A Hydrologic Similarity-Based Parameters Dynamic Matching Framework: Application to Enhance the Real-Time Flood Forecasting.

Conventional hydrological modeling is usually based on the assumption that parameters are statically time -invariant. However, recent studies suggest that the influences of climate change and human interventions have made this hypothesis questionable. Meanwhile, machine learning techniques are increasingly used to extract patterns and insights from the ever-increasing hydrometeorological data. Here, we proposed a hybrid framework (HSPDM) to improve the precision of real-time flood forecasting in response to nonstationary con-ditions. It utilizes multiple machine learning techniques to dynamically retrieve calibrated hydrological model parameters from historical similar floods, thus continuously obtaining hourly time-variant parameters in real-time flood forecasting operations. Using the Quzhou Basin in China as a case study, the effectiveness and advancement of HSPDM framework was examined. Three schemes, including traditional time-invariant pa-rameters (scheme 1), hourly time-variant parameters (scheme 2), and probabilistic forecasting scheme (scheme 3), were built for comparison purpose. The results were summarized as follows: (1) The proposed framework can successfully identify continuous flood subsequence with a high retrieval accuracy (1.74) and acceptable time consumption (175.05 s) by adopting k-means, K-Nearest Neighbor (KNN), and embedding-based subsequence matching (EBSM) method. (2) Compared to scheme 1, scheme 2 provided more reliable forecasting results with higher accuracies, in terms of the general goodness-of-fit (higher NSE value) and reproducing flood peak and flood process. (3) The streamflow hydrographs forecasted by scheme 2 fell exactly in the predictive uncertainty bounds of scheme 3 and even showed superiority compared to a preferred deterministic forecast (Q50) of scheme 3. The major scientific contribution of this study lies in advancing the technique of real-time flood forecasting based on the hourly time-variant model parameters, thereby strengthening our understanding of model behav-iors under changing conditions. The proposed framework can also act as a new alternative for flood control and ultimately contribute to the mitigation of flooding disasters.