Automating Emulator Construction for Geophysical Hazard Maps

Spiller, Elaine; Bayarri, M. J.; Berger, James O.; Calder, Eliza S.; Patra, Abani; Pitman, E. Bruce; Wolpert, Robert L.

doi:10.1137/120899285

Cited by 60 publications

(106 citation statements)

References 17 publications

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“…Secondly, our multi-hazard framework fundamentally relies on the strategy of separating the calculation of the probability of a specific event (e.g., medium-size lahars at catchment #195, given the occurrence of a large eruption at Somma-Vesuvius; see Figure 7) and the hazard footprint that ensues from this specific event (e.g., Spiller et al, 2014). This is a great advantage because it implies that changes in the probability distributions of Multihaz, for instance due to a modification in the prior table of RI, do not inevitably require many more simulations to be performed.…”

Section: Probabilistic Framework To Model Cascading Volcanic Hazardsmentioning

confidence: 99%

“…However, our probabilistic output distributions still consider too few "realizations" of the hazard (they are based on 3 values, one per scenario) and, therefore, we do not have enough information about the exceedance probabilities over the whole possible domain of the hazard-intensity measure. This limitation could be overcome by building a BBN model that used continuous PDFs at each of its nodes (e.g., Hanea et al, 2006) as well as by propagating uncertainty through performing a larger number of lahar simulations and/or using sophisticated uncertainty quantification techniques (e.g., Spiller et al, 2014).…”

Section: Probabilistic Framework To Model Cascading Volcanic Hazardsmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Probabilistic Framework To Model Cascading Volcanic Hazardsmentioning

confidence: 99%

Section: Probabilistic Framework To Model Cascading Volcanic Hazardsmentioning

confidence: 99%

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

et al. 2017

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“…The majority of these hazard assessments have quantified the probability of PDC invasion around the target volcano without considering other hazard intensity measures such as the flow depth or speed (e.g., Bevilacqua et al, ; Neri et al, ; Sandri et al, , , ; Tierz, Sandri, Costa, Sulpizio, et al, ; Tierz, Sandri, Costa, Zaccarelli, et al, ). Some studies that have quantified uncertainty in relation with intensity measures have mostly focused on dividing the PDC model parameter space into regions that do or do not lead to a catastrophe (defined as a given flow depth being overcome) occurring at selected locations (e.g., Bayarri et al, ; Spiller et al, ). Other studies have provided probability maps displaying the exceedance probability associated with a given threshold of flow depth as the hazard intensity measure (e.g., Dalbey et al, ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Pyroclastic density currents (PDCs) are hot flowing mixtures of gas and pyroclasts that can cause widespread loss of life and structural damage around the erupting volcano. Hazard assessments that include quantification of aleatory and epistemic uncertainty are a necessary step toward calculating volcanic risk of PDCs in an accurate and complete manner. We develop a three‐stage procedure to quantify such uncertainties for dense PDCs. First, the TITAN2D model is parameterized to simulate the PDC phenomenology at the target volcano. Second, TITAN2D is coupled with Polynomial Chaos Quadrature to propagate aleatory uncertainty from model parameters to hazard intensity measures (flow depth and speed). Third, the TITAN2D‐PCQ analysis is merged with the Bayesian Event Tree for Volcanic Hazard to include other volcano‐specific aleatory uncertainty and estimates of epistemic uncertainty. A comprehensive collection of probabilistic hazard curves and maps for flow depth and speed around the volcano is obtained through this methodology and its application is illustrated at Somma‐Vesuvius (Italy). Our results indicate that, given an eruption from the current central crater, exceedance probabilities are around 30% (aleatory uncertainty only) and between 10% and 60% (aleatory and epistemic uncertainty), for flow depth = 1 m and flow speed = 2 m/s, over the first 2–3 km around the vent. Dense PDCs faster than 30 m/s may cover areas about 50 km2 around the vent, on average, 1 every 10 eruptions. This type of probabilistic hazard assessment represents a crucial step toward quantitative volcanic risk of dense PDCs at Somma‐Vesuvius and worldwide.

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“…Samples from regression lines can be used directly for empirical models such as the energy line/cone method or for estimating the basal friction input parameter for a geophysical model like TITAN2D (Bayarri et al, 2009;Ogburn, 2014;Spiller et al, 2014) or the constant resisting shear stress input parameter in VolcFlow (Ogburn, 2014). Furthermore, using regression samples generated by this method allows one to account for uncertainty in probabilistic assessments of PDC inundation.…”

Section: Geophysical Results and Discussionmentioning

confidence: 99%

“…This work has been driven by our specific interest in constraining the basal Statistics in Volcanology friction input parameter required by TITAN2D when undertaking ensemble runs for generating probabilistic hazards maps (Bayarri et al, 2009;Spiller et al, 2014;Bayarri et al, 2015), by using the (H/L)-volume mobility relationships for block-and-ash flows from the FlowDat database. The application of the method developed can, however, be applied widely.…”

Section: Mobility Metrics For Flow Modelingmentioning

confidence: 99%

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2016

SIV

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Pooling strength amongst limited datasets using hierarchical Bayesian analysis, with application to pyroclastic density current mobility metrics, Statistics in Volcanology 2.1 : 1 − 26. DOI: http://dx.doi.org/10.5038/2163-338X.2.1Authors retain copyright of their material under a Creative Commons Non-Commercial Attribution 3.0 License. Ogburn et al.Pooling strength among limited data using hierarchical Bayesian analysis 2 AbstractIn volcanology, the sparsity of datasets for individual volcanoes is an important problem, which, in many cases, compromises our ability to make robust judgments about future volcanic hazards. In this contribution we develop a method for using hierarchical Bayesian analysis of global datasets to combine information across different volcanoes and to thereby improve our knowledge at individual volcanoes. The method is applied to the assessment of mobility metrics for pyroclastic density currents in order to better constrain input parameters and their related uncertainties for forward modeling. Mitigation of risk associated with such flows depends upon accurate forecasting of possible inundation areas, often using empirical models that rely on mobility metrics measured from the deposits of past flows, or on the application of computational models, several of which take mobility metrics, either directly or indirectly, as input parameters. We use hierarchical Bayesian modeling to leverage the global record of mobility metrics from the FlowDat database, leading to considerable improvement in the assessment of flow mobility where the data for a particular volcano is sparse. We estimate the uncertainties involved and demonstrate how they are improved through this approach. The method has broad applicability across other areas of volcanology where relationships established from broader datasets can be used to better constrain more specific, sparser, datasets. Employing such methods allows us to use, rather than shy away from, limited datasets, and allows for transparency with regard to uncertainties, enabling more accountable decision-making.

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Automating Emulator Construction for Geophysical Hazard Maps

Cited by 60 publications

References 17 publications

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

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

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

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