Identification of extreme events using drought indices and their impact on the Danube lower basin discharge

Abstract

In this study extreme droughts and extremely wet periods in the Danube upper and middle basin (DUMB) have been highlighted with specific indices. The most widely used indices have been considered to estimate both the dry and wet phenomena severity and the frequency or spatio‐temporal extension. The climatic condition of 15 meteorological stations situated in the Danube basin has been evaluated using four indices: Palmer Drought Severity Index (PDSI), Palmer Hydrological Drought Index (PHDI), Weighted PDSI (WPLM) and Palmer Z‐index (ZIND). The four indices have been analysed separately for each of the four seasons between 1901 and 2000. First the internal structure of the time series of the four indices has been analysed separately. Then the overall temporal characteristic has been analysed by means of the principal component of the Multivariate Empirical Orthogonal Functions decomposition of the four indices (PC1‐MEOF). For the discharge in the Danube lower basin, station Orsova has been chosen, representing an integrator of the discharges from the DUMB. A very close connection has been found between Palmer indices and Danube discharge in all seasons (with correlation coefficients greater then 0.80) excepting the spring season. A classification in five classes of both the four indices separately and the PC1‐MEOF has been achieved in order to highlight extreme events. The impact of phenomena quantified by Palmer indices in DUMB upon discharges in Danube lower basin is evident. It was demonstrated in this study that the Greenland‐Balkan Oscillation (GBO) influences the south‐east European hydro‐climatic regime more than the North Atlantic Oscillation (NAO) Index. Copyright © 2016 John Wiley & Sons, Ltd.

Spatial–Temporal Evolution of Drought Characteristics Over Hungary Between 1961 and 2010

“…Drought in Hungary is one of the aspects of global climate change that could mainly be affected by various atmospheric factors such as the El Niño-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), the Greenland-Balkan Oscillation (GBO) and the Pacific decadal oscillation (PDO). However, Mares et al (2016) demonstrate that the role of the Greenland-Balkan Oscillation (GBO) in influencing the south-east European hydro-climatic regime is greater than that of the NAO and the ENSO. In particular, the GBO captures both movements of air masses with a meridional component and a zonal component, while the ENSO and the NAO were less dominant in the Danube middle basin (Mares et al 2016).…”

“…However, Mares et al (2016) demonstrate that the role of the Greenland-Balkan Oscillation (GBO) in influencing the south-east European hydro-climatic regime is greater than that of the NAO and the ENSO. In particular, the GBO captures both movements of air masses with a meridional component and a zonal component, while the ENSO and the NAO were less dominant in the Danube middle basin (Mares et al 2016). In a similar vein, Bartholy et al (2013) emphasized that the most significant factors affecting drought trends in Hungary are atmospheric circulation, lack of precipitation, changes in soil moisture and evapotranspiration and other elements of the hydrological cycle.…”

Selection of Optimal Palmer Predictors for Increasing the Predictability of the Danube Discharge: New Findings Based on Information Theory and Partial Wavelet Coherence Analysis

“…In the same publication, the authors describe a calibration procedure, i.e., introduce a self-calibrating Palmer drought severity index (SC-PDSI). The authors of this study compared, in the work of Mares et al (2016) [ 44 ], the influence of the four original Palmer indices on the discharge of the Danube River at Orsova with the influence of the self-calibrating ones. Compared to the self-calibrating indices, the original ones for seasons are more suitable from a statistical point of view, regarding the influence on Danube discharge in the lower basin.…”

“…The next negative RSI value (high in absolute value) was for the autumn season ( Figure 2 ). Additionally, for this season, the highest correlation between the PC1 of the development in the MEOF of the 4 predictors and the Danube discharge was obtained (Mares et al, 2016) [ 44 ]. Therefore, the investigation based on wavelet analysis in the present study focused on the autumn season.…”

“…This study is based on time series from the twentieth century (1901–2000), and investigations were performed separately for each of the four seasons. This time interval and the seasonal averages were considered in order to be able to compare the results obtained here with those obtained in our previous investigations [ 18 , 19 , 20 , 44 ].…”