A hydrologic similarity-based parameters dynamic matching framework: Application to enhance the real-time flood forecasting

Hongshi Wu; Peng Shi; Simin Qu; Xiaoqiang Yang; Hongxue Zhang; Le Wang; Song Ding; Zichun Li; Meixia Lu; Chao Qiu

doi:10.1016/j.scitotenv.2023.167767

A hydrologic similarity-based parameters dynamic matching framework: Application to enhance the real-time flood forecasting

Sci Total Environ. 2024 Jan 10:907:167767. doi: 10.1016/j.scitotenv.2023.167767. Epub 2023 Oct 11.

Authors

Hongshi Wu¹, Peng Shi², Simin Qu³, Xiaoqiang Yang⁴, Hongxue Zhang⁵, Le Wang¹, Song Ding¹, Zichun Li¹, Meixia Lu¹, Chao Qiu⁶

Affiliations

¹ College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
² College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China; Cooperative Innovation Center for Water Safety & Hydro Science, Nanjing 210098, China. Electronic address: ship@hhu.edu.cn.
³ College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China. Electronic address: wanily@hhu.edu.cn.
⁴ Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, China.
⁵ School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China.
⁶ Zhejiang Hydrologic Management Center, Hangzhou 310009, China.

PMID: 37832671
DOI: 10.1016/j.scitotenv.2023.167767

Abstract

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 conditions. 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 parameters (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 behaviors 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.

Keywords: Hydrologic similarity; Hydrological modeling; Machine learning techniques; Real-time flood forecasting; Time-variant parameters.