[{"department":[{"_id":"FrPe"}],"corr_author":"1","date_updated":"2026-06-02T09:24:00Z","citation":{"short":"A. Fontrodona-Bach, B. Schaefli, R. Woods, J.R. Larsen, Hydrology and Earth System Sciences 30 (2026) 2613–2636.","ista":"Fontrodona-Bach A, Schaefli B, Woods R, Larsen JR. 2026. Estimating robust melt factors and temperature thresholds for snow modelling across the Northern Hemisphere. Hydrology and Earth System Sciences. 30(9), 2613–2636.","apa":"Fontrodona-Bach, A., Schaefli, B., Woods, R., &#38; Larsen, J. R. (2026). Estimating robust melt factors and temperature thresholds for snow modelling across the Northern Hemisphere. <i>Hydrology and Earth System Sciences</i>. Copernicus Publications. <a href=\"https://doi.org/10.5194/hess-30-2613-2026\">https://doi.org/10.5194/hess-30-2613-2026</a>","chicago":"Fontrodona-Bach, Adrià, Bettina Schaefli, Ross Woods, and Joshua R. Larsen. “Estimating Robust Melt Factors and Temperature Thresholds for Snow Modelling across the Northern Hemisphere.” <i>Hydrology and Earth System Sciences</i>. Copernicus Publications, 2026. <a href=\"https://doi.org/10.5194/hess-30-2613-2026\">https://doi.org/10.5194/hess-30-2613-2026</a>.","mla":"Fontrodona-Bach, Adrià, et al. “Estimating Robust Melt Factors and Temperature Thresholds for Snow Modelling across the Northern Hemisphere.” <i>Hydrology and Earth System Sciences</i>, vol. 30, no. 9, Copernicus Publications, 2026, pp. 2613–36, doi:<a href=\"https://doi.org/10.5194/hess-30-2613-2026\">10.5194/hess-30-2613-2026</a>.","ieee":"A. Fontrodona-Bach, B. Schaefli, R. Woods, and J. R. Larsen, “Estimating robust melt factors and temperature thresholds for snow modelling across the Northern Hemisphere,” <i>Hydrology and Earth System Sciences</i>, vol. 30, no. 9. Copernicus Publications, pp. 2613–2636, 2026.","ama":"Fontrodona-Bach A, Schaefli B, Woods R, Larsen JR. Estimating robust melt factors and temperature thresholds for snow modelling across the Northern Hemisphere. <i>Hydrology and Earth System Sciences</i>. 2026;30(9):2613-2636. doi:<a href=\"https://doi.org/10.5194/hess-30-2613-2026\">10.5194/hess-30-2613-2026</a>"},"publisher":"Copernicus Publications","status":"public","publication_identifier":{"issn":["1027-5606"],"eissn":["1607-7938"]},"article_processing_charge":"Yes","has_accepted_license":"1","author":[{"last_name":"Fontrodona-Bach","id":"f06891fd-9f42-11ee-8632-a20971c43046","full_name":"Fontrodona-Bach, Adrià","first_name":"Adrià"},{"first_name":"Bettina","last_name":"Schaefli","full_name":"Schaefli, Bettina"},{"full_name":"Woods, Ross","last_name":"Woods","first_name":"Ross"},{"first_name":"Joshua R.","last_name":"Larsen","full_name":"Larsen, Joshua R."}],"OA_place":"publisher","tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"volume":30,"date_published":"2026-05-04T00:00:00Z","day":"04","month":"05","oa_version":"Published Version","scopus_import":"1","OA_type":"gold","quality_controlled":"1","issue":"9","file_date_updated":"2026-06-02T09:22:26Z","license":"https://creativecommons.org/licenses/by/4.0/","PlanS_conform":"1","publication":"Hydrology and Earth System Sciences","acknowledgement":"AFB acknowledges funding from the UK's Natural Environment Research Council (NERC) CENTA2 doctoral training program, grant number NE/S007350/1. AFB acknowledges support from the School of Geography, Earth and Environmental Science research fund. The computations described in this paper were performed using the University of Birmingham's BlueBEAR HPC service, which provides a High Performance Computing service to the University's research community. See http://www.birmingham.ac.uk/bear (last access: 15 December 2025) for more details. This research has been supported by the Natural Environment Research Council (grant no. CENTA2 NE/S007350/1).","type":"journal_article","_id":"21915","ddc":["550"],"doi":"10.5194/hess-30-2613-2026","intvolume":"        30","date_created":"2026-05-24T22:01:32Z","DOAJ_listed":"1","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2026","publication_status":"published","article_type":"original","oa":1,"file":[{"content_type":"application/pdf","checksum":"8bde4775545f9e049ea3806144b0d5f1","file_name":"2026_HydrologyEarthSystemSciences_FontrodonaBach.pdf","date_created":"2026-06-02T09:22:26Z","file_id":"21940","date_updated":"2026-06-02T09:22:26Z","creator":"dernst","file_size":11250378,"access_level":"open_access","relation":"main_file","success":1}],"title":"Estimating robust melt factors and temperature thresholds for snow modelling across the Northern Hemisphere","page":"2613-2636","abstract":[{"lang":"eng","text":"Hydrological models commonly use very simple snow accumulation and melt models based on air temperature information, namely, a temperature threshold for snow accumulation as well as for snowmelt, and a melt factor. This utility emerges due to the simplicity, efficiency, and generally good performance of such models if sufficient calibration information is available. At scales beyond single gauged catchments, the estimation and evaluation of the temperature thresholds and the melt factor has been difficult due to a lack of observations on snow accumulation and melt. Using a recently published Northern Hemisphere snow water equivalent dataset (NH-SWE) and co-located climate station observations of temperature and precipitation (4736 stations across the Northern Hemisphere), this work estimates melt factors and temperature thresholds for snow modelling based on station observations and provides the first large-scale and long-term (1950–2023) evaluation of a simple temperature-index snow model and its parameters across a diverse range of snow climates. Our study reveals that the 0 °C as precipitation-phase threshold captures most snowfall days (89 %) and the 0 °C as snowmelt initiation threshold captures most snowmelt days (76 %). Adjusting large-scale uniform threshold values does not consistently improve performance across all snow accumulation and melt metrics. Estimated melt factors based on observations converge towards 3–5 mm (°C d)−1 for deeper snowpack climates (peak snow water equivalent >300 mm), but their estimation may be more challenging for colder climates with shallower snowpacks (<300 mm), conditions where the derived melt factors cover a wider range (1 to 12 mm (°C d)−1) and a much higher interannual and spatial variability. The temperature-index snow model performs consistently well, on average, across the available Northern Hemisphere data set for estimating long-term mean values of seasonal snow cover onset, snowmelt season onset, mean snow accumulation and snowmelt rates, but challenges may arise due to biases in temperature records or solid precipitation undercatch. Peak snow water equivalent is likely underestimated for deep or alpine snowpacks, while it is likely overestimated for shallow snowpacks in the coldest and continental climates. The best median performance of the temperature-index approach lies on relatively shallow snowpacks in temperate climates. This study provides valuable insights into temperature-threshold snowfall modelling and temperature-index melt modelling for applications across diverse climates and environments, and the results should help refine regional modelling approaches to enhance our understanding of snowpack responses to global warming."}]}]