An Adaptive Method to Estimate Evapotranspiration using Satellite and Reanalysis Products

Abstract. Accurate estimation of evapotranspiration (ET) is critical for hydrological, agricultural, and climate-related applications. However, spatially and temporally consistent ET datasets are often limited, particularly in regions like Ireland, where cloud cover is high and ground-based observations are sparse. This study evaluates ten global, operational open-access ET products by comparing them to Penman-Monteith (PM) reference values derived from weather station data across 22 locations in Ireland between 2019 and 2023. Systematic errors were identified in all ET products, varying across sites, seasons, and years. An adaptive bias correction (AB) method was applied, which dynamically adjusts each product based on recent errors. Although the AB method significantly improved individual ET estimates, no single product consistently exhibited superior performance under all conditions. To further enhance ET accuracy, a novel Combination (COM) method was introduced. This method assigns dynamic weights to each bias-corrected ET product based on recent skill scores, enabling the creation of an optimally merged ET estimate. Unlike traditional static statistical methods, which are interpretable but inflexible, and machine learning approaches, which are adaptive but opaque and data-intensive, the COM method offers a transparent, computationally efficient, and interpretable solution. It requires minimal historical data and runs efficiently on non-specialised systems, making it particularly suitable for operational settings. Results show that the merged COM product outperformed all individual ET datasets, achieving lower errors and stronger correlations with PM observations. Given the persistent cloud cover and variable satellite retrieval accuracy in regions like the Ireland, the ability to adapt to recent performance represents a significant advancement. Overall, the proposed adaptive merging framework provides a scalable, lightweight solution for improving ET monitoring. This method holds promise for enhancing operational hydrology, agricultural decision-making, and climate impact assessments in Ireland and other regions facing similar challenges.