Articles | Volume 30, issue 4
https://doi.org/10.5194/hess-30-1189-2026
© Author(s) 2026. 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-30-1189-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Mar 2026
Research article |
| 03 Mar 2026
| 03 Mar 2026
Assessing the impact of Earth Observation data-driven calibration of the melting coefficient on the LISFLOOD snow module
Valentina Premier
CORRESPONDING AUTHOR
Institute for Earth Observation, Eurac Research, Bolzano, Italy
Francesca Moschini
CORRESPONDING AUTHOR
European Commission, Joint Research Centre (JRC), Ispra, Italy
Rey Juan Carlos University, Madrid, Spain
Jesús Casado-Rodríguez
European Commission, Joint Research Centre (JRC), Ispra, Italy
Davide Bavera
Arcadia SIT, Milano, Italy
Carlo Marin
Institute for Earth Observation, Eurac Research, Bolzano, Italy
Alberto Pistocchi
European Commission, Joint Research Centre (JRC), Ispra, Italy
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Cited articles
Asaoka, Y. and Kominami, Y.: Incorporation of satellite-derived snow-cover area in spatial snowmelt modeling for a large area: determination of a gridded degree-day factor, Ann. Glaciol., 54, 205–213, 2013. a
Avanzi, F., Maurer, T., Glaser, S. D., Bales, R. C., and Conklin, M. H.: Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters, J. Hydrol., 582, 124478, https://doi.org/10.1016/j.jhydrol.2019.124478, 2020. a
Avanzi, F., Gabellani, S., Delogu, F., Silvestro, F., Pignone, F., Bruno, G., Pulvirenti, L., Squicciarino, G., Fiori, E., Rossi, L., Puca, S., Toniazzo, A., Giordano, P., Falzacappa, M., Ratto, S., Stevenin, H., Cardillo, A., Fioletti, M., Cazzuli, O., Cremonese, E., Morra di Cella, U., and Ferraris, L.: IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations, Zenodo [data set], https://doi.org/10.5281/zenodo.14093436, 2024. a
Barella, R., Marin, C., Gianinetto, M., and Notarnicola, C.: A novel approach to high resolution snow cover fraction retrieval in mountainous regions, in: IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, 3856–3859, https://doi.org/10.1109/IGARSS46834.2022.9884177, 2022. a, b
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, 2005. a
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Miralles, D. G., McVicar, T. R., Schellekens, J., and Bruijnzeel, L. A.: Global‐scale regionalization of hydrologic model parameters, Water Resour. Res., 52, 3599–3622, 2016. a
Beniston, M., Stoffel, M., and Hill, M.: Impacts of climatic change on water and natural hazards in the Alps: can current water governance cope with future challenges? Examples from the European “ACQWA” project, Environ. Sci. Policy, 14, 734–743, 2011. a
Beven, K.: How to make advances in hydrological modelling, Hydrol. Res., 50, 1481–1494, 2019. a
Beven, K. J.: Rainfall-runoff modelling: the primer, John Wiley & Sons, https://doi.org/10.1002/9781119951001, 2012. a
Blöschl, G., Hall, J., Parajka, J., Perdigão, R. A. P., Merz, B., Arheimer, B., Aronica, G. T., Bilibashi, A., Bonacci, O., Borga, M., Čanjevac, I., Castellarin, A., Chirico, G. B., Claps, P., Fiala, K., Frolova, N., Gorbachova, L., Gül, A., Hannaford, J., Harrigan, S., Kireeva, M., Kiss, A., Kjeldsen, T. R., Kohnová, S., Koskela, J. J., Ledvinka, O., Macdonald, N., Mavrova-Guirguinova, M., Mediero, L., Merz, R., Molnar, P., Montanari, A., Murphy, C., Osuch, M., Ovcharuk, V., Radevski, I., Rogger, M., Salinas, J. L., Sauquet, E., Šraj, M., Szolgay, J., Viglione, A., Volpi, E., Wilson, D., Zaimi, K., and Živković, N.: Changing climate shifts timing of European floods, Science, 357, 588–590, 2017. a
Bormann, K. J., Brown, R. D., Derksen, C., and Painter, T. H.: Estimating snow-cover trends from space, Nat. Clim. Change, 8, 924–928, 2018. a
Burek, P., van der Knijff, J., and Roo, A. D.: LISFLOOD DistributedWater Balance and Flood Simulation Model e Revised User Manual 2013, JRC Technical Reports, Joint Research Centre of the European Commission, Luxembourg, https://doi.org/10.2788/24719, 2013. a, b
Cammalleri, C., Micale, F., and Vogt, J.: On the value of combining different modelled soil moisture products for European drought monitoring, J. Hydrol., 525, 547–558, 2015. a
Cammalleri, C., Vogt, J., and Salamon, P.: Development of an operational low-flow index for hydrological drought monitoring over Europe, Hydrolog. Sci. J., 62, 346–358, 2017. a
Casado-Rodríguez, J., de Wiart, C. C., Grimaldi, S., Zsoter, E., Baugh, C., Bosshard, N., Mikuličková, M., Pechlivanidis, I., Prudhomme, C., and Salamon, P.: Optimizing Warnings for Hydrologic Ensemble Prediction Systems for Improved Decision-Making, J. Hydrometeorol., 26, 675–689, https://doi.org/10.1175/JHM-D-24-0054.1, 2025. a
De Roo, A., Wesseling, C., and Deursen, W. V.: Physically based river basin modelling within a GIS: The LISFLOOD model, Hydrol. Process., 14, 1981–1992, https://doi.org/10.1002/1099-1085(20000815/30)14:11/12<1981::aid-hyp49>3.0.co;2-f, 2000. a, b
Engel, M., Notarnicola, C., Endrizzi, S., and Bertoldi, G.: Snow model sensitivity analysis to understand spatial and temporal snow dynamics in a high-elevation catchment, Hydrol. Process., 31, 4151–4168, https://doi.org/10.1002/hyp.11314, 2017. 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, https://doi.org/10.1029/2010WR009824, 2011. a, b
Finger, D., Vis, M., Huss, M., and Seibert, J.: The value of multiple data set calibration versus model complexity for improving the performance of hydrological models in mountain catchments, Water Resour. Res., 51, 1939–1958, 2015. a
Fortin, F.-A., Rainville, F.-M. D., Gardner, M.-A., Parizeau, M., and Gagné, C.: DEAP: Evolutionary Algorithms Made Easy, J. Mach. Learn. Res., 13, 2171–2175, 2012. 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
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
Hall, D. and Riggs, G.: MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 61, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD10A1.061, 2021. 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
Helbig, N., van Herwijnen, A., Magnusson, J., and Jonas, T.: Fractional snow-covered area parameterization over complex topography, Hydrol. Earth Syst. Sci., 19, 1339–1351, https://doi.org/10.5194/hess-19-1339-2015, 2015. a
Hock, R.: Temperature index melt modelling in mountain areas, J. Hydrol., 282, 104–115, 2003. a
HR-S&I consortium: Product User Manual for Snow Products, https://land.copernicus.eu/user-corner/technical-library/hrsi-snow-pum (last access: 8 November 2024), 2020. a
H-SAF Team: Algorithm Theoretical Baseline Document (ATBD) for product H35 – FSC-H Effective snow cover by VIS/IR radiometry, Tech. rep., EUMETSAT H SAF, https://hsaf.meteoam.it/Products/Detail?prod=H35 (last access: 23 January 2026), 2020. a
JRC: European Flood Awareness System v5.3, https://confluence.ecmwf.int/display/CEMS/Latest+operational+EFAS+release (last access: 23 January 2026), 2024a. a
JRC: Global Flood Awareness System v4.2, https://confluence.ecmwf.int/display/CEMS/Latest+operational+GloFAS+release (last access: 23 January 2026), 2024b. a
Kelleher, C., McGlynn, B., and Wagener, T.: Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding, Hydrol. Earth Syst. Sci., 21, 3325–3352, https://doi.org/10.5194/hess-21-3325-2017, 2017. 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, 1–5, 2006. a
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, 2012. a
Lee, W. Y., Gim, H.-J., and Park, S. K.: Parameterizations of Snow Cover, Snow Albedo and Snow Density in Land Surface Models: A Comparative Review, Asia-Pac. J. Atmos. Sci., 60, 185–210, 2024. a
Liston, G. E.: Representing subgrid snow cover heterogeneities in regional and global models, J. Climate, 17, 1381–1397, 2004. a
Matthews, G., Baugh, C., Barnard, C., Carton De Wiart, C., Colonese, J., Grimaldi, S., Ham, D., Hansford, E., Harrigan, S., Heiselberg, S., Hooker, H., Hossain, S., Mazzetti, C., Milano, L., Moschini, F., O’Regan, K., Pappenberger, F., Pfister, D., Rajbhandari, R. M., Salamon, P., Ramos, A., Shelton, K., Stephens, E., Tasev, D., Turner, M., van den Homberg, M., Wittig, J., Zsótér, E., and Prudhomme, C.: Chapter 15 – On the operational implementation of the Global Flood Awareness System (GloFAS), in: Flood Forecasting, Second Edition, edited by: Adams, T. E., Gangodagamage, C., and Pagano, T. C., Academic Press, second edition, 299–350, ISBN 978-0-443-14009-9, https://doi.org/10.1016/B978-0-443-14009-9.00014-6, 2025a. a
Matthews, G., Baugh, C., Barnard, C., De Wiart, C. C., Colonese, J., Decremer, D., Grimaldi, S., Hansford, E., Mazzetti, C., O’Regan, K., Pappenberger, F., Ramos, A., Salamon, P., Tasev, D., and Prudhomme, C.: Chapter 14 – On the operational implementation of the European Flood Awareness System (EFAS), in: Flood Forecasting (Second Edition), edited by: Adams, T. E., Gangodagamage, C., and Pagano, T. C., Academic Press, second edition, 251–298, ISBN 978-0-443-14009-9, https://doi.org/10.1016/B978-0-443-14009-9.00005-5, 2025b. a
Molotch, N. P. and Margulis, S. A.: Estimating the distribution of snow water equivalent using remotely sensed snow cover data and a spatially distributed snowmelt model: A multi-resolution, multi-sensor comparison, Adv. Water Resour., 31, 1503–1514, 2008. a
Mott, R.: Climatological snow data since 1998, OSHD, EnviDat [data set], https://doi.org/10.16904/envidat.401, 2023. a
Mott, R., Winstral, A., Cluzet, B., Helbig, N., Magnusson, J., Mazzotti, G., Quéno, L., Schirmer, M., Webster, C., and Jonas, T.: Operational snow-hydrological modeling for Switzerland, Front. Earth Sci., 11, 1228158, https://doi.org/10.3389/feart.2023.1228158, 2023. a
Niu, G.-Y. and Yang, Z.-L.: An observation-based formulation of snow cover fraction and its evaluation over large North American river basins, J. Geophys. Res.-Atmos., 112, https://doi.org/10.1029/2007JD008674, 2007. 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
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, 2005. a
Pimentel, R., Herrero, J., and Polo, M. J.: Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography, Hydrol. Earth Syst. Sci., 21, 805–820, https://doi.org/10.5194/hess-21-805-2017, 2017. a
Pistocchi, A., Bagli, S., Callegari, M., Notarnicola, C., and Mazzoli, P.: On the direct calculation of snow water balances using snow cover information, Water, 9, 848, https://doi.org/10.3390/w9110848, 2017. a
Premier, V.: vpremier/SCA4LISFLOOD: v1.0.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.18800162, 2026. a
Premier, V., Moschini, F., Casado Rodríguez, J., Bavera, D., Marin, C., and Pistocchi, A.: Daily Snow Cover Fraction (SCF) for the nine river basins across Europe (1st October 2017–30th September 2023) (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.14961639, 2025. a
Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T., et al.: Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018, Nature, 581, 294–298, 2020. a
Ragettli, S. and Pellicciotti, F.: Calibration of a physically based, spatially distributed hydrological model in a glacierized basin: On the use of knowledge from glaciometeorological processes to constrain model parameters, Water Resour. Res., 48, https://doi.org/10.1029/2011WR010559, 2012. a
Riboust, P., Thirel, G., Moine, N. L., and Ribstein, P.: Revisiting a Simple Degree-Day Model for Integrating Satellite Data: Implementation of Swe-Sca Hystereses, J. Hydrol. Hydromech., 67, 70–81, https://doi.org/10.2478/johh-2018-0004, 2019. a
Riggs, G. and Hall, D.: VIIRS/NPP CGF Snow Cover Daily L3 Global 375m SIN Grid, Version 2, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/PN50Y51IVNLE, 2022. a
Riggs, G. and Hall, D.: VIIRS/NPP Snow Cover Daily L3 Global 375m SIN Grid, Version 2, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/45VDCKJBXWEE, 2023. a
Riggs, G. A., Hall, D. K., and Román, M. O.: MODIS snow products collection 6 user guide, National Snow and Ice Data Center, Boulder, CO, USA, p. 66, https://modis-snow-ice.gsfc.nasa.gov/uploads/C6_MODIS_Snow_User_Guide.pdf? (last access: 23 January 2026) 2015. a
Roesch, A., Wild, M., Gilgen, H., and Ohmura, A.: A new snow cover fraction parametrization for the ECHAM4 GCM, Clim. Dynam., 17, 933–946, 2001. a
Ruelland, D.: Development of the snow-and ice-accounting routine (SIAR), J. Hydrol., 624, 129867, https://doi.org/10.1016/j.jhydrol.2023.129867, 2023. a, b, c
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.2023.129867, 2024. a, b, c, d
Speers, D. and Versteeg, J.: Runoff forecasting for reservoir operations-the past and the future, Proceedings 52nd Western Snow Conference, 149–156, 1982. a
Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J., Kärnä, J.-P., Koskinen, J., and Bojkov, B.: Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements, Remote Sens. Environ., 115, 3517–3529, 2011. a
Tedesco, M.: Remote sensing and the cryosphere, John Wiley & Sons, 1–16, https://doi.org/10.1002/9781118368909, 2015. a
Thiemig, V., Gomes, G. N., Skøien, J. O., Ziese, M., Rauthe-Schöch, A., Rustemeier, E., Rehfeldt, K., Walawender, J. P., Kolbe, C., Pichon, D., Schweim, C., and Salamon, P.: EMO-5: a high-resolution multi-variable gridded meteorological dataset for Europe, Earth Syst. Sci. Data, 14, 3249–3272, https://doi.org/10.5194/essd-14-3249-2022, 2022. a
Thirel, G., Notarnicola, C., Kalas, M., Zebisch, M., Schellenberger, T., Tetzlaff, A., Duguay, M., Mölg, N., Burek, P., and De Roo, A.: Assessing the quality of a real-time Snow Cover Area product for hydrological applications, Remote Sens. Environ., 127, 271–287, 2012. a
Umirbekov, A., Essery, R., and Müller, D.: GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers, Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, 2024. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Viviroli, D., Dürr, H. H., Messerli, B., Meybeck, M., and Weingartner, R.: Mountains of the world, water towers for humanity: Typology, mapping, and global significance, Water Resour. Res., 43, https://doi.org/10.1029/2006WR005653, 2007. a
Xiao, M., Mascaro, G., Wang, Z., Whitney, K. M., and Vivoni, E. R.: On the value of satellite remote sensing to reduce uncertainties of regional simulations of the Colorado River, Hydrol. Earth Syst. Sci., 26, 5627–5646, https://doi.org/10.5194/hess-26-5627-2022, 2022. a, b
Zhu, Z., Tang, X., Zhang, J., Liu, L., Gao, C., Zhang, S., Wang, G., Jin, J., Liu, C., Xu, H., et al.: Enhanced runoff simulation by precise capture of snowmelt variation signals with satellite-based snow products in a high-elevation basin, J. Hydrol., 643, 131920, https://doi.org/10.1016/j.jhydrol.2024.131920, 2024. a
Short summary
This study evaluates the snow module of LISFLOOD by replacing the discharge-calibrated snowmelt coefficient with pixel-wise values calibrated using EO (Earth Observation) snow cover fraction after standard streamflow calibration. Using a gap-filled high-resolution daily snow dataset, the approach improves snow cover representation across European basins while largely preserving discharge performance, without requiring full model recalibration.
This study evaluates the snow module of LISFLOOD by replacing the discharge-calibrated snowmelt...
