EcoService Models Library (ESML)
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Compare EMs
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.
EM Identity and Description
EM ID
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EM-379 | EM-466 |
EM Short Name
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VELMA soil temperature, Oregon, USA | Yasso 15 - soil carbon model |
EM Full Name
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VELMA (Visualizing Ecosystems for Land Management Assessments) soil temperature, Oregon, USA | Yasso 15 - soil carbon |
EM Source or Collection
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US EPA | None |
EM Source Document ID
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317 |
342 ?Comment:Webpage pdf users manual for model. |
Document Author
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Abdelnour, A., McKane, R. B., Stieglitz, M., Pan, F., and Chen, Y. | Repo, A., Jarvenpaa, M., Kollin, J., Rasinmaki, J. and Liski, J. |
Document Year
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2013 | 2016 |
Document Title
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Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment | Yasso 15 graphical user-interface manual |
Document Status
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Peer reviewed and published | Other or unclear (explain in Comment) |
Comments on Status
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Published journal manuscript | Not applicable |
EM ID
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EM-379 | EM-466 |
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. |
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Contact Name
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Alex Abdelnour | Jari Liski |
Contact Address
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Department of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA | Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki |
Contact Email
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abdelnouralex@gmail.com | jari.liski@ymparisto.fi |
EM ID
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EM-379 | EM-466 |
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" | 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." |
Specific Policy or Decision Context Cited
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None identified | None identified |
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. | Not applicable |
EM Scenario Drivers
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No scenarios presented | No scenarios presented |
EM ID
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EM-379 | EM-466 |
Method Only, Application of Method or Model Run
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Method + Application | Method Only |
New or Pre-existing EM?
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Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-379 | EM-466 |
Document ID for related EM
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Doc-13 | Doc-317 | Doc-343 | Doc-344 |
EM ID for related EM
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EM-375 | EM-380 | EM-884 | EM-883 | EM-887 | EM-467 | EM-469 | EM-480 | EM-485 |
EM Modeling Approach
EM ID
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EM-379 | EM-466 |
EM Temporal Extent
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1969-2008 | Not applicable |
EM Time Dependence
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time-dependent | time-dependent |
EM Time Reference (Future/Past)
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future time | Not applicable |
EM Time Continuity
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discrete | discrete |
EM Temporal Grain Size Value
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1 | 1 |
EM Temporal Grain Size Unit
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Day | Year |
EM ID
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EM-379 | EM-466 |
Bounding Type
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Watershed/Catchment/HUC | Not applicable |
Spatial Extent Name
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H. J. Andrews LTER WS10 | Not applicable |
Spatial Extent Area (Magnitude)
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10-100 ha | Not applicable |
EM ID
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EM-379 | EM-466 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:See below, grain includes vertical, subsurface dimension. |
spatially lumped (in all cases) |
Spatial Grain Type
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volume, for 3-D feature | Not applicable |
Spatial Grain Size
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30 m x 30 m surface pixel and 2-m depth soil column | Not applicable |
EM ID
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EM-379 | EM-466 |
EM Computational Approach
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Numeric | Numeric |
EM Determinism
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deterministic | stochastic |
Statistical Estimation of EM
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EM ID
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EM-379 | EM-466 |
Model Calibration Reported?
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No | Not applicable |
Model Goodness of Fit Reported?
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No | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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No | Not applicable |
Model Uncertainty Analysis Reported?
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No | Not applicable |
Model Sensitivity Analysis Reported?
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No | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-379 | EM-466 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-379 | EM-466 |
None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-379 | EM-466 |
Centroid Latitude
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44.25 | Not applicable |
Centroid Longitude
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-122.33 | Not applicable |
Centroid Datum
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WGS84 | Not applicable |
Centroid Coordinates Status
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Provided | Not applicable |
EM ID
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EM-379 | EM-466 |
EM Environmental Sub-Class
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Forests | Forests | Grasslands | Scrubland/Shrubland | Tundra |
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). | Not applicable |
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 |
Scale of differentiation of organisms modeled
EM ID
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EM-379 | EM-466 |
EM Organismal Scale
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Not applicable | Species |
Taxonomic level and name of organisms or groups identified
EM-379 | EM-466 |
None Available | None Available |
EnviroAtlas URL
EM-379 | EM-466 |
Average Annual Precipitation | Average Annual Precipitation, Carbon storage by tree biomass (kg/m2), Carbon Storage by Tree Biomass |
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-379 | EM-466 |
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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-379 | EM-466 |
None | None |