A stochastic weather generator has been developed to simulate long daily sequences of rainfall and temperature for the Belgian and French part of the Meuse basin. The weather generator is based on the principle of nearest-neighbour resampling. In this method rainfall and temperature data are sampled simultaneously from multiple historic records with replacement such that the temporal and spatial correlations are preserved. Particular emphasis is given to the problem that these records have different lengths. The distribution of the 10-day winter maxima of area-average rainfall is quite well reproduced. The generated sequences were used as input for hydrological simulations with a semi-distributed HBV model. Though this model is capable of reproducing the flood peaks of December 1993 and January 1995, it tends to underestimate the less extreme daily peak discharges. This underestimation does not show up in the 10-day average discharges. The hydrological simulations with the generated daily rainfall and temperature data reproduce the distribution of the winter maxima of the 10-day average discharges well. Resampling based on long station records resulted in lower rainfall and discharge extremes than resampling from the data over a shorter period for which area-average rainfall was available.
R Leander, TA Buishand, MJM de Wit, P Aalders. Estimation extreme floods of the river Meuse using a stochastic weather generator and a rainfall-runoff model
published, Hydrological Sciences Journal, 2005, 50