Articles | Volume 29, issue 6
https://doi.org/10.5194/hess-29-1725-2025
© Author(s) 2025. 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-29-1725-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Mar 2025
Research article |
| 26 Mar 2025
| 26 Mar 2025
Scale dependency in modeling nivo-glacial hydrological systems: the case of the Arolla basin, Switzerland
Faculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy
Pascal Horton
Institute of Geography, and Oeschger Center on Climate Change Research, University of Bern, Bern, Switzerland
Bettina Schaefli
Institute of Geography, and Oeschger Center on Climate Change Research, University of Bern, Bern, Switzerland
Jamal Shokory
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
Felix Pitscheider
Faculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy
Leona Repnik
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
Mattia Gianini
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
Simone Bizzi
Department of Geosciences, University of Padua, Padua, Italy
Stuart N. Lane
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
Francesco Comiti
Faculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy
Department of Land, Environment, Agriculture and Forestry, University of Padua, Padua, Italy
Related authors
No articles found.
Pau Wiersma, Jan Magnusson, Nadav Peleg, Bettina Schaefli, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 30, 3331–3350, https://doi.org/10.5194/hess-30-3331-2026, https://doi.org/10.5194/hess-30-3331-2026, 2026
Short summary
Short summary
Streamflow observations contain information about snow, but their potential to constrain seasonal snow mass reconstructions remains underexplored. Using inverse hydrological modeling, we show that streamflow is particularly effective at constraining catchment-aggregated melt rates, but that non-uniqueness in the snow–streamflow relationship and uncertainties in the inverse modeling chain can easily limit inversion performance.
Malve Heinz, Annelie Holzkämper, Rohini Kumar, Sélène Ledain, Pascal Horton, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 30, 2879–2911, https://doi.org/10.5194/hess-30-2879-2026, https://doi.org/10.5194/hess-30-2879-2026, 2026
Short summary
Short summary
Droughts increasingly threaten agriculture. Improving soils to store more water, for example by increasing soil organic carbon, can help. We simulated this in a Swiss catchment and found that more soil carbon slightly increased soil water storage and evapotranspiration, modestly reduced floods, and shortened periods with very little streamflow. However in warmer, drier areas, these periods with little streamflow could sometimes last longer.
Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, and Joshua R. Larsen
Hydrol. Earth Syst. Sci., 30, 2613–2636, https://doi.org/10.5194/hess-30-2613-2026, https://doi.org/10.5194/hess-30-2613-2026, 2026
Short summary
Short summary
Investigating changing snow in response to global warming can be done with a simple model and only temperature and precipitation data, simplifying snow dynamics with assumptions and parameters. We provide a large-scale and long-term evaluation of this approach and its performance across diverse climates. Temperature thresholds are more robust over cold climates but melt parameters are more robust over warmer climates with deep snow. The model performs well across climates despite its simplicity.
Justine Berg, Pascal Horton, Martina Kauzlaric, Alexandra von der Esch, and Bettina Schaefli
EGUsphere, https://doi.org/10.5194/egusphere-2026-439, https://doi.org/10.5194/egusphere-2026-439, 2026
Short summary
Short summary
Snow and glacier melt are essential for water supply in mountain regions, but it is challenging to determine how much each source contributes to streamflow. To improve these estimates, we simulated 14 headwater catchments in Switzerland using a hydrological model modified to include snow redistribution and integrating glacier melt from a glacier evolution model. This allowed more accurate estimates of snow and glacier melt contributions, supporting future predictions of water availability.
Jonas Paccolat, Pietro de Anna, Stuart Nicholas Lane, Hannes Markus Peter, and Tom Battin
Hydrol. Earth Syst. Sci., 29, 7201–7216, https://doi.org/10.5194/hess-29-7201-2025, https://doi.org/10.5194/hess-29-7201-2025, 2025
Short summary
Short summary
With glacier retreat, barren areas are exposed to life settlement. Biofilms, surface attached colonies of microbes, are amongst the first colonizers. In low flow streams, they grow into millimeter thick mats gluing fine sediments together. We studied how such bio-clogging enhance water availability on lateral terraces, suffering from water scarcity. Mat permeability was quantified from streamside flume experiments and an idealized terrace model was conceived to estimate stream elongation.
Xinyang Fan, Florentin Hofmeister, Bettina Schaefli, and Gabriele Chiogna
EGUsphere, https://doi.org/10.5194/egusphere-2025-6065, https://doi.org/10.5194/egusphere-2025-6065, 2025
Short summary
Short summary
We adopt a physics-based, fully-integrated hydrological modeling approach to understand streamflow variations and their interactions with groundwater in a high-elevation glaciated environment. We demonstrate opportunities and challenges of integrating point-scale groundwater observations into a distributed model. This study sheds new lights on surface-subsurface processes in high alpine environments and highlights the importance of improving subsurface representation in hydrological modeling.
David Dorthe, Michael Pfister, and Stuart Nicholas Lane
Hydrol. Earth Syst. Sci., 29, 6309–6331, https://doi.org/10.5194/hess-29-6309-2025, https://doi.org/10.5194/hess-29-6309-2025, 2025
Short summary
Short summary
This study explores how climate change affects river temperatures under hydropower influence using numerical modeling. While average warming is similar to natural rivers, hydropower both increases vulnerability in low-flow areas and helps limit extreme temperatures through cold lake releases in summer. This research helps adapt hydropower production to protect aquatic species in a changing climate.
Florentin Hofmeister, Xinyang Fan, Madlene Pfeiffer, Ben Marzeion, Bettina Schaefli, and Gabriele Chiogna
EGUsphere, https://doi.org/10.5194/egusphere-2025-3256, https://doi.org/10.5194/egusphere-2025-3256, 2025
Short summary
Short summary
We use the WRF model for dynamically downscaling a global reanalysis product for the period 1850 to 2015 for the central European Alps. We demonstrate a workflow for transferring coarse-resolution (2 km) WRF temperature and precipitation to a much finer spatial resolution (25 m) of a physics-based hydrological model (WaSiM) and evaluate the results in a multi-data approach covering different simulation periods. Our results highlight the need for plausible and consistent elevation gradients.
Xinyang Fan, Florentin Hofmeister, Bettina Schaefli, and Gabriele Chiogna
EGUsphere, https://doi.org/10.5194/egusphere-2025-1500, https://doi.org/10.5194/egusphere-2025-1500, 2025
Preprint archived
Short summary
Short summary
We adopt a fully-distributed, physics-based hydrological modeling approach, to understand streamflow variations and their interactions with groundwater in a high-elevation glaciated environment. We demonstrate opportunities and challenges of integrating point-scale groundwater observations into a distributed model. This study sheds new lights on surface-subsurface processes in high alpine environments and highlights the importance of improving subsurface representation in hydrological modeling.
Malve Heinz, Maria Eliza Turek, Bettina Schaefli, Andreas Keiser, and Annelie Holzkämper
Hydrol. Earth Syst. Sci., 29, 1807–1827, https://doi.org/10.5194/hess-29-1807-2025, https://doi.org/10.5194/hess-29-1807-2025, 2025
Short summary
Short summary
Potato farmers in Switzerland are facing drier conditions and water restrictions. We explored how improving soil health and planting early-maturing potato varieties might help them to adapt. Using a computer model, we simulated potato yields and irrigation water needs under water scarcity. Our results show that earlier-maturing potato varieties reduce the reliance on irrigation but result in lower yields. However, improving soil health can significantly reduce yield losses.
Tom Müller, Mauro Fischer, Stuart N. Lane, and Bettina Schaefli
The Cryosphere, 19, 423–458, https://doi.org/10.5194/tc-19-423-2025, https://doi.org/10.5194/tc-19-423-2025, 2025
Short summary
Short summary
Based on extensive field observations in a highly glacierized catchment in the Swiss Alps, we develop a combined isotopic and glacio-hydrological model. We show that water stable isotopes may help to better constrain model parameters, especially those linked to water transfer. However, we highlight that separating snow and ice melt for temperate glaciers based on isotope mixing models alone is not advised and should only be considered if their isotopic signatures have clearly different values.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Proc. IAHS, 385, 121–127, https://doi.org/10.5194/piahs-385-121-2024, https://doi.org/10.5194/piahs-385-121-2024, 2024
Short summary
Short summary
This study assesses the impact of climate change on the timing, seasonality and magnitude of mean annual minimum (MAM) flows and annual maximum flows (AMF) in the Volta River basin (VRB). Several climate change projection data are use to simulate river flow under multiple greenhouse gas emission scenarios. Future projections show that AMF could increase with various magnitude but negligible shift in time across the VRB, while MAM could decrease with up to 14 days of delay in occurrence.
Tom Müller, Matteo Roncoroni, Davide Mancini, Stuart N. Lane, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 28, 735–759, https://doi.org/10.5194/hess-28-735-2024, https://doi.org/10.5194/hess-28-735-2024, 2024
Short summary
Short summary
We investigate the role of a newly formed floodplain in an alpine glaciated catchment to store and release water. Based on field measurements, we built a numerical model to simulate the water fluxes and show that recharge occurs mainly due to the ice-melt-fed river. We identify three future floodplains, which could emerge from glacier retreat, and show that their combined storage leads to some additional groundwater storage but contributes little additional baseflow for the downstream river.
Lindsay Marie Capito, Enrico Pandrin, Walter Bertoldi, Nicola Surian, and Simone Bizzi
Earth Surf. Dynam., 12, 321–345, https://doi.org/10.5194/esurf-12-321-2024, https://doi.org/10.5194/esurf-12-321-2024, 2024
Short summary
Short summary
We propose that the pattern of erosion and deposition from repeat topographic surveys can be a proxy for path length in gravel-bed rivers. With laboratory and field data, we applied tools from signal processing to quantify this periodicity and used these path length estimates to calculate sediment transport using the morphological method. Our results highlight the potential to expand the use of the morphological method using only remotely sensed data as well as its limitations.
Marvin Höge, Martina Kauzlaric, Rosi Siber, Ursula Schönenberger, Pascal Horton, Jan Schwanbeck, Marius Günter Floriancic, Daniel Viviroli, Sibylle Wilhelm, Anna E. Sikorska-Senoner, Nans Addor, Manuela Brunner, Sandra Pool, Massimiliano Zappa, and Fabrizio Fenicia
Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, https://doi.org/10.5194/essd-15-5755-2023, 2023
Short summary
Short summary
CAMELS-CH is an open large-sample hydro-meteorological data set that covers 331 catchments in hydrologic Switzerland from 1 January 1981 to 31 December 2020. It comprises (a) daily data of river discharge and water level as well as meteorologic variables like precipitation and temperature; (b) yearly glacier and land cover data; (c) static attributes of, e.g, topography or human impact; and (d) catchment delineations. CAMELS-CH enables water and climate research and modeling at catchment level.
Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, Adriaan J. Teuling, and Joshua R. Larsen
Earth Syst. Sci. Data, 15, 2577–2599, https://doi.org/10.5194/essd-15-2577-2023, https://doi.org/10.5194/essd-15-2577-2023, 2023
Short summary
Short summary
We provide a dataset of snow water equivalent, the depth of liquid water that results from melting a given depth of snow. The dataset contains 11 071 sites over the Northern Hemisphere, spans the period 1950–2022, and is based on daily observations of snow depth on the ground and a model. The dataset fills a lack of accessible historical ground snow data, and it can be used for a variety of applications such as the impact of climate change on global and regional snow and water resources.
Alessio Gentile, Davide Canone, Natalie Ceperley, Davide Gisolo, Maurizio Previati, Giulia Zuecco, Bettina Schaefli, and Stefano Ferraris
Hydrol. Earth Syst. Sci., 27, 2301–2323, https://doi.org/10.5194/hess-27-2301-2023, https://doi.org/10.5194/hess-27-2301-2023, 2023
Short summary
Short summary
What drives young water fraction, F*yw (i.e., the fraction of water in streamflow younger than 2–3 months), variations with elevation? Why is F*yw counterintuitively low in high-elevation catchments, in spite of steeper topography? In this paper, we present a perceptual model explaining how the longer low-flow duration at high elevations, driven by the persistence of winter snowpacks, increases the proportion of stored (old) water contributing to the stream, thus reducing F*yw.
Anthony Michelon, Natalie Ceperley, Harsh Beria, Joshua Larsen, Torsten Vennemann, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 27, 1403–1430, https://doi.org/10.5194/hess-27-1403-2023, https://doi.org/10.5194/hess-27-1403-2023, 2023
Short summary
Short summary
Streamflow generation processes in high-elevation catchments are largely influenced by snow accumulation and melt. For this work, we collected and analyzed more than 2800 water samples (temperature, electric conductivity, and stable isotopes of water) to characterize the hydrological processes in such a high Alpine environment. Our results underline the critical role of subsurface flow during all melt periods and the presence of snowmelt even during the winter periods.
Tom Müller, Stuart N. Lane, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 26, 6029–6054, https://doi.org/10.5194/hess-26-6029-2022, https://doi.org/10.5194/hess-26-6029-2022, 2022
Short summary
Short summary
This research provides a comprehensive analysis of groundwater storage in Alpine glacier forefields, a zone rapidly evolving with glacier retreat. Based on data analysis of a case study, it provides a simple perceptual model showing where and how groundwater is stored and released in a high Alpine environment. It especially points out the presence of groundwater storages in both fluvial and bedrock aquifers, which may become more important with future glacier retreat.
Feiko Bernard van Zadelhoff, Adel Albaba, Denis Cohen, Chris Phillips, Bettina Schaefli, Luuk Dorren, and Massimiliano Schwarz
Nat. Hazards Earth Syst. Sci., 22, 2611–2635, https://doi.org/10.5194/nhess-22-2611-2022, https://doi.org/10.5194/nhess-22-2611-2022, 2022
Short summary
Short summary
Shallow landslides pose a risk to people, property and infrastructure. Assessment of this hazard and the impact of protective measures can reduce losses. We developed a model (SlideforMAP) that can assess the shallow-landslide risk on a regional scale for specific rainfall events. Trees are an effective and cheap protective measure on a regional scale. Our model can assess their hazard reduction down to the individual tree level.
Alexandre Tuel, Bettina Schaefli, Jakob Zscheischler, and Olivia Martius
Hydrol. Earth Syst. Sci., 26, 2649–2669, https://doi.org/10.5194/hess-26-2649-2022, https://doi.org/10.5194/hess-26-2649-2022, 2022
Short summary
Short summary
River discharge is strongly influenced by the temporal structure of precipitation. Here, we show how extreme precipitation events that occur a few days or weeks after a previous event have a larger effect on river discharge than events occurring in isolation. Windows of 2 weeks or less between events have the most impact. Similarly, periods of persistent high discharge tend to be associated with the occurrence of several extreme precipitation events in close succession.
Stefan Brönnimann, Peter Stucki, Jörg Franke, Veronika Valler, Yuri Brugnara, Ralf Hand, Laura C. Slivinski, Gilbert P. Compo, Prashant D. Sardeshmukh, Michel Lang, and Bettina Schaefli
Clim. Past, 18, 919–933, https://doi.org/10.5194/cp-18-919-2022, https://doi.org/10.5194/cp-18-919-2022, 2022
Short summary
Short summary
Floods in Europe vary on time scales of several decades. Flood-rich and flood-poor periods alternate. Recently floods have again become more frequent. Long time series of peak stream flow, precipitation, and atmospheric variables reveal that until around 1980, these changes were mostly due to changes in atmospheric circulation. However, in recent decades the role of increasing atmospheric moisture due to climate warming has become more important and is now the main driver of flood changes.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 26, 1481–1506, https://doi.org/10.5194/hess-26-1481-2022, https://doi.org/10.5194/hess-26-1481-2022, 2022
Short summary
Short summary
Climate change impacts on water resources in the Volta River basin are investigated under various global warming scenarios. Results reveal contrasting changes in future hydrological processes and water availability, depending on greenhouse gas emission scenarios, with implications for floods and drought occurrence over the 21st century. These findings provide insights for the elaboration of regional adaptation and mitigation strategies for climate change.
Adrien Michel, Bettina Schaefli, Nander Wever, Harry Zekollari, Michael Lehning, and Hendrik Huwald
Hydrol. Earth Syst. Sci., 26, 1063–1087, https://doi.org/10.5194/hess-26-1063-2022, https://doi.org/10.5194/hess-26-1063-2022, 2022
Short summary
Short summary
This study presents an extensive study of climate change impacts on river temperature in Switzerland. Results show that, even for low-emission scenarios, water temperature increase will lead to adverse effects for both ecosystems and socio-economic sectors throughout the 21st century. For high-emission scenarios, the effect will worsen. This study also shows that water seasonal warming will be different between the Alpine regions and the lowlands. Finally, efficiency of models is assessed.
Cited articles
Arnold, N.: Investigating the sensitivity of glacier mass-balance/elevation profiles to changing meteorological conditions: Model experiments for Haut Glacier D'Arolla, Valais, Switzerland, Arc. Antarct. Alp. Res., 37, 139–145, https://doi.org/10.1657/1523-0430(2005)037[0139:ITSOGE]2.0.CO;2, 2005. a
Bárdossy, A. and Das, T.: Influence of rainfall observation network on model calibration and application, Hydrol. Earth Syst. Sci., 12, 77–89, https://doi.org/10.5194/hess-12-77-2008, 2008. a
Bárdossy, A. and Singh, S. K.: Robust estimation of hydrological model parameters, Hydrol. Earth Syst. Sci., 12, 1273–1283, https://doi.org/10.5194/hess-12-1273-2008, 2008. a
Bezinge, A., Clark, M. J., Gurnell, A. M., and Warburton, J.: The management of sediment transported by glacial melt-water streams and its significance for the estimation of sediment yield, Ann. Glaciol., 13, 1–5, https://doi.org/10.3189/S0260305500007527, 1989. a, b
Bradley, R. S., Vuille, M., Diaz, H. F., and Vergara, W.: Threats to Water Supplies in the Tropical Andes, Science, 312, 1753–1755, https://doi.org/10.1126/science.1114856, 2006. a
Brock, B. W., Willis, I. C., and Sharp, M. J.: Measurement and parameterization of albedo variations at haut glacier d'arolla, Switzerland, J. Glaciol., 46, 675–688, https://doi.org/10.3189/172756500781832675, 2000. a
Carenzo, M., Pellicciotti, F., Rimkus, S., and Burlando, P.: Assessing the transferability and robustness of an enhanced temperature-index glacier-melt model, J. Glaciol., 55, 258–274, https://doi.org/10.3189/002214309788608804, 2009. a
Carlstein, E.: The Use of Subseries Values for Estimating the Variance of a General Statistic from a Stationary Sequence, Ann. Stat., 14, 1171–1179, 1986. a
Clark, M. P. and Slater, A. G.: Probabilistic Quantitative Precipitation Estimation in Complex Terrain, J. Hydrometeorol., 7, 3–22, https://doi.org/10.1175/JHM474.1, 2006. a
Clark, M. P., Schaefli, B., Schymanski, S. J., Samaniego, L., Luce, C. H., Jackson, B. M., Freer, J. E., Arnold, J. R., Dan Moore, R., Istanbulluoglu, E., and Ceola, S.: Improving the theoretical underpinnings of process-based hydrologic models, Water Resour. Res., 52, 2350–2365, https://doi.org/10.1002/2015wr017910, 2016. a
Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J., Tang, G., Gharari, S., Freer, J. E., Whitfield, P. H., Shook, K. R., and Papalexiou, S. M.: The Abuse of Popular Performance Metrics in Hydrologic Modeling, Water Resour. Res., 57, e2020WR029001, https://doi.org/10.1029/2020WR029001, 2021. a, b, c, d
Dadic, R., Mott, R., Lehning, M., and Burlando, P.: Wind influence on snow depth distribution and accumulation over glaciers, J. Geophys. Res.-Earth, 115, F01012, https://doi.org/10.1029/2009JF001261, 2010. a
Ebtehaj, M., Moradkhani, H., and Gupta, H. V.: Improving robustness of hydrologic parameter estimation by the use of moving block bootstrap resampling, Water Resour. Res., 46, W07515, https://doi.org/10.1029/2009WR007981, 2010. a, b, c
Efron, B.: Bootstrap methods: another look at the jackknife, Ann. Stat., 7, 1–26, https://doi.org/10.1214/aos/1176344552, 1979. a, b
Efstratiadis, A. and Koutsoyiannis, D.: One decade of multi-objective calibration approaches in hydrological modelling: a review, Hydrolog. Sci. J., 55, 58–78, https://doi.org/10.1080/02626660903526292, 2010. a
Engel, M., Penna, D., Bertoldi, G., Vignoli, G., Tirler, W., and Comiti, F.: Controls on spatial and temporal variability in streamflow and hydrochemistry in a glacierized catchment, Hydrol. Earth Syst. Sci., 23, 2041–2063, https://doi.org/10.5194/hess-23-2041-2019, 2019. a
Fatichi, S., Rimkus, S., Burlando, P., Bordoy, R., and Molnar, P.: High-resolution distributed analysis of climate and anthropogenic changes on the hydrology of an Alpine catchment, J. Hydrol., 525, 362–382, https://doi.org/10.1016/j.jhydrol.2015.03.036, 2015. a
Fernandez, W., Vogel, R. M., and Sankarasubramanian, A.: Calage régional d'un modèle de bassin hydrologique, Hydrolog. Sci. J., 45, 689–707, https://doi.org/10.1080/02626660009492371, 2000. a
Finger, D., Pellicciotti, F., Konz, M., Rimkus, S., and Burlando, P.: The value of glacier mass balance, satellite snow cover images, and hourly discharge for improving the performance of a physically based distributed hydrological model, Water Resour. Res., 47, W07519, https://doi.org/10.1029/2010WR009824, 2011. a
Fischer, M., Huss, M., Barboux, C., and Hoelzle, M.: The new Swiss Glacier Inventory SGI2010: Relevance of using high-resolution source data in areas dominated by very small glaciers, Arct. Antarct. Alp. Res., 46, 933–945, https://doi.org/10.1657/1938-4246-46.4.933, 2014. a
Follum, M. L., Niemann, J. D., and Fassnacht, S. R.: A comparison of snowmelt-derived streamflow from temperature-index and modified-temperature-index snow models, Hydrol. Process., 33, 3030–3045, https://doi.org/10.1002/hyp.13545, 2019. a
Frenierre, J. L. and Mark, B. G.: A review of methods for estimating the contribution of glacial meltwater to total watershed discharge, Prog. Phys. Geog., 38, 173–200, https://doi.org/10.1177/0309133313516161, 2014. a
Gabbi, J., Carenzo, M., Pellicciotti, F., Bauder, A., and Funk, M.: A comparison of empirical and physically based glacier surface melt models for long-term simulations of glacier response, J. Glaciol., 60, 1199–1207, https://doi.org/10.3189/2014JoG14J011, 2014. a, b
Gabbud, C., Micheletti, N., and Lane, S. N.: Instruments and methods: Lidar measurement of surface melt for a temperate Alpine glacier at the seasonal and hourly scales, J. Glaciol., 61, 963–974, https://doi.org/10.3189/2015JoG14J226, 2015. a, b, c
Gabbud, C., Micheletti, N., and Lane, S. N.: Response of a temperate alpine valley glacier to climate change at the decadal scale, Geogr. Ann. A, 98, 81–95, https://doi.org/10.1111/geoa.12124, 2016. a, b
GLAMOS: Swiss Glacier Inventory 2016, release 2020, Glacier Monitoring Switzerland [data set], https://doi.org/10.18750/inventory.sgi2016.r2020, 2020. a, b, c, d
Götzinger, J. and Bárdossy, A.: Comparison of four regionalisation methods for a distributed hydrological model, J. Hydrol. 333, 374–384, https://doi.org/10.1016/j.jhydrol.2006.09.008, 2007. a, b
Griessinger, N., Seibert, J., Magnusson, J., and Jonas, T.: Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments, Hydrol. Earth Syst. Sci., 20, 3895–3905, https://doi.org/10.5194/hess-20-3895-2016, 2016. a, b
Guo, Y., Zhang, Y., Zhang, L., and Wang, Z.: Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review, WIREs Water, 8, e1487, https://doi.org/10.1002/wat2.1487, 2021. a, b, c
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a, b
Gyawali, D. R. and Bárdossy, A.: Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS, Hydrol. Earth Syst. Sci., 26, 3055–3077, https://doi.org/10.5194/hess-26-3055-2022, 2022. a
Hamon, W. R.: Estimating potential evapotranspiration, T. Am. Soc. Civ. Eng., 128, 324–338, 1963. a
He, Z. H., Parajka, J., Tian, F. Q., and Blöschl, G.: Estimating degree-day factors from MODIS for snowmelt runoff modeling, Hydrol. Earth Syst. Sci., 18, 4773–4789, https://doi.org/10.5194/hess-18-4773-2014, 2014. a, b
Hingray, B., Schaefli, B., Mezghani, A., and Hamdi, Y.: Signature-based model calibration for hydrological prediction in mesoscale Alpine catchments, Hydrolog. Sci. J., 55, 1002–1016, https://doi.org/10.1080/02626667.2010.505572, 2010. a, b, c
Hock, R.: A distributed temperature-index ice- and snowmelt model including potential direct solar radiation, J. Glaciol., 45, 101–111, https://doi.org/10.3189/s0022143000003087, 1999. a, b, c
Hock, R.: Temperature index melt modelling in mountain areas, J. Hydrol., 282, 104–115, https://doi.org/10.1016/S0022-1694(03)00257-9, 2003. a
Horton, P. and Argentin, A.-L.: Hydrobricks: v0.7.2, Zenodo [code], https://doi.org/10.5281/zenodo.11082505, 2024. a, b
Horton, P., Schaefli, B., Mezghani, A., Hingray, B., and Musy, A.: Assessment of climate-change impacts on alpine discharge regimes with climate model uncertainty, Hydrol. Process., 20, 2091–2109, https://doi.org/10.1002/hyp.6197, 2006. a
Horton, P., Schaefli, B., and Kauzlaric, M.: Why do we have so many different hydrological models? A review based on the case of Switzerland, WIREs Water, 9, e1574, https://doi.org/10.1002/wat2.1574, 2022. a, b
Houska, T., Kraft, P., Chamorro-Chavez, A., and Breuer, L.: SPOTting model parameters using a ready-made python package, PLoS ONE, 10, e0145180, https://doi.org/10.1371/journal.pone.0145180, 2015. a
Hundecha, Y. and Bárdossy, A.: Modeling of the effect of land use changes on the runoff generation of a river basin through parameter regionalization of a watershed model, J. Hydrol., 292, 281–295, https://doi.org/10.1016/j.jhydrol.2004.01.002, 2004. a
Hurni, M.: Assimilation of snow depth data from Sentinel-1 to improve the hydrological model of the Borgne catchment (VS), PhD thesis, EPFL, CREALP, https://infoscience.epfl.ch/handle/20.500.14299/189169 (last access: 18 March 2025), 2021. a
Huss, M., Farinotti, D., Bauder, A., and Funk, M.: Modelling runoff from highly glacierized alpine drainage basins in a changing climate, Hydrol. Process., 22, 3888–3902, https://doi.org/10.1002/hyp.7055, 2008. a
Immerzeel, W. W., Droogers, P., de Jong, S. M., and Bierkens, M. F.: Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing, Remote Sens. Environ., 113, 40–49, https://doi.org/10.1016/j.rse.2008.08.010, 2009. a
Ismail, M. F., Bogacki, W., Disse, M., Schäfer, M., and Kirschbauer, L.: Estimating degree-day factors of snow based on energy flux components, The Cryosphere, 17, 211–231, https://doi.org/10.5194/tc-17-211-2023, 2023. a, b
Jansson, P., Hock, R., and Schneider, T.: The concept of glacier storage: A review, J. Hydrol., 282, 116–129, https://doi.org/10.1016/S0022-1694(03)00258-0, 2003. a
Kirchner, J. W.: Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology, Water Resour. Res., 42, W03S04, https://doi.org/10.1029/2005WR004362, 2006. a
Kling, H. and Gupta, H.: On the development of regionalization relationships for lumped watershed models: The impact of ignoring sub-basin scale variability, J. Hydrol., 373, 337–351, https://doi.org/10.1016/j.jhydrol.2009.04.031, 2009. a
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019. a, b, c
Koboltschnig, G. R., Schöner, W., Zappa, M., Kroisleitner, C., and Holzmann, H.: Runoff modelling of the glacierized Alpine Upper Salzach basin (Austria): Multi-criteria result validation, Hydrol. Process., 22, 3950–3964, https://doi.org/10.1002/hyp.7112, 2008. a
Konz, M. and Seibert, J.: On the value of glacier mass balances for hydrological model calibration, J. Hydrol., 385, 238–246, https://doi.org/10.1016/j.jhydrol.2010.02.025, 2010. a
Künsch, H. R.: The Jackknife and the Bootstrap for General Stationary Observations, Ann. Stat., 17, 1217–1241, 1989. a
Lambiel, C., Maillard, B., Kummert, M., and Reynard, E.: Geomorphology of the Hérens valley (Swiss Alps), J. Maps, 12, 160–172, https://doi.org/10.1080/17445647.2014.999135, 2016. a
Lane, S. N. and Nienow, P. W.: Decadal-Scale Climate Forcing of Alpine Glacial Hydrological Systems, Water Resour. Res., 55, 2478–2492, https://doi.org/10.1029/2018WR024206, 2019. a, b, c
Lane, S. N., Bakker, M., Gabbud, C., Micheletti, N., and Saugy, J. N.: Sediment export, transient landscape response and catchment-scale connectivity following rapid climate warming and Alpine glacier recession, Geomorphology, 277, 210–227, https://doi.org/10.1016/j.geomorph.2016.02.015, 2017. a
Liang, X., Guo, J., and Leung, L. R.: Assessment of the effects of spatial resolutions on daily water flux simulations, J. Hydrol., 298, 287–310, https://doi.org/10.1016/j.jhydrol.2003.07.007, 2004. a
Linsbauer, A., Huss, M., Hodel, E., Bauder, A., Fischer, M., Weidmann, Y., Bärtschi, H., and Schmassmann, E.: The New Swiss Glacier Inventory SGI2016: From a Topographical to a Glaciological Dataset, Front. Earth Sci., 9, https://doi.org/10.3389/feart.2021.704189, 2021. a
Mair, D., Nienow, P., Sharp, M., Wohlleben, T., and Willis, I.: Influence of subglacial drainage system evolution on glacier surface motion: Haut Glacier d'Arolla, Switzerland, J. Geophys. Res.-Sol. Ea., 107, EPM 8-1–EPM 8-13, https://doi.org/10.1029/2001jb000514, 2002. a
Mair, D., Willis, I., Fischer, U. H., Hubbard, B., Nienow, P., and Hubbard, A.: Hydrological controls on patterns of surface, internal and basal motion during three ”spring events”: Haut Glacier d' Arolla, Switzerland, J. Glaciol., 49, 555–567, https://doi.org/10.3189/172756503781830467, 2003. a
Martinec, J.: The degree-day factor for snowmelt-runoff forecasting, in: IAHS Publication, in: IUGG General Assembly of Helsinki, Helsinki, Finland, 51, 468–477, 1960. a
MeteoSwiss: Documentation of MeteoSwiss Grid-Data Products: Daily Precipitation (final analysis): RhiresD, Tech. Rep., MeteoSwiss, https://www.meteoswiss.admin.ch/dam/jcr:4f51f0f1-0fe3-48b5-9de0-15666327e63c/ProdDoc_RhiresD.pdf (last access: 18 March 2025), 2019a. a
MeteoSwiss: Documentation of MeteoSwiss Grid-Data Products: Daily Mean, Minimum and Maximum Temperature: TabsD, TminD, TmaxD, Tech. Rep., MeteoSwiss, https://www.meteoswiss.admin.ch/dam/jcr:818a4d17-cb0c-4e8b-92c6-1a1bdf5348b7/ProdDoc_TabsD.pdf (last access: 18 March 2025), 2019b. a
MeteoSwiss: Spatial Climate Analyses, MeteoSwiss [data set], https://www.meteoswiss.admin.ch/climate/the-climate-of-switzerland/spatial-climate-analyses.html, last access: 18 March 2025. a
Michelon, A., Benoit, L., Beria, H., Ceperley, N., and Schaefli, B.: Benefits from high-density rain gauge observations for hydrological response analysis in a small alpine catchment, Hydrol. Earth Syst. Sci., 25, 2301–2325, https://doi.org/10.5194/hess-25-2301-2021, 2021. a
Mosley, M.: Delimitation of New Zealand hydrologic regions, J. Hydrol., 49, 173–192, 1981. a
Nasab, M. T. and Chu, X.: Do sub-daily temperature fluctuations around the freezing temperature alter macro-scale snowmelt simulations?, J. Hydrol., 596, 125683, https://doi.org/10.1016/j.jhydrol.2020.125683, 2021. a
Ohmura, A.: Physical basis for the temperature-based melt-index method, J. Appl. Meteorol., 40, 753–761, https://doi.org/10.1175/1520-0450(2001)040<0753:PBFTTB>2.0.CO;2, 2001. a
Parajka, J. and Blöschl, G.: Validation of MODIS snow cover images over Austria, Hydrol. Earth Syst. Sci., 10, 679–689, https://doi.org/10.5194/hess-10-679-2006, 2006. a
Parajka, J. and Blöschl, G.: The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models, J. Hydrol., 358, 240–258, https://doi.org/10.1016/j.jhydrol.2008.06.006, 2008. a, b
Parajka, J., Merz, R., and Blöschl, G.: A comparison of regionalisation methods for catchment model parameters, Hydrol. Earth Syst. Sci., 9, 157–171, https://doi.org/10.5194/hess-9-157-2005, 2005. a, b
Pellicciotti, F., Brock, B., Strasser, U., Burlando, P., Funk, M., and Corripio, J.: An enhanced temperature-index glacier melt model including the shortwave radiation balance: Development and testing for Haut Glacier d'Arolla, Switzerland, J. Glaciol., 51, 573–587, https://doi.org/10.3189/172756505781829124, 2005. a
Penna, D., Engel, M., Bertoldi, G., and Comiti, F.: Towards a tracer-based conceptualization of meltwater dynamics and streamflow response in a glacierized catchment, Hydrol. Earth Syst. Sci., 21, 23–41, https://doi.org/10.5194/hess-21-23-2017, 2017. a
Pokhrel, P., Gupta, H. V., and Wagener, T.: A spatial regularization approach to parameter estimation for a distributed watershed model, Water Resour. Res., 44, W12419, https://doi.org/10.1029/2007WR006615, 2008. a
Rango, A. and Martinec, J.: Revisiting the degree-day method for snowmelt computations, J. Am. Water Resour. As., 31, 657–669, https://doi.org/10.1111/j.1752-1688.1995.tb03392.x,, 1995. a
Rinaldo, A., Marani, A., and Rigon, R.: Geomorphological dispersion, Water Resour. Res., 27, 513–525, https://doi.org/10.1029/90WR02501, 1991. a
Rinaldo, A., Botter, G., Bertuzzo, E., Uccelli, A., Settin, T., and Marani, M.: Transport at basin scales: 1. Theoretical framework, Hydrol. Earth Syst. Sci., 10, 19–29, https://doi.org/10.5194/hess-10-19-2006, 2006. a
Ritter, A. and Muñoz-Carpena, R.: Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments, J. Hydrol., 480, 33–45, https://doi.org/10.1016/j.jhydrol.2012.12.004, 2013. a
Ruelland, D.: Potential of snow data to improve the consistency and robustness of a semi-distributed hydrological model using the SAFRAN input dataset, J. Hydrol., 631, 130820, https://doi.org/10.1016/j.jhydrol.2024.130820, 2024. a, b
Samaniego, L., Kumar, R., and Attinger, S.: Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., 46, W05523, https://doi.org/10.1029/2008WR007327, 2010. a, b
Schaefli, B. and Gupta, H. V.: Do Nash values have value?, Hydrol. Process., 21, 2075–2080, https://doi.org/10.1002/hyp.6825, 2007. a
Schaefli, B. and Huss, M.: Integrating point glacier mass balance observations into hydrologic model identification, Hydrol. Earth Syst. Sci., 15, 1227–1241, https://doi.org/10.5194/hess-15-1227-2011, 2011. a, b
Schaefli, B., Hingray, B., Niggli, M., and Musy, A.: A conceptual glacio-hydrological model for high mountainous catchments, Hydrol. Earth Syst. Sci., 9, 95–109, https://doi.org/10.5194/hess-9-95-2005, 2005. a, b, c, d
Seibert, J.: Regionalisation of parameters for a conceptual rainfall-runoff model, Agr. Forest Meteorol., 98–99, 279–293, 1999. a
Sharp, M., Richards, K., Willis, I., Arnold, N., Nienow, P., Lawson, W., and Tison, J.: Geometry, bed topography and drainage system structure of the haut glacier d'Arolla, Switzerland, Earth Surf. Proc. Land., 18, 557–571, https://doi.org/10.1002/esp.3290180608, 1993. a
Shokory, J. A. N. and Lane, S. N.: Patterns and drivers of glacier debris-cover development in the Afghanistan Hindu Kush Himalaya, J. Glaciol., 69, 1260–1274, https://doi.org/10.1017/jog.2023.14, 2023. a, b
Singh, P. and Kumar, N.: Impact assessment of climate change on the hydrological response of a snow and glacier melt runoff dominated Himalayan river, J. Hydrol., 193, 316–350, https://doi.org/10.1016/S0022-1694(96)03142-3, 1997. a
Sivapalan, M., Blöschl, G., Zhang, L., and Vertessy, R.: Downward approach to hydrological prediction, Hydrol. Process., 17, 2101–2111, https://doi.org/10.1002/hyp.1425, 2003. a
Şorman, A. A., Şensoy, A., Tekeli, A. E., Şorman, A. Ü., and Akyürek, Z.: Modelling and forecasting snowmelt runoff process using the HBV model in the eastern part of Turkey, 23, 1031–1040, https://doi.org/10.1002/hyp.7204, 2009. a
Srinivas, V. V. and Srinivasan, K.: Hybrid moving block bootstrap for stochastic simulation of multi-site multi-season streamflows, J. Hydrol., 302, 307–330, https://doi.org/10.1016/j.jhydrol.2004.07.011, 2005. a, b
Swift, D. A., Nienow, P. W., Spedding, N., and Hoey, T. B.: Geomorphic implications of subglacial drainage configuration: rates of basal sediment evacuation controlled by seasonal drainage system evolution, Sediment. Geol., 149, 5–19, https://doi.org/10.1016/S0037-0738(01)00241-X, 2002. a
Swift, D. A., Nienow, P. W., Hoey, T. B., and Mair, D. W.: Seasonal evolution of runoff from Haut Glacier d'Arolla, Switzerland and implications for glacial geomorphic processes, J. Hydrol., 309, 133–148, https://doi.org/10.1016/j.jhydrol.2004.11.016, 2005. a
Tague, C. L., Papuga, S. A., Gerlein-Safdi, C., Dymond, S., Morrison, R. R., Boyer, E. W., Riveros-Iregui, D., Agee, E., Arora, B., Dialynas, Y. G., Hansen, A., Krause, S., Kuppel, S., Loheide, S. P., Schymanski, S. J., and Zipper, S. C.: Adding our leaves: A community-wide perspective on research directions in ecohydrology, Hydrol. Process., 34, 1665–1673, https://doi.org/10.1002/hyp.13693, 2020. a
Thibert, E., Sielenou, P. D., Vionnet, V., Eckert, N., and Vincent, C.: Causes of Glacier Melt Extremes in the Alps Since 1949, Geophys. Res. Lett., 45, 817–825, https://doi.org/10.1002/2017GL076333, 2018. a
Tiwari, D., Trudel, M., and Leconte, R.: On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow, Hydrol. Earth Syst. Sci., 28, 1127–1146, https://doi.org/10.5194/hess-28-1127-2024, 2024. a
Tobias, W., Manfred, S., Klaus, J., Zappa, M., and Bettina, S.: The future of Alpine Run-of-River hydropower production: Climate change, environmental flow requirements, and technical production potential, Sci. Total Environ., 890, 163934, https://doi.org/10.1016/j.scitotenv.2023.163934, 2023. a
Tobin, C., Schaefli, B., Nicótina, L., Simoni, S., Barrenetxea, G., Smith, R., Parlange, M., and Rinaldo, A.: Improving the degree-day method for sub-daily melt simulations with physically-based diurnal variations, Adv. Water Resour., 55, 149–164, https://doi.org/10.1016/j.advwatres.2012.08.008, 2013. a
Troy, T. J., Wood, E. F., and Sheffield, J.: An efficient calibration method for continental-scale land surface modeling, Water Resour. Res., 44, W09411, https://doi.org/10.1029/2007WR006513, 2008. a, b
USGS: Landsat, USGS [data set], https://earthexplorer.usgs.gov/, last access: 18 March 2025. a
van Tiel, M., Stahl, K., Freudiger, D., and Seibert, J.: Glacio-hydrological model calibration and evaluation, WIREs Water, 7, e1483, https://doi.org/10.1002/wat2.1483, 2020. a, b
Vogel, R. M. and Shallcross, A. L.: The moving blocks bootstrap versus parametric time series models, Water Resour. Res., 32, 1875–1882, https://doi.org/10.1029/96WR00928, 1996. a
Wagener, T. and Wheater, H. S.: Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty, J. Hydrol., 320, 132–154, https://doi.org/10.1016/j.jhydrol.2005.07.015, 2006. a
Zuecco, G., Carturan, L., Blasi, F. D., Seppi, R., Zanoner, T., Penna, D., Borga, M., Carton, A., and Fontana, G. D.: Understanding hydrological processes in glacierized catchments: Evidence and implications of highly variable isotopic and electrical conductivity data, Hydrol. Process., 33, 816–832, https://doi.org/10.1002/hyp.13366, 2019. a
Short summary
In this article, we show that by taking the optimal parameters calibrated with a semi-lumped model for the discharge at a catchment's outlet, we can accurately simulate runoff at various points within the study area, including three nested and three neighboring catchments. In addition, we demonstrate that employing more intricate melt models, which better represent physical processes, enhances the transfer of parameters in the simulation, until we observe overparameterization.
In this article, we show that by taking the optimal parameters calibrated with a semi-lumped...
