Global Assessment of the Capability of Satellite Precipitation Products to Retrieve Landslide-Triggering Extreme Rainfall Events

Earth Interactions

article

Odin Marc¹  Romulo A. Jucá Oliveira²  Marielle Gosset¹  Robert Emberson³  Jean-Philippe Malet⁶ 

[1] Geosciences Environnement Toulouse;Laboratoire d’Etudes en Geophysique et Oc ´ eanographie Spatiales, Universit ´ e de Toulouse III/CNRS/CNES/IRD;Hydrological Sciences Laboratory, NASA Goddard Space Flight Center;Universities Space Research Association;NASA Goddard Earth Sciences Technology and Research;Institut Terre et Environnement de Strasbourg, CNRS/UMR7063, EOST/Universite de Strasbourg

关键词: Extreme events;    Satellite observations;    Anomalies;    Atmosphere–land interaction;   

DOI  :  10.1175/EI-D-21-0022.1

学科分类：地球科学(综合)

来源: American Geophysical Union

【 摘 要 】

Rainfall-induced landsliding is a global and systemic hazard that is 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 the availability of relevant rainfall measurements in many landslide prone areas. In the last decade, global satellite multisensor precipitation products (SMPP) 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 two SMPPs (IMERG and GSMaP) and one hybrid product [Multi-Source Weighted-Ensemble Precipitation (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-yr 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. In addition, the few (four) landslide events caused by short and localized storms are most often undetected. We also show that, in at least five cases, the SMPP’s spatial pattern of rainfall anomaly matches landsliding less well than does 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.

【 授权许可】

Unknown