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-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
|
EM Short Name
em.detail.shortNameHelp
?
|
ACRU, South Africa | Area and hotspots of flow regulation, South Africa | Reduction in pesticide runoff risk, Europe | FORCLIM v2.9, Western OR, USA | InVEST crop pollination, Costa Rica | VELMA hydro, Oregon, USA | Esocid spawning, St. Louis River, MN/WI, USA | Carbon sequestration, Guánica Bay, Puerto Rico | Presence of Euchema sp., St. Croix, USVI | Yasso07 - Land use SOC dynamics, China | Chinook salmon value, Yaquina Bay, OR | VELMA v2.0, Ohio, USA | Waterfowl pairs, CREP wetlands, Iowa, USA | ARIES: Crop pollination in Rwanda and Burundi | Human well being index for U.S. | HWB indicator-Adult success, Great Lakes, USA | OpenNSPECT v. 1.2 |
|
EM Full Name
em.detail.fullNameHelp
?
|
ACRU (Agricultural Catchments Research Unit), South Africa | Area and hotspots of water flow regulation, South Africa | Reduction in pesticide runoff risk, Europe | FORCLIM (FORests in a changing CLIMate) v2.9, Western OR, USA | InVEST crop pollination, Costa Rica | VELMA (visualizing ecosystems for land management assessments) hydro, Oregon, USA | Esocid spawning, St. Louis River estuary, MN & WI, USA | Carbon sequestration, Guánica Bay, Puerto Rico, USA | Relative presence of Euchema sp. (on reef), St. Croix, USVI | Yasso07 - Land use dynamics of Soil Organic Carbon in the Loess Plateau, China | Economic value of Chinook salmon by angler effort method, Yaquina Bay, OR | Visualizing Ecosystems for Land Management Assessments (VELMA) v2.0, Shayler Crossing watershed, Ohio, USA | Waterfowl pairs, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | ARIES; Crop pollination in Rwanda and Burundi | Human well being index for multiple scales, United States | Human well being indicator-Adult financial success, Great Lakes waterfront, USA | OpenNSPECT v. 1.2 |
|
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
None | None | None | US EPA | InVEST | US EPA | US EPA | US EPA | US EPA | None | US EPA | US EPA | None | ARIES | US EPA | US EPA | None |
|
EM Source Document ID
|
271 | 271 | 255 |
23 ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
279 | 13 | 332 | 338 | 335 | 344 | 324 |
359 ?Comment:Document #366 is a supporting document for this EM. McKane et al. 2014, VELMA Version 2.0 User Manual and Technical Documentation. |
372 | 411 | 421 |
422 ?Comment:Has not been submitted to Journal yet, but has been peer reviewed by EPA inhouse and outside reviewers |
431 |
|
Document Author
em.detail.documentAuthorHelp
?
|
Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S. | Lautenbach, S., Maes, J., Kattwinkel, M., Seppelt, R., Strauch, M., Scholz, M., Schulz-Zunkel, C., Volk, M., Weinert, J. and Dormann, C. | Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A. | Lonsdorf, E., Kremen, C., Ricketts, T., Winfree, R., Williams, N., and S. Greenleaf | Abdelnour, A., Stieglitz, M., Pan, F. and McKane, R. B. | Ted R. Angradi, David W. Bolgrien, Jonathon J. Launspach, Brent J. Bellinger, Matthew A. Starry, Joel C. Hoffman, Mike E. Sierszen, Anett S. Trebitz, and Tom P. Hollenhorst | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu | Stephen J. Jordan, Timothy O'Higgins and John A. Dittmar | Hoghooghi, N., H. E. Golden, B. P. Bledsoe, B. L. Barnhart, A. F. Brookes, K. S. Djang, J. J. Halama, R. B. McKane, C. T. Nietch, and P. P. Pettus | 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 | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Smith, L.M., Harwell, L.C., Summers, J.K., Smith, H.M., Wade, C.M., Straub, K.R. and J.L. Case | Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick | Eslinger, David L., H. Jamieson Carter, Matt Pendleton, Shan Burkhalter, Margaret Allen |
|
Document Year
em.detail.documentYearHelp
?
|
2008 | 2008 | 2012 | 2007 | 2009 | 2011 | 2016 | 2017 | 2014 | 2015 | 2012 | 2018 | 2010 | 2018 | 2014 | None | 2012 |
|
Document Title
em.detail.sourceIdHelp
?
|
Mapping ecosystem services for planning and management | Mapping ecosystem services for planning and management | Mapping water quality-related ecosystem services: concepts and applications for nitrogen retention and pesticide risk reduction | Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model | Modelling pollination services across agricultural landscapes | Catchment hydrological responses to forest harvest amount and spatial pattern | Mapping ecosystem service indicators of a Great Lakes estuarine Area of Concern | Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico | Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement | Ecosystem Services of Coastal Habitats and Fisheries: Multiscale Ecological and Economic Models in Support of Ecosystem-Based Management | Cumulative effects of low impact development on watershed hydrology in a mixed land-cover system | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Towards globally customizable ecosystem service models | A U.S. Human Well-being index (HWBI) for multiple scales: linking service provisioning to human well-being endpoints (2000-2010) | Human well-being and natural capital indictors for Great Lakes waterfront revitalization | “OpenNSPECT: The Open-source Nonpoint Source Pollution and Erosion Comparison Tool.” NOAA Office for Coastal Management, Charleston, South Carolina. Accessed (11/2022) at https://coast.noaa.gov/digitalcoast/tools/opennspect.html |
|
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 but unpublished (explain in Comment) | Peer reviewed and published |
|
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published report | Published journal manuscript | Published EPA report | Journal manuscript submitted or in review | Webpage |
|
EM ID
em.detail.idHelp
?
|
EM-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
| Not applicable | Not applicable | Not applicable | Not applicable | http://www.naturalcapitalproject.org/models/crop_pollination.html | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | Not applicable | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | Not applicable | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | Not applicable | https://github.com/integratedmodelling/im.aries.global | Not applicable | Not applicable | https://coast.noaa.gov/digitalcoast/tools/opennspect.html | |
|
Contact Name
em.detail.contactNameHelp
?
|
Roland E Schulze | Benis Egoh | Sven Lautenbach | Richard T. Busing | Eric Lonsdorf | A. Abdelnour | Ted R. Angradi | Susan H. Yee | Susan H. Yee | Xing Wu | Stephen Jordan | Heather Golden | David Otis | Javier Martinez | Lisa Smith | Ted Angradi | Not reported |
|
Contact Address
|
School of Bioresources Engineering and Environmental Hydrology, University of Natal, South Africa | Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy | Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany | U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA | Conservation and Science Dept, Linclon Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA | Dept. of Civil and Environmental Engineering, Goergia Institute of Technology, Atlanta, GA 30332-0335, USA | United States Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboraty, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804 USA | U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA | Chinese Academy of Sciences, Beijing 100085, China | U.S. EPA, Gulf Ecology Div., 1 Sabine Island Dr., Gulf Breeze, FL 32561, USA | National Exposure Research Laboratory, Office of Research and Development, US EPA, Cincinnati, OH 45268, USA | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | BC3-Basque Centre for Climate Chan ge, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | 1 Sabine Island Dr, Gulf Breeze, FL 32561 | USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804 | NOAA Coastal Services Center, 2234 South Hobson Avenue Charleston, South Carolina 29405-2413 |
|
Contact Email
|
schulzeR@nu.ac.za | Not reported | sven.lautenbach@ufz.de | rtbusing@aol.com | ericlonsdorf@lpzoo.org | abdelnouralex@gmail.com | angradi.theodore@epa.gov | yee.susan@epa.gov | yee.susan@epa.gov | xingwu@rceesac.cn | jordan.steve@epa.gov | Golden.Heather@epa.gov | dotis@iastate.edu | javier.martinez@bc3research.org | smith.lisa@epa.gov | tedangradi@gmail.com | Not reported |
|
EM ID
em.detail.idHelp
?
|
EM-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
|
Summary Description
em.detail.summaryDescriptionHelp
?
|
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: "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…Water flow regulation is a function of the storage and retention components of the water supply service (de Groot et al., 2002). The ability of a catchment to regulate flows is directly related to the volume of water that is retained or stored in the soil and underlying aquifers as moisture or groundwater; and the infiltration rate of water which replenishes the stored water (Kittredge, 1948; Farvolden, 1963). Groundwater contribution to surface runoff is the most direct measure of the water regulation function of a catchment. Data on the percentage contribution of groundwater to baseflows were obtained from DWAF (2005) per quaternary catchment and expressed as a percentage of total surface runoff, the range and hotspot being defined as areas with at least 10% and 30%, respectively (Colvin et al., 2007)." | AUTHOR'S DESCRIPTION: "We used a spatially explicit model to predict the potential exposure of small streams to insecticides (run-off potential – RP) as well as the resulting ecological risk (ER) for freshwater fauna on the European scale (Schriever and Liess 2007; Kattwinkel et al. 2011)...The recovery of community structure after exposure to insecticides is facilitated by the presence of undisturbed upstream stretches that can act as sources for recolonization (Niemi et al. 1990; Hatakeyama and Yokoyama 1997). In the absence of such sources for recolonization, the structure of the aquatic community at sites that are exposed to insecticides differs significantly from that of reference sites (Liess and von der Ohe 2005)...Hence, we calculated the ER depending on RP for insecticides and the amount of recolonization zones. ER gives the percentage of stream sites in each grid cell (10 × 10 km) in which the composition of the aquatic community deviated from that of good ecological status according to the WFD. In a second step, we estimated the service provided by the environment comparing the ER of a landscape lacking completely recolonization sources with that of the actual landscape configuration. Hence, the ES provided by non-arable areas (forests, pastures, natural grasslands, moors and heathlands) was calculated as the reduction of ER for sensitive species. The service can be thought of as a habitat provisioning/nursery service that leads to an improvement of ecological water quality." | ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range…The simulation of both stand-replacing and partial-stand disturbances across western Oregon improved agreement between simulated and actual data." Western Oregon forested ecoregions (Omernick classification) were Coastal Volcanics (1d), Mid-coastal Sedimentary (1g), Willamette Valley (3), West Cascade Lowlands (4a), West Cascade Montane (4b), Cascade Crest (4c), East Cascade Ponderosa Pine (9d), and East Cascade Pumice Plateau (9e). | 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: "Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery." AUTHOR'S DESCRIPTION: "…Lacking information on seasonality, a single flight season was assumed for all species..." | AUTHOR'S DESCRIPTION: "VELMA uses a distributed soil column framework to simulate the movement of water and nutrients (NH4, NO3, DON, DOC) within the soil, between the soil and the vegetation, and between the soil surface and vegetation to the atmosphere. The soil column model consists of three coupled submodels: (1) a hydrological model that simulates vertical and lateral movement of water within soil, losses of water from soil and vegetation to the atmosphere, and the growth and ablation of the seasonal snowpack, (2) a soil temperature model that simulates daily soil layer temperatures from surface air temperature and snow depth, and (3) a plant-soil model that simulates C and N dynamics. (Note: for the purposes of this paper we describe only the hydrologic aspects of the model.) Each soil column consists of n soil layers. Soil water balance is solved for each layer (equations (A1)–(A6)). We employ a simple logistic function that is based on the degree of saturation to capture the breakthrough characteristics of soil water drainage (equations (A7)–(A9)). Evapotranspiration increases exponentially with increasing soil water storage and asymptotically approaches the potential evapotranspiration rate (PET) as water storage reaches saturation [Davies and Allen, 1973; Federer, 1979, 1982; Spittlehouse and Black, 1981] (equation (A12)). PET is estimated using a simple temperature-based method [Hamon, 1963] (equation (A13)). An evapotranspiration recovery function is used to account for the effects of changes in stand-level transpiration rates during succession, e.g., after fire or harvest (equation (B2)). Snowmelt is estimated using the degree-day approach [Rango and Martinec, 1995] and accounts for the effects of rain on snow [Harr, 1981] (equation (A10)). [15] The soil column model is placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other through the downslope lateral transport of water (Figures A1 and A2). Surface and subsurface lateral flow are routed using a multiple flow direction method [Freeman, 1991; Quinn et al., 1991]. As with vertical drainage of soil water, lateral subsurface downslope flow is modeled using a simple logistic function multiplied by a factor to account for the local topographic slope angle (equation (A16))… The model is forced with daily temperature and precipitation. Daily observed streamflow data is used to calibrate and validate simulated discharge." "Model calibration is needed to accurately capture the pre- and postharvest dynamics at WS10. This model calibration consists of two simulations: an old-growth simulation for the period 1969-1974 and a post-harvest simulation for the period 1975-2008." Two additional sets of VELMA simulations examining changes in streamflow are presented in the paper, but not included here. Twenty simulations were conducted varying the location across the watershed of a 20% har | ABSTRACT: "Estuaries provide multiple ecosystem services from which humans benefit…We described an approach, with examples, for assessing how local-scale actions affect the extent and distribution of coastal ecosystem services, using the St. Louis River estuary (SLRE) of western Lake Superior as a case study. We based our approach on simple models applied to spatially explicity biophysical data that allows us to map the providing area of ecosystem services at high resolution (10-m^2 pixel) across aquatic and riparian habitats…Aspects of our approach can be adapted by communities for use in support of local decision-making." AUTHOR'S DESCRIPTION: "We derived the decision criteria used to map the IEGS habitat proxy of esocid spawning from habitat suitability information for two species that have similar but not identical spawning habitat and behavior." | AUTHOR'S DESCRIPTION: "In addition to affecting water quality, the ecosystem services of nitrogen retention, phosphorous retention, and sediment retention were also considered to contribute to stakeholder goals of maintaining the productivity of agricultural land and reducing soil loss. Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class, applying the mean of rates for natural sub-tropical ecosystems obtained from the literature." | ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j:...(4) density of Euchema sp. seaweed," | ABSTRACT: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau." | ABSTRACT:"Critical habitats for fish and wildlife are often small patches in landscapes, e.g., aquatic vegetation beds, reefs, isolated ponds and wetlands, remnant old-growth forests, etc., yet the same animal populations that depend on these patches for reproduction or survival can be extensive, ranging over large regions, even continents or major ocean basins. Whereas the ecological production functions that support these populations can be measured only at fine geographic scales and over brief periods of time, the ecosystem services (benefits that ecosystems convey to humans by supporting food production, water and air purification, recreational, esthetic, and cultural amenities, etc.) are delivered over extensive scales of space and time. These scale mismatches are particularly important for quantifying the economic values of ecosystem services. Examples can be seen in fish, shellfish, game, and bird populations. Moreover, there can be wide-scale mismatches in management regimes, e.g., coastal fisheries management versus habitat management in the coastal zone. 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 recreational Chinook (Oncorhynchus tshawytscha) salmon fisheries in the U.S. Pacific Northwest (Washington and Oregon) and commercial blue crab (Callinectes sapidus) and penaeid shrimp fisheries in the Gulf of Mexico. | ABSTRACT: "Low Impact Development (LID) is an alternative to conventional urban stormwater management practices, which aims at mitigating the impacts of urbanization on water quantity and quality. Plot and local scale studies provide evidence of LID effectiveness; however, little is known about the overall watershed scale influence of LID practices. This is particularly true in watersheds with a land cover that is more diverse than that of urban or suburban classifications alone. We address this watershed-scale gap by assessing the effects of three common LID practices (rain gardens, permeable pavement, and riparian buffers) on the hydrology of a 0.94 km2 mixed land cover watershed. We used a spatially-explicit ecohydrological model, called Visualizing Ecosystems for Land Management Assessments (VELMA), to compare changes in watershed hydrologic responses before and after the implementation of LID practices. For the LID scenarios, we examined different spatial configurations, using 25%, 50%, 75% and 100% implementation extents, to convert sidewalks into rain gardens, and parking lots and driveways into permeable pavement. We further applied 20 m and 40 m riparian buffers along streams that were adjacent to agricultural land cover…" AUTHOR'S DESCRIPTION: "VELMA’s modeling domain is a three-dimensional matrix that includes information regarding surface topography, land use, and four soil layers. VELMA uses a distributed soil column framework to model the lateral and vertical movement of water and nutrients through the four soil layers. A soil water balance is solved for each layer. The soil column model is placed within a watershed framework to create a spatially distributed model applicable to watersheds (Figure 2, shown here with LID practices). Adjacent soil columns interact through down-gradient water transport. Water entering each pixel (via precipitation or flow from an adjacent pixel) can either first infiltrate into the implemented LID and the top soil layer, and then to the downslope pixel, or continue its downslope movement as the lateral surface flow. Surface and subsurface lateral flow are routed using a multiple flow direction method, as described in Abdelnour et al. [21]. A detailed description of the processes and equations can be found in McKane et al. [32], Abdelnour et al. [21], Abdelnour et al. [40]." | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "Number of duck pairs per site was estimated for 6 species of ducks: Mallard (Anas platyrhynchos), Blue-winged Teal (Anas discors), Northern Shoveler (Anas clypeata), Gadwall (Anas strepera), Northern Pintail (Anas acuta), and Wood Duck (Aix sponsa), using models developed by Cowardin et al. (1995). Pair abundance was based on wetland class (i.e., temporary, seasonal, semi-permanent, lake, or river), wetland size, and a set of species specific regression coefficients. All CREP wetlands were considered semi-permanent for this analysis; therefore only coefficients associated with the semipermanent wetland pair model were used in calculations. The general equation used to estimate the pairs per wetland was: Pairs = e (a + bx + α) * p where, e = mathematical constant ≈ 2.718, a = species specific regression coefficient a, b = species specific regression coefficient b, x = the natural log of wetland size, α = species specific alpha value, and p = proportion of the basin containing water (assumed to be 0.90 for this analysis)" | [Abstract:Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The Artificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multicriteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed.] | Executive summary: "The HWBI is a composite assessment covering 8 domains based on 25 indicators measured using 80 different metrics. Service flow and stock assessments include 7 economic services (23 indicators, 40 metrics), 5 ecosystem services (8 indicators, 24 metrics) and 10 social services (37 indicators, 76 metrics). Data from 64 data sources were included in the HWBI and services provisioning characterizations (Fig. ES-3). For each U.S. county, state, and GSS region, data were acquired or imputed for the 2000-2010 time period resulting in over 1.5 million data points included in the full assessment linking service flows to well-being endpoints. The approaches developed for calculation of the HWBI, use of relative importance values, service stock characterization and functional modeling are transferable to smaller scales and specific population groups. Additionally, tracked over time, the HWBI may be useful in evaluating the sustainability of decisions in terms of EPA’s Total Resources Impact Outcome (TRIO) approaches. " | ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context. " | "This open-source version of the Nonpoint Source Pollution and Erosion Comparison Tool is used to investigate potential water quality impacts from climate change and development to other land uses. The downloadable tool is designed to be broadly applicable for coastal and noncoastal areas alike. Tool functions simulate erosion, pollution, and the accumulation from overland flow. OpenNSPECT uses spatial elevation data to calculate flow direction and flow accumulation throughout a watershed. To do this, land cover, precipitation, and soils data are processed to estimate runoff volume at both the local and watershed levels. Coefficients representing the contribution of each land cover class to the expected pollutant load are also applied to land cover data to approximate total pollutant loads. These coefficients are taken from published sources or can be derived from local water quality studies. The output layers display estimates of runoff volume, pollutant loads, pollutant concentration, and total sediment yield. Requires MapWindow GIS v.4.8.8 (open source software)" |
|
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | None identified | European Commission Water Framework Directive (WFD, Directive 2000/60/EC) | None Identified | None identified | None identified | Federal delisting of an area of concern (AOC) | None identified | None identified | None identified | None reported | None identified | None identified | None identified | None reported | None identified | None identified |
|
Biophysical Context
|
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. | Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential. | Not applicable | Coastal to montane, Pacific Northwest US (Oregon) forests. | No additional description provided | Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter. Mean annual precipitation is 2300 mm and falls primarily as rain between October and April. Total rainfall during June– September averages 200 mm. Snow rarely persists longer than a couple of weeks and usually melts within 1 to 2 days. Average annual streamflow is 1600 mm, which is approximately 70% of annual precipitation. Soils are of the Frissel series, classified as Typic Dystrochrepts with fine loamy to loamy-skeletal texture that are generally deep and well drained. These soils quickly transmit subsurface water to the stream. Prior to the 1975 100% clearcut, WS10 was a 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). The dominant vegetation of WS10 today is a 35 year old mixed Douglasfir and western hemlock stand. | No additional description provided | No additional description provided | No additional description provided | Agricultural plain, hills, gulleys, forest, grassland, Central China | Yaquina Bay estuary | The Shayler Crossing (SHC) watershed is a subwatershed of the East Fork Little Miami River Watershed in southwest Ohio, USA and falls within the Till Plains region of the Central Lowland physiographic province. The Till Plains region is a topographically young and extensive flat plain, with many areas remaining undissected by even the smallest stream. The bedrock is buried under a mantle of glacial drift 3–15 m thick. The Digital Elevation Model (DEM) has a maximum value of ~269 m (North American_1983 datum) within the watershed boundary (Figure 1). The soils are primarily the Avonburg and Rossmoyne series, with high silty clay loam content and poor to moderate infiltration. Average annual precipitation for the period, 1990 through 2011, was 1097.4 _ 173.5 mm. Average annual air temperature for the same period was 12 _C Mixed land cover suburban watershed. The primary land uses consist of 64.1% urban or developed area (including 37% lawn, 12% building, 6.5% street, 6.4% sidewalk, and 2.1% parking lot and driveway), 23% agriculture, and 13% deciduous forest. Total imperviousness covers approximately 27% of the watershed area. | Prairie pothole region of north-central Iowa | Entire countries of Rwanda and Burundi considered | Not applicable | Waterfront districts on south Lake Michigan and south lake Erie | No additional description provided |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
No scenarios presented | No scenarios presented | No scenarios presented | Two scenarios modelled, forests with and without fire | No scenarios presented | Stand age; old-growth (pre-harvest), and harvested (postharvest) | The effect of habitat restoration on esocid spawning area was simulated by varying biophysical changes. | No scenarios presented | No scenarios presented | Land use change | N/A | Three types of Low Impact Development (LID) practices (rain gardens, permeable pavements, forested riparian buffers) applied a different conversion levels. | No scenarios presented | N/A | geographic region | N/A | No scenarios presented |
|
EM ID
em.detail.idHelp
?
|
EM-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Related document ID 22 is a secondary source providing tree species specific parameters in appendix. |
Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method Only |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
Application of existing model | New or revised model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | New or revised model | New or revised model | New or revised model | New or revised model | 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-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
Doc-272 ?Comment:Doc ID 272 was also used as a source document for this EM |
Doc-271 |
Doc-254 | Doc-256 ?Comment:Document 254 was also used as a source document for this EM |
Doc-22 | Doc-23 ?Comment:Related document ID 22 provides tree species specific parameters in appendix. |
Doc-279 | Doc-317 | None | None | None | Doc-343 | Doc-342 | None | Doc-13 | Doc-366 | None |
?Comment:Supplemental Information to this article can be found online at https://doi.org/10.1016/j.scitotenv.2018.09.371. |
Doc-418 | Doc-422 | None |
|
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
None | EM-86 | EM-87 | EM-88 | None | EM-146 | EM-208 | EM-224 | EM-338 | EM-339 | EM-379 | EM-380 | EM-605 | EM-884 | EM-883 | EM-887 | None | None | None | EM-466 | EM-467 | EM-469 | EM-485 | EM-604 | EM-397 | EM-375 | EM-377 | EM-378 | EM-884 | EM-883 | EM-887 | EM-705 | EM-703 | EM-702 | EM-701 | EM-700 | EM-859 | EM-880 | EM-886 | EM-888 | EM-889 | EM-890 | EM-891 | EM-893 | EM-895 | EM-940 |
EM Modeling Approach
|
EM ID
em.detail.idHelp
?
|
EM-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
1950-1993 | Not reported | 2000 | >650 yrs | 2001-2002 | 1969-2008 | 2013 | 1978 - 2013 | 2006-2007, 2010 | 1969-2011 | 2003-2008 | Jan 1, 2009 to Dec 31, 2011 | 2002-2007 | 2010 | 2000-2010 | 2022 | Not applicable |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-stationary |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
future time | Not applicable | Not applicable | past time | Not applicable | future time | Not applicable | Not applicable | Not applicable | past time | Not applicable | past time | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
discrete | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | discrete | Not applicable | discrete | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
1 | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | 1 | Not applicable | 1 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Day | Not applicable | Not applicable | Year | Not applicable | Day | Not applicable | Not applicable | Not applicable | Year | Not applicable | Day | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
|
EM ID
em.detail.idHelp
?
|
EM-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
|
Bounding Type
em.detail.boundingTypeHelp
?
|
Geopolitical | Geopolitical | Geopolitical | Physiographic or ecological | Other | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Geopolitical | Watershed/Catchment/HUC | Multiple unrelated locations (e.g., meta-analysis) | Geopolitical | Geopolitical | Geopolitical | Not applicable |
|
Spatial Extent Name
em.detail.extentNameHelp
?
|
South Africa | South Africa | EU-27 | Western Oregon, north of 43.00 N to Washington border | Large coffee farm, Valle del General | H. J. Andrews LTER WS10 | St. Louis River estuary | Guanica Bay watershed | Coastal zone surrounding St. Croix | Yangjuangou catchment | Pacific Northwest | Shayler Crossing watershed, a subwatershed of the East Fork Little Miami River Watershed | CREP (Conservation Reserve Enhancement Program) wetland sites | Rwanda and Burndi | Continental U.S. | Great Lakes waterfront | Not applicable |
|
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
>1,000,000 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 | 10-100 km^2 | 10-100 ha | 10-100 km^2 | 1000-10,000 km^2. | 100-1000 km^2 | 1-10 km^2 | >1,000,000 km^2 | 10-100 ha | 1-10 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 | 1000-10,000 km^2. | Not applicable |
|
EM ID
em.detail.idHelp
?
|
EM-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all 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 | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | area, for pixel or radial feature |
|
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
Distributed by catchments with average size of 65,000 ha | Distributed by catchments with average size of 65,000 ha | 10 km x 10 km | 0.08 ha | 30 m x 30 m | 30 m x 30 m surface pixel and 2-m depth soil column | 10 m x 10 m | 30 m x 30 m | 10 m x 10 m | 30m x 30m | Not applicable | 10m x 10m | multiple, individual, irregular shaped sites | 1km | county | Not applicable | 30 m |
|
EM ID
em.detail.idHelp
?
|
EM-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
Numeric | Analytic | Analytic | Numeric | Analytic | Numeric | Analytic | Analytic | Analytic | Numeric | Numeric | Numeric | Analytic | Analytic | Numeric | Logic- or rule-based | Analytic |
|
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic |
|
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
EM ID
em.detail.idHelp
?
|
EM-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
|
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | No | No | No | Unclear | Yes | No | No | Yes | No | No | Yes | Unclear | Unclear | No | No | Not applicable |
|
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | No | No | No | No | Yes | No | No | No |
Yes ?Comment:p value: p<0.001 |
No |
Yes ?Comment:Goodness of fit for calibrated (2009-2010) and observed streamflow. |
No | No | 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 | None | None |
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
No | No | Yes | Yes | Yes | No | No | No | Yes | No |
Yes ?Comment:Compared to a second methodological approach |
Yes | Unclear | No | No | No | Not applicable |
|
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No | No | No | No | No | No | No | No | No | No | No | No | No | Unclear | No | Not applicable |
|
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | No | No | No | Yes | No | No | No | No | No | No | No | No | No | Yes | Yes | Not applicable |
|
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
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 | Yes | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
|
|
|
|
|
|
|
|
None |
|
|
|
|
|
|
|
None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
| 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-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
-30 | -30 | 50.53 | 44.66 | 9.13 | 44.15 | 46.74 | 17.96 | 17.73 | 36.7 | 44.62 | 39.19 | 42.62 | -2.59 | 39.83 | 42.26 | Not applicable |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
25 | 25 | 7.6 | -122.56 | -83.37 | -122.2 | -92.14 | -67.02 | -64.77 | 109.52 | -124.02 | -84.29 | -93.84 | 29.97 | -98.58 | -87.84 | Not applicable |
|
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Provided | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Not applicable |
|
EM ID
em.detail.idHelp
?
|
EM-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Forests | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Rivers and Streams | Ground Water | Forests | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Inland Wetlands | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Agroecosystems | Near Coastal Marine and Estuarine | Rivers and Streams | Ground Water | Forests | Agroecosystems | Created Greenspace | Inland Wetlands | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
Not reported | Not reported | Streams and near upstream environments | Primarily conifer forest | Cropland and surrounding landscape | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | freshwater estuary | 13 LULC were used | Coral reefs | Loess plain | Yaquina Bay | Mixed land cover suburban watershed | Wetlands buffered by grassland set in agricultural land | varied | All land of the continental US | Lake Michigan & Lake Erie waterfront | Coastal and non-coastal |
|
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
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 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 corresponds to the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | 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 |
Scale of differentiation of organisms modeled
|
EM ID
em.detail.idHelp
?
|
EM-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Not applicable | Not applicable | Species | Species | Not applicable | Not applicable | Not applicable | Species | Not applicable | Individual or population, within a species | Not applicable | Species | Guild or Assemblage | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
| 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-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
|
|
|
|
|
|
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-84 | EM-85 | EM-94 |
EM-186 |
EM-340 |
EM-375 |
EM-415 | EM-430 | EM-461 |
EM-480 |
EM-603 |
EM-605 |
EM-632 |
EM-855 | EM-882 | EM-894 | EM-938 |
|
None | None | None |
|
None |
|
None |
|
None | None |
|
|
None | None | None | None |
Home
Search EMs
My
EMs
Learn about
ESML
Show Criteria
Hide Criteria