TELECONNECTIONS OF AO, NAO, SO, AND QBO WITH INTERANNUAL STREAMFLOW FLUCTUATION IN THE HRON BASIN

J. Hydrol. Hydromech., 52, 2004, 4, 279–290

279

TELECONNECTIONS OF AO, NAO, SO, AND QBO WITH INTERANNUAL STREAMFLOW FLUCTUATION IN THE HRON BASIN PAVLA PEKAROVA1) , JAN PEKAR2) 1)Institute of Hydrology, Slovak Academy of Sciences, Racianska 75, 831 02 Bratislava 3, Slovak Republic; mailto: [email protected] 2)Department of Applied Mathematics and Statistics, Faculty of Mathematics, Physics and Informatics, Comenius University Bratislava, Mlynská dolina, 842 48 Bratislava, Slovak Republic; mailto: [email protected]

The aim of the paper is to analyse a possible teleconnection of AO (Arctic Oscillation), SO (Southern Oscillation), PDO (Pacific Decade Oscillation), NAO (North Atlantic Oscillation) and QBO (Quasi Bien-nial Oscillation) phenomena with long-term streamflow fluctuation in Hron River basin (Central Slovakia). The spectral analysis shows that for the series of AO, NAO, SO, and PDO indexes we can identify the ca 2.4-; 3.6-; 7.8-; 14-; 21-; 30- and 36-yr cycles. The coincident cycles were found in the monthly discharge time series from the Hron basin (period 1931–2000) using combined periodogram method. As these periods were found in almost all discharge series analysed within very different geographical zones, it can be con-sidered as the general regularity on the earth. The regularity is related to general oceanic and atmospheric circulation, part of which are also the SO, AO, PDO and NAO phenomena. KEY WORDS: Long-term Streamflow Fluctuation, Spectral Analysis, Teleconnection, Discharge, AO, NAO, SO, QBO.

Pavla Pekárová, Ján Pekár: TELEKONEKCIA AO, NAO, SO A QBO S VIACROČNÝMI FLUK-TUÁCIAMI PRIETOKOV V POVODÍ HRONA. Vodohosp. Čas., 52, 2004, 4; 43 lit., 8 obr., 3 tab.

Cieľom predloženej štúdie je analýza možných telekonekcií Arktickej oscilácie (AO), Južnej oscilácie

(SO), Tichomorskej dekádnej oscilácie (PDO), Severoatlantickej oscilácie (NAO) a Kvázi dvojročnej os-cilácie (QBO) s viacročnými cyklami priemerných ročných prietokov v povodí rieky Hron (stredné Sloven-sko). Spektrálnou analýzou časových radov AO, NAO, SO, a PDO indexov boli nájdené nasledujúce viacročné cykly kolísania indexov: ca 2,4; 3,6; 7,8; 14; 21; 30 a 36 rokov. Metódou kombinovaného perio-dogramu boli nájdené zhodné cykly kolísania viacročných suchých a mokrých období i v mesačných prie-tokových radoch z povodia Hrona (1930–2000). Keďže tieto periódy boli nájdené vo všetkých prietokových radoch z rôznych geografických zón, môžu byť považované za všeobecný jav na Zemi. Toto pravidelné opakovanie mokrých a suchých období súvisí so všeobecnou cirkuláciou oceánov a atmosféry, súčasťou ktorých sú i SO, AO, PDO, NAO a QBO javy.

KĽÚČOVÉ SLOVÁ: viacročné kolísanie odtoku, spektrálna analýza, telekonekcia, prietoky, AO, NAO, SO, QBO.

1. Introduction

Interannual discharge series fluctuations have their natural origin. Apart from it river discharge may have changed due to a range of human activi-ties. Dams and artificial reservoirs dramatically change the natural flow regime. Nowadays main problem of hydrology and design support for water projects connects with climate change and its im-pact on hydrological characteristics as observed as well as designed. According to Lobanov & Lo-

banova (2004), there are three main stages of this problem: i) how to extract a climate variability and climate

change from complex hydrological records; ii) how to assess the contribution of climate change

and its significance for the point and area scale; iii) how to use the detected climate change for

computation of design hydrological characteris-tics.

Currently to the climate change problems of the long-term streamflow trends a number of studies was published all over the world. For example,

P. Pekarova, J. Pekar

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a special section of Hydrological Sciences Journal in February 2004 dealt with the issue of detecting changes in hydrological data. Authors Kundzewicz & Robson (2004), Sheng & Pilon (2004), and Xiong & Guo (2004) focused on detecting changes in hy-drological long-time series. In Radziejewski & Kundzewicz (2004) a new concept of visualisation of the comprehensive change detection is demon-strated. In Burn et al. (2004) the trends in the Liard River (northern Canada) were investigated using Mann-Kendall test. They showed that the observed trends are related to both, trends in meteorological data and a large-scale oceanic and atmospheric process. In Slovakia, Hlavcova et al. (1999) studied the possible impact of climate change upon stream-flow regime and analysed the unfavourable de-creasing streamflow trend in 1981–1995. Kostka & Holko (2000) studied the impact of climate changes to hydrological regime in small mountainous basin. Halmova (2000) studied the water changes of stor-age in reservoirs under climate change. Pekarova & Miklanek (2004) analysed long-term course of 27 discharge series from the database of National Cli-mate Programme SR (period 1931–2000). Regard-less the fact that they showed the decrease of dis-charge in South Slovakia in decade 1982–1993, they emphasize from the long-term point of view the necessity to identify hidden periods in the long-term series. As mentioned in Pekarova (2003) and Pekarova et al. (2003), it is necessary to come out from sufficiently long-time discharge series be-cause of possible confusion between long-term trend and variability.

The variability of streamflow results from the global system of oceanic streams, the global circu-lation of the atmosphere, and the transport of mois-ture (precipitation). In recent few years many scien-tists have studied relationships between the atmo-spheric phenomena (as Arctic Oscillation (AO), Southern Oscillation (SO), Pacific Decade Oscilla-tion (PDO) and North Atlantic Oscillation (NAO)) and some hydroclimatic characteristics (as total precipitation, air temperature, discharge, snow and ice cover, flood risk, see levels series, or coral oxy-gen isotope records, dendrochronological series etc).

E.g., Compagnucci et al. (2000) employed a wavelet filter for removing the strong annual wave in the Atuel river streamflow data to analyze for other wavelength phenomena and to examine the influence of the ENSO events.

Jevrejeva & Moore (2001) and Jevrejeva et al. (2003) studied variability in time series of ice con-

ditions in the Baltic Sea within the context of North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) winter indices using the Singular Spectrum Analysis (SSA) and wavelet approach. According these authors cross-wavelet power for the time se-ries indicates that the times of largest variance in ice conditions are in excellent agreement with sig-nificant power in the AO at 2.2–3.5-, 5.7–7.8-, and 12–20-yr periods, similar patterns are also seen with the Southern Oscillation Index (SOI) and Nino-3 sea surface temperature (Nino-3) series. Wavelet coherence shows in-phase linkages be-tween the 2.2–7.8- and 12–20-yr period signals in both, tropical and Arctic atmospheric circulation and also with ice conditions in the Baltic Sea.

In Kiem et al. (2003) the variability of flood risk across New South Wales (Australia) is analyzed with respect to the observed modulation of ENSO event magnitude. This is achieved through the use of a simple index of regional flood risk. The results indicate that cold ENSO events (La Nina) are the dominant drivers of elevated flood risk. An analysis of multi-decadal modulation of flood risk is achieved using the inter-decadal Pacific Oscillation (IPO) index. The analysis reveals that IPO modula-tion of ENSO events leads to multi-decadal epochs of elevated flood risk, however this modulation appears to affect not only the magnitude of individ-ual ENSO events, but also the frequency of their occurrence. These results have marked implications for achieving robust flood frequency analysis as well as providing a strong example of the role of natural climate variability.

Anctil & Coulibaly (2003) described the local in-terannual variability in southern Québec streamflow based on wavelet analysis, and to identify plausible climatic teleconnections that could explain these local variations. The span of available observations, 1938–2000, allows depicting the variance for peri-ods up to about 12 yr. The most striking feature, in the 2–3-yr band, in the 3–6-yr band, and the 6–12-yr band is dominated by white noise and is not con-sidered further – is a net distinction between the timing of the interannual variability in local western and eastern streamflows, which may be linked to the local climatology. This opens up the opportu-nity to construct two regional time series using principal component (PC) analysis. Then, for each band, linear relationships are sought between the regional streamflow and five selected climatic indi-ces: the Pacific–North America (PNA), the North Atlantic Oscillation (NAO), the Northern Hemi-sphere annular mode (NAM), the Baffin Island–

Teleconnections of AO, NAO, SO, and QBO with interannual streamflow fluctuation in the Hron basin

281

West Atlantic (BWA) and the sea surface tempera-ture anomalies over the Niño-3 region (ENSO-3).

Turkes & Erlat (2003) and Uvo (2003) studied teleconnection of NAO variability with precipita-tion variability in Turkey, and in Northen Europe, resp.

Felis et al. (2000) studied a 245-yr coral oxygen isotope record from the northern Red Sea in bi-monthly resolution. A similar to 70-yr oscillation of probably North Atlantic origin dominates the coral time series. Interannual to interdecadal variability is correlated with instrumental indices of the North Atlantic Oscillation (NAO), the El Nino-Southern Oscillation (ENSO), and North Pacific climate variability. The results suggest that these modes contributed consistently to Middle East climate variability since at least 1750, preferentially at a period of similar to 5.7 years.

Tardif et al. (2003) studied variations in peri-odicities of the radial growth response of black ash exposed to yearly spring flooding in relation to hydrological fluctuations at Lake Duparquet in northwestern Québec. They detected about 3.5-, 3.75-, and 7.5-yr periodicities in all the dendro-chronological series. According to authors, the 3.75- and 7.5-yr components are harmonics of a 15-yr periodicity. In this study we try: 1. to identify an interannual variability analysis of

AO (Arctic Oscillation), NAO (North Atlantic Oscillation), SO (Southern Oscillation), PDO (Pacific Decade Oscillation) and QBO (Quasi Biennial Oscillation) phenomena by combined periodogram method;

2. to perform an interannual variability analysis of discharge time series in the Hron River basin;

3. and to find teleconnection between AO, NAO, SO, PDO, and QBO phenomena with long-term streamflow dynamics in the Hron River basin. The mountainous Hron River basin (Central Slo-

vakia) was chosen due to its relatively natural con-ditions uninfluenced by man activity (especially in the upper part of the basin). 2. Interannual variability analysis of AO, NAO, PDO, SO and QBO phenomena 2.1 Material North Atlantic Oscillation Index

North Atlantic Oscillation is one of the major modes of atmospheric circulation variability of the Northern Hemisphere over the middle and high

latitudes. According to Hurrell et al. (2003), the NAO refers to swings in the atmospheric sea level pressure difference between the Arctic and sub-tropical Atlantic that are associated with changes in the mean wind speed and direction. There exist several time series of NAO Indexes (NAOI), e.g. according to Stephenson et al. (2000) (Azory-Reykjavik), according to Hurrell (2000) (Lisabon-Reykjavik), or according to Jones et al. (1997) (Gibraltar-Island). In this study, for forecast purposes, the winter NOAI time series (December – March) according to Jones et al. (1997) are used (period 1825–2002). Dickson et al. (2000) or Ko-dera and Kuroda (2003) suggest, that NAO is the regional manifestation of a larger-scale (hemi-spheric) mode of variability known as the Arctic Oscillation. Arctic Oscillation Index

Arctic Oscillation (AO) Index (AOI) is defined as the normalized difference in zonal-averaged sea level pressure anomalies between 35°N and 65°N, and it is a measure of hemispheric-wide fluctua-tions in air mass between zones of high and low pressure anomalies centered around these two lati-tudes of Northern Hemisphere. The AOI captures an optimal representation of the temporal-spatial features of the AO, and the latitudial zones centered at 35°N and 65°N are denoted as annular belts of action for the AO. The longest AOI time series exhibits a major source of low-frequency variability in the Northern Hemispheric climate. We used the winter AOI according to Thompson (www.atmos. colostate.edu/ao/) for period 1899–2002. Pacific Decadal Oscillation Index

Pacific Decadal Oscillation (PDO) is defined as leading anomalies of mean November through March sea surface temperatures for the Pacific Ocean to the north of 20°N latitude (Mitchell, 2004). Positive values of PDOI indicate months of above normal SSTs along the west coast of the North and Central America and on the equator and below normal SSTs in the central and western north Pacific at about the latitude of Japan. Fluctuations in this pattern are dominated by variability on de-cadal time scales. We used the data according to Mitchell, (2004). Southern Oscillation Index

The SO (Southern Oscillation) pattern represents large-scale fluctuation of ocean temperatures, rain-fall, atmospheric circulation, vertical motion and air

http://www.atmos/


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pressure across the tropical Pacific. El Nino epi-sodes (also called Pacific warm episodes or ENSO) and La Nina episodes (also called Pacific cold epi-sodes) represent opposite extremes of the SO cycle (Gershunov et al., 2001). Quasi-Biennial Oscillation Index

Quasi-Biennial Oscillation Index (QBOI) repre-sents variability in equatorial lower stratospheric zonal wind. The values for period 1953–2001 were provided by Naujokat (1986) and Marquardt & Naujokat (1997). This index is the concatenation of values at Canton Island (3S, 172W) for Jan 1953–Aug 1967; Maledives (1S, 73E) for Sept 1967–Dec 1975; and Singapore (1N, 104 E) for Jan 1976–Sep 2001. 2.2 Spectral Analysis

Time series analysis includes many useful meth-ods to identify periodicity in time series, e.g.

Maximum Entropy Spectrum Analysis (MESA), Power Spectrum Analysis (PSA), Singular Spectrum Analysis (SSA), Empirical Orthogonal Functions Method (EOFs)/Fourier Analysis (FA), Autocorrelation Analysis (AC), Method of Main Components (MMC), etc. (Nobre & Shukla, 1996; Jevrejeva & Moore, 2001; Rao & Hamed, 2003; Liritzis & Fairbridge, 2003; Van Gelder et al., 2000; Prochazka et al., 2001). In this study we used both, combined periodogram method described by Pekarova et al. (2003) and AC method to identify interannual dynamics pattern of AO, NAO, PDO, SO as well as QBO phenomena.

In Fig. 1 there are the filtered (double 48-months moving averages) month indexes of the AO, NAO, and SO phenomena between 1900 and 2002. The circles indicate the about 28-yrs periods of El Niño occurrence. The graph below represents course of monthly QBO index for period 1953–2000.

AO

-0.5

0

0.5

1

I.1900 I.1910 I.1920 I.1930 I.1940 I.1950 I.1960 I.1970 I.1980 I.1990 I.2000

NAO

-0.5

0

0.5

1

I.1900 I.1910 I.1920 I.1930 I.1940 I.1950 I.1960 I.1970 I.1980 I.1990 I.2000

SO

-10

-5

0

5

10

I.1900 I.1910 I.1920 I.1930 I.1940 I.1950 I.1960 I.1970 I.1980 I.1990 I.2000

El Nino

La Nina

QBO

-4

-2

0

2

4

I.1900 I.1910 I.1920 I.1930 I.1940 I.1950 I.1960 I.1970 I.1980 I.1990 I.2000

Fig. 1. The filtered monthly indexes of the AO, NAO, and SO phenomena between 1900 and 2002. Course of the monthly QBO index, period 1953–2000. Obr. 1. Priebeh filtrovaných hodnôt indexov AO, NAO a SO v období r. 1900–2002. Priebeh mesačných hodnôt QBO indexu v období r. 1953–2000.


283

The autocorrelograms of SOI and NAOI are on Fig. 2a). The cyclicity of both indexes is more evi-dent if we plot the 3-years moving averages of the autocorrelation coefficients (Fig. 2b). SOI shows the 6–7-yr cycle, the NAOI 7–8-yr cycle. The auto-correlogram of yearly values is not very suitable for identification periodicities because it works in one year steps only. For the accurate calculation of the lengths of the periodicities we use the combined periodogram method.

On the Fig. 3 the combined periodograms of mentioned indexes are presented. In QBOI time

series a significant 28-month (ca 2.4-yr) periodicity was found. This period occurs in both, AOI and SOI time series. In the AOI time series all periods are presented. Similarly, in the NAOI time series there exists a significant 7.8-yr period, presented in the AO series.

Fourier analysis shows, that all periods found in all other indexes are presented in the AOI time series. It means, the AO pattern covers variability of all mentioned oscillations around the North Hemisphere.

a)

SOI

-0.3-0.2-0.10.00.10.2

0 7 14 21 28 35 42 49 56lag

k

b)

SOI

-0.1

0.0

0.1

0 7 14 21 28 35 42 49 56lag

km

NAOI

-0.3-0.2-0.10.00.10.20.3

0 7 14 21 28 35 42 49 56lag

k

NAOI

-0.1

0.0

0.1

0 7 14 21 28 35 42 49 56lag

km

Fig. 2. a) The autocorrelograms and b) 3-years moving averages of the autocorrelation coefficients of the SOI and NAOI. Obr. 2. a) Autokorelogramy zdrojových hodnôt a b) autokorelogramy 3-ročných kĺzavých priemerov SOI a NAOI. 3. Interannual variability analysis of discharge time series

Statistical analysis of the streamflow oscillations depends on availability of long-term data series. In Slovakia there exist four 100-year discharge time series, namely Danube at Bratislava station, Mo-rava: at Moravsky Jan, Vah: at Sala, and Bodrog: at Streda n. Bodrogom station. Systematic measure-ments of water levels in Hron river basin (Central Slovakia) started after 1930.

In order to identify long-term variability (wet and dry periods dynamics) of discharge in the Hron basin, eight monthly discharge time series were collected. The data series were obtained from the SHMI archive. Basic hydrological characteristics of analysed discharge series are given in Tab. 1.

Our goal is to identify periods of long-term dry and wet conditions occurred in the Hron basin. For

this purpose, the standardised monthly average discharge time series was computed according to the formula:

it

y

y yYσ

−= , (1)

where y – mean of the analysed series,

yσ – standard deviation of the analysed series,

tY – element of the standardised series. Then the standardized data were filtered by dou-

ble MA-filter from 84 terms. On the Fig. 4 the course of the filtered monthly standardized dischar-ge time series are presented. From the figure it fol-lows, that after 1965 the variance of series is lower. The discharge series of Bystra brook before 1965 are probably influenced by systematic errors, there-


284

0.0E+00

1.0E+00

2.0E+00

3.0E+00

4.0E+00

5.0E+00

6.0E+00

7.0E+00

5147433934

.73229

.326

.724

.522

.520

.418

.81715

.714

.3131210

.9109.

098.

337.

696.

936.

385.

785.2

4.73

4.334

3.47

3.06

2.74

2.48

2.26

2.08

AOI7.85.7

13.621

36

3.62.4

2.262.7

4.5

0.0E+00

5.0E-01

1.0E+00

1.5E+00

2.0E+00

2.5E+00

3.0E+00

3.5E+00

51.3

46.7

42.539

35.2

32.4

29.327

24.6

22.6

20.8

18.9

17.2

15.8

14.5

13.2

12.111

10.1

9.16

8.38

7.65

6.96

6.36

5.74

5.18

4.68

4.29

3.87

3.36

2.97

2.66

2.412.2

2.02

NAOI7.8

5

21 30

13.5

0.0E+00

3.0E+00

6.0E+00

9.0E+00

1.2E+01

1.5E+01

5147433934

.73229

.326

.724

.522

.520

.418

.81715

.714

.3131210

.9109.

098.

337.

696.

936.

385.

785.2

4.73

4.334

3.47

3.06

2.74

2.48

2.26

2.08

PDOI

2926

14.511.89

5.7

4.4 18.421

0.0E+00

1.0E+02

2.0E+02

3.0E+02

4.0E+02

5.0E+02

6.0E+0251

.546

.541

.738

.33531

.328

.826

.323

.821

.819

.818

.216

.715

.113

.912

.811

.710

.69.

628.

798.

077.

246.

586.

055.

434.

924.

464.1

3.57

3.13

2.782.5

2.27

2.08

SOI3.64

56.5

7

13.5

16.8

22

43

26

9

0.0E+001.0E+062.0E+063.0E+064.0E+065.0E+066.0E+067.0E+068.0E+069.0E+06

48.0

036

.00

24.0

018

.00

14.5

012

.00

10.3

39.

008.

007.

256.

606.

005.

605.

204.

864.

564.

294.

003.

833.

633.

433.

253.

082.

932.

792.

652.

532.

402.

282.

172.

051.

961.

871.

781.

701.

621.

541.

47

QBOIm

6.6

2.65

2.39

2.17

3

2

Fig. 3. The combined periodograms of AO, PDO, NAO, SO and QBO indexes. Obr. 3. Kombinované periodogramy AO, PDO, NAO, SO a QBO indexov.


285

T a b l e 1. Basic hydrological characteristics of yearly discharge, period of observation (1931–2000); A – area [km2], Qa – average annual discharge [m3s-1], qa – mean annual specific yield [l s-1 km-2], cs – coefficient of asymmetry, cv – coefficient of variation, min/max – minimal/maximal mean multiannual discharge [m3 s-1]. T a b u ľ k a 1. Základné hydrologické charakteristiky priemerných ročných prietokov, obdobie 1931–2000; A – plocha [km2], Qa – priemerný ročný prietok [m3 s-1], qa – priemerný ročný špecifický odtok [l s-1 km-2], cs – koeficient asymetrie, cv – koeficient variácie, min/max – minimálny/maximálny priemerný viacročný prietok [m3 s-1].

River Station A Rkm a.s.l Qa qa cs cv min max 7070 D. Lehota Vajskovský p. 53 2.7 495 1.4 26.3 0.82 0.24 0.73 2.5 7060 Bystrá Bystrianka 36 7 573 0.9 26.2 0.71 0.29 0.53 1.6 7065 Mýto p. Ď. Štiavnička 47.1 2.9 616 1.1 22.9 -0.05 0.24 0.43 1.6 6950 Hron Zlatno 83.7 263 733 1.4 17.3 0.94 0.32 0.65 2.9 7015 Hron Brezno 582 223 490 7.7 13.2 0.53 0.29 3.33 13.9 7045 Č. Hron Hronec 239 2.4 480 3.0 12.4 0.34 0.32 1.05 5.5 7160 Hron B. Bystrica 1766 175 334 26.5 15.0 0.46 0.26 12.49 46.8 7290 Hron Brehy 3821 93.9 194 47.3 12.4 0.44 0.28 22.02 85.4

fore this station was removed from statistical analy-sis.

On the Figs. 4–6, the extreme dry period 1982–1994 is presented on monthly (Fig. 4 and 5) runoff and on average annual discharge time series (Fig. 6) from period 1931–2000. As follows from the Fig. 6, (course of 10-yr moving averages of coeeficients

of variation cv), the dry periods are characterized by lower variability of discharge time series. On Fig. 7, the combined periodograms of yearly discharge are drawn. In discharge time series, the periods of 2.4-; 3.6-; 5-; 7-; 9-; 13.5- 22-; and 33-yrs were found.

-0.4

-0.2

0.0

0.2

0.4

0.6

XI-30 XI-40 XI-50 XI-60 XI-70 XI-80 XI-90 XI-00

1 Vajskov sky 2 Stiav nicka 3 H: Zlatno 4 H: Brezno5 C. Hron 6 H: B.By str. 7 H: Brehy By stra

Fig. 4. Course of the filtered monthly standardized discharge time series. Double MA-filter from 84 terms. Comparison of input data. Obr. 4. Priebeh filtrovaných priemerných mesačných prietokov. Dvojnásobný MA filter z 84 členov. Porovnanie vstupných údajov – grafická kontrola.

8

13

18

23

28

33

XI-30 XI-40 XI-50 XI-60 XI-70 XI-80 XI-90 XI-00

O[l.s-1km-2] 1 Vajskov sky 2 Stiav nicka 3 H: Zlatno 4 H: Brezno5 C. Hron 6 H: B.By str. 7 H: Brehy

Fig. 5. Course of the filtered monthly specific runoff [l s-1 km-2], double MA-filter from 84 terms. Identification of dry and wet period, as well as of long-term trend. Obr. 5. Priebeh filtrovaných priemerných mesačných špecifických odtokov [l s-1 km-2], dvojnásobný MA filter z 84 členov. Identi-fikácia suchých a mokrých období, ako aj dlhodobého trendu.


286

a)

0.5

1.0

1.5

2.0

2.5

1930 1950 1970 1990

Q [m

3 .s-1]

Vajskov sky p. Qa

-2.50

-1.50

-0.50

0.50

1.50

1930 1950 1970 1990

cs

0.10

0.20

0.30

0.40

0.50cv

Vajskov sky p. cv

cs

b)

2030405060708090

1930 1950 1970 1990

Q [m

3 .s-1]

Hron: Brehy Qa

-4.50

-3.50

-2.50-1.50

-0.50

0.50

1.50

1930 1950 1970 1990cs

0.10

0.20

0.30

0.40

0.50

cv

cv

cs

Fig. 6. Course of average annual discharge time series Qa (left), (differences from 5-yr moving averages values), course of 10-yr moving averages of coefficient of variation cv and symmetry cs, a) Vajskovsky brook, b) Hron: Brehy. Obr. 6. Priebeh priemerných ročných prietokov Qa (vľavo), (rozdiely v porovnaní s 5-ročnými kĺzavými priemermi), priebehy 10-ročných kĺzavých priemerov koeficientov variácie cv a symetrie cs.

0.0

0.1

0.2

0.3

0.4

0.5

68584838312621

.317

.51512

.810

.79.

147.

566.

365.

234.

383.5

2.8

2.33

Stiavnicka

2.43.65

5

913.5

22 33

0.0

0.2

0.4

0.6

0.8

1.0

68584838312621

.317

.51512

.810

.79.

147.

566.

365.

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68584838312621

.317

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Hron B. Bystrica

2.43.6

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Fig. 7. The combined periodograms of discharge time series (1931–2000). Obr. 7. Kombinované periodogramy prietokových radov (1931–2000).


287

4. Teleconnection AO, NAO, SO, PDO and QBO phenomena with long-term streamflow fluctuation in Hron River basin

In this part the teleconnection of some atmo-spheric phenomena with long-term discharge fluc-tuation is studied in relatively uninfluenced moun-tainous basin in the Central Slovakia.

The cross-correlations of the Southern Oscilla-tion Index (SOI) and mean annual discharge of the Hron River shows, that there exists a 3–4 years shift between these time series. Therefore, for fol-lowing analysis, we used winter SOI data series with 3 years shift (SOIw-3). In Tab. 2, correlation matrix coefficients of annual discharge time series from selected sites in the Hron basin and winter

indexes of AO, SO and NAO phenomena are pre-sented. There is a direct relationship between dis-charge and SOIw-3, and an indirect relationship between the discharge series and AOIw, as well as NAOIw. The average annual Hron River discharge is lower during higher AO and NAOI periods.

Based on AOIw and SOIw-3 the relationship (2) for simple estimation of average annual discharge in Hron: Brehy station in the current year was de-rived. The multiple coefficient of correlation was 0.68. In Tab. 3 other statistical characteristics of es-timated parameters are presented.

Qa = 46.84 –7.5 AOIw + 0.209 SOIw-3, (2)

where Qa – annual discharge in Hron: Brehy.

T a b l e 2. Correlation matrix coefficients of annual discharge time series from selected sites in the Hron basin and winter indexes of AOw, SOw and NAOw phenomena. Period 1951–2000. T a b u ľ k a 2. Korelačná matica koeficientov závislostí ročných prietokov vo vybraných profiloch Hronu a AOw, Sow a NAOw javov. Obdobie 1951–2000.

AOw SOIw-3 NAOw Vajsk. b. Stiavnic. H. Zlatno C. Hron H. Brezno H. B.Bystr H. Brehy AOw 1.00 SOIw-3 -0.28 1.00 NAOw 0.72 -0.33 1.00 Vajsk. b. -0.58 0.45 -0.38 1.00 Stiavnic. -0.55 0.37 -0.31 0.82 1.00 H. Zlatno -0.45 0.37 -0.27 0.75 0.73 1.00 C. Hron -0.59 0.40 -0.51 0.82 0.82 0.75 1.00 H. Brezno -0.60 0.42 -0.44 0.84 0.88 0.75 0.94 1.00 H. B.Bystr. -0.59 0.31 -0.41 0.83 0.85 0.73 0.91 0.90 1.00 H. Brehy -0.67 0.33 -0.46 0.83 0.88 0.68 0.91 0.92 0.95 1.00

T a b l e 3. Basic statistical characteristics of multiple regression parameters. T a b u ľ k a 3. Základné štatistické charakteristiky viacnásobnej regresnej analýzy.

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 46.83926 1.222826 38.30411 4.27E-37 44.37925 49.29926 AOIw –7.49234 1.340237 –5.59031 1.12E-06 –10.1886 –4.79614 SOIw-3 0.209859 0.154164 1.361268 0.17992 –0.10028 0.519997

On the Fig. 8a), the combined periodogram of Hron: Brehy yearly discharge is presented and on Fig. 8b) there is the autocorrelogram of monthly adjusted discharge (12-monthly seasonality was removed from raw data). On the basis of the previ-ous analyses we suppose that the 28-month period in the discharge time series is connected to the QBO cycle. While all the periods 3.65-; 7–8- and 14–15-yr are connected to the AO phenomenon, only some of them are connected to the NAO and SO phenomena.

5. Conclusions

In the study, the teleconnection of AO, SO, NAO, and QBO on the interannual streamflow cy-cles in Hron basin (Central Europe) was found. The ca 2.4-; 3.6-; 7.8-; 14-; 21-;30- and 36-yr periods of SOI, NAOI, AOI time series were identified by AC and the combined periodogram. Such periods were found also in most of the analysed discharge series.

The mutual teleconnection of the temperature, discharge, precipitation, ice cover, see level, den-drochronological and other time series and AO,


288

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.321

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O AO

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k

Fig. 8. Combined periodogram of yearly discharge (top), Hron River at Brehy station, 1931–2000. Calculated values of the autocor-relation function and 95% confidence intervals for monthly adjusted discharge (12-monthly seasonality was removed) below. Obr. 8. Kombinovaný periodogram ročných prietokov Hronu v stanici Brehy, 1931–2000 (hore). Autokorelogram časového radu upravených mesačných prietokov (12-mesačný cyklus bol odstránený) dole. NAO, PDO, SO is sufficiently proved, now. There must be a reason for why lengths of the found in-terannual cycles coincide in all analyzed time series all over the world. They have probably an identical origin. Unfortunately, the source of this interannual cyclicity is unknown, yet.

As a source of these cycles, the fluctuations of solar activity (known 11- and 22-yrs cycles of sun-spot numbers) was discussed in a lot of studies, e.g. Palus et al. (2000) showed that there is a weak in-teraction of gravity and solar activity. On the other hand, Garric & Huber (2003) considered, there is no compelling reason to invoke solar cycles as a cause of quasi-decadal (QD) variability in paleo-climatic records. Important results were obtained by Charvatova & Strestik (1995). Authors employed the inertial motion of the Sun around the barycentre of the Solar System as the base in searching for possible influence of the Solar System as a whole on climatic processes, especially on the changes in surface air temperature. Charvatova (2000) ex-plained a solar activity cycle of about 2400 years by solar inertial motion. She described the 178.7-year basic cycle of solar motion. The longer cycle, over an 8000 year interval, is found to average 2402.2 years. This corresponds to the Jupiter/Heliocentre/ /Barycentre alignments (9.8855-yr x 243). Simi-

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Received 28. September 2004

Scientific paper accepted 25. October 2004

TELEKONEKCIA AO, NAO, SO A QBO S VIACROČNÝMI FLUKTUÁCIAMI PRIETOKOV V POVODÍ HRONA Pavla Pekárová, Ján Pekár

Štúdia analyzovala možné telekonekcie Arktickej os-cilácie (AO), Južnej oscilácie (SO), Tichomorskej dekádnej oscilácie (PDO), Severoatlantickej oscilácie (NAO) a Kvázi dvojročnej oscilácie (QBO) s viacročnými cyklami suchých a mokrých období (vy-jadrených priemernými ročnými prietokmi) v povodí rieky Hron (stredné Slovensko). Spektrálnou analýzou časových radov AO, NAO, SO, a PDO indexov boli nájdené tieto viacročné cykly kolísania spomenutých javov: ca 2,4; 3,6; 7,8; 14; 21; 30 a 36 rokov. Metódou kombinovaného periodogramu boli nájdené zhodné cykly kolísania viacročných suchých a mokrých období i v mesačných prietokových radoch z povodia Hronu (1930–2000). Keďže tieto periódy boli nájdené vo všetkých prietokových radoch z rôznych geografických zón, môžu byť považované za všeobecný jav na Zemi. Toto pravidelné opakovanie mokrých a suchých období súvisí so všeobecnou cirkuláciou oceánov a atmosféry, súčasťou ktorých sú i SO, AO, PDO, NAO a QBO javy.

TELECONNECTIONS OF AO, NAO, SO, AND QBO WITH INTERANNUAL STREAMFLOW FLUCTUATION IN THE HRON BASIN

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