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Depth-Dependent Seasonal Variation of Soil Water in a Thick Vadose Zone in the Badain Jaran Desert, China

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Abstract

In a vadose zone the soil water content can change seasonally, driven by seasonal variations of meteorological factors. This dynamic behavior is depth-dependent, which controls the groundwater recharge from infiltration, and plays an essential role in the environments in arid and semi-arid regions. In particular, the depth-dependent seasonal variations of soil water were investigated in the Badain Jaran Desert (BJD), China, where the vadose zone is thick. The monitoring results showed that the amplitudes of temperature and soil moisture content in the shallow vadose zone (depth < 3 m) significantly decrease with depth. For the deep vadose zone (depth >3 m), the depth-dependent dynamic was synthetically estimated with both numerical and analytical models. Results show that the penetration depth of seasonal fluctuation is about 47 m, below which the infiltration flux stabilizes at a level of 30.7 ± 4 mm/yr. The depth to water table in the BJD is generally larger than 50 m, up to 480 m. As a consequence, groundwater recharge from infiltration in this area almost does not change in different seasons.
water
Article
Depth-Dependent Seasonal Variation of
Soil Water in a Thick Vadose Zone in the
Badain Jaran Desert, China
Yanyi Zhou , Xu-Sheng Wang * and Peng-Fei Han
MOE Key Laboratory of Groundwater Circulation and Environmental Evolution, China University of
Geosciences (Beijing), Beijing 100083, China; zhouyanyi@cugb.edu.cn (Y.Z.); hpf0328@126.com (P.-F.H.)
*Correspondence: wxsh@cugb.edu.cn; Tel.: +86-13718590051
Received: 9 October 2018; Accepted: 21 November 2018; Published: 24 November 2018


Abstract:
In a vadose zone the soil water content can change seasonally, driven by seasonal variations
of meteorological factors. This dynamic behavior is depth-dependent, which controls the groundwater
recharge from infiltration, and plays an essential role in the environments in arid and semi-arid
regions. In particular, the depth-dependent seasonal variations of soil water were investigated in the
Badain Jaran Desert (BJD), China, where the vadose zone is thick. The monitoring results showed that
the amplitudes of temperature and soil moisture content in the shallow vadose zone (
depth < 3 m
)
significantly decrease with depth. For the deep vadose zone (depth >3 m), the depth-dependent
dynamic was synthetically estimated with both numerical and analytical models. Results show that
the penetration depth of seasonal fluctuation is about 47 m, below which the infiltration flux stabilizes
at a level of 30.7
±
4 mm/yr. The depth to water table in the BJD is generally larger than 50 m, up to
480 m. As a consequence, groundwater recharge from infiltration in this area almost does not change
in different seasons.
Keywords:
soil water fluctuation; penetration depth; groundwater recharge; arid and semi-arid
region; Badain Jaran Desert
1. Introduction
Periodic variations of meteorological factors at the land surface, such as precipitation and
evapotranspiration, can be transmitted downward into the vadose zone by soil water movements,
causing fluctuations of available soil water for plants and of groundwater recharge from infiltration.
A typical behavior of this kind of dynamic is the seasonal variation of soil water content; rising in
some seasons and falling in the other seasons. Such a seasonal fluctuation of soil water content
is accompanied by the seasonal fluctuation of vertical flow rate, which is of great significance for
groundwater recharge from infiltration, especially in arid and semi-arid regions. Generally, a buffer
effect exists in the vadose zone so that fluctuation amplitudes of soil water content and flux decay with
increasing depth.
Depth-dependent soil water fluctuation has been investigated by Klute and Heermann (1974) [
1
]
with numerical models for a shallow depth (<3 m) and a short cycle (<2 h), which showed the decay
trend of fluctuation amplitude. Assuming a cosine type of time-varying infiltration rate at land surface,
Bakker and Nieber (2009) [
2
] presented an analytical formula for the amplitude of vertical soil water
flux damping with depth, and found that the decay rate is positively related to the fluctuation frequency.
This theoretical formula was used by Dickinson et al. (2014) [
3
], who defined the damping depth as
the depth at which the variation range of flux is only 5% of that at the land surface. According to their
estimation, when the seasonal fluctuation (a cycle of 365 d) is accounted for, the damping depth is
Water 2018,10, 1719; doi:10.3390/w10121719 www.mdpi.com/journal/water
Water 2018,10, 1719 2 of 15
generally 1 to 30 m in clay soils, but is up to 10 to 1000 m in sandy soils. These results highlighted the
depth-dependent fluctuation of soil water for groundwater recharge from precipitation infiltration
through a thick vadose zone, which widely exist in arid and semi-arid regions.
The Badain Jaran Desert (BJD), in China, is characterized by a thick vadose zone in mega sand
dunes that are generally 50 to 400 m higher than local depressions with lakes. The lakes are groundwater
fed and have been there for thousands of years or more [
4
]. Some highly controversial conjectures
of water origination in this desert were presented in the literature [
5
7
]. A key question that arose
was that could precipitation provide sufficient groundwater recharge via infiltration through the
thick unsaturated zone [
8
]? A paleorecharge rate of less than 5 mm/yr was suggested after analyzing
environmental tracer on unsaturated profiles [
7
,
9
12
], which seemed to be too small to counterbalance
the evaporation loss of lake water, which is about 940–1300 mm/yr [
4
,
13
]. The tracer method relies on
hydrochemistry information with a lot of uncertainties. It is necessary to solve the problem with direct
observation and analysis on the dynamic process of soil water. In the last decade, several researchers
carried out experimental studies on moving wetting fronts and varying soil water content in the vadose
zone after rains in the desert [
14
18
]. They found that the wetting depth was seldom more than 3 m
and thus set a negative conclusion for groundwater recharge from precipitation infiltration. In fact,
we should not speculate that there is no infiltration recharge when wetting fronts after rains could not
touch the water table at the zone below 3 m. The rain events behaved like a high frequency infiltration
fluctuation, which could only be transmitted, moving as wetting front, into a shallow zone due to
the damping effect [
2
,
3
]. This does not mean a zero-infiltration recharge, in contrast, is a result of the
environment whereby the deep unsaturated zone yields almost uniform downward water flux [
19
].
In recent years, numerical modeling of unsaturated flow in the desert has been carried out to analyze
the precipitation infiltration process according to long-term observation data [
20
,
21
]. It was found
that the infiltration rate at 3 m depth was 5 to 33 mm/yr, which was a plausible and more accurate
estimation of groundwater recharge. However, the thickness of the numerical model was limited to
3 m; therefore, less than the general thickness of the vadose zone in the desert.
In this paper, we analyze the seasonal variation of soil water moisture content, suction and
temperature at different depths according to the long-term monitoring data in the BJD. The general
details of study region and observation methods are presented in Section 2. The decay of fluctuation
amplitude with depth for soils in the shallow zone (depth <3 m) is identified in Section 3. For the
seasonal variation of soil water in the deep vadose zone (depth >3 m), which is hard to observe,
the theory of Bakker and Nieber (2009) [
2
] and the numerical modeling method for unsaturated flow
are used in the analysis and presented in Section 4. A new estimation of groundwater recharge in the
BJD is obtained with the method.
2. Study Area and Observation Methods
2.1. Study Area
The BJD is located in the Alxa Plateau in the western Inner Mongolia, China (Figure 1a),
between longitudes 99
48
0
E and 104
14
0
E, and between latitudes 39
30
0
N and 41
38
0
N, covering
an area of about 50,000 km
2
[
22
]. The desert is bounded by several mountains on the southern
(Beida Mountains) and eastern (Yabulai Mountains, Zongnai Mountains) sides. To the West and
North, it extends to the plains of Gurinai and Guaizihu. The Gurinai plain belongs to the Heihe River
Basin, where the second largest inland river in China, the Heihe River, transports water from the
Qilian Mountains to the Ejina basin, which lies on the Northwest of the BJD. The desert has been
well-known for having the world’s highest sand dunes, which can be up to 480 m high [
23
], and more
than 100 lakes of different sizes and salinity are distributed among the megadunes. These dunes and
lakes form a unique landscape of the Alxa Desert Geological Park. The BJD is a typical arid zone
characterized by low precipitation and high potential evaporation. The annual precipitation generally
ranged between 50 and 100 mm from 1957–2000. The mean annual potential evaporation observed by
Water 2018,10, 1719 3 of 15
the 20-cm-diameter evaporation pan was higher than 3000 mm from 1957–2000 [
24
], which indicates
an extremely arid climate in the desert.
For the hydrogeological conditions, the BJD is a part of the Yingen-Ejina basin [
25
,
26
], bounded by
mountains of magmatic rocks, in which the thick sedimentary formations function as the aquifer system
for groundwater flow, partly shown in Figure 1b. The Cretaceous sandstones are a porous-fractured
aquifer, with a thickness that normally ranges between 1000 and 3000 m, up to 4000 m [
24
,
27
].
The overlying Neogene sandstones are semi-consolidated and function as a confined porous aquifer,
of which the thickness is generally less than 400 m. On the top are the Quaternary sediments, with fine
to coarse sands, which function as an unconfined aquifer. The thickness of the saturated zone in the
Quaternary system is generally larger than 100 m. Groundwater level could be higher than 1200 m
near the mountains and less than 1000 m at the places of Gurinai and Guaizihu. This difference in
groundwater level triggers a regional groundwater flow from the South and East to the North and
West. Groundwater-fed lakes in the BJD are connected with the unconfined Quaternary aquifer and
have attracted a lot of research interest. However, the hydraulic properties of the aquifer are poorly
known because of limited hydrogeological surveys.
Water 2018, 10, x FOR PEER REVIEW 3 of 14
evaporation pan was higher than 3000 mm from 1957–2000 [24], which indicates an extremely arid
climate in the desert.
For the hydrogeological conditions, the BJD is a part of the Yingen-Ejina basin [25,26], bounded
by mountains of magmatic rocks, in which the thick sedimentary formations function as the aquifer
system for groundwater flow, partly shown in Figure 1b. The Cretaceous sandstones are a porous-
fractured aquifer, with a thickness that normally ranges between 1000 and 3000 m, up to 4000 m
[24,27]. The overlying Neogene sandstones are semi-consolidated and function as a confined porous
aquifer, of which the thickness is generally less than 400 m. On the top are the Quaternary sediments,
with fine to coarse sands, which function as an unconfined aquifer. The thickness of the saturated
zone in the Quaternary system is generally larger than 100 m. Groundwater level could be higher
than 1200 m near the mountains and less than 1000 m at the places of Gurinai and Guaizihu. This
difference in groundwater level triggers a regional groundwater flow from the South and East to the
North and West. Groundwater-fed lakes in the BJD are connected with the unconfined Quaternary
aquifer and have attracted a lot of research interest. However, the hydraulic properties of the aquifer
are poorly known because of limited hydrogeological surveys.
Figure 1. (a) Location of the Badain Jaran Desert; (b) a schematic geological profile between the
Gurinai and Yabulai Mountains, modified from Wang and Zhou (2018) [8].
2.2. Observation Methods
The data used in this study were obtained from a soil water monitoring station that was set up
by China University of Geosciences (Beijing) in the southeast of the BJD in 2012. It was located on a
sand dune to the north of Sumujaran South lake, with the longitude and latitude coordinates:
39°47′44.30″ N and 102°25′19.93″ E. The ground surface was relatively flat at the site and the altitude
was 1192 m a.s.l., which was 13 m higher than the water level in the nearby lake. Instruments for the
soil water monitoring experiments are shown in Figure 2. At the site, the soil water contents at the
depths of 0.2 m, 0.5 m, and 1.0 m (denoted as MS1, MS2, and MS3, respectively, in Figure 2) were
Figure 1.
(
a
) Location of the Badain Jaran Desert; (
b
) a schematic geological profile between the Gurinai
and Yabulai Mountains, modified from Wang and Zhou (2018) [8].
2.2. Observation Methods
The data used in this study were obtained from a soil water monitoring station that was set
up by China University of Geosciences (Beijing) in the southeast of the BJD in 2012. It was located
on a sand dune to the north of Sumujaran South lake, with the longitude and latitude coordinates:
39
47
0
44.30” N and 102
25
0
19.93” E. The ground surface was relatively flat at the site and the altitude
was 1192 m a.s.l., which was 13 m higher than the water level in the nearby lake. Instruments for the
Water 2018,10, 1719 4 of 15
soil water monitoring experiments are shown in Figure 2. At the site, the soil water contents at the
depths of 0.2 m, 0.5 m, and 1.0 m (denoted as MS1, MS2, and MS3, respectively, in Figure 2) were
measured using two-needle capacitance probes (AVALON, Dallas, TX, USA), at the accuracy level of
±
1%. In addition, the temperature and soil water matrix suction were monitored using pF-meters
(GeoPrecision GmbH, Ettlingen, Germany) installed at the depths of 0.2 m, 0.5 m, 1.0 m, 1.5 m, 2.0 m,
and 3.0 m (denoted as P1–P6 in Figure 2), at the accuracy level of
±
0.05
C for temperature and
±
1 cm
for suction. The distance between the sites of the capacitance probe and the pF-meter probe was less
than 2 m. Since the aeolian sand on the dune was relatively uniform, the monitoring data at the two
sites could be regarded as obtained at the same place. The instruments were powered by a solar panel.
1
Figure 2
Figure 5
1.5
P1
P2
P3
P4
P5
P6
MS1
MS2
MS3
AV-EC5 probes
monitoring profile
of water content
Depth (m)
1.0
2.0
0
3.0
0.5
2.5
monitoring profile of soil
suction and temperature
pF-meter suction and temperature probes
1192 m a.s.l.
……
1179 m a.s.l.
y = 2.055x + 11.003
R² = 0.9744
y = 2.9614x + 6.9273
R² = 0.8317
0
6
12
18
24
0 2 4 6
A
w
of temperature (°C)
A
w
of volumetric water content (%)
Depth <1 m
Depth >1 m
(b)
y = 0.8749ln(x) + 9.6655
R² = 0.9967
y = 1E-05x + 10.337
R² = 0.5402
0
6
12
18
24
10 1000 100000 10000000
A
w
of temperature (°C)
A
w
of soil suction (cm)
Depth <1 m
Depth >1 m
(a)
10
1
10
3
10
5
10
7
y=0.00001x+10.337
R
2
=0.5402
Figure 2. Schematic diagram of the instruments used for soil water observation.
A data logger was set up at the monitoring station to store all data that was extracted at the
frequency of 30 min. To capture the seasonal variations of soil water, at least one year of data has to
be collected. In this study, observation data in the period of 2013–2016 were selected, and their daily
averages were calculated to analyze the seasonal variations. The variations in the soil temperature and
suction during this period are shown in Figure 3a,b, respectively. However, it should be noted that
some of the suction data were lost during the winter of 2014 due to instrument failure.
Water 2018,10, 1719 5 of 15
16
Figure 3.
Daily data during the 2013–2016 period with respect to various depths (0.2 to 3.0 m) at
the soil monitoring station in the Badain Jaran Desert (BJD): (
a
) Temperature; (
b
) soil matrix suction;
and (c) volumetric soil water content.
3. Observed Soil Water Variation in the Shallow Zone
The soil monitoring data used in this study were within the depth of 3 m, which was relatively
shallow in comparison with the general thickness of the desert unsaturated zone, hence the observed
soil water variation only represents the characteristics of the shallow vadose zone in the BJD.
This section is focused on the depth-dependent features of the seasonal soil water variation within 3 m,
and the characteristics for deeper depths are analyzed in Section 4.
Water 2018,10, 1719 6 of 15
3.1. Data Processing
In the soil water monitoring station, the maximum depth of installed capacitance probes was 1 m,
so that only the soil water content variation within 1 m could be observed directly. In order to obtain
the water content at the depth of 1–3 m, converting the soil suction observed by pF-meter into the
water content was needed. The transformation was performed by using the van Genuchten model [
28
],
which is known as V-G formula for short, and can be written as follows:
θ=θr+(θsθr)1+|αh|nm,h<0; θ=θs,h0 (1)
where
θr
and
θs
denote the residual and saturated volumetric water contents (m
3
/m
3
), respectively;
his the soil water pressure head represented by the negative value of soil suction (m) for unsaturated
conditions;
α
is a parameter related to the air entry pressure (m
1
); nis a non-dimensional parameter
and n>1; and mis equal to (11/n).
To obtain the appropriate parameters of the V-G model (
θr
,
θs
,
α
,n), the synchronous soil water
content and suction data, measured at 0.2 m, 0.5 m, and 1.0 m, were used to adjust the parameters until
the moisture content converted from the suction data best matched the measured moisture content.
The results of the parameter optimization were:
θr
= 0.02,
θs
= 0.39,
α
= 5.2 m
1
,n= 2.8. The soil
moisture data at different depths are shown in Figure 3c.
To assess the seasonal variation of soil water, some quantitative indices were extracted from the
daily data, including the annual maximum value, the annual minimum value, the annual mean value,
and the times when the maximum and minimum values appear. The fluctuation amplitude of the
seasonal variation was defined as:
Aw=(Wmax Wmin)/2 (2)
where Wdenotes a physical variable with respect to the soil water (temperature, water content, suction,
etc.); W
max
and W
min
are the annual maximum and minimum values, respectively; and A
w
is the
fluctuation amplitude. The absent suction data in the winter of 2014 had to be speculated in order
to determine some of the indices in that year. The soil suction would increase significantly due to
the frozen effect when the temperature was less than 0
C. During the years studied, the variation of
temperature in the winter of 2013 was mostly close to that in the winter of 2014. Therefore, we used
the suction data in the winter of 2013 as an approximate replica for that in 2014.
3.2. Characteristics of the Seasonal Variations
It is shown in Figure 3a that the soil temperature changed in a large range. At the depth of 0.2 m,
the temperature varied from
20 to 40
C, the extreme low value occurred in December and the
maximum occurred in July, indicating a significant seasonal fluctuation on land surface. At deeper
positions, the seasonal fluctuation of temperature became weaker, and the occurrence times of the
maximum and minimum temperatures were delayed. The time lag at the depth of 1.5 m was about one
month, whereas it was larger than two months at the depth of 3 m. The changes in the extreme values,
mean value, and fluctuation amplitude of temperature with depth are shown in Figure 4a,b. It can be
seen that the minimum temperature increased, whereas the maximum value decreased with depth.
The annual mean temperature at different depths varied between 5.4 and 14.8
C. The mean annual
temperature was close to 12.4
C, and showed a slight increasing trend with depth, which may be
caused by the slight decrease of atmospheric temperature in the 2013–2016 period. Below the depth of
1 m, the temperature was always higher than 0
C, indicating the frozen depth at the site was limited
and the maximum frozen depth was 1 m. The fluctuation amplitude of temperature significantly
decreased with depth, from a value more than 20
C near the land surface to a value less than 7
C at
the depth of 3 m.
Water 2018,10, 1719 7 of 15
Water 2018, 10, x FOR PEER REVIEW 7 of 14
Figure 4. Depth-dependent indices in the shallow vadose zone: (a) Temperature; (b) fluctuation
amplitude of temperature; (c) soil suction; (d) fluctuation amplitude of soil suction; (e) volumetric
water content; and (f) fluctuation amplitude of volumetric water content.
Figure 3b shows that the soil suction also changed dramatically. The suction at depth of 0.2 m
ranged between 102 and 106 cm, indicating a normally dry condition. In fact, the suction at the depth
of 0.2 m could be reduced to a value below 10 cm in the summer by rains, but such low suction only
persisted for a few hours. After a rain, the suction increased rapidly by strong evaporation, so that
the daily average suction was still above 100 cm. Soil suction was also significantly altered by changes
in temperature around 0 °C due to the soil water freeze–thaw effect. Consequently, the maximum
soil suction occurred either in summer (by intense evaporation) or in winter (by freeze). In Figure
4c,d, the depth-dependent variations of extreme values, mean value, and fluctuation amplitude of
soil suction are shown. The seasonal variation of soil suction showed a significant decay trend with
depth when within the depth of 1 m; however, almost showed a depth-independent pattern in the
0
1
2
3
0 10 20 30
Depth (m)
A
w
of temperature (°C)
(b)
(c)
0
1
2
3
-20 0 20 40
Depth (m)
Temperature (°C)
maximum frozen depth
(a)
0
1
2
3
10 1000 100000 10000000
Depth(m)
Soil suction(cm H
2
O)
10
1
10
3
10
5
10
7
Soil suction (cm)
Depth (m)
0
1
2
3
1 100 10000 1000000
Depth(m)
Fluctuation amplitude of soil suction(cm
H
2
O)
10
0
10
2
10
4
10
6
(d)
A
w
of soil suction (cm)
Depth (m)
0
1
2
3
0 2 4 6 8
Depth(m)
Fluctuation amplitude of water
Fluctuation amplitude of volumetric water content(%)
Mean annual
Annual minimum
Annual mean
Annual maximum
Fluctuation amplitude
(f)
A
w
of volumetric water content (%)
Depth (m)
0
1
2
3
0 5 10 15
Depth(m)
Water content(%)
Volumetric water content(%)
(e)
Depth (m)
Volumetric water content (%)
Figure 4.
Depth-dependent indices in the shallow vadose zone: (
a
) Temperature; (
b
) fluctuation
amplitude of temperature; (
c
) soil suction; (
d
) fluctuation amplitude of soil suction; (
e
) volumetric
water content; and (f) fluctuation amplitude of volumetric water content.
Figure 3b shows that the soil suction also changed dramatically. The suction at depth of 0.2 m
ranged between 10
2
and 10
6
cm, indicating a normally dry condition. In fact, the suction at the depth
of 0.2 m could be reduced to a value below 10 cm in the summer by rains, but such low suction only
persisted for a few hours. After a rain, the suction increased rapidly by strong evaporation, so that the
daily average suction was still above 100 cm. Soil suction was also significantly altered by changes in
temperature around 0
C due to the soil water freeze–thaw effect. Consequently, the maximum soil
suction occurred either in summer (by intense evaporation) or in winter (by freeze). In Figure 4c,d,
the depth-dependent variations of extreme values, mean value, and fluctuation amplitude of soil
suction are shown. The seasonal variation of soil suction showed a significant decay trend with depth
Water 2018,10, 1719 8 of 15
when within the depth of 1 m; however, almost showed a depth-independent pattern in the zone
deeper than 1 m. This seemed to imply that, below the depth of 1 m, the soil water movement was
not dominated by the soil suction gradient, but rather driven by gravity. An unexpected increase in
A
w
was exhibited when the depth increased from 2 to 3 m, which may have been caused by the soil
heterogeneity in this zone. At the depth of 3 m, the mean annual fluctuation amplitude of soil suction
was close to 39 cm, and the maximum value was a bit higher than 50 cm, indicating that a 3 m depth
was not the extinction depth of seasonal soil water variations. However, the difference of A
w
between
depths of 2 and 3 m was not significant, and did not change the general decay trend of A
w
with depth.
The volumetric soil water content varied in a relatively small range (2–15%), as shown in Figure 3c.
The water content at the depth of 0.2 m was significantly influenced by rains and evaporation on land
surface, such that a dramatic fluctuation was seen. With the increasing depth, the fluctuation of water
content decayed gradually, or even disappeared. The moisture content at depths between 1–2 m slowly
declined in autumn and winter; however, slowly increased in spring and summer. Figure 4e,f show the
depth-dependent variation of water content and its fluctuation amplitude, respectively. The annual
mean value changed slightly with depth, but in an irregular pattern, varying in the range between
4% and 6%. Within a depth of 1 m, with the increase of depth, the maximum and minimum values
of water content quickly converged to the mean value. Accordingly, the fluctuation amplitude of
volumetric water content decreased from a value close to 6%, near the land surface, to about a value
that was less than 1% (below the accuracy of capacitance measurement), at the depth of 1 m. In the
zone deeper than 1 m, it seemed that the seasonal fluctuation of water content did not significantly
depend on depth. In addition, the increase in A
w
value at depths from 2 to 3 m seemed to be caused by
the soil heterogeneity; however, the effect was not significant.
The above results showed that there were similar depth-dependent features between temperature
and soil water content, for instance, both the fluctuations of temperature and soil water content decayed
sharply with depth in the upper 1-m-thickness layer. This seemed to indicate that temperature was a
control of the seasonal variation of soil water in the near surface zone. To understand the relationship
between them, the correlation analysis was carried out. It is clearly shown in Figure 5that both A
w
of
the logarithmic soil suction and A
w
of the volumetric water content were positively correlated with the
A
w
of temperature. The linear correlation coefficients (R
2
) were 0.540 and 0.831, respectively, for the
temperature–suction relationship and the temperature–water content relationship, as represented by
the red lines in Figure 5. However, the data points for that observed below the 1 m depth seemed to be
poorly correlated with the temperature because they were not significantly influenced by the seasonal
freezing–thawing process. Therefore, it was better to do the correlation analysis only on the data points
within the maximum frozen depth zone, as represented by the blue lines in Figure 5. In this shallow
zone, the A
w
of the logarithmic soil suction, shown in Figure 5a, had the highest positive correlation
with the A
w
of temperature (R
2
= 0.997), whereas the positive correlation was also high (R
2
= 0.974),
shown in Figure 5b, for the Awof the volumetric water content versus temperature.
Water 2018,10, 1719 9 of 15
1
Figure 2
Figure 5
1.5
P1
P2
P3
P4
P5
P6
MS1
MS2
MS3
AV-EC5 probes
monitoring profile
of water content
Depth (m)
1.0
2.0
0
3.0
0.5
2.5
monitoring profile of soil
suction and temperature
pF-meter suction and temperature probes
1192 m a.s.l.
……
1179 m a.s.l.
y = 2.055x + 11.003
R² = 0.9744
y = 2.9614x + 6.9273
R² = 0.8317
0
6
12
18
24
0 2 4 6
A
w
of temperature (°C)
A
w
of volumetric water content (%)
Depth <1 m
Depth >1 m
(b)
y = 0.8749ln(x) + 9.6655
R² = 0.9967
y = 1E-05x + 10.337
R² = 0.5402
0
6
12
18
24
10 1000 100000 10000000
A
w
of temperature (°C)
A
w
of soil suction (cm)
Depth <1 m
Depth >1 m
(a)
10
1
10
3
10
5
10
7
y=0.00001x+10.337
R
2
=0.5402
Figure 5.
The relationship plots for fluctuation amplitudes of different factors in the shallow vadose
zone: (a) temperature vs soil suction; (b) temperature vs volumetric soil water content.
4. Computing Soil Water Variation in the Deep Vadose Zone
In Section 3the attenuation of soil water fluctuations with depth in the shallow zone was identified
according to the observation data of soil water and temperature. However, for soil water in the deep
vadose zone the observation data were not available so that the seasonal variation had to be identified
with indirect approaches. In this section, we used both an analytical formula and a numerical model to
solve this problem, considering the depth of 3 m as a known boundary of the unsaturated flow in the
deep vadose zone.
4.1. Existing Analytical Formula
Generally, the one-dimensional vertical flow of soil water can be described by the Richards
equation [29] as follows:
C(h)h
t=
zK(h)h
zK
z(3)
where zis the relative depth (m); tis the time (d); K(h) is the hydraulic conductivity (m/d), which varies
with the pressure head; and Cis the soil moisture capacity (m
1
) that is estimated as C(h) =
∂θ
/
h.
The variation of
θ
with his expressed as the soil–water retention curve. Source and sink terms, due to
factors like root water uptake, were not included in Equation (3) because plants in the sand dunes
were sparse and the deep soil zone was not significantly influenced by roots.
An approximate analytical solution of Equation (3) was proposed by Bakker and Nieber [
2
],
who did not use the V-G formulas but rather used the Gardner-Kozeny model (G-K formulas for
short) to represent the soil–water retention curve and K(h) function. The G-K formulas can be written
as follows:
K=Ksexp[β(hhe)],θ=θsexp[µ(hhe)],h<he(4)
where K
s
is the saturated hydraulic conductivity (m/d); h
e
is the pressure head at air entry point
(m);
β
(m
1
) is a parameter dependent on the pore size distribution; and
µ
(m
1
) is a fitting
parameter. In their mathematical model, the top boundary condition is characterized by a sinusoidal
time-dependent infiltration flux (positive downward), q, which is expressed as:
q(z=0, t)=qs+Aqsin(ωt)(5)
where q
s
(m/d) is the average infiltration flux; A
q
(m/d) is the fluctuation amplitude of the periodic
infiltration flux at the top; and
ω
(d
1
) is the angular frequency. At the infinite depth, the flow is
uniform and expressed as q(z,t) = qs.
Water 2018,10, 1719 10 of 15
According to the analytical solution derived by Bakker and Nieber ([
2
], the variation of the
infiltration flux with depth and time can be expressed by a formula as follow:
q(z,t)=qs+Aqexp(z/λ)sin(ωtkz)(6)
where
λ
is the characteristic length of the amplitude attenuation, kis a coefficient of the phase shift,
and both of them depend on the parameters in Equations (4) and (5). The value of
λ
increases with the
increasing ωvalue, which can be calculated by [3]:
λ=2
β1+16ω2
β4D21/4 cosh1
2arctan4ω
β2Diβ
(7)
where Dis defined as
D=Ks
θsµqs
Ks(βµ)/β
(8)
It can be seen that the characteristic length is related to the average infiltration flux. The periodic
solution of hcan be also obtained from Equation (6), but does not show a sinusoidal fluctuation.
4.2. Numerical Model Based on Hydrus-1D
There were two limitations in the analytical formula proposed by Bakker and Nieber [
2
]:
(1) The soil-water retention curve was not described with the more widely used V-G formulas; and (2)
the top of model was settled as a known flux boundary with a sinusoidal time-dependent infiltration
flux, whereas the direct observation of the flux is quite difficult. In practice, it is not easy to use the
formula because only the data of soil moisture contents or pressure heads are available. To obtain
more realistic results, we established a numerical model of the unsaturated flow in the deep zone
with Hydrus-1D [
30
]. The modeling results were then used in comparison with the above analytical
model. Hydrus-1D solves the unsaturated flow with the Richards equation but describes the soil–water
retention curve with the V-G formulas, where the relationship between water content and pressure
head is shown in Equation (1), while the hydraulic conductivity is expressed as:
K(θ)=KsSelh11Se
1
mmi2,Se=θθr
θsθr(9)
where lis a pore connectivity parameter, which has been generally assumed to be 0.5 [28].
In order to avoid the complexity of the precipitation–evaporation and freeze–thaw processes,
only the soil water flow below the 3 m depth was simulated, and the actual measurement of the
pressure head at the depth of 3 m was taken as the upper boundary condition. The thickness of the
vertical one-dimensional model was 100 m. The pressure head gradient of the lower boundary was
assumed to be zero. The boundary conditions were then described as follows
h(z,t)=hsAhsin(ωt),z=3 m, t>0 (10)
h
z=0, z=103 m, t>0 (11)
where h
s
and A
h
are the mean annual and fluctuation amplitude of the pressure head, respectively.
According to the monitored data, which was presented in Section 3, we specified the control parameters
as: h
s
=
1.38 m, A
h
= 0.39 m, and
ω
=2
π
/365 d
1
. The model needed to be preheated, for which the
mean annual value of the pressure head was taken as the initial condition, and the repeated simulations
of many periodic processes were carried out until the simulation results were completely in a state of
periodic repetition and not affected by the initial condition. The preheating period should be at least
20 years according to the test results.
Water 2018,10, 1719 11 of 15
In the Hydrus-1D model, the variable time-step iterative method was used to match the
convergence, with the minimum of 0.00001 d and the maximum of 5 d. The convergence accuracy of
moisture content in the model was 0.1%. The effective interval of the element nodes were optimized
to balance the accuracy of the results and the computation cost, which was finally specified as 0.1 m.
The parameters of the soil–water retention curve have been given in Section 3.1, but the saturated
permeability coefficient K
s
has not been determined yet. The K
s
of aeolian sands obtained from
previous infiltration tests undertaken in the BJD were 0.5–75.0 m/d [
31
]. One test zone with an area
of 25 m
2
near the soil water monitoring station yielded K
s
values in the range of 6.6–46.0 m/d with
a Gaussian distribution [
31
]. In this study, the average infiltration effect at the site was focused on,
so that the arithmetic average Ksvalue, 25 m/d, was used in the Hydrus-1D model.
The seasonal variations of the soil water at the deep zone are exhibited in Figure 6, according to
the modeling results. With increasing depth, both the maximum and minimum pressure head values
changed and finally converged to a steady value of about
119 cm, as shown in Figure 6a, which was
close to the h
s
value. Since the measurement accuracy of soil suction by pF-meter was
±
1 cm, we took
the maximum ignorable change in pressure head to be 1 cm in order to define the penetration depth of
seasonal variation in this study. The penetration depth is the depth at which the fluctuation amplitude
of pressure head damps to 1 cm. As indicated in Figure 6a, it was 47.1 m for the seasonal soil water
variation simulated in the model. Correspondingly, the maximum and minimum volumetric water
contents moved to the value of 3.4% with increasing depth, as shown in Figure 6b. The fluctuation
amplitude of the water content was less than 0.1% in the zone below the penetration depth.
Water 2018, 10, x FOR PEER REVIEW 11 of 14
amplitude of pressure head damps to 1 cm. As indicated in Figure 6a, it was 47.1 m for the seasonal
soil water variation simulated in the model. Correspondingly, the maximum and minimum
volumetric water contents moved to the value of 3.4% with increasing depth, as shown in Figure 6b.
The fluctuation amplitude of the water content was less than 0.1% in the zone below the penetration
depth.
Figure 6. The depth-dependent maximum and minimum values of the pressure head (a) and
volumetric water content (b) in the deep zone, according to numerical modeling results.
4.3. Depth-Dependent Flux in the Deep Zone
The vertical soil water flux in the deep zone could be estimated with the analytical model
developed by Bakker and Nieber [2] or the numerical model presented in Section 4.2. The upper
boundary condition was specified in different ways: A sinusoidal time-dependent infiltration flux
was assumed in the analytical model, whereas a sinusoidal time-dependent pressure head was used
in the Hydrus-1D model. In order to compare the results of these two models, we applied the
following formula to calculate the flux for the numerical model:
 
 
 
 
 
 
, 1
h
q z t K h
z
(12)
where z is also the depth related to the top of the model.
According to the modeling results of Hydrus-1D, the infiltration fluxes at different depths were
obtained with Equation (12), including the infiltration flux at the top (where the real depth was 3 m),
as represented by the dashed line in Figure 7a. Obviously, variation of the infiltration flux with time
did not follow a sine function. The maximum q, which was 0.28 mm/d, occurred on the 264th day of
the year cycle. The average q was 0.08 mm/d and the fluctuation amplitude of q was 0.14 mm/d. No
negative q value existed, indicating the permanent downward flow of soil water was at the depth of
3 m below ground surface. The annual infiltration was 30.7 mm, which was estimated from the
average q value.
If the Bakker-Nieber model is used, the q value at the top has to be determined as described in
Equation (5). For comparison, we specified the average value and fluctuation amplitude for the
analytical model as the same of the numerical modeling results (i.e., qs = 0.08 mm/d and Aq = 0.14
mm/d). Therefore, the sinusoidal time-dependent variation of q for the analytical model was obtained,
as represented by the solid line in Figure 7a. Note that there existed a short term of negative q values
(upward soil water flow). In this situation, the depth-dependent of infiltration flux could be estimated
with Equation (6) after determining the parameters of the G-K formulas. By fitting the soil–water
retention curve with respect to the V-G formulas, the optimum matching parameters of the G-K
formulas were obtained, that were: he = 0, μ = 2.2 m−1, and β = 9.0 m−1. The other parameters were the
3
28
53
78
103
2.5 3 3.5 4
Depth (m)
Volumetric water content (%)
3
28
53
78
103
-180 -150 -120 -90
Depth (m)
Pressure head (cm)
(a) (b)
Figure 6.
The depth-dependent maximum and minimum values of the pressure head (
a
) and volumetric
water content (b) in the deep zone, according to numerical modeling results.
4.3. Depth-Dependent Flux in the Deep Zone
The vertical soil water flux in the deep zone could be estimated with the analytical model
developed by Bakker and Nieber [
2
] or the numerical model presented in Section 4.2. The upper
boundary condition was specified in different ways: A sinusoidal time-dependent infiltration flux was
assumed in the analytical model, whereas a sinusoidal time-dependent pressure head was used in
the Hydrus-1D model. In order to compare the results of these two models, we applied the following
formula to calculate the flux for the numerical model:
q(z,t)=K(h)1h
z(12)
where zis also the depth related to the top of the model.
According to the modeling results of Hydrus-1D, the infiltration fluxes at different depths were
obtained with Equation (12), including the infiltration flux at the top (where the real depth was 3 m),
Water 2018,10, 1719 12 of 15
as represented by the dashed line in Figure 7a. Obviously, variation of the infiltration flux with time
did not follow a sine function. The maximum q, which was 0.28 mm/d, occurred on the 264th day
of the year cycle. The average qwas 0.08 mm/d and the fluctuation amplitude of qwas 0.14 mm/d.
No negative qvalue existed, indicating the permanent downward flow of soil water was at the depth
of 3 m below ground surface. The annual infiltration was 30.7 mm, which was estimated from the
average qvalue.
If the Bakker-Nieber model is used, the qvalue at the top has to be determined as described
in Equation (5). For comparison, we specified the average value and fluctuation amplitude for
the analytical model as the same of the numerical modeling results (i.e., q
s
= 0.08 mm/d and
Aq= 0.14 mm/d
). Therefore, the sinusoidal time-dependent variation of qfor the analytical model
was obtained, as represented by the solid line in Figure 7a. Note that there existed a short term of
negative qvalues (upward soil water flow). In this situation, the depth-dependent of infiltration flux
could be estimated with Equation (6) after determining the parameters of the G-K formulas. By fitting
the soil–water retention curve with respect to the V-G formulas, the optimum matching parameters
of the G-K formulas were obtained, that were: h
e
= 0,
µ
= 2.2 m
1
, and
β
= 9.0 m
1
. The other
parameters were the same as those used in the Hydrus-1D model. By substituting the parameters into
Equations (7) and (8), the characteristic length was estimated as
λ
= 10.9 m. Subsequently, the change
in the fluctuation amplitude of the infiltration flux with depth, following Equation (6), could be
determined as Aqexp(z/λ).
Water 2018, 10, x FOR PEER REVIEW 12 of 14
same as those used in the Hydrus-1D model. By substituting the parameters into Equations (7) and
(8), the characteristic length was estimated as λ = 10.9 m. Subsequently, the change in the fluctuation
amplitude of the infiltration flux with depth, following Equation (6), could be determined as
Aqexp(−z/λ).
Figure 7. Infiltration flux in the deep zone: (a) The periodic variation of the infiltration flux at the
depth of 3 m; and (b) the change in the fluctuation amplitude of the infiltration flux along depth.
Two Aw-depth curves are shown in Figure 7b, which were obtained from the analytical and
numerical model. Overall, the two curves showed similar attenuation characteristics. The analytical
model yielded an extremely small value of the fluctuation amplitude for the zone below 50 m, which
was close to zero and significantly smaller than that in the numerical model. At the penetration depth,
47.1 m, of the seasonal variation that was determined from Section 4.2, the fluctuation amplitude of
the infiltration flux in the numerical model was damped to 7.8% of Aq, which was only 0.01 m/d.
Accordingly, below the depth of 47.1 m the infiltration flux was basically steady at the level of 30.7 ±
4 mm/yr. In the southern BJD, the depth from the sand dunes to water table was generally larger than
50 m. Figure 7a indicates that the infiltration recharge in the BJD did not show significant seasonal
variation, even the near-surface soil water dramatically changed.
5. Conclusion Remarks
In the BJD, the groundwater recharge from precipitation infiltration is highly dependent on the
hydrodynamic processes in the vadose zone, which can be investigated by analyzing the seasonal
variation of soil water. Monitoring data of soil water and temperature at depths of 0–3 m in the BJD
are presented and analyzed in this study. The seasonal fluctuations of soil temperature and moisture
in the shallow zone were significant. With the increase in depth, the fluctuation amplitudes of the
temperature, soil suction, and moisture content showed an obvious decreasing trend. Under the
depth of 3 m, the seasonal fluctuation of soil water could still propagate downward, but would
attenuate to an ignorable level after reaching a certain depth.
By using the monitoring results at the depth of 3 m, as the boundary condition, the seasonal
variation of soil water in the deep zone was evaluated with a numerical model of Hydrus-1D and the
analytical model developed by Bakker and Nieber [2]. Taking 1 cm as the maximum ignorable value
of change in the soil water pressure head, the penetration depth of the seasonal fluctuation was about
47 m. Below this depth, the infiltration flux was almost steady at 0.08 mm/d, with a small fluctuation
amplitude that was less than 0.01 mm/d. This indicated an infiltration recharge of groundwater at the
level of 30.7 ± 4 mm/yr if the depth to the water table was larger than 47 m. The thickness of the
vadose zone in the BJD is generally larger than 50 m; thus, the groundwater recharge from
precipitation infiltration in the desert almost has no seasonal change even though the near-surface
soil water dramatically changes.
-0.1
0
0.1
0.2
0.3
0 73 146 219 292 365
Infiltration flux at top
Time (day)
(a) (b)
Infiltration flux at depth 3 m (mm/d)
3
28
53
78
103
0 0.04 0.08 0.12
Depth (m)
A
w
(mm/d)
Hydrus-1D Model
Bakker-Nieber Model
Figure 7.
Infiltration flux in the deep zone: (
a
) The periodic variation of the infiltration flux at the depth
of 3 m; and (b) the change in the fluctuation amplitude of the infiltration flux along depth.
Two A
w
-depth curves are shown in Figure 7b, which were obtained from the analytical and
numerical model. Overall, the two curves showed similar attenuation characteristics. The analytical
model yielded an extremely small value of the fluctuation amplitude for the zone below 50 m,
which was close to zero and significantly smaller than that in the numerical model. At the penetration
depth, 47.1 m, of the seasonal variation that was determined from Section 4.2, the fluctuation amplitude
of the infiltration flux in the numerical model was damped to 7.8% of A
q
, which was only 0.01 m/d.
Accordingly, below the depth of 47.1 m the infiltration flux was basically steady at the level of
30.7 ±4 mm/yr
. In the southern BJD, the depth from the sand dunes to water table was generally
larger than 50 m. Figure 7a indicates that the infiltration recharge in the BJD did not show significant
seasonal variation, even the near-surface soil water dramatically changed.
5. Conclusion Remarks
In the BJD, the groundwater recharge from precipitation infiltration is highly dependent on the
hydrodynamic processes in the vadose zone, which can be investigated by analyzing the seasonal
Water 2018,10, 1719 13 of 15
variation of soil water. Monitoring data of soil water and temperature at depths of 0–3 m in the BJD
are presented and analyzed in this study. The seasonal fluctuations of soil temperature and moisture
in the shallow zone were significant. With the increase in depth, the fluctuation amplitudes of the
temperature, soil suction, and moisture content showed an obvious decreasing trend. Under the depth
of 3 m, the seasonal fluctuation of soil water could still propagate downward, but would attenuate to
an ignorable level after reaching a certain depth.
By using the monitoring results at the depth of 3 m, as the boundary condition, the seasonal
variation of soil water in the deep zone was evaluated with a numerical model of Hydrus-1D and the
analytical model developed by Bakker and Nieber [
2
]. Taking 1 cm as the maximum ignorable value
of change in the soil water pressure head, the penetration depth of the seasonal fluctuation was about
47 m. Below this depth, the infiltration flux was almost steady at 0.08 mm/d, with a small fluctuation
amplitude that was less than 0.01 mm/d. This indicated an infiltration recharge of groundwater
at the level of 30.7
±
4 mm/yr if the depth to the water table was larger than 47 m. The thickness
of the vadose zone in the BJD is generally larger than 50 m; thus, the groundwater recharge from
precipitation infiltration in the desert almost has no seasonal change even though the near-surface soil
water dramatically changes.
Due to the limitations in the observations and modeling methods, the estimated recharge from
infiltration in this study is just a preliminary result. Uncertainties of the results could be caused by the
soil heterogeneity, which should be highlighted in further studies. Phreatophyte effects and the vertical
vapor flow are ignored in this study, which could introduce additional uncertainties. Nevertheless,
due to the sparsely vegetated land surface on the sand dunes, the phreatophyte effects is potentially
insignificant in the general infiltration process. The numerical modeling of soil water and vapor
movements undertaken by Hou et al. (2016) [
21
] resulted in an estimated infiltration rate between
11–30 mm/yr at the depth of 3 m, which was less than the result in our study. This difference may
be caused by additional loss of soil moisture from vapor flow. Further investigations are expected to
address this problem.
Author Contributions:
Y.Z. designed and carried out this research; X.-S.W. supervised and instructed this
research; Y.Z. and X.-S.W. wrote this paper; P.-F.H. provided help for the field work; and all authors have approved
the manuscript.
Funding: This research was funded by the National Natural Science Foundation of China (Nos. 41772249).
Acknowledgments:
The authors are grateful to the editors and four anonymous reviewers for their
constructive comments.
Conflicts of Interest: The authors declare no conflicts of interest.
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2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
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... Assessing soil water content (θ) at various spatial and temporal scales is important for a wide range of applications, such as water dynamics and hydrological modeling (Bertoldi et al., 2014;Zhou et al., 2018), management of water resources (Dobriyal et al., 2012), and irrigation planning (Hillel, 2013). Electromagnetic (EM) sensors are a well-established and widely available technique for measuring θ (Mittelbach et al., 2012). ...
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Electromagnetic sensors are widely used to monitor soil water content (θ); however, site-specific calibrations are necessary for accurate measurements. This study compares regression models used for calibration of soil moisture sensors and investigates the relation between soil attributes and the adjusted parameters of the specific calibration equations. Undisturbed soil samples were collected in the A and B horizons of two Ultisols and two Inceptisols from the Mantiqueira Range in Southeastern Brazil. After saturation, the Theta Probe ML2X was used to obtain the soil dielectric constant (ε). Several readings were made, ranging from saturation to oven-dry. After each reading, the samples were weighted to calculate θ (m 3 m-3). Fourteen regression models (linear, linearized, and nonlinear) were adjusted to the calibration data and checked for their residue distribution. Only the exponential model with three parameters met the regression assumptions regarding residue distribution. The stepwise regression was used to obtain multiple linear equations to estimate the adjusted parameters of the calibration model from soil attributes, with silt and clay contents providing the best relations. Both the specific and the general calibrations performed well, with RMSE values of 0.02 and 0.03 m 3 m-3 , respectively. Manufacturer calibration and equations from the literature were much less accurate, reinforcing the need to develop specific calibrations.
... For example, the spatial pattern of soil moisture between two identical seasons in an oasis in northwestern China usually has a high temporal stability . As environmental pressure increases, the interaction between plants in dryland ecosystems shifts from competition to promotion (Butterfield et al. 2016), and the temporal stability of soil moisture in typical subalpine ecosystems in Northwest China and the Badain Jaran Desert also increases with increasing soil depth (Zhou et al. 2018;Zhu et al. 2020). In addition, terrain also has a certain effect on soil moisture (Majdar et al. 2018;Yu et al. 2019). ...
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PurposeSoil moisture is the main factor limiting the growth of vegetation in semiarid areas. A large area of the Loess Plateau has undergone vegetation restoration efforts following an afforestation program initiated in 1999. Understanding how soil moisture responded to afforestation is important for long-term sustainability of ecological restoration measures in this area, especially because the tree planted were non-native species.Methods The effects on soil moisture content (SMC) of afforestation (Robinia pseudoacacia and Caragana korshinskii) were measured for different plantation ages (10, 20, 30, and 40 years) on the Loess Plateau. Meanwhile, a comparative with natural restoration grassland for the same age intervals was conducted.ResultsSMC of R. pseudoacacia plots on south-facing slopes and R. pseudoacacia and C. korshinskii plots on north-facing slopes was lowest when vegetation coverage was greatest after 20 and 30 years, respectively; SMC increases over time following natural grassland restoration; soil moisture consumption of all vegetation types was greater in the shallow soil layer (20–200 cm) than in the deep soil layer (200–500 cm) in each recovery period; and based on a three-way ANOVA, the interaction among afforestation year, vegetation type, and soil depth had significant effects on SMC.Conclusion In response to societal demand for wood, existing plantations should be thinned, with afforested lands located on north-facing slopes being thinned every 10–30 years (approximately 20 years).
... These two tasks are closely related, since they both require knowledge of water velocity in the vadose zone, which is generally variable in space and time. In order to achieve this goal, numerical models of unsaturated flow and transport are often used [1][2][3][4][5][6][7][8][9][10][11][12]. They can be considered as an approach alternative or complementary to other, more costly and time-consuming methods (e.g., lysimeters, tracer experiments). ...
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The Badain Jaran Desert (BJD) in China is a desert with impressive sand dunes and a groundwater situation that has attracted numerous researchers. This paper gives an overview of the mysteries of groundwater in the BJD that are exhibited as five key problems identified in previous studies. These problems relate to the origin of the groundwater, the hydrological connection between the BJD and the Heihe River Basin (HRB), the infiltration recharge, the lake–groundwater interactions, and the features of stable isotope analyses. The existing controversial analyses and hypotheses have caused debate and have hindered effective water resources management in the region. In recent years, these problems have been partly addressed by additional surveys. It has been revealed that the Quaternary sandy sediments and Neogene-Cretaceous sandstones form a thick aquifer system in the BJD. Groundwater flow at the regional scale is dominated by a significant difference in water levels between the surrounding mountains and lowlands at the western and northern edges. Discharge of groundwater from the BJD to the downstream HRB occurs according to the regional flow. Seasonal fluctuations of the water level in lakes are less than 0.5 m due to the quasi-steady groundwater discharge. The magnitude of infiltration recharge is still highly uncertain because significant limitations existed in previous studies. The evaporation effect may be the key to interpreting the anomalous negative deuterium-excess in the BJD groundwater. Further investigations are expected to reveal the hydrogeological conditions in more detail.
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Groundwater-fed lakes are essential for the ecology in arid and semiarid regions. As a typical arid region, the Badain Jaran Desert (BJD) is famous in the world for the presence of a large number of groundwater-fed saline lakes among the mega dunes. Based on the up to date geological surveys and observations, this study analyzed the groundwater contributions in water-salt balances of the lakes in the desert. We found different types of springs, including the sublacustrine springs that indicate an upward flow of groundwater under the lakebed. A simplified water balance model was developed to analyze the seasonal variations of water level in the SumuBarunJaran Lake, which revealed an approximately steady groundwater discharge in the lake and explained why the amplitude of seasonal changes in lake level is less than 0.5 m. In addition, a salt balance model was developed to evaluate the salt accumulations in the groundwater-fed lakes. The relative salt accumulation time is 800–7,000 years in typical saline lakes, which were estimated from the concentration of Cl−, indicating a long history evolution for the lakes in the BJD. Further researches are recommended to provide comprehensive investigations on the interactions between the lakes and groundwater in the BJD.
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