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

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Comparing:

EM-70
EM-80
EM-86
EM-97
EM-104
EM-196
EM-315
EM-403
EM-704
EM-713
EM-945
EM-998
EM-1007

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

EM Short Name

Plant species diversity, Central French Alps

Agronomic ES and plant traits, Central French Alps

Area and hotspots of soil retention, South Africa

AnnAGNPS, Kaskaskia River watershed, IL, USA

SPARROW, Northeastern USA

N removal by wetlands, Contiguous USA

ARIES open Space, Puget Sound Region, USA

HexSim, tule elk, California, USA

Northern Pintail recruits, CREP wetlands, IA, USA

ESII Tool, Michigan, USA

Air pollution removal by green roofs, Chicago, USA

CAESAR landscape evolution model

N-SPECT land-sea planning submodel

EM Full Name

Plant species diversity, Central French Alps

Agronomic ecosystem service estimated from plant functional traits, Central French Alps

Area and hotspots of soil retention, South Africa

AnnAGNPS (Annualized Agricultural Non-Point Source Pollution Model), Kaskaskia River watershed, IL, USA

SPARROW (SPAtially Referenced Regressions On Watershed Attributes), Northeastern USA

Nitrogen removal by wetlands as a function of loading, Contiguous USA

ARIES (Artificial Intelligence for Ecosystem Services) Open Space Proximity for Homeowners, Puget Sound Region, Washington, USA

HexSim, tule elk, California, USA

Northern Pintail duck recruits, CREP (Conservation Reserve Enhancement Program) wetlands, Iowa, USA

ESII (Ecosystem Services Identification and Inventory) Tool, Michigan, USA

Air pollution removal by green roofs, Chigago, USA

Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model

A technical guide to the integrated land-sea planning toolkit

EM Source or Collection

EU Biodiversity Action 5

None

US EPA

ARIES

US EPA

None

EM Source Document ID

260

271

137

302

328 ?

372 ?

392 ?

438 ?

468

473

Document Author

Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R.

Egoh, B., Reyers, B., Rouget, M., Richardson, D.M., Le Maitre, D.C., and van Jaarsveld, A.S.

Yuan, Y., Mehaffey, M. H., Lopez, R. D., Bingner, R. L., Bruins, R., Erickson, C. and Jackson, M.

Moore, R. B., Johnston, C.M., Smith, R. A. and Milstead, B.

Jordan, S., Stoffer, J. and Nestlerode, J.

Bagstad, K.J., Villa, F., Batker, D., Harrison-Cox, J., Voigt, B., and Johnson, G.W.

Huber, P. R., S. E. Greco, N. H. Schumaker, and J. Hobbs

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

Guertin, F., K. Halsey, T. Polzin, M. Rogers, and B. Witt

Yang, J., Q. Yu and P. Gong

Van De Wiel, M. J., Coulthard, T. J., Macklin, M. G., & Lewin, J.

Crist, P., Madden, K., Varley, I., Eslinger, D., Walker, D., Anderson, A., Morehead, S. and Dunton, K.,

Document Year

2011

2008

2011

2014

2010

2019

2008

2007

2009

Document Title

Using plant functional traits to understand the landscape distribution of multiple ecosystem services

Mapping ecosystem services for planning and management

AnnAGNPS model application for nitrogen loading assessment for the Future Midwest Landscape study

Source and delivery of nutrients to receiving waters in the northeastern and mid-Atlantic regions of the United States

Wetlands as sinks for reactive nitrogen at continental and global scales: A meta-analysis

From theoretical to actual ecosystem services: mapping beneficiaries and spatial flows in ecosystem service assessments

A priori assessment of reintroduction strategies for a native ungulate: using HexSim to guide release site selection

Assessment of environmental services of CREP wetlands in Iowa and the midwestern corn belt

From ash pond to riverside wetlands: Making the business case for engineered natural technologies

Quantifying air pollution removal by green roofs in Chicago

Embedding reach-scale fluvial dynamics within the CAESAR cellular automaton landscape evolution model

Integrated Land-Sea Planning: A Technical Guide to the Integrated Land-Sea Planning Toolkit.

Document Status

Peer reviewed and published

Comments on Status

Published journal manuscript

Published report

Published journal manuscript

Published report

Software and Access

EM ID

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

EM Software Access info Exit

Not applicable

https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/annagnps-pollutant-loading-model/

Not applicable

http://aries.integratedmodelling.org/

http://www.hexsim.net/download

Not applicable

https://www.esiitool.com/

Not applicable

http://www.coulthard.org.uk/

https://repositories.lib.utexas.edu/bitstreams/3dee92a8-9373-4bcc-be25-eda74e81fabf/download

Contact Name

Sandra Lavorel

Benis Egoh

Yongping Yuan

Richard Moore

Steve Jordan

Ken Bagstad

P. R. Huber

David Otis

Not reported

Jun Yang

Marco J. Van De Wiel

Patrick Crist ?

Contact Address

Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France

Water Resources Unit, Institute for Environment and Sustainability, European Commission - Joint Research Centre, Ispra, Italy

U.S. Environmental Protection Agency Office of Research and Development, Environmental Sciences Division, 944 East Harmon Ave., Las Vegas, NV 89119, USA

U.S. Environmental Protection Agency, 27 Tarzwell Drive, Narragansett, Rhode Island 02882

Gulf Ecology Division U.S. Environmental Protection Agency, 1 Sabine Island Drive, Gulf Breeze, Florida 32561

Geosciences and Environmental Change Science Center, US Geological Survey

University of California, Davis, One Shields Ave., Davis, CA 95616, USA

U.S. Geological Survey, Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University

Not reported

Department of Landscape Architecture and Horticulture, Temple University, 580 Meetinghouse Road, Ambler, PA 19002, USA.

Department of Geography, University of Western Ontario, London, Ontario, Canada

None provided

Contact Email

sandra.lavorel@ujf-grenoble.fr

Not reported

yuan.yongping@epa.gov

rmoore@usgs.gov

steve.jordan@epa.gov

kjbagstad@usgs.gov

prhuber@ucdavis.edu

dotis@iastate.edu

Not reported

juny@temple.edu

mvandew3@uwo.ca

patrick@planitfwd.com

EM Description

EM ID

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

Summary Description

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."

ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The Agronomic ecosystem service map is a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to agronomic ecosystem services are based on stakeholders’ perceptions, given positive or negative contributions."

AUTHOR'S DESCRIPTION: "We define the range of ecosystem services as areas of meaningful supply, similar to a species’ range or area of occupancy. The term ‘‘hotspots’’ was proposed by Norman Myers in the 1980s and refers to areas of high species richness, endemism and/or threat and has been widely used to prioritise areas for biodiversity conservation. Similarly, this study suggests that hotspots for ecosystem services are areas of critical management importance for the service. Here the term ecosystem service hotspot is used to refer to areas which provide large proportions of a particular service, and do not include measures of threat or endemism…Soil retention was modelled as a function of vegetation or litter cover and soil erosion potential. Schoeman et al. (2002) modelled soil erosion potential and derived eight erosion classes, ranging from low to severe erosion potential for South Africa. The vegetation cover was mapped by ranking vegetation types using expert knowledge of their ability to curb erosion. We used Schulze (2004) index of litter cover which estimates the soil surface covered by litter based on observations in a range of grasslands, woodlands and natural forests. According to Quinton et al. (1997) and Fowler and Rockstrom (2001) soil erosion is slightly reduced with about 30%, significantly reduced with about 70% vegetation cover. The range of soil retention was mapped by selecting all areas that had vegetation or litter cover of more than 30% for both the expert classified vegetation types and litter accumulation index within areas with moderate to severe erosion potential. The hotspot was mapped as areas with severe erosion potential and vegetation/litter cover of at least 70% where maintaining the cover is essential to prevent erosion. An assumption was made that the potential for this service is relatively low in areas with little natural vegetation or litter cover."

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"

AUTHOR'S DESCRIPTION: "SPAtially Referenced Regressions On Watershed attributes (SPARROW) nutrient models were developed for the Northeastern and Mid-Atlantic (NE US) regions of the United States to represent source conditions for the year 2002. The model developed to examine the source and delivery of nitrogen to the estuaries of nine large rivers along the NE US Seaboard indicated that agricultural sources contribute the largest percentage (37%) of the total nitrogen load delivered to the estuaries"

ABSTRACT: "We compiled published data from wetland studies worldwide to estimate total Nr removal and to evaluate factors that influence removal rates. Over several orders of magnitude in wetland area and Nr loading rates, there is a positive, near-linear relationship between Nr removal and Nr loading. The linear model (null hypothesis) explains the data better than either a model of declining Nr removal efficiency with increasing Nr loading, or a Michaelis–Menten (saturation) model."

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: "For open space proximity, we mapped the relative value of open space, highways that impede walking access or reduce visual and soundscape quality, and housing locations, connected by a flow model simulating physical access to desirable spaces. We used reviews of the hedonic valuation literature (Bourassa et al. 2004, McConnell and Walls 2005) to inform model development, ranking the influence of different open space characteristics on property values to parameterize the source and sink models. The model includes a distance decay function that accounts for changes with distance in the value of open space. We then computed the ratio of actual to theoretical provision of open space to compare the values accruing to homeowners relative to those for the entire landscape."

AUTHOR'S DESCRIPTION: "HexSim is a simulation framework within which PVA and other models are constructed. HexSim simulations can range from simple and parsimonious, at one extreme, to complex, data intensive, and biologically realistic at the other. Our tule elk simulations were moderately complex, capturing major life history events such as survival, reproduction and movement, while ignoring other details such as impact of environmental stochasticity or the spread of diseases through the population." "One of the features that distinguishes HexSim from its predecessor is the ability to model group, or herd, movement. This is accomplished through use of a ‘‘proto-disperser’’, an imaginary individual that explores the landscape, finds resources, and then serves as a movement target for the other group members who converge on this target. This feature allows for modeling of both individuals and groups. Another useful feature of HexSim is the barriers component. Multiple types of movement barriers can be included in the model, reflecting likely responses to various kinds of blockages to wildlife. Because many of these barriers tend to be human-related, this feature allows for assessing the potential impacts of multiple types of human infrastructure and landscape features on modeled species. This paper examines several reintroduction scenarios for returning an endemic elk subspecies (tule elk; Cervus elaphus nannodes) to a portion of its native range in California, USA."

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 = 2*R*n 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 = H*Z*B/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: "The 2015 announcement of The Dow Chemical Company's (Dow) Valuing Nature Goal, which aims to identify $1 billion in business value from projects that are better for nature, gives nature a spot at the project design table. To support this goal, Dow and The Nature Conservancy have extended their long-standing collaboration and are now working to develop a defensible methodology to support the implementation of the goal. This paper reviews the nature valuation methodology framework developed by the Collaboration in support of the goal. The nature valuation methodology is a three-step process that engages Dow project managers at multiple stages in the project design and capital allocation processes. The three-step process identifies projects that may have a large impact on nature and then promotes the use of ecosystem service tools, such as the Ecosystem Services Identification and Inventory Tool, to enhance the project design so that it better supports ecosystem health. After reviewing the nature valuation methodology, we describe the results from a case study of redevelopment plans for a 23-acre site adjacent to Dow's Michigan Operations plant along the Tittabawassee River." AUTHOR'S DESCRIPTION: "The ESII Tool measures the environmental impact or proposed land changes through eight specific ecosystem services: (i) water provisioning, (ii) air quality control (nitrogen and particulate removal), (iii) climate regulation (carbon uptake and localized air temperature regulation), (iv) erosion regulation, (v) water quality control (nitrogen and filtration), (vi) water temperature regulation, (vii) water quantity control, and (viii) aesthetics (noise and visual). The ESII Tool allows for direct comparison of the performance of these eight ecosystem services both across project sites and across project design proposals within a site." "The team was also asked to use an iterative design process using the ESII Tool to create alternative restoration scenarios…The project team developed three alternative restoration designs: i) standard brownfield restoration (i.e., cap and plant grass) on the ash pond and 4-D property (referred to as SBR); ii) ecological restoration (i.e., excavate ash and associated soil for secured disposal in approved landfill and restore historic forest, prairie, wetland) of the ash pond only, with SBR on the 4-D property (referred to as ER); and iii) ecological restoration on the ash pond and 4- D property (referred to as ER+)."

ABSTRACT: "The level of air pollution removal by green roofs in Chicago was quantified using a dry deposition model. The result showed that a total of 1675 kg of air pollutants was removed by 19.8 ha of green roofs in one year with O3 accounting for 52% of the total, NO2 (27%), PM10 (14%), and SO2 (7%). The highest level of air pollution removal occurred in May and the lowest in February. The annual removal per hectare of green roof was 85 kg/ha/yr. The amount of pollutants removed would increase to 2046.89 metric tons if all rooftops in Chicago were covered with intensive green roofs. Although costly, the installation of green roofs could be justified in the long run if the environmental benefits were considered. The green roof can be used to supplement the use of urban trees in air pollution control, especially in situations where land and public funds are not readily available."

We introduce a new computational model designed to simulate and investigate reach-scale alluvial dynamics within a landscape evolution model. The model is based on the cellular automaton concept, whereby the continued iteration of a series of local process ‘rules’ governs the behaviour of the entire system. The model is a modified version of the CAESAR landscape evolution model, which applies a suite of physically based rules to simulate the entrainment, transport and deposition of sediments. The CAESAR model has been altered to improve the representation of hydraulic and geomorphic processes in an alluvial environment. In-channel and overbank flow, sediment entrainment and deposition, suspended load and bed load transport, lateral erosion and bank failure have all been represented as local cellular automaton rules. Although these rules are relatively simple and straightforward, their combined and repeatedly iterated effect is such that complex, non-linear geomorphological response can be simulated within the model. Examples of such larger-scale, emergent responses include channel incision and aggradation, terrace formation, channel migration and river meandering, formation of meander cutoffs, and transitions between braided and single-thread channel patterns. In the current study, the model is illustrated on a reach of the River Teifi, near Lampeter, Wales, UK.

The Nonpoint-Source Pollution and Erosion Comparison Tool (N-SPECT) is a screening tool developed to help land use planners and mangers understand the potential impacts of land use change decisions on erosion and water quality. The tool runs as an extension within the ESRI ArcGIS software package. It utilizes digital elevation maps, soils and precipitation information from data sets that are available nationally. However, it also lets users take advantage of local higher resolution and/or more accurate data sets when available. For example, the N-SPECT pollution coefficients used are similar to those in the EPA’s BASINS suite of tools and provide a good starting point for quick comparisons between management scenarios, but the coefficients can still be easily customized as users develop more localized data. The real utility of N-SPECT does not lie in the user’s ability to examine the accuracy of any particular run’s results, but in the comparison of runs between different development (or restoration) scenarios. By allowing users to modify multiple land uses and providing the results of those changes in a GIS environment, N-SPECT enables managers to quickly understand the overall consequences of different land use scenarios. The primary role of N-SPECT in this toolkit is to predict sedimentation and pollution changes from different land use scenarios and identify areas that are key contributors of these inputs.

Specific Policy or Decision Context Cited

None identified

Not reported

water-quality assessment, total maximum daily load(TMDL) determination

None identified

As part of an ongoing restoration program, HexSim was used to evaluate a portion of the former range of tule elk to identify the release scenario producing the most elk and fewest human conflicts.

None identified

Use ESII to answer the following business decision question: how can Dow close the ash pond while enhancing ecosystem services to Dow and the community and creating local habitat, for a lesser overall cost to Dow than the option currently defined?

None identified

None provided

Biophysical Context

Elevation ranges from 1552 to 2442 m, predominantly on south-facing slopes

Elevation ranges from 1552 to 2442 m, on predominantly south-facing slopes

Semi-arid environment. Rainfall varies geographically from less than 50 to about 3000 mm per year (annual mean 450 mm). Soils are mostly very shallow with limited irrigation potential.

Upper Mississipi River basin, elevation 142-194m,

Norteneastern region (U.S.); Mid-Atlantic region (U.S.)

Estuarine Emergent; Agricultural; Salt Marsh; Palustrine Emergent; Palustrine Forested

No additional description provided

Located in the Central Valley of California.

Prairie Pothole Region of Iowa

No additional description provided

River Teifi, Lampeter, Wales

Not applicable

EM Scenario Drivers

No scenarios presented

Alternative agricultural land use (type and crop management (fertilizer application) towards a future biofuel target

No scenarios presented

Four release sites; Kesterson, Arena Plains, San Luis, and East Bear Creek.

No scenarios presented

Alternative restoration designs

No scenarios presented

Varying flow velocities and durations

No scenarios presented

EM Relationship to Other EMs or Applications

EM ID

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

Method Only, Application of Method or Model Run

Method + Application

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method Only

New or Pre-existing EM?

New or revised model

Application of existing model

New or revised model

Application of existing model

New or revised model

Application of existing model

New or revised model

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

EM ID

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

Document ID for related EM

Doc-260

Doc-260 | Doc-270

Doc-271 ?

Doc-142

None

Doc-303 | Doc-305

Doc-327 | Doc-2 | Doc-337 ?

Doc-372 | Doc-373

Doc-391

Doc-439

Doc-467

Doc-473

EM ID for related EM

EM-65 | EM-66 | EM-68 | EM-69 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83

EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-81 | EM-82 | EM-83

EM-85 | EM-87 | EM-88

None

EM-98 | EM-422

EM-705 | EM-703 | EM-702 | EM-701 | EM-700

EM-712

None

EM-997

EM-1003 | EM-1005 | EM-1006 | EM-1008 | EM-1017

EM Modeling Approach

EM Relationship to Time

EM ID

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

EM Temporal Extent

2007-2009

Not reported

1980-2006

2002 ?

2004

2000-2011

25 years

1987-2007

Not reported

July 2006 to July 2007

Not applicable

EM Time Dependence

time-stationary

time-dependent

time-stationary

time-dependent

EM Time Reference (Future/Past)

Not applicable

future time

Not applicable

EM Time Continuity

Not applicable

discrete

Not applicable

discrete

continuous

other or unclear (comment)

EM Temporal Grain Size Value

Not applicable

EM Temporal Grain Size Unit

Not applicable

Year

Not applicable

Month

Not applicable

EM Spatial Extent

EM ID

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

Bounding Type

Physiographic or Ecological

Geopolitical

Watershed/Catchment/HUC

Geopolitical

Multiple unrelated locations (e.g., meta-analysis)

Physiographic or ecological

Geopolitical

Multiple unrelated locations (e.g., meta-analysis)

Physiographic or ecological

Geopolitical

Watershed/Catchment/HUC

Not applicable

Spatial Extent Name

Central French Alps

South Africa

East Fork Kaskaskia River watershed basin

NE U.S. Regions

Contiguous U.S.

Puget Sound Region

Grasslands Ecological Area

CREP (Conservation Reserve Enhancement Program

Dow Midland Operations facility ash pond and Posey Riverside (4-D property)

Chicago

River Teifi

Not applicable

Spatial Extent Area (Magnitude)

10-100 km^2

>1,000,000 km^2

100-1000 km^2

>1,000,000 km^2

100,000-1,000,000 km^2

10,000-100,000 km^2

100-1000 km^2

10,000-100,000 km^2

10-100 ha

100-1000 km^2

1000-10,000 km^2.

Not applicable

Spatial Distribution of Computations

EM ID

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

EM Spatial Distribution

spatially distributed (in at least some cases)

spatially lumped (in all cases)

spatially distributed (in at least some cases)

spatially lumped (in all cases)

other or unclear (comment)

Spatial Grain Type

area, for pixel or radial feature

other (specify), for irregular (e.g., stream reach, lake basin)

length, for linear feature (e.g., stream mile)

area, for pixel or radial feature

Not applicable

area, for pixel or radial feature

other (specify), for irregular (e.g., stream reach, lake basin)

Not applicable

Spatial Grain Size

20 m x 20 m

Distributed across catchments with average size of 65,000 ha

1 km^2

30 x 30 m

Not applicable

200m x 200m

Not reported

multiple, individual, irregular sites

map unit

plot (green roof) size

Not applicable

EM Structure and Computation Approach

EM ID

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

EM Computational Approach

Analytic

Numeric

Analytic

Numeric

Analytic

EM Determinism

deterministic

stochastic

deterministic

Statistical Estimation of EM

Conventional (frequentist) statistics

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Bayesian statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Bayesian statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Model Checking Procedures Used

EM ID

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

Model Calibration Reported?

Yes

Unclear

Not applicable

Model Goodness of Fit Reported?

Yes

Yes ?

Yes

Not applicable

Goodness of Fit (metric| value | unit)

R squared | 31 | Not applicable

None

R^2 | 0.97 | Not applicable
Mean Square Error | 0.12 | Not applicable
Root Mean Square Error | 0.35 | Not applicable
R-squared | 0.83 | Not applicable

R^2 | 0.86 | none

None

Model Operational Validation Reported?

Yes

Unclear

Not applicable

Model Uncertainty Analysis Reported?

Yes

Unclear

Yes

Not applicable

Model Sensitivity Analysis Reported?

Unclear

Yes

Not applicable

Model Sensitivity Analysis Include Interactions?

Not applicable

Unclear

Yes

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-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

Europe
- France

Europe
- France

Africa
- South Africa

North America
- United States
  - Illinois

North America
- United States
  - Vermont
  - New York
  - West Virginia
  - Massachusetts
  - Maine
  - Connecticut
  - New Hampshire
  - Delaware
  - Maryland
  - New Jersey
  - Pennsylvania
  - Virgina

None

North America
- United States
  - Washington

North America
- United States
  - California

North America
- United States
  - Iowa

North America
- United States
  - Michigan

North America
- United States
  - Illinois

Europe
- United Kingdom

None

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

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

None

Temperate North Pacific
- Northeast Pacific
  - Cold Temperate Northeast Pacific
    - Oregon, Washington, Vancouver Coast and Shelf

None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

Centroid Latitude

45.05

-30

38.69

-9999

37.25

42.62

43.6

41.88

52.04

Not applicable

Centroid Longitude

6.4

-89.1

-73

-9999

-123

-120.8

-93.84

-84.24

87.65

-4.39

Not applicable

Centroid Datum

WGS84

None provided

WGS84

Not applicable

Centroid Coordinates Status

Provided

Estimated

Provided

Estimated

Not applicable

Estimated

Provided

Estimated

Not applicable

Environments and Scales Modeled

EM ID

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

EM Environmental Sub-Class

Agroecosystems | Grasslands

Terrestrial Environment (sub-classes not fully specified)

Agroecosystems

Rivers and Streams | Ground Water | Terrestrial Environment (sub-classes not fully specified) | Agroecosystems | Atmosphere

Inland Wetlands | Near Coastal Marine and Estuarine

Inland Wetlands | Forests | Agroecosystems | Grasslands

Inland Wetlands | Agroecosystems | Grasslands

Rivers and Streams | Inland Wetlands | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified)

Created Greenspace

Rivers and Streams

Not applicable

Specific Environment Type

Subalpine terraces, grasslands, and meadows

Subalpine terraces, grasslands, and meadows.

Not reported

Row crop agriculture in Kaskaskia river basin

none

Wetlands (multiple types)

Terrestrial environment surrounding a large estuary

Terrestrial mosaic

Wetlands buffered by grassland within agroecosystems

Ash pond and surrounding environment

urban green roofs

River

None

EM Ecological Scale

Not applicable

Ecological scale is coarser 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 coarser 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

Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

EM Organismal Scale

Community

Not applicable

Individual or population, within a species

Not applicable

Community

Taxonomic level and name of organisms or groups identified

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

None Available

Species
- Cervus elaphus nannodes

Species
- Northern pintail (Anas acuta)

None Available

EnviroAtlas URL

EM-70

EM-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

None Available

GAP Ecological Systems, Carbon storage by tree biomass (kg/m2)

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)

National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, U.S. EPA (Omernik) ecoregions, Total Annual Nitrogen Deposition

National Hydrography Dataset Plus (NHD PlusV2), Total Annual Nitrogen Deposition

GAP Ecological Systems, Green Space per Capita, Percent GAP Status 1 & 2, Residential Density

None Available

Acres of Land Enrolled in the Conservation Reserve Program (CRP)

GAP Ecological Systems, The National Hydrography Dataset (NHD), Average Annual Precipitation, Stream Length, Waterbody area, Agricultural water use (million gallons/day)

Green Space per Capita, Average Annual Daily Potential Solar Energy, Average Annual Precipitation, Average Annual Daily Potential Wind Energy

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-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

None

[Ecosystem] Provisioning
- Nutrition
  - Biomass
    - Cultivated crops
- Materials
  - Biomass
    - Materials from plants, algae and animals for agricultural use

[Ecosystem] Regulation & Maintenance
- Mediation of flows
  - Mass flows
    - Mass stabilisation and control of erosion rates

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Water conditions
    - Chemical condition of freshwaters

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Water conditions
    - Chemical condition of freshwaters
- Mediation of waste, toxics and other nuisances
  - Mediation by biota
    - Bio-remediation by micro-organisms, algae, plants, and animals
    - Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals
  - Mediation by ecosystems
    - Filtration/sequestration/storage/accumulation by ecosystems

[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 waste, toxics and other nuisances
  - By natural chemical and physical processes
    - Not applicable
[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Water conditions
    - Chemical condition of freshwaters

[Ecosystem] Cultural
- Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings]
  - Physical and experiential interactions
    - Physical use of land-/seascapes in different environmental settings
  - Intellectual and representative interactions
    - Aesthetic
    - Entertainment

[Ecosystem] Provisioning
- Materials
  - Biomass
    - Fibres and other materials from plants, algae and animals for direct use or processing
[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

[Ecosystem] Provisioning
- Nutrition
  - Biomass
    - Wild animals and their outputs

[Ecosystem] Provisioning
- Materials
  - Water
    - Surface water for non-drinking purposes
[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Atmospheric composition and climate regulation
    - Micro and regional climate regulation
    - Global climate regulation by reduction of greenhouse gas concentrations
  - Water conditions
    - Chemical condition of freshwaters
- Mediation of flows
  - Mass flows
    - Mass stabilisation and control of erosion rates
- Mediation of waste, toxics and other nuisances
  - Mediation by ecosystems
    - Mediation of smell/noise/visual impacts
    - Filtration/sequestration/storage/accumulation by ecosystems

[Ecosystem] Regulation & Maintenance
- Mediation of waste, toxics and other nuisances
  - Mediation by biota
    - Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals

[Ecosystem] Regulation & Maintenance
- Mediation of flows
  - Liquid flows
    - Hydrological cycle and water flow maintenance
  - Mass flows
    - Mass stabilisation and control of erosion rates

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Soil formation and composition
    - Decomposition and fixing processes
    - Weathering processes
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats
- Mediation of flows
  - Liquid flows
    - Hydrological cycle and water flow maintenance
  - Mass flows
    - Buffering and attenuation of mass flows
    - Mass stabilisation and control of erosion rates
- Mediation of waste, toxics and other nuisances
  - Mediation by ecosystems
    - Filtration/sequestration/storage/accumulation by ecosystems
    - Dilution by atmosphere, freshwater and marine 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-80

EM-86

EM-97

EM-104

EM-196

EM-315

EM-403

EM-704

EM-713

EM-945

EM-998

EM-1007

None

Grasslands (23)
- Flora (5)
  - Fodder
    - Quality Indicator
  - Green biomass
    - Stock Indicator
Agroecosystems (22)
- Flora (5)
  - Fodder
    - Quality Indicator
  - Green biomass
    - Stock Indicator
- Composite (8)
  - Presence of environmental class/subclass
    - Quality Indicator

None

Rivers and Streams (111)
- Water (3)
  - Liquid water
    - Quality Indicator

Rivers and Streams (111)
- Water (3)
  - Liquid water
    - Quality Indicator

Rivers and Streams (111)
- Water (3)
  - Liquid water
    - Quality Indicator

Near Coastal Marine/Estuarine (113)
- Composite (8)
  - Presence of environmental class/subclass
    - Stock Indicator
Rivers and Streams (111)
- Composite (8)
  - Presence of environmental class/subclass
    - Stock Indicator
Forests (21)
- Composite (8)
  - Presence of environmental class/subclass
    - Stock Indicator
Lakes and Ponds (112)
- Composite (8)
  - Presence of environmental class/subclass
    - Stock Indicator
Wetlands (12)
- Composite (8)
  - Presence of environmental class/subclass
    - Stock Indicator
Urban/Suburban (27)
- Composite (8)
  - Presence of environmental class/subclass
    - Stock Indicator

None

Wetlands (12)
- Fauna (4)
  - Duck
    - Stock Indicator

Wetlands (12)
- Water (3)
  - Liquid water
    - Quality Indicator
- Composite (8)
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Quality Indicator

None

Rivers and Streams (111)
- Water (3)
  - Liquid water
    - Flow Indicator
- Soil (2)
  - Specific types of soil
    - Flow Indicator

None

US EPA

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:

Comment:

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

Comment:

Comment:

EM Modeling Approach

Comment:

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)