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Over-pumping of groundwater resources is a serious problem world-wide. In addition to depleting this valuable water supply resource, hydraulically connected wetlands and surface water bodies are often impacted and even destroyed by over-pumping. Effectively managing groundwater resources in a way that satisfy human needs while preserving natural resources is a daunting problem that will only worsen with growing populations and climate change. What further complicates management of these systems is that even when pumping rates of wells are held fairly constant, their hydraulic effects are often highly transient due to variable weather and hydrologic conditions. Despite this, transient conditions are rarely if ever accounted for by management models due to the difficulties in separating pumping effects from natural factors like weather. To address this shortcoming, a conceptual real-time decision support system for managing complex groundwater/surface water systems affected by variable weather, hydrologic, and pumping conditions over space and time is presented in this chapter. For the hypothetical but realistic groundwater/surface water system presented here, the decision support system, based upon previous work by Coppola et al. (2003a, b, 2005a, b, c, 2007, 2014) consists of real-time data streams combined with artificial neural network (ANN) prediction models and formal optimization. Time variable response coefficients derived from ANN prediction models are used by an optimization model to maximize total groundwater pumping in a multi-layered aquifer system while protecting against aquifer over-draft, streamflow depletion, and dewatering of riparian areas. Optimization is performed for different management constraint sets for both the wet and dry seasons, resulting in significantly different groundwater pumping extraction solutions. Stochastic optimization is also performed for different precipitation forecast events to address corresponding uncertainty associated with weather-dependent irrigation pumping demand. This data-driven support system can continuously adapt in real-time to existing and forecasted hydrological and weather conditions, as well as water demand, providing superior management solutions.
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Barlow, P. M., & Leake, S. A. (2012). Streamflow depletion by wells—understanding and managing the effects of groundwater pumping on streamflow. Circular 1376, United States Geological Survey, Reston, Virginia.
Brown, L. R. (2011). World on the edge. New York: W. W. Norton & Company.
Coppola, E., Szidarovszky, F., Poulton, M., & Charles, E. (2003a). Artificial neural network approach for predicting transient water levels in a multilayered groundwater system under variable state, pumping, and climate conditions. Journal of Hydrologic Engineering, 8(6), 348–359. CrossRef
Coppola, E., Poulton, M., Charles, E., Dustman, J., & Szidarovszky, F. (2003b). Application of artificial neural networks to complex groundwater management problems. Journal of Natural Resources Research, 12(4), 303–320. CrossRef
Coppola, E., Rana, A., Poulton, M., Szidarovszky, F., & Uhl, V. (2005a). A neural network model for predicting water table elevations. Journal of Ground Water, 43(2), 231–241. CrossRef
Coppola, E., McLane, C., Poulton, M., Szidarovszky, F., & Magelky, R. (2005b). Predicting conductance due to upcoming using neural networks. Journal of Ground Water, 43(6), 827–836. CrossRef
Coppola, E., Szidarovszky, F., & Poulton, M. (2005c). Application of artificial neural networks to complex groundwater prediction and management problems. Journal of Southwest Hydrology, 4(3).
Coppola, E., Szidarovszky, F., Davis, D., Spayd, S., Poulton, M., & Roman, E. (2007). Multiobjective analysis of a public wellfield using artificial neural networks. Journal of Ground Water, 45(1), 53–61. CrossRef
Coppola, E., Szidarovszky, A., & Szidarovszky, F. (2014). Artificial neural network based modeling of hydrologic processes. In Handbook of engineering hydrology. CRC Press.
Karamouz, M., Szidarovszky, F., & Zahraie, B. (2003). Water resources systems analysis. Boca Raton, Florida: Lewis Publishers.
Mays, L. W. (2007). Water resources sustainability. New York: The McGraw Hill Companies.
Peralta, R. C., & Kalwij, I. M. (2012). Groundwater optimization handbook. Boca Raton, Florida: CRC Press. CrossRef
Poulton, M. (Ed.). (2001). Computational neural networks for geophysical data processing. Amsterdam: Pergamon, 335 p.
Winter, T. C., Harvey, J. W., Franke, O. L., & Alley, W. M. (1998). Groundwater and surface water, a single resource. U.S. Geological Survey Circular 1139, Denver, Colorado.
- A Decision Support System for Managing Water Resources in Real-Time Under Uncertainty
Emery A. Coppola
- Springer Singapore
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