Infrastructure Engineering - Theses

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Subject: Victoria × Subject: estuaries × Subject: mathematical models ×

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    Stochastic joint probability modelling of estuarine flood levels
    TAN, KIM SEONG ( 2004)
    The determination of the annual exceedence probability (AEP) of extreme water levels, such as the 1% AEP flood level, in complex estuarine systems is an important and yet highly challenging issue. Water level AEP is required for land and water resources planning, emergency management and flood insurance underwriting. Extreme water levels in estuaries are caused by the combined effects of environmental forcings (river floods, winds and coastal ocean levels (COLs)), estuarine hydrodynamics, floodplain topography and catchment conditions. A comprehensive flood study should therefore entail a detailed hydrological, hydraulic and terrain modelling of the entire system. Unfortunately, there is currently no standard procedure for undertaking such a study. The question asked in this thesis is: "Is it possible to estimate, in a scientifically rigorous but computationally efficient way, the AEP of extreme water levels in large and complex estuarine systems such that the spatial and temporal forcing characteristics ranging from catchment to synoptic scales are preserved?" This question is addressed by developing a generic modelling method for application to any estuaries, and testing it on the Gippsland Lakes in southeast Australia - a coastal lagoon system having water surface area of almost 400 km2 and contributing catchment area of over 20,000 km2. The new method is a stratified Monte-Carlo stochastic-deterministic hydro-climatic modelling-based joint probability (MBJP) method. Conceptually, two thousand years of stochastic event-based concurrent hourly forcing sequences (river flows, winds and COLs) that preserve the space-time cross-correlations are generated using a sequence of hydro-climatic models developed in this thesis. Monte-Carlo (MC) simulation of event-based water levels around the estuarine system is then carried out using a calibrated hydrodynamic model (HDM) driven by the generated stochastic forcing sequences. (From Abstract)