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-379 | EM-449 |
EM-467 
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EM-846 | EM-962 |
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EM Short Name
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VELMA soil temperature, Oregon, USA | Decrease in erosion (shoreline), St. Croix, USVI | Yasso07 v1.0.1, Switzerland | Indigo bunting abund, Piedmont region, USA | RZWQM2, Quebec, Canada |
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EM Full Name
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VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | Decrease in erosion (shoreline) by reef, St. Croix, USVI | Yasso07 v1.0.1 forest litter decomposition, Switzerland | Indigo bunting abundance, Piedmont ecoregion, USA | Root zone water quality model 2 mitigation of greenhouse gases, Quebec, Canada |
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EM Source or Collection
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US EPA | US EPA | None | None | None |
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EM Source Document ID
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317 | 335 | 343 | 405 | 447 |
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Document Author
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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 | Riffel, S., Scognamillo, D., and L. W. Burger | Jiang, Q., Zhiming, Q., Madramootoo, C.A., and Creze, C. |
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Document Year
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2013 | 2014 | 2014 | 2008 | 2018 |
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Document Title
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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 | Effects of the Conservation Reserve Program on northern bobwhite and grassland birds | Mitigating greenhouse gas emisssions in subsurface-drained field using RZWQM2 |
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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 | 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 | Published journal manuscript |
Software and Access
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EM ID
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EM-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
| 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 | Not applicable | |
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Contact Name
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Alex Abdelnour | Susan H. Yee |
Markus Didion ?Comment:Tel.: +41 44 7392 427 |
Sam Riffell | Zhiming Qi |
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Contact Address
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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 | Department of Wildlife & Fisheries, Mississippi State University, Mississippi State, MS 39762, USA | Department of Bioresource Engineering, McGill University, Sainte-Anne-de-Bellevue, QC H9X 3V9, Canada |
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Contact Email
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abdelnouralex@gmail.com | yee.susan@epa.gov | markus.didion@wsl.ch | sriffell@cfr.msstate.edu | zhiming.qi@mcgill.ca |
EM Description
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EM ID
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EM-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
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Summary Description
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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...Shoreline protection as an ecosystem service has been defined in a number of ways including protection from shoreline erosion...and can thus be estimated as % Decrease in erosion due to reef = 1 - (Ho/H)^2.5 where Ho is the attenuated wave height due to the presence of the reef and H is wave height in the absence of the reef." | 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:"The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds." | 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." |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None reported | None |
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Biophysical Context
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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). | Conservation Reserve Program lands left to go fallow | None |
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EM Scenario Drivers
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No scenarios presented | No scenarios presented |
No scenarios presented ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
N/A | None |
EM Relationship to Other EMs or Applications
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EM ID
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EM-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
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Method Only, Application of Method or Model Run
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Method + Application | Method + Application |
Method + Application (multiple runs exist) View EM Runs ?Comment:Yasso model simulations were run using 3 different parameter sets from: 1) Tuomi et al., 2009 (P09), 2) Tuomi et al., 2011 (P11), and 3) Rantakari et al., 2012 (P12). |
Method + Application | None |
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New or Pre-existing EM?
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Application of existing model | Application of existing model | Application of existing model | New or revised model | None |
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-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
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Document ID for related EM
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Doc-13 | Doc-317 | Doc-335 | Doc-342 | Doc-344 | Doc-405 | None |
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EM ID for related EM
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EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | EM-447 | EM-448 | EM-466 | EM-469 | EM-480 | EM-485 | EM-831 | EM-838 | EM-839 | EM-840 | EM-841 | EM-842 | EM-843 | EM-844 | EM-845 | EM-847 | None |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
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EM Temporal Extent
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1969-2008 | 2006-2007, 2010 | 1993-2013 | 2008 | 2012-2015 |
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EM Time Dependence
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time-dependent | time-stationary | time-dependent | time-stationary | time-dependent |
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EM Time Reference (Future/Past)
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future time | Not applicable | future time | Not applicable | past time |
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EM Time Continuity
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discrete | Not applicable | discrete | Not applicable | discrete |
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EM Temporal Grain Size Value
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1 | Not applicable | 1 | Not applicable | 1 |
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EM Temporal Grain Size Unit
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Day | Not applicable | Year | Not applicable | Year |
EM Spatial Extent
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EM ID
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EM-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
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Bounding Type
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Watershed/Catchment/HUC | Physiographic or ecological | Geopolitical | Physiographic or ecological | Point or points |
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Spatial Extent Name
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H. J. Andrews LTER WS10 | Coastal zone surrounding St. Croix | Switzerland | Piedmont Ecoregion | Corn field |
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Spatial Extent Area (Magnitude)
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10-100 ha | 100-1000 km^2 | 10,000-100,000 km^2 | 100,000-1,000,000 km^2 | 1-10 ha |
Spatial Distribution of Computations
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EM ID
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EM-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
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EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:See below, grain includes vertical, subsurface dimension. |
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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volume, for 3-D feature | area, for pixel or radial feature | other (specify), for irregular (e.g., stream reach, lake basin) | Not applicable | Not applicable |
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Spatial Grain Size
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30 m x 30 m surface pixel and 2-m depth soil column | 10 m x 10 m | 5 sites | Not applicable | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
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EM Computational Approach
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Numeric | Analytic | Numeric | Analytic | * |
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EM Determinism
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deterministic | deterministic | stochastic | deterministic | None |
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Statistical Estimation of EM
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None |
Model Checking Procedures Used
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EM ID
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EM-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
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Model Calibration Reported?
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No | Yes | No | Yes | None |
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Model Goodness of Fit Reported?
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No | No | No | No | None |
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Goodness of Fit (metric| value | unit)
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None | None | None | None | None |
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Model Operational Validation Reported?
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No | Yes | Yes | No | None |
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Model Uncertainty Analysis Reported?
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No | No | No | No | None |
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Model Sensitivity Analysis Reported?
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No | No | No | Yes | None |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | Not applicable | None |
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-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| EM-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
| None |
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None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
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Centroid Latitude
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44.25 | 17.73 | 46.82 | 36.23 | 45.32 |
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Centroid Longitude
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-122.33 | -64.77 | 8.23 | -81.9 | 74.17 |
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Centroid Datum
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WGS84 | WGS84 | WGS84 | WGS84 | None provided |
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Centroid Coordinates Status
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Provided | Estimated | Estimated | Estimated | Provided |
Environments and Scales Modeled
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EM ID
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EM-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
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EM Environmental Sub-Class
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Forests | Near Coastal Marine and Estuarine | Forests | Grasslands | None |
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Specific Environment Type
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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 | grasslands | None |
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EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is finer than that of the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | Ecological scale corresponds to the Environmental Sub-class | None |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
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EM Organismal Scale
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Not applicable | Not applicable | Community | Species | None |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| EM-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
| None Available | None Available | None Available |
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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)
em.detail.cicesHelp
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| EM-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
| None |
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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>
fegs1Help
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
fegs2Help
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| EM-379 | EM-449 |
EM-467
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EM-846 | EM-962 |
| None |
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None |
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None |