The Relationship between Sap Flow Density and  Environmental Factors in the Yangtze River Delta  Region of China

Abstract

Canopy transpiration is an important component of evapotranspiration, integrating physical and biological processes within the water and energy cycles of forests. Quercus acutissima and Cunninghamia lanceolata are two important, fast-growing and commercial tree species that have been extensively used for vegetation restoration, water conservation and building artificial forests in the Yangtze River Delta region of China. The primary objective of this study was to characterize sap flow densities of the two species by comparing daytime and nocturnal sap flow patterns and their relationships with environmental factors. Sap flow densities (S_d) were measured between September 2012 and August 2013 using the commercially-available thermal dissipation probes. Hourly meteorological data were measured in an open field, located 200 m away from the study site, including photosynthetically-active radiation (P_ar), air temperature (T_a), relative air humidity (R_h), vapor pressure deficit (V_pd) and precipitation (P). Soil water content (S_wc) data were logged hourly in different layers at Q. acutissima and C. lanceolata forests. Results indicated that the mean S_d in summer was higher than that in spring and autumn. Both the S_d of Q. acutissima and C. lanceolata showed distinct diurnal patterns. Nocturnal sap flow densities (S_dn) were noticeable, and both species followed similar declining patterns during our study period. The daytime sap flow density (S_dd) was more sensitive to environmental factors than S_dn. Sap flow density was significant linearly correlated with P_ar, V_pd and T_a, and P_ar and V_pd explained the greatest amount of variation in daytime sap flow of Q. acutissima and C. lanceolata, respectively. Our study will enrich knowledge of plantation forest physical and biological processes and provide valuable information for plantation forest management in the Yangtze River Delta region of China.

1. Introduction

In a forest ecosystem, canopy transpiration, which can be derived empirically from sap flow measurements in the trunk xylem [1], is a physiological metabolism process that is essential to the vitality of tree and also is an important component of evapotranspiration [2,3,4]. It exerts great influence on the weather patterns, Earth’s surface energy balance and water supply to downstream systems [5,6].

Most prior studies indicated that sap flow is strongly influenced by environmental factors [7,8,9], including solar radiation, vapor pressure deficit, rainfall, air temperature, wind speed and soil water content [10]. For example, in a comparative study, the effect of a flat-roof screen house on banana transpiration showed that reduction in transpiration in the screen house is mainly due to the combined reductions of wind speed and global radiation [11]. There were significant differences in sap flow dynamics of a drought-tolerant plant between the two periods of the day (0:00–15:00 and the remainder) due to the meteorological factors [12]. In the future, climate change likely will increase air temperature, and its variation may be accompanied by similar changes in vapor pressure deficit (V_pd), which would influence the canopy transpiration [6]. Therefore, an improved understanding of how these environmental factors interact to influence the sap flow is essential in the Yangtze River Delta region of China.

Transpiration occurs through the stomata, and researchers formerly thought that leaf stomata close at night, so it is generally assumed that transpiration only occurs during the day [13,14], which led to the underestimation of total sap flow [15]. Now, there is a greater awareness that nocturnal sap flow can in fact have a significant contribution to total daily sap flow [16,17,18,19]. Most previous work on the nocturnal sap flow has shown by analysis of 98 species that its averaged proportion of the total daily sap flow was 12.03% [16]. Daley and Phillips found that over 10% of the total daily sap flux during the growing season was due to transpiration at night in paper birch [20]. Nighttime sap flow was over 8% of the total daily flow in red oak and 2% in red maple. Integrated crop water loss during the dark, non-photosynthetic hours measured on the lysimeter was 3%–10.8% of total daily water loss [21]. Hence the nocturnal sap flow is significant and needs to be carefully considered in sap flow and related studies [16].

With the rapid growth of the population, China underwent deforestation at an alarming rate during the second half of the 20th century to meet the needs for timber and food production. Almost all natural primary forests have been replaced with secondary forest and fast-growing tree plantations. Plantations currently account for 40% of the total forest area in China [22,23]. Quercus acutissima and Cunninghamia lanceolata are the most important plantation tree species in China in terms of planted area, yield and timber usage [24,25,26,27]. In recent decades, a great deal of pure Q. acutissima stands and pure C. lanceolata stands were established in the Yangtze River Delta region of China. With the maturation of these plantations, the forest farms faced many problems of management and sustainable development [28,29,30], such as reasonable use of water. Therefore, accurate calculation of water consumption for the plantation ecosystems is important for forest farm management, which depends on our capacity to quantify the transpiration in plantations. To our knowledge, however, none of the existing studies considered the transpiration of Q. acutissima and C. lanceolata [31] in the Yangtze River Delta region of China.

Therefore, in this study, we monitored stem sap flow density using thermal dissipation probes (TDPs) in the pure Q. acutissima forest and pure C. lanceolata forest in the Yangtze River Delta region of China. Our primary objectives were (1) to compare the difference of the sap flow density among the diameters at breast height (DBH), (2) to examine monthly variations of the daily sap flow density and (3) to compare the diurnal and nocturnal sap flow patterns and their relationships with the environmental factors. Our study will enrich knowledge of plantation forest hydrological processes and provide valuable information for plantation forest management in the Yangtze River Delta region of China.

2. Materials and Methods

2.1. Study Site

The study site was located in the Tong Shan forest farm (31°37′ N, 118°51′ E), Nanjing, China. The altitude of the Tong Shan forest farm ranged from 38 m–388 m. The research area is a mature artificial pure forest of, respectively, Q. acutissima pure forest, C. lanceolata pure forest or Phyllostachys edulis pure forest. It has a subtropical monsoon climate; the annual mean temperature is 16.5 °C, with a monthly mean temperature reaching a maximum of 28.6 °C in July and a minimum of 3.1 °C in January (Figure 1). Annual cumulative hours of sunshine are 2199.5 h. The annual mean precipitation is 1126.9 mm, and the average precipitation during the rainy season, from June to August is 587.6 mm (Figure 1).

The experimental plots were located in Q. acutissima deciduous forest and C. lanceolata evergreen forest. At the time of the study (2012–2013), the trees were about 45 years old. The averaged tree heights of Q. acutissima and C. lanceolata were, respectively, 13.8 m and 12.8 m. The stand densities were, respectively, 425 trees ha^–1 and 850 trees ha^–1. The average diameters at breast height (DBH) were, respectively, 25.8 cm and 21.0 cm. The leaf area indices (LAI) of Q. acutissima deciduous forest, estimated by an LAI-2200 plant canopy analyzer (LI-COR, Lincoln, USA), was 2.49–3.37 during the growing season, and the LAI of C. lanceolata evergreen forest was 1.71–3.12 throughout the year.

2.2. Sap Flow Measurements

Sap flow densities per unit sapwood area (S_d, g·cm^–2·h^–1) were measured from July 2012 to August 2013 using the commercially-available thermal dissipation probes (Model TDP-30, Dynamax Inc., Houston, TX, USA) [32]. Eight Q. acutissima trees and eleven C. lanceolata trees, relatively well-grown, were selected based on the growing condition in the experimental site (

Table 1. Basic parameters of the samples in the two forests.

No.	DBH (mm)	Depth (mm) and Aspects	SA (cm2)	DBH (mm)	Depth (mm) and Aspects	SA (cm2)
	Q. acutissima			C. lanceolata
1	187	0–25 at N	142.69	122	0–20 at N	73.64
2	200	0–25 at N	166.49	137	0–20 at N	92.14
3	242	0–25 at N	257.86	188	0–20 at N	169.86
4	252	0–25 at N	282.97	196	0–20 at N	184.11
5	268	0–25 at N	325.91	204	0–20 at N	198.92
6	278	0–25 at N	354.50	208	0–20 at N	206.52
7	308	0–25 at N	448.49	215	0–20 at N	220.17
8	318	0–25 at N	482.61	218	0–20 at N	226.15
9	-	-	-	223	0–20 at N	236.28
10	-	-	-	227	0–20 at N	244.54
11	-	-	-	249	0–20 at N	292.42

DBH, diameter at breast height (1.3m); SA, sapwood area; N, north.

). We took a separate sample of 20 trees of the same species distributed across the study area to determine the sapwood area (SA) of the samples. The trees used for the determination of sapwood area were not the same as those used for continuous monitoring of sap flow density, in order to avoid the effects of plant injury on our measurements. Approximately 20 tree ring cores with an average height of 1.3 m were drilled by an increment borer (SY-CO, Haglöf, Långsele, Sweden), after which their heartwood and sapwood were measured [33]. The sapwood area values of the samples were estimated according to the data (Figure 2).

Pairs of probes with lengths of 30 mm were inserted in the northern trunk directions at breast height of each sample tree in order to avoid the sun-exposed side of the trunk, the TDP sensor installed according to the instructions recommended by the manufacturer (Copyright 2007, Dynamax Inc.) at the same sapwood depth of the same tree species (Table 1). These probes operated on the constant power principle [34], and the sap flow density was recorded at 10-min intervals. An average value was calculated every 60 min and then recorded in Campbell CR1000 data loggers. Due to loss of electricity supply, the sap flow densities of C. lanceolata in May and December were lost.

Sap flow density on a sapwood area basis was calculated based on the temperature difference between the heated and non-heated probe by an empirical equation [34]:

$$S_d=0.0119\times 3600\times {\left[\left(dTM–dT\right)/dT\right]}^{1.231}$$

(1)

where $S_d$ is the sap flow density (g·cm^–2·h⁻¹ or cm·h⁻¹), $dT$ is differential temperature between the two probes and $dTM$ is the temperature difference at zero flow. Zeppel et al. used two methods to determine the zero set of each sensor: (1) examining the flow rates at night when V_pd and wind speed were ca. zero during the periods and (2) at the end of the study, cutting into the sapwood beneath the sensors in each tree to determine the zero flow rates [35]. They found that there was no significant difference (p < 0.05) between the two different methods used to estimate zero flow rates [35]. In our study, zero flow measurements were automatically conducted every six days between the hours of 02:00 and 05:00 because the sap flow velocity can reasonably be considered to be zero or minimal during this period [36]. According Oishi et al., to account for potential nocturnal sap flows due to both transpiration and recharge, we selected the highest daily $dT$ to represent $dTM$ if two conditions are satisfied simultaneously: (1) the average, minimum two hours V_pd < 0.05 kPa, thus assuring that water loss to the atmosphere is negligible, and (2) the standard deviation of the highest $dT$ values <0.5% of the mean of these values; such stable measurement of maximum $dT$ ensures that recharge of water above the sensor height is completed or negligible. In our sap flow time series, zero flow nighttime conditions were often not met for several consecutive days [37].

In addition, transpiration was scaled up to the stand level using the following equation [38]:

$$S_t=10\times S_{dm}\times S{A}_{stand}/SG$$

(2)

where $S_t$ is stand transpiration (mm·h^–1), $S_{dm}$ is the mean $S_d$ of stand (g·cm^–2·h^–1 or cm·h^–1), $S{A}_{stand}$ is stand sapwood area (cm²) and $SG$ is the ground area (cm²).

2.3. Meteorological and Soil Water Measurements

Hourly meteorological data (EM50, Decagon Devices, Pullman, WA, USA) were measured in an open field, which is 200 m away from the studied stand, including photosynthetically-active radiation (P_ar, μmol·m^–2·s^–1), air temperature (T_a, °C), relative air humidity (R_h, %) and precipitation (P, mm) (Figure 3A,B,C,F). These T_a and R_h data were used to calculate the vapor pressure deficits (V_pd, kPa, Figure 3D).

Soil water content at different soil layers (10 cm and 40 cm) was measured continuously with ECH2O probes (Decagon Devices Inc., Pullman, WA, USA) (Figure 3F).

2.4. Statistical Analyses

All statistical analyses were performed using the SPSS Version 21.0 software (IBM Inc., Armonk, NC, USA). Data plotting was completed with software OriginPro 8.5 (OriginLab Inc., Northampton, MA, USA).

3. Results

3.1. Environmental Variables

The seasonal patterns of environmental variables obtained during the study period (1 September 2012 to 31 August 2013) were representative of the climatic conditions at the study site (Figure 3). The daily maximum photosynthetically-active radiation (P_ar), mean air temperature (T_a), mean air relative humidity (R_h) and mean vapor pressure deficit (V_pd) had seasonal patterns (Figure 3A,B,C,D). The daily maximum P_ar varied from 12.82 μmol·m^–2·s^–1 during December to 1034.55 μmol·m^–2·s^–1 during May (Figure 3A). The daily mean T_a and V_pd were highest in August, and the values of July were only a little lower than August. Daily averaged T_a was 15.87 °C and varied between –3.31 °C (28 February 2013) and 33.28 °C (10 August 2013) (Figure 3B,D). The R_h (Figure 3C) was significantly negatively correlated with Ta, and daily averaged R_h was 80.6%.

Rainfall data for the study region were collected from 2012–2013 (Figure 3E). During the data collection period, there were 115 rainy days, which produced a total rainfall of 973.2 mm and an average rainfall of 9.5 mm per day, with individual events ranging from 0.2–129 mm. The distribution of rainfall is shown in

Table 2. The distribution of rainfall during our study period.

	The Percentage of Amounts	The Percentage of Events
<10 mm	26.86%	77.39%
10–25 mm	23.28%	14.78%
25–50 mm	16.67%	4.35%
50–100 mm	19.93%	2.61%
>100 mm	13.26%	0.87%

. A light rain event (<10 mm) was most frequent and had the greatest contribution to total rainfall.

Soil moisture depended significantly on rainfall. Soil water content (S_wc) increased with increasing rainfall amount, and the dynamic variation of S_wc increased intensely after rainfall, then gradually decreased as a result of evaporation (Figure 3F). Soil water content for the upper 10 cm (S_wc10) in most study periods was higher than S_wc40. In Q. acutissima forest, S_wc10 averaged 14.97%, and S_wc40 averaged 13.81%. In C. lanceolata forest, S_wc10 averaged 17.80%, and S_wc40 averaged 16.59%.

3.2. Sap Flow Densities

Q. acutissima is a deciduous species, and C. lanceolata is an evergreen species. We found that the sap flow of Q. acutissima obviously rose starting on 8 April and declined starting on 20 November. The sap flow of C. lanceolata obviously rose starting in March and declined starting in December. Therefore, the data of Q. acutissima used for analyses was from 8 April to 20 November and C. lanceolata was from March to November. Mean sap flow density of the two DBH classes (Q. acutissima: ≤25.8 cm and >25.8 cm (mean DBH of Q. acutissima stand); C. lanceolata: ≤21.0 cm and >21.0 cm (mean DBH of C. lanceolata stand)), averaged over the entire study period, is shown for three seasons (Figure 4). The sap flow densities showed no significant difference between two DBH classes of Q. acutissima (S_dQ) and C. lanceolata (S_dC), respectively, and the S_dQ was higher than that of S_dC. In addition, the mean S_dQ and S_dC in summer were significantly higher than those of the spring and autumn. However, the mean S_dQ and S_dC showed no significant difference between spring and autumn.

Daytime sap flow density (S_dd) of Q. acutissima (S_ddQ) started at about 7:00–9:00 a.m. and presented a sustained rise along midday and usually reached a peak at 12 p.m. (Figure 5). Daytime sap flow density of C. lanceolata (S_ddC) started about one or two hours later than S_ddQ. The time that it reached the peak was about two hours later (about 3:00 p.m.). Both the sap flows for Q. acutissima and for C. lanceolata were highest in July and were lowest in November. To further clarify the differences between Q. acutissima and C. lanceolata, we calculated the frequency of the S_d peak with sunny days throughout all study periods. The results are presented in Figure 6. On 80.63% of all days over the study period, the maximum S_d (S_d,max) for Q. acutissima occurred between 11:00 a.m. and 1:00 p.m. In contrast, the most frequent occurrence of S_d,max for C. lanceolata occurred between 2:00 p.m. and 4:00 p.m. (77.23%).

Both the nocturnal sap flows of Q. acutissima and C. lanceolata were noticeable and declined slowly at 8:00 a.m.–9:00 p.m. (Figure 7). The nocturnal sap flow density (S_dn) showed a strongly declining power function relationship through nights, and all of these power functions passed the variance analysis (

Table 3. The change trends of nocturnal sap flow density from sunset to sunrise.

Month	Q. acutissima	R2	C. lanceolata	R2
March	/	/	y = 3.438 n–1.57	0.927
April	y = 3.281 n–1.04	0.978	y = 2.889 n–1.18	0.979
May	y = 2.274 n–1.08	0.986	/	/
June	y = 2.037 n–1.11	0.929	y = 1.878 n–0.96	0.932
July	y = 1.468 n–0.94	0.937	y = 2.547 n–-0.87	0.986
August	y = 1.497 n–0.69	0.937	y = 2.671 n–1.16	0.979
September	y = 1.351 n–0.93	0.829	y = 3.861 n–1.76	0.951
October	y = 2.268 n–1.09	0.975	y = 7.988 n–2.31	0.883
November	y = 1.041 n–0.81	0.882	y = 1.428 n–1.14	0.862

The time from sunset to sunrise: March and September (7 p.m.–6 a.m., n = 1–12); April and May (7 p.m.–5 a.m., n = 1–11); June and August (8 p.m.–5 a.m., n = 1–10); July (8 p.m.–4 a.m., n = 1–9); October and November (6 p.m.–6 a.m., n = 1–13); n is the integer.

). The nocturnal sap flow density of Q. acutissima (S_dnQ) and C. lanceolata (S_dnC) was highest in April (9.30 ± 0.85 g·cm^–2·d^–1) and July (8.16 ± 1.21 g·cm^–2·d^–1), respectively. The proportions of nocturnal sap flow to the whole-day sap flow for Q. acutissima and C. lanceolata were, respectively, 2.71% (July)–12.56% (November) and 8.00% (July)–24.29% (November).

The canopy transpirations of both of Q. acutissima and C. lanceolata stands were highest in July, and the values were, respectively, 67.42 mm and 56.52 mm (

Table 4. The canopy transpiration of the stands from March–November (mm).

Month	Q. acutissima	C. lanceolata
March	/	25.25
April	28.18	27.46
May	41.16	43.34
June	40.23	35.47
July	62.72	56.52
August	58.10	39.71
September	40.57	38.56
October	33.52	28.87
November	11.49	8.38
Total	315.99	303.55

). The total canopy transpirations were 315.99 mm and 303.55 mm, which were about 32.47% and 31.19% of rainfall, respectively.

3.3. The Relationships between Sap Flow Densities and Environmental Factors

The relationship between daily sap flow density and environmental factors (P_ar, T_a, V_pd and S_wc) in the study periods is shown in Figure 8, in which both the daily sap flow density of Q. acutissima and C. lanceolata increased with increases in P_ar, T_a and V_pd (Figure 8A,B,C). There were statistically-significant linear correlations between sap flow density and P_ar, T_a or V_pd (p < 0.001). However, there was no significant correlation between sap flow density and soil water content. To further elucidate the influence of environmental factors on sap flow density, we used the partial regression analysis (

Table 5. Partial regression analysis of sap flow density versus various explanatory factors.

Factors	SddQ		SdnQ		SddC		SdnC
	PC	VE (%)	PC	VE (%)	PC	VE (%)	PC	VE (%)
Par	0.51	25.91			–0.07	0.49
Ta	0.46	20.88	0.01	0.00	0.25	6.45	0.09	0.76
Vpd	0.26	6.71	0.44	19.45	0.59	35.28	0.20	3.88
Swc10	0.08	0.67	0.26	6.92	0.06	0.35	0.00	0.00
Swc40	–0.05	0.23	–0.14	1.93	0.03	0.08	–0.01	0.01

S_ddQ, daytime sap flow density of Q. acutissima; S_dnQ, nocturnal sap flow density of Q. acutissima; S_ddC, daytime sap flow density of C. lanceolata; S_dnC, nocturnal sap flow density of C. lanceolata; PC, partial correlation; VE, variance explained.

), which showed that P_ar explained 25.91% of the variation in daytime hourly sap flow density of Q. acutissima and that T_a was a secondary explanatory variable (20.88%). In comparison, V_pd was the main explanatory variable for both nocturnal hourly sap flow density of Q. acutissima (19.45%) and daytime hourly sap flow density of C. lanceolata (35.28%). The nocturnal sap flow density of C. lanceolata was not obviously influenced by environmental factors, and the V_pd explained only 3.88% of the variation. During the study period, the significance linear relationship between S_dnC and S_ddC was shown in Figure 9B (p < 0.01); but no observed significant correlation of S_dnQ to S_ddQ was found (Figure 9A).

In our study, the P_ar, T_a and V_pd were selected to model and calculate the daily sap flow density. The regression equations are given by:

$$S_{dQ}=0.103P_{ar}+1.579T_a+33.178V_{pd}\left(R^2=0.948\right)$$

(3)

$$S_{dC}=0.032P_{ar}+1.051T_a+38.148V_{pd}\left(R^2=0.908\right)$$

(4)

where $S_{dQ}$ and $S_{dC}$ are the daily sap flow density of Q. acutissima and C. lanceolata (g·cm^–2·d^–1), $P_{ar}$ is the maximum photosynthetically-active radiation (μmol·m^–2·s^–1), $T_a$ is the mean air temperature (°C) and $V_{pd}$ is the mean vapor pressure deficit (kPa).

4. Discussion

In China, there has been an increasing research focus on the sap flow characteristics in arid and semi-arid regions, where soil water is limited [39,40,41,42]. Many people think that water shortage problems only exist in arid or semi-arid regions. However, with the development of regional economy and population, water pollution has gradually increased, and many regions face the serious problem of water quality-induced water shortage [43,44], especially in the Yangtze River Delta region of China. Hence, it is also important and necessary to do research on the sap flow characteristics in water-rich regions in order to plan rational use of the water resources and manage the forest farms.

Measurements of sap flow have been widely used to study the characteristics of transpiration by using the thermal dissipation probe method (TDP) [42,45]. In our study, there was no significant difference between the two DBH classes of Q. acutissima and C. lanceolata. In spring, summer and autumn, the sap flow density of Q. acutissima was greater than that of C. lanceolata. The sap flow of Q. acutissima and C. lanceolata showed similar patterns with solar radiation, which was consistent with the research on Abies georgei [33], Quercus ilex and Phillyrea latifolia [46]. S_ddQ started at about 7:00–9:00 a.m., presented a sustained rise during the middle of the day and usually reached a peak at 12:00 p.m. Sap flow velocity of Abies georgei increased gradually after 7:00 and reached a peak at approximately 14:00, as shown in Guo et al. [33]. S_ddC started later, about one or two hours than S_ddQ. The time that it reached the peak was about two hours later (about 3:00 p.m.). Doronila and Forster [47] and Du et al. [48] suggested that species with a peak sap flow at earlier times of the day have greater stomatal sensitivity to V_pd, which, in turn, can also affect patterns of carbon uptake and growth rates. Our study species show distinct sap flow peaks at 12:00 p.m. (Q. acutissima) and 3:00 p.m. (C. lanceolata), respectively. How these times of peak sap flow are related to stomatal opening, photosynthesis, growth rates and even patterns of species co-existence and interaction provides grounds for further research.

In previous studies, nocturnal sap flow has been documented in a wide range of species [16,17]. Zeppel et al. [49] used data from many ecosystems to examine the abiotic factors that may affect the S_dn and found that S_dn was higher in broad-leaved compared with needle-leaved plants and in tropical compared with temperate species. Our study also demonstrates that S_dn accounted for 2.71%–12.56% (Q. acutissima) and 8.00%–24.29% (C. lanceolata) of the whole-day sap flow throughout the study period, respectively. S_dn was higher in broad-leavedthan in evergreen plants. In addition, the proportions of S_dn in summer were lower than other seasons, which maybe because the nighttime period during spring and autumn is longer than summer, and therefore, there is more time for nocturnal sap flow to accumulate [16]. Previous studies have also shown that S_dn accounted for up to 31%–47% of total daily sap flow in a desert ecosystem [19] and 22.6% ± 7.5% for Q. ilex and 18.2% ± 8.9% for P. latifolia in a Mediterranean ecosystem [46]. Comparing the drought regions, the S_dn proportion in the Yangtze River Delta region, with its greater water sources, was less, but it was also an important fraction to sap flow of the whole day.

As in previous studies carried out in forests [8,50,51], at our study site, sap flow was also effected by environmental factors, such as P_ar, V_pd and T_a. Our results indicate that sap flow density was significant linearly correlated with P_ar, V_pd and T_a. Chang and Zhao, studying a Qinghai spruce forest in northwestern China, reported that daily variation in sap flow was also closely related to changes in P_ar, V_pd and T_a, and sap flow velocity rose with increasing P_ar, V_pd and T_a when P_ar < 800 W·m⁻², V_pd < 1.4 kPa and T_a < 18.0 °C [10]. Among these factors, V_pd is a well-known driver of transpiration owing to the water potential gradient and the ability of dry air to pull water from a source of higher water potential [13,19]. Wilson et al. reported that when soil water is plentiful, evaporation generally increases with vapor pressure deficit (V_pd) over a wide range of V_pd [32]. In our study, P_ar and V_pd explained the greatest amount of variation in daytime sap flow of Q. acutissima and C. lanceolata, respectively. In addition, V_pd and soil water content (S_wc) were the abiotic drivers of nocturnal sap flow [49]. Causally, V_pd is also used to determine whether S_dn is related to either nocturnal transpiration or stem refilling [16,19,52,53]. Most studies over a wide range of forests indicate that increasing S_wc is associated with increased S_dn [54]. High S_wc will lead to less negative water potentials [54]. For our study site, the V_pd was the main explanatory variable for both S_dnQ (19.45%) and S_dnC (3.88%), and S_wc10 explained 6.92% of the variation in S_dnQ. In addition, the nocturnal sap flow density of C. lanceolata was influenced by the S_ddC (R² = 0.160, p < 0.01, Figure 9B), but there was no significant correlation between S_dnQ and S_ddQ (R² = 0.01, p > 0.05, Figure 9A). The results suggest that S_dnQ and S_dnC were mainly used for transpiration and stem refilling, respectively.

The sap flow density increased gradually with the increase of V_pd, but the stomata usually closed with V_pd increase [55]. Chen et al. reported that low transpiration was governed by strict physiological regulation and daily V_pd exceeding 1 kPa [12]. Sap flow velocity of Qinghai spruce was heightened by increasing the V_pd when V_pd < 1.4 kPa [10]. Deng et al., studying Hedysarum scoparium in relation to environmental factors in semiarid northwestern China, reported that the minimum threshold for V_pd as a driving force was seen to be around 1.5 kPa [45]. In our study, the S_dd also increased gradually with the increase of V_pd. When V_pd < 0.6 kPa, it increased significantly. When the V_pd > 1.6 kPa, S_dd increased just a little higher than V_pd in the interval 1.4 kPa ≤ V_pd < 1.6 kPa (Figure 10A). When the V_pd > 2.4 kPa (V_pd > 2.2 kPa), the S_ddQ (S_ddC) decreased with the increase of V_pd. During the nighttime, the V_pd in this site ranged from 0–1.5 kPa. When 0.6 < V_pd ≤ 0.8kPa, the S_dnQ decreased and then increased when V_pd > 1.0 kPa (Figure 10B). However, when the V_pd > 1.0 kPa, the S_dnC decreased. Our studies were carried out on S_ddQ and S_ddC in the Yangtze River Delta region where optimal V_pd values reached 2.4 kPa and 2.2 kPa, respectively.

With these fluctuating rain events, the soil water content frequently increased and diminished (Figure 3E,F). Light rain events (≤10 mm) were most frequent and had the greatest influence on total rainfall in our study site. In a temperate deciduous forest of Korea, the relationship between sap flow of Quercus serrata Thunb change and S_wc was relatively strong during the drought period, and this relationship was stronger during sunny days [38]. Zhang et al. reported that there was no obvious relationship between monthly sap flow and monthly S_wc during the full-leaf periods in a semi-arid black locust plantation, Loess Plateau, China [41]. On the contrary, in aridnorthwestern China, Chang et al. had established a logistic functional relationship between sap flow and S_wc. Sap flow showed an increasing tendency when S_wc was increasing, which explained 84% of the variation in sap flow velocity and exhibited great sensitivity [10]. In our study, there was no significant correlation between both S_dQ and S_dC (all data) with S_wc, because the soil is usually moist during rainy days (115 rain days), when sap flow is low as a result of low P_ar, V_pd and T_a, which determines the energy available for transpiration [38]. However, in the continuous sunny days (Figure 11), the correlation coefficients between the daily sap flow and soil water contents display very strong positive correlations (Figure 11A,C), which indicates that sap flow decreased with the S_wc decreased. Figure 11B,D show that soil water contents gradually decreased with the increase of the cumulative sap flow. All of these results supported the conclusion that transpiration and soil water content influence each other.

5. Conclusions

Q. acutissima and C. lanceolata are two important, fast-growing, commercial tree species that have been extensively used for vegetation restoration, water conservation and building artificial forests in the Yangtze River Delta region of China. The sap flow densities of trees with different DBH show no significant difference. The sap flow density showed distinct patterns in daytime and nighttime, and S_dC reached the peak time about two hours later than S_dQ. P_ar (25.91%) and V_pd (35.28%) explained the greatest amount of variation in daytime sap flow of Q. acutissima and C. lanceolata, respectively. The amount of nocturnal sap flow was noticeable; the proportions of nocturnal sap flow for Q. acutissima and C. lanceolata were, respectively, 2.71%–12.56% and 8.00%–24.29%. The total canopy transpirations of the Q. acutissima and C. lanceolata stands were 315.99 mm and 303.55 mm, which were about 32.47% and 31.19% of rainfall, respectively. The results of this study can be used to estimate the transpiration of Q. acutissima and C. lanceolata by the regression equations with environmental factors in the Yangtze River Delta region of China.