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

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

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)

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

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

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

Seasonal ET - 1989

Comparison of Seasonal ET by METRICtm with Lysimeter ET (mm) - April-Sept., Kimberly, 1989 Sugar Beets METRIC METRIC 714 mm Lysimeter 718 mm

Comparison of Seasonal ET by SEBAL2000 with Lysimeter ET (mm) - July-Oct., Montpelier, ID 1985 Lysimeter 388 mm SEBAL 405 mm

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

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

Comparison to Kc Curves

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

Kc near 1.0 indicating high production agriculture 516 fields

564 fields

325 fields

Approaches – 1 (METRIC) Base ET estimates on METRIC “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%?)

Approaches – 2 (Satellite NDVI) “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

Approaches – 3 (no satellite) “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

“basal” Kc “mean” Kc “mean” Kc “basal” Kc

“mean” Kc “mean” Kc “basal” Kc

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

“mean” Kc vs. NDVI Well-watered fields

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)

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

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

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.

“Performance” of Irrigation Projects

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

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

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?

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

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

Approaches – 1 (METRIC) Base ET estimates on METRIC “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%?)

Approaches – 2 (Satellite NDVI) “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

Approaches – 3 (no satellite) “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

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

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

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