Estimating Near Real-Time Hourly Evapotranspiration Using Numerical Weather Prediction Model Output and GOES Remote Sensing Data in Iowa

• Main Authors: Wonsook S. Ha, George R. Diak, Witold F. Krajewski
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Remote Sensing
• Subjects: evapotranspiration; numerical weather prediction; remote sensing; GOES; eddy covariance
• Online Access: https://www.mdpi.com/2072-4292/12/14/2337

This study evaluates the applicability of numerical weather prediction output supplemented with remote sensing data for near real-time operational estimation of hourly evapotranspiration (ET). Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) systems were selected to provide forcing data for a Penman-Monteith model to calculate the Actual Evapotranspiration (AET) over Iowa. To investigate how the satellite-based remotely sensed net radiation (<inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi>n</mi> </msub> </mrow> </semantics> </math> </inline-formula>) estimates might potentially improve AET estimates, Geostationary Operational Environmental Satellite derived <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi>n</mi> </msub> </mrow> </semantics> </math> </inline-formula> (GOES-<inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi>n</mi> </msub> </mrow> </semantics> </math> </inline-formula>) data were incorporated into each dataset for comparison with the RAP and HRRR <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mi>n</mi> </msub> </mrow> </semantics> </math> </inline-formula>-based AET evaluations. The authors formulated a total of four AET models—RAP, HRRR, RAP-GOES, HRRR-GOES, and validated the respective ET estimates against two eddy covariance tower measurements from central Iowa. The implementation of HRRR-GOES for AET estimates showed the best results among the four models. The HRRR-GOES model improved statistical results, yielding a correlation coefficient of 0.8, a root mean square error (mm hr⁻¹) of 0.08, and a mean bias (mm hr⁻¹) of 0.02 while the HRRR only model results were 0.64, 0.09, and 0.04, respectively. Despite limited in situ observational data to fully test a proposed AET estimation, the HRRR-GOES model clearly showed potential utility as a tool to predict AET at a regional scale with high spatio-temporal resolution.