Spatial and temporal distributions of waterlogging disasters in the summer of 2021 in Mainland China and their possible impacts

LI Mengqian; FANG Shibo; ZHU Yongchao; WU Yingjie; CAO Yun; ZHUO Wen; E Youhao

doi:10.11834/jrs.20221782

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Spatial and temporal distributions of waterlogging disasters in the summer of 2021 in Mainland China and their possible impacts
Vol. 26, Issue 9, Pages: 1886-1894(2022)
Published： 07 September 2022 ，
DOI： 10.11834/jrs.20221782

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李梦倩，房世波，朱永超，武英洁，曹云，卓文，俄有浩.2022.2021年夏季中国大陆涝渍灾害时空分布分析.遥感学报，26（9）： 1886-1894

Li M Q，Fang S B，Zhu Y C，Wu Y J，Cao Y，Zhuo W and E Y H. 2022. Spatial and temporal distributions of waterlogging disasters in the summer of 2021 in Mainland China and their possible impacts. National Remote Sensing Bulletin， 26（9）：1886-1894
李梦倩，房世波，朱永超，武英洁，曹云，卓文，俄有浩.2022.2021年夏季中国大陆涝渍灾害时空分布分析.遥感学报，26（9）： 1886-1894 DOI： 10.11834/jrs.20221782.

Li M Q，Fang S B，Zhu Y C，Wu Y J，Cao Y，Zhuo W and E Y H. 2022. Spatial and temporal distributions of waterlogging disasters in the summer of 2021 in Mainland China and their possible impacts. National Remote Sensing Bulletin， 26（9）：1886-1894 DOI： 10.11834/jrs.20221782.

摘要

涝渍害是中国除干旱以外对农作物影响最严重的气象灾害，涝渍害的发生和发展对人民的生命和财产安全以及农作物的生长发育产生巨大的影响。2021年7月至8月间，中国北方多地降水达到历史观测极值，而相应时期地面的涝渍害发生和发展状况及其时空特征并未得到有效研究。因此，本研究首先利用中国大陆土壤水分站点日数据和被动微波遥感卫星SMAP反演土壤水分日产品获取中国高精度表层土壤湿度数据（0—10 cm）；随后，结合田间持水量数据计算土壤表层相对含水量。在此基础上，以连续10 d土壤相对含水量大于等于90%为标准，分析中国大陆2021年7月1日—8月25日的涝渍害时空分布情况。结果表明：（1）融合后的土壤水分产品较原始的SMAP微波土壤水分产品精度具有明显的提高；（2）中国东北地区水稻田种植区的土壤相对含水量大于等于90%的最长持续天数均为56 d，土壤的水分长期处于饱和状态，说明了本文方法能够较为准确的反应出土壤涝渍害的情况；（3）中国东北及华北地区受灾较为严重，其中黑龙江的西部和河北、河南、山东发生的涝渍面积最大。中国大陆耕地部分受涝渍灾害区域占到总耕地面积的1/2左右，重灾区面积为1.940×10

；（4）黑龙江的西部及河北、河南、山东等地较往年降水偏多，这与涝渍害受灾区基本吻合。

Abstract

Waterlogging is the most serious meteorological disaster affecting crops in China besides drought. The occurrence of waterlogging has a great impact on the safety of people’s life and properties and the growth and development of crops. From July to August 2021

the precipitation in many places in northern China reached the historical observation extreme value

while the occurrence and development of surface waterlogging in the corresponding period and its temporal and spatial characteristics have not been effectively studied. In this study

the high-precision soil water data (0—10 cm) obtained from the daily soil water data of the soil water stations in Mainland China and the soil water daily products retrieved from passive microwave remote sensing satellite SMAP were used to calculate the soil surface relative water content combined with the soil field capacity data. The soil’s relative water content of greater than or equal to 90% for 10 consecutive days was taken as the standard. The spatial-temporal distribution of the waterlogging damage in Mainland China from July 1 to August 25 in 2021 was analyzed

and the results were comprehensively analyzed on the basis of the cultivated land distribution and precipitation data in Northeast China. The results show the following. (1) Compared with the original SMAP microwave soil moisture product

the accuracy of the fused soil moisture product is significantly improved. (2) The longest duration of soil relative water content greater than or equal to 90% in paddy fields in Northeast China was 56 days

indicating that the proposed method could accurately reflect the situation of relative soil water content. (3) Northeast and Northern China were severely affected

with the most extensive waterlogging in the west part of Heilongjiang Province and the entire area of the Hebei

Henan

and Shandong provinces. The arable area affected by waterlogging accounted for approximately half of the total arable land area in China

and the area of the worst-hit area was 1.940 ×10

. (4) The west part of the Heilongjiang province and the Hebei

Henan

and Shandong provinces received more precipitation than in previous years

which is consistent with the waterlogging disaster areas.

关键词

涝渍害微波遥感土壤相对含水量降水时空分布

Keywords

waterlogged disastermicrowave remote sensingrelative soil water contentprecipitationspatial and temporal distribution

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