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:

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Comparing:

EM-65
EM-71
EM-193
EM-414
EM-421
EM-455
EM-458
EM-590
EM-718
EM-862
EM-890
EM-968
EM-1001
EM-1009

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

EM Short Name

Green biomass production, Central French Alps

Community flowering date, Central French Alps

Cultural ecosystem services, Bilbao, Spain

SAV occurrence, St. Louis River, MN/WI, USA

Water storage in Lago Lucchetti, Puerto Rico, USA

Value of a reef dive site, St. Croix, USVI

Reef density of P. argus, St. Croix, USVI

Fish species richness, Puerto Rico, USA

WESP: Riparian & stream habitat, ID, USA

Recreational fishery index, USA

HWB Blood pressure, Great Lakes waterfront, USA

EPA Stormwater Manamgement Model

NBS benefits explorer

ETDOT carbon contaminant removal

EM Full Name

Green biomass production, Central French Alps

Community weighted mean flowering date, Central French Alps

Cultural ecosystem services, Bilbao, Spain

Predicting submerged aquatic vegetation occurrence, St. Louis River Estuary, MN & WI, USA

Water storage in Lago Lucchetti, Puerto Rico, USA

Value of a dive site (reef), St. Croix, USVI

Relative density of Panulirus argus (on reef), St. Croix, USVI

Fish species richness, Puerto Rico, USA

WESP: Riparian and stream habitat focus projects, ID, USA

Recreational fishery index for streams and rivers, USA

Human well being indicator- Blood pressure, Great Lakes waterfront, USA

Storm Water Management Model User's Manual Version 5.2

Benefit Accounting of Nature-Based Solutions for Watersheds: Guide

nvironmental Technologies Design Option Tool for contaminant removal

EM Source or Collection

EU Biodiversity Action 5

None ?

US EPA

None

US EPA

None

US EPA

None

US EPA

EM Source Document ID

260

191

330

336

335

355

393 ?

414

422 ?

452

471

475

Document Author

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

Casado-Arzuaga, I., Onaindia, M., Madariaga, I. and Verburg P. H.

Ted R. Angradi, Mark S. Pearson, David W. Bolgrien, Brent J. Bellinger, Matthew A. Starry, Carol Reschke

Bousquin, J., W.S. Fisher, J. Carriger, and E. Huertas

Yee, S. H., Dittmar, J. A., and L. M. Oliver

Pittman, S.J., Christensen, J.D., Caldow, C., Menza, C., and M.E. Monaco

Murphy, C. and T. Weekley

Lomnicky. G.A., Hughes, R.M., Peck, D.V., and P.L. Ringold

Ted R. Angradi, Jonathon J. Launspach, and Molly J. Wick

Rossman, L. A., M., Simon

Brill, G., T. Shiao, C. Kammeyer, S. Diringer, K. Vigerstol, N. Ofosu-Amaah, M. Matosich, C. Müller-Zantop, W. Larson and T. Dekker

National Center for Clean Industrial and Treatment Technologies at Michigan Technological University (MTU)

Document Year

2011

2013

2014

2007

2012

2021

None

2022

2019

Document Title

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

Mapping recreation and aesthetic value of ecosystems in the Bilbao Metropolitan Greenbelt (northern Spain) to support landscape planning

Predicting submerged aquatic vegetation cover and occurrence in a Lake Superior estuary

A Bayesian belief network approach to explore alternative decisions for sediment control and water storage capacity at Lago Lucchetti, Puerto Rico

Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI

Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean

Measuring outcomes of wetland restoration, enhancement, and creation in Idaho-- Assessing potential functions, values, and condition in a watershed context.

Correspondence between a recreational fishery index and ecological condition for U.S.A. streams and rivers.

Human well-being and natural capital indictors for Great Lakes waterfront revitalization

Storm Water Management Model User's Manual Version 5.2

Benefit Accounting of Nature-Based Solutions for Watersheds: Guide

Environmental Technologies Design Option Tool

Document Status

Peer reviewed and published

Peer reviewed but unpublished (explain in Comment)

Not peer reviewed but is published (explain in Comment)

Peer reviewed and published

Comments on Status

Published journal manuscript

Published EPA report

Published journal manuscript

Published report

Published journal manuscript

Journal manuscript submitted or in review

Published EPA report

Published report

Software and Access

EM ID

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

EM Software Access info Exit

Not applicable

https://www.epa.gov/water-research/storm-water-management-model-swmm

https://nbsbenefitsexplorer.net/tool

https://github.com/USEPA/Environmental-Technologies-Design-Option-Tool

Contact Name

Sandra Lavorel

Izaskun Casado-Arzuaga

Ted R. Angradi

Susan Yee

Susan H. Yee

Simon Pittman

Chris Murphy

Gregg Lomnicky

Ted Angradi

David Burden

Gregg Brill

David Hokanson

Contact Address

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

Plant Biology and Ecology Department, University of the Basque Country UPV/EHU, Campus de Leioa, Barrio Sarriena s/n, 48940 Leioa, Bizkaia, Spain

U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, 6201 Congdon Blvd., Duluth, MN 55804, USA

NHEERL Gulf Ecology Division, Gulf Breeze FL 32561

US EPA, Office of Research and Development, NHEERL, Gulf Ecology Division, Gulf Breeze, FL 32561, USA

1305 East-West Highway, Silver Spring, MD 20910, USA

Idaho Dept. Fish and Game, Wildlife Bureau, Habitat Section, Boise, ID

200 SW 35th St., Corvallis, OR, 97333

USEPA, Center for Computational Toxicology and Ecology, Great Lakes Toxicology and Ecology Division, Duluth, MN 55804

U.S. EPA Research Center for Environmental Solutions and Emergency Response (CESER) Mail Drop: 314 P.O. Box #1198 Ada, OK 74821-1198

Not reported

224 N. Fair Oaks Ave., Floor 2 Pasadena, CA. 91103

Contact Email

sandra.lavorel@ujf-grenoble.fr

izaskun.casado@ehu.es

angradi.theodore@epa.gov

yee.susan@epa.gov

simon.pittman@noaa.gov

chris.murphy@idfg.idaho.gov

lomnicky.gregg@epa.gov

tedangradi@gmail.com

burden.david@epa.gov

Not reported

administrator@trusselltech.com

EM Description

EM ID

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

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. Vegetative height and leaf traits such as leaf dry matter content were response traits strongly influenced by land use and abiotic environment, with follow-on effects on several ecosystem properties (e.g., green biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in green biomass production was modelled using…traits community-weighted mean (CWM) and functional divergence (FD) and abiotic variables (continuous variables; trait + abiotic) following Diaz et al. (2007). …The comparison between this model and the land-use alone model identifies the need for site-based information beyond a land use or land cover proxy, and the comparison with the land use + abiotic model assesses the value of additional ecological (trait) information…Green biomass production for each pixel was calculated and mapped using model estimates for…regression coefficients on abiotic variables and traits. For each pixel these calculations were applied to mapped estimates of abiotic variables and trait CWM and FD. This step is critically novel as compared to a direct application of the model by Diaz et al. (2007) in that we explicitly modelled the responses of trait community-weighted means and functional divergences to environment prior to evaluating their effects on ecosystem properties. Such an approach is the key to the explicit representation of functional variation across the landscape, as opposed to the use of unique trait values within each land use (see Albert et al. 2010)."

ABSTRACT "This paper presents a method to quantify cultural ecosystem services (ES) and their spatial distribution in the landscape based on ecological structure and social evaluation approaches. The method aims to provide quantified assessments of ES to support land use planning decisions. A GIS-based approach was used to estimate and map the provision of recreation and aesthetic services supplied by ecosystems in a peri-urban area located in the Basque Country, northern Spain. Data of two different public participation processes (frequency of visits to 25 different sites within the study area and aesthetic value of different landscape units) were used to validate the maps. Three maps were obtained as results: a map showing the provision of recreation services, an aesthetic value map and a map of the correspondences and differences between both services. The data obtained in the participation processes were found useful for the validation of the maps. A weak spatial correlation was found between aesthetic quality and recreation provision services, with an overlap of the highest values for both services only in 7.2 % of the area. A consultation with decision-makers indicated that the results were considered useful to identify areas that can be targeted for improvement of landscape and recreation management."

ABSTRACT: “Submerged aquatic vegetation (SAV) provides the biophysical basis for multiple ecosystem services in Great Lakes estuaries. Understanding sources of variation in SAV is necessary for sustainable management of SAV habitat. From data collected using hydroacoustic survey methods, we created predictive models for SAV in the St. Louis River Estuary (SLRE) of western Lake Superior. The dominant SAV species in most areas of the estuary was American wild celery (Vallisneria americana Michx.)…” AUTHOR’S DESCRIPTION: “The SLRE is a Great Lakes “rivermouth” ecosystem as defined by Larson et al. (2013). The 5000-ha estuary forms a section of the state border between Duluth, Minnesota and Superior, Wisconsin…In the SLRE, SAV beds are often patchy, turbidity varies considerably among areas (DeVore, 1978) and over time, and the growing season is short. Given these conditions, hydroacoustic survey methods were the best option for generating the extensive, high resolution data needed for modeling. From late July through mid September in 2011, we surveyed SAV in Allouez Bay, part of Superior Bay, eastern half of St. Louis Bay, and Spirit Lake…We used the measured SAV percent cover at the location immediately previous to each useable record location along each transect as a lag variable to correct for possible serial autocorrelation of model error. SAV percent cover, substrate parameters, corrected depth, and exposure and bed slope data were combined in Arc-GIS...We created logistic regression models for each area of the SLRE to predict the probability of SAV being present at each report location. We created models for the training data set using the Logistic procedure in SAS v.9.1 with step wise elimination (?=0.05). Plots of cover by depth for selected predictor values (Supplementary Information Appendix C) suggested that interactions between depth and other predictors were likely to be significant, and so were included in regression models. We retained the main effect if their interaction terms were significant in the model. We examined the performance of the models using the area under the receiver operating characteristic (AUROC) curve. AUROC is the probability of concordance between random pairs of observations and ranges from 0.5 to 1 (Gönen, 2006). We cross-validated logistic occurrence models for their ability to classify correctly locations in the validation (holdout) dataset and in the Superior Bay dataset… Model performance, as indicated by the area under the receiver operating characteristic (AUROC) curve was >0.8 (Table 3). Assessed accuracy of models (the percent of records where the predicted probability of occurrence and actual SAV presence or absence agreed) for split datasets was 79% for Allouez Bay, 86% for St. Louis Bay, and 78% for Spirit Lake."

AUTHORS DESCRIPTION: " A Bayesian belief network (BBN) was developed to characterize the effects of sediment accumulation on the water storage capacity of Lago Lucchetti (located in southwest Puerto Rico) and to forecast the life expectancy (usefulness) of the reservoir under different management scenarios. The conceptual approach included water and sediment delivery from two sources, from the Lucchetti watershed and from a tunnel linking Lago Lucchetti to three upstream reservoirs. Variables in the model included precipitation and erosion factors (soil type, landscape slope, and land use) applied to the Lucchetti watershed and to watersheds of the upstream reservoirs. The lack of available data for water and sediment flows in the watershed and through tunnels connecting the reservoirs led to several unique methods for network data acquisition. Status quo model runs demonstrated that sediment trapping has continuously declined in all four reservoirs since their construction and that every year a greater proportion of sediment is moving downstream through tunnels or spillways. The model compared favorably with incidental measured data in the region. Sensitivity analysis demonstrated that current sediment accumulation in Lago Lucchetti can be attributed in large part to sediment erosion from the Lucchetti watershed with only minor influence (~8%) from upstream reservoirs. Two decision scenarios were explored by including additional nodes for (1) partial and full conversion of sungrown land use to shadegrown coffee cultivation in the Lucchetti watershed and (2) partial or complete dredging the reservoir. Both management actions were examined singly and in combination for effects on reservoir life expectancy (beyond the year 2000) with varying water capacity targets. Using 50% water storage capacity as a target, model runs for status quo (no decision implemented) resulted in a probability range that averaged 5.57 years (or 50% water storage capacity in the year 2005). Partial and full conversion of land from sungrown to shadegrown coffee cultivation raised the life expectancy to 5.89 and 6.57 years, respectively. Partial and complete dredging of the reservoir resulted in a life expectancy of 37.3 and 40.7 years, and full implementation of both management options resulted in a life expectancy of 44.4 years."

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 recreational activities are associated directly or indirectly with coral reefs including scuba diving, snorkeling, surfing, underwater photography, recreational fishing, wildlife viewing, beach sunbathing and swimming, and beachcombing (Principe et al., 2012)…Another method to quantify recreational opportunities is to use survey data of tourists and recreational visitors to the reefs to generate statistical models to quantify the link between reef condition and production of recreation-related ecosystem services. Wielgus et al. (2003) used interviews with SCUBA divers in Israel to derive coefficients for a choice model in which willingness to pay for higher quality dive sites was determined in part by a weighted combination of factors identified with dive quality: Relative value of dive site = 0.1227(Scoral+Sfish+Acoral+Afish)+0.0565V where Scoral, Sfish are coral and fish richness, Acoral, Afish are abundances of fish and coral per square meter, and V is water visibility (meters)."

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…We broadly consider fisheries production to include harvesting of aquatic organisms as seafood for human consumption (NOAA (National Oceanic and Atmospheric Administration), 2009; Principe et al., 2012), as well as other non-consumptive uses such as live fish or coral for aquariums (Chan and Sadovy, 2000), or shells or skeletons for ornamental art or jewelry (Grigg, 1989; Hourigan, 2008). The density of key commercial fisheries species and the value of finfish can be associated with the relative cover of key benthic habitat types on which they depend (Mumby et al., 2008). For each grid cell, we estimated the contribution of coral reefs to fisheries production as the overall weighted average of relative magnitudes of contribution across habitat types within that grid cell: Relative fisheries production j = ΣiciMij where ci is the fraction of area within each grid cell for each habitat type i (dense, medium dense, or sparse seagrass, mangroves, sand, macroalgae, A. palmata, Montastraea reef, patch reef, and dense or sparse gorgonians),and Mij is the magnitude associated with each habitat for a given metric j: (1) density of the spiny lobster Panulirus argus"

ABSTRACT: "Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01 km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems."

A wetland restoration monitoring and assessment program framework was developed for Idaho. The project goal was to assess outcomes of substantial governmental and private investment in wetland restoration, enhancement and creation. The functions, values, condition, and vegetation at restored, enhanced, and created wetlands on private and state lands across Idaho were retrospectively evaluated. Assessment was conducted at multiple spatial scales and intensities. Potential functions and values (ecosystem services) were rapidly assessed using the Oregon Rapid Wetland Assessment Protocol. Vegetation samples were analyzed using Floristic Quality Assessment indices from Washington State. We compared vegetation of restored, enhanced, and created wetlands with reference wetlands that occurred in similar hydrogeomorphic environments determined at the HUC 12 level.

ABSTRACT: [Sport fishing is an important recreational and economic activity, especially in Australia, Europe and North America, and the condition of sport fish populations is a key ecological indicator of water body condition for millions of anglers and the public. Despite its importance as an ecological indicator representing the status of sport fish populations, an index for measuring this ecosystem service has not been quantified by analyzing actual fish taxa, size and abundance data across the U.S.A. Therefore, we used game fish data collected from 1,561 stream and river sites located throughout the conterminous U.S.A. combined with specific fish species and size dollar weights to calculate site-specific recreational fishery index (RFI) scores. We then regressed those scores against 38 potential site-specific environmental predictor variables, as well as site-specific fish assemblage condition (multimetric index; MMI) scores based on entire fish assemblages, to determine the factors most associated with the RFI scores. We found weak correlations between RFI and MMI scores and weak to moderate correlations with environmental variables, which varied in importance with each of 9 ecoregions. We conclude that the RFI is a useful indicator of a stream ecosystem service, which should be of greater interest to the U.S.A. public and traditional fishery management agencies than are MMIs, which tend to be more useful for ecologists, environmentalists and environmental quality agencies.]

ABSTRACT: "Revitalization of natural capital amenities at the Great Lakes waterfront can result from sediment remediation, habitat restoration, climate resilience projects, brownfield reuse, economic redevelopment and other efforts. Practical indicators are needed to assess the socioeconomic and cultural benefits of these investments. We compiled U.S. census-tract scale data for five Great Lakes communities: Duluth/Superior, Green Bay, Milwaukee, Chicago, and Cleveland. We downloaded data from the US Census Bureau, Centers for Disease Control and Prevention, Environmental Protection Agency, National Oceanic and Atmospheric Administration, and non-governmental organizations. We compiled a final set of 19 objective human well-being (HWB) metrics and 26 metrics representing attributes of natural and 7 seminatural amenities (natural capital). We rated the reliability of metrics according to their consistency of correlations with metric of the other type (HWB vs. natural capital) at the census-tract scale, how often they were correlated in the expected direction, strength of correlations, and other attributes. Among the highest rated HWB indicators were measures of mean health, mental health, home ownership, home value, life success, and educational attainment. Highest rated natural capital metrics included tree cover and impervious surface metrics, walkability, density of recreational amenities, and shoreline type. Two ociodemographic covariates, household income and population density, had a strong influence on the associations between HWB and natural capital and must be included in any assessment of change in HWB benefits in the waterfront setting. Our findings are a starting point for applying objective HWB and natural capital indicators in a waterfront revitalization context."

EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. Running under Windows, SWMM 5 provides an integrated environment for editing study area input data, running hydrologic, hydraulic and water quality simulations, and viewing the results in a variety of formats. These include color coded drainage area and conveyance system maps, time series graphs and tables, profile plots, and statistical frequency analyses. This user’s manual describes in detail how to run SWMM 5.2. It includes instructions on how to build a drainage system model, how to set various simulation options, and how to view results in a variety of formats. It also describes the different types of files used by SWMM and provides useful tables of parameter values. Detailed descriptions of the theory behind SWMM 5 and the numerical methods it employs can be found in a separate set of reference manuals. ?

Watersheds around the world are in peril and risk further decline from climate change and human impacts, like pollution, degrading landscapes, and unsustainable water use. These impacts can inhibit the ability of ecosystems to regulate water flows, sequester carbon to reduce atmospheric greenhouse gas levels, maintain biodiversity and healthy waterways, promote social well-being, offer economic opportunities, and sustain agricultural productivity. Climate change is exacerbating these impacts by shifting weather and precipitation patterns, degrading habitats, and increasing the recurrence and severity of natural disasters. Urgent action is needed to address these impacts by implementing nature-based solutions (NBS). NBS protect, sustainably manage, and restore natural or modified watersheds, to address societal challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits (IUCN, 2016). Investment in NBS offers a mechanism to restore degraded watersheds and protect intact ones, leading to improved water quality and quantity, improved carbon sequestration and increased biodiversity, among many other social and economic benefits. NBS also support climate mitigation and adaptation efforts and reduce the impacts from other shocks, such as floods, droughts, and extreme weather events. Implementing NBS can also help advance progress toward achieving the United Nations Sustainable Development Goals (SDGs), particularly SDG 2 (zero hunger), SDG 6 (water), SDG 11 (sustainable cities and communities), SDG 13 (climate action), and SDG 15 (life on land). NBS therefore support social, economic and environmental objectives, and may be particularly important in supporting vulnerable communities.

Authors description: "The Environmental Technologies Design Option Tool (ETDOT) is a suite of software models that provides engineers with the capability to evaluate and design systems that use granular activated carbon or ion exchange resins for the removal of contaminants, including PFAS, from drinking water and wastewater. Information generated from ETDOT models will provide states and utilities with a better understanding of the fundamentals of carbon adsorption and what that means to the operation, performance, and costs associated with this technology. Even though carbon adsorption can be an effective treatment technology for removing organic compounds, such as PFAS, from water, it can be expensive or may not achieve desired removal objectives if improperly designed. Proper full-scale design of this adsorption process typically results from carefully controlled pilot-scale studies that are used to determine important design variables, such as the type of adsorbent, empty bed contact time, and bed configuration. However, these studies can be time consuming and expensive if they are not properly planned. Information generated from the ETDOT models can be used to help design pilot treatment systems and provide a first-cut prediction of full-scale results.]

Specific Policy or Decision Context Cited

None identified

Land management, ecosystem management, response to EU 2020 Biodiversity Strategy

None identified

None reported

None identified

None provided

None identified

Not Applicable

Biophysical Context

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

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

Northern Spain; Bizkaia region

submerged aquatic vegetation

No additional reported

No additional description provided

Hard and soft benthic habitat types approximately to the 33m isobath

restored, enhanced and created wetlands

None

Waterfront districts on south Lake Michigan and south lake Erie

Not applicable

EM Scenario Drivers

No scenarios presented

Management alternatives

No scenarios presented

Sites, function or habitat focus

N/A

No scenarios presented

Not applicable

EM Relationship to Other EMs or Applications

EM ID

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

Method Only, Application of Method or Model Run

Method + Application

Method + Application (multiple runs exist)

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

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

EM ID

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

Document ID for related EM

Doc-260

Doc-260 | Doc-269

None

Doc-355

Doc-390

None

Doc-422

None

EM ID for related EM

EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83

EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-79 | EM-80 | EM-81 | EM-82 | EM-83

None

EM-698 | EM-699

EM-706 | EM-729 | EM-730 | EM-734 | EM-743 | EM-749 | EM-750 | EM-756 | EM-757 | EM-758 | EM-759 | EM-760 | EM-761 | EM-763 | EM-764 | EM-766 | EM-767 | EM-732 | EM-737 | EM-738 | EM-739 | EM-741 | EM-742 | EM-751 | EM-768

None

EM-886 | EM-888 | EM-889 | EM-891 | EM-893 | EM-894 | EM-895

EM-971

None

EM Modeling Approach

EM Relationship to Time

EM ID

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

EM Temporal Extent

2007-2009

2007-2008

2000 - 2007

2010 - 2012

1962-2006

2006-2007, 2010

2000-2005

2010-2011

2013-2014

2022

Not applicable

EM Time Dependence

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

Not applicable

EM Time Reference (Future/Past)

Not applicable

future time

Not applicable

past time

Not applicable

both

Not applicable

EM Time Continuity

Not applicable

discrete

Not applicable

discrete

Not applicable

continuous

Not applicable

EM Temporal Grain Size Value

Not applicable

EM Temporal Grain Size Unit

Not applicable

Year

Not applicable

Year

Not applicable

EM Spatial Extent

EM ID

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

Bounding Type

Physiographic or Ecological

Geopolitical

Physiographic or ecological

Watershed/Catchment/HUC

Physiographic or ecological

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

Geopolitical

No location (no locational reference given)

Not applicable

Spatial Extent Name

Central French Alps

Bilbao Metropolitan Greenbelt

St. Louis River Estuary

Guanica Bay watershed

Coastal zone surrounding St. Croix

SW Puerto Rico,

Wetlands in idaho

United States

Great Lakes waterfront

Not applicable

Spatial Extent Area (Magnitude)

10-100 km^2

100-1000 km^2

10-100 km^2

100-1000 km^2

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

>1,000,000 km^2

1000-10,000 km^2.

Not applicable

Spatial Distribution of Computations

EM ID

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

EM Spatial Distribution

spatially distributed (in at least some cases)

spatially distributed (in at least some cases) ?

spatially distributed (in at least some cases)

spatially lumped (in all cases)

spatially distributed (in at least some cases)

spatially lumped (in all cases)

spatially distributed (in at least some cases)

spatially lumped (in all cases)

Spatial Grain Type

area, for pixel or radial feature

Not applicable

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

Not applicable

area, for pixel or radial feature

Not applicable

Spatial Grain Size

20 m x 20 m

2 m x 2 m

0.07 m^2 to 0.70 m^2

Not reported

10 m x 10 m

not reported

Not applicable

stream reach (site)

Not applicable

EM Structure and Computation Approach

EM ID

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

EM Computational Approach

Analytic

Numeric

Analytic

Numeric

Analytic

Numeric

Analytic

EM Determinism

deterministic

Statistical Estimation of EM

Conventional (frequentist) statistics

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

Bayesian statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Bayesian statistics

Other or unclear (comment)

Model Checking Procedures Used

EM ID

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

Model Calibration Reported?

Yes

Not applicable

Model Goodness of Fit Reported?

Yes

Not applicable

Goodness of Fit (metric| value | unit)

Adjusted R | 69.9 | Not applicable

R squared | 45.2 | Not applicable

None

p-value | <0.001 | unitless
Nagelkerke r2 | 0.30-0.62 | unitless

None

R squared | 0.55 | unitless
Cohen's Kappa statistic | 0.54 | unitless
AUC | 0.79 | unitless

None

Model Operational Validation Reported?

Yes

Not applicable

Unclear

Model Uncertainty Analysis Reported?

Not applicable

Model Sensitivity Analysis Reported?

Yes

Not applicable

Model Sensitivity Analysis Include Interactions?

Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

Europe
- France

Europe
- France

Europe
- Spain

North America
- United States
  - Minnesota
  - Wisconsin

North America
- U.S. Caribbean Territories

None

North America
- United States
  - Idaho

North America
- United States
  - North Carolina
  - Indiana
  - Wyoming
  - Northern Mariana Islands
  - Utah
  - Guam
  - Arizona
  - Montana
  - Kentucky
  - California
  - Kansas
  - Delaware
  - Florida
  - Pennsylvania
  - Iowa
  - Mississippi
  - Illinois
  - Texas
  - Connecticut
  - Georgia
  - Maryland
  - Idaho
  - Oregon
  - Vermont
  - Puerto Rico
  - Maine
  - Oklahoma
  - Tennessee
  - American Samoa
  - Alabama
  - Arkansas
  - South Carolina
  - Washington
  - Nebraska
  - West Virginia
  - Colorado
  - Massachusetts
  - Missouri
  - Alaska
  - U.S. Virgin Islands
  - Washington D.C.
  - North Dakota
  - Wisconsin
  - Nevada
  - New York
  - Rhode Island
  - Hawaii
  - South Dakota
  - Minnesota
  - New Jersey
  - Michigan
  - Virgina
  - New Mexico
  - New Hampshire
  - Louisiana
  - Ohio

North America
- United States
  - Illinois
  - Minnesota
  - Ohio
  - Wisconsin

None

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

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

None

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Eastern Caribbean

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Eastern Caribbean

Tropical Atlantic
- West Tropical Atlantic
  - Tropical Northwestern Atlantic
    - Greater Antilles

None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

Centroid Latitude

45.05

43.25

46.72

18.09

17.73

17.9

44.06

36.21

42.26

Not applicable

Centroid Longitude

6.4

-2.92

-96.13

-66.86

-64.77

67.11

-114.69

-113.76

-87.84

Not applicable

Centroid Datum

WGS84

Not applicable

Centroid Coordinates Status

Provided

Estimated

Not applicable

Environments and Scales Modeled

EM ID

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

EM Environmental Sub-Class

Agroecosystems | Grasslands

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

Lakes and Ponds

Near Coastal Marine and Estuarine

Inland Wetlands

Rivers and Streams

Terrestrial Environment (sub-classes not fully specified)

Not applicable

Specific Environment Type

Subalpine terraces, grasslands, and meadows

Subalpine terraces, grasslands, and meadows.

none

Freshwater estuarine system

Lago Luchetti

Coral reefs

shallow coral reefs

created, restored and enhanced wetlands

reach

Lake Michigan & Lake Erie waterfront

User-defined catchments

None

Not applicable

EM Ecological Scale

Not applicable

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

Other or unclear (comment)

Ecological scale corresponds to the Environmental Sub-class

Not applicable

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

EM Organismal Scale

Community

Not applicable

Guild or Assemblage

Species

Guild or Assemblage

Not applicable

Guild or Assemblage

Not applicable

Taxonomic level and name of organisms or groups identified

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

None Available

Species
- Panulirus argus

other
- (fish class)

None Available

EnviroAtlas URL

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

GAP Ecological Systems

None Available

Percent IUCN Status II, Percent GAP Status 1 & 2

Average Annual Precipitation

National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation

None Available

Total Annual Reduced Nitrogen Deposition, Carbon Storage by Tree Biomass

None Available

GAP Ecological Systems, Enabling Conditions

None Available

GAP Ecological Systems, Average Annual Precipitation, U.S. EPA (Omernik) ecoregions, Residential Density

None Available

EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)

EM-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

None

[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
    - Aesthetic

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats

[Ecosystem] Provisioning
- Nutrition
  - Water
    - Surface water for drinking
- Materials
  - Water
    - Surface water for non-drinking purposes
[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
    - Experiential use of plants, animals and land-/seascapes in different environmental settings

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

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats

[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats
  - Water conditions
    - Chemical condition of freshwaters
- Mediation of flows
  - Liquid flows
    - Flood protection
    - Hydrological cycle and water flow maintenance
  - Mass flows
    - Mass stabilisation and control of erosion rates
- Mediation of waste, toxics and other nuisances
  - Mediation by biota
    - Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals

[Ecosystem] Provisioning
- Nutrition
  - Biomass
    - Wild animals and their outputs
[Ecosystem] Cultural
- Physical and intellectual interactions with biota, ecosystems, and land-/seascapes [environmental settings]
  - Physical and experiential interactions
    - Experiential use of plants, animals and land-/seascapes in different environmental settings

None

[Ecosystem] Provisioning
- Materials
  - Water
    - Ground water for non-drinking purposes
    - Surface water for non-drinking purposes
[Ecosystem] Regulation & Maintenance
- Mediation of flows
  - Liquid flows
    - Flood protection

[Ecosystem] Provisioning
- Materials
  - Water
    - Ground water for non-drinking purposes
    - Surface water for non-drinking purposes
[Ecosystem] Regulation & Maintenance
- Maintenance of physical, chemical, biological conditions
  - Soil formation and composition
    - Weathering processes
  - Pest and disease control
    - Pest control
  - Lifecycle maintenance, habitat and gene pool protection
    - Maintaining nursery populations and habitats
  - Water conditions
    - Chemical condition of salt waters
    - Chemical condition of freshwaters
- Mediation of flows
  - Liquid flows
    - Hydrological cycle and water flow maintenance
  - Mass flows
    - Mass stabilisation and control of erosion rates

None

<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-65

EM-71

EM-193

EM-414

EM-421

EM-455

EM-458

EM-590

EM-718

EM-862

EM-890

EM-968

EM-1001

EM-1009

Grasslands (23)
- Flora (5)
  - Green biomass
    - Stock Indicator
Agroecosystems (22)
- Flora (5)
  - Green biomass
    - Stock Indicator

None

Near Coastal Marine/Estuarine (113)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Site Characteristic Indicator
Forests (21)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Site Characteristic Indicator
Grasslands (23)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Site Characteristic Indicator
Scrubland/Shrubland (24)
- Composite (8)
  - Presence of environmental class/subclass
    - Site Characteristic Indicator
  - Scapes: views, sounds and scents of land, sea, sky or a combination
    - Site Characteristic Indicator

None

Lakes and Ponds (112)
- Water (3)
  - Liquid water
    - Stock Indicator
- Composite (8)
  - Regulation of extreme events
    - Extreme Event Indicator

Near Coastal Marine/Estuarine (113)
- Composite (8)
  - Presence of environmental class/subclass
    - Quality Indicator

Near Coastal Marine/Estuarine (113)
- Fauna (4)
  - Fish
    - Stock Indicator

Near Coastal Marine/Estuarine (113)
- Fauna (4)
  - Fish
    - Stock Indicator

None

Rivers and Streams (111)
- Fauna (4)
  - Fish
    - Quality Indicator

None

Rivers and Streams (111)
- Composite (8)
  - Regulation of extreme events
    - Flow Indicator
Groundwater
- Water (3)
  - Liquid water
    - Flow Indicator
Lakes and Ponds (112)
- Composite (8)
  - Regulation of extreme events
    - Flow Indicator
Urban/Surburban (27)
- Composite (8)
  - Regulation of extreme events
    - 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:

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)