Joint probability and design storms at the crossroads

Abstract

The joint probability problem inherent in flood estimation is complex. Although the design storm approach has a long tradition it lacks the fundamental rigour of joint probability analysis. The use of average values for random inputs other than rainfall intensity and duration can be justified from a joint probability perspective provided variations in the input affect the peak flow density in a linear fashion. However, the assignment of the average value for initial conditions is problematic. A case study involving a detention basin demonstrates large biases arising from mis-specification of initial conditions in volume-sensitive systems. It is suggested that the current revision of ARR needs to articulate the shortcomings of the design storm approach, identify calibration strategies that ensure closure and give guidance about its reliability in different applications. Looking to the future, ARR needs to move towards event and total joint probability approaches that are underpinned by a rigorous joint probability framework. Continuous simulation is emerging as a practical tool and remains the most rigorous tool available. Event joint probability methods based on Monte Carlo simulation are computationally less demanding but require specification of the probability distribution of initial conditions. Stochastic rainfall models are on the verge of practical application to service Monte Carlo methods.

Keywords:

• Flood risk estimation
• desgin storms
• joint probability
• continuous simulation
• Monte Carlo
• stochastic rainfall models
• volume-sensitive systems