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Title: Structural Constraints in Current Stomatal Conductance Models Preclude Accurate Prediction of Evapotranspiration
Abstract Evapotranspiration (ET) plays a critical role in water and energy budgets at regional to global scales. ET is composed of direct evaporation (E) and plant transpiration (T) where the latter is regulated via stomatal conductance (gsc), which depends on a multitude of plant physiological processes and hydrometeorological forcings. In recent years, significant advances have been made toward estimatinggscusing a variety of models, ranging from relatively simple empirical models to more complex and data‐intensive plant hydraulic models. Using machine learning (ML) and eddy covariance flux tower data of 642 site years across 84 sites distributed across 10 land covers globally, here we show that structural constraints inherent in current empirical and plant hydraulic models ofgsclimit their effectiveness for predicting ET. These constraints also prevent the models from fully utilizing the available hydrometeorological data at eddy covariance sites. Even if thesegscmodels are calibrated locally, structural simplifications inherent in them limit their capability to accurately capturegscdynamics. In contrast, a ML approach, wherein the model structure is learned from the data, outperforms traditional models, thus highlighting that there still is significant room for improvement in the structure of traditional models for predicting ET. These results underscore the need to prioritize improvements ingscmodels for more accurate ET estimation. This, in turn, will help reduce uncertainties in the assessments of plants' role in regulating the Earth's climate.  more » « less
Award ID(s):
2317819 2019561 1856054
PAR ID:
10542574
Author(s) / Creator(s):
; ;
Publisher / Repository:
American Geophysical Union
Date Published:
Journal Name:
Water Resources Research
Volume:
60
Issue:
8
ISSN:
0043-1397
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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