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In this study, we develop more accurate hyper-arid evapotranspiration (ET) models to help improve irrigation water conservation. We examine five ET models (one combination model, three radiation-based models, and one temperature-based model) under hyper-arid condition at three center-pivot fields in the Kingdom of Saudi Arabia. These models were evaluated and calibrated for the alfalfa crop of 2010 and validated for the wheat and potato crops of 2011. The FAO-56 Penman-Monteith (PM) was the most accurate ET model for estimating crop water irrigation needs. The Turc and the Makkink solar radiation-based ET models provided the least accurate estimates even after calibration, while the calibrated Hargreaves-Samani temperature-based model provided the second most accurate estimates for irrigation scheduling in hyper-arid environments. Unlike the FAO-56 PM model, Hargreaves-Samani does not require wind speed or relative humidity data. The most sensitive parameter for this model is air temperature, which is readily available at most sites. The Priestley-Taylor model is highly sensitive to solar radiation data that may not be locally available. The main drawback of the FAO-56 PM model is that it requires extensive list of meteorological data. Weather forecasts are often limited to air temperature data that limit the use of the FAO-56 PM model for irrigation scheduling compared to the calibrated Hargreaves-Samani model.