Rainfall Uncertainty in Flood Forecasting: Belgian Case Study of Rivierbeek

Abstract

Rainfall forecast errors are the key sources of uncertainty in flood forecasting. To quantify this uncertainty operational flood forecasting centers make use of rainfall forecasts obtained by ensemble predicting systems (EPS). The EPS forecasts are generated by perturbing the initial conditions of numerical weather prediction models. Question, however, remains whether these EPS cover the real forecast uncertainty range and whether the EPS-based uncertainty estimates are similar to the ones obtained by statistical methods. Both questions are addressed in this research based on data of a flood forecasting system in Belgium. Comparison is made between the uncertainty bounds generated by EPS and by a Monte Carlo-based statistical method after historical forecasted rainfall uncertainty analysis. The latter analysis calculates the error between forecasted and observed catchment rainfall, taking into account the dependency on lead time and rainfall depth. The forecasted rainfall errors are described by truncated normal distributions, which allow to calculate the full uncertainty distribution on a deterministic rainfall forecast. It is concluded that the EPS may underestimate the influence of the total forecasted rainfall uncertainty. For the Belgian case study of the Rivierbeek, the forecasted rainfall uncertainty explains 30% of the total river flow forecast uncertainty.