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-133 | EM-972 |
EM Short Name
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Flood regulation supply-demand, Etropole, Bulgaria | NC HUC-12 conservation prioritization tool |
EM Full Name
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Flood regulation supply vs. demand, Municipality of Etropole, Bulgaria | NC HUC-12 conservation prioritization tool v. 1.0, North Carolina, USA |
EM Source or Collection
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EU Biodiversity Action 5 | None |
EM Source Document ID
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248 |
443 ?Comment:Doc 444 is an additional source for this EM |
Document Author
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Nedkov, S., Burkhard, B. | Warnell, K., I. Golden, and C. Canfield |
Document Year
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2012 | 2023 |
Document Title
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Flood regulating ecosystem services - Mapping supply and demand, in the Etropole municipality, Bulgaria | Conservation planning tools for NC's people & nature |
Document Status
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Peer reviewed and published | Peer reviewed and published |
Comments on Status
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Published journal manuscript | Webpage |
EM ID
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EM-133 | EM-972 |
Not applicable | https://prioritizationcobenefitstool.users.earthengine.app/view/nc-huc-12-conservation-prioritizer | |
Contact Name
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Stoyan Nedkov | Katie Warnell |
Contact Address
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National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, bl.3, 1113 Sofia, Bulgaria | Not reported |
Contact Email
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snedkov@abv.bg | katie.warnell@duke.edu |
EM ID
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EM-133 | EM-972 |
Summary Description
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ABSTRACT: "Floods exert significant pressure on human societies. Assessments of an ecosystem’s capacity to regulate and to prevent floods relative to human demands for flood regulating ecosystem services can provide important information for environmental management. Maps of demands for flood regulating ecosystem services in the study region were compiled based on a digital elevation model, land use information and accessibility data. Finally, the flood regulating ecosystem service supply and demand data were merged in order to produce a map showing regional supply-demand balances.The flood regulation ecosystem service demand map shows that areas of low or no relevant demands far exceed the areas of high and very high demands, which comprise only 0.6% of the municipality’s area. According to the flood regulation supply-demand balance map, areas of high relevant demands are located in places of low relevant supply capacities" AUTHOR'S DESCRIPTION: "A similar relative scale ranging from 0 to 5 was applied to assess the demands for flood regulation. A 0-value indicates that there is no relevant demand for flood regulation and 5 would indicate the highest demand for flood regulation within the case study region. Values of 2, 3 and 4 represent respective intermediate demands. The calculations were based on the assumption that the most vulnerable areas would have the highest demand for flood regulation. The vulnerability, defined as “the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard” (UN/ISDR, 2009), has different dimensions (e.g. social, economic, environmental, institutional). The most vulnerable places in the case study area were defined by using different sources of demographic, statistical, topographic and economic data (Nikolova et al., 2009). These areas will have the highest (5-value) demand for flood regulation…For analyzing source and sink dynamics and to identify flows of ecosystem services, the information in the matrixes and in the maps of ecosystem service supply and demand can be merged (Burkhard et al., 2012). As the landscapes’ flood regulation supply and demand are not analyzed and modeled in the same units it is not possible to calculate the balance between them quantitatively. Using the relative scale (0–5) it becomes possible to compare them and to calculate supply-demand budgets. Although this does not providea clear indication of whether there is excess supply or demand, the resulting map shows where areas of qualitatively high demand correspond with low supply and vice versa." | ABSTRACT: "Conservation organizations and land trusts in North Carolina are increasingly focused on how their work can contribute to both human and ecosystem resilience and adaptation to climate change, as well as directly mitigate climate change through carbon storage and sequestration. Recent state executive and legislative actions also underscore the importance of natural systems for climate adaptation and mitigation, and may provide additional funding for conservation and restoration for those purposes in the near term. To make it more efficient for conservation organizations working in North Carolina to consider a broad suite of conservation benefits in their work, the Conservation Trust for North Carolina and the Nicholas Institute for Energy, Environment & Sustainability at Duke University have developed two online tools for identifying priority areas for conservation action and estimating benefit metrics for specific properties. The conservation prioritization tool finds the sub-watersheds in North Carolina with the greatest potential to provide a set of user-selected conservation benefits. It allows users to identify priority areas for future conservation work within the entire state or a defined region. This high-level tool allows for quick and easy exploration without the need for spatial analysis expertise." |
Specific Policy or Decision Context Cited
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None identified | Allows users to prioritize HUCs within their area of interest based on their conservation goals. |
Biophysical Context
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Average elevation is 914 m. The mean annual temperatures gradually decrease from 9.5 to 2 degrees celcius as the elevation increases. The annual precipitation varies from 750 to 800 mm in the northern part to 1100 mm at the highest part of the mountains. Extreme preipitation is intensive and most often concentrated in certain parts of the catchment areas. Soils are represented by 5 main soil types - Cambisols, Rankers, Lithosols, Luvisols, ans Eutric Fluvisols. Most of the forest is deciduous, represented mainly by beech and hornbeam oak. | No additional description provided |
EM Scenario Drivers
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No scenarios presented | No scenarios presented |
EM ID
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EM-133 | EM-972 |
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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New or revised 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-133 | EM-972 |
Document ID for related EM
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Doc-248 |
Doc-444 ?Comment:The secondary source, document 444, is the website for running the tool. |
EM ID for related EM
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EM-130 | EM-132 | None |
EM Modeling Approach
EM ID
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EM-133 | EM-972 |
EM Temporal Extent
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Not reported | Not applicable |
EM Time Dependence
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time-stationary | time-stationary |
EM Time Reference (Future/Past)
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Not applicable | Not applicable |
EM Time Continuity
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Not applicable | Not applicable |
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-133 | EM-972 |
Bounding Type
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Geopolitical | Not applicable |
Spatial Extent Name
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Municipality of Etropole | Not applicable |
Spatial Extent Area (Magnitude)
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100-1000 km^2 | Not applicable |
EM ID
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EM-133 | EM-972 |
EM Spatial Distribution
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spatially distributed (in at least some cases) ?Comment:Distributed by land cover and soil type polygons |
spatially distributed (in at least some cases) |
Spatial Grain Type
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other (specify), for irregular (e.g., stream reach, lake basin) | map scale, for cartographic feature |
Spatial Grain Size
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Distributed by irregular land cover and soil type polygons | HUC 12 |
EM ID
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EM-133 | EM-972 |
EM Computational Approach
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Analytic | Other or unclear (comment) |
EM Determinism
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deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-133 | EM-972 |
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-133 | EM-972 |
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None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-133 | EM-972 |
None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-133 | EM-972 |
Centroid Latitude
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42.8 | Not applicable |
Centroid Longitude
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24 | Not applicable |
Centroid Datum
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WGS84 | Not applicable |
Centroid Coordinates Status
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Estimated | Not applicable |
EM ID
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EM-133 | EM-972 |
EM Environmental Sub-Class
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Rivers and Streams | Lakes and Ponds | Terrestrial Environment (sub-classes not fully specified) | Forests | Agroecosystems | Grasslands | Scrubland/Shrubland | Aquatic Environment (sub-classes not fully specified) | Terrestrial Environment (sub-classes not fully specified) |
Specific Environment Type
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Mountainous flood-prone region | Terrestrial and freshwater aquatic |
EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Ecological scale is coarser than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-133 | EM-972 |
EM Organismal Scale
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Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-133 | EM-972 |
None Available | None Available |
EnviroAtlas URL
EM-133 | EM-972 |
None Available | GAP Ecological Systems, Green Space per Capita, Ecosystem Markets: Imperiled Species and Habitats, Percent GAP Status 1 & 2, 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-133 | EM-972 |
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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>
(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
EM-133 | EM-972 |
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None |