Review on Estimation and Forecast of Reference Evapotranspiration

Abstract

The evapotranspiration process plays an essential role in the hydrological cycle, exerting a great influence on various aspects of life, including agricultural practices, water resource management, environmental assessments, and drought indices. However, many regions around the world lack reference sites or costly physical networks for direct evapotranspiration measurements, making estimation a vital criterion in these water-scarce areas. Over the years, researchers have employed empirical and/or physics-based models to compute reference evapotranspiration, a fundamental parameter for crop water requirements in diverse climatic regions. Moreover, forecasting reference evapotranspiration using climate data provides on-time information for irrigation scheduling and water resource management. Consequently, an array of techniques, from conventional statistical approaches to advanced deep learning techniques, have been employed by researchers to predict reference evapotranspiration. In this comprehensive review, a wide range of estimation and forecasting models for reference evapotranspiration is presented, encompassing both traditional techniques and cutting-edge forecasting methods.