CMIP6 multi-model ensemble projection of reference evapotranspiration using machine learning algorithms

Abstract

Changes in reference crop evapotranspiration (ET_o) due to climate change (CC) can severely impact food and water security, emphasizing the need for integrating ET_o projections into agricultural water management strategies. In this study, ET_o changes were projected for two future time slices with respect to the baseline using several machine learning techniques, incorporating minimum and maximum temperature, diurnal temperature range, and extraterrestrial radiation across Iran. Additionally, an ensemble of 10 CMIP6 Global Climate Models, downscaled by the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6), was employed. The X-means clustering algorithm was also exploited to classify ET_o based on various characteristics, including minimum, maximum, average, skewness, and standard deviation, as well as ET_o ranges of 0–5, 5–10, and greater than 10 mm d⁻¹. This clustering approach divided the study area into five distinct clusters. Apart from cluster I, where the Support Vector Machine outperformed, the Random Forest technique provided more accurate ET_o predictions. The findings project an average ET_o increase of 4.8 % and 5.3 % during 2030–2049, and 8.0 % and 13.3 % for 2080–2099 under SSP245 and SSP585, respectively. Geographically, the highest ET_o increases are anticipated primarily in the northern and western parts of the country, predominantly within clusters I and II. Notably, the ET_o rise will exceed 40 % relative to the baseline during the late century under the SSP585. Furthermore, the most significant ET_o increment is expected during winter. Future projections also indicate that cluster V, which already experiences significant daily ET_o peaks, will face even more ET_o extremes. Given the critical importance of these regions for sustaining food and water security and preserving natural resources, the substantial rise in ET_o under future CC poses a significant threat to natural sustainability in Iran. This highlights the critical necessity for adaptive strategies in agricultural water management to mitigate the adverse CC effects. In this context, the current findings can assist decision-makers in identifying hotspots and quantifying CC impacts, thereby enabling the design of crucial adaptations.