Solene Pouget

Computation of probabilistic hazard maps and source parameter estimation for volcanic ash transport and dispersion

Volcanic ash advisory centers are charged with forecasting the movement of volcanic ash plumes, for aviation, health and safety preparation. Deterministic mathematical equations model the advection and dispersion of these plumes. However initial plume conditions - height, profile of particle location, volcanic vent parameters - are known only approximately at best, and other features of the governing system such as the windfield are stochastic. These uncertainties make forecasting plume motion difficult. As a result of these uncertainties, ash advisories based on a deterministic approach tend to be conservative, and many times over/under estimate the extent of a plume. This paper presents an end-to-end framework for generating a probabilistic approach to ash plume forecasting. This framework uses an ensemble of solutions, guided by Conjugate Unscented Transform (CUT) method for evaluating expectation integrals. This ensemble is used to construct a polynomial chaos expansion that can be sampled cheaply, to provide a probabilistic model forecast. The CUT method is then combined with a minimum variance condition, to provide a full posterior pdf of the uncertain source parameters, based on observed satellite imagery.The April 2010 eruption of the Eyjafjallajokull volcano in Iceland is employed as a test example. The puff advection/dispersion model is used to hindcast the motion of the ash plume through time, concentrating on the period 14-16 April 2010. Variability in the height and particle loading of that eruption is introduced through a volcano column model called bent. Output uncertainty due to the assumed uncertain input parameter probability distributions, and a probabilistic spatial-temporal estimate of ash presence are computed.

Interpretation of umbrella cloud growth and morphology: implications for flow regimes of short-lived and long-lived eruptions

New numerical and analytical modeling shows that the growth of a volcanic umbrella cloud, expressed as the increase of radius with time, proceeds through regimes, dominated by different force balances. Four regimes are identified: Regime Ia is the long-time behavior of continuously-supplied intrusions in the buoyancy-inertial regime; regime IIa is the long-time behavior of continuously-supplied, turbulent drag-dominated intrusions; regime Ib is the long-time behavior of buoyancy-inertial intrusions of constant volume; and regime IIb that of turbulent drag-dominated intrusions of constant volume. Power-law exponents for spreading time in each regime are 3/4 (Ia), 5/9 (IIa), 1/3 (Ib), and 2/9 (IIb). Both numerical modeling and observations indicate that transition periods between the regimes can be long-lasting, and during these transitions, the spreading rate does not follow a simple power law. Predictions of the new model are consistent with satellite data from seven eruptions and, together with observations of umbrella cloud structure and morphological evolution, support the existence of multiple spreading regimes.

Challenges in developing DDDAS based methodology for volcanic ash hazard analysis - Effect of numerical weather prediction variability and parameter estimation

In this paper, we will present ongoing work on using a dynamic data driven application system (DDDAS) based approach to the forecast of volcanic ash transport and dispersal. Our primary modeling tool will be a new code puffin formed by the combination of a plume eruption model Bent and the ash transport model Puff. Data from satellite imagery, observation of vent parameters and windfields will drive our simulations. We will use ensemble based uncertainty quantification and parameter estimation methodology – polynomial chaos quadrature in combination with data integration to complete the DDDAS loop.

Temporal, probabilistic mapping of ash clouds using wind field stochastic variability and uncertain eruption source parameters: Example of the 14 April 2010 Eyjafjallajökull eruption

Uncertainty in predictions from a model of volcanic ash transport in the atmosphere arises from uncertainty in both eruption source parameters and the model wind field. In a previous contribution, we analyzed the probability of ash cloud presence using weighted samples of volcanic ash transport and dispersal model runs and a reanalysis wind field to propagate uncertainty in eruption source parameters alone. In this contribution, the probabilistic modeling is extended by using ensemble forecast wind fields as well as uncertain source parameters. The impact on ash transport of variability in wind fields due to unresolved scales of motion as well as model physics uncertainty is also explored. We have therefore generated a weighted, probabilistic forecast of volcanic ash transport with only a priori information, exploring uncertainty in both the wind field and the volcanic source.

Comparing landslide susceptibility models in the Río El Estado watershed on the SW flank of Pico de Orizaba volcano, Mexico

A comprehensive study of landslide susceptibility models is carried out in the Río El Estado watershed on the SW flank of Pico de Orizaba volcano. A detailed multitemporal landslide inventory map in the watershed is used as a framework for the quantitative comparison of three landslide susceptibility models. The first landslide susceptibility map is created by using the Stability Index MAPping model. The second and the third landslide susceptibility maps are created using multiple logistic regression (MLR) and multicriteria evaluation models. The validation of the resulting susceptibility maps is performed by comparing them with an inventory map in a contingency table and through the area under the receiver operating characteristic curve. The results point out that the models tend to over-predict and have a moderate to high match with the landslide areas. In this research, MLR is preferred over the other two models because MLR obtains similar or better results with fewer significant variables.

Fast Construction of Surrogates for UQ Central to DDDAS– Application to Volcanic Ash Transport

In this paper, we present new ideas to greatly enhance the quality of uncertainty quantification in the DDDAS framework. We build on ongoing work in large scale transport of geophysical mass of volcanic origin – a danger to both land based installations and airborne vehicles. The principal new idea introduced is the concept of a localized Bayes linear model as a surrogate for the expensive simulator. Probability of ash presence is compared to earlier work.

Mapping landforms for landslide hazards assessment on the SW flank of Pico de Orizaba volcano, Puebla-Veracruz, Mexico

Landslides that occur along stream systems are very common and have the potential to damage human settlements and economic activities. On the highest mountains in Mexico the potential for landslides and debris flows is great because of the large area of weakened rocks at high altitudes and under high seasonal rainfall. In Mexico, in spite of the effort to represent and assess slope stability by local authorities and scientists, there is a lack of standardized and systematized landslide inventory maps, landslide hazard maps, and related geo-databases that support the prediction of future slope instability. The present work illustrates a method to analyze the distribution of landslides and characterize landforms that are prone to slope instability. For the Río Chiquito-Barranca del Muerto watershed on the southwestern flank of Pico de Orizaba volcano, landforms and landslide distribution were ascertained through a landslide inventory map created from multi-temporal aerial photographs, field investigations and, an adaptation of the Landslide Hazard Zonation Protocol of the Washington State Department of Natural Resources, Forest Practices Division, in a GIS-based technology. This analysis divided the watershed into 12 mass-wasting landforms that were assigned slope-stability hazard ratings from low to very high. The overall hazard rating for this watershed was very high.