中国农业气象 ›› 2016, Vol. 37 ›› Issue (02): 158-165.doi: 10.3969/j.issn.1000-6362.2016.02.005

• 论文 • 上一篇    下一篇

水稻蒸散特征及日尺度作物系数估算

高磊,申双和,邵立瑛,褚荣浩,谭诗琪   

  1. 江苏省农业气象重点实验室/气象灾害预警预报与评估协同创新中心,南京信息工程大学,南京 210044
  • 收稿日期:2015-06-30 出版日期:2016-04-20 发布日期:2016-04-18
  • 作者简介:高磊(1991-),硕士生,主要从事农业气象研究。E-mail:gaoleisg@sina.com
  • 基金资助:
    国家重点基础研究发展计划(“973”计划)项目(2010CB950702);公益性行业(气象)科研专项(GYHY201106043;GYHY201306046);干旱气象科学研究-我国北方干旱致灾过程及机理(GYHY201506001)

Evapotranspiration Characteristics and Crop Coefficient Estimation of Paddy Rice

GAO Lei, SHEN Shuang-he, SHAO Li-ying, CHU Rong-hao, TAN Shi-qi   

  1. Jiangsu Key Laboratory of Agricultural Meteology/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • Received:2015-06-30 Online:2016-04-20 Published:2016-04-18

摘要: 基于南京2012年水稻生长季蒸渗仪水稻实际蒸散数据及相应生物、气象环境资料,对水稻生长季的参考作物蒸散量、实际蒸散量及作物系数进行分析,并建立作物系数估计模型。结果表明:水稻生长季内逐日参考作物蒸散量呈单峰曲线变化,峰值出现在分蘖-拔节期;逐日实际蒸散量变化则表现为双峰型,耗水双高峰发生于分蘖-抽穗期。日参考作物蒸散量和实际蒸散量均有明显的季节性变化特征。水稻生长季内实际作物系数趋势变化特征与FAO修正作物系数较一致,但二者在数值上具有较大差异,建立的水稻作物系数与其影响因子(叶面积指数、气温、净辐射)的关系模型检验表明,其拟合度为0.887,将模型应用于计算水稻农田蒸散量,其拟合度为0.943,说明模型能较精确地估算稻田日蒸散量。该模型基于日尺度影响因子,在一定程度上简化了水稻作物系数的计算过程,明确了不同类型因子对水稻作物系数的影响程度,可应用于水稻作物系数的连续动态估算。

关键词: 蒸渗仪, 水稻, 蒸散, 作物系数模型

Abstract: Based on evapotranspiration data in a lysimeter of paddy rice, the data of biological and meteorological during the growth season in Nanjing in 2012, the reference evapotranspiration (ET0), actual evapotranspiration (ETc), and actual crop coefficient (Kc) of paddy rice were calculated, and the crop coefficient model was established. The results showed that the daily ET0 changed as a single peak curve, with the peak at tillering-jointing stage. While the daily ETc changed as a two-peak type, with the peak at tillering and heading stages respectively. Both ET0 and ETc obvious varied as seasonal. The actual crop coefficient (Kc) was consistent with the K corrected by FAO, but their values generally differed. The relationship between establishing model and its affected factors was well, with their R2 = 0.887. A model describing relationships between daily Kc and biological factor (LAI) as well as environmental factors (air temperature, net solar radiation) was established and its estimate agreed very well with the actual crop coefficient, and the model was capable of predicting ETc of paddy rice (R2 = 0.943) from a formula ETc=KcET0 by use of the Kc estimates. This model could help simplify computation of crop coefficient and define the effect of different factors and know well its dynamic characteristics.

Key words: Lysimeter, Paddy rice, Evapotranspiration, Crop coefficient model