Abstract. Adequate estimation of the spatial distribution of snowfall is critical in hydrologic modelling. However, this is a well-known problem in estimating basin-scale snowfall, especially in mountainous basins with data scarcity. This study focuses on correction and estimation of this spatial distribution, which considers topographic effects within the basin. A method is proposed that optimises an altitude-based snowfall correction factor (Cfsnow). This is done through multi-objective calibration of a spatially distributed, multilayer energy and water balance-based snowmelt model (WEB-DHM-S) with observed discharge and remotely sensed snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The Shuffled Complex Evolution–University of Arizona (SCE–UA) automatic search algorithm is used to obtain the optimal value of Cfsnow for minimum cumulative error in discharge and snow cover simulations. Discharge error is quantified by Nash–Sutcliffe efficiency and relative volume deviation, and snow cover error was estimated by pixel-by-pixel analysis. The study region is the heavily snow-fed Yagisawa Basin of the Upper Tone River in northeast Japan. First, the system was applied to one snow season (2002–2003), obtaining an optimised Cfsnow of 0.0007 m−1. For validation purposes, the optimised Cfsnow was implemented to correct snowfall in 2004, 2002 and 2001. Overall, the system was effective, implying improvements in correlation of simulated versus observed discharge and snow cover. The 4 yr mean of basin-average snowfall for the corrected spatial snowfall distribution was 1160 mm (780 mm before correction). Execution of sensitivity runs against other model input and parameters indicated that Cfsnow could be affected by uncertainty in shortwave radiation and setting of the threshold air temperature parameter. Our approach is suitable to correct snowfall and estimate its distribution in poorly gauged basins, where elevation dependence of snowfall amount is strong.