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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (12): 1664-1685    DOI: 10.1007/s40333-024-0112-1    
Research article     
Estimation of evapotranspiration from artificial forest in mountainous areas of western Loess Plateau based on HYDRUS-1D model
LU Rui1,2, ZHANG Mingjun1,2,*(), ZHANG Yu1,2, QIANG Yuquan1,2, CHE Cunwei1,2, SUN Meiling1,2, WANG Shengjie1,2
1College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
2Key Laboratory of Resource Environment and Sustainable Development of Oasis, Gansu Province, Lanzhou 730070, China
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Abstract  

Evapotranspiration is the most important expenditure item in the water balance of terrestrial ecosystems, and accurate evapotranspiration modeling is of great significance for hydrological, ecological, agricultural, and water resource management. Artificial forests are an important means of vegetation restoration in the western Loess Plateau, and accurate estimates of their evapotranspiration are essential to the management and development of water use strategies for artificial forests. This study estimated the soil moisture and evapotranspiration based on the HYDRUS-1D model for the artificial Platycladus orientalis (L.) Franco forest in western mountains of Loess Plateau, China from 20 April to 31 October, 2023. Moreover, the influence factors were identified by combining the correlation coefficient method and the principal component analysis (PCA) method. The results showed that HYDRUS-1D model had strong applicability in portraying hydrological processes in this area and revealed soil water surplus from 20 April to 31 October, 2023. The soil water accumulation was 49.64 mm; the potential evapotranspiration (ETp) was 809.67 mm, which was divided into potential evaporation (Ep; 95.07 mm) and potential transpiration (Tp; 714.60 mm); and the actual evapotranspiration (ETa) was 580.27 mm, which was divided into actual evaporation (Ea; 68.27 mm) and actual transpiration (Ta; 512.00 mm). From April to October 2023, the ETp, Ep, Tp, ETa, Ea, and Ta first increased and then decreased on both monthly and daily scales, exhibiting a single-peak type trend. The average ratio of Ta/ETa was 0.88, signifying that evapotranspiration mainly stemmed from transpiration in this area. The ratio of ETa/ETp was 0.72, indicating that this artificial forest suffered from obvious drought stress. The ETp was significantly positively correlated with ETa, and the R2 values on the monthly and daily scales were 0.9696 and 0.9635 (P<0.05), respectively. Furthermore, ETa was significantly positively correlated with temperature, solar radiation, and wind speed, and negatively correlated with relative humidity and precipitation (P<0.05); and temperature exhibited the highest correlation with ETa. Thus, ETp and temperature were the decisive contributors to ETa in this area. The findings provide an effective method for simulating regional evapotranspiration and theoretical reference for water management of artificial forests, and deepen understanding of effects of each influence factors on ETa in arid areas.

Key words： potential evapotranspiration      actual evapotranspiration      evaporation      transpiration      HYDRUS-1D model      Loess Plateau      soil water content     
Received: 13 July 2024      Published: 31 December 2024
Corresponding Authors: *ZHANG Mingjun (mjzhang2004@163.com)
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LU Rui
ZHANG Mingjun
ZHANG Yu
QIANG Yuquan
CHE Cunwei
SUN Meiling
WANG Shengjie
Cite this article:

LU Rui, ZHANG Mingjun, ZHANG Yu, QIANG Yuquan, CHE Cunwei, SUN Meiling, WANG Shengjie. Estimation of evapotranspiration from artificial forest in mountainous areas of western Loess Plateau based on HYDRUS-1D model. Journal of Arid Land, 2024, 16(12): 1664-1685.

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http://jal.xjegi.com/10.1007/s40333-024-0112-1     OR     http://jal.xjegi.com/Y2024/V16/I12/1664

Fig. 1 Geographical location, landscape, and climate of the Jiaoyuting Forestry Farm. (a and b), location of the Jiaoyuting Forestry Farm in the Loess Plateau; (c), unmanned aerial vehicle (UAV) landscape of the Jiaoyuting Forestry Farm; (d), daily temperature, precipitation, and relative humidity during the study period. The flight height of the UAV is 200.00 m.
Plot Plant height (m) Canopy area (m2) Basal diameter (m) Soil water content (cm3/cm3)
S1 4.14 4.32 12.04 5.95
S2 4.62 3.06 13.22 7.49
S3 4.96 2.32 13.97 5.90
S4 5.33 2.16 15.89 5.76
S5 5.40 2.00 14.32 5.59
Average 4.89 2.77 13.89 6.14
Table 1 Information on forest stand survey and soil moisture content
Soil depth (cm) Clay (%) Silt (%) Sand (%) Soil bulk density (g/cm3)
10.00 8.50 70.58 20.93 1.10
30.00 7.90 71.80 20.30 1.12
50.00 8.75 70.19 21.05 1.17
70.00 7.97 70.88 21.15 1.25
90.00 6.45 70.20 23.36 1.19
110.00 6.31 67.92 25.76 1.10
130.00 7.02 67.90 25.08 1.03
150.00 6.28 66.35 27.38 0.94
170.00 6.64 66.69 26.68 1.05
190.00 6.10 66.43 27.48 1.18
Table 2 Soil physical property at different depths in the Platycladus orientalis (L.) Franco forest
Soil depth (cm) Qr (cm3/cm3) Qs (cm3/cm3) α (/cm) σ Ks (cm/d) l
10.00 0.0585 0.3210 0.0038 1.1860 90.80 0.5
30.00 0.0657 0.4770 0.0041 1.3590 122.40 0.5
50.00 0.0695 0.3260 0.0040 1.2225 100.04 0.5
70.00 0.0723 0.3740 0.0044 1.2230 76.61 0.5
90.00 0.0723 0.3280 0.0042 1.1784 107.29 0.5
110.00 0.0745 0.3570 0.0039 1.2358 152.43 0.5
130.00 0.0687 0.3790 0.0036 1.2310 189.56 0.5
150.00 0.0669 0.3990 0.0040 1.2276 157.50 0.5
170.00 0.0698 0.4010 0.0041 1.2354 180.31 0.5
190.00 0.0752 0.4270 0.0039 1.2431 112.95 0.5
Table 3 Value of model parameter at different soil depths
Fig. 2 Comparison of simulated and observed values of soil water content
Statistic index Soil depth (cm)
10.00 30.00 50.00 70.00 90.00 110.00 130.00 150.00 170.00 190.00
R2 0.7186 0.9604 0.9706 0.6023 0.5036 0.6843 0.8394 0.9326 0.9069 0.9458
Root mean square error (RMSE) 0.0188 0.0060 0.0038 0.0074 0.0066 0.0058 0.0052 0.0016 0.0015 0.0017
Table 4 Statistic results of simulation verification
Model parameter Change rate of soil water content (%)
D(10.00%) D(20.00%) D(30.00%) D(-10.00%) D(-20.00%) D(-30.00%)
Qr 1.10 2.16 3.27 -1.07 -2.06 -3.15
Qs 7.56 15.06 15.06 -7.87 -14.89 -23.21
α -0.48 -0.94 -1.35 -1.35 1.47 2.49
σ -17.46 -29.02 -36.57 31.51 - -
Ks -0.19 -0.38 -0.42 0.22 0.51 0.77
l 0.10 0.18 0.24 -0.13 -0.20 -0.31
Table 5 Average change rate of simulation results under different parameter perturbations
Fig. 3 Change rate of simulation results under different parameter perturbations. (a), Qr; (b), Qs; (c), α; (d), σ; (e), Ks; (f), l. Qr, soil residual water content; Qs, soil saturated water content; Ks, saturated hydraulic conductivity; α, reciprocal of the soil intake value; σ, soil pore-size distribution parameter; l, pore-connectivity parameter. The change rates of simulation results were same when the perturbation degree of Qs was at 20.00% and 30.00%, so the Figure 3b only shows five perturbations. The change rates of simulation results were same when the perturbation degree of α was at -10.00% and 30.00%, so the Figure 3c also shows five perturbations. Due to the limitation of parameter value range, perturbation degree of σ cannot set to -20.00% and -30.00%, the Figure 3d only shows four perturbations.
Fig. 4 Soil water budget and dynamic changes simulated by HYDRUS-1D model. (a), soil water accumulation; (b), soil water consumption; (c), variation of soil water storage (ΔW); (d), change of soil water content. P, amount of precipitation; Gr, upward recharge of deep soil water below the rhizosphere; I, amount of irrigation; ETa, actual evapotranspiration; Dr, deep percolation.
Month P (mm) I (mm) Gr (mm) Dr (mm) ETa (mm) ΔW (mm)
April 12.40 0.00 15.88 0.75 18.39 9.13
May 30.20 0.00 23.81 1.93 80.93 -28.85
June 34.20 0.00 27.39 5.92 90.61 -34.94
July 59.00 14.30 37.74 11.22 111.53 -11.71
August 31.80 14.30 41.49 15.39 117.23 -45.03
September 123.00 0.00 75.54 20.89 92.03 85.62
October 70.40 0.00 93.41 18.85 69.54 75.42
Total 361.00 28.60 315.26 74.95 580.27 49.64
Table 6 Soil water balance of the P. orientalis forest from April to October in 2023
Fig. 5 Dynamic change of evapotranspiration on the monthly (a and b) and daily (c and d) scales. ETp, potential evapotranspiration; Ep, potential evaporation; Tp, potential transpiration; Ea, actual evaporation; Ta, actual transpiration.
Fig. 6 Relationship between ETp and ETa on the monthly (a) and daily (b) scales. The light blue area that is symmetric on both sides of function represents the 95% confidence interval.
Fig. 7 Variation of ETa/ETp on the monthly (a) and daily (b) scales
Fig. 8 Principal component analysis (PCA) diagram of influence factors of ETa. The ellipse represents 95.00% confidence ellipse, and its color is same with the points of the same month. PC, principal component.
Fig. 9 Correlation analysis between ETa and environmental factors on the daily scale. (a), temperature; (b), relative humidity; (c), precipitation; (d), solar radiation; (e), wind speed; (f), soil water content. The light blue area that is symmetric on both sides of function represents the 95% confidence interval.
Fig. 10 PCA load diagram of influence factors of ETa/ETp on the monthly (a) and daily (b) scales
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