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A.V.M. Ines 1 , R. Clemente 1 , P. Droogers 2 , A.D. Gupta 1 and K. Honda 1

Exploring Water Management Options Using Genetic Algorithms: A Case Study of Bata Minor, Kaithal, Haryana, India. A.V.M. Ines 1 , R. Clemente 1 , P. Droogers 2 , A.D. Gupta 1 and K. Honda 1 1 Asian Institute of Technology, P.O. Box 4 Klong Luang 12120 Pathumthani, Thailand

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A.V.M. Ines 1 , R. Clemente 1 , P. Droogers 2 , A.D. Gupta 1 and K. Honda 1

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  1. Exploring Water Management Options Using Genetic Algorithms: A Case Study of Bata Minor, Kaithal, Haryana, India A.V.M. Ines1, R. Clemente1, P. Droogers2, A.D. Gupta1 and K. Honda1 1 Asian Institute of Technology, P.O. Box 4 Klong Luang 12120 Pathumthani, Thailand 2 FutureWater, Eksterstraat 7, 6823 DH Arnhem, The Netherlands

  2. RESOURCES AVAILABLE We can explore options in water management Problems and Opportunities EXTERNAL STIMULI field level Physical properties (soil, water quality, GW depth…) Management practices (water, crop mgt…) Capable of Regional scale application To characterize this complexity in the system System We need a ROBUST model

  3. Water Management Optimization Model RS/GIS data Water Management Options Regional model General Framework of the Study System characterization DATA Genetic Algorithm

  4. System Characterization [x, y] Irrigation dates, depths Spatial distribution yield t+2t t+t ETa water balance Extended SWAP SEBAL By Genetic Algorithm water productivity . . . t t+2t … t+t t+nt Past Time The future

  5. Formulation of the Water Management Model Objective function: Subject to:

  6. …Water Management Model Water management variables: Crop management variables:

  7. …Water Management Model Where:

  8. …Water Management Model By definition:

  9. STUDY AREA Study Area After Sakthivadivel et al., 1999 Bhakra Irrigation System, Haryana, India

  10. Snapshot of Kaithal Irrigation Circle (Landsat 7ETM+) Kaithal Sirsa branch Bata minor (inset)

  11. 2.90 2.48 2.06 1.64 1.22 4.20 0.80 3.44 2.68 1.92 1.16 0.40 ETa in Bata Minor from SEBAL analysis February 4, 2001 March 8, 2001 ETa, mm ETa, mm m m

  12. Classification Cropped area Cropped area February 4, 2001 March 8, 2001

  13. 60 60 SEBAL 50 50 SWAPGA 40 40 SEBAL 30 30 Rel. frequency, % SWAPGA Rel. frequency 20 20 10 10 0 0 <=1.9 1.9-2.1 2.1-2.3 2.3-2.5 2.5-2.7 >2.7 <=2.9 2.9-3.1 3.1-3.3 3.3-3.5 3.5-3.7 3.7-3.9 >3.9 ETa, mm ETa, mm GA solution to the regional inverse modeling February 4, 2001 March 8, 2001

  14. System characteristics derived by GA * The mean and standard deviation were derived independently, so the values depended on the range between their prescribed maximum and minimum values. ** Sowing dates were represented by emergence dates in Extended SWAP.

  15. Option1 Option2 Individual Water mgt. Crop mgt.     Unconstrained Form: Penalty Method Penalty function Penalty coefficient

  16. How GA traps the solution QaveS: 200 mm Max. fitness Yield Irrigation

  17. Water Management Options Note: A rainfall of 91 mm was recorded during the simulation period a Irrigation scheduling criterion, Ta/Tp, the level of water stress allowed before irrigation. b Sowing dates, represented here by the emergence dates (eDate); Std. Dev. is in number of days.

  18. WatProdGA optimum solutions to the water management problem

  19. Optimized distribution of irrigation, yield, PWIrrigated, PWDepleted and PWProcess when the average water supply is around 300 mm.

  20. Optimized distribution of irrigation, yield, PWIrrigated, PWDepleted and PWProcess when the average water supply is around 500 mm.

  21. Conclusions • Genetic Algorithm is powerful in both parameter estimation and water management optimization • When water is scarce, equitable water distribution increases the overall performance of the system. • There is an optimal water distribution (volume and timing) to achieve the best possible yield; beyond this level of supply water can be saved for other purpose. • Water and crop management practices should be synchronized to achieve the best possible outcome from the system.

  22. Conclusions • If the system is operated under optimum conditions with the present case, the expected regional yield will increase to about 8.5% (i.e from 4.4 to 4.8 t ha-1). The expected water productivity (process) would be about 1.6 kg m-3. • The developed methodologies (i.e. GA-Regional IM, WatProdGA) in this study can contribute to an improved management and operations of irrigation systems. These methodologies can also enhance spatial and temporal analyses in regional water management studies. THANK YOU FOR YOUR ATTENTION…

  23. A universe of options Water availability Yieldorwater productivity    Crop management option  Water management option

  24. A1 B5 B1 Selection Reproduction Crossover Mating Pool A5 B1 B5 Mutation . : GA in a nutshell Fitness (Measure) Variable1 Variable2 A1 B1 : (t+1) Population (t) . An Bn   A3

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