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Exploring the Mechanism of Surface and Ground Water through Data-Driven Techniques with Sensitivity Analysis for Water Resources Management

Author

Listed:
  • Wen-Ping Tsai

    (National Taiwan University)

  • Yen-Ming Chiang

    (Zhejiang University)

  • Jun-Lin Huang

    (National Taiwan University)

  • Fi-John Chang

    (National Taiwan University)

Abstract

The over extraction of groundwater in central-western and southwestern Taiwan has resulted in serious land subsidence for decades. For making countermeasures in response to land subsidence, this study collects long-term hydrological data to explore the relationships between surface water and groundwater in various monitoring stations, and then constructs one-month-ahead forecast models by using data-driven techniques for the water resources management of the Zhuoshui River basin in Taiwan. The results demonstrate that the constructed models can accurately forecast monthly groundwater levels. The sensitivity analysis is next conducted on the input variables of the constructed models by using the partial derivative method. The analysis results reveal that streamflow is a predominant factor for groundwater level variation, and therefore streamflow management made by the upstream weir of the river would influence groundwater level variations. This study further implements several scenario analyses based on the interactive mechanism between groundwater and surface water in response to future climatic conditions and weir discharge management, respectively. The results of scenario analyses indicate that the groundwater recharge zone spreads along the Zhuoshui River while lateral and vertical recharge sources would cause different quantities and distribution patterns of groundwater recharge. Besides, an increase in weir discharge would improve groundwater recharge quantities with groundwater level variations at 0.12 m and 0.06 m in wet and dry seasons, respectively. As a consequence, the operation of weir discharge would play an import role in sustainable development of water resources management in the study area.

Suggested Citation

  • Wen-Ping Tsai & Yen-Ming Chiang & Jun-Lin Huang & Fi-John Chang, 2016. "Exploring the Mechanism of Surface and Ground Water through Data-Driven Techniques with Sensitivity Analysis for Water Resources Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4789-4806, October.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:13:d:10.1007_s11269-016-1453-0
    DOI: 10.1007/s11269-016-1453-0
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    References listed on IDEAS

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    1. Jenq-Tzong Shiau & Hui-Ting Hsu, 2016. "Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin, Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1499-1513, March.
    2. A. Osei-Twumasi & R. Falconer & R. Ahmadian, 2016. "Coupling Surface Water and Groundwater Flows in a Laboratory Model Using Foam as Artificial Groundwater Material," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1449-1463, March.
    3. S. Mohanty & Madan Jha & S. Raul & R. Panda & K. Sudheer, 2015. "Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5521-5532, December.
    4. J Sreekanth & Bithin Datta, 2014. "Stochastic and Robust Multi-Objective Optimal Management of Pumping from Coastal Aquifers Under Parameter Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 2005-2019, May.
    5. Santiago Castaño & David Sanz & Juan Gómez-Alday, 2013. "Sensitivity of a Groundwater Flow Model to Both Climatic Variations and Management Scenarios in a Semi-arid Region of SE Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2089-2101, May.
    6. Omid Bozorg-Haddad & Mahboubeh Zarezadeh-Mehrizi & Mehri Abdi-Dehkordi & Hugo A. Loáiciga & Miguel A. Mariño, 2016. "A self-tuning ANN model for simulation and forecasting of surface flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 2907-2929, July.
    7. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    8. T. Fowe & I. Nouiri & B. Ibrahim & H. Karambiri & J. Paturel, 2015. "OPTIWAM: An Intelligent Tool for Optimizing Irrigation Water Management in Coupled Reservoir–Groundwater Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3841-3861, August.
    9. A. Osei-Twumasi & R. A. Falconer & R. Ahmadian, 2016. "Coupling Surface Water and Groundwater Flows in a Laboratory Model Using Foam as Artificial Groundwater Material," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1449-1463, March.
    10. Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    11. Shishir Gaur & Sudheer Ch & Didier Graillot & B. Chahar & D. Kumar, 2013. "Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 927-941, February.
    12. Bagher Shirmohammadi & Mehdi Vafakhah & Vahid Moosavi & Alireza Moghaddamnia, 2013. "Application of Several Data-Driven Techniques for Predicting Groundwater Level," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 419-432, January.
    13. Purna Nayak & Y. Rao & K. Sudheer, 2006. "Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(1), pages 77-90, February.
    14. Gabor Bekesi & Megan McGuire & Dean Moiler, 2009. "Groundwater Allocation Using a Groundwater Level Response Management Method—Gnangara Groundwater System, Western Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(9), pages 1665-1683, July.
    15. Salim Djerbouai & Doudja Souag-Gamane, 2016. "Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Stochastic Models: Case of the Algerois Basin in North Algeria," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(7), pages 2445-2464, May.
    16. Jenq-Tzong Shiau & Hui-Ting Hsu, 2016. "Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin, Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1499-1513, March.
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