EcoService Models Library (ESML)

Compare EMs

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.

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Comparing:

EM-59
EM-79
EM-81
EM-125
EM-432
EM-598
EM-701
EM-788
EM-937
EM-1021

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
EM Short Name em.detail.shortNameHelp ?	EnviroAtlas-Air pollutant removal	Divergence in flowering date, Central French Alps	Cultural ES and plant traits, Central French Alps	Land-use change and recreation, Europe	Nitrogen fixation rates, Guánica Bay, Puerto Rico	DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland	Blue-winged Teal recruits, CREP wetlands, IA, USA	Wild bees over 26 yrs of restored prairie, IL, USA	EPA national stormwater calculator tool	CMAQ chemical transport model, UK
EM Full Name em.detail.fullNameHelp ?	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	Cultural ecosystem service estimated from plant functional traits, Central French Alps	Land-use change effects on recreation, Europe	Nitrogen fixation rates, Guánica Bay, Puerto Rico, USA	DeNitrification-DeComposition simulation of N2O flux Ireland	Blue-winged Teal duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Wild bee community change over a 26 year chronosequence of restored tallgrass prairie, IL, USA	Environmental Protection Agency National stormwater calculator tool	Application of chemical transport model CMAQ to policy decisions regarding PM2.5 in the UK
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA \| EnviroAtlas \| i-Tree ? Comment: EnviroAtlas uses an application of the i-Tree Eco model.	EU Biodiversity Action 5	EU Biodiversity Action 5	EU Biodiversity Action 5	US EPA	None	None	None	US EPA	None
EM Source Document ID	223	260	260	228	338 ? Comment: WE received a draft copy prior to journal publication that was agency reviewed.	358	372 ? Comment: Document 373 is a secondary source for this EM.	401	428 ? Comment: This is a tool available on the web for downloading to personal computers. A manual is also available for further documentation of the tool.	483
Document Author em.detail.documentAuthorHelp ?	US EPA Office of Research and Development - National Exposure Research Laboratory	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Haines-Young, R., Potschin, M. and Kienast, F.	Amelia Smith, Susan Harrell Yee, Marc Russell, Jill Awkerman and William S. Fisher	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	Griffin, S. R, B. Bruninga-Socolar, M. A. Kerr, J. Gibbs and R. Winfree	Rossman, L.A., Bernagros, J.T., Barr, C.M., and M.A. Simon	Chemel, C., Fisher, B.E.A., Kong, X., Francis, X.V., Sokhi, R.S., Good, N., Collins, W.J. and Folberth, G.A.
Document Year em.detail.documentYearHelp ?	2013	2011	2011	2012	2017	2010	2010	2017	2022	2014
Document Title em.detail.sourceIdHelp ?	EnviroAtlas - Featured Community	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs	Linking ecosystem services supply to stakeholder concerns on both land and sea: An example from Guanica Bay watershed, Puerto Rico	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	Wild bee community change over a 26-year chronosequence of restored tallgrass prairie	EPA National Stormwater Calculator Web App users guide-Version 3.4.0.	Application of chemical transport model CMAQ to policy decisions regarding PM2.5 in the UK
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published on US EPA EnviroAtlas website	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published report	Published journal manuscript	Published EPA report	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	https://www.epa.gov/enviroatlas	Not applicable	Not applicable	Not applicable	Not applicable	http://www.dndc.sr.unh.edu	Not applicable	Not applicable	https://www.epa.gov/water-research/national-stormwatercalculator	https://www.epa.gov/cmaq/download-cmaq
Contact Name em.detail.contactNameHelp ?	EnviroAtlas Team	Sandra Lavorel	Sandra Lavorel	Marion Potschin	Susan H. Yee	M. Abdalla	David Otis	Sean R. Griffin	Lewis Rossman	B.E.A. Fisher
Contact Address	Not reported	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom	U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	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	Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901, U.S.A.	Center for environmental solutions and emergency response, Cincinnati, Ohio	Little Beeches, Headley Road, Leatherhead KT22 8PT, UK.
Contact Email	enviroatlas@epa.gov	sandra.lavorel@ujf-grenoble.fr	sandra.lavorel@ujf-grenoble.fr	marion.potschin@nottingham.ac.uk	yee.susan@epa.gov	abdallm@tcd.ie	dotis@iastate.edu	srgriffin108@gmail.com	n.a.	None provided

EM Description

EM ID em.detail.idHelp ?	EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
Summary Description em.detail.summaryDescriptionHelp ?	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: "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." AUTHOR'S DESCRIPTION: "The Cultural ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to cultural ecosystem services were based on stakeholders’ perceptions, given positive or negative contributions."	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 (Recreation); 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: " 'Recreation' is broadly defined as all areas where landscape properties are favourable for active recreation purposes….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."	AUTHOR'S DESCRIPTION: " …In Guánica Bay watershed, Puerto Rico, deforestation and drainage of a large lagoon have led to sediment, contaminant, and nutrient transport into the bay, resulting in declining quality of coral reefs. A watershed management plan is currently being implemented to restore reefs through a variety of proposed actions…After the workshops, fifteen indicators of terrestrial ecosystem services in the watershed and four indicators in the coastal zone were identified to reflect the wide range of stakeholder concerns that could be impacted by management decisions. Ecosystem service production functions were applied to quantify and map ecosystem services supply in the Guánica Bay watershed, as well as an additional highly engineered upper multi-watershed area connected to the lower watershed via a series of reservoirs and tunnels,…” AUTHOR''S DESCRIPTION: "The U.S. Coral Reef Task Force (CRTF), a collaboration of federal, state and territorial agencies, initiated a program in 2009 to better incorporate land-based sources of pollution and socio-economic considerations into watershed strategies for coral reef protection (Bradley et al., 2016)...Baseline measures for relevant ecosystem services were calculated by parameterizing existing methods, largely based on land cover (Egoh et al., 2012; Martinez- Harms and Balvanera, 2012), with relevant rates of ecosystem services production for Puerto Rico, and applying them to map ecosystem services supply for the Guánica Bay Watershed...The Guánica Bay watershed is a highly engineered watershed in southwestern Puerto Rico, with a series of five reservoirs and extensive tunnel systems artificially connecting multiple mountainous sub-watersheds to the lower watershed of the Rio Loco, which itself is altered by an irrigation canal and return drainage ditch that diverts water through the Lajas Valley (PRWRA, 1948)...For each objective, a translator of ecosystem services production, i.e., ecological production function, was used to quantify baseline measurements of ecosystem services supply from land use/land cover (LULC) maps for watersheds across Puerto Rico...Two additional metrics, nitrogen fixation and rates of carbon sequestration into soil and sediment, were also calculated as potential measures of soil quality and agricultural productivity. Carbon sequestration and nitrogen fixation rates were assigned to each land cover class"	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: "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…" AUTHOR'S DESCRIPTION: "The first phase of the U.S. Fish and Wildlife Service task was to evaluate the contribution of the 27 approved sites to migratory birds breeding in the Prairie Pothole Region of Iowa. To date, evaluation has been completed for 7 species of waterfowl and 5 species of grassland birds. All evaluations were completed using existing models that relate landscape composition to bird populations. As such, the first objective was to develop a current land cover geographic information system (GIS) that reflected current landscape conditions including the incorporation of habitat restored through the CREP program. The second objective was to input landscape variables from our land cover GIS into models to estimate various migratory bird population parameters (i.e. the number of pairs, individuals, or recruits) for each site. Recruitment for the 27 sites was estimated for Mallards, Blue-winged Teal, Northern Shoveler, Gadwall, and Northern Pintail according to recruitment models presented by Cowardin et al. (1995). Recruitment was not estimated for Canada Geese and Wood Ducks because recruitment models do not exist for these species. Variables used to estimate recruitment included the number of pairs, the composition of the landscape in a 4-square mile area around the CREP wetland, species-specific habitat preferences, and species- and habitat-specific clutch success rates. Recruitment estimates were derived using the following equations: Recruits = 2Rn where, 2 = constant based on the assumption of equal sex ratio at hatch, n = number of breeding pairs estimated using the pairs equation previously outlined, R = Recruitment rate as defined by Cowardin and Johnson (1979) where, R = HZB/2 where, H = hen success (see Cowardin et al. (1995) for methods used to calculate H, which is related to land cover types in the 4-mile2 landscape around each wetland), Z = proportion of broods that survived to fledge at least 1 recruit (= 0.74 based on Cowardin and Johnson 1979), B = average brood size at fledging (= 4.9 based on Cowardin and Johnson 1979)." ENTERER'S COMMENT: The number of breeding pairs (n) is estimated by a separate submodel from this paper, and as such is also entered as a separate model in ESML (EM 632).	ABSTRACT: "Restoration efforts often focus on plants, but additionally require the establishment and long-term persistence of diverse groups of nontarget organisms, such as bees, for important ecosystem functions and meeting restoration goals. We investigated long-term patterns in the response of bees to habitat restoration by sampling bee communities along a 26-year chronosequence of restored tallgrass prairie in north-central Illinois, U.S.A. Specifically, we examined how bee communities changed over time since restoration in terms of (1) abundance and richness, (2) community composition, and (3) the two components of beta diversity, one-to-one species replacement, and changes in species richness. Bee abundance and raw richness increased with restoration age from the low level of the pre-restoration (agricultural) sites to the target level of the remnant prairie within the first 2–3 years after restoration, and these high levels were maintained throughout the entire restoration chronosequence. Bee community composition of the youngest restored sites differed from that of prairie remnants, but 5–7 years post-restoration the community composition of restored prairie converged with that of remnants. Landscape context, particularly nearby wooded land, was found to affect abundance, rarefied richness, and community composition. Partitioning overall beta diversity between sites into species replacement and richness effects revealed that the main driver of community change over time was the gradual accumulation of species, rather than one-to-one species replacement. At the spatial and temporal scales we studied, we conclude that prairie restoration efforts targeting plants also successfully restore bee communities."	"Abstract: EPA’s National Stormwater Calculator (SWC) is a software application tool that estimates the annual amount of rainwater and frequency of runoff from a specific site using green infrastructure as low impact development controls. The SWC is designed for use by anyone interested in reducing runoff from a property, including site developers, landscape architects, urban planners, and homeowners. This User’s guide contains information on the SWC web application. SWC Version 3.4 contains has updated historical meteorological data (from 1970 - 2006 to 1990 - 2019), updated Bureau of Labor Statistics Cost Data (from 2018 to 2020), and the 5.1.015 Stormwater Management Model (SWMM) engine (from 5.1.007). Evaporation was calculated by the Hargreaves method (EPA, 2015), based on historical or future daily temperature data."	This paper shows how the advanced chemical transport model CMAQ can be used to estimate future levels of PM2.5 in the UK, the key air pollutant in terms of human health effects, but one which is largely made up from the formation of secondary particulate in the atmosphere. By adding the primary particulate contribution from typical urban roads and including a margin for error, it is concluded that the current indicative limit value for PM2.5 will largely be met in 2020 assuming 2006 meteorological conditions. Contributions to annual average regional PM2.5 concentration from wild fires in Europe in 2006 and from possible climate change between 2006 and 2020 are shown to be small compared with the change in PM2.5 concentration arising from changes in emissions between 2006 and 2020. The contribution from emissions from major industrial sources regulated in the UK is estimated from additional CMAQ calculations. The potential source strength of these emissions is a useful indicator of the linearity of the response of the atmosphere to changes in emissions. Uncertainties in the modelling of regional and local sources are taken into account based on previous evaluations of the models. Future actual trends in emissions mean that exceedences of limit values may arise, and these and further research into PM2.5 health effects will need to be part of the future strategy to manage PM2.5 concentrations.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	None identified	None identified	None identified	None provided	climate change	None identified	None identified	None given	None identified
Biophysical Context	No additional description provided	Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes	Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes	No additional description provided	No additional description provided	Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C	Prairie Pothole Region of Iowa	The Nachusa Grasslands consists of over 1,900 ha of restored prairie plantings, prairie remnants, and other habitats such as wetlands and oak savanna. The area is generally mesic with an average annual precipitation of 975 mm, and most precipitation occurs during the growing season.	Sites up to 12 acres	United kingdom atmosphere
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	No scenarios presented	Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use change (2000-2030)	No scenarios presented	fertilization	No scenarios presented	No scenarios presented	Climate change scenarios	2020 European emissions scenario

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method Only	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	Application of existing model	New or revised model	New or revised model	New or revised model	Application of existing model	Application of existing model	New or revised model	New or revised model	New or revised model	Application of existing model

Related EMs (for example, other versions or derivations of this EM) described in ESML

EM ID em.detail.idHelp ?	EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-345	Doc-260 \| Doc-269	None	Doc-238 \| Doc-239 \| Doc-240 \| Doc-241 \| Doc-242 \| Doc-228	None	None	Doc-372 \| Doc-373	None	None	Doc-478 \| Doc-481 \| Doc-482
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-80 \| EM-81 \| EM-82 \| EM-83	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-82 \| EM-83	EM-122 \| EM-123 \| EM-124 \| EM-162 \| EM-164 \| EM-165 \| EM-166 \| EM-170 \| EM-171 \| EM-99 \| EM-119 \| EM-120 \| EM-121	None	EM-593	EM-705 \| EM-704 \| EM-703 \| EM-702 \| EM-700 \| EM-632	None	None	EM-1012 \| EM-1019 \| EM-1020

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
EM Temporal Extent em.detail.tempExtentHelp ?	2008-2010	2007-2008	Not reported	1990-2030	1978 - 2009	1961-1990	1987-2007	1988-2014	Not applicable	2006-2020
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	time-stationary	time-stationary	time-stationary	time-stationary	time-dependent	time-stationary	time-stationary	time-stationary	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	future time	Not applicable	Not applicable	Not applicable	Not applicable	both	Not applicable	Not applicable	Not applicable	both
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	Not applicable	Not applicable	Not applicable	Not applicable	discrete	Not applicable	Not applicable	Not applicable	discrete
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	1	Not applicable	Not applicable	Not applicable	Not applicable	1	Not applicable	Not applicable	Not applicable	14
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Hour	Not applicable	Not applicable	Not applicable	Not applicable	Day	Not applicable	Not applicable	Not applicable	Year

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or Ecological	Physiographic or Ecological	Geopolitical	Watershed/Catchment/HUC	Point or points	Multiple unrelated locations (e.g., meta-analysis)	Physiographic or ecological	Not applicable	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	Durham NC and vicinity	Central French Alps	Central French Alps	The EU-25 plus Switzerland and Norway	Guanica Bay watershed	Oak Park Research centre	CREP (Conservation Reserve Enhancement Program	Nachusa Grasslands	Not applicable	United Kingdom
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	10-100 km^2	10-100 km^2	>1,000,000 km^2	100-1000 km^2	1-10 ha	10,000-100,000 km^2	10-100 km^2	Not applicable	100,000-1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
EM Spatial Distribution em.detail.distributeLumpHelp ?	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 lumped (in all 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)
Spatial Grain Type em.detail.spGrainTypeHelp ?	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	irregular	20 m x 20 m	20 m x 20 m	1 km x 1 km	HUC	Not applicable	multiple, individual, irregular sites	Area varies by site	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
EM Computational Approach em.detail.emComputationalApproachHelp ?	Numeric	Analytic	Analytic	Logic- or rule-based	Analytic	Numeric	Analytic	Analytic	Analytic	Numeric
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
Model Calibration Reported? em.detail.calibrationHelp ?	Unclear	No	No	No	No	Yes	Unclear	No	Not applicable	Yes
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes	No	No	No	Yes ? Comment: Actual value was not given, just that results were very poor. Simulation results were 258% of observed	No	No	Not applicable	Yes ? Comment: Two versions of CMAQ (v4.6 and v4.7) were used to assess performance. Both values are provided here respectively.
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	R squared \| 35.5 \| Not applicable	None	None	None	r squared \| 00.0 \| unitless	None	None	None	FAC2 \| 88.2, 100 \| %
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	No	No	Yes	No	No	Not applicable	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	No	No	No	No	Not applicable	Unclear
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	No	No	No	No	No	Not applicable	Unclear
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

EM Locations, Environments, Ecology

Location of EM Application

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
North America United States North Carolina	Europe France	Europe France	Europe Cyprus Portugal Malta Greece Austria Latvia Netherlands Sweden Ireland Luxembourg Poland Slovakia Slovenia France Lithuania Hungary Switzerland Spain Czech Republic Belgium Norway Finland Denmark Italy Germany Estonia	North America U.S. Caribbean Territories	Europe Ireland	North America United States Iowa	North America United States Illinois	North America United States	Europe United Kingdom

Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)

EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
None	None	None	None	None	None	None	None	None	Temperate North Atlantic Northeast Atlantic Northern European Seas North Sea

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
Centroid Latitude em.detail.ddLatHelp ?	35.99	45.05	45.05	50.53	17.96	52.86	42.62	41.89	Not applicable	54
Centroid Longitude em.detail.ddLongHelp ?	-78.96	6.4	6.4	7.6	-67.02	6.54	-93.84	-89.34	Not applicable	4
Centroid Datum em.detail.datumHelp ?	None provided	WGS84	WGS84	WGS84	WGS84	None provided	WGS84	WGS84	Not applicable	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Provided	Provided	Estimated	Estimated	Provided	Estimated	Provided	Not applicable	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Created Greenspace \| Atmosphere	Agroecosystems \| Grasslands	Agroecosystems \| Grasslands	Aquatic Environment (sub-classes not fully specified) \| Terrestrial Environment (sub-classes not fully specified)	Inland Wetlands \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands \| Scrubland/Shrubland \| Barren	Agroecosystems	Inland Wetlands \| Agroecosystems \| Grasslands	Agroecosystems \| Grasslands	Terrestrial Environment (sub-classes not fully specified)	Atmosphere
Specific Environment Type em.detail.specificEnvTypeHelp ?	Urban and vicinity	Subalpine terraces, grasslands, and meadows	Subalpine terraces, grasslands, and meadows.	Not applicable	Tropical terrestrial	farm pasture	Wetlands buffered by grassland within agroecosystems	Restored prairie, prairie remnants, and cropland	Terrrestrial landcover	United Kingdom atmosphere
EM Ecological Scale em.detail.ecoScaleHelp ?	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 coarser than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Community	Community	Not applicable	Not applicable	Not applicable	Individual or population, within a species	Species	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
None Available	None Available	None Available	None Available	None Available	None Available	Species Blue-winged teal (Anas discors)	other Bees	None Available	None Available

EnviroAtlas URL

EM-59	EM-79	EM-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
Average Annual Precipitation	None Available	GAP Ecological Systems	None Available	Total Annual Reduced Nitrogen Deposition, The Watershed Boundary Dataset (WBD), Percent Impervious Area	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	Acres of Land Enrolled in the Conservation Reserve Program (CRP)	GAP Ecological Systems	Average Annual Precipitation, Water supply from NID reservoirs (million gallons), Enabling Conditions	Average Annual Precipitation, Average Annual Daily Potential Wind Energy

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-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Micro and regional climate regulation Mediation of waste, toxics and other nuisances Mediation by biota Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals	None	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings Intellectual and representative interactions Aesthetic Heritage, cultural	[Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings Experiential use of plants, animals and land-/seascapes in different environmental settings Intellectual and representative interactions Aesthetic Entertainment	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Pollination and seed dispersal	None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Micro and regional climate regulation

<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-81	EM-125	EM-432	EM-598	EM-701	EM-788	EM-937	EM-1021
Atmosphere Atmosphere (1) Air Quality Indicator Urban/Suburban (27) Composite (8) Presence of environmental class/subclass Quality Indicator	None	None	Rivers and Streams (111) Composite (8) Presence of environmental class/subclass Quality Indicator Barren / Rock and Sand (28) Composite (8) Presence of environmental class/subclass Quality Indicator Forests (21) Composite (8) Presence of environmental class/subclass Quality Indicator Tundra (25) Composite (8) Presence of environmental class/subclass Quality Indicator Grasslands (23) Composite (8) Presence of environmental class/subclass Quality Indicator Scrubland/Shrubland (24) Composite (8) Presence of environmental class/subclass Quality Indicator Lakes and Ponds (112) Composite (8) Presence of environmental class/subclass Quality Indicator Wetlands (12) Composite (8) Presence of environmental class/subclass Quality Indicator Urban/Suburban (27) Composite (8) Presence of environmental class/subclass Quality Indicator	Agroecosystems (22) Soil (2) Specific types of soil Quality Indicator	Agroecosystems (22) Flora (5) Green biomass Stock Indicator	Wetlands (12) Fauna (4) Duck Stock Indicator	None	Urban/Surburban (27) Composite (8) Regulation of extreme events Flow Indicator	Atmosphere Atmosphere (1) Air Quality Indicator

EcoService Models Library (ESML)

Compare EMs

EM Variables by Variable Role

EM Variables by Category

All EM Descriptors

EM Descriptors by Modeling Concepts

Comparing:

EM Identity and Description

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Related EMs (for example, other versions or derivations of this EM) described in ESML

EM Modeling Approach

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EM Locations, Environments, Ecology

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Taxonomic level and name of organisms or groups identified

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)

<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)