A method is presented to address model state uncertainty in hydrologic model simulation. This is achieved by introducing tuneable parameters that allow adjustments to the model states. Excessive dimensionality is avoided by introducing only a limited number of parameters that control the index (timing) and size of the state adjustments. The method is designed to compensate for issues with hydrologic model structures, particularly those relevant to the soil moisture state in a rainfall-runoff model. In the context of water resource planning and management, errors in the model states have often been overlooked as an important source of uncertainty and have the potential to significantly degrade model simulations. A synthetic study shows that a classical parameter estimation approach will produce biased distributions when state errors exist, and that the proposed state and parameter uncertainty estimation (SPUE) can remove the bias in parameter estimates for improved model simulations. In a real case study, SPUE and the classical approach are implemented in 46 sites around Australia. The results show that hydrologic parameter distributions for a selected conceptual model can be significantly different when accounting for state uncertainty. This has large implications for scenario modeling since it puts into dispute how to determine appropriate parameters for such studies. SPUE outperforms the classical approach in a range of calibration and validation metrics, particularly for sites that contain zero flows. Future work involves testing SPUE with different hydrologic models and likelihood formulations, and enhancing rigor by explicitly accounting for observational data uncertainty.

中文翻译：

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联合标定水文模型参数和状态调整

提出了一种解决水文模型模拟中模型状态不确定性的方法。这是通过引入允许调整模型状态的可调参数来实现的。通过只引入有限数量的参数来控制状态调整的索引（时间）和大小，避免了过多的维度。该方法旨在补偿水文模型结构的问题，特别是与降雨径流模型中土壤湿度状态相关的问题。在水资源规划和管理的背景下，模型状态中的错误经常被忽略为不确定性的重要来源，并且有可能显着降低模型模拟的性能。一项综合研究表明，当存在状态误差时，经典的参数估计方法会产生有偏的分布，并且提出的状态和参数不确定性估计（SPUE）可以消除参数估计中的偏差，以改进模型模拟。在一个真实的案例研究中，SPUE 和经典方法在澳大利亚的 46 个站点中实施。结果表明，在考虑状态不确定性时，所选概念模型的水文参数分布可能会有显着差异。这对情景建模具有重大意义，因为它对如何确定此类研究的适当参数存在争议。SPUE 在一系列校准和验证指标方面优于经典方法，特别是对于包含零流量的站点。未来的工作包括使用不同的水文模型和可能性公式测试 SPUE，并通过明确考虑观测数据的不确定性来提高严谨性。