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
1
International Workshop ADVANCES IN STATISTICAL HYDROLOGY May 23-25, 2010 Taormina, Italy
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.
2
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
2
International Workshop ADVANCES IN STATISTICAL HYDROLOGY May 23-25, 2010 Taormina, Italy
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.
3
CASE OF STUDY AND INFORMATION
4100000.0000004200000.0000004300000.0000004400000.0000004500000.0000004600000.0000004700000.0000004800000.000000
-70000.000000
-70000.000000
130000.000000
1619
!
330000.000000
530000.000000
1427 !1395 1378
1215
!
!
!
1626 !
1727
2076 !
9093
2104
!
!
!
9071
9050
!
!
2015
9040 !
9025
!
!
9055
9177
!
3060
20523002 !
3235 ! ! 3245 !
3163 !
!
!
! 2046
3193
3231
! 9111 !
2000!
2005
!
9022
9013 !
9043
!
!
! !9018
9197 9003 !
!
9061
!
!
2030
2074 ! 2126
2818
930000.000000
1080
!
! 1404
!
730000.000000
!
9042 !
3005
9030 8028 !
!
!
!
!
8030
3161 ! 31473224 3251 ! 3276 ! 3244 3213 ! ! ! 3220 ! 4251
!
4201
8090 80878139 ! 8140 ! !!
!
4205
!
!
4014 !
!
8060 8025 ! ! 8112 8089 ! !
4008 !
8151 !
7050 !
4174
7016
!
5107 ! !5004
4176 !
4172
!
!
5024
!
5097 !
130000.000000
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.
( !
( !
( !
( !
( !
( !
( !
( ! ! (
( !
( !
( !
( !
( ! ( !
( !
( ! ( ! ( !
( !
( !
( !
( ! ( !
( ! ( !
( !
( ! ( !
( !
( !
( ! ( !
( !
( !
( !
! ( ( (! !
( !
( !
( !
( ! ! (
( !
( !
( !
( !
( !
( ! ( !
( !
( !
930000.000000
( !
( !
( !
( !
( !
( !
( ! ( !
( ! ( ! ( ! ( ! ( !
( ! ( !
( ! ( !
( ! ( !
Invierno
( !
Otoño Primavera Verano
130000 .000000
330000.000000
530000.000000
730000.000000
200000.000000
600000.000000
1000000 .000000
S 4600000.000000
( !
( ! ( ! ( !
730000.000000
Winter
930000.000000
Spring Autum
200000.000000
600000.000000
4200000.000000
( !
( ! ( !
530000.000000
4600000.000000
330000.000000
4200000.000000
130000 .000000
4000000.0000004100000.0000004200000.0000004300000 .0000004400000 .0000004500000 .0000004600000.0000004700000.0000004800000 .0000004900000.000000
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
Lopez et al., Influence of the NAO and WeMO in the Maximun flow Events in Spain
3
International Workshop ADVANCES IN STATISTICAL HYDROLOGY May 23-25, 2010 Taormina, Italy
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
2.0
Islandia
1986
1993
2000
19 96
2007
1979
19 89
1972
19 82
1965
19 75
1958
1951
1944
1937
1930
1923
1916
1909
1902
1895
1888
1881
1874
1867
1860
1853
1846
1839
1832
0.0
1825
1.0
-1.0
-2.0
-3.0
NAO
WEMO index (Sep - Dec 1821 - 2009) 2.50 2.00
Padua
1.50 1.00
WEMO
0.50
19 68
19 61
19 54
19 47
19 40
19 33
19 26
19 19
19 12
19 05
18 98
18 91
18 84
18 77
18 70
18 63
18 56
18 49
18 42
18 35
18 28
18 21
0.00 -0.50 -1.00 -1.50
Gibraltar
-2.00
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
1 lag
4 lags
2 lags
5 lags
3 lags
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 -0.22
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
Lopez et al., Influence of the NAO and WeMO in the Maximun flow Events in Spain
4
International Workshop ADVANCES IN STATISTICAL HYDROLOGY May 23-25, 2010 Taormina, Italy
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
6
7
Lags
Figure 5 – Stations with significant correlation
Lopez et al., Influence of the NAO and WeMO in the Maximun flow Events in Spain
5
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
Ebro
Cantabrico
1.4
Miño - Sil Duero
1.2
Tajo 1.0
Guadiana Guadalquivir
0.8
Segura Jucar
0.6
Ebro
0.4
0.5 0.2
0.0
0.0
Sin retardo
Retardo - 1
Retardo - 2
Retrado - 3
Retardo - 4
Retardo - 5
Retrado - 6
Retardo - 7
Sin retardo
Retardo - 1
Retardo - 2
Retrado - 3
Retardo - 4
Retardo - 5
Retrado - 6
Retardo - 7
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).
0.4 0.4
0.3
0.4
0.5
0.2
0.3
0.4
0.1
0.2
0.3
0
0.1
0.2
-0.1
0
0.1
-0.2
-0.1
0
-0.3
-0.2
-0.1
-0.4
-0.3
-0.2
-0.5
-0.4
-0.3
-0.5
-0.4
0.3 0.2 0.1 0 -0.1 -0.2 -0.3 -0.4 -0.5
None delay
-0.6
1 lag
2 lag
Lopez et al., Influence of the NAO and WeMO in the Maximun flow Events in Spain
3 lag
6
International Workshop ADVANCES IN STATISTICAL HYDROLOGY May 23-25, 2010 Taormina, Italy
0.5
0.6
0.4
0.4
0.4
0.5
0.3
0.3
0.4
0.2
0.2
0.1
0.1
0
0
-0.1
-0.1
-0.2
-0.2
-0.3
-0.3
-0.3
-0.4
-0.4
-0.4
-0.5
-0.5
-0.5
0.3
0.3
0.2 0.2
0.1
0.1 0
0 -0.1
-0.1
-0.2
-0.2
Noene delay
1 lag
2 lag
-0.3
3 lag
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
0. 1
-0. 5 0.1
-0.4
0.2 0.3 0
-0.3
-0.1
-0.2
Influence NAO (Negative Phase) 0.2 0.1
-0.5 0 0.1
-0.5
-0.5 -0.5
Influence WeMO (Positive Phase)
0.1 0.4 0. 2 0.1
0.3 -0.3
0 -0.1 -0.2
-0.3
-0.4
-0.1 0.3 0
0 0
0.2
-0.3 -0.1
0.1
Influence WeMO (Negative Phase)
-0.3
0.1
-0.1 0
Figure 8– Interpolation results of the correlation indices for the two ! ( ! (
! (
! ( ! ! ( ! ! ( !
! (
! ( !
! ( !
! (
! ( ! ! ( !
! ( !
! ( ! ! (
! ( !
! ( ! ! ( ! ( ! ! ! ( ! ! ( !
! ( ! ( !
! (! (
! ( !
! ( !
! ( !
! (
! ( ! ! ( ! ! ! ( ( ! ! ! (
! ( !
! ( ! ! ( !
! ( !
! ! ( ( ! ! ! (
! ( ! ! ( ! ! ( ! ! ( ! ! ( !
! (
! (
! ( !
! ( !
! ( !
( !! ( ! ( ! ! ( !
! ( ! ! ( !
! ( !
! ( !
! ( ! ! ( ! ! ( !
! ( ! ( ! ! ( !
! ( !
! ( !
! ( !
! (
! ( ! ! ( !
! ( ! ! ( !
! ( !
! ( !
! ( !
! ( ! ( ! (
! ( !
Stations with maxims correlations with te leconnection indices
NAO MOI No Significativa
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
7
International Workshop ADVANCES IN STATISTICAL HYDROLOGY May 23-25, 2010 Taormina, Italy
in the correlation matrices. For the months of March to July and September showed the highest correlations with one lag (fig. 10). 0.8
0.6
0.6
0.4
Coeficientes de Pearson
Coeficiente de Pearson
0.4 0.2 0.0 -0.2 -0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.6
Median 25%-75% Min-Max
-0.8 Oct Nov Dic Ene Feb Mar Abr May Jun
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
20 0
800
700
1000
700
600
600
500
500
400
Caudales (m 3/s)
800
3
Caudales (m /s)
3
40
10 0
0
Caudales (m /s)
20
400 300 200
300 200 100
100 0
0
400 200 0
-100
-100 -200 -6
600
-4
-2
0
2
Indice NAO
4
6
8
0
20
40
-200 -6
-200 -4
-2
0
2
4
6
8
0
10
20
-8
-6
-4
-2
0
2
4
Indice NAO
Indice NAO
95% confidence
6
0
20
40
95% confidence
95% confidence
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
0.8
0.8
0.6 0.4
0.4
Coeficiente de Pearson
Coeficiente de Pearson
0.6
0.2 0.0 -0.2 -0.4
0.2 0.0 -0.2 -0.4 -0.6
-0.6 -0.8
Median 25%-75% Min-Max
-1.0 Oct Nov Dic Ene Feb Mar Abr May Jun
Jul Ago Sep
-0.8
Median 25%-75% Min-Max
-1.0 Oct Nov Dic Ene Feb Mar Abr May Jun
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
Lopez et al., Influence of the NAO and WeMO in the Maximun flow Events in Spain
8
International Workshop ADVANCES IN STATISTICAL HYDROLOGY May 23-25, 2010 Taormina, Italy
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
20
Guadalquivir Miño-Sil 15
Ebro Segura
10
Jucar
5
0
0
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
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).
0.2
0.2
0.1
0.1 0
0
0.3
0.1
0.2
0
0.1
-0.1
0
-0.1
-0.1
-0.2
-0.1
-0.2
-0.2
-0.3
-0.3
-0.4
-0.4
-0.5
-0.2
-0.3 -0.3
-0.4 -0.4
-0.5
-0.5
-0.6
-0.6
-0.7
0.1 -0.3 0
-0.2
-0.4
-0.1
-0.5
-0.3 -0.3
-0.5
-0.5
-0.3
-0.4
-0.4 -0.5
-0.1
-0.5
-0.4
-0.3 -0.1
-0.4 -0.1 0 -0.3 -0.2
-0.4
0
-0.3
-0.1
-0.4
0.1 -0.5
-0.5 -0.5 -0.5
-0.4 -0.5 -0.6
-0.5 -0.6
-0.4
-0.2
-0.3
-0.3 -0.2
-0.1
0
-0.4
-0.2
0 0.1 -0.4 -0.5
-0.3
-0.5
0
-0.3 -0.4
-0.2
-0.6 -0.5
-0.5 -0.4
0.2
-0.4
-0.5
-0.2
-0.4 -0.1 0
-0.1 0
-0.5
-0.2
-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
Lopez et al., Influence of the NAO and WeMO in the Maximun flow Events in Spain
9
International Workshop ADVANCES IN STATISTICAL HYDROLOGY May 23-25, 2010 Taormina, Italy
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
5
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.
6
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.
7
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
10
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.
Lopez et al., Influence of the NAO and WeMO in the Maximun flow Events in Spain
11