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.

Compare Criteria:

Show Criteria

Hide Criteria

Comparing:

EM-70
EM-97
EM-337
EM-359
EM-418
EM-605
EM-774
EM-940

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
EM Short Name em.detail.shortNameHelp ?	Plant species diversity, Central French Alps	AnnAGNPS, Kaskaskia River watershed, IL, USA	Rate of Fire Spread	InVEST (v1.004) sediment retention, Indonesia	SIRHI, St. Croix, USVI	VELMA v2.0, Ohio, USA	Plant-pollinator networks at reclaimed mine, USA	OpenNSPECT v. 1.1, California, U.S.
EM Full Name em.detail.fullNameHelp ?	Plant species diversity, Central French Alps	AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA	Rate of Fire Spread	InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) sediment retention, Sumatra, Indonesia	SIRHI (SImplified Reef Health Index), St. Croix, USVI	Visualizing Ecosystems for Land Management Assessments (VELMA) v2.0, Shayler Crossing watershed, Ohio, USA	Restoration of plant-pollinator networks at reclaimed strip mine, Ohio, USA	OpenNSPECT v. 1.1, California, U.S.
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	US EPA	None	InVEST	US EPA	US EPA	None	None
EM Source Document ID	260	137	306	309	335	359 ? Comment: Document #366 is a supporting document for this EM. McKane et al. 2014, VELMA Version 2.0 User Manual and Technical Documentation.	397	433 ? Comment: Additional source for this EM: NOAA, 2012. National Oceanic and Atmospheric Administration. Technical Guide for OpenNSPECT, Version 1.1, p. 44. http://www.csc.noaa.gov/digitalcoast/tools/opennspect.
Document Author em.detail.documentAuthorHelp ?	Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.	Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M.	Rothermel, Richard C.	Bhagabati, N. K., Ricketts, T., Sulistyawan, T. B. S., Conte, M., Ennaanay, D., Hadian, O., McKenzie, E., Olwero, N., Rosenthal, A., Tallis, H., and Wolney, S.	Yee, S. H., Dittmar, J. A., and L. M. Oliver	Hoghooghi, N., H. E. Golden, B. P. Bledsoe, B. L. Barnhart, A. F. Brookes, K. S. Djang, J. J. Halama, R. B. McKane, C. T. Nietch, and P. P. Pettus	Cusser, S. and K. Goodell	Morrison, K. D. and C. A. Kolden
Document Year em.detail.documentYearHelp ?	2011	2011	1972	2014	2014	2018	2013	2015
Document Title em.detail.sourceIdHelp ?	Using plant functional traits to understand the landscape distribution of multiple ecosystem services	AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study	A Mathematical model for predicting fire spread in wildland fuels	Ecosystem services reinforce Sumatran tiger conservation in land use plans	Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI	Cumulative effects of low impact development on watershed hydrology in a mixed land-cover system	Diversity and distribution of floral resources influence the restoration of plant-pollinator networks on a reclaimed strip mine	Modeling the impacts of wildfire on runoff and pollutant transport from coastal watersheds to the nearshore environment
Document Status em.detail.statusCategoryHelp ?	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 journal manuscript	Published journal manuscript	Published USDA Forest Service report	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/	http://firelab.org/project/farsite	https://www.naturalcapitalproject.org/invest/	Not applicable	https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20	Not applicable	https://coast.noaa.gov/digitalcoast/tools/opennspect.html
Contact Name em.detail.contactNameHelp ?	Sandra Lavorel	Yongping Yuan	Charles McHugh	Nirmal K. Bhagabati	Susan H. Yee	Heather Golden	Sarah Cusser ? Comment: Department of Evolution, Ecology, and Organismal Biology, Ohio State University, 318 West 12th Avenue, Columbus, OH 43202, U.S.A.	Crystal A. Kolden
Contact Address	Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France	U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA	RMRS Missoula Fire Sciences Laboratory, 5775 US Highway 10 West, Missoula, MT 59808	The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018	US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA	National Exposure Research Laboratory, Office of Research and Development, US EPA, Cincinnati, OH 45268, USA	Department of Evolution, Ecology, and Behavior, School of Biological Sciences, The University of Texas at Austin, 100 East 24th Street Stop A6500, Austin, TX 78712-1598, U.S.A.	Not reported
Contact Email	sandra.lavorel@ujf-grenoble.fr	yuan.yongping@epa.gov	cmchugh@fs.fed.us	nirmal.bhagabati@wwfus.org	yee.susan@epa.gov	Golden.Heather@epa.gov	sarah.cusser@gmail.com	ckolden@uidaho. Edu

EM Description

EM ID em.detail.idHelp ?	EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
Summary Description em.detail.summaryDescriptionHelp ?	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: "Simpson species diversity was modelled using the LU + abiotic [land use and all abiotic variables] model given that functional diversity should be a consequence of species diversity rather than the reverse (Lepsˇ et al. 2006)…Species diversity for each pixel was calculated and mapped using model estimates for effects of land use types, and for regression coefficients on abiotic variables. For each pixel these calculations were applied to mapped estimates of abiotic variables."	AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198"	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.	Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. ABSTRACT: "...Here we use simple spatial analyses on readily available datasets to compare the distribution of five ecosystem services with tiger habitat in central Sumatra. We assessed services and habitat in 2008 and the changes in these variables under two future scenarios: a conservation-friendly Green Vision, and a Spatial Plan developed by the Indonesian government..." AUTHOR'S DESCRIPTION: "We used a modeling tool, InVEST (Integrated Valuation of Environmental Services and Tradeoffs version 1.004; Tallis et al., 2010), to map and quantify tiger habitat quality and five ecosystem services. InVEST maps ecosystem services and the quality of species habitat as production functions of LULC using simple biophysical models. Models were parameterized using data from regional agencies, literature surveys, global databases, site visits and prior field experience (Table 1)... The sediment retention model is based on the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978). It estimates erosion as ton y^-1 of sediment load, based on the energetic ability of rainfall to move soil, the erodibility of a given soil type, slope, erosion protection provided by vegetated LULC, and land management practices. The model routes sediment originating on each land parcel along its flow path, with vegetated parcels retaining a fraction of sediment with varying efficiencies, and exporting the remainder downstream. ...Although InVEST reports ecosystem services in biophysical units, its simple models are best suited to understanding broad patterns of spatial variation (Tallis and Polasky, 2011), rather than for precise quantification. Additionally, we lacked field measurements against which to calibrate our outputs. Therefore, we focused on relative spatial distribution across the landscape, and relative change to scenarios."	ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati	ABSTRACT: "Low Impact Development (LID) is an alternative to conventional urban stormwater management practices, which aims at mitigating the impacts of urbanization on water quantity and quality. Plot and local scale studies provide evidence of LID effectiveness; however, little is known about the overall watershed scale influence of LID practices. This is particularly true in watersheds with a land cover that is more diverse than that of urban or suburban classifications alone. We address this watershed-scale gap by assessing the effects of three common LID practices (rain gardens, permeable pavement, and riparian buffers) on the hydrology of a 0.94 km2 mixed land cover watershed. We used a spatially-explicit ecohydrological model, called Visualizing Ecosystems for Land Management Assessments (VELMA), to compare changes in watershed hydrologic responses before and after the implementation of LID practices. For the LID scenarios, we examined different spatial configurations, using 25%, 50%, 75% and 100% implementation extents, to convert sidewalks into rain gardens, and parking lots and driveways into permeable pavement. We further applied 20 m and 40 m riparian buffers along streams that were adjacent to agricultural land cover…" AUTHOR'S DESCRIPTION: "VELMA’s modeling domain is a three-dimensional matrix that includes information regarding surface topography, land use, and four soil layers. VELMA uses a distributed soil column framework to model the lateral and vertical movement of water and nutrients through the four soil layers. A soil water balance is solved for each layer. The soil column model is placed within a watershed framework to create a spatially distributed model applicable to watersheds (Figure 2, shown here with LID practices). Adjacent soil columns interact through down-gradient water transport. Water entering each pixel (via precipitation or flow from an adjacent pixel) can either first infiltrate into the implemented LID and the top soil layer, and then to the downslope pixel, or continue its downslope movement as the lateral surface flow. Surface and subsurface lateral flow are routed using a multiple flow direction method, as described in Abdelnour et al. [21]. A detailed description of the processes and equations can be found in McKane et al. [32], Abdelnour et al. [21], Abdelnour et al. [40]."	ABSTRACT: "Plant–pollinator mutualisms are one of the several functional relationships that must be reinstated to ensure the long-term success of habitat restoration projects. These mutualisms are unlikely to reinstate themselves until all of the resource requirements of pollinators have been met. By meeting these requirements, projects can improve their long-term success. We hypothesized that pollinator assemblage and structure and stability of plant–pollinator networks depend both on aspects of the surrounding landscape and of the restoration effort itself. We predicted that pollinator species diversity and network stability would be negatively associated with distance from remnant habitat, but that local floral diversity might rescue pollinator diversity and network stability in locations distant from the remnant. We created plots of native prairie on a reclaimed strip mine in central Ohio, U.S.A. that ranged in floral diversity and isolation from the remnant habitat. We found that the pollinator diversity declined with distance from the remnant habitat. Furthermore, reduced pollinator diversity in low floral diversity plots far from the remnant habitat was associated with loss of network stability. High floral diversity, however, compensated for losses in pollinator diversity in plots far from the remnant habitat through the attraction of generalist pollinators. Generalist pollinators increased network connectance and plant-niche overlap. Asa result, network robustness of high floral diversity plots was independent of isolation. We conclude that the aspects of the restoration effort itself, such as floral community composition, can be successfully tailored to incorporate the restoration of pollinators and improve success given a particular landscape context."	ABSTRACT: "Wildfire is a common disturbance that can significantly alter vegetation in watersheds and affect the rate of sediment and nutrient transport to adjacent nearshore oceanic environments. Changes in runoff resulting from heterogeneous wildfire effects are not well-understood due to both limitations in the field measurement of runoff and temporally-limited spatial data available to parameterize runoff models. We apply replicable, scalable methods for modeling wildfire impacts on sediment and nonpoint source pollutant export into the nearshore environment, and assess relationships between wildfire severity and runoff. Nonpoint source pollutants were modeled using a GIS-based empirical deterministic model parameterized with multi-year land cover data to quantify fire-induced increases in transport to the nearshore environment. Results indicate post-fire concentration increases in phosphorus by 161 percent, sediments by 350 percent and total suspended solids (TSS) by 53 percent above pre-fire years. Higher wildfire severity was associated with the greater increase in exports of pollutants and sediment to the nearshore environment, primarily resulting from the conversion of forest and shrubland to grassland. This suggests that increasing wildfire severity with climate change will increase potential negative impacts to adjacent marine ecosystems. The approach used is replicable and can be utilized to assess the effects of other types of land cover change at landscape scales. It also provides a planning and prioritization framework for management activities associated with wildfire, including suppression, thinning, and post-fire rehabilitation, allowing for quantification of potential negative impacts to the nearshore environment in coastal basins."
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	Not reported	None identified	This analysis provided input to government-led spatial planning and strategic environmental assessments in the study area. This region contains some of the last remaining forest habitat of the critically endangered Sumatran tiger, Panthera tigris sumatrae.	None identified	None identified	None identified	None identified
Biophysical Context	Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes	Upper Mississipi River basin, elevation 142-194m,	Not applicable	Six watersheds in central Sumatra covering portions of Riau, Jambi and West Sumatra provinces. The Barisan mountain range comprises the western edge of the watersheds, while peat swamps predominate in the east.	No additional description provided	The Shayler Crossing (SHC) watershed is a subwatershed of the East Fork Little Miami River Watershed in southwest Ohio, USA and falls within the Till Plains region of the Central Lowland physiographic province. The Till Plains region is a topographically young and extensive flat plain, with many areas remaining undissected by even the smallest stream. The bedrock is buried under a mantle of glacial drift 3–15 m thick. The Digital Elevation Model (DEM) has a maximum value of ~269 m (North American_1983 datum) within the watershed boundary (Figure 1). The soils are primarily the Avonburg and Rossmoyne series, with high silty clay loam content and poor to moderate infiltration. Average annual precipitation for the period, 1990 through 2011, was 1097.4 _ 173.5 mm. Average annual air temperature for the same period was 12 _C Mixed land cover suburban watershed. The primary land uses consist of 64.1% urban or developed area (including 37% lawn, 12% building, 6.5% street, 6.4% sidewalk, and 2.1% parking lot and driveway), 23% agriculture, and 13% deciduous forest. Total imperviousness covers approximately 27% of the watershed area.	The site was surface mined for coal until the mid-1980s and soon after recontoured and seeded with a low diversity of non-native grasses and forbes. The property is grassland in a state of arrested succession, unable to support tree growth because of shallow, infertile soils.	Central California coast includes twelve adjacent watersheds covering 87,638 ha and rises steeply from sea level to just below 1800 m within a few km from the coast, and experiences a Mediterranean climate, with fire season typically lasting from June to November. Precipitation is dependent on elevation ranging from 65 cm near the coast to over 130 cm at ridge top. Three ecological zones occur within the study area. These zones are comprised of grasslands, coastal sage scrub, chaparral, oak forests, mixed broadleaf evergreen forest, and coniferous forests.
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target	No scenarios presented	Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan	No scenarios presented	Three types of Low Impact Development (LID) practices (rain gardens, permeable pavements, forested riparian buffers) applied a different conversion levels.	No scenarios presented	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method Only	Method + Application (multiple runs exist) View EM Runs	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method + Application (multiple runs exist) View EM Runs	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	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	Application of existing model

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

EM ID em.detail.idHelp ?	EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-260	Doc-142	None	Doc-338	None	Doc-13 \| Doc-366	None	Doc-431
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-65 \| EM-66 \| EM-68 \| EM-69 \| EM-71 \| EM-79 \| EM-80 \| EM-81 \| EM-82 \| EM-83	None	None	EM-435	None	EM-375 \| EM-377 \| EM-378 \| EM-884 \| EM-883 \| EM-887	None	EM-938

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
EM Temporal Extent em.detail.tempExtentHelp ?	2007-2009	1980-2006	Not applicable	2008-2020	2006-2007, 2010	Jan 1, 2009 to Dec 31, 2011	2009-2010	2005-2008
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	Not applicable	time-stationary	time-stationary	time-dependent	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	past time	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	discrete	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	1	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Day	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
Bounding Type em.detail.boundingTypeHelp ?	Physiographic or Ecological	Watershed/Catchment/HUC	Not applicable	Watershed/Catchment/HUC	Physiographic or ecological	Watershed/Catchment/HUC	Physiographic or ecological	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	Central French Alps	East Fork Kaskaskia River watershed basin	Not applicable	central Sumatra	Coastal zone surrounding St. Croix	Shayler Crossing watershed, a subwatershed of the East Fork Little Miami River Watershed	The Wilds	Big Sur region, central California
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	10-100 km^2	100-1000 km^2	Not applicable	100,000-1,000,000 km^2	100-1000 km^2	10-100 ha	1-10 km^2	100-1000 km^2

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)	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)
Spatial Grain Type em.detail.spGrainTypeHelp ?	area, for pixel or radial feature	length, for linear feature (e.g., stream mile)	Not applicable	area, for pixel or radial feature	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)
Spatial Grain Size em.detail.spGrainSizeHelp ?	20 m x 20 m	1 km^2	Not applicable	30 m x 30 m	10 m x 10 m	10m x 10m	10 m radius	irregular

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Numeric	Analytic	Analytic	Analytic	Numeric	Analytic	Analytic
EM Determinism em.detail.deterStochHelp ?	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic	deterministic
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	Not applicable	No	Yes	Yes	Not applicable	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes	No	Not applicable	No	No	Yes ? Comment: Goodness of fit for calibrated (2009-2010) and observed streamflow.	Not applicable	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	R squared \| 31 \| Not applicable	None	None	None	None	None	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	Yes	No	No	Yes	Yes	Yes	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	Yes	Not applicable	No	No	No	Yes	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	Unclear	Not applicable	No	No	No	No	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

EM Locations, Environments, Ecology

Location of EM Application

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

EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
Europe France	North America United States Illinois	None	Asia Indonesia	None	North America United States Ohio	North America United States Ohio	North America United States California

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

EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
None	None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Northern California

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
Centroid Latitude em.detail.ddLatHelp ?	45.05	38.69	-9999	0	17.73	39.19	39.82	35.96
Centroid Longitude em.detail.ddLongHelp ?	6.4	-89.1	-9999	102	-64.77	-84.29	-81.75	-121.43
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	Not applicable	WGS84	WGS84	WGS84	WGS84	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	Provided	Not applicable	Provided	Estimated	Provided	Provided	Estimated

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems \| Grasslands	Agroecosystems	Terrestrial Environment (sub-classes not fully specified)	Inland Wetlands \| Lakes and Ponds \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands \| Scrubland/Shrubland \| Barren	Near Coastal Marine and Estuarine	Rivers and Streams \| Ground Water \| Forests \| Agroecosystems \| Created Greenspace	Grasslands	Rivers and Streams \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified)
Specific Environment Type em.detail.specificEnvTypeHelp ?	Subalpine terraces, grasslands, and meadows	Row crop agriculture in Kaskaskia river basin	Not applicable	104 land use land cover classes	Coral reefs	Mixed land cover suburban watershed	Grassland	Coastal watersheds
EM Ecological Scale em.detail.ecoScaleHelp ?	Not applicable	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	Ecological scale is finer than that of the Environmental Sub-class	Ecological scale corresponds to the Environmental Sub-class	Ecological scale is finer than that of the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
EM Organismal Scale em.detail.orgScaleHelp ?	Community	Not applicable	Not applicable	Community	Guild or Assemblage	Not applicable	Species	Not applicable

Taxonomic level and name of organisms or groups identified

EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
None Available	None Available	None Available	None Available	other snapper Family grouper parrotfish surgeonfish	None Available	Species Planted plant species Pollinator species	None Available

EnviroAtlas URL

EM-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
None Available	National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Agricultural water use (million gallons/day), Total Annual Nitrogen Deposition, The Watershed Boundary Dataset (WBD)	Average Annual Precipitation	The National Hydrography Dataset (NHD), Average Annual Precipitation	None Available	Average Annual Precipitation, Average Annual Daily Potential Wind Energy	GAP Ecological Systems	Average Annual Precipitation

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-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters	None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Weathering processes	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems

<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-70	EM-97	EM-337	EM-359	EM-418	EM-605	EM-774	EM-940
None	Rivers and Streams (111) Water (3) Liquid water Quality Indicator	None	None	None	Rivers and Streams (111) Water (3) Liquid water Flow Indicator	None	None

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

Comment:

Comment:

Comment:

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

EM Modeling Approach

Comment:

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)