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Flood risk has been increasing in many basins of the world, due to the global water cycle change driving by the global climate warming. To deal with the nonstationary properties of hydrological extremes, some new concepts, methods and models on flood frequency analysis and risk assessment are developed and applied. However, the robustness of nonstationary frequency analysis models, e.g. those based on the Generalized Additive Models for Location, Scale and Shape, is yet a big concern because the uncertainty of the parameters introduced by the methods and its impact on design flood values are difficult to quantify. This study aims to develop sensitivity degree indexes to assess the robustness of the nonstationary estimation of flood risk rates and their attributions, based on classical and Bayesian statistics, respectively. The results of the case study showed that the proposed method was efficient in identifying significant driving factors of nonstationary flood frequency; the results of the sensitivity index based on the Bayesian statistics showed that the uncertain degree of the nonstationary flood risk estimation increases with uncertain degree of the nonstationary model parameters as expected, but the sensitivity degree is decreased. It is indicated that the degree of influence of model parameters uncertainty on the risk estimation results is model dependent. This study will benefit the application of nonstationary frequency analysis methods in the flood risk assessment and flood design inference fields.Keywords: Flood frequency analysis; Flood risk; Non-stationarity; Attribution*This work was supported by the Research Council of Norway (FRINATEK Project 274310). |
