黑河中游绿洲区玉米冠层阻抗的环境响应及模拟

Response of maize canopy to environmental factors in the middle reach oasis of Heihe River Basin

蒸散发(ET)是区域能量平衡以及水量平衡的关键环节,精确估算蒸散发,对于提高水分利用效率以及优化区域用水结构具有重要意义,而冠层阻抗则是准确估算蒸散发的一个重要变量。为了确定冠层阻抗模型区域适用性、解决其参数化问题,本研究基于黑河重大研究计划已有的通量观测数据,以Irmak模型为基础,考虑微气象因子与冠层阻抗之间的关系,增加了大气CO_2浓度对冠层阻抗的影响,构建了未考虑CO_2和考虑CO_2影响的两种Irmak模型,并将其与Penman-Monteith(P-M)模型耦合,利用已有涡度相关数据,分析和检验了两种冠层阻抗模型对环境变量和大气CO_2浓度响应的模拟结果,并对模型参数进行敏感性分析。结果表明:将考虑大气CO_2浓度影响的Irmak模型与Penman-Monteith模型耦合,能够更好地模拟玉米冠层阻抗和蒸散量对外部环境变量的响应过程。在参数率定期该模型所模拟的冠层阻抗和蒸散量与实测值之间的R2分别达0.76和0.95,RMSE分别达33.1 s·m-1和34.5 W·m-2;模型验证期冠层阻抗和蒸散量模拟值与实测值之间的R2分别达0.68和0.90,RMSE分别达63.2 s·m-1和49.0 W·m-2。两个独立验证点结果表明考虑了大气CO_2浓度影响的Irmak模型具有较好的空间可移植性和适应性,模型能够较为准确地模拟玉米在整个生长季半小时时间尺度上的农田耗水过程。敏感性分析表明玉米冠层阻抗及其蒸散量对净辐射和相对湿度变化最为敏感,其次是气温、叶面积指数和大气CO_2浓度。本文所构建的考虑大气CO_2浓度对于玉米冠层阻抗影响的Irmak模型能够较为准确地估算作物蒸散量,并可为种植结构调整、土地利用方式改变以及大气CO_2浓度变化环境下的农田耗水研究提供一定的研究依据。

Evapotranspiration （ET） is critical for energy and water balance in agricultural systems. Accurate estimation or measurement of ET is therefore important in improving water use efficiency and optimizing the structure of regional water use. Canopy resistance is one of the most important variables in the estimation of ET. The accuracy of simulation of the response process of canopy resistance to environmental variables is critically important for crop ET research. A convenient approach to simulate the response process of canopy resistance to multiple factors is based on the relationship between measured latent heat, climatic variables and by using the modified Penman-Monteith （P-M） equation. However, this method has certain limitations in some practical applications due to the lack of a few effective parameters. Another approach is to construct empirical and semi-empirical models using multiple factors （such as the Irmak model） based on measured data combined with the rearranged P-M equation. Based on canopy resistance values calculated by the rearranged P-M equation and on maize data （for the period May to September 2012） collected from the three eddy covariance observation stations in Heihe River Basin, this study constructed Irmak model taking into account the effect of atmospheric CO2 concentration of half-hourly and daily time-steps to simulate the response processes of maize to environmental variables such as net radiation （Rn）, air temperature （Ta）, leaf area index （LAI）, relative humidity （RH）, wind speed （U3）, aerodynamic resistance （ra）, effective soil water content （0） and atmospheric CO2 concentration. In the study, the performance of the two Irmak models were tested with measured values of latent heat from the eddy covariance systems of the other two verification points. Besides, the sensitivity of environmental variables was analyzed. The results indicated that the improved Irmak model which took into account the effect of atmospheric CO2 concentration well estimated canopy resistance and ET. The coefficients of determination （R2） for canopy resistance and ET were respectively 0.76 and 0.95 for the calibration phase, with root mean square errors （RMSE） of 33.1 s·m^-1 and 34.5 W·m^-2. Meanwhile, R^2 for canopy resistance and ET were respectively 0.68 and 0.90 for the validation phase, with RMSE of 63.2 s·m^-1 and 49.0 W·m^-2. The two verification points showed that the improved Irmak model had a good performance and strong regional applicability and spatial portability. The model also simulated the response processes of canopy resistance to environmental variables and reflected the effect of the variations in atmospheric CO2 concentration on ET. Sensitivity analysis of the improved Irmak model showed that canopy and ET were the most sensitive to net radiation and relative humidity, followed by air temperature, leaf area index and atmospheric CO2 concentration. The improved Irmak model used in this study was applicable in estimating crop water consumption and the accuracy of ET of maize, in providing scientific basis for improvements in water use efficiency and in optimizing the structure of regional water use under increased future atmospheric CO2 concentration.