China experiences one of the most frequent flood disasters in the world.Establishing accurate and reliable flood prediction program is the key to deal with flood disasters.Nanshui Reservoir Basin,in southern China,bel...China experiences one of the most frequent flood disasters in the world.Establishing accurate and reliable flood prediction program is the key to deal with flood disasters.Nanshui Reservoir Basin,in southern China,belongs to subtropical monsoon climate,with more rain in spring,concentrated rainstorm in summer and typhoon storm in autumn.Floods at dam site are mostly small and medium-sized floods with steep rise and slow fall as typical mountain flood.In order to explore the applicability of Liuxihe model in flood prediction of Nanshui Reservoir,this paper builds up Liuxihe model for Nanshui Reservoir based on DEM,land use and soil type data,and selects a typical flood event to optimize the parameters using particle swarm optimization(PSO)algorithm and verifies the accuracy of the model by simulating the other floods.Liuxihe model established in this paper indicates a satisfactory performance for flood prediction for Nanshui Reservoir,which can meet the accuracy requirement of flood prediction.Finally,the effects of different river grading and PSO algorithm on flood prediction are discussed.The results show that the PSO algorithm can obviously improve the accuracy of the Liuxihe model for flood forecast in Nanshui Reservoir.The simulation based on four-level channel grading has better results than that based on three-level channel,which indicates increased peak flood value,delayed peak time and closer simulation to the measured value.展开更多
Urbanization has been a worldwide development trend,which regulates river courses,impervious surfaces and drainage systems.Urbanization causes hydrological effects,including increased runoff volumes,peak discharges an...Urbanization has been a worldwide development trend,which regulates river courses,impervious surfaces and drainage systems.Urbanization causes hydrological effects,including increased runoff volumes,peak discharges and flow concentrations.This manuscript selects the Malaysian Sungai Pinang watershed as a case study to illustrate these land use,channel and flooding changes of Asian coastal cities.The Landsat satellite remote sensing images were first used to estimate the land use/land cover changes of the Sungai Pinang watershed by using SVM algorithm,and the results shows the urbanization was very rapid in the past decades,with the urbanization rate reached 46.41%in 2018 based on the build area rate.River channel characteristics also changed significantly,from natural river to concrete channel.Some flood resilience measures for coastal cities experiencing urbanization are also proposed for development and flood mitigation.Moreover,a flood forecasting model of the Sungai Pinang watershed is established herein.The simulation results of the Liuxihe model constructed in this study conforms to hydrological regularities and can provide a technical reference for flood control and disaster reduction.However,it is necessary to pay attention to the uncertainty of the forecast results.展开更多
Forecasting flooding hazards is a very effective non-engineering measure for flood control.Presently,the commonly used forecasting method in simulating flash flood events is through a watershed hydrological model.Many...Forecasting flooding hazards is a very effective non-engineering measure for flood control.Presently,the commonly used forecasting method in simulating flash flood events is through a watershed hydrological model.Many Asia-Pacific countries like the Philippines are prone to frequent hydrometeorological hazards such as tropical cyclones,resulting in frequent heavy rainfall events.However,most rivers in the many basins lack water flow observation data,which makes it challenging to use lumped and data-driven models for flash flood forecasting.With the continuous progress of remote sensing(RS)and geographic information system(GIS)techniques,the physically-based distributed hydrological model(PBDHMs)has rapidly advanced.PBDHMs can directly determine the model parameters according to the underlying surface characteristics from remotely-sensed data,which makes it possible for flood forecasting in areas with little to virtually no data.In this study,the Matina River basin in Davao City was selected as a case study in simulating a small data-poor basin in the region.The Liuxihe model was used to formulate a forecasting scheme and simulated the past flash flood events.The results show that there is a good correspondence between the past heavy rainfall events and their corresponding simulated river discharges.The results conform to the hydrological regularities,which can be used for flood forecasting and can serve as a baseline for the development of a flood forecasting system in the rivers of Davao City and elsewhere.展开更多
基金supported by the National Key Research and Development Program of China(funding no.2017YFC1502702)
文摘China experiences one of the most frequent flood disasters in the world.Establishing accurate and reliable flood prediction program is the key to deal with flood disasters.Nanshui Reservoir Basin,in southern China,belongs to subtropical monsoon climate,with more rain in spring,concentrated rainstorm in summer and typhoon storm in autumn.Floods at dam site are mostly small and medium-sized floods with steep rise and slow fall as typical mountain flood.In order to explore the applicability of Liuxihe model in flood prediction of Nanshui Reservoir,this paper builds up Liuxihe model for Nanshui Reservoir based on DEM,land use and soil type data,and selects a typical flood event to optimize the parameters using particle swarm optimization(PSO)algorithm and verifies the accuracy of the model by simulating the other floods.Liuxihe model established in this paper indicates a satisfactory performance for flood prediction for Nanshui Reservoir,which can meet the accuracy requirement of flood prediction.Finally,the effects of different river grading and PSO algorithm on flood prediction are discussed.The results show that the PSO algorithm can obviously improve the accuracy of the Liuxihe model for flood forecast in Nanshui Reservoir.The simulation based on four-level channel grading has better results than that based on three-level channel,which indicates increased peak flood value,delayed peak time and closer simulation to the measured value.
基金supported by the National Key Research and Development Program of China(funding no.2017YFC1502702)
文摘Urbanization has been a worldwide development trend,which regulates river courses,impervious surfaces and drainage systems.Urbanization causes hydrological effects,including increased runoff volumes,peak discharges and flow concentrations.This manuscript selects the Malaysian Sungai Pinang watershed as a case study to illustrate these land use,channel and flooding changes of Asian coastal cities.The Landsat satellite remote sensing images were first used to estimate the land use/land cover changes of the Sungai Pinang watershed by using SVM algorithm,and the results shows the urbanization was very rapid in the past decades,with the urbanization rate reached 46.41%in 2018 based on the build area rate.River channel characteristics also changed significantly,from natural river to concrete channel.Some flood resilience measures for coastal cities experiencing urbanization are also proposed for development and flood mitigation.Moreover,a flood forecasting model of the Sungai Pinang watershed is established herein.The simulation results of the Liuxihe model constructed in this study conforms to hydrological regularities and can provide a technical reference for flood control and disaster reduction.However,it is necessary to pay attention to the uncertainty of the forecast results.
基金supported by the Natural Science Foundation of China(No.51961125206)
文摘Forecasting flooding hazards is a very effective non-engineering measure for flood control.Presently,the commonly used forecasting method in simulating flash flood events is through a watershed hydrological model.Many Asia-Pacific countries like the Philippines are prone to frequent hydrometeorological hazards such as tropical cyclones,resulting in frequent heavy rainfall events.However,most rivers in the many basins lack water flow observation data,which makes it challenging to use lumped and data-driven models for flash flood forecasting.With the continuous progress of remote sensing(RS)and geographic information system(GIS)techniques,the physically-based distributed hydrological model(PBDHMs)has rapidly advanced.PBDHMs can directly determine the model parameters according to the underlying surface characteristics from remotely-sensed data,which makes it possible for flood forecasting in areas with little to virtually no data.In this study,the Matina River basin in Davao City was selected as a case study in simulating a small data-poor basin in the region.The Liuxihe model was used to formulate a forecasting scheme and simulated the past flash flood events.The results show that there is a good correspondence between the past heavy rainfall events and their corresponding simulated river discharges.The results conform to the hydrological regularities,which can be used for flood forecasting and can serve as a baseline for the development of a flood forecasting system in the rivers of Davao City and elsewhere.