Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity

Abstract

Abstract. Landslides causes severe damages to the road network of a hit zone, in terms of both direct (partial or complete destruction of a road trait, blockages) and indirect (traffic restriction, cut-off of a certain area) costs. Thus, the identification of the parts of the road network which are more susceptible to landslides is fundamental to reduce the risk to the population potentially exposed and the money expense caused by road damaging. For these reasons, this paper aimed to develop and test a data-driven model based on the Genetic Algorithm Method for the identification of road sectors that are susceptible to be hit 15 by shallow landslides triggered in slopes upstream to the infrastructure. This work also analyzed the importance of considering or not the sediment connectivity on the estimation of the susceptibility. The study was carried out in a catchment of northeastern Oltrepò Pavese (northern Italy), where several shallow landslides affected roads in the last 8 years. The random partition of the dataset used for building the model in two parts (training and test subsets), within a 100-fold bootstrap procedure, allowed to select the most significant explanatory variables, providing a better description of the occurrence and 20 distribution of the road sectors potentially susceptible to damages induced by shallow landslides. The presented methodology allows the identification, in a robust and reliable way, of the most susceptible road sectors that could be hit by sediments delivered by landslides. The best predictive capability was obtained using a model which took into account also the index of connectivity, calculated according to a linear relationship. Most susceptible road traits resulted to be located below steep slopes with a limited height (lower than 50 m), where sediment connectivity is high. Different scenarios of land use were implemented 25 in order to estimate possible changes in road susceptibility. Land use classes of the study area were characterized by similar connectivity features with a consequent loss of variations also on the susceptibility of the road networks according to different scenarios of distribution of land cover. Larger effects on sediment connectivity and, as a consequence on road susceptibility, could be due to modifications in the morphology of the slopes (e.g. drainage system, modification of the slope angle) caused by the abandonment or by the recovery of cultivations. The results of this research demonstrate the ability of the developed 30 methodology in the assessment of susceptible roads. This could give to the managers of an infrastructure information on the Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2017-457 Manuscript under review for journal Nat. Hazards Earth Syst. Sci. Discussion started: 24 January 2018 c Author(s) 2018. CC BY 4.0 License. 2 criticality of the different road traits, thereby allowing attention and economic budgets to be shifted towards the most critical assets, where structural and non-structural mitigation mea...

Indices of sediment connectivity: opportunities, challenges and limitations

“…In any case, care must be taken when setting up and interpreting colour-scales (e.g., a bluewhite-red one as in Cavalli et al, 2013) for index maps. The inter-catchment comparison of IC can be facilitated by standardising (z-transforming;Persichillo et al, 2018) or normalising (to the range [0,1],…”

“…The latter two studies suggest that IC could be used for a general geomorphological classification of (sub-)catchments. Bordoni et al (2018) While not representing proper indices of connectivity, ECA or SCA (section 3.2) approaches are closely related because they implement concepts of sediment connectivity; they can be used in models to predict sediment yield. The SCA (following Heinimann et al, 1998;see also Wichmann et al, 2009) was demonstrated to correlate well with the mean annual sediment yield measured by sediment traps in different alpine catchments (Haas et al, 2011;Sass et al, 2012;Huber et al, 2015;Neugirg et al, 2016; Figure 13).…”

“…If a process/change is occurring at multiple locations within a catchment, connectivity assessment can help to identify, rank, and prioritise areas to which funding should be allocated best/first because they generate the most severe impact (e.g. landslides potentially affecting the road network; Bordoni et al 2018). Similarly, connectivity analysis may support the identification of upstream causes of processes (or changes) observed upstream/upslope of a particular location; this is related to the…”

“…The IC modified according toKalantari et al (2017) is the only example of an index that accounts for spatially and temporally varying external forcing. However, some studies have computed the IC index for different points in time to investigate seasonal (Foerster et al, 2014;Figure 7) to decadal(Lopez-Vicente et al, 2017a; Lizaga et al, 2017;Persichillo et al, 2018) changes of structural connectivity due to landuse/landcover changes, based on multitemporal landcover maps. Such analyses are facilitated by the increasing opportunities to gather remotely sensed topographic and landuse/landcover data (c.f.…”

A near-real-time model for estimating probability of road obstruction due to earthquake-triggered landslides

“…This simple model benefits from an intuitive explanation where obstruction probabilities rise with distance and increased landslide probability, but relies on selecting a specific set of model pixels to calculate the hazard value for each road segment. Although we considered using methods that reflect some measure of landslide mobility (Bordoni et al, 2018; Robinson et al, 2018), we found that using the average probability along each road segment provided good first-order estimates of obstruction risk without requiring supplementary datasets or additional segment-level calculations that could significantly slow down processing time. While each segment might ideally be assigned a hazard value proportional to its upslope contributing area and the potential for undercut failure, these types of calculations are currently impractical at the resolution of regional or global models and likely not warranted given the underlying coarseness of the GFP landslide model.…”