标题： 时空建模用于干旱分类和预测 显示英文标题

标题： Space time modeling for drought classification and prediction

摘要： 标准化降水指数（SPI）是监测干旱状况的关键工具，通常依赖于标准化的累积降水量。 虽然更长的历史降水记录可以提供更准确的边缘分布参数估计，但它们往往反映了非平稳性影响，如人类引起的气候变化和几十年的自然变异性。 传统方法要么忽略这种非平稳性，要么用准平稳参考期来处理它。 本研究引入了一个新颖的非平稳SPI框架，利用广义可加模型（GAMs）来灵活地建模干旱过程中固有的时空变异。 GAMs被用于估计伽马分布的参数，同时整合了双极端尾部灵活模型，以稳健地捕捉极端干旱事件的概率风险。 未来在重现期方面的干旱和湿润极端事件使用扩展的广义帕累托分布进行计算，该分布在建模数据整个分布的同时，避免了阈值选择步骤。 结果表明，所提出的非平稳SPI模型既稳定又能再现已知的非平稳干旱模式，同时为干旱演变的动力学提供了新的见解。 这种方法在变化的气候条件下对干旱建模具有重大进展。 显示英文摘要

摘要： The Standardized Precipitation Index (SPI) is a critical tool for monitoring drought conditions, typically relying on normalized accumulated precipitation. While longer historical records of precipitation yield more accurate parameter estimates of marginal distribution, they often reflect nonstationary influences such as anthropogenic climate change and multidecadal natural variability. Traditional approaches either overlook this nonstationarity or address it with quasi-stationary reference periods. This study introduces a novel nonstationary SPI framework that utilizes generalized additive models (GAMs) to flexibly model the spatiotemporal variability inherent in drought processes. GAMs are employed to estimate parameters of the Gamma distribution, while dual extreme tails flexible models are integrated to robustly capture the probabilistic risk of extreme drought events. Future drought and wet extremes events in terms of return levels are calculated using an extended generalized Pareto distribution, which offers flexibility in modeling the entire distribution of the data while bypassing the threshold selection step. Results demonstrate that the proposed nonstationary SPI model is both stable and capable of reproducing known nonstationary drought patterns, while also providing new insights into the evolving dynamics of drought. This approach represents a significant advancement in drought modeling under changing climatic conditions.