Landslides triggered by heavy rains are increasing in number and creating severe losses in hilly regions across the world. Rainfall thresholds on regional and local-scales are being used for forecasting such events, for efficient early warning. Empirical and probabilistic approaches for defining rainfall thresholds are traditional tools which are being used as part of the forecasting system for rainfall induced landslides. Such methods are easy-to-use and are based on statistical analyses. They can be derived without looking into the complex hydro-geological processes involved in slope failures, but are often associated with the disadvantage of higher false alarms, limiting their applications in a regional landslide early warning system (LEWS). This study is an attempt to improve the performance of conventional meteorological thresholds by considering the effect of soil moisture, using a probabilistic approach. Idukki district in southern part of India is highly susceptible to landslides and has witnessed major socio-economical setbacks in the recent disasters happened in 2018 and 2019. This tourist hub is now in need of a landslide forecasting system, which can help in landslide risk reduction. This study attempts to understand the effect of averaged soil moisture estimates derived from passive microwave remote sensing data, for improving the performance of conventional empirical and probabilistic thresholds. For defining empirical thresholds, an algorithm-based approach such as Calculation of Thresholds for Rainfall-induced Landslides Tool (CTRL-T) has been used. Probabilistic thresholds were defined using a Bayesian approach, finding the posterior probability of occurrence using the marginal and conditional probabilities of the control parameters along with the prior probability of occurrence of landslide. The derived rainfall thresholds were quantitatively compared with the Bayesian probabilistic threshold derived using rainfall severity and soil wetness using an area under the curve (AUC) based on receiver operating characteristics (ROC) curve method. The results show that when the antecedent moisture content in soil is less, only severe rainfall events can trigger landslides in the study area; while less severe rainfall events can also trigger landslides when the soil is wet. The role of soil wetness in the initiation is used to improve the performance of the conventional methods, and a ROC approach was used for the statistical comparison of different models. Further, the results indicated that the probabilistic threshold using rainfall severity and soil wetness outperformed the conventional approaches with AUC of 0.96, being the most sensitive and specific among the models considered. This result opens new promising perspectives for the development of an operational LEWS in the Idukki district based on a combination of rainfall and soil moisture data. Moreover, this work contributes to strengthen the advancing trend of hydro-meteorological thresholds based on soil moisture, which is gaining a growing attention in landslide studies and that, to date, was lacking evidences in monsoon regions.

由大雨引发的滑坡数量正在增加，并在世界各地的丘陵地区造成了严重损失。区域和地方尺度的降雨阈值被用于预报此类事件，以进行有效的预警。定义降雨阈值的经验和概率方法是传统工具，它们被用作降雨诱发滑坡预测系统的一部分。这样的方法易于使用并且基于统计分析。它们的产生无需考虑斜坡失灵所涉及的复杂水文地质过程，但通常伴随着较高的虚假警报，限制了它们在区域滑坡预警系统（LEWS）中的应用。这项研究是尝试通过一种概率方法，通过考虑土壤水分的影响来改善常规气象阈值的性能。印度南部的Idukki地区非常容易发生滑坡，并且在2018年和2019年发生的最近灾难中见证了重大的社会经济挫折。这个旅游枢纽现在需要滑坡预测系统，可以帮助降低滑坡风险。这项研究试图了解从被动微波遥感数据得出的平均土壤湿度估算值的影响，以改善常规经验阈值和概率阈值的性能。为了定义经验阈值，已使用基于算法的方法，例如降雨诱发的滑坡工具的阈值计算（CTRL-T）。概率阈值是使用贝叶斯方法定义的，使用控制参数的边际和条件概率以及滑坡先发概率来找到后发概率。使用基于接收器工作特征（ROC）曲线方法的曲线下面积（AUC），将得出的降雨阈值与使用降雨强度和土壤湿度得出的贝叶斯概率阈值进行定量比较。结果表明，当土壤中的前期含水量较少时，只有严重的降雨事件才能触发研究区的滑坡。而不太严重的降雨事件也可能在土壤潮湿时引发滑坡。土壤湿度在引发过程中的作用是用来改善常规方法的性能，ROC方法用于不同模型的统计比较。此外，结果表明，使用降雨强度和土壤湿度的概率阈值优于传统方法（AUC为0.96），这是所考虑模型中最敏感和最具体的模型。这一结果为结合降雨和土壤湿度数据在Idukki地区开发LEWS作业开辟了新的前景。此外，这项工作有助于加强基于土壤水分的水文气象阈值的发展趋势，这在滑坡研究中越来越受到重视，迄今为止，在季风地区尚缺乏证据。结果表明，使用降雨强度和土壤湿度的概率阈值优于传统方法，其AUC为0.96，在所考虑的模型中是最敏感和最特异的。这一结果为结合降雨和土壤湿度数据在Idukki地区开发LEWS作业开辟了新的前景。此外，这项工作有助于加强基于土壤水分的水文气象阈值的发展趋势，这在滑坡研究中越来越受到重视，迄今为止，在季风地区尚缺乏证据。结果表明，使用降雨强度和土壤湿度的概率阈值优于传统方法，其AUC为0.96，在所考虑的模型中是最敏感和最特异的。这一结果为Idukki地区基于降雨和土壤湿度数据的LEWS的发展开辟了新的前景。此外，这项工作有助于加强基于土壤水分的水文气象阈值的发展趋势，这在滑坡研究中越来越受到重视，迄今为止，在季风地区尚缺乏证据。这一结果为结合降雨和土壤湿度数据在Idukki地区开发LEWS作业开辟了新的前景。此外，这项工作有助于加强基于土壤水分的水文气象阈值的发展趋势，这在滑坡研究中越来越受到重视，迄今为止，在季风地区尚缺乏证据。这一结果为结合降雨和土壤湿度数据在Idukki地区开发LEWS作业开辟了新的前景。此外，这项工作有助于加强基于土壤水分的水文气象阈值的发展趋势，这在滑坡研究中越来越受到重视，迄今为止，在季风地区尚缺乏证据。