2. TSM 331 Introduction Amor VM Ines

3. What is Irrigation? artificial application of water to crops why?

4. "Photosynthesis" by At09kg - Own work. Licensed under CC BY-SA 3.0 via Commons - https://commons.wikimedia.org/wiki/File:Photosynthesis.gif #/media/File:Photosynthesis.gif Photosynthesis Why 1? According to crop physiologist Energy-water-carbon interaction

5. Photosynthesis "Photosynthesis equation" by ZooFari - Own work. Licensed under Public Domain via Commons - https://commons.wikimedia.org/wiki/File:Photosynthesis_equation.svg#/media/File:Photosynthesis_equation.svg

6. Transpiration (cooling system) Why 2: according to biophysicist wikipedia

7. Water-energy interaction Why 3: According to you: Allen et al., 1998, FAO 56

8. Water Stress Indicator Ines, 2003 (Honda Lab)

9. Courtesy: Steve Miller

10. Partitioning of Evapotranspiration to Soil Evaporation and Transpiration Allen et al., 1998, FAO 56

11. That’s at the plant level, how about at system level (surface irrigation) – a glimpse of what you would expect from this class… Case study: INDIA Bhakra Irrigation System (Northwest, India) Distribution Canal Bhakra Dam

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

13. 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.

14. Optimization of Water Management Strategies Objective function: Subject to either : Formulation of the Water Management Model: (strict constraint) (relaxed constraint)

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

16. …Water Management Model Where:

17. By definition:

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

19. Genetic Algorithms What are they? GAs are mathematical models of natural genetics What they do? They try to mimic the mechanisms of nature in developing well-fitted species that could survive in the current state of the environment How they do it? By exploring good traits (genes) of an individual that can be transmitted from generation to generation

20. Survival of the fittest low jumpers high jumpers gluttons low IQ high IQ low jumper low jumper eaten eaten eaten eaten down to hole eaten Just married

21. . GA in a nutshell A1A1A1A1 B1B1B1B1 Reproduction Selection Crossover Mutation AnAnAnAn BnBnBnBn : Population (t) Fitness (Measure) A1A1A1A1 B1B1B1B1 A5A5A5A5 B5B5B5B5 :. A3A3A3A3 (t+1) Option1Option2 B1B1B1B1 B5B5B5B5 Mating Pool  

22. Regional Model: The Extended SWAP model N ( ,  ) Distributed N ( ,  ) Distributed

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

24. Development of WatProdGA WatProdGA Decision Support Model Regional mode SWAP model Genetic Algorithm Water Management Options DATA

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

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

27. offtake tail Sirsa branch Bata Minor

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

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

30. GA solution to the regional inverse modeling February 4, 2001 March 8, 2001

31. 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.

32. Areal water balance and depth to groundwater from regional inverse modeling.

33. Universe of options Water management option Crop management option     Yield or water productivity Water availability

34. Water Management Options Note: a rainfall of 91 mm was recorded during the simulation period a Irrigation scheduling criterion, T a /T p, 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.

35. WatProdGA optimum solutions to the water management problem

36. Optimized distribution of irrigation, yield, PW Irrigated, PW Depleted and PW Process when the average water supply is around 300 mm.

37. Optimized distribution of irrigation, yield, PW Irrigated, PW Depleted and PW Process when the average water supply is around 500 mm.