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Article

Estimation of Evapotranspiration in the Desert–Oasis Transition Zone Using the Water Balance Method and Groundwater Level Fluctuation Method—Taking the Haloxylon ammodendron Forest at the Edge of the Gurbantunggut Desert as an Example

1
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830000, China
2
School of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100000, China
3
National Field Scientific Observation and Research Station of Akesu Oasis Farmland Ecosystem, Aksu 843000, China
*
Author to whom correspondence should be addressed.
Water 2023, 15(6), 1210; https://doi.org/10.3390/w15061210
Submission received: 17 February 2023 / Revised: 13 March 2023 / Accepted: 16 March 2023 / Published: 20 March 2023
(This article belongs to the Special Issue Evapotranspiration Measurements and Modeling II)

Abstract

:
Shallow groundwater is an important water source for Haloxylon ammodendron (H. ammodendron). The accurate estimation of evapotranspiration (ETg) from groundwater is of great significance for the water cycle and the maintenance of ecological stability. Using a combination of the water balance method and the groundwater level fluctuation method (WTF), the water balance components (precipitation, soil moisture, groundwater depth, and Bowen ratio meteorological data) in the desert–oasis transition zone were continuously monitored from 2015 to 2018 and the ETg was estimated The results showed that the closed degree of Bowen specific energy after data screening was higher, and the annual actual evapotranspiration (ETa) value could be reliably calculated at 260.87 mm. As the main contributor to water consumption in the growing season, latent heat accounted for 70.16~91.86% of the energy balance. Precipitation had no significant impact on water consumption for H. ammodendron vegetation growth, and the precipitation in the main growing season accounted for 59.44% of the ETa. The groundwater depth in the study area decreased yearly and had a significant impact on the growth of H. ammodendron vegetation. Although the groundwater depth in the study area was greater than 9 m, the ETg, as an important part of the water balance, was found to participate in the evapotranspiration process brought about by H. ammodendron due to the strong root system and supporting capillary water in the soil. The actual evapotranspiration ETa for H. ammodendron in the main growing season was 244.32 mm, and the contribution rate for ETg was as high as 74.78% or approximately 182.35 mm. After the ETg was verified using the water balance method and WTF, R was greater than 0.96, the RMSE range was 1.5931~4.5706, the bias range was −0.15~0.11, and the IOA value was greater than 0.95. The accuracy of the estimation model was high, and the results were relatively accurate. The model can be applied in the desert–oasis transition zone to obtain accurate ETg estimations and provide theoretical guidance and a scientific basis for local water resource management and ecological protection.

1. Introduction

The fifth report of the IPCC pointed out that the global temperature has risen by 0.85 °C in the past 130 years. Against the background of global warming, drought, and desertification have become increasingly serious. Since the 1990s, in particular, the intensification of global warming has seriously affected all aspects of production and life in human societies [1,2]. As in other regions in the world, persistent high-temperature events have increased significantly in most regions of China, with the most serious in the northwest. The current challenge is determining how to effectively manage water resources and accurately assess climate events [3]. On the one hand, global warming is leading to the acceleration of the water cycle and the increase in surface evaporation is leading to a decrease in the effective available water, resulting in the intensification of extreme drought events and desertification. On the other hand, as the temperature rises, the water holding capacity of the atmosphere is increasing, which means that more water vapour is needed to saturate the air and form effective precipitation [4,5]. In this context, it is of great significance for local water resource allocation and scientific management to consider the evapotranspiration from groundwater brought about by Haloxylon ammodendron (H. ammodendron) in the desert–oasis transition zones.
The desert–oasis transition zone refers to the ecological ecotone between the oasis ecosystem and the desert ecosystem, and it is a region that is very sensitive to climate change [6,7,8,9]. Such zones demonstrate a plant gradient phenomenon generated by an environmental gradient with water as the leading factor, and they are ecologically fragile zones with a sensitive degradation trend. The desert–oasis transition zone at the edge of the Gurbantunggut Desert is home to approximately 68% of the H. ammodendron in China. The H. ammodendron forest fixes a large amount of the quicksand around the oasis, which plays an important role in protecting the security of the oasis [10]. In recent years, due to the overexploitation and utilization of water resources, the groundwater level has dropped sharply, the process of desertification has accelerated, the H. ammodendron community has been seriously degraded, and a large area of death has developed [11,12], all of which seriously threaten oasis security and the ecological balance of the desert–oasis transition zone [13].
Many studies on the relationship between ecological hydrological processes and vegetation composition and climate have been carried out in the desert–oasis transition zone at the edge of the Gurbantunggut Desert [14,15,16]. Many scholars have evaluated the importance of groundwater for various types of vegetation and the effects of vegetation on groundwater decline [17,18,19,20]. However, this research has mostly been limited to vegetation with good soil water conditions [21], farmland crops that rely on irrigation and water replenishment [22,23,24,25], and riparian forest vegetation with river water replenishment [26,27]. There is a consensus concerning vegetation change under extreme drought conditions without irrigation or river water replenishment. Therefore, although the importance of soil water and shallow groundwater for vegetation in the desert–oasis transition zone has been recognized, estimations of the components of the water balance and the contribution rate of shallow groundwater to evapotranspiration are still lacking [28].
The importance of shallow groundwater for maintaining vegetation has been proposed in various water balance studies, which further underlines the importance of various ecosystems and crop systems for shallow groundwater [29]. As an important process of energy exchange between the land and atmosphere, evapotranspiration is also a key node in the water balance cycle [30]. The survival of vegetation in the desert–oasis transition zone mainly depends on precipitation, and more than 60% of precipitation returns to the atmosphere in the form of evaporation [31,32]. Evapotranspiration from shallow groundwater (ETg) leads to significant losses in groundwater reserves [28,33,34]. When water moves upwards through capillarity to replenish soil water reserves, surface soil water storage is exhausted through surface evaporation and water absorption by the surface roots of vegetation. For crops, depths within 200 cm of the surface are usually effective [35,36,37], and the capillary rise rate increases with the development of crop roots. Therefore, it is reasonable to assume that the H. ammodendron forest in the desert–oasis transition zone can absorb soil water and shallow groundwater at depths of more than 200 cm below the surface due to the influence of root distribution [38].
Research on ETg can be traced back to the beginning of the 20th century. The spatiotemporal variability of ETg makes it difficult to estimate directly [39]. Research shows that soil moisture and groundwater level have certain memory effects on important ecological hydrological processes (such as evapotranspiration and leakage processes) occurring in the aeration zone [40,41]. The question of how to use relatively convenient data and obtain a large amount of conventional observation data to estimate ETg is a research hotspot and a new direction in ecology–hydrology, and there are still technical challenges [23,42]. As a typical hydrological method, the water balance method is widely used and has the advantages of low cost and high accuracy [43,44], but its credibility is poor in the desert–oasis transition zone, which has low precipitation and high evaporation intensity [45]. The groundwater level fluctuation method is mainly applicable for phreatic aquifers. It is not necessary to calculate the source and sink items for groundwater. Only the dynamic monitoring data for the regional groundwater level need to be used to obtain the total recharge of the region. Therefore, this method is practical and easy to employ in areas where the source and sink items are unknown and the data are incomplete [46,47].
Therefore, this study used Bowen ratio meteorological data, soil moisture data, and groundwater level fluctuation field monitoring data from the desert–oasis ecosystem at the Fukang National Experimental Station. The ETg and its rate of contribution to the H. ammodendron shrub forest at the edge of the Gurbantunggut Desert were estimated with the water balance method and groundwater level fluctuation method, and the results can provide theoretical guidance and a scientific basis for water resource planning in the region.

2. Overview of the Study Area and Data

2.1. Overview of the Study Area

The Gurbantunggut Desert region is located in the northern part of Xinjiang (44°15′~46°50′ N, 84°50′~91°20′ E) and has an area of about 9.81 × 104 km2 (Figure 1). The study area is a typical temperate desert climatic zone with important seasonal changes. Through an analysis of differing land uses and the vegetation cover gap between the Gurbantunggut Desert and northern Xinjiang (Table 1), it can be concluded that grassland and sparse vegetation represent the absolute gap. The sparse vegetation in the Gurbantunggut Desert area has the widest distribution area of about 9 × 104 km2, accounting for 91.74% of the whole Gurbantunggut Desert area (Table 1). Through field investigations, it was found that the sparse vegetation mainly comprised H. ammodendron forest. The naturally occurring growth density of H. ammodendron is 0.25 plant·m−2, the average diameter of the trees is 3.3 cm, the average height is 192 cm, the average width of the crowns is 199 cm × 192 cm, and the vegetation cover is 20~30%.

2.2. Data

From 2015 through 2018, soil moisture and Bowen ratio meteorological data for H. ammodendron shrubs were monitored at the Fukang Desert Ecosystem National Monitoring Station of the Chinese Academy of Sciences.
(1) Micrometeorological data for the Bowen ratio
Data were acquired with a CR1000X data collector (Campbell Scientific Inc., Logan, UT, USA). An NR-lite net radiometer (Campbell Scientific Inc., Logan, UT, USA) was used to measure Rn net radiation. An HMP45C (Campbell Scientific Inc., Logan, UT, USA) air temperature and humidity probe was used to measure the air temperature T and relative humidity RH at 3 m and 5 m. An HFP01SC soil heat flux plate (Campbell Scientific Inc., Logan, UT, USA) was used to measure the soil heat flux at 3 cm and 5 cm below the soil surface. The rainfall P and wind speed (2 m high position) data were monitored using an additional rainfall meter. The data were acquired once every 30 s, and the mean values were automatically saved every 30 min (Figure 2).
(2) Monitoring of soil moisture and H. ammodendron root system
Figure 3a shows the surface area distribution of Haloxylon ammodendron root system, and Figure 3b shows the soil moisture profile. Soil moisture was monitored automatically with a Hydra Plant Sensing (Aoda Plant Sensing Instruments Inc., Osborne, SA, Australia) instrument and a neutron detector, both at the roots of H. ammodendron shrubs (Figure 4). The Hydra soil moisture meter measures the soil moisture content of soil at depths of 10 cm, 30 cm, 50 cm, 70 cm, 90 cm, 110 cm, 130 cm, and 150 cm below the root surface by means of a pre-buried soil moisture probe. The automatic data acquisition instrument was connected to the probe by a cable, collected data every 5 s, and computed and stored the 10 min mean values. Neutron monitoring of soil moisture was undertaken at depths of 30 cm, 50 cm, 70 cm, 90 cm, 110 cm, 130 cm, 150 cm, …, 950 cm, 970 cm, and 990 cm below the surface (Figure 3b). A total of 23 neutron tubes were buried at a depth of 1050 cm. Each layer was monitored three times, and the mean values were recorded. Soil moisture monitoring with a neutron tube was synchronized with groundwater monitoring, which started in early March each year and took place every 10 days. In this study, the multipoint curve fitting method was used to calibrate the soil sensor.
The H. ammodendron forest in the study area has developed roots. According to the layered excavation method, the root distribution range is 0~10 m, and the depth of the main roots reaches 8.33 m, with 66.6% of the roots being distributed in the 0~4 m soil layer (Figure 3a).
(3) Groundwater burial depth
Monitoring of the groundwater burial depth was primarily undertaken using a groundwater observation well constructed in advance approximately 0.5 m from the neutron tube (Figure 5). There were three groundwater observation wells: at the foot of the eastern slope, at the foot of the western slope, and in the bare earth between the hills. The heights of the control points were 400.95 m, 398.40 m, and 399.04 m, respectively. Figure shows the groundwater level fluctuation.

3. Materials and Methods

3.1. Water Balance Method

The ET0 reference evapotranspiration was computed using the FAO 56 Penman Monteith model [48]:
E T 0 = 0.408 Δ R n G + γ 900 T + 273 U 2 e s e a Δ + γ 1 + 0.34 U 2
where ET0 is the reference evapotranspiration in mm day−1, Rn is the net radiation in MJm−2day−1, G is the soil heat flux in MJm−2 day−1, γ is the psychometric constant in kPa °C−1), T is the mean air temperature in °C, U2 is the wind speed at a 2 m height in m s−1, es is the saturation vapour pressure in kPa, ea is the actual vapour pressure in kPa, and ∆ is the slope of the vapour pressure curve in kPa °C−1.
(1)
Energy balance method using the Bowen ratio
The energy balance method using the Bowen ratio can be used to indirectly estimate the sensible heat flux H and the latent heat flux λET of the canopy surface and then to obtain the actual evapotranspiration ETa, which can be expressed as follows:
λ E T + H = R n G
β = H λ E T = γ Δ T Δ e
where β is the Bowen ratio, H is the sensible heat flux in MJ·m−2·d−1, λET is the latent heat flux in MJ·m−2·d−1, λ is the latent heat of vaporisation in MJ·kg−1, ΔT is the two-stage air temperature difference in °C, and Δe is the two-stage air vapour pressure difference in kPa.
H and λET may be estimated using the following equations:
H = β 1 + β R n G
λ E T = 1 1 + β R n G
where G is the soil heat flux in MJ·m−2·d−1, and Rn is the net radiation of the canopy surface in MJ·m−2·d−1.
(2)
Water balance method
The groundwater in the desert–oasis transition zone is located at a deep level (>9 m). The average texture of the soil makes it ideal as a unit, and it shows no surface runoff or deep leakage. Based on water balance theory:
P + E T g = E T a + Δ S
where P is the precipitation in the period in mm, ETg is the evapotranspiration from groundwater in mm, ΔS is the soil water storage in the period in mm, and ETa is the actual annual evapotranspiration in mm.
The accumulation of water in the soil ΔS is calculated as follows:
Δ S = i = 1 n θ 2 θ 1 i Δ d i
where n is the number of layers from the effective root zone to the depths studied (0~1000 cm); θ1 and θ2 are the volumetric water contents of the first and second samplings, respectively, in m3·m−3; and Δdi is the thickness of each soil layer in mm.

3.2. Water Table Fluctuation Method (WTF)

The groundwater level fluctuation method (WTF) is mainly used in arid and semiarid areas to estimate the ETg for groundwater by analysing the groundwater level changes in unconfined aquifers [49,50]. The calculation formula is as follows:
E T g = S y Δ H Δ h Δ t
where Sy is the specific yield, ∆H is the change in the groundwater table in the period in cm, ∆h is the lateral runoff in cm, and ∆t is the time interval in days.
Evapotranspiration of groundwater is caused by the combined action of soil evaporation and plant transpiration. The capillary rise flux of the groundwater table [51] is mainly affected by soil texture and the distribution of plant roots. According to this research, the upward flux of capillary water should eventually be consumed by evapotranspiration, regardless of lateral soil loss. Therefore, Equation (1) can be simplified to:
E T g = S y Δ Z y Δ t
where ∆Zy is the seasonal decline in the groundwater table in cm.
The mean apparent specific yield Sya was used in this research, and it can be calculated from the parameters of the van Genuchten model [52]:
S y a = S y u S y u 1 + z i + z f 2 n 1 1 n
S y u = θ r θ s
where θs and θr are the saturated soil moisture content and residual soil moisture content in cm3·cm−3, respectively; zi and zf are the initial and final depths of the groundwater table in cm; and α and n are the parameters of the van Genuchten model. The model parameters are shown in Table 2.
Equation (10) was used to calculate the average apparent specific yield Sya. When the groundwater table fluctuates in the 0–10 m soil layer, the soil temperature increases.
S y a ¯ = i = 1 m Δ h i S y i i = 1 m Δ h i i = 1 , 2 , , m
where Syi is the specific yield of the corresponding soil layer and ∆hi is the fluctuation amplitude of the groundwater table in the i-th layer in cm.
Soil texture affects the water yield Sy and thus the accuracy of the ETg estimation in the WTF model [27,53]. Therefore, we considered the impacts of different soil textures on water yield Sy, as shown in Table 3.The soil texture in the study area was mainly sandy.

3.3. Data Preprocessing and Model Evaluation

3.3.1. Data Preprocessing

The accuracy β of the Bowen specific energy balance method determines its accuracy in estimating the latent heat flux β. When it approaches −1, the λET tends to infinity, which has no practical physical significance. Therefore, the calculation of latent heat flux λET using the measured Bowen ratio required data preprocessing and the selection of the Bowen ratio data. Research shows that the unreasonable data range for the value of β is dynamic [54] and mainly related to the water pressure gradient and the resolution of the sensor. Under the condition that the energy flux directionality of the Bowen ratio data is correct, the accuracy of the temperature and humidity probes can be used to construct the β value, and the dynamic rejection field β error limit is ε; thus, we obtain:
ε = δ β = β Δ T δ Δ T + β Δ e δ Δ e = γ Δ e δ Δ T + γ Δ T ( Δ e ) 2 δ Δ e
When β is close to −1, it can be seen that:
ε δ Δ e + γ δ Δ T Δ e
The accuracies of the temperature and humidity probes used in this study (δT and δe) were 0.1 °C and 0.02 kPa, respectively. Thus:
ε = 0.0026 Δ e
Therefore, the β = −1 ± ε functions constitute β as shown in the figure; the shaded part represents the β value rejection field. As can be seen from Figure 6, the β value rejection domain mainly changes due to changes in the water pressure gradient ∆e. When ∆e → 0, β is larger in the reject field near −1.

3.3.2. Model Validation

In this research, the correlation coefficient (R), root-mean-square error (RMSE), bias, and index of agreement (IOA) were used to estimate the performance of the actual evapotranspiration model.
R = i = 1 N M i M ¯ G i G ¯ i = 1 N M i M ¯ 2 i = 1 N G i G ¯ 2
R M A E = i = 1 N M i G i 2 N
B I A S = i = 1 N M i G i N
I O A = 1 i = 1 N M i G i 2 i = 1 N M i G ¯ + G i G ¯ 2
where Mi and Gi are the estimated value and the measured value in the model, respectively; the subscript i indicates the i-th sample; N is the total number of data pairs in the study period; and M ¯ and G ¯ are the means of the estimated values and the measured values in the model, respectively.

4. Results and Analysis

4.1. Meteorological Conditions and Observation Data

4.1.1. Meteorological Conditions

According to the in situ observation of Bowen ratio meteorological data in the study area, the annual mean P for the studied years was 177.93 mm, and the average annual P in the main growth season was 126.58 mm (Figure 2a), representing 70.84% of the yearly average precipitation. The rainfall was unevenly distributed in time. The interannual high temperature Tmax was 48.74 °C, the extreme low temperature Tmin was −44.11 °C, and the annual mean temperature T was 8.15 °C. The surface soil temperature in the study area was usually above the average temperature, and the annual average surface soil temperature T(topsoil) was 12.43 °C (Figure 2b). The annual mean wind speed at 2 m WS was 1.55 m/s, with a maximum wind speed WS of 4.4 m/s (Figure 2c). The trends for the air temperature, surface soil temperature, and wind speed in the study area were consistent, showing a unique peak curve throughout the year. Relative humidity RH showed a trend opposite to those of air temperature, surface soil temperature, and wind speed, with the peak appearing in December each year (Figure 2d).

4.1.2. Distribution of the Haloxylon ammodendron Root System

The H. ammodendron forest in the study area has developed roots. According to the layered excavation method, the root distribution range is 0~10 m, and the depth of the main roots reaches 8.33 m, with 66.6% of the roots being distributed in the 0~4 m soil layer (Figure 4a). The surface H. ammodendron roots mainly absorb the soil water transformed by precipitation infiltration, while the deeper taproot distribution provides conditions for the development of deep absorption roots (root diameter < 2 mm), which in turn provides the conditions for H. ammodendron vegetation to use groundwater. Moreover, some studies have shown that, when the groundwater level drops, H. ammodendron continues to extend downwards to ensure its survival due to its water-seeking tendency [10,54]. Xu and Li [5] noted that, when the groundwater depth was 18 m, the distribution of the water-absorbing roots of H. ammodendron could still be observed with the excavation method.

4.1.3. Soil Water Capillarity Support

Soil capillary action drives the movement from groundwater to soil water. The rise height of the capillary determines whether the capillary water can recharge the soil layers available to plants, directly affecting the survival of desert vegetation [8,9]. Soil water capillarity support is the only way for soil to be supplied with groundwater. From the determination of the intersection position of the maximum molecular water capacity line and the average soil moisture content of the profile, the maximum rise height of the capillary water was judged to be 600 cm (Figure 4b). In the distribution range of the H. ammodendron root system, although less precipitation can penetrate into the middle and deep layers of the soil and the level of infiltration of surface precipitation is low, H. ammodendron can still absorb the water in the soil through its strong root system via the support of capillarity (Figure 4). This finding is consistent with the conclusion provided by Xu et al. [35]; that is, the distribution of vegetation roots affects capillarity, allowing H. ammodendron vegetation to absorb shallow groundwater and survive.

4.1.4. Groundwater Depth Fluctuation

Groundwater is considered the main water source for desert vegetation growth in arid areas [17,18,19,20]. The interannual groundwater depth in the study area decreased in a fluctuating manner. From 2015 to 2018, the groundwater depth decreased by an average of 11.21 cm per year, and the maximum decline increased to 15.75 cm per year (Figure 5). The main period of groundwater consumption in the year is from May to October, and the period of groundwater recharge is from October to May of the following year (Figure 5). The recovery of groundwater depth was found to be mainly achieved through the melting and recharge of snow in winter and the lateral recharge from farmland flood irrigation in spring and winter near the desert–oasis transition zone, which is consistent with the conclusions of other scholars [55,56]. The period of decline for the groundwater depth is mainly from May to October. The reason for the decline is that this period is the main growing season for vegetation. The evapotranspiration associated with vegetation is strong. The main water source for vegetation is groundwater. ETg leads to significant losses of groundwater reserves. When the water moves upwards from the capillary zone, it supplements the soil water reserves. At this time, soil water storage becomes exhausted through surface evaporation and root water absorption [8,9].

4.2. β Value Exclusion Domain

After calculation and screening of λET and H with the Bowen Ratio Data Screening Program in MATLAB software, the total numbers of micrometeorological data samples for the Bowen ratio in 2015, 2016, 2017, and 2018 were 52,561, 51,912, 52,552, and 56,880, respectively. There was some missing data for the weather monitoring over the year, and the data qualification rates were 81.82%, 77.50%, 76.37%, and 78.4%, respectively (Table 4). Finally, the eliminated data were supplemented using a linear interpolation formula; accurate Bowen ratio data could be obtained, and the energy balance closure for the filtered data was significantly improved.

4.3. Energy Balance and Evapotranspiration

4.3.1. Energy Composition and Proportions

The Bowen ratio closed better than the energy balance after data screening [49], which provided the conditions for the subsequent accurate calculation of the important term in the water balance—the actual evapotranspiration ETa.
The energy components of the shuttle were different in different growth and development periods. The peak latent heat flux λET and net radiation Rn appeared in the summer dormancy period, and a trend for the sensible heat flux was not obvious in any of the growth periods. The negative value for the soil heat flux G in the late stage of branch growth indicated that the soil heat released to the atmosphere began to freeze [57]. The proportions of the energy budget in the growing season were as follows: latent heat flux > sensible heat flux > soil heat flux. As an important component of the energy flux, latent heat was the main contributor to water consumption in the growing season [58,59], with its energy balance accounting for 70.16~91.86%, while the sensible heat flux energy balance accounted for 15.80~33.52%. The proportions of soil heat flux were −15.47~9.377% (Table 5).

4.3.2. Evapotranspiration

The precipitation in the main growing season of the vegetation in the study area was low, and the evapotranspiration was high. The annual average potential evapotranspiration reached as high as 1237.50 mm, while the annual average actual evapotranspiration ETa was 260.87 mm, and the annual precipitation accounted for 68.21% of the actual evapotranspiration (Figure 7). Precipitation cannot meet the needs of desert vegetation growth [54]. Therefore, in desert–oasis transition zones without irrigation or water replenishment, groundwater is considered to be important for the survival of vegetation [18,20]. However, this is only true for deep-rooted vegetation, such as shuttles; furthermore, in June and July, when the temperature is relatively hot, plants with shallow roots die [16]. Other scholars have also noted that short-lived plants are significantly affected by precipitation and that groundwater evapotranspiration has no significant effect on plants with shallow roots [38].

4.4. Groundwater Evapotranspiration

The water balance method and groundwater level fluctuation method were used to estimate the evapotranspiration ETg for H. ammodendron vegetation from groundwater. The validation accuracy R of the model was greater than 0.96, the RMSE range was 1.5931~4.5706, the bias range was −0.15~−0.11, and the IOA value was greater than 0.95. The model estimation accuracy was high (Table 6).
Table 7 shows the components of the water balance during the growth period of H. ammodendron. The annual average value for the actual evapotranspiration ETa during the growth period of H. ammodendron was 244.32 mm, the annual average value of precipitation in the growth period was 145.24 mm, and the precipitation accounted for 59.44% of the actual evapotranspiration. The average annual value for ETg from groundwater evapotranspiration in the growth period was 182.35 mm (Table 7), and the contribution rate for groundwater reached up to 74.78%. The results of the water balance method were relatively underestimated compared to those of the WTF model. In summary, it was concluded that groundwater is an important component of the water balance in the desert–oasis transition zone and the main water source for the growth and survival of H. ammodendron vegetation [12,25,60].

5. Discussion

5.1. Error Analysis

The error sources for the Bowen ratio energy balance method include: (1) Errors in RnG. The observation accuracy for RnG is mainly reflected in the measurement accuracy for Rn. When we control the observation accuracy for ΔT and Δe, we can ensure that the calculation errors generated by them are as small as possible, so the observation accuracy for RnG must be strictly controlled and must be kept within 5~10%; (2) ΔT and Δe observation errors. If the accuracy of the temperature and humidity gradienter is improved to σΔT = 0.05 °C and σΔe = 0.05 hPa, the calculation error in the Bowen energy balance model can be greatly reduced under conditions of non-advection or advection inversion; (3) The turbulence exchange coefficient (KH) and water vapour exchange coefficient (KW). The most basic assumption of the general Bowen ratio energy balance model is currently KH = KW. Although a large number of studies suggest that KH = KW, there are also studies that show that KH < KW under stable conditions; (4) The influence of the length of the wind wave zone under advection and inversion conditions. The wind wave zone refers to the length range of the horizontal uniformity in the windward direction from the observation point. It is generally believed that the length of the wind wave zone should be more than 100 times the installation height of the temperature and humidity sensor and anemometer.

5.2. Uncertainty Analysis

The uncertainty analysis was undertaken by calculating the relative error limit of ETa. For the effects of RnG and β on the measured evapotranspiration value, it was assumed that the error caused by Rn was 5% Rn, and the error caused by G was 30% G [55].
δ β = β Δ T δ Δ T + β Δ e δ Δ e = γ Δ e δ Δ T + γ Δ T Δ e 2 δ Δ e = β δ Δ T Δ T + δ Δ e Δ e
δ β β = δ Δ T Δ T + δ Δ e Δ e
δ R n + δ G R n G = 0.05 R n + 0.3 G R n G
δ E T E T = δ R n + δ G R n G + δ β 1 + β
The Bowen ratio energy balance method is an indirect method for calculating latent heat flux, and its accuracy is affected by RnG. The uncertainty of RnG was analysed by calculating the relative error limit for the available energy, as shown in Figure 8. Between 6:00 and 20:00 in the daytime, the relative uncertainty of RnG was below 50%; the uncertainty at noon was about 20% and that at 18:00 was only 10%. The uncertainty at night was greater than 100%, making it unfavourable for the accurate determination of evapotranspiration. The change rule for ETa uncertainty is consistent with that for RnG uncertainty. It can be seen that whether RnG can be accurately measured determines the measurement accuracy for ETa to a certain extent. According to the uncertainty analysis for β, during the day (11:00~16:00), the uncertainty was less than 20%, and at night, it increased to about 40%. The uncertainty of β had two peaks in the morning (8:00) and evening (20:00) at respectively 65% and 56%. During those times, the uncertainty of β had a great impact on the uncertainty of ETa, which restricted the accurate estimation of ETa.

6. Summary and Conclusions

The Bowen specific energy closure after data screening was high, and the calculated annual ETa value was relatively reliable, with a value of 260.87 mm. As the main contributor to water consumption in the growing season, latent heat accounted for 70.16~91.86% of the energy balance. The study found that precipitation had no significant impact on water consumption for H. ammodendron vegetation growth, and the precipitation in the main growing season accounted for 59.44% of the ETa. From 2015 to 2018, the groundwater depth in the study area showed a decreasing trend each year, and the groundwater depth had a significant impact on the growth of H. ammodendron vegetation. Although the groundwater depth in the study area was greater than 9 m, ETg, as an important part of the water balance, participated in the evapotranspiration process associated with the H. ammodendron forest due to the strong root system and supporting capillary water in the soil. The actual evapotranspiration ETa in the desert–oasis transition zone in the main growing season was 244.32 mm, and the contribution rate for ETg was as high as 74.78% or approximately 182.35 mm.
In the verification of ETg using the water balance method and the groundwater level fluctuation method (WTF), R was greater than 0.96, the RMSE range was 1.5931~4.5706, the bias range was −0.15~0.11, and the IOA value was greater than 0.95. The accuracy of the estimation model was high, and the results were relatively accurate. The Bowen energy balance method has better applicability for H. ammodendron forests in transitional desert–oasis zones. The model can be applied in desert–oasis transition zones to obtain accurate ETg estimations and can provide theoretical guidance and a scientific basis for local water resource management and ecological protection.

Author Contributions

Conceptualization, P.J. and S.-J.H.; methodology, P.J.; software, P.J.; validation, P.J.; formal analysis, P.J.; investigation, P.J. and S.-J.H.; resources, S.-J.H.; data curation, P.J. and S.-J.H.; writing—original draft preparation, P.J.; writing—review and editing, P.J. and S.-J.H.; supervision, S.-J.H. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key R&D Program of China (No. 2021YFD1900801), the National Natural Science Foundation of China (No. 41671032), and the special fund project of Xinjiang Water Conservancy Science and Technology (nos YF2020-08 and XSKJ-2021-05).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Map of China (a), City and County Map of Northern Xinjiang (b) and Landcover in Northern Xinjiang (ESA 10 m land cover data (c). Drawing review no.: GS (2020) no. 4619.
Figure 1. Map of China (a), City and County Map of Northern Xinjiang (b) and Landcover in Northern Xinjiang (ESA 10 m land cover data (c). Drawing review no.: GS (2020) no. 4619.
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Figure 2. Precipitation (a), air temperature and soil temperature (b), wind speed (c), and relative humidity (d) in the study area from 2015 to 2018 (P, precipitation in mm/d; T, temperature in °C; WS, wind speed in m/s; RH, the relative humidity in %).
Figure 2. Precipitation (a), air temperature and soil temperature (b), wind speed (c), and relative humidity (d) in the study area from 2015 to 2018 (P, precipitation in mm/d; T, temperature in °C; WS, wind speed in m/s; RH, the relative humidity in %).
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Figure 3. (a,b) Distribution of the H. ammodendron root system in the test area.
Figure 3. (a,b) Distribution of the H. ammodendron root system in the test area.
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Figure 4. Monitoring of soil moisture in the H. ammodendron root zone.
Figure 4. Monitoring of soil moisture in the H. ammodendron root zone.
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Figure 5. Dynamic diagram of groundwater burial depth. The red lines are the annual mean value for the groundwater depth, the red dotted line is the trend line for the annual groundwater level fluctuation, and the points represent the groundwater level monitoring depths.
Figure 5. Dynamic diagram of groundwater burial depth. The red lines are the annual mean value for the groundwater depth, the red dotted line is the trend line for the annual groundwater level fluctuation, and the points represent the groundwater level monitoring depths.
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Figure 6. Rejection domain dynamic graph.
Figure 6. Rejection domain dynamic graph.
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Figure 7. Reference evapotranspiration ET0 and actual evapotranspiration ETa for H. ammodendron vegetation.
Figure 7. Reference evapotranspiration ET0 and actual evapotranspiration ETa for H. ammodendron vegetation.
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Figure 8. Uncertainty analysis for the determination of ET using the Bowen ratio energy balance method.
Figure 8. Uncertainty analysis for the determination of ET using the Bowen ratio energy balance method.
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Table 1. Gap analysis of land use/land cover in Gurbantunggut Desert area in northern Xinjiang in 2020 (unit: 104 km2).
Table 1. Gap analysis of land use/land cover in Gurbantunggut Desert area in northern Xinjiang in 2020 (unit: 104 km2).
Land CoverGurbantunggut DesertNorthern XinjiangAbsolute GapRelative Gap
Tree cover0.000,2091.392,5741.392,3656663
Shrubland0.011,0530.380,5420.369,48934
Grassland0.731,08416.607,70315.876,61923
Cropland0.051,9194.151,7474.099,82880
Built-up0.002,2250.282,1210.279,896127
Bare/sparse vegetation9.004,97419.013,85410.00,8882
Snow and ice00.309,6460.309,646-
Permanent water bodies0.013,8850.468,0680.454,18334
Herbaceous wetland0.000,4150.062,4350.06,202150
Moss and lichen0.000,0121.777,6391.777,627148,137
Table 2. Basic physical parameters of soil.
Table 2. Basic physical parameters of soil.
Soil Depth (cm)Soil Particle SizeSoil TextureSBDθrθsθcθfcθhcαnKs
Clay (0~0.002 mm)Silt (0.002~0.05)Sand (0.05~2 mm)
0~201.085.7693.16Sand1.580.04650.38270.380.220.020.03763.1716582.70
20~401.6312.3186.061.580.03900.38880.380.230.020.04352.2.48208.71
40~601.106.9092.001.580.04520.38400.390.260.030.03862.9900491.38
60~801.217.0791.721.530.04510.38400.390.260.030.03872.9415470.20
80~1001.237.0791.711.560.04510.38400.390.260.030.03872.9384468.97
100~1501.238.7989.881.500.04290.38570.410.290.030.04032.6814359.66
Average1.267.9890.761.560.04400.38490.390.250.030.03763.1716582.70
Note(s): SBD is the dry bulk density of soil in g·cm−3; θr is the residual soil moisture content in cm3·cm−3; θs is the saturated moisture content of soil in cm3·cm−3; θc is the capillary water holding capacity in cm3·cm−3; θfc is the field water holding capacity in cm3·cm−3; θhc is the moisture absorption coefficient in cm3·cm−3; α is a parameter in the soil water characteristic function in cm−1; n is another parameter in the soil moisture characteristic function; Ks is the saturated hydraulic conductivity in cm·day−1.
Table 3. Water contents of different soil textures.
Table 3. Water contents of different soil textures.
Soil TypeθsθrDepths CompensatedSy
Sand0.3910.35–0.360.47
Sandy loam0.3450.31–0.320.38
Loam0.3520.24–0.250.25
Silt loam0.3830.18–0.190.19
Silt0.4260.16–0.180.16
Table 4. β value rejection domain types and data disqualification rates.
Table 4. β value rejection domain types and data disqualification rates.
Typeβ Value Exclusion Domain2015201620172018
ab (%)ab (%)ab (%)ab (%)
ARn − G > 0 Δ e > 0 β < 1 + ε 15562.969591.8520413.973701.2
BRn − G > 0 Δ e < 0 β > 1 + ε 29055.53627712.0916153.149583.11
CRn − G < 0 Δ e > 0 β > 1 ε 45868.7335646.87847316.48494516.05
DRn − G < 0 Δ e < 0 β < 1 + ε 5080.978821.70230.04260.08
Amounting to955518.1811,68222.501215223.63665621.6
Note(s): a indicates the number of nonconforming data samples and b the nonconforming rate.
Table 5. The change in energy flux during the phenophase of H. ammodendron in 2015–2016. Rn is the net radiation, λET is the latent heat flux, H is the sensible heat flux, and G is the geothermal flux.
Table 5. The change in energy flux during the phenophase of H. ammodendron in 2015–2016. Rn is the net radiation, λET is the latent heat flux, H is the sensible heat flux, and G is the geothermal flux.
YearsPhenological PeriodTime SlotDaysEnergy Component (MJ· m 2 · d 1 Proportion of Energy Balance (%)β
HλETRnGH/RnλET/RnG/Rn
2015Early budding12 April–22 April1122.0168.8598.137.2722.4370.167.414.97
Budding23 April–30 April818.8167.7094.367.8519.9371.758.321.90
Assimilating branch growth1 May–14 May1517.2668.2094.318.8418.3072.319.371.92
Late summer dormancy15 May–24 September13220.2291.16116.284.8917.3978.404.211.33
Late shoot growth25 September–29 October258.3148.3152.59−4.0315.8091.86−7.662.1
2016Early budding18 May–2 May1521.5168.0494.685.1322.7271.865.424.15
Budding3 May–12 May1017.3668.9091.765.5018.9275.095.992.01
Assimilating branch growth13 May–22 May1019.4077.95101.94.5519.0476.504.471.94
Late summer dormancy23 May–10 October14120.1088.34112.794.3517.8278.323.861.26
Late shoot growth11 October–2 November2315.1437.0245.17−6.9933.5281.96−15.473.69
Table 6. Verification of ETg using water balance method and groundwater level fluctuation method (WTF).
Table 6. Verification of ETg using water balance method and groundwater level fluctuation method (WTF).
YearPhenological PeriodValidation Indicators
RRMAEBiasIOA
2015Early budding0.97804.5706−0.150.9585
Budding0.96692.1989−0.110.9507
Assimilating branch growth0.97184.5657−0.110.9548
Late summer dormancy0.97712.7667−0.140.9831
Late shoot growth0.96833.6789−0.110.9654
2016Early budding0.96463.7134−0.120.9771
Budding0.97032.3849−0.120.9688
Assimilating branch growth0.97132.4510−0.140.9719
Late summer dormancy0.96543.2468−0.130.9570
Late shoot growth0.96381.5931−0.110.9892
Table 7. Water balance in the H. ammodendron growth period.
Table 7. Water balance in the H. ammodendron growth period.
YearPhenological PeriodTime SlotDaysP (mm)ET0 (mm)ETa (mm)ΔS (mm)ETg (mm)ETg (WTF) (mm)
2015Early budding12 April–22 April1014.049.1111.5343.8241.3544.1
Budding23 April–30 April80.045.818.9737.9246.8949.4
Assimilating branch growth1 May–14 May150.585.3415.6432.5447.6848.11
Late summer dormancy15 May–24 September13297.5874.97189.54−79.8112.2313.38
Late shoot growth25 September–29 October2513.071.7816.7123.8127.5227.09
Total1901251127.02242.3858.28175.67182.08
2016Early budding18 May–2 May1450.465.7417.1156.0722.7823.56
Budding3 May–12 May100.054.3415.6640.3956.0560.01
Assimilating branch growth13 May–22 May1013.655.3617.0241.5144.9345.27
Late summer dormancy23 May–10 October14197.6849.69190.90−76.8216.4818.77
Late shoot growth11 October–2 November233.825.895.5739.3241.0944.12
Total198165.471051.02246.26100.47181.33191.73
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Jiao, P.; Hu, S.-J. Estimation of Evapotranspiration in the Desert–Oasis Transition Zone Using the Water Balance Method and Groundwater Level Fluctuation Method—Taking the Haloxylon ammodendron Forest at the Edge of the Gurbantunggut Desert as an Example. Water 2023, 15, 1210. https://doi.org/10.3390/w15061210

AMA Style

Jiao P, Hu S-J. Estimation of Evapotranspiration in the Desert–Oasis Transition Zone Using the Water Balance Method and Groundwater Level Fluctuation Method—Taking the Haloxylon ammodendron Forest at the Edge of the Gurbantunggut Desert as an Example. Water. 2023; 15(6):1210. https://doi.org/10.3390/w15061210

Chicago/Turabian Style

Jiao, Ping, and Shun-Jun Hu. 2023. "Estimation of Evapotranspiration in the Desert–Oasis Transition Zone Using the Water Balance Method and Groundwater Level Fluctuation Method—Taking the Haloxylon ammodendron Forest at the Edge of the Gurbantunggut Desert as an Example" Water 15, no. 6: 1210. https://doi.org/10.3390/w15061210

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