Evaluation of uncalibrated energy balance model (BAITSSS) for estimating evapotranspiration in a semiarid, advective climate
2019
Dhungel, Ramesh | Aiken, Robert | Colaizzi, Paul D. | Lin, Xiaomao | O'Brien, Dan | Baumhardt, R Louis | Brauer, David K. | Marek, Gary W.
The backward‐averaged iterative two‐source surface temperature and energy balance solution (BAITSSS) model was developed to calculate evapotranspiration (ET) at point to regional scales. The BAITSSS model is driven by micrometeorological data and vegetation indices and simulates the water and energy balance of the soil and canopy sources separately, using the Jarvis model to calculate canopy resistance. The BAITSSS model has undergone limited testing in Idaho, United States. We conducted a blind test of the BAITSSS model without prior calibration for ET against weighing lysimeter measurements, net radiation, and surface temperature of drought‐tolerant corn (Zea mays L. cv. PIO 1151) in a semiarid, advective climate (Bushland, Texas, United States) in 2016. Later in the season (20 days), BAITSSS consistently overestimated ET by up to 3 mm d⁻¹. For the entire growing season (127 days), simulated versus measured ET resulted in a 7% error in cumulative ET, RMSE = 0.13 mm h⁻¹, and 1.70 mm d⁻¹; r² = 0.66 (daily) and r² = 0.84 (hourly); MAE = 0.08 mm h⁻¹ and 1.24 mm d⁻¹; and MBE = 0.02 mm h⁻¹ and 0.58 mm d⁻¹. The results were comparable with thermally driven instantaneous ET models that required some calibration. Next, the initial soil water boundary condition was reduced, and model revisions were made to resistance terms related to incomplete cover and assumption of canopy senescence. The revisions reduced discrepancies between measured and modelled ET resulting in <1% error in cumulative ET, RMSE = 0.1 mm h⁻¹, and 1.09 mm d⁻¹; r² = 0.86 (daily) and r² = 0.90 (hourly); MAE = 0.06 mm h⁻¹ and 0.79 mm d⁻¹; and MBE = 0.0 mm h⁻¹ and 0.17 mm d⁻¹ and generally mitigated the previous overestimation. The advancement in ET modelling with BAITSSS assists to minimize uncertainties in crop ET modelling in a time series.
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