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Journal of Arid Land
Journal of Arid Land  2026, Vol. 18 Issue (8): 1354-1377    DOI: 10.1016/j.jaridl.2026.08.004    
Research article     
Ecozone-dependent reorganization of multifactor interactions controls actual evapotranspiration across arid mountain-basin systems
MA Zhenrong1,2, WANG Yongdong1,2,*(), ZHOU Zhibin1,2, CHEN Yusen1,2
1 National Engineering Research Center for Desert-Oasis Ecological Construction Technology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Xinjiang 830011, China
2 University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract  

Actual evapotranspiration (ETa) is a key regulator of land-atmosphere water and energy exchanges in arid regions. However, the coupled effects of its controlling factors along mountain-basin gradients remain poorly quantified, limiting precise water resource management. Based on multi-source data (2001-2022) from Xinjiang Uygur Autonomous Region, China, this study analyzed the spatiotemporal patterns of ETa across five eco-zones: the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, and Kunlun Mountains. Using Mann-Kendall and Theil-Sen trend analyses, GeoDetector, and Random Forest model, we quantified the contributions and interactions of seven environmental drivers: air temperature, precipitation, soil moisture, wind speed, net radiation, vapour pressure deficit, and leaf area index. The multi-year mean ETa over Xinjiang was 193.15(±21.16) mm/a, showing a distinct ''high in mountains and low in basins'' spatial pattern. A significant increasing trend of 1.60 mm/a was observed regionally, yet with clear spatial divergence, i.e., ETa increased in the major mountain ranges but decreases or stabilized in the hyper-arid Tarim Basin and transitional Junggar Basin. Water-supply factors (precipitation and soil moisture) explained about 40% of the spatial variance individually, while factor interactions enhanced the explanatory power substantially (maximum interaction gain (Δq)=0.18), indicating that bivariate and nonlinear combinations captured ETa patterns more effectively than single driver. Interaction regimes were strongly ecozone-dependent and varied between wet and dry years. Through comparing the wet year (2003) with the dry year (2022), we found a reorganization of dominant interaction pairs: the hyper-arid Tarim Basin shifted from hydrothermal control to vegetation-water-stress coupling; the Junggar Basin maintained persistent vegetation-energy coupling; the Altay and Tianshan Mountains strengthened vegetation-mediated interactions during drought; and the Kunlun Mountains transition from hydrothermal to moisture-aerodynamic control. These results clarify how complex topography and vegetation physiology modulate macroclimatic forcing in this arid mountain-basin system. These findings offer a process-based foundation for ecohydrological zoning, and provide critical insights for adaptive water ecosystem management and sustainable development in response to the "warming and wetting" trend in global drylands.

Key words： actual evapotranspiration      arid region      mountain-basin systems, GeoDetector      Random Forest model     
Received: 12 January 2026      Published: 31 August 2026
Corresponding Authors: *WANG Yongdong (E-mail: wangyd@ms.xjb.ac.cn)
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Conceptualization, methodology, software, formal analysis, investigation, data curation, visualization, and writing - original draft preparation: MA Zhenrong; Writing - review and editing: WANG Yongdong; ZHOU Zhibin, CHEN Yusen; Supervision: CHEN Yushen. All authors approved the manuscript.
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MA Zhenrong
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MA Zhenrong, WANG Yongdong, ZHOU Zhibin, CHEN Yusen. Ecozone-dependent reorganization of multifactor interactions controls actual evapotranspiration across arid mountain-basin systems. Journal of Arid Land, 2026, 18(8): 1354-1377.

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http://jal.xjegi.com/10.1016/j.jaridl.2026.08.004     OR     http://jal.xjegi.com/Y2026/V18/I8/1354

Fig. 1 Division of ecozones (R1-R5) of the study area, China. DEM, digital elevation model. The figure is based on the standard map (GS(2024)0650) provided by the National Platform for Common Geospatial Information Services (http://bzdt.ch.mnr.gov.cn/), and the boundary of the base map has not been modified.
No. Ecozone name Geographic name Landscape
R1 Altay-western Junggar mountain forest-steppe zone Altay Mountains Mountain
R2 Junggar Basin desert zone Junggar Basin Basin
R3 Tianshan mountain forest-steppe zone Tianshan Mountains Mountain
R4 Tarim Basin-eastern Xinjiang desert zone Tarim Basin Basin
R5 Pamir-Kunlun-Altun alpine frigid desert-steppe zone Kunlun Mountains Mountain
Table 1 Classification of the five ecozones
Dataset Data product Spatial resolution Temporal resolution Source
Administrative boundary Xinjiang administrative boundary (GS(2024)0650) - - Tianditu (http://
cloudcenter.tianditu.gov.cn)
Ecozones R1-R5 ecozones - - Peking University GeoData (http://geodata.pku.edu.cn)
ETa PML_V2 v0.1.8
(coupled ET and GPP) via GEE		 500 m (resampled to 1 km) 8-d, monthly, and annual scales GEE (Zhang et al., 2016; Gan et al., 2018; Zhang et al., 2019)
Meteorology ERA5-Land monthly means
(T, P, SM, Wind, Rn, and VPD)		 Native grid (resampled to 1 km) Monthly Copernicus Data Store (Muñoz-Sabater et al., 2021)
Drought index HSPEI 1 km Multi-scale (1/12-month) 10.57760/sciencedb.ecodb.00090
Topography DEM 30 m (resampled to 1 km) - USGS/NASA LP DAAC (Farr et al., 2007)
Vegetation MOD15A2H.061 500 m (resampled to 1 km) 8-d GEE/LP DAAC
Table 2 Information of the data used in the study
Data range Trend Probability (P') ΔP'
SPEI≥2.00 Extremely wet (EW) 0.977-1.000 0.023
1.50≤SPEI≤1.99 Severely wet (SW) 0.933-0.977 0.044
1.00≤SPEI≤1.49 Moderately wet (MW) 0.841-0.933 0.092
-0.99≤SPEI≤0.99 Near normal (N) 0.159-0.841 0.682
-1.49≤SPEI≤ -1.00 Moderate drought (MD) 0.067-0.159 0.092
-1.99≤SPEI≤ -1.50 Severe drought (SD) 0.023-0.067 0.044
SPEI≤ -2.00 Extreme drought (ED) 0.000-0.023 0.023
Table 3 Classification of dry and wet periods
Description Interaction
q12<min(q1, q2) Weakened and nonlinear
min(q1, q2)<q12<max(q1, q2) Weakened and univariate
max(q1, q2)<q12<q1+q2 Enhanced and bivariate
q12=q1+q2 Independent
q12>q1+q2 Enhanced and nonlinear
Table 4 Categories of interactions between two factors and their interactive relationship
Fig. 2 Spatial and temporal patterns of actual evapotranspiration (ETa) in Xinjiang (XJ) and its five ecozones during 2001-2022. (a), ETa; (b), ETa trend; (c-f), annual ETa time series and linear trends for XJ as a whole and its five ecozones (R1-R5).
Fig. 3 Inter-annual variation of standardized precipitation evapotranspiration index (SPEI) in XJ and its five ecozones during 2001-2022
Fig. 4 Spatial patterns and monthly trends of SPEI (a and c) and ETa (b and d) in 2003 (the wet year) and 2022 (the dry year). ED, extreme drought; SD, severe drought; MD, moderate drought; N, near normal; MW, moderately wet. The details of classification criteria are shown in Table 3.
Fig. 5 Multi-year mean GeoDetector contributions of individual driver across to the spatial heterogeneity of ETa in XJ and its five ecozones during 2001-2022. P, precipitation; SM, soil moisture; Rn, net radiation; T, temperature; LAI, leaf area index; VPD, vapor pressure deficit.
Fig. 6 Contribution of GeoDetector to ETa drivers in XJ (a) and its ecozones (b-f) during 2001-2022
Fig. 7 Interaction strength and type between ETa drivers in XJ (a) and its five ecozones (b-f) during 2001-2022
Fig. 8 Contributions of GeoDetector to intra-annual ETa drivers for XJ (a) and its five ecozones (b-f) in 2003 (the wet year)
Fig. 9 Interaction strength and type between ETa driver in XJ (a) and its five ecozones (b-f) in 2003 (the wet year)
Fig. 10 Comparison of the contribution of GeoDetector and Random Forest (RF) models to ETa drivers for XJ (a1 and b1) and its five ecozones (a2-a6 and b2-b6) in 2003 (the wet year) and 2022 (the dry year). +, positive association; -, negative association; ±, no significant directional association; *, P<0.05 level; ▲, positive accumulated local effect (ALE); ▼, negative ALE.
Region and ecozone Period No. of model R2val R2OOB Average sample size
XJ Annual (2003-2022) 20 0.779±0.052 0.781±0.051 100,000
Monthly (2003, 2022) 24 0.790±0.072 0.792±0.075 100,000
R1
(Altay Mountains)		 Annual (2003-2022) 20 0.898±0.013 0.888±0.014 100,000
Monthly (2003, 2022) 24 0.868±0.105 0.860±0.097 100,000
R2
(Junggar Basin)		 Annual (2003-2022) 20 0.868±0.022 0.890±0.018 100,000
Monthly (2003, 2022) 24 0.870±0.057 0.878±0.056 100,000
R3
(Tianshan Mountains)		 Annual (2003-2022) 20 0.868±0.039 0.872±0.039 100,000
Monthly (2003, 2022) 24 0.865±0.050 0.860±0.049 100,000
R4
(Tarim Basin)		 Annual (2003-2022) 20 0.708±0.049 0.700±0.050 100,000
Monthly (2003, 2022) 24 0.765±0.113 0.767±0.105 100,000
R5
(Kunlun Mountains)		 Annual (2003-2022) 20 0.763±0.078 0.761±0.081 100,000
Monthly (2003, 2022) 24 0.786±0.106 0.786±0.106 100,000
Table S1 Predictive performance metrics of Random Forest (RF) model for actual evaporation (ETa) across Xinjiang (XJ) and its five ecozones (R1-R5)
Fig. S1 Contribution of permutation importance (PI) from Random Forest (RF) model to actual evaporation (ETa) drivers for Xinjiang (XJ; a) and its ecozones (b-f) during 2001-2022. P, precipitation; SM, soil moisture; Rn, net radiation; T, temperature; LAI, leaf area index; VPD, vapor pressure deficit.
Region and ecozone ρ ALE vs. risk-detector
XJ 0.54 [0.36, 0.71] 71 [57, 71]
R1 0.64 [0.51, 0.75] 67 [60, 75]
R2 0.45 [0.37, 0.71] 80 [76, 100]
R3 0.48 [0.37, 0.71] 69 [67, 83]
R4 0.41 [0.29, 0.54] 83 [67, 96]
R5 0.43 [0.29, 0.53] 75 [53, 79]
Table S2 Consistency between RF and GeoDetector for ETa drivers across XJ and its five ecozones during 2001-2022
Fig. S2 Contributions of GeoDetector to intra-annual ETa drivers for XJ (a) and its five ecozones (b-f) in 2022 (the dry year)
Fig. S3 Pairwise interactions between ETa drivers in XJ (a) and its five ecozones (b-f) in 2022 (the dry year)
Fig. S4 Consistency between GeoDetector and RF model in the strength (ρ; Spearman's rank correlation) and direction (agreement rate) of monthly ETa drivers for XJ (a1 and b1) and its five ecozones (a2-a6 and b2-b6) in 2003 (the wet year) and 2022 (the dry year)
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