Preprints
https://doi.org/10.5194/tc-2022-146
https://doi.org/10.5194/tc-2022-146
 
22 Aug 2022
22 Aug 2022
Status: this preprint is currently under review for the journal TC.

Exploring the Use of Multi-source High-Resolution Satellite Data for Snow Water Equivalent Reconstruction over Mountainous Catchments

Valentina Premier1,3, Carlo Marin1, Giacomo Bertoldi2, Riccardo Barella1, Claudia Notarnicola1, and Lorenzo Bruzzone3 Valentina Premier et al.
  • 1Institute for Earth Observation, Eurac Research, Viale Druso, 1 - 39100 Bolzano, Italy
  • 2Institute for Alpine Environment, Eurac Research, Viale Druso, 1 - 39100 Bolzano, Italy
  • 3Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, 9 I - 38123 Povo, Italy

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.

Valentina Premier et al.

Status: open (until 17 Oct 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on tc-2022-146', Pau Wiersma, 29 Aug 2022 reply
    • AC1: 'Reply on CC1', Valentina Premier, 13 Sep 2022 reply
  • RC1: 'Comment on tc-2022-146', Anonymous Referee #1, 13 Sep 2022 reply

Valentina Premier et al.

Valentina Premier et al.

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Short summary
The large amount of information regularly acquired by satellites can provide important information about SWE. We explore the use of multi-source data, in-situ observations and a degree-day melting model to reconstruct daily SWE at 25 m. The results show spatial patterns that are consistent with the geomorphological features as well as with a reference product. Being able to also reproduce inter-annual variability, the method has great potentiality for hydrological and ecological applications.