模型结构与参数化差异对蒸散发估算的影响

基于2012年黑河绿洲HiWATER高密度通量观测数据, 对比研究模型结构差异(单源Penman-Monteith/PM公式与双源PM公式、双源PM 公式与双源三温模型)以及PM公式中阻抗参数化差异对蒸散发估算的影响。结果表明: 1) 与模型结构相对复杂的双源PM公式相比, 单源PM公式计算的蒸散发平均相对误差(MAPE)为34%, 略优于双源PM公式的40%; 2) 对于两种模型结构差异显著的双源模型, 模型中不含阻抗参数的三温模型比模型中含阻抗参数的PM公式具有更高的估算精度, 前者的MAPE为18% (R²=0.85), 后者为40% (R²=0.34); 3) 两种单源和一种双源阻抗参数化方法导致PM公式计算的蒸散发出现不同程度的差异, MAPE可相差6%; 4) 使用先验知识/数据事前率定阻抗参数化方法, 可显著地提高单源PM公式的计算精度(MAPE可降低22%), 但随着模型结构与参数化复杂度增加, 事前率定双源PM公式的阻抗参数化方法难以提高计算精度(MAPE仅减小0.8%)。

Impact of Model Structure and Parameterization Differences on Evapotranspiration Estimation

Based on the HiWATER high-density eddy covariance (EC) tower observations in Heihe Oasis in 2012, the impact of model structure differences (comparison between one-source Penman-Monteith / PM equation and two-source PM equation, or comparison between two-source PM equation and two-source three-temperature model) and parameterization differences on the evapotranspiration estimation were evaluated. The results show that, 1) compared with the two-source PM equation with a relatively complex model structure, the mean absolute percent error (MAPE) estimated by the one-source PM equation is 34%, which is more accurate than that by the two-source PM equation (40%); 2) for two kinds of two-source model with significant differences in model structure, the three-temperature model without resistance parameters has higher estimation accuracy than the PM-based equation with resistance parameters. The former has a MAPE of 18% (R²=0.85), while the PM-based equation has that of 40% (R²=0.34); 3) two one-source and one two-source resistance parameterization methods lead to different evapotranspiration estimation accuracy for the PM-based equation, with a MAPE difference of up to 6%; 4) using prior knowledge / dataset to calibrate resistance parameterization can significantly improve the estimation accuracy of one-source PM equation (MAPE can be reduced by 22%), but as model structure and parameterization complexity increase, two-source PM equation hasn’t been improved significantly after resistance parameterization calibration (MAPE is only reduced by 0.8%).