Abstract Global precipitation products are very important datasets in flow simulations, especially in poorly gauged regions. Uncertainties resulting from precipitation products, hydrological models and their combinations vary with time and data magnitude, and undermine their application to flow simulations. However, previous studies have not quantified these uncertainties individually and explicitly. This study developed an ensemble-based dynamic Bayesian averaging approach (e-Bay) for deterministic discharge simulations using multiple global precipitation products and hydrological models. In this approach, the joint probability of precipitation products and hydrological models being correct is quantified based on uncertainties in maximum and mean estimation, posterior probability is quantified as functions of the magnitude and timing of discharges, and the law of total probability is implemented to calculate expected discharges. Six global fine-resolution precipitation products and two hydrological models of different complexities are included in an illustrative application. e-Bay can effectively quantify uncertainties and therefore generate better deterministic discharges than traditional approaches (weighted average methods with equal and varying weights and maximum likelihood approach). The mean Nash-Sutcliffe Efficiency values of e-Bay are up to 0.97 and 0.85 in training and validation periods respectively, which are at least 0.06 and 0.13 higher than traditional approaches. In addition, with increased training data, assessment criteria values of e-Bay show smaller fluctuations than traditional approaches and its performance becomes outstanding. The proposed e-Bay approach bridges the gap between global precipitation products and their pragmatic applications to discharge simulations, and is beneficial to water resources management in ungauged or poorly gauged regions across the world.

中文翻译：

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一种基于集合的动态贝叶斯平均方法，用于使用多个全球降水产品和水文模型的流量模拟

摘要 全球降水产品是流动模拟中非常重要的数据集，尤其是在测量较差的地区。降水产品、水文模型及其组合产生的不确定性随时间和数据量级而变化，并削弱了它们在流动模拟中的应用。然而，以前的研究并没有单独和明确地量化这些不确定性。本研究开发了一种基于集合的动态贝叶斯平均方法 (e-Bay)，用于使用多个全球降水产品和水文模型进行确定性流量模拟。在这种方法中，降水产品和水文模型正确的联合概率是基于最大值和平均值估计的不确定性来量化的，后验概率被量化为排放量和时间的函数，并实施总概率定律来计算预期排放。一个说明性应用程序包括六个全球精细分辨率降水产品和两个不同复杂性的水文模型。e-Bay 可以有效地量化不确定性，因此比传统方法（具有相等和不同权重的加权平均方法和最大似然方法）产生更好的确定性排放。e-Bay 的平均 Nash-Sutcliffe Efficiency 值在训练和验证期间分别高达 0.97 和 0.85，比传统方法至少高 0.06 和 0.13。此外，随着训练数据的增加，e-Bay的评估标准值比传统方法波动更小，性能更加突出。提议的 e-Bay 方法弥合了全球降水产品与其在排放模拟中的实用应用之间的差距，并有利于世界各地未测量或测量不足的地区的水资源管理。