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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (11): 1463-1483    DOI: 10.1007/s40333-024-0034-y    
Research article     
Understanding and simulating of three-dimensional subsurface hydrological partitioning in an alpine mountainous area, China
ZHANG Lanhui*(), TU Jiahao, AN Qi, LIU Yu, XU Jiaxin, ZHANG Haixin
Key Laboratory of West China's Environmental System (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
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Abstract  

Critical zone (CZ) plays a vital role in sustaining biodiversity and humanity. However, flux quantification within CZ, particularly in terms of subsurface hydrological partitioning, remains a significant challenge. This study focused on quantifying subsurface hydrological partitioning, specifically in an alpine mountainous area, and highlighted the important role of lateral flow during this process. Precipitation was usually classified as two parts into the soil: increased soil water content (SWC) and lateral flow out of the soil pit. It was found that 65%-88% precipitation contributed to lateral flow. The second common partitioning class showed an increase in SWC caused by both precipitation and lateral flow into the soil pit. In this case, lateral flow contributed to the SWC increase ranging from 43% to 74%, which was notably larger than the SWC increase caused by precipitation. On alpine meadows, lateral flow from the soil pit occurred when the shallow soil was wetter than the field capacity. This result highlighted the need for three-dimensional simulation between soil layers in Earth system models (ESMs). During evapotranspiration process, significant differences were observed in the classification of subsurface hydrological partitioning among different vegetation types. Due to tangled and aggregated fine roots in the surface soil on alpine meadows, the majority of subsurface responses involved lateral flow, which provided 98%-100% of evapotranspiration (ET). On grassland, there was a high probability (0.87), which ET was entirely provided by lateral flow. The main reason for underestimating transpiration through soil water dynamics in previous research was the neglect of lateral root water uptake. Furthermore, there was a probability of 0.12, which ET was entirely provided by SWC decrease on grassland. In this case, there was a high probability (0.98) that soil water responses only occurred at layer 2 (10-20 cm), because grass roots mainly distributed in this soil layer, and grasses often used their deep roots for water uptake during ET. To improve the estimation of soil water dynamics and ET, we established a random forest (RF) model to simulate lateral flow and then corrected the community land model (CLM). RF model demonstrated good performance and led to significant improvements in CLM simulation. These findings enhance our understanding of subsurface hydrological partitioning and emphasize the importance of considering lateral flow in ESMs and hydrological research.

Key words： subsurface hydrological partitioning      lateral flow      random forest model      community land model (CLM)      alpine mountainous area     
Received: 27 February 2024      Published: 30 November 2024
Corresponding Authors: *ZHANG Lanhui (E-mail: lhzhang@lzu.edu.cn)
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ZHANG Lanhui
TU Jiahao
AN Qi
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ZHANG Haixin
Cite this article:

ZHANG Lanhui, TU Jiahao, AN Qi, LIU Yu, XU Jiaxin, ZHANG Haixin. Understanding and simulating of three-dimensional subsurface hydrological partitioning in an alpine mountainous area, China. Journal of Arid Land, 2024, 16(11): 1463-1483.

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http://jal.xjegi.com/10.1007/s40333-024-0034-y     OR     http://jal.xjegi.com/Y2024/V16/I11/1463

Parameter In situ observation site
Arou Dashalong Dayekou Jingyangling Yakou
Longitude 100°27′E 98°56′E 100°17′E 101°07′E 100°14′E
Latitude 38°03′N 38°50′N 38°33′N 37°50′N 38°00′N
Elevation (m) 3033 3739 2703 3750 4010
Slope (°) 2 0 29 12 4
Aspect (°) 321 101 156 154 221
Vegetation type Subalpine meadow Marsh alpine meadow Grassland Alpine meadow Alpine meadow
Filed capacity (m3/m3) 0.36 0.28 0.40 0.42 0.30
Saturated hydraulic conductivity (cm/h) 0.26 0.29 0.29 0.26 0.29
Total organic carbon (TOC; g/100 g) 5.68 0.69 6.18 6.98 6.52
Sand (%) 28.66 26.87 19.90 45.11 43.81
Silt (%) 61.03 57.66 73.60 42.45 43.60
Clay (%) 10.30 15.47 6.50 12.44 12.58
Soil texture Silt loam Silt loam Silt loam Loam Loam
Observed period 2013-2020 2014-2020 2020-2021 2018-2020 2016-2020
Table 1 Information of five in situ observation sites
Fig. 1 Hourly changes of soil water content (ΔSWC) at five in situ observation sites. (a), Arou; (b), Dashalong; (c), Dayekou; (d), Jingyangling; (e), Yakou. Boxes in the figure indicate the IQR (interquartile range, 75th to 25th of the data). The median value is shown as a line within the box. Red diamond is shown as mean. Outlier is shown as blue circle. Whiskers extend to the most extreme value within 1.5×IQR.
In situ observation site P (mm) ΔSWC (mm) ET/P ratio Runoff ratio ((Rs+RL)/P)
Arou 455.80±110.07 -11.50±25.23 0.7789 0.2463
Dashalong 302.94±51.63 129.19±81.93 0.6852 -0.1116
Dayekou 122.20±8.77 -8.40±25.88 0.9194 0.1493
Jingyangling 162.23±84.00 28.23±33.24 0.3941 0.4319
Yakou 193.42±55.96 8.64±18.24 0.3384 0.6169
Table 2 Annual mean values of hydrological components during growth periods at five in situ observation sites
Fig. 2 Subsurface hydrological partitioning during precipitation process. (a), Arou; (b), Dashalong; (c), Dayekou; (d), Jingyangling; (e), Yakou.
In situ observation site Class 1 Class 2 Class 3
Arou 0.28 0.02 0.70
Dashalong 0.33 0.01 0.65
Dayekou 0.33 0.00 0.67
Jingyangling 0.47 0.05 0.48
Yakou 0.36 0.08 0.56
Table 3 Probabilities of different subsurface hydrological partitioning classes during precipitation process
Fig. 3 Environmental factors on subsurface hydrological partitioning classes during precipitation process. (a), relative importance; (b), Class 1; (c), Class 2; (d), Class 3. SD, saturation degree; NDVI, normalized difference vegetation index; TOC, total organic carbon.
Fig. 4 Subsurface hydrological partitioning during evapotranspiration (ET) process. (a), Arou; (b), Dashalong; (c), Dayekou; (d), Jingyangling; (e), Yakou.
In situ observation site Class 4 Class 5 Class 6
Arou 1.00 0.00 0.00
Dashalong 0.97 0.00 0.02
Dayekou 0.78 0.09 0.12
Jingyangling 0.99 0.01 0.00
Yakou 0.96 0.01 0.03
Table 4 Probabilities of different subsurface hydrological partitioning classes during ET process
Fig. 5 Environmental factors on subsurface hydrological partitioning classes during ET process. (a), relative importance; (b), Class 4; (b), Class 5; (d), Class 6.
Fig. 6 Simulated result of lateral flow through random forest model. (a-d), Arou, Dashalong, Jingyangling, and Yakou sites during precipitation process; (e-i), Arou, Dashalong, Dayekou, Jingyangling, and Yakou sites during ET process. RMSE, root mean square errors.
Fig. 7 Simulated results of ΔSWC and ET by CLM and bias corrected CLM. (a-d), ΔSWC at Arou, Dashalong, Jingyangling, and Yakou sites during precipitation process; (e-i), ET at Arou, Dashalong, Dayekou, Jingyangling, and Yakou during ET process. CLM, community land model.
Fig. 8 SD of antecedent SWC for two subsurface hydrological partitioning classes (Class 1 and Class 3) during precipitation process. (a), Arou; (b), Dashalong; (c), Dayekou; (d), Jingyangling; (e), Yakou. Boxes in the figure indicate the IQR (interquartile range, 75th to 25th of the data). The median value is shown as a line within the box. Outlier is shown as blue or red circle. Whiskers extend to the most extreme value within 1.5×IQR.
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