A non-parametric data-based approach for probabilistic flood forecasting in support of uncertainty communication

Abstract

In addition to structural measures, governmental authorities have set up flood forecasting systems to be used as early warning systems, to minimize the damage of future floods. These flood forecasting systems make use of hydrological and hydrodynamic models and input time series (measured and predicted rainfall, evapotranspiration, water levels and discharges). The uncertainty of these models and time series, certainly the predicted rainfall, is high and not always known. Consequently the prediction power of the flood forecasting systems is often unclear. To calculate the predictive uncertainty in the forecasts, a method has been set up, which involves computation of the exceedance probability of alert and alarm levels. The uncertainty results allow far more complete information to be provided to decision makers (in comparison with deterministic model-based forecasts).The uncertainty estimation is based on the statistical analysis of historical flood forecasting results. The forecast residuals (differences between predictions and measurements at river gauging stations) have been analysed using a non parametric technique. Because the residuals are correlated with the value of the simulated water level and time horizon, the residuals are split up into discrete classes of simulated water levels and time horizons. For each class, percentile values of the residuals are calculated and stored in a so called ‘three dimensional error matrix’. Based on 3D interpolation in the error matrix, confidence intervals on forecasted water levels are calculated and visualised. The method is implemented in software for post processing of the forecast results, and is connected to the database of a river flood forecasting system in Belgium. Hereby it is possible to update the error matrix in real time, based on new simulations.