A Framework for Probabilistic Multi-Hazard Assessment of Rain-Triggered Lahars Using Bayesian Belief Networks

Abstract

Volcanic water-sediment flows, commonly known as lahars, can often pose a higher threat to population and infrastructure than primary volcanic hazardous processes such as tephra fallout and Pyroclastic Density Currents (PDCs). Lahars are volcaniclastic flows of water, volcanic debris and entrained sediments that can travel long distances from their source, causing severe damage by impact and burial. Lahars are frequently triggered by intense or prolonged rainfall occurring after explosive eruptions, and their occurrence depends on numerous factors including the spatio-temporal rainfall characteristics, the spatial distribution and hydraulic properties of the tephra deposit, and the pre-and post-eruption topography. Modeling (and forecasting) such a complex system requires the quantification of aleatory variability in the lahar triggering and propagation. To fulfill this goal, we develop a novel framework for probabilistic hazard assessment of lahars within a multi-hazard environment, based on coupling a versatile probabilistic model for lahar triggering (a Bayesian Belief Network: Multihaz) with a dynamic physical model for lahar propagation (LaharFlow). Multihaz allows us to estimate the probability of lahars of different volumes occurring by merging varied information about regional rainfall, scientific knowledge on lahar triggering mechanisms and, crucially, probabilistic assessment of available pyroclastic material from tephra fallout and PDCs. LaharFlow propagates the aleatory variability modeled by Multihaz into hazard footprints of lahars. We apply our framework to Somma-Vesuvius (Italy) because: (1) the volcano is strongly lahar-prone based on its previous activity, (2) there are many possible source areas for lahars, and (3) there is high density of population nearby. Our results indicate that the size of the eruption preceding the lahar occurrence and the spatial distribution of tephra accumulation have a paramount role in the lahar initiation and potential impact. For instance, lahars with initiation volume ≥10 5 m 3 along the volcano flanks are almost 60% probable to occur after large-sized eruptions (∼VEI ≥ 5) but 40% after medium-sized eruptions (∼VEI4). Some simulated lahars can propagate for 15 km or reach combined flow depths of 2 m and speeds of 5-10 m/s, even over flat terrain. Probabilistic multi-hazard frameworks like the one presented here can be invaluable for volcanic hazard assessment worldwide.

Towards Quantitative Volcanic Risk of Pyroclastic Density Currents: Probabilistic Hazard Curves and Maps Around Somma‐Vesuvius (Italy)

“…For instance, the hazard curves of dense‐PDC flow depth and speed at locations along the coastline southward and southwestward of Somma‐Vesuvius could be integrated into quantitative hazard analyses of tsunamis triggered by dense PDCs entering the sea (e.g., Watts & Waythomas, ). Similarly, the probabilistic assessment of flow depth presented here has been extended by Tierz et al (), who have calculated hazard curves of pyroclastic volume, from dense PDCs and tephra fallout, available over the catchments around Somma‐Vesuvius, to evaluate the likelihood of rain‐triggered lahars of different volumes to occur on the area of study (Tierz et al, ). In this respect, the discontinuity in flow speed observed approximately on the limits of the caldera floor (see Figures d–f, for instance) implies two opposite consequences.…”

“…Even if the perfect PDC data-generating model is found in the near future, probabilistic volcanic hazard assessments of PDCs will still have to incorporate the specific characteristics of the volcanic system under study in terms of probability of eruption, vent-opening probability, etc. This can be accomplished, as it has been done in recent years, through event trees (e.g., Newhall & Pallister, 2015;Sandri et al, 2012Sandri et al, , 2014Sandri et al, , 2018Wright et al, 2018) and/or Bayesian Belief Networks (e.g., Hincks et al, 2014;Tierz et al, 2017).…”

Mapping the susceptibility of syn-eruptive rain-triggered lahars at Vulcano island (Italy) combining field characterization and numerical modelling

“…tephra, PDC), geological and geomorphological characteristics of the area and climatic conditions (mostly precipitation). Tierz et al (2017) have already show the importance of assessing the effect of cascading hazards by compiling a probabilistic lahar hazard assessment through the Bayesan belief network "Multihaz" based on a combination of probabilistic hazard assessment of both tephra fallout and PDCs and a dynamic physical model for lahar propagation.…”

Multisource Bayesian Probabilistic Tsunami Hazard Analysis for the Gulf of Naples (Italy)

“…In general, hazards due to multiple events may exponentially increase the impact of disasters on society with respect to the hazards that consider separately different events (Faenza et al, ; Marzocchi et al, ; Selva, ; Tierz et al, ). Similarly, the probabilistic tsunami hazard assessment should not be focused on one type of tsunami source only, but different potential sources should be considered and combined together to provide a complete and unbiased assessment (Grezio et al, ).…”

“…The study has the main following purposes: (a) dealing simultaneously with the different principal types of tsunamigenic sources in a coherent multidisciplinary probabilistic framework and (b) evaluating the long‐term comprehensive tsunami hazard in a analysis, hence merging prior information—for example, modellng—and past/historical data. This scheme is here applied in the context of the multirisk problem in the Campania region (Faenza et al, ; Perfetti et al, ; Sandri et al, ; Tierz et al, , ). Natural hazards have posed relevant threats to the city of Naples and the surrounding areas.…”