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-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
EM Short Name
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EnviroAtlas-Air pollutant removal | Divergence in flowering date, Central French Alps | ROS (Recreation Opportunity Spectrum), Europe | ARIES open Space, Puget Sound Region, USA | ARIES sediment regulation, Puget Sound Region, USA | InVEST carbon storage and sequestration (v3.2.0) | VELMA soil temperature, Oregon, USA | Retained rainwater, Guánica Bay, Puerto Rico | Decrease in erosion (shoreline), St. Croix, USVI | Yasso07 v1.0.1, Switzerland | DayCent N2O flux simulation, Ireland | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | Alwife phosphorus flux in lakes, Connecticut, USA | C sequestration in grassland restoration, England | Wildflower mix supporting bees, CA, USA | InVEST Coastal Vulnerability | ARIES Sediment regulation, Santa Fe, NM | NBS benefits explorer |
EM Full Name
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US EPA EnviroAtlas - Pollutants (air) removed annually by tree cover; Example is shown for Durham NC and vicinity, USA | Functional divergence in flowering date, Central French Alps | ROS (Recreation Opportunity Spectrum), Europe | ARIES (Artificial Intelligence for Ecosystem Services) Open Space Proximity for Homeowners, Puget Sound Region, Washington, USA | ARIES (Artificial Intelligence for Ecosystem Services) Sediment Regulation for Reservoirs, Puget Sound Region, Washington, USA | InVEST v3.2.0 Carbon storage and sequestration | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | Retained rainwater, Guánica Bay, Puerto Rico, USA | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland | DayCent simulation N2O flux and climate change, Ireland | DeNitrification-DeComposition simulation of N2O flux Ireland | Net phosphorus flux in freshwater lakes from alewives, Connecticut, USA | Carbon sequestration in grassland diversity restoration, England | Wildflower planting mix supporting bees in agricultural landscapes, CA, USA | InVEST Coastal Vulnerability | Artificial Intelligence for Ecosystem Services (ARIES); Sediment regulation, Santa Fe, New Mexico | Benefit Accounting of Nature-Based Solutions for Watersheds: Guide |
EM Source or Collection
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US EPA | EnviroAtlas | i-Tree ?Comment:EnviroAtlas uses an application of the i-Tree Eco model. |
EU Biodiversity Action 5 | EU Biodiversity Action 5 | ARIES | ARIES | InVEST | US EPA | US EPA | US EPA | None | None | None | None | None | None | InVEST | None | None |
EM Source Document ID
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223 | 260 | 293 | 302 | 302 | 315 | 317 | 338 | 335 | 343 | 358 | 358 | 383 | 396 | 400 | 408 | 411 | 471 |
Document Author
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US EPA Office of Research and Development - National Exposure Research Laboratory | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Paracchini, M.L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J.P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P.A., and Bidoglio, G. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W. | The Natural Capital Project | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher | Yee, S. H., Dittmar, J. A., and L. M. Oliver | Didion, M., B. Frey, N. Rogiers, and E. Thurig | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | West, D. C., A. W. Walters, S. Gephard, and D. M. Post | De Deyn, G. B., R. S. Shiel, N. J. Ostle, N. P. McNamara, S. Oakley, I. Young, C. Freeman, N. Fenner, H. Quirk, and R. D. Bardgett | Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters | The Natural Capital Project.org | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Brill, G., T. Shiao, C. Kammeyer, S. Diringer, K. Vigerstol, N. Ofosu-Amaah, M. Matosich, C. Müller-Zantop, W. Larson and T. Dekker |
Document Year
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2013 | 2011 | 2014 | 2014 | 2014 | 2015 | 2013 | 2017 | 2014 | 2014 | 2010 | 2010 | 2010 | 2011 | 2015 | None | 2018 | 2022 |
Document Title
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EnviroAtlas - Featured Community | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments | Carbon storage and sequestration - InVEST (v3.2.0) | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | 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 | Validating tree litter decomposition in the Yasso07 carbon model | 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 | 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 | Nutrient loading by anadromous alewife (Alosa pseudoharengus): contemporary patterns and predictions for restoration efforts | Additional carbon sequestration benefits of grassland diversity restoration | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | InVEST Coastal Vulnerability | Towards globally customizable ecosystem service models | Benefit Accounting of Nature-Based Solutions for Watersheds: Guide |
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 |
Comments on Status
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Published on US EPA EnviroAtlas website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Website | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | Website users guide | Published journal manuscript | Published report |
EM ID
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EM-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
https://www.epa.gov/enviroatlas | Not applicable | Not applicable | http://aries.integratedmodelling.org/ | http://aries.integratedmodelling.org/ | 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 | Not applicable | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | Not applicable | https://naturalcapitalproject.stanford.edu/software/invest |
https://integratedmodelling.org/hub/#/register ?Comment:Need to set up an account first and then can access the main integrated modelling hub page: |
https://nbsbenefitsexplorer.net/tool | |
Contact Name
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EnviroAtlas Team | Sandra Lavorel | Maria Luisa Paracchini | Ken Bagstad | Ken Bagstad | The Natural Capital Project | Alex Abdelnour | Susan H. Yee | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
M. Abdalla | M. Abdalla | Derek C. West | Gerlinde B. De Deyn | Neal Williams | Not applicable | Javier Martinez-Lopez | Gregg Brill |
Contact Address
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Not reported | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Joint Research Centre, Institute for Environment and Sustainability, Via E.Fermi, 2749, I-21027 Ispra (VA), Italy | Geosciences and Environmental Change Science Center, US Geological Survey | Geosciences and Environmental Change Science Center, US Geological Survey | 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 | 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 | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Dept. of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, CT 06511, USA | Dept. of Terrestrial Ecology, Netherlands Institute of Ecology, P O Box 40, 6666 ZG Heteren, The Netherlands | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 | Not applicable | BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | Not reported |
Contact Email
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enviroatlas@epa.gov | sandra.lavorel@ujf-grenoble.fr | luisa.paracchini@jrc.ec.europa.eu | kjbagstad@usgs.gov | kjbagstad@usgs.gov | invest@naturalcapitalproject.org | abdelnouralex@gmail.com | yee.susan@epa.gov | yee.susan@epa.gov | markus.didion@wsl.ch | abdallm@tcd.ie | abdallm@tcd.ie | derek.west@yale.edu | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com | nmwilliams@ucdavis.edu | Not applicable | javier.martinez@bc3research.org | Not reported |
EM ID
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EM-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
Summary Description
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The Air Pollutant Removal model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina. ... pollution removal ... 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: The maps, estimate and illustrate the variation in the amount of six airborne pollutants, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM10), and particulate matter (PM2.5), removed by trees. PM10 is for particulate matter greater than 2.5 microns and less than 10 microns. DATA FACT SHEET: "The data for this map are based on the land cover derived for each EnviroAtlas community and the pollution removal models in i-Tree, a toolkit developed by the USDA Forest Service. The land cover data were created from aerial photography through remote sensing methods; tree cover was then summarized as the percentage of each census block group. The i-Tree pollution removal module uses the tree cover data by block group, the closest hourly meteorological monitoring data for the community, and the closest pollution monitoring data... hourly estimates of pollution removal by trees were combined with atmospheric data to estimate hourly percent air quality improvement due to pollution removal for each pollutant." | 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." | ABSTRACT: "Research on ecosystem services mapping and valuing has increased significantly in recent years. However, compared to provisioning and regulating services, cultural ecosystem services have not yet beenfully integrated into operational frameworks. One reason for this is that transdisciplinarity is required toaddress the issue, since by definition cultural services (encompassing physical, intellectual, spiritual inter-actions with biota) need to be analysed from multiple perspectives (i.e. ecological, social, behavioural).A second reason is the lack of data for large-scale assessments, as detailed surveys are a main sourceof information. Among cultural ecosystem services, assessment of outdoor recreation can be based ona large pool of literature developed mostly in social and medical science, and landscape and ecologystudies. This paper presents a methodology to include recreation in the conceptual framework for EUwide ecosystem assessments (Maes et al., 2013), which couples existing approaches for recreation man-agement at country level with behavioural data derived from surveys and population distribution data.The proposed framework is based on three components: the ecosystem function (recreation potential),the adaptation of the Recreation Opportunity Spectrum framework to characterise the ecosystem serviceand the distribution of potential demand in the EU." | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "For open space proximity, we mapped the relative value of open space, highways that impede walking access or reduce visual and soundscape quality, and housing locations, connected by a flow model simulating physical access to desirable spaces. We used reviews of the hedonic valuation literature (Bourassa et al. 2004, McConnell and Walls 2005) to inform model development, ranking the influence of different open space characteristics on property values to parameterize the source and sink models. The model includes a distance decay function that accounts for changes with distance in the value of open space. We then computed the ratio of actual to theoretical provision of open space to compare the values accruing to homeowners relative to those for the entire landscape." | ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We mapped sediment regulation as the location of sediment sinks (depositional areas in floodplains), which can absorb sediment transported by hydrologic flows from upstream sources (erosionprone areas) prior to reaching users. In this case the benefit of avoided sedimentation is provided to 29 major reservoirs. Avoided sedimentation helps maintain the ability of reservoirs to provide benefits including hydroelectric power generation, flood control, recreation, and water supply to beneficiaries through the region. Avoided reservoir sedimentation likely helps to protect each of these benefits in different ways, i.e., increased turbidity or the loss of reservoir storage capacity may have a greater impact on some provision of some benefit types than others. For our purposes we ended the modeling and mapping exercise at the reservoirs. Reservoir sedimentation reduces their storage capacity, typically decreasing their ability to provide these benefits without costly dredging. We thus used a probabilistic Bayesian model of soil erosion incorporating vegetation, soils, and rainfall influences and calibrated using regional data from coarser scale and/or RUSLE derived erosion models (Bagstad et al. 2011). We probabilistically modeled sediment deposition in floodplains using data for floodplain vegetation, floodplain width, and stream gradient, which can influence rates of deposition. We calculated the ratio of actual to theoretical sediment regulation using the aggregated sink values upstream of reservoirs in the Puget Sound region, divided by aggregated theoretical sink values for the entire landscape." | 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" | AUTHOR'S DESCRIPTION: "In total, 19 ecosystem services metrics were identified as relevant to stakeholder objectives in the Guánica Bay watershed identified during the 2013 Public Values Forum (Table 2)...Ecological production functions were applied to translate LULC measures of ecosystem condition to supply of ecosystem services…The volume of retained rainwater per unit area (in^3/in^2) includes both the maximum soil moisture retention and the initial abstraction of water before runoff due to infiltration, evaporation, or interception by vegetation…" | 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 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." | 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. | 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. 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. | 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: "A major aim of European agri-environment policy is the management of grassland for botanical diversity conservation and restoration, together with the delivery of ecosystem services including soil carbon (C) sequestration. To test whether management for biodiversity restoration has additional benefits for soil C sequestration, we investigated C and nitrogen (N) accumulation rates in soil and C and N pools in vegetation in a long-term field experiment (16 years) in which fertilizer application and plant seeding were manipulated. In addition, the abundance of the legume Trifolium pratense was manipulated for the last 2 years. To unravel the mechanisms underlying changes in soil C and N pools, we also tested for effects of diversity restoration management on soil structure, ecosystem respiration and soil enzyme activities…" AUTHOR'S DESCRIPTION: "Measurements were made on 36 plots of 3 x 3 m comprising two management treatments (and their controls) in a long-term multifactorial grassland restoration experiment which have successfully increased plant species diversity, namely the cessation of NPK fertilizer application and the addition of seed mixtures…" | Abstract: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm | Faced with an intensification of human activities and a changing climate, coastal communities need to better understand how modifications of the biological and physical environment (i.e. direct and indirect removal of natural habitats for coastal development) can affect their exposure to storm-induced erosion and flooding (inundation). The InVEST Coastal Vulnerability model produces a qualitative estimate of such exposure in terms of a vulnerability index, which differentiates areas with relatively high or low exposure to erosion and inundation during storms. By coupling these results with global population information, the model can show areas along a given coastline where humans are most vulnerable to storm waves and surge. The model does not take into account coastal processes that are unique to a region, nor does it predict long- or short-term changes in shoreline position or configuration. Model inputs, which serve as proxies for various complex shoreline processes that influence exposure to erosion and inundation, include: a polyline with attributes about local coastal geomorphology along the shoreline, polygons representing the location of natural habitats (e.g., seagrass, kelp, wetlands, etc.), rates of (observed) net sea-level change, a depth contour that can be used as an indicator for surge level (the default contour is the edge of the continental shelf), a digital elevation model (DEM) representing the topography of the coastal area, a point shapefile containing values of observed storm wind speed and wave power, and a raster representing population distribution. Outputs can be used to better understand the relative contributions of these different model variables to coastal exposure and highlight the protective services offered by natural habitats to coastal populations. This information can help coastal managers, planners, landowners and other stakeholders identify regions of greater risk to coastal hazards, which can in turn better inform development strategies and permitting. The results provide a qualitative representation of coastal hazard risks rather than quantifying shoreline retreat or inundation limits. | 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. " | 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. |
Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | None identified | None identified | None identified | Meeting water demands for agriculture and domestic purposes. | None identified | None identified | climate change | climate change | Restoration and management of diadromous fish runs in coastal New England | None identified | None identified | None identified | None identified | None identified |
Biophysical Context
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No additional description provided | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | No additional description provided | No additional description provided | No additional description provided | 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 descriptions provided | No additional description provided | Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa). | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C | Bride Lake is 28.7 ha and linked to Long Island Sound by the 3.3 km Bride Brook. | Lolium perenne-Cynosorus cristatus grassland; The soil is a shallow brown-earth (average depth 28 cm) over limestone of moderate-high residual fertility. | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca | Not applicable | Watersheds surrounding Santa Fe and Albuquerque, New Mexico | NA |
EM Scenario Drivers
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No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | No scenarios presented | 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). |
air temperature, precipitation, Atmospheric CO2 concentrations | fertilization | current and historical run size | Additional benefits due to biodiversity restoration practices | Varied wildflower planting mixes of annuals and perennials | Options for future sea level change and population change | N/A | No scenarios presented |
EM ID
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EM-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application | Method + Application | Method Only | Method + Application | Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment: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 (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 | Method Only |
New or Pre-existing EM?
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Application of existing model | New or revised model | Application of existing model | New or revised model | New or revised model | New or revised model | Application of existing model | Application of existing model | Application of existing model | 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 | Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
Document ID for related EM
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Doc-345 | Doc-260 | Doc-269 | Doc-290 | Doc-291 | Doc-289 | Doc-303 | Doc-305 | Doc-303 | Doc-305 | Doc-309 | Doc-13 | Doc-317 | None | Doc-335 | Doc-342 | Doc-344 | None | None | None | None | Doc-400 | Doc-410 | Doc-411 | None |
EM ID for related EM
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None | EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | None | None | None | EM-349 | EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | None | EM-447 | EM-448 | EM-466 | EM-469 | EM-480 | EM-485 | EM-598 | EM-593 | EM-667 | EM-672 | EM-674 | EM-673 | None | EM-784 | EM-793 | EM-851 | None | None |
EM Modeling Approach
EM ID
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EM-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
EM Temporal Extent
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2008-2010 | 2007-2008 | Not reported | 2000-2011 | 1971-2005 | Not applicable | 1969-2008 | 2006 - 2012 | 2006-2007, 2010 | 1993-2013 | 1961-1990 | 1961-1990 | 1960"s and early 2000's | 1990-2007 | 2011-2012 | Not applicable | 2011 | Not applicable |
EM Time Dependence
em.detail.timeDependencyHelp
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time-dependent | time-stationary | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-dependent | time-dependent | time-stationary | time-stationary | time-dependent | time-stationary | time-stationary | time-stationary |
EM Time Reference (Future/Past)
em.detail.futurePastHelp
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future time | Not applicable | Not applicable | Not applicable | Not applicable | future time | future time | Not applicable | Not applicable | future time | both | both | Not applicable | Not applicable | past time | Not applicable | Not applicable | Not applicable |
EM Time Continuity
em.detail.continueDiscreteHelp
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discrete | Not applicable | Not applicable | Not applicable | Not applicable | discrete | discrete | Not applicable | Not applicable | discrete | discrete | discrete | Not applicable | Not applicable | discrete | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
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1 | Not applicable | Not applicable | Not applicable | Not applicable | 1 | 1 | Not applicable | Not applicable | 1 | 1 | 1 | Not applicable | Not applicable | 1 | Not applicable | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Hour | Not applicable | Not applicable | Not applicable | Not applicable | Year | Day | Not applicable | Not applicable | Year | Day | Day | Not applicable | Not applicable | Year | Not applicable | Not applicable | Not applicable |
EM ID
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EM-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
Bounding Type
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Geopolitical | Physiographic or Ecological | Geopolitical | Physiographic or ecological | Physiographic or ecological | Not applicable | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Point or points | Point or points | Watershed/Catchment/HUC | Other |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
Not applicable | Watershed/Catchment/HUC | Not applicable |
Spatial Extent Name
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Durham NC and vicinity | Central French Alps | European Union countries | Puget Sound Region | Puget Sound Region | Not applicable | H. J. Andrews LTER WS10 | Guanica Bay watershed | Coastal zone surrounding St. Croix | Switzerland | Oak Park Research centre | Oak Park Research centre | Bride Lake and Linsley Pond | Colt Park meadows, Ingleborough National Nature Reserve, northern England | Agricultural plots | Not applicable | Santa Fe Fireshed | Not applicable |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | 10-100 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 | 10,000-100,000 km^2 | Not applicable | 10-100 ha | 1000-10,000 km^2. | 100-1000 km^2 | 10,000-100,000 km^2 | 1-10 ha | 1-10 ha | 10-100 ha | <1 ha | 10-100 km^2 | Not applicable | 100-1000 km^2 | Not applicable |
EM ID
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EM-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Spatial grain type is census block group. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment: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 lumped (in all cases) | spatially lumped (in all cases) | spatially lumped (in all 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) |
Spatial Grain Type
em.detail.spGrainTypeHelp
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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 | 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) | Not applicable | Not applicable | Not applicable | area, for pixel or radial feature | Not applicable | area, for pixel or radial feature | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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irregular | 20 m x 20 m | 100 m x 100 m | 200m x 200m | 200m x 200m | application specific | 30 m x 30 m surface pixel and 2-m depth soil column | 30 m x 30 m | 10 m x 10 m | 5 sites | Not applicable | Not applicable | Not applicable | 3 m x 3 m | Not applicable | user defined | 30 m | Not applicable |
EM ID
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EM-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
EM Computational Approach
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Numeric | Analytic | Analytic | Analytic | Analytic | Analytic | Numeric | Analytic | Analytic | Numeric | Numeric | Numeric | Analytic | Analytic | Numeric | Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | stochastic | deterministic | deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
Model Calibration Reported?
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Unclear | No | No | No | Yes | Not applicable | No | No | Yes | No | No | Yes | Yes | Not applicable | No | Not applicable | Unclear | Not applicable |
Model Goodness of Fit Reported?
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No | Yes | No | No | No | Not applicable | No | No | No | No |
Yes ?Comment:for N2O fluxes |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
No | Not applicable | No | Not applicable | No | Not applicable |
Goodness of Fit (metric| value | unit)
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None |
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None | None | None | None | None | None | None | None |
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None | None | None | None | None | None |
Model Operational Validation Reported?
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No | No | No | No | No | Not applicable | No | No | Yes | Yes | Yes | Yes | No | No | No | Not applicable | No | Unclear |
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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No | No | No | No | No | Not applicable | No | No | No | No | No | No | No | No | No | Not applicable | No | Not applicable |
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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No | No | No | No | No | Not applicable | No | No | No | No | No | No | Yes | No | No | Not applicable | No | Not applicable |
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 | Not applicable | Not applicable | Not applicable | Unclear | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
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None |
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None |
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None |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
None | None | None |
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None | None | None | None |
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None | None | None | None | None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
Centroid Latitude
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35.99 | 45.05 | 48.2 | 48 | 48 | -9999 | 44.25 | 17.96 | 17.73 | 46.82 | 52.86 | 52.86 | 41.33 | 54.2 | 29.4 | Not applicable | 35.86 | Not applicable |
Centroid Longitude
em.detail.ddLongHelp
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-78.96 | 6.4 | 16.35 | -123 | -123 | -9999 | -122.33 | -67.02 | -64.77 | 8.23 | 6.54 | 6.54 | -72.24 | -2.35 | -82.18 | Not applicable | -105.76 | Not applicable |
Centroid Datum
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None provided | WGS84 | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | WGS84 | WGS84 | WGS84 | None provided | None provided | WGS84 | WGS84 | WGS84 | Not applicable | WGS84 | Not applicable |
Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Estimated | Provided | Estimated | Estimated | Estimated | Not applicable | Provided | Estimated | Estimated | Estimated | Provided | Provided | Estimated | Provided | Provided | Not applicable | Estimated | Not applicable |
EM ID
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EM-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Created Greenspace | Atmosphere | Agroecosystems | Grasslands | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Created Greenspace | Rivers and Streams | Lakes and Ponds | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Not applicable | Forests | Inland Wetlands | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Barren | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Agroecosystems | Rivers and Streams | Lakes and Ponds | Agroecosystems | Grasslands | Agroecosystems | Near Coastal Marine and Estuarine | Terrestrial 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 |
Specific Environment Type
em.detail.specificEnvTypeHelp
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Urban and vicinity | Subalpine terraces, grasslands, and meadows | Not applicable | Terrestrial environment surrounding a large estuary | Terrestrial environment surrounding a large estuary | 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). | 13 LULC were used | Coral reefs | forests | farm pasture | farm pasture | Coastal lakes and ponds and associated streams | fertilized grassland (historically hayed) | Agricultural landscape | Coastal environments | watersheds | None |
EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is finer 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 corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | 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 corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale is finer than that of 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-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
EM Organismal Scale
em.detail.orgScaleHelp
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Not applicable | Community | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Community | Not applicable | Not applicable | Individual or population, within a species | Community | Species | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available | None Available |
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None Available |
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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-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
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None |
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None |
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<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-59 ![]() |
EM-79 | EM-184 | EM-315 | EM-327 | EM-374 | EM-379 | EM-428 | EM-449 |
EM-467 ![]() |
EM-593 ![]() |
EM-598 |
EM-661 ![]() |
EM-735 ![]() |
EM-812 ![]() |
EM-849 | EM-860 | EM-1001 |
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None |
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None | None | None | None |
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None |
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None | None |
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None | None | None |