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-434
EM-627
EM-660
EM-705
EM-709
EM-862
EM-983
EM-1019

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
EM Short Name em.detail.shortNameHelp ?	EnviroAtlas - Natural biological nitrogen fixation	Land capability classification	N removal by wetland restoration, Midwest, USA	RUM: Valuing fishing quality, Michigan, USA	Total duck recruits, CREP wetlands, Iowa, USA	Pollinators on landfill sites, United Kingdom	Recreational fishery index, USA	Atlantis ecosystem physics submodel	SMOKE emissions model, Asia
EM Full Name em.detail.fullNameHelp ?	US EPA EnviroAtlas - BNF (Natural biological nitrogen fixation), USA	Land capability classification	Nitrate removal by potential wetland restoration, Mississippi River subbasins, USA	Random utility model (RUM) Valuing Recreational fishing quality in streams and rivers, Michigan, USA	Total duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA	Pollinating insects on landfill sites, East Midlands, United Kingdon	Recreational fishery index for streams and rivers, USA	Atlantis user's guide part I: general overview, physics & ecology	Development of an anthropogenic emissions processing system for Asia using SMOKE
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	US EPA \| EnviroAtlas	None	None	None	None	None	US EPA	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.	340	370 ? Comment: Final project report to U.S. Department of Agriculture; Project number: IOW06682. December 2006.	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.	372 ? Comment: Document 373 is a secondary source for this EM.	389	414	461	481
Document Author em.detail.documentAuthorHelp ?	US EPA Office of Research and Development - National Exposure Research Laboratory	United States Department of Agriculture - Natural Resources Conservation Service	Crumpton, W. G., G. A. Stenback, B. A. Miller, and M. J. Helmers	Melstrom, R. T., Lupi, F., Esselman, P.C., and R. J. Stevenson	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	Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin	Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold	Audzijonyte, A., Gorton, R., Kaplan, I., & Fulton, E. A.	Woo, J.H., Choi, K.C., Kim, H.K., Baek, B.H., Jang, M., Eum, J.H., Song, C.H., Ma, Y.I., Sunwoo, Y., Chang, L.S. and Yoo, S.H.
Document Year em.detail.documentYearHelp ?	2013	2013	2006	2014	2010	2013	2021	2017	2012
Document Title em.detail.sourceIdHelp ?	EnviroAtlas - National	National Soil Survey Handbook - Part 622 - Interpretative Groups	Potential benefits of wetland filters for tile drainage systems: Impact on nitrate loads to Mississippi River subbasins	Valuing recreational fishing quality at rivers and streams	Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt	Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects	Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers.	Atlantis user’s guide part I: general overview, physics & ecology	Development of an anthropogenic emissions processing system for Asia using SMOKE
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Neither peer reviewed nor published (explain in Comment)	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Not peer reviewed but is published (explain in Comment)	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published on US EPA EnviroAtlas website	Published report	Published report	Published journal manuscript	Published report	Published journal manuscript	Published journal manuscript	Published report	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	https://www.epa.gov/enviroatlas	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	https://research.csiro.au/atlantis/home/links/	https://www.cmascenter.org/smoke/
Contact Name em.detail.contactNameHelp ?	EnviroAtlas Team ? Comment: Additional contact: Jana Compton, EPA	United States Department of Agriculture	William G. Crumpton	Richard Melstrom	David Otis	Sam Tarrant	Gregg Lomnicky	Asta Audzijonyte	Jung-Hun Woo
Contact Address	Not reported	Not reported	Dept. of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA 50011	Department of Agricultural Economics, Oklahoma State Univ., Stillwater, Oklahoma, USA	U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University	RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K.	200 SW 35th St., Corvallis, OR, 97333	University of Tasmania (Australia); Nature Research Centre (Lithuania)	Department of Advanced Technology Fusion, Room 812, San-Hak Bldg., Konkuk University, Seoul, South Korea
Contact Email	enviroatlas@epa.gov	http://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/contactus/	crumpton@iastate.edu	melstrom@okstate.edu	dotis@iastate.edu	sam.tarrant@rspb.org.uk	lomnicky.gregg@epa.gov	Asta.Audzijonyte@utas.edu.au	jwoo@konkuk.ac.kr

EM Description

EM ID em.detail.idHelp ?	EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
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."	AUTHOR'S DESCRIPTION: "Definition. Land capability classification is a system of grouping soils primarily on the basis of their capability to produce common cultivated crops and pasture plants without deteriorating over a long period of time." "Class I (1) soils have slight limitations that restrict their use. Class II (2) soils have moderate limitations that reduce the choice of plants or require moderate conservation practices. Class III (3) soils have severe limitations that reduce the choice of plants or require special conservation practices, or both. Class IV (4) soils have very severe limitations that restrict the choice of plants or require very careful management, or both. Class V (5) soils have little or no hazard of erosion but have other limitations, impractical to remove, that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VI (6) soils have severe limitations that make them generally unsuited to cultivation and that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VII (7) soils have very severe limitations that make them unsuited to cultivation and that restrict their use mainly to rangeland, forestland, or wildlife habitat. Class VIII (8) soils and miscellaneous areas have limitations that preclude their use for commercial plant production and limit their use mainly to recreation, wildlife habitat, water supply, or esthetic purposes." [More information can be found at: http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054226#ex2]	ABSTRACT: "The primary objective of this project was to estimate the nitrate reduction that could be achieved using restored wetlands as nitrogen sinks in tile-drained regions of the upper Mississippi River (UMR) and Ohio River basins. This report provides an assessment of nitrate concentrations and loads across the UMR and Ohio River basins and the mass reduction of nitrate loading that could be achieved using wetlands to intercept nonpoint source nitrate loads. Nitrate concentration and stream discharge data were used to calculate stream nitrate loading and annual flow-weighted average (FWA) nitrate concentrations and to develop a model of FWA nitrate concentration based on land use. Land use accounts for 90% of the variation among stations in long term FWA nitrate concentrations and was used to estimate FWA nitrate concentrations for a 100 ha grid across the UMR and Ohio River basins. Annual water yield for grid cells was estimated by interpolating over selected USGS monitoring station water yields across the UMR and Ohio River basins. For 1990 to 1999, mass nitrate export from each grid area was estimated as the product of the FWA nitrate concentration, water yield and grid area. To estimate potential nitrate removal by wetlands across the same grid area, mass balance simulations were used to estimate percent nitrate reduction for hypothetical wetland sites distributed across the UMR and Ohio River basins. Nitrate reduction was estimated using a temperature dependent, area-based, first order model. Model inputs included local temperature from the National Climatic Data Center and water yield estimated from USGS stream flow data. Results were used to develop a nonlinear model for percent nitrate removal as a function of hydraulic loading rate (HLR) and temperature. Mass nitrate removal for potential wetland restorations distributed across the UMR and Ohio River basin was estimated based on the expected mass load and the predicted percent removal. Similar functions explained most of the variability in per cent and mass removal reported for field scale experimental wetlands in the UMR and Ohio River basins. Results suggest that a 30% reduction in nitrate load from the UMR and Ohio River basins could be achieved using 210,000-450,000 ha of wetlands targeted on the highest nitrate contributing areas." AUTHOR'S DESCRIPTION: "Percent nitrate removal was estimated based on HLR functions (Figure 19) spanning a 3 fold range in loss rate coefficient (Crumpton 2001) and encompassing the observed performance reported for wetlands in the UMR and Ohio River basins (Table 2, Figure 7). The nitrate load was multiplied by the expected percent nitrate removal to estimate the mass removal. This procedure was repeated for each restoration scenario each year in the simulation period (1990 to 1999)… for a scenario with a wetland/watershed area ratio of 2%. These results are based on the assumption that the FWA nitrate concentration versus percent row crop r	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. "	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: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each	ABSTRACT: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.]	Before delving into Atlantis we would like to provide a little bit of background on the modelling framework and this manual. Atlantis is just one of many marine ecosystem models, originally known as BM2 (BoxModel 2) it was christened Atlantis by Villy Christensen in South Africa in 2001. Marine ecosystem models have existed for more than 50 years, though they have only grown in popular use since the advent of (fast) modern computing power. They have grown from a biophysical focus to include more and more of the human dimensions. This is reflected in the structure of this manual, which sequentially works through the physical then biological before getting into the human dimensions. Atlantis was originally developed with an eye to temperate marine ecosystems and fisheries, though it has grown through time.	Air quality modeling is a useful methodology to investigate air quality degradation in various locations and to analyze effectiveness of emission reduction plans. A comprehensive air quality model usually requires a coordinated set of emissions input of all necessary chemical species. We have developed an anthropogenic emissions processing system for Asia in support of air quality modeling and analysis over Asia (named SMOKE-Asia). The SMOKE (Sparse Matrix Operator kernel Emissions) system, which was developed by U.S. EPA and has been maintained by the Carolina Environmental Program (CEP) of the University of North Carolina, was used to develop our emissions processing system. A merged version of INTEX 2006 and TRACE-P 2000 inventories was used as an initial Asian emissions inventory. The IDA (Inventory Data Analyzer) format was used to create SMOKE-ready emissions. Source Classification Codes (SCCs) and country/state/county (FIPS) code, which are the two key data fields of SMOKE IDA data structure, were created for Asia. The 38 SCCs and 2752 FIPS codes were allocated to our SMOKE-ready emissions for more comprehensive processing. US EPA’s MIMS (Multimedia Integrated Modeling System) Spatial Allocator software, along with many global and regional GIS shapes, were used to create spatial allocation profiles for Asia. Temporal allocation and chemical speciation profiles were partly regionalized using Asia-based studies. Initial data production using the developed SMOKE-Asia system was successfully performed. NOx and VOC emissions for the year 2009 were projected to be increased by 50% from those of 1997. The emission hotspots, such as large cities and large point sources, are distinguished in the domain due to spatial allocation. Regional emission peaks were distinguished due to temporally resolved emission information. The PAR (Paraffin carbon bond) and XYL (Xylene and other polyalkyl aromatics) showed the first and second largest emission rate among VOC species. Most of point source emissions are located in layers 3 to 4, which the altitude range reaches 310–550 m AGL. Qualitative inter-comparison between model output and ground/satellite measurement showed good agreements in terms of spatial and temporal patterns. We expect that the result of this study will provide better air quality modeling inputs, which will act as a major step to improve our understanding of Asian air quality.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None Identified	None provided	None identified	None identified	None identified	None identified	None identified	None identified	None provided
Biophysical Context	No additional description provided	No additional description provided	No additional description provided	stream and river reaches of Michigan	Prairie Pothole Region of Iowa	No additional description provided	None	Marine and coastal ecosystems	Asia
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	More conservative, average and less conservative nitrate loss rate	targeted sport fish biomass	No scenarios presented	No scenarios presented	N/A	No scenarios presented	NA

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method Only	Method + Application (multiple runs exist)	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method Only	Method + Application
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	Application of existing model

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

EM ID em.detail.idHelp ?	EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
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.	None	None	None	Doc-372 \| Doc-373	Doc-389	None	Doc-456 \| Doc-459	Doc-478
EM ID for related EM em.detail.relatedEmEmIdHelp ?	None	None	None	None	EM-632 \| EM-700 \| EM-701 \| EM-702 \| EM-703 \| EM-704	EM-697	None	EM-981 \| EM-978 \| EM-985 \| EM-990 \| EM-991	EM-1012 \| EM-1021

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
EM Temporal Extent em.detail.tempExtentHelp ?	2006-2010	Not applicable	1973-1999	2008-2010	1987-2007	2007-2008	2013-2014	Not applicable	1997-2009
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	Not applicable	time-dependent	time-stationary	time-stationary	time-stationary	time-dependent	time-dependent	time-dependent
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	future time	Not applicable	Not applicable	Not applicable	past time	Not applicable	past time
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	discrete	Not applicable	Not applicable	Not applicable	discrete	continuous	continuous
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	1	Not applicable	Not applicable	Not applicable	1	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Day	Not applicable	Not applicable	Not applicable	Year	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Not applicable	Watershed/Catchment/HUC	Watershed/Catchment/HUC	Multiple unrelated locations (e.g., meta-analysis)	Multiple unrelated locations (e.g., meta-analysis)	Geopolitical	Not applicable	Geopolitical
Spatial Extent Name em.detail.extentNameHelp ?	counterminous United States	Not applicable	Upper Mississippi River and Ohio River basins	HUCS in Michigan	CREP (Conservation Reserve Enhancement Program	East Midlands	United States	Not applicable	Asia
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	>1,000,000 km^2	Not applicable	>1,000,000 km^2	100,000-1,000,000 km^2	10,000-100,000 km^2	1000-10,000 km^2.	>1,000,000 km^2	Not applicable	1000-10,000 km^2.

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases) ? Comment: Watersheds (12-digit HUCs).	Not applicable	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	Not applicable	spatially lumped (in all cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	other (specify), for irregular (e.g., stream reach, lake basin)	Not applicable	area, for pixel or radial feature	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	other (specify), for irregular (e.g., stream reach, lake basin)	length, for linear feature (e.g., stream mile)	Not applicable	Not applicable
Spatial Grain Size em.detail.spGrainSizeHelp ?	irregular	Not applicable	1 km2	reach in HUC	multiple, individual, irregular sites	multiple unrelated locations	stream reach (site)	Not applicable	Not applicable

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Not applicable	Numeric	Numeric	Analytic	Analytic	Analytic	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	None	Conventional (frequentist) statistics	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-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
Model Calibration Reported? em.detail.calibrationHelp ?	No	Not applicable	No	No	Unclear	Not applicable	No	Not applicable	Unclear
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	Not applicable	No	Yes	No	Not applicable	No	Not applicable	Unclear
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	R squared \| 0.552 \| unitless	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No ? Comment: However, agreement of submodel and intermediate components; annual discharge (R2=0.79), and nitrate-N load (R2=0.74), based on GIS land use were determined in comparison with USGS NASQAN data.	No	No	Not applicable	No	Not applicable	Yes
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Not applicable	No	No	No	Not applicable	No	Not applicable	Unclear
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Not applicable	No	No	No	Not applicable	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

EM Locations, Environments, Ecology

Location of EM Application

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

EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
North America United States	None	North America United States North Carolina New York Indiana Tennessee Kentucky South Dakota Minnesota Pennsylvania Virgina West Virginia Iowa Illinois Missouri Ohio Maryland Wisconsin	North America United States Michigan	North America United States Iowa	Europe United Kingdom	North America United States North Carolina Indiana Wyoming Northern Mariana Islands Utah Guam Arizona Montana Kentucky California Kansas Delaware Florida Pennsylvania Iowa Mississippi Illinois Texas Connecticut Georgia Maryland Idaho Oregon Vermont Puerto Rico Maine Oklahoma Tennessee American Samoa Alabama Arkansas South Carolina Washington Nebraska West Virginia Colorado Massachusetts Missouri Alaska U.S. Virgin Islands Washington D.C. North Dakota Wisconsin Nevada New York Rhode Island Hawaii South Dakota Minnesota New Jersey Michigan Virgina New Mexico New Hampshire Louisiana Ohio	None	Asia

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

EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
None	None	None	None	None	None	None	None	Western Indo-Pacific

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
Centroid Latitude em.detail.ddLatHelp ?	39.5	Not applicable	40.6	45.12	42.62	52.22	36.21	Not applicable	38.63
Centroid Longitude em.detail.ddLongHelp ?	-98.35	Not applicable	-88.4	85.18	-93.84	-0.91	-113.76	Not applicable	117.79
Centroid Datum em.detail.datumHelp ?	WGS84	Not applicable	WGS84	WGS84	WGS84	WGS84	WGS84	Not applicable	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Not applicable	Estimated	Estimated	Estimated	Estimated	Estimated	Not applicable	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Terrestrial Environment (sub-classes not fully specified)	Terrestrial Environment (sub-classes not fully specified)	Rivers and Streams \| Inland Wetlands \| Agroecosystems	Rivers and Streams	Inland Wetlands \| Agroecosystems \| Grasslands	Created Greenspace \| Grasslands	Rivers and Streams	Aquatic Environment (sub-classes not fully specified) \| Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Near Coastal Marine and Estuarine \| Open Ocean and Seas	Atmosphere
Specific Environment Type em.detail.specificEnvTypeHelp ?	Terrestrial	None identified	Agroecosystems and associated drainage and wetlands	stream reaches	Wetlands buffered by grassland within agroecosystems	restored landfills and grasslands	reach	Multiple	Asian atmosphere
EM Ecological Scale em.detail.ecoScaleHelp ?	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	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	Not applicable

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Guild or Assemblage	Individual or population, within a species	Guild or Assemblage	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
None Available	None Available	None Available	Family panfishes Species Brown trout smallmouth bass Brook trout Walleye	Species Northern Pintail (Anas acuta) Mallard (Anas platyrhynchos) Blue-winged teal (Anas discors) Northern Shoveler (Anas clypeata) Gadwall (Anas strepera)	Genus Calliopum Lasioglossum Species Apis mellifera Bombus lapidarius Oedemera nobilis Episyrphus balteatus Bombus terrestris/lucorum Bombus pascuorum	None Available	None Available	None Available

EnviroAtlas URL

EM-63	EM-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
Average Annual Precipitation, Natural Biological Nitrogen Fixation, The Watershed Boundary Dataset (WBD)	Average Annual Precipitation	GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Total Annual Nitrogen Deposition	The National Hydrography Dataset (NHD), The Watershed Boundary Dataset (WBD), Enabling Conditions, Employment Rate	Acres of Land Enrolled in the Conservation Reserve Program (CRP)	GAP Ecological Systems	None Available	Average Annual Precipitation, Average Annual Daily Potential Wind Energy	None Available

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-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	[Ecosystem] Provisioning Nutrition Biomass Reared animals and their outputs Cultivated crops	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs	[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 Pollination and seed dispersal	[Ecosystem] Provisioning Nutrition Biomass Wild animals and their outputs [Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Experiential use of plants, animals and land-/seascapes in different environmental settings	[Non-ecosystem] Regulation & Maintenance by natural physical structures and processes Maintenance of physical, chemical, abiotic conditions By natural chemical and physical processes Not applicable Mediation of flows by natural abiotic structures By soild (mass), liquid and gaseous (air)flows Not applicable [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of salt waters Mediation of flows Liquid flows Hydrological cycle and water flow maintenance	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations

<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-434	EM-627	EM-660	EM-705	EM-709	EM-862	EM-983	EM-1019
Atmosphere Soil (2) Specific types of soil Quality Indicator	Agroecosystems (22) Soil (2) Specific types of soil Quality Indicator	Rivers and Streams (111) Water (3) Liquid water Quality Indicator	Rivers and Streams (111) Fauna (4) Fish Quality Indicator	Wetlands (12) Fauna (4) Duck Stock Indicator	None	Rivers and Streams (111) Fauna (4) Fish Quality Indicator	Open Oceans and Seas (114) Composite (8) Presence of environmental class/subclass Site Indicator Flow Indicator	Atmosphere Atmosphere (1) Air Quality Indicator

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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)