大气科学学报2025，Vol.48Issue(1)：49-61,13.DOI:10.13878/j.cnki.dqkxxb.20240613001
基于长序列遥感降水融合数据集的黄河源气象干旱特征研究
Meteorological drought characteristics in the source region of the Yellow River based on long-term fused remote sensing precipitation datasets
摘要
Abstract
Remote sensing precipitation products provide near real-time,multi-temporal,and spatially resolved precipitation data,which are essential for accurate meteorological drought monitoring.However,their accuracy is often compromised by complex terrain and extreme climate conditions.Machine learning-based data fusion methods offer a novel solution to for enhancing the precision of remote sensing precipitation products,particularly in challenging environments.This study focuses on the source region of the Yellow River,a data-scarce area with complex topography,to develop a high-resolution gridded precipitation dataset and evaluate its utility in drought monitoring. Using the Random Forest(RF)model,a long-term(1983-2018)high-accuracy precipitation dataset was generated by fusing multiple remote sensing precipitation products.The fused dataset was applied to identify mete-orological drought events using the Standardized Precipitation Index(SPI)and run theory.Temporal and spatial characteristics of drought events were analyzed to assess dataset's capability to capture drought dynamics.Key findings include:1)The fused precipitation dataset outperformed three individual remote sensing precipitation products(PERSIANN-CDR,MS WEP v2.0,and CHIRPS v2.0)at the station scale,exhibiting higher correlation coefficients(CC),lower root mean square errors(RMSE),reduced relative bias,and improved Kling-Gupta effi-ciency(KGE).The dataset accurately captured both monthly and inter annual variations,demonstrating its adapta-bility to the Yellow River source region.2)Precipitation and SPI values across four temporal scales(SPI1,SPI3,SPI6,and SPI12)exhibited statistically significant increasing trends(P＜0.05),indicating increased precipitation and a reduction meteorological drought severity over the past 36 years.3)An abrupt change in precipitation oc-curred in 2006.Prior to this point,the region experienced more frequent and severe droughts with longer durations,higher intensities,and greater extremes.After 2006,drought characteristics became milder.Spatially,the northwest of the source region experienced longer and more severe droughts,while the southeast exhibited higher drought intensity and extremes. This study provides critical insights into precipitation and drought dynamics in the source region of the Yellow River,supporting efforts in meteorological drought early warning,water resource management,and regional climate adaptation.The observed increasing precipitation trend and alleviation of drought conditions are vital for developing sustainable development strategies and disaster mitigation plans. The research underscores the potential of integrating remote sensing products with machine learning tech-niques to improve the accuracy and applicability of climate datasets,especially in regions with limited ground-based observations and complex topography.The fused dataset not only demonstrated enhanced accuracy but also provided a robust foundation for analyzing the spatiotemporal evolution of meteorological drought events.Future work could extend this approach to other regions and incorporate additional hydrometeorological variables for more comprehensive drought assessments.关键词
标准化降水指数/气象干旱/融合降水/黄河源区Key words
standardized precipitation index/meteorological drought/fusion precipitation/the source region of the Yellow River引用本文复制引用
成硕,黄曼捷,余文君,庄稼成,星寅聪,严海文,李艳忠,赵林..基于长序列遥感降水融合数据集的黄河源气象干旱特征研究[J].大气科学学报,2025,48(1):49-61,13.基金项目
第二次青藏高原综合科学考察研究项目(2019QZKK0201) （2019QZKK0201）
国家自然科学基金项目(41901076 （）
41931180) （）
2023年国家级大学生创新创业训练计划支持项目(202310300045Z) （202310300045Z）
南京信息工程大学2024届"优秀本科毕业论文(设计)支持计划"项目 （设计）
