Abstract
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 in this way is the inadequate representation of the autocorrelation. In this paper two cascade model modifications are analysed regarding their ability to improve the autocorrelation in disaggregated time series with 5 min resolution. Both modifications are based on a state-of-the-art reference cascade model (method A). In the first modification, a position dependency is introduced in the first disaggregation step (method B). In the second modification the position of a wet time step is redefined in addition by taking into account the disaggregated finer time steps of the previous time step instead of the previous time step itself (method C). Both modifications led to an improvement of the autocorrelation, especially the position redefinition (e.g. for lag-1 autocorrelation, relative errors of −3 % (method B) and 1 % (method C) instead of −4 % for method A). To ensure the conservation of a minimum rainfall amount in the wet time steps, the mimicry of a measurement device is simulated after the disaggregation process. Simulated annealing as a post-processing strategy was tested as an alternative as well as an addition to the modifications in methods B and C. 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 (e.g. for lag-1 autocorrelation the relative error of −4 % for method A is reduced to 0.9 %). 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.
Highlights
For many applications in hydrology high-resolution rainfall time series are crucial to match the scale of the underlying processes (Blöschl and Sivapalan, 1995). Schilling (1991) concludes that for urban hydrology, in particular for overland flow, a temporal resolution of 5 min is acceptable. Berne et al (2004) point out that the required temporal resolution depends on the catchment size and recommend for urban catchments with area sizes of about 1000 ha a temporal resolution of 6 min and for 10 ha or smaller a temporal resolution of 1 min
A position dependency is introduced in the first disaggregation step
For the non-recording stations the time series lengths are usually sufficient, but the temporal resolution is not fine enough to cope with the dynamics in urban hydrology (Ochoa-Rodriguez et al, 2015)
Summary
For many applications in hydrology high-resolution rainfall time series are crucial (see the review of Cristiano et al, 2017) to match the scale of the underlying processes (Blöschl and Sivapalan, 1995). Schilling (1991) concludes that for urban hydrology, in particular for overland flow, a temporal resolution of 5 min is acceptable. Berne et al (2004) point out that the required temporal resolution depends on the catchment size and recommend for urban catchments with area sizes of about 1000 ha a temporal resolution of 6 min and for 10 ha or smaller a temporal resolution of 1 min. For many applications in hydrology high-resolution rainfall time series are crucial (see the review of Cristiano et al, 2017) to match the scale of the underlying processes (Blöschl and Sivapalan, 1995). Schilling (1991) concludes that for urban hydrology, in particular for overland flow, a temporal resolution of 5 min is acceptable. Lengths of time series with such a high temporal resolution are insufficient for most applications. For the non-recording stations (registration of daily values) the time series lengths are usually sufficient, but the temporal resolution is not fine enough to cope with the dynamics in urban hydrology (Ochoa-Rodriguez et al, 2015).
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