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-434 | EM-1019 |
EM Short Name
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Land capability classification | SMOKE emissions model, Asia |
EM Full Name
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Land capability classification | Development of an anthropogenic emissions processing system for Asia using SMOKE |
EM Source or Collection
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None | None |
EM Source Document ID
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340 | 481 |
Document Author
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United States Department of Agriculture - Natural Resources Conservation Service | Woo, J.H., Choi, K.C., Kim, H.K., Baek, B.H., Jang, M., Eum, J.H., Song, C.H., Ma, Y.I., Sunwoo, Y., Chang, L.S. and Yoo, S.H. |
Document Year
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2013 | 2012 |
Document Title
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National Soil Survey Handbook - Part 622 - Interpretative Groups | Development of an anthropogenic emissions processing system for Asia using SMOKE |
Document Status
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Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published report | Published journal manuscript |
EM ID
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EM-434 | EM-1019 |
Not applicable | https://www.cmascenter.org/smoke/ | |
Contact Name
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United States Department of Agriculture | Jung-Hun Woo |
Contact Address
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Not reported | Department of Advanced Technology Fusion, Room 812, San-Hak Bldg., Konkuk University, Seoul, South Korea |
Contact Email
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http://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/contactus/ | jwoo@konkuk.ac.kr |
EM ID
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EM-434 | EM-1019 |
Summary Description
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AUTHOR'S DESCRIPTION: "Definition. Land capability classification is a system of grouping soils primarily on the basis of their capability to produce common cultivated crops and pasture plants without deteriorating over a long period of time." "Class I (1) soils have slight limitations that restrict their use. Class II (2) soils have moderate limitations that reduce the choice of plants or require moderate conservation practices. Class III (3) soils have severe limitations that reduce the choice of plants or require special conservation practices, or both. Class IV (4) soils have very severe limitations that restrict the choice of plants or require very careful management, or both. Class V (5) soils have little or no hazard of erosion but have other limitations, impractical to remove, that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VI (6) soils have severe limitations that make them generally unsuited to cultivation and that limit their use mainly to pasture, rangeland, forestland, or wildlife habitat. Class VII (7) soils have very severe limitations that make them unsuited to cultivation and that restrict their use mainly to rangeland, forestland, or wildlife habitat. Class VIII (8) soils and miscellaneous areas have limitations that preclude their use for commercial plant production and limit their use mainly to recreation, wildlife habitat, water supply, or esthetic purposes." [More information can be found at: http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054226#ex2] | Air quality modeling is a useful methodology to investigate air quality degradation in various locations and to analyze effectiveness of emission reduction plans. A comprehensive air quality model usually requires a coordinated set of emissions input of all necessary chemical species. We have developed an anthropogenic emissions processing system for Asia in support of air quality modeling and analysis over Asia (named SMOKE-Asia). The SMOKE (Sparse Matrix Operator kernel Emissions) system, which was developed by U.S. EPA and has been maintained by the Carolina Environmental Program (CEP) of the University of North Carolina, was used to develop our emissions processing system. A merged version of INTEX 2006 and TRACE-P 2000 inventories was used as an initial Asian emissions inventory. The IDA (Inventory Data Analyzer) format was used to create SMOKE-ready emissions. Source Classification Codes (SCCs) and country/state/county (FIPS) code, which are the two key data fields of SMOKE IDA data structure, were created for Asia. The 38 SCCs and 2752 FIPS codes were allocated to our SMOKE-ready emissions for more comprehensive processing. US EPA’s MIMS (Multimedia Integrated Modeling System) Spatial Allocator software, along with many global and regional GIS shapes, were used to create spatial allocation profiles for Asia. Temporal allocation and chemical speciation profiles were partly regionalized using Asia-based studies. Initial data production using the developed SMOKE-Asia system was successfully performed. NOx and VOC emissions for the year 2009 were projected to be increased by 50% from those of 1997. The emission hotspots, such as large cities and large point sources, are distinguished in the domain due to spatial allocation. Regional emission peaks were distinguished due to temporally resolved emission information. The PAR (Paraffin carbon bond) and XYL (Xylene and other polyalkyl aromatics) showed the first and second largest emission rate among VOC species. Most of point source emissions are located in layers 3 to 4, which the altitude range reaches 310–550 m AGL. Qualitative inter-comparison between model output and ground/satellite measurement showed good agreements in terms of spatial and temporal patterns. We expect that the result of this study will provide better air quality modeling inputs, which will act as a major step to improve our understanding of Asian air quality. |
Specific Policy or Decision Context Cited
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None provided | None provided |
Biophysical Context
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No additional description provided | Asia |
EM Scenario Drivers
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No scenarios presented | NA |
EM ID
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EM-434 | EM-1019 |
Method Only, Application of Method or Model Run
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Method Only | Method + Application |
New or Pre-existing EM?
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New or revised model | Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
EM ID
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EM-434 | EM-1019 |
Document ID for related EM
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None | Doc-478 |
EM ID for related EM
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None | EM-1012 | EM-1021 |
EM Modeling Approach
EM ID
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EM-434 | EM-1019 |
EM Temporal Extent
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Not applicable | 1997-2009 |
EM Time Dependence
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Not applicable | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | past time |
EM Time Continuity
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Not applicable | continuous |
EM Temporal Grain Size Value
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Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Not applicable | Not applicable |
EM ID
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EM-434 | EM-1019 |
Bounding Type
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Not applicable | Geopolitical |
Spatial Extent Name
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Not applicable | Asia |
Spatial Extent Area (Magnitude)
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Not applicable | 1000-10,000 km^2. |
EM ID
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EM-434 | EM-1019 |
EM Spatial Distribution
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Not applicable | spatially lumped (in all cases) |
Spatial Grain Type
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Not applicable | Not applicable |
Spatial Grain Size
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Not applicable | Not applicable |
EM ID
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EM-434 | EM-1019 |
EM Computational Approach
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Not applicable | Numeric |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-434 | EM-1019 |
Model Calibration Reported?
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Not applicable | Unclear |
Model Goodness of Fit Reported?
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Not applicable | Unclear |
Goodness of Fit (metric| value | unit)
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None | None |
Model Operational Validation Reported?
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No | Yes |
Model Uncertainty Analysis Reported?
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Not applicable | Unclear |
Model Sensitivity Analysis Reported?
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Not applicable | Unclear |
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-434 | EM-1019 |
None |
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-434 | EM-1019 |
None |
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Centroid Lat/Long (Decimal Degree)
EM ID
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EM-434 | EM-1019 |
Centroid Latitude
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Not applicable | 38.63 |
Centroid Longitude
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Not applicable | 117.79 |
Centroid Datum
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Not applicable | WGS84 |
Centroid Coordinates Status
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Not applicable | Estimated |
EM ID
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EM-434 | EM-1019 |
EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) | Atmosphere |
Specific Environment Type
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None identified | Asian atmosphere |
EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class | Not applicable |
Scale of differentiation of organisms modeled
EM ID
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EM-434 | EM-1019 |
EM Organismal Scale
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Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-434 | EM-1019 |
None Available | None Available |
EnviroAtlas URL
EM-434 | EM-1019 |
Average Annual Precipitation | None Available |
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
EM-434 | EM-1019 |
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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-434 | EM-1019 |
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