Similarity of the temporal pattern of soil moisture across soil profile in karst catchments of southwestern China

Accepted Manuscript Research papers Similarity of the temporal pattern of soil moisture across soil profile in karst catchments of southwestern China Xuezhang Li, Xianli Xu, Wen Liu, Liang He, Rongfei Zhang, Chaohao Xu, Kelin Wang PII: DOI: Reference:

S0022-1694(17)30722-9 https://doi.org/10.1016/j.jhydrol.2017.10.045 HYDROL 22325

To appear in:

Journal of Hydrology

Received Date: Revised Date: Accepted Date:

29 October 2016 7 August 2017 21 October 2017

Please cite this article as: Li, X., Xu, X., Liu, W., He, L., Zhang, R., Xu, C., Wang, K., Similarity of the temporal pattern of soil moisture across soil profile in karst catchments of southwestern China, Journal of Hydrology (2017), doi: https://doi.org/10.1016/j.jhydrol.2017.10.045

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Similarity of the temporal pattern of soil moisture across soil profile

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in karst catchments of southwestern China

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Xuezhang Lia,b, Xianli Xua,b,*, Wen Liuc, Liang Hed, Rongfei Zhanga,b,e, Chaohao

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Xu a,b,e, Kelin Wanga,b

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a Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of

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Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China

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b Huanjiang Observation and Research Station for Karst Ecosystem, Chinese

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Academy of Sciences, Huanjiang 547100, China

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c College of Resources and Environmental Sciences, Hunan Normal University,

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Changsha 410125 China

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d National Meteorological Center, Beijing 100081, China

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e University of Chinese Academy of Sciences, Beijing 100049, China

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*Corresponding author at: Institute of Subtropical Agriculture, Chinese Academy of

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Sciences, Changsha 410125, China. Tel.: +86-731-84619760

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E-mail addresses: [email protected] (Xuezhang Li); [email protected]

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(Xianli Xu); [email protected] (Wen Liu); [email protected] (Liang He);

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[email protected] (Rongfei Zhang); [email protected] (Chaohao Xu);

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[email protected] (Kelin Wang).

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Summary:

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Knowledge of dynamics of profile soil moisture is of importance for soil moisture

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prediction and modelling. However, information on the relationships between soil

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moisture at the surface and subsurface layers is relatively inadequate. The objective of

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this study was to examine the similarities in the overall and scale specific temporal

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patterns of soil moisture across soil profile. Soil moisture was measured at five soil

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depths using time-domain reflectometry in two sampling plots over 242 days in a

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karst depression. Spearman rank correlation and wavelet coherency analyzes were

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used to determine overall and scale-dependent similarities of temporal patterns of soil

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moisture, respectively. Results showed that soil moisture in farmland was higher than

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grassland especially in 10-50 cm depths. The surface depth had higher variability for

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both farmland and grassland. Spearman rank correlation coefficients between any two

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soil depths were significant, indicating strong depth persistence in the overall

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temporal pattern of soil moisture. The similarity decreased as the distance between

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depths increased. Wavelet coherency analysis indicated that soil moisture temporal

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pattern had a higher depth persistence in the farmland than grassland. Soil depth had a

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larger influence than land uses on the similarity in soil moisture temporal pattern, and

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the relative humidity and precipitation had larger effect than air temperature

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controlling the change of soil moisture at larger scales. These scale specific similarity

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in the temporal pattern of soil moisture can be used to guide estimating deeper soil

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moisture and to improve sampling strategies for soil moisture prediction with a higher

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prediction accuracy. 2

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Keywords: Soil moisture, scale-dependence, wavelet coherency, temporal scale, karst

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landscape, pedohydrology

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1. Introduction

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Soil moisture is a key variable in understanding a variety of hydrological and

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climatic processes on various spatial and temporal scales (Brocca et al., 2009a;

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Heathman et al., 2009). Soil moisture has been shown to improve streamflow

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prediction (Brocca et al., 2009b; Anctil et al., 2008), irrigation management (Souza et

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al., 2011), and climate modeling (Koster et al., 2004; Berg and Mulroy, 2006).

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In southwest China, karst areas cover approximately 4.26×10 5 km2, and belong to

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one of the largest karst regions in the world (Feng et al., 2016). Karst area is a special

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style of landscape occurring on soluble rocks with high heterogeneity. The unique

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dual system of karst landscape can result in rapid hydrological processes (Nie et al.,

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2012; Butscher and Huggenberger, 2009) such as rapid transport of surface water to

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groundwater. Therefore, the appearance, storage and circulation of water in karst

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aquifers are apparently different than in non-karst areas. It was reported that

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hydrological processes of karst landscape have mainly been investigated in surface

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layers (Chen et al., 2010; Cerdà, 1998). For example, Chen et al. (2010) investigated

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the temporal dynamics of surface soil water under different land uses on a karst

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hillslope and found that land uses had significant effect on surface soil moisture.

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However, knowledge of surface soil moisture is often inadequate to understand

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hydrological processes of the relatively thin soil profile in karst landscape. In addition,

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although the precipitation is abundant, this area experiences a seasonal drought which

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inhibits plant growth (Nie et al., 2011). The frequent and serious droughts were

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harmful to ecosystems and society (Cai et al., 2014). Therefore, the evolution of soil 4

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moisture dynamics across soil profile may be helpful for hydrological modeling,

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water resource management and vegetation restoration.

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Soils were heterogeneous and their water content changed spatially and

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temporally (Western et al., 1999). Nevertheless, the pioneering work of Vachaud et al.

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(1985) had shown that the rank of soil moisture at different times and locations will

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remain the same at a certain probability. In general, this temporal persistence of

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spatial pattern of soil moisture also can be termed as rank persistence (Chen, 2006).

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To date, the rank persistence of soil moisture in spatial dimension were studied

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temporally in time series (Zhao et al., 2010; Liu and Shao, 2014; Li et al., 2016), and

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vertically in soil depth series (Hu and Si, 2014; Wang et al., 2015; Li et al., 2015b).

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For instance, Liu and Shao (2014) found that the most time-stable location can

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estimate soil water storage with a high accuracy of prediction even over multiple

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years. Hu and Si (2014) observed that mean water content of 0-3.8 m profile was

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predicted well by the most time stable depth at a point scale. However, there was little

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knowledge on the similarity of soil moisture in temporal dimension across soil profile.

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The continuous measurement of soil moisture could enable us to completely grasp the

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similarity information of soil moisture across soil profile. It was of importance to

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study temporal pattern of soil moisture for understanding its dynamic characteristics.

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Soil moisture is affected by different factors operating in different intensities and

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scales (Biswas and Si, 2011a; Seyfried, 1998). Kachanoski and de jong (1988) used

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the spatial coherency analysis to identify the similarity of the spatial patterns as a

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function of spatial scale. However, information on the scale-dependent of soil 5

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moisture temporal patterns on the influencing factors was not addressed in other

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literatures. Spearman rank correlation analysis can be used to identify the overall

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persistence of soil moisture temporal patterns at the measurement scale. Unfortunately,

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this analysis lost the scale and occasion information. Because the scale of the

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processes controlling soil moisture may not always be the same as the measurements

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scale (Blöschl and Sivapalan, 1995). Moreover, a factor can have different

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correlations over scales and occasions that may neutralize each other over the entire

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measurement scales (Biswas and Si, 2011b). Spectral analysis and spatial coherency

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can reveal the scale-dependent relationships between variables, but are only

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applicable to stationary systems (Si, 2008). Generally, soil moisture series behavior

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belongs to non-stationary system. Wavelet coherency, however, can deal with

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non-stationary data series, and thereby provide an opportunity to analyze the temporal

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patterns of soil moisture at multiple scales. Wavelet coherency has been used to

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identify the scale-location specific correlations between soil properties (Biswas and Si,

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2011a, b; Yates et al., 2007; Shu et al., 2008). However, there is no information

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available about the scale and occasion specific depth persistence of the temporal

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pattern of soil moisture.

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For a deeper insight on the issues above, this study used soil moisture data

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measured continuously by time domain reflectometry (TDR) probes for 242 days in a

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karst landscape. The overall and scale-dependent similarities of the temporal patterns

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of soil moisture were identified using Spearman rank correlation and wavelet

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coherency analyses, respectively. The specific objectives of this study were: (1) to 6

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analyze the depth persistence of temporal patterns of soil moisture in a karst typical

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depression, and (2) to confirm the scale-dependence of soil moisture on controlling

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factors.

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2. Materials and methods

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2.1 Site description

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The study was carried out in Guzhou catchment located in Huanjiang County of

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northwest Guangxi, China (Fig. 1). Guzhou catchment is a typical karstic peak-cluster

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depression area with a flat depression surrounded by steep mountains. The catchment

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has an area of 1.87 km2 with an elevation of 375 to 816 m a.s.l., and has 0.17 km2 of

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farmland mainly located in the depression. A subtropical mountainous monsoon

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climate dominates with mean annual air temperature of 18.5℃and mean annual

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rainfall of 1389 mm, mostly falling from late April to September. The calcareous soils

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initiated from limestone contain few rock fragments and have an average depth of

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50-80 cm in the depression and 10-30 cm on hillslope, respectively (Chen et al. 2010).

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In late 2002, some farmlands (corn) in the depression were converted into forage

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grassland (hybrid Napiergrass Guimu-1) to feed cattle for economic benefits. In late

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2014, a sample plot (5×5 m) was established from representative farmland and

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grassland for soil moisture measurements, respectively.

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2.2 Sampling and measurements

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2.2.1 Soil moisture measurements

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In each sample plot, the TDR (Hydra Probe II) probes were installed in order to

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better understand profile soil moisture dynamics. The TDR probes were vertically

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installed at depths of 10, 20, 30, 40 and 50 cm, respectively, with soil moisture data

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recorded each 30 min for 242 days from March 12, 2015 to November 8, 2015. There

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were 58080 measurements in total during the sampling period for each sample plot, 8

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and we used daily average value of each depth in this study.

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2.2.2 Meteorological variables measurements

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Meteorological data were collected from a microclimate station (HOBO U30

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station made by Onset Computer Corp., MA, USA) installed near both of the sample

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plots in the depression. The meteorological data included precipitation, wind speed,

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air temperature, relative humidity, and global radiation. For this study, the

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meteorological variables were converted to represent daily average values for air

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temperature and relative humidity. Precipitation was presented as daily accumulated

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values. Considering the significant influence, air temperature, precipitation and

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relative humidity were selected to analyze their effects on profile soil moisture in the

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temporal dimension.

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2.2.3 Measurements of other main characteristics

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A portable global positioning system (GPS) was employed to locate the sampling

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sites and record the corresponding elevation (above sea level). From each sample plot,

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three undisturbed soil core samples were collected by cutting rings (5 cm in height; 20

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cm2 cross section) for measurements of soil saturated hydraulic conductivity (KS)

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using the constant-head method (Klute and Dirksen, 1986) and of soil bulk density

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(BD). In addition, a soil corer was used to collect disturbed soil samples from each of

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the five layers corresponding to those used for soil moisture measurements. The

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disturbed soil samples were air-dried and divided into two sub-samples. One

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sub-sample was passed through a 1-mm sieve to analyze soil particle size distribution

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using a MS 2000 particle size analyzer (Malvern Instruments Ltd., Malvern, UK) 9

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which was calibrated by Sieve-Pipette Method (Feng et al., 2013). The other

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sub-sample was passed through a 0.25 mm sieve for the determination of soil

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organic-carbon (SOC) content by the K2Cr2O7 method. Aboveground biomass (AGB)

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were measured by clipped from three 1×1 m quadrats in each sample plot. The plant

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samples were oven-dried at 75 ℃ for 72 h to obtain dry weights. Detailed

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descriptions of the surface soil (0-10 cm) and vegetation characteristics for the sample

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plots are shown in Table 1.

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2.3 Methodology

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2.3.1 Assessment of the depth persistence of temporal patterns of soil moisture

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(1) Spearman’s rank correlation analysis

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The non-parametric Spearman’s rank correlation test introduced by Vachaud et al.

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(1985) was applied to evaluate the similarly of the overall temporal pattern of soil

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moisture. The Spearman’s rank correlation coefficient is expressed as follows: n

(

6∑ i =1 Rij − Rij '

)

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(1)

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rs = 1 −

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where Rij and Rij’ are the ranks of the soil moisture observed at time i in soil layers j

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and j’, respectively, and n is the number of observations. A Spearman’s rank

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correlation value closer to 1 indicates the stronger tendency of similarity of soil

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moisture in time series between different soil depths.

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(2) Wavelet coherency analysis

n( n 2 − 1)

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Wavelet coherency of two temporal series of soil moisture across soil profile was

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used to describe the depth persistence of temporal pattern in this study. Wavelet

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coherency analysis requires the calculation of wavelet coefficients for each of the two 10

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data series and associated cross-wavelet spectrum. A detailed introduction on the

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theory of the wavelet transform (Farge, 1992; Kumar and Foufoula-Georgiou, 1997)

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and the wavelet coherency (Grinsted et al., 2004; Si and Zeleke, 2005; Si, 2008) can

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be found.

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(3) Significance testing

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The significance test for wavelet coherency can be performed against a Gaussian

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white or red noise or permutation test (Pardo-Iguzquiza and Rodriguez-Tovar, 2000).

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A white noise shows that the data series are independently and identically distributed

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normal random variables (Si and Zeleke, 2005). However, soil properties are usually

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autocorrelated (Isaaks and Srivastava, 1989), thus a white noise is not appropriate. For

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a red noise, the data of variables are dependent on those in a short sampling interval

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with a univariate lag 1 autoregssive (AR1) process (Torrence and Compo, 1998; Si

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and Farrell, 2004). There are many soil properties exhibiting the red noise-like

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behavior ( Grinsted et al., 2004; Torrence and Compo, 1998; Si and Farrell, 2004),

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including soil moisture, soil saturated hydraulic conductivity and so on. Therefore, red

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noise-like behavior is treated as the background or the null hypothesis for a statistical

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test. In this study, soil moisture can be approximated using AR1 processes according

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to Torrence and Compo (1998).

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For a given AR1 process with a lag-1 autocorrelation coefficient r, a temporal

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series can be generated using the Monte Carlo simulation. In this study, we generated

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1000 realizations of a temporal soil moisture series, then computed the wavelet power.

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At each scale, the 1000 wavelet powers were sorted into ascending order. If 95% of 11

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the wavelet power bellow the 950th wavelet coherency value, then 95% level is the

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95% confidence level (Si and Zeleke, 2005). The wavelet coherency can reflect

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relationships between variables at each scale and occasion. The phase information can

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also be identified using the wavelet coherency analysis.

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2.4 Statistical analysis

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Exploratory analysis was conducted by Microsoft Office Excel (Microsoft

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Corporation Inc., Redmond, USA). Spearman’s rank correlation analysis was

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implemented with SPSS 18.0 (SPSS Inc., Chicago, USA). Wavelet coherency analysis

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was performed using the MATLAB R2015a (The MathWorks Inc., Natick, USA)

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software with code written by Grinsted et al. (2004).

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3. Results

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3.1 The temporal pattern of soil moisture in the various soil depths

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Fig. 2 showed the daily dynamics of soil moisture of five selected soil layers for

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both farmland and grassland with corresponding air temperature and precipitation.

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The precipitation has obvious seasonal change, mainly falls during late April to late

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September. However, the change of air temperature was relatively small compared

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with precipitation, ranging from 12.5 to 28.5 ℃ with coefficient of variation of

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14.7% (Table 2). Mean soil moisture ranged from 0.39 to 0.62 cm3 cm-3 across soil

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profile for the farmland. Mean soil moisture in 0-10 cm depth was apparently lower

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than the deeper soil depths (20-50 cm). The standard deviation of soil moisture was

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the highest in the 0-10 cm depth and declined in deeper depths (Table 2). The

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coefficient of variation was the highest (10.7%) in 0-10 cm depth, and gradually

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decreased to 2.7% in 30-40 cm depth. For the grassland, mean soil moisture changed

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relatively small across soil profile, ranging from 0.32 to 0.43 cm3 cm-3 (Table 2). The

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standard deviation and coefficient of variation exhibited similar trend across soil

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profile in comparison with the farmland (Table 2). The difference of mean soil

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moisture in 0-10 cm depth was low between farmland and grassland, however, the

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mean soil moisture of 10-50 cm depths was significantly higher in farmland than

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grassland.

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The response of soil moisture to air temperature was not significant in visual for

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both land uses. However, soil moisture was sensitive to the precipitation, especially in

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shallow soil layers. There were three deep depressions during the sampling period, 13

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including Apr. 25, Jul. 20, and Oct. 30 2015, respectively (Fig. 2). For instance,

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during the period of Apr. 3 to Apr. 25 with little rainfall, the soil moisture in 0-10 cm

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depth decreased from 0.37 to 0.29 cm3 cm-3 and 0.38 to 0.26 cm3 cm-3 for farmland

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and grassland, respectively; but seemed no change (decreased only from 0.57 to 0.56

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cm3 cm-3 and 0.39 to 0.38 cm3 cm-3) for farmland and grassland in the 40-50 cm depth

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(Fig. 2), respectively.

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3.2 Similarity in the overall temporal pattern of soil moisture in the soil profile

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Spearman rank correlation coefficients (rs) between any two soil depths for both

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farmland and grassland were statistically significant (p < 0.001), indicating strong

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similarities between soil depths. The value of rs decreased gradually with increasing

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depth interval for both farmland and grassland (Table 3). For instance, rs was 0.909

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between soil moisture of the 0-10 cm and 10-20 cm and was 0.540 between the 0-10

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cm and 40-50 cm for grassland. However, rs between any two depths of farmland

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were relatively larger than that of grassland (Table 3). In addition, the value of rs

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ranged from 0.929 to 0.980 for farmland, but from 0.540 to 0.952 for grassland.

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3.3 Similarities in the scales of temporal pattern of soil moisture in the soil profile

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The scales were divided into three groups based on the similarities among the

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wavelet coherency graphs between any two soil depths, including small (< 4 d),

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medium (4-16 d), and large (> 16 d) scales.

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For the farmland, the significant correlations existed in most of the time series at

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small scales between soil moisture of the 0-10 and 10-20 cm depths, but gradually

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reduced with increasing depth interval (Fig. 3). Most of the occasions had significant 14

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correlations for soil moisture at medium scales between the 0-10 cm and 10-20, 20-30,

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30-40, and 40-50 cm depths (Fig. 3), respectively, except for a few occasions.

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However, there were significant correlations over the whole time series for the scales

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of 16-32 d between 0-10 cm depth and any other depths (Fig. 3). For the grassland,

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there were no significant correlations for soil moisture only at the beginning of the

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time series between the 0-10 and 10-20 cm depths, but there were more occasions

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with no significant correlations between 0-10 and the deeper depths at small scales

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(Fig. 4). Significant correlations were found nearly across all the time series at

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medium scales between the 0-10 and 10-20 cm depths (Fig. 4a). However, there were

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no significant correlations at medium scales between 0-10 cm and other depths (20-30,

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30-40, and 40-50 cm), mainly located at Mar. 12 to Apr. 15 and Aug. 2 to Oct. 6 (Fig.

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4b, c, d). Soil moisture between 0-10 cm and other two depths (10-20 and 20-30 cm)

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were significantly correlated nearly across the entire time series at scales of 16-32 d

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(Fig. 4a, b), while significant correlations between 0-10 and the other two depths

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(30-40 and 40-50 cm) mainly concentrated between Apr. 25 and Oct. 6 (Fig. 4c, d).

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In terms of wavelet coherency analysis, significantly correlated occasions covered

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most of the time series intermittently at the small scales (Fig. 5). The soil moisture

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was significantly correlated between farmland and grassland at the medium scales for

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each depth across the entire time series in additional to a few occasions (Fig. 5).

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However, the significant correlations between farmland and grassland were observed

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across all over the time series at scales of 16-32 d for all depths (Fig. 5).

15

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The difference in wavelet coherency between different depths or land uses can be

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determined in terms of area of significant correlation (Figs. 3-5; Table 4). The total

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area representing significant correlations at different scales gradually decreased with

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increasing depth interval for both farmland and grassland (Table 4). For instance, the

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significant area was 0.99 between 0-10 and 10-20 cm depths for farmland, and

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gradually decreased to 0.76 between 0-10 and 40-50 cm depths. The total area of

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significant correlation between the same depths was relatively higher for farmland

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compared to grassland (Table 4). However, the total area representing significant

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correlation of each depth between farmland and grassland changed within a small

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extent, ranging from 0.79 to 0.91 (Table 4).

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Wavelet coherency analysis was also carried out to reveal the temporal

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relationships between the soil moisture and air temperature, relative humidity, and

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precipitation (Figs. 6 and 7). Significant correlations between soil moisture and

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temperature in 0-10 cm depth for farmland displayed at some occasions in small and

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medium scales (Fig. 6a). Significant effects of relative humidity on soil moisture in

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the 0-10 cm depth can be found at some occasions in small scales and all over the

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time series at scales of 16-32 d (Fig. 6b). The significant correlations between soil

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moisture and precipitation existed at lots of occasions for all scales (Fig. 6c).

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Moreover, a similar trend was observed between soil moisture and temperature,

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relative humidity, and precipitation for the 40-50 cm depth (Fig. 6d, e, f). For

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grassland, the relationships between soil moisture and influencing factors were similar

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to that of farmland for both 0-10 and 40-50 cm depths (Fig. 7). For farmland, the 16

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significant area between soil moisture and influencing factors for both 0-10 and 40-50

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cm depths increased in the order of temperature, relative humidity, and precipitation,

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respectively (Table 5). However, relative humidity had the largest effect on soil

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moisture in the 0-10 cm depth for grassland, while precipitation in the 40-50 cm depth

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(Table 5).

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Additionally, type of correlation can be examined according to the direction of

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arrows. The left directed arrows of significant areas indicated positive correlation,

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while the right directed arrows suggested negative correlation. Soil moisture was

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observed correlated positively with relative humidity and precipitation but negatively

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with air temperature (Figs. 6 and 7).

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4. Discussion

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In comparison with farmland, soil moisture of the 10-50 cm depth was lower for

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grassland. This can be attributed to that the grassland had much more aboveground

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biomass but much lower soil saturated hydraulic conductivity (Table 1), and then

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consumed more soil water and generated more runoff. Though soil profiles of both

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farmland and grassland in karst depression contained high soil moisture (Table 2), the

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total amount of water stored in soil profile in karst region may be lower even than

320

some semiarid or arid areas such as the Loess Plateau in north of China. There were

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two reasons to explain this phenomenon. One reason was that the soil thickness only

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ranged from 10-30 cm on hillslope to 50-80 cm in depression (Chen et al., 2010),

323

resulting in the lower soil water storage. The other reason was that the unique dual

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structure of soil had strong water permeability (Butscher and Huggenberger, 2009;

325

Nie et al., 2012) in karst regions. Therefore, severe water shortage caused by extreme

326

droughts occurring in this area should be considerable for hydrological management

327

and policy making.

328

Soil moisture was affected by many influencing factors, including static (e.g., soil

329

texture, bulk density, topography) and dynamic (e.g., temperature, precipitation,

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relative humidity) factors (Reynolds, 1970; Crow et al., 2012). Static factors

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controlling soil water holding capacity had major control on variability of soil

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moisture in spatial series (Vachaud et al., 1985; Mohanty and Skaggs, 2001; Starks et

333

al., 2006; Cosh et al., 2008; Li et al., 2015a). For example, Mohanty and Skaggs

334

(2001) found that soil type, as characterized by bulk density, sand and clay content, 18

335

was responsible for nearly 50% of the temporal stability. However, the change of soil

336

moisture in temporal dimension was mainly determined by the dynamic factors such

337

as precipitation. In this study, there was a remarkable response of soil moisture to the

338

precipitation during the period with frequent rains for both farmland and grassland.

339

For instance, the soil moisture fluctuated at higher values with rainfall during the Apr.

340

28 and Jul. 3 2015 (Fig. 2). It was reported that soil moisture can be predicted

341

appropriately by the static factors in spatial dimension (Hu and Si, 2013). Similarly,

342

the dynamic factors may play a more important role in predicting the soil moisture in

343

time dimension.

344

High spearman rank correlation coefficients between soil moisture series at any

345

two depths indicated strong similarity of the overall temporal patterns across soil

346

profile, in accordance with the findings by Li et al. (2015b), and Jia et al. (2013). This

347

may be due to the limited soil texture heterogeneity across the soil profile at the same

348

location. However, the correlation coefficients between the surface depth and

349

subsurface depths gradually decreased with increasing depth interval. These results

350

are in agreement with those from other literatures (Biswas and Si, 2011a; Li et al.,

351

2015b; Zhang and Shao, 2013). For example, Biswas and Si (2011a) found a higher rs

352

of soil water storage with a smaller interval of soil in both recharge and discharge

353

period. This is because the impact of hydrological processes controlling the temporal

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patterns of soil moisture gradually decreased with increasing soil interval, leading to

355

less variability (Table 2) in the subsurface. The range of rs for farmland was higher

356

than for grassland, indicating the effects of plant utilization. In this study, grass can 19

357

consume a large amount of water in comparison with corn and then weaken the

358

overall similarity of soil moisture in temporal series.

359

The depth persistence of temporal pattern for soil moisture can be referred to as

360

rank stability (Chen, 2006). Rank stability of soil moisture presents a practical basis

361

for exploring statistical methods for imputing missing values of soil moisture data.

362

For example, Dumedah and Coulibaly (2011) evaluated the six selected methods

363

based on rank stability conception for infilling missing values of soil water data, and

364

found that the merged method can consistently provide the best evaluation accuracy

365

across all locations. In addition, the easy-to-measure surface soil moisture can be used

366

to estimate soil moisture in the subsurface or soil profile to reduce time and labor cost.

367

Hu and Si (2014) verified that soil moisture at the most time stable depth can predict

368

soil moisture of soil profile well at both point and hillslope scales.

369

The total area of significant coherency at the different scales gradually decreased

370

with increasing depth interval for both farmland and grassland, in agreement with

371

other studies (Biswas and Si, 2011a; Li et al., 2015b). These results implied a

372

reduction in the degree of similarity in soil moisture temporal patterns and coincided

373

with the results of the Spearman rank correlation analysis. However, there was a

374

reduction of total area of significant coherency between the same depths from the

375

farmland to grassland, suggesting that the similarity was lower in grassland. This can

376

be attributed to plant species: on the one hand, the root system of hybrid Napiergrass

377

utilized soil moisture unevenly in soil profile could weaken the depth persistence of

378

soil moisture temporal patterns; on the other hand, the hybrid Napiergrass with higher 20

379

aboveground

biomass

could

affect

crucial

hydrological

process

such

as

380

evapotranspiration and rainfall redistribution. In addition, soil depth had a larger

381

effect than plant series on the similarities in soil moisture temporal patterns. For

382

example, the ranges of area of significant correlation between depths for farmland and

383

grassland were 0.23 and 0.45, respectively (Table 4). However, the range of area of

384

significant correlation between farmland and grassland was 0.12 (Table 4).

385

The correlations of soil moisture between different depths were significant at most

386

of scales and occasions, whereas soil moisture significantly correlated with the

387

meteorological factors at some specific scales. This is because the underlying

388

processes between influencing factors and soil moisture may not operate at the same

389

scales. Therefore, processes that were dominant at one scale may not have a

390

significant effect at other scales (Zeleke and Si, 2005). In this study, for example, soil

391

moisture was more deterministic as affected by the relative humidity at greater scales

392

(Figs. 6 and 7), while soil moisture significantly correlated with temperature at some

393

occasions for small scales. Meteorological factors had more effects on changes of soil

394

moisture in comparison to static factors, and the relations between these

395

meteorological factors and soil moisture temporal patterns were scale-dependent. The

396

scale influences of controlling factors were also reported by other researchers (Biswas

397

et al., 2011a, b; Li et al., 2015b; Hu and Si, 2013). Biswas et al. (2011a) found that

398

elevation had obvious effects on soil moisture spatial patterns at greater scales.

399

However, Li et al. (2015b) and Hu and Si (2013) found that soil texture significantly

400

correlated

soil moisture

at

greater

scales. 21

An

important

application of

401

scale-dependence was that some variables can be predicted at specific scale for a

402

higher

403

scale-dependence were successful performed (Hu and Si, 2013; He et al., 2015). Hu

404

and Si (2013) predicted soil water appropriately using multivariate empirical mode

405

decomposition in a Canadian prairie area and a watershed on the Chinese Loess

406

Plateau. He et al. (2015) found that the hybrid wavelet neural network model can

407

improve the monthly rainfall forecasting accuracy over Australia compared with the

408

reference models. Additionally, since soil moisture temporal patterns can be well

409

predicted with more meteorological factors at larger time scales, it was not necessary

410

to carry out sampling activities frequently especially for artificial sampling.

accuracy

of

prediction.

Some

411

22

presented

methods

derived

from

412

6. Conclusions

413

This study investigated the similarities in soil moisture temporal pattern across

414

soil profiles in a karst landscape using Spearman rank correlation and wavelet

415

coherency analyses. Soil moisture was relatively lower in grassland than farmland for

416

each soil depth, which can be attributed to the higher aboveground biomass in

417

grassland and the higher soil saturated hydraulic conductivity in farmland. The change

418

of soil moisture was sensitive to precipitation rather than air temperature, indicating

419

that the precipitation may have a larger effect than air temperature for soil moisture

420

prediction in temporal dimension.

421

The overall soil moisture temporal pattern of the surface depth was more similar

422

to the depths closest to the surface and gradually decreased with the increase in

423

distance between the depths. Therefore, the easy-to-measure surface soil moisture can

424

be used to estimate soil moisture at deeper depths. The relationships between soil

425

moisture and controlling variables were obviously dependent on temporal scales. The

426

significant effect of temperature was found at small temporal scales, while the

427

significant effects of the relative humidity and precipitation were observed at larger

428

temporal scales. The scale information of soil moisture variations may improve the

429

performance of soil moisture estimation.

430

23

431

Acknowledgement

432

This study was financially supported by the National Natural Science Foundation of

433

China (41471233; 41571130073; 41601223; 41501478), Youth Innovation Team

434

Project of ISA, CAS (2017QNCXTD_XXL), and the “100 talents program” (2060299)

435

of the Chinese Academy of Sciences. Special thanks go to the editor and anonymous

436

reviewers for their constructive suggestions which greatly improve the manuscript.

437

24

438

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582

31

Farmland

TDR

Guzhou 583

Grassland

584 585 586 587

Fig. 1 Location of the study area, sampling position and time domain reflectometry

588

(TDR) system in Guzhou catchment in northwest Guangxi, China.

589

32

590 591

592 593

Fig. 2 Evolution of soil moisture in various soil depths for farmland and grassland

594

with corresponding temperature, relative humidity and precipitation above.

33

595 596

Fig. 3 Wavelet coherency of soil moisture in the time series between (a) the 0 to 10

597

and the 10 to 20 cm depths, (b) the 0 to 10 and the 20 to 30 cm depths, (c) the 0 to 10

598

and the 30 to 40 cm depths, and (d) the 0 to 10 and the 40 to 50 cm depths for

599

farmland. The y-axes are the scales in days. The color bars indicate the values of the

600

wavelet coefficients, the solid black lines represent the 5% significance levels, and the

601

arrows indicate the phase relationships. 34

602 603

Fig. 4 Wavelet coherency of soil moisture in the time series between (a) the 0 to 10

604

and the 10 to 20 cm depths, (b) the 0 to 10 and the 20 to 30 cm depths, (c) the 0 to 10

605

and the 30 to 40 cm depths, and (d) the 0 to 10 and the 40 to 50 cm depths for

606

grassland. The y-axes are the scales in days. The color bars indicate the values of the

607

wavelet coefficients, the solid black lines represent the 5% significance levels, and the

608

arrows indicate the phase relationships. 35

609 610

Fig. 5 Wavelet coherency of soil moisture in the time series between farmland and

611

grassland in the (a) 0 to 10, (b) 10 to 20, (c) 20 to 30, (d) 30 to 40, and (e) 40 to 50 cm

612

depths. The y-axes are the scales in days. The color bars indicate the values of the

613

wavelet coefficients, the solid black lines represent the 5% significance levels, and the

614

arrows indicate the phase relationships.

36

615 616

Fig. 6 Wavelet coherency between soil moisture and temperature, relative humidity,

617

and precipitation in the time series for the (a, b, and c) 0-10 and (d, e, and f) 40-50 cm

618

depths for farmland. The y-axes are the scales in days. The color bars indicate the

619

values of the wavelet coefficients, the solid black lines represent the 5% significance

620

levels, and the arrows indicate the phase relationships.

37

621 622

Fig. 7 Wavelet coherency between soil moisture and temperature, relative humidity,

623

and precipitation in the time series for the (a, b, and c) 0-10 and (d, e, and f) 40-50 cm

624

depths for grassland. The y-axes are the scales in days. The color bars indicate the

625

values of the wavelet coefficients, the solid black lines represent the 5% significance

626

levels, and the arrows indicate the phase relationships.

627

38

628

Table 1 Summary statistics of surface soil properties and AGB for farmland and grassland,

629

respectively.

630 631 632

Variabl BD (g es cm-3) Farmlan d 1.43±0.05 Grassla nd 1.45±0.04 BD, bulk density; Ks, aboveground biomass.

Ks (mm min-1)

Clay Silt Sand SOC (g AGB (g -1 (%) (%) (%) kg ) m-2) 11.3±0. 57.4±1. 31.3±1. 0.026±0.024 9 3 0 8.9±1.2 287±19 15.0±1. 50.5±1. 34.6±1. 0.004±0.002 1 4 7 11.1±1.5 907±142 saturated soil hydraulic conductivity; SOC, soil organic carbon; AGB,

39

633 634

Table 2 Statistical summary of meteorological parameters and soil moisture in the various soil

635

depths for farmland and grassland, respectively. Parameter

Max

Min

Mean

SD

CV (%)

Temperature (℃) Precipitation (mm) Relative humidity (%)

28.5 75.9 99.8

12.5 0 69.7

23.0 7.1 89.7

3.4 14.2 7.1

14.7 199.9 7.9

Layer (cm)

Max (cm3 cm-3)

Min (cm3 cm-3)

Mean (cm3 cm-3)

SD (cm3 cm-3)

CV (%)

0.45 0.58 0.54 0.65 0.62

0.28 0.45 0.45 0.55 0.52

0.39 0.54 0.51 0.62 0.58

0.04 0.03 0.02 0.02 0.02

10.7 5.4 4.1 2.7 3.3

0.42 0.38 0.40 0.46 0.45

0.25 0.23 0.29 0.39 0.35

0.36 0.32 0.36 0.43 0.41

0.04 0.03 0.02 0.02 0.02

10.7 10.1 6.6 3.6 4.9

Farmland 0-10 10-20 20-30 30-40 40-50 Grassland 0-10 10-20 20-30 30-40 40-50 636 637

SD, standard deviation; CV, coefficient of variation.

40

638 639

Table 3 Spearman’s rank correlation coefficients between soil moisture of various soil depths for

640

farmland and grassland, respectively. Depth (cm) Farmland 0-10 10-20 20-30 30-40 40-50 Grassland 0-10 10-20 20-30 30-40 40-50

641 642

0-10

10-20

20-30

30-40

40-50

1.000

0.980 1.000

0.952 0.977 1.000

0.929 0.968 0.966 1.000

0.946 0.963 0.964 0.965 1.000

1.000

0.909 1.000

0.802 0.952 1.000

0.613 0.824 0.910 1.000

0.540 0.780 0.846 0.944 1.000

All correlations were significant at P < 0.001.

41

643 644

Table 4 Significant area for soil moisture between the 0-10 cm depth and the other soil depths for

645

farmland and grassland, and between farmland and grassland for various soil depths. Land uses Farmland

Grassland

Farmland vs grassland

Significant area 0-10 vs 10-20 0-10 vs 20-30 0-10 vs 30-40 0-10 vs 40-50 0-10 vs 10-20 0-10 vs 20-30 0-10 vs 30-40 0-10 vs 40-50 0-10 vs 0-10 10-20 vs 10-20 20-30 vs 20-30 30-40 vs 30-40 40-50 vs 40-50

0.99 0.95 0.78 0.76 0.95 0.86 0.68 0.50 0.79 0.81 0.81 0.80 0.91

646 647

42

648 649

Table 5 Significant area between soil moisture and temperature, relative humidity, and

650

precipitation in the 0-10 and 40-50 cm soil depths for farmland and grassland, respectively. Significant area

Depth (cm) 0-10

40-50

Farmland 0.18 0.36 0.44 0.24 0.29 0.39

Soil moisture vs temperature vs relative humidity vs precipitation Soil moisture vs temperature vs relative humidity vs precipitation

651 652

43

Grassland 0.12 0.49 0.41 0.18 0.18 0.50

653

Highlights

654

(1) Investigated depth persistence of temporal pattern of soil moisture.

655

(2) Correlation coefficient decreased with increasing distance between

656 657 658 659 660 661

soil layers. (3) Soil layer had a higher effect on similarity of soil moisture than land use. (4) Precipitation and relative humidity affected soil moisture at large scales. (5) Scale-dependence information was useful for soil moisture prediction.

662

44