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Citation: Liu, D.; Chang, Y.; Sun, L.;
Wang, Y.; Guo, J.; Xu, L.; Liu, X.; Fan,
Z. Multi-Scale Periodic Variations in
Soil Moisture in the Desert Steppe
Environment of Inner Mongolia,
China. Water 2024,16, 123.
https://doi.org/10.3390/w16010123
Academic Editor: Cheng-Zhi Qin
Received: 28 November 2023
Revised: 26 December 2023
Accepted: 27 December 2023
Published: 28 December 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
water
Article
Multi-Scale Periodic Variations in Soil Moisture in the Desert
Steppe Environment of Inner Mongolia, China
Dandan Liu 1, 2, †, Yaowen Chang 3, †, Lei Sun 3 ,*, Yunpeng Wang 3, Jiayu Guo 3, Luyue Xu 3, Xia Liu 3
and Zhaofei Fan 4
1Water Resources Research Institute of Anhui Province and Huaihe River Commission, Ministry of Water
Resources, Hefei 230088, China; liudandan06shuibao@163.com
2Key Laboratory of Water Conservancy and Water Resources of Anhui Province, Bengbu 233000, China
3
Jiangsu Key Laboratory of Soil and Water Conservation and Ecological Restoration, Collaborative Innovation
Center of Sustainable Forestry in Southern China of Jiangsu Province, Forestry College of Nanjing Forestry
University, Nanjing 210037, China; 15148099284@163.com (Y.C.); c1424962540@163.com (Y.W.);
guojiayu9@sina.com (J.G.); xly000414@163.com (L.X.); liuxia@njfu.edu.cn (X.L.)
4
College of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849, USA; zzf0008@auburn.edu
*Correspondence: ls2015@njfu.edu.cn
†These authors contributed equally to this work.
Abstract: Uncovering the complex periodic variations in soil moisture can provide a significant
reference for climate prediction and hydrological process simulation. We used wavelet analysis to
quantify and identify the multi-scale periodic variations of soil moisture in the desert steppe of Inner
Mongolia from 2009 to 2019 and analyzed the differences between the multi-scale periodic changes
in soil moisture at the bottom (BS) and upper slope (US). The results show that the interannual
variability in soil moisture at the BS and US has a significant upward trend. Moreover, the amount
and volatility decrease with the increase in soil depth in the range of 0–30 cm, and the soil moisture at
the BS is 26.4% higher than the US. The soil moisture has periodic changes of 0.5a, 1a, 2a, 3a and 3.5a
in the desert steppe environment of Inner Mongolia. The periodic structure and intensity of different
slope positions are greatly different. Soil moisture at the US has more complex multi-scale periodic
changes, and the periodic oscillations of 3.5a, 3a, and 1a are dominant. The periodic oscillations of
0.5a and 1a are dominant at the BS. At the BS, the intensity of periodic oscillation of 1a after January
2015 has weakened. The weakening of soil moisture by temperature, rainfall and soil temperature
caused the change in the multiple time-scale periodic oscillation of soil moisture.
Keywords: soil moisture; periodic variation; desert steppe; wavelet analysis
1. Introduction
Soil moisture is an important parameter and plays an essential role in characterizing
land–atmosphere coupling and surface hydrology [
1
]. On the one hand, soil moisture
becomes a critical medium that affect the water cycle and atmospheric changes through the
water vapor transfer method of evaporation and transpiration [
2
,
3
]. In surface hydrology,
soil moisture controls the redistribution of surface water. For example, low soil moisture
leads to more surface infiltration and smaller surface runoff after rainfall. On the other hand,
soil moisture affects atmospheric changes by changing land surface characteristics such
as soil heat capacity, vegetation coverage, and surface albedo [
4
,
5
]. Meanwhile, under the
joint action of climate factors and surface characteristics, soil moisture has a complex multi-
scale periodic variation. In addition, soil moisture is an important factor restricting the
growth of vegetation [
6
,
7
]. Soil moisture deficit leads to a significant decline in vegetation
productivity [
8
,
9
], especially in arid and semi-arid desert steppe areas, and the growth and
development of vegetation are more sensitive to the change in soil moisture [
10
–
12
]. Soil
moisture becomes an important factor affecting the structure and function of grassland
Water 2024,16, 123. https://doi.org/10.3390/w16010123 https://www.mdpi.com/journal/water
Water 2024,16, 123 2 of 16
ecosystems [
13
]. The study of soil moisture change characteristics has important scientific
value for soil moisture monitoring, drought risk warning, climate change prediction, and
vegetation ecological management.
The desert steppe of Inner Mongolia, located in the mid-latitudes of the northern
hemisphere, is a transitional area from a typical steppe to the desert. As a typical ground–
atmosphere coupling region in Eurasia, this area is affected by the Siberian winter monsoon
and the Pacific East Asian monsoon in winter and summer, respectively. Accompanied by
climate change, the frequency and uncertainty of extreme weather events have become
increasingly higher [
14
–
16
]. The climate in the desert steppe of Inner Mongolia has shown
a trend of warming and drying [
17
,
18
], and the increasing frequency and intensity of
droughts significantly impact the land–atmosphere coupling [
19
–
21
], which has an impact
on the dynamic process of soil moisture in the desert steppe and its multi-scale period. The
implementation of the “enclosure and grazing prohibition” and the “two-screens, three-
belts” ecological security strategy have dramatically changed the vegetation coverage and
community structure of the desert steppe in recent years [
22
,
23
]. This change increases the
vegetation’s demand for soil moisture, thereby increasing the variability and uncertainty of
the dynamic changes in soil moisture [
24
,
25
] and breaking the original periodic variations
in soil moisture. Therefore, it is necessary to clarify the multi-scale periodic variations in soil
moisture in the desert steppe, which will help us to understand the region’s meteorological
changes and water balance with strong land–atmosphere coupling represented by the
desert steppe and provide a theoretical basis for people to formulate a drought prevention
and animal husbandry development model under climate change.
The dynamic change and multi-scale period of soil moisture has always been an
important basic research area and focus for studying the interaction between soil and the
near-surface atmosphere. Deng et al. [
26
] found that global soil moisture has shown a
significant downward trend in recent decades, and global soil will continue to be dominated
by aridification. Cheng and Huang [
27
] proposed that significant soil drying firstly occurred
in the dry–wet transitional zone, including East Asia. In China, soil moisture has shown
a significant downward trend in the past 30 years [
28
]. In terms of multi-scale periodic
variation studies, Ma et al. [
29
] used 11-year soil moisture data from 98 observatories in
China and found that soil moisture change has a cycle of 3–4 years. Jia et al. [
30
] found that
soil moisture in plain areas has a time cycle of 3–5 years and 1.5–2.5 years. At the same
time, topographic differences affect soil moisture changes through the redistribution of
rainfall, and the difference in slope position is an important factor reflecting the impact
of topography on soil moisture. Su et al. [
31
] showed that slope position has a significant
impact on soil moisture and its change within a certain range of soil depth, and Meng
et al. [
32
] found that soil moisture decreases with the increase in slope position in Maowusu
sandland. Zhang et al. [
33
] showed that the difference in slope position affected the
decay rate of soil moisture. Recent studies [
29
,
34
–
37
] have explored the spatiotemporal
evolution characteristics, multi-scale periodic variations, and response to slope position
of soil moisture in some typical ground–atmosphere coupling regions in China. However,
most researchers only consider the difference in soil moisture and its dynamic changes
at different slope positions, and there are relatively few studies on the difference in soil
moisture periodic variation between different slope positions. Wavelet analysis, known as
the mathematical microscope, can better explore the period frequency and local features of
time series. Wavelet analysis was widely used to analyze long-term climate change [
38
,
39
],
surface runoff characteristics [
40
–
43
] and other fields, and it can effectively identify and
explore the multi-scale periodic variations in soil moisture. Scientific identification and
analysis of the periodic variation in soil moisture in the desert steppe can provide an
essential reference for hydrological process simulation and climate prediction in this area.
Therefore, the aims of this study were to (1) characterize the time series trend charac-
teristics of soil moisture based on the ground high-time-resolution automatic soil moisture
observation data; (2) study soil moisture’s multi-level time-scale structure and periodic
change characteristics using wavelet analysis to determine the main periodic and oscil-
Water 2024,16, 123 3 of 16
lating characteristics; and (3) reveal the multi-scale periodic variations in soil moisture
time series at different slope locations and the differences in influencing factors in different
periods to raise awareness of the differences in soil moisture caused by different slope
positions. It provides a theoretical basis for water cycle simulation and meteorological
prediction of the coupling system of soil and air in the desert steppe. In practice, it provides
management and a decision-making basis for rational allocation of water resources and
vegetation restoration in the steppe.
2. Materials and Methods
2.1. Study Area
The study area (41
◦
20
′
N–42
◦
40
′
N, 109
◦
16
′
E–111
◦
25
′
E) is located on the northern
edge of the Yinshan Mountains, in the transition zone from the Yinshan Mountains to the
Inner Mongolia Plateau. It belongs to Darhan Muminggan United Banner, Baotou, Inner
Mongolia. The topography of the study area is low in the north and high in the south,
with an average elevation of 1367 m. It belongs to the mid-temperate semi-arid continental
monsoon climate, with an arid and windy spring and autumn, abundant rainfall in summer,
and a dry and cold winter. The mean annual precipitation is 284 mm, mainly concentrated
in July to September, accounting for 76–80% of the annual precipitation. The mean annual
evaporation is 2305 mm, and the moisture coefficient ranges from 0.13 to 0.31. The mean
annual temperature is 2.5
◦
C, the annual accumulated temperature ranges from 1985
◦
C to
2800
◦
C, the annual average sunshine duration is 3100–3300 h, and the area experiences
a frost-free period of about 83 d. The average wind speed is 4.5 m/s, and the maximum
speed is 27.0 m/s. The main wind directions throughout the year are north and northwest.
The study area is located in the small Shangdong River watershed, a tributary of
the Tabu River. Shangdong River is a seasonal river, and 3–5 floods flow into the Tabu
River in summer under the influence of rainfall. The thickness of the aquifer is generally
about 3–8 m, the buried depth of the roof is less than 10 m, the buried depth of the
floor is 6–20 m, and the water level is 3–6 m. Plants depend almost entirely on natural
precipitation for their water needs. The soil type is millet, and the soil texture is mainly
sandy loam and light loam. The effective soil layer thickness is about 40 cm. The average soil
porosity is 69.13%, and there were significant differences between BS and US in soil porosity
(p< 0.05, Table 1). The zonal vegetation in the study area is mainly shallow root grass and
grasses. Stipa krylovii Roshev is the founding vegetation group. The dominant vegetation
species are Artemisia frigida,Cleistogenes squarrosa,Convolvulus ammannii Desr,Heteropappus
altaicus,Agropyron cristatum, and Leymus chinensis. The vegetation height is 30–50 cm, and
the coverage is 25–45%. The plant roots are mainly distributed in the 0–30 cm soil layer.
Table 1. Statistical characteristics of soil physical properties in different slope positions.
Soil Bulk Density
/g·cm−3Soil Porosity/% Clay Sand/%
(<0.05 mm)
Fine Sand/%
(0.05–0.1 mm)
Coarse Sand/%
(0.1–2 mm)
US 1.346 ±0.122 65.711 ±2.338 36.238 ±4.95 20.905 ±4.113 42.857 ±6.068
BS 1.34 ±0.143 72.549 ±2.388 41.053 ±9.036 18.082 ±5.56 40.865 ±7.672
Mean 1.343 ±0.127 69.13 ±4.222 38.646 ±7.387 19.493 ±4.891 41.861 ±6.676
2.2. Data
Soil moisture and meteorological data come from the National Field Scientific Observa-
tion and Research Station of the Eco-hydrology of the Desert Steppe on the Southern Edge
of the Inner Mongolia Plateau. In the study area, we set up two different slope positions
on a typical slope (the slope is 3
◦
, the slope direction is northeast-southwest): the upper
slope (US, 41
◦
21
′
10
′′
N, 111
◦
12
′
34
′′
E, the altitude is 1610 m) and the bottom slope (BS,
4
◦
20
′
55
′′
N, 111
◦
12
′
22
′′
E, the altitude is 1600 m) (Figure 1). Soil moisture observation
stations were established at two slope positions to monitor soil moisture for a long time
(began in 2008). The two observation stations are 541 m apart. The observation instrument
Water 2024,16, 123 4 of 16
is an AZ-DT soil moisture monitoring station (produced by IMKO company, Germany, and
the data collector is DT-80 produced in Australia) to collect soil volume water content. The
soil moisture sensors were placed at soil depths of 5 cm, 15 cm, and 25 cm, respectively,
representing the soil moisture of 0–10 cm, 10–20 cm, and 20–30 cm. This study selected soil
moisture data from 25 May 2009 to 16 August 2019, with a time resolution of 30 min.
Water 2024, 16, 123 4 of 16
bottom slope (BS, 4°20′55″ N, 111°12′22″ E, the altitude is 1600 m) (Figure 1). Soil moisture
observation stations were established at two slope positions to monitor soil moisture for
a long time (began in 2008). The two observation stations are 541 m apart. The observation
instrument is an AZ-DT soil moisture monitoring station (produced by IMKO company,
Germany, and the data collector is DT-80 produced in Australia) to collect soil volume
water content. The soil moisture sensors were placed at soil depths of 5 cm, 15 cm, and 25
cm, respectively, representing the soil moisture of 0–10 cm, 10–20 cm, and 20–30 cm. This
study selected soil moisture data from 25 May 2009 to 16 August 2019, with a time
resolution of 30 min.
Aberrant values of soil moisture that were outlierswere eliminated, and then the
average value of the data in the adjacent period was calculated for interpolation. Missing
and abnormal data were less than 5% of the total data volume, and their impact was
negligible. To ensure the reliability and consistency of the data, we used the soil moisture
observed by the UGT at a depth of 5 cm to verify the soil moisture observed by the AZ-
DT. The two soil moisture data sets were highly correlated and showed good consistency.
Meteorological data come from the UGT Automatic Meteorological Station
(produced by UGT Germany, 41°21′13″ N, 111°12′27″ E, the altitude is 1600 m, Figure 1)
in this study area. It is located on the same slope and has similar site conditions to the soil
moisture monitoring station. Meteorological data include air temperature, rainfall, soil
temperature, wind speed, and solar radiation. The air temperature sensor is 1.5 m from
the ground with a measurement accuracy of 0.1 °C, the rain gauge is a non-heated type
with an accuracy of 0.1 mm, and the buried depth of the soil temperature sensor is 5 cm.
The measurement range of wind speed is 0.5–40 m/s, and the measurement range of solar
radiation data is 0–1400 W/m
2
with an accuracy of 1 W/m
2
. The meteorological data’s
research period and time resolution are consistent with the soil moisture data.
Figure 1. The location of each monitoring station in the study area: (a) UGT Automatic
Meteorological Station (UGT AMS), (b) AZ-DT soil moisture monitoring station at BS (AZ-DT SMS
at BS), (c) AZ-DT soil moisture monitoring station at US (AZ-DT SMS at US).
2.3. Data Analysis
Firstly, we used the original soil moisture data to calculate the daily average and
monthly average values, and the Z-score method was used to standardize the soil
moisture time series:
𝑋 𝑋−𝑥
𝜎 (1)
where 𝑋 is the value after standardization, 𝑋 is the value to be standardized in the data,
and 𝑥 and 𝜎 are the mean and standard deviation of the time series respectively.
Then, we analyzed intermonthly periodic variation characteristics of soil moisture
using the wavelet analysis method, which can simultaneously realize time- and
frequency-domain analysis to reveal multiple cycles’ changes hidden in time series.
Compared with traditional time series analysis methods, it can characterize the local
Figure 1. The location of each monitoring station in the study area: (a) UGT Automatic Meteorological
Station (UGT AMS), (b) AZ-DT soil moisture monitoring station at BS (AZ-DT SMS at BS), (c) AZ-DT
soil moisture monitoring station at US (AZ-DT SMS at US).
Aberrant values of soil moisture that were outlierswere eliminated, and then the
average value of the data in the adjacent period was calculated for interpolation. Missing
and abnormal data were less than 5% of the total data volume, and their impact was
negligible. To ensure the reliability and consistency of the data, we used the soil moisture
observed by the UGT at a depth of 5 cm to verify the soil moisture observed by the AZ-DT.
The two soil moisture data sets were highly correlated and showed good consistency.
Meteorological data come from the UGT Automatic Meteorological Station (produced
by UGT Germany, 41
◦
21
′
13
′′
N, 111
◦
12
′
27
′′
E, the altitude is 1600 m, Figure 1) in this study
area. It is located on the same slope and has similar site conditions to the soil moisture
monitoring station. Meteorological data include air temperature, rainfall, soil temperature,
wind speed, and solar radiation. The air temperature sensor is 1.5 m from the ground with
a measurement accuracy of 0.1
◦
C, the rain gauge is a non-heated type with an accuracy
of 0.1 mm, and the buried depth of the soil temperature sensor is 5 cm. The measurement
range of wind speed is 0.5–40 m/s, and the measurement range of solar radiation data is
0–1400 W/m
2
with an accuracy of 1 W/m
2
. The meteorological data’s research period and
time resolution are consistent with the soil moisture data.
2.3. Data Analysis
Firstly, we used the original soil moisture data to calculate the daily average and
monthly average values, and the Z-score method was used to standardize the soil moisture
time series:
Xst =(X−x)
σ(1)
where
Xst
is the value after standardization,
X
is the value to be standardized in the data,
and xand σare the mean and standard deviation of the time series respectively.
Then, we analyzed intermonthly periodic variation characteristics of soil moisture
using the wavelet analysis method, which can simultaneously realize time- and frequency-
domain analysis to reveal multiple cycles’ changes hidden in time series. Compared with
traditional time series analysis methods, it can characterize the local characteristics of
time series at a different time and frequency for accurate frequency positioning for non-
stationary time series affected by multiple factors. The soil moisture time series can be
decomposed into discrete signals. The basic principle of wavelet analysis is to use a cluster
of wavelet functions to represent or approximate the signal. Therefore, the key to wavelet
Water 2024,16, 123 5 of 16
analysis is the wavelet function. The wavelet function refers to a type of function that
is oscillating and can quickly decay to zero, that is, the wavelet function
ψ(t)∈L2(R)
and satisfies: Z+∞
−∞ψ(t)dt =0 (2)
where
ψ(t)
is the fundamental wavelet function, which can form a cluster of functions
through scale expansion and translation on the time axis:
ψa,b(t)=|a|−1
2ψt−b
aa,b∈R,a=0 (3)
where
ψa,b(t)
is a sub-wavelet,
a
is a scale factor, which reflects the period length of the
wavelet, and
b
is the translation factor, which reflects the amount of translation in time. If
ψa,b(t)
is the sub-wavelet given by Formula (3), then for a given finite signal
f(t)∈L2(R)
,
the continuous wavelet transform equation is:
Wf(a,b)=|a|−1
2ZRf(t)ψt−b
adt (4)
where
Wf(a,b)
is the wavelet transform coefficient,
f(t)
is a signal or square integrable
function,
a
is the scaling scale,
b
is a translation parameter, and
ψt−b
a
is the complex
conjugate function of
ψt−b
a
. In actual situations, the time series are usually discrete. Set
the function
f(k∆t)
,
(k=1, 2, . . . , N;∆t is the sampling interval)
, then the discrete form of
the above formula is:
Wf(a,b)=|a|−1
2∆t∑N
k=1f(k∆t)ψk∆t−b
a(5)
The wavelet basis function selected in this study is a Morlet continuous complex
wavelet function, which can satisfy the multiple time-scale characteristics of soil moisture
in this experiment. Moreover, the phase difference between the real part and the imaginary
part of the complex wavelet function is
π
/2, which can eliminate the false oscillation
generated by using the real wavelet coefficient as the judgment basis. This study used
Morlet wavelet analysis in the Matlab wavelet toolbox to carry out continuous wavelet
transformation for soil moisture time series and calculate the wavelet coefficients under
every
a
and
b
value. The real part and modulus of the wavelet coefficients were used
to draw the contour map of the real part and wavelet coefficient modulus, respectively.
The wavelet real-part contour map represents the distribution characteristics of signals
at different times and frequencies. Positive and negative wavelet coefficients indicate
relatively high and low periods of soil moisture, respectively, and a wavelet coefficient of 0
indicates a sudden change point. The wavelet coefficient modulus represents the periodic
oscillation intensity of the corresponding period and scale, which is used to verify the
wavelet coefficient’s real part distribution.
Calculating the wavelet variance can further reflect the distribution of time series
fluctuation energy on each time scale and is used to determine the primary cycle in the
process of soil moisture change. The
x
value corresponding to the maximum peak value
of the curve in the wavelet variance map is primary cycle, and each primary cycle has
a corresponding real-part process line of the wavelet coefficient, which can identify the
fluctuation characteristics of soil moisture under the primary cycle. Integrate the square
value of the wavelet coefficient in the bdomain to get the wavelet variance:
Var(a)=Z+∞
−∞
Wf(a,b)
2db (6)
IBM SPSS
®
Statistics 25 software was used to conduct outlier tests and eliminate
abnormal data, while standardizing time series. Wavelet analysis was calculated by Matlab
Water 2024,16, 123 6 of 16
2016 and Office Excel software 2021. Correlation analysis was done by undertaken with
IBM SPSS statistics 25 software. Images were drafted in Origin 2018 software.
3. Results
3.1. Time Series of Soil Moisture
Figure 2shows the interannual variation trend of soil moisture in each soil layer of
0–30 cm at BS and US from May 2009 to August 2019, displaying a similar and consistent
fluctuation trend between the slope positions. Soil moisture in each soil layer showed a
significant upward trend (p< 0.05). From the upper to the lower layer, soil moisture at
the BS was consistently higher than at the US, averaging 20.22%, 12.94%, and 11.16%, in
contrast to 15.03%, 10.43%, and 9.59%, respectively. Soil moisture gradually decreases with
the deeper soil. The mean soil moisture in the 0–30 cm soil layer of BS was 1.26 times that of
US, the standard deviation of BS was 4.97, the standard deviation of US was 2.97, and there
were significant differences in soil moisture between different slope positions (p< 0.05).
Surface soil moisture at the BS and US fluctuates wildly, and the fluctuation range and
standard deviation decreases with deeper soil (Table 2). The correlation between rainfall
and the monthly variation in soil moisture (Table 2) shows that rainfall’s uncertainty and
pulse characteristics make the soil surface moisture more volatile, especially at the BS.
Water 2024, 16, 123 6 of 16
𝑉𝑎𝑟(𝑎)=𝑊
(𝑎,𝑏)
𝑑𝑏 (6)
IBM SPSS® Statistics 25 software was used to conduct outlier tests and eliminate
abnormal data, while standardizing time series. Wavelet analysis was calculated by
Matlab 2016 and Office Excel software 2021. Correlation analysis was done by undertaken
with IBM SPSS statistics 25 software. Images were drafted in Origin 2018 software.
3. Results
3.1. Time Series of Soil Moisture
Figure 2 shows the interannual variation trend of soil moisture in each soil layer of
0–30 cm at BS and US from May 2009 to August 2019, displaying a similar and consistent
fluctuation trend between the slope positions. Soil moisture in each soil layer showed a
significant upward trend (p < 0.05). From the upper to the lower layer, soil moisture at the
BS was consistently higher than at the US, averaging 20.22%, 12.94%, and 11.16%, in
contrast to 15.03%, 10.43%, and 9.59%, respectively. Soil moisture gradually decreases
with the deeper soil. The mean soil moisture in the 0–30 cm soil layer of BS was 1.26 times
that of US, the standard deviation of BS was 4.97, the standard deviation of US was 2.97,
and there were significant differences in soil moisture between different slope positions
(p < 0.05). Surface soil moisture at the BS and US fluctuates wildly, and the fluctuation
range and standard deviation decreases with deeper soil (Table 2). The correlation
between rainfall and the monthly variation in soil moisture (Table 2) shows that rainfall’s
uncertainty and pulse characteristics make the soil surface moisture more volatile,
especially at the BS.
Table 2. Statistical characteristics of soil moisture and correlation between monthly variation (MV)
and precipitation in each layer.
BS 0–10/% BS 10–20/% BS 20–30/% US 0–10/% US 10–20/% US 20–30/%
Average variation 3.57 3.04 2.45 3.18 1.76 1.52
Standard deviation 5.46 4.86 4.54 3.81 2.6 2.47
Correlation between
MV and precipitation 0.265 ** 0.267 ** 0.299 ** 0.086 0.204 * 0.207 *
* Significant correlation at the 0.05 level; ** Significant correlation at the 0.01 level.
Figure 2. Interannual variation in soil moisture in each layer.
Figure 2. Interannual variation in soil moisture in each layer.
Table 2. Statistical characteristics of soil moisture and correlation between monthly variation (MV)
and precipitation in each layer.
BS 0–10/% BS 10–20/% BS 20–30/% US 0–10/% US 10–20/% US 20–30/%
Average variation 3.57 3.04 2.45 3.18 1.76 1.52
Standard deviation 5.46 4.86 4.54 3.81 2.6 2.47
Correlation between MV
and precipitation 0.265 ** 0.267 ** 0.299 ** 0.086 0.204 * 0.207 *
Note(s): * Significant correlation at the 0.05 level; ** Significant correlation at the 0.01 level.
Water 2024,16, 123 7 of 16
3.2. Periodicity of Soil Moisture Characterized by Wavelet Analysis
Wavelet real-part contour maps of the six time series (0–10 cm, 10–20 cm, 20–30 cm
at BS, and 0–10 cm, 10–20 cm, 20–30 cm at the US) show the multi-time-scale periodic
distribution characteristics of the soil moisture (Figure 3).
Water 2024, 16, 123 7 of 16
3.2. Periodicity of Soil Moisture Characterized by Wavelet Analysis
Wavelet real-part contour maps of the six time series (0–10 cm, 10–20 cm, 20–30 cm
at BS, and 0–10 cm, 10–20 cm, 20–30 cm at the US) show the multi-time-scale periodic
distribution characteristics of the soil moisture (Figure 3).
Overall, there are multiple time-scale characteristics of the change process of soil
moisture from May 2009 to August 2019. There are cyclical changes in soil moisture on
scales of 6–11 months, 12–24 months, 25–42 months, and 51–64 months at the BS. On the
time scale of 51–64 months, the cyclical change in soil moisture experienced a global
alternation of four high-value and three low-value periods, and on the time scale of 25–42
months, the cyclical change in soil moisture experienced a global alternation of six high-
value and six low-value periods, but the intensity of cyclical oscillations on these two time
scales was weak. As time increases, the periodic oscillation of soil moisture on this time
scale gradually increases, and the phenomenon of increased periodic oscillation is more
obvious at 10–30 cm (Figure 3c,e). On the time scale of 6–11 months and 12–24 months,
the intensity of cyclical oscillations of soil moisture was high, and on the time scale of 12–
24 months, soil moisture underwent five “high–low” oscillation alternations between
January 2010 and June 2015.
Figure 3. Wavelet real-part contour map of each layer of soil moisture: (a) BS 0–10 cm, (b) US 0–10
cm, (c) BS 10–20 cm, (d) US 10–20 cm, (e) BS 20–30 cm, and (f) US 20–30 cm.
There are cyclical changes in soil moisture on the scales of 6 to 24 months and 30 to
64 months at the US. Compared with the BS, the periodic distribution on each time scale
is complex at the US, and the periodic oscillations on each time scale transform with each
other. At 0–30 cm, the periodic oscillations on the 30- to 54-month time scale were obvious,
and became weaker after January 2014. The periodic oscillations on the 48- to 64-month
Figure 3. Wavelet real-part contour map of each layer of soil moisture: (a) BS 0–10 cm, (b) US 0–10 cm,
(c) BS 10–20 cm, (d) US 10–20 cm, (e) BS 20–30 cm, and (f) US 20–30 cm.
Overall, there are multiple time-scale characteristics of the change process of soil moisture
from May 2009 to August 2019. There are cyclical changes in soil moisture on scales of
6–11 months, 12–24 months, 25–42 months, and 51–64 months at the BS. On the time scale of
51–64 months, the cyclical change in soil moisture experienced a global alternation of four
high-value and three low-value periods, and on the time scale of 25–42 months, the cyclical
change in soil moisture experienced a global alternation of six high-value and six low-value
periods, but the intensity of cyclical oscillations on these two time scales was weak. As time
increases, the periodic oscillation of soil moisture on this time scale gradually increases, and
the phenomenon of increased periodic oscillation is more obvious at 10–30 cm (Figure 3c,e).
On the time scale of 6–11 months and 12–24 months, the intensity of cyclical oscillations of
soil moisture was high, and on the time scale of 12–24 months, soil moisture underwent five
“high–low” oscillation alternations between January 2010 and June 2015.
There are cyclical changes in soil moisture on the scales of 6 to 24 months and 30 to
64 months at the US. Compared with the BS, the periodic distribution on each time scale is
complex at the US, and the periodic oscillations on each time scale transform with each
other. At 0–30 cm, the periodic oscillations on the 30- to 54-month time scale were obvious,
and became weaker after January 2014. The periodic oscillations on the 48- to 64-month
time scale were enhanced, and the periodic oscillations on the 30- to 54-month time scale
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shifted to 48 to 64 months. Meanwhile, compared with the BS, the cyclical change in soil
moisture experienced a global alternation on the time scale of 6 to 24 months (Figure 3b,d,f).
Figure 4shows the contour map of the wavelet coefficient modulus of the six soil mois-
ture time series. The modulus variation is consistent with the strong periodic oscillation
distribution in the real-part contour map. The periodic intensity on the 25- to 42-month
time scale is global during the study period at the BS; however, as the soil deepens, the
cyclical changes in soil moisture on this time scale slightly weaken, and the weakening
trend is more evident before January 2015. Meanwhile, January 2015 was also the time node
when the modulus disappeared on the 9-month and 18-month time scales. The periodic
intensity on the 36- to 54-month and 48- to 64-month time scales was apparent during the
study period at the US. In particular, with the deepening of the soil, the 30- to 54-month
periodic oscillation shifted to the 48- to 64-month periodic oscillation more obviously. The
periodic change on other time scales was not apparent.
Water 2024, 16, 123 8 of 16
time scale were enhanced, and the periodic oscillations on the 30- to 54-month time scale
shifted to 48 to 64 months. Meanwhile, compared with the BS, the cyclical change in soil
moisture experienced a global alternation on the time scale of 6 to 24 months (Figure
3b,d,f).
Figure 4 shows the contour map of the wavelet coefficient modulus of the six soil
moisture time series. The modulus variation is consistent with the strong periodic
oscillation distribution in the real-part contour map. The periodic intensity on the 25- to
42-month time scale is global during the study period at the BS; however, as the soil
deepens, the cyclical changes in soil moisture on this time scale slightly weaken, and the
weakening trend is more evident before January 2015. Meanwhile, January 2015 was also
the time node when the modulus disappeared on the 9-month and 18-month time scales.
The periodic intensity on the 36- to 54-month and 48- to 64-month time scales was
apparent during the study period at the US. In particular, with the deepening of the soil,
the 30- to 54-month periodic oscillation shifted to the 48- to 64-month periodic oscillation
more obviously. The periodic change on other time scales was not apparent.
Figure 4. Contour map of wavelet coefficient modulus of each layer of soil moisture: (a) BS 0–10 cm,
(b) US 0–10 cm, (c) BS 10–20 cm, (d) US 10–20 cm, (e) BS 20–30 cm, and (f) US 20–30 cm.
The soil moisture at the BS has four primary cycles: 9-month, 18-month, and 34 to 36-
month, respectively. The 18-month cycle is the strongest, followed by the 9-month, and
34- to 36-month. The first primary cycle of soil moisture at the US is 63 months for all
layers, but the second and third primary cycles are different by layer (Figure 5). The
second primary cycles of 0–10 cm, 10–20 cm and 20–30 cm were 9-month, 19-month and
52-month, and the third primary cycles of 0–10 cm, 10–20 cm and 20–30 cm were 19-month,
Figure 4. Contour map of wavelet coefficient modulus of each layer of soil moisture: (a) BS 0–10 cm,
(b) US 0–10 cm, (c) BS 10–20 cm, (d) US 10–20 cm, (e) BS 20–30 cm, and (f) US 20–30 cm.
The soil moisture at the BS has four primary cycles: 9-month, 18-month, and 34 to
36-month, respectively. The 18-month cycle is the strongest, followed by the 9-month, and
34- to 36-month. The first primary cycle of soil moisture at the US is 63 months for all
layers, but the second and third primary cycles are different by layer (Figure 5). The second
primary cycles of 0–10 cm, 10–20 cm and 20–30 cm were 9-month, 19-month and 52-month,
and the third primary cycles of 0–10 cm, 10–20 cm and 20–30 cm were 19-month, 11-month
and 19-month, respectively. Compared with the BS, the distribution of primary cycles in
the soil layers of the US is inconsistent.
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11-month and 19-month, respectively. Compared with the BS, the distribution of primary
cycles in the soil layers of the US is inconsistent.
The real-part process line of wavelet coefficients (Figure 6) shows that on the 18-, 9-,
34 to 36-month time scale, the average cycle of soil moisture change at the BS is about 12
(1a), 6 (0.5a), and 24 months (2a), respectively. For the 18-month time scale, the intensity
of cyclical oscillations was relatively small before July 2011 and after January 2015. The
periodic fluctuation of soil moisture in 10–20 cm and 20–30 cm is relatively weak
compared to 0–10 cm on this 34- to 36-month time scale in the early period of this study.
At the US, the average cycle of soil moisture change is about 42 (3.5a), 36 (3a), 12 (1a), and
6 (0.5a) months on the 63-, 52-, 19-, 9-month time scales, respectively. On the 63-month
time scale, the intensity of cyclical oscillations increases gradually over time, and cyclical
oscillations of soil moisture on the 19- and 9-month time scales are similar to that of BS. It
is worth noting that the periodic oscillation of the 52-month time scale only exists in 20–
30 cm of US, and the periodic variation waveform of soil moisture is better (Figure 7).
Figure 5. Wavelet variance map of soil moisture in each layer.
Figure 6. The real-part process line of wavelet coefficients of the BS soil moisture on each time
scale.
Figure 5. Wavelet variance map of soil moisture in each layer.
The real-part process line of wavelet coefficients (Figure 6) shows that on the 18-, 9-,
34 to 36-month time scale, the average cycle of soil moisture change at the BS is about
12 (1a), 6 (0.5a), and 24 months (2a), respectively. For the 18-month time scale, the intensity
of cyclical oscillations was relatively small before July 2011 and after January 2015. The
periodic fluctuation of soil moisture in 10–20 cm and 20–30 cm is relatively weak compared
to 0–10 cm on this 34- to 36-month time scale in the early period of this study. At the US,
the average cycle of soil moisture change is about 42 (3.5a), 36 (3a), 12 (1a), and 6 (0.5a)
months on the 63-, 52-, 19-, 9-month time scales, respectively. On the 63-month time scale,
the intensity of cyclical oscillations increases gradually over time, and cyclical oscillations
of soil moisture on the 19- and 9-month time scales are similar to that of BS. It is worth
noting that the periodic oscillation of the 52-month time scale only exists in 20–30 cm of US,
and the periodic variation waveform of soil moisture is better (Figure 7).
Water 2024, 16, 123 9 of 16
11-month and 19-month, respectively. Compared with the BS, the distribution of primary
cycles in the soil layers of the US is inconsistent.
The real-part process line of wavelet coefficients (Figure 6) shows that on the 18-, 9-,
34 to 36-month time scale, the average cycle of soil moisture change at the BS is about 12
(1a), 6 (0.5a), and 24 months (2a), respectively. For the 18-month time scale, the intensity
of cyclical oscillations was relatively small before July 2011 and after January 2015. The
periodic fluctuation of soil moisture in 10–20 cm and 20–30 cm is relatively weak
compared to 0–10 cm on this 34- to 36-month time scale in the early period of this study.
At the US, the average cycle of soil moisture change is about 42 (3.5a), 36 (3a), 12 (1a), and
6 (0.5a) months on the 63-, 52-, 19-, 9-month time scales, respectively. On the 63-month
time scale, the intensity of cyclical oscillations increases gradually over time, and cyclical
oscillations of soil moisture on the 19- and 9-month time scales are similar to that of BS. It
is worth noting that the periodic oscillation of the 52-month time scale only exists in 20–
30 cm of US, and the periodic variation waveform of soil moisture is better (Figure 7).
Figure 5. Wavelet variance map of soil moisture in each layer.
Figure 6. The real-part process line of wavelet coefficients of the BS soil moisture on each time
scale.
Figure 6. The real-part process line of wavelet coefficients of the BS soil moisture on each time scale.
Water 2024,16, 123 10 of 16
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Figure 7. The real-part process line of wavelet coefficients of the US soil moisture on each time scale.
3.3. The Relationship between Soil Moisture and Influencing Factors
It is worth noting that the soil moisture changes for the period of 1a on the 18-month
time scale, and the intensity of periodic oscillation reflects the intensity of the seasonal
variation in soil moisture. As mentioned previously, January 2015 is an important time
node for periodic oscillation of soil moisture. At the BS, the periodic oscillation on the 18-
month time scale suddenly disappeared in this time node (Figures 4 and 6). Taking
January 2015 as the boundary, two time periods were divided: May 2009–January 2015
(pre-period) and February 2015–August 2019 (post-period) to examine the abrupt changes
in soil water oscillation intensity.
There were no significant differences in soil moisture (SM), air temperature (AT),
rainfall (R), wind speed (WS), soil temperature (ST), or solar radiation (SR) between the
two periods (p > 0.05) (Table 3). As the primary factor affecting soil moisture change, the
change in air temperature, rainfall, and soil temperature in the study area showed
noticeable seasonal change (Figure 8) In the pre-period, soil temperature, air temperature,
and rainfall were significantly positively correlated with soil moisture (p > 0.05), while in
the post-period, soil moisture was not significantly correlated with any influencing factor
(Table 4). Under the influence of seasonal meteorological factors, the periodic oscillation
on the 63-month time scale was weakened in the pre-period. The periodic oscillation on
the 18-month time scale was prominent in the pre-period. As the correlation became
insignificant in the post-period, the periodic oscillation on the 18-month time scale also
disappeared in January 2015. Therefore, the variation in soil moisture oscillation intensity
at the BS might not result from the mutation of soil moisture, but from the influence of
temperature and rainfall after 2015 nonlinearly.
Table 3. Statistical characteristics of BS soil moisture and its influencing factors in two time periods.
SM 0–10/% SM 10–20/% SM 20–30/% AT/°C R/mm WS/m·s−1 ST/°C SR/W·m−2
Total$$(n =
124)
Mean 20.22 12.94 11.16 3.29 20.15 2.52 7.03 189.88
SD 5.49 4.88 4.55 12.98 24.63 0.7 12.38 62.44
CV 0.27 0.38 0.41 3.94 1.22 0.28 1.76 0.33
Pre-
period$$(n =
69)
Mean 19.99 12.38 10.94 2.82 21.66 2.62 6.51 186.6
SD 6.05 5.29 4.94 13.21 25.52 0.78 12.2 61.73
CV 0.3 0.41 0.45 4.68 1.18 0.3 1.87 0.33
Figure 7. The real-part process line of wavelet coefficients of the US soil moisture on each time scale.
3.3. The Relationship between Soil Moisture and Influencing Factors
It is worth noting that the soil moisture changes for the period of 1a on the 18-month
time scale, and the intensity of periodic oscillation reflects the intensity of the seasonal
variation in soil moisture. As mentioned previously, January 2015 is an important time
node for periodic oscillation of soil moisture. At the BS, the periodic oscillation on the
18-month time scale suddenly disappeared in this time node (Figures 4and 6). Taking
January 2015 as the boundary, two time periods were divided: May 2009–January 2015
(pre-period) and February 2015–August 2019 (post-period) to examine the abrupt changes
in soil water oscillation intensity.
There were no significant differences in soil moisture (SM), air temperature (AT),
rainfall (R), wind speed (WS), soil temperature (ST), or solar radiation (SR) between the
two periods (p> 0.05) (Table 3). As the primary factor affecting soil moisture change,
the change in air temperature, rainfall, and soil temperature in the study area showed
noticeable seasonal change (Figure 8) In the pre-period, soil temperature, air temperature,
and rainfall were significantly positively correlated with soil moisture (p> 0.05), while in
the post-period, soil moisture was not significantly correlated with any influencing factor
(Table 4). Under the influence of seasonal meteorological factors, the periodic oscillation
on the 63-month time scale was weakened in the pre-period. The periodic oscillation
on the 18-month time scale was prominent in the pre-period. As the correlation became
insignificant in the post-period, the periodic oscillation on the 18-month time scale also
disappeared in January 2015. Therefore, the variation in soil moisture oscillation intensity
at the BS might not result from the mutation of soil moisture, but from the influence of
temperature and rainfall after 2015 nonlinearly.
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Table 3. Statistical characteristics of BS soil moisture and its influencing factors in two time periods.
SM
0–10/%
SM
10–20/%
SM
20–30/% AT/◦C R/mm
WS/m
·
s
−1ST/◦C
SR/W
·
m
−2
Total
(n= 124)
Mean 20.22 12.94 11.16 3.29 20.15 2.52 7.03 189.88
SD 5.49 4.88 4.55 12.98 24.63 0.7 12.38 62.44
CV 0.27 0.38 0.41 3.94 1.22 0.28 1.76 0.33
Pre-period
(n= 69)
Mean 19.99 12.38 10.94 2.82 21.66 2.62 6.51 186.6
SD 6.05 5.29 4.94 13.21 25.52 0.78 12.2 61.73
CV 0.3 0.41 0.45 4.68 1.18 0.3 1.87 0.33
Post-period
(n= 55)
Mean 20.51 13.08 11.44 3.88 18.27 2.39 7.68 194
SD 4.71 4.36 4.05 12.76 23.56 0.58 12.69 63.64
CV 0.23 0.33 0.35 3.29 1.29 0.24 1.65 0.33
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Post-
period$$(n =
55)
Mean 20.51 13.08 11.44 3.88 18.27 2.39 7.68 194
SD 4.71 4.36 4.05 12.76 23.56 0.58 12.69 63.64
CV 0.23 0.33 0.35 3.29 1.29 0.24 1.65 0.33
Table 4. Correlation between BS soil moisture and its influencing factors in two time periods.
Soil Layer/cm AT/°C R/mm WS/m·s−1 ST/°C SR/W·m−2
Total$$(n = 124)
0–10 0.293 ** 0.258 ** −0.083 0.291 ** 0.175
10–20 0.17 0.142 −0.077 0.162 0.072
20–30 0.291 ** 0.231 ** −0.099 0.283 ** 0.163
Pre-period$$(n =
69)
0–10 0.321 ** 0.301 * −0.21 0.331 ** 0.16
10–20 0.19 0.19 −0.19 0.2 0.04
20–30 0.316 ** 0.305 * −0.21 0.326 ** 0.13
Post-period$$(n =
55)
0–10 0.25 0.2 0.22 0.23 0.2
10–20 0.13 0.07 0.16 0.1 0.11
20–30 0.25 0.12 0.15 0.22 0.21
* Significant correlation at the 0.05 level; ** Significant correlation at the 0.01 level.
Figure 8. Variation characteristics of soil temperature, air temperature and rainfall during the study
period.
4. Discussion
4.1. The Time-Series Trend Characteristics of Soil Moisture and Influencing Factors
Soil moisture in the desert steppe in the study area increased significantly. Wang et
al. [44] also found that soil moisture showed an overall upward trend in the Mongolian
Plateau but an insignificant one, probably due to the large spatial scale. During the study
period, there were significant differences in soil moisture between different slope
positions and different depths, and the total amount and volatility of soil moisture
gradually decreased with the deepening of soil depth, consistent with Fang et al. [45] and
Zhang et al. [46]. Compared with deep soil, the surface soil is more sensitive to the
supplement of rainfall and the surface evaporation dominated by temperature change,
which leads to the fluctuation in surface soil moisture. The correlation analysis of this
study also proves this point. The correlation between surface soil moisture and
temperature and precipitation is more significant. The correlation between rainfall and
monthly changes in soil moisture (Table 2) shows similar results to those reported by Zou
et al. [47].
In arid and semi-arid areas, rainfall is the main source of soil moisture, the
uncertainty and pulse characteristics of rainfall signal itself are transmitted from the soil
surface to the lower layer, and the rainfall signal is attenuated in the downward
Figure 8. Variation characteristics of soil temperature, air temperature and rainfall during the
study period.
Table 4. Correlation between BS soil moisture and its influencing factors in two time periods.
Soil
Layer/cm AT/◦C R/mm WS/m·s−1ST/◦C SR/W·m−2
Total
(n= 124)
0–10 0.293 ** 0.258 ** −0.083 0.291 ** 0.175
10–20 0.17 0.142 −0.077 0.162 0.072
20–30 0.291 ** 0.231 ** −0.099 0.283 ** 0.163
Pre-period
(n= 69)
0–10 0.321 ** 0.301 * −0.21 0.331 ** 0.16
10–20 0.19 0.19 −0.19 0.2 0.04
20–30 0.316 ** 0.305 * −0.21 0.326 ** 0.13
Post-
period
(n= 55)
0–10 0.25 0.2 0.22 0.23 0.2
10–20 0.13 0.07 0.16 0.1 0.11
20–30 0.25 0.12 0.15 0.22 0.21
Note(s): * Significant correlation at the 0.05 level; ** Significant correlation at the 0.01 level.
4. Discussion
4.1. The Time-Series Trend Characteristics of Soil Moisture and Influencing Factors
Soil moisture in the desert steppe in the study area increased significantly. Wang
et al. [
44
] also found that soil moisture showed an overall upward trend in the Mongolian
Plateau but an insignificant one, probably due to the large spatial scale. During the
study period, there were significant differences in soil moisture between different slope
Water 2024,16, 123 12 of 16
positions and different depths, and the total amount and volatility of soil moisture gradually
decreased with the deepening of soil depth, consistent with Fang et al. [
45
] and Zhang
et al. [
46
]. Compared with deep soil, the surface soil is more sensitive to the supplement of
rainfall and the surface evaporation dominated by temperature change, which leads to the
fluctuation in surface soil moisture. The correlation analysis of this study also proves this
point. The correlation between surface soil moisture and temperature and precipitation is
more significant. The correlation between rainfall and monthly changes in soil moisture
(Table 2) shows similar results to those reported by Zou et al. [47].
In arid and semi-arid areas, rainfall is the main source of soil moisture, the uncertainty
and pulse characteristics of rainfall signal itself are transmitted from the soil surface to the
lower layer, and the rainfall signal is attenuated in the downward transmission process,
resulting in the decrease in the value and fluctuation of soil moisture with the deepening of
soil, which is more volatile in BS 0–10 cm soil moisture. Consistently with the results of this
study, Sun et al. [
35
] also believed that the amplitude of soil moisture signal will decrease
with the increasing soil depth.
The mean value and standard deviation of soil moisture between BS and US were
different, which was consistent with the results found by Meng et al. [
32
] and Zhao et al. [
48
]
In addition to the supplement of precipitation, the soil moisture at the lower slope position
is also supplemented by the surface runoff and interflow of other high terrains. When a
rainfall event of >25 cm occurs, the soil moisture content at the BS increases or even reaches
supersaturation to form surface confluence [
49
]. Furthermore, surface soil moisture at
the BS is more volatile than at the US due to surface runoff and soil flow from other high
terrains, increasing the soil moisture content and uncertainty [
45
]. As a static influencing
factor of soil moisture, although there is little difference in the physical properties of soil at
the BS and US, soil porosity and clay content at the BS are higher (Table 1), which have an
important impact on soil water storage and holding capacity [13].
4.2. Multi-Scale Periodic Variation Characteristics of Soil Moisture
The periodic variations in soil moisture are complex, and the smaller periodic changes
are nested within the larger periodic changes. The real-part process line of the wavelet
coefficient (Figures 7and 8) shows that the average cycle of soil moisture change at
9 months, 18 months, 34 to 36 months, 52 months and 63 months were 0.5a, 1a, 2a, 3a
and 3.5a, respectively. This means that the soil moisture has alternating dry and wet
cycles of 0.5a, 1a, 2a, 3a and 3.5a. Huang and Ding [
50
] found that soil surface moisture
had periodic fluctuations of 5.5 years around the research area, consistent with the 63-
month first primary cycle of soil moisture obtained in this study. On a larger spatial scale,
Ma et al. [
29
] found that soil moisture has a 3- to 4-year cycle in the mid-latitude region,
which is basically consistent with the periodic variation (3a) of soil moisture in this study.
Some scholars have also found that atmospheric factors have similar periodic fluctuations
with soil moisture in the region. Sun [
51
] showed that temperature changed periodically
for three years, consistent with the periodic change (3a) of soil moisture at the US in this
research. Jiang et al. [
52
] found that the periodic fluctuation in precipitation within 4 to
5 years was larger than the fluctuation period of soil moisture in this study, which may be
caused by the difference in rainfall in the study area.
Soil moisture was positively correlated with temperature and precipitation. Influenced
by seasonal atmospheric factors (Figure 8), soil moisture also changed in a seasonal period
of 1a. Compared with deep soils, surface soil moisture is more susceptible to periodic
atmospheric factors, radiation fluxes and vegetation communities, especially in temperate
regions with alternating seasons [
49
,
53
]. The 2a periodic change in soil moisture mainly
shows the interannual alternation between the high-value period and the low-value period,
and a low-value year and a high-value year constitute a 2a variation cycle of soil moisture. It
is worth noting that the periodic oscillation on the 63-month time scale at the US gradually
increases with time, and it is speculated that this time scale will remain for the main cycle
and the periodic oscillation will continue to increase in the next few years. For the 9- and
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18-month time scales, the strength of the periodic oscillations was relatively small before
July 2011 and after January 2015. The periodic oscillation of soil moisture reflects the
replacement of dry and wet soil in this region, while in the BS, the periodic oscillation
waveform is poor and the periodic oscillation is relatively weak on all time scales except
the 18- and 9-month ones (Figure 7).
4.3. The Difference in Soil Moisture Periodic Variations at Different Slope Positions
The characteristics of periodic variation in soil moisture on different slope positions
are obviously different, including the differences in the primary cycle of soil moisture and
the intensity of periodic oscillation. The periodic oscillation of soil moisture, dominated
by the 18-month and 9-month time scales at the BS is more robust than the 9- to 19-
month one at the US; however, the periodic oscillation of soil moisture, dominated by the
63-month and 52-month time scales at the US, is more robust than the same time scales at
the BS. The periodic variation in soil moisture is the result of the comprehensive action
of climate, soil, vegetation and topography, etc. As a dynamic influencing factor of soil
moisture, the dynamic change in climate factors at various scales is the main influencing
factor for the periodic oscillation of soil moisture. The precipitation redistribution caused
by slope position resulted in differences in vegetation (root distribution, water holding
capacity of plants, etc.), soil properties (bulk density, soil permeability, etc.), and surface
heat characteristics (soil water–heat flux, etc.), which weakened or strengthened the effect
of atmospheric factors on the periodic variation in soil moisture. However, with the
increase in the research scale, this effect would gradually decrease. Temperature (air
temperature and soil temperature) is the critical factor causing soil moisture fluctuation in
the dry–wet climate transition zone [
27
]. Studies have shown that the positive feedback
effect of temperature change on soil moisture in global climate change is greater than the
supplementary effect of rainfall on soil moisture. Under the long-term disturbance of
climate change and SST anomalies, the coupling mechanism between land and air in the
dry–wet climate transition zone will become more complex and uncertain [54].
This study provides a scientific basis for quantitative monitoring of drought and
flood disasters in desert steppe areas in the future. However, a complete understanding
of drought evolution still needs to combine meteorological drought and socioeconomic
indicators. As an essential link in the water cycle, under the influence of the atmospheric
circulation, soil properties, human activities, soil moisture reserves and periodicity show
large variability. In the larger space and water system, soil moisture and variability influ-
ence factors will become more complex. The future impact on the soil water cycle should
be focused on the whole regional water cycle system to examine periodic characteristics of
soil moisture through model simulation.
5. Conclusions
This paper analyzed the periodic variation in soil temperature in the desert steppe from
2009 to 2019 by applying a wavelet analysis method. The inherent multi-scale period in the
soil moisture time series was determined, and the difference in the structure and intensity
of soil moisture periodic variations at different slope positions was analyzed. These results
provide a new understanding of soil moisture evolution in the desert steppe environment.
The interannual variation in soil moisture has a noticeable upward trend. Moreover,
the volatility decreases with the increase in soil depth. Soil moisture was significantly
different at different slope positions. The soil moisture of BS is higher than that of US,
and the periodic structure and intensity of different slope positions are greatly different.
The soil moisture has periodic changes of 0.5a, 1a, 2a, 3a and 3.5a in the desert steppe
environment of Inner Mongolia. The soil moisture at the BS mainly has three types of
periodic variations: 0.5a, 1a, and 2a. The periodic oscillations of 0.5a and 1a are dominant
and stable. Compared with the BS, the soil moisture at the US has more complex multi-scale
periodic changes, and the soil moisture of the three soil layers mainly has four types of
periodic variations: 3.5a, 3a, 1a, and 0.5a. The periodic oscillations of 3.5a, 3a, and 1a are
Water 2024,16, 123 14 of 16
dominant and stable. On the periodic variations of 1a at the BS, the oscillation intensity
was relatively weakened after 2015. The weakening effect of temperature, rainfall, and soil
temperature on soil moisture is the main reason for changing soil moisture multi-time scale
cycle oscillation. Rainfall and soil moisture in this region have similar periodic fluctuations,
and the fluctuation in rainfall is still the main influencing factor of soil moisture in the
desert steppe region.
Author Contributions: Conceptualization, D.L. and Y.C.; methodology, D.L. and Y.C.; software,
D.L., Y.C. and Y.W.; validation, D.L., Y.C. and J.G.; formal analysis, D.L. and Y.C.; investigation,
D.L. and Y.C.; resources, L.S.; data curation, L.S.; writing—original draft preparation, D.L. and Y.C.;
writing—review and editing, X.L. and Z.F.; visualization, Y.C. and L.X.; supervision, L.S.; project
administration, X.L.; funding acquisition, L.S. All authors have read and agreed to the published
version of the manuscript.
Funding: This study was funded by the National Key Research and Development Program of China
(2018YFC0507005), the National Natural Science Foundation of China (32071840), Jiangsu Province
“333 Project” scientific research project (BRA2019069) and the Funding Project for advantageous
disciplines construction of Jiangsu higher education institutions.
Data Availability Statement: The datasets generated during the current study are available from the
corresponding author upon reasonable request.
Conflicts of Interest: The authors declare no conflicts of interest.
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