基于DCCA方法分析气候变化对四川省粮食产量的影响

doi:10.3969/j.issn.1000-6362.2016.01.006

摘要/Abstract

摘要：

选取四川省23个气象台站1961-2012年的年气温距平、降水量距平数据等气象资料以及四川省粮食产量数据，应用非线性时间序列的去趋势互相关分析（DCCA）方法对四川省粮食产量与年平均气温和年平均降水量两个主要气候因子的相关性及其随时间变化特征进行分析。结果表明：四川省粮食产量与年平均气温和年平均降水量这两个主要气候因子之间均表现出强烈的长期持续的正相关性特征，标度不变区间在52a以上。进一步对1990-2012年气候变化对四川省粮食产量的影响进行DCCA滑移分析，结果显示，尽管受到区域性气候暖干化趋势的影响，四川省粮食产量出现不稳定波动现象，但粮食产量整体受气候变化的影响程度在逐渐减弱，这可能得益于近年来四川省农业资金及科技力度的大幅投入。

Abstract:

The impacts of climate change on grain output in Sichuan province was assessed based on monthly mean temperature and precipitation data from 23 meteorological stations and grain output data of Sichuan province from 1961 to 2012 by the method of detrended cross-correlation analysis(DCCA method), while the temporal variation characteristics of Sichuan grain yield and the average annual temperature and annual precipitation of two major climatic factors were analyzed, respectively. The results showed that the correlations between temperature, precipitation and grain yield were characterized by long-term cross-correlation. The high persistence signifies that the correlations, from small time intervals to larger ones (up to 52 years at least) were positively correlated in a power-law fashion. At the same time, the DCCA exponent curves of moving series from 1990-2012 were studied. The results showed that, despite the regional climate warming and drying trend was appeared, the fluctuation of grain yield in Sichuan province is not stable, the impact of climate change on grain yield is gradually weakened. The increments of agriculture investment, which have an active role on grain output facing to harmful influence of climate change, were contributed to the DCCA exponent curves.

Key words: Detrended cross-correlation analysis, Long-term persistence, Grain output, Climate change, Sichuan

刘春琼，刘萍，吴生虎，史凯. 基于DCCA方法分析气候变化对四川省粮食产量的影响[J]. 中国农业气象, 2016, 37(01): 43-50.

LIU Chun-qiong, LIU Ping, WU Sheng-hu, SHI Kai. Impacts of Climate Change on Grain Output in Sichuan Based on the Detrended Cross-correlation Analysis Method[J]. Chinese Journal of Agrometeorology, 2016, 37(01): 43-50.

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