EcoService Models Library (ESML)
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Compare EMs
Which comparison is best for me?EM Variables by Variable Role
One quick way to compare ecological models (EMs) is by comparing their variables. Predictor variables show what kinds of influences a model is able to account for, and what kinds of data it requires. Response variables show what information a model is capable of estimating.
This first comparison shows the names (and units) of each EM’s variables, side-by-side, sorted by variable role. Variable roles in ESML are as follows:
- Predictor Variables
- Time- or Space-Varying Variables
- Constants and Parameters
- Intermediate (Computed) Variables
- Response Variables
- Computed Response Variables
- Measured Response Variables
EM Variables by Category
A second way to use variables to compare EMs is by focusing on the kind of information each variable represents. The top-level categories in the ESML Variable Classification Hierarchy are as follows:
- Policy Regarding Use or Management of Ecosystem Resources
- Land Surface (or Water Body Bed) Cover, Use or Substrate
- Human Demographic Data
- Human-Produced Stressor or Enhancer of Ecosystem Goods and Services Production
- Ecosystem Attributes and Potential Supply of Ecosystem Goods and Services
- Non-monetary Indicators of Human Demand, Use or Benefit of Ecosystem Goods and Services
- Monetary Values
Besides understanding model similarities, sorting the variables for each EM by these 7 categories makes it easier to see if the compared models can be linked using similar variables. For example, if one model estimates an ecosystem attribute (in Category 5), such as water clarity, as a response variable, and a second model uses a similar attribute (also in Category 5) as a predictor of recreational use, the two models can potentially be used in tandem. This comparison makes it easier to spot potential model linkages.
All EM Descriptors
This selection allows a more detailed comparison of EMs by model characteristics other than their variables. The 50-or-so EM descriptors for each model are presented, side-by-side, in the following categories:
- EM Identity and Description
- EM Modeling Approach
- EM Locations, Environments, Ecology
- EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
EM Descriptors by Modeling Concepts
This feature guides the user through the use of the following seven concepts for comparing and selecting EMs:
- Conceptual Model
- Modeling Objective
- Modeling Context
- Potential for Model Linkage
- Feasibility of Model Use
- Model Certainty
- Model Structural Information
Though presented separately, these concepts are interdependent, and information presented under one concept may have relevance to other concepts as well.
EM Identity and Description
EM ID
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EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
EM Short Name
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Divergence in flowering date, Central French Alps | EnviroAtlas - Water recharge | InVEST - Water Yield (v3.0) | InVEST carbon storage and sequestration (v3.2.0) | VELMA soil temperature, Oregon, USA | Decrease in erosion (shoreline), St. Croix, USVI | Decrease in wave runup, St. Croix, USVI | Yasso07 v1.0.1, Switzerland | EnviroAtlas - Crops with no pollinator habitat | Fish species richness, Puerto Rico, USA | DayCent N2O flux simulation, Ireland | WaterWorld v2, Santa Basin, Peru | LUCI, New Zealand | Alwife phosphorus flux in lakes, Connecticut, USA | ESII Tool, Michigan, USA | SLAMM, Tampa Bay, FL, USA | VELMA v. 2.0 Hydro | Visitation to natural areas, New England, USA | RZWQM2, Quebec, Canada | NC HUC-12 conservation prioritization tool | NBS benefits explorer | CommunityViz, Albany county, Wyoming | CMAQ chemical transport model, UK |
EM Full Name
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Functional divergence in flowering date, Central French Alps | US EPA EnviroAtlas - Annual water recharge by tree cover; Example is shown for Durham NC and vicinity, USA | InVEST v3.0 Reservoir Hydropower Projection, aka Water Yield | InVEST v3.2.0 Carbon storage and sequestration | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Decrease in wave runup (by reef), St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland | US EPA EnviroAtlas - Acres of pollinated crops with no nearby pollinator habitat, USA | Fish species richness, Puerto Rico, USA | DayCent simulation N2O flux and climate change, Ireland | WaterWorld v2, Santa Basin, Peru | LUCI (Land Utilisation and Capability Indicator), New Zealand | Net phosphorus flux in freshwater lakes from alewives, Connecticut, USA | ESII (Ecosystem Services Identification and Inventory) Tool, Michigan, USA | SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA | Visualizing Ecosystems for Land Management Assessments (VELMA) Version 2.0 Hydro | Estimating natural area use with cell phone data, Narragansett Beach, New England, USA | Root zone water quality model 2 mitigation of greenhouse gases, Quebec, Canada | NC HUC-12 conservation prioritization tool v. 1.0, North Carolina, USA | Benefit Accounting of Nature-Based Solutions for Watersheds: Guide | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming. | Application of chemical transport model CMAQ to policy decisions regarding PM2.5 in the UK |
EM Source or Collection
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EU Biodiversity Action 5 |
US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
InVEST | InVEST | US EPA | US EPA | US EPA | None | US EPA | EnviroAtlas | None | None | None | None | None | None | None | US EPA | US EPA | None | None | None | None | None |
EM Source Document ID
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260 |
223 ?Comment:Parameter default values used in the i-Tree Hydro model were obtained from the i-Tree website (Document ID 198, EM 137). |
311 | 315 | 317 | 335 | 335 | 343 | 262 | 355 | 358 | 368 |
380 ?Comment:Document 381 is an additional source for this EM. |
383 |
392 ?Comment:Document 391 is an additional source for this EM. |
415 ?Comment:Secondary sources: Documents 412 and 413. |
366 | 436 | 447 |
443 ?Comment:Doc 444 is an additional source for this EM |
471 |
479 ?Comment:Published as a report by the University of Wyoming, but no record of peer review. |
483 |
Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | US EPA Office of Research and Development - National Exposure Research Laboratory | Natural Capital Project | The Natural Capital Project | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Didion, M., B. Frey, N. Rogiers, and E. Thurig | US EPA Office of Research and Development - National Exposure Research Laboratory | Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Van Soesbergen, A. and M. Mulligan | Trodahl, M. I., B. M. Jackson, J. R. Deslippe, and A. K. Metherell | West, D. C., A. W. Walters, S. Gephard, and D. M. Post | Guertin, F., K. Halsey, T. Polzin, M. Rogers, and B. Witt | Sherwood, E. T. and H. S. Greening | McKane, R. B., A. Brookes, K. Djang, M. Stieglitz, A. G. Abdelnour, F. Pan, J. J. Halama, P. B. Pettus and D. L. Phillips | Merrill, N.H., Atkinson, S.F., Mulvaney, K.K., Mazzotta, K.K., and J. Bousquin | Jiang, Q., Zhiming, Q., Madramootoo, C.A., and Creze, C. | Warnell, K., I. Golden, and C. Canfield | Brill, G., T. Shiao, C. Kammeyer, S. Diringer, K. Vigerstol, N. Ofosu-Amaah, M. Matosich, C. Müller-Zantop, W. Larson and T. Dekker | Lieske, S. N., Mullen, S., Knapp, M., & Hamerlinck, J. D. | Chemel, C., Fisher, B.E.A., Kong, X., Francis, X.V., Sokhi, R.S., Good, N., Collins, W.J. and Folberth, G.A. |
Document Year
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2011 | 2013 | 2015 | 2015 | 2013 | 2014 | 2014 | 2014 | 2013 | 2007 | 2010 | 2018 | 2017 | 2010 | 2019 | 2014 | 2014 | 2020 | 2018 | 2023 | 2022 | 2003 | 2014 |
Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | EnviroAtlas - Featured Community | Water Yield: Reservoir Hydropower Production- InVEST (v3.0) | Carbon storage and sequestration - InVEST (v3.2.0) | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | 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 | Validating tree litter decomposition in the Yasso07 carbon model | EnviroAtlas - National | Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean | Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture | Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Investigating trade-offs between water quality and agricultural productivity using the Land Utilisation and Capability Indicator (LUCI)-A New Zealand application | Nutrient loading by anadromous alewife (Alosa pseudoharengus): contemporary patterns and predictions for restoration efforts | From ash pond to riverside wetlands: Making the business case for engineered natural technologies | Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats | VELMA Version 2.0 User Manual and Technical Documentation | Using data derived from cellular phone locations to estimate visitation to natural areas: An application to water recreation in New England, USA | Mitigating greenhouse gas emisssions in subsurface-drained field using RZWQM2 | Conservation planning tools for NC's people & nature | Benefit Accounting of Nature-Based Solutions for Watersheds: Guide | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming | Application of chemical transport model CMAQ to policy decisions regarding PM2.5 in the UK |
Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Published on US EPA EnviroAtlas website | Web published | Website | 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 journal manuscript | Published journal manuscript | Published EPA report | Published journal manuscript | Published journal manuscript | Webpage | Published report | Published report | Published journal manuscript |
EM ID
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EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
Not applicable | https://www.epa.gov/enviroatlas | https://www.naturalcapitalproject.org/invest/ | https://www.naturalcapitalproject.org/invest/ | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov | Not applicable | Not applicable | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | https://www.epa.gov/enviroatlas | Not applicable | Not applicable | www.policysupport.org/waterworld |
info@lucitools.org ?Comment:To obtain LUCI, email us your enquiry at info@lucitools.org with information about: The problem you are seeking to solve or your research question. The country and region you wish to apply LUCI in. What data you have with as much detail as possible about the data sources. Your timeframe or deadlines. |
Not applicable | https://www.esiitool.com/ | http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html | https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | https://github.com/USEPA/Recreation_Benefits.git | Not applicable | https://prioritizationcobenefitstool.users.earthengine.app/view/nc-huc-12-conservation-prioritizer | https://nbsbenefitsexplorer.net/tool | https://communityviz.com/ | https://www.epa.gov/cmaq/download-cmaq | |
Contact Name
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Sandra Lavorel | EnviroAtlas Team | Natural Capital Project | The Natural Capital Project | Alex Abdelnour | Susan H. Yee | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
EnviroAtlas Team | Simon Pittman | M. Abdalla | Arnout van Soesbergen | Martha I. Trodahl | Derek C. West | Not reported | Edward T. Sherwood | Robert B. McKane | Nathaniel Merrill | Zhiming Qi | Katie Warnell | Gregg Brill | Scott Lieske | B.E.A. Fisher |
Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Not reported | 371 Serra Mall, Stanford University, Stanford, Ca 94305 | 371 Serra Mall Stanford University Stanford, CA 94305-5020 USA | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, 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 | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Not reported | 1305 East-West Highway, Silver Spring, MD 20910, USA | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | School of Geography, Environment & Earth Sciences, Victoria University of Wellington, New Zealand | Dept. of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, CT 06511, USA | Not reported | Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA | USEPA Office of Research and Development National Health and Environmental Effects Research Laboratory Western Ecology Division Corvallis, Oregon 97333 | Atlantic Coastal Environmental Sciences Division, U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Narragansett, Rhode Island, United States of America, | Department of Bioresource Engineering, McGill University, Sainte-Anne-de-Bellevue, QC H9X 3V9, Canada | Not reported | Not reported | Department of Agricultural & Applied Economics University of Wyoming, Laramie WY 82071 | Little Beeches, Headley Road, Leatherhead KT22 8PT, UK. |
Contact Email
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sandra.lavorel@ujf-grenoble.fr | enviroatlas@epa.gov | invest@naturalcapitalproject.org | invest@naturalcapitalproject.org | abdelnouralex@gmail.com | yee.susan@epa.gov | yee.susan@epa.gov | markus.didion@wsl.ch | enviroatlas@epa.gov | simon.pittman@noaa.gov | abdallm@tcd.ie | arnout.van_soesbergen@kcl.ac.uk | Not reported | derek.west@yale.edu | Not reported | esherwood@tbep.org | mckane.bob@epa.gov | merrill.nathaniel@epa.gov | zhiming.qi@mcgill.ca | katie.warnell@duke.edu | Not reported | lieske@uwyo.edu | None provided |
EM ID
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EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
Summary Description
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ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables." | 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. | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. AUTHOR'S DESCRIPTION: "The InVEST Reservoir Hydropower model estimates the relative contributions of water from different parts of a landscape, offering insight into how changes in land use patterns affect annual surface water yield and hydropower production. Modeling the connections between landscape changes and hydrologic processes is not simple. Sophisticated models of these connections and associated processes (such as the WEAP model) are resource and data intensive and require substantial expertise. To accommodate more contexts, for which data are readily available, InVEST maps and models the annual average water yield from a landscape used for hydropower production, rather than directly addressing the affect of LULC changes on hydropower failure as this process is closely linked to variation in water inflow on a daily to monthly timescale. Instead, InVEST calculates the relative contribution of each land parcel to annual average hydropower production and the value of this contribution in terms of energy production. The net present value of hydropower production over the life of the reservoir also can be calculated by summing discounted annual revenues. The model runs on a gridded map. It estimates the quantity and value of water used for hydropower production from each subwatershed in the area of interest. It has three components, which run sequentially. First, it determines the amount of water running off each pixel as the precipitation less the fraction of the water that undergoes evapotranspiration. The model does not differentiate between surface, subsurface and baseflow, but assumes that all water yield from a pixel reaches the point of interest via one of these pathways. This model then sums and averages water yield to the subwatershed level. The pixel-scale calculations allow us to represent the heterogeneity of key driving factors in water yield such as soil type, precipitation, vegetation type, etc. However, the theory we are using as the foundation of this set of models was developed at the subwatershed to watershed scale. We are only confident in the interpretation of these models at the subwatershed scale, so all outputs are summed and/or averaged to the subwatershed scale. We do continue to provide pixel-scale representations of some outputs for calibration and model-checking purposes only. These pixel-scale maps are not to be interpreted for understanding of hydrological processes or to inform decision making of any kind. | Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product." | ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation" | 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...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | 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...Wave run-up, R, can be estimated as R = H(tan α/(√H/Ho) where H is the wave height nearshore, Ho is the deep water wave height, and α is the angle of the beach slope. R may be corrected by a multiplier depending on the porosity of the shoreline surface...The contribution of each grid cell to reduction in wave run-up would depend on its contribution to wave height attenuation (Eq. (S3))." | ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data." | DATA FACT SHEET: "This EnviroAtlas national map estimates the total acres of agricultural crops within each 12-digit hydrologic unit (HUC) that have varying amounts of nearby forested pollinator habitat. The crop types selected from the U.S. Department of Agriculture Cropland Data Layer (CDL) require (or would benefit from) the presence of pollinators, but crops may have no nearby native pollinator habitat. This metric is based on the average flight distance of native bees, both social and solitary, that nest in woodland habitats and forage on native plants and cultivated crops." "The metric was generated using the ESRI ArcMap Neighborhood Distance tool in conjunction with a blended landcover grid, which included the 2006 National Land Cover Dataset (NLCD) and U.S. Department of Agriculture National Agricultural Statistics Service Cropland Data Layer (CDL). Pollinator habitat is defined as trees (fruit, nut, deciduous, and evergreen) for nesting and associated woodland for additional pollen sources. Crops that either require or benefit from pollination were selected and a distance measure of 2.8 kilometers (the average bee species’ foraging distance from the nest4) was used to assess presence or absence of nearby native pollinator habitat. The total area of crops without nearby pollinator habitat was summarized by 12-digit HUC boundaries taken from the NHDPlusV2 Watershed Boundary Dataset (WBD Snapshot)." | ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems." | Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. | ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | ABSTRACT: "...The Land Utilisation & Capability Indicator (LUCI) is a GIS framework that considers impacts of land use on multiple ecosystem services in a holistic and spatially explicit manner. Due to its fine spatial scale and focus on the rural environment, LUCI is well-placed to help both farm and catchment managers to explore and quantify spatially explicit solutions to improve water quality while also maintaining or enhancing other ecosystem service outcomes. LUCI water quality and agricultural productivity models were applied to a catchment in the Bay of Plenty, New Zealand. Nitrogen (N) and phosphorus (P) sources, sinks and pathways in the landscape were identified and trade-offs and synergies between water quality and agricultural productivity were investigated. Results indicate that interventions to improve water quality are likely to come at the expense of agriculturally productive land. Nonetheless, loss of agriculturally productive land can be minimised by using LUCI to identify, at a fine spatial scale, the most appropriate locations for nutrient intervention. Spatially targeted and strategic nutrient source management and pathway interception can improve water quality, while minimising negative financial impacts on farms. Our results provide spatially explicit solutions to optimize agricultural productivity and water quality, which will inform better farm, land and catchment management as well as national and international policy." AUTHOR'S DESCRIPTION (of OVERSEER submodel): "Water quality models within LUCI use an enhanced, spatially representative export co-efficient (EC) approach to model total nitrogen (TN) and total phosphorus (TP) exports to water… Here, ECs for pastoral land cover are calculated by LUCI using algorithms derived from a large ( > 14 000 samples), pastorally based national dataset. The dataset consists of detailed farm nutrient input and management variables that have been entered and run using OVERSEER® to generate nutrient loss predictions, which are also included in the dataset." NOTE: The LUCI model, is a second-generation extension and software implementation of the Polyscape framework, as described in EM-658. https://esml.epa.gov/detail/em/658 | ABSTRACT: "Anadromous alewives (Alosa pseudoharengus) have the potential to alter the nutrient budgets of coastal lakes as they migrate into freshwater as adults and to sea as juveniles. Alewife runs are generally a source of nutrients to the freshwater lakes in which they spawn, but juveniles may export more nutrients than adults import in newly restored populations. A healthy run of alewives in Connecticut imports substantial quantities of phosphorus; mortality of alewives contributes 0.68 g P_fish–1, while surviving fish add 0.18 g P, 67% of which is excretion. Currently, alewives contribute 23% of the annual phosphorus load to Bride Lake, but this input was much greater historically, with larger runs of bigger fish contributing 2.5 times more phosphorus in the 1960s..." AUTHOR'S DESCRIPTION: "Here, we evaluate the patterns of net nutrient loading by alewives over a range of population sizes. We concentrate on phosphorus, as it is generally the nutrient that limits production in the lake ecosystems in which alewives spawn (Schindler 1978). First, we estimate net alewife nutrient loading and parameterize an alewife nutrient loading model using data from an existing run of anadromous alewives in Bride Lake. We then compare the current alewife nutrient load to that in the 1960s when alewives were more numerous and larger. Next, since little is known about the actual patterns of nutrient loading during restoration, we predict the net nutrient loading for a newly restored population across a range of adult escapement… Anadromous fish move nutrients both into and out of freshwater ecosystems, although inputs are typically more obvious and much better studied (Moore and Schindler 2004). Net loading into freshwater ecosystems is fully described as inputs due to adult mortality, gametes, and direct excretion of nutrients minus the removal of nutrients from freshwater ecosystems by juvenile fish when they emigrate… Our research was conducted at Bride Lake and Linsley Pond in Connecticut. Bride Lake contains an anadromous alewife population that we used to both evaluate contemporary and historic net nutrient loading by an alewife population and parameterize our general alewife nutrient loading model." | ABSTRACT: "The 2015 announcement of The Dow Chemical Company's (Dow) Valuing Nature Goal, which aims to identify $1 billion in business value from projects that are better for nature, gives nature a spot at the project design table. To support this goal, Dow and The Nature Conservancy have extended their long-standing collaboration and are now working to develop a defensible methodology to support the implementation of the goal. This paper reviews the nature valuation methodology framework developed by the Collaboration in support of the goal. The nature valuation methodology is a three-step process that engages Dow project managers at multiple stages in the project design and capital allocation processes. The three-step process identifies projects that may have a large impact on nature and then promotes the use of ecosystem service tools, such as the Ecosystem Services Identification and Inventory Tool, to enhance the project design so that it better supports ecosystem health. After reviewing the nature valuation methodology, we describe the results from a case study of redevelopment plans for a 23-acre site adjacent to Dow's Michigan Operations plant along the Tittabawassee River." AUTHOR'S DESCRIPTION: "The ESII Tool measures the environmental impact or proposed land changes through eight specific ecosystem services: (i) water provisioning, (ii) air quality control (nitrogen and particulate removal), (iii) climate regulation (carbon uptake and localized air temperature regulation), (iv) erosion regulation, (v) water quality control (nitrogen and filtration), (vi) water temperature regulation, (vii) water quantity control, and (viii) aesthetics (noise and visual). The ESII Tool allows for direct comparison of the performance of these eight ecosystem services both across project sites and across project design proposals within a site." "The team was also asked to use an iterative design process using the ESII Tool to create alternative restoration scenarios…The project team developed three alternative restoration designs: i) standard brownfield restoration (i.e., cap and plant grass) on the ash pond and 4-D property (referred to as SBR); ii) ecological restoration (i.e., excavate ash and associated soil for secured disposal in approved landfill and restore historic forest, prairie, wetland) of the ash pond only, with SBR on the 4-D property (referred to as ER); and iii) ecological restoration on the ash pond and 4- D property (referred to as ER+)." | ABSTRACT: "The Tampa Bay estuary is a unique and valued ecosystem that currently thrives between subtropical and temperate climates along Florida’s west-central coast. The watershed is considered urbanized (42 % lands developed); however, a suite of critical coastal habitats still persists. Current management efforts are focused toward restoring the historic balance of these habitat types to a benchmark 1950s period. We have modeled the anticipated changes to a suite of habitats within the Tampa Bay estuary using the sea level affecting marshes model (SLAMM) under various sea level rise (SLR) scenarios. Modeled changes to the distribution and coverage of mangrove habitats within the estuary are expected to dominate the overall proportions of future critical coastal habitats. Modeled losses in salt marsh, salt barren, and coastal freshwater wetlands by 2100 will significantly affect the progress achieved in ‘‘Restoring the Balance’’ of these habitat types over recent periods…" | ABSTRACT: "VELMA – Visualizing Ecosystems for Land Management Assessments – is a spatially distributed, eco-hydrological model that links a land surface hydrology model with a terrestrial biogeochemistry model for simulating the integrated responses of vegetation, soil, and water resources to interacting stressors. For example, VELMA can simulate how changes in climate and land use interact to affect soil water storage, surface and subsurface runoff, vertical drainage, evapotranspiration, vegetation and soil carbon and nitrogen dynamics, and transport of nitrate, ammonium, and dissolved organic carbon and nitrogen to water bodies. VELMA differs from other existing eco-hydrology models in its simplicity, flexibility, and theoretical foundation. The model has a user-friendly Graphics User Interface (GUI) for easy input of model parameter values. In addition, advanced visualization of simulation results can enhance understanding of results and underlying concepts. VELMA’s visualization and interactivity features are packaged in an open-source, open-platform programming environment (Java / Eclipse). The development team for VELMA version 2.0 includes Dr. Bob McKane and coworkers at the U.S. Environmental Protection Agency’s Western Ecology Division, Dr. Marc Stieglitz and coworkers at the Georgia Institute of Technology, and Dr. Feifei Pan at the University of North Texas." | ABSTRACT: "We introduce and validate the use of commercially available human mobility datasets based on cell phone locations to estimate visitation to natural areas. By combining this data with on-the-ground observations of visitation to water recreation areas in New England, we fit a model to estimate daily visitation for four months to more than 500 sites. The results show the potential for this new big data source of human mobility to overcome limitations in traditional methods of estimating visitation and to provide consistent information at policy-relevant scales. However, the data providers’ opaque and rapidly developing methods for processing locational information required a calibration and validation against data collected by traditional means to confidently reproduce the desired estimates of visitation. We found that with this calibration, the high-resolution information in both space and time provided by cell phone location-derived data creates opportunities for developing next-generation models of human interactions with the natural environment. " | Abstract: "Greenhouse gas (GHG) emissions from agricultural soils are affected by various environmental factors and agronomic practices. The impact of inorganic nitrogen (N) fertilization rates and timing, and water table management practices on N2O and CO2 emissions were investigated to propose mitigation and adaptation efforts based on simulated results founded on field data. Drawing on 2012–2015 data measured on a subsurface-drained corn (Zea mays L.) field in Southern Quebec, the Root Zone Water Quality Model 2 (RZWQM2) was calibrated and validated for the estimation of N2O and CO2 emissions under free drainage (FD) and controlled drainage with sub-irrigation (CD-SI). Long term simulation from 1971 to 2000 suggested that the optimal N fertilization should be in the range of 125 to 175 kg N ha−1 to obtain higher NUE (nitrogen use efficiency, 7–14%) and lower N2O emission (8–22%), compared to 200 kg N ha−1 for corn-soybean rotation (CS). While remaining crop yields, splitting N application would potentially decrease total N2O emissions by 11.0%. Due to higher soil moisture and lower soil O2 under CD-SI, CO2 emissions declined by 6% while N2O emissions increased by 21% compared to FD. The CS system reduced CO2 and N2O emissions by 18.8% and 20.7%, respectively, when compared with continuous corn production. This study concludes that RZWQM2 model is capable of predicting GHG emissions, and GHG emissions from agriculture can be mitigated using agronomic management." | ABSTRACT: "Conservation organizations and land trusts in North Carolina are increasingly focused on how their work can contribute to both human and ecosystem resilience and adaptation to climate change, as well as directly mitigate climate change through carbon storage and sequestration. Recent state executive and legislative actions also underscore the importance of natural systems for climate adaptation and mitigation, and may provide additional funding for conservation and restoration for those purposes in the near term. To make it more efficient for conservation organizations working in North Carolina to consider a broad suite of conservation benefits in their work, the Conservation Trust for North Carolina and the Nicholas Institute for Energy, Environment & Sustainability at Duke University have developed two online tools for identifying priority areas for conservation action and estimating benefit metrics for specific properties. The conservation prioritization tool finds the sub-watersheds in North Carolina with the greatest potential to provide a set of user-selected conservation benefits. It allows users to identify priority areas for future conservation work within the entire state or a defined region. This high-level tool allows for quick and easy exploration without the need for spatial analysis expertise." | Watersheds around the world are in peril and risk further decline from climate change and human impacts, like pollution, degrading landscapes, and unsustainable water use. These impacts can inhibit the ability of ecosystems to regulate water flows, sequester carbon to reduce atmospheric greenhouse gas levels, maintain biodiversity and healthy waterways, promote social well-being, offer economic opportunities, and sustain agricultural productivity. Climate change is exacerbating these impacts by shifting weather and precipitation patterns, degrading habitats, and increasing the recurrence and severity of natural disasters. Urgent action is needed to address these impacts by implementing nature-based solutions (NBS). NBS protect, sustainably manage, and restore natural or modified watersheds, to address societal challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits (IUCN, 2016). Investment in NBS offers a mechanism to restore degraded watersheds and protect intact ones, leading to improved water quality and quantity, improved carbon sequestration and increased biodiversity, among many other social and economic benefits. NBS also support climate mitigation and adaptation efforts and reduce the impacts from other shocks, such as floods, droughts, and extreme weather events. Implementing NBS can also help advance progress toward achieving the United Nations Sustainable Development Goals (SDGs), particularly SDG 2 (zero hunger), SDG 6 (water), SDG 11 (sustainable cities and communities), SDG 13 (climate action), and SDG 15 (life on land). NBS therefore support social, economic and environmental objectives, and may be particularly important in supporting vulnerable communities. | The Wyoming Community VizTM Partnership was established in 2001 to promote the use of geographic information system-based planning support systems and related decision support technologies in community land-use planning and economic development activities in the State of Wyoming. Partnership members include several state agencies, local governments and several nongovernment organizations. Partnership coordination is provided by the Wyoming Rural Development Council. Research and technical support is coordinated by the Wyoming Geographic Information Science Center’s Spatial Decision Support System Research Program at the University of Wyoming. In June 2002, the Partnership initiated a three-phase plan to promote Community VizTM based planning support systems in Wyoming. Phase I of the Partnership plan was a “proof of concept” pilot project set in Albany County in southeastern Wyoming. The goal of the project was to demonstrate the application of Community VizTM to a Wyoming-specific issue (in this case, aquifer protection) and to determine potential challenges for broader adoption in terms of data requirements, computing infrastructure and technological expertise. The results of the Phase I pilot project are detailed in this report. Efforts are currently underway to secure funding for Phase II of the plan, which expands the use of Community VizTM into four additional Wyoming communities. Specific Phase II objectives are to expand the type and number of issues addressed by Community VizTM and increase the use of Community VizTM in the planning process. As a part of Phase II the Partnership will create a technical assistance network aimed at assisting communities with the technical challenges in applying the software to their planning issues. The third phase will expand the program to more communities in the state, maintain the technical assistance network, and monitor the impact of Community VizTM on the planning process. | This paper shows how the advanced chemical transport model CMAQ can be used to estimate future levels of PM2.5 in the UK, the key air pollutant in terms of human health effects, but one which is largely made up from the formation of secondary particulate in the atmosphere. By adding the primary particulate contribution from typical urban roads and including a margin for error, it is concluded that the current indicative limit value for PM2.5 will largely be met in 2020 assuming 2006 meteorological conditions. Contributions to annual average regional PM2.5 concentration from wild fires in Europe in 2006 and from possible climate change between 2006 and 2020 are shown to be small compared with the change in PM2.5 concentration arising from changes in emissions between 2006 and 2020. The contribution from emissions from major industrial sources regulated in the UK is estimated from additional CMAQ calculations. The potential source strength of these emissions is a useful indicator of the linearity of the response of the atmosphere to changes in emissions. Uncertainties in the modelling of regional and local sources are taken into account based on previous evaluations of the models. Future actual trends in emissions mean that exceedences of limit values may arise, and these and further research into PM2.5 health effects will need to be part of the future strategy to manage PM2.5 concentrations. |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | None identified | None identified | None identified | None identified | None Identified | None provided | climate change | None identified | Land management trade off between agricultural productivity and water quality | Restoration and management of diadromous fish runs in coastal New England | Use ESII to answer the following business decision question: how can Dow close the ash pond while enhancing ecosystem services to Dow and the community and creating local habitat, for a lesser overall cost to Dow than the option currently defined? | None identified | None identified | None identified | None | Allows users to prioritize HUCs within their area of interest based on their conservation goals. | None identified | None provided | None identified |
Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | Range of tree and impervious covers in urban setting | None applicable | Not applicable | 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. | No additional description provided | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | No additional description provided | Hard and soft benthic habitat types approximately to the 33m isobath | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | Groundwater dominated, volcanic caldera catchment, largely comprised of porous allophanic and pumice soils. | Bride Lake is 28.7 ha and linked to Long Island Sound by the 3.3 km Bride Brook. | No additional description provided | No additional description provided | No additional description provided | Natural area water bodies | None | No additional description provided | NA | Groundwater recharge area, City of Laramie | United kingdom atmosphere |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | N/A | Optional future scenarios for changed LULC and wood harvest | No scenarios presented | No scenarios presented | No scenarios presented |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
No scenarios presented | No scenarios presented | air temperature, precipitation, Atmospheric CO2 concentrations | Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | No scenarios presented | current and historical run size | Alternative restoration designs | Varying sea level rise (baseline - 2m), and two habitat adaption strategies | No scenarios presented | N/A | None | No scenarios presented | No scenarios presented | Aquifer protection | 2020 European emissions scenario |
EM ID
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EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
Method Only, Application of Method or Model Run
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Method + Application | Method + Application | Method Only | Method Only | Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method + Application (multiple runs exist) View EM Runs | Method Only | Method + Application | None | Method Only | Method Only | Method + Application (multiple runs exist) | Method + Application |
New or Pre-existing EM?
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New or revised model |
Application of existing model ?Comment:EnviroAtlas uses an application of the i-Tree Hydro model. |
New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | Application of existing 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 | Application of existing model | Application of existing model | New or revised model | New or revised model | None | New or revised model | New or revised model | Application of existing model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM Modeling Approach
EM ID
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EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
EM Temporal Extent
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2007-2008 | 2008-2010 | Not applicable | Not applicable | 1969-2008 | 2006-2007, 2010 | 2006-2007, 2010 | 1993-2013 | 2001-2015 | 2000-2005 | 1961-1990 | 1950-2071 | 1930-2013 | 1960"s and early 2000's | Not reported | 2002-2100 |
Not applicable ?Comment:User defined model duration. |
2017 | 2012-2015 | Not applicable | Not applicable | 2000 | 2006-2020 |
EM Time Dependence
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time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-stationary | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | Not applicable | future time | future time | future time | Not applicable | Not applicable | future time | Not applicable | Not applicable | both | both | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | past time | past time | Not applicable | Not applicable | Not applicable | both |
EM Time Continuity
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Not applicable | Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | Not applicable | discrete |
EM Temporal Grain Size Value
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Not applicable | Not applicable | 1 | 1 | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | 1 | Not applicable | Not applicable | Not applicable | 14 |
EM Temporal Grain Size Unit
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Not applicable | Not applicable | Year | Year | Day | Not applicable | Not applicable | Year | Not applicable | Not applicable | Day | Month | Not applicable | Not applicable | Not applicable | Not applicable | Day | Day | Year | Not applicable | Not applicable | Not applicable | Year |
EM ID
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EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
Bounding Type
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Physiographic or Ecological | Geopolitical | Not applicable | Not applicable | Watershed/Catchment/HUC | Physiographic or ecological | Physiographic or ecological | Geopolitical | Geopolitical | Physiographic or ecological | Point or points | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Watershed/Catchment/HUC | Not applicable | Point or points | Point or points | Not applicable | Not applicable | Watershed/Catchment/HUC | Geopolitical |
Spatial Extent Name
em.detail.extentNameHelp
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Central French Alps | Durham, NC and vicinity | Not applicable | Not applicable | H. J. Andrews LTER WS10 | Coastal zone surrounding St. Croix | Coastal zone surrounding St. Croix | Switzerland | conterminous United States | SW Puerto Rico, | Oak Park Research centre | Santa Basin | Lake Rotorua catchment | Bride Lake and Linsley Pond | Dow Midland Operations facility ash pond and Posey Riverside (4-D property) | Tampa Bay estuary watershed | Not applicable | Cape Cod | Corn field | Not applicable | Not applicable | Laramie City's aquifer protection area | United Kingdom |
Spatial Extent Area (Magnitude)
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10-100 km^2 | 100-1000 km^2 | Not applicable | Not applicable | 10-100 ha | 100-1000 km^2 | 100-1000 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 | 100-1000 km^2 | 1-10 ha | 10,000-100,000 km^2 | 100-1000 km^2 | 10-100 ha | 10-100 ha | 1000-10,000 km^2. | Not applicable | 1000-10,000 km^2. | 1-10 ha | Not applicable | Not applicable | 10-100 km^2 | 100,000-1,000,000 km^2 |
EM ID
em.detail.idHelp
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EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
EM Spatial Distribution
em.detail.distributeLumpHelp
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:pixel is likely 30m x 30m |
spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:See below, grain includes vertical, subsurface dimension. |
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) ?Comment:User defined scale, from plot to basin size. |
spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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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 | volume, for 3-D feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | map scale, for cartographic feature | Not applicable | Not applicable | Not applicable |
Spatial Grain Size
em.detail.spGrainSizeHelp
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20 m x 20 m | irregular | Not specified | application specific | 30 m x 30 m surface pixel and 2-m depth soil column | 10 m x 10 m | 10 m x 10 m | 5 sites | irregular | not reported | Not applicable | 1 km2 | 5m x 5m | Not applicable | map unit | 10 x 10 m | user defined | water feature edge (beach) | Not applicable | HUC 12 | Not applicable | Not applicable | Not applicable |
EM ID
em.detail.idHelp
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EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
EM Computational Approach
em.detail.emComputationalApproachHelp
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Analytic | Numeric | Numeric | Analytic | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Numeric | Numeric | Analytic | Analytic | Analytic | Analytic | Numeric | Numeric | * | Other or unclear (comment) | Analytic | Numeric | Analytic |
EM Determinism
em.detail.deterStochHelp
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deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | deterministic | None | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
em.detail.idHelp
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EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
Model Calibration Reported?
em.detail.calibrationHelp
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No | Yes |
Yes ?Comment:Annual Yield can be calibrated with actual yield based up 10 year average input data though this was an "optional" part of the model. Calibrate with total precipitation and potential evapotranspiration. Before the calibration process is commenced, the modelers suggest performing a sensitivity analysis with the observed runoff data to define the parameters that influence model outputs the most. The calibration can then focus on highly sensitive parameters followed by less sensitive ones. |
Not applicable | No | Yes | Yes | No | No | No | No | No | No | Yes | Unclear | No | Not applicable | Yes | None | Not applicable | Not applicable | Unclear | Yes |
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | Yes | Not applicable | Not applicable | No | No | No | No | No | Yes |
Yes ?Comment:for N2O fluxes |
No | No | No | No | No | Not applicable |
Yes ?Comment:Random forest model performance statistics |
None | Not applicable | Not applicable | No |
Yes ?Comment:Two versions of CMAQ (v4.6 and v4.7) were used to assess performance. Both values are provided here respectively. |
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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None | None | None | None | None | None | None |
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None | None | None | None | None | None |
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None | None | None | None |
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Model Operational Validation Reported?
em.detail.validationHelp
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No | No | No | Not applicable | No | Yes | Yes | Yes | No | Yes | Yes | Yes | No | No | Unclear | No | Not applicable | Yes | None | Not applicable | Unclear | Unclear | No |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | Not applicable | No | No | No | No | No | No | No | No | No | No | No | No | Not applicable | Unclear | None | Not applicable | Not applicable | Unclear | Unclear |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | Unclear | Not applicable | Not applicable | No | No | No | No | No | Yes | No | No | No | Yes | No | No | Not applicable | Yes | None | Not applicable | Not applicable | Unclear | Unclear |
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Unclear | None | 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-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
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None | None |
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None | None |
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None |
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None |
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None | None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
None | None | None | None | None |
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None | None |
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None | None | None | None | None |
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None | None | None | None | None | None |
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Centroid Lat/Long (Decimal Degree)
EM ID
em.detail.idHelp
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EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
Centroid Latitude
em.detail.ddLatHelp
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45.05 | 35.99 | -9999 | -9999 | 44.25 | 17.73 | 17.73 | 46.82 | 39.5 | 17.9 | 52.86 | -9.05 | -38.14 | 41.33 | 43.6 | 27.76 | Not applicable | 41.72 | 45.32 | Not applicable | Not applicable | 41.31 | 54 |
Centroid Longitude
em.detail.ddLongHelp
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6.4 | -78.96 | -9999 | -9999 | -122.33 | -64.77 | -64.77 | 8.23 | -98.35 | 67.11 | 6.54 | -77.81 | 176.25 | -72.24 | -84.24 | -82.54 | Not applicable | -70.29 | 74.17 | Not applicable | Not applicable | -105.46 | 4 |
Centroid Datum
em.detail.datumHelp
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WGS84 | WGS84 | Not applicable | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | None provided | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | None provided | Not applicable | Not applicable | WGS84 | WGS84 |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | Estimated | Not applicable | Not applicable | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Provided | Estimated | Estimated | Estimated | Estimated | Estimated | Not applicable | Estimated | Provided | Not applicable | Not applicable | Estimated | Estimated |
EM ID
em.detail.idHelp
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EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Rivers and Streams | Ground Water | Created Greenspace | Rivers and Streams | Not applicable | Forests | Near Coastal Marine and Estuarine | Near Coastal Marine and Estuarine | Forests | Terrestrial Environment (sub-classes not fully specified) | Near Coastal Marine and Estuarine | Agroecosystems | Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Ground Water | Forests | Agroecosystems | Scrubland/Shrubland | Rivers and Streams | Lakes and Ponds | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Lakes and Ponds | Near Coastal Marine and Estuarine | None | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Atmosphere |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Urban areas including streams | Watershed | Terrestrial environments, but not specified for methods | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Coral reefs | Coral reefs | forests | Terrestrial | shallow coral reefs | farm pasture | tropical, coastal to montane | Largely agricultural, commercial forestry, non-commercial forest and shrubland and urban | Coastal lakes and ponds and associated streams | Ash pond and surrounding environment | Esturary and associated urban and terrestrial environment | Terrestrial | beaches | None | Terrestrial and freshwater aquatic | None | watershed | United Kingdom atmosphere |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Not applicable | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale 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 |
Other or unclear (comment) ?Comment:Variable data was derived from multiple climate data stations distrubuted across the study area. The location and distribution of the data stations was not provided. |
Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale 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 | None | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
em.detail.idHelp
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EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
EM Organismal Scale
em.detail.orgScaleHelp
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Community | Community | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Community | Guild or Assemblage | Guild or Assemblage | Not applicable | Not applicable | Not applicable | Individual or population, within a species | Not applicable | Not applicable | Not applicable | Not applicable | None | Not applicable | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available | None Available | None Available |
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None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
EnviroAtlas URL
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
None |
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None |
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None | None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-79 | EM-142 | EM-368 | EM-374 | EM-379 | EM-449 | EM-450 |
EM-467 ![]() |
EM-491 | EM-590 |
EM-593 ![]() |
EM-618 ![]() |
EM-659 |
EM-661 ![]() |
EM-713 ![]() |
EM-863 ![]() |
EM-884 | EM-943 | EM-962 | EM-972 | EM-1001 | EM-1015 | EM-1021 |
None |
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None | None |
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None |
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None |
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None | None |
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None | None | None |
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