Comment on tc-2022-146

Abstract

Seasonal snow accumulation and release are so crucial for the hydrological cycle to the point that mountains have been claimed as the "water towers" of the world. A key variable in this sense is the snow water equivalent (SWE). However, the complex accumulation and snow redistribution processes render its quantification and prediction very challenging. In this work, we explore the use of multi-source data to reconstruct SWE at a high spatial resolution (HR) of 25 m by proposing a novel approach designed for mountainous catchments. To this purpose, we exploit i) daily HR time-series of snow cover area (SCA) derived by high- and low-resolution optical images to define the days of snow presence, ii) a degree-day model driven by in-situ temperature to determine the potential melting, and iii) in-situ snow depth and Synthetic Aperture Radar (SAR) images to determine the state of the catchment (i.e., accumulation or ablation) that is needed to add or remove SWE to the reconstruction. Given the typical high spatial heterogeneity of snow in mountainous areas, HR data sample more adequately its distribution thus resulting in a highly detailed spatialized information that represents an important novelty. The proposed SWE reconstruction approach also foresees a novel SCA time-series regularization from impossible transitions. Moreover it reconstructs SWE for all the hydrological season without the need of spatialized precipitation information as input, that is usually affected by uncertainty. Despite the simple approach based on a set of empirical assumptions, it shows good performances when tested in two different catchments: the South Fork catchment, California, and the Schnals catchment, Italy, showing a good agreement with an average bias of -40 mm when evaluated against a HR spatialized reference product and of 38 mm when evaluated against manual measurements. The main sources of error introduced by each step of the method have been finally discussed to provide insights about the applicability and future improvements of the method that may be of great interest for several hydrological and ecological applications.