Optimal Multi-Reservoir Operation for Hydropower Production in the Nam Ngum River Basin

This research aims to investigate optimal hydropower production of multi-reservoirs in Lao PDR and develop optimal reservoir rule curves. The Nam Ngum 1 and 2 (NN1 and NN2, respectively) reservoirs in the Nam Ngum River basin (NNRB), which is located in the middle of Laos, are selected as study areas. Mixed integer nonlinear programming (MINLP) is developed as an optimization model to maximize the hydropower production of joint reservoir operation of NN1 and NN2. The optimal operation rule curves are established by using the storage level estimated by the optimization model. Given the limited sideflow data, an integrated flood analysis system (IFAS) and water balance equation are used to simulate the sideflow into NN1 reservoir. A good fit is observed between the monthly streamflow simulated by IFAS and that calculated by the water balance equation. Compared with the observed data, the MINLP model can increase the annual and monthly hydropower production by 20.22% (6.01% and 14.21% for NN1 and NN2, respectively). The water storage level estimated by the MINLP model is used to build the operation rule curves. Results show that the MINLP model of multi-reservoir is a useful and effective approach for multi-reservoir operations and is expected to hold high application value for similar reservoirs in NNRB

Inferring efficient operating rules in multireservoir water resource systems: A review

[EN] Coordinated and efficient operation of water resource systems becomes essential to deal with growing demands and uncertain resources in water-stressed regions. System analysis models and tools help address the complexities of multireservoir systems when defining operating rules. This paper reviews the state of the art in developing operating rules for multireservoir water resource systems, focusing on efficient system operation. This review focuses on how optimal operating rules can be derived and represented. Advantages and drawbacks of each approach are discussed. Major approaches to derive optimal operating rules include direct optimization of reservoir operation, embedding conditional operating rules in simulation-optimization frameworks, and inferring rules from optimization results. Suggestions on which approach to use depend on context. Parametrization-simulation-optimization or rule inference using heuristics are promising approaches. Increased forecasting capabilities will further benefit the use of model predictive control algorithms to improve system operation. This article is categorized under: Engineering Water > Water, Health, and Sanitation Engineering Water > MethodsThe study has been partially funded by the ADAPTAMED project (RTI2018-101483-B-I00) from the Ministerio de Ciencia, Innovacion Universidades (MICINN) of Spain, and by the postdoctoral program (PAID-10-18) of the Universitat Politecnica de Valencia (UPV).Macian-Sorribes, H.; Pulido-Velazquez, M. (2019). Inferring efficient operating rules in multireservoir water resource systems: A review. Wiley Interdisciplinary Reviews Water. 7(1):1-24. https://doi.org/10.1002/wat2.1400S12471Aboutalebi, M., Bozorg Haddad, O., & Loáiciga, H. A. (2015). Optimal Monthly Reservoir Operation Rules for Hydropower Generation Derived with SVR-NSGAII. Journal of Water Resources Planning and Management, 141(11), 04015029. doi:10.1061/(asce)wr.1943-5452.0000553Ahmad, A., El-Shafie, A., Razali, S. F. M., & Mohamad, Z. S. (2014). Reservoir Optimization in Water Resources: a Review. Water Resources Management, 28(11), 3391-3405. doi:10.1007/s11269-014-0700-5Ahmadi, M., Bozorg Haddad, O., & Mariño, M. A. (2013). Extraction of Flexible Multi-Objective Real-Time Reservoir Operation Rules. Water Resources Management, 28(1), 131-147. doi:10.1007/s11269-013-0476-zAndreu, J., Capilla, J., & Sanchís, E. (1996). AQUATOOL, a generalized decision-support system for water-resources planning and operational management. Journal of Hydrology, 177(3-4), 269-291. doi:10.1016/0022-1694(95)02963-xAndreu, J., & Sahuquillo, A. (1987). Efficient Aquifer Simulation in Complex Systems. Journal of Water Resources Planning and Management, 113(1), 110-129. doi:10.1061/(asce)0733-9496(1987)113:1(110)Ashbolt, S. C., Maheepala, S., & Perera, B. J. C. (2016). Using Multiobjective Optimization to Find Optimal Operating Rules for Short-Term Planning of Water Grids. Journal of Water Resources Planning and Management, 142(10), 04016033. doi:10.1061/(asce)wr.1943-5452.0000675Ashbolt, S. C., & Perera, B. J. C. (2018). Multiobjective Optimization of Seasonal Operating Rules for Water Grids Using Streamflow Forecast Information. Journal of Water Resources Planning and Management, 144(4), 05018003. doi:10.1061/(asce)wr.1943-5452.0000902Azari, A., Hamzeh, S., & Naderi, S. (2018). Multi-Objective Optimization of the Reservoir System Operation by Using the Hedging Policy. Water Resources Management, 32(6), 2061-2078. doi:10.1007/s11269-018-1917-5Becker, L., & Yeh, W. W.-G. (1974). Optimization of real time operation of a multiple-reservoir system. Water Resources Research, 10(6), 1107-1112. doi:10.1029/wr010i006p01107Bellman, R. E., & Dreyfus, S. E. (1962). Applied Dynamic Programming. doi:10.1515/9781400874651Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust Optimization. doi:10.1515/9781400831050Bessler, F. T., Savic, D. A., & Walters, G. A. (2003). Water Reservoir Control with Data Mining. Journal of Water Resources Planning and Management, 129(1), 26-34. doi:10.1061/(asce)0733-9496(2003)129:1(26)Bhaskar, N. R., & Whitlatch, E. E. (1980). Derivation of monthly reservoir release policies. Water Resources Research, 16(6), 987-993. doi:10.1029/wr016i006p00987Bianucci, P., Sordo-Ward, Á., Moralo, J., & Garrote, L. (2015). Probabilistic-Multiobjective Comparison of User-Defined Operating Rules. Case Study: Hydropower Dam in Spain. Water, 7(12), 956-974. doi:10.3390/w7030956Biglarbeigi, P., Giuliani, M., & Castelletti, A. (2018). Partitioning the Impacts of Streamflow and Evaporation Uncertainty on the Operations of Multipurpose Reservoirs in Arid Regions. Journal of Water Resources Planning and Management, 144(7), 05018008. doi:10.1061/(asce)wr.1943-5452.0000945Bolouri-Yazdeli, Y., Bozorg Haddad, O., Fallah-Mehdipour, E., & Mariño, M. A. (2014). Evaluation of Real-Time Operation Rules in Reservoir Systems Operation. Water Resources Management, 28(3), 715-729. doi:10.1007/s11269-013-0510-1Borgomeo, E., Mortazavi-Naeini, M., Hall, J. W., O’Sullivan, M. J., & Watson, T. (2016). Trading-off tolerable risk with climate change adaptation costs in water supply systems. Water Resources Research, 52(2), 622-643. doi:10.1002/2015wr018164Bozorg-Haddad, O., Azarnivand, A., Hosseini-Moghari, S.-M., & Loáiciga, H. A. (2017). WASPAS Application and Evolutionary Algorithm Benchmarking in Optimal Reservoir Optimization Problems. Journal of Water Resources Planning and Management, 143(1), 04016070. doi:10.1061/(asce)wr.1943-5452.0000716Bozorg-Haddad, O., Karimirad, I., Seifollahi-Aghmiuni, S., & Loáiciga, H. A. (2015). Development and Application of the Bat Algorithm for Optimizing the Operation of Reservoir Systems. Journal of Water Resources Planning and Management, 141(8), 04014097. doi:10.1061/(asce)wr.1943-5452.0000498Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Brown, C., Ghile, Y., Laverty, M., & Li, K. (2012). Decision scaling: Linking bottom-up vulnerability analysis with climate projections in the water sector. Water Resources Research, 48(9). doi:10.1029/2011wr011212Brown, C. M., Lund, J. R., Cai, X., Reed, P. M., Zagona, E. A., Ostfeld, A., … Brekke, L. (2015). The future of water resources systems analysis: Toward a scientific framework for sustainable water management. Water Resources Research, 51(8), 6110-6124. doi:10.1002/2015wr017114Cai, X., McKinney, D. C., & Lasdon, L. S. (2001). Piece-by-Piece Approach to Solving Large Nonlinear Water Resources Management Models. Journal of Water Resources Planning and Management, 127(6), 363-368. doi:10.1061/(asce)0733-9496(2001)127:6(363)Cai, X., Vogel, R., & Ranjithan, R. (2013). Special Issue on the Role of Systems Analysis in Watershed Management. Journal of Water Resources Planning and Management, 139(5), 461-463. doi:10.1061/(asce)wr.1943-5452.0000341Cancelliere, A., Giuliano, G., Ancarani, A., & Rossi, G. (2002). Water Resources Management, 16(1), 71-88. doi:10.1023/a:1015563820136Caseri, A., Javelle, P., Ramos, M. H., & Leblois, E. (2015). Generating precipitation ensembles for flood alert and risk management. Journal of Flood Risk Management, 9(4), 402-415. doi:10.1111/jfr3.12203Castelletti, A., Galelli, S., Restelli, M., & Soncini-Sessa, R. (2010). Tree-based reinforcement learning for optimal water reservoir operation. Water Resources Research, 46(9). doi:10.1029/2009wr008898Castelletti, A., Pianosi, F., & Restelli, M. (2013). A multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run. Water Resources Research, 49(6), 3476-3486. doi:10.1002/wrcr.20295Castelletti, A., Pianosi, F., & Soncini-Sessa, R. (2008). Water reservoir control under economic, social and environmental constraints. Automatica, 44(6), 1595-1607. doi:10.1016/j.automatica.2008.03.003Castelletti, A., & Soncini-Sessa, R. (2007). Bayesian networks in water resource modelling and management. Environmental Modelling & Software, 22(8), 1073-1074. doi:10.1016/j.envsoft.2006.06.001Castelletti, A., & Soncini-Sessa, R. (2007). Bayesian Networks and participatory modelling in water resource management. Environmental Modelling & Software, 22(8), 1075-1088. doi:10.1016/j.envsoft.2006.06.003Celeste, A. B., & Billib, M. (2009). Evaluation of stochastic reservoir operation optimization models. Advances in Water Resources, 32(9), 1429-1443. doi:10.1016/j.advwatres.2009.06.008Celeste, A. B., Curi, W. F., & Curi, R. C. (2009). Implicit Stochastic Optimization for deriving reservoir operating rules in semiarid Brazil. Pesquisa Operacional, 29(1), 223-234. doi:10.1590/s0101-74382009000100011Chandramouli, V., & Raman, H. (2001). Multireservoir Modeling with Dynamic Programming and Neural Networks. Journal of Water Resources Planning and Management, 127(2), 89-98. doi:10.1061/(asce)0733-9496(2001)127:2(89)Chang, L.-C., & Chang, F.-J. (2001). Intelligent control for modelling of real-time reservoir operation. Hydrological Processes, 15(9), 1621-1634. doi:10.1002/hyp.226Chazarra, M., García-González, J., Pérez-Díaz, J. I., & Arteseros, M. (2016). Stochastic optimization model for the weekly scheduling of a hydropower system in day-ahead and secondary regulation reserve markets. Electric Power Systems Research, 130, 67-77. doi:10.1016/j.epsr.2015.08.014Chen, D., Leon, A. S., Fuentes, C., Gibson, N. L., & Qin, H. (2018). Incorporating Filters in Random Search Algorithms for the Hourly Operation of a Multireservoir System. Journal of Water Resources Planning and Management, 144(2), 04017088. doi:10.1061/(asce)wr.1943-5452.0000876Coerver, H. M., Rutten, M. M., & van de Giesen, N. C. (2018). Deduction of reservoir operating rules for application in global hydrological models. Hydrology and Earth System Sciences, 22(1), 831-851. doi:10.5194/hess-22-831-2018Côté, P., & Leconte, R. (2016). Comparison of Stochastic Optimization Algorithms for Hydropower Reservoir Operation with Ensemble Streamflow Prediction. Journal of Water Resources Planning and Management, 142(2), 04015046. doi:10.1061/(asce)wr.1943-5452.0000575Cui, L., & Kuczera, G. (2005). Optimizing water supply headworks operating rules under stochastic inputs: Assessment of genetic algorithm performance. Water Resources Research, 41(5). doi:10.1029/2004wr003517Culley, S., Noble, S., Yates, A., Timbs, M., Westra, S., Maier, H. R., … Castelletti, A. (2016). A bottom-up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate. Water Resources Research, 52(9), 6751-6768. doi:10.1002/2015wr018253Cunha, M. C., & Antunes, A. (2012). Simulated annealing algorithms for water systems optimization. WIT Transactions on State of the Art in Science and Engineering, 57-73. doi:10.2495/978-1-84564-664-6/04Dariane, A. B., & Momtahen, S. (2009). Optimization of Multireservoir Systems Operation Using Modified Direct Search Genetic Algorithm. Journal of Water Resources Planning and Management, 135(3), 141-148. doi:10.1061/(asce)0733-9496(2009)135:3(141)Das, B., Singh, A., Panda, S. N., & Yasuda, H. (2015). Optimal land and water resources allocation policies for sustainable irrigated agriculture. Land Use Policy, 42, 527-537. doi:10.1016/j.landusepol.2014.09.012Davidsen, C., Liu, S., Mo, X., Rosbjerg, D., & Bauer-Gottwein, P. (2016). The cost of ending groundwater overdraft on the North China Plain. Hydrology and Earth System Sciences, 20(2), 771-785. doi:10.5194/hess-20-771-2016Ehteram, M., Karami, H., & Farzin, S. (2018). Reservoir Optimization for Energy Production Using a New Evolutionary Algorithm Based on Multi-Criteria Decision-Making Models. Water Resources Management, 32(7), 2539-2560. doi:10.1007/s11269-018-1945-1Eisel, L. M. (1972). Chance constrained reservoir model. Water Resources Research, 8(2), 339-347. doi:10.1029/wr008i002p00339European Commission(2007). Communication from the Commission to the European Parliament and the Council: Addressing the challenge of water scarcity and droughts in the European Union COM(2007) 414 final. Brussels Belgium.European Commission. (2012a). Communication from the Commission to the European Parliament the Council the European Economic and Social Committee and the Committee of the Regions: A Blueprint to Safeguard Europe's Water Resources COM(2012) 673 final. Brussels Belgium.European Commission. (2012b). Communication from the Commission to the European Parliament the Council the European Economic and Social Committee and the Committee of the Regions: Report on the Review of the European Water Scarcity and Droughts Policy COM(2012) 672 final. Brussels Belgium.Fallah-Mehdipour, E., Bozorg Haddad, O., & Mariño, M. A. (2012). Real-Time Operation of Reservoir System by Genetic Programming. Water Resources Management, 26(14), 4091-4103. doi:10.1007/s11269-012-0132-zFazlali, A., & Shourian, M. (2017). A Demand Management Based Crop and Irrigation Planning Using the Simulation-Optimization Approach. Water Resources Management, 32(1), 67-81. doi:10.1007/s11269-017-1791-6Ficchì, A., Raso, L., Dorchies, D., Pianosi, F., Malaterre, P.-O., Van Overloop, P.-J., & Jay-Allemand, M. (2016). Optimal Operation of the Multireservoir System in the Seine River Basin Using Deterministic and Ensemble Forecasts. Journal of Water Resources Planning and Management, 142(1), 05015005. doi:10.1061/(asce)wr.1943-5452.0000571Fu, Q., Li, T., Cui, S., Liu, D., & Lu, X. (2017). Agricultural Multi-Water Source Allocation Model Based on Interval Two-Stage Stochastic Robust Programming under Uncertainty. Water Resources Management, 32(4), 1261-1274. doi:10.1007/s11269-017-1868-2Galelli, S., Goedbloed, A., Schwanenberg, D., & van Overloop, P.-J. (2014). Optimal Real-Time Operation of Multipurpose Urban Reservoirs: Case Study in Singapore. Journal of Water Resources Planning and Management, 140(4), 511-523. doi:10.1061/(asce)wr.1943-5452.0000342Giuliani, M., Castelletti, A., Pianosi, F., Mason, E., & Reed, P. M. (2016). Curses, Tradeoffs, and Scalable Management: Advancing Evolutionary Multiobjective Direct Policy Search to Improve Water Reservoir Operations. Journal of Water Resources Planning and Management, 142(2), 04015050. doi:10.1061/(asce)wr.1943-5452.0000570Giuliani, M., Herman, J. D., Castelletti, A., & Reed, P. (2014). Many-objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management. Water Resources Research, 50(4), 3355-3377. doi:10.1002/2013wr014700Giuliani, M., Li, Y., Castelletti, A., & Gandolfi, C. (2016). A coupled human-natural systems analysis of irrigated agriculture under changing climate. Water Resources Research, 52(9), 6928-6947. doi:10.1002/2016wr019363Giuliani, M., Quinn, J. D., Herman, J. D., Castelletti, A., & Reed, P. M. (2018). Scalable Multiobjective Control for Large-Scale Water Resources Systems Under Uncertainty. IEEE Transactions on Control Systems Technology, 26(4), 1492-1499. doi:10.1109/tcst.2017.2705162Grüne, L., & Semmler, W. (2004). Using dynamic programming with adaptive grid scheme for optimal control problems in economics. Journal of Economic Dynamics and Control, 28(12), 2427-2456. doi:10.1016/j.jedc.2003.11.002Guariso, G., Rinaldi, S., & Soncini-Sessa, R. (1986). The Management of Lake Como: A Multiobjective Analysis. Water Resources Research, 22(2), 109-120. doi:10.1029/wr022i002p00109Gundelach, J., & ReVelle, C. (1975). Linear decision rule in reservoir management and design: 5. A general algorithm. Water Resources Research, 11(2), 204-207. doi:10.1029/wr011i002p00204Guo, X., Hu, T., Zeng, X., & Li, X. (2013). Extension of Parametric Rule with the Hedging Rule for Managing Multireservoir System during Droughts. Journal of Water Resources Planning and Management, 139(2), 139-148. doi:10.1061/(asce)wr.1943-5452.0000241Haddad, O. B., Afshar, A., & Mariño, M. A. (2006). Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization. Water Resources Management, 20(5), 661-680. doi:10.1007/s11269-005-9001-3Hadka, D., Herman, J., Reed, P., & Keller, K. (2015). An open source framework for many-objective robust decision making. Environmental Modelling & Software, 74, 114-129. doi:10.1016/j.envsoft.2015.07.014Haguma, D., & Leconte, R. (2018). Long-Term Planning of Water Systems in the Context of Climate Non-Stationarity with Deterministic and Stochastic Optimization. Water Resources Management, 32(5), 1725-1739. doi:10.1007/s11269-017-1900-6Haguma, D., Leconte, R., & Côté, P. (2018). Evaluating Transition Probabilities for a Stochastic Dynamic Programming Model Used in Water System Optimization. Journal of Water Resources Planning and Management, 144(2), 04017090. doi:10.1061/(asce)wr.1943-5452.0000883Houck, M. H. (1979). A Chance Constrained Optimization Model for reservoir design and operation. Water Resources Research, 15(5), 1011-1016. doi:10.1029/wr015i005p01011Ji, C., Zhou, T., & Huang, H. (2014). Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression. Water Resources Management, 28(9), 2435-2451. doi:10.1007/s11269-014-0610-6Karamouz, M., & Houck, M. H. (1982). Annual and monthly reservoir operating rules generated by deterministic optimization. Water Resources Research, 18(5), 1337-1344. doi:10.1029/wr018i005p01337Karamouz, M., & Houck, M. H. (1987). COMPARISON OF STOCHASTIC AND DETERMINISTIC DYNAMIC PROGRAMMING FOR RESERVOIR OPERATING RULE GENERATION. Journal of the American Water Resources Association, 23(1), 1-9. doi:10.1111/j.1752-1688.1987.tb00778.xKaramouz, M., & Vasiliadis, H. V. (1992). Bayesian stochastic optimization of reservoir operation using uncertain forecasts. Water Resources Research, 28(5), 1221-1232. doi:10.1029/92wr00103Kasprzyk, J. R., Nataraj, S., Reed, P. M., & Lempert, R. J. (2013). Many objective robust decision making for complex environmental systems undergoing change. Environmental Modelling & Software, 42, 55-71. doi:10.1016/j.envsoft.2012.12.007Kelman, J., Stedinger, J. R., Cooper, L. A., Hsu, E., & Yuan, S.-Q. (1990). Sampling stochastic dynamic programming applied to reservoir operation. Water Resources Research, 26(3), 447-454. doi:10.1029/wr026i003p00447Keshtkar, A. R., Salajegheh, A., Sadoddin, A., & Allan, M. G. (2013). Application of Bayesian networks for sustainability assessment in catchment modeling and management (Case study: The Hablehrood river catchment). Ecological Modelling, 268, 48-54. doi:10.1016/j.ecolmodel.2013.08.003Kim, T., Heo, J.-H., Bae, D.-H., & Kim, J.-H. (2008). Single-reservoir operating rules for a year using multiobjective genetic algorithm. Journal of Hydroinformatics, 10(2), 163-179. doi:10.2166/hydro.2008.019Koutsoyiannis, D., & Economou, A. (2003). Evaluation of the parameterization-simulation-optimization approach for the control of reservoir systems. Water Resources Research, 39(6). doi:10.1029/2003wr002148Kumar, D. N., & Reddy, M. J. (2006). Ant Colony Optimization for Multi-Purpose Reservoir Operation. Water Resources Management, 20(6), 879-898. doi:10.1007/s11269-005-9012-0Nagesh Kumar, D., & Janga Reddy, M. (2007). Multipurpose Reservoir Operation Using Particle Swarm Optimization. Journal of Water Resources Planning and Management, 133(3), 192-201. doi:10.1061/(asce)0733-9496(2007)133:3(192)Kumar, K., & Kasthurirengan, S. (2018). Generalized Linear Two-Point Hedging Rule for Water Supply Reservoir Operation. Journal of Water Resources Planning and Management, 144(9), 04018051. doi:10.1061/(asce)wr.1943-5452.0000964Kwakkel, J. H., Haasnoot, M., & Walker, W. E. (2016). Comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty. Environmental Modelling & Software, 86, 168-183. doi:10.1016/j.envsoft.2016.09.017Labadie, J. W. (2004). Optimal Operation of Multireservoir Systems: State-of-the-Art Review. Journal of Water Resources Planning and Management, 130(2), 93-111. doi:10.1061/(asce)0733-9496(2004)130:2(93)Labadie J. W. Baldo M. &Larson R.(2000).MODSIM: Decision support system for river basin management. Documentation and user manual.Lee, J.-H., & Labadie, J. W. (2007). Stochastic optimization of multireservoir systems via reinforcement learning. Water Resources Research, 43(11). doi:10.1029/2006wr005627Lei, X., Tan, Q., Wang, X., Wang, H., Wen, X., Wang, C., & Zhang, J. (2018). Stochastic optimal operation of reservoirs based on copula functions. Journal of Hydrology, 557, 265-275. doi:10.1016/j.jhydrol.2017.12.038Lerma, N., Paredes-Arquiola, J., Andreu, J., & Solera, A. (2013). Development of operating rules for a complex multi-reservoir system by coupling genetic algorithms and network optimization. Hydrological Sciences Journal, 58(4), 797-812. doi:10.1080/02626667.2013.779777Lerma, N., Paredes-Arquiola, J., Andreu, J., Solera, A., & Sechi, G. M. (2015). Assessment of evolutionary algorithms for optimal operating rules design in real Water Resource Systems. Environmental Modelling & Software, 69, 425-436. doi:10.1016/j.envsoft.2014.09.024Li, Y., Giuliani, M., & Castelletti, A. (2017). A coupled human–natural system to assess the operational value of weather and climate services for agriculture. Hydrology and Earth System Sciences, 21(9), 4693-4709. doi:10.5194/hess-21-4693-2017Lin, N. M., & Rutten, M. (2016). Optimal Operation of a Network of Multi-purpose Reservoir: A Review. Procedia Engineering, 154, 1376-1384. doi:10.1016/j.proeng.2016.07.504Liu, P., Cai, X., & Guo, S. (2011). Deriving multiple near-optimal solutions to deterministic reservoir operation problems. Water Resources Research, 47(8). doi:10.1029/2011wr010998Loucks, D. P. (1970). Some Comments on Linear Decision Rules and Chance Constraints. Water Resources Research, 6(2), 668-671. doi:10.1029/wr006i002p00668Loucks

Water supply reservoir operation in the framework of climate variability and change

The optimal planning and operation of a reservoir system is getting more crucial particularly in view of the recent awareness of potential climate change. In particular, the incorporation of hydrologic uncertainties due to climate change into reservoir operation system requires comprehensive and long-term hydrological database which rarely available in most of the conventional reservoir design. The prime objective of the study is to formulate a multiple approach on the long-term reservoir operation optimization under the scarcity of observed hydrological data and with the influence of climate change. A combined research method using IHACRES for hydrological simulation, HadCM3 for emission scenario and Statistical Downscaling Model were developed along with a Mixed Integer Linear Programming (MILP) for reservoir operation optimization. These approaches were applied to a single purpose Sg Layang Resevoir, that is one of the most prominent water supply reservoir located in Johor State, Malaysia. The climatic variables obtained from general circulation model (GCM) were downscaled corresponding to HadCM3 emission scenario and used in climate change impact analysis. The SDSM was used to produce 100 synthetic climate time-series for 90 years of the participating station, representing the climate change projection and baseline period. With respect to the baseline data, an apparent increase in temperature (1.2 degree Celsius between time periods) and rainfall was observed. The deterministic optimization exercise is performed repetitively for a number of case scenarios based on weekly reservoir’s inflows derived from the projected climate change in a way to determine the optimal operation rule and policy which are based on total pumping volume and pumping cost. Corresponded to the future inflows, the pumping volume has shown an increase trend particularly during southwest monsoon, transition between seasons and autumn. Judged from the decreasing rate of the streamflows, a 34 to 40% increase in the projected monthly pumping volume is anticipated. An opposite scenario is observed during northeast monsoon season which shows a decreasing trend of 28% to 46%. At various degree of statistical reliability, the optimal operational pumping curves of the reservoir were established. These curves provide some basic information on the monthly pumping requirement from various sources of inflow to sustain the reservoir storage and demand. These operation curves are of very useful guidelines for reservoir operators in making decision to follow an optimal pumping operations schedule onsite. Such research findings were expected to generate a general awareness to the public water authorities on the potential long term effect of climate change to the reliability of reservoir operating system

A simulation-based multi-criteria management system for optimal water supply under uncertainty

For cost and reliability efficiency, optimal design and operation of pressurized water distribution networks is highly important. However, optimizing such networks is still a challenge since it requires an appropriate determination of: (1) dimension of pipe / pump / tank - decision variables (2) cost / network reliability - objective functions and (3) limits or restrictions within which the network must operate - a given set of constraints. The costs mentioned here consist in general of capital, construction, and operation costs. The reliability of a network mainly refers to the intrinsic capability of providing water with adequate volume and a certain pressure to consumers under normal and extreme conditions. These contradicting objective functions are functions of network configuration regarding component sizes and network layout. Because considerable uncertainties finally render the overall task to a highly complex problem, most recent approaches mainly focus only on finding a trade-off between minimizing cost and maximizing network reliability. To overcome these limitations, a novel model system that simultaneously considers network configuration, its operation and the relevant uncertainties is proposed in this study. For solving this multi-objective design problem, a simulation-based optimization approach has been developed and applied. The approach couples a hydraulic model (Epanet) with the covariance matrix adaptation evolution strategy (CMA-ES) and can be operated in two different modes. These modes are (1) simulation–based Single-objective optimization and (2) simulation-based multi-objective optimization. Single-objective optimization yields the single best solution with respect to cost or network reliability, whereas multi-objective optimization produces a set of non-dominated solutions called Pareto optimal solutions which are trade-offs between cost and reliability. In addition, to prevent a seriously under-designed network, demand uncertainties was also taken into account through a so called “robustness probability” of the network. This consideration may become useful for a more reliable water distribution network. In order to verify the performance of the proposed approach, it was systematically tested on a number of different benchmark water distribution networks ranging from simple to complex. These benchmark networks are either gravity-fed or pumped networks which need to be optimally designed to supply urban or irrigation water demand under specific constraints. The results show that the new approach is able: • to solve optimization problems of pressurized water distribution network design and operation regarding cost and network reliability; • to directly determine the pumping discharge and head, thus allowing to select pumps more adequately; • to simulate time series of tank water level; • to eliminate redundant pipes and pumps to generate an optimal network layout; • to respond well to complex networks other than only to simple networks; • to perform with multiple demand loading; • to produce reliable Pareto optimal solutions regarding multi-objective optimization. In conclusion, the new technique can be successfully applied for optimization problems in pressurized water distribution network design and operation. The new approach has been demonstrated to be a powerful tool for optimal network design not only for irrigation but also for an urban water supply

Water Management for Sustainable Food Production

The agricultural community is face with the challenge of increasing food production by more than 70% to meet demand from the global population increase by the mid-21st century. Sustainable food production involves the sustained availability of resources, such as water and energy, to agriculture. The key challenges to sustainable food production are population increase, increasing demands for food, climate change, climate variability, and decreasing per capita land and water resources. To discuss more details on (a) the challenges for sustainable food production and (b) mitigation options available, a Special Issue on “Water Management for Sustainable Food Production” was assembled. This Special Issue focused on issues such as irrigation using brackish water, virtual water trade, allocation of water resources, consequences of excess precipitation on crop yields, strategies to increase water productivity, rainwater harvesting, irrigation water management, deficit irrigation, fertilization, environmental and socio-economic impacts, and irrigation water quality. The articles in the Special Issue cover several water-related issues across the U.S., Asia, Middle East, Africa, and Pakistan concerning sustainable food production. The articles in this Special Issue highlight the substantial impacts on agricultural production, water availability, and water quality in the face of increasing demands for food and energy

Innovation Issues in Water, Agriculture and Food

In a worldwide context of ever-growing competition for water and land, climate change, droughts and man-made water scarcity, and less-participatory water governance, agriculture faces the great challenge of producing enough food for a continually increasing population. In this line, this book provides a broad overview of innovation issues in the complex water–agriculture–food nexus, thus also relative to their interconnections and dependences. Issues refer to different spatial scales, from the field or the farm to the irrigation system or the river basin. Multidisciplinary approaches are used when analyzing the relationships between water, agriculture, and food security. The covered issues are quite diverse and include: innovation in crop evapotranspiration, crop coefficients and modeling; updates in research relative to crop water use and saving; irrigation scheduling and systems design; simulation models to support water and agricultural decisions; issues to cope with water scarcity and climate change; advances in water resource quality and sustainable uses; new tools for mapping and use of remote sensing information; and fostering a participative and inclusive governance of water for food security and population welfare. This book brings together a variety of contributions by leading international experts, professionals, and scholars in those diverse fields. It represents a major synthesis and state-of-the-art on various subjects, thus providing a valuable and updated resource for all researchers, professionals, policymakers, and post-graduate students interested in the complex world of the water–agriculture–food nexus

Performance assessment of heterogeneous irrigation schemes in India

Most irrigation schemes in India are performing poorly as seen from the average irrigation efficiency in the range of 30-40% for these projects. Hence it is necessary to study the performance assessment of these schemes to investigate the reasons and improve the performance subsequently. There are different kinds of performance measures that may vary spatially over the irrigation scheme. Hence it is necessary to use a framework for finding out the final performance index (FPI) that combines important performance measures. Hence this study was undertaken. Mula Irrigation Scheme in Ahmednagar District of Maharashtra State, India was identified after verifying that most of the needed data was available. The six performance indicators viz. Productivity, Equity, Adequacy Reliability, Flexibility and Sustainability were identified as the important one for obtaining the information on the relative preference from the farmers and first three were considered for obtaining the allocation plans. The performance of different irrigation schemes is assessed with the help of Area and Water Allocation Model (AWAM). The performance measures viz. productivity, equity, adequacy and excess were obtained by formulating the irrigation strategies based on 1. Irrigation amount: Full depth irrigation (FDI), Fixed depth irrigation (FxDI) and Variable depth irrigation (VDI), 2. Irrigation frequency (14 days, 21 days, 28 days and 35 days), 3. Water distribution: Free water distribution (FWD), Equitable distribution of seasonal water (EDSW) and Equitable distribution of intra-seasonal water (EDIW) and 4. Cropping distribution (Free cropping distribution and Fixed cropping distribution). The yield response of crops to different criteria such as soil, irrigation interval, irrigation strategy and irrigation depth, were analysed. It is found for wheat grown on all considered soils, the variable irrigation depth strategy provided better performance of irrigation scheme in terms of productivity and results in higher irrigation water use efficiency. It is concluded though that the application of water according to the variable irrigation depth strategy is operationally and from a management point of view not convenient and in current situation may not be adoptable. Though the fixed depth irrigation strategy is found to be less productive based on this research for Mula irrigation scheme, it is more convenient for operation compared to other strategies as it does not involve adoption of separate schedules for different crops. In general the area and net benefit productivity values are higher in fixed depth irrigation followed by variable depth and then full depth. The productivity values are higher in case of free cropping distribution compared to fixed cropping distribution. The equitable water distribution resulted in lower productivity compared to free water distribution. No specific trend of equity with the irrigation interval was found. Equity values are higher in case of fixed depth of irrigation compared to full depth. The equity values are higher in case of fixed cropping distribution compared to free cropping. The equity values are as expected higher or unity for equitable water distribution compared to free water distribution. The adequacy values are higher in full depth of irrigation followed by variable depth irrigation and fixed depth irrigation. It is observed that the productivity and equity are almost inversely proportional to each other. Hence the hypothesis that productivity and equity conflicts with each other holds true. Further, Analytic Hierarchy Process (AHP) was used to assign weights of different performance measures by determining the farmers relative preference of different performance measures. The average weights of different performance measures (monetary productivity, equity in water distribution and adequacy) were obtained for farmers from different reaches from the weights obtained from AHP analysis, and considerable differences were found between the weights for the head, middle and tail reaches. The values of the performance indicators were obtained from the simulation-optimization modeling (AWAM model). The different indicators were combined into a final overall performance indicator (FPI) of irrigation management in an irrigation scheme from the farmers perspective. The FPI was computed for head, middle and tail reach farmers using the weights obtained from AHP by compromise programming. It is interesting to note that the strategies that best met the farmers preferences (highest FPI), were same for middle reach and tail reach farmers however it is different for head reach. It is also interesting to note that the preferences of the head, middle and tail reach farmers, irrespective of their relative location in irrigation scheme, were best met by strategies which include the equitable distribution of water. For middle and tail reach farmers, full depth irrigation would give the highest FPI, while for head reach farmers optimised fixed depth would be best. It is also seen that for head and middle reach farmers a strategy with fixed cropping distribution and free water distribution would be worst for meeting the preferences of head and middle reach farmers while for tail reach farmers a strategy with free water and free cropping distribution would be worst. The mean values of the weights for head, middle and tail reach farmers were Productivity = 0.33, Equity = 0.31 and Adequacy = 0.36. With these weights, the highest FPI (0.85) was obtained with an irrigation strategy of Full depth irrigation with free cropping and annual equity at irrigation interval of 35 days in winter and 28 days in summer . Considering the different depth of irrigations (FxDI, VDI and FDI) the VDI and FDI are practically difficult to execute due to the data required for calculations and operational requirements of the irrigation canals. Using FxDI, a strategy with high FPI (0.83) was identified as the best feasible irrigation strategy to implement for the entire irrigation scheme: Fixed depth irrigation with free cropping and annual equity at irrigation interval of 35 days in winter and 28 days in summer . It was found that this best feasible irrigation strategy for the entire scheme was not sensitive to the weights assigned to the performance measures

Monthly Optimal Reservoirs Operation for Multicrop Deficit Irrigation under Fuzzy Stochastic Uncertainties

An uncertain monthly reservoirs operation and multicrop deficit irrigation model was proposed under conjunctive use of underground and surface water for water resources optimization management. The objective is to maximize the total crop yield of the entire irrigation districts. Meanwhile, ecological water remained for the downstream demand. Because of the shortage of water resources, the monthly crop water production function was adopted for multiperiod deficit irrigation management. The model reflects the characteristics of water resources repetitive transformation in typical inland rivers irrigation system. The model was used as an example for water resources optimization management in Shiyang River Basin, China. Uncertainties in reservoir management shown as fuzzy probability were treated through chance-constraint parameter for decision makers. Necessity of dominance (ND) was used to analyse the advantages of the method. The optimization results including reservoirs real-time operation policy, deficit irrigation management, and the available water resource allocation could be used to provide decision support for local irrigation management. Besides, the strategies obtained could help with the risk analysis of reservoirs operation stochastically

Model evolution for the realization of complex systems

George Box said, “All models are wrong, but some are useful.” In the design of complex systems, types of complexity need to be managed. Giving the complexities that a decision maker may encounter, corresponding adjustments or improvements should be made to the design. In this dissertation, it is defined that all kinds of engineering design are comprised of four stages – formulation, approximation, exploration and evaluation – and the four stages form the model evolution loop or design evolution loop. By running the design evolution loop iteratively, a designer can handle the complexities and improve the design. Such improvements include but not limited to more robust to uncertainties, more efficient in design evolutions, easier interpretations of phenomena, etc. In the design of complex systems, as lack of data and information, heuristics are used to proceed the design, so that designers can explore the solution space and gain insight to improve the design. Those heuristics include but not limit to model structures, sub-problems identification and integration, approximation rules, and scale of details incorporated in the model. There is lacking mechanisms to evaluate the quality of the design associated with the heuristics. In this dissertation, it is hypothesized that by running the design evolution loop and exploring the solution space, designers can do the things as follows to improve the design. • Evaluating system performances associated with various heuristics (structure of the model, critical parameter setting, rules making, etc.). • Replacing the heuristics with insight obtained from exploration of the solution space to improve the design. • Managing the complexity of module structure, such as analyzing and simplifying the structure of a large number of goals. • Interpreting the behavior and the property of the model into the knowledge that supports the decision making. • Capturing and managing newly observed properties or a more detailed complexity that are not incorporated into the modeling at first – the emergent properties. • Automating the steps in the above. The intellectual merits in this dissertation are the expandable computational framework for designing complex systems and managing multiple types of uncertainty– the design evolution loop, and the methods fitting into it. By using satisficing strategy and incorporating machine learning to explore the solution space, heuristics in each of the four stages (formulation, approximation, exploration, and evaluation) can be updated or replaced by knowledge gained from experiments, calculations and analyses. In addition, knowledge on tradeoffs between different categories of design requirement – such as (but not limited to) approximation accuracy, computational complexity, design preference diversity, reformulation flexibility, and the degree of design automation – can be collected, stored and reused