An automated weather station located in the Teaching Botanical Garden was used to measure meteorological parameters, using an air temperature (Ta) and relative humidity (RH) probe (HMP45C; Vaisala, Helsinki, Finland), a wind (w) sensor (034B Met One Windset; Campbell Scientific, Logan, UT, USA), a quantum sensor (PAR Lite; Kipp and Zonen, Delft, Netherlands), a soil temperature (Ts) probe at a depth of 10 cm (109; Campbell Scientific), and a rainfall (P) gauge (TE525MM; Campbell Scientific). Soil moisture content was measured at depths of 10 and 30 cm (SWC10, SWC30) using soil moisture sensors (ECH2O; Decagon Devices, Pullman, WA, USA) in the center of the sampled Chinese pine trees. All these meteorological data were sampled and recorded at the same frequency as the sap flow measurements. Vapor pressure deficit (D) was calculated using the 10-min averages of temperature and relative humidity as follows:
D = a x exp(bTa/(Ta + c)) x (1 – RH) (4.3)
where a, b, and c are fixed parameters equal to 0.611 kPa, 17.502, and 240.97 °C, respectively (Campbell and Norman 1998).
4.2.3 Statistical Analyses
Statistical analyses were performed using SPSS 11.5 (SPSS, Chicago, IL, USA) and Sigmaplot 10.0 (Systat Software, San Jose, CA, USA). A paired-samples t test was performed in SPSS with a significance level of p = 0.05 for the comparison of mean Et. The relationships between Et and Ta, PAR, D, and SWC on both the diurnal and daily scale were investigated using curve estimation analyses performed with Sigmaplot. Linear regression analyses were conducted to study the influences of climate variables on Et at multiple time scales using the stepwise procedure in SPSS with a significance level of p = 0.05.
