Monitoring of Extreme Drought in the Yangtze River Basin in 2022 Based on Multi-Source Remote Sensing Data

被引:0
|
作者
Yu, Mingxiao [1 ]
He, Qisheng [1 ]
Jin, Rong [2 ]
Miao, Shuqi [1 ]
Wang, Rong [2 ]
Ke, Liangliang [2 ]
机构
[1] Hohai Univ, Coll Geog & Remote Sensing, Nanjing 211100, Peoples R China
[2] Hydrol & Water Resources Monitoring Ctr Xiuhe Rive, Jiujiang 332000, Peoples R China
基金
国家重点研发计划;
关键词
GRACE; drought; Yangtze river basin; GROUNDWATER DEPLETION; GRACE DATA;
D O I
10.3390/w16111502
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The Yangtze River Basin experienced a once-in-a-century extreme drought in 2022 due to extreme weather, which had a serious impact on the local agricultural production and ecological environment. In order to investigate the spatial distribution and occurrence of the extreme drought events, this study used multi-source remote sensing data to monitor the extreme drought events in the Yangtze River Basin in 2022. In this study, the gravity satellite data product CSR_Mascon was used to calculate the GRACE Drought Intensity Index (GRACE-DSI), which was analyzed and compared with the commonly used meteorological drought indices, relative soil humidity, and soil water content data. The results show that (1) terrestrial water storage change data can well reflect the change in water storage in the Yangtze River Basin. Throughout the year, the average change in terrestrial water storage in the Yangtze River Basin from January to June is higher than the average value of 33.47 mm, and the average from July to December is lower than the average value of 48.17 mm; (2) the GRACE-DSI responded well to the intensity and spatial distribution of drought events in the Yangtze River Basin region in 2022. From the point of view of drought area, the Yangtze River Basin showed a trend of extreme drought increasing first, and then decreasing in the area of different levels of drought, and the range of drought reached a maximum in September with a drought area of 175.87 km2, which accounted for 97.71 per cent of the total area; at the same time, the area of extreme drought was the largest, with an area of 85.69 km2; (3) the spatial and temporal variations of the GRACE-DSI and commonly used meteorological drought indices were well correlated, with correlation coefficients above 0.750, among which the correlation coefficient of the SPEI-3 was higher at 0.937; (4) the soil moisture and soil relative humidity products from the CLDAS, combined with soil moisture products from the GLDAS, reflect the starting and ending times of extreme drought events in the Yangtze River Basin in 2022 well, using the information from the actual stations. In conclusion, gravity satellite data, analyzed in synergy with data from multiple sources, help decision makers to better understand and respond to drought.
引用
收藏
页数:20
相关论文
共 50 条
  • [1] Temporal and spatial evolution of flash drought events in the Yangtze River basin from 1982 to 2022 based on multi-source data∗
    Xiong L.
    Li S.
    Zha X.
    Shuikexue Jinzhan/Advances in Water Science, 2024, 35 (01): : 24 - 37
  • [2] SONGHUA RIVER BASIN FLOOD MONITORING USING MULTI-SOURCE SATELLITE REMOTE SENSING DATA
    Zheng, Wei
    Shao, Jiali
    Gao, Hao
    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 9760 - 9763
  • [3] Global Drought-Wetness Conditions Monitoring Based on Multi-Source Remote Sensing Data
    Wei, Wei
    Wang, Jiping
    Ma, Libang
    Wang, Xufeng
    Xie, Binbin
    Zhou, Junju
    Zhang, Haoyan
    LAND, 2024, 13 (01)
  • [4] A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data
    Wang, Qian
    Zhao, Lin
    Wang, Mali
    Wu, Jinjia
    Zhou, Wei
    Zhang, Qipeng
    Deng, Meie
    REMOTE SENSING, 2022, 14 (19)
  • [5] The Evolution of the Urban Spatial Pattern in the Yangtze River Economic Belt: Based on Multi-Source Remote Sensing Data
    Li, Yang
    Shao, Hua
    Jiang, Nan
    Shi, Ge
    Cheng, Xin
    SUSTAINABILITY, 2018, 10 (08)
  • [6] Drought Monitoring of Winter Wheat in Henan Province, China Based on Multi-Source Remote Sensing Data
    Tian, Guizhi
    Zhu, Liming
    AGRONOMY-BASEL, 2024, 14 (04):
  • [7] Construction of a drought monitoring model using deep learning based on multi-source remote sensing data
    Shen, Runping
    Huang, Anqi
    Li, Bolun
    Guo, Jia
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2019, 79 : 48 - 57
  • [8] Changes of glacier mass balance in Manas river basin based on multi-source remote sensing data
    Zhao G.
    Zhang Z.
    Liu L.
    Xu L.
    Wang P.
    Li L.
    Ning S.
    Dili Xuebao/Acta Geographica Sinica, 2020, 75 (01): : 98 - 112
  • [9] Impacts and countermeasures of extreme drought in the Yangtze River Basin in 2022
    Xia J.
    Chen J.
    She D.
    Shuili Xuebao/Journal of Hydraulic Engineering, 2022, 53 (10): : 1143 - 1153
  • [10] Drought Monitoring of Spring Maize in the Songnen Plain Using Multi-Source Remote Sensing Data
    Pei, Zhifang
    Fan, Yulong
    Wu, Bin
    ATMOSPHERE, 2023, 14 (11)