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Optimizing Water Management Strategy under Limited/Non-limited Water Conditions and a Heterogeneous Environment A.V.M. Ines1, K. Honda1, A.D. Gupta1, P. Droogers2 and R. Clemente1 1 Asian Institute of Technology, P.O. Box 4 Klong Luang 12120 Pathumthani, Thailand 2 FutureWater, Eksterstraat 7, 6823 DH Arnhem, The Netherlands
EXTERNAL CONSTRAINTS Need to characterize and understand these complexities We can explore options in water management System and Heterogeneity INPUT WEATHER Management practices (water, crop mgt…) Physical properties (soil, water quality, GW depth…) OUTPUT IRRIGATION SYSTEM Yield, water balance, water productivities…
RS/GIS data Regional model WatProdGA model System characterization DATA Genetic Algorithm Water Management Optimization Model Water Management Options
~~~~~~APPLICATIONS~~~~~ 1. System Characterization 2. Water Management Optimization
System Characterization [x, y] Irrigation dates, depths Spatial distribution yield t+2t t+t ETa water balance Extended SWAP SEBAL By Genetic Algorithm water productivity . . . t t+2t … t+t t+nt Past Time The future
Water Management Model Objective function: Subject to:
Water management variables: Crop management variables:
By definition: HALT
STUDY AREA Study Area After Sakthivadivel et al., 1999 Bhakra Irrigation System, Haryana, India
Snapshot of Kaithal Irrigation Circle (Landsat 7ETM+) Kaithal Sirsa branch Bata minor (inset)
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
Classification Cropped area Cropped area February 4, 2001 March 8, 2001
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
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. HALT
Unconstrained Form: Penalty Method Option1 Option2 Individual Water mgt. Crop mgt. Penalty function Penalty coefficient
How GA traps the solution QaveS: 200 mm Max. fitness Yield Irrigation
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.
Optimized distribution of yield, PWIrrigated, PWDepleted and PWProcess when the average water supply is around 300 mm.
Optimized distribution of yield, PWIrrigated, PWDepleted and PWProcess when the average water supply is around 500 mm.
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.
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…
Water Management Options Water availability Yieldorwater productivity Crop management option Water management option
The Challenge in Regional Agro-hydrology Schematic of a regional analysis. Schematic of a system characterization by exploring the dependency of the measured hydrological data with the system properties.
A1 B5 B1 Selection Reproduction Crossover Mating Pool A5 B1 B5 Mutation . : Genetic Algorithm in a nutshell Fitness (Measure) Variable1 Variable2 A1 B1 : (t+1) Population (t) . An Bn A3