Evaluating the Feasibility of the Liuxihe Model for Forecasting Inflow Flood to the Fengshuba Reservoir

Because of differences in the underlying surface, short flood confluence times, extreme precipitation, and other dynamic parameters, it is difficult to forecast an inflow flood to a basin reservoir, and traditional hydrological models do not achieve the forecast accuracy required for flood control operations. This study of the Fengshuba Reservoir in China evaluated the capacity of the Liuxihe model, which is based on a physically distributed hydrological model, to predict inflow floods in the Fengshuba Reservoir. The results show that the Liuxihe model has good applicability for flood forecasting in the basin. The use of different river classifications influenced the simulation results. The Liuxihe model can take into account the temporal and spatial inhomogeneity of precipitation and model parameters can be optimized using particle swarm optimization; this greatly improves the accuracy. The results show that the Liuxihe model can be used for real-time flood forecasting in the Fengshuba Reservoir watershed.

[1]  Jun Liu,et al.  Remote Sensing-Supported Flood Forecasting of Urbanized Watersheds - A Case Study in Southern China , 2022, Remote. Sens..

[2]  K. Takeuchi,et al.  Comprehensive Evaluation of Parameter Importance and Optimization Based on the Integrated Sensitivity Analysis System: A Case Study of the BTOP Model in the Upper Min River Basin, China , 2022, Journal of Hydrology.

[3]  Yangbo Chen,et al.  Flood forecasting scheme of Nanshui reservoir based on Liuxihe model , 2021, Tropical Cyclone Research and Review.

[4]  Yangbo Chen,et al.  Baipenzhu Reservoir Inflow Flood Forecasting Based on a Distributed Hydrological Model , 2021, Water.

[5]  Keith Beven,et al.  A history of TOPMODEL , 2020, Hydrology and Earth System Sciences.

[6]  Dehua Zhu,et al.  Hydrological evaluation of hourly merged satellite–station precipitation product in the mountainous basin of China using a distributed hydrological model , 2020, Meteorological Applications.

[7]  Yangbo Chen,et al.  Identifying Key Hydrological Processes in Highly Urbanized Watersheds for Flood Forecasting with a Distributed Hydrological Model , 2019, Water.

[8]  S. Sorooshian,et al.  Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model , 2019, Hydrology and Earth System Sciences.

[9]  J. Fleckenstein,et al.  A New Fully Distributed Model of Nitrate Transport and Removal at Catchment Scale , 2018, Water Resources Research.

[10]  Dehua Zhu,et al.  Hydrological Appraisal of Climate Change Impacts on the Water Resources of the Xijiang Basin, South China , 2017 .

[11]  K. C. Patra,et al.  Evaluating the Uncertainties in the SWAT Model Outputs due to DEM Grid Size and Resampling Techniques in a Large Himalayan River Basin , 2017 .

[12]  Mohamed El Alfy,et al.  Assessing the impact of arid area urbanization on flash floods using GIS, remote sensing, and HEC-HMS rainfall–runoff modeling , 2016 .

[13]  Yangbo Chen,et al.  Large-watershed flood forecasting with high-resolution distributed hydrological model , 2016 .

[14]  Chong-Yu Xu,et al.  A comparative study of different objective functions to improve the flood forecasting accuracy , 2016 .

[15]  Shenglian Guo,et al.  Impact of Cascaded Reservoirs Group on Flow Regime in the Middle and Lower Reaches of the Yangtze River , 2016 .

[16]  J. Li,et al.  Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization , 2015 .

[17]  M. Gusyev,et al.  Uncertainty Estimation During the Process of Flood Risk Assessment in Developing Countries – Case Study in the Pampanga River Basin – , 2014 .

[18]  Okke Batelaan,et al.  A distributed model for water and energy transfer between soil, plants and atmosphere (WetSpa) , 1996 .

[19]  E. Todini The ARNO rainfall-runoff model , 1996 .

[20]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[21]  P. E. O'connell,et al.  An introduction to the European Hydrological System — Systeme Hydrologique Europeen, “SHE”, 1: History and philosophy of a physically-based, distributed modelling system , 1986 .

[22]  John F. O'Callaghan,et al.  The extraction of drainage networks from digital elevation data , 1984, Comput. Vis. Graph. Image Process..

[23]  Jack F. Paris,et al.  A Physicoempirical Model to Predict the Soil Moisture Characteristic from Particle-Size Distribution and Bulk Density Data 1 , 1981 .

[24]  Xu Huijun Application of SCE-UA Algorithm to Parameter Optimization of Liuxihe Model , 2012 .

[25]  Ian Cluckie,et al.  Liuxihe Model and Its Modeling to River Basin Flood , 2011 .