Abstract
Abstract. Rainfall threshold determination is a pressing issue in the landslide scientific community. While major improvements have been made towards more reproducible techniques for the identification of triggering conditions for landsliding, the now well-established rainfall intensity or event-duration thresholds for landsliding suffer from several limitations. Here, we propose a new approach of the frequentist method for threshold definition based on satellite-derived antecedent rainfall estimates directly coupled with landslide susceptibility data. Adopting a bootstrap statistical technique for the identification of threshold uncertainties at different exceedance probability levels, it results in thresholds expressed as AR = (α±Δα)⋅S(β±Δβ), where AR is antecedent rainfall (mm), S is landslide susceptibility, α and β are scaling parameters, and Δα and Δβ are their uncertainties. The main improvements of this approach consist in (1) using spatially continuous satellite rainfall data, (2) giving equal weight to rainfall characteristics and ground susceptibility factors in the definition of spatially varying rainfall thresholds, (3) proposing an exponential antecedent rainfall function that involves past daily rainfall in the exponent to account for the different lasting effect of large versus small rainfall, (4) quantitatively exploiting the lower parts of the cloud of data points, most meaningful for threshold estimation, and (5) merging the uncertainty on landslide date with the fit uncertainty in a single error estimation. We apply our approach in the western branch of the East African Rift based on landslides that occurred between 2001 and 2018, satellite rainfall estimates from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA 3B42 RT), and the continental-scale map of landslide susceptibility of Broeckx et al. (2018) and provide the first regional rainfall thresholds for landsliding in tropical Africa.
Highlights
Rainfall is widely recognized as an important trigger for landslides (Sidle and Bogaard, 2016), posing an increased threat to people and economies worldwide under climate change conditions (Gariano and Guzzetti, 2016)
A 5 % exceedance probability, for instance, means that any landslide occurring in the field has a 0.05 probability of being triggered by an antecedent rainfall AR lower than that defined by the threshold curve, with about weighted 5 % of the data points effectively lying below the curve
We propose a new rainfall threshold approach fundamentally different from previous research and based on the relation between antecedent rainfall and landslide susceptibility through a modified frequentist approach with bootstrapping
Summary
Rainfall is widely recognized as an important trigger for landslides (Sidle and Bogaard, 2016), posing an increased threat to people and economies worldwide under climate change conditions (Gariano and Guzzetti, 2016). Rainfall thresholds, defined as the best separators for triggering and non-triggering known rainfall conditions (Crozier, 1997), are the most used instrument in landslide hazard assessment and early warning tools (Segoni et al, 2018). The most common parameters used to define empirical thresholds are the combinations of rainfall intensityduration, rainfall event-duration, and antecedent rainfallconditions (Guzzetti et al, 2007). No unanimous definition of triggering antecedent rainfall (AR) conditions is currently achieved. This is related to the complexity and process dependence of environmental
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
