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Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate multi-step-ahead flood forecasting is beneficial for flood control and disaster reduction efforts, as well as optimizing water resource management. This study proposed a runoff process vectorization method (RPV) and combined it with three machine learning (ML) models to construct the RPV-ML flood forecasting series models. The research area comprises three typical river basins: Gushanchuan River, Jialu River, and Zuli River, in the middle and upper reaches of the Yellow River. Rainfall-runoff data from 43, 28, and 37 flood events were respectively used for model training and validation, with a ratio of 7∶ 3 for flood events. The research shows that: ① Under the same lead time conditions, RPV-ML has higher Nash efficiency coefficients(ENS ), lower root mean square errors(ERMS ), and lower relative errors(ER) of peak in flood forecasting for the Gushanchuan River, Jialu River, and Zuli River basins. RPV-ML outperforms ML models in terms of predictive performance, especially in lead times of 4-6 hours. ② The accuracy of RPV-ML and ML models gradually decreases as the lead time increases, but RPV-ML exhibits a slower decline in accuracy and demonstrates better robustness. ③ The RPV- improved Temporal Convolutional Network (TCN) can better overcome forecasting errors, and the RPV- TCN model performs the best in terms of predictive performance among the three basins. The research findings can provide the scientific basis for flood control and disaster reduction efforts in river basins. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Current study incorporates both the watershed and urban areas into a unified spatial context in order to address the problem of coordinated flood forecasting in watershed- urban compound systems. Based on the proposed framework for distinguishing easily generated runoff patterns, a hybrid forecasting model, called GRGM-LSTM, and is developed by coupling the Grid- based Runoff Generation Model (GRGM) with Long Short- Term Memory neural networks (LSTM). The model is tested using 18 observed flood events in the control basin of the Jialu River at Zhongmou station. In addition, the forecast results are compared and analyzed against the Storm Water Management Model ( SWMM) and GRGM- SWMM model. The study reveals that: ① The relative error and coefficient of determination obtained from the GRGM for simulating runoff are 8. 41% and 0. 976, respectively. This indicates that considering the spatial distribution of runoff patterns results in more accurate runoff calculations. ② For forest period of less than 6 hours, the GRGM-LSTM hybrid model outperforms physical mechanism models such as GRGM-SWMM and SWMM, yielding Nash- Sutcliffe efficiency coefficients greater than 0. 8, indicating superior simulation performance. ③ However, for a forest period exceeding 6 hours, the GRGM-LSTM hybrid model experiences some accuracy loss, and when the forest period increases to 12 hours, the simulation accuracy of GRGM-SWMM surpasses that of GRGM- LSTM. The research findings can serve as a scientific basis for coordinated management of flood prevention and disaster reduction in watershed- urban areas. [ABSTRACT FROM AUTHOR]

Urban flood scenario deduction is the basis of urban flood control and drainage work. Taking Luoyang downtown area as the study area, comprehensively considering the urban pipeline network, inland rivers, terrain and other complex underlying surface factors, a 1 D river-1 D pipe network-2 D surface coupled urban flood simulation model based on MIKE FLOOD was constructed. The model was calibrated and tested by field rainfall investigations on the depth of the accumulated water and the measured flow of the river channel. The simulated qualification rate of the accumulated water point reached 81.25% and 93.75%, and the NSE of the simulated flow process was 0.79-0.96. It also simulated and analyzed the inundation process under different scenarios with different durations and different return periods. The results show that when the designed rainstorm return period is less than once in 20 years, the inundation area caused by the rainstorm is relatively small, mostly concentrated in the downtown area with dense building. When the design rainstorm return period is greater than once in 20 years, the inundation area caused by the rainstorm will increase significantly; when the return period is less than once in 10 years, the submerged area will increase significantly as the return period increases; the return period is greater than when it encounters once in 10 years, the growth of the submerged area tends to be flat. [ABSTRACT FROM AUTHOR]