Bridging groundwater models and decision support with a Bayesian network
Article first published online: 9 OCT 2013
©2013. American Geophysical Union. All Rights Reserved.
Water Resources Research
Volume 49, Issue 10, pages 6459–6473, October 2013
How to Cite
2013), Bridging groundwater models and decision support with a Bayesian network, Water Resour. Res., 49, 6459–6473, doi:10.1002/wrcr.20496., , , , and (
- Issue published online: 27 NOV 2013
- Article first published online: 9 OCT 2013
- Accepted manuscript online: 27 AUG 2013 10:20AM EST
- Manuscript Accepted: 18 AUG 2013
- Manuscript Revised: 31 JUL 2013
- Manuscript Received: 21 DEC 2012
- Bayesian Network;
- decision support
 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.