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 proc...
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| Language: | English |
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Wiley
2024-08-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR037652 |
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| author | Pushpendra Raghav Mukesh Kumar Yanlan Liu |
| author_facet | Pushpendra Raghav Mukesh Kumar Yanlan Liu |
| author_sort | Pushpendra Raghav |
| collection | DOAJ |
| description | 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 estimating gsc using 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 of gsc limit their effectiveness for predicting ET. These constraints also prevent the models from fully utilizing the available hydrometeorological data at eddy covariance sites. Even if these gsc models are calibrated locally, structural simplifications inherent in them limit their capability to accurately capture gsc dynamics. 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 in gsc models for more accurate ET estimation. This, in turn, will help reduce uncertainties in the assessments of plants' role in regulating the Earth's climate. |
| format | Article |
| id | doaj-art-bd966addc7c44005b3992c7db6c3e55e |
| institution | DOAJ |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-bd966addc7c44005b3992c7db6c3e55e2025-08-20T02:58:21ZengWileyWater Resources Research0043-13971944-79732024-08-01608n/an/a10.1029/2024WR037652Structural Constraints in Current Stomatal Conductance Models Preclude Accurate Prediction of EvapotranspirationPushpendra Raghav0Mukesh Kumar1Yanlan Liu2Department of Civil, Construction, and Environmental Engineering University of Alabama Tuscaloosa AL USADepartment of Civil, Construction, and Environmental Engineering University of Alabama Tuscaloosa AL USASchool of Earth Sciences The Ohio State University Columbus OH USAAbstract 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 estimating gsc using 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 of gsc limit their effectiveness for predicting ET. These constraints also prevent the models from fully utilizing the available hydrometeorological data at eddy covariance sites. Even if these gsc models are calibrated locally, structural simplifications inherent in them limit their capability to accurately capture gsc dynamics. 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 in gsc models for more accurate ET estimation. This, in turn, will help reduce uncertainties in the assessments of plants' role in regulating the Earth's climate.https://doi.org/10.1029/2024WR037652evaporationtranspirationeddy covarianceempiricalplant hydraulicsmachine learning |
| spellingShingle | Pushpendra Raghav Mukesh Kumar Yanlan Liu Structural Constraints in Current Stomatal Conductance Models Preclude Accurate Prediction of Evapotranspiration Water Resources Research evaporation transpiration eddy covariance empirical plant hydraulics machine learning |
| title | Structural Constraints in Current Stomatal Conductance Models Preclude Accurate Prediction of Evapotranspiration |
| title_full | Structural Constraints in Current Stomatal Conductance Models Preclude Accurate Prediction of Evapotranspiration |
| title_fullStr | Structural Constraints in Current Stomatal Conductance Models Preclude Accurate Prediction of Evapotranspiration |
| title_full_unstemmed | Structural Constraints in Current Stomatal Conductance Models Preclude Accurate Prediction of Evapotranspiration |
| title_short | Structural Constraints in Current Stomatal Conductance Models Preclude Accurate Prediction of Evapotranspiration |
| title_sort | structural constraints in current stomatal conductance models preclude accurate prediction of evapotranspiration |
| topic | evaporation transpiration eddy covariance empirical plant hydraulics machine learning |
| url | https://doi.org/10.1029/2024WR037652 |
| work_keys_str_mv | AT pushpendraraghav structuralconstraintsincurrentstomatalconductancemodelsprecludeaccuratepredictionofevapotranspiration AT mukeshkumar structuralconstraintsincurrentstomatalconductancemodelsprecludeaccuratepredictionofevapotranspiration AT yanlanliu structuralconstraintsincurrentstomatalconductancemodelsprecludeaccuratepredictionofevapotranspiration |