Bridging groundwater models and decision support with a Bayesian network
Water Resources Research
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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.
Suggested Citation
Fienen, M., Masterson, J., Plant, N.G., Gutierrez, B.T., Thieler, E.R., 2013, Bridging groundwater models and decision support with a Bayesian network: Water Resources Research, v. 49, no. 10, p. 6459-6473, https://doi.org/10.1002/wrcr.20496.
ISSN: 1944-7973 (online)
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Bridging groundwater models and decision support with a Bayesian network |
| Series title | Water Resources Research |
| DOI | 10.1002/wrcr.20496 |
| Volume | 49 |
| Issue | 10 |
| Publication Date | October 09, 2013 |
| Year Published | 2013 |
| Language | English |
| Publisher | Wiley |
| Contributing office(s) | New England Water Science Center, St. Petersburg Coastal and Marine Science Center, Wisconsin Water Science Center, Woods Hole Coastal and Marine Science Center |
| Description | 15 p. |
| Larger Work Type | Article |
| Larger Work Subtype | Journal Article |
| Larger Work Title | Water Resources Research |
| First page | 6459 |
| Last page | 6473 |