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A Multi-sensor Evaluation of Precipitation Uncertainty for Landslide-triggering Storm Events
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  • Elsa Culler,
  • Andrew Badger,
  • J. Minear,
  • Kristy Tiampo,
  • Spencer Zeigler,
  • Ben Livneh
Elsa Culler
University of Colorado Boulder

Corresponding Author:[email protected]

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Andrew Badger
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J. Minear
University of Colorado Boulder
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Kristy Tiampo
University of Colorado Boulder
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Spencer Zeigler
University of Colorado Boulder
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Ben Livneh
University of Colorado Boulder
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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.
24 Nov 2020Submitted to Hydrological Processes
24 Nov 2020Submission Checks Completed
24 Nov 2020Assigned to Editor
26 Nov 2020Reviewer(s) Assigned
14 Jan 2021Review(s) Completed, Editorial Evaluation Pending
20 Jan 2021Editorial Decision: Revise Major
16 Mar 20211st Revision Received
16 Mar 2021Submission Checks Completed
16 Mar 2021Assigned to Editor
16 Mar 2021Reviewer(s) Assigned
15 Apr 2021Review(s) Completed, Editorial Evaluation Pending
19 Apr 2021Editorial Decision: Revise Minor
08 May 20212nd Revision Received
10 May 2021Reviewer(s) Assigned
10 May 2021Submission Checks Completed
10 May 2021Assigned to Editor
28 May 2021Review(s) Completed, Editorial Evaluation Pending
29 May 2021Editorial Decision: Accept
Jul 2021Published in Hydrological Processes volume 35 issue 7. 10.1002/hyp.14260