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Volume 27 Issue 2

Mar.  2010

Article Contents
Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2010 >  27(2): 274-284

Downscaling GCMs Using the Smooth Support Vector Machine Method to Predict Daily Precipitation in the Hanjiang Basin

  • 1.

    State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072,State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072,Radar and Avionics Institute of Aviation Industry Corporation of China, Wuxi 214063,State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072,Department of Geosciences, University of Oslo, PO Box 1047 Blindern, NO-0316 Oslo, Norway


doi: 10.1007/s00376-009-8071-1

  • General circulation models (GCMs) are often used in assessing the impact of climate change at global and continental scales. However, the climatic factors simulated by GCMs are inconsistent at comparatively smaller scales, such as individual river basins. In this study, a statistical downscaling approach based on the Smooth Support Vector Machine (SSVM) method was constructed to predict daily precipitation of the changed climate in the Hanjiang Basin. NCEP/NCAR reanalysis data were used to establish the statistical relationship between the larger scale climate predictors and observed precipitation. The relationship obtained was used to project future precipitation from two GCMs (CGCM2 and HadCM3) for the A2 emission scenario. The results obtained using SSVM were compared with those from an artificial neural network (ANN). The comparisons showed that SSVM is suitable for conducting climate impact studies as a statistical downscaling tool in this region. The temporal trends projected by SSVM based on the A2 emission scenario for CGCM2 and HadCM3 were for rainfall to decrease during the period 2011--2040 in the upper basin and to increase after 2071 in the whole of Hanjiang Basin.
  • [1] Deliang CHEN, Christine ACHBERGER, Jouni R¨AIS¨ANEN, Cecilia HELLSTR¨OM, 2006: Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios: A Case Study of Swedish Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 54-60.  doi: 10.1007/s00376-006-0006-5
    [2] Dangfu YANG, Shengjun LIU, Yamin HU, Xinru LIU, Jiehong XIE, Liang ZHAO, 2023: Predictor Selection for CNN-based Statistical Downscaling of Monthly Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1117-1131.  doi: 10.1007/s00376-022-2119-x
    [3] Donglin GUO, Huijun WANG, 2016: Comparison of a Very-fine-resolution GCM with RCM Dynamical Downscaling in Simulating Climate in China, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 559-570.  doi: 10.1007/s00376-015-5147-y
    [4] Akio KITOH, Masahiro HOSAKA, Yukimasa ADACHI, Kenji KAMIGUCHI, 2005: Future Projections of Precipitation Characteristics in East Asia Simulated by the MRI CGCM2, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 467-478.  doi: 10.1007/BF02918481
    [5] JIE Weihua, WU Tongwen, WANG Jun, LI Weijing, LIU Xiangwen, 2014: Improvement of 6-15 Day Precipitation Forecasts Using a Time-Lagged Ensemble Method, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 293-304.  doi: 10.1007/s00376-013-3037-8
    [6] FAN Lijun, Deliang CHEN, FU Congbin, YAN Zhongwei, 2013: Statistical downscaling of summer temperature extremes in northern China, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1085-1095.  doi: 10.1007/s00376-012-2057-0
    [7] LIU Ge, WU Renguang, ZHANG Yuanzhi, and NAN Sulan, 2014: The Summer Snow Cover Anomaly over the Tibetan Plateau and Its Association with Simultaneous Precipitation over the Mei-yu-Baiu region, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 755-764.  doi: 10.1007/s00376-013-3183-z
    [8] Kun-Hui YE, Chi-Yung TAM, Wen ZHOU, Soo-Jin SOHN, 2015: Seasonal Prediction of June Rainfall over South China: Model Assessment and Statistical Downscaling, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 680-689.  doi: 10.1007/s00376-014-4047-x
    [9] JIA Xiaolong, LI Chongyin, LING Jian, Chidong ZHANG, 2008: Impacts of a GCM's Resolution on MJO Simulation, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 139-156.  doi: 10.1007/s00376-008-0139-9
    [10] Xinyu LI, Riyu LU, 2018: Subseasonal Change in the Seesaw Pattern of Precipitation between the Yangtze River Basin and the Tropical Western North Pacific during Summer, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1231-1242.  doi: 10.1007/s00376-018-7304-6
    [11] Xiaying ZHU, Mingzhu YANG, Ge LIU, Yanju LIU, Weijing LI, Sulan NAN, Linhai SUN, 2023: A Precursory Signal of June–July Precipitation over the Yangtze River Basin: December–January Tropospheric Temperature over the Tibetan Plateau, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1986-1997.  doi: 10.1007/s00376-022-2079-1
    [12] REN Guoyu, DING Yihui, ZHAO Zongci, ZHENG Jingyun, WU Tongwen, TANG Guoli, XU Ying, 2012: Recent Progress in Studies of Climate Change in China, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 958-977.  doi: 10.1007/s00376-012-1200-2
    [13] Ruian TIE, Chunxiang SHI, Gang WAN, Xingjie HU, Lihua KANG, Lingling GE, 2022: CLDASSD: Reconstructing Fine Textures of the Temperature Field Using Super-Resolution Technology, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 117-130.  doi: 10.1007/s00376-021-0438-y
    [14] WEN Xinyu, ZHOU Tianjun, WANG Shaowu, WANG Bin, WAN Hui, LI Jian, 2007: Performance of a Reconfigured Atmospheric General Circulation Model at Low Resolution, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 712-728.  doi: 10.1007/s00376-007-0712-7
    [15] Athanassios A. ARGIRIOU, Zhen LI, Vasileios ARMAOS, Anna MAMARA, Yingling SHI, Zhongwei YAN, 2023: Homogenised Monthly and Daily Temperature and Precipitation Time Series in China and Greece since 1960, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1326-1336.  doi: 10.1007/s00376-022-2246-4
    [16] WU Tongwen, WU Guoxiong, 2004: An Empirical Formula to Compute Snow Cover Fraction in GCMs, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 529-535.  doi: 10.1007/BF02915720
    [17] Tian FENG, Fumin REN, Da-Lin ZHANG, Guoping LI, Wenyu QIU, Hui YANG, 2020: Sideswiping Tropical Cyclones and Their Associated Precipitation over China, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 707-717.  doi: 10.1007/s00376-020-9224-5
    [18] Meng YAN, Johnny C. L. CHAN, Kun ZHAO, 2020: Impacts of Urbanization on the Precipitation Characteristics in Guangdong Province, China, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 696-706.  doi: 10.1007/s00376-020-9218-3
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    [20] ZHANG Xinping, JIN Huijun, SUN Weizhen, 2006: Stable Isotopic Variations in Precipitation in Southwest China, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 649-658.  doi: 10.1007/s00376-006-0649-2
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    • Manuscript History

    Manuscript received: 10 March 2010
    Manuscript revised: 10 March 2010
    通讯作者: 陈斌, bchen63@163.com
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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    Downscaling GCMs Using the Smooth Support Vector Machine Method to Predict Daily Precipitation in the Hanjiang Basin

    • 1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072,State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072,Radar and Avionics Institute of Aviation Industry Corporation of China, Wuxi 214063,State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072,Department of Geosciences, University of Oslo, PO Box 1047 Blindern, NO-0316 Oslo, Norway
    • Manuscript received: 2010-03-10
    • Manuscript revised: 2010-03-10

    Abstract: General circulation models (GCMs) are often used in assessing the impact of climate change at global and continental scales. However, the climatic factors simulated by GCMs are inconsistent at comparatively smaller scales, such as individual river basins. In this study, a statistical downscaling approach based on the Smooth Support Vector Machine (SSVM) method was constructed to predict daily precipitation of the changed climate in the Hanjiang Basin. NCEP/NCAR reanalysis data were used to establish the statistical relationship between the larger scale climate predictors and observed precipitation. The relationship obtained was used to project future precipitation from two GCMs (CGCM2 and HadCM3) for the A2 emission scenario. The results obtained using SSVM were compared with those from an artificial neural network (ANN). The comparisons showed that SSVM is suitable for conducting climate impact studies as a statistical downscaling tool in this region. The temporal trends projected by SSVM based on the A2 emission scenario for CGCM2 and HadCM3 were for rainfall to decrease during the period 2011--2040 in the upper basin and to increase after 2071 in the whole of Hanjiang Basin.

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