| 基于被动微波遥感反演雪深的时间序列分析我国积雪时空变化特征 |
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| 引用本文： | 戴声佩,张勃,程峰,王海军,王鹏.基于被动微波遥感反演雪深的时间序列分析我国积雪时空变化特征[J].冰川冻土,2010,32(6). |
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| 作者姓名： | 戴声佩 张勃 程峰 王海军 王鹏 |
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| 作者单位： | 西北师范大学,地理与环境科学学院,甘肃,兰州,730070;云南师范大学,旅游与地理科学学院,云南,昆明,650092;成都理工大学,工程技术学院,四川,乐山,614007 |
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| 基金项目： | 国家自然科学基金项目，中国科学院知识创新工程重要方向项目，公益性行业(气象)科研专项，生态经济学省级重点学科 |
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| 摘 要： | 利用1978-2005年逐日中国积雪深度数据集,分析了我国积雪空间分布特征和季节时空分布特征,并运用趋势线分析方法和均方根差模拟了积雪深度和积雪日数的变化趋势及异常空间变化特征.结果表明:青藏高原东南、青藏高原西部和南部、新疆北部和东北山区为我国积雪空间分布四大高值区.近28 a来,积雪深度和积雪日数呈增加趋势,20世纪80年代青藏高原明显增加和明显减少趋势并存,90年代整体明显增加,2000-2005年整体基本不变.青藏高原中东部、新疆北部以及东北山区为积雪深度异常变化敏感区,而青藏高原西部则为积雪日数异常变化敏感区.
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| 关 键 词： | 积雪 被动微波 遥感 时空变化 中国 |
The Spatio-Temporal Variations of Snow Cover in China from the Snow Depth Time Series Dataset Based on Passive Microwave Remote Sensing |
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| DAI Sheng-pei,ZHANG Bo,CHENG Feng,WANG Hai-jun,WANG Peng.The Spatio-Temporal Variations of Snow Cover in China from the Snow Depth Time Series Dataset Based on Passive Microwave Remote Sensing[J].Journal of Glaciology and Geocryology,2010,32(6). |
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| Authors: | DAI Sheng-pei ZHANG Bo CHENG Feng WANG Hai-jun WANG Peng |
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| Institution: | DAI Sheng-pei1,ZHANG Bo1,CHENG Feng2,WANG Hai-jun3,WANG Peng1(1.The College of Geography and Environmental Science,Northwest Normal University,Lanzhou Gansu 730070,China,2.The College of Tourism and Geography Science,Yunnan Normal University,Kunming Yunnan 650092,3.The Engineering and Technical College of Chengdu University of Technology,Leshan Sichuan 614007,China) |
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| Abstract: | Using the long-term(1978-2005) snow depth dataset of China,the spatial distribution,the seasonal snow cover changes and the inter-annual spatio-temporal variations of snow depth and snow cover days over China were analyzed.It is found that the Southeast Tibetan Plateau,the western and southern margins of the Tibetan Plateau,the Northern Xinjiang Region and the mountain regions of Northeast China are the four areas of deeper snow cover over China.In the period from 1978 to 2005,when the snow depth and snow c... |
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| Keywords: | snow cover passive microwave remote sensing spatio-temporal variation China |
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