| 2010—2100年淮河径流量变化情景预估 |
| |
| 引用本文： | 高超,曾小凡,苏布达,闻余华,朱进,吴必文.2010—2100年淮河径流量变化情景预估[J].气候变化研究进展,2010,6(1):15-21. |
| |
| 作者姓名： | 高超 曾小凡 苏布达 闻余华 朱进 吴必文 |
| |
| 作者单位： | 1. 中国科学院南京地理与湖泊研究所；安徽师范大学国土资源与旅游学院2. 华中科技大学水电与数字化工程学院3. 中国气象局国家气候中心4. 江苏省水文局5. 安徽省气候中心 |
| |
| 基金项目： | 国家自然科学基金项目，国家重点基础研究发展计划，"气候变化对我国东部季风区陆地水循环与水资源安全影响及适应对策" |
| |
| 摘 要： | 根据淮河流域14个气象站点1964—2007年观测降水量与温度数据和ECHAM5/MPI-OM模式在3种排放情景下对该流域2001—2100年的气候预估,利用人工神经网络模型预估淮河蚌埠站2010—2100年逐月径流量变化。计算结果表明:3种排放情景下2010—2100年淮河径流量年际变化幅度差异较大,SRES-A2情景总体处于波动上升趋势,其中2051—2085年上升趋势显著;SRES-A1B情景2024—2037年年平均流量显著降低;SRES-B1情景年平均流量的变率甚小。季节分析表明:春季径流量在2010—2100年变幅最小,距平百分率在-15.1%～18.6%之间小幅波动。夏季平均流量在2040年代前呈下降趋势,之后小幅波动上升。秋、冬季平均流量SRES-A2和SRES-A1B情景变幅显著,其中,秋季SRES-A2情景2060年代距平百分率下降达50.6%,为3种情景下各季节径流量降幅之最;冬季SRES-A1B情景2050年代其增幅达到54.7%,亦为上升幅度之最。
|
| 关 键 词： | 气候变化影响 径流量 预估 人工神经网络 淮河流域 |
| 收稿时间： | 2009-2-16 |
| 修稿时间： | 2009-4-19
|
Projected Stream Flow in the Huaihe River in 2010-2100 |
| |
| Gao Chao,Zeng Xiaofan,Su Buda,Wen Yuhua,Zhu Jin,Wu Biwen.Projected Stream Flow in the Huaihe River in 2010-2100[J].Advances in Climate Change,2010,6(1):15-21. |
| |
| Authors: | Gao Chao Zeng Xiaofan Su Buda Wen Yuhua Zhu Jin Wu Biwen |
| |
| Institution: | 1. Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences2. National Climate Center |
| |
| Abstract: | Based on the observed precipitation and temperature data at 14 meteorological stations of the Huaihe River basin from 1964 to 2007, and the climate projection from 2001 to 2100 by the ECHAM5/MPI-OM model, the streamflow for the basin from 2010 to 2100 were projected under the SRES-A2, -A1B, and-B1 scenarios by applying artificial neural network (ANN) hydrological models. The results show that differences in annual streamflow from 2010 to 2100 are significant under the three scenarios; the streamflow under the SRES-A2 scenario displays a general increasing trend, especially significant from 2051 to 2085, it declines gradually in fluctuation from 2024 to 2037 under the SRES-A1B scenario, and shows no obvious trend under the SRES-B1 scenario. Fluctuations of spring streamflow in 2010-2100 are the smallest in all four seasons, ranging from -15.1% to 18.6% under the three scenarios. Summer average streamflow decreases before the 2040s and increases subsequently whilst the fluctuations are not significant. Autumn streamflow has obvious fluctuations under the SRES-A2 and SRES-A1B scenarios; it drops in the 2060s under the SRES-A2 scenario by 50.6%, which is the maximum decreasing range for all projected seasons. Winter streamflow increases in 2050s under SRES-A1B scenario by 54.7%, which is the maximum increasing range in four seasons. |
| |
| Keywords: | climate change impact streamflow projection artificial neural networks 21st century Huaihe catchment |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《气候变化研究进展》浏览原始摘要信息 |
| 点击此处可从《气候变化研究进展》下载免费的PDF全文 |
|
