Spatial and temporal runoff oscillation analysis of the main rivers of the world during the 19th–20th centuries

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Spatial and temporal runoff oscillation analysis of the main rivers of the world during the 19th–20th centuries

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  Spatial and temporal runoff oscillation analysis of the main riversof the world during the 19th–20th centuries Pavla Peka´rova´ a, *, Pavol Mikla´nek  b,1 , Ja´n Peka´r c,2 a  Institute of Hydrology of Slovak Academy of Science, Racianska 75, 838 11 Bratislava, Slovakia b  Institute of Hydrology of Slovak Academy of Science, Racianska 75, 838 11 Bratislava, Slovakia c  Department of Economic and Financial Models, Comenius University, Mlynska dolina, 842 48 Bratislava, Slovakia Received 19 July 2001; revised 4 November 2002; accepted 15 November 2002 Abstract The annual discharge time series of selected large rivers in the world were tested for wet and dry periods. The 28–29-yearscycle, as well as 20–22-years cycle of extremes occurrence were identified. From the trend analysis it follows that thehydrological characteristics of the rivers must be stated at least for one 28-year period. If we want to identify any trenduninfluenced by the 28-year periodicity of the discharge time series, we must determine the trend during a single or multiplecurve cycle, starting and terminating by either minima (e.g.1861–1946 inWest/Central Europe) or maxima (e.g.1847–1930 or1931–1984 in West/Central Europe). Trends determined for other periods are influenced by the periodicity of the series anddepend on the position of the starting point on the increasing or recession curve. Long-term trends during the period 1860–1990have not been detected for the West/Central European runoff.Further, the temporal shift in the discharge extremes occurrence (both, maxima and minima) was shown to depend on thelongitude and latitude. The time shift between Neva and Amur discharge time series is about four years, between Amur and StLawrence is about 16 years, and between St. Lawrence and Neva is about nine years. The time shift between Congo andAmazon is about seven years. q 2003 Elsevier Science B.V. All rights reserved. Keywords:  Long-term runoff fluctuation; Discharge; Time series analysis; Spectral analysis; Temporal pattern; Teleconnection 1. Introduction The development of mankind has depended onavailability of water resources. Already the firstagricultural civilisations noticed the temporal varia-bility of water resources and oscillation of the dry andwet periods.Statistical analysis of the runoff oscillationsdepends on availability of long time series of data. Systematic measurements of discharge inmodern era started relatively late. The longesttime series are available in Europe, but they do notexceed 200 years (Probst and Tardy, 1987). Suchlong series are exceptional and in most of theworld only much shorted series exist. Journal of Hydrology 274 (2003) 62–79www.elsevier.com/locate/jhydrol0022-1694/03/$ - see front matter q 2003 Elsevier Science B.V. All rights reserved.PII: S0022-1694(02)00397-9 1 Tel/Fax:  þ 4212-44259311. 2 Tel.:  þ 4212-60295713; fax:  þ 4212-65412305.* Corresponding author. Tel.:  þ 421-2-44259311; fax:  þ 421-2-44259311. E-mail addresses:  pekarova@uh.savba.sk (P. Peka´rova´),miklanek@uh.savba.sk (P. Mikla´nek), pekar@fmph.uniba.sk (J.Peka´r).  Forty years ago, Williams (1961) investigatedthe nature and causes of cyclical changes inhydrological data of the world. He attemptedcorrelation between hydrologic data and sunspotwith varying success. Bra´zdil and Tam (1990),Walanaus and Soja (1995), Sosedko (1997), Smithet al. (1997) and Lukjanetz and Sosedko (1998)found several different dry and moisture periods(2.6; 3.5; 5; 13.3-year) in the precipitation anddischarge time series in Europe. Using an improvednumerical procedure the variance contribution of both, the luni-solar 18.6-year and 10–11-year solarcycle signals to 3234 yearly sampled climaterecords were studied by Currie (1996) and Probstand Tardy (1987) studied mean annual dischargefluctuations of fifty major rivers distributed aroundthe world by filtering methods. They showed, thatNorth American and European runoffs fluctuate inopposition while South American and Africanrunoffs present synchronous fluctuations.The changes of runoff in last decades may byrelated to climate change, but there exist also othernatural factors that influence the runoff variabilityand may reinforce the runoff changes. Thehydrologists and climatologists concentrate on therelationship between both, precipitation and runoff variability and large air pressure oscillations overthe oceans during the last 15 years. A typicalexample is the SO (Southern Oscillation) over thePacific (Rodriguez-Puebla et al., 1998) and NAO(North Atlantic Oscillation) over the Atlantic Ocean(Hurrell, 1995; Stephenson, 1999; Stephenson et al.,2000). The change of air pressure fields over largeareas influences the transport of amount of precipitation over neighbouring continents.For example Shorthouse and Arnell (1997)analysed relationships between inter-annual climaticvariability—as measured by the NAOI—and spatialpatterns of anomalous hydrological behaviour acrossEurope. The analysis was based on regional averagemonthly discharge series, derived from 477 drainagebasins on the FRIEND European Water Archivebetween 1961 and 1990. It was shown thatEuropean river flows are strongly correlated, mostparticularly in winter, with the NAO and that thisrelationship exhibits a strong spatial pattern. North-ern European river flows, particularly inthe Scandinavian region, tend to be positivelycorrelated with the NAOI, and rivers in southernEurope reveal negative correlation with the index.This result is consistent with the previously exploredcorrelation between the NAO and precipitation.Cluis (1998) shows that in most areas of the Asia-Pacific region, a strong El Nin˜o related signal can befound in the historical river series stored at the GlobalRunoff Data Center (GRDC). This signal is particu-larly strong in the Australian rivers whose regimes areknown to be highly contrasted. Cluis showed thatduring El Nin˜o the runoff is lower in these areas, whileduring La Nin˜a it is higher than the mean runoff (despite the fact that for some of the New Zealandrivers the results were contrary).Yang et al. (2000) investigated the ENSO tele-connection with annual precipitation series (TiberianPlateau, China) from 1690 to 1987 (nearly 300 years).The results showed that negative precipitationanomalies are significantly associated with El Nin˜oyears.On the other hand Kane (1997) investigated, thatthe relationship between El Nin˜o and droughts innorth–east Brazil is poor. Thus, forecasts of droughtsbased on the appearance of El Nin˜o alone would bewrong half the time. Instead, predictions based onsignificant periodicities (ca 13 and ca 26 years) givereasonably good results.Our previous analysis of the long-term runoff oscillation shows the regular dry and wet periodsoccurrence in central Europe (Peka´rova´, 2002;Peka´rova´ and Peka´r, 2002; Svoboda et al., 2000).The scope of the study is:(i) To demonstrate the existence of the long-termdischarge fluctuations (20–30 years) in rivers of all continents;(ii) To pronounce the hypothesis of the shift in long-term runoff extremes occurrence over the earth. 2. Temporal and spatial discharge analysis of mainworld rivers 2.1. Material The longest available time series of mean annualdischarge of the selected world largest rivers wereused to analyse the long-term runoff oscillation. P. Peka´ rova´  et al. / Journal of Hydrology 274 (2003) 62–79  63  The annual precipitation time series are usually thebasis for study of the long-term oscillation of dry andwet periods in the basin. We will analyse the annualdischarge time series due to following reasons: †  The increase of precipitation by one third mayincrease the runoff by one half. Therefore changesin precipitation series are even more evident indischarge series. †  The water balance of the basin depends not only onprecipitation, but on temperature as well (evapo-transpiration). The discharge series combine boththese influences. †  The problems of precipitation measurements andevaluation of the areal precipitation in mountainbasins are well known. Discharge measurement inthe outlet profile of the basin is simpler and moreaccurate incomparison to areal precipitation. †  The analyses of the long-term runoff oscillationsof the large rivers eliminates the local disturb-ances in precipitation and temperature series dueto local orographic peculiarities.The long annual discharge data series of all thecontinents were obtained from following data sources:(i) GlobalRunoffDataCenterinKoblenz,Germany;(ii) CD ROM of the Hydro-Climatic Data Network (HCDN),USGeologicalSurveyStreamflowDataSet for the United States;(iii) CD-ROM World Freshwater Resourcesprepared by Shiklomanov in the framework of the International Hydrological Programme (IHP)of UNESCO;(iv) URL http://waterdata.usgs.gov.A set of more than a hundred of annual dischargetime series with long periods of observation in allcontinents were analysed in the study. The riverbasins were grouped into two regions:I. Extra tropics zone of the Northern Hemisphere(between 30 –75 8 N);II. Equatorial zone and mild zone of the SouthernHemisphere (30 8 N–40 8 S).For the final analysis twenty river basins wereselected in each region. The selected rivers andstations are in Fig. 1. In Table 1 there are basic hydrologic characteristics of the series and basins.In Fig. 2 there are shown the smoothed yearlydischarge of selected rivers of all the continents byresistant non-linear smoothing technique. The rawdata were filtered by two filters in order to attenuatethe short-range fluctuations and to extract the long-range climatic variations. In the first step, the 5-years moving medians were computed from theoriginal data. (Medians are not as sensitive onisolated extreme values as the averages are). In thesecond step, the 5-years weighted moving averageswere computed from the medians according to Fig. 1. Gauging stations localisation on selected rivers (legend in Table 1). P. Peka´ rova´  et al. / Journal of Hydrology 274 (2003) 62–79 64  formulae:  y i  ¼  1  =  16 ð  x i 2 2  þ  4 ·x i 2 1  þ  6 ·x i  þ  4 ·x i þ 1  þ  x i þ 2 Þ ð 1 Þ The influence of different methods on data filtrationwas studied by Currie (1996) and Probst and Tardy(1987). Probst and Tardy (1987) compared three complementary filtering methods. They found adifference of one or two years for the localisation of maxima and minima discharges in filtered timeseries. Table 1Gauging stationslocalisationand basic hydrologic characteristics: C—country of the station,A—area [10 3 km 2 ], periodof observation(since—to), Qa—mean annual discharge [m 3 s 2 1 ], qa—mean annual yield [l.s 2 1 km 2 ], cs—coefficient of asymmetry, cv—coefficient of variation,min/max–minimal/maximal mean annual discharge [m 3 s 2 1 ]River Station C a A Since To Qa qa cs cv min max1 Yukon River Mouth b US 850 1945 1988 6189 7.2 0.46 0.25 2617 102492 Mackenzie River Mouth CN 1790 1948 1988 10338 5.8 0.75 0.09 8799 132453 Fraser River Hope CN 217 1912 1984 2722 12.5 0.29 0.13 1939 36734 Columbia Mouth US 668 1878 1989 7454 11.2  2 0.23 0.18 4510 103755 St.Lawrence Ogdensburg, N.Y. US 765 1860 1998 6986 9.1 0.04 0.10 5219 89466 Mississippi Mouth US 2980 1914 1988 16069 5.4 0.48 0.23 8830 276577 Thjorsa Urridafoss IC 7 1947 1993 364 50.6 0.55 0.12 289 4778 Loire Mouth FR 120 1921 1986 838 7.0 0.72 0.33 282 19679 Rhine Koeln DE 144 1816 1997 2089 14.5  2 0.03 0.19 920 322710 Vaenern–Goeta Vaenersborg SE 47 1807 1992 535 11.4  2 0.10 0.19 225 76811 Danube Orsova (1971:Turnu Severin) RO 576 1840 1988 5438 9.4 0.48 0.17 3339 805312 Neva Novosaratovka RS 281 1859 1984 2503 8.9 0.18 0.17 1341 367413 Dniepr Locmanskaja Kamjanka UA 495 1818 1984 1627 3.3 0.77 0.33 673 337514 Ob Salekhard RS 2950 1930 1994 12532 4.2 0.39 0.15 8791 1781215 Yenisei Igarka RS 2440 1936 1995 18050 7.4 0.20 0.08 15543 2096616 Lena Kusur RS 2430 1935 1994 16619 6.8 0.48 0.12 12478 2262617 Songhua Harbin CH 391 1898 1987 1202 3.1 0.53 0.40 386 267118 Amur Khabarovsk RS 1630 1896 1985 8569 5.3 1.15 0.25 4281 1859319 Kolyma Sredne–Kolymsk RS 361 1927 1988 2199 6.1 0.36 0.22 1337 348120 Amguema Mouth of South Brook RS 27 1944 1984 338 12.7 0.79 0.32 168 6371 Magdelena Mouth CO 260 1904 1990 7139 27.5 0.26 0.08 5361 95872 Sao Francisco Juazeiro BZ 511 1929 1994 2692 5.3 1.03 0.30 1603 47983 Amazon Obidos BZ 4640 1928 1996 174069 37.5  2 0.24 0.10 138555 2069414 Orinoco Puente Angostura VN 836 1923 1989 30932 37 0.42 0.10 21245 447025 La Plata Mouth AR 3100 1904 1985 25583 8.3 1.71 0.26 14191 586576 Oubangui Bangui CA 500 1911 1994 4116 8.2  2 0.03 0.28 782 73607 Chari Ndjamena(Fort Lamy) CD 600 1933 1991 1119 2 1.63 0.48 236 33448 Niger Mouth NG 2090 1920 1985 9275 4 0.44 0.27 3931 152009 Congo Kinshasa CG 3475 1903 1983 39536 11.4 0.89 0.10 32253 5390810 Blue Nile Roseires Dam SU 210 1912 1982 1548 7.4  2 0.06 0.18 652 219911 White Nile Malakal SU 1080 1912 1982 939 0.9 1.53 0.19 714 153712 Zambezi Mouth MO 1330 1921 1985 4852 3.6 0.45 0.19 2551 810513 Oranje Vioolsdrif SA 851 1964 1986 150 0.2 1.08 0.80 30 44914 Indus Mouth IN 960 1921 1985 7127 7.4 0.42 0.20 3974 1132115 Ganges c Mouth BA 1730 1921 1985 43704 25.3 3.15 0.03 38222 5317016 Mekong Mouth VI 810 1921 1985 15924 19.7 0.01 0.12 11794 1923717 Yang–tze Hankou CH 1488 1865 1986 23266 15.6 0.12 0.10 14313 3198318 Mary River Miva AU 5 1910 1995 38 7.9 1.28 0.90 4 14719 Darling River Bourke Town AU 386 1943 1994 126 0.3 2.84 1.40 5 85620 Murray Mouth AU 3520 1877 1988 760 0.2 2.59 0.75 38 4091 a AR—Argentina, AU—Australia, BA—Bangladesh, BZ—Brasilia, CA—Central Africa, CD—Chad, CG—Congo (Democratic Republicof), CN—Canada, CO—Colombia, DE—Germany, FR—France, CH—China, IN—India, IC—Iceland, MO—Mozambique, NG—Nigeria,RO—Romania, RS—Russia, SA—South Africa, SE—Sweden, SU—Sudan, UA—Ukraine, US—United States of America, VI—Vietnam,VN—Venezuela. b Mouth specifies the data from Shiklomanov CD World Freshwater Resources, other data were provided by GRDC Koblenz. c Ganges: delta of Ganges—Brahmaputra—Meghna. P. Peka´ rova´  et al. / Journal of Hydrology 274 (2003) 62–79  65  2.2. Identification of the long-term runoff trend 2.2.1. Europe In Europe, the longest discharge data series havebeen available since beginning of the 19th century.Therefore these series are particularlysuitable to study the long-term runoff oscillationsand trends.In order to identify trends for some Europeanrivers, discharge time series of eleven rivers forWest/Central Europe were used (Goeta: Vaeners-borg, SE (1807–1992), Rhine: Koeln, DE (1816–1997), Neman: Smalininkai, LT (1912–1993),Loire: Montjean, FR (1863–1986) Weser: Hann–Muenden, DE (1831–1994), Danube, RO (1840–1988), Elbe: Decin, CZ (1851–1998), Oder: Goz-dowice, PL (1900–1993), Vistule: Tczew, PL(1900–1994), Rhone: mouth, FR (1921–1986),and Po: Pontelagoscuro, IT (1918–1979)) and sixtime series for East Europe (Dniepr: LocmanskajaKamjanka (1818–1984), Neva: Novosaratovka(1859–1984), N. Dvina: Ust-Pinega (1881–1990),Don: Razdorskaya (1891–1984), Pechora: mouth(1921–1987), and Volga: mouth (1882–1998)). Fig. 2. Smoothed yearly discharge of selected rivers over the continents using two resistant non-linear smoothing techniques (the 5-yearsmoving medians and the 5-years weighted moving averages). P. Peka´ rova´  et al. / Journal of Hydrology 274 (2003) 62–79 66
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