial feature |
|
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
irregular | 20 m x 20 m | 20 m x 20 m | 20 m x 20 m | Distributed by catchments with average size of 65,000 ha | NHDplus v1 | 1 km^2 | 1 km x 1 km | 30 m x 30 m | 25 m x 25 m | irregular | Not applicable | 10 m x 10 m | 10 m x 10 m | Not applicable | 30m2 | multiple, individual, irregular shaped sites | beach length | 20 ft x 28 ft |
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric | Logic- or rule-based | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Analytic | Numeric | 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
?
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
|
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | No | No | No | No | Yes | No | No | Yes | Yes | Yes | No | Yes | Yes | Yes | No | Unclear | Yes | Not applicable |
|
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | Yes | Yes | Yes | No | Yes | No | No | No | No | Yes | No | No | No | No | Yes | No | No | Not applicable |
|
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None |
|
|
|
None |
|
None | None | None | None |
|
None | None | None | None |
|
None | None | None |
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | Yes | Yes | Yes | No | No | Yes | Yes | Yes | No | No | No | Yes | Yes | No | No | Unclear | No | No |
|
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No | No | No | No | Yes | No | No | No | No | No | No | No | No | No | No | No | Not applicable |
|
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | No | No | No | No |
No ?Comment:Some model coefficients serve, by their magnitude, to indicate the proportional impact on the final result of variation in the parameters they modify. |
Unclear | No | Yes | No | Unclear | No | No | No | No | No | No | No | Not applicable |
|
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
|
|
|
|
|
|
|
|
|
|
|
|
None | None |
|
|
|
None |
|
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
| None | None | None | None | None | None | None | None |
|
None | None | None |
|
|
None | None | None |
|
None |
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
39.5 | 45.05 | 45.05 | 45.05 | -30 | 42.5 | 38.69 | 50.53 | 47.8 | 42.8 | 35.99 | 46.75 | 17.73 | 17.73 | 32.09 | 43.93 | 42.62 | 41.62 | 42.93 |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
-98.35 | 6.4 | 6.4 | 6.4 | 25 | -90.63 | -89.1 | 7.6 | -122.7 | 24 | -78.96 | -92.08 | -64.77 | -64.77 | -98.12 | 110.24 | -93.84 | -70.42 | -92.57 |
|
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Provided | Provided | Provided | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Estimated | Provided |
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere | Agroecosystems | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Forests | Rivers and Streams | Ground Water | Created Greenspace | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Agroecosystems | Forests | Inland Wetlands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine | Agroecosystems | Grasslands |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Terrestrial | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | Not reported | None | Row crop agriculture in Kaskaskia river basin | Not applicable | glacier-carved saltwater fjord | Primarily forested watershed | Urban areas including streams | Freshwater estuary | Coral reefs | Coral reefs | Rangeland and forage fields for dairy | Montain forest | Grassland buffering inland wetlands set in agricultural land | Beaches | Research farm in historic grassland |
|
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Not applicable | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecosystem | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is 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 corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
|
EM ID
em.detail.idHelp
?
|
EM-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Community | Community | Community | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Community | Not applicable | Not applicable | Guild or Assemblage | Not applicable | Not applicable | Species | Not applicable | Community |
Taxonomic level and name of organisms or groups identified
| EM-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
| None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
|
None Available | 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-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
|
None |
|
None |
|
|
|
|
|
None |
|
|
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-63 | EM-66 | EM-68 | EM-69 | EM-84 | EM-91 | EM-97 | EM-99 |
EM-111 |
EM-130 | EM-142 | EM-416 | EM-448 | EM-456 |
EM-584 |
EM-628 | EM-650 |
EM-686 |
EM-719 |
|
None |
|
None |
|
None |
|
|
|
None |
|
None | None |
|
|
|
|
|
|
Home
Search EMs
My
EMs
Learn about
ESML
Show Criteria
Hide Criteria