【目的】实现在气候变化背景下对粮食产区农业水资源的优化配置和水分的高效利用。【方法】利用Python、 ArcGIS软件和MODIS遥感估算的MOD17和MOD16数据产品,研究了中国粮食主产区(东北、内蒙古、华北、南部) 20002014年植被生态系统水分利用效率(water use efficiency,WUE)的时空分布规律。【结果】①年WUE(以碳计,下同)整体呈上升趋势,增速为0.013 g(/ mm·m~2 · a)(P<0.01),2个转折点分别出现在2003、2010年;②年内WUE的变化主要呈M型的双峰模式,峰值出现在45月、910月,而季节尺度上WUE的变化具有显著差异;③不同时间尺度的WUE空间分布上具有明显的异质性;④20002014年来全区年WUE整体呈上升趋势,其中呈上升趋势的面积占84.01%,但上升幅度以轻度、中度为主;⑤各季WUE整体呈上升趋势,但是不同季节WUE发生上升的区域位置、面积和显著性的大小都不同。【结论】在气候变化背景下,20002014年中国粮食主产区植被生态系统WUE呈上升趋势,且年内呈M型的双峰结构,季节和空间尺度上的分布具有显著差异。
英文摘要:
【Objective】Climate change will alert hydrological cycling. This paper analyzes the spatiotemporal change in crop production in main agricultural production areas in China in attempts to analyze the optimal reallocation of water resources for crop production and improve water use efficiency under globally changing climate. 【Method】The analysis was based on data from 20002014. Python and ArcGIS were used to calculate the spatiotemporal variation of water use efficiency (WUE) of the crops in the ecosystems within the major agricultural production areas (Northeast, Inner Mongolia, North, South); we also estimated the MOD17 (GPP) and MOD16 (ET) using the MODIS remote sensing data.【Result】①The annual WUE in major crop production areas from 2000 to 2014 in China was rising at an annual rate of 0.013 g(/ mm·m~2)(P<0.01), with two turning points occurring in 2003 and 2010 respectively. ②The intra-annual change in WUE was a M-type bimodal mode, with two peaks occurring in April to May and September to October, respectively. The seasonal WUE distribution differed significantly. ③The spatial distribution of WUE also varied seasonally. ④From 20002014, the overall WUE showed an increase, with areas seeing increase and significant increase (P<0.05) accounting for 84.01% and 39.91% of the total areas respectively. ⑤The WUE over the whole season was also in rise, although varying with location and time【. Conclusion】Under climate change, the WUE of the vegetation ecosystems in the main agricultural production areas from 2000 to 2014 in China had been rising with the intra-annual change being a M-type bimodal mode, and its distribution at seasonal and spatial scales varied considerably.