引用本文:龙学智,刘苏峡,莫兴国,陈学娟. 基于Copula的京津冀平原作物水分利用效率驱动因子分析[J]. 中国生态农业学报(中英文), 2019, 27(12): 1833-1845
LONG Xuezhi,LIU Suxia,MO Xingguo,CHEN Xuejuan. Analysis of water use efficiency and driving factors in the Beijing-Tianjin-Hebei Province Plain using the Copula method[J]. Chinese Journal of Eco-Agriculture, 2019, 27(12): 1833-1845
DOI:10.13930/j.cnki.cjea.190340
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基于Copula的京津冀平原作物水分利用效率驱动因子分析
龙学智1,2, 刘苏峡1,2, 莫兴国1,2, 陈学娟1,2
1.中国科学院地理科学与资源研究所陆地水循环及地表过程重点实验室 北京 100101;2.中国科学院大学资源与环境学院/中丹学院 北京 100049
摘要:  农业是京津冀地区最主要的用水部门,提高农业用水效率有助于缓解京津冀水资源压力,实现可持续发展。基于VIP模型模拟的1980-2013年京津冀平原作物水分利用效率(WUE)、作物净初级生产力(NPP)、作物实际蒸散发(ETa),结合同期年平均气温(Tmean)、年降水量(Pre)和年日照时数(Sun),应用Copula函数理论分别建立WUE与NPP、ETa、Tmean、Pre、Sun的5组联合概率分布函数,计算各驱动因子在低、中、高取值条件下WUE大于任一特定取值的可能性(定义为WUE条件概率),探索WUE的驱动关系。结果表明:1)驱动因子NPP、ETa、Sun取值越大,WUE大于任一特定取值的可能性越大;而驱动因子Tmean和Pre取值越小,WUE大于任一特定取值的可能性越大。2)若以各驱动因子分别在高、低取值条件下的WUE条件概率的差值来反映WUE对各驱动因子大小的敏感程度,WUE对NPP的大小最为敏感,而后依次是Sun、ETa、Pre、Tmean。3)对比不同驱动因子相同取值条件下的WUE条件概率,较低的NPP会明显抑制WUE的大小,提高NPP对WUE的提升有明显的保障作用。综上所述,作物WUE同时受光合作用和蒸腾作用两个生理过程控制,较难确定光合和蒸腾对WUE的驱动关系;WUE与驱动因子的联合概率分布和条件概率分析指出,在京津冀平原可以采用在控制耗水的条件下提高NPP的策略,该策略可能比在控制产量的条件下减少耗水的策略更有效。
关键词:  京津冀平原  作物水分利用效率  驱动因子  Copula函数  VIP模型  作物净初级生产力(NPP)
中图分类号:K903
基金项目:国家重点研发计划课题(2016YPC0401402)和国家自然科学基金项目(41471026)资助
Analysis of water use efficiency and driving factors in the Beijing-Tianjin-Hebei Province Plain using the Copula method
LONG Xuezhi1,2, LIU Suxia1,2, MO Xingguo1,2, CHEN Xuejuan1,2
1.Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;2.School of Natural Resources and Environment/Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China
Abstract:  Agricultural irrigation accounts for>65% of water use in the Beijing-Tianjin-Hebei Province Plain. Improving the agricultural water use efficiency will help relieve the pressure on the water resources found in the Beijing-Tianjin-Hebei Plain and promote sustainable development. Based on water use efficiency (WUE), net primary productivity (NPP), and actual evapotranspiration (ETa) from 1980 to 2013 simulated by the VIP model, combined with the annual mean air temperature (Tmean), annual precipitation (Pre), and annual sunshine duration (Sun), the Copula method was used to create five groups of joint probability distributions:WUE and NPP, ETa, Tmean, Pre, and Sun. Conditional probability was calculated based on the hypothesis that WUE was greater than any particular value under low, medium, and high value ranges of each driving factor. The findings showed that the greater the values of NPP, ETa, and Sun, the more likely was WUE to be greater than any particular value. However, the lower the values of Tmean and Pre, the more likely was WUE to be greater than any particular value. The sensitivity of WUE to variation in the value of each driving factor was reflected by the difference of the conditional probability of WUE under high and low value ranges, suggesting that WUE was most sensitive to the variation in the value of NPP followed by those of Sun, ETa, Pre, and Tmean. Comparison of the conditional probabilities of WUE under the same value conditions of NPP, ETa, Tmean, Pre, and Sun showed that a lower NPP clearly suppressed WUE and that improvement in NPP guaranteed a higher value of WUE. Crop WUE is controlled simultaneously by photosynthesis and transpiration, which makes it difficult to ascertain the driving mechanism underlying WUE. Based on the joint probability distribution determined using the Copula method and conditional probability analysis, we concluded that improving NPP when water consumption is controlled may be a more effective strategy than reducing water consumption when grain yield is controlled to adopt in the Beijing-Tianjin-Hebei Province Plain.
Keyword:  Beijing-Tianjin-Hebei Province Plain  Water use efficiency  Driving factors  Copula  VIP model  Net primary productivity (NPP)
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