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-132
EM-326
EM-359
EM-368
EM-417
EM-598
EM-617
EM-655

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID em.detail.idHelp ?	EM-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
EM Short Name em.detail.shortNameHelp ?	Flood regulation capacity, Etropole, Bulgaria	ARIES flood regulation, Puget Sound Region, USA	InVEST (v1.004) sediment retention, Indonesia	InVEST - Water Yield (v3.0)	SWAT, Guanica Bay, Puerto Rico, USA	DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland	RBI Spatial Analysis Method	Hunting recreation, Wisconsin, USA
EM Full Name em.detail.fullNameHelp ?	Flood regulation capacity of landscapes, Municipality of Etropole, Bulgaria	ARIES (Artificial Intelligence for Ecosystem Services) Flood Regulation, Puget Sound Region, Washington, USA	InVEST (Integrated Valuation of Environmental Services and Tradeoffs v1.004) sediment retention, Sumatra, Indonesia	InVEST v3.0 Reservoir Hydropower Projection, aka Water Yield	SWAT (Soil and Water Assessment Tool) Guánica Bay, Puerto Rico, USA	DeNitrification-DeComposition simulation of N2O flux Ireland	Rapid Benefit Indicator (RBI) Spatial Analysis Toolset Method	Hunting recreation, Wisconsin, USA
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	EU Biodiversity Action 5	ARIES	InVEST	InVEST	US EPA	None	None	None
EM Source Document ID	248	302	309	311	334	358	367	376
Document Author em.detail.documentAuthorHelp ?	Nedkov, S., Burkhard, B.	Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W.	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.	Natural Capital Project	Hu, W. and Y. Yuan	Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M.	Bousquin, J., Mazzotta M., and W. Berry	Qiu, J. and M. G. Turner
Document Year em.detail.documentYearHelp ?	2012	2014	2014	2015	2013	2010	2017	2013
Document Title em.detail.sourceIdHelp ?	Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria	From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments	Ecosystem services reinforce Sumatran tiger conservation in land use plans	Water Yield: Reservoir Hydropower Production- InVEST (v3.0)	Evaluation of Soil Erosion and Sediment Yield for the Ridge Watersheds in the Guanica Bay Watershed, Puerto Rico, Using the SWAT Model	Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture	Rapid Benefit Indicators (RBI) Spatial Analysis Toolset - Manual.	Spatial interactions among ecosystem services in an urbanizing agricultural watershed
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published	Peer reviewed and published
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	Published journal manuscript	Published journal manuscript	Web published	Published EPA report	Published journal manuscript	Published EPA report	Published journal manuscript

Software and Access

EM ID em.detail.idHelp ?	EM-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	http://aries.integratedmodelling.org/	https://www.naturalcapitalproject.org/invest/	https://www.naturalcapitalproject.org/invest/	Not applicable	http://www.dndc.sr.unh.edu	Not applicable	Not applicable
Contact Name em.detail.contactNameHelp ?	Stoyan Nedkov	Ken Bagstad	Nirmal K. Bhagabati	Natural Capital Project	Yongping Yuan	M. Abdalla	Justin Bousquin	Monica G. Turner
Contact Address	National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl.3, 1113 Sofia, Bulgaria	Geosciences and Environmental Change Science Center, US Geological Survey	The Nature Conservancy, 1107 Laurel Avenue, Felton, CA 95018	371 Serra Mall, Stanford University, Stanford, Ca 94305	USEPA, ORD, NERL, Environmental sciences Division, Las Vegas, Nevada	Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland	US EPA, Office of Research and Development, National health and environmental Effects Lab, Gulf Ecology Division, Gulf Breeze, FL 32561	Not reported
Contact Email	snedkov@abv.bg	kjbagstad@usgs.gov	nirmal.bhagabati@wwfus.org	invest@naturalcapitalproject.org	Yuan.Yongping@epa.gov	abdallm@tcd.ie	bousquin.justin@epa.gov	turnermg@wisc.edu

EM Description

EM ID em.detail.idHelp ?	EM-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
Summary Description em.detail.summaryDescriptionHelp ?	ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. In this study, the capacities of different ecosystems to regulate floods were assessed through investigations of water retention functions of the vegetation and soil cover. Based on spatial land cover units originating from CORINE and further data sets, these regulating ecosystem services were quantified and mapped. Resulting maps show the ecosystems’ flood regulating service capacities in the case study area of the Malki Iskar river basin above the town of Etropole in the northern part of Bulgaria...The resulting map of flood regulation supply capacities shows that the Etropole municipality’s area has relatively high capacities for flood regulation. Areas of high and very high relevant capacities cover about 34% of the study area." AUTHOR'S DESCRIPTION: "The capacities of the identified spatial units were assessed on a relative scale ranging from 0 to 5 (after Burkhard et al., 2009). A 0-value indicates that there is no relevant capacity to supply flood regulating services and a 5-value indicates the highest relevant capacity for the supply of these services in the case study region. Values of 2, 3 and 4 represent respective intermediate supply capacities. Of course it depends on the observer’s estimation and knowledge which function–service relations in general are supposed to be relevant. But, this scale offers an alternative relative evaluation scheme, avoiding the presentation of monetary or normative value-transfer results. The 0–5 capacity values’ classifications for the different land cover types were based on the spatial analyses of different biogeophysical and land use data combined with hydrological modeling as described before…The supply capacities of the land cover classes and soil types in the study area were assigned to every unit in their databases. GIS map layers, containing information about the capacity to supply flood regulation for every polygon, were created. The map of supply capacities of flood regulating ecosystem services was elaborated by overlaying the GIS map layers of the land cover and the soils’ capacities."	ABSTRACT: "...new modeling approaches that map and quantify service-specific sources (ecosystem capacity to provide a service), sinks (biophysical or anthropogenic features that deplete or alter service flows), users (user locations and level of demand), and spatial flows can provide a more complete understanding of ecosystem services. Through a case study in Puget Sound, Washington State, USA, we quantify and differentiate between the theoretical or in situ provision of services, i.e., ecosystems’ capacity to supply services, and their actual provision when accounting for the location of beneficiaries and the spatial connections that mediate service flows between people and ecosystems... Using the ARtificial Intelligence for Ecosystem Services (ARIES) methodology we map service supply, demand, and flow, extending on simpler approaches used by past studies to map service provision and use." AUTHOR'S NOTE: "We estimated flood sinks, i.e., the capacity of the landscape to intercept, absorb, or detain floodwater, using a Bayesian model of vegetation, topography, and soil influences (Bagstad et al. 2011). This green infrastructure, the ecosystem service that we used for subsequent analysis, can combine with anthropogenic gray infrastructure, such as dams and detention basins, to provide flood regulation. Since flood regulation implies a hydrologic connection between sources, sinks, and users, we simulated its flow through a threestep process. First, we aggregated values for precipitation (sources of floodwater), flood mitigation (sinks), and users (developed land located in the 100-year floodplain) within each of the 502 12-digit Hydrologic Unit Code (HUC) watersheds within the Puget Sound region. Second, we subtracted the sink value from the source value for each subwatershed to quantify remaining floodwater and the proportion of mitigated floodwater. Third, we multiplied the proportion of mitigated floodwater for each subwatershed by the number of developed raster cells within the 100-year floodplain to yield a ranking of flood mitigation for each subwatershed...We calculated the ratio of actual to theoretical flood sinks by dividing summed flood sink values for subwatersheds providing flood mitigation to users by summed flood sink values for the entire landscape without accounting for the presence of at-risk structures."	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."	Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. AUTHOR'S DESCRIPTION: "The InVEST Reservoir Hydropower model estimates the relative contributions of water from different parts of a landscape, offering insight into how changes in land use patterns affect annual surface water yield and hydropower production. Modeling the connections between landscape changes and hydrologic processes is not simple. Sophisticated models of these connections and associated processes (such as the WEAP model) are resource and data intensive and require substantial expertise. To accommodate more contexts, for which data are readily available, InVEST maps and models the annual average water yield from a landscape used for hydropower production, rather than directly addressing the affect of LULC changes on hydropower failure as this process is closely linked to variation in water inflow on a daily to monthly timescale. Instead, InVEST calculates the relative contribution of each land parcel to annual average hydropower production and the value of this contribution in terms of energy production. The net present value of hydropower production over the life of the reservoir also can be calculated by summing discounted annual revenues. The model runs on a gridded map. It estimates the quantity and value of water used for hydropower production from each subwatershed in the area of interest. It has three components, which run sequentially. First, it determines the amount of water running off each pixel as the precipitation less the fraction of the water that undergoes evapotranspiration. The model does not differentiate between surface, subsurface and baseflow, but assumes that all water yield from a pixel reaches the point of interest via one of these pathways. This model then sums and averages water yield to the subwatershed level. The pixel-scale calculations allow us to represent the heterogeneity of key driving factors in water yield such as soil type, precipitation, vegetation type, etc. However, the theory we are using as the foundation of this set of models was developed at the subwatershed to watershed scale. We are only confident in the interpretation of these models at the subwatershed scale, so all outputs are summed and/or averaged to the subwatershed scale. We do continue to provide pixel-scale representations of some outputs for calibration and model-checking purposes only. These pixel-scale maps are not to be interpreted for understanding of hydrological processes or to inform decision making of any kind.	AUTHOR'S DESCRIPTION: " SWAT is a physically-based continuous watershed simulation model that operates on a daily time step. It is designed for long-term simulations. The U.S. Department of Agriculture-Agriculture Research Station (USDA-ARS) Grassland, Soil and Water Research Laboratory in Temple, Texas created SWAT in the early 1990s. It has undergone continual review and expansion of capabilities since it was created (Arnold et al., 1998; Neitsch, et al., 2011a and b). This model has the ability to predict changes in water, sediment, nutrient and pesticide loads with respect to the different management conditions in watershed. Major components of the SWAT model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides and agricultural management practices (Neitsch et al., 2011b). SWAT subdivides a watershed into multiple sub-watersheds, and the subwatersheds are further divided into Hydrologic Response Units (HRUs) that consist of homogeneous land use, soils, slope, and management (Gassman et al., 2007; Neitsch, et al., 2011b; Williams et al., 2008).	Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC.	AUTHOR DESCRIPTION: "The Rapid Benefits Indicators (RBI) approach consists of five steps and is outlined in Assessing the Benefits of Wetland Restoration – A Rapid Benefits Indicators Approach for Decision Makers, hereafter referred to as the “guide.” The guide presents the assessment approach, detailing each step of the indicator development process and providing an example application in the “Step in Action” pages. The spatial analysis toolset is intended to be used to analyze existing spatial information to produce metrics for many of the indicators developed in that guide. This spatial analysis toolset manual gives directions on the mechanics of the tool and its data requirements, but does not detail the reasoning behind the indicators and how to use results of the assessment; this information is found in the guide. "	AUTHOR'S DESCRIPTION (from Supporting Information): "The hunting recreation service was estimated as a function of the extent of wildlife areas open for hunting, the number of game species, proximity to population center, and accessibility. Similar assumptions were made for this assessment: larger areas and places with more game species would support more hunting, areas closer to large population centers would be used more than remote areas, and proximity to major roads would increase access and use of an area. We first obtained the boundary of public wild areas from Wisconsin DNR and calculated the amount of areas for each management unit. The number of game species (Spe) for each area was derived from Dane County Parks Division (70). We used the same population density (Pop) and road buffer layer (Road) described in the previous forest recreation section. The variables Spe, Pop, and Road were weighted to ranges of 0–40, 0–40, and 0–20, respectively, based on the relative importance of each in determining this service. We estimated overall hunting recreation service for each 30-m grid cell with the following equation: HRSi = Ai Σ(Spei + Popi +Roadi), where HRS is hunting recreation score, A is the area of public wild areas open for hunting/fishing, Spe represents the number of game species, Pop stands for the proximity to population centers, and Road is the distance to major roads. To simplify interpretation, we rescaled the original hunting recreation score (ranging from 0 to 28,000) to a range of 0–100, with 0 representing no hunting recreation service and 100 representing highest service.
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	None identified	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	climate change	None identified	None identified
Biophysical Context	Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak.	No additional description provided	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.	None applicable	Need to fill in	Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C	wetlands	No additional description provided
EM Scenario Drivers em.detail.scenarioDriverHelp ?	No scenarios presented	No scenarios presented	Baseline year 2008, future LULC Sumatra 2020 Roadmap (Vision), future LULC Government Spatial Plan	N/A	Planting type, fertilizing rate, harvest rate	fertilization	N/A	No scenarios presented

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application	Method + Application	Method + Application (multiple runs exist) View EM Runs	Method Only	Method + Application	Method + Application	Method Only	Method + Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	New or revised model	New or revised model	Application of existing model	New or revised model	New or revised model	Application of existing 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-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	Doc-248 \| Doc-251 \| Doc-252	Doc-303 \| Doc-305	Doc-338	Doc-307 \| Doc-280 \| Doc-338 \| Doc-205	None	None	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-130 \| EM-133	None	EM-435	EM-437 \| EM-148 \| EM-344 \| EM-111	None	EM-593	None	None

EM Modeling Approach

EM Relationship to Time

EM ID em.detail.idHelp ?	EM-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
EM Temporal Extent em.detail.tempExtentHelp ?	Not reported	1971-2006	2008-2020	Not applicable	1981-2004	1961-1990	Not applicable	2000-2006
EM Time Dependence em.detail.timeDependencyHelp ?	time-stationary	time-stationary	time-stationary	time-dependent	time-dependent	time-dependent	time-stationary	time-stationary
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	Not applicable	Not applicable	Not applicable	future time	future time	both	Not applicable	Not applicable
EM Time Continuity em.detail.continueDiscreteHelp ?	Not applicable	Not applicable	Not applicable	discrete	discrete	discrete	Not applicable	Not applicable
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	Not applicable	Not applicable	Not applicable	1	1	1	Not applicable	Not applicable
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Not applicable	Not applicable	Not applicable	Year	Day	Day	Not applicable	Not applicable

EM Spatial Extent

EM ID em.detail.idHelp ?	EM-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
Bounding Type em.detail.boundingTypeHelp ?	Geopolitical	Physiographic or ecological	Watershed/Catchment/HUC	Not applicable	Watershed/Catchment/HUC	Point or points	Not applicable	Watershed/Catchment/HUC
Spatial Extent Name em.detail.extentNameHelp ?	Municipality of Etropole	Puget Sound Region	central Sumatra	Not applicable	Guanica Bay, Puerto Rico watersheds	Oak Park Research centre	Not applicable	Yahara Watershed, Wisconsin
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	100-1000 km^2	10,000-100,000 km^2	100,000-1,000,000 km^2	Not applicable	100-1000 km^2	1-10 ha	Not applicable	1000-10,000 km^2.

Spatial Distribution of Computations

EM ID em.detail.idHelp ?	EM-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
EM Spatial Distribution em.detail.distributeLumpHelp ?	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) ? Comment: pixel is likely 30m x 30m	spatially distributed (in at least some cases)	spatially lumped (in all cases)	spatially distributed (in at least some cases)	spatially distributed (in at least some cases)
Spatial Grain Type em.detail.spGrainTypeHelp ?	other (specify), for irregular (e.g., stream reach, lake basin)	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	area, for pixel or radial feature	Not applicable	area, for pixel or radial feature	area, for pixel or radial feature
Spatial Grain Size em.detail.spGrainSizeHelp ?	Distributed by land cover and soil type polygons	200m x 200m	30 m x 30 m	Not specified	30m x 30m	Not applicable	Not reported	30m x 30m

EM Structure and Computation Approach

EM ID em.detail.idHelp ?	EM-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
EM Computational Approach em.detail.emComputationalApproachHelp ?	Analytic	Analytic	Analytic	Numeric	Numeric	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	Bayesian statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	Conventional (frequentist) statistics	None	Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID em.detail.idHelp ?	EM-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
Model Calibration Reported? em.detail.calibrationHelp ?	No	No	No	Yes ? Comment: Annual Yield can be calibrated with actual yield based up 10 year average input data though this was an "optional" part of the model. Calibrate with total precipitation and potential evapotranspiration. Before the calibration process is commenced, the modelers suggest performing a sensitivity analysis with the observed runoff data to define the parameters that influence model outputs the most. The calibration can then focus on highly sensitive parameters followed by less sensitive ones.	Yes ? Comment: Used 1981 and 1982 data to calibrate hydrology.	Yes	Not applicable	No
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	No	No	No	Not applicable	No ? Comment: Calibration for both the stream flow and Sediment concentration of the mode	Yes ? Comment: Actual value was not given, just that results were very poor. Simulation results were 258% of observed	Not applicable	No
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	None	None	None	None	Nash-Sutcliffe \| 0.82 \| unitless Rsquared \| 0.89 \| unitless Rsquared for sed \| 0.15 \| unitless NSE for sed \| -0.02 \| unitless RME for sed \| 0.15 \| unitless	r squared \| 00.0 \| unitless	None	None
Model Operational Validation Reported? em.detail.validationHelp ?	No	No	No	No	Yes ? Comment: Validation with 1983-1984 data from USGS. Used streamflow and water quality data from two stations	Yes	Not applicable	No
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	No	No	No	Unclear	No	Not applicable	No
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	No	No	Not applicable	Yes ? Comment: Yes for both runoff and sediment	No	Not applicable	No
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	Not applicable	Not applicable	Not applicable	No	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-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
Europe Bulgaria	North America United States	Asia Indonesia	None	North America United States Puerto Rico U.S. Caribbean Territories	Europe Ireland	None	North America United States Wisconsin

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

EM-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
None	None	None	None	None	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
Centroid Latitude em.detail.ddLatHelp ?	42.8	48	0	-9999	18.19	52.86	Not applicable	43.1
Centroid Longitude em.detail.ddLongHelp ?	24	-123	102	-9999	-66.76	6.54	Not applicable	-89.4
Centroid Datum em.detail.datumHelp ?	WGS84	WGS84	WGS84	Not applicable	WGS84	None provided	Not applicable	WGS84
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Estimated	Estimated	Provided	Not applicable	Estimated	Provided	Not applicable	Provided

Environments and Scales Modeled

EM ID em.detail.idHelp ?	EM-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Rivers and Streams \| Lakes and Ponds \| Forests \| Agroecosystems \| Grasslands \| Scrubland/Shrubland	Rivers and Streams \| Near Coastal Marine and Estuarine \| Terrestrial Environment (sub-classes not fully specified)	Inland Wetlands \| Lakes and Ponds \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands \| Scrubland/Shrubland \| Barren	Rivers and Streams	Terrestrial Environment (sub-classes not fully specified)	Agroecosystems	Inland Wetlands	Rivers and Streams \| Inland Wetlands \| Lakes and Ponds \| Forests \| Agroecosystems \| Created Greenspace \| Grasslands
Specific Environment Type em.detail.specificEnvTypeHelp ?	Mountainous flood-prone region	Terrestrial environment surrounding a large estuary	104 land use land cover classes	Watershed	watershed	farm pasture	Restored wetlands	Mixed environment watershed of prairie converted to predominantly agriculture and urban landscape
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 is finer than that of the Environmental Sub-class	Not applicable	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

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	Not applicable	Community	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

Taxonomic level and name of organisms or groups identified

EM-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
None Available	None Available	None Available	None Available	None Available	None Available	None Available	None Available

EnviroAtlas URL

EM-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
National Hydrography Dataset Plus (NHD PlusV2)	GAP Ecological Systems, National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Percent Impervious Area	The National Hydrography Dataset (NHD), Average Annual Precipitation	Average Annual Precipitation, Agricultural water use (million gallons/day), The Watershed Boundary Dataset (WBD)	National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Agricultural water use (million gallons/day), The Watershed Boundary Dataset (WBD)	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	Dasymetric Allocation of Population, The National Hydrography Dataset (NHD), GAP Ecological Systems, Green Space per Capita, Percent GAP Status 1 & 2, The Watershed Boundary Dataset (WBD)	Dasymetric Allocation of Population

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-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Weathering processes	[Ecosystem] Provisioning Materials Water Surface water for non-drinking purposes	[Ecosystem] Regulation & Maintenance 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 Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection [Ecosystem] Cultural Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings] Physical and experiential interactions Physical use of land-/seascapes in different environmental settings Experiential use of plants, animals and land-/seascapes in different environmental settings Intellectual and representative interactions Educational	[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-132	EM-326	EM-359	EM-368	EM-417	EM-598	EM-617	EM-655
Rivers and Streams (111) Composite (8) Regulation of extreme events Extreme Event Indicator Forests (21) Composite (8) Regulation of extreme events Extreme Event Indicator Grasslands (23) Composite (8) Regulation of extreme events Extreme Event Indicator Scrubland/Shrubland (24) Composite (8) Regulation of extreme events Extreme Event Indicator Agroecosystems (22) Composite (8) Regulation of extreme events Extreme Event Indicator	Rivers and Streams (111) Composite (8) Regulation of extreme events Flow Indicator Near Coastal Marine/Estuarine (113) Composite (8) Regulation of extreme events Flow Indicator Forests (21) Composite (8) Regulation of extreme events Flow Indicator	None	Rivers and Streams (111) Water (3) Liquid water Stock Indicator	None	Agroecosystems (22) Flora (5) Green biomass Stock Indicator	Wetlands (12) Water (3) Liquid water Extreme Event Indicator Composite (8) Scapes: views, sounds and scents of land, sea, sky or a combination Quality Indicator Fauna (4) Birds Quality Indicator	None

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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)