Statistical modelling of rainfall intensity-frequency-duration curves using regional frequency analysis and Bayesian hierarchical modelling

Abstract

There is a predicted increase in extreme rainfall due to climate change. This may lead to an increase in natural hazards such as flooding, which can result in damage to infrastructure, farming, and may even result in injury or loss of life. Therefore, understanding rainfall extremes is important for assessing and adapting to the potential impacts of climate change. Thus, there is a need for accurate analysis and projection of extreme rainfall and its potential impacts. Understanding the relationship between rainfall intensity, frequency, and duration is important for the design and safety of infrastructure so that it can withstand extreme rainfall events. This relationship is described graphically through intensity-frequency-duration (IFD) curves. Estimating IFD curves, and their associated uncertainty as accurately as possible is critical as it may reduce the human and economic impacts that result from such extreme rainfall events. In this paper, we examine two methods for modelling extreme rainfall spatially: regional frequency analysis (RFA), and the Bayesian hierarchical model (BHM). We produce IFD estimates for both methods and compare the results. We find that for some locations, the RFA and BHM estimates are similar, and for other locations, they differ. We discuss the importance of uncertainty estimates and demonstrate the flexibility of the BHM for producing such measures of uncertainty.