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-367 | EM-968 | EM-981 |
EM Short Name
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InVEST Coastal Blue Carbon | EPA Stormwater Manamgement Model | Atlantis ecosystem biology submodel |
EM Full Name
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InVEST v3.0 Coastal Blue Carbon | Storm Water Management Model User's Manual Version 5.2 | Calibrating process-based marine ecosystem models: An example case using Atlantis |
EM Source or Collection
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InVEST | US EPA | None |
EM Source Document ID
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310 | 452 | 459 |
Document Author
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Natural Capital Project | Rossman, L. A., M., Simon | Pethybridge, H. R., Weijerman, M., Perrymann, H., Audzijonyte, A., Porobic, J., McGregor, V., … & Fulton, E. |
Document Year
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2014 | 2022 | 2019 |
Document Title
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Blue Carbon model - InVEST (v3.0) | Storm Water Management Model User's Manual Version 5.2 | Calibrating process-based marine ecosystem models: An example case using Atlantis |
Document Status
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Documented, not peer reviewed | Not peer reviewed but is published (explain in Comment) | Peer reviewed and published |
Comments on Status
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other | Published EPA report | Published journal manuscript |
EM ID
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EM-367 | EM-968 | EM-981 |
http://ncp-dev.stanford.edu/~dataportal/invest-releases/documentation/current_release/blue_carbon.html#running-the-model | https://www.epa.gov/water-research/storm-water-management-model-swmm | https://noaa-fisheries-integrated-toolbox.github.io/Atlantis | |
Contact Name
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Gregg Verutes | David Burden | Heidi R. Pethybridge |
Contact Address
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Stanford University | U.S. EPA Research Center for Environmental Solutions and Emergency Response (CESER) Mail Drop: 314 P.O. Box #1198 Ada, OK 74821-1198 | CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, Tasmania, 7000, Australia |
Contact Email
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gverutes@stanford.edu | burden.david@epa.gov | Heidi.Pethybridge@csiro.au |
EM ID
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EM-367 | EM-968 | EM-981 |
Summary Description
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Please note: This ESML entry describes an InVEST model version that was current as of 2014. More recent versions may be available at the InVEST website. "InVEST Coastal Blue Carbon models the carbon cycle through a bookkeeping-type approach (Houghton, 2003). This approach simplifies the carbon cycle by accounting for storage in four main pools (aboveground biomass, belowground biomass, standing dead carbon and sediment carbon… Accumulation of carbon in coastal habitats occurs primarily in sediments (Pendleton et al., 2012). The model requires users to provide maps of coastal ecosystems that store carbon, such as mangroves and seagrasses. Users must also provide data on the amount of carbon stored in the four carbon pools and the rate of annual carbon accumulation in the sediments. If local information is not available, users can draw on the global database of values for carbon stocks and accumulation rates sourced from the peer-reviewed literature that is included in the model. If data from field studies or other local sources are available, these values should be used instead of those in the global database. The model requires land cover maps, which represent changes in human use patterns in coastal areas or changes to sea level, to estimate the amount of carbon lost or gained over a specified period of time. The model quantifies carbon storage across the land or seascape by summing the carbon stored in these four carbon pools. |
EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality from primarily urban areas. The runoff component of SWMM operates on a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. Running under Windows, SWMM 5 provides an integrated environment for editing study area input data, running hydrologic, hydraulic and water quality simulations, and viewing the results in a variety of formats. These include color coded drainage area and conveyance system maps, time series graphs and tables, profile plots, and statistical frequency analyses. This user’s manual describes in detail how to run SWMM 5.2. It includes instructions on how to build a drainage system model, how to set various simulation options, and how to view results in a variety of formats. It also describes the different types of files used by SWMM and provides useful tables of parameter values. Detailed descriptions of the theory behind SWMM 5 and the numerical methods it employs can be found in a separate set of reference manuals. ?Comment:The variables used for this ESML entry were derived from the quick tutorial section of the SWMM manual. |
Calibration of complex, process-based ecosystem models is a timely task with modellers challenged by many parameters, multiple outputs of interest and often a scarcity of empirical data. Incorrect calibration can lead to unrealistic ecological and socio-economic predictions with the modeller’s experience and available knowledge of the modelled system largely determining the success of model calibration. Here we provide an overview of best practices when calibrating an Atlantis marine ecosystem model, a widely adopted framework that includes the parameters and processes comprised in many different ecosystem models. We highlight the importance of understanding the model structure and data sources of the modelled system. We then focus on several model outputs (biomass trajectories, age distributions, condition at age, realised diet proportions, and spatial maps) and describe diagnostic routines that can assist modellers to identify likely erroneous parameter values. We detail strategies to fine tune values of four groups of core parameters: growth, predator-prey interactions, recruitment and mortality. Additionally, we provide a pedigree routine to evaluate the uncertainty of an Atlantis ecosystem model based on data sources used. Describing best and current practices will better equip future modellers of complex, processed-based ecosystem models to provide a more reliable means of explaining and predicting the dynamics of marine ecosystems. Moreover, it promotes greater transparency between modellers and end-users, including resource managers. |
Specific Policy or Decision Context Cited
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None identified | NA | N/A |
Biophysical Context
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Land use land class; habitat type | NA | Marine ecosystem |
EM Scenario Drivers
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Land use land cover changes; habitat disturbance | NA | No scenarios presented |
EM ID
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EM-367 | EM-968 | EM-981 |
Method Only, Application of Method or Model Run
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Method Only | Method Only | Method Only |
New or Pre-existing EM?
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New or revised model | 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-367 | EM-968 | EM-981 |
Document ID for related EM
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None | None | Doc-456 |
EM ID for related EM
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None | EM-971 | EM-978 | EM-983 | EM-985 | EM-990 | EM-991 |
EM Modeling Approach
EM ID
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EM-367 | EM-968 | EM-981 |
EM Temporal Extent
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Not applicable | Not applicable | Not applicable |
EM Time Dependence
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time-dependent | time-dependent | time-dependent |
EM Time Reference (Future/Past)
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Not applicable | both | Not applicable |
EM Time Continuity
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discrete | continuous | continuous |
EM Temporal Grain Size Value
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1 | Not applicable | Not applicable |
EM Temporal Grain Size Unit
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Year | Not applicable | Not applicable |
EM ID
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EM-367 | EM-968 | EM-981 |
Bounding Type
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Not applicable | No location (no locational reference given) | Not applicable |
Spatial Extent Name
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Not applicable | Not applicable | Not applicable |
Spatial Extent Area (Magnitude)
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Not applicable | Not applicable | Not applicable |
EM ID
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EM-367 | EM-968 | EM-981 |
EM Spatial Distribution
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spatially distributed (in at least some cases) | spatially distributed (in at least some cases) | Not applicable |
Spatial Grain Type
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volume, for 3-D feature | area, for pixel or radial feature | Not applicable |
Spatial Grain Size
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user-specified | mm | Not applicable |
EM ID
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EM-367 | EM-968 | EM-981 |
EM Computational Approach
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Analytic | Analytic | Analytic |
EM Determinism
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deterministic | deterministic | deterministic |
Statistical Estimation of EM
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EM ID
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EM-367 | EM-968 | EM-981 |
Model Calibration Reported?
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Not applicable | Not applicable | Yes |
Model Goodness of Fit Reported?
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Not applicable | Not applicable | Not applicable |
Goodness of Fit (metric| value | unit)
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None | None | None |
Model Operational Validation Reported?
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Not applicable | Not applicable | Not applicable |
Model Uncertainty Analysis Reported?
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Not applicable | Not applicable | Not applicable |
Model Sensitivity Analysis Reported?
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Not applicable | Not applicable | Not applicable |
Model Sensitivity Analysis Include Interactions?
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Not applicable | Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
EM-367 | EM-968 | EM-981 |
None | None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
EM-367 | EM-968 | EM-981 |
None | None | None |
Centroid Lat/Long (Decimal Degree)
EM ID
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EM-367 | EM-968 | EM-981 |
Centroid Latitude
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-9999 | Not applicable | Not applicable |
Centroid Longitude
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-9999 | Not applicable | Not applicable |
Centroid Datum
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Not applicable | Not applicable | Not applicable |
Centroid Coordinates Status
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Not applicable | Not applicable | Not applicable |
EM ID
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EM-367 | EM-968 | EM-981 |
EM Environmental Sub-Class
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Inland Wetlands | Near Coastal Marine and Estuarine | Terrestrial Environment (sub-classes not fully specified) | Aquatic Environment (sub-classes not fully specified) | Rivers and Streams | Inland Wetlands | Lakes and Ponds | Near Coastal Marine and Estuarine | Open Ocean and Seas |
Specific Environment Type
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user specified | User-defined catchments | Multiple |
EM Ecological Scale
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Ecological scale is finer than that of the Environmental Sub-class | Other or unclear (comment) | Ecological scale corresponds to the Environmental Sub-class |
Scale of differentiation of organisms modeled
EM ID
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EM-367 | EM-968 | EM-981 |
EM Organismal Scale
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Not applicable | Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
EM-367 | EM-968 | EM-981 |
None Available | None Available | None Available |
EnviroAtlas URL
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Carbon Storage by Tree Biomass | None Available | 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-367 | EM-968 | EM-981 |
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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-367 | EM-968 | EM-981 |
None |
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