Temporal Variability of Annual Rainfall in the Macta and Tafna Catchments, Northwestern Algeria

Water Resour Manage (2010) 24:3817–3833DOI 10.1007/s11269-010-9635-7

Temporal Variability of Annual Rainfall in the Mactaand Tafna Catchments, Northwestern Algeria

Mohamed M. Meddi · Ali Arkamose Assani ·Hind Meddi

Received: 25 March 2009 / Accepted: 18 March 2010 /Published online: 16 April 2010© Springer Science+Business Media B.V. 2010

Abstract We analyzed the temporal variability of annual precipitation measured atfive stations in the Macta (14,380 km2) and Tafna (4,949 km2) catchments located innorthwestern Algeria, in connection with climate indices over the 1950–2004 period.Analysis of the temporal variability using linear regression and Pettitt test revealed asignificant decline in annual rainfall in the mid-1970s in both basins. Furthermore, adecrease of at least 20% of total annual rainfall is also observed at all five stations. Asfor the relationship between climate indices and variability of annual precipitation,canonical correlation analysis shows that, from 1950 to 2004, precipitation at the fivestations are negatively correlated with NINO4 and, to a lesser degree, with NAO.

Keywords Annual rainfall · Drought · Climate indices · Linear regression ·Pettitt test · Macta and Tafna catchments · Algeria

1 Introduction

Studies looking at the interannual variability of rainfall in the Mediterranean basinhave shown that this variability is characterized by a significant decline in precipita-tion (e.g. Knippertz et al. 2003a; New et al. 2001; Rodigro and Trigo 2007). Analysisof this drought has mostly been concerned with the European side (shore) of the

M. M. Meddi · H. MeddiÉcole Nationale Supérieure d’Hydraulique, Blida, Algeria

M. M. Meddie-mail: [email protected]

A. A. Assani (B)Laboratoire d’hydro-climatologie et de géomorphologie fluviale, section de Géographie,Pavillon Léon-Provencher, Université du Québec à Trois-Rivières,3351, Boulevard des Forges, Trois-Rivières, Québec, Canada, G9A 5H7e-mail: [email protected]

3818 M.M. Meddi et al.

basin (e.g. Bonaccorso et al. 2003; Brunetti et al. 2001; Esteban-Parra et al. 1998;Goodess and Jones 2002; Iglesias et al. 2007; Lana et al. 2004; Longobardi and Villani2001; Karabök et al. 2005; Martin-Vide 2004; Mizyed 2009; Mourato et al. 2009;Rodriguez-Puebla et al. 1998; Rodriguez-Puebla and Nieto 2009; Rodigro and Trigo2007; Rodriguez et al. 1999; Sumner et al. 2001; Trigo and DaCamara 2000; Trigo etal. 2002, 2004; Tsakiris et al. 2007; Valero et al. 2004; Vicente-Serrano 2005). There islittle study on the African and Asian sides (e.g. Ellouze and Abiba 2008; Knippertzet al. 2003a, b; Shaban 2009).

Most of these studies, however, have shown that the drought varies widely in timeand in space in the Mediterranean basin, so that conclusions relating to a given areamay not be generalized to the whole basin, even for areas on the same side of it.Moreover, although the rainfall deficit has been well quantified in most studies andthe underlying meteorological conditions have been adequately analyzed (e.g. Geb2000; Monkam 2003; Romero et al. 1999; Sumner et al. 2001; Trigo and DaCamara2000; Valero et al. 2004), the nature of the underlying climatic factors is still cause fordebate. Some authors have attributed it to the North Atlantic Oscillation (NAO; e.g.Goodess and Jones 2002; Hurell and Van Loon 1997; Karabök et al. 2005; Knippertzet al. 2003a; Mariotti et al. 2002; Marshall et al. 2001; Rodigro and Trigo 2007;Rodriguez-Puebla and Nieto 2009; Trigo et al. 2002, 2004), while others link it toENSO (El Niño/Southern Oscillation; e.g. Kiladis and Diaz 1989; Knippertz et al.2003b; Lloyd-Hughes and Saunders 2002; Rodo et al. 1997; Van Oldenborgh et al.2000). However, as demonstrated by Mariotti et al. (2002), the relationship betweenprecipitation and climate indices is notably unstable in the Euro–Mediterraneandomain. Indeed, this relationship may or may not be observable according to theperiod being analyzed.

Given the above, here are the two main objectives of this study:

1) To analyze the interannual variability trend of annual rainfall in the Macta andTafna catchments of western Algeria to see if there is a significant decreasein annual precipitation over time, on the one hand, and if such a decline issynchronous in both catchments at the annual and decadal scale, on the otherhand.

2) To determine which climate indices are significantly correlated with annualprecipitation in these two catchments, in order to test whether or not theNAO and SOI climate indices can account for the interannual variability ofannual precipitation. To date, there has been no other analysis of the correlationbetween climate indices and precipitation in Algeria.

2 Methodology

2.1 Description of Watersheds and Data Sources

Located in far western Algeria, the Tafna catchment has a drainage area of4,949 km2, and comprises eight sub-basins, two of which are located upstreamin Moroccan territory. Culminating at 1,843 m at Jebel Tenouchfi, the watershedis bounded on the north by the main topographic feature (Tlemcen Mountains)between the Mediterranean and the high plains, on the west, by the Moroccan Atlasand on the east by the Daïa Mountains (Saida). The Macta catchment, located in

Temporal Variability of Annual Rainfall in Northwestern Algeria 3819

northern Algeria, has a drainage area of 14,380 km2. It is bounded on the north bythe Tessala Mountains, on the south, by the Maalif high plateau, on the west, by theTelagh plateau, and on the east, by the Saida Mountains (Fig. 1).

In both catchments, the general rainfall regime is similar to that of the semiaridMediterranean areas of northern Algeria, and is characterized by winter precip-itation with maxima in December, January and February, and a long period ofdrought from June to September. This system is also characterized by high spatialand temporal variability of total rainfall: rainfall during the wettest years may bethree to four times higher than during the driest years. In the Macta watershed,annual rainfall varies from 280 mm in the south to 600 mm in the Beni ChougraneMountains in the north (Laborde 1993). Five rainfall stations were selected in thetwo catchments, for which continuous rainfall measurements starting in 1950 areavailable and which are representative of the spatial variability of rainfall in theregion (Fig. 1 and Table 1). Rainfall data are from the National Agency of HydraulicResources and the National Meteorology Office. Available rainfall data are verydisparate both in terms of measurement reliability and time series duration (Laborde1993). The times series are characterized by errors from different possible sources(measurement, reporting, etc.). The five stations were selected based on the quality

Fig. 1 Location of rainfall stations in the Macta and Tafna catchments


Table 1 Locationof rainfall stations

Station Latitude (N) Longitude (W) Altitude (m)

Bouhanifia 35◦16′43′′ 00◦03′33′′ 295Sidi Bel Abbes 35◦10′39′′ 00◦39′34′′ 486Sidi Ali Ben Youb 34◦58′38′′ 00◦44′04′′ 635Lalla Setti 34◦51′89′′ 01◦19′29′′ 1,007Beni Bahdel 34◦42′34′′ 01◦29′48′′ 645

of rainfall data and the very small proportion of missing monthly data (less than 10%of all data). Monthly precipitation data (September to August) were summed up toobtain total annual rainfall values.

As for climate indices, five were chose which have been shown to affect theinterannual variability of precipitation and temperature in North Africa and/orWestern Europe (Mediterranean basin). These are the Atlantic Multidecadal Os-cillation (AMO), the Arctic Oscillation (AO), the North Atlantic Oscillation(NAO), the El Niño Southern Oscillation (NINO4) and the Southern Oscilla-tion Index (SOI). Data for these indices were taken from the NOAA website(http://www.cdc.noaa.gov/ClimateIndices/List, downloaded on March 30th, 2008). Abrief description of the indices is provided in Table 2. For each index, annual meanvalues were calculated from the twelve monthly values (September to August) foreach year.

2.2 Statistical Analysis of the Data

2.2.1 Analysis of the Trend of the Interannual Variability of Annual Precipitation

Two types of statistical methods are commonly used in the literature to analyze theinterannual variability trend of rainfall data time series, namely parametric and non-parametric methods. Of the parametric methods, linear regression is the most widelyused in the scientific literature (e.g. Kundzewicz et al. 2005). It involves fitting aregression line through rainfall measurement data as a function of time in years. The

Table 2 Description of climate indices

Indices Location Index calculation mode

Atlantic Multi-Decadal North Atlantic Mean SST north of the equator in the NorthOscillation (AMO) Ocean Atlantic Ocean, between 0◦ N–60◦ N

and 75◦ W–7.5◦ WArctic Oscillation Extratropical Northern Difference in SLP between the North Pole

(AO) Hemisphere and the 45◦ N parallelNorth Atlantic Extratropical North Difference in SLP between the northern

Oscillation (NAO) Atlantic zone (Reykjavik, Iceland) and the southern(Ponta Delgada, Spain) of the NorthAtlantic Basin

Southern Oscillation Tropical South Difference in SLP between theIndex (SOI) Pacific South West Pacific (Darwin) and The

Central South Pacific (Tahiti)NINO4 Central Tropical Central Tropical Pacific SST between

Pacific 5◦ N–5◦ S and 160◦ E–150◦ W

http://www.cdc.noaa.gov/ClimateIndices/List


statistical significance of the regression line is calculated using variance analysis withthe following equation:

Fo = R2 (n − 2) /(1 − R2) (1)

where R2 is the coefficient of determination of the regression line fitted to the dataand n is the total number of years (sample size). The null hypothesis (Ho) is rejectedwhen Fo ≥ Fα for 1 and (n − 2) degrees of freedom. The value of Fα is taken fromthe Fisher–Snedecor distribution table.

Although the oft-described Mann–Kendall test (e.g. Hamed and Rao 1998) isthe most commonly used non-parametric method, it does not allow very precisedetermination of the mean shift date (Vanitsem and Demarée 1991) and is sensitiveto autocorrelation (serial correlation; e.g. Khaliq et al. 2009). Therefore, anothernon-parametric method, the Pettitt test (Pettitt 1979), was used in this study.

Given x1, . . . , xt, . . . , xT , a sequence of random events (measurements). Thissequence shows a shift in population at t, if the set of events (measurements)x1, . . . , xt has a distribution function F1(x) whose mean is significantly different fromthat of F2(x), the distribution function over xt+1, . . . , xT , the goal being to test thenull hypothesis, which is the absence of a shift in the trend of the variable.

The statistic associated with this test is as follows:

Kt = max1<t<T

∣∣Ut,T∣∣ (2)

where

Ut, T =t∑

i=1

T∑

j=i+1

sgn(xi − x j

)(3)

sgn being equal to 1 if the sign of the difference between the two values inparentheses is positive, −1 if the sign is negative, and 0 if xi and x j are identical.If a shift in only one direction (positive or negative) is considered, it is as follows:

K+T = max

1<t<TUt, T (4)

or

K−T = min

1<t<TUt, T (5)

Thus, K+T and K−

T are large if, respectively,

< F1 (x)> < < F2 (x)>, where < > is the mean of the set.

The Pettitt test may be used to determine the significance probability associatedwith the various statistics: the probability of detecting a shift in the mean whereno such shift occurs in the series. Hence, it may be used to estimate, at a givenconfidence level, whether or not a population shift did occur. It should be notedthat the critical thresholds of the test, which are based on the asymptotic expressionof the Kolmogorov–Smirnov distribution, are not well suited to small samples (≤ 20;Vanitsem and Demarée 1991). In this study, series size is larger than 50. Hence, theexpression put forth by Pettitt (1979) is retained. In order to quantify the extent ofthe shift (increase or decrease) in total annual rainfall, Student’s t-test was used tocompare the arithmetic means of the sub-series preceding and following the year


Fig. 2 Interannual variabilityof annual precipitation overthe period 1950–2004.a Bouhanifia station; b SidiBel Abbes station; c Sidi AliBen Youb station; d Lalla Settistation; e Beni Bahdel station.In the regression equation,P annual rainfall, T year

(a)

(b)

(c)

(d)

(e)


when the shift took place. To confirm results derived for the observed time series,a Monte Carlo resampling approach was used to randomly generate 1,000 annualrainfall series for each of the five stations. These series were subjected to the sametests as those applied to the measured time series (McCabe et al. 2004).

2.2.2 Analysis of the Link Between Climate Indices and Annual Precipitation

This was a two-step analysis. First, the trend of the interannual variability of annualprecipitation was compared to climate indices over the period 1950–2004 using linearregression. Then, canonical correlation analysis was used to calculate the relationshipbetween climate indices and annual precipitation. This method was chosen becauseit allows calculation of the correlation between variables of two different groups,on one hand, and between variables of the same group, on the other hand, whilemaximizing correlation coefficients. As such, it provides the general theoreticalframework for factorial discriminant analysis, multivariate regression and correspon-dence analysis techniques (Ouarda et al. 2001). In this study, the five climate indicesare the independent variables and measurements from the five rainfall stationsare the dependent variables. Canonical correlation analysis is performed first byextracting the canonical factors from both groups of variables. Canonical factorsderived from the independent variables group (climate indices) are labeled Wq andthose derived from the dependent variables group (rainfall stations) are labeled Vp.The point of the analysis is to maximize the correlation coefficients between thecanonical factors (W and V) for both groups. Note that Wq and Vp are linear com-binations of originally independent and dependent variables. Thus, the correlationcoefficients are calculated at two levels. First, correlation coefficients are calculatedbetween the canonical factors Vp and Wq. These coefficients are called canonicalcorrelation coefficients. Second, simple correlation coefficients between canonicalfactors Vp and the original dependant variables on the one hand, and betweencanonical factors Wq and the original independent variables, on the other hand,are calculated. These simple correlation coefficients are called structure coefficients.These coefficients (the correlation coefficients calculated between canonical factorsVp and precipitation) may be used to check for the synchroneity of the interannualvariability of precipitation measured at the five stations. The mathematical detailsof the canonical correlation analysis are described in Afifi and Clark (1996), amongothers.

Finally, comparison of the sequence of positive and negative anomaly intervalsusing a smoothing method based on the calculation of simple moving averages over10 years (11 terms) was used to assess the synchroneity of the temporal variability of

Table 3 Significance threshold associated with the maximum U value (Umax) calculated from theannual precipitation data with the Pettitt test

Stations Umax p-value

Bouhanifia 517 0.0001Sidi Bel Abbes 435 0.0025Sidi Ali Ben Youb 362 0.0123Lalla Setti 593 0.0000Beni Bahdel 371 0.0083

Umax = maximum U value calculated. All p-values are statistically significant


Fig. 3 Pettitt test results.a Sidi Ali Ben Youb station;b Sidi Bel Abbes station;c Bouhanifia station;d Beni Bahdel station;e Lalla Setti station. Thetest is significant (1%) forall five stations

(a)

(b)

(c)

(d)

(e)


precipitation at the decadal scale in the two watersheds. This smoothing was appliedto the standardized precipitation values.

3 Results

3.1 Analysis of the Temporal Variability of Rainfall and Climate Indices

Analysis of the temporal variability of annual precipitation through linear regressionreveals a continuing decline in total rainfall for the five stations (Fig. 2), thesignificance of which is confirmed by the Pettitt test. Thus, after reaching a maximum,U values show a progressive decrease over time, and the probability associated withthis maximum is less than 0.01 (Table 3). In addition, according to this test, thedecline took place around the year 1975 for all stations except at Bouhanifia, wherethe shift centers around 1980 (Fig. 3), although precipitation at this station did startdeclining during the 1970s. Comparison of total annual rainfall values before andafter this shift using Student’s t-test reveals a greater than 20% decrease in totalrainfall at all five stations. The largest decrease was observed at the Lalla Setti station(Table 4).

The sequence of positive and negative anomaly intervals at the decadal scale inboth catchments is shown in Fig. 4. Overall, the fact that two distinct periods areobserved at all stations confirms the fact that the temporal variability of precipitationin the two catchments was synchronous: a period of generally positive anomaliesduring the 1950s and 1960s, followed by a period of negative anomalies during the1980s and 1990s. It is worth noting that the most recent negative anomaly period issustained at all five stations, as opposed to the preceding period of positive anomaly,which is interrupted by a short interval of negative anomaly. This is particularlyobvious for the Sidi Bel Abbes station. The sequence of positive and negativeanomalies highlights a significant point, which is that the change in the sign of thesmoothed and standardized values occurred in the mid-1970s for all five stations.This change in sign from positive to negative values, which reflects a decline inprecipitation in the catchments, also reflects the change in the mean of the rainfalltime series. This confirms the results of the three previous tests (linear regression,Pettitt test and Student’s t-test) pertaining to the change in the mean of the rainfalltime series and the timing of this change.

Table 4 Comparison of annual precipitation before and after the shift in precipitation mean usingStudent’s t-test

Stations 1950–2004 Before the After the Rangemean (mm) shift (mm) shift (mm) (%)

Bouhanifia (1982)a 305 350 (76.1) 244 (62.9) −30Sidi Bel Abbes (1976) 339 385 (101.4) 299 (73.5) −22Sidi Ali Ben Youb (1976) 356 400 (97.1) 311 (86.1) −22Lalla Setti (1976) 606 738 (160.7) 484 (109.1) −34Beni Bahdel (1975) 471 556 (122.5) 392 (97.5) −29

(1976) year of shift in mean; − decrease; () standard deviationaA second maximum occurred in 1976


Fig. 4 Decadal variability.Standardized and smoothed(10-years) annual rainfallvalues over the period1950–2004. a Bouhanifiastation; b Sidi Bel Abbesstation; c Sidi Ali Ben Youbstation; d Lalla Setti station;e Beni Bahdel station

(a)

(b)

(c)

(d)

(e)


Fig. 5 Interannual variabilityof climate indices. a AMO;b AO; c NAO; d NINO4;e SOI

(a)

(b)

(c)

(d)

(e)


Table 5 Coefficients of canonical correlation calculated between climate indices and the annualprecipitation measured at five stations (1950–2004)

Coefficients Canonical correlation value Eigenvalues F value p > F

CC1 0.663 0.784 2.42 0.0005CC2 0.531 0.393 1.81 0.0357CC3 0.433 0.230 1.34 0.2230CC4 0.188 0.036 0.50 0.7382CC5 0.077 0.006 0.29 0.9522

Statistically significant canonical correlation coefficients and their significance threshold are shownin bold.

As for climate indices, linear regression analysis shows that the interannual vari-ability is characterized by an increase in the annual values of AMO, AO, NAO andNINO4 (Fig. 5) during the period 1950–2004. By contrast, the annual values of SOIshow a decreasing trend which is not, however, statistically significant (p < 0.05).Either way, interannual variability analysis of these five climate indices indicates thata shift took place during the mid-1970s. Indeed, the annual values of the first fourindices tend to increase after the 1970s. Thus, there exists some similarity betweenthe interannual variability of the climate indices and of precipitation.

3.2 Relationship Between Annual Precipitation and Climate Indices

Canonical analysis results are shown in Tables 5 and 6. As for canonical correlationcoefficients, only the first two (CC1 and CC2) are statistically significant (p < 0.05;Table 5). However, CC1 and CC2 values are relatively moderate (<0.7), which is dueto the fact that correlation was calculated at the annual scale. In any case, becausethe first two canonical correlation coefficients are significant, interpretation of thestructure coefficients is straightforward. Recall that the structure coefficients are thesimple correlation coefficients between the canonical factors (V and W) and theirrespective original variables (annual rainfall and climate indices). The five rainfall

Table 6 Correlation between climate stations and their canonical factors (V), and between climateindices and their canonical factors (W) (1950–2004)

Stations V1 V2 V3 V4 V5 W1 W2 W3 W4 W5and indices

A 0.644 0.627 −0.177 0.381 −0.126B 0.782 0.499 0.340 0.088 −0.129C 0.407 0.402 0.289 0.619 0.455D 0.881 0.130 −0.274 0.312 0.186E 0.755 −0.041 −0.026 0.649 −0.080AMO 0.079 −0.201 0.823 −0.524 −0.013AO −0.454 −0.217 0.166 0.838 −0.130NAO −0.680 −0.391 0.156 0.587 −0.126NINO4 −0.906 −0.294 −0.189 −0.191 0.143SOI 0.433 −0.057 0.110 0.426 0.784EV (%) 50.7 16.5 6.1 21.1 5.6 33.7 6.6 15.6 30.8 13.4

EV explained variance of canonical factor. A Sidi Ali Ben Youb station; B Sidi Bel Abbes station;C Bouhanifia station; D Beni Bahdel station; E Lalla Setti station


stations all show a better correlation to V1 when, of course, the last three canonicalfactors (V3 and W3, V4 and W4, V5 and W5), which are not statistically significant,are excluded. The explained variance associated with the first canonical factor V1(derived from the rainfall stations group of variables) is ∼50%. As for the climateindices, W1, which is derived from the climate indices group of variables, showsa strong correlation with NINO4 and, to a lesser degree, with NAO, and has anexplained variance of 33%. The other three climate indices are strongly correlatedto the other three canonical factors (W3, W4 and W5), which are not statisticallysignificant. Given that V1 and W1 are significantly correlated, the five climatestations, which are correlated to V1, are significantly correlated to NINO4 and NAO,which are significantly correlated to W1. Since V1 shows a positive correlation withdata from all five stations and W1, a negative correlation with the NINO4 andNAO indices, these indices are negatively correlated to precipitation measurementsfrom the five stations. It follows that, if NINO4 and NAO values increase, annualprecipitation values at all five stations decrease. Thus, the positive anomalies shownby the two indices correspond with the negative anomalies of annual rainfall in thetwo watersheds.

4 Discussion and Conclusion

Analysis of the interannual variability of rainfall in the Macta and Tafna catchmentsof northwestern Algeria highlights the following three main findings:

1. The interannual variability of annual precipitation is characterized by a sig-nificant decrease (>20%) in total rainfall in both watersheds. This declineoccurred during the second half of the 1970s. At the decadal scale, rainfallvariability in both catchments is characterized by two distinct periods: a period ofpositive anomaly during the 1950s and 1960s (the two wettest decades) followedby a period of negative anomaly in the 1980s and 1990s (the two driest decades).Thus, the temporal variability of precipitation in both watersheds is synchronousat both the annual and the decadal scale. Although many studies have alsohighlighted the decline in precipitation in North Africa in particular, and in thewestern Mediterranean in general (e.g. Brunetti et al. 2001; Knippertz et al.2003a, b; Longobardi and Villani 2001; López-Moreno et al. 2009; Mouratoet al. 2009; Rodigro and Trigo 2007; Sumner et al. 2001; Valero et al. 2004),they did not quantify the extent of this decline. In addition, most of these studieshave only looked at seasonal precipitation. At the decadal scale, a study of thevariability of Fall precipitation in the western Mediterranean basin (includingwestern North Africa) and its relationship to large-scale atmospheric variabilityduring the period 1948–1989 (Valero et al. 2004) concluded that the late 1970sand 1980s constitute the driest period and the late 1950s and early 1960s werethe wettest years of the century. Chbouki et al. (1995) also identified droughtperiods using tree-ring chronology in Morocco, including a rather wet period inthe interval 1950–1970 and relatively drier periods in the interval 1925–1950 andduring the 1970s. Although the data used in this last study do not extend beyond1974, this succession of wet and dry decades is consistent with that observed inthe Macta and Tafna catchments.


2. Annual precipitation measured in these two catchments shows a better cor-relation with the NINO4 climate index than with NAO. Although NAO isregarded by many authors (see Section 1) as the main climate (temperature andprecipitation) variability factor in the Mediterranean basin, all these studies onlylooked at the relationship between this index and precipitation at the seasonalscale. In addition, because these studies rarely compare the effect of NAO tothat of other climate indices, the effect of NINO4 as the main factor affecting theinterannual variability of precipitation has never before been documented forthe Mediterranean basin. This study shows that, in both catchments analyzed,NINO4 is the main factor affecting the interannual variability of annual pre-cipitation. Recall that this index is a measure of the temperature variability inthe tropical South Pacific Ocean and hence, represents the oceanic componentof the El Niño/Southern Oscillation (ENSO) phenomenon. The effect of thisoceanic component (NINO3.4) on the interannual variability of precipitation atthe seasonal scale for North Africa has already been addressed by Knippertzet al. (2003b) for the period 1962–1987 more specifically. Precipitation shows anegative correlation to both climate indices (NINO4 and NAO). In other words,when tropical South Pacific and North Atlantic surface temperatures are abovenormal, there is a decrease in precipitation in North Africa. This decline may beassigned to persistent anticyclonic conditions which hinder cloud developmentand precipitation in the western Mediterranean. For instance, Hasanean (2004)showed that subtropical anticyclonic cells are characterized by a significantincrease in strength (pressure) since the 1970s, which coincides with the declinein precipitation in the western Mediterranean. The presence of a correlationbetween NINO4 and NAO highlighted here suggests that a teleconnectionbetween the tropical South Pacific and the North Atlantic Oceans affects theinterannual variability of precipitation in North Africa. Knippertz et al. (2003b),among others, have already addressed the existence of such a teleconnection(a correlation between NAO and NINO3).

3. The change in precipitation averages took place during the second half of the1970s, which is consistent with prior results from the Mediterranean basin (e.g.Brunetti et al. 2001; Knippertz et al. 2003a; Rodigro and Trigo 2007). However,the actual date of this change may vary from one region to another even inNorth Africa. For instance, Knippertz et al. (2003a) noted that the decreasein precipitation took place in the early 1970s in the Mediterranean region ofnorthern Morocco, whereas in the Altantic region of northeast Morocco, it tookplace at the end of the decade. The variation may not be attributed to rainfallmeasurement error since, in many areas of the world, a significant change in gen-eral atmospheric circulation was observed during that decade (e.g. Ivanov andEvtimov 2009; Pillai and Mohankumar 2010; Trenberth and Hurrell 1994; Wanget al. 2010). For instance, according to Wang et al. (2010), “in the mid-1970s, theatmospheric circulation underwent significant changes in many regions of theworld”. This change in general atmospheric circulation affected the variabilityof climate indices and of hydroclimatic variables (temperature, precipitation,wind, streamflow, etc.). As for the tropical region, Pillai and Mohankumar (2010)noted that “the tropical climate system has undergone many secular changeson interdecadal timescales as the climate shift occurred in 1976. Along with theshift, a significant increase in sea surface temperature was found in the tropicalcentral and eastern Pacific and Indian Ocean”. The current study highlights


a change in climate indices, particularly NINO4, and precipitation during the1970s. Although some authors have attributed this change to the increase inanthropogenic greenhouse gas concentrations (e.g. Kein-Tank et al. 2005), thisinterpretation is still cause for debate.

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Temporal Variability of Annual Rainfall in the Macta and Tafna Catchments, Northwestern Algeria

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