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EM Identification (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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EM Short Name
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Divergence in flowering date, Central French Alps | Land-use change and crop-based production, Europe | ROS (Recreation Opportunity Spectrum), Europe | ARIES open Space, Puget Sound Region, USA | InVEST carbon storage and sequestration (v3.2.0) | VELMA soil temperature, Oregon, USA | Decrease in erosion (shoreline), St. Croix, USVI | Yasso07 v1.0.1, Switzerland | EnviroAtlas - Restorable wetlands | DayCent N2O flux simulation, Ireland | DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland | Waterfowl pairs, CREP wetlands, Iowa, USA | Sedge Wren density, CREP, Iowa, USA | Savannah Sparrow density, CREP, Iowa, USA | C sequestration in grassland restoration, England | Wildflower mix supporting bees, CA, USA | ARIES Sediment regulation, Santa Fe, NM | SLAMM, Tampa Bay, FL, USA | COBRA v 4.1 | Atlantis ecosystem biology submodel | CommunityViz, Albany county, Wyoming |
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EM Full Name
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Functional divergence in flowering date, Central French Alps | Land-use change effects on crop-based production, Europe | ROS (Recreation Opportunity Spectrum), Europe | ARIES (Artificial Intelligence for Ecosystem Services) Open Space Proximity for Homeowners, Puget Sound Region, Washington, USA | 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 | Yasso07 v1.0.1 forest litter decomposition, Switzerland | US EPA EnviroAtlas - Percent potentially restorable wetlands, USA | DayCent simulation N2O flux and climate change, Ireland | DeNitrification-DeComposition simulation of N2O flux Ireland | Waterfowl pairs, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Sedge Wren population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Savannah Sparrow population density, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA | Carbon sequestration in grassland diversity restoration, England | Wildflower planting mix supporting bees in agricultural landscapes, CA, USA | Artificial Intelligence for Ecosystem Services (ARIES); Sediment regulation, Santa Fe, New Mexico | SLAMM (sea level affecting marshes model), Tampa Bay, Florida, USA | COBRA (CO–Benefits Risk Assessment) v 4.1 | Calibrating process-based marine ecosystem models: An example case using Atlantis | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming. |
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EM Source or Collection
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EU Biodiversity Action 5 | EU Biodiversity Action 5 | EU Biodiversity Action 5 | ARIES | InVEST | US EPA | US EPA | * | US EPA | EnviroAtlas | * | * | * | * | * | * | * | * | None | US EPA | * | * |
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EM Source Document ID
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260 | 228 | 293 | 302 | 315 | 317 | 335 | 343 | 262 | 358 | 358 | 372 | 372 | 372 | 396 | 400 | 411 |
415 ?Comment:Secondary sources: Documents 412 and 413. |
437 ?Comment:User's manual is provided at the webpage. |
459 |
479 ?Comment:Published as a report by the University of Wyoming, but no record of peer review. |
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Document Author
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Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Haines-Young, R., Potschin, M. and Kienast, F. | 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. | 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 | Didion, M., B. Frey, N. Rogiers, and E. Thurig | US EPA Office of Research and Development - National Exposure Research Laboratory | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M. | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | Otis, D. L., W. G. Crumpton, D. Green, A. K. Loan-Wilsey, R. L. McNeely, K. L. Kane, R. Johnson, T. Cooper, and M. Vandever | 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 | Martinez-Lopez, J.M., Bagstad, K.J., Balbi, S., Magrach, A., Voigt, B. Athanasiadis, I., Pascual, M., Willcock, S., and F. Villa. | Sherwood, E. T. and H. S. Greening | US EPA | Pethybridge, H. R., Weijerman, M., Perrymann, H., Audzijonyte, A., Porobic, J., McGregor, V., … & Fulton, E. | Lieske, S. N., Mullen, S., Knapp, M., & Hamerlinck, J. D. |
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Document Year
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2011 | 2012 | * | * | 2015 | 2013 | * | * | 2013 | 2010 | 2010 | 2010 | 2010 | 2010 | 2011 | 2015 | 2018 | 2014 | 2021 | 2019 | 2003 |
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Document Title
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Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | 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 | 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 | Validating tree litter decomposition in the Yasso07 carbon model | EnviroAtlas - National | 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 | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt | Additional carbon sequestration benefits of grassland diversity restoration | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States | Towards globally customizable ecosystem service models | Potential impacts and management implications of climate change on Tampa Bay estuary critical coastal habitats | CO-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA) | Calibrating process-based marine ecosystem models: An example case using Atlantis | Wyoming Community Viz TM Partnership Phase I Pilot: Aquifer Protection and Community Viz TM in Albany County, Wyoming |
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Document Status
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* | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * | Peer reviewed and published | * | * | Not peer reviewed but is published (explain in Comment) |
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Comments on Status
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* | * | * | * | Website | * | * | * | Published on US EPA EnviroAtlas website | * | * | Published report | Published report | Published report | * | * | * | Published journal manuscript | Webpage | * | Published report |
Software and Access (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
| Not applicable | Not applicable | Not applicable | 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 | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | https://www.epa.gov/enviroatlas | Not applicable | http://www.dndc.sr.unh.edu | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
https://integratedmodelling.org/hub/#/register ?Comment:Need to set up an account first and then can access the main integrated modelling hub page: |
http://warrenpinnacle.com/prof/SLAMM/index.html com/prof/SLAMM/index.html | https://www.epa.gov/cobra | https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | https://communityviz.com/ | |
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Contact Name
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Sandra Lavorel | Marion Potschin | Maria Luisa Paracchini | Ken Bagstad | The Natural Capital Project | Alex Abdelnour | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
EnviroAtlas Team | M. Abdalla | M. Abdalla | David Otis | David Otis | David Otis | Gerlinde B. De Deyn | Neal Williams | Javier Martinez-Lopez | Edward T. Sherwood | Emma Zinsmeister | Heidi R. Pethybridge | Scott Lieske |
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Contact Address
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Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | 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 | 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 | Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland | Not reported | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University | 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 | BC3-Basque Centre for Climate Change, Sede Building 1, 1st floor, Scientific Campus of the Univ. of the Basque Country, 48940 Leioa, Spain | Tampa Bay Estuary Program, 263 13th Avenue South, St. Petersburg, FL 33701, USA | EPA’s Office of Atmospheric Programs’ Climate Protection Partnerships Division | CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, Tasmania, 7000, Australia | Department of Agricultural & Applied Economics University of Wyoming, Laramie WY 82071 |
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Contact Email
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sandra.lavorel@ujf-grenoble.fr | marion.potschin@nottingham.ac.uk | luisa.paracchini@jrc.ec.europa.eu | kjbagstad@usgs.gov | invest@naturalcapitalproject.org | abdelnouralex@gmail.com | yee.susan@epa.gov | markus.didion@wsl.ch | enviroatlas@epa.gov | abdallm@tcd.ie | abdallm@tcd.ie | dotis@iastate.edu | dotis@iastate.edu | dotis@iastate.edu | g.dedeyn@nioo.knaw.nl; gerlindede@gmail.com | nmwilliams@ucdavis.edu | javier.martinez@bc3research.org | esherwood@tbep.org | zinsmeister.emma@epa.gov | Heidi.Pethybridge@csiro.au | lieske@uwyo.edu |
EM Description (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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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." | ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Crop-based production); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: "The analysis for “Crop-based production” maps all the areas that are important for food crops produced through commercial agriculture….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." | 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." | 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 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 depicts the percent potentially restorable wetlands within each subwatershed (12-digit HUC) in the U.S. Potentially restorable wetlands are defined as agricultural areas that naturally accumulate water and contain some proportion of poorly-drained soils. The EnviroAtlas Team produced this dataset by combining three data layers - land cover, digital elevation, and soil drainage information." "To map potentially restorable wetlands, 2006 National Land Cover Data (NLCD) classes pasture/hay and cultivated crops were reclassified as potentially suitable and all other landcover classes as unsuitable. Poorly- and very poorly drained soils were identified using Natural Resources Conservation Service (NRCS) Soil Survey information mainly from the higher resolution Soil Survey Geographic (SSURGO) Database. The two poorly drained soil classes, expressed as percentage of a polygon in the soil survey, were combined to create a raster layer. A wetness index or Composite Topographic Index (CTI) was developed to identify areas wet enough to create wetlands. The wetness index grid, calculated from National Elevation Data (NED), relates upstream contributing area and slope to overland flow. Results from previous studies suggested that CTI values ≥ 550 captured the majority of wetlands. The three layers, when combined, resulted in four classes: unsuitable, low, moderate, and high wetland restoration potential. Areas with high potential for restorable wetlands have suitable landcover (crop/pasture), CTI values ≥ 550, and 80–100% poorly- or very poorly drained soils (PVP). Areas with moderate potential have suitable landcover, CTI values ≥ 550, and 1–79% PVP. Areas with low potential meet the landcover and 80–100% PVP criteria, but do not have CTI values ≥ 550 to corroborate wetness. All other areas were classed as unsuitable. The percentage of total land within each 12-digit HUC that is covered by potentially restorable wetlands was estimated and displayed in five classes for this map." | 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: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "Number of duck pairs per site was estimated for 6 species of ducks: Mallard (Anas platyrhynchos), Blue-winged Teal (Anas discors), Northern Shoveler (Anas clypeata), Gadwall (Anas strepera), Northern Pintail (Anas acuta), and Wood Duck (Aix sponsa), using models developed by Cowardin et al. (1995). Pair abundance was based on wetland class (i.e., temporary, seasonal, semi-permanent, lake, or river), wetland size, and a set of species specific regression coefficients. All CREP wetlands were considered semi-permanent for this analysis; therefore only coefficients associated with the semipermanent wetland pair model were used in calculations. The general equation used to estimate the pairs per wetland was: Pairs = e (a + bx + α) * p where, e = mathematical constant ≈ 2.718, a = species specific regression coefficient a, b = species specific regression coefficient b, x = the natural log of wetland size, α = species specific alpha value, and p = proportion of the basin containing water (assumed to be 0.90 for this analysis)" | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Sedge Wren (Cistothorus platensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SEWR density = 1-1/1+e^(-0.8015652 + 0.08500569 * grass400) *e^(-0.7982511 + 0.0285891 * bbspath + 0.0105094 *grass400) | ABSTRACT: "This final project report is a compendium of 3 previously submitted progress reports and a 4th report for work accomplished from August – December, 2009. Our initial primary objective (Progress Report I) was prediction of environmental services provided by the 27 Iowa Conservation Reserve Enhancement Program (CREP) wetland sites that had been completed by 2007 in the Prairie Pothole Region of northcentral Iowa. The sites contain 102.4 ha of wetlands and 377.4 ha of associated grassland buffers... With respect to wildlife habitat value, USFWS models predicted that the 27 wetlands would provide habitat for 136 pairs of 6 species of ducks, 48 pairs of Canada Geese, and 839 individuals of 5 grassland songbird species of special concern..." AUTHOR'S DESCRIPTION: "The migratory bird benefits of the 27 CREP sites were predicted for Savannah Sparrow (Passerculus sandwichensis)... Population estimates for these species were calculated using models developed by Quamen (2007) for the Prairie Pothole Region of Iowa (Table 3). The “neighborhood analysis” tool in the spatial analysis extension of ArcGIS (2008) was used to create landscape composition variables (grass400, grass3200, hay400, hay3200, tree400) needed for model input (see Table 3 for variable definitions). Values for the species-specific relative abundance (bbspath) variable were acquired from Diane Granfors, USFWS HAPET office. The equations for each model were used to calculate bird density (birds/ha) for each 15-m2 pixel of the land coverage. Next, the “zonal statistics” tool in the spatial analyst extension of ArcGIS (ESRI 2008) was used to calculate the average bird density for each CREP buffer. A population estimate for each site was then calculated by multiplying the average density by the buffer size." Equation: SASP density = e^(-1.581362 + 0.0229603 *bbspath + 0.01024* grass3200 + 0.0255867 * hay3200) | 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 | 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. " | 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…" | Introduction: "COBRA is a screening tool that provides preliminary estimates of the impact of air pollution emission changes on ambient particulate matter (PM) air pollution concentrations, translates this into health effect impacts, and then monetizes these impacts, as illustrated below. The model does not require expertise in air quality modeling, health effects assessment, or economic valuation. Built into COBRA are emissions inventories, a simplified air quality model, health impact equations, and economic valuations ready for use, based on assumptions that EPA currently uses as reasonable best estimates. COBRA also enables advanced users to import their own datasets of emissions inventories, population, incidence, health impact functions, and valuation functions. Analyses can be performed at the state or county level and across the 14 major emissions categories (these categories are called “tiers”) included in the National Emissions Inventory. COBRA presents results in tabular as well as geographic form, and enables policy analysts to obtain a first-order approximation of the benefits of different mitigation scenarios under consideration. However, COBRA is only a screening tool. More sophisticated, albeit time- and resource-intensive, modeling approaches are currently available to obtain a more refined picture of the health and economic impacts of changes in emissions. EPA initially developed COBRA as a desktop application. In 2021, EPA released a web-based version of the tool, known as the COBRA Web Edition. Although the desktop version and web versions of COBRA both use the same methodology to calculate outdoor air quality and health impacts from changes in air pollution emissions, the desktop version offers additional advanced features that are not included in the more streamlined Web Edition. In particular, the desktop version is preloaded with input data on emissions, population, and baseline health incidence for 2016, 2023, and 2028; the Web Edition includes data only for 2023. Similarly, the desktop version allows users to import custom input datasets, while the Web Edition does not. The Web Edition, however, does not require the user to download or install additional software, and it runs more quickly than the desktop version. Users might choose to use the desktop version if they would like to use advanced features, such as custom input data and/or use the preloaded data for 2016 or 2028. Otherwise, users may choose to use the Web Edition for data analysis relevant to 2023. The process for entering emissions input data into COBRA is very similar for the desktop and web versions of the tool. The remainder of this User’s Manual focuses on the steps required to run the desktop version of the tool. The same general process can be used with the Web Edition." | Calibration of complex, process-based ecosystem models is a timely task with modellers challenged by many parameters, multiple outputs of interest and often a scarcity of empirical data. Incorrect calibration can lead to unrealistic ecological and socio-economic predictions with the modeller’s experience and available knowledge of the modelled system largely determining the success of model calibration. Here we provide an overview of best practices when calibrating an Atlantis marine ecosystem model, a widely adopted framework that includes the parameters and processes comprised in many different ecosystem models. We highlight the importance of understanding the model structure and data sources of the modelled system. We then focus on several model outputs (biomass trajectories, age distributions, condition at age, realised diet proportions, and spatial maps) and describe diagnostic routines that can assist modellers to identify likely erroneous parameter values. We detail strategies to fine tune values of four groups of core parameters: growth, predator-prey interactions, recruitment and mortality. Additionally, we provide a pedigree routine to evaluate the uncertainty of an Atlantis ecosystem model based on data sources used. Describing best and current practices will better equip future modellers of complex, processed-based ecosystem models to provide a more reliable means of explaining and predicting the dynamics of marine ecosystems. Moreover, it promotes greater transparency between modellers and end-users, including resource managers. | 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. |
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Specific Policy or Decision Context Cited
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* | * | * | * | * | * | * | * | * | climate change | climate change | * | * | * | * | * | * | None identified | * | N/A | None provided |
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Biophysical Context
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Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | * | * | * | 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. | * | 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 | Prairie pothole region of north-central Iowa | Prairie pothole region of north-central Iowa | Prairie pothole region of north-central Iowa | 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 | Watersheds surrounding Santa Fe and Albuquerque, New Mexico | No additional description provided | * | Marine ecosystem | Groundwater recharge area, City of Laramie |
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EM Scenario Drivers
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No scenarios presented | Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use changes (2000-2030) | No scenarios presented | No scenarios presented | Optional future scenarios for changed LULC and wood harvest | 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 | air temperature, precipitation, Atmospheric CO2 concentrations | fertilization | No scenarios presented | No scenarios presented | No scenarios presented | Additional benefits due to biodiversity restoration practices | Varied wildflower planting mixes of annuals and perennials | N/A | Varying sea level rise (baseline - 2m), and two habitat adaption strategies | No scenarios presented | No scenarios presented | Density transfer |
EM Relationship to Other EMs or Applications
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EM ID
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New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application (multiple runs exist) | Method + Application | Method + Application | Method Only | Method + Application | Method + Application |
Method + Application (multiple runs exist) ?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 (multiple runs exist) | Method + Application | Method + Application (multiple runs exist) | Method + Application | Method + Application | Method + Application (multiple runs exist) | Method + Application (multiple runs exist) | Method + Application | Method + Application (multiple runs exist) | Method Only | Method Only | Model Run Associated with a Specific EM Application |
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New or Pre-existing EM?
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New or revised model | New or revised 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 |
Application of existing model ?Comment:Models developed by Quamen (2007). |
Application of existing model ?Comment:Models developed by Quamen (2007). |
New or revised model | New or revised model | Application of existing model | Application of existing model | New or revised model | Application of existing model | Density transfer |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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Document ID for related EM
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Doc-260 | Doc-269 | Doc-238 | Doc-239 | Doc-240 | Doc-241 | Doc-242 | Doc-228 | Doc-290 | Doc-291 | Doc-289 | Doc-303 | Doc-305 | Doc-309 | Doc-13 | Doc-317 | Doc-335 | Doc-342 | Doc-344 | None | None | None | None | Doc-372 | Doc-372 | None | Doc-400 | Doc-411 | Doc-412 | Doc-413 | None | Doc-456 | Doc-473 |
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EM ID for related EM
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EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83 | EM-123 | EM-124 | EM-125 | EM-162 | EM-164 | EM-165 | EM-166 | EM-170 | EM-171 | EM-99 | EM-119 | EM-120 | EM-121 | None | None | EM-349 | EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | EM-447 | EM-448 | EM-466 | EM-469 | EM-480 | EM-485 | None | EM-598 | EM-593 | EM-705 | EM-703 | EM-702 | EM-701 | EM-700 | EM-652 | EM-651 | EM-649 | EM-648 | EM-648 | EM-649 | EM-650 | EM-651 | None | EM-784 | EM-793 | None | EM-857 | None | EM-978 | EM-983 | EM-985 | EM-990 | EM-991 | None |
EM Modeling Approach
EM Relationship to Time (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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EM Temporal Extent
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2007-2008 | 1990-2030 | Not reported | 2000-2011 | Not applicable | 1969-2008 | 2006-2007, 2010 | 1993-2013 | 2006-2013 | 1961-1990 | 1961-1990 | 2002-2007 | 1992-2007 | 1992-2007 | 1990-2007 | 2011-2012 | 2011 | 2002-2100 | Not applicable | Not applicable | 2000 |
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EM Time Dependence
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* | time-dependent | * | * | time-dependent | time-dependent | * | time-dependent | * | time-dependent | time-dependent | * | * | * | * | time-dependent | * | time-stationary | Not applicable | time-dependent | * |
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EM Time Reference (Future/Past)
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* | future time | * | * | future time | future time | * | future time | * | both | both | * | * | * | * | past time | * | Not applicable | * | * | * |
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EM Time Continuity
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* | discrete | * | * | discrete | discrete | * | discrete | * | discrete | discrete | * | * | * | * | discrete | * | Not applicable | * | continuous | * |
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EM Temporal Grain Size Value
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* | 6, 10, and 30 | * | * | 1 | 1 | * | 1 | * | 1 | 1 | * | * | * | * | 1 | * | Not applicable | * | * | * |
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EM Temporal Grain Size Unit
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* | Year | * | * | Year | Day | * | Year | * | Day | Day | * | * | * | * | Year | * | Not applicable | * | * | * |
EM spatial extent (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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Bounding Type
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Physiographic or Ecological | Geopolitical | Geopolitical | Physiographic or ecological | Not applicable | * | Physiographic or ecological | Geopolitical | Geopolitical | Point or points | Point or points | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Multiple unrelated locations (e.g., meta-analysis) | Other |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
* | Watershed/Catchment/HUC | Geopolitical | Not applicable | * |
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Spatial Extent Name
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Central French Alps | The EU-25 plus Switzerland and Norway | European Union countries | Puget Sound Region | Not applicable | H. J. Andrews LTER WS10 | Coastal zone surrounding St. Croix | Switzerland | conterminous United States | Oak Park Research centre | Oak Park Research centre | CREP (Conservation Reserve Enhancement Program) wetland sites | CREP (Conservation Reserve Enhancement Program) wetland sites | CREP (Conservation Reserve Enhancement Program) wetland sites | Colt Park meadows, Ingleborough National Nature Reserve, northern England | Agricultural plots | Santa Fe Fireshed | Tampa Bay estuary watershed | Not applicable | Not applicable | Laramie City's aquifer protection area |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | >1,000,000 km^2 | >1,000,000 km^2 | 10,000-100,000 km^2 | Not applicable | 10-100 ha | 100-1000 km^2 | 10,000-100,000 km^2 | >1,000,000 km^2 | 1-10 ha | 1-10 ha | 1-10 km^2 | 1-10 km^2 | 1-10 km^2 | <1 ha | 10-100 km^2 | 100-1000 km^2 | 1000-10,000 km^2. | Not applicable | Not applicable | 10-100 km^2 |
Spatial Distribution of Computations (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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EM Spatial Distribution
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* | * | * | * | * |
* ?Comment:See below, grain includes vertical, subsurface dimension. |
* | * | * | spatially lumped (in all cases) | spatially lumped (in all cases) | * | * | * | * | spatially lumped (in all cases) | * | spatially distributed (in at least some cases) | * | Not applicable | spatially lumped (in all cases) |
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Spatial Grain Type
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* | * | * | * | * | volume, for 3-D feature | * | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | other (specify), for irregular (e.g., stream reach, lake basin) | * | Not applicable | * | area, for pixel or radial feature | map scale, for cartographic feature | Not applicable | Not applicable |
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Spatial Grain Size
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20 m x 20 m | 1 km x 1 km | 100 m x 100 m | 200m x 200m | application specific | 30 m x 30 m surface pixel and 2-m depth soil column | 10 m x 10 m | 5 sites | irregular | Not applicable | Not applicable | multiple, individual, irregular shaped sites | multiple, individual, irregular shaped sites | multiple, individual, irregular shaped sites | 3 m x 3 m | Not applicable | 30 m | 10 x 10 m | user defined | Not applicable | Not applicable |
EM Structure and Computation Approach (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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EM Computational Approach
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* | Logic- or rule-based | * | * | * | Numeric | * | Numeric | * | Numeric | Numeric | * | * | * | * | Numeric | * | Analytic | * | * | Numeric |
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EM Determinism
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* | * | * | stochastic | * | * | * | stochastic | * | * | * | * | * | * | stochastic | * | * | deterministic | stochastic | * | * |
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Statistical Estimation of EM
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* | * |
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Model Checking Procedures Used (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
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New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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Model Calibration Reported?
em.detail.calibrationHelp
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* | * | * | * | Not applicable | * | Yes | * | * | * | Yes | Unclear | Unclear | Unclear | Not applicable | * | Unclear | No | Not applicable | Yes | Unclear |
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Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
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Yes | * | * | * | Not applicable | * | * | * | * |
Yes ?Comment:for N2O fluxes |
Yes ?Comment:Actual value was not given, just that results were very poor. Simulation results were 258% of observed |
* | * | * | Not applicable | * | * | No | Not applicable | Not applicable | * |
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Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
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* | * | * | * | * | * | * | * |
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* | * | * | * | * | * | None | * | * | * |
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Model Operational Validation Reported?
em.detail.validationHelp
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* | * | * | * | Not applicable | * | Yes | Yes | * | Yes | Yes | Unclear | Unclear | Unclear | * | * | * | No | Not applicable | Not applicable | Unclear |
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Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
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* | * | * | * | Not applicable | * | * | * | * | * | * | * | * | * | * | * | * | No | Not applicable | Not applicable | Unclear |
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Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
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* | * | * | * | Not applicable | * | * | * | * | * | * | * | * | * | * | * | * | No | Not applicable | Not applicable | Unclear |
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Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
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* | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * | Not applicable | * | * | * |
EM Locations, Environments, Ecology
Location of EM Application (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
em.detail.relationToSpaceTerrestrialHelp
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| New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
em.detail.relationToSpaceMarineHelp
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| New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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Centroid Lat/Long (Decimal Degree)
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EM ID
em.detail.idHelp
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New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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Centroid Latitude
em.detail.ddLatHelp
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45.05 | 50.53 | 48.2 | 48 | -9999 | 44.25 | 17.73 | 46.82 | 39.5 | 52.86 | 52.86 | 42.62 | 42.62 | 42.62 | 54.2 | 29.4 | 35.86 | 27.76 | Not applicable | Not applicable | 41.31 |
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Centroid Longitude
em.detail.ddLongHelp
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6.4 | 7.6 | 16.35 | -123 | -9999 | -122.33 | -64.77 | 8.23 | -98.35 | 6.54 | 6.54 | -93.84 | -93.84 | -93.84 | -2.35 | -82.18 | -105.76 | -82.54 | Not applicable | Not applicable | -105.46 |
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Centroid Datum
em.detail.datumHelp
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* | * | * | * | Not applicable | * | * | * | * | None provided | None provided | * | * | * | * | * | * | WGS84 | Not applicable | Not applicable | * |
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Centroid Coordinates Status
em.detail.coordinateStatusHelp
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Provided | * | * | * | Not applicable | Provided | * | * | * | Provided | Provided | * | * | * | Provided | Provided | * | Estimated | Not applicable | Not applicable | * |
Environments and Scales Modeled (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
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EM ID
em.detail.idHelp
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New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
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Agroecosystems | Grasslands | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Created Greenspace | Not applicable | Forests | Near Coastal Marine and Estuarine | Forests | Agroecosystems | Agroecosystems | Agroecosystems | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Inland Wetlands | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Agroecosystems | 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) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas | Ground Water | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
em.detail.specificEnvTypeHelp
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Subalpine terraces, grasslands, and meadows | Not applicable | Not applicable | 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). | Coral reefs | forests | Terrestrial | farm pasture | farm pasture | Wetlands buffered by grassland set in agricultural land | Grassland buffering inland wetlands set in agricultural land | Grassland buffering inland wetlands set in agricultural land | fertilized grassland (historically hayed) | Agricultural landscape | watersheds | Esturary and associated urban and terrestrial environment | Not applicable | Multiple | watershed |
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EM Ecological Scale
em.detail.ecoScaleHelp
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Ecological scale is coarser than that of the Environmental Sub-class | * | * | Ecological scale corresponds to the Environmental Sub-class | Not applicable | * | * | Ecological scale corresponds to the Environmental Sub-class | * | * | * | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | 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 | Ecological scale corresponds to the Environmental Sub-class |
Organisms modeled (* Note that run information is shown only where run data differ from the "parent" entry shown at left)
Scale of differentiation of organisms modeled
em.detail.nameOfOrgsOrGroupsHelp
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EM ID
em.detail.idHelp
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New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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EM Organismal Scale
em.detail.orgScaleHelp
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Community | * | * | * | * | * | * | Community | * | * | * | Species | Species | Species | Community | Species | * | Not applicable | * | * | * |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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EnviroAtlas URL
em.detail.enviroAtlasURLHelp
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EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
* Note that run information is shown only where run data differ from the "parent" entry shown at left
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
em.detail.cicesHelp
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| New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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EM Variable Names (and Units)
* Note that for runs, variable name is displayed only where data for that variable differed by run AND those differences were reported in the source document. Where differences occurred but were not reported, the variable is not listed. Click on variable name to view details.
Predictor
em.detail.variablesPredictorHelp
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Intermediate
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EM ID
em.detail.idHelp
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New or revised model | EM-122 | Application of existing model | New or revised model | New or revised model | Application of existing model | Application of existing model | EM-467 | New or revised model | EM-593 | Application of existing model | EM-632 | Application of existing model | Application of existing model | EM-735 | EM-812 | Application of existing model | EM-863 | New or revised model | Application of existing model | Density transfer |
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Intermediate (Computed) Variables (and Units)
em.detail.intermediateVariableHelp
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None | None |
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None | None | None | None | None | None | None | None | None | None | None | * |
Response
em.detail.variablesResponseHelp
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