Representing low‐frequency variability in continuous rainfall simulations: A hierarchical random Bartlett Lewis continuous rainfall generation model

Abstract

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.

If Precipitation Extremes Are Increasing, Why Aren't Floods?

“…Decreasing flooding in larger catchments may also be coupled with a shift to more frequent, higher intensity but shorter convective storms (Lenderink & van Meijgaard, ; Molnar et al, ; Wasko & Sharma, ), which may have smaller spatial extents (Peleg et al, ; Wasko et al, ). Any shift in atmospheric circulation will result in changes in the dominant storm mechanism or frequency of events, changing persistence characteristics, which will correspondingly change precipitation extremes and antecedent conditions causing changes in flood magnitude as well (Hirsch & Archfield, ; Lu et al, ; Mallakpour & Villarini, ; Wasko, Pui, et al, ), a point which imparts large uncertainty in climate model simulations (Shepherd, ). Any of the above listed changes will affect flooding irrespective of the temporal or spatial scale considered (Pathiraja et al, ; Saft et al, ; Stephens et al, ).…”

The unusual 2013–2015 drought in South Korea in the context of a multicentury precipitation record: Inferences from a nonstationary, multivariate, Bayesian copula model

“…In particular, the role of the El Niño-Southern Oscillation (ENSO) for understanding and predicting hydrologic variables [Barlow et al, 2001;Boyd et al, 2006;Khalil et al, 2007;Rajagopalan et al, 2000;Tong et al, 2006;Wang et al, 2000;Wasko et al, 2015] has been explored extensively. Nonstationary frequency analyses using time-varying climate variables (e.g., sea surface temperature and sea level pressure) and time trend, as predictors have been pursued [He et al, 2006;Jain and Lall, 2001;Khaliq et al, 2006;Kwon et al, 2008;Sankarasubramanian and Lall, 2003;Villarini et al, 2009].…”

“…Over the past decade, there has been mounting evidence indicating that the frequency of hydrologic extreme events is modulated by low‐frequency climate variability [ Franks and Kuczera , ; Kwon et al , ; Lall et al ., ; Merz et al, ; Milly et al, ; Pui et al, ; Verdon‐Kidd and Kiem , ]. In particular, the role of the El Niño–Southern Oscillation (ENSO) for understanding and predicting hydrologic variables [ Barlow et al, ; Boyd et al, ; Khalil et al, ; Rajagopalan et al, ; Tong et al, ; Wang et al, ; Wasko et al, ] has been explored extensively. Nonstationary frequency analyses using time‐varying climate variables (e.g., sea surface temperature and sea level pressure) and time trend, as predictors have been pursued [ He et al, ; Jain and Lall , ; Khaliq et al, ; Kwon et al, ; Sankarasubramanian and Lall , ; Villarini et al, ].…”

A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures

“…The literature reports numerous empirical investigations that examine the performance of the Poissoncluster models in a wide range of rainfall types and climatic conditions. The models have been successfully fitted to data of various fine time scales from England (Cameron et al, 2000;Cowpertwait, 1991;Entekhabi et al, 1989;Onof and Wheater, 1994a, 1994b, Scotland (Glasbey et al, 1995), Belgium (Verhoest et al, 1997), Switzerland (Paschalis et al, 2014), Germany (Kaczmarska et al, 2014), Spain (Khaliq and Cunnane, 1996), Ireland (Khaliq and Cunnane, 1996), South Africa (Smithers et al, 2002), New Zealand (Cowpertwait et al, 2007), Australia (Gyasi-Agyei and Willgoose, 1997;Gyasi-Agyei, 1999;Wasko et al, 2015), Greece (Derzekos et al, 2005;Kossieris et al, 2015Kossieris et al, , 2013, Italy (Bo et al, 1994;Islam et al, 1990), United States (Bo et al, 1994;Kim et al, 2016Kim et al, , 2013bRodriguez-Iturbe et al, 1988Velghe et al, 1994) and Korean Peninsula (Kim et al, 2014). Further to the single-site case, Poisson-cluster models have been also developed for the simulation of rainfall in space and time (e.g.…”