Stochastic Event-Based Approach to Generate Concurrent Hourly Mean Sea Level Pressure and Wind Sequences for Estuarine Flood Risk Assessment

Abstract

The determination of the annual exceedence probability (AEP) of extreme water levels in complex estuarine systems is an important and challenging issue in flood management. Extreme estuarine levels are caused by the combined effects of river flows, local winds, and coastal ocean levels. This paper describes a stochastic, event-based approach for generating concurrent hourly mean sea level pressure (MSLP) and wind speed, which are needed in a larger project to derive stochastic hourly river flows, winds, and coastal ocean levels to drive a hydrodynamic model for estuarine flood level simulation. The minimum MSLP versus rainfall and maximum wind speed versus rainfall relationships for the extreme flood events at the daily time scale are first used to generate minimum daily MSLP and maximum daily wind speed using a nonparametric resampling method. The generated minimum daily MSLP and maximum daily wind speed are then used to scale concurrent hourly MSLP and wind speed sequences resampled from the historical record. This approach is novel because it is simple, generic, and computationally efficient for generating stochastic cross-correlated forcing time series for any estuarine hydrodynamic and flood risk study. The approach is tested using 50 years of forcing data from the Gippsland Lakes system in southeast Australia. The results indicate that the approach produces realistic stochastic concurrent hourly sequences of MSLP and wind speed that preserve and distinguish the MSLP-rainfall and wind speed-rainfall relationships for different synoptic weather conditions and times of the year. The approach also produces hourly and daily minimum MSLP and maximum wind speed with similar AEP characteristics as the historical data.