Regional early warning model for rainfall induced landslide based on slope unit in Chongqing, China

Recent advances in the diversity, precision and systematization of design methods and real-time data have led to a general elevation in spatio-temporal accuracy for regional landslide early-warning (LEW). However, the heterogeneity of the geo-environment and the differences in landslide mechanisms are always neglected in the design of LEW models, which hinder the implementation of LEW systems. This study proposes a slope-unit (SU) based regional LEW model for forecasting the real-time probability of rainfall-induced landslides, combing landslide susceptibility assessment and rainfall threshold modeling, taking Chongqing, China as the study case. The SU is adopted to discretize the study area concerning the concurrent occurrence of rainfall-induced shallow and deep-seated landslides, in view of the limitations of grid cells, which are more appropriate for shallow landslides with homogeneous materials and structures. In addition, four distinct subregions are identified based on the geo-environmental heterogeneity of the study area. For each subregion, specific landslide susceptibility models and rainfall thresholds are developed to account for the different landslide mechanisms. Landslide susceptibility maps integrate data-driven methods with the latest 1:50,000 field surveys to achieve accurate predictions of future landslides. Rainfall threshold models are constructed based on a correlation analysis of 2142 historical rainfall events and associated landslides. By using 9-day antecedent rainfall records from 2103 rain gauges and numerical rainfall forecast products for the next 24 h as input data, the LEW model can dynamically release warning information based on evolving rainfall conditions. To validate the performance of the LEW model, the daily warnings for rainfall events that induced groups of landslides were retrieved over a consecutive period from 2013 to 2021. The results demonstrate an overall satisfactory warning effect, with over 70% of the total rainfall-induced landslides exceeding the yellow alert warning level and a low rate of miss-alarms (<15%). It indicates that the mapping unit partition based on the characteristics of rainfall-induced landslides and region division according to geological heterogeneity could effectively contribute to accurate LEW, especially over large areas. Furthermore, the findings reveal that early warnings of landslides induced by persistent rainfall over large area are more prone to generate false or miss alarms compared to local concentrated rainstorms. The LEW framework proposed in this study is expected to provide valuable technical support to the local authorities in effectively mitigating landslide risks in a time-efficient manner.