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-122 
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EM-379 | EM-466 |
EM-605
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EM-812
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EM Short Name
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Land-use change and crop-based production, Europe | VELMA soil temperature, Oregon, USA | Yasso 15 - soil carbon model | VELMA v2.0, Ohio, USA | Wildflower mix supporting bees, CA, USA |
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EM Full Name
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Land-use change effects on crop-based production, Europe | VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | Yasso 15 - soil carbon | Visualizing Ecosystems for Land Management Assessments (VELMA) v2.0, Shayler Crossing watershed, Ohio, USA | Wildflower planting mix supporting bees in agricultural landscapes, CA, USA |
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EM Source or Collection
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EU Biodiversity Action 5 | US EPA | None | US EPA | None |
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EM Source Document ID
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228 | 317 |
342 ?Comment:Webpage pdf users manual for model. |
359 ?Comment:Document #366 is a supporting document for this EM. McKane et al. 2014, VELMA Version 2.0 User Manual and Technical Documentation. |
400 |
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Document Author
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Haines-Young, R., Potschin, M. and Kienast, F. | Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Repo, A., Jarvenpaa, M., Kollin, J., Rasinmaki, J. and Liski, J. | Hoghooghi, N., H. E. Golden, B. P. Bledsoe, B. L. Barnhart, A. F. Brookes, K. S. Djang, J. J. Halama, R. B. McKane, C. T. Nietch, and P. P. Pettus | Williams, N.M., Ward, K.L., Pope, N., Isaacs, R., Wilson, J., May, E.A., Ellis, J., Daniels, J., Pence, A., Ullmann, K., and J. Peters |
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Document Year
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2012 | 2013 | 2016 | 2018 | 2015 |
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Document Title
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Indicators of ecosystem service potential at European scales: Mapping marginal changes and trade-offs | Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Yasso 15 graphical user-interface manual | Cumulative effects of low impact development on watershed hydrology in a mixed land-cover system | Native wildflower Plantings support wild bee abundance and diversity in agricultural landscapes across the United States |
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Document Status
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Peer reviewed and published | Peer reviewed and published | Other or unclear (explain in Comment) | 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 | Not applicable | Published journal manuscript | Published journal manuscript |
Software and Access
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EM ID
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EM-122
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EM-379 | EM-466 |
EM-605
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EM-812
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| Not applicable | Bob McKane, VELMA Team Lead, USEPA-ORD-NHEERL-WED, Corvallis, OR (541) 754-4631; mckane.bob@epa.gov |
http://en.ilmatieteenlaitos.fi/yasso-download-and-support ?Comment:User's manual states that the software will be downloadable at this site. |
https://www.epa.gov/water-research/visualizing-ecosystem-land-management-assessments-velma-model-20 | Not applicable | |
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Contact Name
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Marion Potschin | Alex Abdelnour | Jari Liski | Heather Golden | Neal Williams |
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Contact Address
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Centre for Environmental Management, School of Geography, University of Nottingham, NG7 2RD, United Kingdom | Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki | National Exposure Research Laboratory, Office of Research and Development, US EPA, Cincinnati, OH 45268, USA | Department of Entomology and Mematology, Univ. of CA, One Shilds Ave., Davis, CA 95616 |
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Contact Email
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marion.potschin@nottingham.ac.uk | abdelnouralex@gmail.com | jari.liski@ymparisto.fi | Golden.Heather@epa.gov | nmwilliams@ucdavis.edu |
EM Description
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EM ID
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EM-122
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EM-379 | EM-466 |
EM-605
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EM-812
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Summary Description
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ABSTRACT: "The study focuses on the EU-25 plus Switzerland and Norway, and develops the methodology proposed by Kienast et al. (2009), which uses expert-and literature-driven modelling methods. The novel aspect of this work is an analysis of whether the historical and the projected land use changes for the periods 1990–2000, 2000–2006, and 2000–2030 are likely to be supportive or degenerative in the capacity of ecosystems to deliver (Crop-based production); we refer to these as ‘marginal’ or incremental changes. The latter are assessed by using land account data for 1990–2000 and 2000–2006 (LEAC, EEA, 2006) and EURURALIS 2.0 land use scenarios for 2000–2030. The results are reported at three spatial reporting units, i.e. (1) the NUTS-X regions, (2) the bioclimatic regions, and (3) the dominant landscape types." AUTHOR'S DESCRIPTION: "The analysis for “Crop-based production” maps all the areas that are important for food crops produced through commercial agriculture….The historic assessment of marginal changes was undertaken using the Land and Ecosystem Accounting database (LEAC) created by the EEA using successive CORINE Land Cover data. The analysis of these incremental changes was included in the study in order to examine whether recent trend data could add additional insights to spatial assessment techniques, particularly where change against some base-line status is of interest to decision makers…The futures component of the work was based on EURURALIS 2.0 land use scenarios for 2000–2030, which are based on the four IPCC SRES land use scenarios." | 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" | AUTHOR'S DESCRIPTION: "The Yasso15 calculates the stock of soil organic carbon, changes in the stock of soil organic carbon and heterotrophic soil respiration. Applications the model include, for example, simulations of land use change, ecosystem management, climate change, greenhouse gas inventories and education. The Yasso15 is a relatively simple soil organic carbon model requiring information only on climate and soil carbon input to operate... In the Yasso15 model litter is divided into five soil organic carbon compound groups (Fig. 1). These groups are compounds hydrolysable in acid (denoted with A), compounds soluble in water (W) or in a non-polar solvent, e.g. ethanol or dichloromethane (E), compounds neither soluble nor hydrolysable (N) and humus (H). The AWEN form the group of labile fractions whereas H fraction contains humus, which is more recalcitrant to decomposition. Decomposition of the fractions results in carbon flux out of soil and carbon fluxes between the compartments (Fig. 1). The basic idea of Yasso15 is that the decomposition of different types of soil carbon input depends on the chemical composition of the input types and climate conditions. The effects of the chemical composition are taken into account by dividing carbon input to soil between the four labile compartments explicitly according to the chemical composition (Fig. 1). Decomposition of woody litter depends additionally on the size of the litter. The effects of climate conditions are modelled by adjusting the decomposition rates of the compartments according to air temperature and precipitation. In the Yasso15 model separate decomposition rates are applied to fast-decomposing A, W and E compartments, more slowly decomposing N and very slowly decomposing humus compartment H. The Yasso is a global-level model meaning that the same parameter values are suitable for all applications for accurate predictions. However, the current GUI version also includes possibility to use earlier parameterizations. The parameter values of Yasso15 are based on measurements related to cycling of organic carbon in soil (Table 1). An extensive set of litter decomposition measurements was fundamental in developing the model (Fig. 2). This data set covered, firstly, most of the global climate conditions in terms of temperature precipitation and seasonality (Fig 3.), secondly, different ecosystem types from forests to grasslands and agricultural fields and, thirdly, a wide range of litter types. In addition, a large set of data giving information on decomposition of woody litter (including branches, stems, trunks, roots with different size classes) was used for fitting. In addition to woody and non-woody litter decomposition measurements, a data set on accumulation of soil carbon on the Finnish coast and a large, global steady state data sets were used in the parameterization of the model. These two data sets contain information on the formation and slow decomposition of humus." | ABSTRACT: "Low Impact Development (LID) is an alternative to conventional urban stormwater management practices, which aims at mitigating the impacts of urbanization on water quantity and quality. Plot and local scale studies provide evidence of LID effectiveness; however, little is known about the overall watershed scale influence of LID practices. This is particularly true in watersheds with a land cover that is more diverse than that of urban or suburban classifications alone. We address this watershed-scale gap by assessing the effects of three common LID practices (rain gardens, permeable pavement, and riparian buffers) on the hydrology of a 0.94 km2 mixed land cover watershed. We used a spatially-explicit ecohydrological model, called Visualizing Ecosystems for Land Management Assessments (VELMA), to compare changes in watershed hydrologic responses before and after the implementation of LID practices. For the LID scenarios, we examined different spatial configurations, using 25%, 50%, 75% and 100% implementation extents, to convert sidewalks into rain gardens, and parking lots and driveways into permeable pavement. We further applied 20 m and 40 m riparian buffers along streams that were adjacent to agricultural land cover…" AUTHOR'S DESCRIPTION: "VELMA’s modeling domain is a three-dimensional matrix that includes information regarding surface topography, land use, and four soil layers. VELMA uses a distributed soil column framework to model the lateral and vertical movement of water and nutrients through the four soil layers. A soil water balance is solved for each layer. The soil column model is placed within a watershed framework to create a spatially distributed model applicable to watersheds (Figure 2, shown here with LID practices). Adjacent soil columns interact through down-gradient water transport. Water entering each pixel (via precipitation or flow from an adjacent pixel) can either first infiltrate into the implemented LID and the top soil layer, and then to the downslope pixel, or continue its downslope movement as the lateral surface flow. Surface and subsurface lateral flow are routed using a multiple flow direction method, as described in Abdelnour et al. [21]. A detailed description of the processes and equations can be found in McKane et al. [32], Abdelnour et al. [21], Abdelnour et al. [40]." | Abstract: " Global trends in pollinator-dependent crops have raised awareness of the need to support managed and wild bee populations to ensure sustainable crop production. Provision of sufficient forage resources is a key element for promoting bee populations within human impacted landscapes, particularly those in agricultural lands where demand for pollination service is high and land use and management practices have reduced available flowering resources. Recent government incentives in North America and Europe support the planting of wildflowers to benefit pollinators; surprisingly, in North America there has been almost no rigorous testing of the performance of wildflower mixes, or their ability to support wild bee abundance and diversity. We tested different wildflower mixes in a spatially replicated, multiyear study in three regions of North America where production of pollinatordependent crops is high: Florida, Michigan, and California. In each region, we quantified flowering among wildflower mixes composed of annual and perennial species, and with high and low relative diversity. We measured the abundance and species richness of wild bees, honey bees, and syrphid flies at each mix over two seasons. In each region, some but not all wildflower mixes provided significantly greater floral display area than unmanaged weedy control plots. Mixes also attracted greater abundance and richness of wild bees, although the identity of best mixes varied among regions. By partitioning floral display size from mix identity we show the importance of display size for attracting abundant and diverse wild bees. Season-long monitoring also revealed that designing mixes to provide continuous bloom throughout the growing season is critical to supporting the greatest pollinator species richness. Contrary to expectation, perennials bloomed in their first season, and complementarity in attraction of pollinators among annuals and perennials suggests that inclusion of functionally diverse species may provide the greatest benefit. Wildflower mixes may be particularly important for providing resources for some taxa, such as bumble bees, which are known to be in decline in several regions of North America. No mix consistently attained the full diversity that was planted. Further study is needed on how to achieve the desired floral display and diversity from seed mixes. " Additional information in supplemental Appendices online: http://dx.doi.org/10.1890/14-1748.1.sm |
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Specific Policy or Decision Context Cited
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None identified | None identified | None identified | None identified | None identified |
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Biophysical Context
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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. | Not applicable | The Shayler Crossing (SHC) watershed is a subwatershed of the East Fork Little Miami River Watershed in southwest Ohio, USA and falls within the Till Plains region of the Central Lowland physiographic province. The Till Plains region is a topographically young and extensive flat plain, with many areas remaining undissected by even the smallest stream. The bedrock is buried under a mantle of glacial drift 3–15 m thick. The Digital Elevation Model (DEM) has a maximum value of ~269 m (North American_1983 datum) within the watershed boundary (Figure 1). The soils are primarily the Avonburg and Rossmoyne series, with high silty clay loam content and poor to moderate infiltration. Average annual precipitation for the period, 1990 through 2011, was 1097.4 _ 173.5 mm. Average annual air temperature for the same period was 12 _C Mixed land cover suburban watershed. The primary land uses consist of 64.1% urban or developed area (including 37% lawn, 12% building, 6.5% street, 6.4% sidewalk, and 2.1% parking lot and driveway), 23% agriculture, and 13% deciduous forest. Total imperviousness covers approximately 27% of the watershed area. | field plots near agricultural fields (mixed row crop, almond, walnuts), central valley, Ca |
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EM Scenario Drivers
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Recent historical land-use change (1990-2000 and 2000-2006) and projected land-use changes (2000-2030) | No scenarios presented | No scenarios presented | Three types of Low Impact Development (LID) practices (rain gardens, permeable pavements, forested riparian buffers) applied a different conversion levels. | Varied wildflower planting mixes of annuals and perennials |
EM Relationship to Other EMs or Applications
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EM ID
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EM-122
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EM-379 | EM-466 |
EM-605
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EM-812
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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 Only | Method + Application (multiple runs exist) View EM Runs | 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 | Application of existing model | New or revised model | New or revised model | New or revised 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-122
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EM-379 | EM-466 |
EM-605
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EM-812
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Document ID for related EM
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Doc-238 | Doc-239 | Doc-240 | Doc-241 | Doc-242 | Doc-228 | Doc-13 | Doc-317 | Doc-343 | Doc-344 | Doc-13 | Doc-366 | Doc-400 |
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EM ID for related EM
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EM-123 | EM-124 | EM-125 | EM-162 | EM-164 | EM-165 | EM-166 | EM-170 | EM-171 | EM-99 | EM-119 | EM-120 | EM-121 | EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | EM-467 | EM-469 | EM-480 | EM-485 | EM-375 | EM-377 | EM-378 | EM-884 | EM-883 | EM-887 | EM-784 | EM-793 |
EM Modeling Approach
EM Relationship to Time
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EM ID
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EM-122
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EM-379 | EM-466 |
EM-605
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EM-812
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EM Temporal Extent
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1990-2030 | 1969-2008 | Not applicable | Jan 1, 2009 to Dec 31, 2011 | 2011-2012 |
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EM Time Dependence
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time-dependent | time-dependent | time-dependent | time-dependent | time-dependent |
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EM Time Reference (Future/Past)
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future time | future time | Not applicable | past time | past time |
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EM Time Continuity
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discrete | discrete | discrete | discrete | discrete |
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EM Temporal Grain Size Value
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6, 10, and 30 | 1 | 1 | 1 | 1 |
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EM Temporal Grain Size Unit
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Year | Day | Year | Day | Year |
EM Spatial Extent
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EM ID
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EM-122
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EM-379 | EM-466 |
EM-605
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EM-812
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Bounding Type
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Geopolitical | Watershed/Catchment/HUC | Not applicable | Watershed/Catchment/HUC |
Point or points ?Comment:This is a guess based on information in the document. 3 field sites were separated by up to 9km |
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Spatial Extent Name
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The EU-25 plus Switzerland and Norway | H. J. Andrews LTER WS10 | Not applicable | Shayler Crossing watershed, a subwatershed of the East Fork Little Miami River Watershed | Agricultural plots |
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Spatial Extent Area (Magnitude)
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>1,000,000 km^2 | 10-100 ha | Not applicable | 10-100 ha | 10-100 km^2 |
Spatial Distribution of Computations
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EM ID
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EM-122
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EM-379 | EM-466 |
EM-605
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EM-812
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EM Spatial Distribution
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spatially distributed (in at least some cases) |
spatially distributed (in at least some cases) ?Comment:See below, grain includes vertical, subsurface dimension. |
spatially lumped (in all cases) | spatially distributed (in at least some cases) | spatially lumped (in all cases) |
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Spatial Grain Type
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area, for pixel or radial feature | volume, for 3-D feature | Not applicable | area, for pixel or radial feature | Not applicable |
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Spatial Grain Size
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1 km x 1 km | 30 m x 30 m surface pixel and 2-m depth soil column | Not applicable | 10m x 10m | Not applicable |
EM Structure and Computation Approach
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EM ID
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EM-122
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EM-379 | EM-466 |
EM-605
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EM-812
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EM Computational Approach
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Logic- or rule-based | Numeric | Numeric | Numeric | Numeric |
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EM Determinism
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deterministic | deterministic | stochastic | deterministic | deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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EM ID
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EM-122
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EM-379 | EM-466 |
EM-605
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EM-812
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Model Calibration Reported?
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No | No | Not applicable | Yes | No |
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Model Goodness of Fit Reported?
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No | No | Not applicable |
Yes ?Comment:Goodness of fit for calibrated (2009-2010) and observed streamflow. |
No |
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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 | No | Not applicable | Yes | No |
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Model Uncertainty Analysis Reported?
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No | No | Not applicable | No | No |
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Model Sensitivity Analysis Reported?
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No | No | Not applicable | No | No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable | 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-122
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EM-379 | EM-466 |
EM-605
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EM-812
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None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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EM-122
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EM-379 | EM-466 |
EM-605
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EM-812
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| None | None | None | None | None |
Centroid Lat/Long (Decimal Degree)
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EM ID
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EM-122
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EM-379 | EM-466 |
EM-605
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EM-812
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Centroid Latitude
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50.53 | 44.25 | Not applicable | 39.19 | 29.4 |
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Centroid Longitude
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7.6 | -122.33 | Not applicable | -84.29 | -82.18 |
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Centroid Datum
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WGS84 | WGS84 | Not applicable | WGS84 | WGS84 |
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Centroid Coordinates Status
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Estimated | Provided | Not applicable | Provided | Provided |
Environments and Scales Modeled
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EM ID
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EM-122
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EM-379 | EM-466 |
EM-605
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EM-812
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EM Environmental Sub-Class
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Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) | Forests | Forests | Grasslands | Scrubland/Shrubland | Tundra | Rivers and Streams | Ground Water | Forests | Agroecosystems | Created Greenspace | Agroecosystems |
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Specific Environment Type
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Not applicable | 400 to 500 year old forest dominated by Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). | Not applicable | Mixed land cover suburban watershed | Agricultural landscape |
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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 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 |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM ID
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EM-122
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EM-379 | EM-466 |
EM-605
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EM-812
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EM Organismal Scale
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Not applicable | Not applicable | Species | Not applicable | Species |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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EM-122
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EM-379 | EM-466 |
EM-605
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EM-812
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| None Available | None Available | None Available | None Available |
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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-122
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EM-379 | EM-466 |
EM-605
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EM-812
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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>
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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EM-122
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EM-379 | EM-466 |
EM-605
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EM-812
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None | None |
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