The importance of model horizontal resolution for improved estimation of snow water equivalent in a mountainous region of western Canada

Abstract. Accurate estimation of snow water equivalent (SWE) over high mountainous regions is essential to support water resource management. Due to the sparse distribution of in situ observations in these regions, weather forecast models have been used to estimate SWE. However, the influence of horizontal resolution on the accuracy of the snow simulation remains poorly understood. The objective of this study is to evaluate the potential of the Weather Research and Forecasting (WRF) model run at horizontal resolutions of 9, 3, and 1 km to estimate the daily values of SWE over the mountainous South Saskatchewan River Basin (SSRB) in western Canada for a representative water year, 2017–2018. Special focus is given to investigating the impact of the WRF model grid cell size on accurate estimation of the peak time and value of SWE across the watershed. Observations from manual snow surveys show an accumulation period from October 2017 to the annual peak in April 2018, followed by a melting period to the end of water year. All WRF simulations underestimated the annual SWE. The largest errors occurred in two conditions: at higher elevations and when using coarser horizontal resolution. These biases reached up to 58 kg m−2 (24 % relative error). The two higher-resolution simulations capture the magnitude (and timing) of peak SWE very accurately, with only a 3 % to 6 % low bias for 1 and 3 km simulations, respectively. This demonstrates that a 1 km resolution may be appropriate for estimating SWE accumulation across the region. A relationship is identified between model elevation bias and SWE biases, suggesting that the smoothing of topographic features at lower horizontal resolution leads to lower grid cell elevations, warmer temperatures, and lower SWE. Overall, this study indicates that high-resolution WRF simulations can provide reliable SWE values as an accurate input for hydrologic modeling over a sparsely monitored mountainous catchment.