| 黄淮海地区植被活动对气候变化的响应特征 |
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| 引用本文： | 陈怀亮,徐祥德,杜子璇,邹春辉.黄淮海地区植被活动对气候变化的响应特征[J].应用气象学报,2009,20(5):513-520. |
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| 作者姓名： | 陈怀亮 徐祥德 杜子璇 邹春辉 |
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| 作者单位： | 1.河南省气象科学研究所, 郑州 450003 |
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| 基金项目： | "十一五"国家科技支撑计划重点项目，中国气象局气候变化专项 |
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| 摘 要： | 基于1982 -2003年GIMMSNDVI遥感数据和气象资料, 综合运用趋势分析、相关分析、奇异值分解等方法, 分析我国黄淮海地区植被活动对气候变化响应的时空特征。结果表明:黄淮海地区整体气候变暖趋势比较明显, 干旱化尚不显著, 年平均植被NDVI表现为略微增加的趋势。在年尺度上, 温度是敏感性最强的气候因子, 全年温度、降水、相对湿度对植被NDVI动态变化具有正效应, 而蒸发量具有负效应; 在季尺度上, 温度、降水的敏感性最强。自然植被对降水的敏感性最强, 其次是温度; 农业植被对温度的敏感性最强, 其次是降水。植被对气候变化响应的空间特征表现为, 植被主要生长季平均NDVI与温度距平场空间结构一致, 与蒸发量距平场反位相对应, 与降水量距平场呈北、南部正负相反分布, 与相对湿度距平场呈南、北向正负相反的空间分布。
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| 关 键 词： | 植被覆盖变化 气候变化 时空变化 响应特征 奇异值分解 |
| 收稿时间： | 9/2/2008 12:00:00 AM |
Vegetation Activity Responses to Climate Change in the Huang-Huai-Hai Area Based on GIMMS NDVI Dataset |
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| Chen Huailiang,Xu Xiangde,Du Zixuan and Zou Chunhui.Vegetation Activity Responses to Climate Change in the Huang-Huai-Hai Area Based on GIMMS NDVI Dataset[J].Quarterly Journal of Applied Meteorology,2009,20(5):513-520. |
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| Authors: | Chen Huailiang Xu Xiangde Du Zixuan and Zou Chunhui |
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| Institution: | 1.Henan Institute of Meteorological Sciences, Zhengzhou 4500032.Key Laboratory of Agrometeorological Safeguard and Applied Technique, CMA, Zhengzhou 4500033.Chinese Academy of Meteorological Sciences, Beijing 100081 |
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| Abstract: | Based on 1982-2003 GIMMS NDVI sounding and climate data by use of techniques for the trend, correlation and singular value decomposition (SVD) analysis, the space and time patterns of vegetation activity response to climate change in the Huan-Huai-Hai Area (HHHA) is investigated. Results suggest that this area shows a more significant warming trend and less distinct aridization, on the whole, with annual mean NDVI displaying a marginally increasing trend. In the spatial distribution figure of the correlation coefficient between annual average and climate factors, the annual average temperature is positive correlated to the annual NDVI in most area, which indicates that the increasing temperature is beneficial to the vegetation growing in most region of the study area. On the other hand, the annual precipitation is negatively correlated to the annual NDVI in south region but positively correlated to the annual NDVI in north region of the study area. On the yearly basis, temperature is the most sensitive climate factor. Annual temperature, rainfall and relative humidity exert positive effect on the dynamic variation in vegetation NDVI while evaporation exerts negative effect. On the seasonal scale, temperature and rainfall are the most strongly influencing factors, with autumn climate having heavier impact on yearly mean NDVI. Natural vegetation is predominantly sensitive to rainfall and, to a less degree, to temperature; agricultural vegetation is sensitive dominantly to temperature and, to less extent, to rainfall. The grassland vegetation is more sensitive to the precipitation and other climate factors than other kinds of natural vegetation. A-mong the agricultural vegetation, the rain-fed vegetation of one crop per annual and paddy-upland rotation agricultural vegetation of two crops per annual are more sensitive to the temperature and precipitation, but the vegetation of two crops per annual in irrigated farmland is less sensitive to the climate factors. The precipitation of autumn, spring and winter and the temperature of spring and summer are the main factors affecting natural vegetation. The temperature of spring and winter, the precipitation of spring and summer are the main climate factors affecting the agriculture vegetation. April-September vegetation response to climate has the spatial patterns as follows. The anomaly field of NDVI has the same structure as that of temperature, an anti-correlation structure with anomalies of evaporation, and a see-saw distribution with positive (negative) correlation in the north (south) with that of rainfall anomalies, and an opposite distribution with positive (negative) correlations in the south (north) to that of relative humidity. |
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| Keywords: | change in vegetation cover climate change spatial and temporal change characteristic response singular value decomposition (SVD) |
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