Keywords:Rainfall disaggregation Random multiplicative cascades Randomized Bartlett-Lewis model Method of fragments resampling Australia s u m m a r y This paper evaluates three distinct approaches for disaggregating daily rainfall to sub-daily sequences:(1) random multiplicative cascades (microcanonical and canonical versions), (2) point process (randomized Bartlett-Lewis model -RBLM), and (3) resampling (method of fragments). These methods are used to perform disaggregation of daily rainfall to hourly rainfall at four point locations across Australia (Sydney, Perth, Cairns, and Hobart), which are associated with different climatic regimes. The methods are evaluated based on parameter estimation procedures applied (including introduction of the sequential Monte Carlo sampler in RBLM), the capability of the resulting sequences to reproduce standard validation statistics, and the representation of observed rainfall variability and intermittency, within-day wet spells, and extreme rainfall percentiles. The results generally indicate that the method of fragments outperforms the other models. While all the models are found to simulate reasonably well the commonly used statistical measures (e.g. mean and dry proportions) of rainfall at the hourly timestep, the microcanonical model is found to significantly overestimate the hourly rainfall variance. With respect to extreme value characteristics, the resampling approach is found to match well the observed intensity-frequency relationship at an hourly scale, with the cascade models underestimating (canonical) and overestimating (microcanonical) extreme rainfall. The point process model's performance is poor in Cairns but reasonably good at other locations. An analysis of the empirical within-day wet-and dry-spell distributions further reveals that the cascade-based models are not robust for observed wet and dry spells.

[1] Previous studies have shown significant dependence of annual maximum floods in eastern Australia on the Interdecadal Pacific Oscillation (IPO), a low-frequency mode of natural climatic variability. However, the causative factors behind differences in flood risk observed during contrasting phases of the IPO remain poorly understood. In particular, an important question that stems from a practical flood hydrology perspective is: Does maximum precipitation exhibit a similar level of dependence on the IPO as that observed in the floods? If not, then what are the factors responsible for this disparity, and are there significant ramifications for the conventional approach of estimating design floods through a predefined design rainfall? This paper investigates the possible reasons for the disparate relationship of IPO on rainfall and streamflow design events; that is, why do flood characteristics for contrasting IPO phases differ significantly compared to less notable changes in the corresponding rainfall intensities? We hypothesize that this difference in flood characteristics as a function of the IPO is a result of catchment antecedent or wetness conditions prior to the design storm. This hypothesis is tested using data from 166 high-quality daily rainfall stations across Australia along with catchment-averaged rainfall and resulting flows across an additional 128 catchments from the same region. In addition, 35 subdaily rainfall stations with long records in east Australia were also used to support our arguments. The results of these tests suggest that catchment antecedent conditions prior to design storm events were found to vary significantly across the two IPO phases, leading to the significant differences in flood characteristics across the two phases.

The relationship between seasonal aggregate rainfall and large-scale climate modes, particularly the El Niño–Southern Oscillation (ENSO), has been the subject of a significant and ongoing research effort. However, relatively little is known about how the character of individual rainfall events varies as a function of each of these climate modes. This study investigates the change in rainfall occurrence, intensity, and storm interevent time at both daily and subdaily time scales in east Australia, as a function of indices for ENSO, the Indian Ocean dipole (IOD), and the southern annular mode (SAM), with a focus on the cool season months. Long-record datasets have been used to sample a large variety of climate events for better statistical significance. Results using both the daily and subdaily rainfall datasets consistently show that it is the occurrence of rainfall events, rather than the average intensity of rainfall during the events, which is most strongly influenced by each of the climate modes. This is shown to be most likely associated with changes to the time between wet spells. Furthermore, it is found that despite the recent attention in the research literature on other climate modes, ENSO remains the leading driver of rainfall variability over east Australia, particularly farther inland during the winter and spring seasons. These results have important implications for how water resources are managed, as well as how the implications of large-scale climate modes are included in rainfall models to best capture interannual and longer-scale variability.

Low-frequency variability, in the form of the El Niño-Southern Oscillation, plays a key role in shaping local weather systems. However, current continuous stochastic rainfall models do not account for this variability in their simulations. Here a modified Random Pulse Bartlett Lewis stochastic generation model is presented for continuous rainfall simulation exhibiting low-frequency variability. Termed the Hierarchical Random Bartlett Lewis Model (HRBLM), the model features a hierarchical structure to represent a range of rainfall characteristics associated with the El Niño-Southern Oscillation with parameters conditioned to vary as functions of relevant climatic states. Long observational records of near-continuous rainfall at various locations in Australia are used to formulate and evaluate the model. The results indicate clear benefits of using the hierarchical climate-dependent structure proposed. In addition to accurately representing the wet spells characteristics and observed low-frequency variability, the model replicates the interannual variability of the antecedent rainfall preceding the extremes, which is known to be of considerable importance in design flood estimation applications.

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