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.

Impacts of observational uncertainty on analysis and modelling of hydrological processes: Preface

“…A correct design of the modelling study should consider a proper selection of the input (precipitation) products for hydrological models because the model parameters and modelling uncertainty will largely depend on this forcing product. Based on papers from this issue, it could be recommended to either test different products to choose the most appropriate one for the purpose of the study (Culler et al, 2021), or to use an ensemble of input forcings instead of using only a single product (Liu et al, 2021).…”

“…In our special issue, all three papers dealing with input uncertainty similarly focus largely on precipitation uncertainty. The papers investigate input (precipitation) uncertainty and its impact on parameter identification in hydrological models (Liu et al, 2021), input uncertainty in observed and generated variables for hydrological models and process representation (Beven, 2021b), and precipitation uncertainty impact on the rainfall‐triggered landslide events (Culler et al, 2021). Beven (2021b) also discusses uncertainties in other input variables, that is, temperature and evapotranspiration.…”

“…Culler et al (2021) investigate the effect that different precipitation products can have on the accuracy in rainfall‐triggered landslide event prediction. They compare different precipitation products such as satellite, radar and rain gauge data, to assess the effect of the uncertainty in precipitation data on predicted landslide magnitudes in the continental US and Canada.…”

Research on the Prediction of Infiltration Depth of Xiashu Loess Slopes Based on Particle Swarm Optimized Back Propagation (PSO-BP) Neural Network

“…At present, in the prediction of slopes, most scholars focus on the prediction of slope deformation and stability [11][12][13][14][15], and there are few studies on the prediction of landslide damage depth [16]. Landslide damage depth is affected by a variety of factors [17], its uncertainty is higher, and it is more difficult to predict directly. Some scholars believe that shallow landslides parallel to the surface of soil slopes often occur (failure at wet fronts) [18,19]; thus, the range of landslide damage depths can be indirectly determined from rainfall infiltration depths and the prediction of slope infiltration depths is better implementable.…”