Improving the Operational Simplified Surface Energy Balance Evapotranspiration Model Using the Forcing and Normalizing Operation

Actual evapotranspiration modeling is providing useful information for researchers and resource managers in agriculture water resources around the world. The performance of models depends on accuracy forcing inputs model parameters. We developed an improved approach to parameterization Operational Simplified Surface Energy Balance (SSEBop) using Forcing Normalizing Operation (FANO). SSEBop has two key parameters that define boundary conditions. FANO algorithm computes wet-bulb condition a linear Equation relating surface temperature, psychrometric constant, Normalized Difference Vegetation Index (NDVI). was implemented computing platforms Landsat gridded meteorological datasets: (1) Google Earth Engine (GEE) (2) Resources Observation Science (EROS) Center Processing Architecture (ESPA). Evaluation conducted by comparing modeled actual (ETa) estimates with AmeriFlux eddy covariance (EC) balance ETa from level-8 Hydrologic Unit Code sub-basins conterminous United States. brought substantial improvements operational implementation. Compared earlier version (v0.1.7), (v0.2.6) reduced grassland bias 47% −2% while maintaining comparable croplands (11% versus −7%) against EC data. A balance-based evaluation showed overall improvement 7% −1%. Climatology annual reference (ETr) produced results, justifying use climatology ETr global accessible through ESPA website. Besides accuracy, increases spatiotemporal coverage ET due elimination high NDVI requirements parameterization. Because existence potential biases parameters, continued corrections are necessary improve absolute magnitude localized budget applications.