Evaluating and Calibrating Reference Evapotranspiration Models Using Water Balance under Hyper-Arid Environment

Abstract

This research investigates five reference evapotranspiration models (one combined model, one temperature-based model, and three radiation-based models) under hyper-arid environmental conditions at the operational field level. These models were evaluated and calibrated using the weekly water balance of alfalfa by EnviroSCAN to calculate crop evapotranspiration (ET c ). Calibration models were evaluated and validated using wheat and potatoes, respectively, on the basis of weekly water balance. Based on the results and discussion, the FAO-56 Penman-Monteith model proved to be superior in estimating ET c with a slight underestimation of 2 %. Meanwhile, the Hargreaves-Samani (HS) model (temperaturebased) underestimated ET c by 20 % and the Priestley-Taylor (PT) and Makkink (MK) models (radiation-based) had similar performances underestimating by up to 35 % of the measured ET c . The Turc (TR) model had the lowest performance compared with other models, demonstrating values underestimated by up to 60 % of the measured ET c . Local calibration based on alfalfa evapotranspiration measurements was used to rectify these underestimations. The surprisingly good performance of the calibrated simple HS model, with a new coefficient 0.0029, demonstrated its favorable potential to improve irrigation scheduling. The MK and PT models were in third and fourth rank, respectively, reflecting minor differences between one another. The new coefficients obtained for the MK and PT models were 1.99 and 0.963, Water Resour Manage respectively. One important observation was that the calibrated TR model performed poorly, with an increase in its coefficient from 0.013 to 0.034 to account for hyper-arid environmental conditions; moreover, it required additional seasonal calibration to adequately improve its performance.

Changes in Temperature and Moisture Content of an Extensive-Type Green Roof

“…The range of water capacity recovery between events is connected with the evapotranspiration and retention capacity of the profile. These parameters are dependent on the history of rainfall (intensity, duration, time between events), climatic conditions (net radiation, temperature, humidity, wind) vegetation characteristics (species, leaf area index, growth stadium) and the properties of the substrate (porosity, permeability, water-holding capacity) [35][36][37][38]. Some of these parameters are strictly connected with the climatic conditions on a local scale.…”

Effectiveness of introducing crop coefficient and leaf area index to enhance evapotranspiration simulations in hydrologic models

“…Kite and Droogers () reported the consistent performance of the FAO‐56 method based on a comparison of eight different AET estimation results using the hydrological model, satellite, and field data. Similarly, Mattar et al () demonstrated the superiority of the FAO‐56 Penman–Monteith method in estimating the AET after a comparison with field data using five reference evapotranspiration (ET o ) estimation methods under standard and hyper‐arid climatic conditions. Gassmann et al () compared the results of the soil water content model with experimental data considering the K c and LAI using the FAO‐56 Penman–Monteith method to estimate the potential evapotranspiration (PET) and found that the LAI approach predicts the AET better than the K c .…”

“…ET is the largest component, contributing more than 65% of the precipitation that is lost to the atmosphere; yet ET remains the most difficult component to simulate and measure (Crow, Wood, & Pan, 2003;Mao & Wang, 2017;Oki & Kanae, 2006). ET integrates the water on land with the atmosphere, carbon cycle, and energy (Mao & Wang, 2017;Wang & Dickinson, 2012) and is the key component of global agricultural water use (Mattar, Alazba, Alblewi, Gharabaghi, & Yassin, 2016). Therefore, accurate ET estimation and comprehension of the associated dynamics play a crucial role in efficient water resource management and will help to reduce water balance errors and improve the reliability of climate change studies.…”

“…Kite and Droogers (2000) reported the consistent performance of the FAO-56 method based on a comparison of eight different AET estimation results using the hydrological model, satellite, and field data. Similarly, Mattar et al (2016) Penman-Monteith method to estimate the potential evapotranspiration (PET) and found that the LAI approach predicts the AET better than the K c . The K c value is the ratio of the crop evapotranspiration to the reference evapotranspiration and combines the effects of crop height, albedo of the crop-soil surface, canopy resistance, and soil evaporation (Allen, Pereira, Raes, & Smith, 1998).…”

Investigating the Spatial and Temporal Variability of Precipitation using Entropy Theory

“…Prediction of precipitation variability and its influence on the hydrological cycle and water quality is an increasingly active area in hydrological research (Zhang et al 2000;Zhang et al 2001;Lemmen et al 2008;Finney et al 2010;Ahmed et al 2013;Disley et al 2015;Liu et al 2015;Mattar et al 2016;Atieh et al 2017). The distribution of precipitation spatially and temporally directly affects the hydrological cycle within a watershed, altering flow regimes and complicating the accurate predictions of flow (Das et al 2008;Boyer et al 2010;Coulibaly 2006;Khan and Coulibaly 2010;Liu and Cui 2011;Trenouth et al 2013;Thompson et al 2016).…”