14 Jun 2022
14 Jun 2022
Status: this preprint is open for discussion.

Snow data assimilation for seasonal streamflow supply prediction in mountainous basins

Sammy Metref1, Emmanuel Cosme1, Matthieu Le Lay2, and Joël Gailhard2 Sammy Metref et al.
  • 1Université Grenoble Alpes, Centre National de la Recherche Scientifique, Institut de Recherche pour le Développement, Institut des Géosciences de l’Environnement, Grenoble, France
  • 2Électricité de France – Division Technique Générale, Saint-Martin-le-Vinoux, France

Abstract. Accurately predicting the seasonal inflow into a reservoir accumulated during the snowmelt season, for instance the total aggregated inflow between April and August (A48), in a hydrological basin is critical to anticipate the operation of hydroelectric damns and avoid hydrology-related hazard. Such forecasts generally involve numerical models that simulate the hydrological evoluation of a basin. The operational department of the French electric company EDF implements a semi-distributed model and carry out such forecasts for several decades, on about fifty basins. However, both scarse observation data and over-simplified physics representatioin may leed to significant forecasts errors. Data assimilation has been shown beneficial to improve predictions in various hydrological applications, yet very few have addressed the seasonal streamflow supply prediction problem. More specifically, the assimilation of snow observations, though available in various forms, has been rarely studied, despite the possible sensitivity of the streamflow supply to snow stock. This is the goal of the present paper. In three mountainous basins, a serie of four ensemble data assimilation experiments – assimilating (i) the streamflow (Q) alone, (ii) Q and fractional snow cover (FSC) data, (iii) Q and local cosmic ray snow sensor data (CRS) and (iv) all the data combined – are compared to the climatologic ensemble and an ensemble of free simulations. The experiments compare the accuracy of the estimated streamflows during the reanalysis (or assimilation) period, September to March; during the forecast period, April to August; and the A48 estimation. The results show that Q assimilation notably improves streamflow estimations during both reanalysis and forecast period. Also, the additional combination of CRS and FSC data to the assimilation further ameliorates the A48 prediction in two of the three basins. In the last basin, the experiments highlight a poor representativity of the CRS observations during some years and reveals the need for an enhanced observation system.

Sammy Metref et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-385', Anonymous Referee #1, 03 Oct 2022 reply

Sammy Metref et al.

Sammy Metref et al.


Total article views: 354 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
270 76 8 354 3 2
  • HTML: 270
  • PDF: 76
  • XML: 8
  • Total: 354
  • BibTeX: 3
  • EndNote: 2
Views and downloads (calculated since 14 Jun 2022)
Cumulative views and downloads (calculated since 14 Jun 2022)

Viewed (geographical distribution)

Total article views: 296 (including HTML, PDF, and XML) Thereof 296 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 30 Nov 2022
Short summary
Predicting the seasonal streamflow supply of water in a mountainous basin is critical to anticipate the operation of hydroelectric damns and avoid hydrology-related hazard. This quantity partly depends on the snowpack accumulated during winter. The study addresses this prediction problem using information from streamflow data and both direct and indirect snow measurements. In this study, the prediction is improved by integrating the data information into a basin scale hydrological model.