Temporal rainfall disaggregation using a micro-canonical cascade model: Possibilities to improve the autocorrelation
Abstract. In urban hydrology rainfall time series of high resolution in time are crucial. Such time series with sufficient length can be generated through the disaggregation of daily data with a micro-canonical cascade model. A well-known problem of time series generated so is the underestimation of the autocorrelation. In this paper two cascade model modifications are analysed regarding their ability to improve the autocorrelation. Both modifications are based on a state-of-the-art reference cascade model. In the first modification, a position-dependency is introduced in the first disaggregation step. In the second modification the position of a wet time step is redefined in addition. Both modifications led to an improvement of the autocorrelation, especially the position redefinition. Simultaneously, two approaches are investigated to avoid the generation of time steps with too small rainfall intensities, the conservation of a minimum rainfall amount during the disaggregation process itself and the mimicry of a measurement device after the disaggregation process. The mimicry approach shows slight better results for the autocorrelation and hence was kept for a subsequent resampling investigation using Simulated Annealing. For the resampling, a special focus was given to the conservation of the extreme rainfall values. Therefore, a universal extreme event definition was introduced to define extreme events a priori without knowing their occurrence in time or magnitude. The resampling algorithm is capable of improving the autocorrelation, independent of the previously applied cascade model variant. Also, the improvement of the autocorrelation by the resampling was higher than by the choice of the cascade model modification. The best overall representation of the autocorrelation was achieved by method C in combination with the resampling algorithm. The study was carried out for 24 rain gauges in Lower Saxony, Germany.