Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data
The Penman-Monteith-Leuning version 2 (PML-V2) evapotranspiration (ET) model, an advanced iteration of the classic Penman-Monteith (PM) model, is available globally via Google Earth Engine with a spatio-temporal resolution of 500 m and 8 days. PML-V2 improves canopy conductance estimation and incorp...
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Elsevier
2025-01-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24014699 |
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| author | Vahid Nourani Ramin Ahmadi Yongqiang Zhang Dominika Dąbrowska |
| author_facet | Vahid Nourani Ramin Ahmadi Yongqiang Zhang Dominika Dąbrowska |
| author_sort | Vahid Nourani |
| collection | DOAJ |
| description | The Penman-Monteith-Leuning version 2 (PML-V2) evapotranspiration (ET) model, an advanced iteration of the classic Penman-Monteith (PM) model, is available globally via Google Earth Engine with a spatio-temporal resolution of 500 m and 8 days. PML-V2 improves canopy conductance estimation and incorporates carbon dioxide effects on transpiration via gross primary production. However, it faces limitations, particularly in calibration and the lack of pre-2000 data. This study applies several machine learning (ML) models—including a backpropagation neural network (BPNN), an adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and long short-term memory (LSTM)—to simulate PML-V2 ET in the Ahar Chay basin, Northwestern Iran. The Seto mixed forest site in Japan, characterized by a contrasting ecosystem, served as a cross-validation site to further validate the methodology. Sensitivity analysis was performed to optimize the input variables and reduce uncertainty. Among the models, LSTM demonstrated superior performance, while an ensemble of shallow ML models increased prediction accuracy by up to 24 %. The optimal model was applied to extrapolate PML-V2 ET data for the period from 1983 to 2000. In the Ahar Chay basin, actual ET (AET) was estimated using the water balance equation, as direct observations were unavailable, and was evaluated via dynamic time warping from 2002 to 2016. Notably, neural ensemble ET and PML-V2 improved ET estimates by 55 % and 41 %, respectively, over the PM model, particularly during the growing season (April–September). At the Seto site, the methodology yielded a 39 % improvement over the PM model based on observed AET data. These findings have significant implications for ecohydrology, offering improved ET estimates for future projections and historical periods prior to 2000. |
| format | Article |
| id | doaj-art-4f40d177392c499d8a46f232a2f3ad57 |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-4f40d177392c499d8a46f232a2f3ad572025-01-31T05:10:29ZengElsevierEcological Indicators1470-160X2025-01-01170113012Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration dataVahid Nourani0Ramin Ahmadi1Yongqiang Zhang2Dominika Dąbrowska3Center of Excellence in Hydroinformatics and Faculty of Civil Engineering, University of Tabriz, Tabriz 166616471, Iran; Faculty of Civil and Environmental Engineering, Near East University, Via Mersin 10, TurkeyCenter of Excellence in Hydroinformatics and Faculty of Civil Engineering, University of Tabriz, Tabriz 166616471, IranInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaUniversity of Silesia, Faculty of Natural Sciences, Sosnowiec, Bedzinska 60, Poland; Corresponding author.The Penman-Monteith-Leuning version 2 (PML-V2) evapotranspiration (ET) model, an advanced iteration of the classic Penman-Monteith (PM) model, is available globally via Google Earth Engine with a spatio-temporal resolution of 500 m and 8 days. PML-V2 improves canopy conductance estimation and incorporates carbon dioxide effects on transpiration via gross primary production. However, it faces limitations, particularly in calibration and the lack of pre-2000 data. This study applies several machine learning (ML) models—including a backpropagation neural network (BPNN), an adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and long short-term memory (LSTM)—to simulate PML-V2 ET in the Ahar Chay basin, Northwestern Iran. The Seto mixed forest site in Japan, characterized by a contrasting ecosystem, served as a cross-validation site to further validate the methodology. Sensitivity analysis was performed to optimize the input variables and reduce uncertainty. Among the models, LSTM demonstrated superior performance, while an ensemble of shallow ML models increased prediction accuracy by up to 24 %. The optimal model was applied to extrapolate PML-V2 ET data for the period from 1983 to 2000. In the Ahar Chay basin, actual ET (AET) was estimated using the water balance equation, as direct observations were unavailable, and was evaluated via dynamic time warping from 2002 to 2016. Notably, neural ensemble ET and PML-V2 improved ET estimates by 55 % and 41 %, respectively, over the PM model, particularly during the growing season (April–September). At the Seto site, the methodology yielded a 39 % improvement over the PM model based on observed AET data. These findings have significant implications for ecohydrology, offering improved ET estimates for future projections and historical periods prior to 2000.http://www.sciencedirect.com/science/article/pii/S1470160X24014699EvapotranspirationPML-V2Sensitivity analysisMachine learningNeural ensemble techniqueAhar Chay Basin |
| spellingShingle | Vahid Nourani Ramin Ahmadi Yongqiang Zhang Dominika Dąbrowska Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data Ecological Indicators Evapotranspiration PML-V2 Sensitivity analysis Machine learning Neural ensemble technique Ahar Chay Basin |
| title | Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data |
| title_full | Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data |
| title_fullStr | Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data |
| title_full_unstemmed | Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data |
| title_short | Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data |
| title_sort | ensemble machine learning based extrapolation of penman monteith leuning evapotranspiration data |
| topic | Evapotranspiration PML-V2 Sensitivity analysis Machine learning Neural ensemble technique Ahar Chay Basin |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24014699 |
| work_keys_str_mv | AT vahidnourani ensemblemachinelearningbasedextrapolationofpenmanmonteithleuningevapotranspirationdata AT raminahmadi ensemblemachinelearningbasedextrapolationofpenmanmonteithleuningevapotranspirationdata AT yongqiangzhang ensemblemachinelearningbasedextrapolationofpenmanmonteithleuningevapotranspirationdata AT dominikadabrowska ensemblemachinelearningbasedextrapolationofpenmanmonteithleuningevapotranspirationdata |