Bridging groundwater models and decision support with a Bayesian network
Bridging groundwater models and decision support with a Bayesian network
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DOI：
10.1002/wrcr.20496
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发表时间：
2013-10-01
影响因子：
5.4
通讯作者：
Thieler, E. Robert
中科院分区：
文献类型：
--
作者：
Fienen, Michael N.;Masterson, John P.;Thieler, E. Robert
面对巨大的不确定性，资源管理者需要做出决定，为未来的环境状况，特别是海平面上升做出规划。许多相互作用的过程都是他们所面临的决策的因素。过程模型的进步和不确定性的量化使模型成为这一目的的宝贵工具。长时间的模拟运行时间和经常的数值不稳定性使得链接过程模型在许多情况下是不切实际的。一种模拟模型输入和预测之间重要联系的方法，在传播不确定性的同时，有可能在复杂的数值过程模型与决策所需的效率和稳定性之间建立一座桥梁。我们使用贝叶斯网络(BN)来模拟地下水流动模型来探索这一点。我们扩展了以前的方法，通过使用美国弗吉尼亚州和马里兰州阿萨蒂格岛地下水模型的交叉验证来计算预测技能来验证BN。由于其与岛屿形态和海平面的联系，这种BN模拟显示了地下水系统的重要地下水流动特征和不确定性。与多个可选BN设计的验证相关联的预测功率度量指导选择最佳水平的BN复杂性。阿萨蒂格岛是探索基于当前条件的预报工具的理想测试案例，因为该岛独特的水文地貌变化包括一系列指示过去、当前和未来条件的环境。由此产生的BN是探索地下水条件对海平面上升的反应的宝贵工具，有助于决策支持。
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.