Daily evapotranspiration estimation using limited meteorological data across diverse geographic regions of China

Evapotranspiration (ET) is a key component of the water cycle, and accurate estimation of reference crop evapotranspiration (ET_o) is essential for irrigation management. To build a precise, lightweight ET_o estimation model, this study takes key meteorological factors as inputs and applies machine learning models and hybrid models Crested Porcupine Optimizer kernel extreme learning machine (CPO-KELM), the Dung Beetle Optimizer Algorithm KELM (DBO-KELM), and Particle Swarm Optimization KELM (PSO-KELM) to estimate ET_o at 38 meteorological stations across China’s seven major geographical regions. The results indicate that maximum temperature (T_max), average temperature (T_ave), and relative humidity (RH) are the primary factors affecting ET_o and were therefore used as model inputs. The standalone kernel extreme learning machine (KELM) model shows acceptable ET_o estimation performance, with R², RMSE, MAE, and NSE ranging from 0.802-0.885, 0.512-0.911, 0.464-0.970, and 0.802-0.885, respectively. Hybrid models outperform the standalone KELM, among which CPO-KELM is the most accurate: its R², RMSE, MAE, and NSE range from 0.881-0.942, 0.413-1.147, 0.284-0.763, and 0.881-0.942. At the regional scale, the CPO-KELM model exhibits its best performance in the Northeast and North China regions, with R², RMSE, MAE, and NSE ranging from 0.923-0.936, 0.413-0.511, 0.284-0.358, and 0.923-0.936, respectively. In contrast, its weakest performance is observed in parts of South China and Northwest China, with R², RMSE, MAE, and NSE ranging from 0.881-0.905, 0.675-1.147, 0.506-0.763, and 0.881-0.905. Compared to standalone KELM, CPO-KELM improves accuracy significantly: R² and NSE rise by 6.4%-9.9%, while MAE drops by 21.3%-38.8%. Thus, the hybrid CPO-KELM model effectively enhances ET_o estimation accuracy across China’s regions. Therefore, the proposed CPO-KELM hybrid model provides a high-accuracy and lightweight alternative for ET_o estimation in data-scarce regions, and offers reliable technical support for intelligent water resources management and irrigation optimization across diverse climatic zones in China.