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Upscaling Soil Salinization in Keriya Oasis Using Bayesian Belief Networks

Author

Listed:
  • Hong Chen

    (College of Geographical Science and Tourism, Xinjiang Normal University, Urumqi 830017, China)

  • Jumeniyaz Seydehmet

    (College of Geographical Science and Tourism, Xinjiang Normal University, Urumqi 830017, China
    Xinjiang Arid Area Lake Environment and Resources Laboratory, Key Laboratory of Xinjiang Uygur Autonomous Region, Urumqi 830017, China)

  • Xiangyu Li

    (College of Geographical Science and Tourism, Xinjiang Normal University, Urumqi 830017, China)

Abstract

Soil salinization in oasis areas of arid regions is recognized as a dynamic and multifaceted environmental threat influenced by both natural processes and human activities. In this study, 13 spatiotemporal predictors derived from field surveys and remote sensing are utilized to construct a spatial probabilistic model of salinization. A Bayesian Belief Network is integrated with spline interpolation in ArcGIS to map the likelihood of salinization, while Partial Least Squares Structural Equation Modeling (PLS-SEM) is applied to analyze the interactions among multiple drivers. The test results of this model indicate that its average sensitivity exceeds 80%, confirming its robustness. Salinization risk is categorized into degradation (35–79% probability), stability (0–58%), and improvement (0–48%) classes. Notably, 58.27% of the 1836.28 km 2 Keriya Oasis is found to have a 50–79% chance of degradation, whereas only 1.41% (25.91 km 2 ) exceeds a 50% probability of remaining stable, and improvement probabilities are never observed to surpass 50%. Slope gradient and soil organic matter are identified by PLS-SEM as the strongest positive drivers of degradation, while higher population density and coarser soil textures are found to counteract this process. Spatially explicit probability maps are generated to provide critical spatiotemporal insights for sustainable oasis management, revealing the complex controls and limited recovery potential of soil salinization.

Suggested Citation

  • Hong Chen & Jumeniyaz Seydehmet & Xiangyu Li, 2025. "Upscaling Soil Salinization in Keriya Oasis Using Bayesian Belief Networks," Sustainability, MDPI, vol. 17(15), pages 1-23, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:15:p:7082-:d:1717682
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    References listed on IDEAS

    as
    1. Jumeniyaz Seydehmet & Guang-Hui Lv & Abdugheni Abliz, 2019. "Landscape Design as a Tool to Reduce Soil Salinization: The Study Case of Keriya Oasis (NW China)," Sustainability, MDPI, vol. 11(9), pages 1-17, May.
    2. Yu, Yang & Yu, Ruide & Chen, Xi & Yu, Guoan & Gan, Miao & Disse, Markus, 2017. "Agricultural water allocation strategies along the oasis of Tarim River in Northwest China," Agricultural Water Management, Elsevier, vol. 187(C), pages 24-36.
    3. Li, Wenhao & Gao, Shuanglong & Pei, Dongjie & Wen, Yue & Mu, Xiaoguo & Liu, Mengjie & Wang, Zhenhua, 2025. "Spatio-temporal evolution and simulation of soil salinization in typical oasis water-saving irrigation area based on long series data," Agricultural Water Management, Elsevier, vol. 307(C).
    4. Chen, Baili & Duan, Quntao & Zhao, Wenzhi & Wang, Lixin & Zhong, Yanxia & Zhuang, Yanli & Chang, Xueli & Dong, Chunyuan & Du, Wentao & Luo, Lihui, 2023. "Oasis sustainability is related to water supply mode," Agricultural Water Management, Elsevier, vol. 290(C).
    5. Garvey, Myles D. & Carnovale, Steven & Yeniyurt, Sengun, 2015. "An analytical framework for supply network risk propagation: A Bayesian network approach," European Journal of Operational Research, Elsevier, vol. 243(2), pages 618-627.
    6. Rahman, Muhammad Muhitur & Hagare, Dharma & Maheshwari, Basant, 2016. "Bayesian Belief Network analysis of soil salinity in a peri-urban agricultural field irrigated with recycled water," Agricultural Water Management, Elsevier, vol. 176(C), pages 280-296.
    7. Thiam, Habibatou I. & Owusu, Victor & Villamor, Grace B. & Schuler, Johannes & Hathie, Ibrahima, 2024. "Farmers’ intention to adapt to soil salinity expansion in Fimela, Sine-Saloum area in Senegal: A structural equation modelling approach," Land Use Policy, Elsevier, vol. 137(C).
    8. Yi Liu & Jie Xue & Dongwei Gui & Jiaqiang Lei & Huaiwei Sun & Guanghui Lv & Zhiwei Zhang, 2018. "Agricultural Oasis Expansion and Its Impact on Oasis Landscape Patterns in the Southern Margin of Tarim Basin, Northwest China," Sustainability, MDPI, vol. 10(6), pages 1-12, June.
    9. Tayierjiang Aishan & Jian Song & Ümüt Halik & Florian Betz & Asadilla Yusup, 2024. "Predicting Land-Use Change Trends and Habitat Quality in the Tarim River Basin: A Perspective with Climate Change Scenarios and Multiple Scales," Land, MDPI, vol. 13(8), pages 1-25, July.
    10. Shiqin Li & Ilyas Nurmemet & Jumeniyaz Seydehmet & Xiaobo Lv & Yilizhati Aili & Xinru Yu, 2024. "Spatiotemporal Dynamics and Driving Factors of Soil Salinization: A Case Study of the Yutian Oasis, Xinjiang, China," Land, MDPI, vol. 13(11), pages 1-23, November.
    11. Aadhityaa Mohanavelu & Sujay Raghavendra Naganna & Nadhir Al-Ansari, 2021. "Irrigation Induced Salinity and Sodicity Hazards on Soil and Groundwater: An Overview of Its Causes, Impacts and Mitigation Strategies," Agriculture, MDPI, vol. 11(10), pages 1-17, October.
    12. Xu, Xiao-Ke & Wang, Xue & Xiao, Jing, 2018. "Inferring parent–child relationships by a node-remove centrality framework in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 222-232.
    13. Wang, Ruoshui & Wan, Shuqin & Kang, Yaohu & Dou, Chaoyin, 2014. "Assessment of secondary soil salinity prevention and economic benefit under different drip line placement and irrigation regime in northwest China," Agricultural Water Management, Elsevier, vol. 131(C), pages 41-49.
    14. R. G. Cowell & R. J. Verrall & Y. K. Yoon, 2007. "Modeling Operational Risk With Bayesian Networks," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 74(4), pages 795-827, December.
    15. Jumeniyaz Seydehmet & Guang Hui Lv & Ilyas Nurmemet & Tayierjiang Aishan & Abdulla Abliz & Mamat Sawut & Abdugheni Abliz & Mamattursun Eziz, 2018. "Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China," Sustainability, MDPI, vol. 10(3), pages 1-22, February.
    16. Gao, Zitian & Peña-Arancibia, Jorge L. & Ahmad, Mobin-ud-Din & Siyal, Altaf Ali, 2025. "Three-dimensional soil salinity mapping with uncertainty using Bayesian Hierarchical Modelling, Random Forest Regression and remote sensing data," Agricultural Water Management, Elsevier, vol. 309(C).
    17. Xue, Bing & Jiang, Yan & Wang, Qijie & Ma, Bin & Hou, Zhen’an & Liang, Xue & Cui, Yirui & Li, Fangfang, 2024. "Seasonal transpiration dynamics and water use strategy of a farmland shelterbelt in Gurbantunggut Desert oasis, northwestern China," Agricultural Water Management, Elsevier, vol. 295(C).
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