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    基于Penman-Monteith模型和Shuttleworth-Wallace模型对太行山南麓人工林蒸散的模拟

    Evapotranspiration simulated by Penman-Monteith and Shuttleworth-Wallace models over a mixed plantation in the southern foot of the Taihang Mountain, northern China

    • 摘要: 蒸散作为陆地生态系统能量平衡和水分循环的一个关键环节,其改变会影响区域气候的变化。森林蒸散模拟研究在评价森林在区域水分循环中的作用具有重要的意义。本文采用Penman-Monteith(P-M)模型和Shuttleworth-Wallace(S-W)模型模拟了太行山南麓栓皮栎-侧柏-刺槐人工混交林的蒸散(ET),对模型模拟的ET与涡度相关法所得的ET进行了比较,评价了P-M模型和S-W模型模拟人工混交林ET的适用性,讨论了这两种模型对各阻力的敏感性。研究结果表明,P-M模型和S-W模型模拟所得的ET的季节变化和日变化类似。S-W模型和P-M模型模拟的ET均低于实测的ET,S-W模型模拟的ET比实测的ET偏低6%,P-M模型模拟的ET比实测值偏低21%,因此,P-M模型模拟的ET偏低更明显。与P-M模型相比,S-W模型模拟的ET与实测值的相关系数、一致性指数(IA)、均方根误差(RMSE)、相对误差(RE)较小。与P-M模型相比,S-W模型模拟的ET与实测值的拟合直线更加趋近1:1线。S-W模型模拟ET的效果优于P-M模型,S-W模型更适合于本研究区人工混交林蒸散的模拟。P-M模型模拟的2009年生长季的ET偏低更明显,将S-W模型模拟的ET分为蒸腾(T)和土壤蒸发(E),其中土壤E与ET比值为11.3%。土壤E约占ET的10%左右。P-M模型模拟ET偏低的原因可能与P-M模型中未考虑土壤表面阻力有关。S-W模型模拟的ET和T对冠层阻力(rsc)最敏感,其次为植物冠层高度至参考高度间的空气动力学阻力(raa),对土壤表面至冠层高度间的空气动力学阻力(ras)相对不敏感;土壤E对土壤表面阻力(rss)最敏感,对rsc最不敏感。P-M模型模拟的ET对rsc最敏感,对空气动力学阻力(ra)敏感性较弱。

       

      Abstract: Evapotranspiration (ET) is one of the key processes of terrestrial energy/water transfer and carbon exchange, and it is also an important factor affecting regional climate and global carbon cycle. It is important to simulate ET over forest ecosystem to evaluate the role of forests in surface water cycle. In this paper, the Penman-Monteith (P-M) model and the Shuttleworth-Wallace (S-W) model were used to simulate ET over a mixed plantation in the southern foot of the Taihang Mountain, northern China. The mixed plantation is mainly composed of Quercus variabilis, Platycladus orientalis and Robinia pseudoacacia. In order to evaluate the model applicability, ET simulated by the S-W model and the P-M model was compared with the measured one obtained by the eddy covariance technique. In addition, the sensitivities of P-M and S-W models to their resistance parameters were analyzed and the diurnal and seasonal variations in ET were explored. ET simulated by the S-W model and the P-M model was about 6% and 21% lower than the measured ET, respectively. Compared with the S-W model, ET simulated by the P-M model was less than the measured one. Compared with the P-M model, the correlation coefficient, agreement index, root mean square error, mean absolute error between measured ET and simulated ET by the S-W model were lower. The fitted curve of the relation between ET simulated by the S-W model and measured ET was closer to the 1:1 line than P-M model. Accordingly, ET simulated by the S-W model was higher than P-M model. It was suggested that the S-W model was more suitable for simulating ET in this region, and ET simulated by the P-M model was low. ET simulated by the P-M model and the S-W model was most sensitive to the change of canopy resistance. The second sensitive parameter was the aerodynamic resistance from canopy to reference height for the S-W model, and it was insensitive to the aerodynamic resistance from soil to the canopy. Transpiration and evaporation were most sensitive to the canopy resistance and soil surface resistance, respectively. There were no significant differences between the diurnal and seasonal variations in ET simulated by the S-W model and the P-M model, but the simulation accuracy of the S-W model was higher than P-M model. ET simulated by the P-M model in 2009 was obviously lower than the measured one. The ratio of E to ET was 11.3%, and E accounted for 10% of total ET simulated by the S-W model. Therefore, the possible reason was that soil surface resistance was not taken into account in the P-M model. The reason of the deference between simulated ET by the S-W model and the P-M model and measured ET may be that the empirical coefficients in the models are different for any vegetation types, especially how to choose the suitable resistance parameters. Moreover, meteorological factors are different in varied regions, which will influence the model accuracy. Accordingly, in order to improve the model accuracy, the climate and environment situation in study area should be taken into account to find a suitable resistance parameter of the model.

       

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