Temporal Rainfall Disaggregation with a Cascade Model: From Single-Station Disaggregation to Spatial Rainfall
Publication: Journal of Hydrologic Engineering
Volume 20, Issue 11
Abstract
Temporal rainfall disaggregation is an important tool to obtain high-resolution rainfall data, which are needed in many fields of hydrology and water resources management. The multiplicative random cascade model can be used for temporal rainfall disaggregation of daily time series. A resampling algorithm is introduced to implement spatial consistence in disaggregated time series. Spatial consistence is assumed to be represented by four bivariate and distance-dependent rainfall characteristics that complement each other. Relative diurnal cycles of the disaggregated time series are resampled with the aim to reproduce these spatial characteristics while preserving the structure generated by the cascade model. Also, to achieve a final resolution of 1 h the traditional cascade model has been modified. A modification called uniform splitting with a branching number of 3 in the first step is introduced. Results are compared with observations and an approach called diversion. In total 22 recording stations in Northern Germany with hourly resolution were used for the validation of the disaggregation procedure, starting with daily values. Investigation areas are two catchments considering different station densities. The results show that for the disaggregation, errors of time series characteristics between 3 and 12% occur. The nonexceedance curves of rainfall intensities are slightly overestimated. Extreme values are well represented. The uniform splitting method outperforms the diversion method. Spatial rainfall characteristics can be reproduced by the simulating annealing algorithm. However, with an increasing number of stations the reproduction performance declines for some rainfall characteristics. Nonexceedance curves of areal rainfall based on disaggregated and not resampled time series are generally underestimated. By application of the resampling algorithm, a better performance regarding the spatial characteristics can be achieved. The presented resampling algorithm also has the potential to be used for implementing spatial consistence for time series generated by other disaggregation models.
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Acknowledgments
The authors thank Jennifer Ullrich for calibration of the simulated annealing parameters and Christian Berndt and Ross Pidoto for useful comments on an early draft of the manuscript. The authors are also thankful for the permission to use the data of the German National Weather Service (Deutscher Wetterdienst DWD) and Meteomedia AG. Finally, the two reviewers and the editor are gratefully acknowledged for their contributions to improve this publication.
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Journal of Hydrologic Engineering
Volume 20 • Issue 11 • November 2015
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Jul 31, 2014
Accepted: Jan 29, 2015
Published online: Apr 3, 2015
Discussion open until: Sep 3, 2015
Published in print: Nov 1, 2015
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