High-Resolution Snow Water Equivalent Estimation: A Data-Driven Method for Localized Downscaling of Climate Data

Abstract. Estimating high-resolution daily Snow Water Equivalent (SWE) in mountainous regions is challenging due to geographical complexity and the irregular availability of high-resolution meteorological data. This study introduces a method for downscaling SWE. It is based on the dependence between meteorological estimators and SWE, and the fact that while SWE can change rapidly within days, its patterns may exhibit year-to-year analogies under similar meteorological conditions. We implement this principle to downscale SWE to a 500 m resolution using a K-nearest neighbor algorithm with a customized distance metric.

To evaluate the performance of our approach, we conduct tests in two regions of interest in the western United States. A cross-validation analysis is performed, and comparisons are made with commonly used SWE datasets as well as against in-situ data. The results demonstrate that our approach enables the generation of downscaled SWE that closely matches that observed in reanalysis data in terms of statistical properties. This opens up possibilities for applications in regions with limited in-situ data or meteorological data. The approach also has the potential to recreate unmeasured historical SWE values and could be extended to future periods using climate projections.