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EM: WaterWorld v2, Santa Basin, Peru (EM-618)
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EM Identity and Description
EM Identification
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EM ID
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EM-618 |
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EM Short Name
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WaterWorld v2, Santa Basin, Peru |
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EM Full Name
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WaterWorld v2, Santa Basin, Peru |
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EM Source or Collection
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None |
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EM Source Document ID
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368 |
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Document Author
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Van Soesbergen, A. and M. Mulligan |
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Document Year
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2018 |
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Document Title
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Potential outcomes of multi-variable climate change on water resources in the Santa Basin, Peru |
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Document Status
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Peer reviewed and published |
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Comments on Status
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Published journal manuscript |
Software and Access
| www.policysupport.org/waterworld | |
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Contact Name
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Arnout van Soesbergen |
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Contact Address
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Environmental Dynamics Research Group, Dept. of Geography, King's College London, Strand, London WC2R 2LS, UK |
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Contact Email
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arnout.van_soesbergen@kcl.ac.uk |
EM Description
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Summary Description
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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 |
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Specific Policy or Decision Context Cited
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None identified |
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Biophysical Context
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Large river valley located on the western slope of the Peruvian Andes between the Cordilleras Blanca and Negra. Precipitation is distinctly seasonal. |
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EM Scenario Drivers
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Scenarios base on high growth and 3.5oC warming by 2100, and scenarios based on moderate growth and 2.5oC warming by 2100 |
EM Relationship to Other EMs or Applications
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Method Only, Application of Method or Model Run
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Method + Application (multiple runs exist) View EM Runs |
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New or Pre-existing EM?
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Application of existing model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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Document ID for related EM
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None |
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EM ID for related EM
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None |
EM Modeling Approach
EM Relationship to Time
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EM Temporal Extent
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1950-2071 |
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EM Time Dependence
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time-dependent |
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EM Time Reference (Future/Past)
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both |
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EM Time Continuity
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discrete |
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EM Temporal Grain Size Value
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1 |
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EM Temporal Grain Size Unit
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Month |
EM Spatial Extent
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Bounding Type
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Watershed/Catchment/HUC |
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Spatial Extent Name
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Santa Basin |
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Spatial Extent Area (Magnitude)
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10,000-100,000 km^2 |
Spatial Distribution of Computations
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EM Spatial Distribution
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spatially distributed (in at least some cases) |
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Spatial Grain Type
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area, for pixel or radial feature |
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Spatial Grain Size
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1 km2 |
EM Structure and Computation Approach
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EM Computational Approach
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Numeric |
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EM Determinism
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deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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Model Calibration Reported?
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No |
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Model Goodness of Fit Reported?
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No |
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Goodness of Fit (metric| value | unit)
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None |
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Model Operational Validation Reported?
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Yes |
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Model Uncertainty Analysis Reported?
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No |
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Model Sensitivity Analysis Reported?
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No |
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Model Sensitivity Analysis Include Interactions?
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Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| None |
Centroid Lat/Long (Decimal Degree)
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Centroid Latitude
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-9.05 |
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Centroid Longitude
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-77.81 |
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Centroid Datum
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WGS84 |
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Centroid Coordinates Status
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Estimated |
Environments and Scales Modeled
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EM Environmental Sub-Class
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Rivers and Streams | Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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tropical, coastal to montane |
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EM Ecological Scale
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Other or unclear (comment) ?Comment:Variable data was derived from multiple climate data stations distrubuted across the study area. The location and distribution of the data stations was not provided. |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM Organismal Scale
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Not applicable |
Taxonomic level and name of organisms or groups identified
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| None Available |
EnviroAtlas URL
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| Average Annual Precipitation |
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)
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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EM Variable Names (and Units)
Predictor
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Driving Variables (and Units)
view details (2 variables)
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Constant or Factor Variables (and Units)
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None |
Intermediate
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Intermediate (Computed) Variables (and Units)
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None |
Response
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