In recent years, global reanalysis weather data has been widely used in hydrological modeling around the world, but the results of simulations vary greatly. To consider the applicability of Climate Forecast System Rea...In recent years, global reanalysis weather data has been widely used in hydrological modeling around the world, but the results of simulations vary greatly. To consider the applicability of Climate Forecast System Reanalysis（CFSR） data in the hydrologic simulation of watersheds, the Bahe River Basin was used as a case study. Two types of weather data（conventional weather data and CFSR weather data） were considered to establish a Soil and Water Assessment Tool（SWAT） model, which was used to simulate runoff from 2001 to 2012 in the basin at annual and monthly scales. The effect of both datasets on the simulation was assessed using regression analysis, Nash-Sutcliffe Efficiency（NSE）, and Percent Bias（PBIAS）. A CFSR weather data correction method was proposed. The main results were as follows.（1） The CFSR climate data was applicable for hydrologic simulation in the Bahe River Basin（R^2 of the simulated results above 0.50, NSE above 0.33, and ｜PBIAS｜ below 14.8. Although the quality of the CFSR weather data is not perfect, it achieved a satisfactory hydrological simulation after rainfall data correction.（2） The simulated streamflow using the CFSR data was higher than the observed streamflow, which was likely because the estimation of daily rainfall data by CFSR weather data resulted in more rainy days and stronger rainfall intensity than was actually observed. Therefore, the data simulated a higher base flow and flood peak discharge in terms of the water balance, except for some individual years.（3） The relation between the CFSR rainfall data（x） and the observed rainfall data（y） could berepresented by a power exponent equation： y=1.4789x0.8875（R2=0.98,P〈0.001）. There was a slight variation between the fitted equations for each station. The equation provides a theoretical basis for the correction of CFSR rainfall data.展开更多
径流预报是缓解洪水的一种重要方法。基于1978-2010年的水文资料,结合长短期记忆神经网络(Long-Short Term Memory,LSTM),构建了灞河流域径流预测模型,并且评价了模型对同一流域不同特征水文站的差异及不同季度的预测效果差异。结果表明...径流预报是缓解洪水的一种重要方法。基于1978-2010年的水文资料,结合长短期记忆神经网络(Long-Short Term Memory,LSTM),构建了灞河流域径流预测模型,并且评价了模型对同一流域不同特征水文站的差异及不同季度的预测效果差异。结果表明:不同神经元的组合,对LSTM模型预测效果会产生影响,利用最佳的神经元组合可以更加有效预测径流量变化,大峪河大峪(三)站的最佳组合为第一层神经元128个,第二层神经元32个;灞河罗李村(四)站的最佳组合为第一层神经元128个,第二层神经元8个;灞河马渡王站的最佳组合为第一层神经元8个,第二层神经元2个。不同站点的LSTM最佳模型都能较为有效的预测三个水文站2006-2010年的径流量变化,其中大峪河大峪(三)站效果最佳,其余两个站点效果相对较差。LSTM模型对各个季度的预测效果有差异,各个站点大部分第三季度的均方根误差都较大,而对第一、四季度的径流预测相对较准确。展开更多
基金International Partnership Program of Chinese Academy of Sciences,No.131551KYSB20160002 National Natural Science Foundation of China,No.41401602+2 种基金 Natural Science Basic Research Plan in Shaanxi Province of China,No.2014JQ2-4021 Key Scientific and Technological Innovation Team Plan of Shaanxi Province,No.2014KCT-27 Graduate Student Innovation Project of Northwest University,No.YZZ15011
文摘In recent years, global reanalysis weather data has been widely used in hydrological modeling around the world, but the results of simulations vary greatly. To consider the applicability of Climate Forecast System Reanalysis（CFSR） data in the hydrologic simulation of watersheds, the Bahe River Basin was used as a case study. Two types of weather data（conventional weather data and CFSR weather data） were considered to establish a Soil and Water Assessment Tool（SWAT） model, which was used to simulate runoff from 2001 to 2012 in the basin at annual and monthly scales. The effect of both datasets on the simulation was assessed using regression analysis, Nash-Sutcliffe Efficiency（NSE）, and Percent Bias（PBIAS）. A CFSR weather data correction method was proposed. The main results were as follows.（1） The CFSR climate data was applicable for hydrologic simulation in the Bahe River Basin（R^2 of the simulated results above 0.50, NSE above 0.33, and ｜PBIAS｜ below 14.8. Although the quality of the CFSR weather data is not perfect, it achieved a satisfactory hydrological simulation after rainfall data correction.（2） The simulated streamflow using the CFSR data was higher than the observed streamflow, which was likely because the estimation of daily rainfall data by CFSR weather data resulted in more rainy days and stronger rainfall intensity than was actually observed. Therefore, the data simulated a higher base flow and flood peak discharge in terms of the water balance, except for some individual years.（3） The relation between the CFSR rainfall data（x） and the observed rainfall data（y） could berepresented by a power exponent equation： y=1.4789x0.8875（R2=0.98,P〈0.001）. There was a slight variation between the fitted equations for each station. The equation provides a theoretical basis for the correction of CFSR rainfall data.
文摘径流预报是缓解洪水的一种重要方法。基于1978-2010年的水文资料,结合长短期记忆神经网络(Long-Short Term Memory,LSTM),构建了灞河流域径流预测模型,并且评价了模型对同一流域不同特征水文站的差异及不同季度的预测效果差异。结果表明:不同神经元的组合,对LSTM模型预测效果会产生影响,利用最佳的神经元组合可以更加有效预测径流量变化,大峪河大峪(三)站的最佳组合为第一层神经元128个,第二层神经元32个;灞河罗李村(四)站的最佳组合为第一层神经元128个,第二层神经元8个;灞河马渡王站的最佳组合为第一层神经元8个,第二层神经元2个。不同站点的LSTM最佳模型都能较为有效的预测三个水文站2006-2010年的径流量变化,其中大峪河大峪(三)站效果最佳,其余两个站点效果相对较差。LSTM模型对各个季度的预测效果有差异,各个站点大部分第三季度的均方根误差都较大,而对第一、四季度的径流预测相对较准确。
