Fuzzy State Real-Time Reservoir Operation Model for Irrigation with Gridded Rainfall Forecasts
Publication: Journal of Irrigation and Drainage Engineering
Volume 142, Issue 2
Abstract
A short-term real-time operation model with fuzzy state variables is developed for irrigation of multiple crops based on earlier work on long-term steady-state policy. The features of the model that distinguish it from the earlier work are (1) apart from inclusion of fuzziness in reservoir storage and in soil moisture of crops, spatial variations in rainfall and soil moisture of crops are included in the real-time operation model by considering gridded command area with a grid size of 0.5° latitude by 0.5° longitude; (2) the water allocation model and soil moisture balance equations are integrated with the real-time operation model with consideration of ponding water depth for Paddy crop; the model solution specifies reservoir releases for irrigation in a 10-day time period and allocations among the crops on a daily basis at each grid by maintaining soil moisture balance at the end of the day; and (3) the release policy is developed using forecasted daily rainfall data of each grid and is implemented for the current time period using actual 10-day inflow and actual daily rainfall of each grid. The real-time operation model is applied to Bhadra Reservoir in Karnataka, India. The results obtained using the real-time operation model are compared with those of the standard operating policy model. Inclusion of fuzziness in reservoir storage and soil moisture of crops captures hydrologic uncertainties in real time. Considerations of irrigation decisions on a daily basis and the gridded command area result in variations in allocating water to the crops, variations in actual crop evapotranspiration, and variations in soil moisture of the crops on a daily basis for each grid of the command area.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors acknowledge the National Centre for Medium Range Weather Forecasting (NCMRWF) research and development organization for providing the forecasted rainfall data on a daily basis at a grid increment of 0.5° latitude and 0.5° longitude, which has been used in the real-time reservoir operation model.
References
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. (1998). “Chapter 2—FAO Penman-Monteith equation: Crop evapotranspiration—Guidelines for computing crop water requirements.” Food and Agricultural Organization of the United Nations, Rome.
Anbumozhi, V., Yamaji, E., and Tabuchi, T. (1998). “Rice crop growth and yield as influenced by changes in ponding water depth, water regime and fertigation level.” Agric. Water Manage., 37(3), 241–253.
Bhadra, A., Bandyopadhyay, A., Singh, R., and Raghuwanshi, N. S. (2013). “Development of a user friendly water balance model for paddy.” Paddy Water Environ., 11(1–4), 331–341.
Doorenbos, J., and Kassam, A. H. (1979). “Yield response to water.” Food and Agricultural Organization of the United Nations, Rome.
Doorenbos, J., and Pruitt, W. O. (1977). “Crop water requirements.” Food and Agricultural Organization of the United Nations, Rome.
Dubrovin, T., Jolma, A., and Turunen, E. (2002). “Fuzzy model for real time reservoir operation.” J. Water Resour. Plann. Manage., 66–73.
Faybishenko, B. (2010). “Fuzzy-probabilistic calculations of water-balance uncertainty.” Stochastic Environ. Resour. Risk Assess., 24(6), 939–952.
Guo, P., Huang, G. H., Zhu, H., and Wang, X. L. (2010). “A two-stage programming approach for water resources management under randomness and fuzziness.” Environ. Model. Software, 25(12), 1573–1581.
Hajilal, M. S., Rao, N. H., and Sarma, P. B. S. (1998). “Real time operation of reservoir based canal irrigation systems.” Agric. Water Manage., 38(2), 103–122.
Kosko, B. (1996). Neural networks and fuzzy systems, Prentice Hall, Englewood Cliffs, NJ.
Li, Y. P., Huang, G. H., and Nie, S. L. (2011). “Optimization of regional economic and environmental systems under fuzzy and random uncertainties.” J. Environ. Manage., 92(8), 2010–2020.
Loucks, D. P., Stedinger, J. R., and Haith, D. A. (1981). Water resource system planning and analysis, Prentice Hall, Englewood Cliffs, NJ.
Michael, A. M. (1978). Irrigation theory and practice, Vikas Publishing House, New Delhi, India.
Montazar, A., Gheidari, O. N., and Snyder, R. L. (2013). “A fuzzy analytical hierarchy methodology for the performance assessment of irrigation projects.” Agric. Water Manage., 121, 113–123.
Moore, R. E. (1966). Interval analysis, Prentice Hall, Englewood Cliffs, NJ.
Mousavi, S. J., Karamouz, M., and Menhadj, M. B. (2004a). “Fuzzy-state stochastic dynamic programming for reservoir operation.” J. Water Resour. Plann. Manage., 460–470.
Mousavi, S. J., Mahdizadeh, K., and Afshar, A. (2004b). “A stochastic dynamic programming model with fuzzy storage states for reservoir operations.” Adv. Water Resour., 27(11), 1105–1110.
Mousavi, S. J., Ponnambalam, K., and Karray, F. (2005). “Reservoir operation using a dynamic programming fuzzy rule-based approach.” Water Resour. Manage., 19(5), 655–672.
Mujumdar, P. P., and Ramesh, T. V. S. (1997). “Real-time reservoir operation for irrigation.” Water Resour. Res., 33(5), 1157–1164.
Nam, W. H., and Choi, J. Y. (2014). “Development of an irrigation vulnerability assessment model in agricultural reservoirs utilizing probability theory and reliability analysis.” Agric. Water Manage., 142, 115–126.
National Centre for Medium Range Weather Forecasting. (2013). “NCMRWF global ensemble forecast system rainfall forecasts.” 〈http://www.ncmrwf.gov.in〉 (Aug. 15, 2015).
Panigrahi, D. P., and Mujumdar, P. P. (2000). “Reservoir operation modeling with fuzzy logic.” Water Resour. Manage., 14(2), 89–109.
Rao, N. H., Sarma, P. B. S., and Chander, S. (1992). “Real-time adaptive irrigation scheduling under a limited water supply.” Agric. Water Manage., 20(4), 267–279.
Sangeeta, K., and Mujumdar, P. P. (2015). “Reservoir operation with fuzzy state variables for irrigation of multiple crops.” J. Irrig. Drain. Eng., 04015015.
Sivapragasam, C., Vasudevan, G., Vincent, P., Sugendran, P., Marimuthu, M., and Seenivasakan, S. (2007). “Rule reduction in fuzzy logic for better interpretability in reservoir operation.” Hydrol. Process., 21(21), 2835–2844.
Srinivasa Prasad, A., Umamahesh, N. V., and Viswanath, G. K. (2013). “Short-term real-time reservoir operation for irrigation.” J. Water Resour. Plann. Manage, 149–158.
Suresh, K. R., and Mujumdar, P. P. (2004). “A fuzzy risk approach for performance evaluation of an irrigation reservoir system.” Agric. Water Manage., 69(3), 159–177.
Tilmant, A., Fortemps, P., and Vanclooster, M. (2002a). “Effect of averaging operators in fuzzy optimization of reservoir operation.” Water Resour. Manage., 16(1), 1–22.
Tilmant, A., Vanclooster, M., Duckstein, L., and Persoons, E. (2002b). “Comparison of fuzzy and nonfuzzy optimal reservoir operating policies.” J. Water Resour. Plann. Manage., 390–398.
Vedula, S., and Mujumdar, P. P. (1992). “Optimal reservoir operation for irrigation of multiple crops.” Water Resour. Res., 28(1), 1–9.
Vedula, S., and Nagesh Kumar, D. (1996). “An integrated model for optimal reservoir operation for irrigation of multiple crops.” Water Resour. Res., 32(4), 1101–1108.
Xu, K., Zhou, J., Ran, G., and Qin, H. (2011). “Approach for aggregating interval-valued intuitionistic fuzzy information and its application to reservoir operation.” Expert Syst. Appl., 38(7), 9032–9035.
Information & Authors
Information
Published In
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Mar 7, 2015
Accepted: Jul 20, 2015
Published online: Sep 11, 2015
Published in print: Feb 1, 2016
Discussion open until: Feb 11, 2016
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.