Year: 2026
Type: article
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.
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $1,725
Awards: STC: Center for Learning the Earth with Artificial Intelligence and Physics (LEAP), G2701512412, G3537118138, TRANSPIRATION IS ONE OF THE MOST IMPORTANT SURFACE WATER FLUXES BUT CURRENT ESTIMATES VARY WIDELY. ACCURATE ESTIMATES OF TRANSPIRATION ARE ESSENTIAL AS THEY CONTROL PLANT WATER USE AND ITS EFFICIENCY AND AS SUCH CONNECT THE WATER CYCLE TO THE CARBON CYCLE BUT ALSO BECAUSE THEY CAN PROVIDE ESTIMATES OF PLANTS' RESILIENCE TO DROUGHTS.WE PROPOSE TO DEVELOP A NEW REMOTE SENSING BASED ALGORITHM TO BETTER ESTIMATE THE PARTITIONING OF EVAPOTRANSPIRATION TOWARDS TRANSPIRATION. AT PRESENT VERY FEW CONSTRAINED ESTIMATES OF TRANSPIRATION EXIST. WE PROPOSE TO BUILD ON A RECENT MACHINE-LEARNING ESTIMATE OF EVAPOTRANSPIRATION BASED ON ONLY REMOTE SENSING DATA NAMED WECANN [ALEMOHAMMAD ET AL. 2016] TO ESTIMATE TRANSPIRATION. TO CONSTRAIN TRANSPIRATION DURING THE LEARNING PROCESS WE WILL USE TWO MAIN PRODUCTS: 1) GOME-2 SOLAR-INDUCED FLUORESCENCE AN INDICATOR OF PLANT GROSS PRIMARY PRODUCTIVITY THUS DIRECTLY TO THE RATE OF PLANT TRASNPIRATION THROUGH LEAF GAS EXCHANGE AND 2) ISOTOPIC OXYGEN ATMOSPHERIC COMPOSITION AN INDICATOR OF SURFACE WATER PARTITIONING. THE RETRIEVAL WILL BE TESTED AND VALIDATED AGAINST 1) EDDY-COVARIANCE FLUX MEASUREMENTS USING THE CONCEPT OF UNDERLYING WATER USE EFFICIENCY (RELATING TRANSPIRATION TO GPP) AND AGAINST A NEW PRODUCT CALLED 2) SAPFLUXNET: A COLLECTION OF SAPFLOW RECORD ACROSS THE GLOBE. THE NEW PRODUCT WILL BE AN ESSENTIAL TOOL FOR LAND-SURFACE MODEL DEVELOPEMENT AND FOR DROUGHT MONITORING., G6023342543, G6630409430, Understanding and Modelling the Earth System with Machine Learning, G930393356
OpenAlex ID: W4407327392