Establishing Opportunities and Limitations of Forecast Use in the Operational Management of Highly Constrained Multiobjective Water Systems
Publication: Journal of Water Resources Planning and Management
Volume 148, Issue 8
Abstract
The potential utility of forecast information to improve reservoir control is well established, but physical, operational, and institutional constraints inhibit the full realization of these benefits. To help integrate forecast technology into operational water management, it is necessary to establish the opportunities and limitations of forecast use within the current system management framework, so that water managers and planners can identify in practice what constraints or aspects of control policy design most limit forecast utility. In this work, we explore the value of forecasts in the current outflow management plan of the Lake Ontario-St. Lawrence River basin, which is the first regulation policy of this system to use water supply forecasts to guide release decisions. We assess the value of forecasts for many-objective system management (e.g., upstream and downstream flood risk reduction, hydropower production, commercial navigation costs, recreational boating benefits, and wetland restoration), as a function of forecast accuracy, timing, and interactive effects, with system constraints and control rule structures. Results show that a 20%–30% reduction in forecast error over current statistical forecasts leads to observable improvements in most objectives, which grow significantly with additional forecast accuracy. Realizing these benefits is possible by increasing forecast accuracy, particularly in the springtime when tradeoffs between objectives are the greatest. However, we also identify one objective (wetland restoration) that declines with improved forecast skill, as well as operational constraints that limit forecast utility under the current outflow management plan. Overall, these results suggest that key aspects of the current control policy could be revised to better utilize forecast information, especially as forecasts improve over time.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
Tim Hunter from NOAA GLERL provided the historic supply data for the LOSLR Basin. Yin Fan from ECCC provided the stochastically generated dataset of supplies. The GLAM Committee provided feedback on this work to ensure project outcomes would be helpful in future decision-making. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1650441 and the Cooperative Institute for Great Lakes Research Graduate Fellowship Program. This is GLERL contribution Number 2002.
References
Addison, P. F. E., L. Rumpff, S. S. Bau, J. M. Carey, Y. E. Chee, F. C. Jarrad, M. F. McBride, and M. A. Burgman. 2013. “Practical solutions for making models indispensable in conservation decision-making.” Divers. Distrib. 19 (5–6): 490–502. https://doi.org/10.1111/ddi.12054.
Ahmad, A., A. El-Shafie, S. F. Mohd Razali, and Z. S. Mohamad. 2014. “Reservoir optimization in water resources: A review.” Water Resour. Manage. 28 (11): 3391–3405. https://doi.org/10.1007/s11269-014-0700-5.
Ahmad, S. K., and F. Hossain. 2020. “Maximizing energy production from hydropower dams using short-term weather forecasts.” Renewable Energy 146 (Feb): 1560–1577. https://doi.org/10.1016/j.renene.2019.07.126.
Ahmadi, M., O. Bozorg Haddad, and M. A. Mariño. 2014. “Extraction of flexible multi-objective real-time reservoir operation rules.” Water Resour. Manage. 28 (1): 131–147. https://doi.org/10.1007/s11269-013-0476-z.
Alemu, E. T., R. N. Palmer, A. Polebitski, and B. Meaker. 2011. “Decision support system for optimizing reservoir operations using ensemble streamflow predictions.” J. Water Resour. Plann. Manage. 137 (1): 72–82. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000088.
Alley, R. B., K. A. Emanuel, and F. Zhang. 2019. “Advances in weather prediction.” Science 363 (6425): 342–344. https://doi.org/10.1126/science.aav7274.
Anghileri, D., N. Voisin, A. Castelletti, F. Pianosi, B. Nijssen, and D. P. Lettenmaier. 2016. “Value of long-term streamflow forecasts to reservoir operations for water supply in snow-dominated river catchments.” Water Resour. Res. 52 (6): 4209–4225. https://doi.org/10.1002/2015WR017864.
Ashbolt, S. C., and B. J. C. Perera. 2018. “Multiobjective optimization of seasonal operating rules for water grids using streamflow forecast information.” J. Water Resour. Plann. Manage. 144 (4): 05018003. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000902.
Badham, J., et al. 2019. “Effective modeling for integrated water resource management: A guide to contextual practices by phases and steps and future opportunities.” Environ. Modell. Software 116 (Jun): 40–56. https://doi.org/10.1016/j.envsoft.2019.02.013.
Bauer, P., A. Thorpe, and G. Brunet. 2015. “The quiet revolution of numerical weather prediction.” Nature 525 (7567): 47–55. https://doi.org/10.1038/nature14956.
Becker, E., B. P. Kirtman, and K. Pegion. 2020. “Evolution of the North American multi-model ensemble.” Geophys. Res. Lett. 47 (9): 1–9. https://doi.org/10.1029/2020GL087408.
Borowski, I., and M. Hare. 2007. “Exploring the gap between water managers and researchers: Difficulties of model-based tools to support practical water management.” Water Resour. Manage. 21 (7): 1049–1074. https://doi.org/10.1007/s11269-006-9098-z.
Bruno Soares, M., and S. Dessai. 2016. “Barriers and enablers to the use of seasonal climate forecasts amongst organisations in Europe.” Clim. Change 137 (1): 89–103. https://doi.org/10.1007/s10584-016-1671-8.
Carter, E., and S. Steinschneider. 2018. “Hydroclimatological drivers of extreme floods on Lake Ontario.” Water Resour. Res. 54 (7): 4461–4478. https://doi.org/10.1029/2018WR022908.
Chen, L., V. P. Singh, W. Lu, J. Zhang, J. Zhou, and S. Guo. 2016. “Streamflow forecast uncertainty evolution and its effect on real-time reservoir operation.” J. Hydrol. 540 (Sep): 712–726. https://doi.org/10.1016/j.jhydrol.2016.06.015.
Clamen, M., and D. Macfarlane. 2018. “Plan 2014: The historical evolution of Lake Ontario–St. Lawrence River regulation.” Can. Water Resour. J. 43 (4): 416–431. https://doi.org/10.1080/07011784.2018.1475263.
Cohen, J., D. Coumou, J. Hwang, L. Mackey, P. Orenstein, S. Totz, and E. Tziperman. 2019. “S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts.” WIREs Clim. Change 10 (2): e00567. https://doi.org/10.1002/wcc.567.
Denaro, S., D. Anghileri, M. Giuliani, and A. Castelletti. 2017. “Informing the operations of water reservoirs over multiple temporal scales by direct use of hydro-meteorological data.” Adv. Water Resour. 103 (May): 51–63. https://doi.org/10.1016/j.advwatres.2017.02.012.
Doering, K., J. Quinn, P. M. Reed, and S. Steinschneider. 2021. “Diagnosing the time-varying value of forecasts in multiobjective reservoir control.” J. Water Resour. Plann. Manage. 147 (7): 04021031. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001386.
Eldardiry, H., and F. Hossain. 2021. “The value of long-term streamflow forecasts in adaptive reservoir operation: The case of the High Aswan Dam in the transboundary Nile River basin.” J. Hydrometeorol. 22 (5): 1099–1115. https://doi.org/10.1175/JHM-D-20-0241.1.
Faber, B. A., and J. R. Stedinger. 2001. “Reservoir optimization using sampling SDP with ensemble streamflow prediction (ESP) forecasts.” J. Hydrol. 249 (1–4): 113–133. https://doi.org/10.1016/S0022-1694(01)00419-X.
Fagherazzi, L., D. Fay, and J. Salas. 2007. “Synthetic hydrology and climate change scenarios to improve multi-purpose complex water resource systems management. The Lake Ontario–St Lawrence River Study of the International Canada and US Joint Commission.” In Water resources management IV, 163–177. Southampton, UK: WIT.
Fry, L. M., D. Apps, and A. D. Gronewold. 2020. “Operational seasonal water supply and water level forecasting for the Laurentian great lakes.” J. Water Resour. Plann. Manage. 146 (9): 04020072. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001214.
Fu, W., and S. Steinschneider. 2019. “A diagnostic-predictive assessment of winter precipitation over the Laurentian Great Lakes: Effects of ENSO and other teleconnections.” J. Hydrometeorol. 20 (1): 117–137. https://doi.org/10.1175/JHM-D-18-0128.1.
Georgakakos, K. P., and N. E. Graham. 2008. “Potential benefits of seasonal inflow prediction uncertainty for reservoir release decisions.” J. Appl. Meteorol. Climatol. 47 (5): 1297–1321. https://doi.org/10.1175/2007JAMC1671.1.
Gleick, P. H. 2002. “Water management: Soft water paths.” Nature 418 (6896): 373. https://doi.org/10.1038/418373a.
Graham, N. E., and K. P. Georgakakos. 2010. “Toward understanding the value of climate information for multiobjective reservoir management under present and future climate and demand scenarios.” J. Appl. Meteorol. Climatol. 49 (4): 557–573. https://doi.org/10.1175/2009JAMC2135.1.
Great Lakes–St. Lawrence River Adaptive Management Committee. 2020. “Overview of expedited review of Plan 2014.” International Joint Commission. Accessed July 31, 2021. https://www.ijc.org/en/glam/expedited-review.
Gronewold, A. D., A. H. Clites, T. S. Hunter, and C. A. Stow. 2011. “An appraisal of the Great Lakes advanced hydrologic prediction system.” J. Great Lakes Res. 37 (3): 577–583. https://doi.org/10.1016/j.jglr.2011.06.010.
Ham, Y-G., J.-H. Kim, and J.-J. Luo. 2019. “Deep learning for multi-year ENSO forecasts.” Nature 573 (7775): 568–572. https://doi.org/10.1038/s41586-019-1559-7.
Hejazi, M. I., and X. Cai. 2011. “Building more realistic reservoir optimization models using data mining—A case study of Shelbyville Reservoir.” Adv. Water Resour. 34 (6): 701–717. https://doi.org/10.1016/j.advwatres.2011.03.001.
Hejazi, M. I., X. Cai, and B. L. Ruddell. 2008. “The role of hydrologic information in reservoir operation—Learning from historical releases.” Adv. Water Resour. 31 (12): 1636–1650. https://doi.org/10.1016/j.advwatres.2008.07.013.
Ho, M., U. Lall, M. Allaire, N. Devineni, H. H. Kwon, I. Pal, D. Raff, and D. Wegner. 2017. “The future role of dams in the United States of America.” Water Resour. Res. 53 (2): 982–998. https://doi.org/10.1002/2016WR019905.
International Joint Commission. 2014. “Lake Ontario–St. Lawrence River Plan 2014: Protecting against extreme water levels, restoring wetlands, and preparing for climate change.” Accessed July 31, 2021. http://www.ijc.org/files/tinymce/uploaded/LOSLR/IJC_LOSR_EN_Web.pdf.
International Joint Commission. 2016. “Regulation Plan 2014 for Lake Ontario and the St. Lawrence River: Compendium document.” Accessed July 31, 2021. https://www.ijc.org/sites/default/files/Plan2014_CompendiumReport.pdf.
International Lake Ontario–St. Lawrence River Board. 2018. “Observed conditions & regulated outflows in 2017.” Report to the International Joint Commission. Accessed July 31, 2021. https://ijc.org/sites/default/files/2018-08/ILOSLRB_FloodReport2017.pdf.
International Lake Ontario–St. Lawrence River Study Board. 2006. “Options for managing Lake Ontario and St. Lawrence River water levels and flows.” Report to the International Joint Commission. Accessed July 31, 2021. https://ijc.org/sites/default/files/L40.pdf.
Kasprzyk, J. R., R. M. Smith, A. S. Stillwell, K. Madani, D. Ford, D. McKinney, and S. Sorooshian. 2018. “Defining the role of water resources systems analysis in a changing future.” J. Water Resour. Plann. Manage. 144 (12): 01818003. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001010.
Khummongkol, R., D. Sutivong, and S. Kuntanakulwong. 2007. “Water resource management using multi-objective optimization and rainfall forecast.” In Proc., 2007 Int. Conf. on Convergence Information Technology, 2449–2454. Washington, DC: IEEE Computer Society.
Lach, D., and S. Rayner. 2017. “Are forecasts still for wimps?” J. Southwest 59 (1–2): 245–263. https://doi.org/10.1353/jsw.2017.0013.
Lemos, M. C. 2008. “What influences innovation adoption by water managers? Climate information use in Brazil and the United States.” J. Am. Water Resour. Assoc. 44 (6): 1388–1396. https://doi.org/10.1111/j.1752-1688.2008.00231.x.
Liu, Z., Y. Guo, L. Wang, and Q. Wang. 2015. “Streamflow forecast errors and their impacts on forecast-based reservoir flood control.” Water Resour. Manage. 29 (12): 4557–4572. https://doi.org/10.1007/s11269-015-1080-1.
Lu, M., U. Lall, A. W. Robertson, and E. Cook. 2017. “Optimizing multiple reliable forward contracts for reservoir allocation using multitime scale streamflow forecasts.” Water Resour. Res. 53 (3): 2035–2050. https://doi.org/10.1002/2016WR019552.
Mejia, J. M., and J. Rousselle. 1976. “Disaggregation models in hydrology revisited.” Water Resour. Res. 12 (2): 185–186. https://doi.org/10.1029/WR012i002p00185.
National Research Council. 2006. Review of the Lake Ontario-St. Lawrence River Studies. Washington, DC: National Academies Press. https://doi.org/10.17226/11481.
Nayak, M. A., J. D. Herman, and S. Steinschneider. 2018. “Balancing flood risk and water supply in California: Policy search integrating short-term forecast ensembles with conjunctive use.” Water Resour. Res. 54 (10): 7557–7576. https://doi.org/10.1029/2018WR023177.
Nearing, G. S., F. Kratzert, A. K. Sampson, C. S. Pelissier, D. Klotz, J. M. Frame, C. Prieto, and H. V. Gupta. 2021. “What role does hydrological science play in the age of machine learning?” Water Resour. Res. 57 (3): e2020WR028091. https://doi.org/10.1029/2020WR028091.
Newell, P. 1997. “Silenced rivers: The ecology and politics of large dams.” Int. Aff. 73 (4): 799–800. https://doi.org/10.2307/2624501.
Odan, F. K., L. F. Ribeiro Reis, and Z. Kapelan. 2015. “Real-time multiobjective optimization of operation of water supply systems.” J. Water Resour. Plann. Manage. 141 (9): 04015011. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000515.
Rayner, S., D. Lach, and H. Ingram. 2005. “Weather forecasts are for wimps: Why water resource managers do not use climate forecasts.” Clim. Change 69 (2): 197–227. https://doi.org/10.1007/s10584-005-3148-z.
Romoff, J. 2017. “Damages sustained from rising lake water levels and restoration plans for waterfront parks.” Report for Action, City of Toronto. Accessed July 27, 2021. https://www.toronto.ca/legdocs/mmis/2017/cc/bgrd/backgroundfile-105457.pdf.
Sankarasubramanian, A., U. Lall, F. A. Souza Filho, and A. Sharma. 2009. “Improved water allocation utilizing probabilistic climate forecasts: Short-term water contracts in a risk management framework.” Water Resour. Res. 45 (11): W11409. https://doi.org/10.1029/2009WR007821.
Shrubsole, D., R. Kreutzwiser, B. Mitchell, T. Dickinson, and D. Joy. 1993. “The history of flooding damages in Ontario.” Can. Water Resour. J. 18 (2): 133–143. https://doi.org/10.4296/cwrj1802133.
Simonovic, S. P. 1992. “Reservoir systems analysis: Closing gap between theory and practice.” J. Water Resour. Plann. Manage. 118 (3): 262–280. https://doi.org/10.1061/(ASCE)0733-9496(1992)118:3(262).
Sissel, A. 2021. “Predicting the effects of improved forecasting skill on Lake Ontario wetlands.” Master’s thesis, Dept. of Biological and Environmental Engineering, Cornell Univ.
Sowby, R. B., and T. M. Walski. 2021. “Reconnecting water resources research and practice.” J. Water Resour. Plann. Manage. 147 (6): 02521004. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001382.
Stedinger, J. R., D. Pei, and T. A. Cohn. 1985. “A condensed disaggregation model for incorporating parameter uncertainty into monthly reservoir simulations.” Water Resour. Res. 21 (5): 665–675. https://doi.org/10.1029/WR021i005p00665.
Sveinsson, Ó. G. B., and J. D. Salas. 2006. “Multivariate shifting mean plus persistence model for simulating the Great Lakes net basin supplies.” In Proc., 2006 Annual AGU Hydrology Days, 173–184. Fort Collins, CO: Colorado State Univ. http://hydrologydays.colostate.edu/Papers_2006/Sveinsson_Salas.pdf.
Syme, G. J., and B. S. Sadler. 1994. “Evaluation of public involvement in water resources planning: A researcher-practitioner dialogue.” Eval. Rev. 18 (5): 523–542. https://doi.org/10.1177/0193841X9401800501.
Turner, S. W. D., J. C. Bennett, D. E. Robertson, and S. Galelli. 2017. “Complex relationship between seasonal streamflow forecast skill and value in reservoir operations.” Hydrol. Earth Syst. Sci. 21 (9): 4841–4859. https://doi.org/10.5194/hess-21-4841-2017.
Vitart, F. 2014. “Evolution of ECMWF sub-seasonal forecast skill scores.” Q. J. R. Meteorol. Soc. 140 (683): 1889–1899. https://doi.org/10.1002/qj.2256.
Watkins, D. W., and W. Wei. 2008. “The value of seasonal climate forecasts and why water managers don’t use them.” In Proc., World Environmental and Water Resources Congress 2008, 1–5. Reston, VA: ASCE. https://doi.org/10.1061/40976(316)550.
Whateley, S., R. N. Palmer, and C. Brown. 2015. “Seasonal hydroclimatic forecasts as innovations and the challenges of adoption by water managers.” J. Water Resour. Plann. Manage. 141 (5): 04014071. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000466.
White, D. D., A. Wutich, K. L. Larson, P. Gober, T. Lant, and C. Senneville. 2010. “Credibility, salience, and legitimacy of boundary objects: Water managers’ assessment of a simulation model in an immersive decision theater.” Sci. Public Policy 37 (3): 219–232. https://doi.org/10.3152/030234210X497726.
Wilcox, D. A., J. W. Ingram, K. P. Kowalski, J. E. Meeker, M. L. Carlson, Y. Xie, G. P. Grabas, K. L. Holmes, and N. J. Patterson. 2005. “Evaluation of water level regulation influences on Lake Ontario and Upper St. Lawrence River coastal wetland plant communities. Final Report.” Report to the International Joint Commission. Accessed July 31, 2021. https://www.researchgate.net/publication/228477533.
Wilcox, D. A., and Y. Xie. 2007. “Predicting wetland plant community responses to proposed water-level-regulation plans for Lake Ontario: GIS-based modeling.” J. Great Lakes Res. 33 (4): 751–773. https://doi.org/10.3394/0380-1330(2007)33[751:PWPCRT]2.0.CO;2.
Xu, B., P.-A. Zhong, Z. Stanko, Y. Zhao, and W. W.-G. Yeh. 2015. “A multiobjective short-term optimal operation model for a cascade system of reservoirs considering the impact on long-term energy production.” Water Resour. Res. 51 (5): 3353–3369. https://doi.org/10.1002/2014WR015964.
Yang, G., S. Guo, P. Liu, and P. Block. 2020. “Integration and evaluation of forecast-informed multiobjective reservoir operations.” J. Water Resour. Plann. Manage. 146 (6): 04020038. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001229.
Yang, G., S. Guo, P. Liu, and P. Block. 2021. “Sensitivity of forecast value in multiobjective reservoir operation to forecast lead time and reservoir characteristics.” J. Water Resour. Plann. Manage. 147 (6): 04021027. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001384.
Yao, H., and A. Georgakakos. 2001. “Assessment of Folsom Lake response to historical and potential future climate scenarios: 2. Reservoir management.” J. Hydrol. 249 (1–4): 176–196. https://doi.org/10.1016/S0022-1694(01)00418-8.
Zhao, T., X. Cai, and D. Yang. 2011. “Effect of streamflow forecast uncertainty on real-time reservoir operation.” Adv. Water Resour. 34 (4): 495–504. https://doi.org/10.1016/j.advwatres.2011.01.004.
Zhao, T., and J. Zhao. 2014. “Joint and respective effects of long- and short-term forecast uncertainties on reservoir operations.” J. Hydrol. 517 (Sep): 83–94. https://doi.org/10.1016/j.jhydrol.2014.04.063.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 148 • Issue 8 • August 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Sep 3, 2021
Accepted: Apr 14, 2022
Published online: Jun 7, 2022
Published in print: Aug 1, 2022
Discussion open until: Nov 7, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Kyla Semmendinger, Scott Steinschneider, Influence of Subseasonal-to-Annual Water Supply Forecasts on Many-Objective Water System Robustness under Long-Term Change, Journal of Water Resources Planning and Management, 10.1061/JWRMD5.WRENG-6205, 150, 5, (2024).
- Lihong Huang, Research and Application of Business Law Information Management System Based on Big Data, Journal of Sensors, 10.1155/2022/5885451, 2022, (1-12), (2022).