收藏设为首页 广告服务联系我们在线留言

干旱区地理 ›› 2017, Vol. 40 ›› Issue (5): 987-996.

• 气候与水文 • 上一篇    下一篇

基于CMIP5模式的干旱内陆河流域未来气候变化预估

祁晓凡1,2,3, 李文鹏2, 李海涛2, 刘宏伟4   

  1. 1 中国地质大学(北京)水资源与环境学院, 北京 100083;
    2 中国地质环境监测院, 北京 100081;
    3 山东省地质调查院, 山东 济南 250013;
    4 中国地质调查局天津地质调查中心, 天津 300170
  • 收稿日期:2017-05-29 修回日期:2017-07-19 出版日期:2017-09-25
  • 作者简介:祁晓凡,男,工程师,博士研究生,主要从事水文学及水资源研究.Email:xf-q@163.com
  • 基金资助:

    国家自然科学基金重点项目(91025019)

Future climate change prediction of arid inland river basin based on CMIP5 model

QI Xiao-fan1,2,3, LI Wen-peng2, LI Hai-tao2, LIU Hong-wei4   

  1. 1 School of Water Resources and Environment, China University of Geosciences, Beijing 100083, China;
    2 China Institute of Geo-Environment Monitoring, Beijing 100081, China;
    3 Shandong Institute of Geological Survey, Jinan 250013, Shandong, China;
    4 Tianjin Center of China Geological Survey, Tianjin 300170, China
  • Received:2017-05-29 Revised:2017-07-19 Online:2017-09-25

摘要: 我国西北干旱半干旱地区水资源短缺、生态环境脆弱,未来气候变化预估对水资源管理具有重要的现实意义。以黑河流域为研究区,基于1960-2014年月值NCEP再分析资料与气象要素实测资料,建立逐步回归降尺度模型;针对模型不足,提出一种补充逐步回归降尺度模型;经2006-2014年CMIP5中CNRM-CM5模式的区域适用性评价,选取适宜模型进行2016-2060年CNRM-CM5模式下的流域未来气候变化预估。主要结论为:(1)补充逐步回归模型的模拟效果总体要好于逐步回归模型,两模型对流域气温的模拟效果要好于降水。(2)降尺度模型的CNRMCM5模式适用性评价表明,RCP4.5与RCP8.5路径下,补充回归模型的适用性总体好于逐步回归模型。(3)两种路径下,黑河流域上中游未来年均降水量分别为324.94 mm、330.15 mm,未来流域降水分布的不均匀性增强。(4)两种路径下黑河流域中下游未来年均气温分别为10.25℃、10.77℃。

关键词: 气候变化, 统计降尺度, CMIP5模式, 气象要素, 黑河流域

Abstract: Climate change is a global issue of common concern to the international community today. Over the past century, great changes characterized by climate warming have taken place in both global climate and eco-environment. In recent years, with the implementation of NSFC major research plan, integrated study on eco-hydrological processes of Heihe River Basin(HRB)has become a hot spot of arid and semi-arid area research in China and great achievements have been made. Future climate change forecasting is one of the foundations of the research, but currently no forecasting has been made based on Global Climate Models(GCMs)and statistical downscaling methods. Such forecasting is in urgent need. Monthly meteorological monitoring data of the reaches and NCEP reanalysis data from 1960 to 2014, together with CNRM-CM5 data from 2006 to 2060 were used for the study. The research process of the paper is as follows:Firstly, statistical downscaling models for east watershed of HRB based on multivariable linear regression using the NCEP reanalysis data and meteorological monitoring data of air temperature and precipitation were built. Secondly, since the models cannot meet forecasting requirements, new complimentary statistical downscaling models were built based on residual analysis to improve forecasting results. Thirdly, regional applicability evaluation was employed to determine the models used for future climate forecasting based on GCM of Coupled Model Intercomparison Project Phase 5(CMIP5). Finally, future climate change prediction for HRB was made based on the chosen downscaling models and CNRM-CM5 model of CMIP5, and both time variations and regional distribution of future air temperature and precipitation of HRB were analyzed. The results show as follows:(1)The complementary downscaling models simulated better than the multivariable regression model overall, and the simulation results of air temperature were better than that of precipitation for both the two models.(2)Regional applicability evaluation of downscaling models based on the CNRM-CM5 model show that, the applicability of the complementary regression models were better than that of the multivariate regression models in general under both RCP4.5 and RCP8.5 pathways.(3)The future average annual precipitation of upstream and midstream of HRB were 324.94 mm and 330.15 mm under both RCP4.5 and RCP8.5 pathways respectively, and inhomogeneity enhancement was confirmed in the future.(4) The future average annual air temperatures of midstream and downstream of HRB under the two pathways were 10.25℃ and 10.77℃, respectively. It should be clear that, because of the uncertainty of the data, downscaling procedures, as well as the diversity of human emissions, uncertainty exists in the prediction of future climate change in HRB.

Key words: climate change, statistical downscaling model, CMIP5, meteorological elements, HRB

中图分类号: 

  • P467