水科学进展2026，Vol.37Issue(5)：814-824,11.DOI:10.14042/j.cnki.32.1309.2026.05.003
基于特征水体的旱情预报和等级评估方法
Drought forecasting and severity assessment method based on characteristic waterbodies
摘要
Abstract
To enhance the capability for early identification and evaluation of drought occurrence and development process,the research proposes the concept of"characteristic waterbodies"and classifies them into small waterbodies sen-sitive to drought and medium-to-large waterbodies with significant seasonal variations,exploring their application in early prediction and process assessment.For small waterbodies,a normalized small waterbody size transition index(NSWSTI)was developed based on water body size transition relationship for early drought forecasting;For medium-to-large water-bodies,a remote sensing dry-wet index(RSDWI)was proposed and combined with standardized waterbody area anomaly index(SAAI)and multi-source hydro meteorological factors to construct a comprehensive drought index.The drought events in the Poyang Lake,Dongting Lake,and Hongze Lake basins in 2022 was selected as case studies for analysis.The results show that NSWSTI respond approximately half a month earlier than observed water-level changes in the Dongting Lake and Poyang Lake basins,and also demonstrate a certain lead time relative to storage-capacity changes in the Hongze Lake basin.The comprehensive drought index can effectively reflect the development process and spatial differences of typical basin-scale drought events.关键词
旱情预报/特征水体/干旱等级评估/遥感干湿基准值/标准化水体面积异常指数Key words
drought forecasting/characteristic waterbodies/drought severity assessment/remote sensing dry-wet index(RSDWI)/standardized waterbody area anomaly index(SAAI)分类
信息技术与安全科学引用本文复制引用
孟令奎,王熙,张镇,王喆,成建国,李林宜,张文,蔡阳..基于特征水体的旱情预报和等级评估方法[J].水科学进展,2026,37(5):814-824,11.基金项目
国家自然科学基金项目(U24A20587) （U24A20587）
国家重点研发计划项目(2021YFB3900600) The study is financially supported by the National Natural Science Foundation of China(No.U24A20587)and the National Key R&D Program of China(No.2021YFB3900600). （2021YFB3900600）
