Global assessment of the capability of satellite precipitation products to retrieve landslide-triggering extreme rainfall events

Abstract

Rainfall-induced landsliding is a global and systemic hazard, likely to increase with the projections of increased frequency of extreme precipitation with current climate change. However, our ability to understand and mitigate landslide risk is strongly limited by th availability of relevant rainfall measurements in many landslide prone areas. In the last decade, global satellite multi-sensor precipitation products (SMPPs) have been proposed as a solution but very few studies have assessed their ability to adequately characterize rainfall events triggering landsliding. Here, we address this issue by testing the rainfall pattern retrieved by 2 SMPPs (IMERG and GSMaP) and one hybrid product (MSWEP) against a large, global database of 20 comprehensive landslide inventories associated with well-identified storm events. We found that after converting total rainfall amounts to an anomaly relative to the 10-year return rainfall, R*, the three products do retrieve the largest anomaly (of the last 20 years) during the major landslide event for many cases. However, the degree of spatial collocation of R* and landsliding varies from case to case and across products, and we often retrieved R*>1 in years without reported landsliding. Additionally, the few (4) landslide events caused by short and localized storms are most often undetected. We also show that, in at least five cases, the SMPPs spatial pattern of rainfall anomaly matches landsliding less well than ground-based radar rainfall pattern or lightning maps, underlining the limited accuracy of the SMPPs. We conclude on some potential avenues to improve SMPPs retrieval, and their relation to landsliding.