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Evaluating the Utility of Coarse Estimates of ET in Hydrologic Analyses

This presentation discusses the use of coarse estimates of evapotranspiration (ET) in hydrologic analyses, including their strengths and weaknesses. It explores the impact of ET estimation errors on recharge and runoff, as well as the variability in precipitation and ET. Sensitivity analyses and comparisons of hydrologic simulations using measured and coarse estimates of ET are also highlighted.

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Evaluating the Utility of Coarse Estimates of ET in Hydrologic Analyses

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  1. Please note: All data included in these slides are subject to revision. In addition, this presentation has not received Director’s approval.

  2. ET network October 2003 Planned Installed

  3. WRWX ET station

  4. ET station at WRWX 0 1 km

  5. Relation of actual to potential ET

  6. Most models use coarse estimates of ET • MODFLOW, MIKESHE, HSPF ET = f (PET) • Agricultural ET = kc PET • Interpolated measurements

  7. Are coarse estimates of ET “good enough” ? …. for hydrologists, not plant physiologists

  8. Duda Farms, Brevard County

  9. “Foes” of coarse ET estimates • Non-random error • ET > Precip (potential for large absolute error) • ET ~ Precip (potential for large relative error)

  10. Amplification of ET error Small error in ET Large error in recharge/runoff when rainfall and ET are comparable Example: Precip = 50 inches ET = 48 +/- 2 inches Available water = 2 inches +/-100%

  11. “Friends” of coarse ET estimates • Most of variability in Precip - ET is contained within Precip • Most of ET variations explained by variations in potential ET • Temporal variability in Precip and ET

  12. Weekly rain and ET

  13. Monthly rain and ET

  14. Most variability in atmospheric input is explained by rainfall variability

  15. “Friends” of coarse ET estimates • Most of variability in Precip - ET is contained within Precip • Most of ET variations explained by variations in potential ET • Temporal variability in Precip and ET

  16. Most ET variation is explained by PET variation r-squared = 0.81 ET = kc PET r-squared = 0.21

  17. “Friends” of coarse ET estimates • Most of variability in Precip - ET is contained within Precip • Most of ET variations explained by variations in potential ET • Temporal variability in Precip and ET

  18. Simple hydrologic model Runoff – if water table at surface Net atmospheric water = Precip - ET Head-dependent flux from/to source/sink Total flux to aquifer Dh = Specific yield

  19. Simple flow model was effective

  20. Simulated flow terms

  21. Comparison of several coarse ET estimators • Vegetation coefficient • MODFLOW ET module • Constant ET • Biased estimates

  22. Traditional agricultural approach ET = kc PET kc = vegetation coefficient PET = potential ET

  23. kc = actual ET / PET

  24. MODFLOW ET module

  25. Heads least sensitive to error in ET when wet

  26. Monthly recharge not very sensitive to ET estimator

  27. Small ET error  large cumulative error in recharge

  28. Comparison of recharge simulated with coarse ET estimators Annual-invariant, monthly kc(SE = .59; no bias) MODFLOW ET module (SE = .80; bias = -6 %) Constant kc(SE = 1.04; bias = -13%) +10% biased kc(SE = 1.17; bias = -63%) -10% biased kc(SE = 1.42; bias = +35%) Better

  29. Important to evaluate utility of coarse estimates of ET in hydrologic analyses • sensitivity analyses • comparison of hydrologic simulations using measured and coarse estimates of ET

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