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-79
EM-374
EM-379
EM-418
EM-449
EM-467
EM-685
EM-709
EM-940
EM-962
EM-985
EM-1001

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-79

EM-374

EM-379

EM-418

EM-449

EM-467

EM-685

EM-709

EM-940

EM-962

EM-985

EM-1001

EM Short Name

Divergence in flowering date, Central French Alps

InVEST carbon storage and sequestration (v3.2.0)

VELMA soil temperature, Oregon, USA

SIRHI, St. Croix, USVI

Decrease in erosion (shoreline), St. Croix, USVI

Yasso07 v1.0.1, Switzerland

Visitor value lost to a beach closure, MA, USA

Pollinators on landfill sites, United Kingdom

OpenNSPECT v. 1.1, California, U.S.

RZWQM2, Quebec, Canada

Atlantis ecosystem assessment submodel

NBS benefits explorer

EM Full Name

Functional divergence in flowering date, Central French Alps

InVEST v3.2.0 Carbon storage and sequestration

VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA

SIRHI (SImplified Reef Health Index), St. Croix, USVI

Decrease in erosion (shoreline) by reef, St. Croix, USVI

Yasso07 v1.0.1 forest litter decomposition, Switzerland

Visitor value lost to a beach closure, Barnstable, Massachusetts, USA

Pollinating insects on landfill sites, East Midlands, United Kingdon

OpenNSPECT v. 1.1, California, U.S.

Root zone water quality model 2 mitigation of greenhouse gases, Quebec, Canada

Lessons in modelling and management of marine ecosystems: the Atlantis experience

Benefit Accounting of Nature-Based Solutions for Watersheds: Guide

EM Source or Collection

EU Biodiversity Action 5

InVEST

US EPA

None

US EPA

None

EM Source Document ID

260

315

317

335

343

386

389

433 ?

447

463

471

Document Author

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

The Natural Capital Project

Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y.

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

Didion, M., B. Frey, N. Rogiers, and E. Thurig

Lyon, Sarina F., Nathaniel H. Merrill, Kate K. Mulvaney, and Marisa J. Mazzotta

Tarrant S., J. Ollerton, M. L Rahman, J. Tarrant, and D. McCollin

Morrison, K. D. and C. A. Kolden

Jiang, Q., Zhiming, Q., Madramootoo, C.A., and Creze, C.

Fulton, E.A., Link, J.S., Kaplan, I.C., Savina‐Rolland, M., Johnson, P., Ainsworth, C., Horne, P., Gorton, R., Gamble, R.J., Smith, A.D. and Smith, D.C.

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

Document Year

2011

2015

2013

2014

2018

2013

2015

2018

2011

2022

Document Title

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

Carbon storage and sequestration - InVEST (v3.2.0)

Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment

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

Validating tree litter decomposition in the Yasso07 carbon model

Valuing coastal beaches and closures using benefit transfer: An application to Barnstable, Massachusetts

Grassland restoration on landfill sites in the East Midlands, United Kingdom: An evaluation of floral resources and pollinating insects

Modeling the impacts of wildfire on runoff and pollutant transport from coastal watersheds to the nearshore environment

Mitigating greenhouse gas emisssions in subsurface-drained field using RZWQM2

Lessons in modelling and management of marine ecosystems: the Atlantis experience

Benefit Accounting of Nature-Based Solutions for Watersheds: Guide

Document Status

Peer reviewed and published

Comments on Status

Published journal manuscript

Website

Published journal manuscript

Published report

Software and Access

EM ID

EM-79

EM-374

EM-379

EM-418

EM-449

EM-467

EM-685

EM-709

EM-940

EM-962

EM-985

EM-1001

EM Software Access info Exit

Not applicable

https://www.naturalcapitalproject.org/invest/

Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov

Not applicable

http://en.ilmatieteenlaitos.fi/yasso-download-and-support

Not applicable

https://coast.noaa.gov/digitalcoast/tools/opennspect.html

Not applicable

https://noaa-fisheries-integrated-toolbox.github.io/Atlantis

https://nbsbenefitsexplorer.net/tool

Contact Name

Sandra Lavorel

The Natural Capital Project

Alex Abdelnour

Susan H. Yee

Markus Didion ?

Kate K, Mulvaney

Sam Tarrant

Crystal A. Kolden

Zhiming Qi

Elizabeth Fulton

Gregg Brill

Contact Address

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

371 Serra Mall Stanford University Stanford, CA 94305-5020 USA

Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA

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

Swiss Federal Institute for Forest, Snow and Landscape Research WSL, 8903 Birmensdorf, Switzerland

Not reported

RSPB UK Headquarters, The Lodge, Sandy, Bedfordshire SG19 2DL, U.K.

Not reported

Department of Bioresource Engineering, McGill University, Sainte-Anne-de-Bellevue, QC H9X 3V9, Canada

CSIRO Wealth from Oceans Flagship, Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas. 7001, Australia

Not reported

Contact Email

sandra.lavorel@ujf-grenoble.fr

invest@naturalcapitalproject.org

abdelnouralex@gmail.com

yee.susan@epa.gov

markus.didion@wsl.ch

Mulvaney.Kate@EPA.gov

sam.tarrant@rspb.org.uk

ckolden@uidaho. Edu

zhiming.qi@mcgill.ca

beth.fulton@csiro.au

Not reported

EM Description

EM ID

EM-79

EM-374

EM-379

EM-418

EM-449

EM-467

EM-685

EM-709

EM-940

EM-962

EM-985

EM-1001

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, and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Functional divergence of flowering date was modelled using mixed models with land use and abiotic variables as fixed effects (LU + abiotic model) and year as a random effect…and modelled for each 20 x 20 m pixel using GLM estimated effects for each land use category and estimated regression coefficients with abiotic variables."

Please note: This ESML entry describes an InVEST model version that was current as of 2015. More recent versions may be available at the InVEST website. ABSTRACT: "Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST model uses maps of land use and land cover types and data on wood harvest rates, harvested product degradation rates, and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate can be used in an optional model that estimates the value of this environmental service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates." AUTHOR'S DESCRIPTION: "A fifth optional pool included in the model applies to parcels that produce harvested wood products (HWPs) such as firewood or charcoal or more long-lived products such as house timbers or furniture. Tracking carbon in this pool is useful because it represents the amount of carbon kept from the atmosphere by a given product."

ABSTRACT: "We used a new ecohydrological model, Visualizing Ecosystems for Land Management Assessments (VELMA), to analyze the effects of forest harvest on catchment carbon and nitrogen dynamics. We applied the model to a 10 ha headwater catchment in the western Oregon Cascade Range where two major disturbance events have occurred during the past 500 years: a stand-replacing fire circa 1525 and a clear-cut in 1975. Hydrological and biogeochemical data from this site and other Pacific Northwest forest ecosystems were used to calibrate the model. Model parameters were first calibrated to simulate the postfire buildup of ecosystem carbon and nitrogen stocks in plants and soil from 1525 to 1969, the year when stream flow and chemistry measurements were begun. Thereafter, the model was used to simulate old-growth (1969–1974) and postharvest (1975–2008) temporal changes in carbon and nitrogen dynamics…" AUTHOR'S DESCRIPTION: "The soil column model consists of three coupled submodels:...a soil temperature model [Cheng et al., 2010] that simulates daily soil layer temperatures from surface air temperature and snow depth by propagating the air temperature first through the snowpack and then through the ground using the analytical solution of the one-dimensional thermal diffusion equation"

ABSTRACT: "...We investigated and compared a number of existing methods for quantifying ecological integrity, shoreline protection, recreational opportunities, fisheries production, and the potential for natural products discovery from reefs. Methods were applied to mapping potential ecosystem services production around St. Croix, U.S. Virgin Islands. Overall, we found that a number of different methods produced similar predictions." AUTHOR'S DESCRIPTION: "A number of methods have been developed for linking biophysical attributes of reef condition, such as reef structural complexity, fish biomass, or species richness, to provisioning of ecosystem goods and services (Principe et al., 2012). We investigated the feasibility of using existing methods and data for mapping production of reef ecosystem goods and services. We applied these methods toward mapping potential ecosystem goods and services production in St. Croix, U.S. Virgin Islands (USVI)...For each of the five categories of ecosystem services, we chose a suite of models and indices for estimating potential production based on relative ease of implementation, consisting of well-defined parameters, and likely availability of input data, to maximize potential for transferability to other locations. For each method, we assembled the necessary reef condition and environmental data as spatial data layers for St. Croix (Table1). The coastal zone surrounding St. Croix was divided into 10x10 m grid cells, and production functions were applied to quantify ecosystem services provisioning in each grid cell...A number of indicators have been proposed for measuring reef integrity, defined as the capacity to maintain healthy function and retention of diversity (Turner et al., 2000). The Simplified Integrated Reef Health Index (SIRHI) combines four attributes of reef condition into a single index: SIRHI = ΣiGi where Gi are the grades on a scale of 1 to 5 for four key reef attributes: percent coral cover, percent macroalgal cover, herbivorous fish biomass, and commercial fish biomass (Table2; Healthy Reefs Initiative, 2010). For a number of coral reef condition attributes, including fish richness, coral richness, and reef structural complexity, available data were point surveys from field monitoring by the US Environmental Protection Agency (see Oliver et al. (2011)) or the NOAA Caribbean Coral Reef Ecosystem Monitoring Program (see Pittman et al. (2008)). To generate continuous maps of coral condition for St. Croix, we fitted regression tree models to point survey data for St. Croix and then used models to predict reef condition in non-sampled locations (Fig. 1). In general, we followed the methods of Pittman et al. (2007) which generated predictive models for fish richness using readily available benthic habitat maps and bathymetry data. Because these models rely on readily available data (benthic habitat maps and bathymetry data), the models have the potential for high transferability to other locati

ABSTRACT: "...We examined the validity of the litter decomposition and soil carbon model Yasso07 in Swiss forests based on data on observed decomposition of (i) foliage and fine root litter from sites along a climatic and altitudinal gradient and (ii) of 588 dead trees from 394 plots of the Swiss National Forest Inventory. Our objectives were to (i) examine the effect of the application of three different published Yasso07 parameter sets on simulated decay rate; (ii) analyze the accuracy of Yasso07 for reproducing observed decomposition of litter and dead wood in Swiss forests;…" AUTHOR'S DESCRIPTION: "Yasso07 (Tuomi et al., 2011a, 2009) is a litter decomposition model to calculate C stocks and stock changes in mineral soil, litter and deadwood. For estimating stocks of organic C in these pools and their temporal dynamics, Yasso07 (Y07) requires information on C inputs from dead organic matter (e.g., foliage and woody material) and climate (temperature, temperature amplitude and precipitation). DOM decomposition is modelled based on the chemical composition of the C input, size of woody parts and climate (Tuomi et al., 2011 a, b, 2009). In Y07 it is assumed that DOM consists of four compound groups with specific mass loss rates. The mass flows between compounds that are either insoluble (N), soluble in ethanol (E), in water (W) or in acid (A) and to a more stable humus compartment (H), as well as the flux out of the five pools (Fig. 1, Table A.1; Liski et al., 2009) are described by a range of parameters (Tuomi et al., 2011a, 2009)." "For this study, we used the Yasso07 release 1.0.1 (cf. project homepage). The Yasso07 Fortran source code was compiled for the Windows7 operating system. The statistical software R (R Core Team, 2013) version 3.0.1 (64 bit) was used for administrating theYasso07 simulations. The decomposition of DOM was simulated with Y07 using the parameter sets P09, P11 and P12 with the purpose of identifying a parameter set that is applicable to conditions in Switzerland. In the simulations we used the value of the maximum a posteriori point estimate (cf. Tuomi et al., 2009) derived from the distribution of parameter values for each set (Table A.1). The simulations were initialized with the C mass contained in (a) one litterbag at the start of the litterbag experiment for foliage and fine root litter (Heim and Frey, 2004) and (b) individual deadwood pieces at the time of the NFI2 for deadwood. The respective mass of C was separated into the four compound groups used by Y07. The simulations were run for the time span of the observed data. The result of the simulation was an annual estimate of the remaining fraction of the initial mass, which could then be compared with observed data."

ABSTRACT: "Each year, millions of Americans visit beaches for recreation, resulting in significant social welfare benefits and economic activity. Considering the high use of coastal beaches for recreation, closures due to bacterial contamination have the potential to greatly impact coastal visitors and communities. We used readily-available information to develop two transferable models that, together, provide estimates for the value of a beach day as well as the lost value due to a beach closure. We modeled visitation for beaches in Barnstable, Massachusetts on Cape Cod through panel regressions to predict visitation by type of day, for the season, and for lost visits when a closure was posted. We used a meta-analysis of existing studies conducted throughout the United States to estimate a consumer surplus value of a beach visit of around $22 for our study area, accounting for water quality at beaches by using past closure history. We applied this value through a benefit transfer to estimate the value of a beach day, and combined it with lost town revenue from parking to estimate losses in the event of a closure. The results indicate a high value for beaches as a public resource and show significant losses to the town when beaches are closed due to an exceedance in bacterial concentrations." AUTHOR'S DESCRIPTION: "While it might be assumed that the economic value of a beach day and the value of a lost beach day would be symmetric, they are not quite the same in our analysis. This is because the town has many fixed costs for beach management, including staff, facility maintenance, and other amenities. These fixed costs are offset by the daily parking fees charged to out-of-town visitors and the various beach stickers available for town residents. Assuming the town does not make a profit and just breaks even on beach parking fees in relation to the costs incurred to provide the services, the net economic value of a day without a closure (benefits less costs) would simply be the consumer surplus for the public. However, this amount is different than the net economic value lost due to a beach closure, which includes the lost consumer surplus as well as the lost revenue to the town. This revenue is money the town would have collected to cover costs and therefore is considered a loss (negative producer surplus). Therefore, a beach day affected by a closure is valued as a loss of consumer surplus plus lost parking revenue…" Equation 3, page 19, provides the resulting formula for the value lost from a beach closure.

ABSTRACT: "...Restored landfill sites are a significant potential reserve of semi-natural habitat, so their conservation value for supporting populations of pollinating insects was here examined by assessing whether the plant and pollinator assemblages of restored landfill sites are comparable to reference sites of existing wildlife value. Floral characteristics of the vegetation and the species richness and abundance of flower-visiting insect assemblages were compared between nine pairs of restored landfill sites and reference sites in the East Midlands of the United Kingdom, using standardized methods over two field seasons. …" AUTHOR'S DESCRIPTION: "The selection criteria for the landfill sites were greater than or equal to 50% of the site restored (to avoid undue influence from ongoing landfilling operations), greater than or equal to 0.5 ha in area and restored for greater than or equal to 4 years to allow establishment of vegetation. Comparison reference sites were the closest grassland sites of recognized nature conservation value, being designated as either Local Nature Reserves (LNRs) or Sites of Special Scientific Interest (SSSI)…All sites were surveyed three times each during the fieldwork season, in Spring, Summer, and Autumn. Paired sites were sampled on consecutive days whenever weather conditions permitted to reduce temporal bias. Standardized plant surveys were used (Dicks et al. 2002; Potts et al. 2006). Transects (100 × 2m) were centered from the approximate middle of the site and orientated using randomized bearing tables. All flowering plants were identified to species level…In the first year of study, plants in flower and flower visitors were surveyed using the same transects as for the floral resources surveys. The transect was left undisturbed for 20 minutes following the initial plant survey to allow the flower visitors to return. Each transect was surveyed at a rate of approximately 3m/minute for 30 minutes. All insects observed to touch the sexual parts of flowers were either captured using a butterfly net and transferred into individually labeled specimen jars, or directly captured into the jars. After the survey was completed, those insects that could be identified in the field were recorded and released. The flower-visitor surveys were conducted in the morning, within 1 hour of midday, and in the afternoon to sample those insects active at different times. Insects that could not be identified in the field were collected as voucher specimens for later identification. Identifications were verified using reference collections and by taxon specialists. Relatively low capture rates in the first year led to methods being altered in the second year when surveying followed a spiral pattern from a randomly determined point on the sites, at a standard pace of 10 m/minute for 30 minutes, following Nielsen and Bascompte (2007) and Kalikhman (2007). Given a 2-m wide transect, an area of approximately 600m2 was sampled in each

ABSTRACT: "Wildfire is a common disturbance that can significantly alter vegetation in watersheds and affect the rate of sediment and nutrient transport to adjacent nearshore oceanic environments. Changes in runoff resulting from heterogeneous wildfire effects are not well-understood due to both limitations in the field measurement of runoff and temporally-limited spatial data available to parameterize runoff models. We apply replicable, scalable methods for modeling wildfire impacts on sediment and nonpoint source pollutant export into the nearshore environment, and assess relationships between wildfire severity and runoff. Nonpoint source pollutants were modeled using a GIS-based empirical deterministic model parameterized with multi-year land cover data to quantify fire-induced increases in transport to the nearshore environment. Results indicate post-fire concentration increases in phosphorus by 161 percent, sediments by 350 percent and total suspended solids (TSS) by 53 percent above pre-fire years. Higher wildfire severity was associated with the greater increase in exports of pollutants and sediment to the nearshore environment, primarily resulting from the conversion of forest and shrubland to grassland. This suggests that increasing wildfire severity with climate change will increase potential negative impacts to adjacent marine ecosystems. The approach used is replicable and can be utilized to assess the effects of other types of land cover change at landscape scales. It also provides a planning and prioritization framework for management activities associated with wildfire, including suppression, thinning, and post-fire rehabilitation, allowing for quantification of potential negative impacts to the nearshore environment in coastal basins."

Abstract: "Greenhouse gas (GHG) emissions from agricultural soils are affected by various environmental factors and agronomic practices. The impact of inorganic nitrogen (N) fertilization rates and timing, and water table management practices on N2O and CO2 emissions were investigated to propose mitigation and adaptation efforts based on simulated results founded on field data. Drawing on 2012–2015 data measured on a subsurface-drained corn (Zea mays L.) field in Southern Quebec, the Root Zone Water Quality Model 2 (RZWQM2) was calibrated and validated for the estimation of N2O and CO2 emissions under free drainage (FD) and controlled drainage with sub-irrigation (CD-SI). Long term simulation from 1971 to 2000 suggested that the optimal N fertilization should be in the range of 125 to 175 kg N ha−1 to obtain higher NUE (nitrogen use efficiency, 7–14%) and lower N2O emission (8–22%), compared to 200 kg N ha−1 for corn-soybean rotation (CS). While remaining crop yields, splitting N application would potentially decrease total N2O emissions by 11.0%. Due to higher soil moisture and lower soil O2 under CD-SI, CO2 emissions declined by 6% while N2O emissions increased by 21% compared to FD. The CS system reduced CO2 and N2O emissions by 18.8% and 20.7%, respectively, when compared with continuous corn production. This study concludes that RZWQM2 model is capable of predicting GHG emissions, and GHG emissions from agriculture can be mitigated using agronomic management."

Models are key tools for integrating a wide range of system information in a common framework. Attempts to model exploited marine ecosystems can increase understanding of system dynamics; identify major processes, drivers and responses; highlight major gaps in knowledge; and provide a mechanism to ‘road test’ management strategies before implementing them in reality. The Atlantis modelling framework has been used in these roles for a decade and is regularly being modified and applied to new questions (e.g. it is being coupled to climate, biophysical and economic models to help consider climate change impacts, monitoring schemes and multiple use management). This study describes some common lessons learned from its implementation, particularly in regard to when these tools are most effective and the likely form of best practices for ecosystem-based management (EBM). Most importantly, it highlighted that no single management lever is sufficient to address the many trade-offs associated with EBM and that the mix of measures needed to successfully implement EBM will differ between systems and will change through time. Although it is doubtful that any single management action will be based solely on Atlantis, this modelling approach continues to provide important insights for managers when making natural resource management decisions.

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.

Specific Policy or Decision Context Cited

None identified

Economic value of protecting coastal beach water quality from contamination caused closures.

None identified

None

None identified

Biophysical Context

Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes

Not applicable

Basin elevation ranges from 430 m at the stream gauging station to 700 m at the southeastern ridgeline. Near stream and side slope gradients are approximately 24o and 25o to 50o, respectively. The climate is relatively mild with wet winters and dry summer. Mean annual temperature is 8.5 oC. Daily temperature extremes vary from 39 oC in the summer to -20 oC in the winter.

No additional description provided

Different forest types dominated by Norway Spruce (Picea abies), European Beech (Fagus sylvatica) and Sweet Chestnut (Castanea sativa).

Four separate beaches within the community of Barnstable

No additional description provided

Central California coast includes twelve adjacent watersheds covering 87,638 ha and rises steeply from sea level to just below 1800 m within a few km from the coast, and experiences a Mediterranean climate, with fire season typically lasting from June to November. Precipitation is dependent on elevation ranging from 65 cm near the coast to over 130 cm at ridge top. Three ecological zones occur within the study area. These zones are comprised of grasslands, coastal sage scrub, chaparral, oak forests, mixed broadleaf evergreen forest, and coniferous forests.

None

N/A

EM Scenario Drivers

No scenarios presented

Optional future scenarios for changed LULC and wood harvest

No scenarios presented

No scenarios presented ?

No scenarios presented

None

No scenarios presented

EM Relationship to Other EMs or Applications

EM ID

EM-79

EM-374

EM-379

EM-418

EM-449

EM-467

EM-685

EM-709

EM-940

EM-962

EM-985

EM-1001

Method Only, Application of Method or Model Run

Method + Application

Method Only

Method + Application

Method + Application (multiple runs exist) View EM Runs ?

Method + Application

Method + Application (multiple runs exist) View EM Runs

Method + Application

None

Method Only

New or Pre-existing EM?

New or revised model

Application of existing model

New or revised model

Application of existing model

None

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-79

EM-374

EM-379

EM-418

EM-449

EM-467

EM-685

EM-709

EM-940

EM-962

EM-985

EM-1001

Document ID for related EM

Doc-260 | Doc-269

Doc-309

Doc-13 | Doc-317

None

Doc-335

Doc-342 | Doc-344

Doc-386 | Doc-387

Doc-389

Doc-431

None

Doc-456 | Doc-459 | Doc-461

None

EM ID for related EM

EM-65 | EM-66 | EM-68 | EM-69 | EM-70 | EM-71 | EM-80 | EM-81 | EM-82 | EM-83

EM-349

EM-375 | EM-380 | EM-884 | EM-883 | EM-887

None

EM-447 | EM-448

EM-466 | EM-469 | EM-480 | EM-485

EM-682 | EM-684 | EM-683 | EM-686

EM-697

EM-938

None

EM-978 | EM-981 | EM-983 | EM-990 | EM-991

None

EM Modeling Approach

EM Relationship to Time

EM ID

EM-79

EM-374

EM-379

EM-418

EM-449

EM-467

EM-685

EM-709

EM-940

EM-962

EM-985

EM-1001

EM Temporal Extent

2007-2008

Not applicable

1969-2008

2006-2007, 2010

1993-2013

July 1, 2011 to June 31, 2016

2007-2008

2005-2008

2012-2015

Not applicable

EM Time Dependence

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

EM Time Reference (Future/Past)

Not applicable

future time

Not applicable

future time

Not applicable

past time

Not applicable

EM Time Continuity

Not applicable

discrete

Not applicable

discrete

Not applicable

discrete

Not applicable

EM Temporal Grain Size Value

Not applicable

EM Temporal Grain Size Unit

Not applicable

Year

Day

Not applicable

Year

Day

Not applicable

Year

Not applicable

EM Spatial Extent

EM ID

EM-79

EM-374

EM-379

EM-418

EM-449

EM-467

EM-685

EM-709

EM-940

EM-962

EM-985

EM-1001

Bounding Type

Physiographic or Ecological

Not applicable

Watershed/Catchment/HUC

Physiographic or ecological

Geopolitical

Physiographic or ecological

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

Watershed/Catchment/HUC

Point or points

Not applicable

Spatial Extent Name

Central French Alps

Not applicable

H. J. Andrews LTER WS10

Coastal zone surrounding St. Croix

Switzerland

Barnstable beaches (Craigville Beach, Kalmus Beach, Keyes Memorial Beach, and Veteran’s Park Beach)

East Midlands

Big Sur region, central California

Corn field

Not applicable

Spatial Extent Area (Magnitude)

10-100 km^2

Not applicable

10-100 ha

100-1000 km^2

10,000-100,000 km^2

10-100 ha

1000-10,000 km^2.

100-1000 km^2

1-10 ha

Not applicable

Spatial Distribution of Computations

EM ID

EM-79

EM-374

EM-379

EM-418

EM-449

EM-467

EM-685

EM-709

EM-940

EM-962

EM-985

EM-1001

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)

Not applicable

spatially lumped (in all cases)

Spatial Grain Type

area, for pixel or radial feature

volume, for 3-D feature

area, for pixel or radial feature

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

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

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

Not applicable

Spatial Grain Size

20 m x 20 m

application specific

30 m x 30 m surface pixel and 2-m depth soil column

10 m x 10 m

5 sites

by beach site

multiple unrelated locations

irregular

Not applicable

EM Structure and Computation Approach

EM ID

EM-79

EM-374

EM-379

EM-418

EM-449

EM-467

EM-685

EM-709

EM-940

EM-962

EM-985

EM-1001

EM Computational Approach

Analytic

Numeric

Analytic

Numeric

Analytic

EM Determinism

deterministic

stochastic

deterministic

None

deterministic

Statistical Estimation of EM

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

Bayesian statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

None

Conventional (frequentist) statistics

Bayesian statistics

Model Checking Procedures Used

EM ID

EM-79

EM-374

EM-379

EM-418

EM-449

EM-467

EM-685

EM-709

EM-940

EM-962

EM-985

EM-1001

Model Calibration Reported?

Not applicable

Yes

Not applicable

None

Not applicable

Model Goodness of Fit Reported?

Yes

Not applicable

None

Not applicable

Goodness of Fit (metric| value | unit)

R squared | 35.5 | Not applicable

None

Model Operational Validation Reported?

Not applicable

Yes

Not applicable

None

Not applicable

Unclear

Model Uncertainty Analysis Reported?

Not applicable

None

Not applicable

Model Sensitivity Analysis Reported?

Not applicable

No ?

Not applicable

None

Not applicable

Model Sensitivity Analysis Include Interactions?

Not applicable

None

Not applicable

EM Locations, Environments, Ecology

Location of EM Application

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

EM-79	EM-374	EM-379	EM-418	EM-449	EM-467	EM-685	EM-709	EM-940	EM-962	EM-985	EM-1001
Europe France	None	North America United States Oregon	None	None	Europe Switzerland	North America United States Massachusetts	Europe United Kingdom	North America United States California	North America Canada	None	None

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

EM-79	EM-374	EM-379	EM-418	EM-449	EM-467	EM-685	EM-709	EM-940	EM-962	EM-985	EM-1001
None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Eastern Caribbean	None	Temperate North Atlantic Northwest Atlantic Cold Temperate Northwest Atlantic Virginian	None	Temperate North Pacific Northeast Pacific Cold Temperate Northeast Pacific Northern California	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-79

EM-374

EM-379

EM-418

EM-449

EM-467

EM-685

EM-709

EM-940

EM-962

EM-985

EM-1001

Centroid Latitude

45.05

-9999

44.25

17.73

46.82

41.64

52.22

35.96

45.32

Not applicable

Centroid Longitude

6.4

-9999

-122.33

-64.77

8.23

-70.29

-0.91

-121.43

74.17

Not applicable

Centroid Datum

WGS84

Not applicable

WGS84

None provided

Not applicable

Centroid Coordinates Status

Provided

Not applicable

Provided

Estimated

Provided

Not applicable

Environments and Scales Modeled

EM ID

EM-79

EM-374

EM-379

EM-418

EM-449

EM-467

EM-685

EM-709

EM-940

EM-962

EM-985

EM-1001

EM Environmental Sub-Class

Agroecosystems | Grasslands

Not applicable

Forests

Near Coastal Marine and Estuarine

Forests

Near Coastal Marine and Estuarine

Created Greenspace | Grasslands

Rivers and Streams | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified)

None

Specific Environment Type

Subalpine terraces, grasslands, and meadows

Terrestrial environments, but not specified for methods

400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata).

Coral reefs

forests

Saltwater beach

restored landfills and grasslands

Coastal watersheds

None

Multiple

None

EM Ecological Scale

Ecological scale is coarser than that of the Environmental Sub-class

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

None

Ecological scale corresponds to the Environmental Sub-class

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID

EM-79

EM-374

EM-379

EM-418

EM-449

EM-467

EM-685

EM-709

EM-940

EM-962

EM-985

EM-1001

EM Organismal Scale

Community

Not applicable

Guild or Assemblage

Not applicable

Community

Not applicable

Individual or population, within a species

Not applicable

None

Not applicable

Taxonomic level and name of organisms or groups identified

EM-79	EM-374	EM-379	EM-418	EM-449	EM-467	EM-685	EM-709	EM-940	EM-962	EM-985	EM-1001
None Available	None Available	None Available	other snapper Family grouper parrotfish surgeonfish	None Available	None Available	None Available	Genus Calliopum Lasioglossum Species Apis mellifera Bombus lapidarius Oedemera nobilis Episyrphus balteatus Bombus terrestris/lucorum Bombus pascuorum	None Available	None Available	None Available	None Available

EnviroAtlas URL

EM-79	EM-374	EM-379	EM-418	EM-449	EM-467	EM-685	EM-709	EM-940	EM-962	EM-985	EM-1001
None Available	GAP Ecological Systems, Carbon Storage by Tree Biomass, Hectares of cotton crops	Average Annual Precipitation	None Available	National Hydrography Dataset Plus (NHD PlusV2)	GAP Ecological Systems, Average Annual Precipitation, Carbon storage by tree biomass (kg/m2), Carbon Storage by Tree Biomass	Dasymetric Allocation of Population	GAP Ecological Systems	Average Annual Precipitation	None Available	Big game hunting recreation demand, Percent GAP Status 1 & 2, Total Employment	GAP Ecological Systems, Average Annual Precipitation, U.S. EPA (Omernik) ecoregions, Residential Density

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-79	EM-374	EM-379	EM-418	EM-449	EM-467	EM-685	EM-709	EM-940	EM-962	EM-985	EM-1001
None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	None	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	[Ecosystem] Regulation & Maintenance Mediation of flows Liquid flows Flood protection	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Soil formation and composition Decomposition and fixing processes	[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	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Pollination and seed dispersal	[Ecosystem] Regulation & Maintenance Mediation of waste, toxics and other nuisances Mediation by ecosystems Filtration/sequestration/storage/accumulation by ecosystems	None	[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] 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

<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-79	EM-374	EM-379	EM-418	EM-449	EM-467	EM-685	EM-709	EM-940	EM-962	EM-985	EM-1001
None	None	None	None	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Extreme Event Indicator	None	Near Coastal Marine/Estuarine (113) Water (3) Liquid water Quality Indicator	None	None	None	None	None

US EPA

EcoService Models Library (ESML)

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EM Locations, Environments, Ecology

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

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

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Taxonomic level and name of organisms or groups identified

EnviroAtlas URL

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

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

<a target="_blank" rel="noopener noreferrer" href="https://www.epa.gov/eco-research/national-ecosystem-services-classification-system-nescs-plus">National Ecosystem Services Classification System (NESCS) Plus</a>

(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)