基于坡位-地块单元的流域最佳管理措施空间优化配置方法

doi:10.12082/dqxxkx.2021.200335

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (4): 564-575.doi: 10.12082/dqxxkx.2021.200335

• 地球信息科学理论与方法 • 上一篇下一篇

基于坡位-地块单元的流域最佳管理措施空间优化配置方法

史亚星¹^,³^,⁴(), 朱良君¹^,²^,^*(), 秦承志¹^,²^,⁵, 朱阿兴¹^,²^,⁵^,⁶^,⁷^,⁸

1.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
2.中国科学院大学资源环境学院,北京 100049
3.中国-丹麦科研教育中心,北京 100190
4.中国科学院大学中丹学院,北京 100190
5.江苏省地理信息资源开发与利用协同创新中心,南京 210023
6.南京师范大学地理科学学院,南京 210023
7.虚拟地理环境教育部重点实验室（南京师范大学）,南京 210023
8.威斯康星大学麦迪逊分校地理系,麦迪逊 WI53706

收稿日期:2020-06-29 修回日期:2020-08-04 出版日期:2021-04-25 发布日期:2021-06-25
通讯作者: *朱良君（1990— ）,男,山东滕州人,博士,助理研究员,主要从事流域系统综合模拟与情景分析研究。 E-mail: zlj@lreis.ac.cn
作者简介:史亚星（1993— ）,女,山西阳泉人,硕士生,主要从事流域系统综合模拟与情景分析研究。E-mail: shiyx@lreis.ac.cn
基金资助:
国家自然科学基金面上项目(41871362);中国科学院A类战略性先导科技专项课题(XDA23100503)

Spatial Optimization of Watershed Best Management Practices based on Slope Position-Field Units

SHI Yaxing¹^,³^,⁴(), ZHU Liangjun¹^,²^,^*(), QIN Chengzhi¹^,²^,⁵, ZHU Axing¹^,²^,⁵^,⁶^,⁷^,⁸

1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3. Sino-Danish Center for Education and Research, Beijing 100190, China
4. Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
5. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
6. School of Geography, Nanjing Normal University, Nanjing 210023, China
7. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
8. Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA

Received:2020-06-29 Revised:2020-08-04 Online:2021-04-25 Published:2021-06-25
Contact: ZHU Liangjun
Supported by:
National Natural Science Foundation of China(41871362);Chinese Academy of Sciences(XDA23100503)

摘要/Abstract

摘要：

最佳管理措施（BMP）是治理流域土壤侵蚀、非点源污染等环境问题的有效途径,基于流域过程模拟的情景优化方法可得到综合效益近似最优的BMP空间配置方案集。目前用于配置BMP的空间单元（如子流域、水文响应单元、地块、坡位）均不能有效地综合体现BMP与地形部位间的空间关系以及同一地形部位内不同土地利用斑块上的BMP差异。本文提出将坡位单元与地块单元叠加生成的坡位-地块单元作为BMP空间配置单元,结合分布式流域建模框架SEIMS和多目标优化算法NSGA-II建立一套流域BMP空间优化配置方法。以江苏省溧阳市中田舍流域的非点源污染治理为例,选取减量施肥、退耕还林、封山育林和生态林草4种典型BMP,以最大化总氮削减率、最小化经济成本为优化目标,分别采用坡位单元、地块单元、坡位-地块单元进行情景优化。结果表明：相比坡位单元和地块单元,采用坡位-地块单元可获得最多具有近似最优综合效益的BMP情景,定量评价解集分布性和收敛性的Hypervolume指数分别提升了7%和4%,且BMP在空间上分布更加精细、配置更加灵活。本文方法可有效、合理地优化流域最佳管理措施的空间配置,为流域治理提供决策支持。

关键词: 最佳管理措施, 流域过程模拟, 情景优化, 空间配置单元, 土地利用, 坡位, 地块, 智能优化

Abstract:

Best Management Practices (BMPs) are effective ways to control environmental problems in watersheds such as soil erosion and nonpoint source pollution. BMP scenario optimization method based on watershed modeling and intelligent optimization algorithms can obtain near-optimal Pareto solutions with comprehensive cost-effectiveness. Existing spatial units used for BMP configuration in optimizing BMP scenarios (e.g., subbasins, Hydrological Response Units (HRUs), farms, and slope position units) cannot comprehensively represent spatial relationships between BMPs and topographical positions and differences of BMPs configured on various landuse units within the same topographical position. In other words, these spatial units cannot effectively consider both the characteristics of natural processes and the flexibility of BMP configuration during BMP scenario optimization in a watershed. In this paper, a composite type of spatial unit, the so-called "slope position-field" unit, is proposed to be the BMP configuration unit, which can incorporate the advantage of slope positions (i.e., effectively considering spatial allocation relationship between BMP and topographic position at the hillslope scale) and that of landuse fields (i.e., effectively considering the difference of BMP configurations on various landuse units within a topographical location). Based on a distributed watershed modeling framework named SEIMS (Spatially Explicit Integrated Modeling System) and a multi-objective optimization algorithm named NAGA-II ( Non-dominated Sorting Genetic Algorithm II), a methodological spatial optimization framework of BMP configuration based on the slope position-field units is designed. The method was examined by a case study of controlling nonpoint source pollution in the Zhongtianshe watershed, Liyang city, Jiangsu province, China. Four types of BMPs (e.g., reducing fertilizer application, returning farmland to forest, closed forest, and planting ecologic forest and grass) were selected. The optimization objectives are maximizing the reduction rate of total nitrogen output at the watershed outlet and minimizing the economic cost of the BMP scenario. The proposed BMP configuration unit was compared with two existing types of BMP configuration units such as slope position unit and field unit on the effectiveness of watershed BMP scenario optimization. The results present that: (1) compared with slope position units and field units, taking slope position-field units as BMP configuration units can obtain the largest number of optimized BMP scenarios that have the best comprehensive cost-effectiveness, which can be interpreted from the scatter plot of Pareto solutions, and proved by the quantitative Hypervolume index (with an increase of 7% and 4%, respectively); (2) BMP scenarios based on slope position-field units have more fragmented but finer spatial distribution of BMPs. Therefore, slope position-field units are more flexible for BMP configuration and hence maybe more beneficial for actual BMP implementation. In conclusion, the proposed method can optimize the spatial configuration of BMPs effectively and reasonably, so as to support decision making for watershed management.

Key words: best management practice, watershed process simulation, scenario optimization, spatial configuration unit, landuse, slope position unit, field unit, intelligent optimization

史亚星, 朱良君, 秦承志, 朱阿兴. 基于坡位-地块单元的流域最佳管理措施空间优化配置方法[J]. 地球信息科学学报, 2021, 23(4): 564-575.DOI:10.12082/dqxxkx.2021.200335

SHI Yaxing, ZHU Liangjun, QIN Chengzhi, ZHU Axing. Spatial Optimization of Watershed Best Management Practices based on Slope Position-Field Units[J]. Journal of Geo-information Science, 2021, 23(4): 564-575.DOI:10.12082/dqxxkx.2021.200335

图/表 9

图1

图2

图3

表1

图4

表2

图5

图6

图7

参考文献 49

[1]	陈洪波, 王业耀. 国外最佳管理措施在农业非点源污染防治中的应用[J]. 环境污染与防治, 2006,28(4):279-282.
	[ Chen H B, Wang Y Y. Best management practices for controlling non-point source pollution[J]. Environmental Pollution & Control, 2006,28(4):279-282. ]
[2]	张玉珍, 陈能汪, 曹文志, 等. 南方丘陵地区农业小流域最佳管理措施模拟评价[J]. 资源科学, 2005,27(6):151-155.
	[ Zhang Y Z, Chen N W, Cao W Z, et al. Evaluation of optimum management practices of a small agricultural watershed in southeast China[J]. Resources Science, 2005,27(6):151-155. ]
[3]	朱阿兴, 朱良君, 史亚星, 等. 流域系统综合模拟与情景分析——自然地理综合研究的新范式?[J]. 地理科学进展, 2019,38(8):1111-1122.
	[ Zhu A, Zhu L J, Shi Y X, et al. Integrated watershed modeling and scenario analysis: A new paradigm for integrated study of physical geography?[J]. Progress in Geography, 2019,38(8):1111-1122. ]
[4]	耿润哲, 梁璇静, 殷培红, 等. 面源污染最佳管理措施多目标协同优化配置研究进展[J]. 生态学报, 2019,39(8):2667-2675.
	[ Geng R Z, Liang X J, Yin P H, et al. A review: multi-objective collaborative optimization of best management practices for non-point sources pollution control[J]. Acta Ecologica Sinica, 2019,39(8):2667-2675. ]
[5]	Xie H, Chen L, Shen Z Y. Assessment of agricultural best management practices using models: Current issues and future perspectives[J]. Water, 2015,7(12):1088-1108.
[6]	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, et al. Review of spatial optimization algorithms in BMPs placement at watershed scale[J]. Progress in Geography, 2013,32(4):570-579. ]
[7]	高会然, 秦承志, 朱良君, 等. 以坡位为空间配置单元的流域管理措施情景优化方法[J]. 地球信息科学学报, 2018,20(6):781-790.
	[ Gao H R, Qin C Z, Zhu L J, et al. Using slope positions as spatial units for optimizing spatial configuration of watershed management practices[J]. Journal of Geo-Information Science, 2018,20(6):781-790. ]
[8]	Qin C Z, Gao H R, Zhu L J, et al. Spatial optimization of watershed best management practices based on slope position units[J]. Journal of Soil and Water Conservation, 2018,73(5):504-517.
[9]	Maringanti C, Chaubey I, Popp J. Development of a multiobjective optimization tool for the selection and placement of best management practices for nonpoint source pollution control[J]. Water Resources Research, 2009,45(6):W06406.
[10]	Maringanti C, Chaubey I, Arabi M, et al. Application of a multi-objective optimization method to provide least cost alternatives for NPS pollution control[J]. Environmental Management, 2011,48(3):448-461. doi: 10.1007/s00267-011-9696-2 pmid: 21667317
[11]	Rabotyagov S, Campbell T, Jha M, et al. Least-cost control of agricultural nutrient contributions to the Gulf of Mexico hypoxic zone[J]. Ecological Applications, 2010,20(6):1542-1555. doi: 10.1890/08-0680.1 pmid: 20945758
[12]	Wu H, Zhu A, Liu J Z, et al. Best management practices optimization at watershed scale: Incorporating spatial topology among fields[J]. Water Resources Management, 2018,32(1):155-177.
[13]	Zhu L J, Qin C Z, Zhu A X, et al. Effects of different spatial configuration units for the spatial optimization of watershed best management practice scenarios[J]. Water, 2019,11(2):262.
[14]	Chang C L, Chiueh P T, Lo S L. Effect of spatial variability of storm on the optimal placement of best management practices (BMPs)[J]. Environmental Monitoring and Assessment, 2007,135(1/2/3):383-389.
[15]	Srivastava P, Hamlett J M, Robillard P D. Watershed optimization of agricultural best management practices: continuous simulation versus design storms[J]. Journal of the American Water Resources Association, 2003,39(5):1043-1054.
[16]	Kalcic M M, Frankenberger J, Chaubey I. Spatial optimization of six conservation practices using swat in tile-drained agricultural watersheds[J]. Journal of the American Water Resources Association, 2015,51(4):956-972.
[17]	Kalcic M M, Chaubey I, Frankenberger J. Defining Soil and Water Assessment Tool (SWAT) hydrologic response units (HRUs) by field boundaries[J]. International Journal of Agricultural and Biological Engineering, 2015,8(3):69-80.
[18]	Teshager A D, Gassman P W, Secchi S, et al. Modeling agricultural watersheds with the soil and water assessment tool (SWAT): calibration and validation with a novel procedure for spatially explicit HRUs[J]. Environmental Management, 2016,57(4):894-911. doi: 10.1007/s00267-015-0636-4 pmid: 26616430
[19]	Jiang P, Anderson S H, Kitchen N R, et al. Landscape and conservation management effects on hydraulic properties of a claypan-soil toposequence[J]. Soil Science Society of America Journal, 2007,71(3):803-811.
[20]	Arnold J G, Srinivasan R, Muttiah R S, et al. Large area hydrologic modeling and assessment part i: Model development[J]. Journal of the American Water Resources Association, 1998,34(1):73-89.
[21]	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 ASABE, 2010,53(5):1433-1443.
[22]	Zhu L J, Qin C Z, Zhu A X. Spatial optimization of watershed best management practice scenarios based on boundary-adaptive configuration units[J]. Progress in Physical Geography: Earth and Environment, 2020. DOI: 10.1177/0309133320939002.
[23]	蔡强国, 朱阿兴, 毕华兴. 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. Paradigms for integrated soil and water conservation over main water erosion regions in China[M]. Beijing: China Water Power Press, 2012. ]
[24]	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.
[25]	Zhu L J, Zhu A X, Qin C Z, et al. Automatic approach to deriving fuzzy slope positions[J]. Geomorphology, 2018,304:173-183.
[26]	Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002,6(2):182-197.
[27]	Liu J Z, Zhu A X, Liu Y B, et al. A layered approach to parallel computing for spatially distributed hydrological modeling[J]. Environmental Modelling & Software, 2014,51:221-227.
[28]	Liu J Z, Zhu A X, Qin C Z, et al. A two-level parallelization method for distributed hydrological models[J]. Environmental Modelling & Software, 2016,80:175-184.
[29]	Zhu L J, Liu J Z, Qin C Z, et al. A modular and parallelized watershed modeling framework[J]. Environmental Modelling & Software, 2019,122:104526.
[30]	Aston A R. Rainfall interception by eight small trees[J]. Journal of Hydrology, 1979,42(3/4):383-396.
[31]	Allen R G, Jensen M E, Wright J L, et al. Operational estimates of reference evapotranspiration[J]. Agronomy Journal, 1989,81(4):650-662.
[32]	Monteith J L. Evaporation and the environment[C]//The state and movement of water in living organisms. London, UK: Cambridge University Press, 1965:205-234.
[33]	Liu Y B. Development and application of a GIS-based distributed hydrological model for flood prediction and watershed management[D]. Belgium: Vrije Universiteit Brussel, 2004.
[34]	Linsley R K, Kohler M A, Paulhus J L H. Hydrology for engineers 2nd ed[M]. New York: McGraw-Hill, 1975.
[35]	Neitsch S L, Arnold J G, Kiniry J R, et al. Soil and Water Assessment Tool Theoretical Documentation, Version 2009[R]. Texas, USA: Texas A&M University System, College Station, 2011.
[36]	Liu Y B, Gebremeskel S, De Smedt F, et al. A diffusive transport approach for flow routing in GIS-based flood modeling[J]. Journal of Hydrology, 2003,283(1-4):91-106.
[37]	Cunge J A. On the subject of A flood propagation computation method (musklngum method)[J]. Journal of Hydraulic Research, 1969,7(2):205-230.
[38]	Williams J R. Sediment-yield prediction with universal equation using runoff energy factor[G]//Present and Prospective Technology for Predicting Sediment Yield and Sources [C]. Oxford, Mississippi, 1975:244-252.
[39]	Williams J R. Spnm, a model for predicting sediment, phosphorus, and nitrogen yields from agricultural basins[J]. Journal of the American Water Resources Association, 1980,16(5):843-848.
[40]	Williams J R. The EPIC model[G]// Singh V P. Computer models of watershed hydrology. Highlands Ranch, CO, USA: Water Resources Publications, 1995:909-1000.
[41]	Brown L C, Barnwell T O. The enhanced stream water quality models QUAL2E and QUAL2E-UNCAS: Documentation and user manual[R]. EPA document EPA/600/3-87/007, USEPA, Athens, 1987.
[42]	李兆富, 刘红玉, 李恒鹏. 天目湖流域湿地对氮磷输出影响研究[J]. 环境科学, 2012,33(11):3753-3759.
	[ Li Z F, Liu H Y, Li H P. Impact on nitrogen and phosphorous export of wetlands in Tianmu lake watershed[J]. Environmental Science, 2012,33(11):3753-3759. ]
[43]	席庆, 李兆富, 罗川. 基于扰动分析方法的AnnAGNPS模型水文水质参数敏感性分析[J]. 环境科学, 2014,35(5):1773-1780.
	[ Xi Q, Li Z F, Luo C. Sensitivity analysis of AnnAGNPS model's hydrology and water quality parameters based on the perturbation analysis method[J]. Environmental Science, 2014,35(5):1773-1780. ]
[44]	吴俊, 樊剑波, 何园球, 等. 苕溪流域不同施肥条件下稻田田面水氮磷动态特征及产量研究[J]. 土壤, 2013,45(2):207-213.
	[ Wu J, Fan J B, He Y Q, et al. Study on rice yield and dynamics of nitrogen and phosphorus in surface water of paddy field under different fertilizations in tiaoxi river basin[J]. Soils, 2013,45(2):207-213. ]
[45]	王媛, 楚春礼, 刘夏, 等. 率水流域非点源污染分析及施肥措施模拟[J]. 水资源与水工程学报, 2019,30(4):6-13.
	[ Wang Y, Chu C L, Liu X, et al. Non-point source pollution analysis and fertilizer management simulation in Shuaishui Basin[J]. Journal of Water Resources and Water Engineering, 2019,30(4):6-13. ]
[46]	Nash J E, Sutcliffe J V. River flow forecasting through conceptual models part I: A discussion of principles[J]. Journal of Hydrology, 1970,10(3):282-290.
[47]	Moriasi D N, Arnold J G, Liew M W V, et al. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations[J]. Transactions of the ASABE, 2007,50(3):885-900.
[48]	Engel B, Storm D, White M, et al. A hydrologic/water quality model Applicati1[J]. Journal of the American Water Resources Association, 2007,43(5):1223-1236.
[49]	Zitzler E, Thiele L. Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach[J]. IEEE Transactions on Evolutionary Computation, 1999,3(4):257-271.

BMP	简介	适宜坡位	适宜土地利用类型
减量施肥	在保证作物产量的前提下合理减少施肥量,降低造成污染的可能性	背坡、沟谷	农田
退耕还林	从保护和改善生态环境目的出发,在易造成水土流失的耕地上,逐步停止耕种,因地制宜植树造林	背坡、沟谷	农田
封山育林	山区定期封山,禁止砍柴、垦荒、放牧等人为活动,利用自然的更新能力,恢复森林植被	山脊、背坡	林地
生态林草	通过种植草、乔木、灌木等方式降低土壤侵蚀,减少养分随土壤迁移	山脊、背坡、沟谷	林地

率定变量	率定结果(2014-01-01—2014-12-31)			月尺度模拟的可接受范围^[35]
率定变量	NSE	RSR	PBIAS /%	NSE	RSR	\|PBIAS\| /%
流量/m³s^-1	0.65	0.59	-2.62	>0.50	≤0.70	<25
泥沙/kg	0.43	0.75	22.83	>0.50	≤0.70	<55
总氮/kg	0.59	0.64	33.70	>0.50	≤0.70	<70

基于坡位-地块单元的流域最佳管理措施空间优化配置方法

Spatial Optimization of Watershed Best Management Practices based on Slope Position-Field Units

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 49

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	林炫歆, 肖桂荣, 周侯伯. 顾及土地利用动态变化的滑坡易发性评估方法[J]. 地球信息科学学报, 2023, 25(5): 953-966.
[2]	俞钦平, 吴振华, 王亚蓓. 一种耦合进化算法与FLUS模型的土地利用变化模拟模型[J]. 地球信息科学学报, 2023, 25(3): 510-528.
[3]	夏俊楠, 魏伟, 尹力, 洪梦谣, 薄立明. 基于四叉树算法的“三区空间”形态效率评价方法[J]. 地球信息科学学报, 2023, 25(3): 450-467.
[4]	秦肖伟, 程博, 杨志平, 李林, 董文, 张新, 杨树文, 靳宗义, 薛庆. 基于时序遥感影像的西南山区地块尺度作物类型识别[J]. 地球信息科学学报, 2023, 25(3): 654-668.
[5]	张轲, 魏伟, 周婕, 尹力, 夏俊楠. 三江源地区“三区空间”时空演化及驱动机制分析（1992―2020年）[J]. 地球信息科学学报, 2022, 24(9): 1755-1770.
[6]	吴亚楠, 郭长恩, 于东平, 段爱民, 刘玉, 董士伟, 单东方, 吴耐明, 李西灿. 基于不确定性分析的遥感分类空间分层及评估方法[J]. 地球信息科学学报, 2022, 24(9): 1803-1816.
[7]	尹文萍, 高宸, 樊辉, 谢菲, 张鑫. 一种融合文本中地理位置和土地利用/覆被信息的野生动物活动细粒度定位方法[J]. 地球信息科学学报, 2022, 24(7): 1363-1374.
[8]	舒弥, 杜世宏. 国土调查遥感40年进展与挑战[J]. 地球信息科学学报, 2022, 24(4): 597-616.
[9]	朱昱, 潘耀忠, 张杜娟. 基于深度卷积网络和分水岭分割的耕地地块识别方法[J]. 地球信息科学学报, 2022, 24(12): 2389-2403.
[10]	王旭东, 姚尧, 任书良, 史绪国. 耦合FLUS和Markov的快速发展城市土地利用空间格局模拟方法[J]. 地球信息科学学报, 2022, 24(1): 100-113.
[11]	孙定钊, 梁友嘉. 基于改进Markov-CA模型的黄土高原土地利用多情景模拟[J]. 地球信息科学学报, 2021, 23(5): 825-836.
[12]	杨帅, 杨娜, 陈传法, 常兵涛, 高原, 郑婷婷. 顾及数据配准的江西省SRTM DEM精度评价和修正[J]. 地球信息科学学报, 2021, 23(5): 869-881.
[13]	刘稳, 詹庆明, 赵中元, 林苏靖, 肖琨, 李荣. 面向自然资源统一管理的多源土地利用信息一致性分析评价[J]. 地球信息科学学报, 2021, 23(3): 365-376.
[14]	郭紫甜, 王春梅, 刘欣, 庞国伟, 朱梦阳, 王晋卿. 基于小流域抽样单元的中国FROM-GLC30数据精度评价[J]. 地球信息科学学报, 2021, 23(3): 524-535.
[15]	王超, 常勇, 侯西勇, 刘玉斌. 基于土地利用格局变化的胶东半岛生境质量时空演变特征研究[J]. 地球信息科学学报, 2021, 23(10): 1809-1822.