EcoService Models Library (ESML)
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Which comparison is best for me?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
Hide CriteriaEM Identity and Description
EM Identification
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EM ID
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EM-12 
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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EM Short Name
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Evoland v3.5 (bounded growth), Eugene, OR, USA | Litter biomass production, Central French Alps | Cultural ES and plant traits, Central French Alps | InVEST nutrient retention, Hood Canal, WA, USA | WaSSI, Conterminous USA | Yasso07 - Land use SOC dynamics, China |
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EM Full Name
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Evoland v3.5 (with urban growth boundaries), Eugene, OR, USA | Litter biomass production, Central French Alps | Cultural ecosystem service estimated from plant functional traits, Central French Alps | InVEST (Integrated Valuation of Envl. Services and Tradeoffs) nutrient retention, Hood Canal, WA, USA | Water Supply Stress Index, Conterminous USA | Yasso07 - Land use dynamics of Soil Organic Carbon in the Loess Plateau, China |
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EM Source or Collection
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Envision | EU Biodiversity Action 5 | EU Biodiversity Action 5 | InVEST |
USDA Forest Service ?Comment:While the user guide on which model entry is based has not been peer reviewed, several peer reviewed journal articles describing this USA HUC8 version of WaSSI have been published. |
None |
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EM Source Document ID
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47 ?Comment:Doc 183 is a secondary source for the Evoland model. |
260 | 260 | 205 | 341 | 344 |
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Document Author
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Guzy, M. R., Smith, C. L. , Bolte, J. P., Hulse, D. W. and Gregory, S. V. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Lavorel, S., Grigulis, K., Lamarque, P., Colace, M-P, Garden, D., Girel, J., Pellet, G., and Douzet, R. | Toft, J. E., Burke, J. L., Carey, M. P., Kim, C. K., Marsik, M., Sutherland, D. A., Arkema, K. K., Guerry, A. D., Levin, P. S., Minello, T. J., Plummer, M., Ruckelshaus, M. H., and Townsend, H. M. | Peter Caldwell, Ge Sun, Steve McNulty, Jennifer Moore Myers, Erika Cohen, Robert Herring, Erik Martinez | Wu, Xing, Akujarvi, A., Lu, N., Liski, J., Liu, G., Want, Y, Holmberg, M., Li, F., Zeng, Y., and B. Fu |
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Document Year
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2008 | 2011 | 2011 | 2013 | 2013 | 2015 |
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Document Title
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Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | Using plant functional traits to understand the landscape distribution of multiple ecosystem services | From mountains to sound: modelling the sensitivity of dungeness crab and Pacific oyster to land–sea interactions in Hood Canal,WA | WaSSI Ecosystem Services Model | Dynamics of soil organic carbon stock in a typical catchment of the Loess Plateau: comparison of model simulations with measurement |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Peer reviewed and published | Not peer reviewed but is published (explain in Comment) | Peer reviewed and published |
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Comments on Status
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Published journal manuscript | Published journal manuscript | Published journal manuscript | Published journal manuscript | While the user guide on which model entry is based has not been peer reviewed, several peer reviewed journal articles describing this USA HUC8 version of WaSSI have been published. | Published journal manuscript |
Software and Access
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EM ID
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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http://evoland.bioe.orst.edu/ ?Comment:Software is likely available. |
Not applicable | Not applicable | https://www.naturalcapitalproject.org/invest/ | http://www.wassiweb.sgcp.ncsu.edu/ | http://en.ilmatieteenlaitos.fi/yasso-download-and-support | |
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Contact Name
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Michael R. Guzy | Sandra Lavorel | Sandra Lavorel | J.E. Toft | Ge Sun | Xing Wu |
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Contact Address
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Oregon State University, Dept. of Biological and Ecological Engineering | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | Laboratoire d’Ecologie Alpine, UMR 5553 CNRS Université Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France | The Natural Capital Project, Stanford University, 371 Serra Mall, Stanford, CA 94305-5020, USA | Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest Service, 920 Main Campus Dr. Venture II, Suite 300, Raleigh, NC 27606 | Chinese Academy of Sciences, Beijing 100085, China |
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Contact Email
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Not reported | sandra.lavorel@ujf-grenoble.fr | sandra.lavorel@ujf-grenoble.fr | jetoft@stanford.edu | gesun@fs.fed.us | xingwu@rceesac.cn |
EM Description
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EM ID
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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Summary Description
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**Note: A more recent version of this model exists. See Related EMs below for links to related models/applications.** ABSTRACT: "Spatially explicit agent-based models can represent the changes in resilience and ecological services that result from different land-use policies…This type of analysis generates ensembles of alternate plausible representations of future system conditions. User expertise steers interactive, stepwise system exploration toward inductive reasoning about potential changes to the system. In this study, we develop understanding of the potential alternative futures for a social-ecological system by way of successive simulations that test variations in the types and numbers of policies. The model addresses the agricultural-urban interface and the preservation of ecosystem services. The landscape analyzed is at the junction of the McKenzie and Willamette Rivers adjacent to the cities of Eugene and Springfield in Lane County, Oregon." AUTHOR'S DESCRIPTION: "Two general scenarios for urban expansion were created to set the bounds on what might be possible for the McKenzie-Willamette study area. One scenario, fish conservation, tried to accommodate urban expansion, but gave the most weight to policies that would produce resilience and ecosystem services to restore threatened fish populations. The other scenario, unconstrained development, reversed the weighting. The 35 policies in the fish conservation scenario are designed to maintain urban growth boundaries (UGB), accommodate human population growth through increased urban densities, promote land conservation through best-conservation practices on agricultural and forest lands, and make rural land-use conversions that benefit fish. In the unconstrained development scenario, 13 policies are mainly concerned with allowing urban expansion in locations desired by landowners. Urban expansion in this scenario was not constrained by the extent of the UGB, and the policies are not intended to create conservation land uses." | 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., litter biomass production), and could therefore be used as functional markers of ES." AUTHOR'S DESCRIPTION: "Variation in litter 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…Litter biomass production for each pixel was calculated and mapped using model estimates...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 litter mass. 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." | ABSTRACT: "Here, we propose a new approach for the analysis, mapping and understanding of multiple ES delivery in landscapes. Spatially explicit single ES models based on plant traits and abiotic characteristics are combined to identify ‘hot’ and ‘cold’ spots of multiple ES delivery, and the land use and biotic determinants of such distributions. We demonstrate the value of this trait-based approach as compared to a pure land-use approach for a pastoral landscape from the central French Alps, and highlight how it improves understanding of ecological constraints to, and opportunities for, the delivery of multiple services." AUTHOR'S DESCRIPTION: "The Cultural ecosystem service map was a simple sum of maps for relevant Ecosystem Properties (produced in related EMs) after scaling to a 0–100 baseline and trimming outliers to the 5–95% quantiles (Venables&Ripley 2002)…Coefficients used for the summing of individual ecosystem properties to cultural ecosystem services were based on stakeholders’ perceptions, given positive or negative contributions." | InVEST Nutrient Retention Model Please note: This ESML entry describes a specific, published application of an InVEST model. Different versions (e.g. different tiers) or more recent versions of this model may be available at the InVEST website. AUTHOR'S DESCRIPTION: "We modelled discharge and total nitrogen for the 153 perennial sub-watersheds in Hood Canal based on spatial variation in hydrological factors, land and water use, and vegetation.To do this, we reparameterized a set of fresh water models available in the InVEST tool (Tallis and Polasky, 2009; Kareiva et al., 2011)" (2) "We used the InVEST Nutrient Retention model to quantify the total nitrogen load for each subwatershed. Inputs to the Nutrient Retention model include water yield, land use and land cover, and nutrient loading and filtration rates (Table 1; Conte et al., 2011; Tallis et al., 2011). The nutrient model quantifies natural and anthropogenic sources of total nitrogen within each subwatershed, allowing managers to identify subwatersheds potentially at risk of contributing excessive nitrogen loads given the predicted development and climate future." ( P. 4) | AUTHORS DESCRIPTION: "WaSSI simulates monthly water and carbon dynamics at the Hydrologic Unit Code 8 level in the US. Three modules are integrated within the WaSSI model framework. The water balance module computes ecosystem water use, evapotranspiration and the water yield from each watershed. Water yield is sometimes referred to as runoff and can be thought of as the amount of streamflow at the outlet of each watershed due to hydrologic processes in each watershed in isolation without any flow contribution from upstream watersheds. The ecosystem productivity module simulates carbon gains and losses in each watershed or grid cell as functions of evapotranspiration. The water supply and demand module routes and accumulates the water yield through the river network according to topological relationships between adjacent watersheds, subtracts consumptive water use by humans from river flows, and compares water supply to water demand to compute the water supply stress index, or WaSSI." | ABSTRACT: "Land use changes are known to significantly affect the soil C balance by altering both C inputs and losses. Since the late 1990s, a large area of the Loess Plateau has undergone intensive land use changes during several ecological restoration projects to control soil erosion and combat land degradation, especially in the Grain for Green project. By using remote sensing techniques and the Yasso07 model, we simulated the dynamics of soil organic carbon (SOC) stocks in the Yangjuangou catchment of the Loess Plateau. The performance of the model was evaluated by comparing the simulated results with the intensive field measurements in 2006 and 2011 throughout the catchment. SOC stocks and NPP values of all land use types had generally increased during our study period. The average SOC sequestration rate in the upper 30 cm soil from 2006 to 2011 in the Yangjuangou catchment was approximately 44 g C m-2 yr-1, which was comparable to other studies in the Loess Plateau. Forest and grassland showed a more effective accumulation of SOC than the other land use types in our study area. The Yasso07 model performed reasonably well in predicting the overall dynamics of SOC stock for different land use change types at both the site and catchment scales. The assessment of the model performance indicated that the combination of Yasso07 model and remote sensing data could be used for simulating the effect of land use changes on SOC stock at catchment scale in the Loess Plateau." |
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Specific Policy or Decision Context Cited
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Authors Description: " By policy, we mean land management options that span the domains of zoning, agricultural and forest production, environmental protection, and urban development, including the associated regulations, laws, and practices. The policies we used in our SES simulations include urban containment policies…We also used policies modeled on agricultural practices that affect ecoystem services and capital…" | None identified | None identified | Land use change | WaSSI can be used to project the regional effects of forest land cover change, climate change, and water withdrawals on river flows, water supply stress, and ecosystem productivity (i.e., carbon sequestration).WaSSI can be used to evaluate trade-offs among management strategies that influence multiple ecosystem services | None identified |
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Biophysical Context
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No additional description provided | Elevation ranges from 1552 to 2442 m, on predominately south-facing slopes | Elevations ranging from 1552 m to 2442 m, on predominantly south-facing slopes | No additional description provided | Conterminous US | Agricultural plain, hills, gulleys, forest, grassland, Central China |
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EM Scenario Drivers
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Five scenarios that include urban growth boundaries and various combinations of unconstrainted development, fish conservation, agriculture and forest reserves. ?Comment:Additional alternatives included adding agricultural and forest reserves, and adding or removing urban growth boundaries to the three main scenarios. |
No scenarios presented | No scenarios presented | Future land use and land cover; climate change |
No scenarios presented ?Comment:Model can be run from WaSSI website using a historic data set (1961 - 2010) or projections from various climate models representing different emissions scenarios and time periods from recent past to 2099. |
Land use change |
EM Relationship to Other EMs or Applications
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EM ID
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application | Method + Application (multiple runs exist) View EM Runs | Method + Application | Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
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New or revised model | New or revised model | New or revised model | Application of existing model |
Application of existing model ?Comment:. |
Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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EM ID
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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Document ID for related EM
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Doc-47 | Doc-313 | Doc-314 ?Comment:Doc 183 is a secondary source for the Evoland model. |
Doc-260 | None | Doc-309 | Doc-338 | None | Doc-343 | Doc-342 |
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EM ID for related EM
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EM-333 | EM-369 | EM-65 | 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-71 | EM-79 | EM-80 | EM-82 | EM-83 | EM-363 | EM-438 | None | EM-466 | EM-467 | EM-469 | EM-485 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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EM Temporal Extent
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1990-2050 | Not reported | Not reported | 2005-7; 2035-45 | 1961-2009 | 1969-2011 |
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EM Time Dependence
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time-dependent | time-stationary | time-stationary | time-stationary | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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future time | Not applicable | Not applicable | Not applicable | future time | past time |
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EM Time Continuity
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discrete | Not applicable | Not applicable | Not applicable | discrete | discrete |
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EM Temporal Grain Size Value
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2 | Not applicable | Not applicable | Not applicable | 1 | 1 |
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EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable | Not applicable | Month | Year |
EM Spatial Extent
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EM ID
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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Bounding Type
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Geopolitical | Physiographic or Ecological | Physiographic or Ecological | Watershed/Catchment/HUC | Watershed/Catchment/HUC | Watershed/Catchment/HUC |
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Spatial Extent Name
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Junction of McKenzie and Willamette Rivers, adjacent to the cities of Eugene and Springfield, Lane Co., Oregon, USA | Central French Alps | Central French Alps | Hood Canal | All 8-digit hydrologic unit codes (HUC-8) in the conterminous USA | Yangjuangou catchment |
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Spatial Extent Area (Magnitude)
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10-100 km^2 | 10-100 km^2 | 10-100 km^2 | 100,000-1,000,000 km^2 | >1,000,000 km^2 | 1-10 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:Spatial grain for computations is the HUC-8. A HUC-12 version is under development. Spatial grain for computations is comprised of 16,005 polygons of various size covering 7091 ha. |
spatially distributed (in at least some cases) |
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Spatial Grain Type
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area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | area, for pixel or radial feature |
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Spatial Grain Size
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varies | 20 m x 20 m | 20 m x 20 m | 30 m x 30 m | Computations are at the 8-digit HUC scale. MostHUC-8 watersheds are within a range of 800-8000 km^2 (500-5000 mi^2) in size. | 30m x 30m |
EM Structure and Computation Approach
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EM ID
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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EM Computational Approach
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Numeric | Analytic | Analytic | Other or unclear (comment) | Numeric | Numeric |
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EM Determinism
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stochastic | deterministic | deterministic | deterministic | deterministic | deterministic |
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Statistical Estimation of EM
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Comment:Agent based modeling results in response indices. |
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Model Checking Procedures Used
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EM ID
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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Model Calibration Reported?
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Unclear | No | No | Yes | No | No |
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Model Goodness of Fit Reported?
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No | Yes | No | No | No |
Yes ?Comment:p value: p<0.001 |
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Goodness of Fit (metric| value | unit)
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None |
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None | None | None |
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Model Operational Validation Reported?
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No | Yes | No | Yes | No | No |
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Model Uncertainty Analysis Reported?
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No | No | No | No | No | No |
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Model Sensitivity Analysis Reported?
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No ?Comment:Sensitivity analysis performed for agent values only. |
No | No | Yes | No | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | No | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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| None | None | None |
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None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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Centroid Latitude
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44.11 | 45.05 | 45.05 | 47.8 | 39.83 | 36.7 |
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Centroid Longitude
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-123.09 | 6.4 | 6.4 | -122.7 | -98.58 | 109.52 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Provided | Provided | Estimated | Estimated | Provided |
Environments and Scales Modeled
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EM ID
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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EM Environmental Sub-Class
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Rivers and Streams | Forests | Agroecosystems | Created Greenspace | Agroecosystems | Grasslands | Agroecosystems | Grasslands | Near Coastal Marine and Estuarine |
Lakes and Ponds ?Comment:Watershed model represents all land areas, major streams and rivers. Since leaf area index, LAI, is an important variable, forests, created greenspaces (e.g., urban forests) and scrub/shrub subclasses are included. |
Agroecosystems |
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Specific Environment Type
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Agricultural-urban interface at river junction | Subalpine terraces, grasslands, and meadows | Subalpine terraces, grasslands, and meadows. | glacier-carved saltwater fjord | Not applicable | Loess plain |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Not applicable | Ecological scale is coarser than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class |
Ecological scale is coarser than that of the Environmental Sub-class ?Comment:Terrestrial characteristics are aggregated at a broad (HUC-8) scale; different types of aquatic sub-classes are not differentiated. |
Ecological scale is finer than that of the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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EM Organismal Scale
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Not applicable | Community | Community | Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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None Available | None Available | None Available | None Available | None Available |
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)
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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None |
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<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>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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EM-12
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EM-66 | EM-81 |
EM-112
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EM-439 |
EM-480
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None | None | None |
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None |