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

  • role： First author第一作者
  • Affiliation：

    Chinese Academy of Meteorological Sciences, Beijing 100081, China

  • Email：1207914744@qq.com
  • Introduction：李梦倩，研究方向为卫星遥感与农业气象。E-mail：1207914744@qq.com
LI Mengqian1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Chinese Academy of Meteorological Sciences, Beijing 100081, China

    Collaborative Innovation Center for Meteorological Disaster Warning, Nanjing University of Information Science & Technology, Nanjing 210044, China

  • Email：fangshibo@cma.gov.cn
  • Introduction：房世波，研究方向为卫星遥感与农业气象。E-mail：fangshibo@cma.gov.cn
FANG Shibo12*,  
  • Affiliation：

    Meteorological Observation Center, China Meteorological Administration, Beijing 100044, China

ZHU Yongchao3,  
  • Affiliation：

    Shandong Meteorological Information Center, Jinan 250031, China

WU Yingjie4,  
  • Affiliation：

    National Meteorological Center, Beijing 100081, China

CAO Yun5,  
  • Affiliation：

    Chinese Academy of Meteorological Sciences, Beijing 100081, China

ZHUO Wen1,  
  • Affiliation：

    Chinese Academy of Meteorological Sciences, Beijing 100081, China

E Youhao1

Resümee

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 ×105 km2. (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.

Schlüsselwort

waterlogged disaster;microwave remote sensing;relative soil water content;precipitation;spatial and temporal distribution

References

  1. 1.
    Azimi S, Dariane A B, Modanesi S, Bauer-Marschallinger B, Bindlish R, Wagner W and Massari C. 2020. Assimilation of Sentinel 1 and SMAP-based satellite soil moisture retrievals into SWAT hydrological model: the impact of satellite revisit time and product spatial resolution on flood simulations in small basins. Journal of Hydrology, 581: 124367
  2. 2.
    Huang S F. 2013. Application of remote sensing technology in flood disaster monitoring and evaluation in China. Disaster Reduction in China, (24): 36-37
  3. 3.
    Huo Z G, Fan Y X, Yang J Y and Shang Y. 2017. Review on agricultural flood disaster in China. Journal of Applied Meteorological Science, 28(6): 641-653
  4. 4.
    Li B X. 2020. Soil Moisture Retrieval in Vegetation Covered Agricultural Areas Based on Optical and Microwave Remote Sensing Data. Nanchang: 95
  5. 5.
    Li J, Yan H P and Zhu Z W. 2020. Quantitative analysis of changes of summer extremes temperature and precipitation days over China with respect to the mean temperature increase. Plateau Meteorology, 39(3): 532-542
  6. 6.
    Liu X N. 1999. Climatic characteristics of extreme rainstorm events in China. Journal of Catastrophology, 14(1): 54-59
  7. 7.
    Ma J W, Sun Y Y, Chen D Q, Huang S F, Li X T, Cui Q and Yong X. 2017. Applications of GF-3 satellite in flood and landslide disasters emergency monitoring. Spacecraft Engineering, 26(6): 161-166
  8. 8.
    Qian L, Wang X G, Luo W B and Wu L. 2013. Experimental study on Morgan model under waterlogging stress. Transactions of the Chinese Society of Agricultural Engineering, 29(16): 92-101
  9. 9.
    Sekhon H S, Setia R, Singh S P, Kingra P K and Ansari J. 2021. Spatio-temporal analysis of the relationship between climate variables and waterlogging using satellite remote sensing. Arabian Journal of Geosciences, 14(14): 1306
  10. 10.
    Sun Q F, Liang J C, Meng H W, Wang M, Huang L P and Shen C M. 2021. Temporal changes of coherent droughts and floods over Yunnan in rainy season during the past five and a half centuries. Quaternary Sciences, 41(2): 379-388
  11. 11.
    Wang J X, Qiu S K, Wang Z, Wang C and Du J. 2021. Flood disaster monitoring based on water body extraction of GF-3 image in Zhengzhou. Henan Science, 39(10): 1701-1706
  12. 12.
    Wang M, Gao G Q, Wang A J, Zhang L, Wan X J and Yuan L W. 2020. Relationship between the area suffered agricultural flood disaster and rainstorm in Tangshan. Journal of Meteorology and Environment, 36(3): 93-97
  13. 13.
    Wu D, Li Z H, Zhu Y C, Li X, Wu Y J and Fang S B. 2021. A new agricultural drought index for monitoring the water stress of winter wheat. Agricultural Water Management, 244: 106599
  14. 14.
    Xie H Y, Zhang S Y, Hou S C and Zheng X. 2018. Comparison research on rainfall interpolation methods for small sample areas. Research of Soil and Water Conservation, 25(3): 117-121
  15. 15.
    Zhai P M and Pan X H. 2003. Change in extreme temperature and precipitation over northern China during the second half of the 20th century. Acta Geographica Sinica, 58(S1): 1-10
  16. 16.
    Zhang A M, Ma X Q, Yang T M, Sheng S X and Huang Y. 2007. The influence of drought and waterlogging disasters on crop yields in Anhui province. Journal of Applied Meteorological Science, 18(5): 619-626
  17. 17.
    Zhang B W and Wan J H. 2013. Assessment and analysis of flood and water logging of China from 2003 to 2012. China Water Resources, (11): 35-37
  18. 18.
    Zhang M M, Li Z, Tian B S, Zhou J M and Tang P P. 2016. The backscattering characteristics of wetland vegetation and water-level changes detection using multi-mode SAR: a case study. International Journal of Applied Earth Observation and Geoinformation, 45: 1-13
  19. 19.
    Zhong Z Z, Zhao J B, Li K H and Yan Y H. 1994. The drought-waterlogging index or the join zone between Qinling Mountain and Huanghuai Plain. Agricultural Research in the Arid Areas, 12(4): 105-112
  20. 20.
    Zhu Y C, Li X, Pearson S, Wu D L, Sun R J, Johnson S, Wheeler J and Fang S B. 2019. Evaluation of Fengyun-3C soil moisture products using in-situ data from the Chinese automatic soil moisture observation stations: a case study in Henan Province, China. Water, 11(2): 248

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