Estimation of Applicability of Soil Model for Rubber (Hevea brasiliensis) Plantations in Xishuangbanna, Southwest China

Abstract

Soil water content (SWC) plays a vital role in the process of evapotranspiration (ET) in rubber plantations. To investigate the influence of the ET process on soil water balance in rubber plantations, we measured SWC at depths of 10, 20, 30, 40, 70, 100, 130 cm, measured the characteristics of root density distribution, and estimated the ET and deep percolation at a rubber plantation in Xishuangbanna using the Hydrus-1D model. Our results indicate the Hydrus-1D model can accurate simulate soil water dynamics in the 0–130 cm of rubber plantations with Nash-Sutcliffe Efficiency Coefficient (NSE) was 0.80–0.96, the Root Mean Squared Error (RMSE) was 0.05–0.02, and the Pearson Correlation Coefficient (R) was 0.82–0.97. Obviously, there were seasonal variation characteristics in soil moisture changes in the rubber plantations of Xishuangbanna. The soil water storage (SWS) dropped to its lowest value of 194 mm and reached its maximum value of 504 mm from the dry season to the rainy season. The simulated ET of the rubber plantation was 1166.1 mm. The large uptake and utilization of soil water by rubber plantations in the dry season affects or exacerbates seasonal drought in Xishuangbanna and leads to a shortage of regional water resources.

1. Introduction

Evapotranspiration (ET) is a complex process in which the soil, vegetation, atmosphere, and other environmental factors interact closely with each other. Hence, it is important to consider maintaining the water cycle and energy balance of ecosystems, which also can function as a key factor in evaluating the regional water balance. Soil water content (SWC) plays a vital role in the ET process of rubber plantation ecosystems [1,2,3]. It is a key factor in ensuring the survival and development of rubber plantations under the water- and heat-limited conditions in Xishuangbanna. The factor under discussion is not SWC, but rather soil water tension. Since part of the SWC is not available for plants, it is not the absolute amount of SWC that controls ET but the availability. Rubber plantations are extremely dependent on the shallow soil layer (≤30 cm) which uptakes about 70.3–81.6% soil water during the year [4]. However, the surface soil moisture level shows great temporal variability, especially during the intense ET period when the shallow soil moisture content remains at a low value and thus cannot provide enough effective water for plant transpiration. On a global scale, it is common for roots to take up deep soil water in forests [5,6].

According to a preliminary survey, there are severe meteorological conditions in the experimental site. For instance, rubber production decreased by 40–55% in 2019 due to drought. It is therefore urgent to obtain an in-depth understanding of the water cycle components in rubber plantations, including ET and deep percolation. Soil conservation in rubber plantations is improved during the rainy season, which enhances water management during the dry season. Water budget models based on runoff, soil erosion, evaporation, and other hydrological process experiments on small plots of land are considered a valuable method of research. Modeling water processes in agricultural systems is a subject of significant recent work in hydrology [7]. Researchers have proposed some effective models, including the Soil Water Balance (SWB) model [8], HYDRUS [9], and the Soil Water Atmosphere Plant (SWAP) model [10].

Among these, the Hydrus-1D model has flexible boundary conditions which simulate the water cycle more accurately. Soil hydrodynamics can be accurately simulated through the consideration of different components of plant/soil/water hydrological processes, such as actual root uptake, canopy retention, deep drainage, and soil storage. Although Hydrus-1D has wide applications in water movement, irrigation systems, and the removal of agricultural pollutants in various crops [11], the model has rarely been used to study rubber plantations under field conditions or in simulations of ET, transpiration, and deep infiltration.

In this paper, a rubber plantation in Xishuangbanna was taken as the experimental site. Based on the field-measured data and laboratory experiments, (1) a rubber plantation soil water transport model using Hydrus-1D was established to simulate the dynamic infiltration process of soil water, (2) the influence of ET processes in rubber plantations on soil water storage (SWS) and deep percolation were analyzed based on the principle of water balance, and (3) we compared and analyzed the effects of soil moisture variation on ET at 0–130 cm (Hydrus-1D simulation) and at 0–40 cm (ECH2O measurement) to prove that rubber plantations tend to absorb deeper soil water when suffering from water stress.

2. Materials and Methods

2.1. Site Description

The experimental site was located in Bubang Village, Mengla County, Xishuangbanna, in the southwest of China (21°34′10″ N, 101°35′24″ E) (Figure 1). The observation plot of the chosen mature rubber plantation was transformed from tropical monsoon forest, with an altitude of 726 m. The average age of the rubber trees was 15 years, as shown in

Table 1. Characteristics of the rubber plantation and the experimental site.

Location	Altitude (m)	Slope (°)	Plot Area (m × m)	Diameter at Breast Height (cm)	Tree Height (m)	Tree Age (year)	Planting Density (trees/ha)
21°34′10″ N 101°35′24″ E	726	22	200 × 200	17 ± 2	11.58 ± 2.3	15	300 ± 50

. The planting distance of the rubber trees was 2–3 m, the row spacing was 4–6 m to make sure the rubber cutting could be carried out, and the width of the reclamation area around the mountains was 1.8–2.5 m. The under-story vegetation consisted of a small amount of weeds and shrubs. Mineral fertilizer containing 15% N, P, and K was applied in April and July. The rubber-tapping period continued from April to November. In response to cold stress, rubber trees start dormancy in December and synchronized defoliation occurs in February. They enter the growing period at the beginning of March.

The temperature in the experimental site was 18–22 °C, with an annual average temperature of 21.5 °C. The annual average sunshine time was 1853.4 h, and the annual average precipitation was 1599.5 mm. The annual precipitation distribution was uneven, and the average relative humidity was about 86%.

The EM50 monitor sensor was deployed in the experimental rubber plantation site from 1 January 2016 to 31 December 2016, with a horizontal distance of 0.5 m from the stem base of the rubber trees. Deep drainage water was measured in the −60 cm soil layer using a G3 drain gauge. Before installation, we performed accuracy correction for the sensors to make sure the error was less than 2%, as shown in Figure 1.

The soil under the rubber plantation was about 2 m deep and well drained, mainly comprising silty loam, which was general red soil (PH = 4.9) mostly formed by siliceous rocks. The soil bulk density varied from 1.39 g·cm⁻³ to 1.67 g·cm⁻³, with obvious stratification characteristics, and increased with soil depth, as shown in

Table 2. Soil physical properties in the study area.

Sampling Depth (cm)	Percentage of Soil Composition (%)			Soil Bulk Density
	Sand (Sa)	Silt (Si)	Clay (Ci)	g·cm−3
0–10	33.23	37.52	30.25	1.39
10–20	32.14	39.88	27.98	1.43
20–30	32.59	37.92	29.49	1.52
30–40	32.73	36.62	30.65	1.59
40–70	31.05	35.27	33.68	1.61
70–100	31.94	36.89	31.17	1.65
100–130	31.91	32.27	33.82	1.67

. Wang [12] and Zou et al. [13] found the similar physical and chemical properties of soil in the rubber plantation in Xishuangbanna.

The daily meteorological data were obtained using small automatic meteorological system stations during the experimental period, including daily precipitation (P) outside the rubber plantation, maximum temperature (T_max), minimum temperature (T_min), relative humidity (RH), sunshine duration (SD), and wind speed (WS). The T_max, T_min and P are show in Figure 2.

Under the influence of the southwest monsoon, Xishuangbanna has low humidity in the hot-dry season. The average temperature was 22.1 °C during the experimental period, the maximum daily temperature was 35.1 °C, and the minimum daily temperature was 6 °C. The mean precipitation values for the cool-dry season (November–February), hot-dry season (March–April), and rainy season (May–October) in 2016 were 186, 138, and 1242 mm, as shown in Figure 2.

2.2. Field Experiments and Measurement

2.2.1. Soil Sample Collection

From January to December, 2016, 20 × 20 m soil sampling areas were randomly selected for rubber plantation soil moisture monitoring (<200 m), and the altitude and slope direction of the selected sampling areas were determined to be consistent. After stripping the surface layer of branches and leaves, soil profiles were dug at the center of each sampling area, and seven soil samples (10, 20, 30, 40, 70, 100, and 130 cm) were collected using the cutting-ring method. After labeling and packaging, they were brought back to the laboratory to complete the tests of soil physical and chemical properties such as field water-holding capacity and SWC. Soil sampling was conducted 13 times a year, with 3 sample points set for each sampling, and a total of 39 sampling sites were selected (the altitude and slope direction of the selected sampling areas were consistent). SWC was measured every 30 days, and the rest of the soil physical properties were measured in January 2016.

The SWC was determined by drying all soil samples at 105 °C for 48 h. The soil physical properties were determined by the pipette method. Soil bulk weight and total porosity were determined by the ring knife method [14].

2.2.2. The Measurement of the Rubber Plantation Growth Index

• Rubber Root Sample

To determine the vertical distribution characteristics of the fine roots of rubber trees in the experimental site, we collected rubber plantation root samples in early May, 2016, and six sampling sites were randomly selected with consistent altitudes and slope directions. Each soil root profile was located at the midpoint of the planting line between two rubber trees, with a cross-section of 60 × 100 cm, and the vertical sampling depth of each sampling site was 130 cm. Then, we trimmed 3–5 mm of soil on the side walls of the profiles, and the number of roots within the 10 × 10 cm grid cells along the groove surface was calculated. The characteristics of the fine roots were recorded. Furthermore, the sampling size was 10 × 10 × 10 cm [15,16,17]. A total of 468 samples from the same soil depth were uniformly labeled at the same horizontal distance to complete the root length density testing in the laboratory. The determination of the length, surface area and volume of rubber fine roots and calculation of root length density were performed by an Epson model V700 root scanner and the WinRhizo root image analysis system (Regent Instrument Inc., Quebec, Canada). To ensure the accuracy of the data, we took the average of the root measurements of all sampling sites.

2.

Leaf Area Index

The plant parameters of the rubber plantation include height, leaf area index (LAI), root distribution, and maximum root depth. The Feddes model was used as the root uptake model and the LAI was observed monthly in the sample plots using an LAI-2000 (LI-COR, Lincorn, NE, USA) from January to December in 2016 for random selections of rubber trees at 5–6 m intervals in the sample sites.

2.3. Model Description

The dynamics of SWC in a rubber plantation were simulated by using Hydrus-1D [18,19] in the experiment. Meanwhile, we also adopted the modified Richards equation, adding a sink term to explain the water uptake by plant roots in terms of its function in one-dimensional unsaturated water flow [20,21]. It is accurate to simulate water movement into the soil using the Hydrus-1D model, after which it was found that the vertical water movement was apparently greater than the lateral movement in the unsaturated area [22,23,24,25]. In 2016, we conducted a simulation of daily water flow movement with regard to the rubber plantation. The whole simulation profile for the experimental site was discretized into 130 nodes and the spatial step was 1 cm. The 130 nodes were divided into 7 layers (0–10, 10–20, 20–30, 30–40, 40–70, 70–100 and 100–130 cm). The nodes with higher density were close to the top of the soil profile in order to make sure the mass balance errors were within 1.0% [26].

2.3.1. Boundary Conditions

Day interval was used for the simulation and the model was selected to simulate the changes in SWC in the 0~130 cm soil profile in the experimental site. The upper boundary condition was a standard atmospheric boundary with a depth of water accumulation on the soil surface of zero. The lower boundary was simulated as the free drainage from a relatively deep soil profile, which was defined by the unit vertical hydraulic gradient, since the water table was deeper than the experimental site (about 1.8–2.2 m) [27].

The meteorological conditions required for Hydrus-1D simulations include T_max, T_min, RH, P, SD and WS. The potential radiation was taken to be the radiation term, the RH was the water vapor pressure input, and the radiation extinction coefficient was taken as 0.463, which was based on the canopy structure.

2.3.2. Plant Parameters

The cyclic defoliation of rubber trees resulted in large intra-annual fluctuations in LAI [28]. The mean LAI of the rubber trees increased from a minimum value of 0.75 ± 0.07 m²/m² between January and February to 2.87 ± 0.11 m²/m² between May and June, and reached an annual maximum of 4.26 ± 0.45 m²/m² from June to September.

The distribution ratio of rubber root length is shown in

Table 3. Distribution ratio of rubber root length.

Soil Depth (cm)	Fine Root Length (10−2 cm/cm3)	Distribution Proportion (%)	Cumulative Root Percentage (%)
0–10	51.25	27.56	27.56
10–20	44.42	23.89	51.44
20–30	34.67	18.64	70.09
30–40	25.18	13.54	83.63
40–70	11.03	5.93	89.56
70–100	8.20	4.41	93.97
100–130	11.22	6.03	100.00

.

The cumulative distribution function of fine roots in the rubber plantation was obtained by fitting the measured root data [29,30]:

$$Y(z)=-0.00008Z^2+0.0162Z+0.2156\begin{array}{cc}& \end{array}{R}^2=0.9086$$

(1)

The root density distribution function was obtained using the derivative of Equation (1)

$$b(z)=-0.00016Z+0.0162$$

(2)

We conducted linear fitting on the measured data of root systems and found that the measured values of the rubber plantation root system are similar to the fitted values of the linear distribution function. Therefore, we chose to use the linear distribution function to construct the rubber root system distribution features in this paper.

The soil substrate potential P0 was 0 cm at the anaerobic point of root uptake, and the soil substrate potential P0pt was −1 cm at the beginning of the root uptake optimum. P2H and P2L were −500 and −900 cm at the end of the optimum point of root water uptake. The withering point corresponds to a soil substrate potential P3 of −16,000 cm. The potential transpiration rates of r2H and r2L were 0.5 and 0.1 cm/d, respectively [31].

2.3.3. Calibration and Validation

The simulation time was from 1 January 2016 to 31 December 2016, a total of 366 days. The calibration period was between days 1–200 and the validation period was between days 201–366 [32].

The measured SWC value (0–130 cm) results were recorded from monthly soil sampling in cylinders and the gravimetric water contents from January to December in 2016. We used the measured SWC values (0–130 cm) to evaluate the simulated SWC outputs of Hydrus-1D.

The measured soil hydraulic parameters were input into the Rosetta transfer function model to derive the initial soil hydraulic parameters, which were expressed as five parameters of the van Genuchten model: residual moisture content (θ_r), saturated moisture content (θ_s), shape parameters of the van Genuchten model (α,n) and saturated hydraulic conductivity (K_s). The measured soil moisture data were used to correct the hydraulic parameters, and the optimized hydraulic parameters were obtained, as shown in

Table 4. Model-optimized soil hydraulics parameters.

Soil Depth (cm)	θr	θs	A	n	Ks
	(cm3 cm−3)	(cm3 cm−3)	cm−1		cm day−1
10	0.0884	0.476	0.0078	1.53	9.46
20	0.0861	0.472	0.0074	1.55	9.35
30	0.0875	0.474	0.0076	1.54	9.59
40	0.0887	0.476	0.0079	1.53	9.71
70	0.0926	0.486	0.0088	1.50	9.20
100	0.0896	0.478	0.0081	1.53	9.38
130	0.0917	0.482	0.0086	1.51	10.47

.

To evaluate the simulation accuracy of Hydrus-1D for the rubber plantation in our study site, we selected the Nash-Sutcliffe efficiency coefficient (NSE), the Root mean squared error (RMSE), and the Pearson correlation coefficient (R) [33,34], and the indices were calculated as follows:

$$RMSE=\sqrt{\frac{{\displaystyle \sum _{i=1}^N{(O_i-P_i)}^2}}{N}}$$

(3)

$$NSE=1-\frac{{\displaystyle \sum _{i=1}^N{(P_i-O_i)}^2}}{{\displaystyle \sum _{i=1}^N{(O_i-\overline{O})}^2}}$$

(4)

$$R=\frac{{\displaystyle {\sum }_{i=1}^N(O_i-\overline{O})(P_i-\overline{P})}}{\sqrt{{\displaystyle {\sum }_{i=0}^N{(O_i-\overline{O})}^2}}\sqrt{{\displaystyle {\sum }_{i=0}^N{(P_i-\overline{P})}^2}}}$$

(5)

where $P$_i is the simulated values, O_i is observed values, $\overline{p}$ is the average of the simulated values, $\overline{O}$ is the average of the observed values and N is the number of the observed data.

2.4. Water Budget

According to the water balance principle, the water balance equation of rubber plantation is:

$$\Delta W=P+I-\Delta S-ET-R_s-\Delta W_{\mathrm{latex}}$$

(6)

where $\Delta W$ is the change of soil water storage in mm, P is precipitation in mm, I is the irrigation recharge in mm, $\Delta S$ is the exchange at the bottom of the soil layer in mm, ET is the ET at the moment in mm, R_s is the runoff in mm and $\Delta$W_latex is the water content in the latex of the rubber tree in mm.

Since there was no irrigation recharge during the test period, I was neglected. Therefore (6) can be simplified as:

$$ET=P-\Delta W-R_s-\Delta S-\Delta W_{\mathrm{latex}}$$

(7)

$$\Delta S_t=S_t{}_{\downarrow }-S_t{}_{\uparrow }$$

(8)

$$W=10\times {\displaystyle \sum _{i=1}^n{\theta }_i}\times h_i$$

(9)

$$\Delta W=W_{t+1}-W_t$$

(10)

where ΔS_t is the exchange water content at the bottom of the soil layer in mm, S_t↓ and S_t↑ are the leakage and recharge water content from the upper part of the soil layer to the lower part in mm, W is soil water storage in mm, ${\theta }_i$ is the soil volume water content at layer I in cm³/cm³, h is soil depth, t is time and t + 1 and t are continuous temporal points.

Considering that ET is divided into two parts: soil evaporation and plant transpiration, the Hydrus-1D model does not sufficiently consider the actual evapotranspiration mechanism under water stress when calculating evapotranspiration [35]. Its output of evapotranspiration data was not used as an ET variable in the water balance, but was obtained by Equation (7).

The regression equation of the annual total rubber plantation runoff depth (Y) and precipitation (X) $(Y=0.41X-253.99)$ was used to calculate the annual runoff depth [36]. The proportion of rubber plantation runoff to precipitation is shown in

Table 5. The proportion of runoff in rubber plantation to precipitation (%).

Season	Cool-Dry Season (November–February)	Hot-Dry Season (March–April)	Initial-Rainy Season	Mid-Rainy Season	Late-Rainy Season
			(May–June)	(July–August)	(September–October)
Proportion of annual runoff	4.6	0.9	9.8	63.6	20.8

.

In rubber plantations, canopy hydrological effects on precipitation redistribution are different from other dry crops. In the dry season, the proportion of stemflow and throughfall to precipitation are 3.20% and 35.73%, respectively. In the rainy season, proportion of stemflow and throughfall to precipitation are 7.33% and 73.80%, respectively [37]. The parameters were selected to estimate the stemflow, throughfall, and the proportion of runoff to precipitation outside the rubber plantation according to the experimental study on the hydrological effects of canopy ecology on a rubber plantation conducted by the Key Laboratory of Tropical Forest Ecology, Xishuangbanna, China.

3. Results and Analysis

3.1. Model Evaluation

The model uncertainty came from the parameter inputs, model structure, and boundary conditions [38,39]. The SWC values (0–130 cm) measured by monthly soil sampling were used to evaluate the simulated SWC outputs of Hydrus-1D (Figure 3).

As shown in Figure 3, the simulation effects of soil moisture in the deeper soil layers from 70 to 130 cm were better than those in the shallow soil layers from 0 to 40 cm. The NES of the soil layers from 0 to 40 cm ranged from 0.80 to 0.92, RMSE ranged from 0.05 to 0.03, and R ranged from 0.82 to 0.94, respectively; while the NES of the soil layers from 70 to 130 cm ranged from 0.92 to 0.96, RMSE ranged from 0.03 to 0.02, and R ranged from 0.95 to 0.87, respectively. As the depth of the soil layer increased, the influence of soil moisture from the external environment decreased and the simulation accuracy improved significantly. Hence, the results showed the suitability of the calibrated Hydrus-1D to simulate the variations in SWC corresponding to the rubber plantation in the experimental field.

3.2. Modeling Soil Water Dynamics in Response to Rubber Plantation

The measured model parameters are usually taken as the initial values of a Hydrus-1D model, and this has been an important way to use the field data to optimize the model parameters [40,41,42].

The daily SWS dynamics in the rubber plantation soil layer (0–130 cm) in 2016 of the experimental area were simulated by the validated Hydrus-1D model, as shown in Figure 4. The simulation was organized into three periods: the hot-dry season (day 90–120), the rainy season (day 150–300) and the cool-dry season (day 300–366, day 1–90).

During the hot-dry season (March to April), the SWS gradually decreased. There was little rainfall, and rubber trees sprouted and took up a large proportion of the soil water. The SWS decreased to its lowest value of 194 mm, and the SWC was 0.1–0.2 cm³cm⁻³. The whole soil layer was in a dry and water-deficient state. Subsequently, at the beginning of the rainy season in May-June (i.e., day 150–180), due to the lack of rainfall in the dry season, the SWC was low. Although there was some rainfall in the early rainy season, most of the rainfall was used to supplement the surface soil moisture, and the SWC increased only in the 0–80 cm soil layer, 0.2–0.3 cm³cm⁻³, and the 80–130 cm soil layer.

When entering July, the middle and late rainy season (i.e., day 180–300), a large amount of rainfall replenished the SWC. The SWS rapidly rose to its maximum value of 504 mm in the summer of 2016 and the SWC of all the layers fell between 0.2 and 0.4 cm³cm⁻³. However, as the rubber tree roots began to take up the water in the shallow soil from the rainfall recharge in the dry season instead of the soil water, which is stored in the rainy season, the SWC of the shallow soil was lower than that of the deeper soil. During the cool-dry season, the SWS slowly decreased due to the decreasing precipitation and lower temperature. Then, rubber trees entered their late reproductive stage, with lower vital activities. Entering the foggy and cool season in November (i.e., day 300–366), due to the decrease in precipitation and temperature, rubber trees entered their late growth stage and their life activities decreased, water consumption decreased, and the SWS decreased slowly.

3.3. Variation of SWC at 0–130 cm and Soil Water Flux at the Bottom

The precipitation distribution in Xishuangbanna is uneven. The dry season accounts for only 15% of the annual precipitation, whereas the rainy season accounts for 85%, which led to an increase in the variation range of the soil moisture. In 2016, the annual variation of soil water in the rubber plantation was −69.8 mm. The 0–130 cm soil water flux exchange at the bottom boundary was 0.21 mm day⁻¹, with a total annual value of 78.3 mm, as shown in Figure 5.

The bottom soil water content exchange fluctuates with precipitation, and positive values indicate that the soil moisture content below 130 cm infiltrates into the lower layer. Otherwise, it would take a negative value. As shown in

Table 6. Amount of soil water content change ($\Delta$W) and soil water flux ($\Delta$S) at the bottom boundary of the rubber plantation at the experimental site (mm).

Season	Month	5	6	7	8	9	10	Total
Rainy season	Soil water content change (ΔW)	−10.6	5.1	11.0	30.8	14.5	−3.3	47.5
	Soil water flux (ΔS)	−3.0	13.0	25.8	40.0	28.0	16.2	120.0
Season	Month	11	12	1	2	3	4	Total
Dry season	Soil water content change (ΔW)	5.7	9.0	−19.4	−25.6	−44.0	−43.0	−117.3
	Soil water flux (ΔS)	−2.8	−5.0	−7.4	−6.3	−10.1	−10.1	−41.7

Note: When soil water infiltrates into the lower layer, $\Delta$S is a positive value.

.

In the rainy season, rainfall is the dominant soil water supplement, and the total soil water flux at the bottom boundary was 120.0 mm. In the dry season, the bottom deep drainage decreases rapidly, and the daily water flux at the bottom boundary of the rubber plantation was negative, at 41.7 mm. This indicates the soil water layer under 130 cm supplies water to the 130 cm layer above. Rubber plantation suffers water stress in the dry season. In order to meet the demands of growth and ET, a rubber plantation not only consumes almost all atmospheric precipitation in the dry season, but also extract an amount of stored water in the soil, especially from deeper soil layers.

3.4. Comparison of Evapotranspiration of Rubber Plantations Based on HYDRUS Simulation and Soil Sample Measurements

In general, it is considered that the SWC change in the 0–40 cm soil layer of the rubber plantation can reflect the absorption and utilization of soil moisture in the ET process of a rubber plantation. However, we compared the SWC variation of the 0–130 cm soil layer simulated by HYDRUS and the 0–40 cm soil layer measured by monthly soil sampling in the ET process of the rubber plantation. The annual soil water variation was −26.7 mm in the 0–40 cm soil layer (soil sampling) and −69.8 mm was simulated by Hydrus-1D in 2016. It is shown in

Table 7. Water balance of ET from the rubber plantation (mm).

Month	Income		Expend							Evapotranspiration
	Throughfall + Stemflow	Effective Precipitation (Pe)	Canopy Interception (Wc)	Rs	$\mathsf{\Delta }$Wlatex	ΔS (0–130 cm)	ΔS (0–40 cm)	$\mathsf{\Delta }$W (0–130 cm)	$\mathsf{\Delta }$W (0–40 cm)	ETWB,0–40	ETWB,0–130
11	20.8	16.3	33.7	4.5	0.1	−2.8	0	5.7	−25.3	47.0	75.2
12	14.9	10.4	24.1	4.5	0	−5.0	0	9.0	−27.6	30.5	62.1
1	19.4	14.9	31.3	4.5	0	−7.4	0	−19.4	−13.1	73.0	59.3
2	15.0	10.5	24.3	4.5	0	−6.3	0	−25.6	−14.0	66.7	48.8
3	19.6	17.9	31.7	1.7	0	−10.1	0	−44.0	−2.4	103.7	52.0
4	30.7	29.0	49.6	1.7	0.1	−10.1	0	−43.0	31.1	131.6	47.4
5	130.5	111.5	29.2	19.0	0.1	−3.0	13.6	−10.6	17.9	154.2	109.1
6	158.4	139.4	35.4	19.0	0.1	13.0	18.1	5.1	58.2	156.6	98.4
7	228.5	105.1	51.2	123.4	0.1	25.8	35.8	11.0	−18.5	119.4	138.9
8	276.0	152.6	61.8	123.4	0.1	40.0	53.6	30.8	−12.8	143.5	173.5
9	156.9	116.5	35.1	40.4	0.1	28.0	22.2	14.5	−8.0	109.0	137.3
10	68.9	28.5	15.4	40.4	0.1	16.2	16.1	−3.3	−12.2	30.9	39.9
Total	1139.6	752.6	422.8	387	0.8	78.3	159.4	−69.8	−26.7	1166.1	1041.9

The water content of latex was estimated from the rubber production data of the rubber factory in Mengla. p = Throughfall + Stemflow + W_c + R_s + $\Delta$W_latex; Pe = Throughfall + Stemflow − R_s; ET_WB = P_e – ΔS − $\Delta$W_latex – ΔW + W_c.

.

Based on the water budget, the soil moisture value ET_WB,0ߝ40 measured by soil sampling was 1041.9 mm (including canopy interception), and the ET_WB,0ߝ130 value calculated by Hydrus-1D was 1166.1 mm (including canopy interception). The results of ET we calculated were close to some related studies on the ET of rubber plantations in Southeast Asia. Tan et al. (2011) estimated ET by catchment water balance (1137 mm) and by eddy covariance (1125 mm) at Xishuangbanna [43]. Giambelluca et al. (2016) found the mean annual rubber ET was 1211 mm in Thailand [44].

The measured value was about 10.65% lower than the calculated value, which indicated that the absorption and utilization of soil moisture by plantations in the process of ET involves far more than the 0–40 cm soil layer. When suffering from water stress, rubber plantations tend to extract deeper soil water, which may contribute to the fact that the variable in the SWC of the 0–40 cm layer did not accurately reflect the characteristics of soil water uptake and utilization in these rubber plantations. Hence, the influence of soil water at depths greater than 40 cm on the ET of rubber plantations should be fully considered.

4. Discussion

In our research, the dynamics of SWS in the 0–130 cm soil profile under a rubber plantation were accurately simulated by the Hydrus-1D model, and the effects of ET on a rubber plantation in Xishuangbanna were examined. According to the soil sampling measurement of the SWC profile and fine root density of the rubber plantation in 2016, the Hydrus-1D model managed to simulate efficiently the dynamics of SWS in the experimental sites.

Plant parameters are one of sensitive parameters of SWC variation in the Hydrus-1D model [26,45], such as plant height, LAI, Feddes root water uptake parameter and root depth. The mean height of rubber trees was 17.7 m. The LAI we measured fluctuated greatly due to the periodic defoliation of rubber plantation. The relative root density distribution function b(z) in the Feddes model determines the simulation results of SWC in different soil layers in the Hydrus-1D. We modified the Feddes model by measured rubber root density and constructed the linear distribution function by using rubber root distribution.

We used the SWC values (0–130 cm) measured by monthly soil sampling to evaluate the simulated SWC outputs of Hydrus-1D. The Hydrus-1D model we modified had high simulation accuracy. The simulation accuracy of soil water dynamics in the 0–130 cm soil layer with NES was 0.80–0.96, the RMSE was 0.02–0.05, and the R was 0.82–0.97. The interference of the external environment on soil moisture decreased with an increase in soil depth, and the simulation accuracy was significantly improved [46].

Despite a significant period of defoliation and dormancy, the results we found when we modelled the uptake of soil water from the deep soil layers maintained rubber plantation growth and ET in the whole dry season and even at the beginning of rainy season. Gonkhamdee et al. (2010) found that there were fine roots (below 300 cm) in the deep soil of the Baan Sila rubber plantation that were active only in the dry season. Guardiola-Claramonte et al. (2008) showed that with the drying of surface soil in Xishuangbanna, the utilization of deep water under the action of rubber plantation increased. Giambelluca et al. (2016) also indicated that more than 50% of the water extracted from the soil by a rubber plantation comes from the deep soil by the end of dry season, and the characteristics of deep roots are similar to those of foreign fast-growing tree species [44,47,48]. Rubber roots can respond to changing soil water distributions and continue to obtain water from the deep soil when the surface soil is dry.

More than 20 years ago, researchers began studying the ability of different tree species to utilize deep soil water sources, such as deciduous tree species in a Brazilian Cerrado savanna. They proved that deciduous species attempt to extract deeper soil water to response dry-hot seasons [49,50]. Liu et al. (2011) also suggested that planting rubber plantations instead of natural forests significantly changed the process of regional runoff production [15]. Rubber plantations use deep soil water to maintain physiological activity in the dry season, which may lead to a decrease to river flows and make the whole catchment drier [51]. Although the precipitation in Xishuangbanna is less than that in the origin sites of rubber plantations, they can survive and grow well, as soil moisture plays a vital role in the ET process of rubber plantations.

We applied a model of soil moisture dynamics to the rubber plantations of Xishuangbanna based on the Hydrus-1D model to compensate for the limitations of the continuity and scale of soil moisture monitored in the field. Under specific climatic conditions, the soil water uptake and utilization status of rubber plantations largely determined the ET of the rubber plantations. Therefore, a multi-dimensional analysis of soil water dynamics is essential in order to further explore the water balance during the ET process in rubber plantations.

5. Conclusions

In our research, the dynamics of SWC in the 0–130 cm soil profile were simulated successfully with the Hydrus-1D model. On the basis of the regular observations of SWC profiles in 2016 under a rubber plantation, we managed to use Hydrus-1D to simulate efficiently the dynamics of SWC in the experiment area. We proved that the calibrated Hydrus-1D model was effective for the simulation of the effective precipitation and water demands, which are essential in order to analyze the water budget of a rubber plantation.

Meanwhile, the high ET of rubber plantations is partly related to deep soil water uptake and utilization by rubber trees. In the late dry season, although affected by soil moisture limitations and canopy stomatal closure, rubber plantations maintain high ET through a well-developed root system that utilizes deep soil water. Therefore, rubber plantations’ uptake amount of deep soil water in the dry season may exacerbate seasonal drought in Xishuangbanna and cause a shortage of regional water resources.