摘要:以辽河三角洲湿地芦苇群落为研究对象，利用涡动相关通量、小气候梯度要素、群落内水面蒸发量以及芦苇群落生长参数等数据，基于FAO-56模型的双作物系数法，分别计算作物系数K_c、基础作物系数K_cb和水面蒸发系数K_w，分析其日变化动态及主导影响因子，建立基于生物因子和环境因子的小时尺度双作物系数模型。结果如下：（1）芦苇生长初期，K_c和K_cb的日变化呈现早晚高、中午略低的多峰波动曲线；在快速生长期和稳定生长期，K_c和K_cb白天波动幅度较小，早晚波动幅度较大；生长末期，K_c和K_cb夜晚波动幅度较大，白天呈现多峰波动曲线；K_w白天较小、夜晚较高，生长初期白天的数值显著高于其他时期。（2）相关分析表明，气温、相对湿度、风速、株高和叶面积指数是K_c、K_cb和K_w的影响因子；基于生物因子和环境因子重新构建双作物系数模型，基于改进的双作物系数法模拟芦苇群落蒸散，决定系数R²达0.894。（3）利用改进的双作物系数模型和FAO-56模型，对辽河三角洲芦苇群落的蒸发与蒸腾过程进行模拟，实现芦苇群落蒸发过程与蒸腾过程的分离，解决了实际观测无法直接获取芦苇群落蒸腾量的问题，同时提高了芦苇群落蒸散的模拟精度。（4）调整了FAO推荐的芦苇单作物系数常数值，调整后的作物系数更适用于辽河三角洲芦苇湿地。



Abstract:Phragmites australis wetland in the Liaohe River Delta of Northeastern China was selected as a research object. Based on the dual crop coefficient of FAO-56 model and the observed data (including flux data, microclimate gradient data, water evaporation, growth parameters of P. australis, etc.) in Panjin Wetland Ecosystem Research Station, the crop coefficient (K_c), the basal crop coefficient (K_cb), and the water evaporation coefficient (K_w) were calculated. The diurnal dynamics of three coefficients and their influencing factors were analyzed. Dual crop coefficient model at hour scale was established using the biological factors and environmental factors. The results are as follows:(1) In the initial growth stage, the diurnal variation of K_c and K_cb showed multi-peak fluctuation with higher values in the morning and evening and lower values at noon; during the rapid development and stable stage, the fluctuation ranges of K_c and K_cb were smaller in the daytime and larger at night; at the end of growth season, K_c and K_cb fluctuated greatly at night and presented multi-peak curves in the daytime. K_w was smaller in the daytime and higher at night, with the daytime values in the initial stage higher than those of the other stages. (2) The correlation analysis showed that the influencing factors of K_c, K_cb, and K_w were air temperature, relative humidity, wind speed, plant height, and leaf area index. The dual coefficient model was reconstructed with the biological and environmental factors. Thus, the simulation of the evapotranspiration in P. australis wetland was improved with the determination coefficient R² reaching 0.894. (3) Using the improved dual crop coefficient model and FAO-56 model, the evaporation and transpiration of P. australis wetland in the Liaohe River Delta were simulated. This could solve the problem that transpiration is unobtainable from direct observation and improve the accuracy of evapotranspiration simulation. (4) We adjusted the constant value of P. australis crop coefficient recommended by FAO which was more suitable for the Liaohe River Delta wetland.





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