Stochastic response surface methods for supporting flood modelling under uncertainty

作者：Ying Huang · 年份：2016 · DOI：10.32657/10356/68480 · 研究领域：Probabilistic and Robust Engineering Design、Flood Risk Assessment and Management、Hydrology and Watershed Management Studies

Flood inundation modelling is a fundamental tool for supporting flood risk assessment and management. However, it is a complex process, involving cascade consideration of meteorological, hydrological, and hydraulic processes. In order to successfully track the flood-related processes, different kinds of models, including stochastic rainfall, rainfall-runoff and hydraulic models are widely employed. However, a variety of uncertainties, originated from model structures, parameters, and inputs, tend to make the simulation results diverge from the real flood situations. Traditional stochastic uncertainty-analysis methods are suffering from time-consuming iterations of model runs based on parameter distributions. It is thus desired that uncertainties associated with flood modelling be more efficiently quantified, without much compromise of model accuracy. This thesis is devoted to developing a series of stochastic response surface methods (SRSMs) and coupled approaches to address forward and inverse uncertainty-assessment problems in flood inundation modelling. Flood forward problem is an important and fundamental issue in flood risk assessment and management. This study firstly investigated the application of a spectral method, namely, Karhunen-Loevè expansion (KLE) to approximate one-dimensional and two-dimensional coupled (1D/2D) heterogeneous random field of roughness. Based on KLE, first-order perturbation (FP-KLE) method was proposed to explore the impact of uncertainty asso...