Global assessment of the skills of satellite precipitation products to retrieve extreme rainfall events causing landsliding

Storm-induced landsliding is a global and recurrent hazard, likely to increase with the strengthening of extreme precipitation events associated with current climate change. Risks associated with landslide hazard could be mitigated, for example with early warning systems or forecasting procedures. However, these approaches require to have constrained a tight relation between rainfall characteristics and the occurrence of landsliding. A traditional approach has been to derive such relationships from the failure of individual landslides, but the development of landslide mapping from satellite imagery allows now to constrain large landslide inventories triggered by single storm. Thus, at regional scale, forecasting the region of occurrence of a widespread landsliding event may be easier than forecasting the failure of individual slopes.

In turn, this regional approach requires spatially and temporally resolved rainfall information about the storms which caused landsliding. In-situ measurements are often too sparse for this and rainfall estimates derived from satellite observations have been proposed as a potential solution to this problem. However, only few studies have assessed the ability of satellite multi-sensor precipitation products (SMPPs) to characterize adequately the rainfall events which caused landsliding. Here, we address this issue by testing the rainfall pattern retrieved by 2 SMPPs (IMERG and GSMAP) and a hybrid product (MSWEP) against a large, global database of 18 comprehensive landslide inventories associated with well identified storm events. We use the nearly 20 years of data of the products to compute local rainfall anomaly over each area during the events and in every year of available data, and assess if the spatial pattern of intense anomaly corresponds to the landslide pattern, and if years without reported landslides have low level of anomalies. We found that after converting event rainfall to anomaly, the three products do retrieve the largest anomaly (of the 20 years) during the major landslide event for a number of cases. Still, the spatial pattern is often at least partially offset from the landslide areas, and that in many cases large anomalies are retrieved in years without substantial landsliding. Typically short, intense and localized storms are often missed by the three products, while large scale storms (e.g., hurricanes) are mostly retrieved, although the quality of the retrieval varies with each product. Using radar measurements or lightning records, we also show that in a number of cases where the SMPPs rainfall anomaly is poorly collocated with the landsliding, this is likely due to a biased retrieval of the rainfall rather than some variations in the landscape propensity to rainfall-induced landslides. We conclude on some potential avenue to improve SMPPs, typically including space-borne lightning measurement and better accounting for orographic precipitations.

In conclusion, rainfall estimates derived from satellite may be helpful in analyzing and understanding the pattern of landsliding, provided they are normalized by local extreme rainfall to obtain rainfall anomaly. Still, to advance toward regional scale landsliding, such methods of rainfall anomaly should also be applied to nowcast products from SMPPs and possibly to forecast issued from modern weather model.