Improving reference evapotranspiration (ETo) calculation under limited data conditions in the high Tropical Andes

2022 ◽  
Vol 262 ◽  
pp. 107439
Author(s):  
Cristina Vásquez ◽  
Rolando Célleri ◽  
Mario Córdova ◽  
Galo Carrillo-Rojas
2015 ◽  
Vol 35 (3) ◽  
pp. 230 ◽  
Author(s):  
Mario Córdova ◽  
Galo Carrillo-Rojas ◽  
Patricio Crespo ◽  
Bradford Wilcox ◽  
Rolando Célleri

2021 ◽  
Vol 7 (3) ◽  
pp. 268-290
Author(s):  
Adeeba Ayaz ◽  
◽  
Maddu Rajesh ◽  
Shailesh Kumar Singh ◽  
Shaik Rehana ◽  
...  

2012 ◽  
Vol 44 (4) ◽  
pp. 706-722 ◽  
Author(s):  
Cosmo Ngongondo ◽  
Chong-Yu Xu ◽  
Lena M. Tallaksen ◽  
Berhanu Alemaw

This study evaluated the performance of the Food and Agriculture Organization (FAO) Penman–Monteith (PM) reference evapotranspiration (ET0) method for various limited data scenarios in southern Malawi. The study further evaluated the full data PM method against the radiation-based Priestley–Taylor (PT) and the temperature-based Hargreaves (HAG) methods, which are less data-intensive approaches commonly used to estimate ET0 in data-scarce situations. A comprehensive daily climate dataset observed at the Nchalo Sugar Estate in southern Malawi for the period 1971–2007 was the basis of the study. The results suggested that lack of data on wind speed and actual vapour pressure did not significantly affect the PM ET0 estimates. However, the estimation of radiation using various combinations of observed wind speed and relative humidity all resulted in significant deviations from the PM ET0. Further, the HAG and PT methods significantly underestimated the PM. However, the PM method computed with estimated climate variables instead of observed climate variables still outperformed both the PT and HAG methods if their original parameters and estimated radiation were used. Thus, new monthly parameters for the PT and the HAG methods are proposed for more accurate daily ET0 estimates.


2020 ◽  
Author(s):  
Cristina Vásquez ◽  
Mario Córdova ◽  
Galo Carrillo ◽  
Rolando Célleri

<p>The correct determination of reference evapotranspiration (ET<sub>o</sub>) is fundamental for countless scientific and management applications such as closing the catchment water balance, the planning of irrigation schemes, and for simulation models. Nevertheless, the records of weather variables are often not available or incomplete. This usually happens when a sensor breaks or malfunctions due to severe weather conditions, lack of maintenance or electronic failure, which leads to data loss and consequently makes it hard to estimate ET<sub>o</sub>. Frequently, that is the case in mountain regions where meteorological sensors are subject to harsh environmental conditions as in the Andes. In case of missing data, the only solution is to estimate the required variable using a given equation. Therefore, these equations need to be calibrated to specific local conditions. The aim of this study was to calibrate and validate equations to estimate Solar Radiation (R<sub>s</sub>) on daily and monthly scales and to evaluate the impact of using these estimations for the calculation of ET<sub>o</sub> through the Penman Monteith (PM) equation in an Andean altitudinal gradient in the páramo ecosystem. The páramo occupies the upper portion of the northern Andes, where the tussock grasslands are the dominant vegetation. In addition, this ecosystem provides essential environmental services for inter-Andean cities. We used six years of observations (2013–2019) from the Quinoas Ecohydrological Observatory. This Observatory has four meteorological stations: Toreadora (3955 m a.s.l), Virgen Cajas (3626 m a.s.l), Chirimachay (3298 m a.s.l) and Balzay (2610 m a.s.l). We evaluated five models to estimate R<sub>s</sub> based on the maximum and minimum daily air temperature. A calibration was performed for each weather station and a simultaneous calibration for the entire gradient. We used four years of data for calibration and validation of the R<sub>s</sub> model, and two years to evaluate the impact on ET<sub>o</sub> calculations. We found that all models yielded estimations that are highly correlated with the observed data. However, no model was able to capture high R<sub>s</sub> values, greater than 185.4 W m<sup>-2</sup> (16 MJ m<sup>-2</sup> d<sup>-1</sup>), found in cloud-free days. The best model to estimate R<sub>s</sub> was the locally calibrated Chen model, which showed a mean error of 2.9 W m<sup>-2 </sup>(0.25 MJ m<sup>-2</sup> d<sup>-1</sup>).  Estimated R<sub>s</sub> values reduced the estimation error of PM-ET<sub>o</sub> and, thus, allows its application for further studies.</p>


2018 ◽  
Vol 137 (1-2) ◽  
pp. 729-743 ◽  
Author(s):  
Koffi Djaman ◽  
Michael O’Neill ◽  
Lamine Diop ◽  
Ansoumana Bodian ◽  
Samuel Allen ◽  
...  

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2203
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga

Reference evapotranspiration (ETo) estimations may be used to improve the efficiency of irrigated agriculture. However, its computation can be complex and could require numerous weather data that are not always available for many locations. Different methods are available to estimate ETo when limited data are available, and the assessment of the most accurate one can be difficult and time consuming. There are some standalone softwares available for computing ETo but none of them allow for the comparison of different methods for the same or different datasets simultaneously. This paper aims to present an application for estimating ETo using several methods that require different levels of data availability, namely FAO-56 Penman–Monteith (PM), the Original and the three modified Hargreaves–Samani (HS and MHS1, MHS2 and MHS3), Trajkovic (TR) and the single temperature procedure (MaxTET). Also, it facilitates the comparison of the accuracy estimation of two selected methods. From an example case, for where the application was used to compute ETo for three different locations, results show that the application can easily and successfully estimate ETo using the proposed methods, allowing for statistical comparison of those estimations. HS proves to be the most accurate method for the studied locations; however, the accuracy of all methods tends to be lower for costal locations than for more continental sites. With this application, users can select the best ETo estimation methods for a specific location and use it for irrigation purposes.


Sign in / Sign up

Export Citation Format

Share Document