不同时间尺度土壤因子与柽柳液流速率关系的差异

Relationships between Soil Factors and Sap Flow of Tamarix chinensis Lour. at Different Time Scales

  • 摘要: 【目的】 研究土壤因子对柽柳液流的影响及存在时间尺度差异,为不同时间尺度下更为准确的分析柽柳液流的变化规律。 【方法】 利用PS-TDP8树木液流监测系统及5TM土壤温度与湿度传感器对库姆塔格沙漠东南部柽柳液流速率及土壤因子(土壤含水量SWC及土壤温度Ts)持续 5个月同步观测。 【结果】 月尺度下,Ts20cm是液流速率变化的主要影响因子,单独能解释91.7%的液流速率变化;日尺度下,进入的土壤因子依次为Ts20cm、Ts200cm、SWC50cm、SWC150cm、SWC20cm,Ts20cm可以解释液流速率变化的72.1%,5个因子共同可以解释82.9%的液流速率变化,日尺度下对柽柳液流速率影响最大的也是20 cm层土壤温度;小时尺度下,对液流影响最大的是Ts20cm,Ts20cm可以解释液流速率变化的37.6%,6个因子共同可以解释55.9%的液流速率变化。随着时间尺度的扩大,与柽柳树干液流显著相关的土壤因子个数有减少的趋势,而对其解释程度则有增加的趋势。 【结论】 土壤因子模拟计算小时尺度柽柳液流速率需要的参数较多,预测月尺度液流速率需要的参数最少,可靠性最大,能够较好地解释柽柳液流速率变化,月尺度预测柽柳液流速率更加准确。

     

    Abstract: 【Objective】 To reveal whether there is a time-scale difference in the influence of soil factors on sap flow of Tamarix chinensis Lour in the hope more accurately analyzing the change law of tamarisk sap flow under different time scales. 【Methods】 The PS-TDP8 tree sap flow monitoring system and 5TM soil temperature and humidity sensor were used to simultaneously observe the tamarisk sap flow rate and soil factors (soil water content SWC and soil temperature Ts) in the southeast of the Kumtag Desert for 5 months. 【Results】 On the monthly scale, Ts20cm, the main factor that influences the change of liquid flow rate, which could explain 91.7% of the change in flow rate alone. On the daily scale, the entered soil factors were Ts20cm,Ts200cm, SWC50cm, SWC150cm, SWC20cm, Ts20cm, which could explain 72.1% of the change in liquid flow rate, and all the five factors could explain 82.9% of the change in sap flow rate. The soil temperature in the 20cm layer had the greatest influence on the tamarisk sap flow rate on the daily scale. On the hour scale, Ts20cm had the greatest impact on liquid flow. Ts20cm could explain 37.6% of the change in liquid flow rate, and six factors together could explain 55.9% of the change in liquid flow rate. 【Conclusion】 Numerous parameters are needed to calculate hourly tamarisk sap flow rate through soil factor simulation, and predicting the monthly sap flow rate requires the fewest parameters and has the greatest reliability. This can better explain the changes in the tamarisk sap flow rate with more accurate prediction.

     

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