Uncertainty decomposition and reduction in river flood forecasting: Belgian case study

• Published in: Journal of flood risk management Vol. 8; no. 3; pp. 263 - 275
• Main Authors: Van Steenbergen, N., Willems, P.
• Format: Journal Article
• Language: English
• Published: London Blackwell Publishing Ltd 01.09.2015; John Wiley & Sons, Inc
• Subjects: Calibration; Case studies; data assimilation; Data collection; Decomposition; Flood forecasting; Floods; hydrological modelling; Meteorology; River forecasting; Rivers; uncertainty decomposition; Weather forecasting; Belgium
• ISSN: 1753-318X; 1753-318X
• DOI: 10.1111/jfr3.12093

Uncertainty is a key factor to be taken into account in river flood forecasting. Every forecast is subject to several sources of uncertainty. Knowledge on the relative importance of the different sources would be useful to determine the most effective improvement actions to reduce the uncertainty. In this paper, three key uncertainty sources are studied for hydrological flood forecasting in the Belgian case study of the Rivierbeek: model uncertainty, forecasted rainfall uncertainty and uncertainty in the initial conditions. A non‐parametric data‐based approach is used to quantify the total uncertainty in the forecasts. By resimulating in the model historical forecasts with optimal initial conditions and observed rainfall, the uncertainty generated by each of the key sources could be identified. In order to reduce the model uncertainty, which was primarily identified as the most important source of uncertainty, a step‐wise physically based calibration technique was suggested. After recalibration of the model with this technique, a significant reduction of the contribution of the model uncertainty to the total forecast uncertainty could be achieved. Further improvement of the initial conditions, identified as the second most important uncertainty source for short lead times, could be obtained by applying data assimilation. Both uncertainty reduction techniques combined led to a reduction of the total forecast uncertainty with 30% to 40%.