Statistical downscaling of extreme daily precipitation, evaporation, and temperature and construction of future scenarios

被引:65
|
作者
Yang, Tao [1 ]
Li, Huihui [1 ]
Wang, Weiguang [1 ]
Xu, Chong-Yu [2 ]
Yu, Zhongbo [3 ]
机构
[1] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Jiangsu, Peoples R China
[2] Univ Oslo, Dept Geosci, N-0316 Oslo, Norway
[3] Univ Nevada Las Vegas, Dept Geosci, Las Vegas, NV 89154 USA
基金
中国国家自然科学基金;
关键词
climate extremes; statistical downscaling; climate change; projection; scenarios; CLIMATE-CHANGE SCENARIOS; RAINFALL; IMPACT; MODEL; RIVER; RECONSTRUCTION; WEATHER; FLOW;
D O I
10.1002/hyp.8427
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
Generally, the statistical downscaling approaches work less perfectly in reproducing precipitation than temperatures, particularly for the extreme precipitation. This article aimed to testify the capability in downscaling the extreme temperature, evaporation, and precipitation in South China using the statistical downscaling method. Meanwhile, the linkages between the underlying driving forces and the incompetent skills in downscaling precipitation extremes over South China need to be extensively addressed. Toward this end, a statistical downscaling model (SDSM) was built up to construct future scenarios of extreme daily temperature, pan evaporation, and precipitation. The model was thereafter applied to project climate extremes in the Dongjiang River basin in the 21st century from the HadCM3 (Hadley Centre Coupled Model version 3) model under A2 and B2 emission scenarios. The results showed that: (1) The SDSM generally performed fairly well in reproducing the extreme temperature. For the extreme precipitation, the performance of the model was less satisfactory than temperature and evaporation. (2) Both A2 and B2 scenarios projected increases in temperature extremes in all seasons; however, the projections of change in precipitation and evaporation extremes were not consistent with temperature extremes. (3) Skills of SDSM to reproduce the extreme precipitation were very limited. This was partly due to the high randomicity and nonlinearity dominated in extreme precipitation process over the Dongjiang River basin. In pre-flood seasons (April to June), the mixing of the dry and cold air originated from northern China and the moist warm air releases excessive rainstorms to this basin, while in post-flood seasons (July to October), the intensive rainstorms are triggered by the tropical system dominated in South China. These unique characteristics collectively account for the incompetent skills of SDSM in reproducing precipitation extremes in South China. Copyright (c) 2011 John Wiley & Sons, Ltd.
引用
收藏
页码:3510 / 3523
页数:14
相关论文
共 50 条
  • [21] Improved statistical downscaling of daily precipitation using SDSM platform and data-mining methods
    Tavakol-Davani, H.
    Nasseri, M.
    Zahraie, B.
    INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2013, 33 (11) : 2561 - 2578
  • [22] Statistical downscaling of daily precipitation over Greece
    Kioutsioukis, Ioannis
    Melas, Dimitrios
    Zanis, Prodromos
    INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2008, 28 (05) : 679 - 691
  • [23] A novel approach for statistical downscaling of future precipitation over the Indo-Gangetic Basin
    Chaudhuri, Chiranjib
    Srivastava, Rajesh
    JOURNAL OF HYDROLOGY, 2017, 547 : 21 - 38
  • [24] A method for deterministic statistical downscaling of daily precipitation at a monsoonal site in Eastern China
    Liu, Yonghe
    Feng, Jinming
    Liu, Xiu
    Zhao, Yadi
    THEORETICAL AND APPLIED CLIMATOLOGY, 2019, 135 (1-2) : 85 - 100
  • [25] Statistical downscaling of daily precipitation using support vector machines and multivariate analysis
    Chen, Shien-Tsung
    Yu, Pao-Shan
    Tang, Yi-Hsuan
    JOURNAL OF HYDROLOGY, 2010, 385 (1-4) : 13 - 22
  • [26] Performance assessment of different data mining methods in statistical downscaling of daily precipitation
    Nasseri, M.
    Tavakol-Davani, H.
    Zahraie, B.
    JOURNAL OF HYDROLOGY, 2013, 492 : 1 - 14
  • [27] Performance comparison of three predictor selection methods for statistical downscaling of daily precipitation
    Yang, Chunli
    Wang, Ninglian
    Wang, Shijin
    Zhou, Liang
    THEORETICAL AND APPLIED CLIMATOLOGY, 2018, 131 (1-2) : 43 - 54
  • [28] Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling
    David W. Pierce
    Tapash Das
    Daniel R. Cayan
    Edwin P. Maurer
    Norman L. Miller
    Yan Bao
    M. Kanamitsu
    Kei Yoshimura
    Mark A. Snyder
    Lisa C. Sloan
    Guido Franco
    Mary Tyree
    Climate Dynamics, 2013, 40 : 839 - 856
  • [29] Future predictions of precipitation and temperature in Iraq using the statistical downscaling model
    Mustafa Al-Mukhtar
    Mariam Qasim
    Arabian Journal of Geosciences, 2019, 12
  • [30] Wavelet-based predictor screening for statistical downscaling of precipitation and temperature using the artificial neural network method
    Baghanam, Aida Hosseini
    Norouzi, Ehsan
    Nourani, Vahid
    HYDROLOGY RESEARCH, 2022, 53 (03): : 385 - 406