Estimating evapotranspiration (ET) is important for water and land resources management because it is an essential component of the water and energy cycles. ET estimates are important for understanding and modeling terrestrial ecosystem productivity because ET is related to the energy transferred between the terrestrial ecosystem and the atmosphere. Remote sensing driven ET models estimates vary drastically with the choice of climate reanalysis data due to biases in these datasets. Thus, our goal was to eliminate the need for climatic reanalysis data by incorporating optical, thermal, and microwave remote sensing information to estimate the required model inputs, such as vapor pressure deficit.
We introduce a different operational approach to estimate 8-day average daily evapotranspiration (ET) using both routinely available data and the Penman-Monteith (P-M) equation for canopy transpiration and evaporation of intercepted water and Priestley and Taylor for soil evaporation. Our algorithm considered the environmental constraints on canopy resistance and ET by (1) including vapor pressure deficit (VPD), incoming solar radiation, soil moisture, and temperature constraints on stomatal conductance; (2) using leaf area index (LAI) to scale from the leaf to canopy conductance; and (3) calculating canopy resistance as a function of environmental variables such as net radiation and VPD.
Results/Conclusions
We found good agreements between our 8-day ET estimates and observations with mean absolute error (MAE) ranges from 0.17 mm/day to 0.94 mm/day compared with MAE ranging from 0.28 mm/day to 1.50 mm/day for MODIS ET. The daily MAE and RMSE were reduced from 0.54 mm/day and 0.68 mm/day from the MODIS ET to 0.33 mm/day and 0.46 mm/day with our model. Compared to MODIS ET, our proposed algorithm has higher correlations and higher Willmott’s index of agreement with observations for the majority of the Ameriflux sites. The strong relationship between the model estimated ET and the flux tower observations implies that our model has the potential to be applied to different ecosystems and at different temporal scales. We have learned from this experiment that capturing the peak of the observed ET in a yearly basis is a challenge to the modeling community. Local site conditions such as, soil type and species composition, might play an important role in determining the peak observed ET.