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  • 1
    Publication Date: 2020-02-05
    Description: Eruption forecasting refers, in general, to the assessment of the occurrence probability of a given erup- tive event, whereas volcanic hazards are normally associated with the analysis of superficial and evident phenomena that usually accompany eruptions (e.g., lava, pyroclastic flows, tephra fall, lahars, etc.). Nevertheless, several hazards of volcanic origin may occur in noneruptive phases dur- ing unrest episodes. Among others, remarkable examples are gas emissions, phreatic explosions, ground deforma- tion, and seismic swarms. Many of such events may lead to significant damages, and for this reason, the “risk” associ- ated to unrest episodes could not be negligible with respect to eruption-related phenomena. Our main objective in this paper is to provide a quantitative framework to calculate probabilities of volcanic unrest. The mathematical frame- work proposed is based on the integration of stochastic mod- els based on the analysis of eruption occurrence catalogs into a Bayesian event tree scheme for eruption forecast- ing and volcanic hazard assessment. Indeed, such models are based on long-term eruption catalogs and in many cases allow a more consistent analysis of long-term tem- poral modulations of volcanic activity. The main result of this approach is twofold: first, it allows to make inferences about the probability of volcanic unrest; second, it allows to project the results of stochastic modeling of the eruptive history of a volcano toward the probabilistic assessment of volcanic hazards. To illustrate the performance of the pro- posed approach, we apply it to determine probabilities of unrest at Miyakejima volcano, Japan.
    Description: Published
    Description: 689
    Description: 4.3. TTC - Scenari di pericolosità vulcanica
    Description: JCR Journal
    Description: open
    Keywords: Volcanic unrest ; Eruption forecasting ; Bayesian event tree ; Stochastic models ; Miyakejima volcano ; 04. Solid Earth::04.08. Volcanology::04.08.08. Volcanic risk
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 2
    Publication Date: 2022-06-09
    Description: In this paper we have put forward a Bayesian framework for the analysis and testing of possible non-stationarities in extreme events. We use the extreme value theory to model temperature and precipitation data in the Dar es Salaam region, Tanzania. Temporal trends are modeled writing the location parameter of the generalized extreme value distribution in terms of deterministic functions of explanatory covariates. The analyses are performed using synthetic time series derived from a Regional Climate Model. The simulations, performed in an area around the Dar es Salaam city, Tanzania, take into account two Representative Concentration Pathways scenarios from the Intergovernmental Panel on Climate Change. Our main interest is to analyze extremes with high spatial and temporal resolution and to pursue this requirement we have adopted an individual grid box analysis approach. The approach presented in this paper is composed of the following key elements: (1) an advanced Bayesian method for the estimation of model parameters, (2) a rigorous procedure for model selection, and (3) uncertainty assessment and propagation. The results of our analyses are intended to be used for quantitative hazard and risk assessment and are presented in terms of hazard curves and probabilistic hazard maps. In the case study we found that for both the temperature and precipitation data, a linear trend in the location parameter was the only model performing better than the stationary one in the areas where evidence against the stationary model exists.
    Description: This research has been developed in the framework of the FP7 European project CLUVA (Climate change and Urban Vulnerability in Africa), Grant No. 265137. This research has been funded by the FP7 European project CLUVA (Climate change and Urban ulnerability in Africa).
    Description: Published
    Description: 289-320
    Description: 4A. Clima e Oceani
    Description: JCR Journal
    Description: open
    Keywords: Non-stationary extreme events ; Climate change ; Multi-hazard ; Bayesian inference ; Extreme precipitation ; Extreme temperature ; Dar es Salaam ; Tanzania ; 01. Atmosphere::01.01. Atmosphere::01.01.02. Climate ; 03. Hydrosphere::03.02. Hydrology::03.02.05. Models and Forecasts ; 03. Hydrosphere::03.03. Physical::03.03.02. General circulation ; 05. General::05.08. Risk::05.08.99. General or miscellaneous
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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