Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2869-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2869-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 May 2021
Research article |
| 27 May 2021
| 27 May 2021
The evaluation of the potential of global data products for snow hydrological modelling in ungauged high-alpine catchments
Michael Weber
Department of Geography, Ludwig-Maximilians-Universität, Munich, Germany
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria
Matthias Bernhardt
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria
Karsten Schulz
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria
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EGUsphere, https://doi.org/10.5194/egusphere-2025-6382, https://doi.org/10.5194/egusphere-2025-6382, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Snow and rainfall measurements in mountain regions often underestimate the true amounts. We developed a method to correct Austrian precipitation data using weather stations and terrain features. The corrected data greatly improves runoff, snow and glacier modeling. This work shows that considering measurement errors is important for accurately assessing water resources, which is increasingly relevant as climate change affects alpine water systems supplying hydropower and downstream communities.
Oliver Konold, Moritz Feigl, Patrick Podest, Christoph Klingler, and Karsten Schulz
EGUsphere, https://doi.org/10.5194/egusphere-2025-4978, https://doi.org/10.5194/egusphere-2025-4978, 2025
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Flood forecasting systems depend on weather forecasts. However, weather forecasts always have an error when compared with historical observations. This causes flood predictions to become less accurate when switching from historical to forecast data. We tested artificial intelligence (AI) methods across 451 European river basins to address this challenge and found that using appropriate model design can turn this accuracy problem into something the system can learn to fix "on the fly".
Hatice Türk, Christine Stumpp, Markus Hrachowitz, Karsten Schulz, Peter Strauss, Günter Blöschl, and Michael Stockinger
Hydrol. Earth Syst. Sci., 29, 3935–3956, https://doi.org/10.5194/hess-29-3935-2025, https://doi.org/10.5194/hess-29-3935-2025, 2025
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Using advances in transit time estimation and tracer data, we tested if fast-flow transit times are controlled solely by soil moisture or if they are also controlled by precipitation intensity. We used soil-moisture-dependent and precipitation-intensity-conditional transfer functions. We showed that a significant portion of event water bypasses the soil matrix through fast flow paths (overland flow, tile drains, preferential-flow paths) in dry soil conditions for both low- and high-intensity precipitation.
Achille Capelli, Franziska Koch, Patrick Henkel, Markus Lamm, Florian Appel, Christoph Marty, and Jürg Schweizer
The Cryosphere, 16, 505–531, https://doi.org/10.5194/tc-16-505-2022, https://doi.org/10.5194/tc-16-505-2022, 2022
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Snow occurrence, snow amount, snow density and liquid water content (LWC) can vary considerably with climatic conditions and elevation. We show that low-cost Global Navigation Satellite System (GNSS) sensors as GPS can be used for reliably measuring the amount of water stored in the snowpack or snow water equivalent (SWE), snow depth and the LWC under a broad range of climatic conditions met at different elevations in the Swiss Alps.
Christian Voigt, Karsten Schulz, Franziska Koch, Karl-Friedrich Wetzel, Ludger Timmen, Till Rehm, Hartmut Pflug, Nico Stolarczuk, Christoph Förste, and Frank Flechtner
Hydrol. Earth Syst. Sci., 25, 5047–5064, https://doi.org/10.5194/hess-25-5047-2021, https://doi.org/10.5194/hess-25-5047-2021, 2021
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A continuously operating superconducting gravimeter at the Zugspitze summit is introduced to support hydrological studies of the Partnach spring catchment known as the Zugspitze research catchment. The observed gravity residuals reflect total water storage variations at the observation site. Hydro-gravimetric analysis show a high correlation between gravity and the snow water equivalent, with a gravimetric footprint of up to 4 km radius enabling integral insights into this high alpine catchment.
Christoph Klingler, Karsten Schulz, and Mathew Herrnegger
Earth Syst. Sci. Data, 13, 4529–4565, https://doi.org/10.5194/essd-13-4529-2021, https://doi.org/10.5194/essd-13-4529-2021, 2021
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LamaH-CE is a large-sample catchment hydrology dataset for Central Europe. The dataset contains hydrometeorological time series (daily and hourly resolution) and various attributes for 859 gauged basins. Sticking closely to the CAMELS datasets, LamaH includes additional basin delineations and attributes for describing a large interconnected river network. LamaH further contains outputs of a conceptual hydrological baseline model for plausibility checking of the inputs and for benchmarking.
Pirmin Philipp Ebner, Franziska Koch, Valentina Premier, Carlo Marin, Florian Hanzer, Carlo Maria Carmagnola, Hugues François, Daniel Günther, Fabiano Monti, Olivier Hargoaa, Ulrich Strasser, Samuel Morin, and Michael Lehning
The Cryosphere, 15, 3949–3973, https://doi.org/10.5194/tc-15-3949-2021, https://doi.org/10.5194/tc-15-3949-2021, 2021
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A service to enable real-time optimization of grooming and snow-making at ski resorts was developed and evaluated using both GNSS-measured snow depth and spaceborne snow maps derived from Copernicus Sentinel-2. The correlation to the ground observation data was high. Potential sources for the overestimation of the snow depth by the simulations are mainly the impact of snow redistribution by skiers, compensation of uneven terrain, or spontaneous local adaptions of the snow management.
Josef Fürst, Hans Peter Nachtnebel, Josef Gasch, Reinhard Nolz, Michael Paul Stockinger, Christine Stumpp, and Karsten Schulz
Earth Syst. Sci. Data, 13, 4019–4034, https://doi.org/10.5194/essd-13-4019-2021, https://doi.org/10.5194/essd-13-4019-2021, 2021
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Rosalia is a 222 ha forested research watershed in eastern Austria to study water, energy and solute transport processes. The paper describes the site, monitoring network, instrumentation and the datasets: high-resolution (10 min interval) time series starting in 2015 of four discharge gauging stations, seven rain gauges, and observations of air and water temperature, relative humidity, and conductivity, as well as soil water content and temperature, at different depths at four profiles.
Moritz Feigl, Katharina Lebiedzinski, Mathew Herrnegger, and Karsten Schulz
Hydrol. Earth Syst. Sci., 25, 2951–2977, https://doi.org/10.5194/hess-25-2951-2021, https://doi.org/10.5194/hess-25-2951-2021, 2021
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In this study we developed machine learning approaches for daily river water temperature prediction, using different data preprocessing methods, six model types, a range of different data inputs and 10 study catchments. By comparing to current state-of-the-art models, we could show a significant improvement of prediction performance of the tested approaches. Furthermore, we could gain insight into the relationships between model types, input data and predicted stream water temperature.
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Short summary
We compared a suite of globally available meteorological and DEM data with in situ data for physically based snow hydrological modelling in a small high-alpine catchment. Although global meteorological data were less suited to describe the snowpack properly, transferred station data from a similar location in the vicinity and substituting single variables with global products performed well. In addition, using 30 m global DEM products as model input was useful in such complex terrain.
We compared a suite of globally available meteorological and DEM data with in situ data for...
