A grid-based distributed hydrological model, the Block-wise use of TOPMODEL （BTOPMC）, which was developed from the original TOPMODEL, was used for hydrological daily rainfall-runoff simulation. In the BTOPMC model, ...A grid-based distributed hydrological model, the Block-wise use of TOPMODEL （BTOPMC）, which was developed from the original TOPMODEL, was used for hydrological daily rainfall-runoff simulation. In the BTOPMC model, the runoff is explicitly calculated on a cell-by-cell basis, and the Muskingum-Cunge flow concentration method is used. In order to test the model＇s applicability, the BTOPMC model and the Xin＇anjiang model were applied to the simulation of a humid watershed and a semi-humid to semi-arid watershed in China. The model parameters were optimized with the Shuffle Complex Evolution （SCE-UA） method. Results show that both models can effectively simulate the daily hydrograph in humid watersheds, but that the BTOPMC model performs poorly in semi-humid to semi-arid watersheds. The excess-infiltration mechanism should be incorporated into the BTOPMC model to broaden the model＇s applicability.展开更多
Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity ana...Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.展开更多
Along with the rapid development of computer and GIS technology, hydrological models have progressed from lumped to distributed models. TOPMODEL, a bridge between lumped and distributed models, is a semi-distributed m...Along with the rapid development of computer and GIS technology, hydrological models have progressed from lumped to distributed models. TOPMODEL, a bridge between lumped and distributed models, is a semi-distributed model in which the predominant factors determining the formation of runoff are derived from the topography of the basin. A test application of TOPMODEL in the Buliu River Basin is presented. For the sake of comprehensively evaluating the TOPMODEL, the Xin’anjiang model, a classic lumped hydrological model, was also applied in the basin. The structural differences and the simulation results of the two models are compared and analyzed.展开更多
中小河流洪水大多位于资料短缺的山丘区,具有突发性强,汇流时间短的特点,已成为当前防洪工作的重点和难点。新安江模型和SWAT(Soil and Water Assessment Tool)模型都是应用广泛的水文模型,但其在中小河流洪水模拟中的对比分析及效果评...中小河流洪水大多位于资料短缺的山丘区,具有突发性强,汇流时间短的特点,已成为当前防洪工作的重点和难点。新安江模型和SWAT(Soil and Water Assessment Tool)模型都是应用广泛的水文模型,但其在中小河流洪水模拟中的对比分析及效果评估方面还鲜有研究。以北潦南河为例,建立了日尺度和小时尺度的新安江模型和SWAT模型,评估2种模型在径流深、洪峰流量和峰现时间等关键要素方面的计算效果,分析各自优缺点。结果表明,在率定期间,新安江模型相对径流误差(RRE)、洪峰流量相对误差(RPE)和峰现时间误差(PTE)平均值分别为-2.6%、-4.3%、-0.3 h, SWAT模型RRE、RPE和PTE平均值分别为-4.3%、-3.3%、-0.1 h,新安江模型在RRE方面优于SWAT模型,但SWAT模型在RPE、PTE和多峰洪水模拟方面则优于新安江模型;在验证期得出了与率定期相同的结论。展开更多
A conceptual hydrological model that links the Xin＇anjiang hydrological model and a physically based snow energy and mass balance model, described as the XINSNOBAL model, was developed in this study for simulating ra...A conceptual hydrological model that links the Xin＇anjiang hydrological model and a physically based snow energy and mass balance model, described as the XINSNOBAL model, was developed in this study for simulating rain-on-snow events that commonly occur in the Pacific Northwest of the United States. The resultant model was applied to the Lookout Creek Watershed in the H. J. Andrews Experimental Forest in the western Cascade Mountains of Oregon, and its ability to simulate streamflow was evaluated. The simulation was conducted at 24-hour and one-hour time scales for the period of 1996 to 2005. The results indicated that runoffand peak discharge could be underestimated if snowpack accumulation and snowmelt under rain-on-snow conditions were not taken into account. The average deterministic coefficient of the hourly model in streamflow simulation in the calibration stage was 0.837, which was significantly improved over the value of 0.762 when the Xin＇anjiang model was used alone. Good simulation performance of the XINSNOBAL model in the WS 10 catchment, using the calibrated parameter of the Lookout Creek Watershed for proxy-basin testing, demonstrates that transplanting model parameters between similar watersheds can orovide a useful tool for discharge forecastin~, in un^au^ed basins.展开更多
Hydrologic models generally represent the most dominant processes since they are mere simplifications of physical reality and thus are subject to many significant uncertainties. As such, a coupling strategy is propose...Hydrologic models generally represent the most dominant processes since they are mere simplifications of physical reality and thus are subject to many significant uncertainties. As such, a coupling strategy is proposed. To this end, the coupling of the artificial neural network （ANN） with the Xin＇anjiang conceptual model with a view to enhance the quality of its flow forecast is presented. The approach uses the latest observations and residuals in runoff/discharge forecasts from the Xin＇anjiang model. The two complementary models （Xin＇anjiang ＆ ANN） are used in such a way that residuals of the Xin＇anjiang model are forecasted by a neural network model so that flow forecasts can be improved as new observations come in. For the complementary neural network, the input data were presented in a patterned format to conform to the calibration regime of the Xin＇anjiang conceptual model, using differing variants of the neural network scheme. The results show that there is a substantial improvement in the accuracy of the forecasts when the complementary model was operated on top of the Xin＇anjiang conceptual model as compared with the results of the Xin＇anjiang model alone.展开更多
基金supported by the Research Fund for Commonweal Trades (Meteorology) (Grants No.GYHY200706037, GYHY (QX) 2007-6-1,GYHY200906007,and GYHY201006038)the National Natural Science Foundation of China (Grants No.50479017 and 40971016)Program for Changjiang Scholars and Innovative Research Team in University (Grant No.IRT0717)
文摘A grid-based distributed hydrological model, the Block-wise use of TOPMODEL （BTOPMC）, which was developed from the original TOPMODEL, was used for hydrological daily rainfall-runoff simulation. In the BTOPMC model, the runoff is explicitly calculated on a cell-by-cell basis, and the Muskingum-Cunge flow concentration method is used. In order to test the model＇s applicability, the BTOPMC model and the Xin＇anjiang model were applied to the simulation of a humid watershed and a semi-humid to semi-arid watershed in China. The model parameters were optimized with the Shuffle Complex Evolution （SCE-UA） method. Results show that both models can effectively simulate the daily hydrograph in humid watersheds, but that the BTOPMC model performs poorly in semi-humid to semi-arid watersheds. The excess-infiltration mechanism should be incorporated into the BTOPMC model to broaden the model＇s applicability.
基金supported by the National Natural Science Foundation of China (Grant No. 41271003)the National Basic Research Program of China (Grants No. 2010CB428403 and 2010CB951103)
文摘Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.
基金supported by the National Natural Science Foundation of China (Grant No 50479017)
文摘Along with the rapid development of computer and GIS technology, hydrological models have progressed from lumped to distributed models. TOPMODEL, a bridge between lumped and distributed models, is a semi-distributed model in which the predominant factors determining the formation of runoff are derived from the topography of the basin. A test application of TOPMODEL in the Buliu River Basin is presented. For the sake of comprehensively evaluating the TOPMODEL, the Xin’anjiang model, a classic lumped hydrological model, was also applied in the basin. The structural differences and the simulation results of the two models are compared and analyzed.
文摘中小河流洪水大多位于资料短缺的山丘区,具有突发性强,汇流时间短的特点,已成为当前防洪工作的重点和难点。新安江模型和SWAT(Soil and Water Assessment Tool)模型都是应用广泛的水文模型,但其在中小河流洪水模拟中的对比分析及效果评估方面还鲜有研究。以北潦南河为例,建立了日尺度和小时尺度的新安江模型和SWAT模型,评估2种模型在径流深、洪峰流量和峰现时间等关键要素方面的计算效果,分析各自优缺点。结果表明,在率定期间,新安江模型相对径流误差(RRE)、洪峰流量相对误差(RPE)和峰现时间误差(PTE)平均值分别为-2.6%、-4.3%、-0.3 h, SWAT模型RRE、RPE和PTE平均值分别为-4.3%、-3.3%、-0.1 h,新安江模型在RRE方面优于SWAT模型,但SWAT模型在RPE、PTE和多峰洪水模拟方面则优于新安江模型;在验证期得出了与率定期相同的结论。
基金supported by the National Natural Science Foundation of China (Grants No. 40901015 and41001011)the Major Program of the National Natural Science Foundation of China (Grants No. 51190090 and 51190091)+3 种基金the Fundamental Research Funds for the Central Universities (Grants No. B1020062 andB1020072)the Ph. D. Programs Foundation of the Ministry of Education of China (Grant No.20090094120008)the Special Fund of State Key Laboratories of China (Grants No. 2009586412 and 2009585412)the Programme of Introducing Talents of Disciplines to Universities of the Ministry of Education and State Administration of the Foreign Experts Affairs of China (the 111 Project, Grant No.B08048)
文摘A conceptual hydrological model that links the Xin＇anjiang hydrological model and a physically based snow energy and mass balance model, described as the XINSNOBAL model, was developed in this study for simulating rain-on-snow events that commonly occur in the Pacific Northwest of the United States. The resultant model was applied to the Lookout Creek Watershed in the H. J. Andrews Experimental Forest in the western Cascade Mountains of Oregon, and its ability to simulate streamflow was evaluated. The simulation was conducted at 24-hour and one-hour time scales for the period of 1996 to 2005. The results indicated that runoffand peak discharge could be underestimated if snowpack accumulation and snowmelt under rain-on-snow conditions were not taken into account. The average deterministic coefficient of the hourly model in streamflow simulation in the calibration stage was 0.837, which was significantly improved over the value of 0.762 when the Xin＇anjiang model was used alone. Good simulation performance of the XINSNOBAL model in the WS 10 catchment, using the calibrated parameter of the Lookout Creek Watershed for proxy-basin testing, demonstrates that transplanting model parameters between similar watersheds can orovide a useful tool for discharge forecastin~, in un^au^ed basins.
文摘Hydrologic models generally represent the most dominant processes since they are mere simplifications of physical reality and thus are subject to many significant uncertainties. As such, a coupling strategy is proposed. To this end, the coupling of the artificial neural network （ANN） with the Xin＇anjiang conceptual model with a view to enhance the quality of its flow forecast is presented. The approach uses the latest observations and residuals in runoff/discharge forecasts from the Xin＇anjiang model. The two complementary models （Xin＇anjiang ＆ ANN） are used in such a way that residuals of the Xin＇anjiang model are forecasted by a neural network model so that flow forecasts can be improved as new observations come in. For the complementary neural network, the input data were presented in a patterned format to conform to the calibration regime of the Xin＇anjiang conceptual model, using differing variants of the neural network scheme. The results show that there is a substantial improvement in the accuracy of the forecasts when the complementary model was operated on top of the Xin＇anjiang conceptual model as compared with the results of the Xin＇anjiang model alone.
