@article{fdi:010086129,
  title = {{G}lobal assessment of the capability of satellite precipitation products to retrieve landslide-triggering extreme rainfall events},
  author = {{M}arc, {O}. and {O}liveira, {R}. {A}. {J}. and {G}osset, {M}arielle and {E}mberson, {R}. and {M}alet, {J}. {P}.},
  editor = {},
  language = {{ENG}},
  abstract = {{R}ainfall-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. {H}owever, our ability to understand and mitigate landslide risk is strongly limited by the availability of relevant rainfall measurements in many landslide prone areas. {I}n 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. {H}ere, we address this issue by testing the rainfall pattern retrieved by two {SMPP}s ({IMERG} and {GSM}a{P}) and one hybrid product [{M}ulti-{S}ource {W}eighted-{E}nsemble {P}recipitation ({MSWEP})] against a large, global database of 20 comprehensive landslide inventories associated with well-identified storm events. {W}e 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. {H}owever, 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. {I}n addition, the few (four) landslide events caused by short and localized storms are most often undetected. {W}e 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 {SMPP}s. {W}e conclude on some potential avenues to improve {SMPP}s' retrieval and their relation to landsliding. {S}ignificance {S}tatement{R}ainfall-induced landsliding is a global hazard that is expected to increase as a result of anthropogenic climate change. {O}ur ability to understand and mitigate this hazard is strongly limited by the lack of rainfall measurements in mountainous areas. {H}ere, we perform the first global assessment of the potential of three high-resolution precipitation datasets, derived from satellite observations, to capture the rainfall characteristics of 20 storms that led to widespread landsliding. {W}e find that, accounting for past extreme rainfall statistics (i.e., the rainfall returning every 10 years), most storms causing landslides are retrieved by the datasets. {H}owever, the shortest storms (i.e., similar to 3 h) are often undetected, and the detailed spatial pattern of extreme rainfall often appears to be distorted. {O}ur work opens new ways to study global landslide hazard but also warns against overinterpreting rainfall derived from satellites.},
  keywords = {{E}xtreme events ; {S}atellite observations ; {A}nomalies ; {A}tmosphere-land ; interaction},
  booktitle = {},
  journal = {{E}arth {I}nteractions},
  volume = {26},
  numero = {1},
  pages = {122--138},
  year = {2022},
  DOI = {10.1175/ei-d-21-0022.1},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086129},
}