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Volume 5 Issue 1
Mar.  2017
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Article Contents
Duong D Bui, Nghia C Nguyen, Nuong T Bui, et al. 2017: Climate change and groundwater resources in Mekong Delta, Vietnam. Journal of Groundwater Science and Engineering, 5(1): 76-90.
Citation: Duong D Bui, Nghia C Nguyen, Nuong T Bui, et al. 2017: Climate change and groundwater resources in Mekong Delta, Vietnam. Journal of Groundwater Science and Engineering, 5(1): 76-90.

Climate change and groundwater resources in Mekong Delta, Vietnam

  • Publish Date: 2017-03-28
  • Groundwater resources have considerable influences on the human population and socioeconomic development of Vietnam and the Mekong River Delta (MRD). This paper presents an overview of the relationship between climate change and groundwater in the MRD, including the challenges, strategies and technical measures. Our results showed that groundwater levels are related to other climate and hydrological variables (i.e., rainfall, river levels, etc.); therefore, the impacts of climate change on the groundwater resources of the Mekong delta are significant, especially on groundwater recharge. Based on the results of this study, it is recommended that groundwater development in the future should focus on reducing groundwater harvesting, enhancing groundwater quantity by establishing artificial works and exploiting surface water. This study suggests that the Artificial Neural Network (ANN) model is an effective tool for forecasting groundwater levels in periods of 1 month and 3 months for aquifers in the natural and tidal regime areas of the delta.
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    Feng S, Kang S, et al. 2008. Neural networks to simulate regional ground water levels affected by human activities. Ground Water, 46(1): 80-90 .
    Mekong Delta. Scentific Institute on Meteorology, Hydrology and Environment (an unpublished report) .
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    DWRPIS. 2010. Report on the groundwater monitoring results for Southern Region. Division for Water Resources Planning and Investigation for the South of Viet Nam .
    Nayak P C, Rao Y R S, et al. 2006. Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resources Management, 20(1): 77-90 .
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