Impact of snow distribution modelling for runoff predictions

• Published in: Hydrology Research
• Main Authors: Clemenzi, Ilaria, Gustafsson, David, Marchand, Wolf-Dietrich, Norell, Björn, Zhang, Jie, Pettersson, Rickard, Pohjola, Veijo
• Format: Article in Journal/Newspaper
• Language: English
• Published: Uppsala universitet, Luft-, vatten- och landskapslära 2023
• Subjects: hydrological modelling; model calibration; mountainous catchment; snow modelling; snow spatial distribution; snowmelt runoff; Oceanography; Hydrology and Water Resources; Oceanografi; hydrologi och vattenresurser; Northern Sweden
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-510521; https://doi.org/10.2166/nh.2023.043

Snow in the mountains is essential for the water cycle in cold regions. The complexity of the snow processes in such an environment makes it challenging for accurate snow and runoff predictions. Various snow modelling approaches have been developed, especially to improve snow predictions. In this study, we compared the ability to improve runoff predictions in the Överuman Catchment, Northern Sweden, using different parametric representations of snow distribution. They included a temperature-based method, a snowfall distribution (SF) function based on wind characteristics and a snow depletion curve (DC). Moreover, we assessed the benefit of using distributed snow observations in addition to runoff in the hydrological model calibration. We found that models with the SF function based on wind characteristics better predicted the snow water equivalent (SWE) close to the peak of accumulation than models without this function. For runoff predictions, models with the SF function and the DC showed good performances (median Nash–Sutcliffe efficiency equal to 0.71). Despite differences among the calibration criteria for the different snow process representations, snow observations in model calibration added values for SWE and runoff predictions.