Snow cover is a crucial driver for plant species distributions in cold environments. The primary source of snow cover data used in distribution models is remotely sensed satellite imagery, which is characterized by coarser spatial resolutions than plot-scale observations of plant distributions. This scale-mismatch was hypothesized to limit model accuracy. Here, we used a common modeling framework to assess the contribution of snow melt-out dates derived from four data sources (satellite imagery, numerical snowpack modeling, webcam imagery and in-situ soil temperature measurements) at 1 m and 20 m spatial resolution to the predictive power of distribution models of 74 plant species in an alpine landscape of the Austrian Alps. We found that >80 % of the distribution models of all species were significantly improved by at least one snow melt-out data set when considering Area Under the Curve (AUC). Satellite-based melt-out led to significantly improved models for the highest number of species (>50 % for AUC) and increased True-Skill-Statistic and AUC on average by 16 % and 5 %, respectively. Surprisingly, fine-scale and in-situ measured melt-out data did not improve models more than the coarser scale (20 m) satellite-based melt-out data. Moreover, numerical snowpack modeling delivered results comparable to the other sources, which supports its use for projecting future species distributions. We conclude that the additional effort needed for producing high resolution, in-situ datasets as compared to commonly used satellite imagery might hence be worthwhile for some species but not for plant distribution modeling in cold ecosystems in general.

Novel climate model data at the kilometer-scale, innovative downscaling techniques, sophisticated snow modelling frameworks, and increasing computational capacities are among the elements currently paving the way for a new phase in high resolution and physically based climate impact studies on snow hydrology in complex mountain terrain. While the assessment of climate model uncertainty is well established, the uncertainty arising from snow model selection typically receives far less attention. To investigate the uncertainty induced by the selection of the snow model configuration, we simulate the seasonal snow cover in the complex mountain area of the Berchtesgaden National Park mountains (Germany) under historical conditions (October 2013-September 2023) and for a 10 year period characterized by a 1 degrees C warming, using a large number of openAMUNDSEN snow model configurations (n=108) with degree-day as well as physically based snowmelt methods and varying land cover maps and spatial resolutions. The analysis of the resulting snow cover durations and snow disappearance days indicates that differences due to the choice of snowmelt method, land cover map and spatial resolution can be comparable in magnitude to the effect of a 1 degrees C warming, with uncertainties particularly pronounced in the forested areas and in the high elevations of the study area. Our results support the identification of critical snow model settings that need careful consideration, especially when employing energy balance instead of degree-day snow models to investigate climate change impacts on snow hydrology in complex mountain terrain.

Abstract. openAMUNDSEN (= the open source version of the Alpine MUltiscale Numerical Distributed Simulation ENgine) is a fully distributed model, designed primarily for calculating the seasonal evolution of a snow cover and melt rates in mountain regions. It resolves the mass and energy balance of snow covered surfaces and layers of the snowpack beneath, thereby including the most important processes that are relevant in such regions. The potential model applications are very versatile; typically, it is applied in areas ranging from the point scale to the regional scale (i.e., up to some thousands of square kilometers), using a spatial resolution of 10–1000 m and a temporal resolution of 1–3 h or daily. Temporal horizons may vary between single events and climate change scenarios. The openAMUNDSEN model has been applied for manyfold applications already which are referenced herein. It features a spatial interpolation of meteorological observations, several layers of snow with different density and liquid water content, wind-induced lateral redistribution, snow-canopy interaction, glacier ice response to climate and more. The model can be configured according to each specific application case. A basic consideration for its development was to include a variety of process descriptions of different complexity to set up individual model runs which best match a compromise between physical detail, transferability, simplicity as well as performance for a certain region in the European Alps, typically a (preferably gauged) hydrological catchment. The Python model code and example data are available for the public as open source project (Hanzer et al., 2023).

This publication presents a comprehensive hydrometeorological data set for three research sites in the upper Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria) and is a companion publication to a data collection published in 2018. The time series presented here comprise data from 2017 to 2023 and originate from three meteorological and snow hydrological stations at 2737, 2805, and 2919 m a.s.l. The fully equipped automatic weather stations include a specific set of sensors to continuously record snow cover properties. These are automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water contents, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow transport. These devices are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. We present data for temperature, precipitation, humidity, wind speed, and radiation fluxes and explore the continuous snow measurements by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations is demonstrated with examples of measured wind speeds, snow drift rates, and redistributed snow amounts during several blowing snow events. The data complement the scientific monitoring infrastructure in the research catchment and represent a unique time series of high-altitude mountain weather and snow observations. They enable comprehensive insights into the dynamics of high-altitude meteorological and snow processes and are collected to support the scientific community, local stakeholders, and the interested public, as well as operational warning and forecasting services. The data are publicly available from the GFZ Data Services repository: https://doi.org/10.5880/fidgeo.2023.037 (Department of Geography, University of Innsbruck, 2024).

The formation and concentration of liquid water (LW) in the snowpack constitute key processes linking snow and runoff. Hence, the LW content of the snowpack represents a crucial target variable to investigate for snowmelt-induced runoff predictions. In this study, we capture the wet snow dynamics at higher than hectometre resolution in the alpine headwater catchment Rofental, Tyrol, Austria (98.1 km2) by means of distributed model simulations and remote sensing data for the 5 year period 10/2017-09/2022. The model simulations are conducted using the intermediate complexity open-source snow-hydrological model openAMUNDSEN. Simulation results are compared to wet snow maps (WSM) derived from Sentinel-1 data. Our investigations indicate that distributed snow models of intermediate complexity, such as openAMUNDSEN and satellite-based wet snow data are well capable of capturing the wet snow dynamics in high spatial and temporal resolutions. The areal extents of wet snow as well as the upward movement of the wet snow line to higher elevation with progressing snowmelt are captured well by both approaches. In order to evaluate the snow simulations, we use fractional snow cover (FSC) data based on Sentinel-2, which proved to provide valuable small-scale snow and snow redistribution patterns in alpine catchments. The comparison of model simulations with FSC maps with more than 50% of the non-glaciated area being cloud-free (i.e. 364 images) results in an accuracy of 0.91. This study represents a further step towards a serviceable operational snow-hydrological monitoring and modelling framework for mountain regions including wet snow dynamics in high spatial and temporal resolutions. We capture the wet snow dynamics in a high alpine headwater catchment Rofental (Tyrol, Austria) at higher than hectometre resolution by means of physically-based snow simulations using the open-source model openAMUNDSEN and remote sensing data based on Sentinel-1. Our analysis represents a further step towards a serviceable operational snow-hydrological monitoring and modelling framework including wet snow dynamics in high spatial and temporal resolutions. image

openAMUNDSEN (the open source version of the Alpine MUltiscale Numerical Distributed Simulation ENgine) is a fully distributed snow-hydrological model, designed primarily for calculating the seasonal evolution of snow cover and melt rates in mountain regions. It resolves the mass and energy balance of snow-covered surfaces and layers of the snowpack, thereby including the most important processes that are relevant in complex mountain topography. The potential model applications are very versatile; typically, it is applied in areas ranging from the point scale to the regional scale (i.e., up to some thousands of square kilometers) using a spatial resolution of 10–1000 m and a temporal resolution of 1–3 h or daily. Temporal horizons may vary between single events and climate change scenarios. The openAMUNDSEN model has already been used for many applications, which are referenced herein. It features a spatial interpolation of meteorological observations, several layers of snow with different density and liquid-water contents, wind-induced lateral redistributions, snow–canopy interactions, glacier ice responses to climate, and more. The model can be configured according to each specific application case. A basic consideration for its development was to include a variety of process descriptions of different complexity to set up individual model runs which best match a compromise between physical detail, transferability, simplicity, and computational performance for a certain region in the European Alps, typically a (preferably gauged) hydrological catchment. The Python model code and example data are available as an open-source project on GitHub (https://github.com/openamundsen/openamundsen, last access: 1 June 2024).

Despite the large socio-economic and ecologic relevance of snow in Austria, no comprehensive assessment of the impact of climate change on snow in Austria existed until recently. Within the project „Future Snow Cover Evolution in Austria” (FuSE-AT, https://fuse-at.ccca.ac.at/) gridded observational datasets and the national climate scenarios (ÖKS15) have been extended by basic variables and user oriented indicators around the topic snow. This has been realized by developing a gridded snow model for climatological time-scales, based on the operational snow model of ZAMG (SNOWGRID-CL) and driving it with gridded meteorological datasets for the past (1961 – 2019) and with the full ensemble of ÖKS15 (including the emission pathways RCP2.6, RCP4.5 and RCP8.5) into the future (1961 – 2100) to generate daily snow variables on a 1 km x 1 km grid. The results are available for users via the Data Centre of the Climate Change Centre Austria (https://fuse-at.ccca.ac.at/). This new dataset includes snow water equivalent, snow depth, new snow, run-off from snow melt and the number of hours with suitable meteorological conditions for technical snow generation (using different wet-bulb-temperatures as threshold criteria). In addition, numerous user-oriented indicators have been analyzed. In close cooperation with stakeholders from the sectors winter tourism, hydropower generation and water supply, case studies to demonstrate socio-economically relevant applications of this new dataset have been conducted. The results show that the natural snow season length has significantly decreased already in the past in virtually all areas and altitude levels of Austria. Future scenarios of snow heavily depend on the emission pathway. The snow season length is expected to decrease by about three weeks (corresponds to -20% to -30% around 1500 m a.s.l.) until the mid-21st century in all scenarios, but it stabilizes on this level in RCP2.6, while it drastically further decreases in RCP8.5 to losses around -80% to -90% below 1500 m a.s.l. Further, we could demonstrate that the meteorological potential for generation of technical snow responds less sensitive to climate change than natural snow, but strongly depends on altitude, exposition, time horizon and emission pathway. More detailed results will be given in the presentation.

The Rofental is a high Alpine environmental research basin in the Ötztal Alps (Austria, 1890 - 3770 m a.s.l.). The existing measurement network has recently been extended by new stations and sensors that focus on automated recordings of snow cover properties. Core of the network are three automatic weather stations (AWS) that incorporate 10 min. recordings of snow depth (SD), snow water equivalent (SWE), layered snow temperatures, snow surface temperature, snow density, as well as solid and liquid water content of the snowpack. One AWS is extended by a particular setup of two SD and SWE measurements at nearby wind-exposed and sheltered locations, complemented by an acoustic-based snow drift sensor to quantify wind-driven snow redistribution.We here present analyses of the publicly available data that focus on snow drift events in an avalanche-prone winter season. The two nearby SWE measurements show differences of around 500% of measured peak SWE at a horizontal distance of only 25 m caused by wind-driven redistribution. In addition, the presented data is used to develop and validate the new open source, distributed snow cover model openAMUNDSEN. We evaluate different integrated energy balance and snow layer schemes and compare the data to results of the ESM-SnowMIP project.

In snow-dominated river basins, floods often occur during early summer, when snowmelt-induced runoff superimposes with rainfall-induced runoff. An earlier onset of seasonal snowmelt as a consequence of a warming climate is often expected to shift snowmelt contribution to river runoff and potential flooding to an earlier date. Against this background, we assess the impact of rising temperatures on seasonal snowpacks and quantify changes in timing, magnitude and elevation of snowmelt. We analyse in situ snow measurements, conduct snow simulations and examine changes in river runoff at key gauging stations. With regard to snowmelt, we detect a threefold effect of rising temperatures: snowmelt becomes weaker, occurs earlier and forms at higher elevations. Due to the wide range of elevations in the catchment, snowmelt does not occur simultaneously at all elevations. Results indicate that elevation bands melt together in blocks. We hypothesise that in a warmer world with similar sequences of weather conditions, snowmelt is moved upward to higher elevation. The movement upward the elevation range makes snowmelt in individual elevation bands occur earlier, although the timing of the snowmelt-induced runoff stays the same. Meltwater from higher elevations, at least partly, replaces meltwater from elevations below.

Trotz der großen sozio-ökonomischen und ökologischen Bedeutung von Schnee in Österreich und trotz der zu erwartenden hohen Sensitivität von Schnee auf die globale Erwärmung gab es bislang keine umfassende Einschätzung der zukünftigen Entwicklung von Schnee in Österreich in einem sich sehr schnell verändernden Klima. Das ACRP Projekt „Future Snow Cover Evolution in Austria” (FuSE-AT) hat diese Lücke geschlossen, indem nicht nur Schneetrends der Vergangenheit flächendeckend analysiert wurden, sondern auch die nationalen österreichischen Klimaszenarien (ÖKS15) um meteorologische und nutzerorientierte Indikatoren rund um das Thema "Schnee" erweitert und für Anwender_innen zur Verfügung gestellt wurden. Im Zuge des Projekts wurden die täglichen Schneehöhen (Naturschnee) und das Potenzial zur Erzeugung von technischem Schnee für die Vergangenheit (1961 bis 2019) sowie für die Zukunft (bis 2100) mithilfe des Schneedeckenmodells SNOWGRID-CL berechnet. Grundlage für diese Berechnungen waren Beobachtungsdatensätze für die Vergangenheit und das gesamte Ensemble der österreichischen Klimaszenarien ÖKS15 für die Zukunft. Es wurden die Variablen Schneehöhe, Schneewasseräquivalent und Abfluss aus Schneeschmelze auf Tagesbasis sowie saisonale Indikatoren (z. B. Schneedeckendauer, Anzahl der Stunden, in denen technische Beschneiung möglich ist) und eine Vielzahl weiterer nutzergerechter Indikatoren ausgewertet. Ein wesentlicher Bestandteil des Projekts war die starke Interaktion mit Stakeholdern von Beginn des Projekts an. Fallstudien in unterschiedlichen wirtschaftlichen Sektoren (Tourismus, Wasserkraft, Wasserversorgung) wurden mit Stakeholdern gemeinsam entworfen und durchgeführt. Die Ergebnisse zeigen, dass die Anzahl der Tage mit Schnee in Österreich bereits in der Vergangenheit in so gut wie allen Lagen signifikant abgenommen hat. Die Szenarien für die Zukunft sind stark vom eingeschlagenen Emissionspfad abhängig. Während sich die Schneesaison im RCP2.6 („Paris-Ziel“) bis Mitte des 21. Jahrhunderts in mittleren Lagen um etwa 3 Wochen verkürzt (entspricht etwa -20% bis -30%), danach aber stabil bleibt, ist unter Annahme des „worst case Szenario“ RCP8.5 unterhalb von 1500 m mit einem fast vollständigen Verlust der natürlichen Schneedecke zu rechnen (-80% bis -90%). Weiters konnte gezeigt werden, dass die Bedingungen für technische Schneeproduktion weniger sensitiv auf den Klimawandel reagiert als Naturschnee, aber je nach Höhenlage, Exposition, Zeithorizont und angenommenen Emissionspfad ganz unterschiedliche Auswirkungen entstehen.

Abstract. According to the living data process in ESSD, this publication presents extensions of a comprehensive hydrometeorological and glaciological data set for several research sites in the Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria). Whereas the original dataset has been published in a first original version in 2018 (https://doi.org/10.5194/essd-10-151-2018), the new time series presented here originate from meteorological and snow-hydrological recordings that have been collected from 2017 to 2020. Some data sets represent continuations of time series at existing locations, others come from new installations complementing the scientific monitoring infrastructure in the research catchment. Main extensions are a fully equipped automatic weather and snow monitoring station, as well as extensive additional installations to enable continuous observation of snow cover properties. Installed at three high Alpine locations in the catchment, these include automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water content, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow redistribution. They are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. The data sets represent a unique time series of high-altitude mountain snow and meteorology observations. We present three years of data for temperature, precipitation, humidity, wind speed, and radiation fluxes from three meteorological stations. The continuous snow measurements are explored by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations are demonstrated with examples of measured wind speeds, snow drift rates and redistributed snow amounts in December 2019 when a tragic avalanche accident occurred in the vicinity of the station. All new data sets are provided to the scientific community according to the Creative Commons Attribution License by means of the PANGAEA repository (https://www.pangaea.de/?q=%40ref104365).

In their manuscript "Enhancing the operational value of snowpack models with visualization design principles", the authors present the application of different visualization design principles in the domain of avalanche forecasting using data from the widely used model SNOWPACK.

In their manuscript "BAYWRF: a convection-resolving, present-day climatological atmospheric dataset for Bavaria", the authors present a new high-resolution RCM simulation using WRF and ERA5 reanalysis data as boundary condition.They evaluate the performance for the target region of Bavaria using station observations.

Snow models that solve coupled energy and mass balances require model parameters to be set, just like their conceptual counterparts. Despite the physical basis of these models, appropriate choices of the parameter values entail a rather high degree of uncertainty as some of them are not directly measurable, observations are lacking, or values are not adaptable from literature. In this study, we test whether it is possible to reach the same performance with energy balance snow models of varying complexity by means of parameter optimization. We utilize a multi-physics snow model which enables the exploration of a multitude of model structures and model complexities with respect to their performance against point-scale observations of snow water equivalent and snowpack runoff observations, and catchment-scale observations of snow cover fraction and spring water balance. We find that parameter uncertainty can compensate structural model deficiencies to a large degree, so that model structures cannot be reliably differentiated within a calibration period. Even with deliberately biased forcing data, comparable calibration performances can be achieved. Our results also show that parameter values need to be chosen very carefully, as no model structure guarantees acceptable simulation results with random (but still physically meaningful) parameters.

The mass balance of very small glaciers is often governed by anomalous snow accumulation, winter precipitation being multiplied by snow redistribution processes (gravitationally or wind driven), or suppressed snow ablation driven by micrometeorological effects lowering net radiation and/or turbulent heat exchange. In this case study, we analysed the relative contribution of snow accumulation and ablation processes governing the long- and short-term mass balance of the lowest perennial ice field of the Alps, the Ice Chapel, located at 870 m a.s.l. in the Berchtesgaden National Park (Germany). This study emphasizes the importance of the local topographic setting for the survival of a perennial ice field located far below the climatic snow line. Although long-term mass balance measurements of the ice field surface showed a dramatic mass loss between 1973 and 2014, the ice field mass balance was rather stable between 2014 and 2017 and even showed a strong mass gain in 2017/2018 with an increase in surface height by 50 %–100 % relative to the ice field thickness. Measurements suggest that the winter mass balance clearly dominated the annual mass balance. At the Ice Chapel surface, 92 % of snow accumulation was gained by snow avalanching, thus clearly governing the 2017/2018 winter mass balance of the ice field with mean snow depths of 32 m at the end of the accumulation period. Avalanche deposition was amplified by preferential deposition of snowfall in the wind-sheltered rock face surrounding the ice field. Detailed micrometeorological measurements combined with a numerical analysis of the small-scale near-surface atmospheric flow field identified the micrometeorological processes driving the energy balance of the ice field. Measurements revealed a katabatic flow system draining down the ice field throughout the day, showing strong temporal and spatial dynamics. The spatial origin of the thermal flow system was shown to be of particular importance for the ice field surface energy balance. Numerical simulation indicates that deep katabatic flows, which developed at higher-elevation shaded areas of the rock face and drained down the ice field, enhance sensible heat exchange towards the ice field surface by enhancing turbulence close to the ice surface. Conversely, the shallow katabatic flow developing at the ice field surface appeared to laterally decouple the local near-surface atmosphere from the warmer adjacent air suppressing heat exchange. Numerical results thus suggest that shallow katabatic flows driven by the cooling effect of the ice field surface are especially efficient in lowering the climatic sensitivity of the ice field to the surrounding rising air temperatures. Such micrometeorological phenomena must be taken into account when calculating mass and energy balances of very small glaciers or perennial ice fields at elevations far below the climatic snow line.

Mountain regions with complex orography are a particular challenge for regional climate simulations. High spatial resolution is required to account for the high spatial variability in meteorological conditions. This study presents a very high-resolution regional climate simulation (5 km) using the Weather Research and Forecasting Model (WRF) for the central part of Europe including the Alps. Global boundaries are dynamically downscaled for the historical period 1980–2009 (ERA-Interim and MPI-ESM), and for the near future period 2020–2049 (MPI-ESM, scenario RCP4.5). Model results are compared to gridded observation datasets and to data from a dense meteorological station network in the Berchtesgaden Alps (Germany). Averaged for the Alps, the mean bias in temperature is about −0.3 °C, whereas precipitation is overestimated by +14% to +19%. R 2 values for hourly, daily and monthly temperature range between 0.71 and 0.99. Temporal precipitation dynamics are well reproduced at daily and monthly scales (R 2 between 0.36 and 0.85), but are not well captured at hourly scale. The spatial patterns, seasonal distributions, and elevation-dependencies of the climate change signals are investigated. Mean warming in Central Europe exhibits a temperature increase between 0.44 °C and 1.59 °C and is strongest in winter and spring. An elevation-dependent warming is found for different specific regions and seasons, but is absent in others. Annual precipitation changes between −4% and +25% in Central Europe. The change signals for humidity, wind speed, and incoming short-wave radiation are small, but they show distinct spatial and elevation-dependent patterns. On large-scale spatial and temporal averages, the presented 5 km RCM setup has in general similar biases as EURO-CORDEX simulations, but it shows very good model performance at the regional and local scale for daily meteorology, and, apart from wind-speed and precipitation, even for hourly values.