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-59
EM-91
EM-97
EM-102
EM-208
EM-380
EM-590
EM-598
EM-667
EM-991
EM-1004
EM-1022

Add EM to Compare:

EM Identity and Description

EM Identification

EM ID

EM-59

EM-91

EM-97

EM-102

EM-208

EM-380

EM-590

EM-598

EM-667

EM-991

EM-1004

EM-1022

EM Short Name

EnviroAtlas-Air pollutant removal

RHyME2, Upper Mississippi River basin, USA

AnnAGNPS, Kaskaskia River watershed, IL, USA

Fish species habitat value, Tampa Bay, FL, USA

FORCLIM v2.9, Santiam watershed, OR, USA

VELMA plant-soil, Oregon, USA

Fish species richness, Puerto Rico, USA

DeNitrification-DeComposition simulation (DNDC) v.8.9 flux simulation, Ireland

Alewife derived nutrients, Connecticut, USA

Atlantis ecosystem harvest submodel

GI toolkit users guide

NU-WRF, USA

EM Full Name

US EPA EnviroAtlas - Pollutants (air) removed annually by tree cover; Example is shown for Durham NC and vicinity, USA

RHyME2 (Regional Hydrologic Modeling for Environmental Evaluation), Upper Mississippi River basin, USA

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

Fish species habitat value, Tampa Bay, FL, USA

FORCLIM (FORests in a changing CLIMate) v2.9, Santiam watershed, OR, USA

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

Fish species richness, Puerto Rico, USA

DeNitrification-DeComposition simulation of N2O flux Ireland

Alewife derived nutrients in stream food web, Connecticut, USA

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

Green Infrastructure valuation toolkit users guide

Nasa Unified Weather Research and Forcasing model

EM Source or Collection

US EPA | EnviroAtlas | i-Tree ?

US EPA

None

EM Source Document ID

223

123

137

187

23 ?

317

355

358

384

463

474

484

Document Author

US EPA Office of Research and Development - National Exposure Research Laboratory

Tran, L. T., O’Neill, R. V., Smith, E. R., Bruins, R. J. F. and Harden, C.

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

Fulford, R., Yoskowitz, D., Russell, M., Dantin, D., and Rogers, J.

Busing, R. T., Solomon, A. M., McKane, R. B. and Burdick, C. A.

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

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

Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M.

Walters, A. W., R. T. Barnes, and D. M. Post

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.

Genecon LLP.

Peters-Lidard, Christa et al. Sujay V. Kumar, , Jossy P. Jacob b , Thomas Clune f , Wei-Kuo Tao g , Mian Chin h , Arthur Hou I , Jonathan L. Case j , Dongchul Kim k , Kyu-Myong Kim l , William Lau m, Yuqiong Liu n , Jainn Shi o , David Starr g , Qian Tan h , Zhining Tao k , Benjamin F. Zaitchik p , Bradley Zavodsky q , Sara Q. Zhang r , Milija Zupanski

Document Year

2013

2011

2016

2007

2013

2007

2010

2009

2011

2010

2015

Document Title

EnviroAtlas - Featured Community

Application of hierarchy theory to cross-scale hydrologic modeling of nutrient loads

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

Habitat and recreational fishing opportunity in Tampa Bay: Linking ecological and ecosystem services to human beneficiaries

Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model

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

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

Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture

Anadromous alewives (Alosa pseudoharengus) contribute marine-derived nutrients to coastal stream food webs

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

Building natural value for sustainable economic development The green infrastructure valuation toolkit user guide

Integrated modeling of aerosol, cloud, precipitation and land processes at satellite-resolved scales

Document Status

Peer reviewed and published

Comments on Status

Published on US EPA EnviroAtlas website

Published journal manuscript

Published report

None

Software and Access

EM ID

EM-59

EM-91

EM-97

EM-102

EM-208

EM-380

EM-590

EM-598

EM-667

EM-991

EM-1004

EM-1022

EM Software Access info Exit

https://www.epa.gov/enviroatlas

Not applicable

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

Not applicable

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

Not applicable

http://www.dndc.sr.unh.edu

Not applicable

https://research.csiro.au/atlantis/home/links/

https://www.merseyforest.org.uk/services/gi-val/

None

Contact Name

EnviroAtlas Team

Liem Tran

Yongping Yuan

Richard Fulford

Richard T. Busing

Alex Abdelnour

Simon Pittman

M. Abdalla

Annika W. Walters

Elizabeth Fulton

The Mercey Forest

Christa

Contact Address

Not reported

Department of Geography, University of Tennessee, 1000 Phillip Fulmer Way, Knoxville, TN 37996-0925, USA

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

USEPA Gulf Ecology Division, Gulf Breeze, FL 32561

U.S. Geological Survey, 200 SW 35th Street, Corvallis, Oregon 97333 USA

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

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

Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland

Dept. of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USA

Division of Marine and Atmospheric Research, GPO Box 1538, Hobart, Tas.

Moss Ln, Woolston, Warrington WA3 6QX, United Kingdom

Hydrospheric and Biospheric Sciences Division, Code 610HB, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

Contact Email

enviroatlas@epa.gov

ltran1@utk.edu

yuan.yongping@epa.gov

Fulford.Richard@epa.gov

rtbusing@aol.com

abdelnouralex@gmail.com

simon.pittman@noaa.gov

abdallm@tcd.ie

annika.walters@yale.edu

beth.fulton@csiro.au

mail@merseyforest.org.uk

christa.peters@nasa.gov

EM Description

EM ID

EM-59

EM-91

EM-97

EM-102

EM-208

EM-380

EM-590

EM-598

EM-667

EM-991

EM-1004

EM-1022

Summary Description

The Air Pollutant Removal model has been used to create coverages for several US communities. An example for Durham, NC is shown in this entry. ABSTRACT: "This EnviroAtlas dataset presents environmental benefits of the urban forest in 193 block groups in Durham, North Carolina. ... pollution removal ... are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas." METADATA: The maps, estimate and illustrate the variation in the amount of six airborne pollutants, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM10), and particulate matter (PM2.5), removed by trees. PM10 is for particulate matter greater than 2.5 microns and less than 10 microns. DATA FACT SHEET: "The data for this map are based on the land cover derived for each EnviroAtlas community and the pollution removal models in i-Tree, a toolkit developed by the USDA Forest Service. The land cover data were created from aerial photography through remote sensing methods; tree cover was then summarized as the percentage of each census block group. The i-Tree pollution removal module uses the tree cover data by block group, the closest hourly meteorological monitoring data for the community, and the closest pollution monitoring data... hourly estimates of pollution removal by trees were combined with atmospheric data to estimate hourly percent air quality improvement due to pollution removal for each pollutant."

ABSTRACT: "We describe a framework called Regional Hydrologic Modeling for Environmental Evaluation (RHyME2) for hydrologic modeling across scales. Rooted from hierarchy theory, RHyME2 acknowledges the rate-based hierarchical structure of hydrological systems. Operationally, hierarchical constraints are accounted for and explicitly described in models put together into RHyME2. We illustrate RHyME2with a two-module model to quantify annual nutrient loads in stream networks and watersheds at regional and subregional levels. High values of R2 (>0.95) and the Nash–Sutcliffe model efficiency coefficient (>0.85) and a systematic connection between the two modules show that the hierarchy theory-based RHyME2 framework can be used effectively for developing and connecting hydrologic models to analyze the dynamics of hydrologic systems." Two EMs will be entered in EPF-Library: 1. Regional scale module (Upper Mississippi River Basin) - this entry 2. Subregional scale module (St. Croix River Basin)

AUTHORS' DESCRIPTION: "AnnAGNPS is an advanced simulation model developed by the USDA-ARS and Natural Resource Conservation Services (NRCS) to help evaluate watershed response to agricultural management practices. It is a continuous simulation, daily time step, pollutant loading model designed to simulate water, sediment and chemical movement from agricultural watersheds.p. 198"

ABSTRACT: "Estimating value of estuarine habitat to human beneficiaries requires that we understand how habitat alteration impacts function through both production and delivery of ecosystem goods and services (EGS). Here we expand on the habitat valuation technique of Bell (1997) with an estimate of recreational angler willingness-to-pay combined with estimates of angler effort, fish population size, and fish and angler distribution. Results suggest species-specific fishery value is impacted by angler interest and stock status, as the most targeted fish (spotted seatrout) did not have the highest specific value (fish−1). Reduced population size and higher size at capture resulted in higher specific value for common snook. Habitat value estimated from recreational fishing value and fish-angler distributions supported an association between seagrass and habitat value, yet this relationship was also impacted by distance to access points. This analysis does not provide complete valuation of habitat as it considers only one service (fishing), but demonstrates a methodology to consider functional equivalency of all habitat features as a part of a habitat mosaic rather than in isolation, as well as how to consider both EGS production and delivery to humans (e.g., anglers) in any habitat valuation, which are critical for a transition to ecosystem management."

ABSTRACT: "The FORCLIM model of forest dynamics was tested against field survey data for its ability to simulate basal area and composition of old forests across broad climatic gradients in western Oregon, USA. The model was also tested for its ability to capture successional trends in ecoregions of the west Cascade Range. It was then applied to simulate present and future (1990-2050) forest landscape dynamics of a watershed in the west Cascades. Various regimes of climate change and harvesting in the watershed were considered in the landscape application." AUTHOR'S DESCRIPTION: "Effects of different management histories on the landscape were incorporated using the land management (conservation, plan, or development trend) and forest age categories…the plan trend was an intermediate alternative, representing the continuation of current policies and trends, whereas the conservation and development trends were possible alternatives…Non-forested areas were given a forest age of zero; forested areas were assigned to one of eight forest age classes: >0-20 yr, 21-40 yr, 41-60 yr, 61-80 yr, 81-200 yr, 201-400 yr, and >600 yr in 1990…two climate change scenarios were used, representing lower and upper extremes projected by a set of global climate models: (1) minor warming with drier summers, and (2) major warming with wetter conditions…For the first scenario, temperature was increased by 0.5°C in 2025 and by 1.5°C in 2045. Precipitation from October to March was increased 2% in 2025 and decreased 2% in 2045. Precipitation from April to September was decreased 4% in 2025 and 7% in 2045. For the second scenario, temperature was by increased 2.6°C in 2025 and by 3.2°C in 2045. Precipitation from October to March was increased 18% in 2025 and 22% in 2045. Precipitation from April to September was increased 14% in 2025 and 9% in 2045.

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 plant-soil model (Figure (A3)) that simulates ecosystem carbon storage and the cycling of C and N between a plant biomass layer and the active soil pools. Specifically, the plant-soil model simulates the interaction among aboveground plant biomass, soil organic carbon (SOC), soil nitrogen including dissolved nitrate (NO3), ammonium (NH4), and organic nitrogen, as well as DOC (equations (A7)–(A12)). Daily atmospheric inputs of wet and dry nitrogen deposition are accounted for in the ammonium pool of the shallow soil layer (equation (A13)). Uptake of ammonium and nitrate by plants is modeled using a Type II Michaelis-Menten function (equation (A14)). Loss of plant biomass is simulated through a density-dependent mortality. The mortality rate and the nitrogen uptake rate mimic the exponential increase in biomass mortality and the accelerated growth rate, respectively, as plants go through succession and reach equilibrium (equations (A14)–(A18)). Vertical transport of nutrients from one layer to another in a soil column is a function of water drainage (equations (A19)–(A22)). Decomposition of SOC follows first-order kinetics controlled by soil temperature and moisture content as described in the terrestrial ecosystem model (TEM) of Raich et al. [1991] (equations (A23)–(A26)). Nitrification (equations (A27)–(A30)) and denitrification (equations (A31)–(A34)) were simulated using the equations from the generalized model of N2 and N2O production of Parton et al. [1996, 2001] and Del Grosso et al. [2000]. [12] The soil column model is placed within a catchment framework to create a spatially distributed model applicable to watersheds and landscapes. Adjacent soil columns interact with each other through the downslope lateral transport of water and nutrients (Figure (A1)). Surface and subsurface lateral flow are routed using a multiple flow direction method [Freeman, 1991; Quinn et al., 1991]. As with vertical drainage of soil water, lateral subsurface downslope flow i

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."

Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC.

ABSTRACT: "Diadromous fish are an important link between marine and freshwater food webs. Pacific salmon (Oncorhynchus spp.) strongly impact nutrient dynamics in inland waters and anadromous alewife (Alosa pseudoharengus) may play a similar ecological role along the Atlantic coast. The annual spawning migration of anadromous alewife contributes, on average, 1050 g of nitrogen and 120 g of phosphorus to Bride Brook, Connecticut, USA, through excretion and mortality each year... There was no significant effect of this nutrient influx on water chemistry, leaf decomposition, or periphyton accrual. Dam removal and fish ladder construction will allow anadromous alewife to regain access to historical freshwater spawning habitats, potentially impacting food web dynamics and nutrient cycling in coastal freshwater systems."

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.

[The toolkit provides a very helpful introduction to the evidence demonstrating the benefits of green infrastructure interventions. It offers a structured argument that speaks the language of regeneration and economic developments. The 11 economic benefits structure provides a relatively simple high level means of presenting and communicating the benefits of green infrastructure projects in economic contexts, although it also brings some risks of double-counting (see Limitations below). The toolkit provides a structured approach to value green infrastructure benefits in monetary, quantitative and qualitative terms, with equal weight being applied to each of these three ways to present existing evidence. It can add value to and inform the decision-making process, particularly when used at an early stage to get broad brush figures and weigh pros and cons.The toolkit relies on current state-of-the-art evidence and valuation techniques for green infrastructure benefits. However, the toolkit also highlights the need for considerable improvement and expansion of the evidence base to enable future iterations to provide improved valuations. The toolkit helps make green infrastructure benefits ‘visible’ to potential funders. The inclusion of environmental benefits in cost benefit analysis is currently very difficult, often requiring professional assistance. Such assistance is frequently beyond the means of many groups seeking project funding. The toolkit is aimed at filling this gap, providing a means of scoping out the indicative benefits of green infrastructure using tools and approaches accessible to many projects and groups. However, whilst the toolkit provides a means of undertaking a broad Value for Money assessment, it must but emphasised that this is only indicative and cannot replace more rigorous formal project appraisal techniques.]

ABSTRACT: "With support from NASA's Modeling and Analysis Program, we have recently developed the NASA Unified-Weather Research and Forecasting model (NU-WRF). NU-WRF is an observation-driven integrated modeling system that represents aerosol, cloud, precipitation and land processes at satelliteresolved scales. “Satellite-resolved” scales (roughly 1e25 km), bridge the continuum between local (microscale), regional (mesoscale) and global (synoptic) processes. NU-WRF is a superset of the National Center for Atmospheric Research (NCAR) Advanced Research WRF (ARW) dynamical core model, achieved by fully integrating the GSFC Land Information System (LIS, already coupled to WRF), the WRF/Chem enabled version of the Goddard Chemistry Aerosols Radiation Transport (GOCART) model, the Goddard Satellite Data Simulation Unit (G-SDSU), and custom boundary/initial condition preprocessors into a single software release, with source code available by agreement with NASA/GSFC. Full coupling between aerosol, cloud, precipitation and land processes is critical for predicting local and regional water and energy cycles "

Specific Policy or Decision Context Cited

None identified

Not reported

None identifed

None identified

None provided

climate change

None identified

None

Biophysical Context

No additional description provided

Upper Mississipi River basin, elevation 142-194m,

shallow bay (mean 3.7m), transition zone between warm temperate and tropical biogeographic provinces. Highly urbanized watershed

No additional description provided

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.

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

Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C

Alewife spawning runs typically occur Mid March - May.

N/A

None

EM Scenario Drivers

No scenarios presented

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

No scenarios presented

Land Management (3); Climate Change (3)

Forest management (harvest/no harvest)

No scenarios presented

fertilization

No scenarios presented

N/A

None

EM Relationship to Other EMs or Applications

EM ID

EM-59

EM-91

EM-97

EM-102

EM-208

EM-380

EM-590

EM-598

EM-667

EM-991

EM-1004

EM-1022

Method Only, Application of Method or Model Run

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method + Application (multiple runs exist) View EM Runs

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

Method + Application (multiple runs exist) View EM Runs

Method + Application

Method + Application (multiple runs exist) View EM Runs

Method Only

None

New or Pre-existing EM?

Application of existing model

New or revised model

Application of existing model

New or revised model

Application of existing model

New or revised model

Application of existing model

New or revised model

None

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

EM ID

EM-59

EM-91

EM-97

EM-102

EM-208

EM-380

EM-590

EM-598

EM-667

EM-991

EM-1004

EM-1022

Document ID for related EM

Doc-345

Doc-123

Doc-142

None

Doc-22 | Doc-23 ?

Doc-13 | Doc-317

Doc-355

None

Doc-383

Doc-456 | Doc-459 | Doc-461 | Doc-463

None

EM ID for related EM

None

EM-146 | EM-186 | EM-224

EM-375 | EM-379 | EM-884 | EM-883 | EM-887

EM-698 | EM-699

EM-593

EM-661 | EM-665 | EM-666 | EM-672 | EM-674 | EM-673

EM-978 | EM-981 | EM-983 | EM-985 | EM-990

None

EM Modeling Approach

EM Relationship to Time

EM ID

EM-59

EM-91

EM-97

EM-102

EM-208

EM-380

EM-590

EM-598

EM-667

EM-991

EM-1004

EM-1022

EM Temporal Extent

2008-2010

1987-1997

1980-2006

2006-2011

1990-2050

1969-2008

2000-2005

1961-1990

1979-2009

Not applicable

2015

EM Time Dependence

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

time-stationary

time-dependent

Not applicable

EM Time Reference (Future/Past)

future time

Not applicable

future time

Not applicable

both

Not applicable

EM Time Continuity

discrete

Not applicable

discrete

Not applicable

discrete

Not applicable

continuous

Not applicable

EM Temporal Grain Size Value

Not applicable

EM Temporal Grain Size Unit

Hour

Not applicable

Year

Day

Not applicable

Day

Not applicable

EM Spatial Extent

EM ID

EM-59

EM-91

EM-97

EM-102

EM-208

EM-380

EM-590

EM-598

EM-667

EM-991

EM-1004

EM-1022

Bounding Type

Geopolitical

Watershed/Catchment/HUC

Physiographic or Ecological

Watershed/Catchment/HUC

Physiographic or ecological

Point or points

Watershed/Catchment/HUC

Not applicable

No location (no locational reference given)

Spatial Extent Name

Durham NC and vicinity

Upper Mississippi River basin; St. Croix River Watershed

East Fork Kaskaskia River watershed basin

Tampa Bay

South Santiam watershed

H. J. Andrews LTER WS10

SW Puerto Rico,

Oak Park Research centre

Bride Brook

Not applicable

None

Spatial Extent Area (Magnitude)

100-1000 km^2

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

100-1000 km^2

1000-10,000 km^2.

100-1000 km^2

10-100 ha

100-1000 km^2

1-10 ha

Not applicable

None

Spatial Distribution of Computations

EM ID

EM-59

EM-91

EM-97

EM-102

EM-208

EM-380

EM-590

EM-598

EM-667

EM-991

EM-1004

EM-1022

EM Spatial Distribution

spatially distributed (in at least some cases) ?

spatially distributed (in at least some cases)

spatially lumped (in all cases)

Not applicable

spatially distributed (in at least some cases)

other or unclear (comment)

Spatial Grain Type

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

NHDplus v1

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

area, for pixel or radial feature

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

area, for pixel or radial feature

Not applicable

area, for pixel or radial feature

Not applicable

Spatial Grain Size

irregular

NHDplus v1

1 km^2

0.08 ha

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

not reported

Not applicable

Not reported

Not applicable

EM Structure and Computation Approach

EM ID

EM-59

EM-91

EM-97

EM-102

EM-208

EM-380

EM-590

EM-598

EM-667

EM-991

EM-1004

EM-1022

EM Computational Approach

Numeric

Analytic

Numeric

Analytic

Numeric

Analytic

Numeric

EM Determinism

deterministic

None

Statistical Estimation of EM

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

Conventional (frequentist) statistics

None

Model Checking Procedures Used

EM ID

EM-59

EM-91

EM-97

EM-102

EM-208

EM-380

EM-590

EM-598

EM-667

EM-991

EM-1004

EM-1022

Model Calibration Reported?

Unclear

Yes

Yes ?

Not applicable

None

Model Goodness of Fit Reported?

Yes

Yes ?

Not applicable

None

Goodness of Fit (metric| value | unit)

None

R^2 | 0.923 | none
Mean Square Error | 25.1 | %

None

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

r squared | 00.0 | unitless

None

Model Operational Validation Reported?

Yes

Not applicable

None

Model Uncertainty Analysis Reported?

Yes

Not applicable

None

Model Sensitivity Analysis Reported?

No ?

Unclear

Yes

Not applicable

None

Model Sensitivity Analysis Include Interactions?

Not applicable

N/A

Not applicable

None

EM Locations, Environments, Ecology

Location of EM Application

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

EM-59	EM-91	EM-97	EM-102	EM-208	EM-380	EM-590	EM-598	EM-667	EM-991	EM-1004	EM-1022
North America United States North Carolina	North America United States Indiana Illinois Missouri South Dakota Minnesota Idaho Wisconsin	North America United States Illinois	North America United States Florida	North America United States Oregon	North America United States Oregon	None	Europe Ireland	North America United States Connecticut	None	None	None

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

EM-59	EM-91	EM-97	EM-102	EM-208	EM-380	EM-590	EM-598	EM-667	EM-991	EM-1004	EM-1022
None	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Floridian	None	None	Tropical Atlantic West Tropical Atlantic Tropical Northwestern Atlantic Greater Antilles	None	Temperate North Atlantic Northwest Atlantic Cold Temperate Northwest Atlantic Virginian	None	None	None

Centroid Lat/Long (Decimal Degree)

EM ID

EM-59

EM-91

EM-97

EM-102

EM-208

EM-380

EM-590

EM-598

EM-667

EM-991

EM-1004

EM-1022

Centroid Latitude

35.99

42.5

38.69

27.74

44.24

44.25

17.9

52.86

41.32

Not applicable

None

Centroid Longitude

-78.96

-90.63

-89.1

-82.57

-122.24

-122.33

67.11

6.54

-72.24

Not applicable

None

Centroid Datum

None provided

WGS84

None provided

WGS84

None provided

WGS84

Not applicable

None

Centroid Coordinates Status

Estimated

Provided

Estimated

Provided

Estimated

Provided

Not applicable

None

Environments and Scales Modeled

EM ID

EM-59

EM-91

EM-97

EM-102

EM-208

EM-380

EM-590

EM-598

EM-667

EM-991

EM-1004

EM-1022

EM Environmental Sub-Class

Created Greenspace | Atmosphere

Agroecosystems

Near Coastal Marine and Estuarine

Forests

Rivers and Streams | Ground Water | Forests

Near Coastal Marine and Estuarine

Agroecosystems

Rivers and Streams

Not applicable

Atmosphere

Specific Environment Type

Urban and vicinity

None

Row crop agriculture in Kaskaskia river basin

Habitat Zones (Low, Med, High, Optimal) around seagrass and emergent marsh

primarily Conifer Forest

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

shallow coral reefs

farm pasture

Coastal stream

Multiple

Regional wealther

EM Ecological Scale

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

Ecosystem

Ecological scale corresponds to the Environmental Sub-class

Zone within an ecosystem

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

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

Scale and taxa of organisms modeled

Scale of differentiation of organisms modeled

EM ID

EM-59

EM-91

EM-97

EM-102

EM-208

EM-380

EM-590

EM-598

EM-667

EM-991

EM-1004

EM-1022

EM Organismal Scale

Not applicable

Species

Not applicable

Guild or Assemblage

Not applicable

Individual or population, within a species

Not applicable

Taxonomic level and name of organisms or groups identified

EM-59	EM-91	EM-97	EM-102	EM-208	EM-380	EM-590	EM-598	EM-667	EM-991	EM-1004	EM-1022
None Available	None Available	None Available	Species spotted seatrout (Cynoscion nebulosus)	Species Pinus ponderosa Abies amabilis Tsuga mertensiana Thuja plicata Abies procera Abies grandis Chamaecyparis nootkatensis Tsuga heterophylla Other (trees) Pseudotsuga menziesii	None Available	other (fish class)	None Available	Species alewife Alosa pseudoharengus	None Available	None Available	None Available

EnviroAtlas URL

EM-59	EM-91	EM-97	EM-102	EM-208	EM-380	EM-590	EM-598	EM-667	EM-991	EM-1004	EM-1022
Average Annual Precipitation	The National Hydrography Dataset (NHD), National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition, The Watershed Boundary Dataset (WBD)	National Hydrography Dataset Plus (NHD PlusV2), Average Annual Precipitation, Agricultural water use (million gallons/day), Total Annual Nitrogen Deposition, The Watershed Boundary Dataset (WBD)	Big game hunting recreation demand	GAP Ecological Systems, Average Annual Precipitation	GAP Ecological Systems, Average Annual Precipitation, Total Annual Reduced Nitrogen Deposition, Total Annual Nitrogen Deposition	None Available	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	The National Hydrography Dataset (NHD)	Big game hunting recreation demand	Dasymetric Allocation of Population, GAP Ecological Systems, The National Hydrography Dataset (NHD), Green Space per Capita, Ecosystem Markets: Imperiled Species and Habitats, Percent GAP Status 1 & 2, Acres of Land Enrolled in the Conservation Reserve Program (CRP), Total Employment, Employment Rate	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-59	EM-91	EM-97	EM-102	EM-208	EM-380	EM-590	EM-598	EM-667	EM-991	EM-1004	EM-1022
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Micro and regional climate regulation Mediation of waste, toxics and other nuisances Mediation by biota Filtration/sequestration/storage/accumulation by micro-organisms, algae, plants, and animals	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters	[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Water conditions Chemical condition of freshwaters	[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 Materials Biomass Fibres and other materials from plants, algae and animals for direct use or processing [Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Lifecycle maintenance, habitat and gene pool protection Maintaining nursery populations and habitats	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 Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	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	None	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-59	EM-91	EM-97	EM-102	EM-208	EM-380	EM-590	EM-598	EM-667	EM-991	EM-1004	EM-1022
Atmosphere Atmosphere (1) Air Quality Indicator Urban/Suburban (27) Composite (8) Presence of environmental class/subclass Quality Indicator	None	Rivers and Streams (111) Water (3) Liquid water Quality Indicator	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	None	None	Near Coastal Marine/Estuarine (113) Fauna (4) Fish Stock Indicator	Agroecosystems (22) Flora (5) Green biomass Stock Indicator	Rivers and Streams (111) Composite (8) Presence of environmental class/subclass Quality Indicator	None	None	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:

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

Comment:

EM Modeling Approach

Comment:

Comment:

Comment:

Comment:

EM Locations, Environments, Ecology

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

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

Centroid Lat/Long (Decimal Degree)

Scale of differentiation of organisms modeled

Taxonomic level and name of organisms or groups identified

EnviroAtlas URL

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

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

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

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