Abstract
Abstract. The use of multiplicative random cascades (MRCs) for temporal rainfall disaggregation has been extensively studied in the past. MRCs are appealing for rainfall disaggregation due to their formal simplicity and the possibility to extract the model parameters directly from observed high resolution rainfall data. These parameters, however, represent the rainfall characteristics of the observation period. Since rainfall characteristics of different time slices are changing due to climate variability, we propose a parameterization approach for MRCs to adjust the parameters according to past (observed) or future (projected) time series. This is done on the basis of circulation patterns (CPs) by extracting a distinct MRC parameterization from high resolution rainfall data, as observed on days governed by each individual CP. The parameterization approach is tested by comparing the statistical properties of disaggregated rainfall time series of two time slices, 1969–1979 and 1989–1999, to the results obtained by two other disaggregation methods (a conceptually similar MRC without CP-based parameterization and a recombination approach) and to the statistical properties of observed hourly rainfall data. In this context, all three approaches use rainfall data of the time slice 1989–1999 for parameterization. We found that the inclusion of CPs into the parameterization of a MRC yields hourly time series that better reproduce the properties of observed rainfall in time slice 1989–1999, as compared to the simple MRC. Despite similar results of both MRCs in the validation period of 1969–1979, we can conclude that the CP-based parameterization approach is applicable for temporal rainfall disaggregation in time slices distinct from the parameterization period. This approach accounts for changes in rainfall characteristics due to changes in the frequency of occurrence of the CPs and allows generating hourly rainfall from daily data, as often provided by a statistical downscaling of global climate change.
Highlights
A great variety of hydrological applications, including the modelling of urban water systems, require continuous rainfall series at hourly or even finer time steps as input
We proposed a circulation pattern based parameterization procedure for a multiplicative random cascades (MRCs), to be used for rainfall disaggregation from daily rainfall values to hourly resolution at point locations
This parameterization attempts to enable the cascade model to account for different rainfall generating mechanisms and, with respect to variations in frequencies of these mechanisms due to climate change, for climate induced fluctuations in rainfall characteristics at high temporal resolutions
Summary
A great variety of hydrological applications, including the modelling of urban water systems, require continuous rainfall series at hourly or even finer time steps as input. While the MRC with its modifications could not be considered as multifractal at all, the results have shown a more accurate simulation of several properties of observed rainfall than could be achieved by the CUM model, which is a more realistic resemblance of a multifractal process This explains the further interest in the study and practical application of MRCs. Despite the progresses made in rainfall modelling and disaggregation based on scale invariance theory, it has to be taken into account that the parameterization process for these models is data driven. Patterns (CPs) and their frequency of occurrence This parameterization approach is meant to enable the user to further disaggregate projected daily precipitation time series, which were generated by scaling down the synoptic scale output of general circulation models (GCMs) for a possible future climate, to hourly resolutions and to use them as input for climate impact studies.
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