Snow depth patterns over glaciers are controlled by precipitation, snow redistribution due to wind and avalanches, and the exchange of energy with the atmosphere that determines snow ablation. While many studies have advanced the understanding of ablation processes, less is known about winter snow patterns and their variability over glaciers. We analyze snow depth on Haut Glacier d'Arolla, Switzerland, in the two winter seasons 2006–2007 and 2010–2011 to (1) understand whether snow depth over an alpine glacier at the end of the accumulation season exhibits a behavior similar to the one observed on single slopes and vegetated areas; and (2) investigate the snow pattern consistency over the two accumulation seasons. We perform this analysis on a data set of high‐resolution lidar‐derived snow depth using variograms and fractal parameters. Our first main result is that snow depth patterns on the glacier exhibit a multiscale behavior, with a scale break around 20 m after which the fractal dimension increases, indicating more autocorrelated structure before the scale break than after. Second, this behavior is consistent over the two years, with fractal parameters and their spatial variability almost constant in the two seasons. We also show that snow depth patterns exhibit a distinct behavior in the glacier tongue and the upper catchment, with longer correlation distances on the tongue in the direction of the main winds, suggesting spatial distinctions that are likely induced by different processes and that should be taken into account when extrapolating snow depth from limited samples.

Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the Överuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas.

<p>A key challenge in continental and global hydro-climate services deals with the incomplete (or even lack of) incorporation of local knowledge and data from the users. Here, we demonstrate the regional skill of seasonal forecasts from large-scale hydro-climate services, while we present a framework that accounts for local data and with the use of machine-learning enhances the seasonal forecasts by better capturing the local information. Five European case studies subject to different hydro-climate conditions and user needs are selected. We test our framework using the E-HYPE hydrological model forced by bias-adjusted ECMWF SEAS5 seasonal meteorological forecasts. We firstly assess the skill of seasonal hydrological forecasts using pseudo-reality and “real” local observations as reference. The skill assessment is driven by the local needs and hence it is conducted for different target hydro-climatic variables and conditions (i.e. floods and droughts). This first evaluation sets the benchmark for quantifying the added value from a machine-learning enhanced hydro-climate service. We next introduce a post-processing workflow to take advantage of the available local observations and potentially improve the forecasting skill. Here, quantile mapping and machine-learning post-processors are tested in the case study areas to further tune the output from the European hydro-climate service towards the local observations. Results from these hybrid seasonal forecasts show potentials to meet the local conditions and consequently address the user expectations from the service. The current work is highlighting the way forward for machine-learning enhanced services that allow tailoring large-scale hydro-climate services using local knowledge and data.</p>

<p>Weather and climate strongly influence the hydrology of natural and managed river basins. Changes in these forcings affect the water resources and hydrological extremes, i.e., floods and droughts, in time and space. Attributing the effect of climatic drivers on present and future runoff allows to understand the hydrological response of river basin to changing climate at the local and regional scale. Here, we investigated the runoff changes, including hydrological extremes, across Europe in the early (2010-2040), mid (2041-2070) and late (2070-2099) century. We used runoff simulations from the E-HYPE hydrological model and the bias-adjusted EURO-CORDEX climate model projections. The sensitivity of runoff changes to the climatic factors (precipitation and evapotranspiration) compared to the reference period (1981-2000) was evaluated with the climatic elasticity method through a Budyko approach. In addition, to address lack of robustness in our insights, we assessed the spatial consistency and uncertainty of the runoff changes due to the ensemble variability. Results showed that the sensitivity of runoff changes to climate change varies depending on the climatic gradient and basin physiographic properties. These results are a step towards enhanced hydro-climate services that allow attribution of (extreme) events to climate change.</p>

<p>Ongoing warming strongly influences the water cycle at the regional scale and increases concerns about future streamflow changes with consequences for water availability and potential intensification of hydrological extremes, i.e. floods and droughts. Detecting and identifying controlling factors of future streamflow changes is a key point to understand the hydrological response to changing climate and develop tailored strategies for the water resources management and risk adaption. In this study we investigate the streamflow changes, including extremes, across Europe in the mid (2041-2070) and late (2070-2099) century. We used streamflow simulations from the E-HYPE hydrological model and the EURO-CORDEX climate model projections. The sensitivity of streamflow changes to the climatic factors compared to the reference period (1981-2010) is evaluated with the climatic elasticity method based on the Budyko framework. Moreover, we assess the spatial consistency and uncertainty of the streamflow changes due to the ensemble variability. Results show that the sensitivity of streamflow changes to climatic change varies depending on the climatic gradient and physiographic properties of the domain.</p>