[1] Runoff generation in Alpine regions is typically affected by snow processes. Snow accumulation, storage, redistribution, and ablation control the availability of water. In this study, several robust parameterizations describing snow processes in Alpine environments were implemented in a fully distributed, physically based hydrological model. Snow cover development is simulated using different methods from a simple temperature index approach, followed by an energy balance scheme, to additionally accounting for gravitational and wind‐driven lateral snow redistribution. Test site for the study is the Berchtesgaden National Park (Bavarian Alps, Germany) which is characterized by extreme topography and climate conditions. The performance of the model system in reproducing snow cover dynamics and resulting discharge generation is analyzed and validated via measurements of snow water equivalent and snow depth, satellite‐based remote sensing data, and runoff gauge data. Model efficiency (the Nash‐Sutcliffe coefficient) for simulated runoff increases from 0.57 to 0.68 in a high Alpine headwater catchment and from 0.62 to 0.64 in total with increasing snow model complexity. In particular, the results show that the introduction of the energy balance scheme reproduces daily fluctuations in the snowmelt rates that trace down to the channel stream. These daily cycles measured in snowmelt and resulting runoff rates could not be reproduced by using the temperature index approach. In addition, accounting for lateral snow transport changes the seasonal distribution of modeled snowmelt amounts, which leads to a higher accuracy in modeling runoff characteristics.

Snow interception in a coniferous forest canopy is an important hydrological feature, producing complex mass and energy exchanges with the surrounding atmosphere and the snowpack below. Subcanopy snowpack accumulation and ablation depends on the effects of canopy architecture on meteorological conditions and on interception storage by stems, branches, and needles. Mountain forests are primarily composed of evergreen conifer species that retain their needles throughout the year and hence intercept snow efficiently during winter. Canopy-intercepted snow can melt, fall to the ground, and/or sublimate into the air masses above and within the canopy. To improve the understanding of snow–canopy interception processes and the associated influences on the snowpack below, a series of model experiments using a detailed, physically based snow–canopy and snowpack evolution model [Alpine Multiscale Numerical Distributed Simulation Engine (AMUNDSEN)] driven with observed meteorological forcing was conducted. A cone-shaped idealized mountain covered with a geometrically regular pattern of coniferous forest stands and clearings was constructed. The model was applied for three winter seasons with different snowfall intensities and distributions. Results show the effects of snow–canopy processes and interactions on the pattern of ground snow cover, its duration, and the amount of meltwater release, in addition to showing under what conditions the protective effect of a forest canopy overbalances the reduced accumulation of snow on the ground. The simulations show considerable amounts of canopy-intercepted snowfall can sublimate, leading to reduced snow accumulation beneath the forest canopy. In addition, the canopy produces a shadowing effect beneath the trees that leads to reduced radiative energy reaching the ground, reduced below-canopy snowmelt rates, and increased snow-cover duration relative to nonforested areas. During snow-rich winters, the shadowing effect of the canopy dominates and snow lasts longer inside the forest than in the open, but during winters with little snow, snow sublimation losses dominate and snow lasts longer in the open areas than inside the forest. Because of the strong solar radiation influence on snowmelt rates, the details of these relationships vary for northern and southern radiation exposures and time of year. In early and high winter, the radiation protection effect of shadowing by the canopy is small. If little snow is available, an intermittent melt out of the snow cover inside the forest can occur. In late winter and spring, the shadowing effect becomes more efficient and snowmelt is delayed relative to nonforested areas.

Mountain regions with complex orography are a particular challenge for regional climate simulations. High spatial resolution is required to account for the high spatial variability in meteorological conditions. This study presents a very high-resolution regional climate simulation (5 km) using the Weather Research and Forecasting Model (WRF) for the central part of Europe including the Alps. Global boundaries are dynamically downscaled for the historical period 1980–2009 (ERA-Interim and MPI-ESM), and for the near future period 2020–2049 (MPI-ESM, scenario RCP4.5). Model results are compared to gridded observation datasets and to data from a dense meteorological station network in the Berchtesgaden Alps (Germany). Averaged for the Alps, the mean bias in temperature is about −0.3 °C, whereas precipitation is overestimated by +14% to +19%. R ${}^2$ values for hourly, daily and monthly temperature range between 0.71 and 0.99. Temporal precipitation dynamics are well reproduced at daily and monthly scales (R ${}^2$ between 0.36 and 0.85), but are not well captured at hourly scale. The spatial patterns, seasonal distributions, and elevation-dependencies of the climate change signals are investigated. Mean warming in Central Europe exhibits a temperature increase between 0.44 °C and 1.59 °C and is strongest in winter and spring. An elevation-dependent warming is found for different specific regions and seasons, but is absent in others. Annual precipitation changes between −4% and +25% in Central Europe. The change signals for humidity, wind speed, and incoming short-wave radiation are small, but they show distinct spatial and elevation-dependent patterns. On large-scale spatial and temporal averages, the presented 5 km RCM setup has in general similar biases as EURO-CORDEX simulations, but it shows very good model performance at the regional and local scale for daily meteorology, and, apart from wind-speed and precipitation, even for hourly values.

Seasonal snow cover is an important temporary water storage in high-elevation regions. Especially in remote areas, the available data are often insufficient to accurately quantify snowmelt contributions to streamflow. The limited knowledge about the spatiotemporal variability of the snowmelt isotopic composition, as well as pronounced spatial variation in snowmelt rates, leads to high uncertainties in applying the isotope-based hydrograph separation method. The stable isotopic signatures of snowmelt water samples collected during two spring 2014 snowmelt events at a north- and a south-facing slope were volume weighted with snowmelt rates derived from a distributed physics-based snow model in order to transfer the measured plot-scale isotopic composition of snowmelt to the catchment scale. The observed δ¹⁸O values and modeled snowmelt rates showed distinct inter- and intra-event variations, as well as marked differences between north- and south-facing slopes. Accounting for these differences, two-component isotopic hydrograph separation revealed snowmelt contributions to streamflow of 35 ± 3 and 75 ± 14 % for the early and peak melt season, respectively. These values differed from those determined by formerly used weighting methods (e.g., using observed plot-scale melt rates) or considering either the north- or south-facing slope by up to 5 and 15 %, respectively.

The mass balance of very small glaciers is often governed by anomalous snow accumulation, winter precipitation being multiplied by snow redistribution processes (gravitationally or wind driven), or suppressed snow ablation driven by micrometeorological effects lowering net radiation and/or turbulent heat exchange. In this case study, we analysed the relative contribution of snow accumulation and ablation processes governing the long- and short-term mass balance of the lowest perennial ice field of the Alps, the Ice Chapel, located at 870 m a.s.l. in the Berchtesgaden National Park (Germany). This study emphasizes the importance of the local topographic setting for the survival of a perennial ice field located far below the climatic snow line. Although long-term mass balance measurements of the ice field surface showed a dramatic mass loss between 1973 and 2014, the ice field mass balance was rather stable between 2014 and 2017 and even showed a strong mass gain in 2017/2018 with an increase in surface height by 50 %–100 % relative to the ice field thickness. Measurements suggest that the winter mass balance clearly dominated the annual mass balance. At the Ice Chapel surface, 92 % of snow accumulation was gained by snow avalanching, thus clearly governing the 2017/2018 winter mass balance of the ice field with mean snow depths of 32 m at the end of the accumulation period. Avalanche deposition was amplified by preferential deposition of snowfall in the wind-sheltered rock face surrounding the ice field. Detailed micrometeorological measurements combined with a numerical analysis of the small-scale near-surface atmospheric flow field identified the micrometeorological processes driving the energy balance of the ice field. Measurements revealed a katabatic flow system draining down the ice field throughout the day, showing strong temporal and spatial dynamics. The spatial origin of the thermal flow system was shown to be of particular importance for the ice field surface energy balance. Numerical simulation indicates that deep katabatic flows, which developed at higher-elevation shaded areas of the rock face and drained down the ice field, enhance sensible heat exchange towards the ice field surface by enhancing turbulence close to the ice surface. Conversely, the shallow katabatic flow developing at the ice field surface appeared to laterally decouple the local near-surface atmosphere from the warmer adjacent air suppressing heat exchange. Numerical results thus suggest that shallow katabatic flows driven by the cooling effect of the ice field surface are especially efficient in lowering the climatic sensitivity of the ice field to the surrounding rising air temperatures. Such micrometeorological phenomena must be taken into account when calculating mass and energy balances of very small glaciers or perennial ice fields at elevations far below the climatic snow line.

The West African monsoon rainfall is essential for regional food production, and decadal predictions are necessary for policy makers and farmers. However, predictions with global climate models reveal precipitation biases. This study addresses the hypotheses that global prediction biases can be reduced by dynamical downscaling with a multimodel ensemble of three regional climate models (RCMs), a RCM coupled to a global ocean model and a RCM applying more realistic soil initialization and boundary conditions, i.e., aerosols, sea surface temperatures (SSTs), vegetation, and land cover. Numerous RCM predictions have been performed with REMO, COSMO‐CLM (CCLM), and Weather Research and Forecasting (WRF) in various versions and for different decades. Global predictions reveal typical positive and negative biases over the Guinea Coast and the Sahel, respectively, related to a southward shifted Intertropical Convergence Zone (ITCZ) and a positive tropical Atlantic SST bias. These rainfall biases are reduced by some regional predictions in the Sahel but aggravated by all RCMs over the Guinea Coast, resulting from the inherited SST bias, increased westerlies and evaporation over the tropical Atlantic and shifted African easterly waves. The coupled regional predictions simulate high‐resolution atmosphere‐ocean interactions strongly improving the SST bias, the ITCZ shift and the Guinea Coast and Central Sahel precipitation biases. Some added values in rainfall bias are found for more realistic SST and land cover boundary conditions over the Guinea Coast and improved vegetation in the Central Sahel. Thus, the ability of RCMs and improved boundary conditions to reduce rainfall biases for climate impact research depends on the considered West African region.