Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches

• Published in: Water resources management 2023-02, Vol.37 (3), p.1013-1032
• Main Authors: T R, Jayashree, Reddy, NV Subba, Acharya, U Dinesh
• Format: Article
• Language: English
• Subjects: Atmospheric Sciences; Bagging; Civil Engineering; Climate change; Climatic zones; Computational efficiency; Computer applications; Computing costs; Daily; Drought; Earth and Environmental Science; Earth Sciences; Environment; Estimation; Evapotranspiration; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrology; Hydrology/Water Resources; Regression models; Water balance; Water management; Water resources
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-022-03399-4

The increasing frequency of droughts and floods due to climate change has severely affected water resources across the globe in recent years. An optimal design for the scheduling and management of irrigation is thus urgently needed to adapt agricultural activities to the changing climate. The accurate estimation of reference crop evapotranspiration (ET0), a vital hydrological component of the water balance and crop water need, is a tiresome task if all the relevant climatic variables are unavailable. This study investigates the potential of four ensemble techniques for estimating precise values of the daily ET0 at representative stations in 10 agro-climatic zones in the state of Karnataka, India, from 1979 to 2014. The performance of these models was evaluated by using several combinations of climatic variables as inputs by using tenfold cross-validation. The outcomes indicated that predictions of ET0 by all four ensemble models based on all climatic variables were the most accurate in comparison with other input combinations. The random forest regressor was found to deliver the best performance among the four models on all measures considered (Nash–Sutcliffe efficiency, 1.0, root-mean-squared error, 0.016 mm/day, and mean absolute error, 0.011 mm/day). However, it incurred the highest computational cost, whereas the computational cost of the bagging model for linear regression was the lowest. The extreme gradient-boosting model delivered the most stable performance with a modified training dataset. The work here shows that these models can be recommended for daily ET 0 estimation based on the users’ interests.