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Journal of Arid Land
Journal of Arid Land  2026, Vol. 18 Issue (5): 774-792    DOI: 10.1016/j.jaridl.2026.05.003     CSTR: 32276.14.JAL.20250544
Research article     
Spatiotemporal patterns of meteorological-soil moisture drought propagation on the Qinghai-Xizang Plateau, China
WANG Zegen1,*(), CHEN Mi1,2, LIU Yaxin1, RAN Yaowen1, YONG Zhiwei1,3, HUANG Yaoxuan1, WANG Yingyang1, ZHAO Junxin4
1 School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
2 Technology Innovation Center for Remote Sensing Monitoring of Natural Resources in Southwest China Mountains, Ministry of Natural Resources, Chengdu 610100, China
3 School of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China
4 School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China
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Abstract  

Drought is among the most destructive and recurrent natural disasters worldwide. In recent decades, the frequency of drought events has increased, exerting significant impacts on socioeconomic development. The propagation of meteorological drought (MD) to soil moisture drought (SMD) is a common natural process; however, its dynamics across different seasons and vegetation types on the Qinghai-Xizang Plateau, as well as the underlying meteorological driving mechanisms, remain insufficiently understood. This study utilized precipitation and soil moisture data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5)-Land reanalysis dataset for the period 1982-2022. The standardized precipitation index (SPI) and standardized soil moisture index (SSMI) were employed to characterize MD and SMD, respectively. By integrating run theory with an optimal parameter geographical detector (OPGD) model, this study systematically analyzed the average duration and propagation time of MD and SMD across the Qinghai-Xizang Plateau, and quantitatively evaluated the explanatory power of various meteorological and topographical factors influencing drought propagation. The results indicated that the mean duration of SMD across the Qinghai-Xizang Plateau from 1982 to 2022 was generally longer than that of MD. Significant seasonal differences in propagation time were observed, with the average propagation time ranked as winter (21 d)>spring (14 d)>autumn (10 d)>summer (8 d). Spatial variability of propagation time was more pronounced in spring and winter than in summer and autumn. Furthermore, the analysis of driving mechanisms revealed that drought propagation from MD to SMD on the Qinghai-Xizang Plateau was primarily influenced by precipitation (relative contribution proportion of 51.9%), followed by evaporation (15.1%) and snowmelt (13.6%), with the strongest interaction effects associated with precipitation. Although the dominant factors across different vegetation types were generally consistent with those for the entire plateau, solar radiation also showed a relatively high contribution (average 13.9%) across vegetation types. In summary, this study provides a scientific basis for improving drought early warning systems and optimizing water resource management strategies.

Key words： meteorological drought      soil moisture drought      drought propagation time      run theory      optimal parameter geographical detector (OPGD) model      standardized precipitation index (SPI)      standardized soil moisture index (SSMI)     
Received: 31 October 2025      Published: 31 May 2026
Corresponding Authors: *WANG Zegen (E-mail: zegen01@126.com)
About author: Author contributions

Conceptualization: WANG Zegen, CHEN Mi, HUANG Yaoxuan; Methodology: WANG Zegen, CHEN Mi, LIU Yaxin, RAN Yaowen, YONG Zhiwei, HUANG Yaoxuan; Supervision: WANG Zegen; Writing - original draft: CHEN Mi; Writing - review & editing: WANG Zegen, RAN Yaowen, YONG Zhiwei, WANG Yingyang; Research: CHEN Mi; Data management: LIU Yaxin; Software: ZHAO Junxin. All authors approved the manuscript.

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WANG Zegen
CHEN Mi
LIU Yaxin
RAN Yaowen
YONG Zhiwei
HUANG Yaoxuan
WANG Yingyang
ZHAO Junxin
Cite this article:

WANG Zegen, CHEN Mi, LIU Yaxin, RAN Yaowen, YONG Zhiwei, HUANG Yaoxuan, WANG Yingyang, ZHAO Junxin. Spatiotemporal patterns of meteorological-soil moisture drought propagation on the Qinghai-Xizang Plateau, China. Journal of Arid Land, 2026, 18(5): 774-792.

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http://jal.xjegi.com/10.1016/j.jaridl.2026.05.003     OR     http://jal.xjegi.com/Y2026/V18/I5/774

Fig. 1 Topography (a) and vegetation type distribution (b) of the Qinghai-Xizang Plateau. DEM, digital elevation model.
Fig. 2 Standardized Taylor diagram of daily total precipitation. RMSD, root mean square deviation.
Fig. 3 Technical flowchart of this study. MD, meteorological drought; SMD, soil moisture drought; SPI, standardized precipitation index; SSMI, standardized soil moisture index; OPGD, optimal parameter geographical detector; IQR, interquartile range.
Fig. 4 Schematic diagram of run theory: illustration of MD to SMD propagation events
Fig. 5 Distribution of MD and SMD interannual (a and b) and seasonal (c-j) durations on the Qinghai-Xizang Plateau from 1982 to 2022. Spring, March-May; summer, June-August; autumn, September-November; winter, December-February.
Fig. 6 Spatial patterns of annual (a) and seasonal (b-e) average frequencies of MD to SMD propagation events on the Qinghai-Xizang Plateau from 1982 to 2022
Fig. 7 Propagation time from MD to SMD on the Qinghai-Xizang Plateau from 1982 to 2022. (a), spatial distribution of average propagation time from MD to SMD; (b), probability density function of MD to SMD propagation time; (c), propagation time from MD to SMD for different vegetation types.
Fig. 8 Spatial distribution of propagation time from MD to SMD across different seasons on the Qinghai-Xizang Plateau from 1982 to 2022. (a), spring; (b), summer; (c), autumn; (d), winter.
Fig. 9 Propagation time from MD to SMD for different vegetation types and seasons. (a), probability density function of propagation time from MD to SMD across different seasons; (b), propagation time from MD to SMD across different seasons for various vegetation types.
Fig. 10 Single-factor explanatory power (a) and contribution to different vegetation types (b)
Fig. 11 Single-factor explanatory power and factor interaction across different vegetation types. (a), forest; (b), grassland; (c), shrub; (d), meadow.
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