scholarly journals Estimating seasonal reference evapotranspiration using limited weather data

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
MAHESH CHAND SINGH ◽  
SHIVAM POONIA ◽  
SANJAY SATPUTE ◽  
VISHNU PRASAD ◽  
SOMPAL SINGH
2017 ◽  
Vol 32 (1) ◽  
pp. 79-86 ◽  
Author(s):  
Samiha A. H. Ouda ◽  
Tahany A. Norledin

Abstract The objective of this paper was to compare between agro-climatic zones developed from 10-year interval of weather data from 2005-2014, 20-year interval of weather data from 1995-2014 and the zoning developed by [NORELDIN et al. 2016] using 30-year interval from 1985-2014 in the old cultivated land of Egypt in the Nile Delta and Valley. Monthly means of weather data were calculated for each year, and then monthly values for 10-year and 20-years were calculated for each governorate. Basic Irrigation scheduling model (BISm) was used to calculate reference evapotranspiration (ETo). Analysis of variance was used and the means was separated and ranked using least significant difference test (LSD0.05). Our results showed that agro-climatic zoning using 20-year values of ETo was similar to the zones developed with 30-year values of ETo, with different values of average ETo in each zone. Furthermore, using 10-year values of ETo resulted in higher values of ETo in each zone, compared to 20-year and 30-year ETo values. However, the average value of ETo over the three classifications was close to each other. Thus, depending on the availability of weather data, either zoning can be sufficient to develop agro-climatic zones.


2018 ◽  
Vol 42 (3) ◽  
pp. 314-324 ◽  
Author(s):  
Daniel Althoff ◽  
Helizani Couto Bazame ◽  
Roberto Filgueiras ◽  
Santos Henrique Brant Dias

ABSTRACT The importance of the precise estimation of evapotranspiration is directly related to sustainable water usage. Since agriculture represents 70% of Brazil’s water consumption, adequate and efficient application of water may reduce the conflicts over the use of water among the multiple users. Considering the importance of accurate estimation of evapotranspiration, the objective of the present study was to model and compare the reference evapotranspiration from different heuristic methodologies. The standard Penman-Monteith method was used as reference for evapotranspiration, however, to evaluate the heuristic methodologies with scarce data, two widely known methods had their performances assessed in relation to Penman-Monteith. The methods used to estimate evapotranspiration from scarce data were Priestley-Taylor and Thornthwaite. The computational techniques Stepwise Regression (SWR), Random Forest (RF), Cubist (CB), Bayesian Regularized Neural Network (BRNN) and Support Vector Machines (SVM) were used to estimate evapotranspiration with scarce and full meteorological data. The results show the robustness of the heuristic methods in the prediction of the evapotranspiration. The performance criteria of machine learning methods for full weather data varied from 0.14 to 0.22 mm d-1 for mean absolute error (MAE), from 0.21 to 0.29 mm d-1 for root mean squared error (RMSE) and from 0.95 to 0.99 coefficient of determination (r²). The computational techniques proved superior performance to established methods in literature, even in scenarios of scarce variables. The BRNN presented the best performance overall.


Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 666 ◽  
Author(s):  
Maryam Bayatvarkeshi ◽  
Binqiao Zhang ◽  
Rojin Fasihi ◽  
Rana Muhammad Adnan ◽  
Ozgur Kisi ◽  
...  

This study evaluates the effect of climate change on reference evapotranspiration (ET0), which is one of the most important variables in water resources management and irrigation scheduling. For this purpose, daily weather data of 30 Iranian weather stations from 1981 and 2010 were used. The HadCM3 statistical model was applied to report the output subscale of LARS-WG and to predict the weather information by A1B, A2, and B1 scenarios in three periods: 2011–2045, 2046–2079, and 2080–2113. The ET0 values were estimated by the Ref-ET software. The results indicated that the ET0 will rise from 2011 to 2113 approximately in all stations under three scenarios. The ET0 changes percentages in the A1B scenario during three periods from 2011 to 2113 were found to be 0.98%, 5.18%, and 12.17% compared to base period, respectively, while for the B1 scenario, they were calculated as 0.67%, 4.07%, and 6.61% and for the A2 scenario, they were observed as 0.59%, 5.35%, and 9.38%, respectively. Thus, the highest increase of the ET0 will happen from 2080 to 2113 under the A1B scenario; however, the lowest will occur between 2046 and 2079 under the B1 scenario. Furthermore, the assessment of uncertainty in the ET0 calculated by the different scenarios showed that the ET0 predicted under the A2 scenario was more reliable than the others. The spatial distribution of the ET0 showed that the highest ET0 amount in all scenarios belonged to the southeast and the west of the studied area. The most noticeable point of the results was that the ET0 differs from one scenario to another and from a period to another.


2020 ◽  
Author(s):  
Deokhwan Kim

<p>In this study, the Hargreaves monthly correction factor is presented to estimate the reference evapotranspiration. For the analysis, I used daily weather data from 1989 to 2018, at 67 meteorological stations located throughout the Korean peninsula.</p><p>A large number of more or less empirical methods have been developed over the last 50 years by numerous scientists and specialists worldwide to estimate evapotranspiration from different climatic variables. The FAO Penman-Monteith method is recommended as the sole ETo method for determining reference evapotranspiration. However, the Penman-Monteith method has the disadvantage of inputting a lot of weather data. In addition, there is a lack of meteorological data when using old historical data or as a test bed for developing countries.</p><p>In the case of the Hargreaves method, the reference evapotranspiration can be estimated only if the latitude, maximum and minimum temperatures of the meteorological station are known. However, the accuracy of the results is not as good as that of the Penman-monteith method. Thus, using the genetic algorithm method suggested the monthly correction factor of the Hargreaves method each station. The reference evapotranspiration amount calculated by Penman-Monteith was set as the true value, and the learning period of genetic algorithm was set from 1989 to 2013, and the validation period was set from 2014 to 2018.</p><p>In order to verify the model efficiency, the root mean square error decreased and the correlation coefficient increased when the monthly correction coefficient was applied to the reference evapotranspiration calculated by the Hargreaves method.</p><p>It is very important to estimate the reference evapotranspiration amount in order to develop the water long-term plan.</p><p>With the development of measuring equipment and technological capabilities, it is now possible to simulate the state of nature as if it were real, but many problems arise when using historical data or analyzing developing countries.</p><p>If the monthly correction coefficient suggested in this study is applied, it is possible to estimate the standard evaporation amount with a more approximate value.</p><p> </p><p>Acknowledgements</p><p> This research is supported by the Research Program (20200041-001) of Korea Institute of Civil Engineering & Building Technology </p>


2018 ◽  
Vol 63 (1) ◽  
pp. 67-81
Author(s):  
Dzenita Idrizovic ◽  
Gordana Matovic ◽  
Enika Gregoric ◽  
Ruzica Stricevic

In order to calculate water deficit of agricultural crops, it is necessary to have an insight into the evapotranspiration process. As for evaluation of reference evapotranspiration, the Penman-Monteith (FAO56-PM) method, suggested by The International Commission on Irrigation and Drainage (ICID) and Food and Agriculture Organization of the United Nations (FAO), requires several climate parameters, which are often unavailable. Thus, in this paper, the methods for computing ETo, which use limited weather data, were tested and then compared to FAO56-PM. The selected methods were those most often used as the replacement for FAO56-PM: Hargreaves, adjusted Hargreaves, Copais, Turc, Priestley-Taylor, Makkink and Hamon. ETo was calculated at the daily and average monthly levels, for the 2010-2013 period, using data from the following meteorological stations: Nis, Belgrade, Novi Sad, Loznica, Valjevo, Zlatibor, Cuprija and Kikinda. Special importance was given to the vegetation period during the dry season due to the application of irrigation. The comparison of methods was based on statistical analysis, using parameters: MXE, MAE, RMSD, ARMSD, WRMSD, b and R2. The highest rate of matching FAO-PM at the average monthly level, as well as during the 2012 growing season, was shown by Copais, Turc and Priestley-Taylor methods, thus these methods may be recommended as the replacement for FAOPM under climate conditions of Serbia. In case only temperature data are available, the results of this research justify the use of the adjusted Hargreaves equation to calculate ETo for the vegetation period.


2011 ◽  
Vol 9 (3) ◽  
pp. 473-480 ◽  
Author(s):  
Slavisa Trajkovic

This study investigates the utility of adaptive Radial Basis Function (RBF) networks for estimating hourly grass reference evapotranspiration (ET0) from limited weather data. Nineteen days of micrometeorological and lysimeter data collected at half-hour intervals during 1962-63 and 1966-67 in the Campbell Tract research site in Davis, California were used in this study. Ten randomly chosen days (234 patterns) were selected for the RBF networks training. Two sequentially adaptive RBF networks with different number of inputs (ANNTR and ANNTHR) and two Penman-Monteith equations with different canopy resistance values (PM42 and PM70) were tested against hourly lysimeter data from remaining nine days (200 patterns). The ANNTR requires only two parameters (air temperature and net radiation) as inputs. Air temperature, humidity, net radiation and soil heat flux were used as inputs in the ANNTHR. PM equations use air temperature, humidity, wind speed, net radiation and soil heat flux density as inputs. The results reveal that ANNTR and PM42 were generally the best in estimating hourly ET0. The ANNTHR performed less well, but the results were acceptable for estimating ET0. These results are of significant practical use because the RBF network with air temperature and net radiation as inputs could be used to estimate hourly ET0 at Davis, California.


2019 ◽  
Vol 35 (5) ◽  
pp. 823-835
Author(s):  
Rogério De Souza Nóia Júnior ◽  
Clyde William Fraisse ◽  
Vinicius Andrei Cerbaro ◽  
Mauricio Alex Z. Karrei ◽  
Noemi Guindin

Abstract. Methods of estimating evapotranspiration require weather variables as their main input data. Thus, the lack of full weather data sets is one of the main challenges for evaluating and mitigating the effects of climate variability and climate change on agricultural production systems. The Hargreaves-Samani method (HS) is one of the ways to estimate reference evapotranspiration (ETo) when only temperature observations are available, which is a common situation in many agricultural enterprises. Another possible option for regions not served by weather stations is the use of gridded weather data (GWD). Based on that, the main objective of this study was to evaluate the performance of the HS method to estimate ETo in different regions of the United States, as well as to assess the suitability of two gridded weather data (PRISM and NOAA-RTMA) sources to estimate ETo by comparing the results obtained to ETo estimated by the Penman-Monteith (FAO-PM) method, which is the recommended methodology by FAO Irrigation and Drainage Paper 56 when all weather variables are available. Weather observations were obtained for 17 locations across the United States representing regions with subtropical humid and semi-arid continental climates, considering the period of one year (2017). These observations were used to estimate daily ETo with the HS and Penman-Monteith methods. Our results demonstrated that the HS method performance varied according to the location and month of the year. Due to the high relative humidity (RH) during the winter, and high air temperature (Ta) during the summer, the locations selected in the state of Florida, presented the worst performance. The HS method performed well in many other locations such as Froid – MT. Also, the estimation of ETo by HS method and by using PRISM and NOAA-RTMA gridded weather databases showed a good agreement with the ETo estimated by FAO-PM based on weather station observations. Keywords: Penman-Monteith, PRISM, RTMA, Water Management.


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