Next Article in Journal
Investigation of Kinetic, Equilibrium, and Thermodynamic Modeling of Perfluorooctanoic Acid (PFOA) Adsorption in the Presence of Natural Organic Matter (NOM) by Dielectric Barrier Discharge Plasma-Modified Granular Activated Carbon (GAC)
Previous Article in Journal
Hydraulic Connectiveness Metric for the Analysis of Criticality in Water Distribution Networks
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Water
Volume 16
Issue 11
10.3390/w16111495
water-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Soil Hydrothermal Dynamics in the Hengduan Mountains of Southeast Tibet and Associated Influencing Factors

by
Lingling Meng
1,2,
Zhaofeng Li
1,2,*,
Qiang Zhang
1,2 and
Xinpeng Zhang
1,2
1
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
2
College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(11), 1495; https://doi.org/10.3390/w16111495
Submission received: 15 April 2024 / Revised: 14 May 2024 / Accepted: 21 May 2024 / Published: 24 May 2024
Browse Figures
Review Reports Versions Notes

Abstract

:
Soil water and soil temperature are important ecological factors and driving forces for ecosystem restoration and sustainable development, possessing great significance for climate modeling and prediction. The Hengduan Mountains in southeastern Tibet, China, are located in a climate-change-sensitive area, and the study of soil hydrothermal dynamics in this area is of great significance for local and global climatic change and water resource utilization. This study, based on the soil hydrothermal and meteorological data of the Hengduan Mountain area in Southeast Tibet, analyzes the dynamic change patterns of soil hydrothermal and meteorological factors and explores their influencing relationships. It was found that the dynamic change in soil water content affected by precipitation was “bimodal” type. Among the meteorological factors, soil water content has the strongest correlation with relative humidity. The intra-annual variation curve of soil temperature is similar to that of the atmospheric temperature, showing a “unimodal” type, and has the highest correlation with atmospheric temperature. Specifically, it takes 70 mm and 170 mm of precipitation to change the soil water content and soil temperature at the 150 cm depth. For every 20 °C change in atmospheric temperature, soil temperature above 150 cm changes by an average of 7.2 °C.

1. Introduction

Soil hydrothermal refers to soil water content (SWC) and soil temperature (TS), which control the soil water cycle and the surface heat balance, respectively. They are important physical factors affecting the material transport and energy transfer between land and the atmosphere [1] and have a critical impact on the exchange of energy between the atmosphere and the soil. Soil water is an important ecological factor and is also holding great significance for climate modeling and prediction [2]. Soil water plays an important role in maintaining biodiversity, promoting vegetation growth, improving the surface environment, and revealing pollution migration [3,4,5,6]. TS is an important indicator of climate change [2] and is one of the key factors affecting the growth and development of plants and soil microorganisms. Only by maintaining suitable soil water and TS conditions can we ensure the health and stability of the ecosystem and promote ecological restoration and sustainable development [7]. TS and SWC are inextricably linked and interact with each other. SWC not only provides the water needed for plant growth but also influences TS. Similarly, TS affects water evaporation and transport, which further impacts water use efficiency and the water cycle of the ecosystem. The study of soil hydrothermal is critical in environmental, agricultural, and earth sciences, involving several disciplines such as hydrology, meteorology, soil science, and ecology.
Both SWC and TS are key variables in land–air interactions [8]. The influencing factors encompass several aspects, collectively contributing to the complexity and variability observed in soil hydrothermal changes. Changes in TS are usually caused by a combination of factors, mainly precipitation [9], air temperature [10], SWC [11], and vegetation [12], and, to a lesser extent, radiation, albedo, evapotranspiration rate [13], soil properties [14], and topographic changes [15]. Similarly, the factors affecting SWC mainly include precipitation intensity, duration [16], vegetation [11], soil texture [17], etc. There is an interaction between TS and SWC. On the one hand, changes in TS affect the evaporation and condensation of water and vary the heat capacity of the soil, thus regulating SWC. On the other hand, changes in SWC also affect TS by altering the heat capacity and heat transfer properties of the soil. In short, TS and SWC are influenced by both natural and anthropogenic factors. These factors are intertwined and jointly determine the physical and chemical properties of the soil, which significantly impacts the circulation of water resources, the maintenance of soil fertility, and the stabilization of the ecological environment.
Soil hydrothermal studies have been conducted in a variety of ways, mostly utilizing a combination of multiple data. For example, Meng et al. [18] used multi-source data (measured, reprocessed, and remotely sensed data) to provide a comprehensive analysis of soil water variability and related factors in the Mongolian Plateau by means of the empirical orthogonal function approach. Their study involved various aspects such as climate, vegetation, soil, and groundwater. Cho et al. [19] used the Kolmogorov–Smirnov test of variance considering meteorological and physical factors to analyze soil water changes and their relationship with meteorological factors in the Korean Peninsula. Fan et al. [20] analyzed soil water dynamics and influencing factors based on multi-source data from 100 stations in three climatic zones dispersed across the Tibetan Plateau. Wang et al. [21] used empirical orthogonal functions to analyze the spatial variability of SWC and its relationship with climate, soil, topography, and vegetation in three regions of the continental United States. In addition, hydrogen and oxygen isotopes [22,23,24], satellite remote sensing inversion [25,26], and numerical analysis methods [27,28,29] have also been employed in previous studies to analyze, simulate and predict water transport patterns and infiltration mechanisms. However, all these methods have limitations when being used individually [30,31], thus a combination of methods is needed to study the problem more deeply and clearly.
The mountainous area of southeast Tibet is a sensitive and critical area of global climate change [32], and its unique alpine environment has become an important factor affecting the climate system in East Asia and even the Northern Hemisphere [33]. The study of SWC and TS in Tibet is a pragmatic topic in the fields of geography, ecology, and environmental science. The unique natural environment and ecological conditions of this region make related research of special significance and challenges. However, most of the current studies on soil water dynamics have focused on arid and semi-arid regions [34]. The depth and breadth of research on the interactions and effects of soil hydrothermal and atmospheric precipitation in alpine mountainous areas and high-altitude regions are still far from adequate [35,36,37]. In particular, the research on TS lags far behind that on SWC. Moreover, the natural environment in the Hengduan Mountains is harsh, and ground observations are difficult to implement; the uncertainty of satellite products [38] and model simulations need to be further enhanced. Therefore, further research on SWC and TS in the Hengduan Mountains of Southeast Tibet holds challenges but of great importance for local and global climate prediction and water utilization.
To the best of our knowledge, there are currently no direct ground observation methods to analyze the soil hydrothermal dynamics for the Hengduan mountainous area in southeast Tibet. In this study, we combine the kriging interpolation and correlation analysis methods, focusing on SWC and TS, to explore soil hydrothermal dynamics in the study area and their influence relationship with meteorological factors. This study provides important theoretical support for exploring the water vapor cycle in the Tibetan Plateau and its influence on the climate environment of surrounding areas.

2. Data and Methodology

2.1. Overview of the Study Area

The study area is located in Bomi County, Nyingchi City, Tibet Autonomous Region, belonging to the alpine valley section of the Hengduan Mountains in Southeast Tibet (95°5′24″~95°6′36″ E, 30°6′~30°6′6″ N) (Figure 1a). It is the most intensely eroded region of the Tibetan Plateau, with seasonal snow and ice covering the summit portion of the mountain. The highest elevation of the study area is 5818 m, and the lowest is 1910 m, with steep slopes. The landforms in the study area are predominantly mountainous, fluvial, and paleo-glacial landforms. The groundwater discharge datum mainly refers to the rivers within the study area, including the Parlung Zangbo River, Yigong Zangbo River, and Bide Zangbo River (Figure 1b).
The main outcrops in the study area are the Nuocuo Formation (C1n) and the Nianqing Tangula Group A Rock Formation (Pt2-3Nqa) [39,40]. The Nianqing Tangula Group A mainly consists of various kinds of gneisses, scattered interspersed with schists, plagioclase, hornblende, metamorphic rocks, and a small amount of marble. The Nuocuo Formation is mainly exposed on the high-altitude steep mountain beams along the north coast of the Parlung Zangbu River. The lithology is dominated by medium-thick layered variable quartz sandstone sandwiched with variable siltstone, slate, and a small amount of graywacke or marble. The Quaternary alluvial deposits in the area are mainly distributed in the river channels and river floodplains. The deposits are mainly silty and muddy layers mixed with sand and gravel layers. The main faults in the study area are the Jiali-Yigong Zangbu Fault (F3-6) and the Jiali–Palong Zangbu Fault (F3-8) (Figure 1c).
The study area has a humid subtropical climate in the southern Himalayas, influenced by the Indian Ocean maritime southwest monsoon. According to the statistics of Bomi County from 1971 to 2000, the average sunshine duration is about 2000 h (the time of the day when the sunlight hits the ground directly without being blocked by clouds), the annual frost-free period is 176 days, the average annual temperature is 8.5 °C, and the average annual precipitation is 977 mm. The maximum temperature during the study period is 24.5 °C, and the minimum temperature is 2.0 °C. The average annual rainfall is 977 mm. The maximum temperature during the study period was 24.9 °C, and the minimum temperature was 2.9 °C. The maximum precipitation for a single day was 52 mm, and the maximum precipitation for a single event was 190.8 mm. The atmospheric temperature and precipitation show obvious seasonal changes, which are mainly reflected in the high temperature and high precipitation in summer and low temperature and low precipitation in winter. The terrain slope varies greatly, ranging from 0 to 55°, mainly 15 to 55°. The study area is well-vegetated with trees, shrubs, and herbs. The main plant types are plantain and calyx aconite, with a coverage of about 60% and a clear vertical zoning. The plant root system ranges from a few centimeters to several meters, and the water content of the vegetation ranges from 20% to 90%, with the moisture content of most vegetation ranging from 50% to 60%.

2.2. Sites and Data Acquisition

A 10 m × 10 m area located in the Hengduan Mountains of Southeast Tibet was selected as the monitoring site. Monitoring devices include a main borehole, an exploratory pit, a small meteorological station, corresponding equipment for soil water content monitoring and meteorological data monitoring, and a data transmitter (Figure 1d). We excavated a 150 cm depth probe pit at the observation site location and installed detection probes at the layer depths of 30 cm, 60 cm, 90 cm, 120 cm, and 150 cm, respectively. Soil particle size analysis yielded a soil type of sandy loam at 0–30 cm depth and a soil type of sand at 30–150 cm depth (Figure 1e).
A full year of observational data spanning from 1 July 2022 to 30 June 2023 was obtained. SWC and TS data covered the depths from 30 cm to 150 cm and were monitored every 15 min using the CS655 Soil Moisture Sensor. Meteorological data, such as atmospheric precipitation, was collected every 15 min using the WS-16 Modular Automatic Weather Station. Groundwater depth and temperature data were recorded every 4 h. All collected data were transmitted wirelessly to the online platform via the CR1000x Data Acquisition Module. Precipitation data were statistically analyzed on a daily basis, and meteorological data such as soil hydrothermal and groundwater temperature and depth were calculated on a daily average basis.

3. Test Results

3.1. Dynamic in Meteorological Factors

The influence of meteorological factors on soil hydrothermal cannot be ignored. Six meteorological factors, i.e., atmospheric temperature, wind speed, wind direction, solar radiation, atmospheric precipitation, and relative humidity, were obtained from the meteorological stations. The annual dynamic fitting curves can be mainly categorized into “unimodal” type and “bimodal” type (Figure 2). The “unimodal” type includes atmospheric temperature, solar radiation, wind direction, and wind speed. The atmospheric temperature, wind speed, and solar radiation ranged at 3–24 °C, 0–5.5 m/s, and 7–53 MJ/m2, respectively, all of which began to decrease from the maximum value in July and reached the minimum in January. The range of wind direction was 36–281°, suggesting that the wind direction in the study area was mostly easterly, southerly, and southeasterly. The “bimodal” type includes precipitation and relative humidity. Daily precipitation and relative humidity varied in the range of 0–54 mm and 43–96%, respectively, with both peaks appearing simultaneously in September–October and March–June. The bimodal dynamic curves of precipitation and relative humidity in the study area represent the regional precipitation pattern in the study area, i.e., the timing of the occurrence of the dry and rainy seasons. The peaks occur in spring and fall, which may be related to factors such as cold air activity or the onset and recession of the Indian Ocean monsoon. In conclusion, the six meteorological factors monitored in the study area are highly variable but still show seasonal correlation and similar variations.

3.2. Dynamic of Groundwater, SWC, and TS

3.2.1. Groundwater Dynamics

The overall trend of groundwater depth is firstly decreasing, then increasing, and then fluctuating slightly (Figure 3). From July to mid-September 2022, the groundwater depth decreased sharply from 31.78 m to 33.88 m due to the dewatering of the tunnel project. Because of the large amount of precipitation during the rainy season, the groundwater depth increased slightly in November, and there was a significant hysteresis effect in the variation in groundwater level. Since then, the depth of groundwater has fluctuated slightly between 33.7 m and 34 m. The groundwater temperature remained unchanged at 17 °C (Figure 4) and was not affected by external factors. The groundwater temperature was close to the average TS.

3.2.2. SWC Dynamics

Soil water is an important component of the water cycle, and the main hydrologic recharge of soil water comes from the atmosphere and groundwater. Based on the soil and atmospheric precipitation monitoring data from July 2022 to June 2023, the correlation between atmospheric precipitation and SWC was analyzed (Figure 3). From the results, the SWC dynamics showed a “bimodal” type, with peaks occurring in September–October and April–June when there was more precipitation. In addition, the SWC at 30 cm depth was higher than that of deeper soil, with a larger range of 0.04–0.28. This is because the soil layer at a depth of 30 cm was the closest to the external environment and was most affected by the replenishment of atmospheric precipitation and condensation water. In addition, most of the plant roots in the study area were distributed in the 0–30 cm soil layer, and the plant roots have a certain ability to lock water.
The SWC trends observed at a depth of 60–150 cm were largely consistent. The overall fluctuation is also a “bimodal” type. Compared with the SWC at 30 cm depth, the fluctuation range of SWC at 60–150 cm depths was narrower, only between 0.01 and 0.08. The fluctuation trend remained basically the same; however, as the depth increased, the SWC peak appeared later. For example, the SWC peaks of 30–120 cm occurred on September 8th, 9th, 14th, 15th, and October 2nd, with the value of 0.26, 0.04, 0.037, 0.032 and 0.07, respectively. When the second peak of other soil layers appeared, the corresponding SWC first peak at the 150 cm depth also appeared.

3.2.3. TS Dynamics

Changes in TS are basically due to a combination of three factors: solar radiation, soil heat balance, and soil thermal properties. The overall TS variations in the soil layer at different depths from the monitoring sites were similar to those of the atmosphere. TS was high in summer and low in winter, showing a “unimodal” type. The maximum values of atmospheric temperature and TS at 30–150 cm depths occurred on August 12th, 13th, 18th, 19th, 20th, and 21st, with temperatures of 25.7 °C, 26.4 °C, 24.1 °C, 23.17 °C, 22.4 °C, and 22.1 °C, respectively. Atmospheric temperature varied considerably, ranging from 2.9 to 25.9 °C, while TS varied from 9.6 to 27.3 °C. The magnitude of TS variations decreased with depth. In winter, the shallow TS was lower than the deeper layer, and in summer, the shallow TS was higher than the deeper layer (Figure 4a). That is, the variation range of TS is wider in the surface layer and narrower in the deep layer.
From May to August, the 30 cm TS was greater than the deep soil temperature, and there was a constant heat flux; the opposite was true from September to April. To further investigate the interaction between TS and the atmosphere, 15 min intervals of data in July (Figure 4b), December (Figure 4d), and September (Figure 4c) in the transition period were selected for further analysis in both time periods.
The overall variations in these three months are similar, with the atmospheric temperature changing rapidly with sunrise and sunset, and the variations are large. The variations in July, September, and December are 24.1 °C, 22.6 °C, and 21.5 °C. The overall variations in TS are small, with the average variations in July, September, and December being 3.3 °C, 3.1 °C, and 2.2 °C. Only 30 cm of the TS produced small changes with sunrise and sunset.
It is noteworthy that the daily trends of TS at 30 cm depth were opposite to atmospheric temperature in the study area. The phenomenon of TS decreasing during the daytime and increasing at night is mainly due to the combined effect of solar radiation, soil heat transfer properties, and heat exchange conditions between the atmosphere and the soil. During the daytime, solar radiation is the main source of soil heat. When the sun strikes the soil, the soil surface layer receives solar radiation and heats up. The soil heat conduction is slow, and heat cannot be immediately transferred to the deeper soil layers. In addition to this, the soil evaporates during the daytime when the temperature is high, and the soil consumes heat in the process. Moreover, the evaporation process increases the soil air content, which reduces the thermal conductivity and temperature conductivity. Consequently, it is more difficult to transfer heat from the upper layers to the deeper layers. At night, the solar radiation disappears, and the soil surface begins to dissipate heat to the atmosphere through long-wave radiation. At this time, the deep soil begins to gradually transfer heat to the surface soil due to the heat accumulated during the daytime. As the atmospheric temperature is lower than the TS at night, the soil releases heat to the atmosphere, which also contributes to the increase in the TS.

4. Analysis and Discussions

4.1. Correlation Analysis

4.1.1. Soil Hydrothermal Correlation

Soil hydrothermal correlation analysis allows for a more in-depth and accurate study of the coupled relationship between TS and SWC in each soil layer (Figure 5). Statistics revealed a significant positive correlation between SWC at different depths, and the correlation coefficient decreased with the increasing distance between soil layers. For example, the Pearson correlation coefficient between the SWC at 30 cm and the SWC at 60–150 cm were 0.82, 0.69, 0.58, 0.55. The main reason is that the SWC was influenced by atmospheric precipitation, and the soil water recharge by the atmospheric precipitation was reduced with the increase in the depth. Similarly, the correlation coefficients between TS at different depths also showed a significant positive correlation and decreased with the increasing spacing between soil layers. This decline is attributable to the diminishing heat transfer to the soil from atmospheric temperature, and solar radiation decreases as depth increases.
SWC and TS were positively correlated, and the correlation coefficient passed the test of significance (p ≤ 0.01), indicating that SWC is significantly and positively correlated with TS. That is, when the water (or temperature) in the soil increases or decreases, the TS (or SWC) also increases or decreases, which is the hydrothermal synchronization phenomenon [41]. The correlation coefficient decreased with increasing soil depth because the influence of meteorological factors on SWC and TS decreased with the increase in soil depth.

4.1.2. Correlation between Soil Hydrothermal and Meteorological Factors

There are many factors affecting soil hydrothermal, such as vegetation [42], soil texture [43], topography [44], climate [45], etc. In particular, the influence of meteorological factors cannot be overlooked. In this study, six types of meteorological data monitored by meteorological observatories were used to analyze the interrelationship of meteorological factors with SWC and TS.
By analyzing the correlation between TS and meteorological factors, the average correlation coefficients of atmospheric temperature, wind speed, precipitation, solar radiation, wind direction, and humidity with TS at each depth were 0.872, 0.772, 0.187, 0.325, 0.213, and 0.174, respectively (Figure 6a). Atmospheric temperature is the meteorological factor with the strongest correlation with TS, followed by wind speed, and other meteorological factors have weaker correlations with TS. Solar radiation affects atmospheric temperature, which in turn leads to barometric pressure differences and affects wind speed. These heat-related factors mainly affect the shallow TS, transferring heat to the deeper soils where heat is lost in the downward transfer process. Therefore, the correlation between solar radiation, atmospheric temperature, wind speed, and TS decreased with depth. In addition, the correlation between precipitation, relative humidity, wind direction, and TS increased with depth. This is mainly due to a combination of the effect of precipitation on the heat capacity and thermal conductivity of the soil, as well as the properties of soil heat transfer.
The correlation pattern of SWC with meteorological factors is more complex (Figure 6b). The results showed that the average correlation coefficients of SWC with atmospheric temperature, wind speed, precipitation, solar radiation, wind direction, and humidity were 0.116, 0.107, 0.078, −0.046, 0.037, and 0.298, respectively. That is, the meteorological factor with the strongest correlation with SWC is the relative humidity, while the rest of the meteorological factors have weak correlations with SWC. The main source of soil water is atmospheric precipitation, but the correlation between precipitation and SWC is not significant. This is because atmospheric precipitation data are non-continuous, while SWC, TS data, and other meteorological data are continuous. Moreover, meteorological data have a cumulative effect that can bias statistical results to some extent. In addition, not all atmospheric precipitation can infiltrate into the soil due to factors such as vegetation interception, evaporation, and soil infiltration rates, which can also affect the statistical results.
The correlation between meteorological factors and SWC was weaker compared to its correlation with TS. This discrepancy arose from the relatively limited variation range of SWC, particularly below 30 cm, where the variation range was only 0–0.1 cm3·cm−3. This limited range of variation in SWC resulted in insufficient data during the analysis process, leading to less accurate and less relevant results compared to those obtained for TS.

4.2. Effect of Meteorological Factors on Soil Hydrothermal

4.2.1. Temporal Changes in Soil Hydrothermal

The previous section has concluded that atmospheric precipitation, relative humidity, and atmospheric temperature are most strongly correlated with SWC and TS. However, due to the continuous relative humidity and atmospheric temperature, and TS usually has a memory [46], there is no better blank control group to compare. The precipitation data are not continuous, but they are highly comparable before and after precipitation. Therefore, this section focuses on atmospheric precipitation to investigate the influence of meteorological factors on soil hydrothermal dynamics.
In order to better analyze the effect of precipitation on SWC and TS, we treated precipitation intervals greater than 24 h as two independent precipitation events. These events were then classified based on precipitation amount [47], as detailed in Table 1.
The source of soil moisture recharge in the study area is mainly atmospheric precipitation, and the dynamic changes in SWC are closely related to atmospheric precipitation. The 190 mm heavy rainstorm (Figure 7a,d), 50 mm heavy rain (Figure 7b,e), and 10 mm light rain (Figure 7c,f) in the study area were selected to analyze the spatial distribution of SWC and TS under the influence of precipitation. According to the changes in the action force and the characteristics of water movement, the water infiltration process can be divided into three stages. At the early stage of precipitation, the precipitation intensity is low, and the SWC and TS are almost unchanged (Figure 7c,f). This phenomenon occurs because that part of the water enters the shallow soil, which is adsorbed by soil particles under the action of molecular and capillary forces, forming thin film water and capillary water. Consequently, the other part is dispersed due to evaporation. When the precipitation intensity is slightly higher, water infiltration enters the seepage stage, the deeper soil is wetted, and water starts to move along the soil pore instability under the combined effect of capillary force and gravity. As a result, the SWC and TS increase (Figure 7b,e). The more precipitation, the greater the change in SWC; the soil reaches a saturated state, all pores are filled with water, and the infiltration stage is reached.
Comparing the SWC and TS under different precipitation intensities, it was found that the larger the precipitation amount, the greater the changes in SWC and TS, and the faster and more significant the response of shallow SWC and TS to precipitation. Specifically, 190 mm of precipitation increased the SWC at 30–150 cm depths by 0.21, 0.04, 0.01, 0.01, and 0, respectively. After precipitation, the water remained on the surface beyond the infiltration capacity of the soil and continued to infiltrate. Infiltration into soil depths of 60 cm and 90 cm occurs within one day and three days, respectively. At 50 mm of precipitation, it took 1, 2, and 3 days for water to infiltrate 90 cm, 120 cm, and 150 cm, respectively. However, a smaller precipitation event of 10 mm does not induce changes in SWC due to evapotranspiration and dissipation. A substantial precipitation event of 190 mm fails to penetrate the 150 cm soil layer even after three days, while a 50 mm precipitation event achieves infiltration. This discrepancy may be due to the initial lower SWC in the former case, leading to water initially being converted into immobile forms such as bound water and capillary water upon infiltration. In addition, there is a cumulative effect of precipitation, with the 50 mm precipitation selected from October 2022 during the region’s rainy season when precipitation levels were higher.
When precipitation is small, and the duration is short, water can only enter the shallow soil or evaporate before it enters the soil. Some studies have shown that precipitation less than 10 mm cannot effectively replenish soil water [48]. When precipitation is large, and the duration is long, precipitation enters the deeper soil, and the infiltration depth and the increment of SWC increase with the increase in precipitation. When precipitation intensity is large, and the duration is short, the amount of precipitation infiltration depends on the infiltration capacity of the soil. Part of the rainfall is also intercepted by the vegetation canopy during its descent [49], affecting the infiltration of precipitation. Secondly, the plant root system affects soil porosity and soil texture. Therefore, vegetation type and cover are important factors affecting precipitation infiltration. Finally, soil composition and particle size determine soil porosity, which in turn determines the time and amount of water infiltration. The soil in this study area is clay, with a small permeability coefficient and a long water infiltration time. The deeper the depth, the longer the lag time in response to precipitation (Figure 7).
Precipitation lowers the atmospheric temperature, which exchanges heat with the soil, thus reducing the TS. For example, 190 mm of precipitation lowered the atmospheric temperature by 4.37 °C and lowered the TS by an average of 3.4 °C. Three days after the precipitation ended, the temperature increased by an average of 1 °C, and the TS increased by an average of 1.25 °C. In addition, the precipitation enters the soil, exchanges heat with the soil water, and removes some of the heat, resulting in a decrease in TS. After the end of precipitation, the shallow TS recovers slightly. The effect of precipitation on TS decreases with depth but affects deeper layer TS with time. For example, TS above 90 cm gradually increased after 190 mm of precipitation, while TS below 90 cm continued to decrease due to the continuous infiltration of water and the removal of heat. Fifty millimeters of precipitation decreased TS by an average of 0.9 °C.
The variation in TS under 50 mm of precipitation exhibited similarities to that under 190 mm of precipitation. However, factors such as the intensity of evaporation and precipitation contribute to smaller changes in TS, with the transition occurring primarily within the 60 cm soil layer. Ten millimeters of precipitation could not enter the soil below 30 cm and could not exchange energy with precipitation, so the change in TS here was determined by the atmospheric temperature. In summary, the more precipitation, the more significant the change in SWC and TS, and the greater the soil depth, the less it is affected by precipitation.
Air temperature mainly affects TS through solar radiation. Solar radiation heats the surface soil and drives heat downward, resulting in heat loss during the process. Vegetation cover can influence TS by shading and reducing solar radiation absorption, leading to lower TS [50]. The distribution, thickness, and size of the vegetative root system affect the soil porosity and structure, thus causing heat transfer in the soil. Soil water also has a significant effect on TS because the specific heat capacity and thermal conductivity of water are higher than that of soil. Therefore, when moisture increases, the soil needs more heat to raise TS.

4.2.2. Spatial Variation in Soil Hydrothermal

The temporal variation in soil hydrothermal can well reflect the response time of soil hydrothermal to precipitation. However, to quantify the degree of influence of meteorological factors on soil hydrothermal, it is also necessary to study the spatial variation pattern of soil hydrothermal. Therefore, based on the precipitation events within the year and the SWC and TS increment data before and after precipitation, the spatial changes and increments of SWC and TS under different precipitation intensities were analyzed (Figure 8).
The results showed that the greater the precipitation, the deeper the soil SWC and TS can be affected. According to the precipitation classification, light rainfall (<10 mm) can only enter the soil layer less than 20 cm depth at the surface; medium rainfall (<25 mm) can enter the soil layer around 30 cm for recharge; 50 mm of precipitation can infiltrate and replenish 60 mm soil; and precipitation above 70 mm can recharge the soil depth of 150 mm or deeper.
The effect of precipitation on TS is similar to the spatial variation in SWC under the influence of precipitation. Precipitation events with 50 mm or less can only reduce the temperature of up to 60 cm depth soil layer, while a precipitation amount of 100 mm can affect the temperature of the soil layer up to 120 cm depth. Changes in TS at deeper depths require more than 100 mm of precipitation.
Comparing the effects of precipitation on SWC and TS, it can be found that the larger the precipitation, the greater the soil water and heat increment. The shallower the soil layer the greater the increment under the same precipitation conditions. Secondly, SWC is more sensitive to the response of precipitation, suggesting that the same precipitation intensity affects the depth of SWC more than TS.
The spatial variation in TS can help us to understand more effectively the extent of the effect of atmospheric temperature on SWC and TS. Therefore, based on the single rise and fall of the atmospheric temperature in the study area and the corresponding SWC and TS changes, Kriging interpolation was used to investigate the spatial variation in SWC and TS and the response to different atmospheric temperature variations in the study area (Figure 9). Atmospheric temperatures varied considerably during the study time, reaching ±20 °C. The average change in TS was 2.2 °C. The average change in TS for atmospheric temperature changes of 10 °C and 20 °C was 2.5 °C and 7.2 °C, respectively. It can be seen that a change of 1 °C in TS at a depth of 30 cm requires the atmospheric temperature to change by at least 3 °C. At a depth of 150 cm, the change in TS by 1 °C needs the atmospheric temperature to change by 10 °C. This shows that the effect of atmospheric temperature on TS decreases with depth. The change in TS in the shallow layer is more obvious than that in the deep layer, and the decrease in TS requires more change in atmospheric temperature than the increase. In addition, there is no significant regularity in the incremental change in SWC at different atmospheric temperatures, indicating that atmospheric temperature has little effect on SWC.

5. Conclusions

This study was carried out in the Hengduan Mountainous area of Southeast Tibet. Six meteorological factors, groundwater temperature and depth, SWC, and TS were monitored from 1 July 2022 to 29 June 2023 in the study area. Through the analysis and reprocessing of measured data using correlation analysis and kriging interpolation techniques, coupled with software such as Origin 2022, IBM SPSS statistics 26, and Surfer 23, this study investigated groundwater, soil water heat, and meteorological factors in the Hengduan Mountainous area of Southeast Tibet. The groundwater was not analyzed in depth because of its large burial depth and weak connection with SWC and TS. The dynamic changes in SWC and TS and the influence of meteorological factors on them were explored, and the main conclusions are as follows:
(1) The dynamics of SWC from July 2022 to July 2023 in the Hengduan Mountains of Southeast Tibet is of a “bimodal” type, with peaks occurring in September–October and April–June. The change in SWC in the shallow layer is greater than that in the deep layer. The dynamic change in TS is “unimodal” type, with the peaks occurring in July–August. The change in TS in the shallow layer is larger than that in the deep layer. The shallow layer TS is higher than the deep layer in summer, and the deep layer TS is higher in winter. TS and atmospheric temperature varied opposite on the daily scale.
(2) The data for the study area from July 2022 to June 2023 show that there is a significant positive correlation between SWC and TS, i.e., the same period of water heat. SWC has the highest correlation with relative humidity, and TS has the strongest correlation with atmospheric temperature.
(3) SWC is mainly affected by atmospheric precipitation, and shallow SWC responds to it faster and to a greater extent. Data from July 2022 through June 2023 in the study area show that without considering the cumulative effect of precipitation, more than 70 mm of precipitation can affect soil layers deeper than 150 cm. Less precipitation is needed when the cumulative effect is considered. The atmospheric temperature has little effect on SWC.
(4) TS in the study area is mainly influenced by atmospheric temperature. Changes in shallow TS during the study period were more pronounced than changes in deep TS, and more changes in atmospheric temperature were required for TS to fall than to rise. When the atmospheric temperature changes by 20 °C, the TS changes by an average of 7.2 °C. In addition, 170 mm of precipitation has the ability to change the TS at depths greater than 150 cm.
The shortcoming of this paper is that one year of data is not enough to deeply understand the impacts of climate change on soil hydrothermal and to predict global climate change. In the future, we will work on multi-point, multi-year studies of hydrothermal dynamics with the aim of making constructive contributions to global climate change and water resource management.

Author Contributions

Data curation, X.Z.; Writing—original draft, L.M.; Writing—review & editing, Z.L.; Supervision, Z.L.; Funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Sichuan Provincial Science and Technology Department Youth Fund (2023NSFSC0802), the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (SKLGP2023Z005), the National Key Research and Development Program of China [Grant number: 2020YFC1808300], and the National Natural Science Foundation of China (41702253).

Data Availability Statement

Data is contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Seneviratne, S.I.; Corti, T.; Davin, E.L.; Hirschi, M.; Jaeger, E.B.; Lehner, I.; Orlowsky, B.; Teuling, A.J. Investigating soil moisture-climate interactions in a changing climate: A review. Earth-Sci. Rev. 2010, 99, 125–161. [Google Scholar] [CrossRef]
  2. Koster, R.D.; Mahanama, S.P.P.; Yamada, T.J.; Balsamo, G.; Berg, A.A.; Boisserie, M.; Dirmeyer, P.A.; Doblas-Reyes, F.J.; Drewitt, G.; Gordon, C.T.; et al. The Second Phase of the Global Land-Atmosphere Coupling Experiment: Soil Moisture Contributions to Subseasonal Forecast Skill. J. Hydrometeorol. 2011, 12, 805–822. [Google Scholar] [CrossRef]
  3. Robertson, J.A.; Gazis, C.A. An oxygen isotope study of seasonal trends in soil water fluxes at two sites along a climate gradient in Washington state (USA). J. Hydrol. 2006, 328, 375–387. [Google Scholar] [CrossRef]
  4. Sun, J.; Sun, X.; Wang, G.; Dong, W.; Hu, Z.; Sun, S.; Wang, F.; Song, C.; Lin, S. Soil water components control plant water uptake along a subalpine elevation gradient on the Eastern Qinghai-Tibet Plateau. Agric. For. Meteorol. 2024, 345, 109827. [Google Scholar] [CrossRef]
  5. Ricks, K.D.; Yannarell, A.C. Soil moisture incidentally selects for microbes that facilitate locally adaptive plant response. Proc. R. Soc. B. 2023, 290, 20230469. [Google Scholar] [CrossRef] [PubMed]
  6. Dougill, A.J.; Heathwaite, A.L.; Thomas, D.S.G. Soil water movement and nutrient cycling in semi-arid rangeland: Vegetation change and system resilience. Hydrol. Process. 1998, 12, 443–459. [Google Scholar]
  7. Zhan, M.-j.; Xia, L.; Zhan, L.; Wang, Y. Recognition of Changes in Air and Soil Temperatures at a Station Typical of China’s Subtropical Monsoon Region (1961–2018). Adv. Meteorol. 2019, 2019, 6927045. [Google Scholar] [CrossRef]
  8. Pu, H.; Song, W.; Wu, J. Using Soil Water Stable Isotopes to Investigate Soil Water Movement in a Water Conservation Forest in Hani Terrace. Water 2020, 12, 3520. [Google Scholar] [CrossRef]
  9. Song, Y.; Huang, A.; Chen, H. The Storage of Antecedent Precipitation and Air Temperature Signals in Soil Temperature over China. J. Hydrometeorol. 2022, 23, 377–388. [Google Scholar] [CrossRef]
  10. Elmi, A.; Anwar, S.A.; Al-Dashti, H. Measurements and Regional Climate Modeling of Soil Temperature with and without Bias Correction Method under Arid Environment: Can Soil Temperature Outperform Air Temperature as a Climate Change Indicator? Environ. Model. Assess. 2023, 29, 279–289. [Google Scholar] [CrossRef]
  11. Yang, Y.; Wu, Z.; He, H.; Du, H.; Wang, L.; Guo, X.; Zhao, W. Differences of the changes in soil temperature of cold and mid-temperate zones, Northeast China. Theor. Appl. Climatol. 2018, 134, 633–643. [Google Scholar] [CrossRef]
  12. Lozano-Parra, J.; Pulido, M.; Lozano-Fondon, C.; Schnabel, S. How do Soil Moisture and Vegetation Covers Influence Soil Temperature in Drylands of Mediterranean Regions? Water 2018, 10, 1747. [Google Scholar] [CrossRef]
  13. Zhang, S.-Y.; Li, X.-Y. Soil moisture and temperature dynamics in typical alpine ecosystems: A continuous multi-depth measurements-based analysis from the Qinghai-Tibet Plateau, China. Hydrol. Res. 2018, 49, 194–209. [Google Scholar] [CrossRef]
  14. Grillakis, M.G.; Koutroulis, A.G.; Papadimitriou, L.V.; Daliakopoulos, I.N.; Tsanis, I.K. Climate-Induced Shifts in Global Soil Temperature Regimes. Soil Sci. 2016, 181, 264–272. [Google Scholar] [CrossRef]
  15. Ceccon, C.; Panzacchi, P.; Scandellari, F.; Prandi, L.; Ventura, M.; Russo, B.; Millard, P.; Tagliavini, M. Spatial and temporal effects of soil temperature and moisture and the relation to fine root density on root and soil respiration in a mature apple orchard. Plant Soil 2011, 342, 195–206. [Google Scholar] [CrossRef]
  16. Wundram, D.; Pape, R.; Loeffler, J. Alpine Soil Temperature Variability at Multiple Scales. Arct. Antarct. Alp. Res. 2010, 42, 117–128. [Google Scholar] [CrossRef]
  17. Zhu, X.; Liu, H.; Li, Y.; Liang, B. Quantifying the role of soil in local precipitation redistribution to vegetation growth. Ecol. Indic. 2021, 124, 107355. [Google Scholar] [CrossRef]
  18. Meng, F.; Luo, M.; Sa, C.; Wang, M.; Bao, Y. Quantitative assessment of the effects of climate, vegetation, soil and groundwater on soil moisture spatiotemporal variability in the Mongolian Plateau. Sci. Total Environ. 2022, 809, 152198. [Google Scholar] [CrossRef]
  19. Cho, E.; Choi, M. Regional scale spatio-temporal variability of soil moisture and its relationship with meteorological factors over the Korean peninsula. J. Hydrol. 2014, 516, 317–329. [Google Scholar] [CrossRef]
  20. Fan, K.; Zhang, Q.; Sun, P.; Song, C.; Zhu, X.; Yu, H.; Shen, Z. Variation, causes and future estimation of surface soil moisture on the Tibetan Plateau. Acta Geogr. Sin. 2019, 74, 520–533. [Google Scholar]
  21. Wang, T.; Franz, T.E.; Li, R.; You, J.; Shulski, M.D.; Ray, C. Evaluating climate and soil effects on regional soil moisture spatial variability using EOFs. Water Resour. Res. 2017, 53, 4022–4035. [Google Scholar] [CrossRef]
  22. Chou, M.-L.; Tseng, H.-Y.; Wang, C.-H.; Hseu, Z.-Y. Identification of the water source and groundwater recharge in a paddy field using stable hydrogen and oxygen isotopes. Water Supply 2022, 22, 6443–6457. [Google Scholar] [CrossRef]
  23. Lyu, S. Variability of δ2H and δ18O in Soil Water and Its Linkage to Precipitation in an East Asian Monsoon Subtropical Forest Plantation. Water 2021, 13, 2930. [Google Scholar] [CrossRef]
  24. Chen, K.; Liu, G.; Xia, C.; Meng, Y.; Tetzlaff, D.; Zhong, Q.; Chang, J. Water cycling and partitioning through the soil-plant-atmosphere continuum in a subtropical, urban woodland inferred by water stable isotopes. Hydrol. Process. 2022, 36, e14746. [Google Scholar] [CrossRef]
  25. Song, Y.; Lu, Y.; Guo, Z.; Xu, X.; Liu, T.; Wang, J.; Wang, W.; Hao, W.; Wang, J. Variations in Soil Water Content and Evapotranspiration in Relation to Precipitation Pulses within Desert Steppe in Inner Mongolia, China. Water 2019, 11, 198. [Google Scholar] [CrossRef]
  26. Zhou, T.; Han, C.; Qiao, L.; Ren, C.; Wen, T.; Zhao, C. Seasonal dynamics of soil water content in the typical vegetation and its response to precipitation in a semi-arid area of Chinese Loess Plateau. J. Arid Land 2021, 13, 1015–1025. [Google Scholar] [CrossRef]
  27. Liang, J.; Xing, X.; Gao, Y. A modified physical-based water-retention model for continuous soil moisture estimation during infiltration: Experiments on saline and non-saline soils. Arch. Agron. Soil Sci. 2020, 66, 1344–1357. [Google Scholar] [CrossRef]
  28. Kader, M.A.; Nakamura, K.; Senge, M.; Mojid, M.A. Two-dimensional numerical simulations of soil-water and heat flow in a rainfed soybean field under plastic mulching. Water Supply 2021, 21, 2615–2632. [Google Scholar] [CrossRef]
  29. Maria Dominguez-Nino, J.; Arbat, G.; Raij-Hoffman, I.; Kisekka, I.; Girona, J.; Casadesus, J. Parameterization of Soil Hydraulic Parameters for HYDRUS-3D Simulation of Soil Water Dynamics in a Drip-Irrigated Orchard. Water 2020, 12, 1858. [Google Scholar] [CrossRef]
  30. Njoku, E.G.; Entekhabi, D. Passive microwave remote sensing of soil moisture. J. Hydrol. 1996, 184, 101–129. [Google Scholar] [CrossRef]
  31. Lakhankar, T.; Ghedira, H.; Temimi, M.; Sengupta, M.; Khanbilvardi, R.; Blake, R. Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data. Remote Sens. 2009, 1, 3–21. [Google Scholar] [CrossRef]
  32. Ma, Y.; Ma, W.; Zhong, L.; Hu, Z.; Li, M.; Zhu, Z.; Han, C.; Wang, B.; Liu, X. Monitoring and Modeling the Tibetan Plateau’s climate system and its impact on East Asia. Sci. Rep. 2017, 7, 44574. [Google Scholar] [CrossRef] [PubMed]
  33. Zhou, X.; Zhao, P.; Chen, J.; Chen, L.; Li, W. Impacts of thermodynamic processes over the Tibetan Plateau on the Northern Hemispheric climate. Sci. China Ser. D-Earth Sci. 2009, 52, 1679–1693. [Google Scholar] [CrossRef]
  34. Shen, Y.; Chen, Y. Global perspective on hydrology, water balance, and water resources management in arid basins. Hydrol. Process. 2010, 24, 129–135. [Google Scholar] [CrossRef]
  35. Liu, W.; Xu, B.; Tang, P.; Li, Z. Research progress on models of soil water and heat and root water uptake and prospect in Tibet. J. Water Resour. Water Eng. 2019, 30, 254–260. [Google Scholar]
  36. Guo, D.; Li, D.; Liu, G. Simulated change in soil temperature on the Tibetan plateau from 1901 to 2010. Quat. Sci. 2017, 37, 1102–1110. [Google Scholar]
  37. Shi, L.; Du, J.; Zhou, K.; Zhuo, G. The temporal-spatial variations of soil moisture over the Tibetan Plateau during 1980–2012. J. Glaciol. Geocryol. 2016, 38, 1241–1248. [Google Scholar]
  38. Jiang, H.; Zheng, G.; Yi, Y.; Chen, D.; Zhang, W.; Yang, K.; Miller, C.E. Progress and Challenges in Studying Regional Permafrost in the Tibetan Plateau Using Satellite Remote Sensing and Models. Front. Earth Sci. 2020, 8, 560403. [Google Scholar] [CrossRef]
  39. Qin, X.; Chen, Q.; Meng, W.; Zhang, C.; Sun, D.; Yang, Y.; Chen, H.; Li, R. Determination of the current in-situ stress field of the Tongmai-Bomi section in the northern margin of the eastern Himalayan syntaxis. Acta Geol. Sin. 2023, 97, 2126–2140. [Google Scholar]
  40. Lou, X.; Fu, H.; Li, H. The Late Palaeozoic stratigraphic sequences and basin evolution in the Bomi region, Xizang. Sediment. Geol. Tethyan Geol. 2003, 23, 54–59. [Google Scholar]
  41. Yang, R.; Ding, Y.; Liu, Y.; Liu, Y.; Tian, L.; Wang, L. Soil Moisture and Temperature in Cherry Orchard:Variation Characteristics and Response to Meteorological Factors. Chin. Agric. Sci. Bull. 2020, 36, 67–72. [Google Scholar]
  42. Ni, J.; Cheng, Y.; Wang, Q.; Ng, C.W.W.; Garg, A. Effects of vegetation on soil temperature and water content: Field monitoring and numerical modelling. J. Hydrol. 2019, 571, 494–502. [Google Scholar] [CrossRef]
  43. Huang, L.; Bao, W.; Hu, H.; Nkrumah, D.T.; Li, F. Rock fragment content alters spatiotemporal patterns of soil water content and temperature: Evidence from a field experiment. Geoderma 2023, 438, 116613. [Google Scholar] [CrossRef]
  44. Rui, W.; Quan, S.U.N.; Xiang-wei, W.U. The Spatial Dynamics of the Soil Moisture under Different Terrain Conditions in the Southeast Edge of Tengger Desert. Acta Agric. Univ. Jiangxiensis 2009, 31, 756–759. [Google Scholar]
  45. Han, S.; Ahn, J.; Kim, S. The Stochastic Behavior of Soil Water and the Impact of Climate Change on Soil Water. J. Korea Water Resour. Assoc. 2009, 42, 433–443. [Google Scholar] [CrossRef]
  46. Yang, K.; Zhang, J. Spatiotemporal characteristics of soil temperature memory in China from observation. Theor. Appl. Climatol. 2016, 126, 739–749. [Google Scholar] [CrossRef]
  47. Hu, A.; Fu, W.; Chen, Y.; Yan, L.; Ai, M.; Chen, R.; Shi, C.; Liu, X. Soil Moisture Dynamics and Its Response to Precipitation in Different Cover Types of the Mu Us Sandy Land. Res. Soil Water Conserv. 2023, 30, 133–142. [Google Scholar]
  48. Li, X.; Zhang, Z.; Huang, L.; Wang, X. Review of the ecohydrological processes and feedback mechanisms controlling sand-binding vegetation systems in sandy desert regions of China. Chin. Sci. Bull. 2013, 58, 1483–1496. [Google Scholar] [CrossRef]
  49. Li, X.-Y.; Zhang, S.-Y.; Peng, H.-Y.; Hu, X.; Ma, Y.-J. Soil water and temperature dynamics in shrub-encroached grasslands and climatic implications: Results from Inner Mongolia steppe ecosystem of north China. Agric. For. Meteorol. 2013, 171, 20–30. [Google Scholar] [CrossRef]
  50. Marsh, P.; Bartlett, P.; MacKay, M.; Pohl, S.; Lantz, T. Snowmelt energetics at a shrub tundra site in the western Canadian Arctic. Hydrol. Process. 2010, 24, 3603–3620. [Google Scholar] [CrossRef]
Water 16 01495 g001
Figure 1. (a) Location of the study area; (b) locations of the field experimental site and major rivers; (c) hydrogeological section of the study area; (d) meteorological monitoring devices in the study area; (e) soil hydrothermal monitoring devices at different depths.
Figure 1. (a) Location of the study area; (b) locations of the field experimental site and major rivers; (c) hydrogeological section of the study area; (d) meteorological monitoring devices in the study area; (e) soil hydrothermal monitoring devices at different depths.
Water 16 01495 g001
Water 16 01495 g002
Figure 2. Dynamics of meteorological factors over the study period. (a) Wind direction (b) Wind speed (c) Wind direction (d) Solar radiation (e) Precipitation (f) Relative humidity.
Figure 2. Dynamics of meteorological factors over the study period. (a) Wind direction (b) Wind speed (c) Wind direction (d) Solar radiation (e) Precipitation (f) Relative humidity.
Water 16 01495 g002
Water 16 01495 g003
Figure 3. Dynamics curve of atmospheric precipitation-SWC-groundwater depth over the study period.
Figure 3. Dynamics curve of atmospheric precipitation-SWC-groundwater depth over the study period.
Water 16 01495 g003
Water 16 01495 g004
Figure 4. Dynamics among TS and atmospheric temperature over the study period. (a) July 2022–June 2023 (b) July (c) September (d) December.
Figure 4. Dynamics among TS and atmospheric temperature over the study period. (a) July 2022–June 2023 (b) July (c) September (d) December.
Water 16 01495 g004
Water 16 01495 g005
Figure 5. Soil hydrothermal correlation thermogram.
Figure 5. Soil hydrothermal correlation thermogram.
Water 16 01495 g005
Water 16 01495 g006
Figure 6. Correlation analysis between TS (a) and SWC (b) with meteorological factors at the monitoring site.
Figure 6. Correlation analysis between TS (a) and SWC (b) with meteorological factors at the monitoring site.
Water 16 01495 g006
Water 16 01495 g007
Figure 7. Redistribution of SWC before and after precipitation (a–c), and redistribution of TS before and after precipitation (d–f).
Figure 7. Redistribution of SWC before and after precipitation (a–c), and redistribution of TS before and after precipitation (d–f).
Water 16 01495 g007
Water 16 01495 g008
Figure 8. Increments of SWC (a) and TS (b) at 0–150 cm soil depth.
Figure 8. Increments of SWC (a) and TS (b) at 0–150 cm soil depth.
Water 16 01495 g008
Water 16 01495 g009
Figure 9. Increments of TS (a) and SWC (b) at 0–150 cm soil depth.
Figure 9. Increments of TS (a) and SWC (b) at 0–150 cm soil depth.
Water 16 01495 g009
Table 1. Classification of precipitation events.
Table 1. Classification of precipitation events.
CategoryPrecipitation (mm)
light rain0–9.9
moderate rain10–24.9
heavy rain25–49.9
rainstorm50–99.9
heavy rainstorm100–199.9
very heavy rainstorm>200
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Meng, L.; Li, Z.; Zhang, Q.; Zhang, X. Soil Hydrothermal Dynamics in the Hengduan Mountains of Southeast Tibet and Associated Influencing Factors. Water 2024, 16, 1495. https://doi.org/10.3390/w16111495

AMA Style

Meng L, Li Z, Zhang Q, Zhang X. Soil Hydrothermal Dynamics in the Hengduan Mountains of Southeast Tibet and Associated Influencing Factors. Water. 2024; 16(11):1495. https://doi.org/10.3390/w16111495

Chicago/Turabian Style

Meng, Lingling, Zhaofeng Li, Qiang Zhang, and Xinpeng Zhang. 2024. "Soil Hydrothermal Dynamics in the Hengduan Mountains of Southeast Tibet and Associated Influencing Factors" Water 16, no. 11: 1495. https://doi.org/10.3390/w16111495

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop