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Probabilistic Characterization Of Drought Events Over Europe

Congress: 2015
Author(s): Brunella Bonaccorso, Giuseppe Rossi, Antonino Cancelliere
Department of Civil, Computer, Construc. and Env. Eng. and App. Math., University of Messina1, Department of Civil Engineering and Architecture, University of Catania2

Keyword(s): Sub-theme 11: Key vulnerabilities and security risks,
AbstractSevere and prolonged drought events have affected many regions all over the world during the last decades, with harmful impacts on public water supply, industrial and agricultural production, as well as on the environment. According to the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC, 2013), in presently dry regions, drought frequency will likely increase by the end of the 21st century under RCP8.5 scenario. In particular, there is high confidence in likely surface drying in the Mediterranean, Southwest US and southern African regions. In this context a common awareness has risen about the need to develop and implement advanced drought risk management strategies. This requires on the one hand a better understanding and modeling of the natural phenomenon and its impacts, and on the other hand the definition and implementation of adequate measures to reduce vulnerability to drought events and to minimize drought impacts. Probabilistic characterization of historical drought events is necessary to achieve the former objective, as it can help to assess drought hazard over a region, which combined with drought vulnerability yields the corresponding drought risk. In particular, estimation of drought return periods can provide useful information for an appropriate water resources planning under drought conditions. Yevjevich (1967) used the theory of runs to characterize droughts as a sequence of consecutive intervals where the water supply variable remains below a threshold level (somehow representative of water demand), preceded and succeeded by values above the threshold. Thus, each drought event can be characterized by two main properties, namely drought duration, and accumulated deficit, i.e. the sum of departures of the variable from the threshold along drought duration intervals. Such characteristics are statistically dependent and therefore a multivariate approach should be employed for their probabilistic analysis. However, due to the relatively limited number of drought events that can be identified on historical records, fitting parametric distributions to observed drought characteristics is unsuitable to model the corresponding marginal or joint probability distributions. A possible solution consists in deriving the marginal and multivariate probability cdf's of drought characteristics as functions of the parameters of the cdf of the underlying variable (e.g. precipitation), whose sample series is usually long enough to obtain reliable estimates in a statistical sense. Once that such probability distributions are derived, return period of different type of drought events can be determined as well. Since drought can span several years, it is not possible to identify a unique time unit (or trial) with respect to which, the exceedance probability P[Xt>xt] can be expressed, as one can usually make in flood frequency analysis, where the return period can be evaluated by the well known formula T=1/P[Xt>xt]. In this regard, Shiau and Shen (2001) developed a procedure for deriving the return period of accumulated deficit, assuming independent and identically distributed events. Return period is defined as the expected value of the average interarrival time between two successive events with accumulated deficit greater than or equal to a fixed value. This procedure has been further extended to the case of drought events characterized by both drought duration and accumulated deficit or drought duration and intensity (Bonaccorso et al. 2003; Gonzalez and Valdes 2003), as well as to the case of periodic series such as monthly or seasonal variables (Cancelliere and Salas 2004). Recently, Cancelliere and Salas (2010) have modified the foregoing methodology in order to deal with autocorrelated series. In the present study, the latter procedure have been applied to investigate space-time variability of meteorological drought occurrences over Europe, by using the annual precipitation series retrieved by the CRU TS3.10.01 gridded dataset for the period 1901--2009 (http://badc.nerc.ac.uk). Different probability distributions, namely normal, lognormal and gamma, were fitted cell by cell to the data, based on the criteria of the lowest value of the Lilliefors test statistic. Historical dry and wet conditions have been identified through the theory of runs with a threshold equal to the theoretical median, and then classified based on the probabilities of occurrence of annual deficit and surplus. The analysis of spatial and temporal distribution of dry and wet periods, although limited to selected cells of the CRU TS3.10.01 grid, has revealed that quite a few severe dry periods, affecting a large part of the continent, have occurred during the period of observation, such as at the beginning of the '20s, during the '40s, in the mid '70s, at the end of the '80s and in 2002/2003. Time series of the spatial coverage of dry conditions for the whole study area has shown a general significant decreasing trend, which leads one to believe that recent severe droughts have a reduced extent with respect to those occurred in the first mid of the past century. Following the proposed procedure, return periods of selected historical droughts, which have heavily affected a large part of Europe, have been analyzed. In particular, the comparison of the spatial distribution of return periods of droughts occurred during 1968--1978, 1985--1995 and 1999--2009, has highlighted worst conditions during the second period, in terms of broader extent of the regions with a corresponding drought return periods greater than 250 years. Finally, the spatial distributions of return periods of design critical droughts (i.e. with fixed accumulated deficit and duration) have revealed that, in addition to Euro-Mediterranean regions, some North Western regions (e.g. from Southern England to Germany) and Central Eastern regions (e.g. countries close to the Black Sea) are more drought prone. 1. Bonaccorso, B., Cancelliere, A. and Rossi, G. (2003) An analytical formulation of return period of drought severity. Stoch. Environ. Res. Risk. Assess. 17, 157–174.
2. Cancelliere, A. and Salas, J.D. (2004) Drought length properties for periodic-stochastic hydrological data. Water Resour. Res., 10(2), 1–13.
3. Cancelliere, A. and Salas, J.D. (2010) Drought probabilities and return period for annual streamflows series. J. Hydrol. 391, 77–89.
4. Gonzalez, J. and Valdes, J.B. (2003) Bivariate drought recurrence analysis using tree ring reconstructions. J. Hydrol. Eng. 8(5), 247–258.
5. IPCC (2013) Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
6. Shiau, J. and Shen, H.W. (2001) Recurrence analysis of hydrologic droughts of differing severity. J. Water. Resour. Plann. Manag. (ASCE) 127(1), 30–40.
7. Yevjevich, V. (1967) An objective approach to definitions and investigations of continental hydrologic droughts. Hydrology Paper 23. Colorado State University, Fort Collins.
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