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 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.

Keywords: extreme precipitation; intensity–duration thresholds; natural hazards; precipitation inter‐comparison; rainfall‐triggered landslides.

FIGURE 1

Map of all landslide sites considered in this analysis: 177 landslide sites coloured by whether the location was approximate (n = 113) or verified using aerial satellite imagery to identify a visible scarp (n = 64); source of landslide locations was the GLC (Kirschbaum et al., 2010), source of the DEM data used for the base map (North America Elevation 1‐Kilometer Resolution, 2007). Elevations above 3000 m are indicated as the highest value on the colour scale

FIGURE 2

Storm characteristics by landslide type: Kernel density estimates for each precipitation product are shown for the duration (h, in panel (a)) and intensity (mm/h, in panel (b)) of landslide‐triggering storms. Distributions are separated by the landslide type, where ‘landslide’ describes unknown types

FIGURE 3

Exposition into the types of precipitation differences leading up to landslide events: Cumulative precipitation measurements at select landslide sites for the 30 days before the event. The date of the landslide event is indicated by a vertical black line in each panel. Precipitation is variable across the different products, and the selected sites each demonstrate diverse types of variability. Panel (a) shows similar measurements among all products throughout the 30 days. In panel (b), all products are well correlated, but the accumulated depths greatly differ. In panel (c) both IMERG products report less precipitation until the landslide‐triggering storm when they reverse and report more precipitation than MRMS and NLDAS‐2. In panel (d) IMERG‐Early reports much more precipitation than the other products. Finally, in panel (e) no landslide‐triggering precipitation was detected by any product, suggesting a location error in the landslide record

FIGURE 4

Relative magnitude of precipitation products on the day of the landslide: Rank among all products for each day, and z‐score of daily precipitation as measured by each product for each of 177 events. Panels (a) and (c) show the entire precipitation record while panels (b) and (d) show only the day‐of‐landslide precipitation for comparison. Z‐scores are plotted on a pseudo‐log scale, a combination of a linear scale near zero and a log scale for higher values

FIGURE 5

Storm characteristics versus the ensemble mean: Depth (mm), duration (h), mean intensity (mm/h), peak intensity (mm/h), and return period (year) for each of the landslide‐triggering storms as measured by four precipitation products. Least‐squares regression lines with $95\%$ confidence intervals are also shown. Panels (a)–(e) show all 177 sites while panels (f)–(j) show the 64 verified locations. Panels (k)–(n) show the depth (mm), duration (h), mean intensity (mm/h) and peak intensity (mm/h) for all storms in the climatology at all locations as a comparison

FIGURE 6

Relationship between peak intensity and return period: Scatter plots of the peak intensity (mm/h) and return period (year) for each of four precipitation products. A least‐squares regression line is also shown

FIGURE 7

Comparison of landslide‐triggering precipitation relative to intensity–duration thresholds: Each storm in the precipitation record and established global or climactic intensity–duration thresholds. Landslides are coloured according to their climate or landslide‐type category and ID threshold curves are coloured by their restrictions. Landslide‐triggering storms are shaded in darker colours. The panels (a)–(h) contains precipitation data for all sites while in panels (i)–(p) only verified sites are included. Points above each threshold are predicted by the threshold to be landslides, and so a larger proportion of landslides plotting above the threshold indicates better performance

FIGURE 8

Comparison of mean intensity values for each precipitation source: The distribution of mean intensity values for each precipitation product shown as boxplots. The data are split into seven duration bands of equal width on a logarithmic scale