4.2 Temperature and discharge trends from linear regression The trends in stream temperature and discharge have been computed using linear regression over the 1999–2018 period for all 52 catchments and over the 1979–2018 period when possible. All trend values are presented in the Appendix in Tables A1 and A2 for water temperature and discharge, and in Tables S4 and S5 for air temperature and precipitation. The plots shown in this section are for the 1999–2018 period, where more catchments are available. Similar plots for the 1979–2018 period are shown in Sect. S2.1. Note that results presented in this section, except for the trends in runoff over the last few decades, also hold for the longer time period, and the results are even more evident over this longer time period. This can be explained by the lower sensitivity to boundary effects and the generally higher robustness of linear regressions over longer time periods. Trends in stream temperature and discharge are compared to trends in air temperature and precipitation in Fig. 5. There is a clear increase in water temperature and a reduction in discharge observed in Swiss rivers over the 1999–2018 period. The mean trends for the last 20 years are + 0.37 ± 0.11 ∘C per decade for water temperature (with a large spread in the distribution), + 0.39 ± 0.14 ∘C per decade for air temperature, - 10.1 ± 4.6 % per decade for discharge, and - 9.3 ± 3.4 % per decade for precipitation. However, the trends in precipitation and runoff have to be considered with caution regarding the long-term variation discussed above. For the 1979–2018 period, the mean trends are as follows: + 0.33 ± 0.03 ∘C per decade for water temperature (again with a large spread in the distribution), +0.46 ∘C ± 0.03 ∘C per decade for air temperature, - 3.0 ± 0.5 % per decade for discharge, and - 1.3 ± 0.5 % per decade for precipitation. Download Download The water temperature and discharge trends for the four different regimes are shown in Fig. 6. Similar plots for air temperature and precipitation are shown in Fig. S14. A two-sided Wilcoxon test is used to assess whether differences between regimes are significant in terms of temperature trends (results shown in Table S3). As some categories only have a few observations and normal distribution can not be assumed, this test is used instead of a t test. Two groups can clearly be identified: the downstream lakes (DLA) regime and the Swiss Plateau/Jura (SPJ) regime on the one hand, and the alpine (ALP) and hydro-peaking-influenced (HYP) regimes on the other hand. Indeed, for both pairs of regimes, the hypothesis of different mean values is clearly rejected with p values > 0.15 (i.e. testing the hypothesis of a different mean between SPJ and DLA and between HYP and ALP). The water temperature trends are significantly lower for alpine catchments and catchments strongly influenced by hydro-peaking. The impact of lakes is discussed in Sect. 4.3. The catchment area is not correlated with trend values (see Fig. 6) despite the fact that area is clearly correlated with the regime (Table 1). To infer the isolated effect of area, only catchments from the Swiss Plateau/Jura regime are used (largest sample of rivers and no major disturbance), but no correlation between water temperature or discharge trends and area can be found (see Fig. S19). Download Elevation and the fraction of glacier coverage in the catchment (which are strongly correlated) clearly correlate with water temperature and discharge trends (see Fig. 6, bottom row). The smaller positive trends in water temperature and reduced negative trends in discharge observed for highly glaciated catchments can be attributed to cold water coming from glacier melt (as discussed in Williamson et al., 2019), as air temperature trends for alpine catchments are similar to lowland catchments (see Fig. S14). For these reasons, discharge and temperature of alpine streams have been the least impacted by climate change to date. However, if this buffer effect induced by glaciers and seasonal snow cover disappears due to the continuation of temperature rise in the future (Bavay et al., 2013; Huss et al., 2014; MeteoSuisse et al., 2018), the alpine catchments will be amply impacted (see Sect. 4.4.4). Lowland catchments, mostly located in the Swiss Plateau/Jura regions, experience the most important decrease in discharge. Unsurprisingly, rivers strongly influenced by hydro-peaking show lower temperature trends compared with undisturbed waterways. This results from large volumes of cold water being released from reservoirs located at high elevation to lowland rivers as discussed, for instance, in Feng et al. (2018). In conclusion, for Swiss Plateau/Jura catchments, air and water temperature trend distributions are similar, and the mean of the trends for this type of catchment is close to the mean air temperature trend (see Figs. 6 and S14). Figures S20 and S21 show water temperature trends for each catchment plotted against trends in air temperature for the 1999–2018 and 1979–2018 periods. Single values (i.e. water and air temperature trends for a given catchment) are poorly correlated. Over the 1979–2018 time period, a better correlation for DLA and SPJ catchments is visible, suggesting that part of the poor correlation in Fig. S20 is due to the noise in the trends obtained with a linear model (boundary effects). For ALP and HYP catchments, the general poor correlation suggests that additional factors, such as snow and glacier melt and anthropogenic disturbances, become predominant in the energy balance, decoupling the mean air temperature and water temperature trends.

4.3 Effect of lakes In the previous section, it was shown that rivers located downstream of lakes have water temperature trends similar to the Swiss Plateau/Jura catchments, in spite of a higher mean elevation and a larger glacier-covered fraction (see Table 1), which typically attenuate the water temperature increase. The effect of lakes located at the foot of mountain ranges on stream temperature is well known (Webb and Nobilis, 2007; Råman Vinnå et al., 2018). The input water originates from alpine rivers (potentially disturbed by hydro-peaking), which are colder than the surrounding environment and not in equilibrium with local air temperature. As water has a certain residence time in the lake, its temperature increases due to atmospheric forcing and the main driver for the outflow water temperature is the air temperature. However, it has currently not been demonstrated if the effect of lakes on river temperature trends is similar. In Schmid and Köster (2016), it is shown that lake temperature trends can exceed air temperature trends due to solar brightening. To investigate the effect of lakes on water temperature trends, five lake systems with measurements at the inflow and the outlet are selected: the Thun–Brienz lake system, Lake Biel, Lake Luzern, Lake Walen, and Lake Geneva. Temperature anomalies with respect to the 1979–2018 period and trends are plotted for water temperature at each station and air temperature at meteorological stations representative of the catchment. The results are shown in Fig. 7 for Lake Geneva and in Figs. S22 to S25 for the other four lakes. The trends for the different inflow and outflow rivers and for air temperature are presented in Table 2. Download Print Version | Download XLSX For Lake Walen and Lake Geneva, the effect is obvious: the outlet trend is almost equal to the co-located air temperature trend. Even if trends in inflows are much smaller, they do not significantly influence the outlet waters (see Table 2). The lake acts as catalyst and the system reaches a quasi-equilibrium. For Lake Geneva, the water temperature of the Arve River is also shown. The Arve River originates from the Mont Blanc massif (France) and flows for about 100 km through the Arve Valley before joining the Rhône in Geneva. Despite flowing through low-lying land, the Arve keeps its alpine characteristics, whereas these characteristics are completely lost in the Rhône River after the lake. In Lake Luzern, a similar effect is observed. Indeed, the three rivers feeding the lake (the Reuss, Muota, and Engelberger Aa rivers) show trends that are considerably lower than that for the Reuss River in Luzern (see Table 2). However, the Kleine-Emme River, which joins the Reuss just after Luzern, shows a similar trend without the presence of a lake along its course; this demonstrates that, for a mid-elevation stream, flowing a certain distance on the Swiss Plateau leads to a similar effect to that induced by lakes. For the Thun–Brienz lake system, the water temperature trend is enhanced as a result of the two subsequent lakes and it gets closer to the air temperature trend. For Lake Biel, no effect is observed. This is not surprising as the Aare input water already has a trend similar to the local air temperature. In addition, the residence time in Lake Biel is very short (58 d, whereas for the five other lakes it ranges from 520 to 4160 d; Bouffard and Dami, 2019), limiting the exposure time of lake waters to atmospheric forcing. This has been described in more detail in Råman Vinnå et al. (2017). In conclusion, despite their higher mean catchment elevation, water temperature trends for stations at lake outlets are similar to Swiss Plateau trends. As lakes have much longer residence times for water than rivers, they smooth out local effects such as snow or glacier melt or precipitation. As a consequence, water temperature trends at the outlet of lakes are generally similar to air temperature trends, which seem to be the main forcing.

4.4 Seasonal trends and relation with air temperature and precipitation In this section, stream temperature and discharge trends and anomalies are analysed at a seasonal scale. The relation between these two variables and the meteorological conditions (air temperature and precipitation) are also discussed on a seasonal basis. Particular seasonal features are then addressed. Finally, the evolution of the intra-annual variability along with the inter-seasonal correlation, or system memory, are discussed. Even if the inter-variable correlation and system memory are not directly linked to observed changes, they are key factors with respect to understanding the system dynamics and, thus, are essential for inferring the impacts of climate change on water temperature and discharge. The analysis below is mostly based on the 1999–2018 period. Seasons are defined as follows: winter is December–January–February (DJF), spring is March–April–May (MAM), summer is June–July–August (JJA), and autumn is September–October–November (SON). Long-term evolution of the seasonal anomalies are shown in Figs. 8 and 9 for water temperature (decades from 1970 to 2010) and discharge (decades from 1960 to 2010). Air temperature and precipitation are shown in Figs. S26 and S27 and exhibit similar behaviour. For all seasons, the water temperature has been significantly rising since 1980. The warming is more important in summer and less pronounced in winter. For discharge, spring and autumn do not show an obvious trend in the long term. There is a clear decrease in summer since 1980, whereas winter shows a slight increase. Download Download Annual and seasonal trends for stream and air temperature, discharge, and precipitation are presented in Fig. 10 for the 1999–2018 period. They confirm the tendencies described above. Mean water temperature trends are slightly smaller than air temperature trends for all seasons except for spring, when they are notably larger. This shows that rivers do not react linearly to a general warming of the atmosphere and additional factors control these complex systems. For discharge, negative trends are found in all seasons except for winter, when they are almost zero. Discharge trends follow precipitation trends in all seasons. In general, precipitation determines the discharge trend; consequently, snow and glacier melt play a minor role in the observed trends. However, for specific catchments, this can be different. When looking at individual catchments, there is only a insignificant correlation between trends in air and water temperature, and between trends in discharge and precipitation (see Table S6). This absence of a correlation results from the noise in the individual trend values due to the short time period available. This is a limitation of the method applied and, thus, trends can not be used for an inter-variable interaction study. Download To explore the correlation between variables, raw values are used. Table 3 shows the correlation between main variables on a yearly and seasonal basis. These values are obtained by computing the correlation of two variables for individual catchments and then averaging these correlations. As a measure of the robustness of the method, the number of catchments where correlations are insignificant (p value > 0.05) is indicated. At an annual scale, air temperature is the main driver of water temperature. The negative correlation between water temperature and discharge is rather weak and is not significant in almost half of the catchments. As expected, discharge and precipitation are strongly correlated. Download Print Version | Download XLSX 4.4.1 Winter The water temperature trends in winter are the lowest of the four seasons and the discharge exhibits a slight positive trend, as opposed to the negative discharge trend in all other seasons (see Fig. 10). The positive trend in winter discharge is mainly driven by the increase in winter precipitation. This is the season where the precipitation and discharge trends are the closest, and the correlation between precipitation and discharge is strong and significant (see Table 3). There is a weak positive correlation between winter discharge and winter water temperature. Even though this correlation is not significant in the majority of the catchments, it indicates a different behaviour compared with spring and summer. An explanation for this could be that increased water input during winter causes a push of relatively warm groundwater. Thus, catchments with increased winter discharge would have a more pronounced temperature trend. In contrast, some catchments show negative water temperature and discharge trends in winter (see Appendix Table A1). In this case, the lower discharge favours a more pronounced water cooling via heat exchange, and this effect might compensate for and even overcome the air temperature trend. Both of these effects would lead to a positive correlation. The annual anomalies in winter water and air temperature, discharge, and precipitation are presented in Fig. S28. 4.4.2 Spring In spring water temperature trends are more pronounced than air temperature trends (see Fig. 10). Looking at individual catchments indicates that those most affected are mainly low-lying, non-glacierized SPJ catchments (see Appendix Table A1). These catchments experience the most significant discharge decrease in spring, probably due to an earlier snow melt period, which possibly explains their higher sensitivity to air temperature. Indeed, snow melt releases cold water that acts as a buffer and reduces the sensitivity to air temperature (Williamson et al., 2019). Figure S29 shows the yearly anomalies in spring. The air temperature remains the main driver; however, high discharge (e.g. 1999 or 2006) or low discharge (e.g. comparing 2013 and 2015) conditions also have a clear anti-correlated impact on water temperature. This can be seen in the negative correlation between air temperature and discharge in spring (Table 3). Download A likely impact of climate change is an earlier and shorter snow melt season. Figure S32 shows the evolution of snow melt in terms of snow water equivalent (SWE) in spring over the last 20 years for Switzerland. There is no clear long-term trend in the total spring melt and, therefore, no contribution to the discharge trend on a seasonal basis (this does not exclude a shift in runoff timing due to earlier snow melt in spring). However, snow melt remains a key factor for spring discharge. For example, in 1999, 2009, 2012, and 2018, precipitation deficits are well compensated for by the above-average snow melt, whereas in 2002 and 2007, the opposite effect is observed. Such discharge variations have a direct impact on water temperature. 4.4.3 Summer, extremes, and autumn Summer exhibits the strongest positive water temperature trends and negative discharge trends, both in the past 20 and 40 years (see Fig. 10 and Appendix Tables A1 and A2). It also has the weakest correlation between water and air temperature and the strongest negative correlation between water temperature and discharge (see Table 3), suggesting that summer is the season when water temperature is the most sensitive to discharge. Moreover, correlation between precipitation and runoff is lowest in summer. This is likely due to the role of evapotranspiration in summer and the variability of the remaining snow at the beginning of summer (see Fig. S31). There is a strong link between extremes in summer air temperature (2003, 2015, and 2018) and extreme summer stream temperature (see Fig. 11), coinciding with a deficit in precipitation and in discharge. A positive air temperature anomaly in summer is generally associated with dry conditions in Switzerland (Fischer et al., 2007a, b). Sometimes, a below-average air temperature but an above-average water temperature is observed, e.g. in summer 2011. This is attributed to the lack of precipitation and the resulting runoff deficit. Therefore, while precipitation deficit favours and enhances summer heat waves, it also has a direct impact on the summer stream temperature. The years 2013 and 2016 show a negative water temperature anomaly, whereas the air temperature is close to the mean. This is likely due to the above-average precipitation and runoff for these years. Therefore, the water temperature to discharge and precipitation negative correlation holds for both high and low values. Summer snow melt, approximated by the amount of snow remaining at the beginning of June, shown in Fig. S21, has an impact on summer stream conditions. Indeed, for high summer snow melt, (e.g. 1999 and 2013) the runoff anomaly is positive and stronger than the precipitation anomaly. The opposite effect is seen in 2005 or 2011: the snow melt is low in summer, with a direct impact on stream temperature. The anomalies in autumn are presented in Fig. S30. Discharge has a very low impact during this season. As air temperature is the main driver, the interannual variability in autumn is lower for water temperature than for air temperature. Download 4.4.4 The case of alpine catchments The analysis in the previous sections did not considered the hydrological regime. However, alpine catchments show a particular behaviour. Over the last 2 decades, higher-elevation catchments have exhibited lower stream temperature trends and less pronounced discharge decreases than lowland rivers (see Fig. 6 and S18). In addition, water temperature trends are notably less important than air temperature trends. Similar behaviour has also been observed in North America (Isaak et al., 2016). In winter, the air temperature trend is higher in the mountains than for the rest of the country, while the water temperature trend is smaller, showing the impact of enhanced snow melt induced by higher air temperatures, and thus cold water advection in rivers, as discussed in Sect. 4.4.1. The same effect is seen in spring. In summer, the temperature trend is mainly driven by the local air temperature trend, which is lower than the median of the whole country, leading to a lower warming in alpine rivers than in lowland waterways (see the top two rows of Fig. S35). Alpine catchments are more preserved from extreme summer temperatures than other catchments (see years 2003, 2015, 2017, and 2018 in Fig. S35, bottom two rows). Despite an important positive anomaly in air temperature, the water temperature anomaly is considerably lower and below the median of catchments of other regimes. This resilience is attributed to many factors impacting alpine river temperatures such as geology, topography, or permafrost (Küry et al., 2017) and, in the case of the extreme 2003 heat wave, additional cold water released from glacier and snow melt during summer (Piccolroaz et al., 2018). This is confirmed by the positive or weakly negative runoff anomaly over this year for alpine catchments, whereas the Swiss median anomaly in discharge is negative and the precipitation anomaly is clearly negative too (see Fig. S35, bottom two rows). In addition, a peak in glacier melting in 2003 is visible in the glacier mass balance of the GLAMOS (Glacier Monitoring Switzerland) records (see Fig. S33). While this low sensitivity is obvious for 2003, when alpine catchments were almost not affected, the sensitivity seems more pronounced in 2015, 2017, and 2018. For these 3 years, the water contribution from glacier melt is lower, as shown by the mass balance glacier record (see Fig. S33) and by the fact that discharge anomalies for these years are closer to the mean of all catchments. Some catchments, e.g. the Lütschine in Gsteig, indicate that the way alpine streams react to summer air temperature and heat waves seems to change. This change is most probably induced by climate change. Note however, that the way alpine rivers respond to heat waves is a recent and not fully explored topic (Piccolroaz et al., 2018). Download In the long term, a shift of the thermal and hydrological regimes of alpine catchments is evident. For example, Fig. 12, obtained by averaging each day of the year (DOY) over an entire decade, shows a clear flattening of the discharge curve over the last 50 years for the Lonza River (glacier surface: 24.7 %). Instead of a peak in the second half of the summer, the last 2 decades show a flatter discharge with a maximum at the end of June. In addition, the entire discharge distribution is shifted towards the beginning of the year, leading to an increase in spring and a decrease in late summer and autumn. There is a clear increase in water temperature, especially between mid-spring and mid-autumn, which is stronger in the middle of the summer, leading to a wider temperature range throughout the summer. This shift in hydrological regime and general warming significantly changes the evolution of the water temperature versus discharge hysteresis curve. While in the 1970s, the amplitude of hysteresis was rather limited (i.e. low sensitivity to summer air temperature), it becomes much wider over the last few decades as a result of lower peak discharge and a higher water temperature. This is an additional evidence that alpine rivers are becoming more sensitive to climate change, and will potentially react in a strongly non-linear way in the future. Similar plots for the Arve in Geneva and the Lütschine in Gsteig are shown in Figs. S36 and S37, respectively (time series from the last two alpine catchments are too short to produce such plots). 4.4.5 Intra-annual water temperature variability, inter-seasonal correlation, and system memory With the summer water temperature trend being stronger than the winter trend, the intra-annual variability, i.e. the summer to winter temperature difference, is expected to increase over time. The topic of the variability of air temperature under climate change is still an open discussion (Vincze et al., 2017). Figure S34 shows the annual difference between summer and winter means for all catchments with data since at least 1980. There is a clear evolution of the intra-annual variability: the computed trend indicates an increase of 0.3±0.1 ∘C per decade, which corresponds to a change of +1.2 ∘C over the studied period and represents an increase of 10 % to 20 % of the variability for individual catchments. The evolution of the summer to winter difference induced by the different seasonal warming rates is thus not negligible and must be considered when assessing the impact of climate change on ecosystems, which will have to cope with warmer conditions but also with an increased variability. It is well known that the 2003 summer heat wave in Europe was enhanced by a long dry spell due to a precipitation deficit in late spring and early summer (Fischer et al., 2007b). The current data set allows for the assessment of whether such robust seasonal connections exist with stream temperature and discharge. The seasonal relation can be studied by comparing Figs. 11, S28, S29, and S30. In addition, the correlations between water temperature and water temperature from previous seasons, between discharge and precipitation from previous seasons, and between water temperature and precipitation from previous seasons are shown in Table S7. For water temperature, there is almost no correlation and calculated values are mostly not significant. There is also no strong correlation between precipitation and discharge more than one season apart. The correlation with the next season is weak and only significant for a few catchments, showing that the groundwater storage plays an important buffer role (see Sect. S2.3 for an extended discussion). Despite this lack of strong correlations in the long term, connections exist for some individual years. A negative relation between spring discharge and summer temperature exists (e.g. 2003 and 2017; see Fig. 11 and S29). However, 2004, 2005, and 2011 have an important precipitation deficit in spring, without any noticeable above-average water temperature in summer, meaning that a spring precipitation deficit can contribute to a positive summer stream temperature anomaly, but the summer conditions (air temperature and precipitation) remain the main controlling factors and can cancel the spring effect. In autumn, impacts of extreme summers such as 2003 or 2018 are no longer noticeable in the mean stream temperature (see Figs. 11 and S30). In summary, no strong memory patterns could be identified in the hydrological system. While it might be important for more complex systems (e.g. the land–atmosphere interaction), the antecedent state of the system is not really relevant for the catchments studied.