Preprints
https://doi.org/10.5194/hess-2021-604
https://doi.org/10.5194/hess-2021-604
 
05 Jan 2022
05 Jan 2022
Status: this discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Comparing seasonal streamflow forecast systems for management of a fresh water reservoir in the Netherlands

Ruud T. W. L. Hurkmans1,2, Bart van den Hurk3, Maurice J. Schmeits1, Fredrik Wetterhall4, and Ilias G. Pechlivanidis5 Ruud T. W. L. Hurkmans et al.
  • 1Royal Netherlands Meteorological Institute, de Bilt, the Netherlands
  • 2HKV Consultants, Lelystad, the Netherlands
  • 3Deltares, Delft, the Netherlands
  • 4ECMWF, Reading, United Kingdom
  • 5Swedish Meteorological and Hydrological Institute (SMHI), Nörrkopping, Sweden

Abstract. For efficient management of the Dutch surface water reservoir Lake IJssel, (sub)seasonal forecasts of the water volumes going in and out of the reservoir are potentially of great interest. Here, streamflow forecasts were analyzed for the river Rhine at Lobith, which is partly routed through the river IJssel, the main influx into the reservoir. We analyzed multiple seasonal forecast data sets derived from EFAS, E-HYPE and HTESSEL, which differ in their underlying hydrological formulation, but are all forced with similar input from the ECMWF SEAS5 meteorological forecasts. We post-processed the streamflow forecasts using quantile matching (QM) and analyzed several forecast quality metrics. Forecast performance was assessed based on the available reforecast period, as well as on individual summer seasons. QM increased forecast skill for nearly all metrics evaluated. Particularly HTESSEL, a land surface scheme that is not optimized for hydrology, needed the largest correction. Averaged over the reforecast period, forecasts were skillful for the longest lead times in spring and early summer. For this period, E-HYPE showed the highest skill; Later in summer, however, skill deteriorated after 1–2 months. When investigating specific years with either low or high flow conditions, forecast skill increased with the extremity of the event. Although raw forecasts for both E-HYPE and EFAS were more skilful than HTESSEL, bias correction based on QM can significantly reduce the difference. In operational mode, the three forecast systems show comparable skill. In general, dry conditions can be forecasted with high success rates up to three months ahead, which is very promising for successful use of Rhine streamflow forecasts in downstream reservoir management.

Ruud T. W. L. Hurkmans et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-604', Anonymous Referee #1, 22 Feb 2022
    • AC1: 'Reply on RC1', Ruud Hurkmans, 06 May 2022
  • RC2: 'Comment on hess-2021-604', Anonymous Referee #2, 02 Mar 2022
    • AC2: 'Reply on RC2', Ruud Hurkmans, 06 May 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-604', Anonymous Referee #1, 22 Feb 2022
    • AC1: 'Reply on RC1', Ruud Hurkmans, 06 May 2022
  • RC2: 'Comment on hess-2021-604', Anonymous Referee #2, 02 Mar 2022
    • AC2: 'Reply on RC2', Ruud Hurkmans, 06 May 2022

Ruud T. W. L. Hurkmans et al.

Ruud T. W. L. Hurkmans et al.

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Short summary
Seasonal forecasts can help in safely and efficiently managing a fresh water reservoir in the Netherlands. We compare hydrological forecast systems of the river Rhine, the lakes most important source and analyze forecast skill for over 1993–2016 and for specific extreme years. On average, forecast skill is high in spring due to Alpine snow and smaller in summer. Dry summers appear to be more predictable, skill increases with event extremity. In those cases, seasonal forecasts are valuable tools.