Remotely assessing tephra fall building damage and vulnerability: Kelud Volcano, Indonesia

Abstract

Tephra from large explosive eruptions can cause damage to buildings over wide geographical areas, creating a variety of issues for post-eruption recovery. This means that evaluating the extent and nature of likely building damage from future eruptions is an important aspect of volcanic risk assessment. However, our ability to make accurate assessments is currently limited by poor characterisation of how buildings perform under varying tephra loads. This study presents a method to remotely assess building damage to increase the quantity of data available for developing new tephra fall building vulnerability models. Given the large number of damaged buildings and the high potential for loss in future eruptions, we use the Kelud 2014 eruption as a case study. A total of 1154 buildings affected by falls 1–10 cm thick were assessed, with 790 showing signs that they sustained damage in the time between pre- and post-eruption satellite image acquisitions. Only 27 of the buildings surveyed appear to have experienced severe roof or building collapse. Damage was more commonly characterised by collapse of roof overhangs and verandas or damage that required roof cladding replacement. To estimate tephra loads received by each building we used Tephra2 inversion and interpolation of hand-contoured isopachs on the same set of deposit measurements. Combining tephra loads from both methods with our damage assessment, we develop the first sets of tephra fall fragility curves that consider damage severities lower than severe roof collapse. Weighted prediction accuracies are calculated for the curves using K-fold cross validation, with scores between 0.68 and 0.75 comparable to those for fragility curves developed for other natural hazards. Remote assessment of tephra fall building damage is highly complementary to traditional field-based surveying and both approaches should ideally be adopted to improve our understanding of tephra fall impacts following future damaging eruptions.

How rainfall influences tephra fall loading — an experimental approach

“…Similarly, following the 1990 eruption of Kelud volcano, Indonesia, all 34 recorded casualties were caused by roof collapse at a single evacuation centre, under the weight of tephra that had been made heavier -by an unmeasured amount -by rain (Bourdier et al 1997;Hidayati et al 2019). More recently at Kelud, Williams et al (2020) conducted a remote building damage and vulnerability assessment, using tephra thicknesses and a published dry deposit density value of 1400 kg m −3 to estimate tephra loading on buildings from Kelud's 2014 eruption. Five days after the eruption began, heavy rainfall measurements were made in an area nearby where building damage was assessed, likely before building residents were able to return and clean tephra from the roofs of all their homes (Dibyosaputro et al 2015).…”

“…Both of these values can be readily influenced by rainfall (Blong 1981;Macedonio and Costa 2012). Unfortunately, our current understanding of how rainfalls can and have influenced tephra fall loading represents a substantial source of uncertainty in both pre-eruption damage estimates and post-eruption damage assessments (Spence et al 1996;Jenkins et al 2015;Biass et al 2016;Williams et al 2020). To improve understanding of this interaction, Editorial responsibility: J. Eychenne.…”

“…Despite the potential value of these measurements having been recognised, in situ tephra deposit densities are rarely recorded during post-eruption building damage surveys. Reasons given for this include time constraints in the field , use of remote damage assessment techniques (Williams et al 2020;Biass et al 2021) or that at the time a damage survey was conducted, it was not intended to compare damage with hazard intensity (Hayes et al 2019). As a result, where deposit densities have been measured, it was typically at a much later date and/or in the laboratory.…”

Tephra deposit inversion by coupling Tephra2 with the Metropolis-Hastings algorithm: algorithm introduction and demonstration with synthetic datasets

“…Tephra2 is a widely-used volcanic ash transport and deposition model (Bonadonna et al 2010;Connor et al 2011). It has been coupled with different statistical and engineering techniques for forward and inverse modeling of tephra fall deposits and volcanic hazard analysis (Connor and Connor 2006;Mannen 2006;Volentik et al 2010;Fontijn et al 2011;Biass et al 2012;Mannen 2014;Magill et al 2015;Biass et al 2016;Biass et al 2017;Takarada 2017;Wild et al 2019;Connor et al 2019;Mannen et al 2020;Williams et al 2020). Tephra2 assumes that tephra particles with different grain sizes are released from a vertical column with column radius increasing with height (accounted for by an additional diffusion term; Suzuki and et al (1983)), and their transport is subject to wind advection, horizontal turbulent diffusion, and falling at terminal velocities.…”

Tephra deposit inversion by coupling Tephra2 with the Metropolis-Hastings algorithm: algorithm introduction and demonstration with synthetic datasets

“…Tephra2 is a widely-used volcanic ash transport and deposition model (Bonadonna et al, 2010;Connor et al, 2011). It has been coupled with different statistical and engineering techniques for forward and inverse modeling of tephra fall deposits and volcanic hazard analysis (Connor and Connor, 2006;Mannen, 2006;Volentik et al, 2010;Fontijn et al, 2011;Biass et al, 2012;Mannen, 2014;Magill et al, 2015;Biass et al, 2016Biass et al, , 2017Takarada, 2017;Wild et al, 2019;Connor et al, 2019;Mannen et al, 2020;Williams et al, 2020). Tephra2 assumes that tephra particles with different grain sizes are released from a vertical column with column radius increasing with height (accounted for by an additional diffusion term; Suzuki et al, 1983), and their transport is subject to wind advection, horizontal turbulent diffusion, and falling at terminal velocities.…”