B. A multi-sensor evaluation of precipitation uncertainty for landslide-triggering storm events

Extreme precipitation can have profound consequences for communities, resulting in natural
hazards such as rainfall-triggered landslides that cause casualties and extensive property
damage. A key challenge to understanding and predicting rainfall-triggered landslides
comes from observational uncertainties in the depth and intensity of precipitation preceding
the event. Practitioners and researchersmust select from a wide range of precipitation
products, often with little guidance. Here we evaluate the degree of precipitation uncertainty
across multiple precipitation products for a large set of landslide-triggering storm
events and investigate the impact of these uncertainties on predicted landslide probability
using published intensity–duration thresholds. The average intensity, peak intensity, duration,
and NOAA-Atlas return periods are compared ahead of 177 reported landslides
across the continental United States and Canada. Precipitation data are taken from four
products that cover disparate measurement methods: near real-time and post-processed
satellite (IMERG), radar (MRMS), and gauge-based (NLDAS-2). Landslide-triggering precipitation
was found to vary widely across precipitation products with the depth of individual
storm events diverging by as much as 296 mm with an average range of 51 mm. Peak
intensity measurements, which are typically influential in triggering landslides, were also
highly variable with an average range of 7.8 mm/h and as much as 57 mm/h. The two
products more reliant upon ground-based observations (MRMS and NLDAS-2) performed
better at identifying landslides according to published intensity–duration storm thresholds,
but all products exhibited hit ratios of greater than 0.56. A greater proportion of landslides
were predicted when including only manually verified landslide locations. We
recommend practitioners consider low-latency products like MRMS for investigating landslides,
given their near-real time data availability and good performance in detecting landslides.
Practitioners would be well-served considering more than one product as a way to
confirm intense storm signals and minimize the influence of noise and false alarms.