Temporal instability of soil moisture at a hillslope scale under subtropical hydroclimatic conditions

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Temporal instability of soil moisture at a hillslope scale under subtropical hydroclimatic conditions ⁎

Lei Gaoa, Xinhua Penga, , Asim Biswasb a b

State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China School of Environmental Sciences, University of Guelph, 50 Stone Road East, Guelph, Ontario N1G 2W1, Canada

A R T I C LE I N FO

A B S T R A C T

Keywords: Soil water content Crop land Humid climate Hillslope hydrology Groundwater table

Temporal instability, a condition of soil moisture with unstable spatiotemporal patterns, may occur when the relative roles of the controlling factors change. It impacts the applicability of the temporal stability in the prediction of soil moisture. However, the occurring frequency, degree and underlying hydrological conditions of the temporal instability are far from clear. This study aimed to examine the presence of temporal instability at the hillslope scale and to characterize and quantify the frequency, degree and underlying hydrological conditions contributing to the phenomenon. Sloping crop lands under subtropical hydrological conditions are known for their frequent transition between dry and wet periods and were chosen for this study. Soil moisture was monitored at 5 locations and at seven soil depths down to 2.2 m along a 160 m hillslope over a period of a year. Relative difference analysis was employed to evaluate the temporal stability of soil moisture. Representative locations (RLs) mainly appeared at locations with a mild slope and relatively high sand content. Daily RLs were not consistent among the study period, which has the highest proportion to be the same to those full dataset based. These proportions were depth dependent, the lowest (40%) at moderate soil layers (i.e. 50 and 80 cm) and the highest (95%) at deep soil layers (i.e. 180 and 220 cm). An obvious temporal instability was observed by the extensive ranges in the daily mean relative difference (MRD) values. If the whole dataset was used to calculate the mean soil moisture of the hillslope, it would be overestimated by 16.1% or underestimated by 18.2%. The frequency of temporal instability was low and varied over time and soil depths. For example, 17% of the time for this period was temporally unstable at 20 cm and 2% time of the study period was unstable at 220 cm. These low proportions confirm that the temporal stability remains a valid and robust tool in mean soil moisture prediction. The rainfall after a long drought period or a storm after successive rainfall probably caused the temporal instability. Therefore, caution should be exercised during the strong dry-wet transition period.

1. Introduction Soil moisture plays a critical role in many hydrological and biochemical processes, such as groundwater flow (Scheliga et al., 2018), nitrogen transport and export (Ocampo et al., 2006) and terrestrial carbon uptake (Green et al., 2019). Strong spatio-temporal variability of soil moisture improves the significance and difficulty of its prediction (Khan et al., 2018). Among many prediction methods, temporal stability received considerable attention due to its low cost and high accuracy (Rivera et al., 2014; Martínez et al., 2014; Hu and Si, 2016; He et al., 2019). Temporal stability of soil moisture was first proposed by Vachaud et al. (1985), who found that the spatial patterns of soil moisture were always similar over time. After that, the temporal stability phenomenon was extensively proven and applied as a tool to

⁎

characterize the behavior of the spatial average soil moisture (Grayson and Western, 1998; Cosh et al., 2006; Li et al., 2017; Nasta et al., 2018; Dari et al., 2019). Spatial patterns of soil moisture were not always perfectly temporally stable. Different strengths of temporal stability were observed over the seasons in a hummocky landscape (Biswas and Si, 2011) and between dry and wet hydrological conditions at two alpine hillslopes (Penna et al., 2013). The differences in the strength of temporal stability were caused by the variable relationships between soil moisture and the controlling factors under certain conditions (Coleman and Niemann, 2013; Lv et al., 2019). For example, Lee and Kim (2019) found that the relative influence of topography and vegetation on temporal stability was season-dependent at the slope scale. Vegetation often acted as a disadvantageous factor to maintain similar spatial

Corresponding author. E-mail address: [email protected] (X. Peng).

https://doi.org/10.1016/j.catena.2019.104362 Received 9 May 2019; Received in revised form 22 October 2019; Accepted 7 November 2019 0341-8162/ © 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Lei Gao, Xinhua Peng and Asim Biswas, Catena, https://doi.org/10.1016/j.catena.2019.104362

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(b)

(a)

Month

51 L1: top position

(c)

Elevation (m)

49

L3: middle-up position L2: shoulder position

47 L5: lower position 45 L4: middle-down position 43 0

20

40

60

80

100

120

140

160

Distance of the locations to the bottom of the slope (m) Fig. 1. The position (a) and meteorological features (b) of the studied hillslope and the spatial distribution of the five investigated locations along the slope (c). Meteorological data from Yingtan weather station from 1954 to 1999.

patterns (Coleman and Niemann, 2013). Similar findings were reported by Gomez-Plaza et al. (2000) from a semi-arid climate where the hillslope with high vegetation cover exhibited a weaker temporal stability than those with sparse vegetation. Antecedent soil moisture status was also regarded widely as a common factor affecting the strength of temporal stability. Jia and Shao (2013) reported that irrespective of vegetation type, only 2–3 points out of 11 maintained the same rank under the wettest and the driest conditions in a semi-arid region. To some extent, the soil moisture status dictated the role of other factors (Penna et al., 2013; Nasta et al., 2018). In general, soil moisture status has a negative correlation with the strength of temporal stability (Martínez-Fernández and Ceballos, 2003). With increasing soil moisture contents, hydrological connectivity becomes increasingly important in promoting the temporal instability of soil moisture (Coleman and Niemann, 2013). More recently, Nasta et al. (2018) proved that dominant hydrological processes would transfer with changing soil moisture status. Specifically, the occurrence of the subsurface flow would disorganize the spatial patterns of soil moisture and result in the subsequent temporal instability (Canton et al., 2016; Lee and Kim, 2019). Although the phenomenon of temporal instability of soil moisture is well known, specific information such as frequency and degree of the occurrences is rare, which is essential to evaluate the temporal stability of an area. One of the major reasons for this was that previous studies on sampling strategies on temporal stability were characterized by sparse interval and long sampling period. Though these sampling strategies were advantageous to capture overall temporal stability, they might miss the information of temporal instability caused by shortperiod hydrological processes (Penna et al., 2013; Martínez et al., 2014).

Sampling period plays a more important role in affecting the behavior of temporal instability than sampling frequency. Several previous studies failed to capture the information of temporal instability through changing sampling frequency. For example, Cosh et al. (2006) found nearly no changes in the strength of temporal stability between hours of the day. Guber et al. (2008) reported a similar result that the sampling frequency could not change the temporal stability behavior. However, differences of the strength of temporal stability were frequently observed among datasets with short durations of the measurement period. The roles of temporal instability might increase with the decrease in sampling period. Conducted at the annual scale, temporal stability of soil moisture generally behaved similarly even in a 10year period (Liu and Shao, 2014). However, differences were observed when sampling periods were shortened to seasons (Penna et al., 2013; Biswas, 2014). Very recently, Gao et al. (2019) found that the overall temporal stability can be over-estimated by 40% when using a 3-month sampling period. It is reasonable to infer that the deviation would increase when the observation duration further decreases. Therefore, to evaluate the similarity of the spatial patterns among short period using high temporal-resolution monitoring is of great value to capture the real time information on temporal instability, but this is rarely stressed (Penna et al., 2013). Characterized by abundant rainfall and frequent transition between dry and wet conditions, the subtropical regions provided an ideal study site to investigate the evolution of temporal stability. The overall objective of this study was to improve our understanding on the temporal instability at the hillslope scale under subtropical hydroclimatic conditions using high-frequency soil moisture measurement. This was achieved through two sub-objectives: 1) characterizing and quantifying the frequency, degree and underlying hydrological conditions of 2

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moisture and the groundwater table along the hillslope. From the top to the bottom of the slope, five sampling points (from L1 to L5) were located at the top, shoulder, middle-up, middle-down and lower position, respectively. Averaged slope gradient at L1 to L5 were 0.0°, 2.9°, 4.4°, 6.6°, and 2.6° respectively (Fig. 1). The profile soil moisture varies dramatically in the upper 200 cm and keeps stable below it (Wu et al., 2019). Therefore, soil moisture along a 220-cm soil profile was measured at 5, 20, 50, 80, 120, 180, and 220 cm soil depths between April, 1st 2017 to March, 31st 2018 at 5-min interval using CS655 soil moisture probes (Campbell Scientific, USA) and data were recorded using CR1000 data loggers (Campbell Scientific, USA). A 2.5-m-depth hole was dug to install soil moisture sensors. Three measures were taken to reduce the influence of the disturbance on soil moisture distribution. Firstly, to reduce the influence of the soil disturbance on the water flow, the sensors were installed at the upslope direction of the hole. Secondly, after the sensors being installed at corresponding depth, the soils were backfilled carefully as previous soil bulk density. Thirdly, the first three-month data from the installation were not employed in the present study. A laboratory calibration was conducted to improve the quality of the data. Disturbed bulk soil samples from 0 to 30 cm soil depths were collected, air-dried at room temperature, sieved to < 2 mm and put into a container with the length, width and height of 47, 31, and 12 cm, respectively. The bulk density was fixed at 1.3 g cm−3, equivalent to the conditions of the top depths in the field. Three CS655 sensors were installed horizontally in the middle of the container or repacked soil profile. Soil moisture of the repacked profile varied between saturation to air-dry representing all the possible field conditions. During the airdried process, soil moisture was recorded both by the sensors and by the weighing methods daily to once every two days. The sensor measured soil moisture was calibrated against the weighing methods using a power function with the R2 of 0.995. The sensor-measured field soil moisture data were calibrated following the relationship before being used for further calculation. Rainfall at 30 min time intervals was obtained from the meteorological station, around 4 km away from the field site. The rainfall on the sampling day (D0), antecedent 3 days (D3) and antecedent 7 days (D7) of the sampling date were calculated from the 30-min data by the arithmetic mean method. We did not employ antecedent precipitation index as it requires more information of soils, such as storage capacity. Moreover, these kinds of information are often scanty. Nevertheless, the information has been reflected by another index to represent hydrological condition, i.e. soil moisture contents. The groundwater table was monitored by pressure gauge with 30-min intervals (Onset Computer Corporation, USA) and aggregated to the daily average using the arithmetic mean method. To avoid the effect of the wells (with a diameter of 25 cm) on soil moisture monitoring, a 3-m separation distance was maintained between the two types of monitoring profiles.

temporal instability; and 2) evaluating the applicability of temporal stability technique under subtropical hydroclimatic conditions. To this end, a high resolution (5-minute interval) measurement of soil moisture at 5 locations and at seven depths down to 2.2 m over a year was monitored. Accordingly, temporal stability analysis was conducted at a very high temporal resolution (at day scale) using 365 daily datasets to investigate the dynamics of temporal instability with changing hydrological conditions. 2. Materials and methods 2.1. Study area The hillslope where this study was undertaken located in Yingtan city, Jiangxi Province, China (Fig. 1). It is a south-facing slope with a length of 160 m, with an elevation ranging from 44 to 50 m above sea level. Studied slope belongs to a typical upland agricultural ecosystem characterized by the following features in climate, soil and vegetation: Climate: The study area is under a typical subtropical monsoon climate with abundant precipitation but highly heterogeneous over the seasons. The mean annual rainfall is about 1800 mm (1674 mm for the study period), 50% (54% in this study) of which falls between April and June. Following the rainy season, a long drought period generally appears between July and September due to strong evapotranspiration and low rainfall (Fig. 1). After the drought period, the soil moisture status increases gradually due to rainfall re-exceeding the actual evapotranspiration. Soil: The soil of the studied slope belongs to a typical red soil, developed from Quaternary red clay. Red soil is an important zonal soil type of China with an area of 218 × 104 km2, characterized by high component of iron oxides and aluminium oxides. Soil layers thickness of the studied hillslope ranged from 5.8 to 7.6 m depending on the slope position. Soil texture of the five 0–220 cm soil profiles ranged from sandy clay loam to clay based on the USDA classification system. The clay content of the slope ranged from 22.7% to 45.1% and sand content ranged from 24.0% to 55.4% (Fig. 2). Soils are sandier at the top and lower portions compared with the shoulder and middle positions. The bulk density showed an increasing trend from 1.3 g cm−3 at 5 cm depth to 1.6 g cm−3 at the 220 cm soil depth. The saturated hydraulic conductivity fitted by van Genutchen equation changed from 0.12 cm h−1 at 180 cm depth at L5 to 1.6 cm h−1 at 20 cm depth at L2, with the mean values of 0.34, 0.88, 0.51, 0.26 and 0.34 cm h−1 for the five soil profiles, respectively (Fig. 2). The retention ability was stronger at shallow soil layers than deep layers, with mean saturated water content of the five locations ranged from 39.1% at 220 cm depth to 45.6% at 5 m soil depth. The retention ability also behaved differently among locations, stronger for location 2 and 3 than the other locations. For example, the field capacity of the five locations ranged from 25.4% at L5 to 29.9% at L2 according to the fitted parameters of van Genutchen equation. The differences of soil properties along the slope and soil profile could be associated with a combination of factors, including soil genesis processes, agricultural management practices (e.g. tillage), as well as intensive soil erosion in this region. Vegetation: The hillslope has a long cultivation history and maintained a rain-fed cropping system for the past 30 years. Peanut (Arachis hypogaea Linn.) was planted evenly along the slope during the study period, which is also the main crop type since the 1990s. The timings for the ploughing, seeding and harvesting of the peanut occurs at the end of March, early April and the middle of August, respectively. The maximum leaf area index (around 0.8) occurs in July. Except for the peanut growing season between April and August, the soils for the rest of the year are generally kept fallow.

2.3. Data analysis To achieve different objectives, the temporal stability analysis was conducted based on three types of datasets at each soil depth. The first type was the ‘full dataset’, from April 1st, 2017 to 31st March 2018 at 5min intervals, and was employed to evaluate the overall temporal stability of soil moisture across the hillslope. The second dataset was characterized by one-day sampling period and 5-min sampling frequency and was termed as ‘daily dataset’. The 365 daily datasets were used to evaluate the daily evolution of temporal instability. The days with MRD ± 5% of the overall MRD were considered as time unstable. To check whether the observed temporal instability affected the overall temporal stability, three datasets, named ‘monthly dataset’, with twelve sampling occasions were created from the full dataset. Sampling dates were around the 5th, 15th, and 25th of each month, respectively. Relative-difference analysis was employed to evaluate the temporal stability of soil moisture in the present study using the metrics of the

2.2. Data collection Five locations with spacing of 35 m were chosen to monitor soil 3

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Clay content (%) 20

30

1.0

1.2

40

Sand content (%)

50

60

20

30

40

50

60

0

Soil depths (cm)

50 100 150 200 250

Bulk density (g cm-3) 1.4

Saturated hydraulic conductivity (cm h-1)

1.6

1.8

0.0

0.5

1.0

1.5

2.0

0

Soil depths (cm)

50 100 150

L1

L2

L4

L5

L3

200 250 Fig. 2. Profile distribution of soil properties, including measured clay content (%), sand content (%), soil bulk density (g cm−3) and fitted Saturated hydraulic conductivity (cm h−1) by van Genutchen equation for the five investigated locations.

Therefore, to identify the representative location (RL) used to predict the average soil moisture of the area, a comprehensive index of temporal stability (ITS) containing both the information of “accuracy” and “precision” was proposed by Jacobs et al. (2004) and Zhao et al. (2010) as:

mean relative difference (MRD) and the standard deviation of relative difference (SDRD) (e.g. Gao and Shao, 2012a). Relative difference (RD) provides an unbiased estimation between individual measurement of SWC at location i time j, SWCj (i) , and the mean SWC, SWCj of the same time:

RDj (i)=

SWCj (i) − SWCj SWCj

ITS = (1)

1 N

N

∑ SWCj (i) i=1

(2)

N (=5 in the present study) is the number of measurement locations. MRD and SDRD are defined as temporally averaged RD and its standard deviation at location i:

MRD (i) =

1 m

m

∑ RDj (i)

3. Results

(3)

j=1

3.1. Dynamics of soil moisture

and

SDRD (i) =

(5)

The locations with the lowest ITS value were identified as RL at each depth in the present study. Considering the similar changing tendency between MRD and SDRD, to save space, the temporal stability was evaluated by the “accuracy” metric only in this study. Pearson correlation was employed to test the influence of hydrological conditions on daily evolution of temporal stability. Five metrics, including D0, D3, and D7, soil moisture and groundwater table were chosen to represent the hydrological conditions of the hillslope in the present study.

where

SWCj =

MRD 2 + SDRD 2

1 m−1

m

The magnitude of soil moisture generally increased, and its temporal variation decreased with increasing soil depths (Fig. 3). Average soil moisture of the slope during the entire study period increased from 0.23 at 5 cm to 0.39 at 220 cm soil depth (Table 1). Temporal variation of soil moisture was much stronger for the depths above 100 cm than those below it, with coefficients of variations (CV) ranging from 17.1% at 5 cm to 1.9% at 220 cm (Table 1). Averaged soil moisture content along the 220-cm profile were the highest at L3 (0.34 cm3 cm−3) and the lowest at L5 (0.31 cm3 cm−3). The lowest moisture content at the lower position could be attributed to the lowest clay content (Fig. 2). In addition to soil texture, slope position also affected the spatial patterns

∑ (RDj (i) − MRD (i))2 j=1

(4)

for which m is the number of measurement occasions. Its value depended on the types of datasets, 105120, 288, and 12 for the ‘full dataset’, ‘daily dataset’ and, ‘sampling monthly dataset’, respectively. The MRD and SDRD represent the bias and the precision of the location when used for predicting the mean SWC, respectively. In general, the location with the lowest MRD value indicates the highest “accuracy” to represent the averaged soil moisture of the area. Similarly, the lowest SDRD value suggests the highest prediction “precision”. 4

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Groundwater table (m)

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Date (MM-YY) Fig. 3. Dynamics of precipitation, profile soil moisture and groundwater table along the hillslope (showing the top, middle-up and lower position only) during the study period from April 1st, 2017 to March 31st, 2018.

5

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Table 1 The mean (M), range (R) and temporal coefficient of variation (CV, %) of soil moisture at seven soil depths for the five investigated locations from April, 1st, 2017 to March, 31st, 2018. 5 cm

L1 L2 L3 L4 L5

20 cm

50 cm

80 cm

120 cm

180 cm

220 cm

M

R

CV

M

R

CV

M

R

CV

M

R

CV

M

R

CV

M

R

CV

M

R

CV

0.23 0.25 0.22 0.25 0.21

0.19 0.20 0.26 0.16 0.30

16.1 17.7 21.5 11.5 18.6

0.26 0.29 0.32 0.30 0.27

0.20 0.23 0.17 0.17 0.21

14.9 12.3 7.5 10.7 16.3

0.31 0.32 0.33 0.32 0.32

0.13 0.19 0.15 0.14 0.14

10.1 8.0 6.4 7.1 7.0

0.33 0.32 0.35 0.34 0.35

0.12 0.20 0.14 0.09 0.11

7.4 9.9 5.4 3.3 3.5

0.36 0.36 0.36 0.37 0.33

0.03 0.08 0.08 0.06 0.11

1.7 2.8 2.2 1.8 3.4

0.37 0.38 0.39 0.36 0.34

0.02 0.04 0.09 0.12 0.08

0.9 1.4 1.6 3.6 2.9

0.36 0.39 0.40 0.37 0.37

0.02 0.02 0.10 0.11 0.06

0.6 0.5 2.1 3.9 2.3

of soil moisture. For example, though the L2 at shoulder position had the highest clay content in the five locations, it only had the second highest soil moisture content. The groundwater table influenced the spatiotemporal patterns of deep soil moisture. The moisture at the deep soil depths (e.g. 180 and 220 cm) showed a sharp increase during some storm events, which matched with the large increases in the groundwater table (Fig. 3). This phenomenon was more frequently observed at middle slope positions with relatively high clay content. One proof of the greater effect at the middle slope position was that the CVs of soil moisture increased with increasing soil depth from 180 to 220 cm for L3 and L4 (Table 1).

180 cm soil depths, respectively or overestimate by 9.1%, 18.0% and 7.2% at 50, 80, and 220 cm soil depths, respectively (Fig. 7). Although MRD values varied across an extensive range, they changed slightly over time. The time unstable days (indicated by the MRD value without in the scope of full dataset based MRD value ± 5%) were lower than 20% at all seven soil depths. The proportions ranged from 0% at 120 cm soil depth to 17% at 20 cm depth (Fig. 8). Interestingly, the temporal instability proportion had a slight increase from 0% at 120 cm to 2% at 180 and 220 cm soil depths. This increase could be attributed to the influence of the groundwater table on the variability of deep soil moisture.

3.2. Overall temporal stability of soil moisture

3.4. Correlations of MRD values to hydrological conditions

The overall temporal stability of soil moisture was evaluated using the full dataset. Small ranges in MRD were observed, especially at deep soil depth, around 20% for the top 20 cm soil depths and 10% for deeper depths (Fig. 4). The RLs were not consistent among the seven soil depths, L1, L2, L5, L4, L1, L1, and L5 for the soil depths at 5, 20, 50, 80, 120, 180, 220 cm, respectively (Fig. 4). The locations at the top (i.e. L1) and lower position (i.e. L5) of the hillslope were probably identified as the RLs. Surprisingly, the location at the middle position (i.e. L3) could not well represent the mean soil moisture status at any soil depth. Slight differences in MRD values were observed among the ‘full dataset’ and three ‘monthly datasets’. Consistent RLs were identified based on the full dataset or based on the monthly datasets at all seven soil depths. The differences in MRD values between the full dataset and monthly datasets were a little larger than those among the three lowresolution datasets. The largest differences were observed at 5 cm depth at L4, with the MRD values of 7.8%, 11.4%, 9.6%, and 10.3% based on the full dataset, monthly sampling around 5th, 15th, and 25th, respectively (Fig. 5).

Significant Pearson correlations between daily MRD values and rainfall, soil moisture and the groundwater table were observed under most of the conditions. The correlation was the strongest with soil moisture except for 5, 120 and 180 cm at L1, and the weakest with the rainfall on the sampling day (D0) except for 20 cm at L2 (Table 2). The correlation between MRD values and groundwater table was generally stronger than between MRD values and rainfall. Stronger correlation with the groundwater table suggested that the heavy rainfall event, which can cause the raising of the groundwater table, played a much important role than slight rainfall event in controlling the variations of soil moisture. Although the strength of the correlation differed among these metrics, the signs of the correlation were exactly consistent due to all the metrics reflecting the dry/wet conditions of soils. The correlations between MRD values and hydrological conditions were depth dependent; generally stronger at deep soil depths (i.e. below 100 cm) than that at the shallow depths (i.e. 0–100 cm). All the correlations were significant at the 0.01 probability levels at soil depths below 100 cm, while only part of them were significant and even nonsignificant at soil depths above that depth. The strongest correlation was observed at 180 cm soil depth except for the correlation with soil moisture at 220 cm soil depth (Table 2). In addition, this depth dependence differed among slope positions. At the top position, a consistent native correlation was observed between MRD value and soil moisture content along the entire profile. However, at lower position, the MRD values exhibited the opposite correlations at different depths. Negative correlation was observed only at shallow soil depths. At deep soil depths (e.g. 220 cm), MRD values increased with increasing soil moisture status (Table 2). These differences indicated that the dominant hydrological process varied among slope positions and soil depths, e.g. groundwater flow mainly affected the temporal stability of deep soil moisture. Dry-wet transition affected the temporal instability of soil moisture. A rainfall event after a long drought period would result in the most under-estimation of daily MRD values. Averaged soil moisture of the most under-estimated days was much lower than those of the whole study period. All the under-estimated conditions, with the differences higher than 5%, were observed just after a rainfall event (Table 3). In addition, the required rainfall to cause temporal instability increased from 5 to 74 mm with increasing soil depth (Table 3). Interestingly,

3.3. Daily dynamics of MRD value Daily RLs were not consistent with those of the full dataset based RLs. On the other hand, RLs based on the full dataset had the highest proportion to be the daily RLs, with about 70%, 40% and 95% in the surface (top 20 cm), middle (50 and 80 cm) and deep depths (180 and 220 cm), respectively (Fig. 6). Daily MRD values showed a depth-dependent temporal variation with the strongest at the top 20 cm soil depths. The range of MRD values was as high as 26.4% at the 5 cm soil depth, ranging from −10.2% to 16.2%. Even at the deep soil depths, the ranges of MRD were as high as 10% (Fig. 7). In addition, a large difference in MRD values between wet and dry seasons was observed with the largest difference occurring at the 20 cm depth. The mean values of the daily MRD at 20 cm depth in June and July of 2017 representing the wettest and driest month during the study period, were 1.7% (from −0.3% to 4.9%) and −5.1% (from −10.9% to 3.0%), respectively (Fig. 7). Based on this, if the full dataset-based MRD values are used to predict the mean soil moisture of the hillslope, it would underestimate the mean soil moisture by 16.1%, 12.3%, 5.0% and 7.7% at the 5, 20, 120, and 6

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20

20

L2 10

L3

L3

L4 10

L1

0

0

L5

L5

L2

L4

L1

-10

-10

20 cm

5 cm -20 20

-20 20

Mean relative difference (%)

0

10

L3

10

L1

L5

L4

L2 0

L2

L1

L4

L5

-10

-10

80 cm

50 cm -20

-20 20

20

10

L1

L2

L3

10

L4

0

0

L5

L2 L4

L3

L1

L5

-10

-10

180 cm

120 cm -20

-20

Rank

20

10

0

L3

L1

L4

L2

L3

L5

-10

220 cm -20

Rank Fig. 4. Rank ordered mean relative differences with standard deviation (vertical bar) of soil moisture for each location at seven soil depths.

4. Discussion

none of the most under-estimated conditions were observed during the rainy season. On the contrary, the over-estimated daily MRD at deep soil depths were mainly observed in the rainy season. The most overestimated conditions at 80–180 cm soil depths occurred during June and July, which were the wettest season of the year (Table 3).

4.1. Features of RL Due to its important role in soil moisture prediction, information on RLs have been the focus of many temporal stability analysis (Gao et al., 7

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Mean relative difference (%) -12

-8

-4

0

4

8

12 -12

-8

-4

0

4

8

12

0

100

200

L1

L2

L3

L4

Soil depth (cm)

300 0

100

200

300 0

100 around 5th Around 15th around 25th full dataset

200

L5 300

Fig. 5. Profile distribution of mean relative difference values based on three datasets with one-month sampling interval, around the 5th, 15th, and 25th of each month, respectively, and the full dataset with 5-min measurement interval during the whole study period. 120

L1

L2

L3

L4

et al., 2017). The reasons for identifying these locations with high sand content in the present study as the RLs may be attributed to the joint effect of slope position and soil texture. Unlike other positions, both the top and lower position were characterized by a mild slope (Fig. 1). Hydrological behaviors at the two positions could offset the deficit in soil moisture caused by soil texture. This could be in the form of either low water loss as surface runoff at the top position or as high influx of water as surface/subsurface flow at the lower position. Therefore, the appearance of the RLs in the present study was a balance between soil texture and slope, the relative roles of which were determined by the degree of heterogeneity of the two metrics. Features of soil property at RLs were always found to be study area specific. Taking the percentage of soil clay content as an example, it performed better than soil texture in temporal stability features prediction (Jacobs et al., 2004; Joshi et al., 2011). Although soil clay content is a very important controlling factor of soil moisture, RLs covered an extensive ranges of clay content. As compared to the field average, the clay content at RLs ranged from low clay content in the present study, to moderate to moderately high clay content by Jacobs et al. (2004), to the highest clay content in the study by Sur et al. (2013). Although more soil property indices (six) were employed by Gao and Shao (2012b), it is still not feasible to predict the RLs using information on soil properties even at adjacent transects. This could be ascribed to the roles of soil properties easily affected by other factors, such as vegetation patterns (He et al., 2019) or topography features (Hu and Si, 2014). Therefore, it is a great challenge to use information on

L5

Percentage (%)

100 80 60 40 20 0 5

20

50

80

120

180

220

Soil depths (cm)

Fig. 6. Proportion of the five locations identified as representative locations (RLs) at seven soil depths based on temporal stability analysis of 365 daily datasets.

2013; Lai et al., 2018). In this study, we found that the locations with high sand content at top or lower slope positions were probably identified as RLs to represent the mean soil moisture of the slope. In general, the location with high sand content may represent a situation with a greater difference between soil moisture of that location and the mean soil moisture of the area (Jacobs et al., 2004; Jia et al., 2013; Wang 8

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5 cm - L1

15 10

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0

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15

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15

10 5 0 -5 -10

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Differences between overall and daily MRD value

-20 Mar-17

Jun-17

Sep-17

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Mar-18

Date (MM/YY) Fig. 7. Daily dynamics of mean relative difference (MRD) values and its differences to the full dataset-based MRD values.

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Percentage (%)

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Under-estimated by 5% Over-estimated by 5%

10

5

0 5 - L1

20 - L2

50 - L5

80 - L4

120 - L1

180 - L1

220 - L5

Soil depths (cm) - RL Fig. 8. The proportion of daily mean relative difference (MRD) values under- and over-estimated by 5% in the absolute value compared to the full dataset-based values at each depth.

in determining the RLs might be ascribed to the weak surface runoff and lateral flow, which is advantageous to maintain an approximately averaged soil moisture status of an area. Though, the RLs were almost always characterized with mild slope, but it does not mean that all mild slopes would be RLs. It must be matched with other conditions, such as features of vegetation and soil texture.

Table 2 Pearson correlation between daily mean relative difference (MRD) values at representative locations (RLs) and hydrological conditions, including rainfall on sampling day (D0), antecedent 3 days (D3) and antecedent 7 days (D7), soil moisture and groundwater table (GWT). Depth (cm) - RL

5 – L1 20 – L2 50 – L5 80 – L4 120 – L1 180 – L1 220 – L5

Antecedent rainfall D0

D3

D7

−0.06 0.17** −0.26** −0.05 −0.48** −0.58** 0.27**

−0.08 0.22** −0.38** −0.16** −0.57** −0.70** 0.57**

−0.14** 0.24** −0.46** −0.29** −0.64** −0.65** 0.47**

Soil moisture

GWT

−0.14** 0.24** −0.46** −0.61** −0.66** −0.64** 0.77**

−0.31** 0.11* −0.39** −0.34** −0.70** −0.72** 0.57**

4.2. Performances of temporal instability High resolution sampling was important in capturing temporal instability of soil moisture. 5-min sampling interval employed in the present study enable the temporal stability analysis conducted daily. Daily temporal stability analysis could get more information of temporal instability than previous conducted at year scale with one-month sampling interval (e.g. Dari et al., 2019). Therefore, to compare the strength of temporal stability day by day, the temporal instability of soil moisture was evaluated based on 365 times temporal stability analysis using 365 daily datasets with 5-min measuring interval. Obvious temporal instability of soil moisture was observed in the present study based on daily MRD analysis. Firstly, if the inner variation of temporal stability among days is ignored, the error of the mean soil moisture prediction can be as high as 18% (Fig. 7). This largely exceeded the generally allowed error of 5% in soil moisture prediction using temporal stability method (e.g. He et al., 2019). Secondly, daily RLs were not consistent across the year, with the proportion of the same RLs only 40% at medium soil depths (Fig. 6). The inconsistent proportion was comparable with the findings of Guber et al. (2008), who reported that the RLs based on one-month observation were the same as those based on the full dataset in 60% of the cases. Similar results were

With“*” indicated that the correlation was significant at 0.05 probability levels and “**” significant at 0.01 levels.

only soil properties to identify RLs using prior experiences from other study areas. Mild slope seems to be a better indicator in determining RLs than soil properties. The locations of mild slope were always preferable to directly represent the mean soil moisture of the hillslope in the present study. This confirmed the finding of Joshi et al. (2011) who found that, at both point and footprint scales, RLs were identified at mild slope, ranging from 0% to 1.85%, but with different soil texture, ranging from sandy loam to clay loam under two hydroclimatic regions. Similar mildslope located RLs were identified at four fields by Jacobs et al. (2004) and at a steep slope by Sur et al. (2013). The critical role of mild slope

Table 3 The hydrological conditions of the most over- and under-estimated conditions for the temporal stability at representative location (RL) at seven soil depths, characterized by the mean relative difference (MRD) values. Depth (cm) -RL

5–L1 20–L2 50–L5 80–L4 120–L1 180–L1 220–L5

Most over-estimated conditions

Most under-estimated conditions

Date D-M-Y

SM-D (SM-Y)

Rainfall on the day

Days to last rain (rainfall)

Date D-M-Y

SM-D

Rainfall on the day

Days to last rain (rainfall)

5-Apr-17 1-Aug-17 18-Oct-17 2-July-17 25-June-17 22-June-17 5-Mar-18

0.24 0.23 0.31 0.36 0.39 0.41 0.38

0 17 0 0 128 117 9

5 (18) 21 (23) 2 (27) 4 (103) 3 (117) 6 (21) 3 (32)

21-Sept-17 29-Sept-17 10-Aug-17 10-Aug-17 9-Aug-17 12-Aug-17 30-Nov-17

0.19 0.24 0.29 0.29 0.34 0.36 0.39

0 0 13 13 36 15 15

1 1 1 1 7 3 1

(0.23) (0.29) (0.32) (0.34) (0.36) (0.37) (0.38)

(5) (11) (36) (36) (20) (36) (74)

The cases in italic were those with differences less than 5% in absolute value between daily MRD and overall MRD values. SM-D is the mean soil moisture of the hillslope on the most over- or under-estimated day. SM-Y is the mean soil moisture of the hillslope during the whole study period. 10

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instability. Therefore, caution should be taken when using the overall temporal stability metric to predict the mean soil moisture of an area during day-wet transition periods.

reported using frequency distribution analysis by Liu et al. (2018), who found that only a few points out of 48 maintained the same rank with time on a plantation hillslope in northwest China. It is notable that the present study was carried out on a slope with limited area and single vegetation. The observed ranges of MRD values among monitoring locations were only one third of those reported from a 480-m hillslope by Liu et al. (2018) and one fifth from the watershed scale by Cosh et al. (2006). Therefore, it is reasonable to infer that corresponding errors caused by ignoring the inner variations would increase in a study area with more complicated environmental conditions. Although temporal instability phenomena were observed based on a daily dataset, temporal stability is still a promising tool in soil moisture prediction in this region. This agrees with previous studies in the same study area but with a different sampling strategy by Gao et al. (2015 and 2016). The applicability of the temporal stability method could be indicated by nearly the same temporal stability features captured based on one-year period datasets of 5-min and one-month sampling intervals (Fig. 5). The negligible influence indicated that those days with strong temporal instability could not change the overall spatial pattern. An important reason for the negligible influences on the overall behavior of temporal stability was the low proportion of temporal instability days, less than 5% for most of the investigated soil depths (Fig. 8). The proportion was much lower than those days with effective rainfall in the red soil region, suggesting that the rainfall event might not necessarily change the spatial pattern of soil moisture.

5. Conclusions Under subtropical hydrological conditions, the behaviors of temporal instability of soil moisture was evaluated at hillslope scale. Obvious temporal instability phenomena was observed based on 365 daily datasets. Firstly, representative locations (RLs) to represent the mean soil moisture of the hillslope varied among days. The degree of the variation was depth-dependent, strongest at moderate soil layers and weakest at deep soil layers. Secondly, mean relative differences (MRD) of RLs showed an obvious temporal variation. Although, the influences of those temporal instability days on overall temporal stability were limited, they can contribute to a considerable prediction error at some occasions. These evolutions of the temporal instability were closely related to the hydrological conditions. The mean soil moisture of the hillslope was easily under-estimated at dry season and over-estimated at wet season by identified RLs. A rainfall event after a long drought period might result in an obvious temporal instability. One of the major contributions of this study is to evaluate the temporal instability of soil moisture with a high temporal resolution. Based on a daily temporal stability analysis in 365 days, we have the opportunity to evaluate the frequency, degree and underlying hydrological conditions of temporal instability. Accordingly, a comprehensive assessment of temporal stability of soil moisture in the red soil region of China was obtained. However, this study presents preliminary results based on only one-year record with limited area and sampling locations. Several issues still need to be addressed in future studies including a multi-year study with more sample points. Although the dry-wet transition period has been recognized as a high-risk period for temporal instability, it was still not clear how to predict the occurrence and the degree of temporal instability during these dry-wet transition periods. In addition, temporal instability behaviors are required to be tested in regions under diverse hydroclimatic conditions.

4.3. Influences of dry-wet status on temporal instability Significant correlation under most of the conditions were observed between the temporal stability metric and soil dry-wet status (Table 2). The relationships between dry-wet status and behavior of temporal stability varied with slope position and soil depths (Table 2). Different performances might be due to the variable roles of subsurface flow in controlling the soil moisture dynamics at different slope positions and soil depths (Wilson et al., 2005). Deep soil moisture was easily affected by subsurface flow and was indicated by the frequency of fluctuation of the groundwater table around deep soil depths in the present study (see Fig. 3). The heterogeneity in the slope position and soil texture was related to the pathways of subsurface flow, which caused different performances of temporal instability among locations and soil depths (Nasta et al., 2018). Rainfall was an important triggering factor to generate temporal instability, the roles of which depended on dry-wet status. The present study showed that the temporal instability probably occurred at the dry season after a rainfall event or a storm after successive rainfall during the wet season (Table 3). The temporal instability of soil moisture was partly caused by different recharge rate and water flux among the locations of the hillslope due to the differences in soil texture and slope position. Compared to the clay soil, the soil with higher sand content was recharged by rainfall with a higher rate and a lower extent due to larger hydraulic conductivity and lower water holding capacity (Keshta et al., 2011; Hu and Si, 2014). The degree of difference was more significant in the dry season than in the wet season. Similar finding was reported by Dari et al. (2019), who found that more RLs were required to represent the mean soil moisture of a catchment in dry season than wet season. Weak temporal stability of dry season could be partly ascribed by the strong variation during this period reported by Sur et al. (2013). In addition, different behaviors of subsurface flow during dry and wet periods might be another important reason for the correlation between temporal instability and soil dry-wet status suggested by Kim et al. (2007). Subsurface flow was more active during the wet period than during the dry period (Zhu and Lin, 2009; Nasta et al., 2018). The functions of subsurface flow in changing the spatial pattern of soil moisture varied between linear and non-linear depending on the features of the locations (Zhu et al., 2017). These irregular changes in the spatial patterns would result in the occurrences of the temporal

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