A Multi-sensor Evaluation of Precipitation Uncertainty for
Landslide-triggering Storm Events
Abstract
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 researchers must select among 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 228 reported landslides across the
continental US 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/hr
and as much as 57 mm/hr. 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.