Groundwater model data assimilation (DA) aims to reduce uncertainty in simulated outcomes of interest to resource management while minimizing the potential for predictive bias. Sequential DA, which can estimate model states along with properties and stresses dynamically in time, offers a potentially powerful alternative to batch DA (i.e., history matching) for reducing bias in decision-relevant predictions in the presence of incorrect model structure and/or processes. This study evaluates the ability of batch and sequential DA approaches to history match and forecast simulated quantities in the presence of model error using a novel ensemble-based paired complex–simple approach that enables the incorporation of stochastic uncertainty and a statistical evaluation of predictive bias. Our findings have implications for groundwater decision support modeling as they underscore the pitfalls of fixing parameters and forcing variables a priori and present a proof of concept for using adjustable model states to cope with model error.

地下水模型数据同化 (DA) 旨在减少对资源管理感兴趣的模拟结果的不确定性，同时最大限度地减少预测偏差的可能性。顺序 DA 可以及时动态地估计模型状态以及属性和应力，为批量 DA（即历史匹配）提供了一种潜在的强大替代方案，用于在存在不正确的模型结构和/或过程的情况下减少决策相关预测中的偏差. 本研究评估了批处理和顺序 DA 方法在存在模型错误的情况下进行历史匹配和预测模拟量的能力，该方法使用一种新的基于集合的配对复简单方法，该方法能够结合随机不确定性和预测偏差的统计评估。先验并提出使用可调整模型状态来处理模型错误的概念证明。