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

Abstract

The Intergovernmental Panel on Climate Change (IPCC, 2007) has predicted an increase in extreme rainfall due to climate change, which may also lead to an increase in natural hazards such as flooding. These hazards can result in damage to infrastructure and agriculture, and may even result in injury or loss of life. Consequently, there is a need for accurate analysis and projection of extreme rainfall and its potential impacts. For example, 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 by intensity-frequency-duration (IFD) curves. Estimating IFD curves and their associated uncertainty as accurately as possible is critical as it may help reduce the human and economic impacts that result from extreme rainfall events. In this paper, we examine two methods for modelling extreme rainfall spatially: regional frequency analysis (RFA) and a Bayesian hierarchical model (BHM). We produce IFD estimates from both methods and compare the results. We find that for some locations, the RFA and BHM estimates are similar, and for other locations, they are different. We discuss the importance of uncertainty estimates and demonstrate the flexibility of the BHM for producing such measures of uncertainty.