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.

Abstract. 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 physicsbased snow model in order to transfer the measured plotscale isotopic composition of snowmelt to the catchment scale. The observed δ 18 O 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.

[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.

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.