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\begin{document}

\title{Land cover changes the soil moisture response to rainfall on the Loess
Plateau}



\author[1]{Ge Fengchi}%
\author[1]{Mingxiang Xu}%
\author[2]{Chen Gong}%
\author[2]{Zuoyuan Zhang}%
\author[1]{Qingyue Tan}%
\affil[1]{Northwest Agriculture and Forestry University}%
\affil[2]{Chinese Academy of Sciences and Ministry of Water Resources Institute of Soil and Water Conservation}%


\vspace{-1em}



  
  \date{May 05, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Insight into the rainfall-soil moisture (SM) response to land cover is
critical for soil hydrological process modeling and management. In this
study, five typical land-cover types (forest, shrub, grass, crop, and
bare land) and four rainfall patterns (heavy, intermediate, light, and
continuous rains) were selected to assess the effects of SM response
characteristics on the Loess Plateau of China. We monitored SM at five
depths on each land-cover type at 1-h intervals over the growing season
of 2019. The results showed that rainfall patterns and land-cover
typestogether determined the SM response process and infiltration
efficiency. A minimum accumulated rainfall amount of 5 mm was the
threshold to trigger a 10-cm SM response. Rain events with higher
intensity and smaller sum triggered a quick surface SM response, while
larger amounts could percolate deeper and faster. Land-cover change
significantly altered the rainfall-SM response dynamics and rainwater
utilization efficiency after 20 years of ecological construction.
Revegetation sites (mean values of forest, shrub, and grass) increased
the soil wetting depth by 14.7\%, shortened the SM response time by
27.3\%, and accelerated the SM wetting front velocity by 67.2\%, which
promoted a 35.2\% rainfall transformation rate (RTR) across the 1-m
profile over all rainfall events (R \textsubscript{1-13}). Moreover,
planted forest showed the highest RTR of R \textsubscript{1-13} and the
maximal increase in soil water storage, which did not aggravate the soil
water deficit across the 1-m profile over the growing season. Therefore,
we present evidence that planted forests, instead of shrubs, may be
beneficial for water conservation if precipitation is greater than 550
mm. The findings of this study prove the role of revegetation on
rainwater infiltration capacity and efficiency and can help improve the
management of afforestation in arid and semiarid regions.%
\end{abstract}%



\sloppy


\textbf{Land cover changes the soil moisture response to rainfall on the
Loess Plateau}

Fengchi Ge \textsuperscript{a}, Mingxiang Xu \textsuperscript{a, b, c,
*}, Chen Gong \textsuperscript{b, c}, Zuoyuan Zhang\textsuperscript{d},
Qingyue Tan \textsuperscript{e},

\textsuperscript{a} College of Forestry, Northwest A\&F University,
Yangling 712100, China

\textsuperscript{b} Institute of Soil and Water Conservation, Northwest
A\&F University, Yangling 712100, China

\textsuperscript{c} State Key Laboratory of Soil Erosion and Dryland
Farming on the Loess Plateau, Institute of Soil and Water Conservation,
Chinese Academy of Sciences and Ministry of Water Resources, Yangling
712100, China

\textsuperscript{d} Institute of Soil and Water Conservation, Northwest
A\&F University, Yangling 712100, China

\textsuperscript{e} College of Forestry, Northwest A\&F University,
Yangling 712100, China

\textbf{Corresponding Author:} Mingxiang Xu, Institute of Soil and Water
Conservation, Northwest A\&F University, Yangling 712100, China.

Email: xumx@nwsuaf.edu.cn

\textbf{Abstract:} Insight into the rainfall-soil moisture (SM) response
to land cover is critical for soil hydrological process modeling and
management. In this study, five typical land-cover types (forest, shrub,
grass, crop, and bare land) and four rainfall patterns (heavy,
intermediate, light, and continuous rains) were selected to assess the
effects of SM response characteristics on the Loess Plateau of China. We
monitored SM at five depths on each land-cover type at 1-h intervals
over the growing season of 2019. The results showed that rainfall
patterns and land-cover typestogether determined the SM response process
and infiltration efficiency. A minimum accumulated rainfall amount of 5
mm was the threshold to trigger a 10-cm SM response. Rain events with
higher intensity and smaller sum triggered a quick surface SM response,
while larger amounts could percolate deeper and faster. Land-cover
change significantly altered the rainfall-SM response dynamics and
rainwater utilization efficiency after 20 years of ecological
construction. Revegetation sites (mean values of forest, shrub, and
grass) increased the soil wetting depth by 14.7\%, shortened the SM
response time by 27.3\%, and accelerated the SM wetting front velocity
by 67.2\%, which promoted a 35.2\% rainfall transformation rate (RTR)
across the 1-m profile over all rainfall events (R\textsubscript{1-13}).
Moreover, planted forest showed the highest RTR of
R\textsubscript{1-13}and the maximal increase in soil water storage,
which did not aggravate the soil water deficit across the 1-m profile
over the growing season. Therefore, we present evidence that planted
forests, instead of shrubs, may be beneficial for water conservation if
precipitation is greater than 550 mm. The findings of this study prove
the role of revegetation on rainwater infiltration capacity and
efficiency and can help improve the management of afforestation in arid
and semiarid regions.

\textbf{Keywords:} soil moisture response; land cover; rainfall;
revegetation; soil water storage; Loess Plateau

\textbf{1. INTRODUCTION}

\textbf{NOTE:} What does the response dynamic of SM after rainfall look
like under different land-cover types? What content and extent of the
soil water percolation process are changed after revegetation? And which
afforestation pattern can make the most effective use of rainfall to
improve the water conservation function of vegetation? There is still no
consensus and definite answer to these questions.

Climate drought and water deficiency are common problems in arid and
semiarid regions around the world. Under the background of global
warming (Williams et al., 2020), the dryland area will continue to
expand (Feng and Fu, 2013; Lickley and Solomon, 2018), from 40\% (Huang
et al., 2016) of the global terrestrial ecosystem at present to 47\% by
the end of the 21st century (Koutroulis, 2019). Although water resources
are scarce in arid and semiarid regions, soil losses in these regions
are extremely serious (Fu et al., 2017; Mamedov and Levy, 2019; Wang et
al., 2020), and this pattern is mainly related to the lack of vegetation
coverage (Zhao et al., 2013), loose soil and easy erosion (Fu et al.,
2017), as well as irregular but high-intensity rainfall in such areas
(Mamedov and Levy, 2019). Therefore, drought and water and soil loss
have become urgent contradictions in arid and semiarid regions of the
world.

To solve this prominent problem, many countries and regions have adopted
a close to natural solution, namely, planting trees (Zheng et al.,
2016). Afforestation is considered to be the most economical and
convenient way to restore the ecological environment and reduce water
loss and soil erosion, so the practice has been widely recognized and
accepted in the world (Bryan et al., 2018; Chirino et al., 2006; Liu et
al., 2018; Wu et al., 2021). Since 1990, the area of artificial forest
has increased by more than 105 million hectares, accounting for 7\% of
the global forest area (Birdsey., 2015). Revegetation has a significant
impact on the hydrological process of an ecosystem by influencing
evapotranspiration, water infiltration, runoff generation, soil erosion,
and solute transportation (Ding et al., 2021; Su and Shangguan, 2019;
Zhu et al., 2021), thus altering the terrestrial water recycling
process.

The relationship between rainfall and SM has always been a core issue in
the study of soil hydrological processes. In recent years, many studies
have been conducted in this research field. For example, Rohit et al.
(2011) assessed~the~impact~of~altered rainfall~on~soil-moisture dynamics
in three annual~grassland~vegetation~types. He et al. (2012)
qualitatively described the influence of precipitation on the soil
moisture of the rainy season in northwestern China's Qilian Mountains.
Su et al. (2019) and Li et al. (2021b) used meta-analysis to illustrate
that the relationship of rainfall and SM changed by vegetation
construction and afforestation led to a decline in SM on the Loess
Plateau of China. Despite efforts to characterize the spatiotemporal
variations in SM related to rainfall, but SM dynamics and infiltration
processes cannot be determined after precipitation. In subsequent
studies, Pan et al. (2019), He et al. (2020) and Mayerhofer et al.
(2017) analyzed soil water infiltration, redistribution and runoff
through laboratory or field experiments using simulated rainfall. Many
studies have adopted continuous monitoring equipment to analyze SM
dynamics after rainfall under different vegetation patterns but have
reached different conclusions (Jin et al., 2018; Wang et al., 2012; Wang
et al., 2013; Yu et al., 2015). For instance, Wang et al. (2013)
suggested that the SM response to rainfall leads to the smallest
accumulated infiltration and largest surface runoff occurring in
grasslands. Jin et al. (2018) showed that forestland has a faster SM
response time and deeper response depth than grassland but the smallest
soil water storage. What does the response dynamic of SM after rainfall
look like under different land-cover types? What content and extent of
the soil water percolation process are changed after revegetation? And
which afforestation pattern can make the most effective use of rainfall
to improve the water conservation function of vegetation? There is still
no consensus and definite answer to these questions.

As a typical arid and semiarid region in the world, the Chinese Loess
Plateau (CLP) has suffered extremely serious soil erosion and water and
soil loss for a long time (Chen et al., 2007b; Fu et al., 2017). To
protect soil and water and curb ecological deterioration, the Chinese
government implemented the `Grain For Green' project at the end of the
20th century. Vegetation reconstruction significantly changed the
land-cover type and vegetation structure, reshaped the mutual-feeding
relationship and cycling process between the soil-vegetation-atmosphere
continuum (SPAC) (Deng and Shangguan, 2017; Jia and Shao, 2014), and
fundamentally contained water and soil loss and restored the ecological
environment on the CLP (Fu et al., 2017). For instance, Zhao et al.
(2017) and Fu et al. (2017) found that 20 years of revegetation reduced
the runoff and sediment load of the Yellow River by 24.8\% and 57\%,
respectively, which controlled soil erosion to a great extent. Moreover,
many studies have confirmed that the conversion of farmland to trees or
grasses enhances vegetation coverage and rainfall interception (Liu et
al., 2020; Murray, 2014), improves soil texture (Li et al., 2006), and
increases soil porosity, thus greatly promoting rainfall infiltration
and soil water storage (Kijowska-Strugala et al., 2018; Sun et al.,
2018). However, artificial vegetation consumes more soil water due to
the larger evapotranspiration (Jia et al., 2017), which intensifies soil
desiccation and even forms a dry soil layer (Wu et al., 2021), thus
limiting plant growth and resulting in advanced senescence and
degradation of artificial vegetation, such as the `little old man tree'
(Wang et al., 2010b). Consequently, the soil water deficit is one of the
most important problems affecting the survival and sustainability of
artificial afforestation (Li et al., 2021a). Improving the conversion
efficiency of rainfall is a critical path to solving the soil water
deficit problem. Therefore, it is particularly important to study the
rainfall-soil moisture (SM) response process and its effect on rainwater
transformation and utilization.

Based on the extensive existence of artificial vegetation and the need
for understand soil water infiltration processes, this paper aimed to
reveal the rainfall-SM response process and mechanism, and evaluate the
effect of soil water infiltration and replenishment by different
rainfall or vegetation land-cover types to clarified the optimal
revegetation type on the semiarid region. To do this, we monitored the
1-h SM at five depths down to the 1 m depth (10, 30, 50, 70, and 100 cm)
in typical land covers (forest, shrub, grass, crop, and bare land) over
the growing season of 2019. We hypothesized that 20 years of
revegetation has changed soil water replenishment patterns, promoted
rainwater use efficiency and enhanced soil water storage in the growing
season. The results of this study are expected to shed insight into
profile soil water infiltration processes related to rainfall after
vegetation restoration on the CLP and to provide design and optimization
solutions for vegetation restoration in similar areas of arid and
semiarid regions around the world.

\textbf{2. MATERIALS AND METHODS}

\textbf{2.1 Study area}

The study area is located in the Zhifanggou watershed (36\selectlanguage{ngerman}°48' N, 109°15'
E) in the loess hilly and gully region of northern Shaanxi Province,
China (Fig. 1). This area belongs to a typical forest-grass zone with
complex terrain, ravine and crisscross, and an elevation ranging from
997\textasciitilde{}1731 m. The region is mainly affected by the
semiarid continental climate in the warm temperate zone, with an average
annual temperature of 8.8 \selectlanguage{english} and an annual rainfall of 500-600 mm. The
natural vegetation of the area has been almost completely destroyed, and
the restored plantations are composed of \emph{Robinia pseudoacacia}
,\emph{Populus simonyi} , \emph{Caragana} korshinskii, and
\emph{Hippophae rhamnoides} . The abandoned slope is mainly composed of
herbs at different stages of succession, such as \emph{Artemisia
gmelinii} ,\emph{Artemisia Giraldi} , \emph{Stipa bungeana} , and
\emph{Bothriochloa ischaemum} . The soil type is loessal soil, of which
silt accounts for over 64\%. In this area, the soil is loose with poor
soil resistance, which leads to serious soil and water erosion.

In the watershed, typical land cover (forest, shrub, grass, crop, and
bare land) was selected (Table 1). The same slope gradient site was
chosen as the experimental plot (22deg\textasciitilde{}25deg), which was a
5x20 m standard experimental plot. In the forest plot, the major tree
species was \emph{Robinia pseudoacacia} . Many herbaceous plants grow
here, such as \emph{Stipa bungeana} , \emph{Setaria viridis} ,
and\emph{Leymus secalinus} . In the shrub plot, the main planted species
was\emph{Caragana korshinskii} , with many undergrowth vegetation types,
such as \emph{Artemisia gmelinii} , \emph{Bothriochloa~ischaemum} ,
and\emph{Heteropappus~altaicus} . In the grass plot, the dominant grass
species were \emph{Lespedeza daurica} , \emph{Bothriochloa~ischaemum}
,\emph{Stipa~bungeana} , \emph{Artemisia gmelinii} ,
and\emph{Leymus~secalinus} . In the crop plot, the major crop species
was\emph{Syringa oblata} . The bare land plot did not have any plant
growth and was weeded once a month.

\textbf{2.2 Methods}

2.2.1 Soil moisture monitoring

SM monitor has been established in typical land-cover sites since June
2018. At each site, an array of SM probes (EC-5 probes with a precision
of +-0.1 vol. \%; The METER Group, Inc., Pullman, Washington) was
installed horizontally at five depths below the soil surface. According
to previous studies, the SM response depth is limited to the 1-m profile
of each land-cover type after the rainy season (Tang et al., 2018; Wang
et al., 2013; Yu et al., 2015). Thus, we installed an SM monitoring
probe in the 0-1-m profile (10, 30, 50, 70, and 100 cm) to investigate
SM infiltration and the response process affected by rainfall.
Meanwhile, this paper set the 10-cm soil layer as the shallowest
monitoring depth to reveal the difference in surface SM response time
and soil permeability after precipitation as a result of revegetation
(Jin et al., 2018; Lee et al., 2017; Liu et al., 2020).

After installation, the pit was carefully refilled with original soil
material and compacted to the original bulk density layer by layer to
avoid perturbation as much as possible. SM was measured every h and
stored in EM50 dataloggers (The METER Group, Inc., USA). The 1-h SM data
from May 1 to October 31 in the growing season were used to explore the
SM dynamics after rainfall.

2.2.2 Identification of rainfall process and soil wetting event

An automatic weather station was set up at the bare land uphill slope
site. Rainfall was recorded every minute by a tipping bucket rain gauge
(0.2 mm/tip; Jiangsu Naiwch Corporation Inc., China). The definition of
a new rainfall process was when the 1-h rainfall amount was larger than
0.4 mm (surpassing the threshold of instrument noise) and recorded after
the period without any rainfall record exceeding 48 h. We defined the
end of the process when the rainfall amount was less than 0.2 mm for 48
h (i.e., the level of instrument noise). Within the rainfall process, we
defined the timing of a rainfall record with an interval of more than 24
h from the previous rainfall record as the initiation of an individual
rainfall event.

The concept of a soil wetting event was adopted, in which a significant
increase in SM as the result of rainfall infiltration into the soil can
be observed (Lozano-Parra et al., 2015; McMillan and Srinivasan, 2015).
For all the probes, we defined the start time of the soil wetting event
if the increase in accumulated SM was \textgreater{}0.2 vol.\% greater
than the initial SM (Hardie et al., 2013; Lin and Zhou, 2008). In our
study, we found that SM was subject to soil temperature fluctuation that
might cause a decrease at a rate of 0.1 vol.\% per h during the daytime.
An SM increment of 0.2 vol.\% was selected to exceed the device noise
(0.1 vol.\% for the EC-5 SM probes) (Rosenbaum et al., 2010).
Furthermore, we used the time without an increase in SM at 6 h as the
end time of the soil wetting event process.

2.2.3 Soil moisture response to rainfall process

The SM response lag time (\emph{RT} , h) was evaluated to characterize
the temporal delay of the soil wetting process with depth. It is
calculated as follows:

\emph{RT = ST\textsubscript{i} - ST\textsubscript{0}} (1)

where \emph{ST\textsubscript{i}} and \emph{ST\textsubscript{0}}represent
the SM response time at layer i (layer1: 0-10 cm, layer2: 10-30 cm,
layer3: 30-50 cm, layer4: 50-70 cm, layer5: 70-100 cm) and rainfall
time, respectively. According to this case, we recorded the accumulated
rainfall amount (\emph{ARA} , mm) that could trigger SM increases in
different soil layers:

\emph{ARA} =\(\ \sum_{t=ST_{0}}^{\text{ST}_{i}}\text{\ R}\) (2)

where \emph{R} represent the and real-time rainfall (mm), respectively.

Definition of the wetting front velocity (\emph{WFV} , cm/h) at
different soil depths used to describe the SM wetting dynamics after
rainfall:

\emph{WFV\textsubscript{ij}} = \emph{SWD\textsubscript{ij} /
RT\textsubscript{ij}} (3)

where \emph{SWD\textsubscript{ij}} and \emph{RT\textsubscript{ij}} are
the soil wetting depth (cm) and the response time (h) at layer i in the
jth soil wetting process, respectively.

2.2.4 Soil water storage variation in the rainfall process and growing
season

In our study, we defined the absolute increase in SM (\emph{AIM} ,
vol.\%) at each measurement location as follows:

\emph{AIM = SM\textsubscript{e} - SM\textsubscript{s}} (4)

where \emph{SM\textsubscript{e}} (vol.\%) and
\emph{SM\textsubscript{s}}(vol.\%) are the SMs at the end time of the
soil wetting process and at the start time of rainfall, respectively.
Additionally, we calculated the soil water storage variation at the 100
cm soil depth (\emph{SWS\textsubscript{0-1m}} , mm) after each rainfall
process:

\emph{SWS\textsubscript{0-1m}} =\(\sum_{i=1}^{5}{\text{AIM}_{i}\times D_{i}}\times 10\) (5)

where \emph{AIM\textsubscript{i}} represents the absolute increase in SM
at layer i. D\textsubscript{i} is the thickness of each depth interval.
Then, the rainfall transformation rate (\emph{RTR} , \%) was calculated
as follows:

\emph{RTR = SWS\textsubscript{0-1m} / R\textsubscript{1-13}} x 100\% (6)

Where \emph{R\textsubscript{1-13}} is the total rainfall amount from the
P1 to P13 rainfall processes. Additionally, we compared the averaged SM
profile and total soil water storage variation
(\emph{SWS\textsubscript{total}} ) for the 6 months among the study
sites:

\emph{SWS\textsubscript{total}}=\(\sum_{i=1}^{5}{\text{AVM}_{i}\times D_{i}}\times 10\) (7)

where AVM\textsubscript{i} represents the absolute variation in SM
across the entire monitoring period at layer i.

2.2.5 Statistical analysis

The Kruskal--Wallis test was used to test the differences in RT,
response duration time, and WFV among different rainfall patterns and
vegetation types, in which data were not normally distributed. The
Bonferroni method was used to correct pairwise comparisons (\selectlanguage{greek}α\selectlanguage{english}=0.05). A
one-way analysis of variance was conducted to compare the difference in
soil water storage over the growing season. Least significant difference
(LSD) was used as a post hoc-test for multiple comparisons of means
(\selectlanguage{greek}α\selectlanguage{english}=0.05). Linear regression models were established between the total
rainfall amount and the change in soil water storage in the 13th
rainfall-soil wetting process. Statistical analyses were conducted using
the SPSS statistical package (SPSS 22.0, SPSS Inc., Chicago, USA) and
Origin 9.3 (OriginLab, Corp., Northampton, MA, USA).

\textbf{3. RESULTS}

\textbf{3.1 Rainfall characteristics and soil moisture dynamics}

During the study period from May 1 to October 31, the precipitation
instrument recorded 1-h rainfall and accumulated rainfall. The total
rainfall reached 565.3 mm, accounting for 94.5\% of the annual rainfall
amount in 2019. A total of 13 rainfall events are described in Fig. 2f
and Table 2, including the total amount, duration, and intensity, which
were divided into four groups. Group I (P1 and P3) represented the heavy
rainfall that was characterized by a relatively large amount and high
peak intensity of rainfall. Group II (P2 and P4) represented
intermediate rains with a short duration and high average intensity.
Group III (P5, P6, P7, P12, and P13) represented light rains
characterized by a short duration and low peak intensity. Group IV (P8,
P9, P10, and P11) represented continuous rains with a long duration and
a low intensity (Table 2).

Fig. 2 shows that the SM was positively correlated with rainfall input
but fluctuated in different levels of different soil layers after
precipitation. The results indicated that distinct cycles of wetting and
drying in response to rainfall were evident in the shallow soil layer
(10 and 30 cm soil depths). However, the deeper layers (70 and 100 cm
soil depths) showed a gentle fluctuation range, which was confined in
response to rainfall (Fig. 2).

\textbf{3.2 Soil moisture response depth to rainfall}

Different rainfall patterns with different rainfall inputs significantly
impacted the soil moisture response depth across the five experimental
sites. Total amounts larger than 15.2 mm, 22.2 mm, and 114.2 mm could
trigger SM responses at depths of 10, 30, and 70 cm, respectively. For
example, heavy rains (Group I) infiltrated to a soil depth of at least
70 cm, while intermediate rains were limited at the 30 cm depth (Fig.
3). In addition, rainfall duration and intensity had a similar impact on
the soil wetting depth. When the rainfall duration was greater than 2.3
h, 13.0 h, and 22.0 h, the rainwater could percolate to soil depths
greater than 10, 30, and 70 cm, respectively. An average rainfall
intensity larger than 0.6 mm/h, 1.3 mm/h, and 6.2 mm/h could stimulate
SM responses at depths of 10, 30, and 70 cm, respectively (Fig. 3).

Land cover also strongly influenced the soil wetting depth after
precipitation. The average response depth was as follows: grass (57.5
cm) \textgreater{} forest (52.5 cm) \textgreater{} bare land (49.7 cm)
\textgreater{} shrub (46.2 cm) \textgreater{} crop (45.4 cm). After
converting crops to forests, shrubs, and grasses, the average soil
wetting depth increased by 15.7\%, 1.7\%, and 26.7\%, respectively
(Supplemental Fig. S1). Two heavy rains (P1 and P3) are obvious, which
permitted rainwater to enter deeper soil in forest and grass sites than
in other sites (Fig. 3). In intermediate rains (P2 and P4), vegetation
recovery prevented rainfall infiltration, leading to a lighter SM
response or nonresponse after rainfall (Fig. 3a, c-e). However, the SM
of planted shrubs and bare land responded more deeply than that of the
other sites (Fig. 3b, e). For light rains and continuous rains, forest
and grass sites had deeper soil wetting depths in some specific response
processes, such as at P6 and P10.

\textbf{3.3 Soil moisture response time at the 10-cm soil depth}

The SM response exhibited a hysteresis effect after rainfall, and the
average response lag time (ART) was 5.6 h at a depth of 10 cm for all
precipitation events. Group IV (continuous rains) showed the most
obvious hysteresis effect (p\textless{}0.05) in which the ART reached
7.8 h, followed by Group III (light rains), Group I (heavy rains), and
Group II (intermediate rains) in Table 3. In addition, the l0 cm SM
response duration showed a similar trend after precipitation, followed
by Group IV (21.9 h) \textgreater{} Group III (14.7 h) \textgreater{}
Group II (13.4 h) \textgreater{} Group I (9.7 h). Therefore, a small
rainfall amount with a lower intensity significantly delayed the surface
SM response time and duration, while a higher rainfall intensity
promoted a quick surface SM response.

Land-cover change significantly influenced the surface SM response time
(p\textless{}0.05). Abandoned grass and planted forest showed a longer
ART of 10 cm SM that surpassed 8 h, which was delayed 69.8\% and 56.6\%,
respectively, compared to the crop type. While the shrub site was the
exception and showed the earliest ART of surface SM, merely 3.4 h (Table
3 and Supplemental Fig. S2). The lasting response time of the 10-cm SM
showed no significant difference among the five vegetation types (Table
3).

In addition, the minimum ARA necessary to trigger the SM response was
approximately the same (5-5.9 mm) at all the monitoring sites (Fig. 4).
The shrub site required the smallest ARA, approximately 5 mm, to trigger
a 10-cm SM response, whereas the other sites required \textasciitilde{}6
mm (the green dashed line in Fig. 4). The median value of the ARA needed
to trigger the SM response was the lowest at the shrub site and highest
at the forest site (the red line of the box plot in Fig. 4).

\textbf{3.4 Soil moisture response velocity to rainfall}

Two rainfall process was selected (P1 and P9) to clarify the percolation
process of rainfall (Table 4 and Fig. 5). Through the analysis of
different rainfall patterns, we found that Group IV has the longest RT
and the slowest wetting front velocity (WFV, cm/h) within the 1-m
profile. Group II had the shortest RT, approximately 6.4 h, which was
significantly lower than that of the other patterns (p\textless{}0.05).
Group I had the slowest WFV of 10 cm but the fastest WFV in the 1-m
profile, which surpassed 6 cm/h (Table 4).

The effect of land-cover change showed that the crop site had the
longest RT and slowest WFV in the 1-m profile at a nonsignificant level
across the five monitoring sites (Table 4). Nevertheless, revegetation
patterns altered this condition, with a smaller RT and a larger WFV.
Specifically, planted shrubs accelerated the WFV by approximately 46.7\%
of the surface SM and reduced the RT by approximately 33.7\% across the
entire profile compared to the crops. Abandoned grass and planted forest
improved the soil permeability to decrease the ART by approximately
17.4\% and 30.8\%, respectively, and increased the WFV by approximately
87.0\% and 77.9\%, respectively, in the 1-m profile compared to the
crops (Supplemental Fig. S3). Hence, revegetation sites decreased the RT
and accelerated the WFV to a great degree.

\textbf{3.5 Soil water storage over the soil wetting process and growing
season}

Regression analysis showed that the P1 to P13 rainfall processes
(R\textsubscript{1-13}) and the change in soil water storage along the
1-m profile (SWS\textsubscript{0-1 m}) had a highly positive linear
correlation at the 0.05 significance level under all land-cover types
(Fig. 5a). The regression model of planted forest exhibited the highest
slope value, approaching 0.78, followed by grass, bare land, shrub, and
crop sites (Fig. 5a). Similarly, planted forest had the largest
increment in SWS\textsubscript{0-1 m} (217.0 mm) and the highest
rainfall transformation rate (RTR), approximately 45\% (Fig. 5b). The
crop site showed the smallest SWS\textsubscript{0-1 m} (129.3 mm) and
RTR (26.8\%). Moreover, our results indicated that revegetation improved
rainwater interception, which increased the SWS\textsubscript{0-1m} by
approximately 67.7\%, 6.1\%, and 31.9\% for planted forest, shrub, and
abandoned grass, respectively, compared to cropland, which promoted soil
water infiltration and soil water storage (Fig. 5b).

The SM dynamic and SWS variation over the growing season showed that the
average soil water content (ASWC) first increased and then decreased
with increasing soil depth (Fig. 6). The ASWC of the grass site (16\%)
was significantly higher than that of the other sites across the entire
profile (p\textless{}0.05), followed by forest (11.6\%), shrub (9.5\%),
bare land (9.4\%), and crop (8.1\%). Moreover, land cover changed the
distribution of the ASWC along the 1-m profile over the growing season.
For the bare land and crop sites, the lowest ASWC occurred at the 0-10
cm depth, while the highest occurred at the 10-30 cm depth. After
planting forest/grass and shrub, the highest ASWC occurred at the 70 cm
and 50 cm soil depths over the growing season (Fig. 6), which was
beneficial to the deep SM sustainability and vegetation growth supply.

A significant difference between the change in soil water storage
(SWS\textsubscript{total}) among the five monitoring sites over the
growing season was discovered (p\textless{}0.05). The forest site had
the largest SWS\textsubscript{total} relative to the initial condition,
approaching 83.3 mm. This was followed by the crop, grass, and shrub,
all with approximately 60 mm increments of SWS\textsubscript{total},
which were not significantly different. The bare land had the minimum
increment over the growing season, merely 46.5 mm (Fig. 6). The above
results illustrated that land cover significantly changed the
infiltration pattern and SWS\textsubscript{total}. In Fig. 6, the
average SWS\textsubscript{total} variation in revegetation construction
(forest, shrub, and grass), approximately 68.1 mm, was higher than that
of crop and bare land in the 0-1 m soil profile (p\textless{}0.05). This
is the crucial implication that vegetation construction and recovery
contributed to water storage and conservation within the 1-m profile in
the growing season.

\textbf{4. DISCUSSION}

\textbf{NOTE:} The manuscript illustrates the response and utilization
of soil moisture to rainfall under different land covers. Currently, the
relationship between artificial vegetation and soil moisture comes to
the critical stage, which is not only related to depletion and
utilization of soil water, but also soil hydrological processes and
sustainability. How to maximum use limited rainfall to replenish soil
moisture and optimize vegetation types is of great guiding significance
for vegetation construction, especially in arid and semiarid regions of
the world.

\textbf{4.1 Effect of rainfall on soil moisture dynamics}

In the Chinese Loess Plateau (CLP), precipitation is the main source of
soil moisture (SM) (Jia et al., 2017; Su and Shangguan, 2019). Although
the average annual precipitation decreased over nearly half a century on
the CLP (Fu et al., 2017), the season-rainstorm occurrence frequency did
not significantly change with vegetation construction (Tang et al.,
2018). However, under rainstorms, the runoff (L\selectlanguage{ngerman}ü et al., 2012) and
sediment (Wang et al., 2016) concentrations decreased significantly
after afforestation, such as the ``Grain For Green Project'', radically
altering the rainfall-SM response relationship, which intercepts
rainfall, delays surface runoff, and increases rainwater infiltration
(Kijowska-Strugala et al., 2018). Nevertheless, the characteristics of
the soil water cycle process have changed, and the extent and process of
this change are still unclear.

Our results concluded that the rainfall-SM response exhibited a positive
but not synchronous correlation. The fluctuation of SM occurred after
precipitation, especially in larger amounts of rainfall. The shallow
soil layers were more susceptible to rainfall than deep layers (Fig. 2).
This phenomenon was more obvious under different rainfall patterns with
different rainfall amounts, durations, and intensities in the CLP (Hou
et al., 2018; Jin et al., 2018; Tang et al., 2019). For example, heavy
rains (Group \selectlanguage{english}I) with a larger amount and intensity had the deepest SM
response depth, at least 70 cm depth (Fig. 3), which was similar to Wang
et al. (2013). Tang et al. (2019) also illustrated that a larger
rainfall amount could promote rainwater percolation into deeper soil.
However, high intensity with a continuous input of rainfall might
surpass the max-rate of soil permeability and limit the soil water
infiltration in the shallow layer (Yan et al., 2021), resulting in the
shortest lasting time of the 10-cm depth and the slowest permeating
velocity of the 100-cm profile (Tables 3 and 4). Compared with heavy
rains, intermediate rains (Group \selectlanguage{english}II) showed the shallowest response depth
(Fig. 3) but the shortest response time (RT) of the entire profile and
the fastest wetting front velocity (WFV) of the 10-cm SM (Tables 3 and
4). It was indicated that small rains with a high rainfall intensity
could penetrate the dense canopy cover and litter layer to trigger a
surface SM response in the most effective way of all the rainfall
patterns, which was similar to the results of Liu et al. (2020).
Continuous rains (Group \selectlanguage{english}IV) were the most lagging and slowest pattern to
cause an SM feedback. For instance, continuous rains not only lagged in
RT of the entire depth but also showed the smallest WFV in the 1-m
profile across all rainfall patterns. This result was mainly due to a
smaller rainfall amount with a longer duration stretching the rainfall
time and decreasing the average rainfall intensity, which made it
difficult to through the canopy and litter layer, and to store rainwater
in the surface soil layer. There has not been enough water to trigger
the SM response and infiltrate into the deep layer at a larger speed of
the soil wetting front, due to the lack of guidance from the
gravitational water potential (Mao et al., 2018). Therefore, a high
average rainfall intensity with a smaller rainfall amount promotes a
quick surface SM response, but a larger rainfall amount facilitates
rainwater percolation into deeper soil with a larger WFV.

In addition, regardless of the vegetation pattern, the surface SM (10-cm
depth) responded only when the minimum accumulated rainfall amount (ARA)
surpassed 5 mm. This conclusion is slightly different from Jin et al.
(2018), who demonstrated that a 9 mm ARA was necessary to trigger
surface SM variation. Perhaps the difference in canopy coverage and
litter depth due to vegetation age, which revegetation after 60 years in
Jin et al. (2018). After 20 years of revegetation in this region, a 5 mm
ARA was the threshold needed to replenish soil water, which was consumed
by plant-inducing dried soil layers (Jia et al., 2017; Wang et al.,
2011). It is suggested that the constraint of a minimum ARA exists in
terms of vegetation restoration in semiarid CLP.

\textbf{4.2 Effect of land cover on the soil wetting process}

In the case of a certain rainfall amount, land-cover type was the key
factor that determined the SM response. Different land-cover patterns
have different vegetation structures, coverage (canopy, herb, litter),
root depths and densities, and soil textures, which influence the soil
wetting process after precipitation (Chen et al., 2007a; Jia et al.,
2017; Li and Shao, 2006; Zhang et al., 2021).

First, the rainfall-SM average response depth increased by 15.7\%,
1.7\%, and 26.7\% when the crop was transformed to forest, shrub, and
grass sites, respectively (Supplemental Fig. S1). Vegetation recovery
altered the plant coverage and structure radically, resulting in changes
in the way and process of soil acceptance from precipitation
(Lozano-Parra et al., 2015; Wang et al., 2013). Specifically, planted
forests, with a higher canopy and greater litter coverage, intercepted
the large rainfall and weakened the dynamic potential energy of heavy
rains in large amounts. One part of the rainfall is intercepted by the
canopy and flows down the trunk to the soil directly, and the part of
rainfall may reach the litter layer as throughfall. The thick and rough
litter hampered the formation of surface runoff and percolated rainwater
infiltration to the deep soil layer. This vegetation structure promoted
an SM response deeper in planted forests after heavy rains (Fig. 3a).
Abandoned grass improved soil texture, decreased soil bulk density, and
increased soil porosity, which was also beneficial for rainfall-SM
infiltration (Sun et al., 2018). Especially in Fig. 3c, permitting
percolation to the 100 cm depth occurred in heavy rains. Planted shrubs
with a high coverage could not reach the deepest monitoring layer,
probably due to SM consumption by root absorption and vegetation
evapotranspiration (Yu et al., 2017). Additionally, the shrub site
showed a shallower average wetting depth than that of bare land across
the 13 rainfall events (Supplemental Fig. S1). This is because smaller
rainfall amounts with short durations have difficulty penetrating dense
canopies and thick litter layers of shrubs to percolate into subsurface
soil (Wang et al., 2013). Compared to afforestation covers, bare land
had a deeper average soil wetting depth than shrub and crop sites after
precipitation. Because no vegetation coverage results in direct rainfall
and surface soil cracks that increased soil water percolation access
together promoted rainwater infiltration into a deeper layer in bare
land. These results suggested that land cover strongly influenced the
rainfall-SM response depth, while revegetation promoted rainwater
percolation to deeper depths across the profile.

Second, the rainfall-surface SM response lag time (RT) and lasting time
changed significantly due to land-cover change. The results showed that
shrub has the smallest RT and lasting time at the 10-cm depth (Table 3
and Fig. 4). This case was correlated to the initial SM that influenced
the speed of rainwater infiltration (Liu et al., 2019). Due to excessive
soil water consumption by vast evapotranspiration (including soil and
plants) and deeper root water absorption, the shrubland showed the
smallest SM across the entire profile, especially in the surface layer.
The extremely dry situation accelerated soil water permeation, which was
demonstrated by Li et al. (2015) and Sun et al. (2018), who illustrated
that a lower initial soil water content accelerated the topsoil
permeability rate. Thus, planted shrubs have the shortest response and
duration. Compared to shrubs, planted forests and abandoned grass show
longer RT and shorter durations. Due to the existence of a dense canopy
and a thick litter layer, rainfall is blocked to a large extent and
postpones the contact time of rainfall-SM, resulting in a longer
response. Meanwhile, much infiltrated rainwater percolates into deep
soil or counterbalanced with plant consumption, also decreasing the SM
duration at the 10-cm depth. Thus, the effect of afforestation on the
surface SM response time or duration after rainfall was significantly
different. However, with no vegetation coverage, bare land showed a
smaller RT and the longest duration. Because bare land does not have
vegetation shade or litter cover, rainwater directly contacts the
surface soil and decreases the SM feedback time. Therefore, vegetation
recovery delays the surface SM response time but shortens the duration
after precipitation, except for shrubs.

Finally, the change in land-cover type influenced the SM response time
and wetting front velocity (WFV). For example, the crop site required
more time to respond to rainfall, resulting in the longest ART and the
slowest WFV across the entire profile. Vegetation restoration altered
this situation. Vegetation restoration no only shortened the ART by
approximately 30.8\%, 33.7\%, and 17.4\% but also accelerated the WFV by
approximately 77.9\%, 36.7\%, and 87.0\% for planted forest, shrub, and
grass, respectively, over the 1-m depth (Table 4 and Supplemental Fig.
S3). This difference in soil permeability was related to soil texture.
Afforestation improved soil texture, decreased soil bulk density and
increased soil porosity, which facilitated rainwater percolation across
the profile (Sun et al., 2018). Thus, revegetation had the shorter RT
and higher WFV, which benifited for alleviating the soil water deficit
caused by plant water consumption.

In conclusion, vegetation restoration has a significant impact on
ecohydrological processes, including rainfall-SM response depth, RT, and
WFV. Despite afforestation consuming more soil water and causing soil
desiccation to a large extent. However, the rainfall-SM response process
showed that revegetation (mean value of forest, shrub, and grass)
increased the soil wetting depth by 14.7\%, shortened the ART by 27.3\%,
and accelerated the WFV by 67.2\% compared to cropland over the entire
profile. Revegetation contributed to rainwater infiltration and deeper
SM replenishment in the rainy season.

\textbf{4.3 Effect of revegetation on the `soil reservoir'}

On the CLP, the thick loess has a very high water storage capacity,
which can accumulate all the natural rainfall and regulate the balance
of soil water to sustain plant growth. Vegetation recovery and
sustainable development are mainly dependent on the level of SM and the
stock amount of the `soil reservoir'. Many previous studies have shown
that SM and SWS decrease significantly after revegetation, causing a
temporary or permanent dry soil layer (Jia et al., 2017; Su and
Shangguan, 2019). This desiccation situation of the soil profile limits
the replenishment of soil water after precipitation. In turn, the lack
of the supply capacity of the soil reservoir further restricts plant
growth, such as the `old-man small tree' (Wang et al., 2010a; Wang et
al., 2010b). For example, Zeng et al. (2017) and Chai et al. (2019)
demonstrated that after planting for 30 yrs, leguminous shrubs (\emph{C.
korshins} ) will decrease the soil quality and continuously reduce the
SM, resulting in premature decay and death. Nevertheless, not all
vegetation recovery types exhibit functional degradation and recession.
The main reason for this situation is whether the soil water of the soil
reservoir can maintain sustainable vegetation growth. In particular, it
is important to note whether the soil water that is consumed by the
plants in the dry season can be replenished by rainfall in the rainy
season. Therefore, we researched the changes in the SM and SWS of each
land-cover type after rainfall events and over the whole plant growing
season.

We concluded that land-cover change, especially vegetation recovery, had
a significant impact on soil water accumulation and utilization after
rainfall (Fig. 5). In our study, revegetation remodeled the
interrelation between SM and rainfall. Letting the plants act as the
intermediate medium and forming a relatively continuous and
discontinuous unity, the roots of plants dig deep into the soil but
block rainfall from coming into direct contact with the soil. These
changes in plant structure and function of land cover promoted much more
rainwater percolating into the `soil reservoir', which increased the SWS
by approximately 67.7\%, 6.1\%, and 31.9\% in the planted forest, shrub,
and grass, respectively, compared to the cropland over the 13 rainfall
events (Fig. 5). The reason is that forest and grass sites with a higher
canopy coverage and a thick litter layers hamper precipitation to a
large extent, reducing the erosion of surface soil and increasing the
retention of rainwater (Jian et al., 2015), especially for heavy rains.
When the amount of rainfall surpasses the interception capacity of
vegetation, rainwater will reach the soil. At this moment, the continuum
that the roots of plants related to soil begin to play their roles. The
dense fine roots form a multi-porous structure and establish a multipath
water infiltration channel, promoting rainwater infiltration into deeper
soil with a short response time. For example, the abandoned grass
exhibited preferential flow in the subsurface soil layer after
continuous rains (Fig. 5h). In addition, the improvement of soil
properties by vegetation, which decreased the bulk density and increased
the SOC, all increased the infiltration rate of soil water. The final
result is that the forest site had the largest response value of slope
correlation (Fig. 5a) and the largest change in the SWS at the 0-1 m
soil depth after all the soil wetting processes, followed by grass (Fig.
5b). Nevertheless, planted shrubs with similar characteristics of
revegetation did not store more soil water after precipitation, mainly
because plant absorption and evapotranspiration exacerbated the entire
soil water depletion profile. The accumulated amount after precipitation
was not enough to compensate for vegetation water consumption. The shrub
site had the shortest RT and fastest WFV in the surface soil but was not
helpful for alleviating soil desiccation and increasing soil water
accumulation. This result suggested that planted shrubs may not be
suitable for this limited rainfall region on the semiarid CLP,
especially after 20 years of revegetation. We also concluded that the
rainfall-SM response pattern determined the utilization and storage
patterns of soil water of different land-cover types. The deeper the
response depth was, the shorter the response time was, and the higher
the velocity of subsurface soil was, the larger the increment of SWS and
rainfall utilization rate (RUR) in rainfall processes.

Further study illustrated that the seasonal SM distribution and SWS
changed significantly across the five monitoring sites over the growing
season. Revegetation improved the average soil water content (ASWC)
across the profile, with a higher RUR and infiltration amount, despite
planted shrubs depleting much more soil water. Meanwhile, planting trees
and grass, instead of cultivation or bare land, promoted rainwater
reaching deeper soil, which showed that the largest ASWC value occurred
at deeper depths after revegetation (Fig. 6). This result was due to
soil permeability promotion, particularly in subsurface profiles, which
accelerated the speed of soil water infiltration and responded in the
deeper layer. Additionally, due to the higher canopy coverage and thick
litter layer of woodland (Jian et al., 2015) and lower
evapotranspiration of grass (Wang et al., 2012), which depleted less
water from deeper soils in the growing season, the soil water of the
revegetation type could sustain a higher content in deep soil. For SWS
variation over the growing season (SWS\textsubscript{total}), planted
forest had the largest increment in SWS\textsubscript{total}, which was
significantly higher than that at the other sites. The utilization and
conservation pattern of forests resulted in greater
SWS\textsubscript{total} accumulation. For example, a deeper soil
wetting depth for infiltration (Fig. 3), a faster WFV of the subsoil
layer (Table 4), a higher utilization rate (RUR) of rainfall (Fig. 5b),
and the roughest surface cover intercepted rainwater and runoff. The
consumption and conservation pattern also confined the condition of
SWS\textsubscript{total}. For example, shrubs with vigorous
evapotranspiration in the growing season depleted a considerable amount
of water in the 1-m profile, causing severe soil desiccation (Wang et
al., 2011) that was counterbalanced with infiltrated rainwater. This
result was limited to storing much more water in the profile over the
rainy season. However, the grass site was the opposite. With less
aboveground biomass and evapotranspiration, the grass site possessed the
lowest soil water consumption and the highest soil water content before
the rainy season. However, the higher initial SM of abandoned grass
limited the range of change in the SWS if the soil was saturated with
water that was not very dry (Su et al., 2019). Thus, planted shrub and
abandoned grass sites showed smaller amplitude variations in the
SWS\textsubscript{total} than did forests and even crop sites over the
growing season (Fig. 6). Unlike revegetation with high canopy recovery
and thick litter to intercept rainfall and increase infiltration, bare
land with no vegetation cover mostly exacerbated the soil erosion of the
soil surface caused by heavy rainfall, which limited the percolation of
water to deep soils. Moreover, the direct solar radiation intensified
the evaporation of the entire soil profile. Together, these factors led
to the smallest increase in the SWS of bare land across the five
monitoring sites over the growing season (Fig. 6e). Therefore,
revegetation types did not aggravate the water deficit of the 0-1 m soil
depth after precipitation in the growing season. In turn, the existence
of vegetation promoted rainwater infiltration into deeper soil layers,
which is beneficial to the SWS increase in `soil reservoir and plant
sustainable development'. This paper concentrated on the rainfall-SM
response process of 20-year revegetation types over the growing season
only, while further research should be conducted on the interaction
between plants and soil water at a longer temporal, a larger spatial,
and deeper profile scales on the CLP.

\textbf{5. CONCLUSIONS}

This paper conducted hly soil moisture monitoring of the plant growth
season on the CLP and found that the constraint of the minimum
accumulated rainfall amount stimulated the surface soil moisture
response. A minimum rainfall amount of 5 mm was required to trigger a
soil moisture response at a depth of 10 cm at the shrub site, while the
other sites demanded \textasciitilde{}6 mm. With a larger rainfall
amount and higher intensity, rainwater can infiltrate deeper and faster
along the 1-m soil profile. Across all the land-cover types,
revegetation sites, especially forest and grass, had a deeper response
depth, shorter response time, faster response velocity, higher rainfall
transformation rate and higher soil water storage of all the rainfall
processes in the 1-m profile. These results suggested that rainfall
patterns, land-cover types, and their interplays together determined the
soil moisture response process. Moreover, after 20 years of vegetation
reconstruction, land-cover change can regulate the rainfall-soil
moisture response relationship (soil moisture response depth, time, and
velocity) and promote rainfall resource utilization efficiency (rainfall
transformation rate and soil water storage), which does not aggravate
the soil water deficit across the 1-m profile over the rainy season.
Therefore, while considering the joint effect of precipitation and
land-cover change on soil moisture and soil water storage, abandoned
grass may be the best choice for sustainable plant--soil water
utilization when the rainfall amount is less than 550 mm, while planted
forest, instead of shrub, is beneficial for rainfall utilization and
ecosystem water conservation if precipitation is greater than 550 mm.

\textbf{ACKNOWLEDGEMENTS}

We thank all the researchers whose data were used in this study and
three anonymous reviewers for theirconstructive comments and suggestions
on this manuscript. This research was supported by the National Natural
Science Foundation of China (Grant No. 42130717).

\textbf{REFERENCES}

Bryan, B. A., Gao, L., Ye, Y. Q., Sun, X. F., Connor, J. D., Crossman,
N. D., Stafford-Smith, M., Wu, J. G., He, C. Y., Yu, D. Y., Liu, Z. F.,
Li, A., Huang, Q. X., Ren, H., Deng, X. Z., Zheng, H., Niu, J. M., Han,
G. D., Hou, X. Y., 2018. China's response to a national land-system
sustainability emergency. Nature 559 (7713), 193-204.
https://doi.org/10.1038/s41586-018-0280-2.

Chai, Q., Ma, Z., An, Q., Wu, G.-L., Chang, X., Zheng, J., Wang, G.,
2019. Does Caragana korshinskii plantation increase soil carbon
continuously in a water-limited landscape on the Loess Plateau, China?
Land Degrad. Dev. 30 (14), 1691-1698. https://doi.org/10.1002/ldr.3373.

Chen, L., Huang, Z., Gong, J., Fu, B., Huang, Y., 2007a. The effect of
land cover/vegetation on soil water dynamic in the hilly area of the
loess plateau, China. Catena 70 (2), 200-208.
https://doi.org/10.1016/j.catena.2006.08.007.

Chen, L., Wei, W., Fu, B., Lu, Y., 2007b. Soil and water conservation on
the Loess Plateau in China: review and perspective. Prog. Phys. Geogr.
31 (4), 389-403. https://doi.org/10.1177/0309133307081290.

Chirino, E., Bonet, A., Bellot, J., Sanchez, J. R., 2006. Effects of
30-year-old Aleppo pine plantations on runoff, soil erosion, and plant
diversity in a semi-arid landscape in south eastern Spain. Catena 65
(1), 19-29. https://doi.org/10.1016/j.catena.2005.09.003.

Deng, L., Shangguan, Z. P., 2017. Afforestation Drives Soil Carbon and
Nitrogen Changes in China. Land Degrad. Dev. 28 (1), 151-165.
https://doi.org/10.1002/ldr.2537.

Ding, W., Wang, F., Dong, Y., Jin, K., Cong, C., Han, J., Ge, W., 2021.
Effects of rainwater harvesting system on soil moisture in rain-fed
orchards on the Chinese Loess Plateau. Agric. Water Manag. 243, 106496.
https://doi.org/10.1016/j.agwat.2020.106496.

Birdsey, R., Pan, Y., 2015. Trends in management of the world's forests
and impacts on carbon stocks. For. Ecol. Manag. 355: 83-90.
http://dx.doi.org/10.1016/j.foreco.2015.04.031.

Feng, S., Fu, Q., 2013. Expansion of global drylands under a warming
climate. Atmos. Chem. Phys. 13 (19), 10081-10094.
https://doi.org/10.5194/acp-13-10081-2013.

Fu, B., Wang, S., Liu, Y., Liu, J., Liang, W., Miao, C., 2017.
Hydrogeomorphic Ecosystem Responses to Natural and Anthropogenic Changes
in the Loess Plateau of China. In: Jeanloz, R., Freeman, K. H., editor,
Annual Review of Earth and Planetary Sciences, Vol 45. Annual Review of
Earth and Planetary Sciences, p. 223-243.
https://doi.org/10.1146/annurev-earth-063016-020552

Hardie, M., Lisson, S., Doyle, R., Cotching, W., 2013. Determining the
frequency, depth and velocity of preferential flow by high frequency
soil moisture monitoring. J. Contam. Hydrol. 144 (1), 66-77.
https://doi.org/10.1016/j.jconhyd.2012.10.008.

He, Z., Jia, G., Liu, Z., Zhang, Z., Yu, X., Hao, P., 2020. Field
studies on the influence of rainfall intensity, vegetation cover and
slope length on soil moisture infiltration on typical watersheds of the
Loess Plateau, China. Hydrol Process 34 (25), 4904-4919.
https://doi.org/10.1002/hyp.13892.

He, Z., Zhao, W., Liu, H., Chang, X., 2012. The response of soil
moisture to rainfall event size in subalpine grassland and meadows in a
semi-arid mountain range: A case study in northwestern China's Qilian
Mountains. J. Hydrol. 420-421, 183-190.
https://doi.org/10.1016/j.jhydrol.2011.11.056.

Hou, G., Bi, H., Wei, X., Kong, L., Wang, N., Zhou, Q., 2018. Response
of Soil Moisture to Single-Rainfall Events under Three Vegetation Types
in the Gully Region of the Loess Plateau. Sustainability 10 (10), 3793.
https://doi.org/10.3390/su10103793.

Huang, J., Yu, H., Guan, X., Wang, G., Guo, R., 2016. Accelerated
dryland expansion under climate change. Nat. Clim. Chang. 6 (2),
166-171. https://doi.org/10.1038/nclimate2837.

Jia, X., Shao, M. A., Zhu, Y., Luo, Y., 2017. Soil moisture decline due
to afforestation across the Loess Plateau, China. J. Hydrol. 546,
113-122. https://doi.org/10.1016/j.jhydrol.2017.01.011.

Jia, Y.-H., Shao, M. A., 2014. Dynamics of deep soil moisture in
response to vegetational restoration on the Loess Plateau of China. J.
Hydrol. 519, 523-531. https://doi.org/10.1016/j.jhydrol.2014.07.043.

Jian, S., Zhao, C., Fang, S., Yu, K., 2015. Effects of different
vegetation restoration on soil water storage and water balance in the
Chinese Loess Plateau. Agric. For. Meteorol. 206, 85-96.
https://doi.org/10.1016/j.agrformet.2015.03.009.

Jin, Z., Guo, L., Lin, H., Wang, Y., Yu, Y., Chu, G., Zhang, J., 2018.
Soil moisture response to rainfall on the Chinese Loess Plateau after a
long-term vegetation rehabilitation. Hydrol Process 32 (12), 1738-1754.
https://doi.org/10.1002/hyp.13143.

Koutroulis, A. G., 2019. Dryland changes under different levels of
global warming. Sci. Total Environ. 655, 482-511.
https://doi.org/10.1016/j.scitotenv.2018.11.215.

Lee, E., Kim, S., 2017. Pattern similarity based soil moisture analysis
for three seasons on a steep hillslope. J. Hydrol. 551, 484-494.
https://doi.org/10.1016/j.jhydrol.2017.06.028.

Li, B., Li, P., Zhang, W., Ji, J., Liu, G., Xu, M., 2021a. Deep soil
moisture limits the sustainable vegetation restoration in arid and
semi-arid Loess Plateau. Geoderma 399, 115122.
https://doi.org/10.1016/j.geoderma.2021.115122.

Li, B., Zhang, W., Li, S., Wang, J., Liu, G., Xu, M., 2021b. Severe
depletion of available deep soil water induced by revegetation on the
arid and semiarid Loess Plateau. For. Ecol. Manage. 491, 119156.
https://doi.org/10.1016/j.foreco.2021.119156.

Li, Q., Zhu, Q., Zheng, J., Liao, K., Yang, G., 2015. Soil moisture
response to rainfall in forestland and vegetable plot in Taihu Lake
Basin, China. Chin Geogr Sci 25 (4), 426-437.
https://doi.org/10.1007/s11769-014-0715-0.

Li, Y. Y., Shao, M. A., 2006. Change of soil physical properties under
long-term natural vegetation restoration in the Loess Plateau of China.
J. Arid Environ. 64 (1), 77-96.
https://doi.org/10.1016/j.jaridenv.2005.04.005.

Lickley, M., Solomon, S., 2018. Drivers, timing and some impacts of
global aridity change. Environ. Res. Lett. 13 (10), 104010.
https://doi.org/10.1088/1748-9326/aae013.

Lin, H., Zhou, X., 2008. Evidence of subsurface preferential flow using
soil hydrologic monitoring in the Shale Hills catchment. Eur J Soil Sci
59 (1), 34-49. https://doi.org/10.1111/j.1365-2389.2007.00988.x.

Liu, M., Wang, Q., Guo, L., Yi, J., Lin, H., Zhu, Q., Fan, B., Zhang,
H., 2020. Influence of canopy and topographic position on soil moisture
response to rainfall in a hilly catchment of Three Gorges Reservoir
Area, China. J. Geogr. Sci. 30 (6), 949-968.
https://doi.org/10.1007/s11442-020-1764-1.

Liu, Y., Miao, H.-T., Huang, Z., Cui, Z., He, H., Zheng, J., Han, F.,
Chang, X., Wu, G.-L., 2018. Soil water depletion patterns of artificial
forest species and ages on the Loess Plateau (China). For. Ecol. Manage.
417, 137-143. https://doi.org/10.1016/j.foreco.2018.03.005.

Liu, Y., Cui, Z., Lopez-Vicente, M., Wu, G.-L., 2019. Influence of soil
moisture and plant roots on the soil infiltration capacity at different
stages in arid grasslands of china - sciencedirect. Catena 182,
104147-104147. https://doi.org/10.1016/j.catena.2019.104147

Lozano-Parra, J., Schnabel, S., Ceballos-Barbancho, A., 2015. The role
of vegetation covers on soil wetting processes at rainfall event scale
in scattered tree woodland of Mediterranean climate. J. Hydrol. 529,
951-961. https://doi.org/10.1016/j.jhydrol.2015.09.018.

Lu, Y., Fu, B., Feng, X., Zeng, Y., Liu, Y., Chang, R., Sun, G., Wu, B.,
2012. A Policy-Driven Large Scale Ecological Restoration: Quantifying
Ecosystem Services Changes in the Loess Plateau of China. PloS One 7
(2), e31782. https://doi.org/10.1371/journal.pone.0031782.

Mamedov, A. I., Levy, G. J., 2019. Soil erosion-runoff relations on
cultivated land: Insights from laboratory studies. Eur J Soil Sci 70
(3), 686-696. https://doi.org/10.1111/ejss.12759.

Mayerhofer, C., Meissl, G., Klebinder, K., Kohl, B., Markart, G., 2017.
Comparison of the results of a small-plot and a large-plot rainfall
simulator -- Effects of land use and land cover on surface runoff in
alpine catchments. Catena 156, 184-196.
http://dx.doi.org/10.1016/j.catena.2017.04.009

McMillan, H. K., Srinivasan, M. S., 2015. Characteristics and controls
of variability in soil moisture and groundwater in a headwater
catchment. Hydrol Earth Syst Sci 19 (4), 1767-1786.
https://doi.org/10.5194/hess-19-1767-2015.

Murray, S. J., 2014. Trends in 20th century global rainfall interception
as simulated by a dynamic global vegetation model: implications for
global water resources. Ecohydrology 7 (1), 102-114.
https://doi.org/10.1002/eco.1325.

Pan, D., Yang, S., Song, Y., Gao, X., Wu, P., Zhao, X., 2019. The
tradeoff between soil erosion protection and water consumption in
revegetation: Evaluation of new indicators and influencing factors.
Geoderma 347, 32-39. https://doi.org/10.1016/j.geoderma.2019.02.003.

Rosenbaum, U., Huisman, J. A., Weuthen, A., Vereecken, H., Bogena, H.
R., 2010. Sensor-to-Sensor Variability of the ECH2O EC-5, TE, and 5TE
Sensors in Dielectric Liquids. Vadose Zone J. 9 (1), 181-186.
https://doi.org/10.2136/vzj2009.0036.

Salve, R., Sudderth, E. A., St Clair, S. B., Torn, M. S., 2011. Effect
of grassland vegetation type on the responses of hydrological processes
to seasonal precipitation patterns. J. Hydrol. 410 (1-2), 51-61.
https://doi.org/10.1016/j.jhydrol.2011.09.003.

Su, B., Shangguan, Z. P., 2019. Decline in soil moisture due to
vegetation restoration on the Loess Plateau of China. Land Degrad. Dev.
30 (3), 290-299. https://doi.org/10.1002/ldr.3223.

Sun, D., Yang, H., Guan, D., Yang, M., Wu, J., Yuan, F., Jin, C., 2018.
The effects of land use change on soil infiltration capacity in China: A
meta-analysis. Sci. Total Environ. 626, 1394-1401.
https://doi.org/10.1016/j.scitotenv.2018.01.104.

Tang, M., Zhao, X., Gao, X., Zhang, C., Wu, P., 2019. Land Use Affects
Soil Moisture Response to Dramatic Short-term Rainfall Events in a
Hillslope Catchment of the Chinese Loess Plateau. Agron. J. 111 (3),
1506-1515. https://doi.org/10.2134/agronj2018.06.0405.

Tang, X., Miao, C., Xi, Y., Duan, Q., Lei, X., Li, H., 2018. Analysis of
precipitation characteristics on the loess plateau between 1965 and
2014, based on high-density gauge observations. Atmos. Res. 213,
264-274. https://doi.org/10.1016/j.atmosres.2018.06.013.

Wang, H., Zhang, G., Li, N., Zhang, B., Yang, H., 2019. Soil erodibility
as impacted by vegetation restoration strategies on the Loess Plateau of
China. Earth Surf. Process. Landf. 44 (3), 796-807.
https://doi.org/10.1002/esp.4531.

Wang, S., Fu, B., Gao, G., Liu, Y., Zhou, J., 2013. Responses of soil
moisture in different land cover types to rainfall events in a
re-vegetation catchment area of the Loess Plateau, China. Catena 101,
122-128. https://doi.org/10.1016/j.catena.2012.10.006.

Wang, S., Fu, B., Gao, G., Yao, X., Zhou, J., 2012. Soil moisture and
evapotranspiration of different land cover types in the Loess Plateau,
China. Hydrol Earth Syst Sci 16 (8), 2883-2892.
https://doi.org/10.5194/hess-16-2883-2012.

Wang, S., Fu, B., Piao, S., Lu, Y., Ciais, P., Feng, X., 2016. Reduced
sediment transport in the Yellow River due to anthropogenic changes.
Nat. Geosci. 9 (1), 38-+. https://doi.org/10.1038/NGEO2602.

Wang, Y., Dong, Y., Su, Z., Mudd, S. M., Zheng, Q., Hu, G., 2020.
Spatial distribution of water and wind erosion and their influence on
the soil quality at the agropastoral ecotone of North China. Int. Soil
Water Conserv. Res. 8 (3), 253-265.
https://doi.org/10.1016/j.iswcr.2020.05.001.

Wang, Y., Shao, M. A., Liu, Z., 2010a. Large-scale spatial variability
of dried soil layers and related factors across the entire Loess Plateau
of China. Geoderma 159 (1-2), 99-108.
https://doi.org/10.1016/j.geoderma.2010.07.001.

Wang, Y., Shao, M. A., Shao, H., 2010b. A preliminary investigation of
the dynamic characteristics of dried soil layers on the Loess Plateau of
China. J. Hydrol 381 (1-2), 9-17.
https://doi.org/10.1016/j.jhydrol.2009.09.042.

Wang, Y., Shao, M. A., Zhu, Y., Liu, Z., 2011. Impacts of land use and
plant characteristics on dried soil layers in different climatic regions
on the Loess Plateau of China. Agric. For. Meteorol. 151 (4), 437-448.
https://doi.org/10.1016/j.agrformet.2010.11.016.

Williams, A. P., Cook, E. R., Smerdon, J. E., Cook, B. I., Abatzoglou,
J. T., Bolles, K., Baek, S. H., Badger, A. M., Livneh, B., 2020. Large
contribution from anthropogenic warming to an emerging North American
megadrought. Science 368 (6488), 314-+.
https://doi.org/10.1126/science.aaz9600.

Wu, G.-L., Cui, Z., Huang, Z., 2021. Contribution of root decay process
on soil infiltration capacity and soil water replenishment of planted
forestland in semi-arid regions. Geoderma 404, 115289.
https://doi.org/10.1016/j.geoderma.2021.115289.

Yan, W., Zhou, Q., Peng, D., Wei, X., Tang, X., Yuan, E., 2021. Soil
moisture responses under different vegetation types to winter rainfall
events in a humid karst region. Environ. Sci. Pollut. Res. 28 (40),
56984-56995. https://doi.org/10.1007/s11356-021-14620-z.

Yu, X., Huang, Y., Li, E., Li, X., Guo, W., 2017. Effects of vegetation
types on soil water dynamics during vegetation restoration in the Mu Us
Sandy Land, northwestern China. J. Arid Land 9 (2), 188-199.
https://doi.org/10.1007/s40333-017-0054-y.

Yu, Y., Wei, W., Chen, L., Jia, F., Yang, L., Zhang, H., Feng, T., 2015.
Responses of vertical soil moisture to rainfall pulses and land uses in
a typical loess hilly area, China. Solid Earth 6 (2), 595-608.
https://doi.org/10.5194/se-6-595-2015.

Zeng, Q., Lal, R., Chen, Y., An, S., 2017. Soil, Leaf and Root
Ecological Stoichiometry of Caragana korshinskii on the Loess Plateau of
China in Relation to Plantation Age. PloS One 12 (1), e0168890.
https://doi.org/10.1371/journal.pone.0168890.

Mao, W., Yang, J., Zhu, Y., Ye, M., Liu, Z., Wu, J., 2018. An efficient
soil water balance model based on hybrid numerical and statistical
methods. J. Hydrol 559, 721-735.
https://doi.org/10.1016/j.jhydrol.2018.02.074.

Zhang, Z.-H., Peng, H.-Y., Kong, Y., 2021. Effects of the ''Grain for
Green'' Program on Soil Water Dynamics in the Semi-Arid Grassland of
Inner Mongolia, China. Water 13 (15), 2034.
https://doi.org/10.3390/w13152034.

Zhao, G., Mu, X., Jiao, J., An, Z., Klik, A., Wang, F., Jiao, F., Yue,
X., Gao, P., Sun, W., 2017. Evidence and cause of spatiotemporal changes
in runoff and sediment yield on the Chinese Loess Plateau. Land Degrad.
Dev. 28 (2), 579-590. https://doi.org/10.1002/ldr.2534.

Zhao, G., Mu, X., Wen, Z., Wang, F., Gao, P., 2013. Soil erosion,
conservation, and eco-environment changes in the Loess Plateau of China.
Land Degrad. Dev. 24 (5), 499-510. https://doi.org/10.1002/ldr.2246.

Zheng, H., Wang, Y., Chen, Y., Zhao, T., 2016. Effects of large-scale
afforestation project on the ecosystem water balance in humid areas: An
example for southern China. Ecol. Eng. 89 103-108.
https://doi.org/10.1016/j.ecoleng.2016.01.013.

Zhu, P., Zhang, G., Wang, H., Zhang, B., Liu, Y., 2021. Soil moisture
variations in response to precipitation properties and plant communities
on steep gully slope on the Loess Plateau. Agric. Water Manag. 256,
107086. https://doi.org/10.1016/j.agwat.2021.107086.

Table 1 Basic characteristics of the monitoring sites\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
\begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Land-cover type\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Land-cover type\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Year (yr)\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Elevation (m)\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Slope (\begin{CJK}{UTF8}{gbsn}\textsuperscript{。}\end{CJK}\selectlanguage{english})\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Aspect\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Position\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Canopy Coverage (\%)\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Herb Coverage (\%)\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Forest\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
25\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1258\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
25\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
East\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Middle\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
74.8\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
41.0\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
86.8\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Shrub\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
28\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1284\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
24\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Northwest\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Bottom\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
40.0\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
59.4\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
71.7\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Grass\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
25\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1309\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
23\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Northwest\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Middle\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
60.5\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
44.2\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Crop\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1312\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
25\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Northwest\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Middle\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
71.2\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
40.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Bareland\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1295\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
25\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Northwest\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Middle\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
-\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Table 2 Characteristic of rainfall-soil wetting process (P1 to P13)
during May to October\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
Rainfall process & Data & Group no.\textsuperscript{a} & Total amount
(mm) & Duration (hr) & Duration (hr) & Peak intensity (mm/h) & Average
intensity (mm/h)\textsuperscript{b} & Average intensity
(mm/h)\textsuperscript{b}\tabularnewline
\midrule
\endhead
P1 & Jul 22 & I & 135.9 & 22.0 & 75.6 & 75.6 & 75.6 &
6.16\tabularnewline
P2 & Jul 10 & II & 15.2 & 2.3 & 75.0 & 75.0 & 75.0 & 6.74\tabularnewline
P3 & Aug 3 & I & 114.2 & 15.7 & 44.4 & 44.4 & 44.4 & 7.30\tabularnewline
P4 & Oct 12 & II & 5.9 & 0.7 & 37.2 & 37.2 & 37.2 & 8.02\tabularnewline
P5 & Jul 20 & III & 24.7 & 12.0 & 25.8 & 25.8 & 25.8 & 2.06\tabularnewline
P6 & Aug 26 & III & 16.6 & 13.0 & 25.2 & 25.2 & 25.2 & 1.28\tabularnewline
P7 & Jun 22 & III & 16.1 & 9.7 & 24.0 & 24.0 & 24.0 & 1.65\tabularnewline
P8 & Oct 15 & IV & 32.6 & 23.4 & 13.8 & 13.8 & 13.8 & 1.39\tabularnewline
P9 & Oct 6 & IV & 26.1 & 20.4 & 12.6 & 12.6 & 12.6 & 1.28\tabularnewline
P10 & Aug 24 & IV & 22.2 & 39.0 & 10.2 & 10.2 & 10.2 &
0.57\tabularnewline
P11 & Jun 27 & IV & 33.1 & 17.4 & 8.4 & 8.4 & 8.4 & 1.91\tabularnewline
P12 & Aug 20 & III & 16.6 & 7.0 & 6.6 & 6.6 & 6.6 & 2.38\tabularnewline
P13 & Sep 19 & III & 20.9 & 8.3 & 5.4 & 5.4 & 5.4 & 2.5\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{a} The soil wetting events are divided into four groups
according to rainfall attributes.

\textsuperscript{b} Average intensity is the total rainfall amount
divided by duration during soil wetting process.

Table 3 Response lag time and lasting time of different patterns at
10-cm depth\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
\begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Pattern\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Group\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Response Lag Time (hr)\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Response Lag Time (hr)\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Response Lag Time (hr)\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Response Lasting Time (hr)\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Response Lasting Time (hr)\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
Response Lasting Time (hr)\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
M\selectlanguage{ngerman}±SE\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Min\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Max\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
M\selectlanguage{ngerman}±SE\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Min\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Max\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Rainfall pattern\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Group I\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
6.1\selectlanguage{ngerman}±0.4 b\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3.5\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
7.7\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
9.7\selectlanguage{ngerman}±0.6 a\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
13\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Group II\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.0\selectlanguage{ngerman}±0.3 a\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
3.1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
13.4\selectlanguage{ngerman}±3.4 ab\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
31\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Group III\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
24\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
6.4\selectlanguage{ngerman}±1.5 b\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.4\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
34.2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
14.7\selectlanguage{ngerman}±2.5 a\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
50\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Group IV\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
7.8\selectlanguage{ngerman}±1.0 b\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.5\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
17.6\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
21.9\selectlanguage{ngerman}±2.7 b\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
55\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Vegetation pattern\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Forest\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
8.3\selectlanguage{ngerman}±1.8 B\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
2.1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
24.2\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
16.1\selectlanguage{ngerman}±3.2 A\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
38\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Shrub\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
13\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
3.4\selectlanguage{ngerman}±0.6 A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
7.6\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
13.5\selectlanguage{ngerman}±2.6 A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
35\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Grass\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
9.0\selectlanguage{ngerman}±2.7 AB\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.9\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
34.2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
14.8\selectlanguage{ngerman}±3.0 A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
40\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Crop\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
13\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5.3\selectlanguage{ngerman}±0.9 AB\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
12.6\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
17.6\selectlanguage{ngerman}±3.7 A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
55\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Bareland\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
13\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5.3\selectlanguage{ngerman}±0.8 AB\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.0\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
11.6\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
17.9\selectlanguage{ngerman}±3.9 A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
50\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Note. N: number; M: mean; SE: standard error. The small letter and upper
letter represent the significant difference in rainfall patterns and
vegetation patterns respectively (p\textless{}0.05).

Table 4 SM response time and wetting front velocity of different
patterns at 1-m profile\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllllll@{}}
\toprule
\begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
Pattern\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
Group\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
RT (hr)\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
RT (hr)\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
RT (hr)\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
WFV (cm/hr)\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
WFV (cm/hr)\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
WFV (cm/hr)\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
WFV (cm/hr)\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
WFV (cm/hr)\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
WFV (cm/hr)\strut
\end{minipage} & \begin{minipage}[b]{0.05\columnwidth}\raggedright\strut
WFV (cm/hr)\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0-100 cm\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0-100 cm\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0-100 cm\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0-100 cm\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0-100 cm\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0-100 cm\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0-10 cm\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0-10 cm\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0-10 cm\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
N\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
M\selectlanguage{ngerman}±SE\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Min\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Max\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
M\selectlanguage{ngerman}±SE\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Min\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Max\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
N\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
M\selectlanguage{ngerman}±SE\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Min\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Max\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Rainfall pattern\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Group I\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
45\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
20.3\selectlanguage{ngerman}±3.3b\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
3.5\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
127.5\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
6.1\selectlanguage{ngerman}±1.3b\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
40.0\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.7\selectlanguage{ngerman}±0.1a\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.9\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Group II\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
6.4\selectlanguage{ngerman}±3.3a\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
1.1\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
42.1\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
5.1\selectlanguage{ngerman}±0.8b\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.7\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
9.5\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
6.0\selectlanguage{ngerman}±0.9b\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
3.2\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
9.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Group III\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
53\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
11.6\selectlanguage{ngerman}±1.3ab\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
1.4\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
41.4\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
4.0\selectlanguage{ngerman}±0.5b\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.3\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
20.0\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
24\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
2.8\selectlanguage{ngerman}±0.4a\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.3\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
7.3\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Group IV\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
68\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
30.8\selectlanguage{ngerman}±3.3c\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
2.5\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
117.0\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
2.0\selectlanguage{ngerman}±0.3a\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
-5.0\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
10.0\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
1.8\selectlanguage{ngerman}±0.2a\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.6\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
4.1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Vegetation pattern\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Forest\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
35\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
18.3\selectlanguage{ngerman}±3.0A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
98.0\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
4.8\selectlanguage{ngerman}±1.1 A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.4\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
30.0\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.9\selectlanguage{ngerman}±0.4A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.4\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
4.9\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Shrub\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
35\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
17.6\selectlanguage{ngerman}±3.0A\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
1.1\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
84.0\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
3.7\selectlanguage{ngerman}±0.6 A\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.7\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
15.0\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
13\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
4.2\selectlanguage{ngerman}±0.8B\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
1.3\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
9.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Grass\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
38\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
21.9\selectlanguage{ngerman}±3.5A\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
2.0\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
86.0\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
5.1\selectlanguage{ngerman}±1.3 A\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
-5.0\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
40.0\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
2.1\selectlanguage{ngerman}±0.5AB\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.3\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
5.3\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Crop\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
34\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
26.5\selectlanguage{ngerman}±5.5A\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
1.1\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
127.5\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
2.7\selectlanguage{ngerman}±0.4 A\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.3\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
10.0\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
13\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
2.9\selectlanguage{ngerman}±0.6AB\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.8\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
9.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
Bareland\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
36\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
19.8\selectlanguage{ngerman}±3.5A\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
2.0\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
90.0\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
2.9\selectlanguage{ngerman}±0.4 A\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.3\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
10.0\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
13\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
2.5\selectlanguage{ngerman}±0.4AB\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
0.9\strut
\end{minipage} & \begin{minipage}[t]{0.05\columnwidth}\raggedright\strut
4.9\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Note. N: number; M: mean; SE: standard error. The different small letter
and upper letter represents the significant difference in rainfall
patterns and vegetation patterns respectively (p\textless{}0.05).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Fig. 1. (a) The study site is located in Zhifanggou watershed of the
Chinese Loess Plateau in northern China. (b) Forest site, (c) Shrub
site, (d) Grass site, (e) Crop site, and (f) Bareland site.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Fig. 2. An overview of 1-hour soil moisture time series and rainfall
over the study period (May 1 to October 31, 2019), (a) Forest site, (b)
Shrub site, (c) Grass site, (d) Crop site, (e) Bareland site. (f) 1-hour
rainfall records over the study period. The blue dashed curve indicates
the accumulated rainfall amount over time.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Fig. 3. 13 rainfall-soil wetting processes to soil wetting depth across
five monitoring sites. The size of circle represents rainfall peak
intensity. The color of each circle indicates different soil wetting
depths.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Fig. 4. The accumulated rainfall amount to soil moisture response at the
10-cm depth of all the soil wetting processes across five monitoring
sites. The green dashed line represent the threshold accumulated
rainfall amount that can trigger 10-cm soil moisture response\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Fig. 5. The relationship between total rainfall amount and the change of
soil water storage in the soil wetting process at 0-100 cm soil depth at
five monitoring sites. (a): Linear regression analysis between total
rainfall amount and the change in soil water storage after each rainfall
process. The colored dot represents different land-cover monitoring
sites. And dashed lines are the linear regression models, showing the
regression equations and coefficients of determination
(\emph{R\textsuperscript{2}} ). (b): Soil water storage variation and
rainfall transformation rate at five monitoring sites across all the
soil wetting processes. The dashed line with brown diamond icon
represents the rainfall transformation rate, which is the ratio of
rainwater into the soil.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Fig. 6. Soil moisture profile dynamic and soil water storage variation
in 0-100 cm soil depth over the growing season. The error bars indicate
the standard deviation of the soil moisture and soil water storage among
the monitoring sites. The letters on the error bars in the inserted
graph is the significant difference in soil water storage across the
monitoring sites in probability of 95\%.

Supplemental material---Fig. S1\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

Fig. S1. The relative change of soil wetting depth after rainfall in
planted forest, shrub, abandoned grassland, and bareland compared with
average cropland soil wetting depth.

Supplemental material---Fig. S2\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

Fig. S2. The relative change of 10-cm soil moisture response lag time
and lasting time in planted forest, shrub, abandoned grassland, and
bareland compared with average cropland response lag time and lasting
time.

Supplemental material---Fig. S3\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

Fig. S3. The relative change of soil moisture response lag time and
wetting front velocity in planted forest, shrub, abandoned grassland,
and bareland compared with cropland.

\selectlanguage{english}
\FloatBarrier
\end{document}

