Knowledge of the ice thickness distribution of glaciers and ice caps is an important prerequisite for many glaciological and hydrological investigations. A wealth of approaches has recently been presented for inferring ice thickness from characteristics of the surface. With the Ice Thickness Models Intercomparison eXperiment (ITMIX) we performed the first coordinated assessment quantifying individual model performance. A set of 17 different models showed that individual ice thickness estimates can differ considerably – locally by a spread comparable to the observed thickness. Averaging the results of multiple models, however, significantly improved the results: on average over the 21 considered test cases, comparison against direct ice thickness measurements revealed deviations on the order of 10 ± 24 % of the mean ice thickness (1σ estimate). Models relying on multiple data sets – such as surface ice velocity fields, surface mass balance, or rates of ice thickness change – showed high sensitivity to input data quality. Together with the requirement of being able to handle large regions in an automated fashion, the capacity of better accounting for uncertainties in the input data will be a key for an improved next generation of ice thickness estimation approaches.

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.

Rock glaciers are capable of storing water in solid (permafrost ice) as well as in liquid form (groundwater within the unfrozen base layer). The latter is relevant no matter the actual state of the rock glacier (intact, containing ice versus relict, no more ice present). A nation-wide comparison of the water equivalent of glacier ice within the Austrian Alps is conducted to evaluate the role of rock glaciers in the hydrological cycle at its current state and potentially in the future. Estimates of ice volumes and their uncertainties, especially related to thickness estimates of permafrost-ice bodies, are discussed in the light of available data and need to be seen as order-of-magnitude estimates. With intact rock glaciers covering almost 123 km² and an assumed ice content of 40% within the permafrost body, ice volumes are estimated to be 0.93 Gigatons. This corresponds to 8.3% of the ice volume estimated for the most recent glacier inventory of the Austrian Alps. Thus, with the currently available data, a water equivalent ratio of ~1: 12 for rock glacier ice versus glacier ice is estimated. In addition to the solid water storage, the dynamic storage potential within rock glaciers in liquid form needs to be considered. While this volume is relatively small compared to the ice volume in glaciers and rock glaciers (ratio of ~1: 20), the time-scales of hydrological relevance (when they become runoff due to ice melt) are very different. This dynamic liquid water storage is replenishable and therefore available over shorter time scales (seasonal drainage pattern), and moreover relatively stable and may potentially even slightly increase as pore space becomes available due to increased melting of permafrost ice. In the light of climate warming and projected glacier recession, the relative hydrological importance of rock glaciers as water stores (in solid as well as in liquid form) in the European Alps is expected to increase and their storage-discharge patterns need to be accounted for in water management considerations.

The density of new snow is operationally monitored by meteorological or hydrological services at daily time intervals, or occasionally measured in local field studies. However, meteorological conditions and thus settling of the freshly deposited snow rapidly alter the new snow density until measurement. Physically based snow models and nowcasting applications make use of hourly weather data to determine the water equivalent of the snowfall and snow depth. In previous studies, a number of empirical parameterizations were developed to approximate the new snow density by meteorological parameters. These parameterizations are largely based on new snow measurements derived from local in situ measurements. In this study a data set of automated snow measurements at four stations located in the European Alps is analysed for several winter seasons. Hourly new snow densities are calculated from the height of new snow and the water equivalent of snowfall. Considering the settling of the new snow and the old snowpack, the average hourly new snow density is 68 kg m ⁻³ , with a standard deviation of 9 kg m ⁻³ . Seven existing parameterizations for estimating new snow densities were tested against these data, and most calculations overestimate the hourly automated measurements. Two of the tested parameterizations were capable of simulating low new snow densities observed at sheltered inner-alpine stations. The observed variability in new snow density from the automated measurements could not be described with satisfactory statistical significance by any of the investigated parameterizations. Applying simple linear regressions between new snow density and wet bulb temperature based on the measurements' data resulted in significant relationships ( r ² > 0.5 and p ≤ 0.05) for single periods at individual stations only. Higher new snow density was calculated for the highest elevated and most wind-exposed station location. Whereas snow measurements using ultrasonic devices and snow pillows are appropriate for calculating station mean new snow densities, we recommend instruments with higher accuracy e.g. optical devices for more reliable investigations of the variability of new snow densities at sub-daily intervals.

The storage of water within the seasonal snow cover is a substantial source of runoff in high mountain catchments. Information about the spatial distribution of snow accumulation is necessary for calibration and validation of hydro-meteorological models. Generally, only a small number of precipitation measurements deliver precipitation input for modelling in mountain areas. The spatial interpolation and extrapolation of measurements of precipitation is still difficult. Multi-temporal application of lidar techniques from aircraft, so-called airborne laser scanning (ALS), provides surface elevations changes even in inaccessible terrain. These ALS surface elevation changes can be used to derive changes in snow depths of the mountain snow cover for seasonal or subseasonal time periods. However, since glacier surfaces are not static over time, ablation, densification of snow, densification of firn and ice flow contribute to surface elevation changes. ALS-derived surface elevation changes were compared to snow depths derived from 35.4 km of ground penetrating radar (GPR) profiles on four glaciers. With this combination of two different data acquisitions, it is possible to evaluate the effect of the summation of these processes on ALS-derived snow depth maps in the high alpine region of the Ötztal Alps (Austria). A Landsat 5 Thematic Mapper image was used to distinguish between snow covered area and bare ice areas of the glaciers at the end of the ablation season. In typical accumulation areas, ALS surface elevation changes differ from snow depths calculated from GPR measurements by −0.4 m on average with a mean standard deviation of 0.34 m. Differences between ALS surface elevation changes and GPR derived snow depths are small along the profiles conducted in areas of bare ice. In these areas, the mean absolute difference of ALS surface elevation changes and GPR snow depths is 0.004 m with a standard deviation of 0.27 m. This study presents a systematic approach to analyze deviations from ALS generated snow depth maps to ground truth measurements on four different glaciers. We could show that ALS can be an important and reliable data source for the spatial distribution of snow depths for most parts of the here investigated glaciers. However, within accumulation areas, just utilizing ALS data may lead to systematic underestimation of total snow depth distribution.