Drought risk assessment and future scenario prediction in agricultural cropping zones of China

doi:10.1007/s40333-025-0113-8

Journal of Arid Land

2025, Vol. 17

Issue (12): 1694-1718 DOI: 10.1007/s40333-025-0113-8 CSTR: 32276.14.JAL.02501138

Research article

Drought risk assessment and future scenario prediction in agricultural cropping zones of China

LIU Xiaohong¹, LIU Chunhui², FAN Jiejie¹, QIU Chunxia¹^,^*(

)

¹College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
²Key Laboratory of Pollution Processes and Environmental Criteria (Ministry of Education), Carbon Neutrality Interdisciplinary Science Centre, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China

Download:

HTML

PDF(9524KB)
Export: BibTeX | EndNote (RIS)

Abstract

With ongoing global climate change, drought has become the primary threat constraining food security in China. Traditional assessment frameworks based on administrative boundaries or macro-climatic zoning overlook variation in vulnerability affected by key agronomic practices, such as crop phenology and cropping systems, thereby limiting their accuracy. To address this research gap, this study developed and validated a novel drought risk assessment framework based on agricultural cropping zones (single-, double-, and triple-cropping zones). The framework coupled a Geographical and Temporal Neural Network Weighted Regression (GTNNWR) model for forecasting future crop vegetation dynamics with the Standardized Precipitation Evapotranspiration Index (SPEI) to assess drought risk under historical (2001-2020) and projected future (2021-2100) scenarios. The GTNNWR model achieved R² values ranging from 0.72 to 0.82 and RMSE values between 0.11 and 0.14 for NDVI prediction, significantly outperforming conventional models. Historical drought risk assessment revealed that drought events were most frequent during summer and concentrated in single-cropping and double-cropping zones. Future projections indicate a substantial intensification of drought risk. Under the Shared Socioeconomic Pathway (SSP)126 scenario, drought risk is projected to increase in the triple-cropping zones of the middle and lower reaches of the Yangtze River Plain. Under the SSP245 scenario, the frequency of spring and winter droughts is anticipated to rise markedly. Under the SSP585 scenario, drought intensity is projected to intensify in central-eastern single-cropping zones and southwestern double-cropping zones. This assessment framework based on agricultural cropping zones can precisely identify drought risks and facilitate adaptation in agricultural management, such as optimizing irrigation systems and adjusting crop structures.

Key words： climate change agricultural cropping zone Geographical and Temporal Neural Network Weighted Regression (GTNNWR) model Standardized Precipitation Evapotranspiration Index (SPEI) run theory drought risk assessment

Received: 24 June 2025 Published: 31 December 2025

Corresponding Authors: ^*QIU Chunxia (E-mail: 000358@xust.edu.cn)

	Service
	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LIU Xiaohong
	LIU Chunhui
	FAN Jiejie
	QIU Chunxia

Cite this article:

LIU Xiaohong, LIU Chunhui, FAN Jiejie, QIU Chunxia. Drought risk assessment and future scenario prediction in agricultural cropping zones of China. Journal of Arid Land, 2025, 17(12): 1694-1718.

URL:

http://jal.xjegi.com/10.1007/s40333-025-0113-8 OR http://jal.xjegi.com/Y2025/V17/I12/1694

Fig. 1 Overview of the study area. Note that the map is based on the standard map (GS(2024)0650) of the National Platform for Common GeoSpatial Infromation Services (https://cloudcenter.tianditu.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the boundary of base map has not been modified.

Table 1 Indicator for the classification of different agricultural cropping zones in China

Table 2 Datasets used in this study

Fig. 2 Heat map of Pearson correlation coefficients between NDVI and multiple meteorological factors. NDVI, Normalized Difference Vegetation Index; r, correlation coefficient; ^**, significance at P<0.01 level.

Fig. 3 Workflow of the drought risk assessment in this study. GTNNWR, Geographically and Temporally Neural Network Weighted Regression; TR, time resolution; SR, spatial resolution; CMIP6, Coupled Model Intercomparison Project 6; SPEI, Standardized Precipitation Evapotranspiration Index.

Fig. 4 Schematic diagram of GTNNWR model solution process. The red dashed box is the Spatio-temporal Proximity Neural Network (STPNN), and the blue dashed box is the Spatio-temporal Weighted Neural Network (STWNN). OLR, Ordinary Linear Regression; P_i, any point in space-time requiring estimation; dS ij, the spatial distance between the estimated point and all other points in the training dataset; dT ij, the temporal distance between the estimated point and all other points in the training dataset; h, neural network hidden layer nodes; q and m, different hidden layer indices in STPNN; dST ij, the temporal and spatial distance between the estimated point and all other points in the training dataset; ω_ip, spatio-temporal non-stationary weights corresponding to the estimated points; β_p, global regression coefficients estimated by the OLR model; x_ip, the independent variable corresponding to the estimated point (relative humidity, precipitation, temperature, and potential evapotranspiration); $\hat{y}_{i}$, the final estimated value of the model.

Fig. 5 Drought event identification based on the three-threshold run theory method. Black shading area indicates a one-month interval between two consecutive drought events; grid shading area denotes events flagged as potential droughts following preliminary screening. R0, the threshold for merging adjacent drought events; R1, the threshold for drought onset; R2, the threshold for excluding minor drought events; a, b, c, d, e, and f denote the identified candidate drought events; g denotes that two adjacent drought events were separated by only one month, and the SPEI value during the intervening month was below R0; h denotes that two adjacent drought events were separated by only one month, but the SPEI value during the intervening month was above R0.

Fig. 6 Performance of GTNNWR (a1-a4), eXtreme Gradient Boosting (XGBoost) (b1-b4), Convolutional Neural Networks (CNN) (c1-c4), and Long Short-Term Memory (LSTM) (d1-d4) models in predicting NDVI for four seasons. The black dashed line represents the ideal 1:1 reference line, while the red solid line denotes the regression fit line between the predicted NDVI and the actual NDVI.

Fig. 7 Temporal trend in annual mean NDVI in agricultural cropping zones of China. (a), historical trends from 2001 to 2020; (b-d), projected future trends from 2021 to 2100 for the (b) single-, (c) double-, and (d) triple-cropping zones under three shared socioeconomic pathway (SSP) scenarios.

Fig. 8 Spatial variation in annual mean NDVI in different cropping zones in China from 2001 to 2020 (a) and 2021-2100 under the SSP126 (b1-b4), SSP245 (c1-c4), and SSP585 (d1-d4) scenarios

Fig. 9 Spatio-temporal analysis of the correlation between monthly NDVI and SPEI across multiple time scales from 2001 to 2020. (a-e), spatial distribution of the correlation between SPEI-1 (a), SPEI-3 (b), SPEI-6 (c), SPEI-9 (d), and SPEI-12 (e) and monthly NDVI; (f), density distribution of correlation coefficient at different time scales. Black dots in Figure 9a-e indicate significant correlations at P<0.05 level, and r denotes the correlation coefficient.

Fig. 10 Spatial distribution of the number (a1-a4), average duration (b1-b4), and average intensity (c1-c4) of drought events in four seasons from 2001 to 2020

Fig. 11 Spatial distribution of the number of drought events in different seasons and agricultural cropping zones under the SSP126 (a1-a16), SSP245 (b1-b16), and SSP585 (c1-c16) scenarios from 2021 to 2100

Fig. 12 Spatial distribution of the average duration of drought events in different seasons and agricultural cropping zones under the SSP126 (a1-a16), SSP245 (b1-b16), and SSP585 (c1-c16) scenarios from 2021 to 2100

Fig. 13 Spatial distribution of the average intensity of drought events in different seasons and agricultural cropping zones under the SSP126 (a1-a16), SSP245 (b1-b16), and SSP585 (c1-c16) scenarios from 2021 to 2100

Fig. 14 Spatial distribution of the correlation between the annual mean NDVI value of China's agricultural zones and SPEI-12 under the SSP126 (a1-a4), SSP245 (b1-b4), and SSP585 (c1-c4) scenarios from 2021 to 2100. Black dots indicate significant correlations at P<0.05 level.


[1]	Bock L, Lauer A, Schlund M, et al. 2020. Quantifying progress across different CMIP phases with the ESMValTool. Journal of Geophysical Research: Atmospheres, 125(21): e2019JD032321, doi:10.1029/2019JD032321.

[2]	Cai S Y, Zuo D P, Wang H X, et al. 2023. Assessment of agricultural drought based on multi-source remote sensing data in a major grain producing area of Northwest China. Agricultural Water Management, 278: 108142, doi:10.1016/j.agwat.2023.108142.

[3]	Chen X X, Wang L C, Cao Q, et al. 2024. Response of global agricultural productivity anomalies to drought stress in irrigated and rainfed agriculture. Science China Earth Sciences, 67(11): 3579-3593.

[4]	Chen Y, Marek G W, Marek T H, et al. 2019. Simulating the impacts of climate change on hydrology and crop production in the Northern High Plains of Texas using an improved SWAT model. Agricultural Water Management, 221: 13-24.

[5]	Cui C L, Zhang W, Hong Z M, et al. 2020. Forecasting NDVI in multiple complex areas using neural network techniques combined feature engineering. International Journal of Digital Earth, 13(12): 1733-1749.

[6]	Du Z H, Wang Z Y, Wu S S, et al. 2020. Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity. International Journal of Geographical Information Science, 34(7): 1353-1377.

[7]	Gao J Q, Yang X G, Zheng B Y, et al. 2019. Effects of climate change on the extension of the potential double cropping region and crop water requirements in northern China. Agricultural and Forest Meteorology, 268: 146-155.

[8]	Han H M, Liu Z H, Barrios B M, et al. 2024. Time series forecasting model for non-stationary series pattern extraction using deep learning and GARCH modeling. Journal of Cloud Computing, 13(1): 2, doi:10.1186/s13677-023-00576-7.

[9]	Han X L, Liu X H, Gao L Z, et al. 1986. Climate zoning of crop planting systems in China. Tillage and Cultivation, Z1: 2-19. (in Chinese)

[10]	He J, Yang X H, Li J Q, et al. 2015. Spatiotemporal variation of meteorological droughts based on the daily comprehensive drought index in the Haihe River Basin, China. Natural Hazards, 75(2): 199-217.

[11]	He J, Yang X H, Li Z, et al. 2016. Spatiotemporal variations of meteorological droughts in China during 1961-2014: An investigation based on multi-threshold identification. International Journal of Disaster Risk Science, 7(1): 63-76.

[12]	He Q, Wang M, Liu K, et al. 2024. High-resolution standardized precipitation evapotranspiration index (SPEI) reveals trends in drought and vegetation water availability in China. Geography and Sustainability, 6(2): 100228, doi:10.1016/j.geosus.2024.08.007.

[13]	Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. 2005. China's Agricultural Zone Classification. [2025-04-28]. https://www.resdc.cn/data.aspx?DATAID=274.

[14]	Javed T, Wang Z H, Liu J, et al. 2025. The influence of agricultural drought on carbon emissions across the four sub-regions of China. Carbon Balance and Management, 20(1): 9, doi:10.1186/s13021-025-00300-9.

[15]	Jia H C, Chen F, Zhang C R, et al. 2022. High emissions could increase the future risk of maize drought in China by 60%-70%. Science of the Total Environment, 852: 158474, doi:10.1016/j.scitotenv.2022.158474.

[16]	Li W, Jiang Z H, Li L. 2023. Anthropogenic influence on the record-breaking compound hot and dry event in summer 2022 in the Yangtze River Basin in China. Bulletin of the American Meteorological Society, 104(11): E1928-E1934.

[17]	Li X Q, Yuan W P, Dong W J. 2021. A machine learning method for predicting vegetation indices in China. Remote Sensing, 13(6): 1147, doi:10.3390/rs13061147.

[18]	Liao J J, Yu C Y, Feng Z, et al. 2021. Spatial differentiation characteristics and driving factors of agricultural eco-efficiency in Chinese provinces from the perspective of ecosystem services. Journal of Cleaner Production, 288: 125466, doi:10.1016/j.jclepro.2020.125466.

[19]	Linh D T, Shabbir M N. 2025. Climate smart agriculture: A path to sustainable farming in China. Food and Humanity, 5: 100872, doi:10.1016/j.foohum.2025.100872.

[20]	Liu Y, Tian J Y, Liu R H, et al. 2021. Influences of climate change and human activities on NDVI changes in China. Remote Sensing, 13(21): 4326, doi:10.3390/rs13214326.

[21]	Liu Z H, Lü A F, Li T H. 2024. Intensified drought threatens future food security in major food-producing countries. Atmosphere, 16(1): 34, doi:10.3390/atmos16010034.

[22]	Long J C, Xu C C, Wang Y Z, et al. 2024. From meteorological to agricultural drought: Propagation time and influencing factors over diverse underlying surfaces based on CNN-LSTM model. Ecological Informatics, 82: 102681, doi:10.1016/j.ecoinf.2024.102681.

[23]	Ma J N, Zhang C, Li S E, et al. 2023. Changes in vegetation resistance and resilience under different drought disturbances based on NDVI and SPEI time series data in Jilin Province, China. Remote Sensing, 15(13): 3280, doi:10.3390/rs15133280.

[24]	Ma L, Long H L, Tang L S, et al. 2021. Analysis of the spatial variations of determinants of agricultural production efficiency in China. Computers and Electronics in Agriculture, 180: 105890, doi:10.1016/j.compag.2020.105890.

[25]	Mi Q C, Huo Z G, Li M X, et al. 2025. Development of a drought monitoring system for winter wheat in the Huang-Huai-Hai region, China, utilizing a machine learning-physical process hybrid model. Agronomy, 15(3): 696, doi:10.3390/agronomy15030696.

[26]	Minoli S, Jägermeyr J, Asseng S, et al. 2022. Global crop yields can be lifted by timely adaptation of growing periods to climate change. Nature Communications, 13(1): 7079, doi:10.1038/s41467-022-34411-5.

[27]	Peng L Q, Sheffield J, Wei Z W, et al. 2024. An enhanced Standardized Precipitation-Evapotranspiration Index (SPEI) drought-monitoring method integrating land surface characteristics. Earth System Dynamics, 15(5): 1277-1300.

[28]	Ren B N, Xiao Y, Liu B, et al. 2025. Exploring the transmission process of carbon sequestration services and its applications: A case study of Hainan. Forests, 16(1): 136, doi:10.3390/f16010136.

[29]	Scarpa G, Gargiulo M, Mazza A, et al. 2018. A CNN-based fusion method for feature extraction from sentinel data. Remote Sensing, 10(2): 236, doi:10.3390/rs10020236.

[30]	Serkendiz H, Tatli H. 2023. Assessment of multidimensional drought vulnerability using exposure, sensitivity, and adaptive capacity components. Environmental Monitoring and Assessment, 195(10): 1154, doi:10.1007/s10661-023-11711-x.

[31]	Su B D, Huang J L, Mondal S K, et al. 2021. Insight from CMIP6 SSP-RCP scenarios for future drought characteristics in China. Atmospheric Research, 250: 105375, doi:10.1016/j.atmosres.2020.105375.

[32]	Sun Y, Lao D Z, Ruan Y J, et al. 2023. A deep learning-based approach to predict large-scale dynamics of normalized difference vegetation index for the monitoring of vegetation activities and stresses using meteorological data. Sustainability, 15(8): 6632, doi:10.3390/su15086632.

[33]	Tefera M L, Giovanna S, Alberto C, et al. 2024. Rainfall variability and drought in West Africa: challenges and implications for rainfed agriculture. Theoretical and Applied Climatology, 156(1): 41, doi:10.1007/s00704-024-05251-8.

[34]	Tuoku L, Wu Z J, Men B H. 2024. Impacts of climate factors and human activities on NDVI change in China. Ecological Informatics, 81: 102555, doi:10.1016/j.ecoinf.2024.102555.

[35]	Ullah F, Wang P Y, Saqib S, et al. 2025. Toxicological complexity of microplastics in terrestrial ecosystems. iScience, 28(2): 111879, doi:10.1016/j.isci.2025.111879.

[36]	Vetter T, Reinhardt J, Flörke M, et al. 2017. Evaluation of sources of uncertainty in projected hydrological changes under climate change in 12 large-scale river basins. Climatic Change, 141(3): 419-433.

[37]	Vicente-Serrano S M, Quiring S M, Peña-Gallardo M, et al. 2020. A review of environmental droughts: Increased risk under global warming? Earth-Science Reviews, 201: 102953, doi:10.1016/j.earscirev.2019.102953.

[38]	Vicente-Serrano S M, Peña-Angulo D, Beguería S, et al. 2022. Global drought trends and future projections. Philosophical Transactions of the Royal Society A, 380(2238): 20210285, doi:10.1098/rsta.2021.0285.

[39]	Wang S N, Xing X G, Wu Y J, et al. 2024a. Seasonal response of the NDVI to the SPEI at different time scales in Yinshanbeilu, Inner Mongolia, China. Land, 13(4): 523, doi:10.3390/land13040523.

[40]	Wang S P, Zhang Q, Wang J S, et al. 2021a. Relationship between drought and precipitation heterogeneity: An analysis across rain-fed agricultural regions in eastern Gansu, China. Atmosphere, 12(10): 1274, doi:10.3390/atmos12101274.

[41]	Wang X, Xu Y, Li W, et al. 2025a. Unequal impacts of future droughts on global croplands: contributions of climate and land-use changes across different income groups. npj Natural Hazards, 2(1): 93, doi:10.1038/s44304-025-00144-w.

[42]	Wang Y G, Wang J X, Gong D H, et al. 2025b. Spatiotemporal heterogeneity and zonal adaptation strategies for agricultural risks of compound dry and hot events in China's middle Yangtze River Basin. Remote Sensing, 17(16): 2892, doi:10.3390/rs17162892.

[43]	Wang Y Q, Shen R P, Huang A Q, et al. 2021b. Spatiotemporal dynamic analysis of MODIS LAI reconstructed in different cultivation areas in China from 2001 to 2017. Journal of Geo-information Science, 23(4): 658-669. (in Chinese)

[44]	Wang Z Y, Ding J L, Tan J, et al. 2024b. UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF. Frontiers in Plant Science, 15: 1358965, doi:10.3389/fpls.2024.1358965.

[45]	Wu C H, Zhong L L, Yeh P J F, et al. 2024. An evaluation framework for quantifying vegetation loss and recovery in response to meteorological drought based on SPEI and NDVI. Science of the Total Environment, 906: 167632, doi:10.1016/j.scitotenv.2023.167632.

[46]	Wu P N, Wang Y L, Li Y M, et al. 2023. Optimizing irrigation strategies for sustainable crop productivity and reduced groundwater consumption in a winter wheat-maize rotation system. Journal of Environmental Management, 348: 119469, doi:10.1016/j.jenvman.2023.119469.

[47]	Wu S S, Wang Z Y, Du Z H, et al. 2021. Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships. International Journal of Geographical Information Science, 35(3): 582-608.

[48]	Yang L S, Feng Q, Zhu M, et al. 2022. Variation in actual evapotranspiration and its ties to climate change and vegetation dynamics in Northwest China. Journal of Hydrology, 607: 127533, doi:10.1016/j.jhydrol.2022.127533.

[49]	Yin R M, Wang Z Q, Xu F. 2023. Multi-scenario simulation of China's dynamic relationship between water-land resources allocation and cultivated land use based on shared socioeconomic pathways. Journal of Environmental Management, 341: 118062, doi:10.1016/j.jenvman.2023.118062.

[50]	Yu G R, Piao S L, Zhang Y J, et al. 2021. Moving toward a new era of ecosystem science. Geography and Sustainability, 2(3): 151-162.

[51]	Yu T Z, Mahe L, Li Y, et al. 2022. Benefits of crop rotation on climate resilience and its prospects in China. Agronomy, 12(2): 436, doi:10.3390/agronomy12020436.

[52]	Zhang X, Fan Y C, Tjiputra J, et al. 2025. Divergent impacts of climate interventions on China's north-south water divide. Communications Earth & Environment, 6: 736, doi:10.1038/s43247-025-02708-0.

[53]	Zhao J Q, Zhang Q, Zhu X D, et al. 2020. Drought risk assessment in China: Evaluation framework and influencing factors. Geography and Sustainability, 1(3): 220-228.

[54]	Zhao W Z, Bo Y C, Chen J G, et al. 2019. Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM). ISPRS Journal of Photogrammetry and Remote Sensing, 151: 237-250.

[55]	Zhao Z Q, Gao J B, Wang Y L, et al. 2015. Exploring spatially variable relationships between NDVI and climatic factors in a transition zone using geographically weighted regression. Theoretical and Applied Climatology, 120(3): 507-519.

[56]	Zou J, Ding J L, Welp M, et al. 2020. Assessing the response of ecosystem water use efficiency to drought during and after drought events across Central Asia. Sensors, 20(3): 581, doi:10.3390/s20030581.

[1]	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.

[2]	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.

[3]	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.

[4]	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.

[5]	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.

[6]	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.

[7]	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.

[8]	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.

[9]	CHAO Yan, ZHU Yonghua, WANG Xiaohan, LI Jiamin, LIANG Li'e. Dynamic evolution of the NDVI and driving factors in the Mu Us Sandy Land of China from 2002 to 2021[J]. Journal of Arid Land, 2025, 17(5): 605-623.

[10]	LYU Leting, JIANG Ruifeng, ZHENG Defeng, LIANG Liheng. Impact of climate change and land use/cover change on water yield in the Liaohe River Basin, Northeast China[J]. Journal of Arid Land, 2025, 17(2): 182-199.

[11]	MA Yutao, GONG Jie, JIN Tiantian, XU Tianyu, KAN Guobin. Comparison of different vegetation indices for estimating vegetation changes and analyzing driving factors in a semi-arid area, China[J]. Journal of Arid Land, 2025, 17(12): 1785-1805.

[12]	Abdelaziz Q BASHABSHEH, Kamel K ALZBOON, Zeyad ALSHBOUL. Spatial trends of extreme temperature events and climate change indicators in climate zones of Jordan[J]. Journal of Arid Land, 2025, 17(11): 1542-1557.

[13]	XIANG Xuemei, DE Kejia, ZHANG Lin, LIN Weishan, FENG Tingxu, LI Fei, WEI Xijie. Response of temporal stability of plant community biomass in alpine meadows of the Qinghai-Xizang Plateau, China to climate warming and nitrogen deposition[J]. Journal of Arid Land, 2025, 17(10): 1425-1442.

[14]	HAO Mingyang, HE Jianuo, HU Weiyin, ZHAO Zhou, LI Can, SONG Shikai, ZOU Xueyong, CHANG Chunping, GUO Zhongling. Effects of dry soil aggregate size on organic carbon, total nitrogen, and soil texture under different land uses[J]. Journal of Arid Land, 2025, 17(10): 1482-1495.

[15]	ZHANG Jing, XU Changchun, WANG Hongyu, WANG Yazhen, LONG Junchen. Runoff simulation and hydropower resource prediction of the Kaidu River Basin in the Tianshan Mountains, China[J]. Journal of Arid Land, 2025, 17(1): 1-18.

Viewed

Full text

Abstract

Cited

Shared

Discussed