Mountain catchments are prone to flash flooding due to heavy rainfall. Enhanced understanding of the generation and evolution processes of flash floods is essential for effective flood risk management. However, tradit...Mountain catchments are prone to flash flooding due to heavy rainfall. Enhanced understanding of the generation and evolution processes of flash floods is essential for effective flood risk management. However, traditional distributed hydrological models based on kinematic and diffusion wave approximations ignore certain physical mechanisms of flash floods and thus bear excessive uncertainty. Here a hydrodynamic model is presented for flash floods based on the full two-dimensional shallow water equations incorporating rainfall and infiltration. Laboratory experiments of overland flows were modelled to illustrate the capability of the model. Then the model was applied to resolve two observed flash floods of distinct magnitudes in the Lengkou catchment in Shanxi Province, China. The present model is shown to be able to reproduce the flood flows fairly well compared to the observed data. The spatial distribution of rainfall is shown to be crucial for the modelling of flash floods. Sensitivity analyses of the model parameters reveal that the stage and discharge hydrographs are more sensitive to the Manning roughness and initial water content in the catchment than to the Green-Ampt head. Most notably, as the flash flood augments due to heavier rainfall, the modelling results agree with observed data better, which clearly characterizes the paramount role of rainfall in dictating the floods. From practical perspectives, the proposed model is more appropriate for modelling large flash floods.展开更多
Flash floods are a natural disaster that occurs annually, especially in the mountainous terrain and steep slopes of northern Thailand. The current flood forecasting systems and tools are available but have low accurac...Flash floods are a natural disaster that occurs annually, especially in the mountainous terrain and steep slopes of northern Thailand. The current flood forecasting systems and tools are available but have low accuracy and efficiency. The numbers of rainfall and runoff stations are less, because the access to the station area is difficult. Additionally, the operation and maintenance costs are high. Hydrological modeling of a SWAT （Soil and Water Assessment Tool） was used in this study with the application of three days weather forecast from the NWP （numerical weather prediction）, which provided temperature, relative humidity, rainfall, sunshine and wind speed. The data from NWP and SWAT were used to simulate the runoff from the Nan River in the last 10 years （2000-2010）. It was found that the simulated flow rate for the main streams using data from NWP were higher than the observations. At the N64 and Nl stations, the ratios of the maximum simulated flow rate to the observations were equal to 108% and 118%, respectively. However, for the tributaries, it was found that the simulated flow rate using NWP data was lower than the observations, but, it was still within the acceptable range of not greater than 20%,6. At N65, D090201 and D090203 stations, the ratio of the maximum simulated flow rate were 90.0%, 83.0% and 86.0%, respectively. This was due to the rainfall from the NWP model being greater than the measured rainfall. The NWP rainfall was distributed all over the area while the rainfall data from the measurements were obtained from specific points. Therefore, the rain from the NWP model is very useful especially for the watershed areas without rain gauge stations. In summary, the data from the NWP can be used with the SWAT model and provides relatively sound results despite the value for the main river being slightly higher than the observed data. Consequently, the output can be used to create a flood map for flash flood warning in the area.展开更多
基金funded by Natural Science Foundation of China (Grants Nos. 51279144 and 11432015)Chinese Academy of Sciences (Grant No. KZZD-EW-05-01-03)
文摘Mountain catchments are prone to flash flooding due to heavy rainfall. Enhanced understanding of the generation and evolution processes of flash floods is essential for effective flood risk management. However, traditional distributed hydrological models based on kinematic and diffusion wave approximations ignore certain physical mechanisms of flash floods and thus bear excessive uncertainty. Here a hydrodynamic model is presented for flash floods based on the full two-dimensional shallow water equations incorporating rainfall and infiltration. Laboratory experiments of overland flows were modelled to illustrate the capability of the model. Then the model was applied to resolve two observed flash floods of distinct magnitudes in the Lengkou catchment in Shanxi Province, China. The present model is shown to be able to reproduce the flood flows fairly well compared to the observed data. The spatial distribution of rainfall is shown to be crucial for the modelling of flash floods. Sensitivity analyses of the model parameters reveal that the stage and discharge hydrographs are more sensitive to the Manning roughness and initial water content in the catchment than to the Green-Ampt head. Most notably, as the flash flood augments due to heavier rainfall, the modelling results agree with observed data better, which clearly characterizes the paramount role of rainfall in dictating the floods. From practical perspectives, the proposed model is more appropriate for modelling large flash floods.
文摘Flash floods are a natural disaster that occurs annually, especially in the mountainous terrain and steep slopes of northern Thailand. The current flood forecasting systems and tools are available but have low accuracy and efficiency. The numbers of rainfall and runoff stations are less, because the access to the station area is difficult. Additionally, the operation and maintenance costs are high. Hydrological modeling of a SWAT （Soil and Water Assessment Tool） was used in this study with the application of three days weather forecast from the NWP （numerical weather prediction）, which provided temperature, relative humidity, rainfall, sunshine and wind speed. The data from NWP and SWAT were used to simulate the runoff from the Nan River in the last 10 years （2000-2010）. It was found that the simulated flow rate for the main streams using data from NWP were higher than the observations. At the N64 and Nl stations, the ratios of the maximum simulated flow rate to the observations were equal to 108% and 118%, respectively. However, for the tributaries, it was found that the simulated flow rate using NWP data was lower than the observations, but, it was still within the acceptable range of not greater than 20%,6. At N65, D090201 and D090203 stations, the ratio of the maximum simulated flow rate were 90.0%, 83.0% and 86.0%, respectively. This was due to the rainfall from the NWP model being greater than the measured rainfall. The NWP rainfall was distributed all over the area while the rainfall data from the measurements were obtained from specific points. Therefore, the rain from the NWP model is very useful especially for the watershed areas without rain gauge stations. In summary, the data from the NWP can be used with the SWAT model and provides relatively sound results despite the value for the main river being slightly higher than the observed data. Consequently, the output can be used to create a flood map for flash flood warning in the area.
