Learning evaporative fraction with memory

Abstract. Evaporative Fraction (EF), the ratio of latent heat flux to the sum of sensible and latent heat flux, is a key metric of surface energy partitioning and water stress. Recognizing the importance of soil moisture and vegetation memory effect, we developed a machine learning (ML) model using Long Short-Term Memory (LSTM) unit, which include memory effects, to predict EF, based on eddy-covariance data from the combined ICOS, AmeriFlux, and FLUXNET2015 Tier 1 dataset across different plant functional types (PFT). The results show that the model can accurately capture and reconstruct the EF dynamics, particularly in the dry season and during drydowns, using routinely available weather observations, e.g., precipitation, net radiation, air temperature, vapor pressure deficit (VPD), and other static variables: PFT and soil properties. Specifically, there is a strong correlation (R2 of 0.72) between the ensemble mean EF predictions and the observations on the test set, across sites spanning a large climate and ecosystem gradient. Second, we employ explainable ML techniques to elucidate the drivers of EF while accounting for the memory effect. Precipitation, VPD are two main drivers for woody savanna (WSA), savanna (SAV), open shrubland (OSH) and grassland (GRA) sites, while air temperature is dominate controlling factor in most forest sites, comprising deciduous broadleaf forest (DBF), evergreen needleleaf forest (ENF) and mixed forest (MF). Additionally, our findings reveal varying memory effects across different PFTs, as indicated by the contributions of antecedent time steps via integrated gradients. Specifically, GRA and WSA exhibited relatively lower memory effect contributions compared to forested sites. A detailed analysis of memory effects indicates their strong relationship with rooting depth, soil water holding capacity, and plant water use strategies, which collectively regulate the time scales of droughts. Notably, the learned memory effect across diverse PFTs could potentially serve as proxies for inferring vegetation rooting depth and assessing the plant water stress conditions. Our findings underscore the crucial influence of meteorological memory effect on EF predictions, particularly important for estimating future water stress, as the frequence and intensity of droughts are expected to rise.