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Assessments of climate change impacts on runoff regimes are essential to climate change adaptation and mitigation planning. Changing runoff regimes and thus changing seasonal patterns of water availability strongly influence various economic sectors such as agriculture, energy production, and fishery and also affect river ecology. In this study, we use new transient hydrological scenarios driven by the most up-to-date local climate projections for Switzerland, the Swiss Climate Change Scenarios. These provide detailed information on changes in runoff regimes and their time of emergence for 93 rivers in Switzerland under three Representative Concentration Pathways (RCPs): RCP2.6, RCP4.5, and RCP8.5. These transient scenarios also allow changes to be framed as a function of global mean temperature. The new projections for seasonal runoff changes largely confirm the sign of changes in runoff from previous hydrological scenarios with increasing winter runoff and decreasing summer and autumn runoff. Spring runoff is projected to increase in high-elevation catchments and to decrease in lower-lying catchments. Despite the increases in winter and some increases in spring, the annual mean runoff is projected to decrease in most catchments. Compared to lower-lying catchments, runoff changes in high-elevation catchments (above 1500 m a.s.l.) are larger in winter, spring, and summer due to the large influence of reduced snow accumulation and earlier snowmelt and glacier melt. The changes in runoff and the agreement between climate models on the sign of change both increase with increasing global mean temperatures and higher-emission scenarios. This amplification highlights the importance of climate change mitigation. The time of emergence is the time when the climate signal emerges significantly from natural variability. Under RCP8.5, times of emergence were found early, before the period 2036–2065, in winter and summer for catchments with mean altitudes above 1500 m a.s.l. Significant changes in catchments below 1500 m a.s.l. emerge later in the century. Not all catchments show significant changes in the distribution of seasonal means; thus, no time of emergence could be determined in these catchments. Furthermore, the significant changes of seasonal mean runoff are not persistent over time in some catchments due to nonlinear changes in runoff.

Future changes in river runoff will impact many sectors such as agriculture, energy production, or ecosystems. Here, we study changes in the seasonality, frequency, and magnitude of moderate low and high flows and their time of emergence. The time of emergence indicates the timing of significant changes in the flow magnitudes. Daily runoff is simulated for 93 Swiss catchments for the period 1981–2099 under Representative Concentration Pathway 8.5 with 20 climate model chains from the most recent transient Swiss Climate Change Scenarios. In the present climate, annual low flows typically occur in the summer half-year in lower-lying catchments ( m a.s.l.) and in the winter half-year in Alpine catchments (>1500 m a.s.l.). By the end of the 21st century, annual low flows are projected to occur in late summer and early autumn in most catchments. This indicates that decreasing precipitation and increasing evapotranspiration in summer and autumn exceed the water contributions from other processes such as snowmelt and glacier melt. In lower-lying catchments, the frequency of annual low flows increases, but their magnitude decreases and becomes more severe. In Alpine catchments, annual low flows occur less often and their magnitude increases. The magnitude of seasonal low flows is projected to decrease in the summer half-year in most catchments and to increase in the winter half-year in Alpine catchments. Early time of emergence is found for annual low flows in Alpine catchments in the 21st century due to early changes in low flows in the winter half-year. In lower-lying catchments, significant changes in low flows emerge later in the century. Annual high flows occur today in lower-lying catchments in the winter half-year and in Alpine catchments in the summer half-year. Climate change will change this seasonality mainly in Alpine catchments with a shift towards earlier seasonality in summer due to the reduced contribution of snowmelt and glacier melt in summer. Annual high flows tend to occur more frequent, and their magnitude increases in most catchments except some Alpine catchments. The magnitude of seasonal high flows in most catchments is projected to increase in the winter half-year and to decrease in the summer half-year. However, the climate model agreement on the sign of change in moderate high flows is weak.

Heavy precipitation on the south side of the central Alps produced a catastrophic flood in October 1868. We assess the damage and societal impacts, as well as the atmospheric and hydrological drivers using documentary evidence, observations and novel numerical weather and runoff simulations.The greatest damage was concentrated close to the Alpine divide and Lago Maggiore. An atmospheric reanalysis emphasizes the repeated occurrence of streamers of high potential vorticity as precursors of heavy precipitation. Dynamical downscaling indicates high freezing levels (4000 m a.s.l.), extreme precipitation rates (max. 270 mm 24 h−1) and weather dynamics that agree well with observed precipitation and damage, and with existing concepts of forced low-level convergence, mid-level uplift and iterative northeastward propagation of convective cells. Simulated and observed peak levels of Lago Maggiore differ by 2 m, possibly because the exact cross section of the lake outflow is unknown. The extreme response of Lago Maggiore cannot be attributed to low forest cover. Nevertheless, such a paradigm was adopted by policy makers following the 1868 flood, and used to implement nationwide afforestation policies and hydraulic structures.These findings illustrate the potential of high-resolution, hydrometeorological models – strongly supported by historical methods – to shed new light on weather events and their socio-economic implications in the 19th century.

Model output statistics (MOS) methods can be used to empirically relate an environmental variable of interest to predictions from earth system models (ESMs). This variable often belongs to a spatial scale not resolved by the ESM. Here, using the linear model fitted by least squares, we regress monthly mean streamflow of the Rhine River at Lobith and Basel against seasonal predictions of precipitation, surface air temperature, and runoff from the European Centre for Medium-Range Weather Forecasts. To address potential effects of a scale mismatch between the ESM's horizontal grid resolution and the hydrological application, the MOS method is further tested with an experiment conducted at the subcatchment scale. This experiment applies the MOS method to 133 additional gauging stations located within the Rhine basin and combines the forecasts from the subcatchments to predict streamflow at Lobith and Basel. In doing so, the MOS method is tested for catchments areas covering 4 orders of magnitude. Using data from the period 1981–2011, the results show that skill, with respect to climatology, is restricted on average to the first month ahead. This result holds for both the predictor combination that mimics the initial conditions and the predictor combinations that additionally include the dynamical seasonal predictions. The latter, however, reduce the mean absolute error of the former in the range of 5 to 12 %, which is consistently reproduced at the subcatchment scale. An additional experiment conducted for 5-day mean streamflow indicates that the dynamical predictions help to reduce uncertainties up to about 20 days ahead, but it also reveals some shortcomings of the present MOS method.

A rain-on-snow flood occurred in the Bernese Alps, Switzerland, on 10 October 2011, and caused significant damage. As the flood peak was unpredicted by the flood forecast system, questions were raised concerning the causes and the predictability of the event. Here, we aimed to reconstruct the anatomy of this rain-on-snow flood in the Lötschen Valley (160 km2) by analyzing meteorological data from the synoptic to the local scale and by reproducing the flood peak with the hydrological model WaSiM-ETH (Water Flow and Balance Simulation Model). This in order to gain process understanding and to evaluate the predictability. The atmospheric drivers of this rain-on-snow flood were (i) sustained snowfall followed by (ii) the passage of an atmospheric river bringing warm and moist air towards the Alps. As a result, intensive rainfall (average of 100 mm day-1) was accompanied by a temperature increase that shifted the 0° line from 1500 to 3200 m a.s.l. (meters above sea level) in 24 h with a maximum increase of 9 K in 9 h. The south-facing slope of the valley received significantly more precipitation than the north-facing slope, leading to flooding only in tributaries along the south-facing slope. We hypothesized that the reason for this very local rainfall distribution was a cavity circulation combined with a seeder-feeder-cloud system enhancing local rainfall and snowmelt along the south-facing slope. By applying and considerably recalibrating the standard hydrological model setup, we proved that both latent and sensible heat fluxes were needed to reconstruct the snow cover dynamic, and that locally high-precipitation sums (160 mm in 12 h) were required to produce the estimated flood peak. However, to reproduce the rapid runoff responses during the event, we conceptually represent likely lateral flow dynamics within the snow cover causing the model to react "oversensitively" to meltwater. Driving the optimized model with COSMO (Consortium for Small-scale Modeling)-2 forecast data, we still failed to simulate the flood because COSMO-2 forecast data underestimated both the local precipitation peak and the temperature increase. Thus we conclude that this rain-on-snow flood was, in general, predictable, but requires a special hydrological model setup and extensive and locally precise meteorological input data. Although, this data quality may not be achieved with forecast data, an additional model with a specific rain-on-snow configuration can provide useful information when rain-on-snow events are likely to occur.