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论地理规律对流域过程模拟并行计算的指导作用

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  • 1. 南京师范大学虚拟地理环境教育部重点实验室, 南京210023;
    2. 江苏省地理信息资源开发与利用协同创新中心, 南京210023;
    3. Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA;
    4. 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;
    5. 中国科学院大学, 北京100049
刘军志(1984-),男,山东海阳人,博士,讲师,研究方向为流域过程模拟及其并行计算。E-mail:liujunzhi@njnu.edu.cn

收稿日期: 2014-12-26

  修回日期: 2015-02-02

  网络出版日期: 2015-05-10

基金资助

水体污染控制与治理科技重大专项(2013ZX07103006-005);国家科技支撑计划项目(2013BAC08B03-4);江苏省高校自然科学研究项目(14KJB170009、13KJB170008);江苏高校优势学科建设工程项目。

Parallel Computing of Watershed Process Simulation Guided by Geographical Laws

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  • 1. Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China;
    2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China;
    3. Department of Geography, University of Wisconsin-Madison, Madison WI 53706, USA;
    4. State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    5. University of Chinese Academy of Sciences, CAS, Beijing 100049, China

Received date: 2014-12-26

  Revised date: 2015-02-02

  Online published: 2015-05-10

摘要

流域过程模拟是地理学研究和流域科学管理的有效工具。在应用需求的推动下,长时段、高空间分辨率的多过程综合模拟逐渐兴起,流域过程模拟所需的计算量越来越大,亟需借助并行计算技术来提高效率。流域过程模拟并行计算的现有研究,大多为针对特定模型的个例研究,缺乏对指导各类流域过程模型并行计算的共性思路和原则进行讨论和梳理。首先,对可指导流域过程模拟并行计算的地理规律进行了分析,从空间、子过程和时间3 个角度,探讨了空间等级层次结构、空间相互作用、子过程间依赖关系、时空动态变化等地理规律,对流域过程模拟并行计算的指导作用;然后,根据地理规律在并行计算中所发挥作用和适用范围的不同对其分类;最后,结合2 个实例,对地理规律的流域过程模拟并行计算进行了分析和验证,旨在对流域过程模拟并行计算理论和方法的发展与应用提供借鉴。

本文引用格式

刘军志, 朱阿兴, 秦承志, 江净超, 朱良君, 沈琳 . 论地理规律对流域过程模拟并行计算的指导作用[J]. 地球信息科学学报, 2015 , 17(5) : 506 -514 . DOI: 10.3724/SP.J.1047.2015.00506

Abstract

Watershed process simulation has become an important tool for geographical researches and decision making of watershed management. For watershed process simulation with long period, high spatial resolution and multi-process integrated modeling, the amount of required computation is so huge that the parallel computing is urgently needed to handle these simulations. Currently, the rapid development of hardware and software in parallel computing provides a good opportunity for solving the computation bottleneck of multi- process and high-resolution watershed process simulation over large regions. In order to take full advantage of the capabilities of new parallel-computing hardware, it is necessary to use geographical laws, which illustrate the characteristics of watershed processes, to guide the design and implementation of parallel computing algorithms. This paper presents that geographical laws can be used to guide the design and implementation of parallel computing algorithms for watershed process simulation from different aspects (i.e. spatial, sub-process, and temporal aspects). The laws that can be used include the spatial hierarchy structure, the interactions among spatial units, the dependences among geographical process, and the spatial-temporal dynamic of geographical processes, etc. At the end of this paper, two parallel computing cases of watershed process simulations guided by geographical laws are illustrated to show how these geographical laws can be used in real-world applications. This paper intends to provide a theoretical and methodology guidance for parallel computing of watershed process simulation and other similar types of geo-computation.

参考文献

[1] Tague C L, Band L E. Evaluating explicit and implicit routing for watershed hydro-ecological models of forest hydrology at the small catchment scale[J]. Hydrological Processes, 2001,15(8):1415-1439.
[2] Borah D K, Bera M. Watershed-scale hydrologic and nonpoint-source pollution models: Review of applications[J]. Transactions of the ASAE, 2004,47(3):789-803.
[3] 芮孝芳,黄国如.分布式水文模型的现状与未来[J].水利 水电科技进展,2004,24(2):55-58.
[4] Meals DW, Dressing S A, Davenport T E. Lag time in water quality response to best management practices: A review[J]. Journal of Environmental Quality, 2010,39(1):85-96.
[5] Chen J M, Chen X F, Ju W. Effects of vegetation heterogeneity and surface topography on spatial scaling of net primary productivity[J]. Biogeosciences, 2013,10:4879-4896.
[6] 徐宗学,程磊.分布式水文模型研究与应用进展[J].水利 学报,2010,41(9):1009-1017.
[7] 陈腊娇,朱阿兴,秦承志,等.流域生态水文模型研究进展[J].地理科学进展,2011,30(5):535-544.
[8] Wood E F, Roundy J K, Troy T J, et al. Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water[J]. Water Resources Research, 2011,47(5): doi:10.1029/2010WR010090.
[9] Vivoni E R, Mascaro G, Mniszewski S, et al. Real-world hydrologic assessment of a fully-distributed hydrological model in a parallel computing environment[J]. Journal of Hydrology, 2011,409(1-2):483-496.
[10] Seo J K, Sugimurab T, Kim A S. OpenMP-accelerated SWAT simulation using Intel C and FORTRAN compilers: Development and benchmark[J]. Computers & Geosciences, 2015,75:66-72.
[11] 王皓,傅旭东,孙其诚.大尺度流域水文并行计算的方法 改进[J].应用基础与工程科学学报,2010,17(11):1-9.
[12] Ran Q H, Su D Y, Fu X D, et al. A physics-based hydrogeomorphologic simulation utilizing cluster parallel computing[J]. Science China (Technological Sciences), 2013, 56(8):1883-1895.
[13] 刘军志,朱阿兴,秦承志,等.分布式水文模型的并行计算 研究进展[J].地理科学进展,2013,32(4):538-547.
[14] Xu R, Huang X X, Luo L, et al. A new grid-associated algorithm in the distributed hydrological model simulations[J]. Science China-Technological Sciences, 2010,53(1): 235-241.
[15] Li T J, Wang G Q, Chen J, et al. Dynamic parallelization of hydrological model simulations[J]. Environmental Modelling & Software, 2011,26(12):1736-1746.
[16] Wang H, Fu X,Wang G, et al. A common parallel computing framework for modeling hydrological processes of river basins[J]. Parallel Computing, 2011, 37(6-7):302-315.
[17] Band L E, Tague C L, Brun S E, et al. Modelling watersheds as spatial object hierarchies: Structure and dynamics[J]. Transactions in GIS, 2000,4:181-196.
[18] Grayson R B, Moore I D, Mcmahon T A. Physically based hydrologic modeling:1. A terrain-based model for investigative purposes[J]. Water Resources Research, 1992,28(10),2639-2658.
[19] Wigmosta M S, Vail L W, Lettenmaier D P. A distributed hydrology-vegetation model for complex terrain[J]. Water Resources Research, 1994,30(6):1665-1679.
[20] 李山,王铮,钟章奇.旅游空间相互的引力模型及其应用[J].地理学报,2012,67(4):526-544.
[21] 李铁键,刘家宏,和杨,等.集群计算在数字流域模型中的 应用[J].水科学进展,2006,17(6):841-845.
[22] Tobler W. A computer movie simulating urban growth in the detroit region[J]. EconomicGeography, 1970,46(2):234-240.
[23] 李小文,曹春香,常超一.地理学第一定律与时空邻近度 的提出[J].自然杂志,2007,29(2):69-71.
[24] Goodchild M F. The validity and usefulness of laws in geographic information science and geography[J]. Annals of the Association of American Geographers, 2004,94: 300-303.
[25] Wang S, Armstrong M P. A theoretical approach to the use of cyberinfrastructure in geographical analysis[J]. International Journal of Geographical Information Science, 2009,23(2):169-193.
[26] Wang H, Fu X D, Wang Y J, et al. A high-performance temporal-spatial discretization method for the parallel computing of river basins[J]. Computer & Geosciences, 2013,58:62-68.
[27] 郑新奇.论地理系统模拟基本模型[J].自然杂志,2012,34 (3):143-149.
[28] 刘军志.分布式水文模型的子流域-基本单元双层并行 计算方法[D].北京:中国科学院地理科学与资源研究所, 2013.
[29] Liu Y B, Gebremeskel S, De Smedt F, et al. Predicting storm runoff from different land use classes using a GISbased distributed model[J]. Hydrological Processes, 2006, 20:533-548.
[30] Liu J Z, Zhu A X, Liu Y B, et al. A layered approach to parallel computing for spatially distributed hydrologic modeling[J]. Environmental Modeling & Software, 2014, 51(1):221-227.

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