引用本文:代慧慧,杨汉波,胡庆芳.基于SDSM的疏勒河流域气候变化统计降尺度研究[J].水利水运工程学报,2015,(5):46-53
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基于SDSM的疏勒河流域气候变化统计降尺度研究
代慧慧1, 杨汉波1, 胡庆芳2
1.清华大学水利水电工程系,水沙科学与水利水电工程国家重点实验室,北京;2.南京水利科学研究院水文水资源与水利工程科学国家重点实验室,江苏南京
摘要:
疏勒河流域属于气候变化敏感区和生态脆弱区,开展该流域未来气候变化研究,对于水资源合理利用及生态环境保护具有重要意义。为预估该流域的未来气候变化,采用SDSM(statistical downscaling model)模型,根据6个地面气象站41年(1961—2001年)的观测数据、NCEP数据和HadCM3模式模拟数据开展未来气温和降水降尺度研究。结果表明: SDSM对气温的月值模拟精度较高,各站月平均气温纳什效率系数均在0.98以上;SDSM对降水的月值模拟值较实测值整体偏高,模拟效果最好的托勒站月累计降水的纳什效率系数达到0.6。SDSM能较好地模拟气温的年际变化,模拟的年际变化趋势与实测值相差不大;但SDSM对降水的年际变化模拟较差,一些站点的变化趋势方向相反,趋势模拟最好的站点为托勒站和瓜州站。根据SDSM预估结果,与1961—2001年平均值相比,2020—2039年各站点的平均气温均有所升高,A2情景下升幅为(0.8~1.9)℃,B2情景下升幅为(1~2)℃;降水在A2和B2情景下差别不大,其中托勒站减少约54 mm,马鬃山站增加6 mm。研究发现,除托勒站外,疏勒河流域与预报变量相关性最高的预报因子并不在站点所在网格,而是其东侧网格,其原因有待进一步研究。
关键词:  统计降尺度  SDSM  平均气温  降水  预报因子
DOI:
分类号:P33
基金项目:“十二五”国家科技支撑计划资助项目(2013BAB05B03); 国家自然科学基金资助项目(51379098,51109136)
Prediction of climate change over Shule River basin based on a statistical downscaling method
DAI Hui-hui1, YANG Han-bo1, HU Qing-fang2
1.State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing;2.State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute
Abstract:
The environment of Shule River basin is sensitive to climate change. Therefore, this study tries to predict the climate change over this basin using the SDSM model (a statistical downscaling model) based on the observation data of 6 meteorological stations in (or adjacent to) the basin and the output of HadCM3. The research results show that the Nash coefficient is all above 0.98 for the air temperature prediction; while the best precipitation prediction occurs at the Tuole meteorological station, with the Nash coefficient of 0.6, and the predicted precipitation is overall higher than the observed precipitation. The comparisons of the inter-annual values between the observed and predicted precipitation show that the SDSM model has a good prediction capacity for the tendancy of the air temperature, but not for the precipitation. The prediction shows that, compared with 1961—2001, the period 2020—2039 has a higher air temperature by (0.8~1.9) ℃ under A2 scenarios and (1~2)℃ under B2; and there is a similar change in the precipitation under both A2 and B2, namely 54 mm decrease at the Tuole meteorological station and 6 mm increase at the Mazongshan meteorological station. Remarkably, the studies indicate that in the Shule River basin, the prediction factors which are interrelated highly with the prediction factors are located not on the grid of the meteorological stations (except the Tuole meteorological station), but on the eastern one, and the reason is to be further studied.
Key words:  statistical downscaling  statistical downscaling model  average temperature  precipitation  prediction factor
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