Improved estimation of extreme floods with data pooling and mixed probability distribution

The accurate estimation of flood probability is crucial for designing water storage and flood retention structures. However, the assumption of identical distribution in flood samples is unrealistic, given the influence of various flood mechanisms. To address this challenge, we proposed a novel framework based on flood clustering and data pooling that encompasses the key steps such as 1) flood event separation based on a peak-detection flood separation algorithm, 2) grouping flood events using the k-prototypes algorithm, 3) application of the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach to pool reforecast ensemble datasets, and 4) statistical mixing approach to derive common quantiles from all the flood groups. We applied the framework to the Dresden gauge in the Elbe River for a detailed case study. Various tests have been performed to assess the applicability of the UNSEEN approach and the reforecast dataset consistently shows the potential for data pooling. The proposed methodology outperformed the classical approach in terms of goodness-of-fit. The relative difference between the classical and the proposed approach ((classical-proposed)/proposed) for the 100-year return level is 0.16, with a reduction in root mean square error (RMSE) value from 163 to 98 m3/s. Further, replication of the approach to the gauges in North Germany exhibited a relative difference ranging from −0.3 to +0.15 and produced better estimates in terms of RMSE compared with the traditional model. In summary, the proposed framework offers a better estimation of flood probability by addressing the inherent sample inhomogeneity along with the inclusion of unprecedented flood samples.