Assessing future drought evolution and driving mechanisms in the Weigan River Basin under CMIP6 climate scenarios

doi:10.1016/j.jaridl.2026.02.003

Journal of Arid Land

2026, Vol. 18

Issue (2): 235-262 DOI: 10.1016/j.jaridl.2026.02.003

Research article

Assessing future drought evolution and driving mechanisms in the Weigan River Basin under CMIP6 climate scenarios

WANG Wenbo¹^,², LIN Li¹^,²^,^*(

), CHEN Dandan¹^,², YANG Jiayun¹^,²

¹ College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
² Xinjiang Key Laboratory of Hydraulic Engineering Security and Water Disasters Prevention, Urumqi 830052, China

Download:

HTML

PDF(4414KB)
Export: BibTeX | EndNote (RIS)

Abstract

In the northern Tarim River Basin, the Weigan River Basin is a critical endorheic system characterized by extreme aridity, where drought poses a major natural hazard to agricultural production and ecological stability. This study assessed the future evolution of drought under climate change by employing the standardized moisture anomaly index (SZI) on the basis of multi-model the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations under historical conditions (1970-2014) and future scenarios (shared socioeconomic pathway (SSP)1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 for 2015-2100). The results show that precipitation-evapotranspiration anomalies are projected to first decline but then increase over time, with increased fluctuations and uncertainty under high-emission scenarios (SSP5-8.5). These trends indicate intensifying drought risks and reveal a strong influence of emission pathways on regional water cycling. Temporal analysis of SZI indicates a transition from wetting to drying under low- and medium-emission pathways (SSP1-2.6 and SSP2-4.5), whereas high-emission scenarios are characterized by persistent drying and increased variability. The significant lower-tail dependence (0.271) observed under SSP2-4.5 and SSP5-8.5 suggests that extreme droughts may be subject to nonlinear co-amplification across scenarios. The frequency of moderate and more severe drought events is expected to increase substantially, especially under SSP5-8.5, where drought occurrence is predicted to extend into spring and autumn and become more evenly distributed throughout the year. Spatially, drought duration shows significant positive autocorrelation across all scenarios, with hot spots consistently concentrated in the southern and southeastern regions of the basin. Random forest analysis, interpreted as association-based pattern attribution, indicates that meteorological variables (precipitation and potential evapotranspiration (PET)) make the greatest contributions to the hot spot pattern, followed by topography and soil moisture. Among land use categories, farmland generally shows higher drought sensitivity than other land use types, as reflected by its relative contribution patterns across scenarios. The spatial pattern of drought is statistically structured by climatic forcing, surface conditions, and soil moisture status, reflecting their coupled associations with hot spot occurrence. In addition, a drought spatial uncertainty index was constructed from multi-scenario hot spot maps, revealing spatially heterogeneous structural variability throughout the basin. Correlation analysis further highlights strong internal couplings among environmental variables (e.g., elevation-linked hydroclimatic gradients and grassland-bare soil contrasts). These findings offer a scientific basis for developing region-specific drought monitoring and adaptation strategies under future climate change conditions.

Key words： Coupled Model Intercomparison Project Phase 6 (CMIP6) Weigan River Basin standardized moisture anomaly index (SZI) drought characteristics climate change random forest Shapley Additive exPlanations (SHAP)

Received: 30 July 2025 Published: 28 February 2026

Corresponding Authors: ^*LIN Li (E-mail: klmhll@126.com)

	Service
	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	WANG Wenbo
	LIN Li
	CHEN Dandan
	YANG Jiayun

Cite this article:

WANG Wenbo, LIN Li, CHEN Dandan, YANG Jiayun. Assessing future drought evolution and driving mechanisms in the Weigan River Basin under CMIP6 climate scenarios. Journal of Arid Land, 2026, 18(2): 235-262.

URL:

http://jal.xjegi.com/10.1016/j.jaridl.2026.02.003 OR http://jal.xjegi.com/Y2026/V18/I2/235

Fig. 1 Regional overview of the digital elevation model (DEM) (a) and distribution of land use types (b) and soil types (c) in the Weigan River Basin

Table 1 Basic information on the seven global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6)

Table 2 Geographic information of meteorological stations

Table 3 Drought classification using the standardized moisture anomaly index (SZI)

Table 4 Z score thresholds and significance levels used to identify hot/cold spots in the Getis-Ord ${\text{G}}_{i}^{\text{*}}$ analysis

Fig. 2 Taylor diagrams for temperature (a) and precipitation (b) after deviation correction for the single and ensemble modes. Filled circles denote bias-corrected simulations, whereas "+" markers indicate raw simulations before bias correction. CRMSE, centered root mean square error; OBS, observation (reference dataset); MME, multi-model ensemble; SD, standard deviation.

Table 5 Performance evaluation of the multi-model ensemble (MME)-simulated daily meteorological variables

Fig. 3 Scatterplot of calendar-month standardized anomalies (z anomalies) between the basin-mean normalized difference vegetation index (NDVI) and standardized moisture anomaly index (SZI; a) and lagged correlations between NDVI (t) and SZI (t lag) for lags of 0-6 months (b) during 1985-2014. LOESS, locally estimated scatterplot smoothing; ACF, autocorrelation function; q_FDR, Benjamini-Hochberg false discovery rate (BH-FDR) adjusted q-values.

Table 6 Autocorrelation- and multiplicity-controlled lagged correlations between normalized difference vegetation index (NDVI) and SZI anomalies

Fig. 4 Anomaly percentages representing the differences between annual precipitation and potential evapotranspiration (PET) in the Weigan River Basin from 1970 to 2100. The shaded envelopes indicate SD. SSP, shared socioeconomic pathway.

Fig. 5 Fitting diagram from the Bayesian piecewise regression model and interannual variation trend of the SZI under different scenarios in 2015-2100. (a), SSP1-2.6; (b), SSP2-4.5; (c), SSP3-7.0; (d), SSP5-8.5. Shaded areas indicate posterior predictive intervals at multiple credibility levels: the darkest shading corresponds to the 50.00% credible interval, intermediate shading represents the 80.00% credible interval, and the lightest shading denotes the 95.00% credible interval.

Fig. 6 Two-dimensional vine copula model results from the copula joint density (a), lower-tail dependence density for u, v<0.15 (b), and upper-tail dependence density for u, v>0.85 (c) between SSP2-4.5 and SSP5-8.5. tll denotes a Student's t copula with nonzero lower- and upper-tail dependence. AIC, Akaike information criterion; u and v, pseudo-observations (uniform margins) used in copula modeling; λ_L, lower-tail dependence coefficient; λ_U, upper-tail dependence coefficient.

Fig. 7 Standardized regression coefficients of precipitation (β_P) and PET (β_PET) anomalies for explaining historical SZI variability at the monthly and seasonal scales during 1970-2014. β, standardized regression coefficient; ^***, significance at P<0.001 level.

Table 7 Monthly and seasonal regression results for basin-mean SZI with precipitation and potential evapotranspiration (PET) anomalies during 1970-2014

Fig. 8 Monthly and seasonally frequency of moderate and above drought events

Table 8 Results of spatial autocorrelation analysis in the Weigan River Basin under different scenarios

Fig. 9 Spatially aggregated significance distribution of drought duration in the Weigan River Basin from the Getis-Ord ${\text{G}}_{i}^{\text{*}}$ analysis for the historical period (a) and future scenarios SSP1-2.6 (b), SSP2-4.5 (c), SSP3-7.0 (d), and SSP5-8.5 (e)

Table 9 Quantification of statistically significant drought-duration hot spots/cold spots and their persistence across scenarios

Fig. 10 Scenario-wise Shapley Additive exPlanations (SHAP) beeswarm plots of feature attributions for drought hot spot patterns from the random forest classifier (RFC) for the historical period (a) and future scenarios SSP1-2.6 (b), SSP2-4.5 (c), SSP3-7.0 (d), and SSP5-8.5 (e). For clarity, only the top 18 features ranked by mean absolute SHAP values are shown in each panel.

Fig. 11 Spatial distribution of drouth uncertainty (DU; a) and Pearson correlation matrix between DU and potential driving factors (b) across the Weigan River Basin. The magnitude of the ellipse reflects the strength of the relationship; large ellipses imply strong correlations. Blue ellipses denote negative correlations, whereas red ellipses indicate positive correlations. ^*, statistical significance at P<0.05 level.


[1]	Ayantobo O O, Wei J. 2019. Appraising regional multi-category and multi-scalar drought monitoring using standardized moisture anomaly index (SZI): a water-energy balance approach. Journal of Hydrology, 579: 124139, doi: 10.1016/j.jhydrol.2019.124139.

[2]	Behzadi F, Javadi S, Yousefi H, et al. 2024. Projections of meteorological drought severity-duration variations based on CMIP6. Scientific Reports, 14: 5027, doi: 10.1038/s41598-024-55340-x. pmid: 38424157

[3]	Brilleman S L, Howe L D, Wolfe R, et al. 2017. Bayesian piecewise linear mixed models with a random change point: an application to BMI rebound in childhood. Epidemiology, 28(6): 827-833. doi: 10.1097/EDE.0000000000000723 pmid: 28817471

[4]	Cannon A J, Sobie S R, Murdock T Q. 2015. Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes? Journal of Climate, 28(17): 6938-6959. doi: 10.1175/JCLI-D-14-00754.1

[5]	Ding X Y, Yu Y, Yang M L, et al. 2024. Investigating the effect of climate change on drought propagation in the Tarim River Basin using multi-model ensemble projections. Atmosphere, 15(1): 50, doi: 10.3390/atmos15010050.

[6]	Dirmeyer P A. 2011. The terrestrial segment of soil moisture-climate coupling. Geophysical Research Letters, 38(16): L16702, doi: 10.1029/2011GL048268.

[7]	Eyring V, Bony S, Meehl G A, et al. 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5): 1937-1958. doi: 10.5194/gmd-9-1937-2016

[8]	Getis A, Ord J K. 1992. The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3): 189-206. doi: 10.1111/gean.1992.24.issue-3

[9]	Guo H, Jiapaer G, Bao A M, et al. 2017. MPDI-based comparison on spatiotemporal variation of drought over oasis and desert in Tarim Basin. Journal of Desert Research, 37(4): 775-783. (in Chinese)

[10]	Hanby G, Zhai L, Mishra B, et al. 2025. A comprehensive outlook on drought caused economic losses and landowner perceptions concerning drought and erratic rainfall patterns. Forest Policy and Economics, 170: 103405, doi: 10.1016/j.forpol.2024.103405.

[11]	Huang W W, Henderson M, Liu B H, et al. 2024. Cumulative and lagged effects: seasonal characteristics of drought effects on East Asian grasslands. Remote Sensing, 16(18): 3478, doi: 10.3390/rs16183478.

[12]	Ke Y M, Shen Z F, Bai J, et al. 2020. Analysis of suitable scale for cultivated land in the Ugan River Basin accounting for water resource constraints. Arid Zone Research, 37(3): 551-561. (in Chinese)

[13]	Lei X N, Xu C C, Liu F, et al. 2023. Evaluation of CMIP6 models and multi-model ensemble for extreme precipitation over arid Central Asia. Remote Sensing, 15(9): 2376, doi: 10.3390/rs15092376.

[14]	Li H B, Wang W G, Fu J Y, et al. 2023a. Spatiotemporal heterogeneity and attributions of streamflow and baseflow changes across the headstreams of the Tarim River Basin, Northwest China. Science of The Total Environment, 856: 159230, doi: 10.1016/j.scitotenv.2022.159230.

[15]	Li J, Bevacqua E, Wang Z L, et al. 2023b. Hydroclimatic extremes contribute to asymmetric trends in ecosystem productivity loss. Communications Earth & Environment, 4: 197, doi: 10.1038/s43247-023-00869-4.

[16]	Li Q Q, Ye A Z, Wada Y, et al. 2024. Climate change leads to an expansion of global drought-sensitive area. Journal of Hydrology, 632: 130874, doi: 10.1016/j.jhydrol.2024.130874.

[17]	Li S N. 2015. Analysis on evolvement characteristics of drought under changing environment in Aksu River Basin. MSc Thesis. Handan: Hebei University of Engineering. (in Chinese)

[18]	Liu J G, Yang H, Gosling S N, et al. 2017. Water scarcity assessments in the past, present, and future. Earth's Future, 5(6): 545-559. doi: 10.1002/eft2.2017.5.issue-6

[19]	Liu X Y, Li X M, Zhang Z R, et al. 2024. A CMIP6-based assessment of regional climate change in the Chinese Tianshan Mountains. Journal of Arid Land, 16(2): 195-219. doi: 10.1007/s40333-024-0053-8

[20]	Meng X Y, Zhang S, Wang G Q, et al. 2025. Decoding agricultural drought resilience: a triple-validated random forest framework integrating multi-source remote sensing for high-resolution monitoring in the North China Plain. Remote Sensing, 17(8): 1404, doi: 10.3390/rs17081404.

[21]	Milly P C D, Dunne K A. 2016. Potential evapotranspiration and continental drying. Nature Climate Change, 6: 946-949. doi: 10.1038/NCLIMATE3046

[22]	Orimoloye I R, Belle J A, Ololade O O. 2021. Drought disaster monitoring using MODIS derived index for drought years: a space-based information for ecosystems and environmental conservation. Journal of Environmental Management, 284: 112028, doi: 10.1016/j.jenvman.2021.112028.

[23]	Pathak R, Dasari H P, Ashok K, et al. 2023. Effects of multi-observations uncertainty and models similarity on climate change projections. npj Climate and Atmospheric Science, 6: 144, doi: 10.1038/s41612-023-00473-5.

[24]	Scheff J, Frierson D M W. 2015. Terrestrial aridity and its response to greenhouse warming across CMIP 5 climate models. Journal of Climate, 28(14): 5583-5600. doi: 10.1175/JCLI-D-14-00480.1

[25]	Seneviratne S I, Nicholls N, Easterling D, et al. 2012. Changes in climate extremes and their impacts on the natural physical environment. In: Field CB, BarrosV, Stocker TF, et al. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Cambridge: Cambridge University Press, 109-230.

[26]	Serinaldi F. 2013. An uncertain journey around the tails of multivariate hydrological distributions. Water Resources Research, 49(10): 6527-6547. doi: 10.1002/wrcr.20531

[27]	Soares P M M, Johannsen F, Lima D C A, et al. 2024. High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia. Geoscientific Model Development, 17(1): 229-259. doi: 10.5194/gmd-17-229-2024

[28]	Stagge J H, Tallaksen L M, Gudmundsson L, et al. 2015. Candidate distributions for climatological drought indices (SPI and SPEI). International Journal of Climatology, 35(13): 4027-4040. doi: 10.1002/joc.2015.35.issue-13

[29]	Tebaldi C, Debeire K, Eyring V, et al. 2021. Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth System Dynamics, 12(1): 253-293. doi: 10.5194/esd-12-253-2021

[30]	Teutschbein C, Seibert J. 2012. Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. Journal of Hydrology, 456-457: 12-29. doi: 10.1016/j.jhydrol.2012.05.052

[31]	Tian P Z, Jian B Y, Li J R, et al. 2023. Land-use-change-induced cooling and precipitation reduction in China: insights from CMIP 6 models. Sustainability, 15(16): 12191, doi: 10.3390/su151612191.

[32]	Vicente-Serrano S M, Gouveia C, Camarero J J, et al. 2013. Response of vegetation to drought time-scales across global land biomes. Proceedings of the National Academy of Sciences of the United States of America, 110(1): 52-57.

[33]	Wang H J, Chen Y N, Li W H, et al. 2013. Runoff responses to climate change in arid region of northwestern China during 1960-2010. Chinese Geographical Science, 23(3): 286-300. doi: 10.1007/s11769-013-0605-x

[34]	Wang Q F, Zhang R R, Qi J Y, et al. 2022. An improved daily standardized precipitation index dataset for mainland China from 1961 to 2018. Scientific Data, 9: 124, doi: 10.1038/s41597-022-01201-z. pmid: 35354842

[35]	Wang T T. 2022. Study on potential evapotranspiration and drought characteristics in Tarim River Basin. MSc Thesis. Shanghai: Shanghai Normal University. (in Chinese)

[36]	Wu H J, Su X L, Singh V P, et al. 2021. Agricultural drought prediction based on conditional distributions of vine copulas. Water Resources Research, 57(8): e2021WR029562, doi: 10.1029/2021WR029562.

[37]	Xiang Y Y, Wang Y, Chen Y N, et al. 2024. Prediction of future hydrological drought risk in the Yarkant River Basin based on CMIP 6 models. Arid Land Geography, 47(5): 798-809. (in Chinese)

[38]	Xiao C W, Zaehle S, Sitch S, et al. 2025. Deforestation increases vegetation vulnerability to drought across biomes. Global Biogeochemical Cycles, 39(5): e2024GB008378, doi: 10.1029/2024GB008378.

[39]	Xu J W, Zhang X T, Feng C J, et al. 2021. Evaluation of surface upward longwave radiation in the CMIP6 models with ground and satellite observations. Remote Sensing, 13(21): 4464, doi: 10.3390/rs13214464.

[40]	Yang P, Xia J, Zhang Y Y, et al. 2019. How is the risk of hydrological drought in the Tarim River Basin, Northwest China? Science of The Total Environment, 693: 133555, doi: 10.1016/j.scitotenv.2019.07.361.

[41]	Zhai J Q, Mondal S K, Fischer T, et al. 2020. Future drought characteristics through a multi-model ensemble from CMIP 6 over South Asia. Atmospheric Research, 246: 105111, doi: 10.1016/j.atmosres.2020.105111.

[42]	Zhang B Q, Zhao X N, Jin J M, et al. 2015. Development and evaluation of a physically based multiscalar drought index: the standardized moisture anomaly index. Journal of Geophysical Research: Atmospheres, 120(22): 11575-11588.

[43]	Zhang G W, Zeng G, Yang X Y, et al. 2021a. Future changes in extreme high temperature over China at 1.5°C-5°C global warming based on CMIP 6 simulations. Advances in Atmospheric Sciences, 38(2): 253-267. doi: 10.1007/s00376-020-0182-8

[44]	Zhang G X, Su X L, Singh V P, et al. 2021b. Appraising standardized moisture anomaly index (SZI) in drought projection across China under CMIP 6 forcing scenarios. Journal of Hydrology: Regional Studies, 37: 100898, doi: 10.1016/j.ejrh.2021.100898.

[45]	Zhang X L, Wang X X, Zhou B T. 2023. CMIP6-projected changes in drought over Xinjiang, Northwest China. International Journal of Climatology, 43(14): 6560-6577. doi: 10.1002/joc.v43.14

[46]	Zhao Z Y, Hao X M, Fan X, et al. 2023. Actual evapotranspiration dominates drought in Central Asia. Remote Sensing, 15(18): 4557, doi: 10.3390/rs15184557.

[47]	Zhong S B, Sun Z H, Di L P. 2021. Characteristics of vegetation response to drought in the CONUS based on long-term remote sensing and meteorological data. Ecological Indicators, 127: 107767, doi: 10.1016/j.ecolind.2021.107767.

[48]	Zhou T J, Zou L W, Chen X L. 2019. Commentary on the coupled model intercomparison project phase 6 (CMIP6). Climate Change Research, 15(5): 445-456. (in Chinese)

[1]	CHU Jiangdong, SU Xiaoling, WANG Lei, WU Nan, Komelle ASKARI, WU Haijiang, ZHANG Te, XU Liujia, ZHANG Qifei. Glacial melting impact on runoff and evapotranspiration based on glacier-coupled SWAT model: A case study in the upper Shiyang River Basin, China[J]. Journal of Arid Land, 2026, 18(2): 216-234.

[2]	XU Xiaona, ZHANG Huayong. Spatiotemporal evolution of net ecosystem productivity and the driving mechanisms in Horqin Sandy Land, China[J]. Journal of Arid Land, 2026, 18(1): 34-55.

[3]	LI Jiani, XU Denghui, XU Zhonglin, WANG Yao, YANG Jianjun. Multi-source remote sensing and machine learning reveal spatiotemporal variations and drivers of NPP in the Tianshan Mountains, China[J]. Journal of Arid Land, 2026, 18(1): 56-83.

[4]	Dianela Alejandra CALVO, Juan José GAITÁN, Juan Manuel ZEBERIO, Ana Inés CASALINI, Guadalupe PETER. Divergent vegetation response to increasing grazing pressure in arid and semi-arid rangelands in Argentina[J]. Journal of Arid Land, 2026, 18(1): 84-100.

[5]	XIU Xiaomin, WU Bo, CHEN Qian, LI Yiran, PANG Yingjun, JIA Xiaohong, ZHU Jinlei, LU Qi. Spatio-temporal dynamics of desertification in China from 1970 to 2019: A meta-analysis[J]. Journal of Arid Land, 2025, 17(9): 1189-1214.

[6]	HE Bing, LI Ying, GAO Fan, XU Hailiang, WU Bin, YANG Pengnian, BAN Jingya, LIU Zeyi, LIU Kun, HAN Fanghong, MA Zhenghu, WANG Lu. Response of vegetation to climate change along the elevation gradient in High Mountain Asia[J]. Journal of Arid Land, 2025, 17(9): 1215-1233.

[7]	GUO Mengjing, BAI Zichen, YUAN Bo, WANG Wen, ZHANG Tiegang, XIANG Ke, ZHANG Jiao, ZHAO Huiyizhe. Spatial and temporal characterization of water quality in Bosten Lake, China based on comprehensive water quality index[J]. Journal of Arid Land, 2025, 17(9): 1234-1251.

[8]	QIU Yuhao, DUAN Limin, CHEN Siyi, WANG Donghua, ZHANG Wenrui, GAO Ruizhong, WANG Guoqiang, LIU Tingxi. Combined application of variable infiltration capacity model and Budyko hypothesis for identification of runoff evolution in the Yellow River Basin, China[J]. Journal of Arid Land, 2025, 17(8): 1048-1063.

[9]	MA Junyao, YANG Kun, ZHANG Xuyang, WANG Leiyu, XUE Yayong. Long-term vegetation dynamics and its drivers in the north of China[J]. Journal of Arid Land, 2025, 17(8): 1064-1083.

[10]	JIA Xu, WEI Baocheng, ZHANG Zhijie, CHEN Lulu, LIU Mengna, ZHAO Yiming, WANG Jing. Probability and spatiotemporal dynamics of active fire occurrence in Inner Mongolia, China from 2000 to 2022[J]. Journal of Arid Land, 2025, 17(8): 1084-1102.

[11]	JIN Zikai, LI Fayuan, LIU Lulu, JIAO Haoyang, CUI Lingzhou. Automatic classification of coastal sand dunes in the Namib Desert through the texture analysis approach[J]. Journal of Arid Land, 2025, 17(8): 1168-1187.

[12]	XI Ruiyun, PEI Tingting, CHEN Ying, XIE Baopeng, HOU Li, WANG Wen. Spatiotemporal dynamic and drivers of ecological environmental quality on the Chinese Loess Plateau: Insights from kRSEI model and climate-human interaction analysis[J]. Journal of Arid Land, 2025, 17(7): 958-978.

[13]	Ilan STAVI, Gal KAGAN, Sivan ISAACSON. Properties, challenges, and opportunities of the loess plains in the northern Negev Desert: A review[J]. Journal of Arid Land, 2025, 17(6): 715-734.

[14]	LIU Huan, YAO Yuyan, AI Zemin, DANG Xiaohu, CAO Yong, LI Qingqing, HOU Mengjia, HU Haoli, ZHANG Yuanyuan, CAO Tian. Spatial and temporal evolution of forage-livestock balance in the agro-pastoral transition zone of northern China[J]. Journal of Arid Land, 2025, 17(6): 754-771.

[15]	LI Wei, WANG Yixuan, DUAN Limin, TONG Xin, WU Yingjie, ZHAO Shuixia. Non-stationary characteristics and causes of extreme precipitation in a desert steppe in Inner Mongolia, China[J]. Journal of Arid Land, 2025, 17(5): 590-604.

Viewed

Full text

Abstract

Cited

Shared

Discussed