Abstract

Interannual-to-decadal variability of Rhine River streamflow and their relationship with large-scale climate anomaly patterns for spring [March–May (MAM)] and autumn [September–November (SON)] are investigated through a statistical analysis of observed streamflow data and global climate anomaly fields. A wavelet analysis reveals that spring streamflow variability is nonstationary with enhanced variability in the 8–16-yr band from 1860 to 1900 and in the 2–8 and 16–30 yr after 1960. A composite analysis reveals that streamflow anomalies during spring are related to a sea surface temperature (SST) pattern that resembles the corresponding El Niño–Southern Oscillation (ENSO) SST pattern. The corresponding atmospheric circulation pattern favors enhanced moisture advection over the Rhine catchment area during positive streamflow anomalies. During autumn, the streamflow variability follows a distribution similar to spring streamflow, but with a strong peak in the 30–60-yr band. Autumn streamflow anomalies are significantly related only with the North Atlantic SST anomalies. The atmospheric circulation pattern associated with high streamflow during autumn, which is more regional than the corresponding spring pattern, shows a deep low pressure system over the British Isles and the northwestern part of Europe and a shift southward of the Atlantic jet axis. The orientation of the axis of the Atlantic and African jets, as well as the advection of the moist air from the ocean, plays a crucial role in the variability of Rhine streamflow both in spring and autumn.

1. Introduction

The availability of water is greatly influenced by climate conditions that vary on seasonal, interannual, and decadal time scales. Characterization of hydrological variability on climatic time scales and identification of connections to climate forcings provide potential improvement for hydrological forecasts when these forcings are predictable or slowly evolving (Souza Filho and Lall 2003; Croley and Luukkonen 2003). Evidence from long hydrological records shows that periods with anomalous hydrological behavior (Arnell et al. 1993) are associated with persistent climate anomalies.

On seasonal time scales, anomalous atmospheric conditions are often linked with seasonal variations in the rivers’ streamflow via variations in precipitation and temperature (Dettinger and Diaz 2000; Cullen et al. 2002). For example, spring and summer rainfall and temperature anomalies across Europe can be forecasted from prior knowledge of varying boundary conditions such as anomalous sea surface temperature (SST) in the North Atlantic (Colman 1997; Colman and Davey 1999; Wilby 2001) and/or the tropical Pacific (Kiladis and Diaz 1989; van Oldenborgh et al. 2000; Lloyd-Hughes and Saunders 2002). Spring precipitation over central Europe is higher than normal following El Niño events combined with cooler SSTs west of Ireland (Lloyd-Hughes and Saunders 2002). Predictability is found to be higher in El Niño–Southern Oscillation (ENSO) extreme years (Branković and Palmer 2000), implying that at least part of the available skill can be attributed to the forcing from the tropical Pacific Ocean.

Interannual-to-decadal variability of the atmosphere over the North Atlantic region is strongly influenced by the North Atlantic Oscillation (NAO) teleconnection pattern (Bjerknes 1964; Hurrell 1995). Different studies focusing on the influence of NAO on different climatic elements at different time scales, from interannual to decadal, showed that NAO is responsible for generating systematic, large-amplitude patterns in the anomalies of temperature, precipitation, wind speed, latent and sensible heat flux, and, hence, SST over much of the extratropical North Atlantic (van Loon and Rogers 1978; Kushnir 1994; Hurrell and van Loon 1997).

Although NAO is one of the most prominent teleconnection patterns in all seasons (Barnston and Livezey 1987), its relative role in regulating the variability of the European climate during nonwinter months is not as clear as for the winter season. At the same time, the mechanisms that drive the European climate variability might vary from one climatic period to another and also might be different for different variables (e.g., precipitation, streamflow, and air temperature).

Since the influence of well-known teleconnection patterns, like NAO or ENSO, is strongest in winter, most studies that have examined the influence of the atmospheric circulation on the river streamflow are confined to the winter season (Rimbu et al. 2004; Déry and Wood 2004; Bouwer et al. 2006; Araneo and Compagnucci 2008). Only a few studies have examined the variability of river streamflow outside the winter months (Kingston et al. 2006a,b; Zanchettin et al. 2008; Ionita et al. 2008, 2011) over the European region. It has been suggested (Kingston et al. 2006a) that the studies of winter relationship, between streamflow variability and climate-related patterns, should not be extrapolated to other seasons because of the fact that streamflows show a monthly variability both in magnitude and direction (Lawler et al. 2003). At the same time, using just annual means should be also regarded with caution, as they can be dominated by the strong winter signal. As such, this paper investigates the nature of spring and autumn variability of Rhine River streamflow and the relationship with large-scale atmospheric circulation.

The paper is organized as follows: in section 2 the study area, the data used in this paper, and the methodology are described. Section 3 deals with the spectral characteristics of Rhine River streamflow, while in section 4 the relationship between the streamflow variability and the large-scale atmospheric circulation is investigated. The discussion and the main conclusions follow in section 5.

2. Data and methods

a. Study area

The primary quantity analyzed in this paper is the Rhine River streamflow, which is a major river in western Europe. The river originates in the Swiss Alps and covers portions of Switzerland, Germany, France, and the Netherlands before draining into the North Sea. The Rhine basin (185 000 km2) can be divided into the Alpine area upstream from Basel (Switzerland) and the middle and lowland parts, downstream. Downstream from Basel, the Rhine is supplied by several large tribu-taries, such as Neckar, the Main, and the Mosel.

The Rhine River has been intensively modified by humans during history. The Lower Rhine (where our gauging station is situated), from Bonn downstream through the Netherlands, was channelized during the latter half of the nineteenth century, with the expansion of this work through the twentieth century (Wetzel 1996; Havinga and Smits 2000). Pinter et al. (2006), testing different mechanisms that can affect the Rhine streamflow (e.g., river regulation or land use and climate change), showed that the regulation of the basin has little or no effect on Rhine streamflow, while the land use and climate change, over the past 100 yr, led to an increase in the magnitude and frequencies of floods.

The monthly time series of Rhine discharge, used in this paper, were recorded at Cologne gauging station (50°57′0″N, 6°58′0″E) and are provided by the German Federal Institute of Hydrology (BfG) in Koblenz, Germany (Fig. 1; this figure is provided with permission from the International Commission for the Hydrology of the Rhine Basin). The hydrological discharge regime is characterized by a pronounced seasonal cycle whose rising limb is situated between January and April and the falling one between September and November, with the highest values being recorded in February. These high discharge values recorded in the spring months are related to the melting of the snow in the catchment area and the soil humidity. We made use of this particular station because it has the longest period of measurements: 1817–2008.

Fig. 1.

Physical map of the Rhine River catchment area.

Fig. 1.

Physical map of the Rhine River catchment area.

From the monthly time series we computed the seasonal spring and autumn means by averaging the months March–May (MAM) and September–November (SON). The time series were linearly detrended, using a first-order polynomial model, and normalized by the corresponding standard deviation (std dev) to obtain the normalized anomalies of Rhine streamflow for MAM and SON.

b. Climatic data

To investigate the relationship of Rhine streamflow variability with global teleconnection patterns we use the Extended Reconstructed Sea Surface Temperature, version 3 (ERSST.v3; Smith et al. 2008). This dataset covers the period 1854–2008.

For the Northern Hemisphere atmospheric circulation we use the geopotential height at 500 hPa (Z500) on a 2° × 2° grid from Twentieth Century Reanalysis (V2) data (Compo et al. 2011, 2006; Whitaker et al. 2004) for the period 1871–2008. The vertically integrated water vapor transport (WVT; Peixoto and Oort 1992) is calculated through zonal wind (u), meridional wind (υ), and specific humidity (q) from the same dataset (Compo et al. 2011, 2006; Whitaker et al. 2004). WVT vectors for latitude (φ) and longitude (λ) are defined as follows:

 
formula

where zonal (Qλ) and meridional (Qφ) components of Q are given by Eq. (2):

 
formula

For each vertical layer and each grid point we calculate the product between the monthly values of horizontal wind and specific humidity (q) corresponding to lower- and upper-pressure levels (p), respectively. The result is multiplied with the pressure difference corresponding to lower and upper layers and divided by gravity. The WVT is obtained by summation of water transport for all layers located between the earth’s surface and the 300-hPa level. Above 300 hPa the specific humidity in the V2 model is zero (Kalnay et al. 1996; Compo et al. 2011, 2006; Whitaker et al. 2004).

We also used the divergence of water vapor K, which is in balance with the surface freshwater flux EP (Starr and Peixoto 1958; Peixoto and Oort 1992):

 
formula

where denotes the two-dimensional nabla operator, E evaporation, and P precipitation. Regions of mean positive divergence (EP > 0) constitute source regions of water vapor; meanwhile, the regions of convergence (EP < 0) are sink regions for water vapor.

c. Methods

To analyze the temporal structure (interannual and decadal variability) of Rhine streamflow, wavelet power spectrum analysis was used. The wavelet analysis used in this paper follows the methods of Torrence and Compo (1998). Statistical significance is determined against a red noise null hypothesis using a chi-squared test. By decomposing a time series into a time–frequency space, it is possible to determine the dominant modes of variability as well as how these modes vary in time.

To assess the links between Rhine River streamflow and large-scale atmospheric circulation we have constructed the composite maps of spring (autumn) SST, Z500, U250, and the WVT for the years of high (std dev > 0.75) and low (std dev < −0.75) Rhine streamflow in spring (autumn).

3. Spectral characteristics of Rhine River streamflow

a. Spring

The time series of spring Rhine streamflow for the period 1817–2005 (Fig. 2a) shows strong interannual-to-decadal variations. The mean Rhine streamflow during this period is 2265.59 m3 s−1 and the standard deviation is 647.65 m3 s−1. To verify the accuracy of the streamflow data, we have calculated a precipitation (PP) index for spring, averaged over Rhine catchment area (45°–53°N, 3°–12°E). The precipitation data were extracted from the reconstruction of Pauling et al. (2006), covering the period 1817–2000. We used this dataset because it goes back in time long enough to make a proper comparison with Rhine streamflow. The PP index follows the temporal evolution of the Rhine streamflow (Fig. 2b). Relatively high values of both streamflow and precipitation are recorded during 1840–50, 1870–80, 1960–70, and 1980–90, while the periods 1940–60 and 1970–80 are dominated by low values. The correlation coefficient between the two time series, before applying the 7-yr running mean, is r = 0.71 (95% confidence level). The precipitation/discharge ratio (Fig. 2c) shows a strong interannual variability but no significant trend over the analyzed period, suggesting that neither the discharge nor precipitation have been affected by major changes over the analyzed period.

Fig. 2.

(a) The time series of spring Rhine discharge (gray line) and the 7-yr running mean (black line), (b) the time series of spring PP index (gray line) and the 7-yr running mean (black line), and (c) the ratio of spring precipitation to spring discharge.

Fig. 2.

(a) The time series of spring Rhine discharge (gray line) and the 7-yr running mean (black line), (b) the time series of spring PP index (gray line) and the 7-yr running mean (black line), and (c) the ratio of spring precipitation to spring discharge.

The wavelet power spectra for Rhine flow (Fig. 3a) reveals that the power is broadly distributed with peaks in the 2–8, 8–16, and 16–30-yr bands. The 95% confidence levels demonstrate that these peaks are not stationary in time, and that the Rhine streamflow variance has varied in time. While the 2–8 and 16–30-yr bands are significant mostly after 1960, the 8–16-yr band was significant from 1860 up to 1900. The wavelet power spectrum for the PP index (Fig. 3b) is quite similar to that of the Rhine flow, except that in the case of the PP index there is a strong and significant peak in the 60-yr band, especially from 1900 up to 2000.

Fig. 3.

(a) The continuous wavelet power spectrum of the time series of spring Rhine discharge, (b) the continuous wavelet power spectrum of the time series of spring PP index, and (c) squared wavelet coherence between the time series of spring Rhine discharge and spring PP index. The thick black line contour is the 5% significance level against red noise. Arrows indicate the phase of the coherence, where right is in phase and left is in out of phase.

Fig. 3.

(a) The continuous wavelet power spectrum of the time series of spring Rhine discharge, (b) the continuous wavelet power spectrum of the time series of spring PP index, and (c) squared wavelet coherence between the time series of spring Rhine discharge and spring PP index. The thick black line contour is the 5% significance level against red noise. Arrows indicate the phase of the coherence, where right is in phase and left is in out of phase.

The cross wavelet transform (XWT) between Rhine flow and the PP index is shown in Fig. 3c. The cross wavelet analysis between the two time series provides a measure of coherence at each frequency (Torrence and Compo 1998). Both time series show high coherency in the 8–16-yr band, from 1860 to 1900, as well as in the 14–20-yr band, starting 1960 until the end of the records. It can also be inferred from XWT that Rhine flow and the PP index, for spring, are in the same phase in the sectors with a significant common power.

b. Autumn

The time series of autumn Rhine streamflow for the period 1817–2005 (Fig. 4a) also shows strong interannual-to-decadal variations. The mean autumn Rhine streamflow during this period is 1716.54 m3 s−1 and the standard deviation is 560.95 m3 s−1. The autumn PP index follows the temporal evolution of the Rhine streamflow (Fig. 4b), both on interannual as well as on decadal time scales. High values of both streamflow and precipitation are recorded during 1820–30, 1870–80, and 1920–40, while relatively low values are recorded during 1860–70, 1900–10, and 1940–50. The correlation coefficient between the two time series is r = 0.69 (95% significance level). In the case of autumn, the precipitation/discharge ratio (Fig. 4c) is characterized by a strong variability, especially after 1900, when the ratio becomes higher. This might be due to an increase in the precipitation amounts that are falling over the Rhine catchment area. Bárdossy and Caspary (1990) noted a rise in the frequency and persistence of some “wet” patterns (in particular the west cyclonic pattern) in southwest Germany during autumn. A similar tendency in precipitation was found by Engel (1997) in the Rhine basin up to Cologne during fall.

Fig. 4.

As in Fig. 2, but for autumn (SON).

Fig. 4.

As in Fig. 2, but for autumn (SON).

The wavelet power spectra for autumn Rhine flow (Fig. 5a) is characterized by strong interannual variability as well as decadal variability. The power is distributed, like in the case of spring streamflow, in the 2–8-yr band as well as in the 8–16-yr band. The main difference, when compared with spring streamflow, is the strong and significant peak in the 30–60-yr band. This band is also present in the autumn PP index (Fig. 5b), where it is the most pronounced peak and remains stable for almost 100 yr (1850 up to 1960) in both time series.

Fig. 5.

As in Fig. 3, but for autumn (SON).

Fig. 5.

As in Fig. 3, but for autumn (SON).

The XWT between autumn Rhine flow and the autumn PP index is shown in Fig. 3e. Both time series show high coherency in the 2–8-yr band, from 1900 to 2000, as well as in the 30–60-yr band, starting from 1850 until 1960. Like in the case of spring, the two time series are in the same phase in the sectors with a significant common power (vectors in Fig. 5c).

4. Large-scale patterns associated with streamflow variability

To identify the physical mechanism associated with the variability of Rhine River streamflow, we constructed the composite maps between the normalized and standardized time series of Rhine streamflow and large-scale atmospheric circulation for the years of high (std dev > 0.75) and low (std dev < −0.75) values of the normalized streamflow, respectively. This threshold was chosen as a compromise between the strength of climate anomalies associated with the streamflow and the number of maps that satisfy this criterion. Further analysis, not shown here, indicates that the results are not sensitive to the exact threshold values used for our composite analysis.

a. Spring

1) Relationship with global sea surface temperature

The difference between the composite maps of global SST associated to high (std dev > 0.75) and low (std dev < −0.75) values of the streamflow is shown in Fig. 6a. Regions with significant anomalies at 95% confidence level on a traditional t test (Helsel and Hirsch 1995) are shown as contour lines in Fig. 6a.

Fig. 6.

(a) The difference (high − low) between the composite maps of global spring SST for the years when the spring discharge was high (std dev >0.75) and low (std dev < −0.75), respectively (the contour lines indicate the SST normalized anomalies significant at 95% confidence level on a standard t test); and (b) squared wavelet coherence between the normalized time series of spring Rhine discharge and the normalized time series of spring Niño-3.4 index.

Fig. 6.

(a) The difference (high − low) between the composite maps of global spring SST for the years when the spring discharge was high (std dev >0.75) and low (std dev < −0.75), respectively (the contour lines indicate the SST normalized anomalies significant at 95% confidence level on a standard t test); and (b) squared wavelet coherence between the normalized time series of spring Rhine discharge and the normalized time series of spring Niño-3.4 index.

Positive anomalies of river streamflow are associated with negative SST anomalies in the central North Pacific and positive SST anomalies in the tropical Eastern Pacific. This pattern resembles the SST pattern associated with ENSO and is consistent with previous studies, which identified positive correlations between ENSO-related indices and river flow from Europe (Dettinger and Diaz 2000; Rimbu et al. 2004; Ionita et al. 2008) as well as precipitation over Europe (Mariotti et al. 2002; van Oldenburgh et al. 2000).

The relationship with ENSO-related anomalies has been captured also by the cross wavelet spectrum between spring Rhine streamflow and the MAM Niño-3.4 (5°N−5°S, 120°–170°W) SST index (Fig. 6b). The two time series show a strong coherency in the 2–6-yr band (ENSO band), especially after 1920. However, at the beginning and the end of the analyzed period the influence of the edge effects (Torrence and Compo 1998) on the common power between the discharge and Niño-3.4 index should be taken into account. There is also a strong coherency in the decadal band, from 1860 up to 1900.

High-streamflow anomalies are also associated with a tripole-like pattern in the North Atlantic region, characterized by negative SST anomalies on the eastern U.S. coast, European coast, and North Sea and positive SST anomalies in the tropical Atlantic extending up to southwestern Greenland. A similar pattern was identified in the North Atlantic Ocean by other authors (Hurrell 1995; Desser and Blackmon 1993). This pattern has been found to have a quasi-decadal variation of about 12 yr, which is consistent with the peak identified by the wavelet analysis in spring streamflow time series.

2) Relationship with 500-hPa geopotential height and 250-hPa wind

High-streamflow anomalies are associated with a wave train pattern in the spring Z500 field (Fig. 7a) characterized by a center of positive Z500 anomalies over the Labrador Sea, a center of negative anomalies over the British Isles and northwestern Europe, and a weaker center of positive anomalies over the northern part of Africa that extends over southern Europe.

Fig. 7.

(a) The difference (high − low) between the composite maps of spring Z500 for the years when the spring discharge was high (std dev >0.75) and low (std dev < −0.75), respectively (the contour lines indicate the Z500 normalized anomalies significant at 95% confidence level on a standard t test); (b) the composite map of the wind speed at 250-hPa level for the years when the discharge was higher than std dev = 0.75; and (c) the composite map of the wind speed at 250-hPa level for the years when the discharge was lower than std dev = −0.75. The jet axes are denoted by thick arrows. For (b) and (c), the contour lines represent the spring climatological mean of U250. For the wind speed the units are m s−1.

Fig. 7.

(a) The difference (high − low) between the composite maps of spring Z500 for the years when the spring discharge was high (std dev >0.75) and low (std dev < −0.75), respectively (the contour lines indicate the Z500 normalized anomalies significant at 95% confidence level on a standard t test); (b) the composite map of the wind speed at 250-hPa level for the years when the discharge was higher than std dev = 0.75; and (c) the composite map of the wind speed at 250-hPa level for the years when the discharge was lower than std dev = −0.75. The jet axes are denoted by thick arrows. For (b) and (c), the contour lines represent the spring climatological mean of U250. For the wind speed the units are m s−1.

To better assess the influence of the jet stream location on the streamflow variability, we have computed the composite maps of the wind speed at a 250-hPa level for the years when the discharge was higher than std dev = +0.75 (Fig. 7b) and when the discharge was lower than std dev = −0.75 (Fig. 7c).

Positive streamflow anomalies are associated with a southward shift of the Atlantic jet orientation (Fig. 7b) relative to its climatological mean (contour lines in Fig. 7b). In the low-streamflow years (Fig. 7c) the jet orientation is turned counter clockwise compared to the orientation in the case of high-streamflow years, so that its northeastern exit points toward Scandinavia rather than toward western Europe, as is found in the case of high-streamflow years. As a result, the degree of separation between the Atlantic jet and the African jet is much more pronounced in low-streamflow years. Furthermore, the southward shift in the jet axis coupled with the position of the African jet are consistent with increased upper-level divergence and midlevel convergence over the Rhine region, which induces strong and deep low pressure anomalies, which is an effective mechanism for rain over the region.

In agreement with Fig. 7a, high-streamflow anomalies are associated with a cyclogenic region because of the fact that the catchment area of the Rhine River is situated to the left of the Atlantic jet exit. Hoskins et al. (1978) suggested that the poleward side of a jet exit region is preferred for cyclonic growth, which in turn can induce heavy precipitation events. In return, low-streamflow anomalies are associated with an anticyclonic region over the northwestern part of Europe, being under both the right sector of the Atlantic jet exit region and the left sector of the African jet entrance region.

3) The relationship with the vertically integrated water vapor transport

The vertically integrated water vapor flow is strongly related to the northeasterly jet. In the exit region of the Atlantic jet a meridional circulation carries negative vorticity southward in the upper troposphere and the return branch of this circulation enhances the baroclinicity in the lower troposphere through the northward thermal advection.

Vector plots of the vertically integrated water vapor transport composites show that during the years of high spring streamflow anomalies the axis of maximum moisture transport is directed from the Atlantic toward the northeastern part of Europe, which causes high-precipitation anomalies over this region (Fig. 8a). At the same time, an intense convergence zone develops over the Alpine region (the headwaters of the Rhine), causing intense precipitation over this region. The convergent zone associated with the cyclonic regime identified in the Z500 field suppresses evaporation and induces intense precipitation and high-flow anomalies. In the case of high-streamflow anomalies both the jet intensity and the water vapor flux tend to be much stronger compared to the years of low streamflow.

Fig. 8.

(a) The composite map of the spring vertically integrated water vapor transport for the years when the discharge was higher than std dev = 0.75. (b) The composite map of the spring vertically integrated water vapor transport for the years when the discharge was lower than std dev = −0.75. The shaded areas (colored legend) indicate the distribution of the horizontal divergence of the total water vapor transport (negative values are associated with net precipitation, units are 10−6 kg m2 s−1). The magnitude of the water vapor transport is indicated by the length of the vectors (kg m s−1).

Fig. 8.

(a) The composite map of the spring vertically integrated water vapor transport for the years when the discharge was higher than std dev = 0.75. (b) The composite map of the spring vertically integrated water vapor transport for the years when the discharge was lower than std dev = −0.75. The shaded areas (colored legend) indicate the distribution of the horizontal divergence of the total water vapor transport (negative values are associated with net precipitation, units are 10−6 kg m2 s−1). The magnitude of the water vapor transport is indicated by the length of the vectors (kg m s−1).

For the case of low-flow anomalies, there is a significant reduction of the water vapor transport from the Atlantic toward Europe (Fig. 8b). The path of the axis of WVT follows the axis of the Atlantic jet (see Fig. 7c).

b. Autumn

1) Relationship with global sea surface temperature

The difference between the composite maps of global autumn SST associated to high (std dev > 0.75) and low (std dev < −0.75) values of the autumn streamflow is characterized by a complete different pattern than in spring. Regions with significant anomalies at 95% confidence level on a traditional t test (Helsel and Hirsch 1995) are shown as contour lines in Fig. 9a. High SON streamflow anomalies are associated with negative SST anomalies on the eastern U.S. coast and the European coast expanding up to the African coast and positive SST anomalies in the Labrador Sea and southwest of Greenland (Fig. 9a). Outside the North Atlantic regions, the SST anomalies are insignificant. A similar SST pattern is found if we apply an EOF analysis over the autumn SST over the North Atlantic region and retain the fourth EOF (EOF4) (Fig. 9b).

Fig. 9.

(a) The difference (high − low) between the composite maps of global autumn SST for the years when the autumn discharge was high (std dev >0.75) and low (std dev < −0.75), respectively; (b) the fourth EOF of the autumn SST from the North Atlantic; (c) the continuous wavelet power spectrum of the time series of autumn SST PC4; and (d) squared wavelet coherence between the normalized time series of autumn Rhine discharge and autumn SST PC4. The thick black line contour is the 5% significance level against red noise. Arrows indicate the phase of the coherence, where right is in phase and left is in out of phase.

Fig. 9.

(a) The difference (high − low) between the composite maps of global autumn SST for the years when the autumn discharge was high (std dev >0.75) and low (std dev < −0.75), respectively; (b) the fourth EOF of the autumn SST from the North Atlantic; (c) the continuous wavelet power spectrum of the time series of autumn SST PC4; and (d) squared wavelet coherence between the normalized time series of autumn Rhine discharge and autumn SST PC4. The thick black line contour is the 5% significance level against red noise. Arrows indicate the phase of the coherence, where right is in phase and left is in out of phase.

To prove that the SON streamflow anomalies are strongly related to the fourth EOF of the North Atlantic SST, we have computed the wavelet spectrum (Fig. 9c) of the fourth principal component of SON SST (corresponding to SON SST EOF4) and the cross wavelet spectrum between autumn streamflow and principal component 4 (PC4). The most striking feature of the wavelet analysis is the strong peak in the wavelet spectrum, of both SON streamflow as well as PC4, in the interdecadal band between 30 and 60 yr. This multidecadal cycle is stationary for almost 100 yr (1850–1940) in both time series. Over this period the two time series are in the same phase in the sectors with a significant common power (vectors in Fig. 9c).

2) Relationship with 500-hPa geopotential height and 250-hPa wind

In autumn, high-streamflow anomalies are associated with a deep center of negative Z500 anomalies over the northwestern part of Europe, which extends over the Atlantic basin, a center of positive Z500 anomalies over Greenland, and a weaker center of positive Z500 anomalies over the Caspian Sea (Fig. 10a). This pattern results in a strong pressure gradient between the centers of action, with the zone of maximum gradient over the northwestern part of Europe. Consequently, strong zonal winds straddling the North Atlantic and increased advection of maritime air masses characterize high-streamflow anomalies over Rhine catchment area.

Fig. 10.

As in Fig. 5, but for autumn (SON). The jet axes are denoted by thick arrows.

Fig. 10.

As in Fig. 5, but for autumn (SON). The jet axes are denoted by thick arrows.

Like in the case of spring, high-streamflow anomalies are associated with a southward shift of the Atlantic jet axis directed toward France. The catchment area of Rhine is found on the left side of the jet exit, under a cyclonic area and intense precipitation (Fig. 10b). For low-flow anomalies, the Atlantic and African jets are clearly separated (Fig. 10c) and the northwestern part of Europe is situated on the right side of the Atlantic jet exit and the left side of the African jet entrance, which favors the development of an anticyclonic area and reduced precipitation.

3) The relationship with the vertically integrated water vapor transport

Vector plots of the vertically integrated water vapor transport composites show that during the years of high autumn streamflow anomalies the axis of maximum moisture transport is directed from the Atlantic toward the northeastern part of Europe, which causes high-precipitation anomalies over this region (Fig. 11a). At the same time, an intense convergence zone develops over the North Sea and part of France and Germany (where part of the catchment area of Rhine is situated), causing intense precipitation over this region. The convergent zone associated with the cyclonic regime identified in the Z500 field suppresses evaporation and induces intense precipitation and high-flow anomalies. During low-streamflow years the moisture transport toward northwestern Europe is relatively weak. The moisture follows mainly two main directions: toward Iceland along the Atlantic jet and toward the Mediterranean region (Fig. 11b).

Fig. 11.

As in Fig. 6, but for autumn (SON).

Fig. 11.

As in Fig. 6, but for autumn (SON).

5. Discussion and conclusions

In summary, this study has investigated the relationship between large-scale atmospheric circulation patterns and Rhine River streamflow variability for spring and autumn, respectively. The research presented here contributes to a better understanding how interannual and decadal phenomenon influences the streamflow variability. We have shown that the streamflow anomalies are correlated to different climate-related patterns during the transition seasons (e.g., spring and autumn) and essential seasonality has been revealed in the temporal evolution of Rhine River streamflow variability and its links to the atmospheric circulation patterns.

Several studies have established that large-scale SST fluctuations can be linked to atmospheric circulation pat-terns that produce precipitation fluctuations and, implicitly, streamflow variability (Dai et al. 1997; Latif et al. 2000; Rajagopalan et al. 1998). Here we show that high spring flow anomalies are associated with negative SST anomalies in the central North Pacific and positive SST anomalies in the eastern and central tropical Pacific and with a tripole-like SST pattern in the Atlantic basin (Fig. 6a). This pattern is reminiscent of the typical ENSO anomaly in the tropical Pacific and the NAO SST anomaly in the Atlantic. A similar SST pattern has been found to be related to the interannual-to-decadal variability of Elbe River streamflow (Ionita et al. 2008, 2011). The physical mechanism through which the Pacific-related phenomenon (ENSO) can affect the North Atlantic region circulation is still a matter of discussion. Müller et al. (2008) found that there is a strong link between the North Atlantic SST tripole and the decadal ENSO-like variations in the Pacific, especially at periods of 10–20 yr, when NAO tends to be out of phase with ENSO. Dima et al. (2001) showed that there is an interaction between phenomenon originating in the tropical Pacific (like ENSO) and the Atlantic basin (like the Atlantic quasi-decadal mode) and the result of this interaction is a coherent mode at the global scale. Marshall et al. (2001) proposed a mechanism for the feedback effect of the tropical Atlantic on the North Atlantic circulation by rearrangement of the Hadley circulation. Although there is a considerable uncertainty regarding the influence of ENSO on the North Atlantic circulation, this Pacific–Atlantic link proves to play an important role in the variability of the river streamflow especially in the central part of Europe (Rimbu et al. 2005; Ionita et al. 2008, 2011). The large-scale connections of Rhine River variability during spring, as established in this study, could be the result of the interaction between different climatic modes like ENSO and NAO. The nonstationarity of the teleconnections of these modes (e.g., Rimbu et al. 2003; Ionita et al. 2008) could explain the nonstationarity of Rhine streamflow variability as established in our analysis using the wavelet method.

In contrast to spring, when Rhine streamflow variability presents nearly global teleconnections, during autumn the streamflow of Rhine River is mainly related to regional climatic patterns (Fig. 9a). Using EOF analysis we identified an Atlantic SST pattern [i.e., EOF4 (Fig. 9b)] that is strongly related to the interannual-to-decadal variability of Rhine River streamflow. The cross wavelet spectrum of streamflow and PC4 (Fig. 7d) shows a relatively stable periodic signal in the 30–60-yr band. This could be due to the proximity of the Rhine catchment area and the Atlantic Ocean, where this pattern take place. A similar peak has been identified in the discharge records of Loire and Vannern Gotta (Labat 2010).

The large-scale SST patterns associated with Rhine streamflow anomalies are accompanied by large-scale atmospheric circulation anomalies. We have shown that the position and intensity of Atlantic and African jet streams are two key elements related to streamflow anomalies both in spring and winter. The Atlantic jet is associated with the transport of water vapor over the western Europe with the maximum transport along the jet axis. Both in spring and autumn, during years of high streamflow, there is a shift in the Atlantic jet axis that is directed over the Rhine region, causing an increase in the water vapor transport over the region (Figs. 7b and 10b). During spring, this shift in the Atlantic jet axis also appears to be accompanied by a stronger jet and elevated moisture transport in the Atlantic Ocean (Fig. 8a). Furthermore, the southward shift in the Atlantic jet axis coupled with the position of the African jet are consistent with increased moisture convergence, low pressure anomalies, and increased precipitation in the Rhine catchment area, which leads to high streamflow. A similar mechanism is associated to high-streamflow anomalies in autumn. However, while the modification in the position and intensity of the Atlantic and African jet streams in the Atlantic European region during spring is accompanied by nearly global climatic anomalies, during autumn the connections are more regional.

The modifications in the atmospheric circulation associated to Rhine streamflow anomalies can be related to climate modes. One important climate mode in the Atlantic European region is the North Atlantic Oscillation (NAO). The European climate is strongly influenced by the NAO (Wanner et al. 2001; Trigo et al. 2002) but the influence of NAO is weaker in nonwinter seasons (Hurrell 2003). In spring and summer, climate variability over Europe is influenced by transitional climate regimes (Seneviratne et al. 2006; Fischer et al. 2007). Zveryaev (2006) found that during periods of weak links to the NAO (both in spring and autumn) precipitation variability over Europe is driven by the Scandinavian teleconnection pattern (Barnston and Livezey 1987). Also, a shift in the axis of the Atlantic jet stream and its effect on different weather systems was found to be responsible for extreme precipitation events over England and Wales (Blackburn and Hoskins 2001) as well as over the Mediterranean region (Ziv et al. 2006).

In conclusion, we have shown that the variability of Rhine River streamflow, during the transition seasons, is strongly related to large-scale atmospheric circulation patterns. At the same time, the wavelet analysis confirmed to be a useful tool in the investigation of highly nonstationary river flow. This analysis is potentially useful to connect long-term hydrological variability to climate forcings.

We have shown the existence of a strong interannual variability of Rhine River streamflow and that this variability of Rhine River flow is driven by climate interactions at a nearly global scale in spring and more regional in autumn. These findings provide a useful basis for the construction of statistical or dynamical prediction models for the evolution of Rhine River discharge, which can lead to a better water resources management in the Rhine region.

Acknowledgments

The authors are grateful to the anonymous reviewers providing thoughtful comments and suggestions. Thanks are due to the German Federal Institute of Hydrology (BfG) for supplying the datasets for the Rhine River discharge.

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