The Use of Rain Gauge Measurements and Radar Data for the Model‐Based Prediction of Runoff‐Generated Debris‐Flow Occurrence in Early Warning Systems

Abstract

Debris flows (DFs) are classified as mixtures of sediments and water that rapidly route along steep channels. In mountainous regions, the impulsive nature of DFs combined with their high devastating power threatens human life and facilities (Jakob & Hungr, 2005) and has a high socioeconomic impact (Thiene et al., 2017). In this work, we refer to runoff-generated DFs, that is, those formed after the entrainment of debris material into abundant runoff descending from rocky cliffs (Coe et al., 2008; Hürlimann et al., 2014; Kean et al., 2013). Doubtless, high-intensity and sub-hourly rainfalls (15-45 minutes) are the predominant factors for the triggering and magnitude of runoff generated DFs. The prediction of such phenomena has great relevance for limiting damages and victims in the threatened areas and increasingly enters the context of social challenges. Many villages, tourist resorts, and linear infrastructures are built on DF fans or intersect DF channels. The expansion of human activities (new settlements, infrastructures, and touristic facilities, often in the extreme alpine environment) increases the vulnerability of society to these phenomena (Franceschinis et al., 2020). Moreover, the frequency of these phenomena is growing due to climate change (Stoffel et al., 2014); it leads to the increasing of both extreme rainfalls (Floris et al., 2010) and rock wall collapses that provide sediments even in locations where they never occurred before (Gatter et al., 2018; Bernard, Berti, et al., 2019). The large number of sites, which are potentially threatened, and the inherent inflexibility of technical countermeasures characterized by high costs make early warning systems the most cost-effective measure to mitigate the risk associated with DFs (Sättele et al., 2015). Because of the short times of DF downstream routing, the prediction of occurrences should occur as soon as possible. The real-time evaluation of triggering rainfalls permits to launch alerts in a time shorter than that given during DF downstream routing by sensors positioned along DF flow paths. The most common way to predict DF occurrences is the use of rainfall-based thresholds in early warning systems (

Investigation of the functional relationship between antecedent rainfall and the probability of debris flow occurrence in Jiangjia Gully, China

“…Based on the collected rainfall data, high-intensity rainstorm or long-duration rainfall processes can cause debris flow occurrence (Zhang et al, 2020). The solid material necessary for a debris flow in a gully may be sourced from shallow landslides (Iverson et al, 1997;Gabet and Mudd, 2006;Zhang et al, 2020;Long et al, 2020) or runoff-induced bed erosions (Berti and Simoni, 2005;Coe et al, 2008;Tang et al, 2020;Bernard and Gregoretti, 2021). In JJG, the solid material is sourced primarily from shallow landslides (Zhang et al, 2014;Liu et al, 2016;Yang et al, 2022), which is consistent with the assumptions of Dens-ID (Zhang et al, 2020).…”

A call for strategic water-quality monitoring to advance assessment and prediction of wildfire impacts on water supplies

“…However, gridded data usually lack sufficient temporal resolution and are less accurate in areas of steep mountains and lack of precipitation gages (Murphy et al, 2017), which is a common scenario in burned forests. On-the-ground precipitation monitoring can also fill gaps in weather radar or better capture highintensity rainfall (e.g., Nikolopoulos et al, 2015;Bernard and Gregoretti, 2021).…”

When instrument location makes the difference on rainfall thresholds definition: Lessons learned at Cancia, Dolomites

“…More in general, gauge location is always one of the key points for reliable thresholds development since having at least one rain gauge close to the catchment centroid is crucial for a proper spatial rainfall calibration even when using distributed measures such as radarderived rainfall maps [11].…”