1. MOISTURE VARIABILITY IN THE DANUBE LOWER BASIN: AN ANALYSIS BASED ON
THE PALMER DROUGHT INDICES AND THE SOLAR/GEOMAGNETIC ACTIVITY INFLUENCE
European Geosciences Union General Assembly 2014
Ileana Mares1, Venera Dobrica1, C. Demetrescu1, C. Mares2
1 Institute of Geodynamics, Romanian Academy, Bucharest, Romania
2National Institute of Hydrology and Water Management, Bucharest, ROMANIA
The climatic condition ( ) of 15 meteorological stations situated in the Danube basin (Fig.1) has been evaluated using four Palmer indices:
Palmer Drought Severity Index (PDSI)
Palmer Hydrological Drought Index (PHDI)
Weighted PDSI (WPLM)
Palmer Z-index (ZIND)
Also, an index easier to estimate was calculated, which depends only on the temperatures and precipitation normalized (TPPI).
For each of the four indices Palmer and for TPP index, decompositions in the empirical orthogonal functions (EOFs) were carried out. For a feature overview of the state of drought or excessive moisture we achieved decompositions in the multivariate EOFs (MEOF) of the four indices Palmer. In all analyzes we used only the first temporal component, namely the principal component (PC1). As representative for the discharge in the Danube lower basin (DLB), station Orsova has been chosen an integrator of the discharges from Danube upper and middle basin. 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)
For each season the internal structure was analyzed:
Limits of variation of these indices (Example, Fig. 2 for PDSI);
3) Quasi-periodicities (Fig.4); b)
FIG 2. PDSI histograms for the 15 stations: a) summer ; b) winter
2) The change points which separating the dry periods and relatively wet periods both in discharge and Palmer indices ( Fig.3 for summer);
4) A classification in 5 classes has been achieved in order to highlight extreme events (Fig.5 for WPLM index).
Fig.1. Localization of 15 precipitation stations situated upstream of Orsova station. a) b)
FIG.3. The summer time evolution of Danube discharge at Orsova compared with PC1-MEOF that represents the four Palmer indices (R=0.85). Bj represents the mean of time series on a time interval (lag) before the occurring of jump (change point) and Aj the mean of time series on the same number of years (the respective lag) after the occurring of the change point.
FIG. 4. The periodogram for PC1-MEOF compared with that of Danube discharge at Orsova:
a) fall, b) winter. (Both time series are standardized)
FIG 5. Percentage of coverage for WPLM index (summer) represented by extreme states (severe droughts and respective excessive moisture).The tendencies of wet periods (green curve) and dry periods (red curve) are estimated using a polynomial regression of order 6.
Solar/geomagnetic activity influence
The influence of large-scale atmospheric circulation
- Quasi-Biennial Oscillation (QBO) ( )
- The 10.7 cm Solar Flux ( )
Greenland Balkan Oscillation Index (GBOI), similar to the NAO index, but GBOI is more efficient for the studied area. The GBO index has a clear influence on the variation of drought indices in winter, especially for the overall index expressed by PC1-MEOF (Fig.6 , Fig. 7).
- Wolf ’s relative sunspot number ( )
- The geomagnetic activity index (aa) ( )
Cross correlation
Lag =0
The most significant correlations have found between the geomagnetic activity index (aa) values in spring and summer and PC1_MEOF and PC1_TT, respectively, for 20th century (Fig. 8).The response of seal level pressure (SLP) to variation in the Wolf numbers is more significant during minimum solar activity than for maximum activity (Fig.9)
Lag ≠0
The best results for the 10.7 cm Solar Flux and TPPI and PC1_MEOF during summer with Lag=3 years (Fig.10). There is a change in the spatial distribution of SLP, for Lag=2 years before and after occurrence the minimum in the solar activity (Fig. 11). a) b)
FIG. 7 . Composite map of winter SLP (departures from average) corresponding to the extreme states of PC1-MEOF : a ) State 1 (extreme droughts) ; b) State 5 ( extremely wet)
FIG. 6. GBOI winter values in comparison with PC1-MEOF. The trends are estimated by the moving average with the span of 5. (R=0.55).
FIG. 10. Time series of the 10.7 cm Solar Flux (standardized) in summer ( ) and: TPPI with Lag=3 years before flux (R=-0.39), PC1_MEOF with Lag=3 years before flux (R=0.44);Statistical significance at 1% level.
CONCLUSIONS
By analyzing the internal structure of series of PC1-MEOF appears that some of the change points or some quasi- periodicities can be found also in the structure of discharge series, especially in fall and winter, such as for example quasi periodicities of 5 and 20 years.
Evaluating possible causes of extreme hydrological events occurring in the lower Danube basin, we found that NAO is not a predictor as good as it is for the west and northwest areas of the European continent.
We introduced a new index GBO, which reflects the Balkan-Greenland Oscillation. It proved to be superior to NAOI, correlating very well with hydro climatic regime of middle and lower Danube basin.
The most significant changes in SLP were highlighted in the case of minimum solar activity quantified by Wolf numbers, simultaneously and with the lags + 2 and -2 years.
- Cross correlations between 10.7 cm solar flux and drought indices revealed that the most significant correlation (1% level) are in summer for the TPPI values (R= ) and for PC1_MEOF (R= 0.44) with Lag=3 years before flux.
FIG.8. Time series of the geomagnetic activity index (aa) in
Spring in comparison with PC1_MEOF (R=-0.29) b) a) a) b)
FIG.11. Spatial distribution of SLP (departure) with :
2-years before minimum Wolf number ;
b) 2-years after maximum Wolf number.
FIG.9. Composite maps of SLP (departures from the average over ) corresponding to extreme Wolf numbers from
nine solar cycles (14-22 cycles): a) maximum; b) minimum