EGU2020-3338
https://doi.org/10.5194/egusphere-egu2020-3338
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

A 20-year reforecast study combining high-resolution hydrological modelling, ensemble forecasting and data assimilation for the 12 largest tributaries of the Rhine

Bart van Osnabrugge1,2, Maarten Smoorenburg1,3, Remko Uijlenhoet2, and Albrecht Weerts1,2
Bart van Osnabrugge et al.
  • 1Deltares, Operational Watermanagement, Delft, Netherlands (bart.vanosnabrugge@deltares.nl)
  • 2Wageningen University, Hydrology and Quantitative Water Management Group, Wageningen, Netherlands
  • 3Ministry of Infrastructure and the Environment, Water Management Centre of The Netherlands, River Forecasting Service, Lelystad, Netherlands

There is an ongoing trend in hydrological forecasting towards both spatially distributed (gridded) models, ensemble forecasting and data assimilation techniques to improve forecasts’ initial states. While in the last years those different aspects have been investigated separately, there are only few studies where the three techniques are combined: ensemble forecasts with state updating of a gridded hydrological model. Additionally, the studies that have addressed this combination of techniques either focus on a small area, a short study period, or both. We here aim to fill this knowledge gap with a 20-year data assimilation and ensemble reforecast experiment with a high resolution gridded hydrological model (wflow_hbv, 1200x1200m) of the full Rhine basin (160 000 km2). To put the impact of state updating in an operational forecasting context, the data assimilation results were compared with AR post-processing as used by the Dutch Forecasting Centre (WMCN).

This data assimilation and reforecast experiment was conducted for the twelve main tributaries of the river Rhine. The effect on forecast skill of state updating with the Asynchronous Ensemble Kalman Filter (AEnKF) and AR error correction are compared for medium-term (15-day) forecasts over a period of 20 years (1996 to 2016). State updating improved the initial state for all subbasins and resulted in lasting skill score increase. AR also improved the forecast skill, but the forecast skill with AR did not always converge towards the uncorrected model skill, and instead can deteriorate for longer lead times. AR correction outperformed the AEnKF state updating for the first two days, after which state updating became more effective and outperformed AR. We conclude that state updating has more potential for medium-term hydrological forecasts than the operational AR procedure.

Further research is underway to investigate the importance, or added value, of long-term reforecasts as opposed to studies covering a short time span which are often more feasible and therefore more often found in literature.

How to cite: van Osnabrugge, B., Smoorenburg, M., Uijlenhoet, R., and Weerts, A.: A 20-year reforecast study combining high-resolution hydrological modelling, ensemble forecasting and data assimilation for the 12 largest tributaries of the Rhine, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3338, https://doi.org/10.5194/egusphere-egu2020-3338, 2020

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