Partitioning Ecosystem Water Fluxes Into Transpiration, Surface Evaporation, and Canopy‐Intercepted Evaporation Using Knowledge‐Guided Machine Learning at NEON Sites

This study introduces KG‐DecompNet, a knowledge‐guided machine learning framework developed to partition total evapotranspiration (ET) into its primary components: transpiration (T), surface evaporation (Es), and canopy‐intercepted evaporation (Ei). Traditional approaches have faced challenges to separate ET components, especially the dynamic, threshold‐based behavior of Ei, leading to likely overestimation of T following rainfall or dew events. KG‐DecompNet addresses this by integrating physical constraints into site‐level machine learning models trained on multi‐year, high‐frequency turbulence and meteorological data from 35 National Ecological Observatory Network sites. The models achieve over 90% agreement with conditional eddy accumulation‐derived T and Es during periods when Ei is likely trivial, and remain robust when compared with flux variance similarity (FVS)‐derived estimates. By isolating Ei, KG‐DecompNet offers new insights into surface‐atmosphere water exchanges and helps set a benchmark for physically grounded ecohydrological modeling.