influence of the north atlantic oscillation and the western ...

International Workshop ADVANCES IN STATISTICAL HYDROLOGY May 23-25, 2010 Taormina, Italy

INFLUENCE OF THE NORTH ATLANTIC OSCILLATION AND THE WESTERN MEDITERRANEAN OSCILLATION IN THE MAXIMUM FLOW EVENTS IN SPAIN by

Jesús López(1) and Félix Frances(1) (1) Institute of Water and Environmental Engineering, Technical University of Valencia, Valencia, Spain ([email protected], [email protected])

ABSTRACT The flow rates in the Spanish peninsula are characterized by large inter-annual variability, especially in the south of Spain. One way to identify this variation with climatic indices, that can have an impact on the study area. This article discusses the possible influence of the levels of the North Atlantic Oscillation (NAO) which has been shown in various studies presented a domain in climate regimes in the region and Western Mediterranean Oscillation (WeMO) in the maximum peak flows. To study the information was taken from 80 monitoring stations for flow in natural regime. The results show that there is a strong correlation between peak events that occur in winter (December to March) with the negative phase of NAO index which is significant to the basins of southern and western Spain, while there are correlations significant for the eastern basin (Mediterranean) and the northern basins. In the case of WEMO index showed significant correlations with the Mediterranean basin and north, which were obtained as for the NAO index for winter. There is a strong impact of the NAO index and WEMO in winter peak flows, and it has an impact almost zero for the periods presented in events outside of winter. Keywords: North Atlantic Oscillation, Wester Mediterranenan Oscillation, Correlation

1

INTRODUCTION

Currently, the understanding of climate variability is one of the most important issues of environmental science. The climate varies from year to year, between decades and even across millennia. The man or nature can change the Earth's climate. However, the complexity of this variation difficult to identify the origin of it. Simultaneous variations in weather and climate on widely separated points on earth have long been in the literature to study weather. These variations are commonly known as "teleconnections". Outside the tropics, teleconnections linking neighboring regions, mainly through the transient behavior of the atmosphere. In the Iberian Peninsula precipitation regimes are characterized by significant variability, presented a great irregularity in their distribution in space and time, and as a consequence the flows have large disparities between wet and dry years. Several investigators have made efforts to find factors that may explain this variability, most of them focusing on the analysis of precipitation, its trend and its strong correlation with patterns of circulation, studies projecting the NAO teleconnection index (Atlantic Oscillation North), which is a pattern of large-scale circulation statistically and physically robust and that characterizes climate variability in the northern hemisphere (Branstator, 2002, Hurrell et al 2001). At present, several studies have found a significant correlation between the NAO index and winter precipitation in the Atlantic coast of the Iberian Peninsula, establishing the same way their negligible correlation with the eastern facade. There are also studies of its influence on the interannual variations of flows in the Tigris, Euphrates (Cullen H. et al, 2002) and Danube (Rimbu, N. et al, 2002). Also the study of the influence of the NAO index on monthly flows of the river Tagus, Douro and Guadiana (Trigo, R. et at 2004) which found that the variability of flows in these three rivers is strongly modulated by this phenomenon. The weak correlation between the NAO index and the rainfall in the eastern façade of the Iberian Peninsula (Guijarro, J. 1999, Martin, V. et al 1999) explains that although much of the monthly Lopez et al., Influence of the NAO and WeMO in the Maximun flow Events in Spain

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rainfall in Spain (Martin V. et al 2001), these results led to study models of variability characteristic of the Mediterranean. As the proposed development of low-frequency index WeMO Mediterranean with its application in its influence on rainfall in eastern Spain (Martín, V. 2002). Finding the results of the index a significant correlation with precipitation for the month of January showed a greater influence than the NAO index. For the foregoing based on past performance which is very attractive, to establish that there is a significant correlation between the instantaneous peak flows on the Iberian Peninsula and teleconnection indices considered have the greatest influence on weather events in itself. Thus able to have their areas of major impact on both time and space, this in order to take teleconnection indices a significant predictor of the behavior of peak flows.

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METHODOLOGY

The guidelines followed in developing the next job consisted primarily in a literature review in relation to teleconnection indices and its influence on weather variables. This was an important part to establish the vision of the subject treated and the lines could be followed and teleconnection patterns with which it could work. And understand their behavior. I then took the collection and analysis of information as both hydrometric teleconnection indices. One way to analyze the influence of teleconnection indices flows in calculating the correlation of the time series. It took place the calculation of correlation matrices annual and monthly, for which test was used Pearson correlations significant for establishing a confidence level of 95%. The Pearson correlation coefficient is specifically a rate (r) for the measurement of linear association between two variables. Unlike the covariance, the Pearson correlation is independent of the measurement scale of the variables (Maidment, 1992). The calculation of linear correlation coefficient is accomplished by dividing the covariance by the product of standard deviations of two variables:

r=

σ XY σ X ∗σ Y

(1)

Where: the covariance of (X, Y), y σ X y σ Y , standard deviations of the marginal distributions. With the correlation coefficient r, can form the basis of a statistical test of independence. The null hypothesis H 0 is that the events Yi are independent and normally distributed random variables and not dependent on the time series X i .. The test statistics TC is defined:

TC =

r n−2 1− r2

(2)

In which r is the Pearson correlation coefficient between the series X and Y , and n is the number of pairs. The null hypothesis H 0 is rejected if TC > T1−α / 2,v , donde T1−α / 2,v , where T1−α / 2,v is a point on the Student t distribution with n-2 degrees of freedom with a probability of exceedance α / 2 .

Lopez et al., Influence of the NAO and WeMO in the Maximun flow Events in Spain

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From the known values were interpolated fields of annual and monthly correlation using Kriging with which we obtained the raster images and contours could be obtained, which could identify the areas where they had more influence the various indices used.

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CASE OF STUDY AND INFORMATION

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530000.000000

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330000.000000

530000.000000

730000.000000

4000000.0000004100000.0000004200000.0000004300000.0000004400000.0000004500000.0000004600000.0000004700000.0000004800000.0000004900000.000000

The study aimed to 80 gauging stations distributed around mainland Spain, the selected sites should have the requirement to natural regimen, fig. 1.

930000.000000

Figure 1 – Zone of study and gaugin sations.

The flow records were consulted in the database CEDEX (Centre for Studies and Experimentation of Public Works) (http://hercules.cedex.es/anuarioaforos/). Information was used from 80 hydrometric stations which were in the natural regime and were distributed throughout the study area.

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330000.000000

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4000000.0000004100000.0000004200000.0000004300000 .0000004400000 .0000004500000 .0000004600000.0000004700000.0000004800000 .0000004900000.000000

With the information flow analysis was performed of the seasonal timing of these, of what it was observed that peak flows occur mostly in winter on the Western Front in Spain, while in the eastern front tend to occur during the fall and in the central part of Spring (fig. 2).

1000000 .000000

Figure 2 – Seasonal distribution of peak flows

The information of the NAO monthly series were obtained of the Climatic Research Unit (CRU) (1821 – 2009), (http://www.cru.uea.ac.uk/) an the series of the WEMO monthly series of the Climatology Group


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Research of the Barcelona University (1821-2009), (http://www.ub.edu/gc/English/wemo.htm). Figure 3, show the dipoles of the teleconnection indices. NAO index (Dec - Apr 1825 - 2009) 3.0

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0.00 -0.50 -1.00 -1.50

Gibraltar

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Figure 3 – Dipoles of the teleconnection indices

4 4.1

RESULTS Correlation analysis of montlhy series

The first analysis was to conduct the analysis correlated with the complete series of monthly maximum flows and monthly series of teleconnections indices. Cross-correlations were measured at different delays seeking the highest correlations for each of the indices. In the analysis of cross-correlations with the NAO index stations were found significantly correlated with the negative phase of NAO, it was observed that significant correlations were detected only in cases of calculation without delays and with a delay of one month, shows the loss of correlation with major step calculation of a delay, which are shown in figure 4.

0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 -0.1 -0.12 -0.14 -0.16 -0.18 -0.2 -0.22

None delay

0.08 0.06 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 -0.1 -0.12 -0.14 -0.16 -0.18 -0.2 -0.22 -0.24 -0.26

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0.08 0.06 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 -0.1 -0.12 -0.14 -0.16 -0.18 -0.2 -0.22

0.08 0.06 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 -0.1 -0.12 -0.14 -0.16

0.1 0.08 0.06 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 -0.1 -0.12 -0.14

0.1 0.08 0.06 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 -0.1 -0.12 -0.14 -0.16 -0.18

0.12 0.1 0.08 0.06 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 -0.1 -0.12 -0.14 -0.16 -0.18 -0.2

0.06 0.02 -0.02 -0.06 -0.1 -0.14 -0.18 -0.22 -0.26

6 lags

7 lags

Negative correlation Positive correlation

Figure 3 – Results cross-correlation between the NAO index and the maximum monthly flow


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The results of the correlations with the index WeMO only seen by failing to consider significant correlations with four months delay and delay in the correlations, we observe a minor influence of this index in the flow, we obtained significant correlations with negative phase index on the Mediterranean coast, in the case of the positive phase showed significant correlations in the northern part, figure 6.

0.1 0.08 0.06 0.04 0.02 0 -0.02 -0.04 -0.08 -0.1 -0.12 -0.14

0.12 0.1 0.08 0.06 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 -0.1 -0.12 -0.14

0.22 0.18 0.14 0.1 0.06 0.02 -0.02 -0.06 -0.1 -0.14 -0.18

None delay

1 lag

2 lags

3 lags

0.22 0.2 0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 -0.1

0.24 0.22 0.2 0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08

4 lags

0.12 0.1 0.08 0.06 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 -0.1 -0.12 -0.14 -0.16

0.12 0.1 0.08 0.06 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 -0.1 -0.12

0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 -0.1

5 lags

6 lags

7 lags

Negative correlation Positive correlation

Figure 4 – Results cross-correlation between the WeMO index and the maximum monthly flow

In this first analysis it was found that the index was found that approximately 50% of the stations showed a significant correlation with the NAO index with 0 and 1 delay and about 25% with the index WeMO with 0 and 4 lags, figure 5. Also observed for the results that the variance explained was less than 5% for most of the hydrographic confederations, figure 6. 45

Number of significant stations

40 35 30 25 NAO

20

WEMO

15 10 5 0 0

1

2

3

4

5

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Lags

Figure 5 – Stations with significant correlation


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International Workshop ADVANCES IN STATISTICAL HYDROLOGY May 23-25, 2010 Taormina, Italy 2.0

3.5

1.8

3.0

Varianza Explicada (%)

Varianza Explicada (%)

1.6

2.5

Cantabrico Miño - Sil Duero

2.0

Tajo Guadiana Guadalquivir

1.5

Segura Jucar 1.0

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Cantabrico

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Miño - Sil Duero

1.2

Tajo 1.0

Guadiana Guadalquivir

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Segura Jucar

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Retardo - 1

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NAO WeMO Figure 5 – Variance explained by each index for each confederation

4.2

Correlation analysis on annual maximum flow

This analysis built the series with annual instantaneous peak flows and the series of teleconnection indices confidants to the flows in the month, also constructed series for the calculation with delays of 1, 2 and 3, and NAO indices WeMO. Having obtained the series we calculated the correlations between the peak flow series and the series of teleconnection indices for different delays. Highest correlations presented a 1 lag month between Maximum instantaneous peak flows and NAO index. The WEMO index didn't show a delay in the calculation of correlations (fig.6a). The NAO index shows a significant influence only in its negative phase, while the index WeMO has it in his two phases positive and negative (fig. 6b). 40 40

35 35 30 Significant Station

Significant Station

30 25 NAO WeMO

20 15

25

NAO

20

WeMO

15 10

10 5

5

0 Significant None delay (Posit ive phase)

0 None delay

1 lag

2 lag

3 lag

Significant None delay (Negative phase)

Significant 1 lag Significant 1 lag Significant 2 lag Significant 2 lag Significant 3 lag Significant 3 lag (Posit ive (Negat ive (Positive (Negative (Posit ive (Negative phase) phase) phase) phase) phase) phase)

Figure 6 – a) Station with significanta correlation and b) Stations significant for each phase of the indices

In the analysis of the annual correlation matrices was observed that after two lags, the correlation is lowered. The NAO index in her negative phase showed significant correlation in the southern and western of the Spanish Peninsula. The NAO index in her negative phase showed significant correlation in the southern and western of the Spanish Peninsula. The WEMO index showed significant correlations in their two phases (Positive and Negative). In the positive phase the significant correlation are in the north of the peninsula. In her negative Phase the significant correlation are in the eastern of the peninsula (fig 7).

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Figure 7 – interpolation results of the correlation indices for the two

Figure 8, shows the areas of influence of the NAO index and WeMO, clearly showing the influence of the negative phase of NAO index on the western front and the positive phase WeMO index in the north and negative phase the eastern front. So also in figure 9 are an analysis of the stations showing the highest correlation with the indices -0.4 0 -0.1 -0.2

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Figure 8– Interpolation results of the correlation indices for the two ! ( ! (

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Stations with maxims correlations with te leconnection indices

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Figure 9– Highest correlation index (annual)

4.3

Correlation analysis on a monthly basis

The results show as in the annual analysis of the effect on flow rates of the negative phase of NAO index. As the months from December to February (during winter) months with the greatest influence of the index (fig. 11). The highest correlations for the months of October to February and August there were no delays Lopez et al., Influence of the NAO and WeMO in the Maximun flow Events in Spain

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in the correlation matrices. For the months of March to July and September showed the highest correlations with one lag (fig. 10). 0.8

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Coeficientes de Pearson

Coeficiente de Pearson

0.4 0.2 0.0 -0.2 -0.4

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Median 25%-75% Min-Max

-0.8 Oct Nov Dic Ene Feb Mar Abr May Jun



Jul Ago Sep

None delay

Jul Ago Sep

1 lag

Figure 10– Interannual coefficient of Pearson (NAO vs Qmax) 40 20

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95% confidence

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Figure 11– Months with more influence of the NAO index (December to February) example station 3161 Tajo Confederation

The results of this WeMO index showed the highest correlations in the great majority for the maximum monthly without delay (fig.12), (January, March, April, June and August to December) showing the greatest influence in the months of September to January in its negative phase especially in the eastern front of the peninsula and the center, the correlation showed positive phase in the months of December through May, particularly in the north of the peninsula.

1.0

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0.2 0.0 -0.2 -0.4 -0.6

-0.6 -0.8



Jul Ago Sep

-0.8



None delay

Jul Ago Sep

1 lag

Figure 12– Interannual coefficient of Pearson (WeMO vs Qmax)

The variance represented by the NAO index in the Confederations most influential (Tajo, Duero, Guadiana, Guadalquivir y Miño-Sil) shows that this is more important for the winter months with average values of 20 to 25% for the Confederations Tajo, Duero , Guadiana and Guadalquivir (fig. 13a). In the case of index


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35

35

30

30

25 Duero 20

Tajo Guadiana Guadalquivir

15

Miño-Sil 10

5

Variance explained (%)

Variance explained (%)

WeMO explained variance is lower than in the NAO, and is even greater during the months of August to January, and showed a greater influence in the confederations Cantabrico, Tajo, Ebro and Jucar (fig.13b).

25

Duero Tajo Cantabrico

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Figure 13– Variance explained by the NAO and WeMO index in the Hydrologic Confederations

The following figures show the results for the spatial distribution of significant stations for month with significant correlations. Stations showing significant for each phase of the index, as well as areas of significant influence to the stations by using Kriging interpolation space. The clear influence of NAO index in winter months and although it was more in the Atlantic area of the study area (fig. 15). In the case of WeMO index showed its biggest influence in cuadales maxima that occur in the Autumn and Winter months, being the most influential areas north and the Atlantic coast (fig. 16).

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-0.4

-0.2

-0.5

-0.4

-0.1

-0.1 -0.4

-0.3

-0.3 -0.2

-0.1

-0.3

-0.3

0 -0.5

-0.4

-0.6

-0.5 -0.6

-0.3

-0.2 -0.3

-0.2

-0.4

-0.4

-0.4

-0.6

-0.3

0

-0.3 -0.2

-0.4

-0.5

-0.7

-0.4 -0.3

-0.2 -0.4

0.1

0

-0.2

-0.6

-0.7

-0.2

-0.1

-0.5

-0.5

-0.6

-0.3

-0.4 0.2

-0.6

0.1 0.2

-0.5

-0.1

December

January

February

March

Figure 15– Area of influence and Isopleths of correlation for months with significant influence of the NAO index

0.3 0.2 0.1 0 -0.1 -0.2 -0.3 -0.4

0.8

0.6

0.7

0.5

0.6

0.4

0.5

0.3

0.4

0.2

0.3

0.1

0.3

0.1 0

0.3

-0.1 -0.2

0.2 -0.3

-0.5

0.4

0.2 0.1

0.5

0.6

-0.2

-0.2

-0.1

-0.3

-0.3

-0.2

-0.4

-0.3

-0.5

-0.4

-0.6

-0.5

-0.7

0.2 -0.1 -0.3

-0.1 0

-0.1 0

-0.3 -0.4

-0.2

-0.2 -0.3 -0.3

-0.4 -0.5

-0.4

-0.1

0

-0.1

-0.3

-0.2 -0.4 -0.3 -0.2

-0.3 -0.4

-0.1

-0.2 -0.2

-0.4 -0.1

0

0

-0.3

-0.5

-0.3

0.2 0 0.1

-0.3 -0.4

0.1

-0.10

-0.1

-0.4

0 -0.4 -0.5

-0.2

-0.2 -0.2 -0.2 -0.3 -0.1

-0.2

0

-0.1

0.3

0.2

-0.2 0.1

0.2

0

0.1

-0.1

-0.2

0.1

-0.2

-0.4

0.1

-0.3

-0.3

0.2

-0.1

-0.1

0

0.1 0.1

-0.3

-0.4 -0.1

-0.6

-0.1 0 -0.2 -0.3

-0.3

-0.5

0.2

-0.1

0

-0.2

-0.3 -0.1 -0.5

-0.4

-0.1

-0.1 -0.2

-0.2

0

0.2 0.1

-0.1

-0.1

0.3 0.3 0.4 0.5 0.2

0.2

0.1 0.3

-0.1

-0.2

0

0 -0.1

0

0

0

0.1

0.2

0.4 0.3

0.1 -0.2

-0.2

-0.3 -0.4

0.2

0.1

-0.5 -0.6

0.4

-0.1 -0.4

-0.2

-0.5

-0.3

-0.4

0 0 0.1

-0.4

-0.1

-0.4 -0.3

-0.1

September

October

November

December

Figure 16– Area of influence and Isopleths of correlation for months with significant influence of the NAO index


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In Figure 17 are the stations that showed significant correlations with each index to a 95% level of significance, which clearly noted the months when peak flows are under the influence of these so as no influence in the months summer. 80 70

N um ber of S tations

60 50 NAO 40

WEMO None Significants

30 20 10 0 Ene

Feb

Mar

Abr

May

Jun

Jul

Ago

Sep

Oct

Nov

Dic

Figure 17– Significant gauging station for each month

Based on results of the interpolated grid for each month of greater influence of each index identified areas of influence of the indices in the analysis each month, building a grid average monthly grid, fig.18. Influence Area of NAO Index (Negative Phase)

0.2

Influence Area of NAO Index (Negative Phase)

NAO

0.15 0.1 0.05 0 -0.05 -0.1 -0.15 -0.2 -0.25 -0.3 -0.35 -0.4 -0.45 -0.5

Influence Area of WeMO Index (Positive Phase)

0.4 0.35 0.3

WeMO

0.25 0.2 0.15 0.1 0.05 0 -0.05 -0.1 -0.15

Influence Area of WeMO Index (Negative Phase)

-0.2 -0.25 -0.3 -0.35 -0.4

Figure 18– Results of spatial analysis in influence of the teleconnection indices

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CONCLUSIONS

Exist a significant correlation between Maximum instantaneous peak flows and teleconnection indices in some areas and seasons in the Spanish Peninsula. The NAO index showed a more extensive area of influence. In the analysis of highest correlations the NAO have more area of influence in the months December, January and February (Winter) and the WEMO index in September, October, November (Autumn). June, July and August present very few stations with correlations significant. These results are in agreement with results obtained by different researches who have relied in the analysis the influence of the teleconnection indices in the rainfalls and temperatures in Spain.

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ACKNOWLEDGMENTS

This study was supported by a grant provided by the National Council for Science and Technology of Mexico (CONACYT) and by the Spanish Ministry of Science and Innovation through the projects CGL2008-06474-C02-02/BTE and Consolider-Ingenio CSD2009-00065.

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REFERENCES

Hurrell, J. W., (1995). Decadal trends in the North Atlantic Oscillation: Regional temperatures and precipitation, Science, 269, pp. 676– 679. Lopez et al., Influence of the NAO and WeMO in the Maximun flow Events in Spain

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International Workshop ADVANCES IN STATISTICAL HYDROLOGY May 23-25, 2010 Taormina, Italy Barnston, A., Livezey R., (1987). Classification, seasonality and persistence of low-frequency atmospheric patterns. Monthly Weather Review, pp. 1083 – 1126. Cullen, H., Kaplan, A., Arkin, P. y Demenocal, P., (2002) Impact of the north Atlantic oscillation on middle eastern climate and streamflow, Climate Change (55), pp. 315-338. Guijarro, J., (1999). Teleconexiones climáticas y precipitación en la España mediterránea, La Climatología española en los albores del siglo XXI, pp. 243-251, Barcelona. Martín, V., Barriendos, M., Peña, J., Raso, J., Llasata, Ma., y Rodríguez, R., (1999). Potencialidad del índice NAO en la previsión de episodios de alta pluviosidad en España, Gerencia de Riesgos, Madrid. Martín, V., (2002). Ensayo sobre la oscilación del mediterráneo occidental y su influencia en la pluviometría del este de España, El Agua y el Clima, publicaciones de la AEC, serie A, nº3, Mallorca, pp. 35-42. Rimbu, N., Boroneant, C., Buta, C. y Dima, M., (2002). Decadal variability of the Danube river flow in the lower basin and its relation with the north Atlantic oscillation. International Journal of Climatology 22, pp. 1169-1179.

Maidment, D., (1992). Handbook of Hydrology, McGraw-Hill. Martín, V. y Fernández, D., (2001). El índice NAO y la precipitación mensual en la España peninsular, Investigaciones Geográficas, nº26, Universidad de Alicante, pp35-42. Trigo RM, Pozo-V´azquez D, Osborn TJ, Castro-D´ıez Y, G´amiz-Fortis S, Esteban-Parra MJ. (2004). North Atlantic oscillation influence

on precipitation, river flow and water resources in the Iberian Peninsula. International Journal of Climatology 24: 925–944.


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