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-63
EM-68
EM-93
EM-337
EM-375
EM-416
EM-647
EM-657
EM-660

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
EM Short Name em.detail.shortNameHelp ?	EnviroAtlas - Natural biological nitrogen fixation	Fodder crude protein content, Central French Alps	Stream nitrogen removal, Mississippi R. basin, USA	Rate of Fire Spread	VELMA hydro, Oregon, USA	Sed. denitrification, St. Louis River, MN/WI, USA	EcoAIM v.1.0 APG, MD	REQI (River Ecosystem Quality Index), Italy	RUM: Valuing fishing quality, Michigan, USA
EM Full Name em.detail.fullNameHelp ?	US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA	Fodder crude protein content, Central French Alps	Stream nitrogen removal, Upper Mississippi, Ohio and Missouri River sub-basins, USA	Rate of Fire Spread	VELMA (visualizing ecosystems for land management assessments) hydro, Oregon, USA	Sediment denitrification, St. Louis River estuary, Lake Superior, MN & WI, USA	EcoAIM v.1.0, Aberdeen Proving Ground, MD	REQI (River Ecosystem Quality Index), Marecchia River, Italy	Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA \| EnviroAtlas	EU Biodiversity Action 5	US EPA	None	US EPA	US EPA	None	None	None
EM Source Document ID	262 ? Comment: EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM.	260	52	306	13	333	374	378	382 ? Comment: Data collected from Michigan Recreational Angler Survey, a mail survey administered monthly to random sample of Michigan fishing license holders since July 2008. Data available taken from 2008-2010.
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.	Hill, B. and Bolgrien, D.	Rothermel, Richard C.	Abdelnour, A., Stieglitz, M., Pan, F. and McKane, R. B.	Brent J. Bellinger, Terri M. Jicha, LaRae P. Lehto, Lindsey R. Seifert-Monson, David W. Bolgrien, Matthew A. Starry, Theodore R. Angradi, Mark S. Pearson, Colleen Elonen, and Brian H. Hill	Booth, P., Law, S. , Ma, J. Turnley, J., and J.W. Boyd	Santolini, R, E. Morri, G. Pasini, G. Giovagnoli, C. Morolli, and G. Salmoiraghi	Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson
Document Year em.detail.documentYearHelp ?	2013	2011	2011	1972	2011	2014	2014	2014	2014
Document Title em.detail.sourceIdHelp ?	EnviroAtlas - National	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	Nitrogen removal by streams and rivers of the Upper Mississippi River basin	A Mathematical model for predicting fire spread in wildland fuels	Catchment hydrological responses to forest harvest amount and spatial pattern	Sediment nitrification and denitrification in a Lake Superior estuary	Implementation of EcoAIM - A Multi-Objective Decision Support Tool for Ecosystem Services at Department of Defense Installations	Assessing the quality of riparian areas: the case of River Ecosystem Quality Index applied to the Marecchia river (Italy)	Valuing recreational fishing quality at rivers and streams
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Documented, not peer reviewed	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 USDA Forest Service report	Published journal manuscript	Published journal manuscript	Published report	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	https://www.epa.gov/enviroatlas	Not applicable	Not applicable	http://firelab.org/project/farsite	Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov	Not applicable	Not applicable	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	EnviroAtlas Team ? Comment: Additional contact: Jana Compton, EPA	Sandra Lavorel	Brian Hill	Charles McHugh	A. Abdelnour	Brent J. Bellinger	Pieter Booth	Elisa Morri	Richard Melstrom
Contact Address	Not reported	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	Mid-Continent Ecology Division NHEERL, ORD. USEPA 6201 Congdon Blvd. Duluth, MN 55804, USA	RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808	Dept. of Civil and Environmental Engineering, Goergia Institute of Technology, Atlanta, GA 30332-0335, USA	U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA	Exponent Inc., Bellevue WA	Dept. of Earth, Life, and Environmental Sciences, Urbino university, via ca le suore, campus scientifico Enrico Mattei, Urbino 61029 Italy	Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA
Contact Email	enviroatlas@epa.gov	sandra.lavorel@ujf-grenoble.fr	hill.brian@epa.gov	cmchugh@fs.fed.us	abdelnouralex@gmail.com	bellinger.brent@epa.ogv	pbooth@ramboll.com	elisa.morri@uniurb.it	melstrom@okstate.edu

EM Description

EM ID em.detail.idHelp ?	EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
Summary Description em.detail.summaryDescriptionHelp ?	DATA FACT SHEET: "This EnviroAtlas national map displays the rate of biological nitrogen (N) fixation (BNF) in natural/semi-natural ecosystems within each watershed (12-digit HUC) in the conterminous United States (excluding Hawaii and Alaska) for the year 2006. These data are based on the modeled relationship of BNF with actual evapotranspiration (AET) in natural/semi-natural ecosystems. The mean rate of BNF is for the 12-digit HUC, not to natural/semi-natural lands within the HUC." "BNF in natural/semi-natural ecosystems was estimated using a correlation with actual evapotranspiration (AET). This correlation is based on a global meta-analysis of BNF in natural/semi-natural ecosystems. AET estimates for 2006 were calculated using a regression equation describing the correlation of AET with climate and land use/land cover variables in the conterminous US. Data describing annual average minimum and maximum daily temperatures and total precipitation at the 2.5 arcmin (~4 km) scale for 2006 were acquired from the PRISM climate dataset. The National Land Cover Database (NLCD) for 2006 was acquired from the USGS at the scale of 30 x 30 m. BNF in natural/semi-natural ecosystems within individual 12-digit HUCs was modeled with an equation describing the statistical relationship between BNF (kg N ha-1 yr-1) and actual evapotranspiration (AET; cm yr–1) and scaled to the proportion of non-developed and non-agricultural land in the 12-digit HUC." EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM."	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 (e.g., fodder crude protein content), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in fodder crude protein content was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy…Fodder crude protein for each pixel was calculated and mapped using model estimates...This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on fodder protein content. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use."	ABSTRACT: "We used stream chemistry and hydrogeomorphology data from 549 stream and 447 river sites to estimate NO3–N removal in the Upper Mississippi, Missouri, and Ohio Rivers. We used two N removal models to predict NO3–N input and removal. NO3–N input ranged from 0.01 to 338 kg/kmd in the Upper Mississippi River to 0.01–54 kg/ kmd in the Missouri River. Cumulative river network NO3–N input was 98700–101676 Mg/year in the Ohio River, 85,961–89,288 Mg/year in the Upper Mississippi River, and 59,463–61,541 Mg/year in the Missouri River. NO3–N output was highest in the Upper Mississippi River (0.01–329 kg/kmd ), followed by the Ohio and Missouri Rivers (0.01–236 kg/kmd ) sub-basins. Cumulative river network NO3–N output was 97,499 Mg/year for the Ohio River, 84,361 Mg/year for the Upper Mississippi River, and 59,200 Mg/year for the Missouri River. Proportional NO3–N removal (PNR) based on the two models ranged from 0.01 to 0.28. NO3–N removal was inversely correlated with stream order, and ranged from 0.01 to 8.57 kg/kmd in the Upper Mississippi River to 0.001–1.43 kg/kmd in the Missouri River. Cumulative river network NO3–N removal predicted by the two models was: Upper Mississippi River 4152 and 4152 Mg/year, Ohio River 3743 and 378 Mg/year, and Missouri River 2,277 and 197 Mg/year. PNR removal was negatively correlated with both stream order (r = −0.80–0.87) and the percent of the catchment in agriculture (r = −0.38–0.76)."	ABSTRACT: "The development of a mathematical model for predicting rate of fire spread and intensity applicable to a wide range of wildland fuels is presented from the conceptual stage through evaluation and demonstration of results to hypothetical fuel models. The model was developed for and is now being used as a basis for appraising fire spread and intensity in the National Fire Danger Rating System. The initial work was done using fuel arrays composed of uniform size particles. Three fuel sizes were tested over a wide range of bulk densities. These were 0.026-inch-square cut excelsior, 114-inch sticks, and 112-inch sticks. The problem of mixed fuel sizes was then resolved by weighting the various particle sizes that compose actual fuel arrays by either surface area or loading, depending upon the feature of the fire being predicted. The model is complete in the sense that no prior knowledge of a fuel's burning characteristics is required. All that is necessary are inputs describing the physical and chemical makeup of the fuel and the environmental conditions in which it is expected to burn. Inputs include fuel loading, fuel depth, fuel particle surface-area-to-volume ratio, fuel particle heat content, fuel particle moisture and mineral content, and the moisture content at which extinction can be expected. Environmental inputs are mean wind velocity and slope of terrain. For heterogeneous mixtures, the fuel properties are entered for each particle size. The model as originally conceived was for dead fuels in a uniform stratum contiguous to the ground, such as litter or grass. It has been found to be useful, however, for fuels ranging from pine needle litter to heavy logging slash and for California brush fields." FARSITE4 will no longer be supported or available for download or further supported. FlamMap6 now includes FARSITE.	AUTHOR'S DESCRIPTION: "VELMA uses a distributed soil column framework to simulate the movement of water and nutrients (NH4, NO3, DON, DOC) within the soil, between the soil and the vegetation, and between the soil surface and vegetation to the atmosphere. The soil column model consists of three coupled submodels: (1) a hydrological model that simulates vertical and lateral movement of water within soil, losses of water from soil and vegetation to the atmosphere, and the growth and ablation of the seasonal snowpack, (2) a soil temperature model that simulates daily soil layer temperatures from surface air temperature and snow depth, and (3) a plant-soil model that simulates C and N dynamics. (Note: for the purposes of this paper we describe only the hydrologic aspects of the model.) Each soil column consists of n soil layers. Soil water balance is solved for each layer (equations (A1)–(A6)). We employ a simple logistic function that is based on the degree of saturation to capture the breakthrough characteristics of soil water drainage (equations (A7)–(A9)). Evapotranspiration increases exponentially with increasing soil water storage and asymptotically approaches the potential evapotranspiration rate (PET) as water storage reaches saturation [Davies and Allen, 1973; Federer, 1979, 1982; Spittlehouse and Black, 1981] (equation (A12)). PET is estimated using a simple temperature-based method [Hamon, 1963] (equation (A13)). An evapotranspiration recovery function is used to account for the effects of changes in stand-level transpiration rates during succession, e.g., after fire or harvest (equation (B2)). Snowmelt is estimated using the degree-day approach [Rango and Martinec, 1995] and accounts for the effects of rain on snow [Harr, 1981] (equation (A10)). [15] The soil column model is placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other through the downslope lateral transport of water (Figures A1 and A2). Surface and subsurface lateral flow are routed using a multiple flow direction method [Freeman, 1991; Quinn et al., 1991]. As with vertical drainage of soil water, lateral subsurface downslope flow is modeled using a simple logistic function multiplied by a factor to account for the local topographic slope angle (equation (A16))… The model is forced with daily temperature and precipitation. Daily observed streamflow data is used to calibrate and validate simulated discharge." "Model calibration is needed to accurately capture the pre- and postharvest dynamics at WS10. This model calibration consists of two simulations: an old-growth simulation for the period 1969-1974 and a post-harvest simulation for the period 1975-2008." Two additional sets of VELMA simulations examining changes in streamflow are presented in the paper, but not included here. Twenty simulations were conducted varying the location across the watershed of a 20% har	ABSTRACT: "Inorganic nitrogen (N) transformations and removal in aquatic sediments are microbially mediated, and rates influence N-transport. In this study we related physicochemical properties of a large Great Lakes embayment, the St. Louis River Estuary (SLRE) of western Lake Superior, to sediment N-transformation rates. We tested for associations among rates and N-inputs, vegetation biomass, and temperature.We measured rates of nitrification (NIT), unamended base denitrification (DeNIT), and potential denitrification [denitrifying enzyme activity (DEA)] in 2011 and 2012 across spatial and depth zones. In vegetated habitats, NIT and DeNIT rateswere highest in deep (ca. 2 m) water (249 and 2111 mg N m−2 d−1, respectively) and in the upper and lower reaches of the SLRE (N126 and 274 mg N m−2 d−1, respectively). Rates of DEA were similar among zones. In 2012, NIT, DeNIT, and DEA rateswere highest in July, May, and June, respectively. System-wide, we observed highest NIT (223 and 287 mgNm−2 d−1) and DeNIT (77 and 64 mgNm−2 d−1) rates in the harbor and from deep water, respectively. Amendment with NO3 − enhanced DeNIT rates more than carbon amendment; however, DeNIT and NIT rates were inversely related, suggesting the two processes are decoupled in sediments. Average proportion of N2O released during DEA (23–54%) was greater than from DeNIT (0–41%). Nitrogen cycling rates were spatially and temporally variable, but we modeled how alterations to water depth and N-inputs may impact DeNIT rates. A large flood occurred in 2012 which temporarily altered water chemistry and sediment nitrogen cycling." ? Comment: BH: I pasted the entire abstract because there is not specific mention of the combined sediment nitrification model.	[ABSTRACT: "This report describes the demonstration of the EcoAIM decision support framework and GIS-based tool. EcoAIM identifies and quantifies the ecosystem services provided by the natural resources at the Aberdeen Proving Ground (APG). A structured stakeholder process determined the mission and non-mission priorities at the site, elicited the natural resource management decision process, identified the stakeholders and their roles, and determine the ecosystem services of priority that impact missions and vice versa. The EcoAIM tool was customized to quantify in a geospatial context, five ecosystem services – vista aesthetics, landscape aesthetics, recreational opportunities, habitat provisioning for biodiversity and nutrient sequestration. The demonstration included a Baseline conditions quantification of ecosystem services and the effects of a land use change in the Enhanced Use Lease parcel in cantonment area (Scenario 1). Biodiversity results ranged widely and average scores decreased by 10% after Scenario 1. Landscape aesthetics scores increased by 10% after Scenario 1. Final scores did not change for recreation or nutrient sequestration because scores were outside the boundaries of the baseline condition. User feedback after the demonstration indicated positive reviews of EcoAIM as being useful and usable for land use decisions and particularly for use as a communication tool. "	ABSTRACT: "Riparian areas support a set of river functions and of ecosystem services (ESs). Their role is essential in reducing negative human impacts on river functionality. These aspects could be contained in the River Basin Management Plan, which is the tool for managing and planning freshwater ecosystems in a river basin. In this paper, a new index was developed, namely the River Ecosystem Quality Index (REQI). It is composed of five ecological indices, which assess the quality of riparian areas, and it was first applied to the Marecchia river (central Italy). The REQI was also compared with the Italian River Functionality Index (IFF) and the ESs measured as the capacity of land cover in providing human benefits. Data have shown a decrease in the quality of riparian areas, from the upper to lower part of river, with 53% of all subareas showing medium-quality values…" AUTHOR'S DESCRIPTION: "The evaluation of the quality of the riparian areas is based on the analysis of two fundamental elements of riparian areas: vegetation (characteristics and distribution) and wild birds, measured with standardized methodology and used as indicators of environmental quality and changes...To represent the REQI, each of the five indicators was initially scored with its own range (Figure 3(a)—(e)). Then, all results were redistributed in ranges from 1 to 5, where 5 is the best condition of all indices. Redistributed results were finally summed."	ABSTRACT: " This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence‐absence indicators or average self‐reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008–2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100–200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. "
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None Identified	None identified	Not applicable	None identified	None identified	None identified	None reported	None identified	None identified
Biophysical Context	No additional description provided	Elevation ranges from 1552 to 2442 m, on predominantely south-facing slopes	Agricultural landuse , 1st-10th order streams	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. Mean annual precipitation is 2300 mm and falls primarily as rain between October and April. Total rainfall during June– September averages 200 mm. Snow rarely persists longer than a couple of weeks and usually melts within 1 to 2 days. Average annual streamflow is 1600 mm, which is approximately 70% of annual precipitation. Soils are of the Frissel series, classified as Typic Dystrochrepts with fine loamy to loamy-skeletal texture that are generally deep and well drained. These soils quickly transmit subsurface water to the stream. Prior to the 1975 100% clearcut, WS10 was a 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). The dominant vegetation of WS10 today is a 35 year old mixed Douglasfir and western hemlock stand.	Estuarine system	Chesapeake bay coastal plain, elev. 60ft.	No additional description provided	stream and river reaches of Michigan
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Not applicable	No scenarios presented	Stand age; old-growth (pre-harvest), and harvested (postharvest)	No scenarios presented	N/A	No scenarios presented	targeted sport fish biomass

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application	Method Only	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	New or revised model	New or revised model	New or revised model	New or revised model	New or revised model	New or revised model	New or revised model

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

EM ID em.detail.idHelp ?	EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-346 \| Doc-347 ? Comment: EnviroAtlas maps BNF based on a correlation with AET modeled by Cleveland et al. 1999, and modified by land use (% natural vs. ag/developed) within each HUC. AET was modeled using climate and land use parameters (equation from Sanford and Selnick 2013). For full citations of these related models, see below, "Document ID for related EM.	Doc-260 \| Doc-269	Doc-154 \| Doc-155	None	Doc-317	None	None	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	EM-65 \| EM-66 \| EM-69 \| EM-70 \| EM-71 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	None	None	EM-379 \| EM-380 \| EM-605 \| EM-884 \| EM-883 \| EM-887	None	None	None	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
EM Temporal Extent em.detail.tempExtentHelp ?	2006-2010	2007-2009	2000-2008	Not applicable	1969-2008	2011 - 2012	2014	1996-2003 ? Comment: All the ecological analyses are based on the production of a 1:10,000 scale map of land cover with detailed classes for the vegetation obtained by overlapping the photogrammetric analysis (AIMA flight 1996) and the 2003 land-use map.	2008-2010
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	Not applicable	time-dependent	time-stationary	time-stationary	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	future time	Not applicable	Not applicable	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	discrete	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	1	Not applicable	Not applicable	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Day	Not applicable	Not applicable	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or Ecological	Watershed/Catchment/HUC	Not applicable	Watershed/Catchment/HUC	Watershed/Catchment/HUC	Geopolitical	Watershed/Catchment/HUC	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	counterminous United States	Central French Alps	Upper Mississippi, Ohio and Missouri River sub-basins	Not applicable	H. J. Andrews LTER WS10	St. Louis River estuary	Aberdeen Proving Ground	Marecchia river catchment	HUCS in Michigan
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	10-100 km^2	>1,000,000 km^2	Not applicable	10-100 ha	10-100 km^2	100-1000 km^2	100-1000 km^2	100,000-1,000,000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases) ? Comment: Watersheds (12-digit HUCs).	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	Not applicable	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases) ? Comment: 500m x 500m is also used for some computations. The evaluation does include some riparian buffers which are linear features along streams.	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	length, for linear feature (e.g., stream mile)	Not applicable	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	area, for pixel or radial feature	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)
Spatial Grain Size em.detail.spGrainSizeHelp ?	irregular	20 m x 20 m	1 km	Not applicable	30 m x 30 m surface pixel and 2-m depth soil column	Not applicable	100m x 100m	500 m x 1000 m	reach in HUC

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Analytic	Numeric	Analytic	Numeric	Analytic	Numeric
EM Determinism em.detail.deterStochHelp ?	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

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	Not applicable	Yes	No	No ? Comment: Nutrient sequestion submodel ( EPA's P8 model has been long used)	Not applicable	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Yes	No	Not applicable	Yes	No	Not applicable	Not applicable	Yes
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	Adjusted R \| 62.4 \|	None	None	Nash-Sutcliffe coefficient \| Varies by Run; view Runs to see values \| unitless Correlation coefficient \| Varies by Run; view Runs to see values \| unitless Index of agreement \| Varies by Run; view Runs to see values \| unitless	None	None	None	R squared \| 0.552 \| unitless
Model Operational Validation Reported? em.detail.validationHelp ?	No	Yes	No	No	No	No	No	Yes ? Comment: R2 values of the analysis between the REQI, the capacity of land cover to provide ESs, and the Italian River Functionality Quality Index ? IFF.	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	Yes	Not applicable	No	No	No	Not applicable	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	Unclear	Not applicable	No	No	Unclear ? Comment: Just cannot tell, but no mention of sensitivity was made.	Not applicable	No
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

EM Locations, Environments, Ecology

Location of EM Application

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

EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
North America United States	Europe France	North America United States New York Montana Indiana Kentucky Minnesota Kansas Alabama Nebraska Pennsylvania Iowa Colorado Illinois Missouri Ohio Georgia	None	North America United States Oregon	North America United States Minnesota Wisconsin	North America United States Maryland	Europe Italy	North America United States Michigan

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

EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
None	None	None	None	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
Centroid Latitude em.detail.ddLatHelp ?	39.5	45.05	36.98	-9999	44.15	46.75	39.46	43.89	45.12
Centroid Longitude em.detail.ddLongHelp ?	-98.35	6.4	-89.13	-9999	-122.2	-92.08	76.12	12.3	85.18
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	Not applicable	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Provided	Estimated	Not applicable	Provided	Estimated	Estimated	Estimated	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	Agroecosystems \| Grasslands	Rivers and Streams	Terrestrial Environment (sub-classes not fully specified)	Rivers and Streams \| Ground Water \| Forests	Rivers and Streams \| Inland Wetlands \| Lakes and Ponds	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Forests \| Created Greenspace \| Grasslands \| Scrubland/Shrubland	Rivers and Streams \| Inland Wetlands \| Terrestrial Environment (sub-classes not fully specified)	Rivers and Streams
Specific Environment Type em.detail.specificEnvTypeHelp ?	Terrestrial	Subalpine terraces, grasslands, and meadows	Not applicable	Not applicable	400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata).	Freshwater estuary	Coastal Plain	Riparian zone along major river	stream reaches
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	Not applicable	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 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

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Community	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Species ? Comment: Bird species for faunistic index of conservation.	Not applicable

Taxonomic level and name of organisms or groups identified

EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
None Available	None Available	None Available	None Available	None Available	None Available	None Available	None Available	Family panfishes Species Brown trout smallmouth bass Brook trout Walleye

EnviroAtlas URL

EM-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
Average Annual Precipitation, Natural Biological Nitrogen Fixation, The Watershed Boundary Dataset (WBD)	GAP Ecological Systems, Carbon storage by tree biomass (kg/m2)	National Hydrography Dataset Plus (NHD PlusV2), Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition	Average Annual Precipitation	GAP Ecological Systems, Average Annual Precipitation, Average Annual Daily Potential Wind Energy	Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition	GAP Ecological Systems, Percent IUCN Status II, Ecosystem Markets: Imperiled Species and Habitats, Total Annual Reduced Nitrogen Deposition	Ecosystem Markets: Imperiled Species and Habitats	The National Hydrography Dataset (NHD), The Watershed Boundary Dataset (WBD), Enabling Conditions, Employment Rate

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-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	[Ecosystem] Provisioning Materials Biomass Materials from plants, algae and animals for agricultural use	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters	None	[Ecosystem] Provisioning Materials Water Surface water for non-drinking purposes	[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 ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters [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	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats Water conditions Chemical condition of freshwaters	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs

<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-63	EM-68	EM-93	EM-337	EM-375	EM-416	EM-647	EM-657	EM-660
Atmosphere Soil (2) Specific types of soil Quality Indicator	Agroecosystems (22) Flora (5) Fodder quality Quality Indicator	Rivers and Streams (111) Water (3) Liquid water Quality Indicator	None	None	None	Forests (21) Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Quality Indicator Wetlands (12) Composite (8) Regulation of extreme events Quality Indicator Scapes: views, sounds and scents of land, sea, sky or a combination Quality Indicator	None	Rivers and Streams (111) Fauna (4) Fish Quality Indicator

EcoService Models Library (ESML)

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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

Comment:

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)