Discussion on using Evapotranspiration for Water Rights Management

1. Discussion on using Evapotranspiration for Water Rights Management Rick Allen -- University of Idaho, Kimberly, Idaho Partners and Collaborators: Jeppe Kjaersgaard, Magali Garcia, R. Trezza – University of Idaho Tony Morse, W. Kramber – Idaho Dept. Water Resources Wim Bastiaanssen – WaterWatch, M. Tasumi --Univ. Miyazaki, Japan James Wright -- USDA-ARS

2. R n ET = R - G - H n METRIC Energy balance • ET is calculated as a “residual” of the energy balance (radiation from sun and sky) ET H (heat to air) Basic Truth: Evaporation consumes Energy The energy balance includes all major sources (Rn) and consumers (ET, G, H) of energy G (heat to ground)

3. Energy balance gives us “actual” ET Therefore, we can account for impacts on ET caused by: • water shortage • disease • crop variety • planting density • cropping dates • salinity • management • (these effects can be converted into a crop coefficient)

4. 1.2 Corn 2000 1 0.8 ETrF 0.6 0.4 0.2 0 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 Splined Satellite Date Interpolation of ETrF (i.e., Kc) for Monthly or Seasonal ET

5. Comparison with Lysimeter Measurements: 1968-1991 Lysimeter at Kimberly (Wright) 12/17/01

6. Period of Partial Cover METRIC ET for period Kimberly, Idaho – Periods between Satellites Sugar Beets Sugar Beets, 1989 Kimberly, Idaho Lysimeter data by Dr. J.L. Wright, USDA-ARS

7. Seasonal ET - 1989

8. METRIC Comparison of Seasonal ET by METRICtm with Lysimeter Sugar Beets ET (mm) - April-Sept., Kimberly, 1989 METRIC 714 mm Lysimeter718 mm

9. Comparison of Seasonal ET by SEBAL2000 with Lysimeter ET (mm) - July-Oct.,Montpelier, ID 1985 Lysimeter388 mm SEBAL405 mm

10. Sharpening of Landsat 5 Thermal Band to 30 m ETrF July 2006 Temp. original (120 m thermal) sharpened (30 m thermal)

11. Sharpening of Landsat 5 Thermal Band to 30 m Growing Season, 2006 – ET aggregated inside CLU’s

12. Comparison to Kc Curves

13. METRIC applied to year 2000 717 fields in the Twin Falls area Average “curve” Vegetation Index

14. Kc near 1.0 indicating high production agriculture 516 fields

15. 564 fields

16. 325 fields

17. “in-season injury assessment” Approaches – 1 (METRIC) • Base ET estimates on METRIC • --7 to 10 day lag time, high expense • --can apply an ‘attainable’ efficiency to derive Diversion requirement • Can normalize to NDVI to estimate stress • Can compare with actual Diversions, ET/NDVI from a few other years (2000, 2003, 2006) • Advantage – gives ‘actual’ ET • Disadvantage • Expensive and with time delay • One ‘look’ each 16 days only, at best • Some native uncertainty in ET estimates (+/-10%?)

18. “in-season injury assessment” Approaches – 2 (Satellite NDVI) • Base ET estimates on NDVI • --quick, one day lag time, low expense • --apply an ‘attainable’ efficiency to derive Diversion requirement • Compare with actual Diversions • Advantage • quick, low cost • can use SPOT, IRS, etc. if the current LS fails • Disadvantage • May not see ET reductions caused by stress (water shortage) • “Injury” based on act. vs. required diversions

19. “in-season injury assessment” Approaches – 3 (no satellite) • Calculate ratio of running average Diversion to running average reference ET (from weather data) • Compare to other years (> 20) • Advantage • quick, inexpensive • longer time series for context (>20 years for Agrimet) • Disadvantage • May need to normalize for cropping patterns • May need to normalize for shift to sprinklers

20. “mean” Kc “basal” Kc “mean” Kc “basal” Kc

21. “mean” Kc “mean” Kc “basal” Kc

22. Kcm “mean” Kc vs. NDVI Well-watered fields Magic Valley, 2000

23. “mean” Kc vs. NDVI Well-watered fields

24. Development of a seasonal Kc curve from NDVI – Comparison against 1989 Lysimeter data at Kimberly for Landsat Overpass Dates (Kc and NDVI were then splined between dates to obtain daily ET estimates)

25. Comparisons between daily ET determined by METRIC for specific crops and ET determined from the general Kcm vs. NDVIsurf relationship, year 2000, Magic Valley, averaged over 100’s of sampled fields

26. Comparisons between 5-day ET determined by METRIC for specific crops and ET determined from the general Kcm vs. NDVIsurf relationship, , year 2000, Magic Valley, averaged over 100’s of sampled fields

27. Error (%) in seasonal ET estimated using Kc estimated using the NDVI (normalized difference vegetation index) relative to seasonal ET calculated by METRIC – positive values indicate overestimation.

28. “Performance” of Irrigation Projects

29. Project wide Crop Coefficient -- METRIC Twin Falls Tract -- 220,000 acres -- Alfalfa Reference Basis 0.7 0.6 0.5 2000 0.4 Kc 2003 0.3 0.2 0.1 0.0 Mar Apr May Jun Jul Aug Sep Oct Irrigation Project Performance -- Idaho March, Sept., and Oct. unavailable for 2003 due to clouds

30. Irrigation Project Performance -- Idaho Twin Falls Canal Company, Idaho

31. Can the NDVI-based Kc pick up ‘stress’ caused by water shortage? “mean” Kc “basal” Kc “mean” Kc “basal” Kc Stress? or Random error in Kc estimate?

35. High because of evaporation from surface flooding or high because of no stress??

36. Issues • If NDVI (and thus ET) is ‘low’ is it because: • shift in crop types due to market • shift in crop types because of perceived water shortage (i.e., internal mitigation) • chronic shortage of water during development • cool spring – late/retarded development • warm summer – accelerated ripening

37. “in-season injury assessment” Approaches – 1 (METRIC) • Base ET estimates on METRIC • --7 to 10 day lag time, high expense • --can apply an ‘attainable’ efficiency to derive Diversion requirement • Can normalize to NDVI to estimate stress • Can compare with actual Diversions, ET/NDVI from a few other years (2000, 2003, 2006) • Advantage – gives ‘actual’ ET • Disadvantage • Expensive and with time delay • One ‘look’ each 16 days only, at best • Some native uncertainty in ET estimates (+/-10%?)

38. “in-season injury assessment” Approaches – 2 (Satellite NDVI) • Base ET estimates on NDVI • --quick, one day lag time, low expense • --apply an ‘attainable’ efficiency to derive Diversion requirement • Compare with actual Diversions • Advantage • quick, low cost • can use SPOT, IRS, etc. if the current LS fails • Disadvantage • May not see ET reductions caused by stress (water shortage) • “Injury” based on act. vs. required diversions

39. “in-season injury assessment” Approaches – 3 (no satellite) • Calculate ratio of running average Diversion to running average reference ET (from weather data) • Compare to other years (> 20) • Advantage • quick, inexpensive • longer time series for context (>20 years for Agrimet) • Disadvantage • May need to normalize for cropping patterns • May need to normalize for shift to sprinklers

40. Impact of Irrigation System Type on ET -- south-central Idaho -- 2003 METRIC Analyses by Lorite, Allen and Robison

41. Impact of Irrigation System Type on ET -- south-central Idaho -- 2003 METRIC Analyses by Lorite, Allen and Robison

42. Impact of Irrigation System Type on ET -- south-central Idaho -- 2003 METRIC Analyses by Lorite, Allen and Robison