EcoService Models Library (ESML)
loading
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
em.detail.idHelp
?
|
EM-630 |
EM-686 |
|
EM Short Name
em.detail.shortNameHelp
?
|
WaterWorld v2, Santa Basin, Peru | Estuary recreational use, Cape Cod, MA |
|
EM Full Name
em.detail.fullNameHelp
?
|
WaterWorld v2, Santa Basin, Peru | Estuary recreational use, Cape Cod, MA |
|
EM Source or Collection
em.detail.emSourceOrCollectionHelp
?
|
None | US EPA |
|
EM Source Document ID
|
368 | 387 |
|
Document Author
em.detail.documentAuthorHelp
?
|
Van Soesbergen, A. and M. Mulligan | Mulvaney, K K., Atkinson, S.F., Merrill, N.H., Twichell, J.H., and M.J. Mazzotta |
|
Document Year
em.detail.documentYearHelp
?
|
2018 | 2019 |
|
Document Title
em.detail.sourceIdHelp
?
|
Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru | Quantifying Recreational Use of an Estuary: A case study of three bays, Cape Cod, USA |
|
Document Status
em.detail.statusCategoryHelp
?
|
Peer reviewed and published | Peer reviewed but unpublished (explain in Comment) |
|
Comments on Status
em.detail.commentsOnStatusHelp
?
|
Published journal manuscript | Draft manuscript-work progressing |
|
EM ID
em.detail.idHelp
?
|
EM-630 |
EM-686 |
| www.policysupport.org/waterworld | Not applicable | |
|
Contact Name
em.detail.contactNameHelp
?
|
Arnout van Soesbergen | Mulvaney, Kate |
|
Contact Address
|
Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK | US EPA, ORD, NHEERL, Atlantic Ecology Division, Narragansett, RI |
|
Contact Email
|
arnout.van_soesbergen@kcl.ac.uk | Mulvaney.Kate@epa.gov |
|
EM ID
em.detail.idHelp
?
|
EM-630 |
EM-686 |
|
Summary Description
em.detail.summaryDescriptionHelp
?
|
ABSTRACT: "Water resources in the Santa basin in the Peruvian Andes are increasingly under pressure from climate change and population increases. Impacts of temperature-driven glacier retreat on stream flow are better studied than those from precipitation changes, yet present and future water resources are mostly dependent on precipitation which is more difficult to predict with climate models. This study combines a broad range of projections from climate models with a hydrological model (WaterWorld), showing a general trend towards an increase in water availability due to precipitation increases over the basin. However, high uncertainties in these projections necessitate the need for basin-wide policies aimed at increased adaptability." AUTHOR'S DESCRIPTION: "WaterWorld is a fully distributed, process-based hydrological model that utilises remotely sensed and globally available datasets to support hydrological analysis and decision-making at national and local scales globally, with a particular focus on un-gauged and/or data-poor environments, which makes it highly suited to this study. The model (version 2) currently runs on either 10 degree tiles, large river basins or countries at 1-km2 resolution or 1 degree tiles at 1-ha resolution utilising different datasets. It simulates a hydrological baseline as a mean for the period 1950-2000 and can be used to calculate the hydrological impact of scenarios of climate change, land use change, land management options, impacts of extractives (oil & gas and mining) and impacts of changes in population and demography as well as combinations of these. The model is ‘self parameterising’ (Mulligan, 2013a) in the sense that all data required for model application anywhere in the world is provided with the model, removing a key barrier to model application. However, if users have better data than those provided, it is possible to upload these to WaterWorld as GIS files and use them instead. Results can be viewed visually within the web browser or downloaded as GIS maps. The model’s equations and processes are described in more detail in Mulligan and Burke (2005) and Mulligan (2013b). The model parameters are not routinely calibrated to observed flows as it is designed for hydrological scenario analysis in which the physical basis of its parameters must be retained and the model is also often used in un-gauged basins. Calibration is inappropriate under these circumstances (Sivapalan et al., 2003). The freely available nature of the model means that anyone can apply it and replicate the results shown here. WaterWorld’s (V2) snow and ice module is capable of simulating the processes of melt water production, snow fall and snow pack, making this version highly suited to the current application. The model component is based on a full energy-balance for snow accumulation and melting based on Walter et al., (2005) with input data provided globally by the SimTerra database (Mulligan, 2011) upon which the model r | [ABSTRACT: "Estimates of the types and number of recreational users visiting an estuary are critical data for quantifying the value of recreation and how that value might change with variations in water quality or other management decisions. However, estimates of recreational use are minimal and conventional intercept surveys methods are often infeasible for widespread application to estuaries. Therefore, a practical observational sampling approach was developed to quantify the recreational use of an estuary without the use of surveys. Designed to be simple and fast to allow for replication, the methods involved the use of periodic instantaneous car counts multiplied by extrapolation factors derived from all-day counts. This simple sampling approach can be used to estimate visitation to diverse types of access points on an estuary in a single day as well as across multiple days. Evaluation of this method showed that when periodic counts were taken within a preferred time window (from 11am-4:30pm), the estimates were within 44 percent of actual daily visitation. These methods were applied to the Three Bays estuary system on Cape Cod, USA. The estimated combined use across all its public access sites is similar to the use at a mid-sized coastal beach, demonstrating the value of estuarine systems. Further, this study is the first to quantify the variety and magnitude of recreational uses at several different types of access points throughout the estuary using observational methods. This model focused on the various use by access point type (beaches, landings and way to water, boat use). This work can be transferred to the many small coastal access points used for recreation across New England and beyond." ] |
|
Specific Policy or Decision Context Cited
em.detail.policyDecisionContextHelp
?
|
None identified | None identified |
|
Biophysical Context
|
Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. | None identified |
|
EM Scenario Drivers
em.detail.scenarioDriverHelp
?
|
Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 | N/A |
|
EM ID
em.detail.idHelp
?
|
EM-630 |
EM-686 |
|
Method Only, Application of Method or Model Run
em.detail.methodOrAppHelp
?
|
Method + Application (multiple runs exist) | Method + Application (multiple runs exist) View EM Runs |
|
New or Pre-existing EM?
em.detail.newOrExistHelp
?
|
Application of existing model | New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
|
EM ID
em.detail.idHelp
?
|
EM-630 |
EM-686 |
|
Document ID for related EM
em.detail.relatedEmDocumentIdHelp
?
|
None | None |
|
EM ID for related EM
em.detail.relatedEmEmIdHelp
?
|
None | EM-682 | EM-684 | EM-685 |
EM Modeling Approach
|
EM ID
em.detail.idHelp
?
|
EM-630 |
EM-686 |
|
EM Temporal Extent
em.detail.tempExtentHelp
?
|
1950-2071 | Summer 2017 |
|
EM Time Dependence
em.detail.timeDependencyHelp
?
|
time-dependent | time-dependent |
|
EM Time Reference (Future/Past)
em.detail.futurePastHelp
?
|
both | past time |
|
EM Time Continuity
em.detail.continueDiscreteHelp
?
|
discrete | discrete |
|
EM Temporal Grain Size Value
em.detail.tempGrainSizeHelp
?
|
1 | 1 |
|
EM Temporal Grain Size Unit
em.detail.tempGrainSizeUnitHelp
?
|
Month | Day |
|
EM ID
em.detail.idHelp
?
|
EM-630 |
EM-686 |
|
Bounding Type
em.detail.boundingTypeHelp
?
|
Watershed/Catchment/HUC | Physiographic or ecological |
|
Spatial Extent Name
em.detail.extentNameHelp
?
|
Santa Basin | Three Bays, Cape Cod |
|
Spatial Extent Area (Magnitude)
em.detail.extentAreaHelp
?
|
10,000-100,000 km^2 | 1000-10,000 km^2. |
|
EM ID
em.detail.idHelp
?
|
EM-630 |
EM-686 |
|
EM Spatial Distribution
em.detail.distributeLumpHelp
?
|
spatially distributed (in at least some cases) | spatially distributed (in at least some cases) |
|
Spatial Grain Type
em.detail.spGrainTypeHelp
?
|
area, for pixel or radial feature | length, for linear feature (e.g., stream mile) |
|
Spatial Grain Size
em.detail.spGrainSizeHelp
?
|
1 km2 | beach length |
|
EM ID
em.detail.idHelp
?
|
EM-630 |
EM-686 |
|
EM Computational Approach
em.detail.emComputationalApproachHelp
?
|
* | Numeric |
|
EM Determinism
em.detail.deterStochHelp
?
|
deterministic | deterministic |
|
Statistical Estimation of EM
em.detail.statisticalEstimationHelp
?
|
None |
|
|
EM ID
em.detail.idHelp
?
|
EM-630 |
EM-686 |
|
Model Calibration Reported?
em.detail.calibrationHelp
?
|
No | Yes |
|
Model Goodness of Fit Reported?
em.detail.goodnessFitHelp
?
|
No | No |
|
Goodness of Fit (metric| value | unit)
em.detail.goodnessFitValuesHelp
?
|
None | None |
|
Model Operational Validation Reported?
em.detail.validationHelp
?
|
Yes | No |
|
Model Uncertainty Analysis Reported?
em.detail.uncertaintyAnalysisHelp
?
|
No | No |
|
Model Sensitivity Analysis Reported?
em.detail.sensAnalysisHelp
?
|
No | No |
|
Model Sensitivity Analysis Include Interactions?
em.detail.interactionConsiderHelp
?
|
Not applicable | Not applicable |
EM Locations, Environments, Ecology
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
| EM-630 |
EM-686 |
| None | None |
Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
| EM-630 |
EM-686 |
| None |
|
Centroid Lat/Long (Decimal Degree)
|
EM ID
em.detail.idHelp
?
|
EM-630 |
EM-686 |
|
Centroid Latitude
em.detail.ddLatHelp
?
|
-9.05 | 41.62 |
|
Centroid Longitude
em.detail.ddLongHelp
?
|
-77.81 | -70.42 |
|
Centroid Datum
em.detail.datumHelp
?
|
WGS84 | WGS84 |
|
Centroid Coordinates Status
em.detail.coordinateStatusHelp
?
|
Estimated | Estimated |
|
EM ID
em.detail.idHelp
?
|
EM-630 |
EM-686 |
|
EM Environmental Sub-Class
em.detail.emEnvironmentalSubclassHelp
?
|
None | Near Coastal Marine and Estuarine |
|
Specific Environment Type
em.detail.specificEnvTypeHelp
?
|
tropical, coastal to montane | Beaches |
|
EM Ecological Scale
em.detail.ecoScaleHelp
?
|
Other or unclear (comment) | Ecological scale is finer than that of the Environmental Sub-class |
Scale of differentiation of organisms modeled
|
EM ID
em.detail.idHelp
?
|
EM-630 |
EM-686 |
|
EM Organismal Scale
em.detail.orgScaleHelp
?
|
Not applicable | Not applicable |
Taxonomic level and name of organisms or groups identified
| EM-630 |
EM-686 |
| None Available | None Available |
EnviroAtlas URL
| EM-630 |
EM-686 |
| 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-630 |
EM-686 |
| 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-630 |
EM-686 |
| None |
|
Home
Search EMs
My
EMs
Learn about
ESML
Show Criteria
Hide Criteria