Flood forecasting methods for a semi‐arid and semi‐humid area in Northern China

Abstract The Double‐Excess (DE) model is a flood forecasting model which was developed to reflect the characteristics of runoff generation in semi‐arid and semi‐humid areas. However, the empirical unit hydrograph used in the subbasin confluence module often has low precision because of the difficult...

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书目详细资料
Main Authors: Xueping Zhu, Yu Zhang, Wei Qi, Yankuan Liang, Xuehua Zhao, Wenjun Cai, Yang Li
格式: 文件
语言:English
出版: Wiley 2022-12-01
丛编:Journal of Flood Risk Management
主题:
在线阅读:https://doi.org/10.1111/jfr3.12831
实物特征
总结:Abstract The Double‐Excess (DE) model is a flood forecasting model which was developed to reflect the characteristics of runoff generation in semi‐arid and semi‐humid areas. However, the empirical unit hydrograph used in the subbasin confluence module often has low precision because of the difficulty associated with parameter quantification. This study improves the subbasin confluence module of the DE model by coupling the geomorphologic instantaneous unit hydrograph (GIUH) to establish the improved DE model (DE‐GIUH) to calculate the subbasin confluence based on topographic physical characteristics. The improved model is applied to the Wangjiahui Hydrological Station Basin in Northern China. Compared with the conventional DE model and the widely used hydrologic modelling system (HEC‐HMS) model, the results show that DE‐GIUH improved flood forecasting, including the component peak discharge, flood peak appearance time and flood progress. The qualified rate (QR) of all three models reached grade A (QR ≥ 85.0%). However, the proportion of the deterministic coefficient (DC) reaching grade B or above (DC ≥ 0.70) improved from 50% (HEC‐HMS) and 30% (DE) to 90% (DE‐GIUH) in the calibration period, and from 62.5% and 25% to 75% in the validation period. In particular, for large and medium floods, the proportion of DC reaching grade B and above is 100% for DE‐GIUH, which is much higher than that of the other two models. The proposed improved model provides an alternative method for flood prediction in ungauged areas.
ISSN:1753-318X