Spatial modelling framework for the characterisation of rainfall extremes at different durations and under climate change

Abstract

dThis paper describes a statistical modelling framework for the characterisation of rainfall extremes over a region of interest. Using a Bayesian hierarchical approach, the data are assumed to follow the generalised extreme value distribution, whose parameters are modelled as spatial Gaussian processes in the latent process layer. We also integrate a parametric relationship between precipitation maxima accumulated over increasing durations. The inference of the model parameters is thus improved by pooling information across both space and accumulation duration. In addition, we propose and investigate two different approaches for the integration of daily and sub-daily rainfall data within the framework. We also demonstrate how information from a regional climate model can be integrated to enable the investigation of future projections of extreme rainfall characteristics. We apply the proposed methodology to precipitation datasets from two large-scale study regions located on the east coast of Australia. The models are fitted using Markov chain Monte Carlo simulations, and we present estimated model parameters and posterior inferences of return levels at various durations and sites of interest. We demonstrate the effectiveness of the framework in spatially extrapolating the inference to locations other than those at which direct rainfall measurements are available. We also provide comparisons between rainfall extremes at various durations obtained for the current climate and those based on future projections from a regional climate model. Both methods proposed for the integration of daily and sub-daily records were found to yield similar results in terms of model performance and computational requirements.

On the Emergence of Anthropogenic Signal in Extreme Precipitation Change Over China

“…It represents a compromise between the block maxima and peak values over threshold. It is a widely used method and shows good performance (Lehmann et al, ; Wang & Zhang, ; Zhang et al, , ).…”

The impact of climate change on extreme precipitation in Sicily, Italy

“…Alam and Elshorbagy (2015) used the K-nearest neighbour technique to disaggregate daily precipitation generated with a stochastic rainfall generator to hourly and sub-hourly scales, and thus evaluate the climate induced changes on DDF curves. Srivastav, Schardong, and Simonovic (2014) proposed the use of the Equidistance Quantile Matching methodology (also known as quantile-quantile mapping) as a downscaling method for GCM data (Lehmann, Phatak, Stephenson, & Lau, 2016;Simonovic, Schardong, Sandink, & Srivastav, 2016;Singh, Arya, Taxak, & Vojinovic, 2016). The idea of this method is to apply a bias correction derived from the differences between observed data and GCM/RCM outputs for a baseline period (quantile mapping functions), which are then used to modify the GCM/RCM outputs in future periods, from which DDF curves are then calculated.…”

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

“…However, rising concentrations of greenhouse gases in the atmosphere and rapid urbanization, which could shorten the heavy rainfall regression cycle, are making this assumption largely untenable. Therefore, the trends in IDF curves calculations are expected to move toward nonstationary or time-varying models to better respond to the changing environment and properly and precisely guide water management and hydraulic infrastructure design [1,10].…”

“…Apart from trend analysis methods, which develop IDF curves by modeling historical rainfall time series data, researchers have incorporated outputs from General Circulation Models (GCMs) or Regional Circulation Models (RCMs) using bias correction and downscaling to simulate non-stationary IDF curves [10,13,14]. Lehmann [10] integrated RCM outputs into spatial Bayesian hierarchical models to investigate the characteristics of future extreme rainfall events stemming from numerous climate change scenarios.…”

“…Lehmann [10] integrated RCM outputs into spatial Bayesian hierarchical models to investigate the characteristics of future extreme rainfall events stemming from numerous climate change scenarios. Srivastav [14] updated the IDF curves using spatial and temporal downscaling, combining the changes in the distributional characteristics of the GCM/RCM between the baseline period and future period to assess the impact of climate change on future extreme precipitation.…”