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Study on the driving factors of spring agricultural drought in Northeast China from the perspective of atmosphere and snow cover

Author

Listed:
  • Pei, Wei
  • Su, Yi
  • Fu, Qiang
  • Ren, Yongtai
  • Li, Tianxiao

Abstract

Drought is closely related to the early stages of the water cycle. Due to the influence of snow cover and soil freezethaw processes, the water cycle process in cold regions presents specific and complex characteristics in the early spring. Therefore, the driving factors of spring drought in cold regions are different from those in warmer regions. This work takes the cold region of Northeast China as the study area. The early spring period was divided into four periods: the prefreezing period, rapid freezing period, stable freezing period, and melting period. Precipitation, evapotranspiration, and snow depth were selected as candidate driving factors in each period. The rough set conditional information entropy method was used to calculate the weights of the driving factors, and the main driving factors were preferably selected. The results indicated that as soil depth increased, the range of drought expanded. Spring drought was mainly concentrated in the western, central, and central western regions of the study area, whereas the northern, eastern, and southeastern regions were relatively humid. The precipitation and evapotranspiration during the prefreezing and melting periods had a considerable impact on spring drought, and for shallow spring drought, the impact of precipitation was greater, whereas the impact of evapotranspiration during deep drought was greater. The impermeability and evaporation suppression function of frozen soil resulted in early water retention in the soil, which in turn affected the soil moisture of the following year. During the rapid freezing period, snow depth was the main factor driving spring drought. The snow during the freezing period helped to suppress soil moisture evaporation, and the infiltration of snowmelt effectively increased the soil moisture content. Studying the driving factors of spring drought can help identify the formation mechanism of agricultural drought in cold regions and provide a reference for disaster prevention.

Suggested Citation

  • Pei, Wei & Su, Yi & Fu, Qiang & Ren, Yongtai & Li, Tianxiao, 2025. "Study on the driving factors of spring agricultural drought in Northeast China from the perspective of atmosphere and snow cover," Agricultural Water Management, Elsevier, vol. 317(C).
  • Handle: RePEc:eee:agiwat:v:317:y:2025:i:c:s0378377425003348
    DOI: 10.1016/j.agwat.2025.109620
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    References listed on IDEAS

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    1. Chengguo Wu & Yin Xu & Juliang Jin & Yuliang Zhou & Boyu Nie & Rui Li & Yi Cui & Fei Tong & Libing Zhang, 2024. "Meteorological to Agricultural Drought Propagation Time Analysis and Driving Factors Recognition Considering Time-Variant Characteristics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(3), pages 991-1010, February.
    2. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.
    3. Weijie Zhang & Hengzhi Guo & Yingjie Wu & Zezhong Zhang & Hang Yin & Kai Feng & Jian Liu & Bin Fu, 2024. "Temporal and Spatial Evolution of Meteorological Drought in Inner Mongolia Inland River Basin and Its Driving Factors," Sustainability, MDPI, vol. 16(5), pages 1-20, March.
    4. Alexander Ly & Maarten Marsman & Eric†Jan Wagenmakers, 2018. "Analytic posteriors for Pearson's correlation coefficient," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(1), pages 4-13, February.
    5. Mingxi Pan & Fang Zhao & Jingyan Ma & Lijuan Zhang & Jinping Qu & Liling Xu & Yao Li, 2022. "Effect of Snow Cover on Spring Soil Moisture Content in Key Agricultural Areas of Northeast China," Sustainability, MDPI, vol. 14(3), pages 1-15, January.
    6. Somnath Mondal & Ashok K. Mishra & Ruby Leung & Benjamin Cook, 2023. "Global droughts connected by linkages between drought hubs," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
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