Geochemical and isotopic tracers were often used in mixing models to estimate glacier melt contributions to streamflow, whereas the spatio‐temporal variability in the glacier melt tracer signature and its influence on tracer‐based hydrograph separation results received less attention. We present novel tracer data from a high‐elevation catchment (17 km², glacierized area: 34%) in the Oetztal Alps (Austria) and investigated the spatial, as well as the subdaily to monthly tracer variability of supraglacial meltwater and the temporal tracer variability of winter baseflow to infer groundwater dynamics. The streamflow tracer variability during winter baseflow conditions was small, and the glacier melt tracer variation was higher, especially at the end of the ablation period. We applied a three‐component mixing model with electrical conductivity and oxygen‐18. Hydrograph separation (groundwater, glacier melt, and rain) was performed for 6 single glacier melt‐induced days (i.e., 6 events) during the ablation period 2016 (July to September). Median fractions (±uncertainty) of groundwater, glacier melt, and rain for the events were estimated at 49±2%, 35±11%, and 16±11%, respectively. Minimum and maximum glacier melt fractions at the subdaily scale ranged between 2±5% and 76±11%, respectively. A sensitivity analysis showed that the intraseasonal glacier melt tracer variability had a marked effect on the estimated glacier melt contribution during events with large glacier melt fractions of streamflow. Intra‐daily and spatial variation of the glacier melt tracer signature played a negligible role in applying the mixing model. The results of this study (a) show the necessity to apply a multiple sampling approach in order to characterize the glacier melt end‐member and (b) reveal the importance of groundwater and rainfall–runoff dynamics in catchments with a glacial flow regime.

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.

Spatio‐temporal variations of precipitation are presumed to influence the displacement rate of slow moving deep‐seated landslides by controlling groundwater recharge, pore‐water pressure and shear strength. Phases of landslide acceleration responding to long lasting rainfall and snowmelt events occur under site‐ and event‐specific time delays. Assessing groundwater recharge and simultaneous recording of landslide's displacement in a sufficient spatial and temporal resolution is essential to deepen the understanding of mechanisms controlling a landslide's deformation behaviour and is indispensable when it comes to identifying and developing target‐oriented mitigation strategies. Objective of this study is to asses hydrologic landslide drivers (solid and liquid precipitation, snowmelt and evapotranspiration) and to investigate their spatio‐temporal distribution in the context of movements recorded at the Vögelsberg landslide (Tyrol, Austria). Hydro‐meteorological variables were simulated using the AMUNDSEN (Alpine MUltiscale Numerical Distributed Simulation ENgine) hydro‐climatological model and landslide movements were continuously monitored using an automated tracking total station. Area‐wide simulated time series of available water were used: (i) to separate them into single landslide triggering hydro‐meteorological events, (ii) to analyse spatio‐temporal patterns of water availability per triggering event including individual response times, (iii) to delineate an effective hydrological landslide catchment, and (iv) to identify relations between assessed water input and landslide displacement rate. For the observation period from 05‐2016 until 06‐2019 we identified three distinctive hydro‐meteorological events causing time‐delayed periods of landslide acceleration. Spatio‐temporal differences in water availability per triggering event result in spatially diverse response times varying from 20 to 60 days for rainfall‐triggered events and between 0 and 8 days for events triggered by snowmelt. Pronounced spatio‐temporal differences of snowmelt within the model domain were identified to offer a unique possibility to delineate the effective hydrological landslide catchment. While considering event‐specific time‐lags, logarithmic correlations between incoming water and landslide displacement rate become apparent.

In diesem Beitrag werden aktuelle tracerhydrologische Forschungsarbeiten an der Rofenache dargelegt, deren hochalpines Einzugsgebiet in den hinteren Ötztaler Alpen liegt. Es wurden Wasserproben von Abfluss, flachgründigen Quellen, Regen, Schnee und Gletscher genommen und auf ihre Sauerstoff-18-Signatur und elektrische Leitfähigkeit analysiert. Die Variabilität der Tracer im Abfluss unterliegt einem deutlichen Jahres- und Tagesgang (letzteres vor allem während der Ablationsperiode). Mit einem tracerbasierten Mischungsmodell konnte ein über den Verlauf der Schneeschmelzperiode variabler Anteil des Schnees am Gesamtabfluss von 35 % im April bis etwa 80 % im Juni abgeschätzt werden. Gletscherschmelzanteile konnten mit bis zu 75 % berechnet werden. Das Wasser der Rofenache ist vor allem durch einen geringen Anteil (16 %) an Wasser, welches weniger als drei Monate zuvor als Niederschlag eingetragen wurde, gekennzeichnet. Dies kann durch die hohen Schnee- und Gletscherschmelzanteile, aber auch durch die Tiefensickerung erklärt werden, welche bedingt, dass der eingetragene Niederschlag erst weiter flussabwärts in den Vorfluter gelangt. This contribution describes recent research in the tracer hydrology of the Rofenache, the mountain stream draining a high-elevation catchment in the European Alps (Oetztal Alps, Austria). Water samples of streamflow, springs, rain, snow and glaciers were analysed for electrical conductivity and oxygen-18. The streamflow tracer variability shows a marked annual and diurnal cycle, especially during the ablation period. By means of a tracer-based mixing model, both, high snowmelt (up to 80%) and glacier melt (up to 75%) contributions to streamflow could be attested. The streamwater is dominated by water older than three months, since only 16% young water fraction was estimated. This could be explained by high fractions of snow and glacier melt, but also occurs due to the deep percolation—typical for some mountainous catchments—which leads to a bypass and contribution to streamflow further downstream.

We determined the streamflow transit time and the subsurface water storage volume in the glacierized high-elevation catchment of the Rofenache (Oetztal Alps, Austria) with the lumped parameter transit time model TRANSEP. Therefore we enhanced the surface energy-balance model ESCIMO to simulate the ice melt, snowmelt and rain input to the catchment and associated δ¹⁸O values for 100 m elevation bands. We then optimized TRANSEP with streamflow volume and δ¹⁸O for a four-year period with input data from the modified version of ESCIMO at a daily resolution. The median of the 100 best TRANSEP runs revealed a catchment mean transit time of 9.5 years and a mobile storage of 13,846 mm. The interquartile ranges of the best 100 runs were large for both, the mean transit time (8.2–10.5 years) and the mobile storage (11,975–15,382 mm). The young water fraction estimated with the sinusoidal amplitude ratio of input and output δ¹⁸O values and delayed input of snow and ice melt was 47%. Our results indicate that streamflow is dominated by the release of water younger than 56 days. However, tracers also revealed a large water volume in the subsurface with a long transit time resulting to a strongly delayed exchange with streamflow and hence also to a certain portion of relatively old water: The median of the best 100 TRANSEP runs for streamflow fraction older than five years is 28%.

Seasonal snow cover is an important temporary water storage in high-elevation regions. Especially in remote areas, the available data is often insufficient to explicitly quantify snowmelt contributions to streamflow. The unknown spatio-temporal variability of the snowmelt isotopic content, as well as pronounced spatial variations of snowmelt rates lead to high uncertainties in applying the isotopic hydrograph separation method. This study presents an approach that uses a distributed snowmelt model to support the traditional isotopic hydrograph separation technique. 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 content of snowmelt water to the catchment scale. The observed δ¹⁸O values and modelled snowmelt rates showed distinct inter- and intra-event variations, as well as marked differences between north- and south-facing slopes. Accounting for those differences, two-component isotopic hydrograph separation revealed snowmelt contributions of 35 ± 3 % and 75 ± 14 % for the early and peak melt season, respectively. Differences to formerly used weighting methods (e.g. using observed plot-scale melt rates) or considering either the north- or south-facing slope were up to 5 and 15 %, respectively.