基于响应面优化的青龙河流域HSPF模型参数校准方法
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摘要
Hydrological Simulation Program-Fortran(HSPF)模型参数多且交互作用复杂,传统参数寻优面临着优化参数不灵敏、优化算法易陷入局部陷阱等问题,影响了优化精度和效率.本文集成青龙河流域、参数抽样、灵敏度分析和参数优化探索新的寻优途径.应用响应面优化软件Design Expert,针对9个HSPF模型参数进行抽样,获得130组参数集,采用多元二次回归模型,建立参数集与纳什效率系数(NSE)的非线性关系,通过等高线和响应面识别最优参数及其密集取值区间.响应面优化参数的NSE平均值、最大值、最小值以及寻优区间缩减率均优于正交极差分析方法;LZSN、INFILT、AGWRC为极灵敏参数,DEEPFR为灵敏参数;LZSN和INFILT、INFILT和AGWRC、INFILT和UZSN、INFILT和IRC的交互作用对结果有显著影响;优化参数的密集取值区间:LZSN[2.00,2.65];INFILT[0.400,0.475];AGWRC[0.870,0.885];DEEPFR[0.101,0.176];BASETP[0.001,0.120];AGWETP[0,083,0.120];CEPSC[0.166,0.244];UZSN[0.83,1.22]; IRC[0.53,0.63].响应面方法综合了参数抽样、参数灵敏度分析以及参数优化等3个方面,考虑了参数非线性关系和参数的交互作用,兼顾了优化精度和效率,为青龙河流域HSPF模型参数优化开拓了新途径.
Hydrological Simulation Program-Fortran(HSPF) model has many parameters and complex interactions. The traditional parameter optimization is insensitive to the optimization parameters and the optimization algorithm is easy to trap into local problems, which affects the precision and efficiency of optimization. In this paper, a new optimization approach is explored by integrating Qinglong River watershed, parameter sampling, sensitivity analysis, and parameter optimization. Response surface optimization software Design Expert was applied to sample the parameters of 9 HSPF models, and 130 sets of parameter sets were obtained. Multiple quadratic regression models were used to establish the nonlinear relationship between the parameter sets and the efficiency coefficient of nash-sutcliffe(NSE), and the optimal parameters and their dense value ranges were identified by contour lines and response surface. The NSE mean value, maximum value, and minimum value of the response surface optimization parameters as well as the optimized interval reduction rate were all superior to the orthogonal range analysis method. LZSN, INFILT, and AGWRC were extremely sensitive parameters, while DEEPFR was sensitive parameters. The interactions between LZSN and INFILT, INFILT and AGWRC, INFILT and UZSN, and INFILT and IRC had significant impacts on the results. The dense value range of parameters were optimized as follows: LZSN[2.00,2.65], INFILT[0.400,0.475], AGWRC[0.870,0.885], DEEPFR[0.101,0.176], BASETP[0.001,0.120], AGWETP[0,083,0.120], CEPSC[0.166,0.244], UZSN[0.83,1.22], IRC[0.53,0.63]. The response surface method synthesized three aspects, i.e., parameter sampling, parameter sensitivity analysis, and parameter optimization, which considers the nonlinear relationship of parameters, the interaction of parameters, and the optimization accuracy and efficiency, thus opening up a new way for parameter optimization of HSPF model in Qinglong River watershed.
Hydrological Simulation Program-Fortran(HSPF) model has many parameters and complex interactions. The traditional parameter optimization is insensitive to the optimization parameters and the optimization algorithm is easy to trap into local problems, which affects the precision and efficiency of optimization. In this paper, a new optimization approach is explored by integrating Qinglong River watershed, parameter sampling, sensitivity analysis, and parameter optimization. Response surface optimization software Design Expert was applied to sample the parameters of 9 HSPF models, and 130 sets of parameter sets were obtained. Multiple quadratic regression models were used to establish the nonlinear relationship between the parameter sets and the efficiency coefficient of nash-sutcliffe(NSE), and the optimal parameters and their dense value ranges were identified by contour lines and response surface. The NSE mean value, maximum value, and minimum value of the response surface optimization parameters as well as the optimized interval reduction rate were all superior to the orthogonal range analysis method. LZSN, INFILT, and AGWRC were extremely sensitive parameters, while DEEPFR was sensitive parameters. The interactions between LZSN and INFILT, INFILT and AGWRC, INFILT and UZSN, and INFILT and IRC had significant impacts on the results. The dense value range of parameters were optimized as follows: LZSN[2.00,2.65], INFILT[0.400,0.475], AGWRC[0.870,0.885], DEEPFR[0.101,0.176], BASETP[0.001,0.120], AGWETP[0,083,0.120], CEPSC[0.166,0.244], UZSN[0.83,1.22], IRC[0.53,0.63]. The response surface method synthesized three aspects, i.e., parameter sampling, parameter sensitivity analysis, and parameter optimization, which considers the nonlinear relationship of parameters, the interaction of parameters, and the optimization accuracy and efficiency, thus opening up a new way for parameter optimization of HSPF model in Qinglong River watershed.
引文
[1]BERNDT M E, RUTELONIS W, REGAN C P. A comparison of results from a hydrologic transport model (HSPF) with distributions of sulfate and mercury in a mine-impacted watershed in northeastern Minnesota[J]. Journal of Environmental Management, 2016, (181): 74. DOI:10.1016/J.JENVMAN.2016.05.067
[2]JEON J H, PARK C G, CHOI D, et al. Characteristics of suspended sediment loadings under Asian summer monsoon climate using the Hydrological Simulation Program-FORTRAN[J]. Sustainability, 2017, 9(1): 1. DOI:10.3390/su9010044
[3]FARZBOD A, FARIDHOSSIENI A, JAVAN K, et al. A comparison of ANN and HSPF models for runoff simulation in Balkhichai River Watershed, Iran[J]. American Journal of Climate Change, 2015, 4(3): 203. DOI:10.4236/ajcc.2015.43016
[4]李兆富, 刘红玉, 李燕. HSPF 水文水质模型应用研究综述[J]. 环境科学, 2012, 33(7): 2217. DOI:10.13227/j.hjkx.2012.07.022LI Zhaofu, LIU Hongyu, LI Yan. Review on HSPF model for simulation of hydrology and water quality processes[J]. Chinese Journal of Environmental Science, 2012, 33(7): 2217. DOI:10.13227/j.hjkx.2012.07.022
[5]LIU Z, TONG S T Y. Using HSPF to model the hydrologic and water quality impacts of riparian land-use change in a small watershed[J]. Journal of Environmental Informatics, 2011, 17(1): 1
[6]CHUNG E S, Park K, Lee K S. The relative impacts of climate change and urbanization on the hydrological response of a Korean urban watershed[J]. Hydrological Processes, 2011, 25(4): 544. DOI:10.1002/hyp.7781
[7]程晓光, 张静, 宫辉力. 半干旱半湿润地区HSPF模型水文模拟及参数不确定性研究[J]. 环境科学学报, 2014, 34(12): DOI:10.13671/j.hjkxxb.2014.0783CHENG Xiaoguang, ZHANG Jing, GONG Huili. HSPF hydrologic simulation and parameter uncertainty in a semi-arid and semi-humid area[J]. Acta Scientiae Circumstantiae, 2014, 34(12): 3179. DOI:10.13671/j.hjkxxb.2014.0783
[8]李燕, 李兆富, 席庆. HSPF径流模拟参数敏感性分析与模型适用性研究[J]. 环境科学, 2013, 34(6): 2139. DOI:10.13227/j.hjkx.2013.06.008LI Yan, LI Zhaofu, XI Qing. Parameter sensitivity analysis of runoff simulation and model adaptability research based on HSPF[J]. Environmental Science, 2013, 34(6): 2139. DOI:10.13227/j.hjkx.2013.06.008
[9]罗川, 李兆富, 席庆, 等. HSPF模型水文水质参数敏感性分析[J]. 农业环境科学学报, 2014, 33(10): 1995. DOI:10.11654/jaes.2014.10.017LUO Chuan, LI Zhaofu, XI Qing, et al. Sensitivity analysis of hydrological and water quality parameters of HSPF model[J]. Journal of Agro-Environment Science, 2014, 33(10): 1995. DOI:10.11654/jaes.2014.10.017
[10]JAVAN K, SALEH F N, SHAHRAIYNI H T. The influences of climate change on the runoff of Gharehsoo River watershed[J]. American Journal of Climate Change, 2013, (2): 296. DOI:10.4236/ajcc.2013.24030
[11]庞树江, 王晓燕, 马文静. 多时间尺度 HSPF 模型参数不确定性研究[J]. 环境科学, 2018, 39(5): 2030. DOI:10.13227/j.hjkx.2017.10.070 PANG Shujiang, WANG Xiaoyan, MA Wenjing. Research of parameter uncertainty for the HSPF model under different temporal scales[J]. Environmental Science, 2018, 39(5): 2030. DOI:10.13227/j.hjkx.2017.10.070
[12]程晓光, 张静, 宫辉力. 基于PEST自动校正的HSPF水文模拟研究[J]. 人民黄河, 2013, 35(12): 33. DOI:10.3969/j.issn.1000-1379.2013.12.012CHENG Xiaoguang, ZHANG Jing, GONG Huili. Research on the HSPF hydrologic simulation based the PEST automatic calibration[J]. Yellow River, 2013, 35(12): 33. DOI:10.3969/j.issn.1000-1379.2013.12.012
[13]高伟, 周丰, 董延军, 等. 基于PEST的HSPF水文模型多目标自动校准研究[J]. 自然资源学报, 2014, 29(5): 885. DOI:10.11849/zrzyxb.2014.05.013GAO Wei, ZHOU Feng, DONG Yanjun, et al. PEST-based multi-objective automatic calibration of hydrologic parameters for HSPF model[J]. Journal of Natural Resources, 2014, 29(5): 885. DOI:10.11849/zrzyxb.2014.05.013
[14]杨博, 陈莹, 陈兴伟, 等. 基于 PEST 的 HSPF 模型径流模拟优化[J]. 中国水土保持科学, 2018, 16(2): 9. DOI:10.16843/j.sswc.2018.02.002YANG Bo, CHEN Ying, CHEN Xingwei, et al. HSPF runoff simulation and optimization based on PEST automatic calibration[J]. Science of Soil and Water Conservation, 2018, 16(2): 9. DOI:10.16843/j.sswc.2018.02.002
[15]李金城, 常学秀, 高伟. 目标函数权重对PEST-HSPF水文率定的影响研究[J]. 水文, 2017, 37(6): 9LI Jincheng, CHANG Xuexiu, GAO Wei. Impact of objective function weight on hydrological calibration of PEST-HSPF[J]. Journal of China Hydrology, 2017, 37(6): 9
[16]刘兴坡, 陈翔, 胡小婷, 等. 基于正交极差分析的青龙河流域HSPF模型参数寻优模式[J]. 哈尔滨工业大学学报, 2018, 50(2): 131. DOI:10.11918/j.issn.0367-6234.2016.07.069LIU Xingpo, CHEN Xiang, HU Xiaoting, et al. Orthogonal range analysis-based HSPF parameter optimization pattern for Qinglong River watershed[J]. Journal of Harbin Institute of Technology, 2018, 50(2): 131. DOI:10.11918/j.issn.0367-6234.201607069
[17]胡小婷. 基于BASINs的青龙河流域水文水质模型应用研究[D]. 上海: 上海海事大学硕士学位论文, 2015HU Xiaoting. BASINs-based hydrological and water quality model and its application at Qinglong River Watershed[D]. Shanghai: Shanghai Maritime University, 2015
[18]刘文斌, 张海涛, 杨海君, 等.辛基酚聚氧乙烯醚混合菌的构建及响应面优化[J]. 中国环境科学, 2016, 36(8): 2444LIU Wenbin, ZHANG Haitao, YANG Haijun, et al. Construction of mixed flora effective degrading octylphenyl polyethoxylates (OPnEO) and optimization of degrading conditions using response surface method[J]. China Environmental Science, 2016, 36(8): 2444
[2]JEON J H, PARK C G, CHOI D, et al. Characteristics of suspended sediment loadings under Asian summer monsoon climate using the Hydrological Simulation Program-FORTRAN[J]. Sustainability, 2017, 9(1): 1. DOI:10.3390/su9010044
[3]FARZBOD A, FARIDHOSSIENI A, JAVAN K, et al. A comparison of ANN and HSPF models for runoff simulation in Balkhichai River Watershed, Iran[J]. American Journal of Climate Change, 2015, 4(3): 203. DOI:10.4236/ajcc.2015.43016
[4]李兆富, 刘红玉, 李燕. HSPF 水文水质模型应用研究综述[J]. 环境科学, 2012, 33(7): 2217. DOI:10.13227/j.hjkx.2012.07.022LI Zhaofu, LIU Hongyu, LI Yan. Review on HSPF model for simulation of hydrology and water quality processes[J]. Chinese Journal of Environmental Science, 2012, 33(7): 2217. DOI:10.13227/j.hjkx.2012.07.022
[5]LIU Z, TONG S T Y. Using HSPF to model the hydrologic and water quality impacts of riparian land-use change in a small watershed[J]. Journal of Environmental Informatics, 2011, 17(1): 1
[6]CHUNG E S, Park K, Lee K S. The relative impacts of climate change and urbanization on the hydrological response of a Korean urban watershed[J]. Hydrological Processes, 2011, 25(4): 544. DOI:10.1002/hyp.7781
[7]程晓光, 张静, 宫辉力. 半干旱半湿润地区HSPF模型水文模拟及参数不确定性研究[J]. 环境科学学报, 2014, 34(12): DOI:10.13671/j.hjkxxb.2014.0783CHENG Xiaoguang, ZHANG Jing, GONG Huili. HSPF hydrologic simulation and parameter uncertainty in a semi-arid and semi-humid area[J]. Acta Scientiae Circumstantiae, 2014, 34(12): 3179. DOI:10.13671/j.hjkxxb.2014.0783
[8]李燕, 李兆富, 席庆. HSPF径流模拟参数敏感性分析与模型适用性研究[J]. 环境科学, 2013, 34(6): 2139. DOI:10.13227/j.hjkx.2013.06.008LI Yan, LI Zhaofu, XI Qing. Parameter sensitivity analysis of runoff simulation and model adaptability research based on HSPF[J]. Environmental Science, 2013, 34(6): 2139. DOI:10.13227/j.hjkx.2013.06.008
[9]罗川, 李兆富, 席庆, 等. HSPF模型水文水质参数敏感性分析[J]. 农业环境科学学报, 2014, 33(10): 1995. DOI:10.11654/jaes.2014.10.017LUO Chuan, LI Zhaofu, XI Qing, et al. Sensitivity analysis of hydrological and water quality parameters of HSPF model[J]. Journal of Agro-Environment Science, 2014, 33(10): 1995. DOI:10.11654/jaes.2014.10.017
[10]JAVAN K, SALEH F N, SHAHRAIYNI H T. The influences of climate change on the runoff of Gharehsoo River watershed[J]. American Journal of Climate Change, 2013, (2): 296. DOI:10.4236/ajcc.2013.24030
[11]庞树江, 王晓燕, 马文静. 多时间尺度 HSPF 模型参数不确定性研究[J]. 环境科学, 2018, 39(5): 2030. DOI:10.13227/j.hjkx.2017.10.070 PANG Shujiang, WANG Xiaoyan, MA Wenjing. Research of parameter uncertainty for the HSPF model under different temporal scales[J]. Environmental Science, 2018, 39(5): 2030. DOI:10.13227/j.hjkx.2017.10.070
[12]程晓光, 张静, 宫辉力. 基于PEST自动校正的HSPF水文模拟研究[J]. 人民黄河, 2013, 35(12): 33. DOI:10.3969/j.issn.1000-1379.2013.12.012CHENG Xiaoguang, ZHANG Jing, GONG Huili. Research on the HSPF hydrologic simulation based the PEST automatic calibration[J]. Yellow River, 2013, 35(12): 33. DOI:10.3969/j.issn.1000-1379.2013.12.012
[13]高伟, 周丰, 董延军, 等. 基于PEST的HSPF水文模型多目标自动校准研究[J]. 自然资源学报, 2014, 29(5): 885. DOI:10.11849/zrzyxb.2014.05.013GAO Wei, ZHOU Feng, DONG Yanjun, et al. PEST-based multi-objective automatic calibration of hydrologic parameters for HSPF model[J]. Journal of Natural Resources, 2014, 29(5): 885. DOI:10.11849/zrzyxb.2014.05.013
[14]杨博, 陈莹, 陈兴伟, 等. 基于 PEST 的 HSPF 模型径流模拟优化[J]. 中国水土保持科学, 2018, 16(2): 9. DOI:10.16843/j.sswc.2018.02.002YANG Bo, CHEN Ying, CHEN Xingwei, et al. HSPF runoff simulation and optimization based on PEST automatic calibration[J]. Science of Soil and Water Conservation, 2018, 16(2): 9. DOI:10.16843/j.sswc.2018.02.002
[15]李金城, 常学秀, 高伟. 目标函数权重对PEST-HSPF水文率定的影响研究[J]. 水文, 2017, 37(6): 9LI Jincheng, CHANG Xuexiu, GAO Wei. Impact of objective function weight on hydrological calibration of PEST-HSPF[J]. Journal of China Hydrology, 2017, 37(6): 9
[16]刘兴坡, 陈翔, 胡小婷, 等. 基于正交极差分析的青龙河流域HSPF模型参数寻优模式[J]. 哈尔滨工业大学学报, 2018, 50(2): 131. DOI:10.11918/j.issn.0367-6234.2016.07.069LIU Xingpo, CHEN Xiang, HU Xiaoting, et al. Orthogonal range analysis-based HSPF parameter optimization pattern for Qinglong River watershed[J]. Journal of Harbin Institute of Technology, 2018, 50(2): 131. DOI:10.11918/j.issn.0367-6234.201607069
[17]胡小婷. 基于BASINs的青龙河流域水文水质模型应用研究[D]. 上海: 上海海事大学硕士学位论文, 2015HU Xiaoting. BASINs-based hydrological and water quality model and its application at Qinglong River Watershed[D]. Shanghai: Shanghai Maritime University, 2015
[18]刘文斌, 张海涛, 杨海君, 等.辛基酚聚氧乙烯醚混合菌的构建及响应面优化[J]. 中国环境科学, 2016, 36(8): 2444LIU Wenbin, ZHANG Haitao, YANG Haijun, et al. Construction of mixed flora effective degrading octylphenyl polyethoxylates (OPnEO) and optimization of degrading conditions using response surface method[J]. China Environmental Science, 2016, 36(8): 2444
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