以坡位为空间配置单元的流域管理措施情景优化方法
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摘要
基于流域过程模型的BMP情景分析是当前流域管理措施评价、非点源污染控制等研究应用中广泛采用的方法,但其通常采用的BMP空间配置单元(地块、农场、水文响应单元或子流域)与坡面上的地形部位关系较弱,难以有效地根据坡面过程特点表达坡面上多种BMP之间的空间配置关系,影响了BMP情景优化效率和结果的合理性。为此,本文提出以坡位单元作为BMP空间配置单元,将各种BMP在不同坡位间合理的空间配置关系显式表达为基于坡位的空间配置规则,通过结合NSGA-II优化算法建立了一套基于坡位单元的BMP空间配置优化方法。应用案例表明,本文构建的基于坡位单元的BMP情景优化方法可有效利用基于坡位的空间配置规则进行BMP情景优化,优化所得的BMP空间配置方案更为合理,优化效率较高。
Scenario analysis based on watershed process model is a widely used method for evaluating watershed management practices(BMP) and controlling non-point source pollution. The commonly used spatial configuration units in current scenario analysis include fields, farms, hydrologic response units, and sub-basins.The weak spatial relationships between these spatial units and the topographic positions along hillslope make the use of these spatial units difficult to effectively represent the effect of different BMP on hillslope processes, and thus affect the efficiency and reasonability of optimized scenarios. In this paper, slope positions are used as the spatial configuration units of BMP under the framework of spatially distributed watershed process model and intelligent optimization method for BMP scenarios. Thus, the knowledge of the spatial relationships between BMP and slope positions can be explicitly considered during optimization. A spatially distributed watershed process model(i.e., SEIMS) and an intelligent optimization algorithm(i.e., the genetic algorithm NSGA-II) were combined in this framework in this paper. A small watershed of red soil dominant region in the east of Hetian county, Changting city, Fujian province, was selected as the case study area. The BMP knowledge base including the relationship between five BMP used in this area and slope positions was built for the study area. The experimental results showed that slope position units can well support the description and application of the knowledge on the spatial configuration of different BMP, compared with the BMP configuration units of fields with upslope-downslope relationship. The proposed method can use BMP spatial configuration knowledge to provide optimal BMP scenarios reasonably and effectively, compared with the random optimization method, a typical BMP scenario optimization method of using NSGA-II optimization algorithm with operations of population initialization, crossover, and mutation randomly.
Scenario analysis based on watershed process model is a widely used method for evaluating watershed management practices(BMP) and controlling non-point source pollution. The commonly used spatial configuration units in current scenario analysis include fields, farms, hydrologic response units, and sub-basins.The weak spatial relationships between these spatial units and the topographic positions along hillslope make the use of these spatial units difficult to effectively represent the effect of different BMP on hillslope processes, and thus affect the efficiency and reasonability of optimized scenarios. In this paper, slope positions are used as the spatial configuration units of BMP under the framework of spatially distributed watershed process model and intelligent optimization method for BMP scenarios. Thus, the knowledge of the spatial relationships between BMP and slope positions can be explicitly considered during optimization. A spatially distributed watershed process model(i.e., SEIMS) and an intelligent optimization algorithm(i.e., the genetic algorithm NSGA-II) were combined in this framework in this paper. A small watershed of red soil dominant region in the east of Hetian county, Changting city, Fujian province, was selected as the case study area. The BMP knowledge base including the relationship between five BMP used in this area and slope positions was built for the study area. The experimental results showed that slope position units can well support the description and application of the knowledge on the spatial configuration of different BMP, compared with the BMP configuration units of fields with upslope-downslope relationship. The proposed method can use BMP spatial configuration knowledge to provide optimal BMP scenarios reasonably and effectively, compared with the random optimization method, a typical BMP scenario optimization method of using NSGA-II optimization algorithm with operations of population initialization, crossover, and mutation randomly.
引文
[1]Arabi M,Govindaraju R S,Hantush M M.Cost-effective allocation of watershed management practices using a genetic algorithm[J].Water Resources Research,2006,42(10):W10429.
[2]Gitau M W,Veith T L,Gburek W J.Farm-level optimization of BMP placement for cost-effective pollution reduction[J].Transactions of the ASAE,2004,47(6):1923-1931.
[3]Turpin N,Bontems P,Rotillon G,et al.Agri BMPWater:Systems approach to environmentally acceptable farming[J].Environmental Modelling&Software,2005,20(2):187-196.
[4]刘永波,吴辉,刘军志.加拿大最佳管理措施流域评价项目评述[J].生态与农村环境学报,2012,28(4):337-342.[Liu Y B,Wu H,Liu J Z.A review of the Canadian watershed evaluation of beneficial management practices project[J].Journal of Ecology and Rural Environment,2012,28(4):337-342.]
[5]蔡强国,朱阿兴,毕华兴,等.中国主要水蚀区水土流失综合调控与治理范式[M].北京:中国水利水电出版社,2012.[Cai Q G,Zhu A X,Bi H X,et al.Paradigms for integrated soil and water conservation over main water erosion regions in China[M].Beijing:China Water Power Press,2012.]
[6]夏军,翟晓燕,张永勇.水环境非点源污染模型研究进展[J].地理科学进展,2012,31(7):941-952.[Xia J,Zhai X Y,Zhang Y Y.Progress in the research of water environmental nonpoint source pollution models[J].Progress in Geography,2012,31(7):941-952.]
[7]Duinker P N,Greig L A.Scenario analysis in environmental impact assessment:Improving explorations of the future[J].Environmental Impact Assessment Review,2007,27(3):206-219.
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[9]朱阿兴,陈腊娇,秦承志,等.水土流失治理新范式:基于流域过程模拟和情景分析的方法[J].应用生态学报,2012,23(7):1883-1890.[Zhu A X,Chen L J,Qin C Z,et al.New paradigm for soil and water conservation:A method based on watershed process modeling and scenario analysis[J].Chinese Journal of Applied Ecology,2012,23(7):1883-1890.]
[10]Berry J,Delgado J,Pierce F,et al.Applying spatial analysis for precision conservation across the landscape[J].Journal of Soil and Water Conservation,2005,60(6):363-370.
[11]Goddard T.An overview of precision conservation in Canada[J].Journal of Soil and Water Conservation,2005,60(6):456-461.
[12]Chang C L,Chiueh P T,Lo S L.Effect of spatial variability storm on the optimal placement of best management practices(BMPs)[J].Environmental Monitoring and Assessment,2007,135(1-3):383-389.
[13]Arnold J G,Srinivasan R,Muttiah R S.Large area hydrologic modeling and assessment-Part 1:Model development[J].Journal of the American Water Resources Association,1998,34(1):73-89.
[14]Bekele E G,Nicklow J W.Multiobjective management of ecosystem services by integrative watershed modeling and evolutionary algorithms[J].Water Resources Research,2005,41(10):3092-3100.
[15]Maringanti C,Chaubey I,Popp J.Development of a multiobjective optimization tool for the selection and placement of best management practices for nonpoint source pollu-tion control[J].Water Resources Research,2009,45(6):51-53.
[16]Arnold J G,Allen P M,Volk M,et al.Assessment of different representations of spatial variability on SWAT model performance[J].Transactions of the American Society of Agricultural and Biological Engineers,2010,53(5):1433-1443.
[17]Srivastava P,Hamlett J M,Robillard P D,et al.Watershed optimization of best management practices using Ann AGNPS and a genetic algorithm[J].Water Resources Research,2002,38(3):1-14.
[18]Qi H H,Altinakar M S.Vegetation buffer strips design using an optimization approach for non-point source pollutant control of an agricultural watershed[J].Water Resources Management,2011,25(2):565-578.
[19]Gaddis E B,Voinov A,Seppelt R,et al.Spatial optimization of best management practices to attain water quality targets[J].Water Resources Management,2014,28(6):1485-1499.
[20]Wu H,Zhu A X,Liu J,et al.Best management practices optimization at watershed scale:incorporating spatial topology among fields[J].Water Resources Management,2018,32(1):155-177.
[21]Rathjens H,Bieger K,Chaubey I,et al.Delineating floodplain and upland areas for hydrologic models:A comparison of methods[J].Hydrological Processes,2016,30(23),4367-4383.
[22]周启鸣,刘学军.数字地形分析[M].北京:科学出版社,2006.[Zhou Q M,Liu X J.Digital terrain analysis[M].Beijing:Science Press,2006.]
[23]吴辉,刘永波,秦承志,等.流域最佳管理措施情景优化算法的并行化[J].武汉大学学报·信息科学版,2016,41(2):202-207.[Wu H,Liu Y B,Qin C Z,et al.Parallelization of an optimization algorithm for beneficial watershed management practices[J].Geomatics and Information Science of Wuhan University,2016,41(2):202-207.]
[24]秦承志,朱阿兴,施迅,等.坡位渐变信息的模糊推理[J].地理研究,2007,26(6):1165-1174,1307.[Qin C Z,Zhu A X,Shi X,et al.Fuzzy inference of spatial gradation of slope positions[J].Geographical Research,2007,26(6):1165-1174,1307.]
[25]Qin C Z,Zhu A X,Shi X,et al.Quantification of spatial gradation of slope positions[J].Geomorphology,2009,110(3-4):152-161.
[26]Zhu L J,Zhu A X,Qin C Z,et al.Automatic approach to deriving fuzzy slope positions[J].Geomorphology,2018,304:173-183.
[27]秦承志,朱阿兴,李宝林,等.坡位的分类及其空间分布信息的定量化[J].武汉大学学报·信息科学版,2009,34(3):374-377.[Qin C Z,Zhu A X,Li B L,et al.Taxonomy of slope positions and quantification of their spatial distribution information[J].Geomatics and Information Science of Wuhan University,2009,34(3):374-377.]
[28]Cover T M,Hart P E.Nearest neighbor pattern classification[J].IEEE Transactions on Information Theory,1967,13(1):21-27.
[29]Tu E,Zhang Y,Zhu L,et al.A graph-based semi-supervised k,nearest-neighbor method for nonlinear manifold distributed data classification[J].Information Sciences,2016,367-368(1):673-688.
[30]Deb K,Agrawal S,Pratap A.A fast elitist nondominated sorting genetic algorithm for multi-objective optimization:NSGA-II[C].Proceedings of the Parallel Problem Solving from Nature VI Conference,Paris,2000,1917:849-858.
[31]吴辉,刘永波,朱阿兴,等.流域最佳管理措施空间配置优化研究进展[J].地理科学进展,2013,32(4):570-579.[Wu H,Liu Y B,Zhu A X,et al.Review of spatial optimization algorithms in BMPs placement at watershed scale[J].Progress in Geography,2013,32(4):570-579.]
[32]刘军志,朱阿兴,刘永波,等.基于栅格分层的逐栅格汇流算法并行化研究[J].国防科技大学学报,2013,35(1):123-129.[Liu J Z,Zhu A X,Liu Y B,et al.Parallelization of a grid-to-grid routing algorithm based on grids layering[J].Journal of National University of Defense Technology,2013,35(1):123-129.]
[33]Liu J Z,Zhu A X,Liu Y B,et al.A layered approach to parallel computing for spatially distributed hydrological modeling[J].Environmental Modeling and Software,2014,51(51):221-227.
[2]Gitau M W,Veith T L,Gburek W J.Farm-level optimization of BMP placement for cost-effective pollution reduction[J].Transactions of the ASAE,2004,47(6):1923-1931.
[3]Turpin N,Bontems P,Rotillon G,et al.Agri BMPWater:Systems approach to environmentally acceptable farming[J].Environmental Modelling&Software,2005,20(2):187-196.
[4]刘永波,吴辉,刘军志.加拿大最佳管理措施流域评价项目评述[J].生态与农村环境学报,2012,28(4):337-342.[Liu Y B,Wu H,Liu J Z.A review of the Canadian watershed evaluation of beneficial management practices project[J].Journal of Ecology and Rural Environment,2012,28(4):337-342.]
[5]蔡强国,朱阿兴,毕华兴,等.中国主要水蚀区水土流失综合调控与治理范式[M].北京:中国水利水电出版社,2012.[Cai Q G,Zhu A X,Bi H X,et al.Paradigms for integrated soil and water conservation over main water erosion regions in China[M].Beijing:China Water Power Press,2012.]
[6]夏军,翟晓燕,张永勇.水环境非点源污染模型研究进展[J].地理科学进展,2012,31(7):941-952.[Xia J,Zhai X Y,Zhang Y Y.Progress in the research of water environmental nonpoint source pollution models[J].Progress in Geography,2012,31(7):941-952.]
[7]Duinker P N,Greig L A.Scenario analysis in environmental impact assessment:Improving explorations of the future[J].Environmental Impact Assessment Review,2007,27(3):206-219.
[8]Yang Q,Zhao Z Y,Benoy G,et al.A watershed-scale assessment of cost-effectiveness of sediment abatement with flow diversion terraces[J].Journal of Environmental Quality,2010,39(1):220-227.
[9]朱阿兴,陈腊娇,秦承志,等.水土流失治理新范式:基于流域过程模拟和情景分析的方法[J].应用生态学报,2012,23(7):1883-1890.[Zhu A X,Chen L J,Qin C Z,et al.New paradigm for soil and water conservation:A method based on watershed process modeling and scenario analysis[J].Chinese Journal of Applied Ecology,2012,23(7):1883-1890.]
[10]Berry J,Delgado J,Pierce F,et al.Applying spatial analysis for precision conservation across the landscape[J].Journal of Soil and Water Conservation,2005,60(6):363-370.
[11]Goddard T.An overview of precision conservation in Canada[J].Journal of Soil and Water Conservation,2005,60(6):456-461.
[12]Chang C L,Chiueh P T,Lo S L.Effect of spatial variability storm on the optimal placement of best management practices(BMPs)[J].Environmental Monitoring and Assessment,2007,135(1-3):383-389.
[13]Arnold J G,Srinivasan R,Muttiah R S.Large area hydrologic modeling and assessment-Part 1:Model development[J].Journal of the American Water Resources Association,1998,34(1):73-89.
[14]Bekele E G,Nicklow J W.Multiobjective management of ecosystem services by integrative watershed modeling and evolutionary algorithms[J].Water Resources Research,2005,41(10):3092-3100.
[15]Maringanti C,Chaubey I,Popp J.Development of a multiobjective optimization tool for the selection and placement of best management practices for nonpoint source pollu-tion control[J].Water Resources Research,2009,45(6):51-53.
[16]Arnold J G,Allen P M,Volk M,et al.Assessment of different representations of spatial variability on SWAT model performance[J].Transactions of the American Society of Agricultural and Biological Engineers,2010,53(5):1433-1443.
[17]Srivastava P,Hamlett J M,Robillard P D,et al.Watershed optimization of best management practices using Ann AGNPS and a genetic algorithm[J].Water Resources Research,2002,38(3):1-14.
[18]Qi H H,Altinakar M S.Vegetation buffer strips design using an optimization approach for non-point source pollutant control of an agricultural watershed[J].Water Resources Management,2011,25(2):565-578.
[19]Gaddis E B,Voinov A,Seppelt R,et al.Spatial optimization of best management practices to attain water quality targets[J].Water Resources Management,2014,28(6):1485-1499.
[20]Wu H,Zhu A X,Liu J,et al.Best management practices optimization at watershed scale:incorporating spatial topology among fields[J].Water Resources Management,2018,32(1):155-177.
[21]Rathjens H,Bieger K,Chaubey I,et al.Delineating floodplain and upland areas for hydrologic models:A comparison of methods[J].Hydrological Processes,2016,30(23),4367-4383.
[22]周启鸣,刘学军.数字地形分析[M].北京:科学出版社,2006.[Zhou Q M,Liu X J.Digital terrain analysis[M].Beijing:Science Press,2006.]
[23]吴辉,刘永波,秦承志,等.流域最佳管理措施情景优化算法的并行化[J].武汉大学学报·信息科学版,2016,41(2):202-207.[Wu H,Liu Y B,Qin C Z,et al.Parallelization of an optimization algorithm for beneficial watershed management practices[J].Geomatics and Information Science of Wuhan University,2016,41(2):202-207.]
[24]秦承志,朱阿兴,施迅,等.坡位渐变信息的模糊推理[J].地理研究,2007,26(6):1165-1174,1307.[Qin C Z,Zhu A X,Shi X,et al.Fuzzy inference of spatial gradation of slope positions[J].Geographical Research,2007,26(6):1165-1174,1307.]
[25]Qin C Z,Zhu A X,Shi X,et al.Quantification of spatial gradation of slope positions[J].Geomorphology,2009,110(3-4):152-161.
[26]Zhu L J,Zhu A X,Qin C Z,et al.Automatic approach to deriving fuzzy slope positions[J].Geomorphology,2018,304:173-183.
[27]秦承志,朱阿兴,李宝林,等.坡位的分类及其空间分布信息的定量化[J].武汉大学学报·信息科学版,2009,34(3):374-377.[Qin C Z,Zhu A X,Li B L,et al.Taxonomy of slope positions and quantification of their spatial distribution information[J].Geomatics and Information Science of Wuhan University,2009,34(3):374-377.]
[28]Cover T M,Hart P E.Nearest neighbor pattern classification[J].IEEE Transactions on Information Theory,1967,13(1):21-27.
[29]Tu E,Zhang Y,Zhu L,et al.A graph-based semi-supervised k,nearest-neighbor method for nonlinear manifold distributed data classification[J].Information Sciences,2016,367-368(1):673-688.
[30]Deb K,Agrawal S,Pratap A.A fast elitist nondominated sorting genetic algorithm for multi-objective optimization:NSGA-II[C].Proceedings of the Parallel Problem Solving from Nature VI Conference,Paris,2000,1917:849-858.
[31]吴辉,刘永波,朱阿兴,等.流域最佳管理措施空间配置优化研究进展[J].地理科学进展,2013,32(4):570-579.[Wu H,Liu Y B,Zhu A X,et al.Review of spatial optimization algorithms in BMPs placement at watershed scale[J].Progress in Geography,2013,32(4):570-579.]
[32]刘军志,朱阿兴,刘永波,等.基于栅格分层的逐栅格汇流算法并行化研究[J].国防科技大学学报,2013,35(1):123-129.[Liu J Z,Zhu A X,Liu Y B,et al.Parallelization of a grid-to-grid routing algorithm based on grids layering[J].Journal of National University of Defense Technology,2013,35(1):123-129.]
[33]Liu J Z,Zhu A X,Liu Y B,et al.A layered approach to parallel computing for spatially distributed hydrological modeling[J].Environmental Modeling and Software,2014,51(51):221-227.