Technical Papers
Mar 17, 2016

Modern Operation of Main Irrigation Canals Suffering from Water Scarcity Based on an Economic Perspective

Publication: Journal of Irrigation and Drainage Engineering
Volume 143, Issue 3

Abstract

The main objective of this study is to determine the optimal distribution of water between upstream and downstream users in a main irrigation canal with a limited supply of water in order to maximize the net revenue derived from existing farming activities. To this end, an economic positive mathematic programming (PMP) model is employed to determine the economic value of the water for each delivery point along the main canal in which there is agricultural activity. This information is added to a model that accounts for the operational aspects of a realistic, large irrigated district in the center of Iran, which is used by a model predictive controller to prioritize the reaches in the main canal according to their potential profit in economic terms. The results show the satisfactory operation of the canal reaches, such that the water levels in the reaches with high economic water value are kept closer to the operational target levels. Accordingly, the water deficit is proportionally divided along the main canals to maximize economic profits from the irrigated district.

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References

Angeli, D., Amrit, R., and Rawlings, J. B. (2012). “On average performance and stability of economic model predictive control.” IEEE Trans. Autom. Control, 57(7), 1615–1626.
Bievre, B. D., Alvarado, A., Timbe, L., Celleri, R., and Feyen, J. (2003). “Night irrigation reduction for water saving in medium-sized systems.” J. Irrig. Drain. Eng., 108–116.
Burt, C. M. (2011). “The irrigation sector shift from construction to modernization: What is required for success?” 21st Int. Congress on Irrigation and Drainage, Wiley, U.K.
Cai, X., Ringler, C., and You, J.-Y. (2008). “Substitution between water and other agricultural inputs: Implications for water conservation in a river basin context.” Ecol. Econ., 66(1), 38–50.
Camacho, E. F., and Bordons, C. (2004). Model predictive control in the process industry, Springer, London.
Chartres, C. J., and Noble, A. (2015). “Sustainable intensification: Overcoming land and water constraints on food production.” Food Secur., 7(2), 235–245.
Clemmens, A., and Schuurmans, J. (2004). “Simple optimal downstream feedback canal controllers: Theory.” J. Irrig. Drain. Eng., 26–34.
Clemmens, A. J. (2012). “Water-level difference controller for main canals.” J. Irrig. Drain. Eng., 1–8.
Clemmens, A. J., Kacerek, T. F., Grawitz, B., and Schuurmans, W. (1998). “Test cases for canal control algorithms.” J. Irrig. Drain. Eng., 23–30.
de Frahan, B., et al. (2007). “Positive mathematical programming for agricultural and environmental policy analysis: Review and practice.” Handbook of operations research in natural resources, A. Weintraub, C. Romero, T. Bjørndal, R. Epstein, and J. Miranda, eds., Springer, Boston, MA, 129–154.
Deltares. (2011). SOBEK 2.12 user manual, Delft, the Netherlands.
Fele, F., Maestre, J. M., Hashemy, S. M., Muñoz de la Peña, D., and Camacho, E. F. (2014). “Coalitional model predictive control of an irrigation canal.” J. Process Control, 24(4), 314–325.
Garay, P. V., Peterson, J. M., Smith, C. M., and Golden, B. B. (2010). “Disaggregated spatial modeling of irrigated land and water use.” Agricultural and Applied Economics Association Annual Meeting, Land Economics/Use, Denver.
González-Dugo, M. P., et al. (2013). “Monitoring evapotranspiration of irrigated crops using crop coefficients derived from time series of satellite images. II: Application on basin scale.” Agric. Water Manage., 125(0), 92–104.
Guan, D., and Hubacek, K. (2008). “A new and integrated hydro-economic accounting and analytical framework for water resources: A case study for North China.” J. Environ. Manage., 88(4), 1300–1313.
Guan, G., Clemmens, A. J., Kacerek, T. F., and Wahlin, B. T. (2011). “Applying water-level difference control to central Arizona project.” J. Irrig. Drain. Eng., 747–753.
Hashemy, S. M., Monem, M. J., Maestre, J., and Van Overloop, P. J. (2013). “Application of an in-line storage strategy to improve the operational performance of main irrigation canals using model predictive control.” J. Irrig. Drain. Eng., 635–644.
Hashemy Shahdany, S. M., Maestre, J. M., and van Overloop, P. J. (2015). “Equitable water distribution in main irrigation canals with constrained water supply.” Water Resour. Manage., 29(9), 3315–3328.
Heckelei, T., Britz, W., and Zhang, Y. (2012). “Positive mathematical programming approaches—Recent developments in literature and applied modelling.” Bio-Based Appl. Econ., 1(1), 109–124.
Heidarinejad, M., Liu, J., and Christofides, P. D. (2012). “Economic model predictive control of nonlinear process systems using Lyapunov techniques.” AIChE J., 58(3), 855–870.
Horváth, K., Galvis, E., Valentín, M. G., and Rodellar, J. (2010). “Comparison of two control algorithms based on different canal models using numerical simulation and experiments on a laboratory canal.” Proc., 10th Int. Conf. on Hydroinformatics, IAHR, Germany, 8.
Horváth, K., Galvis, E., Valentín, M. G., and Rodellar, J. (2015). “New offset-free method for model predictive control of open channels.” Control Eng. Pract., 41(1), 13–25.
Howitt, R. E. (1995). “Positive mathematical programming.” Am. J. Agric. Econ., 77(2), 329–342.
Howitt, R. E., Medellín-Azuara, J., MacEwan, D., and Lund, J. R. (2012). “Calibrating disaggregate economic models of agricultural production and water management.” Environ. Modell. Software, 38(0), 244–258.
Howitt, R. E., and Msangi, S. (2002). “Reconstructing disaggregate production functions.” AAEA-WAEA Annual Meeting, American Agricultural Economics Association, Long Beach, CA.
Isapoor, S., Montazar, A., Van Overloop, P. J., and Van De Giesen, N. (2011). “Designing and evaluating control systems of the Dez main canal.” Irrig. Drain., 60(1), 70–79.
Juhász, Á., and Hrotkó, K. (2014). “Comparison of the transpiration part of two sources evapotranspiration model and the measurements of sap flow in the estimation of the transpiration of sweet cherry orchards.” Agric. Water Manage., 143(0), 142–150.
Maestre, J. M., and Negenborn, R. R. E. (2014). Distributed model predictive control made easy, Springer, Dordrecht, Netherlands.
Malaterre, P.-O., Rogers, D. C., and Schuurmans, J. (1998). “Classification of canal control algorithms.” J. Irrig. Drain. Eng., 3–10.
Maneta, M. P., et al. (2009). “A spatially distributed hydroeconomic model to assess the effects of drought on land use, farm profits, and agricultural employment.” Water Resour. Res., 45(11), W11412.
Marques, G., et al. (2006). “Economically driven simulation of regional water systems: Friant-Kern, California.” J. Water Resour. Plann. Manage., 468–479.
Mattar, M. A., Alazba, A. A., and Zin El-Abedin, T. K. (2015). “Forecasting furrow irrigation infiltration using artificial neural networks.” Agric. Water Manage., 148(0), 63–71.
Medellín-Azuara, J., Harou, J. J., and Howitt, R. E. (2010). “Estimating economic value of agricultural water under changing conditions and the effects of spatial aggregation.” Sci. Total Environ., 408(23), 5639–5648.
Molden, D., and Gates, T. (1990). “Performance measures for evaluation of irrigation-water-delivery systems.” J. Irrig. Drain. Eng., 804–823.
Montazer, A., and Riazi, H. (2008). “Optimization of water allocation in Qazvin irrigation command area.” J. Appl. Irrig. Sci., 43(2), 129–142.
Negenborn, R. R., Overloop, P.-J. V., Keviczky, T., and Schutter, B. D. (2009). “Distributed model predictive control of irrigation canals.” Networks Heterogen. Media, 4(2), 359–380.
Pereira, L. S., Allen, R. G., Smith, M., and Raes, D. (2015). “Crop evapotranspiration estimation with FAO56: Past and future.” Agric. Water Manage., 147(7), 4–20.
Playán, E., and Mateos, L. (2006). “Modernization and optimization of irrigation systems to increase water productivity.” Agric. Water Manage., 80(1–3), 100–116.
Qin, S. J., and Badgwell, T. A. (2003). “A survey of industrial model predictive control technology.” Control Eng. Pract., 11(7), 733–764.
Ringler, C., Huy, N. V., and Msangi, S. (2006). “Water allocation policy modeling for the dong Nai River Basin: An integrated perspective.” J. Am. Water Resour. Assoc., 42(6), 1465–1482.
Schuurmans, W., Brouwer, R., and Wonink, P. (1992). “Identification of control system for canal with night storage.” J. Irrig. Drain. Eng., 360–369.
Torres, M. D. O., et al. (2012). “Economic impacts of regional water scarcity in the São Francisco River Basin, Brazil: An application of a linked hydro-economic model.” Environ. Dev. Econ., 17(02), 227–248.
van Overloop, P. J. (2006a). Model predictive control on open water systems, IOS Press, Delft, Netherlands.
van Overloop, P. J. (2006b). “Drainage control in water management of polders in the Netherlands.” Irrig. Drain. Syst., 20(1), 99–109.
van Overloop, P. J., Clemmens, A. J., Strand, R. J., Wagemaker, R. M. J., and Bautista, E. (2010a). “Real-time implementation of model predictive control on Maricopa-Stanfield irrigation and drainage district’s WM Canal.” J. Irrig. Drain. Eng., 747–756.
van Overloop, P. J., Horváth, K., and Ekin Aydin, B. (2014). “Model predictive control based on an integrator resonance model applied to an open water channel.” Control Eng. Pract., 27(0), 54–60.
van Overloop, P. J., Negenborn, R. R., Schutter, B. D., and Giesen, N. C. (2010b). “Predictive control for national water flow optimization in the Netherlands.” Intelligent infrastructures, R. R. Negenborn, Z. Lukszo, and H. Hellendoorn, eds., Springer, Dordrecht, Netherlands, 439–461.
van Overloop, P. J., Schuurmans, J., Brouwer, R., and Burt, C. M. (2005). “Multiple-model optimization of proportional integral controllers on canals.” J. Irrig. Drain. Eng., 190–196.
van Overloop, P. J., Weijs, S., and Dijkstra, S. (2008). “Multiple model predictive control on a drainage canal system.” Control Eng. Pract., 16(5), 531–540.
Zafra-Cabeza, A., Maestre, J. M., Ridao, M. A., Camacho, E. F., and Sánchez, L. (2011). “A hierarchical distributed model predictive control approach to irrigation canals: A risk mitigation perspective.” J. Process Control, 21(5), 787–799.
Zhang, B., Kang, S., Li, F., Tong, L., and Du, T. (2010). “Variation in vineyard evapotranspiration in an arid region of northwest China.” Agric. Water Manage., 97(11), 1898–1904.

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Go to Journal of Irrigation and Drainage Engineering
Journal of Irrigation and Drainage Engineering
Volume 143Issue 3March 2017

History

Received: Jul 22, 2015
Accepted: Dec 28, 2015
Published online: Mar 17, 2016
Discussion open until: Aug 17, 2016
Published in print: Mar 1, 2017

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Authors

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S. M. Hashemy Shahdany [email protected]
Assistant Professor, Dept. of Water Engineering, College of Aburaihan, Univ. of Tehran, Pakdasht, 3391653755 Tehran, Iran (corresponding author). E-mail: [email protected]
Assistant Researcher, Water Productivity and Economic Studies Office, Ministry of Energy, Kordestan Highway, 1996832611 Tehran, Iran. E-mail: [email protected]
Researcher, Dept. of Master of Business Administration (MBA), Univ. of Science and Culture, Bahar St., Ashrafi Esfahani Blvd., 14619681 Tehran, Iran. E-mail: [email protected]
J. M. Maestre [email protected]
Associate Professor, Dept. of Systems and Automation Engineering, Univ. of Seville, Camino de los Descubrimientos sn, 41092 Sevilla, Spain. E-mail: [email protected]

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