Circulation pattern based parameterization of a multiplicative random cascade for disaggregation of observed and projected daily rainfall time series

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][1990][1991][1992][1993][1994][1995][1996][1997][1998][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.

An hourly‐scale scenario‐neutral flood risk assessment in a mesoscale catchment under climate change

“…In this study, we perturbed the daily rainfall time series and hypothesized that the multifractal parameters are stationary over time. However, given that the rainfall generation mechanisms are subject to changing circulation patterns, the multifractal parameters might not be stationary (Lisniak et al, ). Thus, characterizing the multifractal properties with climatic shifts would improve our approach.…”

“…Based on the successful applications of MRC models to high‐resolution hydrologic analyses (e.g., Ding, Wallner, Müller, & Haberlandt, ; Linznar, Lomotowski, & Rupp, ; Lisniak, Franke, & Bernhofer, ; Müller & Haberlandt, ), we adopted the stochastic MRC proposed by Müller and Haberlandt () for disaggregation of the stress‐induced daily precipitation sets into hourly rainfall (Figure ). This disaggregation scheme uses the multifractal properties that divide a coarser mother rain cell into multiple baby cells at finer scales, enabling analysts to stochastically achieve hourly rainfall series from daily observations.…”

An open-source MEteoroLOgical observation time series DISaggregation Tool (MELODIST v0.1.1)

“…Ideas to cope with changing climatic conditions for disaggregation approaches are currently investigated. Two examples relevant in this context for the statistics-based cascade model are a circulation-based parameterisation in order to better predict changing weather patterns related to changing climate (Lisniak et al, 2013) and an intensity-based categorisation (Anis and Rode, 2014). Current research also focuses on the incorporation of the Clausius-Clapeyron relation to better predict rainfall intensities in future climates (Bürger et al, 2014).…”

Application of Synthetic Meteorological Time Series in BROOK90: A Case Study for the Tharandt Forest in Saxony, Germany

“…The presented weather generator (i.e. ERM) could be extended from a single site to a raster-based multi site weather generator, which might be coupled with a cascade model for the downscaling from daily to 5 min time series [39]. However, this would require a change for the hydrological modelling to a raster based model like WaSim-ETH [37].…”