Impact of rainfall spatial aggregation on the identification of debris flow occurrence thresholds

Abstract

Abstract. The systematic underestimation observed in debris flow early warning thresholds has been associated with the use of sparse rain gauge networks to represent highly non-stationary rainfall fields. Remote sensing products permit concurrent estimates of debris-flow-triggering rainfall for areas poorly covered by rain gauges, but the impact of using coarse spatial resolutions to represent such rainfall fields is still to be assessed. This study uses fine-resolution radar data for ∼ 100 debris flows in the eastern Italian Alps to (i) quantify the effect of spatial aggregation (1-20 km grid size) on the estimation of debris-flow-triggering rainfall and on the identification of early warning thresholds and (ii) compare thresholds derived from aggregated estimates and rain gauge networks of different densities. The impact of spatial aggregation is influenced by the spatial organization of rainfall and by its dependence on the severity of the triggering rainfall. Thresholds from aggregated estimates show 8-21 % variation in the parameters whereas 10-25 % systematic variation results from the use of rain gauge networks, even for densities as high as 1/10 km −2 .

Invited perspectives: Hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: proposing hydro-meteorological thresholds

“…Owing to the high spatial variability of rainfall at scales smaller than 5 km (e.g., Krajewski et al, 2003;Ciach and Krajewski, 2006) and the limited density of operational rain gauge networks, the rainfall intensity observed by rain gauges systematically underestimates the actual triggering rainfall intensity at debris flow initiation locations (Marra et al, 2016), especially for short rain duration and high return period . This issue has been shown to significantly affect the obtained ID thresholds for debris flow initiation (Nikolopoulos et al, 2015;Marra et al, 2017). Additionally, different methods adopted to define the dry period separating rain events have been shown to strongly affect the ID threshold (e.g., Vessia et al, 2014;Melillo et al, 2015).…”

Examples of Application of GASAKe for Predicting the Occurrence of Rainfall-Induced Landslides in Southern Italy

“…Furthermore, available rain gauges are usually far from the sites of interest, and located at either different elevations or aspects. Uncertainty in rainfall estimation has a strong impact on the identification of the triggering rainfall [66,67]. Still, information regarding activation dates is commonly incomplete or poorly accurate [10].…”

High-Resolution Monthly Precipitation Fields (1913–2015) over a Complex Mountain Area Centred on the Forni Valley (Central Italian Alps)

“…The present work is focused on precipitation as its distribution, characteristics, and trend have a great influence as triggering factors for hazard [3,30]. They can represent limiting factors for biological community [31] and they can affect human fruition of the higher areas [32].…”