A reliable assessment of drought return periods is essential to help decision makers in setting effective drought preparedness and mitigation measures. However, often an inferential approach is unsuitable to model the marginal or joint probability distributions of drought characteristics, such as drought duration and accumulated deficit, due to the relatively limited number of drought events that can be observed in the historical records of the hydrological variables of interest. As an alternative, the marginal and multivariate probability cdf’s of drought characteristics can be derived as functions of the parameters of the cdf of the underlying variable (e.g. precipitation), whose sample series is usually long enough to obtain trustworthy estimates in a statistical sense. In this study, the latter methodology is applied to investigate space-time variability of drought occurrences over Europe by using the CRU TS3.10.01 precipitation dataset for the period 1901–2009. In particular, a methodology able to take into account autocorrelation in the underlying precipitation series is adopted. First, a spatial analysis of historical droughts at European level is carried out. Then, the joint probability distributions of drought duration and accumulated deficit are derived for each cell, with reference to both historical and design drought events. Finally, the corresponding bivariate drought return periods are computed, as the expected values of the interarrival time between consecutive critical droughts.Results show that several heavy drought episodes have widely affected the continent. Among the most recent events, drought occurred during the period 1985–1995 was the worst in terms of extent of the regions characterized by return periods greater than 250 years. Besides Euro-Mediterranean regions, North Western and Central Eastern regions appear more drought prone than the rest of Europe, in terms of low values of return periods.

Large Scale Probabilistic Drought Characterization Over Europe

BONACCORSO, Brunella;
2013-01-01

Abstract

A reliable assessment of drought return periods is essential to help decision makers in setting effective drought preparedness and mitigation measures. However, often an inferential approach is unsuitable to model the marginal or joint probability distributions of drought characteristics, such as drought duration and accumulated deficit, due to the relatively limited number of drought events that can be observed in the historical records of the hydrological variables of interest. As an alternative, the marginal and multivariate probability cdf’s of drought characteristics can be derived as functions of the parameters of the cdf of the underlying variable (e.g. precipitation), whose sample series is usually long enough to obtain trustworthy estimates in a statistical sense. In this study, the latter methodology is applied to investigate space-time variability of drought occurrences over Europe by using the CRU TS3.10.01 precipitation dataset for the period 1901–2009. In particular, a methodology able to take into account autocorrelation in the underlying precipitation series is adopted. First, a spatial analysis of historical droughts at European level is carried out. Then, the joint probability distributions of drought duration and accumulated deficit are derived for each cell, with reference to both historical and design drought events. Finally, the corresponding bivariate drought return periods are computed, as the expected values of the interarrival time between consecutive critical droughts.Results show that several heavy drought episodes have widely affected the continent. Among the most recent events, drought occurred during the period 1985–1995 was the worst in terms of extent of the regions characterized by return periods greater than 250 years. Besides Euro-Mediterranean regions, North Western and Central Eastern regions appear more drought prone than the rest of Europe, in terms of low values of return periods.
2013
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/2406621
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