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 formed by 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. Modelling 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 modelled 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 ≥ 105 m3 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.
Highlights
Explosive eruptions can produce volumes of pyroclastic material of up to thousands of cubic kilometers (Newhall and Self, 1982)
We propose a generalizable Bayesian Belief Network (BBN) model (Multihaz) that quantifies the aleatory uncertainty in terms of (i) the pyroclastic volume stored in different catchments around the volcano; (ii) the rainfall characteristics over the catchments; (iii) the response of the catchments to specific conditions of rainfall and pyroclastic volume; and (iv) lahar initiation volumes for each of the catchments defined over the hazard domain
We select this volcanic system for three reasons: (1) there are data available to quantify the aleatory uncertainty linked to tephra fallout (e.g., Sandri et al, 2016) and dense Pyroclastic Density Current (PDC) (e.g., Tierz et al, 2014); (2) the volcano has generated syn-eruptive volcaniclastic flows during/after mid-large explosive eruptions (e.g., Rosi et al, 1993; Sulpizio et al, 2006) and is prone to form them at many locations around it (e.g., Bisson et al, 2010, 2014); and (3) the relatively high population density nearby
Summary
Explosive eruptions can produce volumes of pyroclastic material of up to thousands of cubic kilometers (Newhall and Self, 1982). We present an initial application of our multihazard framework to syn-eruptive (defined as in Sulpizio et al, 2006) rain-triggered lahars at Somma-Vesuvius (Italy) We select this volcanic system for three reasons: (1) there are data available to quantify the aleatory uncertainty linked to tephra fallout (e.g., Sandri et al, 2016) and dense PDCs (e.g., Tierz et al, 2014); (2) the volcano has generated syn-eruptive volcaniclastic flows during/after mid-large explosive eruptions (e.g., Rosi et al, 1993; Sulpizio et al, 2006) and is prone to form them at many locations around it (e.g., Bisson et al, 2010, 2014); and (3) the relatively high population density nearby. The abbreviations used in this table are adopted throughout the manuscript
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