The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany, Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions which match the climatic forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to May) mean snow depth was −8.4 % per decade, for seasonal maximum snow depth −5.6 % per decade, and for seasonal snow cover duration −5.6 % per decade. Stronger and more significant trends were observed for periods and elevations where the transition from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

This paper describes a new distributed hydrological model, called GEOtop. The model accommodates very complex topography and, besides the water balance, unlike most other hydrological models, integrates all the terms in the surface energy balance equation. GEOtop uses a discretization of the landscape based on digital elevation data. These digital elevation data are preprocessed to allow modeling of the effect of topography on the radiation incident on the surface, both shortwave (including shadowing) and longwave (accounting for the sky view factor). For saturated and unsaturated subsurface flow, GEOtop makes use of a numerical solution of the 3D Richards’ equation in order to properly model, besides the lateral flow, the vertical structure of water content and the suction dynamics. These characteristics are deemed necessary for consistently modeling hillslope processes, initiation of landslides, snowmelt processes, and ecohydrological phenomena as well as discharges during floods and interstorm periods. An accurate treatment of radiation inputs is implemented in order to be able to return surface temperature. The motivation behind the model is to combine the strengths and overcome the weaknesses of flood forecasting and land surface models. The former often include detailed spatial description and lateral fluxes but usually lack appropriate knowledge of the vertical ones. The latter are focused on vertical structure and usually lack spatial structure and prediction of lateral fluxes. Outlines of the processes simulated and the methods used to simulate them are given. A series of applications of the model to the Little Washita basin of Oklahoma using data from the Southern Great Plains 1997 Hydrology Experiment (SGP97) is presented. These show the model’s ability to reproduce the pointwise energy and water balance, showing that just an elementary calibration of a few parameters is needed for an acceptable reproduction of discharge at the outlet, for the prediction of the spatial distribution of soil moisture content, and for the simulation of a full year’s streamflow without additional calibration.

Emphasizing the physical intricacies of integrated hydrology and feedbacks in simulating connected, variably saturated groundwater‐surface water systems, the Integrated Hydrologic Model Intercomparison Project initiated a second phase (IH‐MIP2), increasing the complexity of the benchmarks of the first phase. The models that took part in the intercomparison were ATS, Cast3M, CATHY, GEOtop, HydroGeoSphere, MIKE‐SHE, and ParFlow. IH‐MIP2 benchmarks included a tilted v‐catchment with 3‐D subsurface; a superslab case expanding the slab case of the first phase with an additional horizontal subsurface heterogeneity; and the Borden field rainfall‐runoff experiment. The analyses encompassed time series of saturated, unsaturated, and ponded storages, as well as discharge. Vertical cross sections and profiles were also inspected in the superslab and Borden benchmarks. An analysis of agreement was performed including systematic and unsystematic deviations between the different models. Results show generally good agreement between the different models, which lends confidence in the fundamental physical and numerical implementation of the governing equations in the different models. Differences can be attributed to the varying level of detail in the mathematical and numerical representation or in the parameterization of physical processes, in particular with regard to ponded storage and friction slope in the calculation of overland flow. These differences may become important for specific applications such as detailed inundation modeling or when strong inhomogeneities are present in the simulation domain.

Snow-dominated and glacierized catchments are important sources of fresh water for biological communities and for populations living in mountain valleys. Gaining a better understanding of the runoff origin and of the hydrological interactions between meltwater, streamflow and groundwater is critical for natural risk assessment and mitigation as well as for effective water resource management in mountain regions. This study is based on the use of stable isotopes of water and electrical conductivity as tracers to identify the water sources for runoff and groundwater and their seasonal variability in a glacierized catchment in the Italian Alps. Samples were collected from rainfall, snow, snowmelt, ice melt, spring and stream water (from the main stream at different locations and from selected tributaries) in 2011, 2012 and 2013. The tracer-based mixing analysis revealed that, overall, snowmelt and glacier melt were the most important end-members for stream runoff during late spring, summer and early fall. The temporal variability of the tracer concentration suggested that stream water was dominated by snowmelt at the beginning of the melting season (May–June), by a mixture of snowmelt and glacier melt during mid-summer (July–early August), and by glacier melt during the end of the summer (end of August–September). The same seasonal pattern observed in streamflow was also evident for groundwater, with the highest electrical conductivity and least negative isotopic values found during cold or relatively less warm periods, when the melt of snowpack and ice was limited. Particularly, the application of a two-component mixing model to data from different springs showed that the snowmelt contribution to groundwater recharge varied between 21% (±3%) and 93% (±1%) over the season, and the overall contribution during the three study years ranged between 58% (±24%) and 72% (±19%). These results provided new insights into the isotopic characterization of the study catchment presenting further understanding of the spatio-temporal variability of the main water sources contributing to runoff.

Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of (i) the snowmelt onset, (ii) the snow gliding and wet-snow avalanches, (iii) the release of snow contaminants, and (iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to obtain. On the other hand, active microwave sensors such as the synthetic aperture radar (SAR) mounted on board satellites are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 d over several places in the world. In this paper we analyze the correlation between the multitemporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering 2 hydrological years. We found that the multitemporal SAR measurements allow the identification of the three melting phases that characterize the melting process, i.e., moistening, ripening and runoff. In particular, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow us to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.