The Potential of Satellite and Model Derived Variables for Rainfall-Induced Landslide Initiation Thresholds in Rwanda

Abstract

Empirical-statistical rainfall landslide initiation thresholds are popularly used for early warning systems to discriminate between the occurrence and non-occurrence of rainfall-induced landslides. However, the few studies that have derived landslide initiation thresholds for landslide-prone and data-scarce Rwanda rely solely on the limited in situ data. Therefore, our objective is to explore the feasibility of using satellite data and hydrological model derived data to derive both trigger and trigger-cause thresholds for landslides in Rwanda. We firstly evaluated seven precipitation products (TRMM 3B42v7, CHIRPS, PERSIANN-CDR, GLDAS 2.1, CFSv2, IMERG, and ERA5) using the rain gauge data as a reference and found that IMERG was the most suitable product for obtaining rainfall triggering conditions. We then studied the added value of incorporating the antecedent soil moisture from both a high spatial satellite data and from a distributed hydrological model following the trigger-cause framework. The results showed that the event precipitation volume E, the event duration D and the bilinear threshold E-D are the landslide initiation thresholds that accurately predict the highest number of landslide events while keeping the false and the failed alarms low. Including the antecedent soil moisture products as the causal variables -expected to account for the hillslope hydrologic processes predisposing the slopes to near failure- did not lead to any improvement with respect to the trigger only thresholds for predicting landslides in Rwanda.