Due to the significant influence of climate change and human activities on the water cycle, accurately estimating short- and long-term water availability has become imperative. This study introduces a time series model specifically crafted to decompose river flow time series, enabling estimation of trends, seasonality, and long memory components. This decomposition is interesting as it allows to separate permanent patterns, which can be associated with climate change processes, from transient effects on flow patterns. Additionally, this decomposition is incorporated into the quantile regression in quantile regression framework using a gamma function link. The estimation of this model is based on Bayesian inference, exploring the computational efficiency and accuracy of Integrated Nested Laplace Approximations. This methodology is applied to the principal rivers within the Araguaia River basin in Brazil and compared with other alternative time series decompositions with results indicating a remarkable alignment between the model and observed data.

中文翻译：

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溪流时间序列的贝叶斯结构分解


由于气候变化和人类活动对水循环的重大影响，准确估计短期和长期水资源供应量已成为当务之急。本研究引入了一个专门设计的时间序列模型，用于分解河流流量时间序列，从而能够估计趋势、季节性和长记忆成分。这种分解很有趣，因为它允许将与气候变化过程相关的永久模式与对流动模式的瞬态影响分开。此外，此分解使用 Gamma 函数链接合并到分位数回归框架中的分位数回归中。该模型的估计基于贝叶斯推理，探索了集成嵌套拉普拉斯近似的计算效率和准确性。该方法适用于巴西阿拉瓜亚河流域内的主要河流，并与其他替代时间序列分解进行比较，结果表明模型与观测数据之间具有显著的一致性。