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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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
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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.
Christoph Schürz, Bano Mehdi, Jens Kiesel, Karsten Schulz, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 24, 4463–4489, https://doi.org/10.5194/hess-24-4463-2020, https://doi.org/10.5194/hess-24-4463-2020, 2020
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The USLE is a commonly used model to estimate soil erosion by water. It quantifies soil loss as a product of six inputs representing rainfall erosivity, soil erodibility, slope length and steepness, plant cover, and support practices. Many methods exist to derive these inputs, which can, however, lead to substantial differences in the estimated soil loss. Here, we analyze the effect of different input representations on the estimated soil loss in a large-scale study in Kenya and Uganda.
Cited articles
Abimbola, O. P., Wenninger, J., Venneker, R., and Mittelstet, A. R.: The
assessment of water resources in ungauged catchments in Rwanda, J. Hydrol.: Reg. Stud., 13, 274–289, https://doi.org/10.1016/j.ejrh.2017.09.001, 2017. a, b
Abrams, M. and Crippen, R.: ASTER GDEM V3 (Aster Global DEM): User Guide, available at: https://lpdaac.usgs.gov/documents/434/ASTGTM_User_Guide_V3.pdf (last access: 25 March 2021), 2019. a
Adams, M. S., Bühler, Y., and Fromm, R.: Multitemporal Accuracy and
Precision Assessment of Unmanned Aerial System Photogrammetry for Slope-Scale
Snow Depth Maps in Alpine Terrain, Pure Appl. Geophys., 175, 3303–3324, https://doi.org/10.1007/s00024-017-1748-y, 2018. a
Arkin, P., Xie, P., and National Center for Atmospheric Research Staff: The Climate Data Guide: CMAP: CPC Merged Analysis of Precipitation, available at:
https://climatedataguide.ucar.edu/climate-data/cmap-cpc-merged-analysis-precipitation, last access: 24 December 2018. a
Bandyopadhyay, J., Rodda, J. C., Kattelmann, R., Kundzewicz, Z., and Kraemer,
D.: Highland waters – a resource of global significance, in: Mountains of the World. A global priority, edited by: Messerli, B. and Ives, J. D.,
Parthenon Publishing, New York, Carnforth, 131–155, https://doi.org/10.1002/(SICI)1099-145X(200003/04)11:2<197::AID-LDR390>3.0.CO;2-U, 1997. a
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a
warming climate on water availability in snow-dominated regions, Nature, 438,
303–309, https://doi.org/10.1038/nature04141, 2005. a, b
Barrett, A. P.: National Operational Hydrologic Remote Sensing Center SNOw Data Assimilation System (SNODAS) Products at NSIDC: Special Report, NSIDC, Boulder, Colorado, 2003. a
Beniston, M.: Is snow in the Alps receding or disappearing?, Wiley
Interdisciplin. Rev.: Clim. Change, 3, 349–358, https://doi.org/10.1002/wcc.179, 2012. a
Bergstrom, S.: The HBV model (Chapter 13, pp. 443–476), in: Computer models of watershed hydrology, edited by: Singh, V. P., Water Resources Publications, Highlands Ranch, Colorado, USA, 1130 pp., 1995. a
Bernhardt, M., Härer, S., Feigl, M., and Schulz, K.: Der Wert Alpiner
Forschungseinzugsgebiete im Bereich der Fernerkundung, der
Schneedeckenmodellierung und der lokalen Klimamodellierung,
Österreichische Wasser- und Abfallwirtschaft, 70, 515–528,
https://doi.org/10.1007/s00506-018-0510-8, 2018. a
Blöschl, G., Sivapalan, M., Wagener, T., Viglione, A., and Savenije, H. (Eds.): Runoff Predictions in Ungauged Basins: A Synthesis across Processes, Places and Scales, Cambridge University Press, Cambridge,
https://doi.org/10.1017/CBO9781139235761, 2013. a, b
Bormann, K. J., Brown, R. D., Derksen, C., and Painter, T. H.: Estimating
snow-cover trends from space, Nat. Clim. Change, 8, 924–928,
https://doi.org/10.1038/s41558-018-0318-3, 2018. a
Brown, P. D. and Mote, P. W.: The Response of Northern Hemisphere Snow Cover to a Changing Climate, J. Climate, 2008, 2124–2145,
https://doi.org/10.1175/2008JCLI2665.1, 2008. a
Broxton, P. D., Leeuwen, W. J. D., and Biederman, J. A.: Improving Snow Water
Equivalent Maps With Machine Learning of Snow Survey and Lidar Measurements,
Water Resour. Res., 73, 3739–3757, https://doi.org/10.1029/2018WR024146, 2019. a
Brunt, D.: Notes on radiation in the atmosphere. I, Q. J. Roy. Meteorol. Soc., 58, 389–420, https://doi.org/10.1002/qj.49705824704, 1932. a
Bühler, Y., Marty, M., Egli, L., Veitinger, J., Jonas, T., Thee, P., and
Ginzler, C.: Snow depth mapping in high-alpine catchments using digital
photogrammetry, The Cryosphere, 9, 229–243, https://doi.org/10.5194/tc-9-229-2015,
2015. a, b
Buytaert, W., Vuille, M., Dewulf, A., Urrutia, R., Karmalkar, A., and
Célleri, R.: Uncertainties in climate change projections and regional
downscaling in the tropical Andes: implications for water resources
management, Hydrol. Earth Syst. Sci., 14, 1247–1258,
https://doi.org/10.5194/hess-14-1247-2010, 2010. a
Casson, D. R., Werner, M., Weerts, A., and Solomatine, D.: Global re-analysis
datasets to improve hydrological assessment and snow water equivalent
estimation in a sub-Arctic watershed, Hydrol. Earth Syst. Sci., 22, 4685–4697, https://doi.org/10.5194/hess-22-4685-2018, 2018. a, b
Christensen, J. H., Boberg, F., Christensen, O. B., and Lucas-Picher, P.: On
the need for bias correction of regional climate change projections of
temperature and precipitation, Geophys. Res. Lett., 35, L20709, https://doi.org/10.1029/2008GL035694, 2008. a
Currier, W. R., Pflug, J., Mazzotti, G., Jonas, T., Deems, J. S., Bormann,
K. J., Painter, T. H., Hiemstra, C. A., Gelvin, A., Uhlmann, Z., Spaete, L.,
Glenn, N. F., and Lundquist, J. D.: Comparing Aerial Lidar Observations With
Terrestrial Lidar and Snow–Probe Transects From NASA's 2017 SnowEx Campaign,
Water Resour. Res., 55, 6285–6294, https://doi.org/10.1029/2018WR024533, 2019. a
Dadic, R., Mott, R., Lehning, M., and Burlando, P.: Wind influence on snow
depth distribution and accumulation over glaciers, Environ. Res. Lett., 115, 1064, https://doi.org/10.1029/2009JF001261, 2010. a
Danielson, J. J. and Gesch, D. B.: Global Multi-resolution Terrain Elevation
Data 2010 (GMTED2010), available at: https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf (last access: 24 May 2021), 2011. a
Dornes, P. F., Pomeroy, J. W., Pietroniro, A., Carey, S. K., and Quinton, W. L.: Influence of landscape aggregation in modelling snow-cover ablation and snowmelt runoff in a sub-arctic mountainous environment, Hydrolog. Sci. J., 53, 725–740, https://doi.org/10.1623/hysj.53.4.725, 2008. a
Dussaillant, J. A., Buytaert, W., Meier, C., and Espinoza, F.: Hydrological
regime of remote catchments with extreme gradients under accelerated change:
the Baker basin in Patagonia, Hydrolog. Sci. J., 57, 1530–1542,
https://doi.org/10.1080/02626667.2012.726993, 2012. a
DWD – Deutscher Wetterdienst: Wetter und Klima im Überblick, available at: https://www.dwd.de, last access: 28 December 2018. a
Ellis, C. R., Pomeroy, J. W., Brown, T., and MacDonald, J.: Simulations of snow accumulation and melt in needleleaf forest environments, Hydrol. Earth Syst. Sci., 14, 925–940, https://doi.org/10.5194/hess-14-925-2010, 2010. a, b
Essery, R. and Etchevers, P.: Parameter sensitivity in simulations of snowmelt, J. Geophys. Res., 109, D20111, https://doi.org/10.1029/2004JD005036, 2004. a
Essery, R., Rutter, N., Pomeroy, J. W., Baxter, R., Stähli, M., Gustafsson, D., Barr, A., Bartlett, P., and Elder, K.: SNOWMIP2: An Evaluation of Forest Snow Process Simulations, B. Am. Meteorol. Soc., 90, 1120–1135, https://doi.org/10.1175/2009BAMS2629.1, 2009. a
Essou, G. R., Brissette, F., and Lucas-Picher, P.: Impacts of combining
reanalyses and weather station data on the accuracy of discharge modelling, J. Hydrol., 545, 120–131, https://doi.org/10.1016/j.jhydrol.2016.12.021, 2017. a, b
Essou, G. R. C., Sabarly, F., Lucas-Picher, P., Brissette, F., and Poulin, A.: Can Precipitation and Temperature from Meteorological Reanalyses Be Used for Hydrological Modeling?, J. Hydrometeorol., 17, 1929–1950,
https://doi.org/10.1175/JHM-D-15-0138.1, 2016. a, b
Eylander, J. B., Peter-Lidard, C. D., and Kumar, S. V.: The AFWA Next
Generation Land Data Assimilation System, available at:
http://www.nrlmry.navy.mil/BACIMO/2005/Proceedings/5 NWP/5.02 NWP Eylander Land Surface Assimilation Paper.pdf, last access: 24 May 2021. a
Fang, X., Pomeroy, J. W., Ellis, C. R., MacDonald, M. K., DeBeer, C. M., and
Brown, T.: Multi-variable evaluation of hydrological model predictions for a
headwater basin in the Canadian Rocky Mountains, Hydrol. Earth Syst. Sci., 17, 1635–1659, https://doi.org/10.5194/hess-17-1635-2013, 2013. a
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.:
The Shuttle Radar Topography Mission, Rev. Geophys., 45, 1485,
https://doi.org/10.1029/2005RG000183, 2007. a, b, c
Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis, D., Downer, C. W., Camporese, M., Davison, J. H., Ebel, B., Jones, N., Kim, J., Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M., and Tarboton, D.: An overview of current applications, challenges, and future
trends in distributed process-based models in hydrology, J. Hydrol., 537, 45–60, https://doi.org/10.1016/j.jhydrol.2016.03.026, 2016. a
Fekete, B. M., Robarts, R. D., Kumagai, M., Nachtnebel, H.-P., Odada, E., and
Zhulidov, A V.: Time for in situ renaissance, Science, 349, 685–686, https://doi.org/10.1126/science.aac7358, 2015. a
Feki, H., Slimani, M., and Cudennec, C.: Geostatistically based optimization of a rainfall monitoring network extension: case of the climatically
heterogeneous Tunisia, Hydrol. Res., 48, 514–541, https://doi.org/10.2166/nh.2016.256, 2017. a
Flügel, W.: Delineating hydrological response units by geographical
information system analyses for regional hydrological modelling using
PRMS/MMS in the drainage basin of the River Bröl, Germany, Hydrol. Process., 1995, 423–436, 1995. a
Förster, K., Oesterle, F., Hanzer, F., Schöber, J., Huttenlau, M., and Strasser, U.: A snow and ice melt seasonal prediction modelling system for Alpine reservoirs, Proc. Int. Assoc. Hydrol. Sci., 374, 143–150, https://doi.org/10.5194/piahs-374-143-2016, 2016. a, b
Freudiger, D., Kohn, I., Seibert, J., Stahl, K., and Weiler, M.: Snow
redistribution for the hydrological modeling of alpine catchments, Wires Water, 4, e1232, https://doi.org/10.1002/wat2.1232, 2017. a
Fuka, D. R., Walter, M. T., MacAlister, C., Degaetano, A. T., Steenhuis, T. S., and Easton, Z. M.: Using the Climate Forecast System Reanalysis as weather input data for watershed models, Hydrol. Process., 28, 5613–5623,
https://doi.org/10.1002/hyp.10073, 2013. a, b, c
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S.,
Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The
climate hazards infrared precipitation with stations – a new environmental
record for monitoring extremes, Scient. Data, 2, 150066,
https://doi.org/10.1038/sdata.2015.66, 2015. a, b
Gampe, D., Schmid, J., and Ludwig, R.: Impact of Reference Dataset Selection on RCM Evaluation, Bias Correction, and Resulting Climate Change Signals of
Precipitation, J. Hydrometeorol., 20, 1813–1828, https://doi.org/10.1175/JHM-D-18-0108.1, 2019. a
Gao, L., Bernhardt, M., and Schulz, K.: Downscaling ERA-Interim temperature
data in comlex terrain, Hydrol. Earth Syst. Sci., 16, 4661–4673,
https://doi.org/10.5194/hess-16-4661-2012, 2012. a, b, c
Garen, D. C.: Choosing and Assimilation Forcing Data for Hydrological Prediction, in: Putting Prediction in Ungauged Basins into Practice, edited
by: Pomeroy, J. W., Whitfield, P. H., and Spence, C., Canadian Water Resources Association, ISBN 978-1-896513-38-6, 2013. a
Garnier, B. J. and Ohmura, A.: The evaluation of surface variations in solar
radiation income, Sol. Energy, 13, 21–34, https://doi.org/10.1016/0038-092X(70)90004-6, 1970. a
Gascoin, S., Grizonnet, M., Bouchet, M., Salgues, G., and Hagolle, O.: Theia
Snow collection: high-resolution operational snow cover maps from Sentinel-2
and Landsat-8 data, Earth Syst. Sci. Data, 11, 493–514,
https://doi.org/10.5194/essd-11-493-2019, 2019. a
Germann, U. and Joss, J.: Operational Measurement of Precipitation in
Mountainous Terrain, in: Weather Radar, edited by: Meischner, P., Springer, Berlin, Heidelberg, 52–77, 2004. a
Gesch, D. B., Verdin, K. L., and Greenlee, S. K.: New land surface digital
elevation model covers the Earth, Eos Trans. Am. Geophys. Union, 80, 69–70, https://doi.org/10.1029/99EO00050, 1999. a, b
Girons Lopez, M., Vis, M. J. P., Jenicek, M., Griessinger, N., and Seibert,
J.: Complexity and performance of temperature-based snow routines for runoff
modelling in mountainous areas in Central Europe, Hydrol. Earth Syst. Sci. [preprint], https://doi.org/10.5194/hess-2020-57, 2020. a
Granger, R. J. and Pomeroy, J. W.: Sustainability of the western Canadian
boreal forest under changing hydrological conditions. I. Snow accumulation
and ablation, Sustainability of Water Resources under Increasing Uncertainty,
in: IAHS Publ No. 240, edited by: Rosjberg, D., Boutayeb, N., Gustard, A., Kundzewicz, Z., and Rasmussen, P., IAHS Press, Wallingford, 243–250, 1997. a
Grossi, G., Lendvai, A., Peretti, G., and Ranzi, R.: Snow Precipitation
Measured by Gauges: Systematic Error Estimation and Data Series Correction in
the Central Italian Alps, Water, 9, 461, https://doi.org/10.3390/w9070461, 2017. a, b
Grünewald, T., Stötter, J., Pomeroy, J. W., Dadic, R., Moreno Baños, I., Marturià, J., Spross, M., Hopkinson, C., Burlando, P., and Lehning, M.: Statistical modelling of the snow depth distribution in open alpine terrain, Hydrol. Earth Syst. Sci., 17, 3005–3021, https://doi.org/10.5194/hess-17-3005-2013, 2013. a
Guth, P. L.: Geomorphometry from SRTM, Photogram. Eng. Remote Sens., 72, 269–277, https://doi.org/10.14358/PERS.72.3.269, 2006. a
Haberkorn, A.: European Snow Booklet – an Inventory of Snow Measurements in
Europe, EnviDat, https://doi.org/10.16904/ENVIDAT.59, 2019. a
Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., and Bayr, K. J.: MODIS snow-cover products, Remote Sens. Environ., 83, 181–194, https://doi.org/10.1016/S0034-4257(02)00095-0, 2002. a
Härer, S., Bernhardt, M., Corripio, J. G., and Schulz, K.: PRACTISE –
Photo Rectification And ClassificaTIon SoftwarE (V.1.0), Geosci. Model Dev., 6, 837–848, https://doi.org/10.5194/gmd-6-837-2013, 2013. a
Härer, S., Bernhardt, M., and Schulz, K.: PRACTISE – Photo Rectification
And ClassificaTIon SoftwarE (V.2.1), Geosci. Model Dev., 9, 307–321, https://doi.org/10.5194/gmd-9-307-2016, 2016. a
Härer, S., Bernhardt, M., Siebers, M., and Schulz, K.: On the need for a
time- and location-dependent estimation of the NDSI threshold value for
reducing existing uncertainties in snow cover maps at different scales, The
Cryosphere, 12, 1629–1642, https://doi.org/10.5194/tc-12-1629-2018, 2018. a, b
Hay, L. E. and Clark, M. P.: Use of statistically and dynamically downscaled
atmospheric model output for hydrologic simulations in three mountainous
basins in the western United States, J. Hydrol., 282, 56–75,
https://doi.org/10.1016/S0022-1694(03)00252-X, 2003. a
Hersbach, H., Bell, W., Berrisford, P., Horányi, A. J. M.-S., Nicolas,
J., Radu, R., Schepers, D., Simmons, A., Soci, C., and Dee, D.: Global
reanalysis: goodbye ERA-Interim, hello ERA5, European Centre for Medium-Range Weather Forecasts, Reading, UK, https://doi.org/10.21957/VF291HEHD7, 2019. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global
reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/QJ.3803, 2020. a, b, c
Hirsch, R. M. and Costa, J. E.: U.S. stream flow measurement and data
dissemination improve, Eos Trans. AGU, 85, 197–203, https://doi.org/10.1029/2004eo200002, 2004. a
Hirtlreiter, G.: Spät- und postglaziale Gletscherschwankungen im
Wettersteingebirge und seiner Umgebung: Münchner Geographische
Abhandlungen, in: Münchner Geographische Abhandlungen (B), Ludwig-Maximilians-University Munich, Munich, 1992. a
Hopkinson, C., Chasmer, L., Munro, S., and Demuth, M. N.: The influence of DEM resolution on simulated solar radiation-induced glacier melt, Hydrol. Process., 24, 775–788, https://doi.org/10.1002/hyp.7531, 2010. a
Hrachowitz, M. and Weiler, M.: Uncertainty of Precipitation Estimates Caused by Sparse Gauging Networks in a Small, Mountainous Watershed, J. Hydrol. Eng., 16, 460–471, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000331, 2011. a
Hrachowitz, M., Savenije, H., Blöschl, G., McDonnell, J. J., Sivapalan, M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret, U., Fenicia, F., Freer, J. E., Gelfan, A., Gupta, H. V., Hughes, D. A., Hut, R. W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P. A., Uhlenbrook, S.,
Wagener, T., Winsemius, H. C., Woods, R. A., Zehe, E., and Cudennec, C.: A
decade of Predictions in Ungauged Basins (PUB) – a review, Hydrolog. Sci. J., 58, 1198–1255, https://doi.org/10.1080/02626667.2013.803183, 2013. a
Huggel, C., Carey, M., Clague, J. J., and Kaab, A.: The high-mountain
cryosphere: Environmental changes and human risks, Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9781107588653, 2015. a
Hürkamp, K., Zentner, N., Reckerth, A., Weishaupt, S., Wetzel, K.-F.,
Tschiersch, J., and Stumpp, C.: Spatial and Temporal Variability of Snow
Isotopic Composition on Mt. Zugspitze, Bavarian Alps, Germany, J. Hydrol. Hydromech., 67, 49–58, https://doi.org/10.2478/johh-2018-0019, 2019. a, b
Huss, M., Bookhagen, B., Huggel, C., Jacobsen, D., Bradley, R. S., Clague,
J. J., Vuille, M., Buytaert, W., Cayan, D. R., Greenwood, G., Mark, B. G.,
Milner, A. M., Weingartner, R., and Winder, M.: Toward mountains without
permanent snow and ice, Earth's Future, 5, 418–435, https://doi.org/10.1002/2016EF000514, 2017. a, b
Hüttl, C.: Steuerungsfaktoren und Quantifizierung der chemischen
Verwitterung auf dem Zugspitzplatt (Wettersteingebirge, Deutschland),
in: Münchener Geographische Abhandlungen Reihe B, Ludwig-Maximilians-University Munich, Munich, 1999. a
Kahl, A., Winstral, A., Marks, D., and Dozier, J.: Using Satellite Imagery and the Distributed ISNOBAL Energy Balance Model to Derive SWE Heterogeneity in Mountainous Basins, in: Putting Prediction in Ungauged Basins into Practice, edited by: Pomeroy, J. W., Whitfield, P. H., and Spence, C., Canadian Water Resources Association, ISBN 978-1-896513-38-6, 2013. a
Kim, R. S., Durand, M., and Liu, D.: Spectral analysis of airborne passive
microwave measurements of alpine snowpack: Colorado, USA, Remote Sens. Environ., 205, 469–484, https://doi.org/10.1016/j.rse.2017.07.025, 2018. a
Koch, F., Bach, H., Prasch, M., Weber, M., Braun, L., and Mauser, W.: Climate
Change and Energy – Impact of Snow and Glacier Melting on Hydropower in the
Catchment Area of the Upper, Korrespondenz Wasserwirtschaft, 2011, 319–328,
2011. a
Koch, F., Henkel, P., Appel, F., Schmid, L., Bach, H., Lamm, M., Prasch, M.,
Schweizer, J., and Mauser, W.: Retrieval of Snow Water Equivalent, Liquid
Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal
Attenuation and Time Delay, Water Resour. Res., 9, 4465–4487, https://doi.org/10.1029/2018WR024431, 2019. a
König-Langlo, G. and Augstein, F.: Parameterization of the downward
long-wave radiation at the Earth surface in polar regions, Meteorol. Z., 3,
343–347, 1994. a
Konzelmann, T., van de Wal, R., Greuell, W., Bintanja, R., Henneken, E., and
Abeouchi, A.: Parameterization of global and longwave incoming radiation for
the Greenland Ice Sheet, Global Planet. Change, 9, 143–164,
https://doi.org/10.1016/0921-8181(94)90013-2, 1994. a
Kundzewicz, Z. W. and Stakhiv, E. Z.: Are climate models “ready for prime
time” in water resources management applications, or is more research needed?, Hydrolog. Sci. J., 55, 1085–1089, https://doi.org/10.1080/02626667.2010.513211, 2010. a
Kunkel, K. E.: Simple Procedures for Extrapolation of Humidity Variables in the Mountainous Western United States, J. Climate, 2, 656–670,
https://doi.org/10.1175/1520-0442(1989)002<0656:SPFEOH>2.0.CO;2, 1989. a
Kwok, R. and Markus, T.: Potential basin-scale estimates of Arctic snow depth
with sea ice freeboards from CryoSat-2 and ICESat-2: An exploratory analysis,
Adv. Space Res., 62, 1243–1250, https://doi.org/10.1016/j.asr.2017.09.007, 2018. a
Lettenmaier, D. P., Alsdorf, D., Dozier, J., Huffman, G. J., Pan, M., and Wood, E. F.: Inroads of remote sensing into hydrologic science during the WRR era, Water Resour. Res., 51, 7309–7342, https://doi.org/10.1002/2015WR017616, 2015. a
Lievens, H., Demuzere, M., Marshall, H.-P., Reichle, R. H., Brucker, L.,
Brangers, I., de Rosnay, P., Dumont, M., Girotto, M., Immerzeel, W. W.,
Jonas, T., Kim, E. J., Koch, I., Marty, C., Saloranta, T., Schöber, J.,
and de Lannoy, G. J. M.: Snow depth variability in the Northern Hemisphere
mountains observed from space, Nat. Commun., 10, 4629, https://doi.org/10.1038/s41467-019-12566-y, 2019. a, b
Liston, G. E. and Elder, K.: A Meteorological Distribution System for
High-Resolution Terrestrial Modeling (MicroMet), J. Hydrometeorol., 7, 217–234, https://doi.org/10.1175/JHM486.1, 2006. a, b, c, d
Liu, S., Mo, X., Liu, C., Xia, J., and Zhao, W.: How to maximize the predictive values of available data in ungauged basins? – Chinese Lesson, in: Putting Prediction in Ungauged Basins into Practice, edited by: Pomeroy, J. W., Whitfield, P. H., and Spence, C., Canadian Water Resources Association, ISBN 978-1-896513-38-6, 2013. a, b, c, d
López-Moreno, J. I., Pomeroy, J. W., Revuelto, J., and Vicente-Serrano, S. M.: Response of snow processes to climate change: spatial variability in a
small basin in the Spanish Pyrenees, Hydrol. Process., 27, 2637–2650,
https://doi.org/10.1002/hyp.9408, 2013. a
Love, T. B., Kumar, V., Xie, P., and Thiaw, W.: A 20-year daily Africa precipitation climatology using satellite and gauge data, in: Proceedings of the 84th AMS Annual Meeting, vol. Conference on Applied Climatology, Seattle, 4 pp., available at: https://www.cpc.ncep.noaa.gov/products/fews/AFR_CLIM/appl_clim.pdf (last access: 24 May 2021), 2004. a
Ludwig, R. and Schneider, P.: Validation of digital elevation models from SRTM X-SAR for applications in hydrologic modeling, ISPRS J. Photogram. Remote Sens., 60, 339–358, https://doi.org/10.1016/j.isprsjprs.2006.05.003, 2006. a
Lv, Z., Pomeroy, J. W., and Fang, X.: Evaluation of SNODAS Snow Water
Equivalent in Western Canada and Assimilation Into a Cold Region Hydrological
Model, Water Resour. Res., 55, 11166–11187, https://doi.org/10.1029/2019WR025333, 2019. a
MacDonald, M. K., Pomeroy, J. W., and Pietroniro, A.: On the importance of sublimation to an alpine snow mass balance in the Canadian Rocky Mountains, Hydrol. Earth Syst. Sci., 14, 1401–1415, https://doi.org/10.5194/hess-14-1401-2010, 2010. a
Marks, D., Domingo, J., Susong, D., Link, T., and Garen, D.: A spatially
distributed energy balance snowmelt model for application in mountain basins,
Hydrol. Process., 13, 1935–1959,
https://doi.org/10.1002/(SICI)1099-1085(199909)13:12/13<1935::AID-HYP868>3.0.CO;2-C,
1999. a
Marti, R., Gascoin, S., Berthier, E., de Pinel, M., Houet, T., and Laffly, D.: Mapping snow depth in open alpine terrain from stereo satellite imagery, The Cryosphere, 10, 1361–1380, https://doi.org/10.5194/tc-10-1361-2016, 2016. a
Mauser, W., and Prasch, M.: Regional Assessment of Global Change Impacts. The Project GLOWA-Danube, Springer International Publishing, Cham, ISBN 978-3-319-16751-0, https://doi.org/10.1007/978-3-319-16751-0, 2016. a
Merwade, V., Olivera, F., Arabi, M., and Edleman, S.: Uncertainty in Flood
Inundation Mapping: Current Issues and Future Directions, J. Hydrol. Eng., 13, 608–620, https://doi.org/10.1061/(ASCE)1084-0699(2008)13:7(608), 2008. a
Meybeck, M., Green, P., and Vörösmarty, C.: A New Typology for
Mountains and Other Relief Classes, Mt. Res. Dev., 21, 34–45,
https://doi.org/10.1659/0276-4741(2001)021[0034:ANTFMA]2.0.CO;2, 2001. a
Morche, D. and Schmidt, K. H.: Sediment transport in an alpine river before and after a dambreak flood event, Earth Surf. Proc. Land., 37, 347–353, https://doi.org/10.1002/esp.2263, 2012. a
Mott, R., Scipión, D., Schneebeli, M., Dawes, N., Berne, A., and Lehning,
M.: Orographic effects on snow deposition patterns in mountainous terrain,
J. Geophys. Res.-Atmos., 119, 1419–1439, https://doi.org/10.1002/2013JD019880, 2014. a
Muerth, M. J., Gauvin St-Denis, B., Ricard, S., Velázquez, J. A., Schmid,
J., Minville, M., Caya, D., Chaumont, D., Ludwig, R., and Turcotte, R.: On
the need for bias correction in regional climate scenarios to assess climate
change impacts on river runoff, Hydrol. Earth Syst. Sci., 17, 1189–1204, https://doi.org/10.5194/hess-17-1189-2013, 2013. a
Muñoz Sabater, J.: ERA5-Land hourly data from 1981 to present,
ECMWF, https://doi.org/10.24381/CDS.E2161BAC, 2019. a, b
Nagaveni, C., Kumar, K. P., and Ravibabu, M. V.: Evaluation of TanDEMx and SRTM DEM on watershed simulated runoff estimation, J. Earth Syst. Sci., 128, 73, https://doi.org/10.1007/s12040-018-1035-z, 2019. a, b
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models: Part 1 – A discussion of principles, J. Hydrol., 10, 282–290, 1970. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, Environ. Res. Lett., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011. a
Notarnicola, C.: Hotspots of snow cover changes in global mountain regions over 2000–2018, Remote Sens. Environ., 243, 111781, https://doi.org/10.1016/j.rse.2020.111781, 2020. a, b
Piani, C., Haerter, J. O., and Coppola, E.: Statistical bias correction for
daily precipitation in regional climate models over Europe, Theor. Appl. Climatol., 99, 187–192, https://doi.org/10.1007/s00704-009-0134-9, 2010. a
Poli, P., Hersbach, H., Dee, D. P., Berrisford, P., Simmons, A. J., Vitart, F., Laloyaux, P., Tan, D. G. H., Peubey, C., Thépaut, J.-N., Trémolet, Y., Hólm, E. V., Bonavita, M., Isaksen, L., and Fisher, M.: ERA-20C: An Atmospheric Reanalysis of the Twentieth Century, J. Climate, 29,
4083–4097, https://doi.org/10.1175/JCLI-D-15-0556.1, 2016. a, b, c
Pomeroy, J. W. and Li, L.: Prairie and arctic areal snow cover mass balance
using a blowing snow model, J. Geophys. Res., 105, 26619, https://doi.org/10.1029/2000JD900149, 2000. a
Pomeroy, J. W., Gray, D. M., Brown, T., Hedstrom, N. R., Quinton, W. L.,
Granger, R. J., and Carey, S. K.: The cold regions hydrological model: a
platform for basing process representation and model structure on physical
evidence, Hydrol. Process., 21, 2650–2667, https://doi.org/10.1002/hyp.6787, 2007. a, b, c
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004. a, b, c
Rott, H., Nagler, T., Ripper, E., Voglmeier, K., Prinz, R., Fromm, R., Coccia, A., Meta, A., Di Leo, D., and Schuttemeyer, D.: KU- and X-band backscatter analysis and SWE retrieval for Alpine snow, in: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Piscataway, NJ, 2407–2410, https://doi.org/10.1109/IGARSS.2014.6946957, 2014. a
Sabatini, F.: Setting up and Managing Automatic Weather Stations for Remote
Sites Monitoring: From Niger to Nepal, in: Renewing Local Planning to Face
Climate Change in the Tropics, Green Energy and Technology, edited by: Tiepolo, M., Pezzoli, A., and Tarchiani, V., Springer International Publishing, Cham, 21–39, https://doi.org/10.1007/978-3-319-59096-7_2, 2017. a
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R.,
Gayno, G., Wang, J., Hou, Y.-T., Chuang, H.-Y., Juang, H.-M. H., Sela, J.,
Iredell, M., Treadon, R., Kleist, D., van Delst, P., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., van den Dool, H., Kumar, A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z.,
Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and
Goldberg, M.: The NCEP Climate Forecast System Reanalysis, B. Am. Meteorol. Soc., 91, 1015–1058, https://doi.org/10.1175/2010BAMS3001.1, 2010. a, b
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H.-Y., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The NCEP Climate Forecast System Version 2, J. Climate, 27, 2185–2208,
https://doi.org/10.1175/JCLI-D-12-00823.1, 2014. a
Satgé, F., Ruelland, D., Bonnet, M.-P., Molina, J., and Pillco, R.:
Consistency of satellite-based precipitation products in space and over time
compared with gauge observations and snow- hydrological modelling in the Lake
Titicaca region, Hydrol. Earth Syst. Sci., 23, 595–619,
https://doi.org/10.5194/hess-23-595-2019, 2019. a
Schattan, P., Baroni, G., Oswald, S. E., Schöber, J., Fey, C., Kormann, C., Huttenlau, M., and Achleitner, S.: Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing, Water Resour. Res., 53, 3615–3634, https://doi.org/10.1002/2016WR020234, 2017. a
Sedlar, J. and Hock, R.: Testing longwave radiation parameterizations under
clear and overcast skies at Storglaciären, Sweden, The Cryosphere, 3,
75–84, https://doi.org/10.5194/tc-3-75-2009, 2009. a
Seyler, F., Muller, F., Cochonneau, G., Guimarães, L., and Guyot, J. L.:
Watershed delineation for the Amazon sub-basin system using GTOPO30 DEM and a
drainage network extracted from JERS SAR images, Hydrol. Process., 23, 3173–3185, https://doi.org/10.1002/hyp.7397, 2009. a
Shaw, T. E., Gascoin, S., Mendoza, P. A., Pellicciotti, F., and McPhee, J.:
Snow Depth Patterns in a High Mountain Andean Catchment from Satellite
Optical Tristereoscopic Remote Sensing, Water Resour. Res., 56, e2019WR024880, https://doi.org/10.1029/2019WR024880, 2020. a
Sheffield, J., Goteti, G., and Wood, E. F.: Development of a 50-Year
High-Resolution Global Dataset of Meteorological Forcings for Land Surface
Modeling, J. Climate, 19, 3088–3111, https://doi.org/10.1175/JCLI3790.1, 2006. a
Shi, J. and Dozier, J.: Mapping seasonal snow with SIR-C/X-SAR in mountainous
areas, Remote Sens. Environ., 59, 294–307, https://doi.org/10.1016/S0034-4257(96)00146-0, 1997. a
Shi, J. and Dozier, J.: Estimation of snow water equivalence using SIR-C/X-SAR. I. Inferring snow density and subsurface properties, IEEE T. Geosci. Remote, 38, 2465–2474, https://doi.org/10.1109/36.885195, 2000. a
Smiatek, G., Kunstmann, H., Knoche, R., and Marx, A.: Precipitation and temperature statistics in high-resolution regional climate models: Evaluation
for the European Alps, J. Geophys. Res., 114, D19107, https://doi.org/10.1029/2008JD011353, 2009. a
Sørensen, R. and Seibert, J.: Effects of DEM resolution on the calculation
of topographical indices: TWI and its components, J. Hydrol., 347, 79–89, https://doi.org/10.1016/j.jhydrol.2007.09.001, 2007. a
Tadono, T., Ishida, H., Oda, F., Naito, S., Minakawa, K., and Iwamoto, H.:
Precise Global DEM Generation by ALOS PRISM, ISPRS Ann. Photogram. Remote Sens. Spat. Inform. Sci., II-4, 71–76, https://doi.org/10.5194/isprsannals-II-4-71-2014, 2014. a, b
Tauro, F., Selker, J., van de Giesen, N., Abrate, T., Uijlenhoet, R., Porfiri, M., Manfreda, S., Caylor, K., Moramarco, T., Benveniste, J., Ciraolo, G., Estes, L., Domeneghetti, A., Perks, M. T., Corbari, C., Rabiei,
E., Ravazzani, G., Bogena, H., Harfouche, A., Brocca, L., Maltese, A.,
Wickert, A., Tarpanelli, A., Good, S., Lopez Alcala, J. M., Petroselli, A.,
Cudennec, C., Blume, T., Hut, R., and Grimaldi, S.: Measurements and
Observations in the XXI century (MOXXI): innovation and multi-disciplinarity
to sense the hydrological cycle, Hydrolog. Sci. J., 63, 169–196,
https://doi.org/10.1080/02626667.2017.1420191, 2018. a
Teutschbein, C., Wetterhall, F., and Seibert, J.: Evaluation of different
downscaling techniques for hydrological climate-change impact studies at the
catchment scale, Clim. Dynam., 37, 2087–2105, https://doi.org/10.1007/s00382-010-0979-8, 2011. a
Thornton, P. E., Running, S. W., and White, M. A.: Generating surfaces of daily meteorological variables over large regions of complex terrain, J. Hydrol., 190, 214–251, https://doi.org/10.1016/S0022-1694(96)03128-9, 1997. a
USGS: GTPO30 Global Digital Elevation Model, available at: https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-global-30-arc-second-elevation (last access: 24 May 2021), 1996. a, b
van de Giesen, N., Hut, R., and Selker, J.: The Trans-African Hydro-Meteorological Observatory (TAHMO), Wiley Interdisciplin. Rev.: Water, 1, 341–348, https://doi.org/10.1002/wat2.1034, 2014. a
Vaze, J., Teng, J., and Spencer, G.: Impact of DEM accuracy and resolution on
topographic indices, Environ. Model. Softw., 25, 1086–1098, https://doi.org/10.1016/j.envsoft.2010.03.014, 2010. a
Viviroli, D., Archer, D. R., Buytaert, W., Fowler, H. J., Greenwood, G. B.,
Hamlet, A. F., Huang, Y., Koboltschnig, G., Litaor, M. I., López-Moreno,
J. I., Lorentz, S., Schädler, B., Schreier, H., Schwaiger, K., Vuille, M., and Woods, R.: Climate change and mountain water resources: overview and
recommendations for research, management and policy, Hydrol. Earth Syst. Sci., 15, 471–504, https://doi.org/10.5194/hess-15-471-2011, 2011. a
Vuyovich, C. M., Jacobs, J. M., and Daly, S. F.: Comparison of passive
microwave and modeled estimates of total watershed SWE in the continental
United States, Water Resour. Res., 50, 9088–9102, https://doi.org/10.1002/2013WR014734, 2014. a
Weber, M., Bernhardt, M., Pomeroy, J. W., Fang, X., Härer, S., and Schulz, K.: Description of current and future snow processes in a small basin in the Bavarian Alps, Environ. Earth Sci., 75, 962, https://doi.org/10.1007/s12665-016-6027-1, 2016. a, b, c, d
Wesemann, J., Herrnegger, M., and Schulz, K.: Hydrological modelling in the
anthroposphere: predicting local runoff in a heavily modified high-alpine
catchment, J. Mount. Sci., 15, 921–938, https://doi.org/10.1007/s11629-017-4587-5, 2018. a
Wetzel, K.-F.: On the Hydrology of the Partnach Area in the Wetterstein
Mountains (Bavarian Alps), Erdkunde, 58, 172–186, https://doi.org/10.3112/erdkunde.2004.02.05, 2004. a
Whitfield, P. H., Moore, R. D., and Shook, K.: Summary and Synthesis of
Workshop Break Out Group Discussions, in: Putting Prediction in Ungauged
Basins into Practice, edited by: Pomeroy, J. W., Whitfield, P. H., and Spence, C., Canadian Water Resources Association, ISBN 978-1-896513-38-6, 271–304, 2013. a
Winstral, A., Elder, K., and Davis, R. E.: Spatial Snow Modeling of
Wind-Redistributed Snow Using Terrain-Based Parameters, J. Hydrometeorol.,
3, 524–538, https://doi.org/10.1175/1525-7541(2002)003<0524:SSMOWR>2.0.CO;2, 2002. a
Wortmann, M., Bolch, T., Menz, C., Tong, J., and Krysanova, V.: Comparison and Correction of High-Mountain Precipitation Data Based on Glacio-Hydrological Modeling in the Tarim River Headwaters (High Asia), J.
Hydrometeorol., 19, 777–801, https://doi.org/10.1175/JHM-D-17-0106.1, 2018. a
Wrobel, J.-P.: Berichtüber den Markierungsversuch auf dem Zugspitzplatt im Sommer 1980, Bayerisches Geologisches Landesamt, München, 1–6, 1980. a
Zhang, F., Zhang, H., Hagen, S. C., Ye, M., Wang, D., Gui, D., Zeng, C., Tian, L., and Liu, J.: Snow cover and runoff modelling in a high mountain catchment with scarce data: effects of temperature and precipitation parameters, Hydrol. Process., 29, 52–65, https://doi.org/10.1002/hyp.10125, 2015. a, b
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...
