Coupling higher-order probability weighted moments with norming constants method for non-stationary annual maximum flood frequency analysis

Abstract

Non-stationary flood frequency analysis (NFFA) is essential for reducing the risk of hydrologic engineering design and operation, especially when done under the severe influences of climate change and strong human activities. Although different types of methods have been proposed, NFFA is still a challenging task due to the complex characteristics of hydroclimatic data and weaknesses of diverse methods. In this article, a new method, called HPWM-NCM, is proposed for NFFA. The method uses the higher-order probability weighted moments (HPWM) method to accurately estimate parameters of flood data, and then applies the norming constants method (NCM) to precisely calculate the norming constants and further do NFFA. Results of both Monte-Carlo experiments and observed hydrologic data at 62 stations in the Rhine River basin illustrated that HPWM-NCM significantly improved the NFFA results compared to conventional NCM and the probability weighted moments method, as evaluated by overall fitting bias, residuals’ correlation, and residuals’ maximum error. Results highlighted the importance of using the information in high quantiles (rather than low quantiles) of flood data for NFFA, which is the key of the proposed method and ensures its superiority. Overall, the advantages of HPWM-NCM for non-stationary flood frequency analysis were confirmed, and the method has the potential for wide use in hydrologic and climate sciences.