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Extraordinary floods like the one in July 2021 have induced catastrophic consequences on both societal and economic domains. Robust model simulations are crucial for mitigating the adverse effects of such extreme events on human life. However, accurately reproducing and predicting exceptional floods remain a challenge in particular when only one such flood extreme is available in the reference record period. This single flood could be included either in calibration and evaluation period. In both cases, extreme events are missing in the other period. To analyze how to best handle a single extreme flood, we present a framework for calibrating and evaluating the mesoscale Hydrologic Model (mHM) using the July 2021 flood in western Germany as a case study. Hereby, we tested the effect of including the extreme 2021 flood in calibration or evaluation periods. Our study shows that including the exceptional 2021 flood event in model calibration proves crucial for accurately reproducing high streamflow. Without including the 2021 flood in the calibration period, the model cannot learn how to reproduce extreme floods. Our findings reveal that employing the modified weighted Nash-Sutcliffe Efficiency (wNSE) as the objective function significantly improves mHM's performance in capturing flood peaks. This leads to a notable reduction from -35% to -7.8% in the difference between the simulated and observed/reconstructed peaks as demonstrated for the catchment outlet. The hydrological model performance was validated spatially for an independent set of gauges. Spatial validation is necessary for assessing model performance when only one exceptional historical event is available. In conclusion, our framework provides valuable insights into improving hydrologic modeling accuracy, emphasizing the importance of specific calibration strategies and spatial validation in capturing exceptional flood events.
Han et al. (Sat,) studied this question.