Understanding the role of forests on snowmelt processes enables better estimates of snow storages at a catchment scale and contributes to a higher accuracy of spring flood forecasting. A coniferous forest modifies the snowpack energy balance by reducing the total amount of solar shortwave radiation (SWR) and enhancing the role of longwave radiation (LWR) emitted by trees. This study focuses on changes in SWR and LWR at three sites with different canopy structure (Bohemian Forest, Czechia), including one site affected by the bark beetle (Ips typographus). Measurements of incoming and outgoing SWR and LWR were performed at all sites equipped with CNR4 Net Radiometers for three cold seasons. In addition to SWR and LWR, sensible and latent heat, and ground heat and energy supplied by liquid precipitation were calculated. The results showed that net SWR at the healthy forest site represented only 7% of the amount at the open site due to the shading effect of trees. In contrast, net LWR represented a positive component of the snowpack energy balance at the healthy forest site and thus contributed the most to snowmelt. However, the modelled snowmelt rates were significantly lower in the forest than in the open area since the higher LWR in the forest did not compensated for the lower SWR. The progressive decay of disturbed forest caused the decrease in mean net LWR from −3.1 W/ m 2 to −12.9 W/m 2 and the increase in mean net SWR from 31.6 W/m 2 to 96.2 W/ m 2 during the study period. These changes caused an increase in modelled snowmelt rates by 50% in the disturbed forest, compared to the healthy forest site, during the study period. Our findings have important implications for runoff from areas affected by land cover changes due to either human activity or climate change.

The knowledge of water volume stored in the snowpack and its spatial distribution is important to predict the snowmelt runoff. The objective of this study was to quantify the role of different forest types on the snowpack distribution at a plot scale during snow accumulation and snow ablation periods. Special interest was put in the role of the forest affected by the bark beetle (Ips typographus). We performed repeated detailed manual field survey at selected mountain plots with different canopy structure located at the same elevation and without influence of topography and wind on the snow distribution. A snow accumulation and ablation model was set up to simulate the snow water equivalent (SWE) in plots with different vegetation cover. The model was based on degree-day approach and accounts for snow interception in different forest types. The measured SWE in the plot with healthy forest was on average by 41% lower than in open area during snow accumulation period. The disturbed forest caused the SWE reduction by 22% compared to open area indicating increasing snow storage after forest defoliation. The snow ablation in healthy forest was by 32% slower compared to open area. On the contrary, the snow ablation in disturbed forest (due to the bark beetle) was on average only by 7% slower than in open area. The relative decrease in incoming solar radiation in the forest compared to open area was much bigger compared to the relative decrease in snowmelt rates. This indicated that the decrease in snowmelt rates cannot be explained only by the decrease in incoming solar radiation. The model simulated best in open area and slightly worse in healthy forest. The model showed faster snowmelt after forest defoliation which also resulted in earlier snow melt-out in the disturbed forest.

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<p>Seasonal snowpack significantly influences the catchment runoff and thus represents an important input for the hydrological cycle. Changes in the precipitation distribution and intensity, as well as a shift from snowfall to rain is expected in the future due to climate changes. As a result, rain-on-snow events, which are considered to be one of the main causes of floods in winter and spring, may occur more frequently.</p> <p>The objective of this study is 1) to evaluate the frequency, inter-annual variability and extremity of rain-on-snow events in the past based on existing measurements and 2) to simulate the effect of predicted increase in air temperature on the occurrence of rain-on-snow events in the future. We selected 59 near-natural mountain catchments in Czechia with significant snow influence on runoff and with available long-time series (>35 years) of daily hydrological and meteorological variables. A semi-distributed conceptual model, HBV-light, was used to simulate the individual components of the water cycle at a catchment scale. The model was calibrated for each of study catchments by using 100 calibration trials which resulted in respective number of optimized parameter sets. The model performance was evaluated against observed runoff and snow water equivalent. Rain-on-snow events definition by threshold values for air temperature, snow depth, rain intensity and snow water equivalent decrease allowed us to analyze inter-annual variations and trends in rain-on-snow events during the study period 1980-2014 and to explain the role of different catchment attributes.</p> <p>The preliminary results show that a significant change of rain-on-snow events related to increasing air temperature is not clearly evident. Since both air temperature and elevation seem to be an important rain-on-snow drivers, there is an increasing rain-on-snow events occurrence during winter season due to a decrease in snowfall fraction. In contrast, a decrease in total number of events was observed due to the shortening of the period with existing snow cover on the ground. Modelling approach also opened further questions related to model structure and parameterization, specifically how individual model procedures and parameters represent the real natural processes. To understand potential model artefacts might be important when using HBV or similar bucket-type models for impact studies, such as modelling the impact of climate change on catchment runoff.</p>