Ulrich Strasser

Universität Innsbruck

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Thomas Marke

Universität Innsbruck

Florian Hanzer

Universität Innsbruck

Michael Warscher

Universität Innsbruck

Kristian Förster

Leibniz University Hannover

Claudia Notarnicola

Eurac Research

Daniel Günther

GeoSphere Austria

Samuel Morin

Centre National de Recherches Météorologiques

Harald Kunstmann

Karlsruhe Institute of Technology

Marc Zebisch

Eurac Research

Carlo Maria Carmagnola

Centre National de la Recherche Scientifique

Cooperative Institutions

Universität Innsbruck

Centre National de la Recherche Scientifique

ETH Zurich

University of Edinburgh

Eurac Research

Norsk Hydro (Germany)

University of Graz

Delft University of Technology

Innsbruck Medical University

Ludwig-Maximilians-Universität München

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Continuous meteorological observations at station Bella Vista in 2015

Ulrich Strasser Thomas Marke

BELLA

10.1594/pangaea.879210

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An open-source MEteoroLOgical observation time series DISaggregation Tool (MELODIST v0.1.0)

Kristian Förster Florian Hanzer Benjamin Winter Thomas Marke Ulrich Strasser

Abstract. Hourly meteorological time series 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.

10.5194/gmd-2016-51

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Snow Parameters Estimation Through New Data Fusion Approaches Involving a Hydrological Model and Remote Sensing Products

Ludovica De Gregorio Francesca Bovolo Mattia Callegari Daniel Günther Carlo Marín

Sensor Fusion

Snowmelt

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Reply on RC1

Ulrich Strasser

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).

10.5194/egusphere-2024-193-ac1

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Spatial and temporal variability of meteorological variables at Haut Glacier d'Arolla (Switzerland) during the ablation season 2001: Measurements and simulations

Journal of Geophysical Research Atmospheres (2004)

Ulrich Strasser Javier G. Corripio Francesca Pellicciotti Paolo Burlando Ben Brock

During the ablation period 2001 a glaciometeorological experiment was carried out on Haut Glacier d'Arolla, Switzerland. Five meteorological stations were installed on the glacier, and one permanent automatic weather station in the glacier foreland. The altitudes of the stations ranged between 2500 and 3000 m a.s.l., and they were in operation from end of May to beginning of September 2001. The spatial arrangement of the stations and temporal duration of the measurements generated a unique data set enabling the analysis of the spatial and temporal variability of the meteorological variables across an alpine glacier. All measurements were taken at a nominal height of 2 m, and hourly averages were derived for the analysis. The wind regime was dominated by the glacier wind (mean value 2.8 m s −1 ) but due to erosion by the synoptic gradient wind, occasionally the wind would blow up the valley. A slight decrease in mean 2 m air temperatures with altitude was found, however the 2 m air temperature gradient varied greatly and frequently changed its sign. Mean relative humidity was 71% and exhibited limited spatial variation. Mean incoming shortwave radiation and albedo both generally increased with elevation. The different components of shortwave radiation are quantified with a parameterization scheme. Resulting spatial variations are mainly due to horizon obstruction and reflections from surrounding slopes, i.e., topography. The effect of clouds accounts for a loss of 30% of the extraterrestrial flux. Albedos derived from a Landsat TM image of 30 July show remarkably constant values, in the range 0.49 to 0.50, across snow covered parts of the glacier, while albedo is highly spatially variable below the zone of continuous snow cover. These results are verified with ground measurements and compared with parameterized albedo. Mean longwave radiative fluxes decreased with elevation due to lower air temperatures and the effect of upper hemisphere slopes. It is shown through parameterization that this effect would even be more pronounced without the effect of clouds. Results are discussed with respect to a similar study which has been carried out on Pasterze Glacier (Austria). The presented algorithms for interpolating, parameterizing and simulating variables and parameters in alpine regions are integrated in the software package AMUNDSEN which is freely available to be adapted and further developed by the community.

Shortwave radiation

Albedo (alchemy)

Automatic weather station

Shortwave

Lapse rate

10.1029/2003jd003973

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Citations (89)

Water balance estimation in high Alpine terrain by combining distributed modeling and a neural network approach (Berchtesgaden Alps, Germany)

Hydrology and earth system sciences (2012)

G. Kraller Michael Warscher Harald Kunstmann Stefanie Vogl Thomas Marke

Abstract. The water balance in high Alpine regions is often characterized by significant variation of meteorological variables in space and time, a complex hydrogeological situation and steep gradients. The system is even more complex when the rock composition is dominated by soluble limestone, because unknown underground flow conditions and flow directions lead to unknown storage quantities. Reliable distributed modeling cannot be implemented by traditional approaches due to unknown storage processes at local and catchment scale. We present an artificial neural network extension of a distributed hydrological model (WaSiM-ETH) that allows to account for subsurface water transfer in a karstic environment. The extension was developed for the Alpine catchment of the river "Berchtesgadener Ache" (Berchtesgaden Alps, Germany), which is characterized by extreme topography and calcareous rocks. The model assumes porous conditions and does not account for karstic environments, resulting in systematic mismatch of modeled and measured runoff in discharge curves at the outlet points of neighboring high alpine subbasins. Various precipitation interpolation methods did not allow to explain systematic mismatches, and unknown subsurface hydrological processes were concluded as the underlying reason. We introduce a new method that allows to describe the unknown subsurface boundary fluxes, and account for them in the hydrological model. This is achieved by an artificial neural network approach (ANN), where four input variables are taken to calculate the unknown subsurface storage conditions. This was first developed for the high Alpine subbasin Königsseer Ache to improve the monthly water balance. We explicitly derive the algebraic transfer function of an artificial neural net to calculate the missing boundary fluxes. The result of the ANN is then implemented in the groundwater module of the hydrological model as boundary flux, and considered during the consecutive model process. We tested several ANN setups in different time increments to investigate ANN performance and to examine resulting runoff dynamics of the hydrological model. The ANN with 5-day time increment showed best results in reproducing the observed water storage data (r2 = 0.6). The influx of the 20-day ANN showed best results in the hydrological model correction. The boundary influx in the subbasin improved the hydrological model, as performance increased from NSE = 0.48 to NSE = 0.57 for subbasin Königsseetal, from NSE = 0.22 to NSE = 0.49 for subbasin Berchtesgadener Ache, and from NSE = 0.56 to NSE = 0.66 for the whole catchment within the test period. This combined approach allows distributed quantification of water balance components including subsurface water transfer.

Subsurface Flow

Water balance

10.5194/hess-16-1969-2012

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Spatio‐temporal assessment of the hydrological drivers of an active deep‐seated gravitational slope deformation: The Vögelsberg landslide in Tyrol (Austria)

Earth Surface Processes and Landforms (2021)

Jan Pfeiffer Thomas Zieher Jan Schmieder Martin Rutzinger Ulrich Strasser

Summary 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 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. The objective of this study was to assess hydrological 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). Hydrometeorological variables were simulated using the AMUNDSEN (Alpine MUltiscale Numerical Distributed Simulation ENgine) hydroclimatological 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 hydrometeorological 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 hydrometeorological 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.

Hydrometeorology

Snowmelt

10.1002/esp.5129

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Integration of hydro-climatological model and remote sensing for glacier mass balance estimation

Iwona Podsiadlo Claudia Paris Francesca Bovolo Mattia Callegari Ludovica De Gregorio

The accurate monitoring and understanding of glacier dynamics are of high relevance for climate science and water-resources management. The glacier parameters are typically estimated by data assimilation methods which inject field measurements into the numerical simulations with the aim of improving the physical model estimates. However, these methods often are not able to capture and model the complexity of the estimation problem. To solve this problem, this paper proposes a method that integrates remote sensing (RS) data, in-situ observations and a physical-based model to accurately estimate the Glacier Mass Balance (GMB). The RS data are used to represent the physical properties of the glaciers by characterizing their topography and spectral properties. Instead of assimilating the observations into the model, the in-situ measurements are used to perform a data-driven correction of the GMB estimates derived from the physically-based simulations in the informative RS feature space. The method is applied to the Alpine MUltiscale Numerical Distributed Simulation ENgine (AMUNDSEN) hydro-climatological model. In the experimental analysis, the multispectral images used to define the feature space are high-resolution Sentinel-2 images. The method is validated on three glaciers in Tyrol (Hintereis, Kasselwand and Varnagt glaciers), in 2015 and 2016. The obtained results show the effectiveness of the method in improving the GMB estimates.

Glacier mass balance

10.1117/12.2533232

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GLOWA Danube: Integrative Global Change Scenario Simulations for the Upper Danube Catchment First Results

Ulrich Strasser Wolfram Mauser Ralf Ludwig Karl Schneider Vern Lenz

1 Dept. of Earth and Environmental Sciences, Ludwig-Maximilians University (LMU), Luisenstr. 37, D-80333 Munich, Germany, u.strasser@iggf.geo.uni-muenchen.de 2 Department of Geography, University of Cologne, Albert-Magnus-Platz, 50923 Colgne, Germany 3 Institute of Hydraulic Engineering, University of Stuttgart, Pfaffenwaldring 61, 70569 Stuttgart, Germany 4 Division of Economic Geography, University of Regensburg, Universitatsstr. 31, 93053 Regensburg, Germany

Stuttgart

Technical university

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Storylines of combined future land use and climate scenarios and their hydrological impacts in an Alpine catchment (Brixental/Austria)

The Science of The Total Environment (2018)

Ulrich Strasser Kristian Förster Herbert Formayer Florentin Hofmeister Thomas Marke

Water balance

Catchment hydrology

Reforestation

Interception

10.1016/j.scitotenv.2018.12.077

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Citations (25)