IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v203y2018icp37-52.html
   My bibliography  Save this article

Long-term groundwater dynamics affected by intense agricultural activities in oasis areas of arid inland river basins, Northwest China

Author

Listed:
  • Liu, Minghuan
  • Jiang, Yao
  • Xu, Xu
  • Huang, Quanzhong
  • Huo, Zailin
  • Huang, Guanhua

Abstract

Oasis areas of arid inland river basins in northwest China have been facing intensified water use conflicts between agricultural sector and eco-environmental systems since 1990s. The reduction of river water allocation to oasis has resulted in the undesirable declines of groundwater levels (GWLs) with the increase in irrigated area and groundwater pumping. Improving water management and restoring GWLs become a great concern for those areas. In this study, the middle oasis of Heihe River basin (HRB) was selected as the representative case for such an endeavor. A three-dimensional groundwater flow model was established for the Zhangye basin, a sub-basin of HRB to obtain a better understanding of groundwater dynamics in middle oasis, particularly for investigating the effects of agricultural water use. A major advantage of this model is that the spatial and temporal recharge from irrigation has been described in details with considering the result obtained by an ago-hydrological model (SWAP-EPIC) simulation. The model was well calibrated and validated over the period of 1991–2010. Simulation of GWLs matched well with the observed 20-year GWLs in the 50 wells. Then, spatiotemporal groundwater dynamics and groundwater budget were quantitatively analyzed for the Zhangye basin during 1991–2010. In particular, the modeling results revealed three different changing trends of GWLs based on the analysis of groundwater dynamics and budget for four representative zones. Results indicated that negative balance of groundwater was mainly caused by over exploitation of groundwater for irrigation, resulting in a GWL decline of 9 cm a−1 in average and even 2 m decline in some years at local areas. The area with critical groundwater depth (e.g. <5 m) has reduced about 30% in 2010 as compared to that in 1991. Finally, recommendations on how to restore GWLs were proposed with emphasis on irrigation water and land use adjustment and groundwater pumping control. Our results are expected to provide implications for recovering the groundwater status in oasis areas of inland river basins in arid northwest China.

Suggested Citation

  • Liu, Minghuan & Jiang, Yao & Xu, Xu & Huang, Quanzhong & Huo, Zailin & Huang, Guanhua, 2018. "Long-term groundwater dynamics affected by intense agricultural activities in oasis areas of arid inland river basins, Northwest China," Agricultural Water Management, Elsevier, vol. 203(C), pages 37-52.
    • Handle: RePEc:eee:agiwat:v:203:y:2018:i:c:p:37-52
    DOI: 10.1016/j.agwat.2018.02.028
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377418301239
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2018.02.028?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Christopher White & Trevor Tanton & David Rycroft, 2014. "The Impact of Climate Change on the Water Resources of the Amu Darya Basin in Central Asia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5267-5281, December.
    2. Xu, Xu & Huang, Guanhua & Sun, Chen & Pereira, Luis S. & Ramos, Tiago B. & Huang, Quanzhong & Hao, Yuanyuan, 2013. "Assessing the effects of water table depth on water use, soil salinity and wheat yield: Searching for a target depth for irrigated areas in the upper Yellow River basin," Agricultural Water Management, Elsevier, vol. 125(C), pages 46-60.
    3. Jiang, Yao & Xu, Xu & Huang, Quanzhong & Huo, Zailin & Huang, Guanhua, 2016. "Optimizing regional irrigation water use by integrating a two-level optimization model and an agro-hydrological model," Agricultural Water Management, Elsevier, vol. 178(C), pages 76-88.
    4. Li, Jiang & Mao, Xiaomin & Li, Mo, 2017. "Modeling hydrological processes in oasis of Heihe River Basin by landscape unit-based conceptual models integrated with FEFLOW and GIS," Agricultural Water Management, Elsevier, vol. 179(C), pages 338-351.
    5. Jiang, Yao & Xu, Xu & Huang, Quanzhong & Huo, Zailin & Huang, Guanhua, 2015. "Assessment of irrigation performance and water productivity in irrigated areas of the middle Heihe River basin using a distributed agro-hydrological model," Agricultural Water Management, Elsevier, vol. 147(C), pages 67-81.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shi, Xinrui & Batchelor, William D. & Liang, Hao & Li, Sien & Li, Baoguo & Hu, Kelin, 2020. "Determining optimal water and nitrogen management under different initial soil mineral nitrogen levels in northwest China based on a model approach," Agricultural Water Management, Elsevier, vol. 234(C).
    2. Li, Guifang & Shi, Minjun & Zhou, Dingyang, 2021. "How much will farmers be compensated for water reallocation from agricultural water to the local ecological sector on the edge of an oasis in the Heihe River Basin?," Agricultural Water Management, Elsevier, vol. 249(C).
    3. Zheng Lu & Yuan He & Shuyan Peng, 2023. "Assessing Integrated Hydrologic Model: From Benchmarking to Case Study in a Typical Arid and Semi-Arid Basin," Land, MDPI, vol. 12(3), pages 1-23, March.
    4. Lingxiao Sun & Yang Yu & Yuting Gao & Jing He & Xiang Yu & Ireneusz Malik & Malgorzata Wistuba & Ruide Yu, 2021. "Remote Sensing Monitoring and Evaluation of the Temporal and Spatial Changes in the Eco-Environment of a Typical Arid Land of the Tarim Basin in Western China," Land, MDPI, vol. 10(8), pages 1-18, August.
    5. Li, Jiang & Shang, Songhao & Jiang, Hongzhe & Song, Jian & Rahman, Khalil Ur & Adeloye, Adebayo J., 2021. "Simulation-based optimization for spatiotemporal allocation of irrigation water in arid region," Agricultural Water Management, Elsevier, vol. 254(C).
    6. Yu, Haijiao & Wen, Xiaohu & Wu, Min & Sheng, Danrui & Wu, Jun & Zhao, Ying, 2022. "Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture," Agricultural Water Management, Elsevier, vol. 262(C).
    7. Ren, Hourui & Liu, Bin & Zhang, Zirui & Li, Fuxin & Pan, Ke & Zhou, Zhongli & Xu, Xiaoshuang, 2022. "A water-energy-food-carbon nexus optimization model for sustainable agricultural development in the Yellow River Basin under uncertainty," Applied Energy, Elsevier, vol. 326(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xu, Xu & Jiang, Yao & Liu, Minghuan & Huang, Quanzhong & Huang, Guanhua, 2019. "Modeling and assessing agro-hydrological processes and irrigation water saving in the middle Heihe River basin," Agricultural Water Management, Elsevier, vol. 211(C), pages 152-164.
    2. Chen, Shilei & Huo, Zailin & Xu, Xu & Huang, Guanhua, 2019. "A conceptual agricultural water productivity model considering under field capacity soil water redistribution applicable for arid and semi-arid areas with deep groundwater," Agricultural Water Management, Elsevier, vol. 213(C), pages 309-323.
    3. Li, Jiang & Shang, Songhao & Jiang, Hongzhe & Song, Jian & Rahman, Khalil Ur & Adeloye, Adebayo J., 2021. "Simulation-based optimization for spatiotemporal allocation of irrigation water in arid region," Agricultural Water Management, Elsevier, vol. 254(C).
    4. Ren, Dongyang & Xu, Xu & Engel, Bernard & Huang, Quanzhong & Xiong, Yunwu & Huo, Zailin & Huang, Guanhua, 2021. "A comprehensive analysis of water productivity in natural vegetation and various crops coexistent agro-ecosystems," Agricultural Water Management, Elsevier, vol. 243(C).
    5. Wang, Rong & Huang, Guanhua & Xu, Xu & Ren, Dongyang & Gou, Jiachao & Wu, Zhangsheng, 2022. "Significant differences in agro-hydrological processes and water productivity between canal- and well-irrigated areas in an arid region," Agricultural Water Management, Elsevier, vol. 267(C).
    6. Wang, Youzhi & Guo, Shanshan & Yue, Qing & Mao, Xiaomin & Guo, Ping, 2021. "Distributed AquaCrop simulation-nonlinear multi-objective dependent-chance programming for irrigation water resources management under uncertainty," Agricultural Water Management, Elsevier, vol. 247(C).
    7. Liu Liu & Zezhong Guo & Guanhua Huang & Ruotong Wang, 2019. "Water Productivity Evaluation under Multi-GCM Projections of Climate Change in Oases of the Heihe River Basin, Northwest China," IJERPH, MDPI, vol. 16(10), pages 1-17, May.
    8. Jiang, Yao & Xu, Xu & Huang, Quanzhong & Huo, Zailin & Huang, Guanhua, 2016. "Optimizing regional irrigation water use by integrating a two-level optimization model and an agro-hydrological model," Agricultural Water Management, Elsevier, vol. 178(C), pages 76-88.
    9. Huang, Zhenyu & Zhang, Junxiao & Ren, Dongyang & Hu, Jiaqi & Xia, Guimin & Pan, Baozhu, 2022. "Modeling and assessing water and nitrogen use and crop growth of peanut in semi-arid areas of Northeast China," Agricultural Water Management, Elsevier, vol. 267(C).
    10. Li, Jiang & Wang, Xinxin & Bai, Liangliang & Mao, Xiaomin, 2017. "Quantification of lateral seepage from farmland during maize growing season in arid region," Agricultural Water Management, Elsevier, vol. 191(C), pages 85-97.
    11. Ren, Dongyang & Xu, Xu & Engel, Bernard & Huang, Quanzhong & Xiong, Yunwu & Huo, Zailin & Huang, Guanhua, 2019. "Hydrological complexities in irrigated agro-ecosystems with fragmented land cover types and shallow groundwater: Insights from a distributed hydrological modeling method," Agricultural Water Management, Elsevier, vol. 213(C), pages 868-881.
    12. Chen, Shuai & Mao, Xiaomin & Barry, David Andrew & Yang, Jian, 2019. "Model of crop growth, water flow, and solute transport in layered soil," Agricultural Water Management, Elsevier, vol. 221(C), pages 160-174.
    13. Xu, Xu & Sun, Chen & Neng, Fengtian & Fu, Jing & Huang, Guanhua, 2018. "AHC: An integrated numerical model for simulating agroecosystem processes—Model description and application," Ecological Modelling, Elsevier, vol. 390(C), pages 23-39.
    14. Xue, Jing & Ren, Li, 2016. "Evaluation of crop water productivity under sprinkler irrigation regime using a distributed agro-hydrological model in an irrigation district of China," Agricultural Water Management, Elsevier, vol. 178(C), pages 350-365.
    15. Zhang, Chenglong & Li, Xuemin & Guo, Ping & Huo, Zailin, 2020. "An improved interval-based fuzzy credibility-constrained programming approach for supporting optimal irrigation water management under uncertainty," Agricultural Water Management, Elsevier, vol. 238(C).
    16. He, Liuyue & Xu, Zhenci & Wang, Sufen & Bao, Jianxia & Fan, Yunfei & Daccache, Andre, 2022. "Optimal crop planting pattern can be harmful to reach carbon neutrality: Evidence from food-energy-water-carbon nexus perspective," Applied Energy, Elsevier, vol. 308(C).
    17. Madan K. Jha & Richard C. Peralta & Sasmita Sahoo, 2020. "Simulation-Optimization for Conjunctive Water Resources Management and Optimal Crop Planning in Kushabhadra-Bhargavi River Delta of Eastern India," IJERPH, MDPI, vol. 17(10), pages 1-20, May.
    18. Cao, Zhaodan & Zhu, Tingju & Cai, Ximing, 2023. "Hydro-agro-economic optimization for irrigated farming in an arid region: The Hetao Irrigation District, Inner Mongolia," Agricultural Water Management, Elsevier, vol. 277(C).
    19. Bonfante, A. & Monaco, E. & Manna, P. & De Mascellis, R. & Basile, A. & Buonanno, M. & Cantilena, G. & Esposito, A. & Tedeschi, A. & De Michele, C. & Belfiore, O. & Catapano, I. & Ludeno, G. & Salinas, 2019. "LCIS DSS—An irrigation supporting system for water use efficiency improvement in precision agriculture: A maize case study," Agricultural Systems, Elsevier, vol. 176(C).
    20. Deg-Hyo Bae & Toshio Koike & Jehangir Awan & Moon-Hwan Lee & Kyung-Hwan Sohn, 2015. "Climate Change Impact Assessment on Water Resources and Susceptible Zones Identification in the Asian Monsoon Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5377-5393, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:203:y:2018:i:c:p:37-52. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.