Spatio-Temporal Drought Analysis on Example of the Central European Gridded Dataset

Tuesday, 16 December 2014
Petr Stepanek1,2, Miroslav Trnka3,4, Pavel Zahradníček2,4, Daniela Semerádová3,4 and Petr Hlavinka3,4, (1)Organization Not Listed, Washington, DC, United States, (2)Czech Hydrometeorological Institute, Brno, Czech Republic, (3)Mendel University, Department of Agrosystems and Bioclimatology, Brno, Czech Republic, (4)Global Change Research Centre AS CR, v. v. i., Brno, Czech Republic
Drought may have severe impacts on many human activities. Understanding its spatio-temporal variations is thus very important in many research fields. On example of the Central Europe dataset we analyzed and compared some products based on drought analyses, which may help to answer important questions for impact studies:

1) evaluation of the added value coming from inclusion of spatial aspect (not only temporal one) in the drought analysis;

2) comparison of drought indices calculated from various number of available input meteorological elements (backwards into history less and less meteorological elements are available);

3) linking together meteorological drought with agriculture one.

Basis for the study was production of gridded dataset of basic meteorological elements (daily minimum and maximum temperature, precipitation, sunshine duration, relative humidity and wind speed). From the station location time series in the period 1961-2013, gridded dataset was created applying geostatistical methods using both spatial and temporal aspects of the data (spacetime package under R). From such gridded dataset, SPEI (standardized precipitation evaporation index) was calculated using various approaches: Thornthwaite (potential evapotranspiration), Hargreaves and Penman-Monteith (reference evapotranspiration). The outputs were gridded datasets of the SPEIs that were then analyzed for the Central Europe both from temporal (based on station data) and spatial aspects. In order to estimate how this meteorological drought analysis may contribute to drought impact analysis (in our case we chose agriculture), we compared the results with the analysis coming from the agrometeorological drought monitoring system (based on SoilClim – dynamical model of soil water content) which is now used for drought monitoring and analysis in the Central Europe. Such comparison is important either for drought analysis in the past (when soil observations are not available) or also for future climate projections (is it sufficient to use only meteorological information if soil information are not available?).