Abstract
Current practice in flood frequency analysis assumes that the stochastic properties of extreme floods follow that of stationary conditions. As human intervention and anthropogenic climate change influences in hydrometeorological variables are becoming evident in some places, there have been suggestions that nonstationary statistics would be better to represent the stochastic properties of the extreme floods. The probabilistic estimation of non-stationary models, however, is surrounded with uncertainty related to scarcity of observations and modelling complexities hence the difficulty to project the future condition. In the face of uncertain future and the subjectivity of model choices, this study attempts to demonstrate the practical implications of applying a nonstationary model and compares it with a stationary model in flood risk assessment. A fully integrated framework to simulate decision makers’ behaviour in flood frequency analysis is thereby developed. The framework is applied to hypothetical flood risk management decisions and the outcomes are compared with those of known underlying future conditions. Uncertainty of the economic performance of the risk-based decisions is assessed through Monte Carlo simulations. Sensitivity of the results is also tested by varying the possible magnitude of future changes. The application provides quantitative and qualitative comparative results that satisfy a preliminary analysis of whether the nonstationary model complexity should be applied to improve the economic performance of decisions. Results obtained from the case study shows that the relative differences of competing models for all considered possible future changes are small, suggesting that stationary assumptions are preferred to a shift to nonstationary statistics for practical application of flood risk management. Nevertheless, nonstationary assumption should also be considered during a planning stage in addition to stationary assumption especially for areas where future change in extreme flows is plausible. Such comparative evaluations would be of valuable in flood risk management decision-making processes.
Highlights
In hydrology and water resources planning, stochastic methods are routinely applied in the design process
It is assumed that the extreme hydrological events are stationary, which means that the probability distribution of the extremes remains time invariant over the design life/appraisal period of the planned structure
The assumption is, argued due to the fact that the extremes hydrological time series such as extreme precipitation and extreme floods and drought are driven by a complex interaction of different factors that inevitably changes over time
Summary
In hydrology and water resources planning, stochastic methods are routinely applied in the design process. There has been a call to identify nonstationary probabilistic models instead of relying upon the stationary assumption in practical flood risk management problems [6]. The findings from the analysis of change in future flows projections have been taken as the basis of design and planning guidelines for flood risk management in England and Wales [12]. A number of studies have conducted comparison analysis of design estimates from nonstationary probabilistic model to those of stationary [e.g. 13, 14, 15], which lead to emergence of diverse opinions. In the face of uncertain future and the subjectivity of model choice, this study attempts to incorporate nonstationary probabilistic model in a flood risk management decision analysis framework, in addition to the conventional stationary model.
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