Operational snow-hydrological modeling for Switzerland

Abstract

The seasonal evolution of snow cover has significant impacts on the hydrological cycle and microclimate in mountainous regions. However, snow processes also play a crucial role in triggering alpine mass movements and flooding, posing risks to people and infrastructure. To mitigate these risks, many countries use operational forecast systems for snow distribution and melt. This paper presents the Swiss Operational Snow-hydrological (OSHD) model system, developed to provide daily analysis and forecasts on snow cover dynamics throughout Switzerland. The OSHD system is a sophisticated snow hydrological model designed specifically for the high-alpine terrain of the Swiss Alps. It leverages exceptional station data and high-resolution meteorological forcing data, as well as various reanalysis products to combine snow modeling with advanced data assimilation and meteorological downscaling methods. The system offers models of varying complexity, each tailored to specific modeling strategies and applications. For snowmelt runoff forecasting, monitoring snow water resources, and research-grade purposes, the OSHD system employs physics-based modeling chains. For snow climatological assessments, a conceptual model chain is available. We are pleased to present two comprehensive datasets from the conceptual and physics-based models that cover the entirety of Switzerland. The first dataset comprises a snow water equivalent climatology spanning 1998–2022, with a spatial resolution of 1 km. The second dataset includes snow distribution and snow melt data spanning 2016–2022 at a high spatial resolution of 250 m. To meet the needs of a multi-purpose snow hydrological model framework, the OSHD system employs various strategies for process representation and sub-grid parameterizations at the snow-canopy-atmosphere interface, particularly in complex terrain. Recent and ongoing model developments are aimed at accounting for complex forest snow processes, representing slope and ridge-scale precipitation and snow redistribution processes, as well as improving probabilistic snow forecasts and data assimilation procedures based on remote sensing products.

Regionally optimized high resolution input datasets enhance the representation of snow cover and ecophysiological processes in CLM5

“…The 1km forcing data was then upscaled to the desired target resolution (0.25 • and 0.5 • ) with no smoothing applied. We refer to Mott et al (2023) for further details with regards to the Clim OSHD product.…”

“…HS measurements were extracted at a daily timestep and cleaned from obvious outliers (assessed against neighboring stations at similar elevations), which can occur e.g. due to transmission or measurement errors (see Mott et al (2023)).…”

“…The Flexible Snow Model (FSM2, Mazzotti et al (2020)), a recent upgrade of the Factorial Snow Model (FSM, Essery (2015)), is an open-source, spatially distributed, physics-based snow model. Gridded simulations at 250m resolution and point simulations at snow station locations have been specifically set up and calibrated by SLF to run over the extent of Switzerland for the purpose of operational snow water resources monitoring (Griessinger et al, 2019;Mott et al, 2023). In the absence of high-quality, spatially distributed snow depth observations over the entire extent of Switzerland, these FSM2 simulations were served as ground truth for this study.…”

Radar based high resolution ensemble precipitation analyses over the French Alps

“…Surface observations provide reliable local estimates of precipitation. However, the under-sampling of higher elevations (Thornton et al, 2022) means that there is a lack of information on the spatial distribution of elevation-dependent variables, such as precipitation (Mott et al, 2023). On the contrary, high-resolution NWP models produce spatialised estimates of precipitation fields at different spatio-temporal resolutions.…”

Spatio-temporal snow data assimilation with the ICESat-2 laser altimeter

“…In terms of time dynamics, these schemes can be employed either in a strictly sequential forward manner as filters (e.g. for initializing short-term snow hydrological forecasts Mott et al, 2023), or instead as retrospective smoothers that allow information from observations to transfer backward in time, yielding a constrained and consistent reconstruction (ideal for snow reanalysis problems Margulis et al, 2016).…”