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    Bridging groundwater models and decision support with a Bayesian network

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    wrcr20496.pdf (1.247Mb)
    Date
    2013-10-09
    Author
    Fienen, Michael N.  Concept link
    Masterson, John P.  Concept link
    Plant, Nathaniel G.  Concept link
    Gutierrez, Benjamin T.  Concept link
    Thieler, E. Robert  Concept link
    Metadata
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    Citable URI
    https://hdl.handle.net/1912/6757
    As published
    https://doi.org/10.1002/wrcr.20496
    DOI
    10.1002/wrcr.20496
    Keyword
     Groundwater; Hydrology; Bayes; Bayesian Network; Emulation; Decision support 
    Abstract
    Resource managers need to make decisions to plan for future environmental conditions, particularly sea level rise, in the face of substantial uncertainty. Many interacting processes factor in to the decisions they face. Advances in process models and the quantification of uncertainty have made models a valuable tool for this purpose. Long-simulation runtimes and, often, numerical instability make linking process models impractical in many cases. A method for emulating the important connections between model input and forecasts, while propagating uncertainty, has the potential to provide a bridge between complicated numerical process models and the efficiency and stability needed for decision making. We explore this using a Bayesian network (BN) to emulate a groundwater flow model. We expand on previous approaches to validating a BN by calculating forecasting skill using cross validation of a groundwater model of Assateague Island in Virginia and Maryland, USA. This BN emulation was shown to capture the important groundwater-flow characteristics and uncertainty of the groundwater system because of its connection to island morphology and sea level. Forecast power metrics associated with the validation of multiple alternative BN designs guided the selection of an optimal level of BN complexity. Assateague island is an ideal test case for exploring a forecasting tool based on current conditions because the unique hydrogeomorphological variability of the island includes a range of settings indicative of past, current, and future conditions. The resulting BN is a valuable tool for exploring the response of groundwater conditions to sea level rise in decision support.
    Description
    Author Posting. © American Geophysical Union, 2013. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Water Resources Research 49 (2013): 6459–6473, doi:10.1002/wrcr.20496.
    Collections
    • Coastal and Shelf Geology
    Suggested Citation
    Water Resources Research 49 (2013): 6459–6473
     

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