09 Mar 2022
09 Mar 2022
Status: this preprint is currently under review for the journal HESS.

Seasonal forecasting of snow resources at Alpine sites

Silvia Terzago1, Giulio Bongiovanni1,2, and Jost von Hardenberg3,1 Silvia Terzago et al.
  • 1Institute of Atmospheric Sciences and Climate, National Research Council, Torino, Italy
  • 2Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy
  • 3Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Torino, Italy

Abstract. Climate warming in mountain regions is resulting in glacier shrinking, seasonal snow cover reduction, changes in the amount and seasonality of meltwater runoff, with consequences on water availability. Droughts are expected to become more severe in the future with economical and environmental losses both locally and downstream. Effective adaptation strategies involve multiple time scales, and seasonal forecasts can help in the optimization of the available snow/water resources with lead times of several months. In the framework of the ERA4CS MEDSCOPE project we developed a prototype to generate seasonal forecasts of snow depth and snow water equivalent with starting date November 1st and lead times of 7 months, so up to May 31st of the following year. The prototype has been co-designed with end users in the field of water management, hydropower production and of mountain ski tourism, meeting their needs in terms of indicators, time resolution of the forecasts, visualization of the forecast outputs. In this paper we present the modeling chain, based on the seasonal forecasts of ECMWF and Météo-France seasonal prediction systems, made available through the Copernicus Climate Change Service (C3S) Climate Data Store. Seasonal forecasts of precipitation, near-surface air temperature, radiative fluxes, wind and relative humidity are bias-corrected and downscaled to three sites in the Western Italian Alps, and finally used as input for the physically-based multi-layer snow model SNOWPACK. The RainFARM stochastic downscaling procedure is applied to precipitation data in order to allow an estimate of uncertainties due to the downscaling method.

The skill of the prototype in predicting the monthly snow depth evolution from November to May in each season of the hindcast period 1995–2015 are demonstrated using both deterministic and probabilistic metrics. Forecast skills are determined with respect to a simple forecasting method based on the climatology, and station measurements are used as reference data. The prototype shows very good skills at predicting the tercile category, i.e. snow depth below- and above-normal, in the winter (lead time 2-3-4 months) and spring (lead times 5-6-7 months) ahead: snow depth is predicted with higher accuracy (Brier Skill Score) and higher discrimination (Area Under the ROC Curve Skill Score) with respect to a simple forecasting method based on the climatology. Ensemble mean monthly snow depth forecasts are significantly correlated with observations not only at short lead time 1 and 2 months (November and December) but also at lead time 5 and 6 months (March and April) when employing the ECMWFS5 forcing. Moreover the prototype shows skill at predicting extremely dry seasons, i.e. seasons with snow depth below the 10th percentile, while skills at predicting snow depth above the 90th percentile are model-, station- and score-dependent. No remarkable differences are found among the skill scores when the precipitation input is bias-corrected, downscaled or bias-corrected and downscaled compared to the case in which raw data are employed, suggesting that skill scores are weakly sensitive to the treatment of the precipitation input.

Silvia Terzago et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-32', Anonymous Referee #1, 10 Apr 2022
  • RC2: 'Comment on hess-2022-32', Anonymous Referee #2, 27 Apr 2022

Silvia Terzago et al.

Silvia Terzago et al.


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Short summary
Reliable seasonal forecasts of the abundance of mountain snowpack over the winter/spring ahead provide valuable information for water management, hydropower production and ski tourism. We present a climate service prototype to generate multi-model ensemble seasonal forecasts of mountain snow depth, based on Copernicus seasonal forecast system meteorological data used to force the SNOWPACK model. The prototype shows skill at predicting snow depth below- and above-normal and extremely dry seasons.