Evaluation of New Control Structures for Regulating the Great Lakes System: Multiscenario, Multireservoir Optimization Approach
Publication: Journal of Water Resources Planning and Management
Volume 140, Issue 8
Abstract
Water levels in the Great Lakes–St. Lawrence system located in northeastern North America are critically important to the Canadian and U.S. economies. Water managers are concerned that this system, which is currently managed by control structures at the outlets of Lakes Superior and Ontario, is not able to cope with the highly uncertain impacts of climate change. In particular, the frequency of extreme water levels throughout the system might be substantially increased. This study provides an exploratory conceptual analysis to determine the extent that new control structures at the outlet of Lake Huron or Erie (or both) and corresponding excavation along the St. Clair or Niagara River (or both) might mitigate the risks posed by future extreme water supply scenarios. Multilake parametric rule curves were developed to regulate systems enabled with these new control structures as a whole. Multiple stochastic water supply sequences were adopted that represented different future extreme climate scenarios. A multiscenario, multireservoir (multilake), biobjective simulation-optimization methodology was developed to optimize the rule curve parameters in such a way that the risk of experiencing extreme water levels is robustly minimized and fairly distributed across the system. The biobjective setting was designed to embed the secondary objective function so that the cost of the new control structures and excavation generates trade-offs between the costs and the associated achievable risk reductions. The recently developed Pareto Archived Dynamically Dimensioned Search (PA-DDS) multiobjective optimization algorithm was enabled with the efficiency-increasing “deterministic model preemption” strategy and utilized to solve the optimization problem. Results demonstrate that although systemwide regulation with the new control structures could substantially reduce the risk (i.e., by 86% compared to the current or base level of regulation), it could not eliminate such events entirely and would cause adverse effects on the lower St. Lawrence River. Numerical results also suggest that implementing a single new control point at the outlet of Lake Huron would not be effective despite its high cost; however, a single new control point at the outlet of Lake Erie would be very effective, with considerably less associated cost.
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Acknowledgments
This work was a part of Dr. Tolson’s research contract under the International Upper Great Lakes Study (IUGLS) funded by the International Joint Commission (IJC). The authors would like to thank the associate editor and anonymous reviewers for their insightful comments that improved the quality of this paper.
References
Afshar, A., Haddad, O. B., Marino, M. A., and Adams, B. J. (2007). “Honeybee mating optimization (HBMO) algorithm for optimal reservoir operation.” J. Franklin Inst. Eng. Appl. Math., 344(5), 452–462.
Angel, J. R., and Kunkel, K. E. (2010). “The response of Great Lakes water levels to future climate scenarios with an emphasis on Lake Michigan-Huron.” J. Great Lakes Res., 36(2), 51–58.
Arnell, N. W. (2011). “Incorporating climate change into water resources planning in England and Wales.” J. Am. Water Resour. Assoc., 47(3), 541–549.
Asadzadeh, M., and Tolson, B. A. (2009). “A new multi-objective algorithm, Pareto archived DDS.” Genetic and Evolutionary Computation Conf. (GECCO 2009), ACM, New York, 1963–1966.
Asadzadeh, M., and Tolson, B. A. (2012). “Pareto archived dynamically dimensioned search with hypervolume based selection for multi-objective optimization.” J. Eng. Optim., 45(12), 1489–1509.
Baltar, A. M., and Fontane, D. G. (2008). “Use of multiobjective particle swarm optimization in water resources management.” J. Water Resour. Plann. Manage., 257–265.
Brekke, L. D., et al. (2009). “Assessing reservoir operations risk under climate change.” Water Resour. Res., 45, W04411.
Chang, F. J., Chen, L., and Chang, L. C. (2005). “Optimizing the reservoir operating rule curves by genetic algorithms.” Hydrol. Processes, 19(11), 2277–2289.
Chang, L.-C., Chang, F.-J., Wang, K.-W., and Dai, S.-Y. (2010a). “Constrained genetic algorithms for optimizing multi-use reservoir operation.” J. Hydrol., 390(1–2), 66–74.
Chang, L.-C., Ho, C. C., and Chen, Y. W. (2010b). “Applying multiobjective genetic algorithm to analyze the conflict among different water use sectors during drought period.” J. Water Resour. Plann. Manage., 539–546.
Chenoweth, J., et al. (2011). “Impact of climate change on the water resources of the eastern Mediterranean and Middle East region: Modeled 21st-century changes and implications.” Water Resour. Res., 47, W06506.
Cherkauer, K. A., and Sinha, T. (2010). “Hydrologic impacts of projected future climate change in the Lake Michigan region.” J. Great Lakes Res., 36(2), 33–50.
Chiu, Y.-C., Chang, L.-C., and Chang, F.-J. (2007). “Using a hybrid genetic algorithm-simulated annealing algorithm for fuzzy programming of reservoir operation.” Hydrol. Processes, 21(23), 3162–3172.
Clites, A. H., and Lee, D. H. (1998). MIDLAKES: A coordinated hydrologic response model for the Middle Great Lakes NOAA, Great Lakes Environmental Research Laboratory, Ann Arbor, MI.
Dembo, R. S. (1991). “Scenario optimization.” Ann. Operat. Res., 30(1), 63–80.
Fagherazzi, L., Guay, R., Sparks, D., Salas, J., and Sveinsson, O. (2005). “Stochastic modeling and simulation of the Great Lakes–St. Lawrence River system.” Rep. submitted to the International Lake Ontario-St. Lawrence Study.
Fletcher, S. G., and Ponnambalam, K. (1998). “A constrained state formulation for the stochastic control of multireservoir systems.” Water Resour. Res., 34(2), 257–270.
Guo, X., Hu, T., Zeng, X., and Li, X. (2013). “Extension of parametric rule with the hedging rule for managing multi-reservoir system during droughts.” J. Water Resour. Plann. Manage., 139–148.
Hashimoto, T., Stedinger, J. R., and Loucks, D. P. (1982). “Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation.” Water Resour. Res., 18(1), 14–20.
Huang, W. C., Yuan, L. C., and Lee, C. M. (2002). “Linking genetic algorithms with stochastic dynamic programming to the long-term operation of a multireservoir system.” Water Resour. Res., 38(12), 40-1–40-9.
ILOSLRSB. (2006). “Options for managing Lake Ontario–St. Lawrence River water levels and flows.” Rep. to the International Joint Commission, ANNEX 3–Plan Descriptions and Summary Results, International Lake Ontario–St. Lawrence River Study, 160–177.
Jalali, M. R., Afshar, A., and Marino, M. A. (2007). “Multi-colony ant algorithm for continuous multi-reservoir operation optimization problem.” Water Resour. Manage., 21(9), 1429–1447.
Johnson, F., and Sharma, A. (2011). “Accounting for interannual variability: A comparison of options for water resources climate change impact assessments.” Water Resour. Res., 47, 20.
Karamouz, M., Houck, M. H., and Delleur, J. W. (1992). “Optimization and simulation of multiple reservoir systems.” J. Water Resour. Plann. Manage., 71–81.
Kelman, J., Stedinger, J. R., Cooper, L. A., Hsu, E., and Yuan, S. Q. (1990). “Sampling stochastic dynamic programming applied to reservoir operation.” Water Resour. Res., 26(3), 447–454.
Labadie, J. W. (2004). “Optimal operation of multireservoir systems: State-of-the-art review.” J. Water Resour. Plann. Manage., 93–111.
Laird, C. D., and Biegler, L. T. (2008). Large-scale nonlinear programming for multi-scenario optimization, in modeling, simulation and optimization of complex processes, H. Bock, E. Kostina, H. Phu, and R. Rannacher, eds., Springer, Berlin Heidelberg, 323–336.
Li, Y., Zhou, J., Zhang, Y., Qin, H., and Liu, L. (2010). “Novel multiobjective shuffled frog leaping algorithm with application to reservoir flood control operation.” J. Water Resour. Plann. Manage., 217–226.
Lofgren, B. M., Hunter, T. S., and Wilbarger, J. (2011). “Effects of using air temperature as a proxy for potential evapotranspiration in climate change scenarios of Great Lakes basin hydrology.” J. Great Lakes Res., 37(4), 744–752.
Malekmohammadi, B., Kerachian, R., and Zahraie, B. (2009). “Developing monthly operating rules for a cascade system of reservoirs: Application of Bayesian networks.” Environ. Model. Software, 24(12), 1420–1432.
Nalbantis, I., and Koutsoyiannis, D. (1997). “A parametric rule for planning and management of multiple-reservoir systems.” Water Resour. Res., 33(9), 2165–2177.
Quinn, F. H. (1978). “Hydrologic response model of the North American Great Lakes.” J. Hydrol., 37(3), 295–307.
Razavi, S., Tolson, B. A., Matott, L. S., Thomson, N. R., MacLean, A., and Seglenieks, F. R. (2010). “Reducing the computational cost of automatic calibration through model preemption.” Water Resour. Res., 46(11), 17.
Ricciardi, K. L., Pinder, G. F., and Karatzas, G. P. (2007). “Efficient groundwater remediation system design subject to uncertainty using robust optimization.” J. Water Resour. Plann. Manage., 253–263.
Seifi, A., and Hipel, K. W. (2001). “Interior-point method for reservoir operation with stochastic inflows.” J. Water Resour. Plann. Manage., 48–57.
Snyder, S. A., Haight, R. G., and ReVelle, C. S. (2004). “A scenario optimization model for dynamic reserve site selection.” Environ. Model. Assess., 9(3), 179–187.
Taghian, M., Rosbjerg, D., Haghighi, A., and Madsen, H. (2014). “Optimization of conventional rule curves coupled with hedging rules for reservoir operation.” J. Water Resour. Plann. Manage., 693–698.
Tolson, B. A., Razavi, S., and Asadzadeh, M. (2011). “Formulation and evaluation of new control structures in the Great Lakes system.” Tech. Rep. produced for Int. Upper Great Lakes Study– Int. Joint Commission Study, Univ. of Waterloo, Waterloo, ON, Canada, 81.
Tolson, B. A., and Shoemaker, C. A. (2007). “Dynamically dimensioned search algorithm for computationally efficient watershed model calibration.” Water Resour. Res., 43(1), 16.
Wei, C.-C., and Hsu, N.-S. (2008). “Derived operating rules for a reservoir operation system: Comparison of decision trees, neural decision trees and fuzzy decision trees.” Water Resour. Res., 44(2), 11.
Yin, X.-A., Yang, Z.-F., and Petts, G. E. (2011). “Reservoir operating rules to sustain environmental flows in regulated rivers.” Water Resour. Res., 47(8), 13.
Zahraie, B., and Hossein, S. M. (2009). “Development of reservoir operation policies considering variable agricultural water demands.” Expert Syst. Appl., 36(3), 4980–4987.
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Published In
Journal of Water Resources Planning and Management
Volume 140 • Issue 8 • August 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Apr 4, 2012
Accepted: May 7, 2013
Published online: May 8, 2013
Published ahead of production: May 9, 2013
Published in print: Aug 1, 2014
Discussion open until: Sep 18, 2014
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