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Journal of Arid Land
Journal of Arid Land  2026, Vol. 18 Issue (11): 1893-1918    DOI: 10.1016/j.jaridl.2026.11.003    
Research article     
Lagged surface soil salinization responses to groundwater dynamics in the Yinchuan Plain: Stage dependence and spatial differentiation
HAN Xu1, ZHOU Jianwei1,2,*(), GONG Yongfeng3, LI Ran1, LI Jun1, YUE Yanan4
1 School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
2 Key Laboratory of Mine Ecological Effects and Systematic Restoration, Ministry of Natural Resources, Beijing 100081, China
3 Ningxia Hui Autonomous Region Land and Resources Survey and Monitoring Institute, Yinchuan 750002, China
4 Institute of Natural Resources Survey, China University of Geosciences, Wuhan 430074, China
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Abstract  

Shallow groundwater may influence surface soil salinization in arid irrigated plains, but the timing and spatial form of this association remain uncertain. Consequently, this study aimed to identify the monthly timing, seasonal asymmetry, and spatial differentiation of surface soil salinization responses associated with shallow groundwater dynamics. We combined monthly groundwater depth records from 116 monitoring wells (January 2021-August 2024) in the Yinchuan Plain, northwestern China, with multiple other datasets, including groundwater hydrochemical data, surface soil salinity samples, multi-source remote sensing images, model interpretability diagnostics, response-time sensitivity tests, and representative profile observations. A hierarchical classifier was trained using surface soil salinity samples collected in August 2022 and tested externally using independent samples collected in August 2025. The resulting monthly salinization class sequence was used as a model-generated regional indicator. Shallow, relatively saline groundwater was concentrated in the lacustrine-alluvial plain (shallow), whereas severe salinization was mainly found in the localized shallow groundwater areas. A regional class area analysis showed a decrease in the severe salinization area proportion accompanied by an increase in the moderate salinization area proportion within 1.00-3.00 months after the onset of summer irrigation-related groundwater shallowing. Approximately 4.00-5.00 months after the onset of winter irrigation-related groundwater shallowing, a delayed winter-spring resalinization signal was detected. Sensitivity tests showed that the separation between earlier summer responses and later winter responses persisted across the tested cycle subsets and operational settings. The water-salt coupling zone accounted for 56.97% of the classified area, and three response-based groundwater-control forms were identified: threshold-like depth, water-salt sensitive response zone, and groundwater-control instability form. These findings support salinity management that considers both the subregional setting and seasonal stage. The predicted 4.00- to 5.00-month delay in winter-spring resalinization should be understood as an integrated winter-spring resalinization indicator rather than a direct groundwater transit time. More broadly, this response-based paradigm extends regional research on groundwater-related surface soil salinization beyond static depth thresholds by explicitly accounting for temporal lags, seasonal stages, and hydrogeological heterogeneity.

Key words： shallow groundwater      groundwater depth      soil salinity      irrigation      random forest (RF)      gradient tree boosting (GTB)      Yinchuan Plain     
Received: 24 April 2026      Published: 30 November 2026
Corresponding Authors: *ZHOU Jianwei (E-mail: jw.zhou@cug.edu.cn)
About author: Author contributions

Conceptualization: HAN Xu, ZHOU Jianwei; Data curation: HAN Xu, GONG Yongfeng, LI Ran; Formal analysis: HAN Xu; Funding acquisition: ZHOU Jianwei; Investigation: HAN Xu, GONG Yongfeng, LI Ran, LI Jun, YUE Yanan; Methodology: HAN Xu, ZHOU Jianwei; Project administration: ZHOU Jianwei; Resources: GONG Yongfeng, ZHOU Jianwei; Software: HAN Xu; Supervision: ZHOU Jianwei; Validation: HAN Xu, LI Ran, LI Jun; Visualization: HAN Xu; Writing - original draft preparation: HAN Xu; Writing - review and editing: HAN Xu, ZHOU Jianwei, GONG Yongfeng, LI Ran, LI Jun, YUE Yanan. All authors approved the manuscript.

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HAN Xu
ZHOU Jianwei
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Cite this article:

HAN Xu, ZHOU Jianwei, GONG Yongfeng, LI Ran, LI Jun, YUE Yanan. Lagged surface soil salinization responses to groundwater dynamics in the Yinchuan Plain: Stage dependence and spatial differentiation. Journal of Arid Land, 2026, 18(11): 1893-1918.

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http://jal.xjegi.com/10.1016/j.jaridl.2026.11.003     OR     http://jal.xjegi.com/Y2026/V18/I11/1893

Fig. 1 Spatial distribution of groundwater occurrence zones in the Yinchuan Plain
Dataset Temporal coverage Spatial support/sample size Analytical role Interpretive boundary
Shallow groundwater depth monitoring data January 2021-
August 2024		 Monthly records derived from automatic
monitoring at 116 wells		 Generation of monthly groundwater depth fields and analysis of zonal groundwater dynamics
and irrigation-related shallowing phases		 Interpolation uncertainty depended on monitoring well density and spatial coverage, both of which varied among groundwater occurrence zones
Groundwater hydrochemical
data		 August 2021 and August 2024 n=72 (2021) and n=78 (2024) Classification of hydrochemical facies and comparison of TDS and groundwater salinity
among groundwater occurrence zones		 The two August campaigns can capture spatial hydrochemical differences rather than seasonal hydrochemical dynamics
Surface soil salinity model samples
(0-20 cm; model development dataset)		 August 2022 n=144 samples retained after quality control and predictor matching Salinization class label assignment and model development Representing the irrigation regime in August and growing season setting rather than the winter- spring salt-return period
Surface soil salinity model samples
(0-20 cm; external evaluation dataset)		 August 2025 n=92 samples retained
after quality control and predictor matching		 Independent cross-year evaluation under comparable conditions
in August		 Providing an August-based cross-year test: performance differed between the occurrence and severity tasks and did not represent the complete seasonal performance of the 2021-2024 monthly sequence
Multi-source
remote sensing and terrain predictors		 January 2021-
August 2024 for monthly optical and SAR predictors; static for DEM- derived predictors		 Study area coverage on a harmonized 30 m
analytical grid		 Generation of model- derived monthly salinization classes and class area proportions Outputs represented modeled salinization class indicators rather than direct monthly measurements of continuous soil salt content
Ancillary soil medium and geomorphic information Time-invariant spatial layers used
for the January 2021-August 2024 analysis		 Five groundwater occurrence zones and
four soil medium
settings		 Stratification and interpretation of spatially differentiated water-salt response forms Not quantitatively separating all non-groundwater controls
Profile monitoring
of groundwater
depth and soil EC, water content, and temperature		 Daily observations collected during 2024-2025 and averaged by
calendar month		 Two farmland profiles under shallow and medium-deep
groundwater conditions, with automatic groundwater depth monitoring and soil EC, water content, and temperature observations
at 5, 20, 40, 60, 80, and
100 cm depths		 Process-level examination of vadose zone water-salt redistribution and the timing relationship
between groundwater
depth and near-surface salinity		 Providing process- consistent support but not independently validating every regional zone or response form
Table 1 Characteristics, analytical roles, and interpretive boundaries of the datasets used in this study
Fig. 2 Spatial distribution of groundwater depth monitoring wells, groundwater hydrochemical sampling sites, and surface soil sampling sites in the Yinchuan Plain. (a), 116 groundwater depth monitoring wells; (b), groundwater hydrochemical sampling sites in August 2021 (72 samples) and August 2024 (78 samples); (c), surface soil salinity model samples retained after quality control and predictor matching in August 2022 (144 samples) and August 2025 (92 samples). The background shows the groundwater occurrence zones used for zonal analyses.
Fig. 3 Mean monthly groundwater depth of the five groundwater occurrence zones in the Yinchuan Plain during January 2021-August 2024. Calendar month means were calculated from the 43 available monthly gridded surfaces within this period, and April 2021 was excluded because monitoring data were unavailable during equipment inspection and maintenance.
Groundwater occurrence zone Mean shallow groundwater depth during January 2021-August 2024 (m) Median TDS in August 2021 (mg/L) Median TDS in
August 2024 (mg/L)
Piedmont sloping plain 3.96 2689 1430
Lacustrine-alluvial plain (shallow) 1.88 3838 3590
Lacustrine-alluvial plain (medium-deep) 3.41 2527 1411
Yellow River floodplain 3.06 2897 1530
Aeolian sandy land 4.63 1303 891
Table 2 Mean groundwater depth and median TDS concentrations in the five groundwater occurrence zones of the Yinchuan Plain
Fig. 4 Spatial distribution of surface soil salinization classes in the Yinchuan Plain in April (a) and August (b) 2022
Fig. 5 Model interpretability and sensitivity diagnostics for surface soil salinization classification and response-time metrics. (a and b), permutation importance of predictors retained in the gradient tree boosting (GTB) occurrence and random forest (RF) severity models, respectively; (c), leave-one-cycle robustness of leaching response time (LRT) and delayed resalinization time (DRT) estimates for different soil salinization classes under summer and winter irrigation periods; (d), sensitivity across 32 distinct operational settings (64 nominal runs) for different soil salinization classes under summer and winter irrigation periods. Macro-F1, macro-averaged F1 score; NDSI-K, normalized difference salinity index proposed by Khan et al. (2005); VH, vertical-transmit/horizontal-receive polarization; B8, Band 8; ASM, angular second moment; NDSI-S, normalized difference salinity index based on the near-infrared and shortwave-infrared-1 bands; SR B7, Landsat Band 7 shortwave-infrared-2 surface reflectance; NDVI, normalized difference vegetation index; SI2, salinity index 2; sin(aspect), sine-transformed terrain aspect. In Figure 5a and b, bars represent mean macro-F1 loss after predictor permutation; black error bars indicate ±1 standard deviation across spatial block cross-validation folds; blue, orange, and green denote optical, Sentinel-1, and digital elevation model (DEM)-derived predictors, respectively. In Figure 5c, dark blue circles show baseline means, light blue circles show leave-one-cycle means, and gray lines show their minimum-maximum (min-max) ranges. In Figure 5d, purple and orange points show scenario-specific LRT and DRT means, respectively, black circles show scenario medians, and capped gray lines show min-max ranges. Each setting-specific mean used the identified cycles: three or four summer cycles and three winter cycles. Within each category, points were offset horizontally only to reduce overlap.
Classification task Predictor Predictor group Mean permutation importance Mean MDI
L1 occurrence NDSI-K Optical 0.010 0.016
L1 occurrence Sentinel-2 B8 ASM Optical 0.005 0.022
L1 occurrence Sentinel-1 VH contrast Sentinel-1 0.010 0.020
L1 occurrence DEM elevation DEM 0.008 0.026
L2 severity NDSI-S Optical 0.041 0.020
L2 severity Sentinel-1 VH variance Sentinel-1 0.011 0.017
L2 severity sin(aspect) DEM -0.001 0.010
L2 severity SI2 Optical 0.037 0.011
L2 severity SR B7 Optical 0.039 0.011
L2 severity NDVI Optical 0.038 0.016
Table 3 Feature-importance diagnostics for the hierarchical surface soil salinization classification framework
Fig. 6 Class-specific LRT and DRT and severe salinization area proportion offsets during summer and winter irrigation-related phases in the Yinchuan Plain. (a), LRT under summer irrigation and DRT under winter irrigation shown separately for moderate and severe salinization indicators, as well as cycle-specific monthly response times derived from extrema in class area proportions; (b), mean offsets for the severe salinization indicator, expressed in percentage points. The median offsets were -10.49 percentage points for summer irrigation-related phases and 17.52 percentage points for winter irrigation-related phases.
Irrigation period Timing metric Response indicator Response direction Mean time (months) Median time (months) Time range (months) Mean offset (pp) Median offset (pp)
Summer irrigation LRT Moderate salinization area proportion Peak 1.75 2.00 1.00-2.00 - -
Summer irrigation LRT Severe salinization area proportion Trough 1.75 1.50 1.00-3.00 -8.98 -10.49
Winter irrigation DRT Moderate salinization area proportion Trough 4.33 4.00 4.00-5.00 - -
Winter irrigation DRT Severe salinization area proportion Peak 4.67 5.00 4.00-5.00 16.24 17.52
Table 4 Baseline LRT, DRT, and severe salinization area proportion offsets during summer and winter irrigation- related phases
Irrigation period Indicator Baseline cycle values (months) Baseline mean (months) Leave-one mean range (months) Leave-one median range (months)
Summer irrigation Moderate salinization 1.00, 2.00, 2.00, and 2.00 1.75 1.67-2.00 2.00-2.00
Summer irrigation Severe salinization 1.00, 2.00, 1.00, and 3.00 1.75 1.33-2.00 1.00-2.00
Winter irrigation Moderate salinization 4.00, 5.00, and 4.00 4.33 4.00-4.50 4.00-4.50
Winter irrigation Severe salinization 4.00, 5.00, and 5.00 4.67 4.50-5.00 4.50-5.00
Table 5 Leave-one-cycle sensitivity of baseline LRT and DRT estimates based on moderate and severe salinization responses during summer and winter irrigation-related phases
Irrigation period Indicator Scenario mean range (months) Median scenario mean (months)
Summer irrigation Moderate salinization 1.67-2.00 1.71
Summer irrigation Severe salinization 1.75-2.33 2.00
Winter irrigation Moderate salinization 4.00-5.00 4.33
Winter irrigation Severe salinization 4.33-4.67 4.50
Table 6 Sensitivity of LRT and DRT estimates across 32 distinct operational settings (64 nominal runs) for moderate and severe salinization responses during summer and winter irrigation-related phases
Fig. 7 First-level water-salt regulation zones in the Yinchuan Plain. The water-salt coupling zone was further separated into the saline-alkali background subregion, the irrigated silty soil subregion, the zonal xerophytic subregion, and the aeolian sandy subregion.
First-level water-salt regulation zone Area
(km2)		 Proportion of classified area (%) Interpretation
High salinity steady-state zone 25.88 0.54 Persistently high salinity area with long-term salt accumulation and limited seasonal structural change
Low salinity decoupled zone 750.72 15.70 Persistently low salinity area with weak or limited association between groundwater dynamics and surface soil salinization
Multi-factor composite fluctuation zone 1281.57 26.80 Area where salinization dynamics are jointly affected by multiple factors, and the association between groundwater dynamics and surface soil salinization is less stable or less consistently identifiable
Water-salt coupling zone 2724.46 56.97 Zone with more coherent associations between groundwater dynamics and surface soil salinization, comprising the saline-alkali background, irrigated silty soil, zonal xerophytic, and aeolian sandy subregions
Table 7 Area statistics of the first-level water-salt regulation zones in the Yinchuan Plain
Fig. 8 Representative groundwater-control forms statistically identified in typical water-salt coupling subregions of the Yinchuan Plain. (a), threshold-like response in the aeolian sandy subregion with a high salt source condition during the autumn recession and low salinity maintenance stage; (b), water-salt sensitive response zone in the same subregion during the spring recession and resalinization stage; (c), groundwater-control instability forms in the irrigated silty soil subregion under high salt source conditions during the spring recession and resalinization stage. Open circles represent bin-level mean severe salinization proportions; thin gray whiskers indicate the within-bin 10th-90th percentile range; and blue segments indicate the interquartile range. Solid colored lines in Figure 8a and b represent the smoothed response curves; no fitted line is shown in Figure 8c because no coherent monotonic response was identified. The red dashed line in Figure 8a marks the identified groundwater depth of 2.55 m; the beige band in Figure 8b marks the identified groundwater depth interval of 2.51-2.78 m; the gray band in Figure 8c marks the analyzed groundwater depth interval of 1.67-4.44 m. The severe salinization proportion is expressed as a fraction ranging from 0.00 to 1.00.
Subregion class Salt source
background		 Autumn recession and low salinity
maintenance stage		 Groundwater
depth value
or range (m)		 Spring recession
and resalinization stage		 Groundwater
depth range
(m)
Saline-alkali background subregion Low-to-medium Groundwater-control instability form 1.75-2.63 Water-salt sensitive response zone 1.95-2.44
Saline-alkali background subregion High Water-salt sensitive response zone 2.51-2.97 Water-salt sensitive response zone 2.68-3.02
Irrigated silty soil subregion Low-to-medium Water-salt sensitive response zone 2.19-2.75 Water-salt sensitive response zone 4.25-5.55
Irrigated silty soil subregion High Water-salt sensitive response zone 2.48-2.83 Groundwater-control instability form 1.67-4.44
Zonal xerophytic subregion Low-to-medium Water-salt sensitive response zone 3.77-9.40 Water-salt sensitive response zone 3.71-4.64
Zonal xerophytic subregion High Water-salt sensitive response zone 2.53-2.81 Groundwater-control instability form 1.65-2.40
Aeolian sandy
subregion		 Low-to-medium Water-salt sensitive response zone 4.67-5.78 Water-salt sensitive response zone 2.61-3.11
Aeolian sandy
subregion		 High Threshold-like depth 2.55 Water-salt sensitive response zone 2.51-2.78
Table 8 Statistically identified response-based groundwater-control forms within water-salt coupling subregions of the Yinchuan Plain
Fig. 9 Monthly variations in groundwater depth and surface soil relative electrical conductivity (EC) index (dimensionless) at 5 cm depth at two representative farmland profiles in the Yinchuan Plain. (a), shallow groundwater site in the lacustrine-alluvial plain (shallow); (b), medium-deep groundwater site in the lacustrine-alluvial plain (medium-deep). The double-headed arrow in Figure 9a denotes the 2.00-month offset between the shallowest groundwater depth in November 2024 and the minimum surface soil relative EC index in January 2025.
[1]   Aihaiti A, Nurmemet I, Yu X R, et al. 2025. An enhanced soil salinity estimation method for arid regions using multisource remote sensing data and advanced feature selection. CATENA, 256: 109116, doi: 10.1016/j.catena.2025.109116.
[2]   Aksoy S, Sertel E, Roscher R, et al. 2024. Assessment of soil salinity using explainable machine learning methods and Landsat 8 images. International Journal of Applied Earth Observation and Geoinformation, 130: 103879, doi: 10.1016/j.jag.2024.103879.
[3]   Beltrán J M. 1999. Irrigation with saline water: benefits and environmental impact. Agricultural Water Management, 40(2-3): 183-194.
[4]   Ben-Hur M, Yolcu G, Uysal H, et al. 2009. Soil structure changes: aggregate size and soil texture effects on hydraulic conductivity under different saline and sodic conditions. Australian Journal of Soil Research, 47(7): 688-696.
[5]   Cai Z Z, Wang W K, Zhao M, et al. 2020. Interaction between surface water and groundwater in Yinchuan Plain. Water, 12(9): 2635, doi: 10.3390/w12092635.
[6]   Chen S, Mao X M, Shang S H. 2022. Response and contribution of shallow groundwater to soil water/salt budget and crop growth in layered soils. Agricultural Water Management, 266: 107574, doi: 10.1016/j.agwat.2022.107574.
[7]   Chen X, Hu Q. 2004. Groundwater influences on soil moisture and surface evaporation. Journal of Hydrology, 297(1-4): 285-300.
[8]   Feng Z Z, Wang X K, Feng Z W. 2005. Soil N and salinity leaching after the autumn irrigation and its impact on groundwater in Hetao Irrigation District, China. Agricultural Water Management, 71(2): 131-143.
[9]   Forkutsa I, Sommer R, Shirokova Y I, et al. 2009. Modeling irrigated cotton with shallow groundwater in the Aral Sea Basin of Uzbekistan: II. Soil salinity dynamics. Irrigation Science, 27(4): 319-330.
[10]   Foster S, Pulido-Bosch A, Vallejos Á, et al. 2018. Impact of irrigated agriculture on groundwater-recharge salinity: a major sustainability concern in semi-arid regions. Hydrogeology Journal, 26(8): 2781-2791.
[11]   Fu C, Xue J, Chen J F, et al. 2024. Evaluating spatial and temporal variations of soil water, heat, and salt under autumn irrigation in the Hetao Irrigation District based on distributed SHAW model. Agricultural Water Management, 293: 108707, doi: 10.1016/j.agwat.2024.108707.
[12]   Fujimaki H, Shimano T, Inoue M, et al. 2006. Effect of a salt crust on evaporation from a bare saline soil. Vadose Zone Journal, 5(4): 1246-1256.
[13]   Gerke H H. 2006. Preferential flow descriptions for structured soils. Journal of Plant Nutrition and Soil Science, 169(3): 382-400.
[14]   Gowing J W, Konukcu F, Rose D A. 2006. Evaporative flux from a shallow watertable: the influence of a vapour-liquid phase transition. Journal of Hydrology, 321(1-4): 77-89.
[15]   Guo S H, Li X Y, Šimůnek J, et al. 2024. Experimental and numerical evaluation of soil water and salt dynamics in a corn field with shallow saline groundwater and crop-season drip and autumn post-harvest irrigations. Agricultural Water Management, 305: 109119, doi: 10.1016/j.agwat.2024.109119.
[16]   Holland K L, Jolly I D, Overton I C, et al. 2009. Analytical model of salinity risk from groundwater discharge in semi-arid, lowland floodplains. Hydrological Processes, 23(24): 3428-3439.
[17]   Huang X, Rudolph D L. 2023. Numerical study of coupled water and vapor flow, heat transfer, and solute transport in variably-saturated deformable soil during freeze-thaw cycles. Water Resources Research, 59(10): e2022WR032146, doi: 10.1029/2022WR032146.
[18]   Ibrahimi M K, Miyazaki T, Nishimura T, et al. 2014. Contribution of shallow groundwater rapid fluctuation to soil salinization under arid and semiarid climate. Arabian Journal of Geosciences, 7(9): 3901-3911.
[19]   Ivushkin K, Bartholomeus H, Bregt A K, et al. 2019. Global mapping of soil salinity change. Remote Sensing of Environment, 231: 111260, doi: 10.1016/j.rse.2019.111260.
[20]   Khan N M, Rastoskuev V V, Sato Y, et al. 2005. Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Agricultural Water Management, 77(1-3): 96-109.
[21]   Kong J, Xin P, Hua G F, et al. 2015. Effects of vadose zone on groundwater table fluctuations in unconfined aquifers. Journal of Hydrology, 528: 397-407.
[22]   Li C C, Gao X B, Li S Q, et al. 2020. A review of the distribution, sources, genesis, and environmental concerns of salinity in groundwater. Environmental Science and Pollution Research, 27(33): 41157-41174.
[23]   Li H F, Wang J, Liu H, et al. 2022. Quantitative analysis of temporal and spatial variations of soil salinization and groundwater depth along the Yellow River saline-alkali land. Sustainability, 14(12): 6967, doi: 10.3390/su14126967.
[24]   Li H Y, Zhao G Z, Xie S M, et al. 2024. The mechanism of the influence of different irrigation methods on groundwater recharge. Water, 16(11): 1565, doi: 10.3390/w16111565.
[25]   Li R P, Shi H B, Flerchinger G N, et al. 2012. Simulation of freezing and thawing soils in Inner Mongolia Hetao Irrigation District, China. Geoderma, 173-174: 28-33.
[26]   Liu P F, Zhang G H, Cui S J, et al. 2023. Effects of groundwater level control on soil salinity change in farmland around wetlands in arid areas: a case study of the lower reaches of the Shiyang River Basin, China. Water, 15(7): 1308, doi: 10.3390/w15071308.
[27]   Liu S, Huang Q Z, Ren D Y, et al. 2021. Soil evaporation and its impact on salt accumulation in different landscapes under freeze-thaw conditions in an arid seasonal frozen region. Vadose Zone Journal, 20(2): e20098, doi: 10.1002/vzj2.20098.
[28]   Liu Y L, Jin M G, Wang J J. 2018. Insights into groundwater salinization from hydrogeochemical and isotopic evidence in an arid inland basin. Hydrological Processes, 32(20): 3108-3127.
[29]   Min L L, Shen Y J, Pei H W. 2015. Estimating groundwater recharge using deep vadose zone data under typical irrigated cropland in the piedmont region of the North China Plain. Journal of Hydrology, 527: 305-315.
[30]   Moeck C, Collenteur R A, Berghuijs W R, et al. 2024. A global assessment of groundwater recharge response to infiltration variability at monthly to decadal timescales. Water Resources Research, 60(6): e2023WR035828, doi: 10.1029/2023WR035828.
[31]   Nick S M, Copty N K, Saygın S D, et al. 2024. Impact of soil compaction and irrigation practices on salt dynamics in the presence of a saline shallow groundwater: an experimental and modelling study. Hydrological Processes, 38(3): e15135, doi: 10.1002/hyp.15135.
[32]   Nosetto M D, Jobbágy E G, Jackson R B, et al. 2009. Reciprocal influence of crops and shallow ground water in sandy landscapes of the Inland Pampas. Field Crops Research, 113(2): 138-148.
[33]   Nosetto M D, Acosta A M, Jayawickreme D H, et al. 2013. Land-use and topography shape soil and groundwater salinity in central Argentina. Agricultural Water Management, 129: 120-129.
[34]   Pulido-Bosch A, Rigol-Sanchez J P, Vallejos A, et al. 2018. Impacts of agricultural irrigation on groundwater salinity. Environmental Earth Sciences, 77(5): 197, doi: 10.1007/s12665-018-7386-6.
[35]   Ramos T B, Liu M H, Paredes P, et al. 2023. Salts dynamics in maize irrigation in the Hetao Plateau using static water table lysimeters and HYDRUS-1D with focus on the autumn leaching irrigation. Agricultural Water Management, 283: 108306, doi: 10.1016/j.agwat.2023.108306.
[36]   Ren D Y, Wei B Y, Xu X, et al. 2019. Analyzing spatiotemporal characteristics of soil salinity in arid irrigated agro-ecosystems using integrated approaches. Geoderma, 356: 113935, doi: 10.1016/j.geoderma.2019.113935.
[37]   Rose D A, Konukcu F, Gowing J W. 2005. Effect of watertable depth on evaporation and salt accumulation from saline groundwater. Australian Journal of Soil Research, 43(5): 565-573.
[38]   Salama R B, Otto C J, Fitzpatrick R W. 1999. Contributions of groundwater conditions to soil and water salinization. Hydrogeology Journal, 7(1): 46-64.
[39]   Scudiero E, Skaggs T H, Corwin D L. 2015. Regional-scale soil salinity assessment using Landsat ETM+ canopy reflectance. Remote Sensing of Environment, 169: 335-343.
[40]   Shen Z T, Bai Y G, Zheng M, et al. 2026. Spatial and temporal variations in soil salinity and groundwater in the downstream Yarkant River Irrigation District. Water, 18(1): 11, doi: 10.3390/w18010011.
[41]   Shokri N, Salvucci G D. 2011. Evaporation from porous media in the presence of a water table. Vadose Zone Journal, 10(4): 1309-1318.
[42]   Shokri-Kuehni S M S, Raaijmakers B, Kurz T, et al. 2020. Water table depth and soil salinization: from pore-scale processes to field-scale responses. Water Resources Research, 56(2): e2019WR026707, doi: 10.1029/2019WR026707.
[43]   Sprenger M, Leistert H, Gimbel K, et al. 2016. Illuminating hydrological processes at the soil-vegetation-atmosphere interface with water stable isotopes. Reviews of Geophysics, 54(3): 674-704.
[44]   Sultanov M, Ibrakhimov M, Akramkhanov A, et al. 2018. Modelling end-of-season soil salinity in irrigated agriculture through multi-temporal optical remote sensing, environmental parameters, and in situ information. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 86(5-6): 221-233.
[45]   Sun G F, Zhu Y, Ye M, et al. 2019. Development and application of long-term root zone salt balance model for predicting soil salinity in arid shallow water table area. Agricultural Water Management, 213: 486-498.
[46]   Sun G F, Zhu Y, Gao Z L, et al. 2022. Spatiotemporal patterns and key driving factors of soil salinity in dry and wet years in an arid agricultural area with shallow groundwater table. Agriculture, 12(8): 1243, doi: 10.3390/agriculture12081243.
[47]   Sun Y L, Wang L P, Yang S D, et al. 2025. Optimizing irrigation and drainage practices to control soil salinity in arid agroecosystems: a scenario-based modeling approach using SaltMod. Agronomy, 15(9): 2239, doi: 10.3390/agronomy15092239.
[48]   Tang S Q, She D L, Wang H D. 2021. Effect of salinity on soil structure and soil hydraulic characteristics. Canadian Journal of Soil Science, 101(1): 62-73.
[49]   Tsypkin G G. 2003. Accumulation and precipitation of salts during groundwater evaporation and flow. Fluid Dynamics, 38(6): 900-907.
[50]   Tu C W, Wang W R, Wang W H, et al. 2025. Irrigation suitability and interaction between surface water and groundwater influenced by agriculture activities in an arid plain of Central Asia. Agriculture, 15(15): 1704, doi: 10.3390/agriculture15151704.
[51]   Wang F, Han L L, Liu L L, et al. 2024. Advancements and perspective in the quantitative assessment of soil salinity utilizing remote sensing and machine learning algorithms: a review. Remote Sensing, 16(24): 4812, doi: 10.3390/rs16244812.
[52]   Wang H D, She D L, Cardoso R. 2023. Understanding the effect of seasonal climate variability on the salinity in unsaturated agricultural soil. Agronomy, 13(11): 2802, doi: 10.3390/agronomy13112802.
[53]   Wang J Q, Peng J, Li H Y, et al. 2021. Soil salinity mapping using machine learning algorithms with the Sentinel-2 MSI in arid areas, China. Remote Sensing, 13(2): 305, doi: 10.3390/rs13020305.
[54]   Wang J Z, Ding J L, Yu D L, et al. 2019. Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China. Geoderma, 353: 172-187.
[55]   Wang L F, Hu F S, Yin L H, et al. 2013. Hydrochemical and isotopic study of groundwater in the Yinchuan Plain, China. Environmental Earth Sciences, 69(6): 2037-2057.
[56]   Wang W L, Sun J G. 2024. Estimation of soil salinity using satellite-based variables and machine learning methods. Earth Science Informatics, 17(6): 5049-5061.
[57]   Wang Y G, Deng C Y, Liu Y, et al. 2018. Identifying change in spatial accumulation of soil salinity in an inland river watershed, China. Science of The Total Environment, 621: 177-185.
[58]   Wang Y H, Li S E, Qin S J, et al. 2020. How can drip irrigation save water and reduce evapotranspiration compared to border irrigation in arid regions in Northwest China. Agricultural Water Management, 239: 106256, doi: 10.1016/j.agwat.2020.106256.
[59]   Wu M S, Huang J S, Wu J W, et al. 2016. Experimental study on evaporation from seasonally frozen soils under various water, solute and groundwater conditions in Inner Mongolia, China. Journal of Hydrology, 535: 46-53.
[60]   Xiao C, Ji Q Y, Chen J Q, et al. 2023. Prediction of soil salinity parameters using machine learning models in an arid region of Northwest China. Computers and Electronics in Agriculture, 204: 107512, doi: 10.1016/j.compag.2022.107512.
[61]   Xing X G, Li X W, Ma X Y. 2019. Capillary rise and saliferous groundwater evaporation: effects of various solutes and concentrations. Hydrology Research, 50(2): 517-525.
[62]   Xu X, Huang G H, Sun C, et al. 2013. Assessing the effects of water table depth on water use, soil salinity and wheat yield: searching for a target depth for irrigated areas in the upper Yellow River Basin. Agricultural Water Management, 125: 46-60.
[63]   Xue Q. 2011. Groundwater circulation properties and their changes in Yinchuan Plain. Journal of Geological Hazards and Environment Preservation, 22(1): 56-62. (in Chinese)
[64]   Zhang H R, Fu X, Zhang Y N, et al. 2023. Mapping multi-depth soil salinity using remote sensing-enabled machine learning in the Yellow River Delta, China. Remote Sensing, 15(24): 5640, doi: 10.3390/rs15245640.
[65]   Zhang X D, Ye P, Wu Y J, et al. 2022. Experimental study on simultaneous heat-water-salt migration of bare soil subjected to evaporation. Journal of Hydrology, 609: 127710, doi: 10.1016/j.jhydrol.2022.127710.
[66]   Zhang Y, Wu H Q, Kang Y L, et al. 2024. Mapping the soil salinity distribution and analyzing its spatial and temporal changes in Bachu County, Xinjiang, based on Google Earth Engine and machine learning. Agriculture, 14(4): 630, doi: 10.3390/agriculture14040630.
[67]   Zheng Y H, Yang M Z, Liu H J. 2022. The effects of truncating the capillary fringe on water-table dynamics during periodic forcing. Water Resources Research, 58(1): e2021WR031112, doi: 10.1029/2021WR031112.
[68]   Zhong C H, Yang Q C, Ma H Y, et al. 2019. Application of environmental isotopes to identify recharge source, age, and renewability of phreatic water in Yinchuan Basin. Hydrological Processes, 33(16): 2166-2173.
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