Identifying snowfall elevation patterns by assimilating satellite-based snow depth retrievals

Manuela Girotto; Giuseppe Formetta; Shima Azimi; Claire Bachand; Marianne Cowherd; Gabrielle De Lannoy; Hans Lievens; Sara Modanesi; Mark S Raleigh; Riccardo Rigon; Christian Massari

doi:10.1016/j.scitotenv.2023.167312

Identifying snowfall elevation patterns by assimilating satellite-based snow depth retrievals

Sci Total Environ. 2024 Jan 1:906:167312. doi: 10.1016/j.scitotenv.2023.167312. Epub 2023 Sep 25.

Authors

Affiliations

¹ Environmental Science and Policy Management, University of California, Berkeley, CA, USA. Electronic address: mgirotto@berkeley.edu.
² Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy.
³ Environmental Science and Policy Management, University of California, Berkeley, CA, USA; Los Alamos National Laboratory, Los Alamos, USA.
⁴ Environmental Science and Policy Management, University of California, Berkeley, CA, USA.
⁵ Soil and Water Management, Katholieke Universiteit Leuven, Leuven, Belgium.
⁶ Soil and Water Management, Katholieke Universiteit Leuven, Leuven, Belgium; Department of Environment, Ghent University, Ghent, Belgium.
⁷ Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy.
⁸ College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, OR, USA.

PMID: 37758128
DOI: 10.1016/j.scitotenv.2023.167312

Abstract

Precipitation in mountain regions is highly variable and poorly measured, posing important challenges to water resource management. Traditional methods to estimate precipitation include in-situ gauges, Doppler weather radars, satellite radars and radiometers, numerical modeling and reanalysis products. Each of these methods is unable to adequately capture complex orographic precipitation. Here, we propose a novel approach to characterize orographic snowfall over mountain regions. We use a particle batch smoother to leverage satellite information from Sentinel-1 derived snow depth retrievals and to correct various gridded precipitation products. This novel approach is tested using a simple snow model for an alpine basin located in Trentino Alto Adige, Italy. We quantify the precipitation biases across the basin and found that the assimilation method (i) corrects for snowfall biases and uncertainties, (ii) leads to cumulative snowfall elevation patterns that are consistent across precipitation products, and (iii) results in overall improved basin-wide snow variables (snow depth and snow cover area) and basin streamflow estimates.

Keywords: Data assimilation; Mountain hydrology; Sentinel-1; Snow depth; Snowfall correction; Snowfall gradients.