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Using distributed root soil moisture data to enhance the performance of rainfall thresholds for landslide warning

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  • Yuxin Guo

    (China University of Geosciences
    National Engineering Research Center of Geographic Information System)

  • Zhanya Xu

    (China University of Geosciences
    National Engineering Research Center of Geographic Information System)

  • Shuang Zhu

    (China University of Geosciences
    National Engineering Research Center of Geographic Information System)

  • Xiangang Luo

    (China University of Geosciences
    National Engineering Research Center of Geographic Information System)

  • Yinli Xiao

    (China University of Geosciences
    National Engineering Research Center of Geographic Information System)

Abstract

Rainfall-induced landslides are currently one of the most frequent disasters in China. Compared with rainfall, the increase of soil moisture and its continuous infiltration of soil are the direct factors leading to landslides. However, few researches have studied landslide forecasting taking the soil moisture into consideration. In addition, soil moisture data have an important depth attribute. Soil moisture in root zone is difficult to obtain, and the separate comparison of the impact of the root zone and shallow soil moisture on landslide is more scarce. After comparing the commonly used satellite data, this article chose the CLDAS-V2.0 data set as the source of soil moisture with the depths of 0–10 cm and 100–200 cm. One hundred and sixty-six rainfall-induced landslides that occurred in Tongzi and Xishui counties from February to July 2020 were studied. This paper first obtains the effective rainfall that has the strongest correlation with the landslide and then uses the effective rainfall to explore the best combination of rainfall and soil moisture, including separate modeling and joint modeling of rainfall and soil moisture. Then support vector machine, logistic regression and three decision tree models are developed to predict the landslides. The results show that the combined model of rainfall and soil moisture is better than the model that only considers rainfall or soil moisture, and the landslide forecasting accuracy is improved by more than 5%, which is about 30% higher than the traditional ED rainfall threshold method. Landslide prediction model is proposed to be used as a help for urban planning and government decision-making.

Suggested Citation

  • Yuxin Guo & Zhanya Xu & Shuang Zhu & Xiangang Luo & Yinli Xiao, 2023. "Using distributed root soil moisture data to enhance the performance of rainfall thresholds for landslide warning," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(2), pages 1167-1192, January.
    • Handle: RePEc:spr:nathaz:v:115:y:2023:i:2:d:10.1007_s11069-022-05588-1
    DOI: 10.1007/s11069-022-05588-1
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    References listed on IDEAS

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    1. Markus Reichstein & Gustau Camps-Valls & Bjorn Stevens & Martin Jung & Joachim Denzler & Nuno Carvalhais & Prabhat, 2019. "Deep learning and process understanding for data-driven Earth system science," Nature, Nature, vol. 566(7743), pages 195-204, February.
    2. H. Pourghasemi & H. Moradi & S. Fatemi Aghda, 2013. "Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 69(1), pages 749-779, October.
    3. Martin Kuradusenge & Santhi Kumaran & Marco Zennaro, 2020. "Rainfall-Induced Landslide Prediction Using Machine Learning Models: The Case of Ngororero District, Rwanda," IJERPH, MDPI, vol. 17(11), pages 1-20, June.
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