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
The ecological protection of the inter-provincial river basin of Xin’anjiang River takes the ecological compensation mechanism as the starting point and achieves good results.The success of the mechanism is best refl...The ecological protection of the inter-provincial river basin of Xin’anjiang River takes the ecological compensation mechanism as the starting point and achieves good results.The success of the mechanism is best reflected in its innovation of public policy supply,which is not only a vivid practice of Xi Jinping’s ecological civilization thought in the new era,but also establishes a model for absorption,transformation and export of policy supply.The effective supply of public policies on ecological environment is the realistic choice for the construction of ecological civilization in the new era.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
This study evaluated the temporal and spatial variations of water quality data sets for the Xin＇anjiang River through the use of multivariate statistical techniques, including cluster analysis （CA）, discriminant an...This study evaluated the temporal and spatial variations of water quality data sets for the Xin＇anjiang River through the use of multivariate statistical techniques, including cluster analysis （CA）, discriminant analysis （DA）, correlation analysis, and principal component analysis （PCA）. The water samples, measured by ten parameters, were collected every month for three years （2008-2010） from eight sampling stations located along the river. The hierarchical CA classified the 12 months into three periods （First, Second and Third Period） and the eight sampling sites into three groups （Groups 1, 2 and 3） based on seasonal differences and various pollution levels caused by physicochemical properties and anthropogenic activ- ities. DA identified three significant parameters （tempera- ture, pH and E.coli） to distinguish temporal groups with close to 76% correct assignment. The DA also discovered five parameters （temperature, electricity conductivity, total nitrogen, chemical oxygen demand and total phosphorus） for spatial variation analysis, with 80.56% correct assignment. The non-parametric correlation coefficient （Spear- man R） explained the relationship between the water quality parameters and the basin characteristics, and the GIS made the results visual and direct. The PCA identified four PCs for Groups 1 and 2, and three PCs for Group 3. These PCs captured 68.94%, 67.48% and 70.35% of the total variance of Groups 1, 2 and 3, respectively. Although natural pollution affects the Xin＇anjiang River, the main sources of pollution included agricultural activities, industrial waste, and domestic wastewater.展开更多
Practical experience has demonstrated that single objective functions, no matter how carefully chosen, prove to be inadequate in providing proper measurements for all of the characteristics of the observed data. One s...Practical experience has demonstrated that single objective functions, no matter how carefully chosen, prove to be inadequate in providing proper measurements for all of the characteristics of the observed data. One strategy to circumvent this problem is to define multiple fitting criteria that measure different aspects of system behavior, and to use multi-criteria optimization to identify non-dominated optimal solutions. Unfortunately, these analyses require running original simulation models thousands of times. As such, they demand prohibitively large computational budgets. As a result, surrogate models have been used in combination with a variety of multi- objective optimization algorithms to approximate the true Pareto-front within limited evaluations for the original model. In this study, multi-objective optimization based on surrogate modeling （multivariate adaptive regression splines, MARS） for a conceptual rainfall-runoff model （Xin＇anjiang model, XAJ） was proposed. Taking the Yanduhe basin of Three Gorges in the upper stream of the Yangtze River in China as a case study, three evaluation criteria were selected to quantify the goodness-of-fit of observations against calculated values from the simulation model. The three criteria chosen were the Nash-Sutcliffe efficiency coefficient, the relative error of peak flow, and runoff volume （REPF and RERV）. The efficacy of this method is demonstrated on the calibration of the XAJ model. Compared to the single objective optimization results, it was indicated that the multi-objective optimization method can infer the most probable parameter set. The results also demonstrate that the use of surrogate-modeling enables optimization that is much more efficient; and the total computational cost is reduced by about 92.5%, compared to optimization without using surrogate model- ing. The results obtained with the proposed method support the feasibility of applying parameter optimization to computationally intensive simulation models, via reducing the number of simulation runs required in the numerical model considerably.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The ecological protection of the inter-provincial river basin of Xin’anjiang River takes the ecological compensation mechanism as the starting point and achieves good results.The success of the mechanism is best reflected in its innovation of public policy supply,which is not only a vivid practice of Xi Jinping’s ecological civilization thought in the new era,but also establishes a model for absorption,transformation and export of policy supply.The effective supply of public policies on ecological environment is the realistic choice for the construction of ecological civilization in the new era.
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。
文摘This study evaluated the temporal and spatial variations of water quality data sets for the Xin＇anjiang River through the use of multivariate statistical techniques, including cluster analysis （CA）, discriminant analysis （DA）, correlation analysis, and principal component analysis （PCA）. The water samples, measured by ten parameters, were collected every month for three years （2008-2010） from eight sampling stations located along the river. The hierarchical CA classified the 12 months into three periods （First, Second and Third Period） and the eight sampling sites into three groups （Groups 1, 2 and 3） based on seasonal differences and various pollution levels caused by physicochemical properties and anthropogenic activ- ities. DA identified three significant parameters （tempera- ture, pH and E.coli） to distinguish temporal groups with close to 76% correct assignment. The DA also discovered five parameters （temperature, electricity conductivity, total nitrogen, chemical oxygen demand and total phosphorus） for spatial variation analysis, with 80.56% correct assignment. The non-parametric correlation coefficient （Spear- man R） explained the relationship between the water quality parameters and the basin characteristics, and the GIS made the results visual and direct. The PCA identified four PCs for Groups 1 and 2, and three PCs for Group 3. These PCs captured 68.94%, 67.48% and 70.35% of the total variance of Groups 1, 2 and 3, respectively. Although natural pollution affects the Xin＇anjiang River, the main sources of pollution included agricultural activities, industrial waste, and domestic wastewater.
基金Acknowledgements The work was supported by the National Basic Research Program of China （No. 2010CB951103）, the National Natural Science Foundation of China （Grant Nos. 41330854, 41371063 and 51309155） and the National Science ＆ Technology Pillar Program during the 12th Five-year Plan Period （2012BAC21B01 and 2012BAC19B03）. We are also thankful to anonymous reviewers and editors for their helpful comments and suggestions.
文摘Practical experience has demonstrated that single objective functions, no matter how carefully chosen, prove to be inadequate in providing proper measurements for all of the characteristics of the observed data. One strategy to circumvent this problem is to define multiple fitting criteria that measure different aspects of system behavior, and to use multi-criteria optimization to identify non-dominated optimal solutions. Unfortunately, these analyses require running original simulation models thousands of times. As such, they demand prohibitively large computational budgets. As a result, surrogate models have been used in combination with a variety of multi- objective optimization algorithms to approximate the true Pareto-front within limited evaluations for the original model. In this study, multi-objective optimization based on surrogate modeling （multivariate adaptive regression splines, MARS） for a conceptual rainfall-runoff model （Xin＇anjiang model, XAJ） was proposed. Taking the Yanduhe basin of Three Gorges in the upper stream of the Yangtze River in China as a case study, three evaluation criteria were selected to quantify the goodness-of-fit of observations against calculated values from the simulation model. The three criteria chosen were the Nash-Sutcliffe efficiency coefficient, the relative error of peak flow, and runoff volume （REPF and RERV）. The efficacy of this method is demonstrated on the calibration of the XAJ model. Compared to the single objective optimization results, it was indicated that the multi-objective optimization method can infer the most probable parameter set. The results also demonstrate that the use of surrogate-modeling enables optimization that is much more efficient; and the total computational cost is reduced by about 92.5%, compared to optimization without using surrogate model- ing. The results obtained with the proposed method support the feasibility of applying parameter optimization to computationally intensive simulation models, via reducing the number of simulation runs required in the numerical model considerably.
