A physically based hydroclimatological model (AMUNDSEN) is used to assess future climate change impacts on the cryosphere and hydrology of the Ötztal Alps (Austria) until 2100. The model is run in 100 m spatial and 3 h temporal resolution using in total 31 downscaled, bias-corrected, and temporally disaggregated EURO-CORDEX climate projections for the representative concentration pathways (RCPs) 2.6, 4.5, and 8.5 scenarios as forcing data, making this – to date – the most detailed study for this region in terms of process representation and range of considered climate projections. Changes in snow coverage, glacierization, and hydrological regimes are discussed both for a larger area encompassing the Ötztal Alps (1850 km ² , 862–3770 m a.s.l.) as well as for seven catchments in the area with varying size (11–165 km ² ) and glacierization (24–77 %). Results show generally declining snow amounts with moderate decreases (0–20 % depending on the emission scenario) of mean annual snow water equivalent in high elevations (> 2500 m a.s.l.) until the end of the century. The largest decreases, amounting to up to 25–80 %, are projected to occur in elevations below 1500 m a.s.l. Glaciers in the region will continue to retreat strongly, leaving only 4–20 % of the initial (as of 2006) ice volume left by 2100. Total and summer (JJA) runoff will change little during the early 21st century (2011–2040) with simulated decreases (compared to 1997–2006) of up to 11 % (total) and 13 % (summer) depending on catchment and scenario, whereas runoff volumes decrease by up to 39 % (total) and 47 % (summer) towards the end of the century (2071–2100), accompanied by a shift in peak flows from July towards June.

In this study, the fully distributed, physically based hydroclimatological model AMUNDSEN is set up for catchments in the highly glacierized Ötztal Alps (Austria, 558 km² in total). The model is applied for the period 1997–2013, using a spatial resolution of 50 m and a temporal resolution of 1 h. A novel parameterization for lateral snow redistribution based on topographic openness is presented to account for the highly heterogeneous snow accumulation patterns in the complex topography of the study region. Multilevel spatiotemporal validation is introduced as a systematic, independent, complete, and redundant validation procedure based on the observation scale of temporal and spatial support, spacing, and extent. This new approach is demonstrated using a comprehensive set of eight independent validation sources: (i) mean areal precipitation over the period 1997–2006 derived by conserving mass in the closure of the water balance, (ii) time series of snow depth recordings at the plot scale, (iii–iv) multitemporal snow extent maps derived from Landsat and MODIS satellite data products, (v) the snow accumulation distribution for the winter season 2010/2011 derived from airborne laser scanning data, (vi) specific surface mass balances for three glaciers in the study area, (vii) spatially distributed glacier surface elevation changes for the entire area over the period 1997–2006, and (viii) runoff recordings for several subcatchments. The results indicate a high overall model skill and especially demonstrate the benefit of the new validation approach. The method can serve as guideline for systematically validating the coupled components in integrated snow-hydrological and glacio-hydrological models.

Meteorological time series with 1 h time steps are required in many applications in geoscientific modelling. These hourly time series generally cover shorter periods of time compared to daily meteorological time series. We present an open-source MEteoroLOgical observation time series DISaggregation Tool (MELODIST). This software package is written in Python and comprises simple methods to temporally downscale (disaggregate) daily meteorological time series to hourly data. MELODIST is capable of disaggregating the most commonly used meteorological variables for geoscientific modelling including temperature, precipitation, humidity, wind speed, and shortwave radiation. In this way, disaggregation is performed independently for each variable considering a single site without spatial dependencies. The algorithms are validated against observed meteorological time series for five sites in different climates. Results indicate a good reconstruction of diurnal features at those sites. This makes the methodology interesting to users of models operating at hourly time steps, who want to apply their models for longer periods of time not covered by hourly observations.

A hydrometeorological model chain is applied to investigate climate change effects on natural and artificial snow conditions in the Schladming region in Styria (Austria). Four dynamically refined realizations of the IPCC A1B scenario covering the warm/cold and wet/dry bandwidth of projected changes in temperature and precipitation in the winter half-year are statistically downscaled and bias corrected prior to their application as input for a physically based, distributed energy-balance snow model. However, owing to the poor skills in the reproduction of past climate and snow conditions in the considered region, one realization had to be removed from the selection to avoid biases in the results of the climate change impact analysis. The model’s capabilities in the simulation of natural and artificial snow conditions are evaluated and changes in snow conditions are addressed by comparing the number of snow cover days, the length of the ski season, and the amounts of technically produced snow as simulated for the past and the future. The results for natural snow conditions indicate decreases in the number of snow cover days and the ski season length of up to >25 and >35 days, respectively. The highest decrease in the calculated ski season length has been found for elevations between 1600 and 2700 m MSL, with an average decrease rate of ~2.6 days decade−1. For the exemplary ski site considered, the ski season length simulated for natural snow conditions decreases from >50 days at present to ~40 days in the 2050s. Technical snow production allows the season to be prolonged by ~80 days and hence allows ski season lengths of ~120 days until the end of the scenario period in 2050.

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.