scholarly journals Estimation of Reference Evapotranspiration during the Irrigation Season Using Nine Temperature-Based Methods in a Hot-Summer Mediterranean Climate

Agriculture ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 124
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Mediterranean Climate ◽  
Reference Evapotranspiration ◽  
Estimation Error ◽  
Weather Data ◽  
Adjustment Coefficient ◽  
Irrigation Season ◽  
Accurate Model ◽  
Climate Region ◽  
Southern Portugal ◽  
Standard Value

The FAO-56 Penman–Monteith (PM) equation is regarded as the most accurate equation to estimate reference evapotranspiration (ETo). However, it requires a broad range of data that may not be available or of reasonable quality. In this study, nine temperature-based methods were assessed for ETo estimation during the irrigation at fourteen locations distributed through a hot-summer Mediterranean climate region of Alentejo, Southern Portugal. Additionally, for each location, the Hargreaves–Samani radiation adjustment coefficient (kRs) was calibrated and validated to evaluate the appropriateness of using the standard value, creating a locally adjusted Hargreaves–Samani (HS) equation. The accuracy of each method was evaluated by statistically comparing their results with those obtained by PM. Results show that the calibration of the kRs, a locally adjusted HS method can be used to estimate daily ETo acceptably well, with RMSE lower than 0.88 mm day−1, an estimation error lower than 4% and a R2 higher than 0.69, proving to be the most accurate model for 8 (out of 14) locations. A modified Hargreaves–Samani method also performed acceptably for 4 locations, with a RMSE of 0.72–0.84 mm day−1, a slope varying from 0.95 to 1.01 and a R2 higher than 0.78. One can conclude that, when weather data is missing, a calibrated HS equation is adequate to estimate ETo during the irrigation season.

scholarly journals A Simple Procedure to Estimate Reference Evapotranspiration during the Irrigation Season in a Hot-Summer Mediterranean Climate

2021 ◽  
Vol 13 (1) ◽  
pp. 349
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Reference Evapotranspiration ◽  
Simple Procedure ◽  
Irrigation Management ◽  
Weather Data ◽  
Maximum Temperature ◽  
Adjustment Coefficient ◽  
Irrigation Season ◽  
Rural Regions ◽  
Wide Range ◽  
Agricultural Organization

The Food and Agricultural Organization of the United Nations (FAO) Penman–Monteith (PM) method is widely regarded as the most effective reference evapotranspiration (ETo) estimator; however, it requires a wide range of data that may be scarce in some rural regions. When feasible relative humidity, solar radiation and wind speed data are unavailable, a temperature-based method may be useful to estimate ETo and provide suitable data to support irrigation management. This study has evaluated the accuracy of two ETo estimations methods: (1) a locally and monthly adjusted Hargreaves–Samani (HS) equation; (2) a simple procedure that only uses maximum temperature and a temperature adjustment coefficient (MaxTET). Results show that, if a monthly adjusted radiation adjustment coefficient (kRs) is calibrated for each site, acceptable ETo estimations (RMSE and R2 equal to 0.79 for the entire region) can be achieved. Results also show that a procedure to estimate ETo based only on maximum temperature performs acceptably, when compared with ETo estimation using PM equation (RMSE = 0.83 mm day−1 and R2 = 0.77 for Alentejo). When comparing these results with the ones attained when adopting a monthly adjusted HS method, the MaxTET procedure proves to be an accurate ETo estimator. Results also show that both methods can be used to estimate ETo when weather data are scarce.

scholarly journals Estimation of Daily Reference Evapotranspiration from NASA POWER Reanalysis Products in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2077
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Bias Correction ◽  
Reference Evapotranspiration ◽  
Estimation Method ◽  
Accurate Method ◽  
Weather Data ◽  
Maximum Temperature ◽  
Weather Variables ◽  
Local Bias ◽  
Southern Portugal ◽  
Weather Stations

This study aims at assessing the accuracy of estimating daily reference evapotranspiration (ETo) computed with NASA POWER reanalysis products. Daily ETo estimated from local observations of weather variables in 14 weather stations distributed across Alentejo Region, Southern Portugal were compared with ETo derived from NASA POWER weather data, using raw and bias-corrected datasets. Three different methods were used to compute ETo: (a) FAO Penman-Monteith (PM); (b) Hargreaves-Samani (HS); and (c) MaxTET. Results show that, when using raw NASA POWER datasets, a good accuracy between the observed ETo and reanalysis ETo was observed in most locations (R2 > 0.70). PM shows a tendency to over-estimating ETo with an RMSE as high as 1.41 mm d−1, while using a temperature-based ET estimation method, an RMSE lower than 0.92 mm d−1 is obtained. If a local bias correction is adopted, the temperature-based methods show a small over or underestimation of ETo (–0.40 mm d−1≤ MBE < 0.40 mm d−1). As for PM, ETo is still underestimated for 13 locations (MBE < 0 mm d−1) but with an RMSE never higher than 0.77 mm d−1. When NASA POWER raw data is used to estimate ETo, HS_Rs proved the most accurate method, providing the lowest RMSE for half the locations. However, if a data regional bias correction is used, PM leads to the most accurate ETo estimation for half the locations; also, when a local bias correction is performed, PM proved the be the most accurate ETo estimation method for most locations. Nonetheless, MaxTET proved to be an accurate method; its simplicity may prove to be successful not only when only maximum temperature data is available but also due to the low data required for ETo estimation.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

Measurement and estimation of plastic greenhouse reference evapotranspiration in a Mediterranean climate

2010 ◽  
Vol 28 (6) ◽  
pp. 497-509 ◽  
Author(s):  
M. D. Fernández ◽  
S. Bonachela ◽  
F. Orgaz ◽  
R. Thompson ◽  
J. C. López ◽  
...  
Keyword(s):  
Mediterranean Climate ◽  
Reference Evapotranspiration

scholarly journals Evapotranspiration data to determine agro-climatic zones in Egypt

2017 ◽  
Vol 32 (1) ◽  
pp. 79-86 ◽  
Author(s):  
Samiha A. H. Ouda ◽  
Tahany A. Norledin
Keyword(s):  
Nile Delta ◽  
Reference Evapotranspiration ◽  
Irrigation Scheduling ◽  
Weather Data ◽  
Climatic Zones ◽  
Average Value ◽  
Scheduling Model ◽  
Significant Difference ◽  
Difference Test ◽  
Climatic Zoning

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.

scholarly journals Heuristic methods applied in reference evapotranspiration modeling

2018 ◽  
Vol 42 (3) ◽  
pp. 314-324 ◽  
Author(s):  
Daniel Althoff ◽  
Helizani Couto Bazame ◽  
Roberto Filgueiras ◽  
Santos Henrique Brant Dias
Keyword(s):  
Reference Evapotranspiration ◽  
Performance Criteria ◽  
Superior Performance ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector ◽  
Computational Techniques ◽  
Heuristic Methods ◽  
Scarce Data

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.

scholarly journals Investigation into the Effects of Climate Change on Reference Evapotranspiration Using the HadCM3 and LARS-WG

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 666 ◽  
Author(s):  
Maryam Bayatvarkeshi ◽  
Binqiao Zhang ◽  
Rojin Fasihi ◽  
Rana Muhammad Adnan ◽  
Ozgur Kisi ◽  
...  
Keyword(s):  
Climate Change ◽  
Reference Evapotranspiration ◽  
Irrigation Scheduling ◽  
Weather Data ◽  
Base Period ◽  
Resources Management ◽  
Weather Information ◽  
Daily Weather Data ◽  
Weather Stations ◽  
A1b Scenario

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.

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