A comparison of alternatives for daily to sub-daily rainfall disaggregation

Abstract

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.

Application of the stochastic model for temporal rainfall disaggregation for hydrological studies in north western England

“…When examining the method for two case studies in the UK and the USA, Koutsoyiannis and Onof, (2001) found that the BLRP model performed well in preserving the most important statistical properties of the rainfall process. Pui et al (2009) found that the BLRP model performed better on average than the cascade models for Sydney rainfall with a slightly inflated reproduction of dry proportions at an hourly scale. However, complications were encountered during the parameter estimation stage and the choice of statistics remains subjective.…”

“…The BLRP model in its original form was applied strictly as a rainfall simulator (Pui et al, 2009). It has since been modified with an appropriate adjusting procedure to be applied in rainfall disaggregation.…”

An Uncertainty Investigation of RCM Downscaling Ratios in Nonstationary Extreme Rainfall IDF Curves

“…It is widely recognized that variations in the hydrologic cycle significantly affect the ecosystems and water circulation on which human social and economic life depends. In addition to enhanced climate variability, there is strong evidence that climate change is exacerbating the frequency and magnitude of extreme hydroclimatic phenomena, especially heavy precipitation events [1,2]. Therefore, studies of extreme hydrological events based on the nonstationary Extreme Value Theory in a changing environment are increasingly considered as necessary areas of research [1, [3][4][5][6].…”

Disaggregating radar-derived rainfall measurements in East Azarbaijan, Iran, using a spatial random-cascade model

“…Information about precipitation patterns influences the design and management of large and small (especially in urban areas) water-resources systems, ranging from continuous flow simulation and soil-erosion modeling to the evaluation of alternative policies for environmental impact assessments (e.g., Pui et al 2012;Sivakumar and Sharma 2008). Short-term rainfall patterns are one of the most common ones required in hydrologic modeling.…”

“…Licznar et al (2011) tested six randomcascade models for generating synthetic rainfall-time series, with a special focus on the needs of urban hydrology, e.g., hydrodynamic simulations of urban-drainage systems. Pui et al (2012) evaluated the performance of a number of daily to subdaily rainfall disaggregates, such as two versions of random-cascade models and the randomized Bartlett-Lewis model using continuous rainfall data at four cities in Australia.…”