Multiscale spatiotemporal meteorological drought prediction:A deep learning approach
作者机构：Department of Earth System ScienceMinistry of Education Key Laboratory for Earth System ModellingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China
出 版 物：《Advances in Climate Change Research》 (气候变化研究进展（英文版）)
年 卷 期：2024年第15卷第2期
页 面：211-221页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：This work is supported by the National Key Research and Development Program of China(2022YFE0195900,2021YFC3101600,2020YFA0607900,and 2020YFA0608000) the National Natural Science Foundation of China(42125503 and 42075137)
主 题：Meteorological drought Spatiotemporal prediction Multiscale Swim transformer Deep learning
摘 要：Reliable monitoring and thorough spatiotemporal prediction of meteorological drought are crucial for early warning and decision-making regarding drought-related *** utilisation of multiscale methods is effective for a comprehensive evaluation of drought occurrence and progression,given the complex nature of meteorological ***,the nonlinear spatiotemporal features of meteorological droughts,influenced by various climatological,physical and environmental factors,pose significant challenges to integrated prediction that considers multiple indicators and time *** address these constraints,we introduce an innovative deep learning framework based on the shifted window transformer,designed for executing spatiotemporal prediction of meteorological drought across multiple *** formulate four prediction indicators using the standardized precipitation index and the standard precipitation evaporation index as core methods for drought definition using the ERA5 reanalysis *** indicators span time scales of approximately 30 d and one ***-term indicators capture more anomalous variations,whereas long-term indicators attain comparatively higher accuracy in predicting future *** focus on the East Asian region,notable for its diverse climate conditions and intricate terrains,to validate the model s efficacy in addressing the complexities of nonlinear spatiotemporal *** model s performance is evaluated from diverse spatiotemporal viewpoints,and practical application values are analysed by representative drought *** results substantiate the effectiveness of our proposed model in providing accurate multiscale predictions and capturing the spatiotemporal evolution characteristics of *** of the four drought indicators accurately delineates specific facets of the meteorological drought ***,three representative drought events,namely flash drought,sustained drought and severe drought,
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