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Article

Seasonal and Depth Dynamics of Soil Moisture Affect Trees on the Tibetan Plateau

by
Qian Li
1,2,
Liang Jiao
1,2,*,
Ruhong Xue
1,2,
Xichen Che
1,2,
Peng Zhang
1,2,
Xuge Wang
1,2 and
Xin Yuan
1,2
1
College of Geography and Environmental Science, Northwest Normal University, No. 967, Anning East Road, Lanzhou 730070, China
2
Key Laboratory of Resource Environment and Sustainable Development of Oasis, Gansu Province, Northwest Normal University, Lanzhou 730070, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(5), 752; https://doi.org/10.3390/f15050752
Submission received: 24 March 2024 / Revised: 21 April 2024 / Accepted: 23 April 2024 / Published: 25 April 2024
(This article belongs to the Section Forest Soil)
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Abstract

:
The soil moisture (SM) influences tree growth with climate change. However, the spatial and temporal dynamics of tree water use strategies in climate-sensitive areas remain uncertain. Therefore, we collected the tree-ring oxygen isotope (δ18OTR) chronologies and divided the wet–dry gradients according to the precipitation on the Tibetan Plateau (TP). Further, the relationship between the δ18OTR and environmental factors was analyzed across different gradients. We found the following: (1) The SM during the growing season was the most important factor for δ18OTR. (2) The response of the δ18OTR to the SM had a lag in arid areas than in humid areas. (3) Trees absorbed the SM on the surface in humid areas (r = −0.49 to −0.41, p < 0.01), while trees absorbed the SM from deep in the soil in arid areas (r = −0.48 to −0.29, p < 0.01). The results demonstrated that trees were better able to cope with drought stress in arid regions because they used more stable deep soil water than in humid regions. Therefore, the findings will provide a scientific basis for water use of trees using the δ18OTR in complex environmental contexts. Trees with single water use strategies should be given more attention to keep ecosystems healthy.

1. Introduction

Climate change has profound impacts on forest ecosystems [1,2]. Tree growth is mainly influenced by temperature, precipitation, and their interactions [3,4]. It is well known that tree growth in arid regions is controlled by soil moisture (SM), while in humid regions, it is mainly influenced by temperature [5]. Therefore, there is variability in the environmental sensitivity of trees in different climatic contexts.
The SM is the main source to sustain plant growth [6]. It plays a vital role in maintaining ecosystem health. The SM varies with soil seasons and depths [7]. There are also differences in the uptake strategies of SM by trees in different moisture environments [8]. Some studies have found that the SM plays a role in tree growth on the Tibetan Plateau (TP) [9,10], especially during the growing season [11]. Unfortunately, it is not practical to collect soil samples when conducting large-scale studies, making it exceptionally difficult to use measured soil data to study tree water use strategies. However, tree-ring oxygen isotope (δ18OTR) is now used as an indicator to study long-term changes in the SM because of their stability [12]. For example, the SM for nearly a century has been reconstructed using the δ18OTR in the Western Himalayas [13].
The δ18OTR is jointly controlled by the coupled processes of transpiration and water uptake in tree root systems [14]. Therefore, the δ18OTR is an important indicator that can record the response of trees to seasonal climate change [15]. A complex set of δ18OTR formation processes are influenced by external climatic factors [16,17]. Trees absorb precipitation entering the soil and then preserve the precipitation signal in the δ18OTR through transpiration fractionation in the leaves [18,19].
The precipitation in monsoon areas has significant seasonal variations, and similar variations occur in the SM [20,21]. Thus, the δ18O of SM varies at different depths and seasons. The δ18OTR stores SM signals through complex physiological mechanisms [22]. For example, it has been found that the δ18O of Pinus tabulaeformis in the East Asian monsoon (EAM) zone is controlled by the SM [23]. And it has been shown that trees used the shallow SM in the wet season and deep water in the dry season in the EAM region [24].
The TP has been found to have a trend of increasing temperatures [25] and decreasing precipitation in recent years due to shifts in the Asian summer monsoons (ASMs) [26,27]. The δ18OTR was consequently affected in this region [28,29]. At present, there are many articles on the use of δ18OTR to study climate reconstruction and the interpretation of physiological mechanism on the TP [30,31,32]. There are few studies that have been conducted to use the δ18OTR to study water use strategies. Therefore, the purpose of this paper is to explore three main questions on the TP: (1) What are the seasonal dynamics of different gradients of SM use? (2) Is there a difference in the depth of water used by trees at different gradients? (3) Are the seasons as well as soil depths of different gradients of trees water use coupled?

2. Materials and Methods

2.1. Study Areas

As shown in Figure 1, we selected a total of six sampling points. The Delingha region (DE) is in the northeastern part of the TP. The Ganesh (GA) and Wache (WA) regions are in the southern of the TP. The Manali (MA) and Jageshwar (JA) regions are in the southwestern part of the TP. And the Karakorun region (KA) is in the west of the TP. The JA is the wettest region, and the DE is the driest region in our study (Figure 2). The difference in the annual total precipitation between these two regions is 1311 mm. On the basis of the annual total precipitation, the six regions are classified into humid areas (JA, WA, and GA regions) and arid areas (MA, KA, and DE regions).

2.2. Tree-Ring Isotope Data

The δ18OTR data were downloaded from the NOAA Paleoclimatology Datasets (https://www.ncei.noaa.gov, accessed on 26 August 2023). Table 1 provides specific information on the six sampling sites. The experimental methods were checked when downloading data to ensure that the same experimental methods did not affect the accuracy of the study [33]. Some scholars have already found that elevation and age do not affect the δ18OTR [34,35].

2.3. Meteorological Data

The maximum (Tmax), mean(T), and minimum temperatures (Tmin), precipitation (PRCP), and the Palmer drought severity index (PDSI) from Climate Explorer (http://climexp.knmi.nl/start.cgi, accessed on 28 August 2023) were calculated (Figure 2). These were monthly scale data for the subsequent study. The vapor pressure deficit (VPD) was calculated as in Equation (1) [39]. And relative humidity (RH) was calculated as in Equations (2) and (3) [40].
VPD = a c t u a l   w a t e r   v a p o u r   p r e s s u r e = s a t u r a t e d   w a t e r   v a p o u r   p r e s s u r e = e 0 × 10 7.5 T T + 237.3 − actual   water   vapor   pressure
RH = e e s a t × 100
e s a t = 6.108 × exp ( 17.27 × T / T + 273.3
where e0 = 6.11 hpa.

2.4. The SM Data

The SM data was obtained from the Global Land Data Assimilation System (http://Idas.gsfc.nasa.gov/gldas, accessed on 29 August 2023). It spans the period of 1948–2015, with a spatial resolution of 0.25° × 0.25°. Due to its good temporal and spatial resolutions, it has been used for a large number of studies in recent years. The SM data of three depths of 0−100 cm were selected. We transform the SM data (unit: kg/m2) into volumetric water content (VWC, unit: m3/m3) according to Equation (4).
VWC = k g m 2 × m 3 1000 k g × 1000 m m 1 m × 1 t h i c k n e s s   o f   l a y e r   i n   m m

2.5. Data Analysis

Pearson’s correlation analysis as well as partial correlation analysis were used to study the main climatic factors affecting δ18OTR. The variability in the δ18OTR chronology was tested using one-way analysis of variance (ANOVA) in different regions. Meanwhile, the spatial correlation analysis of the δ18OTR and the SM was performed using MATLAB 2023.

3. Results

3.1. The Spatial and Temporal Patterns of the δ18OTR

Figure 3 shows that the values of the δ18OTR in the arid areas as a whole (31.36‰) were larger than those in the humid areas (24.34‰) on the TP. But the dynamics of δ18OTR showed inconsistent spatial and temporal variation characteristics across wet and dry gradients. In addition, the values of δ18OTR showed an increasing trend in all regions except the DE region. And the increasing trend was overall more pronounced in the humid areas.
The spatial dynamics of the δ18OTR showed that, as the climate becomes wetter, the values of the δ18OTR declined (Figure 4). The δ18OTR values are the highest (DE, 32.90‰) on the northern of the TP. On the contrary, the δ18OTR values are the lowest (WA, 19.40‰) on the southern of the TP.

3.2. The SM Dynamics at Different Depth Gradients

Figure 5 shows that the SM at different depths exhibits inconsistent variability characteristics on the TP (Figure 5). Specifically, the SM increased with a deeper soil depth (Figure 5a). And the SM in the DE region is much lower than in other regions.
Figure 5b shows that the seasonal dynamics of SM are more pronounced in humid areas. The high values of SM occur from June to September in humid areas (VMCJun–Sep > 45%). However, this phenomenon does not occur in arid areas. In the MA region, the high values of SM occur from June to September (VMCJun–Sep > 35%). The high values of VMC occur from April to June (VMCApr–Jun > 32%) in the KA region. But no seasonal dynamic was observed in the SM in the driest DE region.
The wettest JA region showed a decreasing trend for the SM, while the driest DE region showed an increasing trend for the SM (Figure 6). Overall, the surface SM increased in the wetter areas, while middle and deeper SM decreased. In arid areas, the SM decreased by 40–100 cm and increased by 0–40 cm. Meanwhile, it was found that the SM in humid areas fitted better with precipitation (Figure 7).

3.3. The Response of δ18OTR to Climatic and SM Factors

It is revealed that the overall δ18OTR responded negatively to PDSI, PRCP, and RH (Figure 8). The δ18OTR is positively correlated with T, Tmax, Tmin, and VPD in study areas (Figure 8). And δ18OTR in the humid areas are mainly influenced by climatic factors during the middle of the growing season in the humid areas and during the late growing season in the arid areas.
The response of the δ18OTR to the SM in terms of season and depth were further investigated (Figure 9). The correlation between the δ18OTR and the SM was greater with SM0–10 cm and SM10–40 cm cm in July in humid areas. However, the δ18OTR was significantly negatively correlated with SM10–40 cm and SM40–100 cm in September in arid areas. The seasonal response of the δ18OTR to the SM lagged in arid areas. Specifically, the δ18OTR was significantly negatively correlated with SM at 0–40 cm (rJun–Aug = −0.49 to −0.41, p < 0.05) in the JA and WA regions, while it was negatively correlated with the SM at all depths (rJun–Aug = −0.44 to −0.26, p < 0.05) in the WA region. However, the δ18OTR was significantly negatively correlated at all depths in the MA region (rJun–Sep = −0.47 to −0.31, p < 0.05), while the δ18OTR was more significantly negatively correlated with SM40–100 cm from July to September (rJun–Sep = −0.48 to −0.29, p < 0.05) in the KA and DE regions.

3.4. The Correlation between δ18OTR and Key Factors during the Growing Season

The δ18OTR and the SM showed high correlations during the growing season (June–September). Therefore, the partial correlation analysis was further used to study the most important climatic factors affecting the δ18OTR (Table 2). The results found that δ18OTR was significantly negatively correlated with the SM at 0–10 cm in the JA region when VPD and RH were controlled. And the δ18OTR was significantly correlated with the SM at 0–10 and 10–40 cm in the WA and GA regions. The δ18OTR was highly significantly negatively correlated with SM at all depths in the MA region when VPD and RH were controlled. However, the δ18OTR was significantly negatively correlated with the SM at 40–100 cm in the KA and DE regions.

3.5. The Spatial Patterns of δ18OTR Response to SM with Soil Depths

The response of the δ18OTR to the SM was generally negatively correlated in the study areas (Figure 10). But the correlation of the δ18OTR to the SM at 40–100 cm is stronger than the correlation to the SM at 0–40 cm (Figure 10). And the responses of the δ18OTR to the SM changed from negative to positive as the soil depth deepened in the southern and northeastern parts of the TP (Figure 10). The correlation decreases from the JA region to the DLH region with the SM at 0–10 cm. That means that, as the climate becomes drier, the correlation decreases between the δ18OTR and the SM at 0–40 cm. However, the positive responses of the δ18OTR to the SM at 40–100 cm increase in the DE region.

4. Discussion

4.1. The δ18OTR Records Soil Water Use by Tree Growth on the TP

The temperature has shown a continuous warming trend [41], and precipitation has increased in the eastern and western parts [42,43] and decreased in the southwestern part of the TP since the 1960s [44]. Trees will adopt multiple water use strategies to increase the chances of survival in this context. It has been shown that the δ18OTR could reflect the water uptake of seasons and the depths in trees [19]. This means that the δ18OTR can record soil water δ18O information for different seasons and depths [31].
The δ18OTR is mainly controlled by the coupled processes of source water and external climate [15]. In our results, the δ18OTR was more sensitive to the SM than to temperature during the growing season (Figure 8 and Figure 9). The δ18OTR is derived from the SM, but the δ18O of SM varies with soil depths [45]. The isotopic signals in the SM are preserved in the δ18OTR through the xylem during the growing season [46]. Therefore, the δ18OTR can be used as one of the indicators of SM use in trees.
At the same time, the values of the δ18OTR were greater in arid areas (Figure 3). Lower SM leads to an increase in the ratio of internal leaf CO2 (Ci) to atmospheric CO2(Ca) in leaves, which enhances the evaporative enrichment of leaf water in arid areas [47]. Then, the tree root system absorbs heavy-source water, and the δ18OTR is increased further [15]. During the conversion of sucrose produced by tree photosynthesis to cellulose, it undergoes a partial oxygen isotope signal exchange in the source water [48]. In other words, the δ18OTR keeps the mixed signal of source water and leaf evaporation [49]. These two processes result in a higher δ18OTR in arid areas. On the contrary, the decrease in the δ18OTR is due to the reduced evaporation and makes leaf water enrichment weaker in humid areas [50].

4.2. Seasonal Use Strategies of SM for Trees across Wet and Dry Gradients

In climate-sensitive areas, the SM is critical for tree growth. The life history stage [24], season [51], tree species [52], ability of plant access to water [53], and the amount of recent rainfall [14] all have an impact on tree water use strategies. In our study, the δ18OTR was significantly correlated with the SM during the growing season, but the δ18OTR responded to the SM later in arid areas (Figure 9).
The δ18OTR is affected by the SM from June to August in humid areas (Figure 9). Because it is influenced by the ASM and receives adequate precipitation from it in these months, which further affects the δ18OTR. These results suggest that monsoon precipitation has a significant effect on tree growth (Figure 8). We believe that precipitation signals in the soil from the ASM are absorbed by tree root systems. Finally, the δ18O of precipitation is retained through the δ18OTR in humid areas. Our results have been confirmed in other studies on the TP [31,32].
The δ18OTR are affected by the SM from July to September in arid areas (Figure 9). It is worth noting that the response of the δ18OTR to the SM lagged in arid areas. We believe this is related to the extended growing season of trees on the TP [36]. However, there are significant differences within the arid areas. The δ18OTR in the MA region was controlled by summer monsoon precipitation and RH [54]. And trees mainly absorb winter snowfall in the KA region, which provides a stable water source [37]. Therefore, the correlation between the δ18OTR and the summer SM is not high in the KA region. In the DE region, the δ18OTR was controlled by the SM from May to September [53], and this observation is consistent with that of our study.
The summer precipitation has a greater effect on the SM in the southeastern part of the TP [38]. And the δ18OTR was significantly negatively correlated with precipitation during the growing season in Southern Mexico [55], Northwestern Tibetan Plateau [56], and Ordos Plateau [57]. This is because summer precipitation replenishes plant water supplies [20]. Therefore, the correlation between the SM and precipitation is higher in the southeastern part of the TP than in its northwestern part.

4.3. Depth Use Strategies of SM for Trees across Wet and Dry Gradients

As a typical climate-sensitive area, the TP has a very high spatial variability in precipitation [58]. As a result, trees flexibly adjust their water use strategies to ensure growth. Trees use the deep SM preferentially in arid areas [44], whereas they use the shallow SM in humid areas [59]. This is consistent with our results. However, the water use strategies for trees in complex environments deserve further research.
The δ18OTR was significantly negatively correlated with the SM at 0–10 cm in the JA region. The δ18OTR were significantly negatively correlated with the SM at 0–10 and 10–40 cm after the VPD or RH was controlled in the WA and GA regions (Table 2). This suggests that trees tended to take up the SM in the surface and middle layers in humid areas. Meanwhile, the surface soil can provide nutrients to the trees [4,60]. In other words, trees preferentially absorb SM on the surface in humid areas.
The significant negative correlation between the δ18OTR and the SM at 40–100 cm in the KA and DE regions was revealed in arid areas (Table 2). In arid areas, capillary water induces trees to form deeper root systems to absorb deep soil water and groundwater to meet higher transpiration needs during the dry season [61,62]. The highly significant negative correlation between the δ18OTR and the SM at all depths in the MA region can be explained by the fact that some SM isotope signals are masked by strong evaporation [63,64]. On the other hand, the “bimodal” precipitation pattern may encourage trees to develop a well-developed root system in this region.
Trees have different coping strategies to climate extremes [60,65]. Trees using a single water strategy will bear a greater risk of death [27,66]. The chances of facing drought stress are increasing on the TP [67]. Trees face greater challenges for survival in humid areas because of the single use of the shallow SM. We argue for the better monitoring of tree growth in the humid regions of this region.

5. Conclusions

In this study, we used the δ18OTR at six sites to investigate whether there are differences in water use strategies of trees across wet and dry gradients on the TP. The results indicated that the SM during the growing season was the main factor influencing tree growth. Trees mainly absorbed the shallow SM in the humid areas, whereas they mainly absorbed the deep SM in the arid areas. And the response of the δ18OTR to the SM in the arid areas lagged behind that in the humid areas. Our study provides a theoretical basis for the further use of δ18OTR to study how tree growth adapts to the SM changes. We argue that shallow soil water uptake is a threat to tree growth in the context of warming on the TP. Therefore, the monitoring of tree growth in the southeastern part of the region should be conducted further.

Author Contributions

Q.L., formal analysis, data curation, visualization, and writing—original draft preparation. L.J., funding acquisition. R.X., writing—original draft. X.C., investigation. P.Z., editing. X.W., supervision. X.Y., investigation. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by National Natural Science Foundation of China (No. 42371038), Basic Research Innovation Group Project of Gansu Province (No. 22JR5RA129), and the 2022 Major Scientific Research Project Cultivation Plan of Northwest Normal University (NWNU-LKZD2022-04). We also thank the anonymous referees for their helpful comments on the manuscript.

Data Availability Statement

The data presented are available on request from the corresponding author of this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Geographical locations of study area and sampling sites.
Figure 1. Geographical locations of study area and sampling sites.
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Figure 2. Meteorological elements of the study areas.
Figure 2. Meteorological elements of the study areas.
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Figure 3. The time series of the δ18OTR for each site.
Figure 3. The time series of the δ18OTR for each site.
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Figure 4. Significance test for study areas. Inter-sample differences in δ18OTR values were assessed using one−way ANOVA, with different letters indicating significant differences (p < 0.05).
Figure 4. Significance test for study areas. Inter-sample differences in δ18OTR values were assessed using one−way ANOVA, with different letters indicating significant differences (p < 0.05).
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Figure 5. Dynamics of SM in different soil layers in different months. The squares in the boxplot diagram represent the median SM, different letters indicating significant differences (p < 0.05), the black dots represent the outliers and the range of the boxes represents the 25%–75% data interval (a). The blue line in the graph represents the 40–100 cm volumetric water content, the red line represents the 10–40 cm volumetric water content and the black line represents the 0–10 cm volumetric water content (b).
Figure 5. Dynamics of SM in different soil layers in different months. The squares in the boxplot diagram represent the median SM, different letters indicating significant differences (p < 0.05), the black dots represent the outliers and the range of the boxes represents the 25%–75% data interval (a). The blue line in the graph represents the 40–100 cm volumetric water content, the red line represents the 10–40 cm volumetric water content and the black line represents the 0–10 cm volumetric water content (b).
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Figure 6. Interannual variation in SM.
Figure 6. Interannual variation in SM.
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Figure 7. Linear fitting of SM and precipitation. The red area in the graph represents the 95% confidence interval and the red line represents the fitted trend line.
Figure 7. Linear fitting of SM and precipitation. The red area in the graph represents the 95% confidence interval and the red line represents the fitted trend line.
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Figure 8. Correlations of δ18OTR with monthly climate variables. “+”: significant correlations at 95%. “*”: significant correlations at 99%.
Figure 8. Correlations of δ18OTR with monthly climate variables. “+”: significant correlations at 95%. “*”: significant correlations at 99%.
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Figure 9. Correlations of δ18OTR with monthly SM. “+”: p < 0.05. ”*”: p < 0.01.
Figure 9. Correlations of δ18OTR with monthly SM. “+”: p < 0.05. ”*”: p < 0.01.
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Figure 10. The spatial correlation between SM and δ18OTR in June–September. The green point in the figure represents δ18OTR sampling points. The colors in the graph from red to blue to indicate that the correlation changes from negative to positive.
Figure 10. The spatial correlation between SM and δ18OTR in June–September. The green point in the figure represents δ18OTR sampling points. The colors in the graph from red to blue to indicate that the correlation changes from negative to positive.
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Table 1. The δ18OTR chronology information.
Table 1. The δ18OTR chronology information.
SitesLocationElevation (m)Tree SpeciesTime SpanAnnual Total Precipitation (mm)Date Source
JA29°38′ N,
79°51′ E		1870Cedrus deodara1621–20081505Xu et al. (2018) [31]
WA27°59′ N,
90° E		3500Larix griffithii1743–20111330Sano et al. (2013) [36]
GA28°10′ N,
85°11′ E		3500Abies spectabilis1801–2000958Xu et al. (2018) [31]
MA32°13′ N,
77°13′ E		2700Abies pindrow1768–2008650Sano et al. (2017) [32]
KA35°54′ N,
74°56′ E		2900Juniperus excelsa1900–1998329Treydte et al. (2006) [37]
DE37°48′ N,
97°78′ E		3500Juniperus przewalskii1168–2011197Yang et al. (2021) [38]
Table 2. Partial correlation analysis between δ18OTR and environmental factors from June–September.
Table 2. Partial correlation analysis between δ18OTR and environmental factors from June–September.
Control VariablesCorrelations with δ18OTRJAWAGAMAKADE
VPDVMC0–10−0.396 **−0.364 *−0.409 *−0.459 *−0.211−0.145
VMC10–40−0.265−0.360 *−0.383 *−0.472 *−0.277−0.286
VMC40–100−0.210−0.225−0.241−0.416 *−0.356*−0.385 *
PDSI−0.161−0.106−0.255−0.264−0.278−0.585 **
RH0.2700.1790.423 *0.151−0.064−0.163
PDSIVMC0–10−0.251−0.459 *−0.365 *−0.527 **−0.0040.173
VMC10–40−0.213−0.451 *−0.317−0.542 **−0.1180.164
VMC40–100−0.14−0.156−0.343−0.500 **−0.2390.119
VPD0.0570.432−0.141−0.3140.003−0.021
RH0.072−0.396 *0.251−0.028−0.042−0.022
RHVMC0–10−0.356 **−0.377 *−0.392 *−0.53 **−0.213−0.130
VMC10–40−0.289−0.380 *−0.365 *−0.542 **−0.276−0.274
VMC40–100−0.214−0.225−0.280−0.499 *−0.352 *−0.365 *
VPD0.2960.2590.367 *0.159−0.042−0.008
PDSI−0.208−0.113−0.242−0.315−0.277−0.570 **
“*” indicates p < 0.05, and “**” indicates p < 0.01.
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Li, Q.; Jiao, L.; Xue, R.; Che, X.; Zhang, P.; Wang, X.; Yuan, X. Seasonal and Depth Dynamics of Soil Moisture Affect Trees on the Tibetan Plateau. Forests 2024, 15, 752. https://doi.org/10.3390/f15050752

AMA Style

Li Q, Jiao L, Xue R, Che X, Zhang P, Wang X, Yuan X. Seasonal and Depth Dynamics of Soil Moisture Affect Trees on the Tibetan Plateau. Forests. 2024; 15(5):752. https://doi.org/10.3390/f15050752

Chicago/Turabian Style

Li, Qian, Liang Jiao, Ruhong Xue, Xichen Che, Peng Zhang, Xuge Wang, and Xin Yuan. 2024. "Seasonal and Depth Dynamics of Soil Moisture Affect Trees on the Tibetan Plateau" Forests 15, no. 5: 752. https://doi.org/10.3390/f15050752

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