SnowQM 1.0: a fast R package for bias-correcting spatial fields of snow water equivalent using quantile mapping

Abstract. Snow plays a crucial role in regional climate systems worldwide. It is a key variable in the context of climate change because of its direct feedback to the climate system, while at the same time being very sensitive to climate change. Long-term spatial data on snow cover and snow water equivalent are scarce, due to the lack of satellite data or forcing data to run land surface models back in time. This study presents an R package, SnowQM, designed to correct for the bias in long-term spatial snow water equivalent data compared to a shorter-term and more accurate dataset, using the more accurate data to calibrate the correction. The bias-correction is based on the widely applied quantile mapping approach. A new method of spatial and temporal grouping of the data points is used to calculate the quantile distributions for each pixel. The main functions of the package are written in C++ to achieve high performance. Parallel computing is implemented in the C++ part of the code. In a case study over Switzerland, where a 60-year snow water equivalent climatology is produced at a resolution of 1 d and 1 km, SnowQM reduces the bias in snow water equivalent from −9 to −2 mm in winter and from −41 to −2 mm in spring. We show that the C++ implementation notably outperforms simple R implementation. The limitations of the quantile mapping approach for snow, such as snow creation, are discussed. The proposed spatial data grouping improves the correction in homogeneous terrain, which opens the way for further use with other variables.