Reference evapotranspiration projections in Southern Spain (until 2100) using temperature-based machine learning models

Abstract

This study firstly examines the performance of temperature-based machine learning models for estimating reference evapotranspiration (ET0), an essential parameter for water management in agriculture, ecosystems, and hydrology. Data from 122 Automated Weather Stations (AWS) across different regions in Southern Spain has been studied and four machine learning models have been developed and assessed: Multilayer Perceptron (MLP), Extreme Learning Machine (ELM), Random Forest (RF), and Support Vector Machine (SVM). The results indicate that all machine learning models outperform the traditional Hargreaves-Samani method in estimating ET0. Specifically, ELM performed, on average, as the best in terms of Global Performance Indicator (GPI) for those locations situated in the first region (GPI = 0.1860), while MLP outperformed the rest for those located in the second (GPI = 0,1162). Besides, the configuration using minimum, mean and maximum air temperature (Tx, Tm, Tn, respectively), the diurnal temperature range (DTR), and Extraterrestrial solar radiation features (Ra) was found to be the fittest for the second region (GPI = 0.0734) and that using Tx, Tn, Tm and Ra in the first one in (GPI = 0.1938). Once the models were validated, they were applied to future 5 km gridded projection datasets, using different Representative Concentration Pathway (RCP) scenarios, in order to estimate ET0 up to the year 2100. In general, the projected ET0 was found to increase significantly in the future, with Mann Kendall Z values that ranged from 7.11 to 10.37 in the RCP4.5 scenario and from 10.84 to 12.57 in the RCP8.5 scenario. Thus, the ET0 is expected to increase from 1300 to 1600 mm to 1500–1700 mm using the RCP4.5 and to 1900 mm using the RCP8.5 in Andalusia, with the highest increase occurring in the south coastal region. This study provides important insights into the application of machine learning models to estimate ET0 and its implications for future water management strategies.