地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (6): 781-790.doi: 10.12082/dqxxkx.2018.170622

• 2017年中国地理信息科学理论与方法学术年会优秀论文专辑 • 上一篇    下一篇

以坡位为空间配置单元的流域管理措施情景优化方法

高会然1,2,3(), 秦承志1,2(), 朱良君1,2, 朱阿兴1,4,5,6,7, 刘军志4,5,6, 吴辉8   

  1. 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,北京100049
    3. 中国科学院遥感与数字地球科学研究所 数字地球重点实验室,北京 100094
    4. 南京师范大学 虚拟地理环境教育部重点实验室,南京 210023
    5. 江苏省地理环境演化国家重点实验室培育建设点,南京 210023
    6. 江苏省地理信息资源开发与利用协同创新中心,南京 210023
    7. 威斯康星大学(麦迪逊)地理系,威斯康星州 WI 53706,美国
    8. 杭州电子科技大学智慧城市研究中心,杭州 310012
  • 收稿日期:2017-12-21 修回日期:2018-02-02 出版日期:2018-06-20 发布日期:2018-06-20
  • 作者简介:

    作者简介:高会然(1992-),男,博士生,现从事遥感与寒区流域水文过程模拟研究。E-mail: gaohr@radi.ac.cn

  • 基金资助:
    国家自然科学基金项目(41431177、41422109);资源与环境信息系统国家重点实验室自主部署项目(O88RA20CYA)

Using Slope Positions as Spatial Units for Optimizing Spatial Configuration of Watershed Management Practices

GAO Huiran1,2,3(), QIN Chengzhi1,2(), ZHU Liangjun1,2, ZHU A-Xing1,4,5,6,7, LIU Junzhi4,5,6, WU Hui8   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094
    4. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
    5. State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
    6. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
    7. Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
    8. Smart City Zhejiang, Hangzhou Dianzi University, Hangzhou 310012, China;
  • Received:2017-12-21 Revised:2018-02-02 Online:2018-06-20 Published:2018-06-20
  • Supported by:
    National Natural Science Foundation of China, No.41431177,41422109;Innovation Project of LREIS, No.O88RA20CYA

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。

关键词: 流域过程模拟, 最佳管理措施, 情景分析, 空间配置单元, 坡位, 遗传算法

Abstract:

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.

Key words: watershed process modeling, best management practices, scenario analysis, spatial units, slope position, genetic algorithm