Regional assessment of daily reference evapotranspiration: Can ground observations be replaced by blending ERA5-Land meteorological reanalysis and CM-SAF satellite-based radiation data?

Reanalysis data are being increasingly used as gridded weather data sources for assessing crop-reference evapotranspiration (ET0) in irrigation water-budget analyses at regional scales. This study assesses the performances of ET0 estimates based on weather data, respectively produced by two high-resolution reanalysis datasets: UERRA MESCAN-SURFEX (UMS) and ERA5-Land (E5L). The study is conducted in Campania Region (Southern Italy), with reference to the irrigation seasons (April–September) of years 2008–2018. Temperature, wind speed, vapor pressure deficit, solar radiation and ET0 derived from reanalysis datasets, were compared with the corresponding estimates obtained by spatially interpolating data observed by a network of 18 automatic weather stations (AWSs). Statistical performances of the spatial interpolations were evaluated with a cross-validation procedure, by recursively applying universal kriging or ordinary kriging to the observed weather data. ERA5-Land outperformed UMS both in weather data and ET0 estimates. Averaging over all 18 AWSs sites in the region, the normalized BIAS (nBIAS) was found less than 5% for all the databases. The normalized RMSE (nRMSE) for ET0 computed with E5L data was 17%, while it was 22% with UMS data. Both performances were not far from those obtained by kriging interpolation, which presented an average nRMSE of 14%. Overall, this study confirms that reanalysis can successfully surrogate the unavailability of observed weather data for the regional assessment of ET0.

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Calculating Sunshine Hours and Reference Evapotranspiration in Arid Regions When Solar Radiation Data are Limited

Irrigation and Drainage ◽

10.1002/ird.1920 ◽

2015 ◽

Vol 64 (3) ◽

pp. 419-425 ◽

Cited By ~ 2

Author(s):

Mohamed H. Abd el-wahed ◽

Richard L. Snyder

Keyword(s):

Solar Radiation ◽

Arid Regions ◽

Reference Evapotranspiration ◽

Radiation Data ◽

Sunshine Hours

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Using remotely sensed solar radiation data for reference evapotranspiration estimation at a daily time step

Agricultural and Forest Meteorology ◽

10.1016/j.agrformet.2007.11.005 ◽

2008 ◽

Vol 148 (4) ◽

pp. 619-630 ◽

Cited By ~ 51

Author(s):

B. Bois ◽

P. Pieri ◽

C. Van Leeuwen ◽

L. Wald ◽

F. Huard ◽

...

Keyword(s):

Solar Radiation ◽

Reference Evapotranspiration ◽

Remotely Sensed ◽

Time Step ◽

Radiation Data ◽

Daily Time Step ◽

Evapotranspiration Estimation ◽

Daily Time

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Evaluation of FAO-56 Procedures for Estimating Reference Evapotranspiration Using Missing Climatic Data for a Brazilian Tropical Savanna

Water ◽

10.3390/w13131763 ◽

2021 ◽

Vol 13 (13) ◽

pp. 1763

Author(s):

Luiz Claudio Galvão do Valle Júnior ◽

George L. Vourlitis ◽

Leone Francisco Amorim Curado ◽

Rafael da Silva Palácios ◽

José de S. Nogueira ◽

...

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Water Resource Management ◽

Reference Evapotranspiration ◽

Meteorological Data ◽

Climatic Conditions ◽

Climatic Variables ◽

Climatic Data ◽

Wet Season ◽

Radiation Data

The Brazilian savanna (Cerrado) has been heavily impacted by agricultural activities over the last four to five decades, and reliable estimates of reference evapotranspiration (ETo) are needed for water resource management and irrigation agriculture. The Penman–Monteith (PM) is one of the most accepted models for ETo estimation, but it requires many inputs that are not commonly available. Therefore, assessing the FAO guidelines to compute ETo when meteorological data are missing could lead to a better understanding of which variables are critically important for reliable estimates of ETo and how climatic variables are related to water requirements and atmospheric demands. In this study, ETo was computed for a grass-dominated part of the Cerrado from April 2010 to August 2019. We tested 12 different scenarios considering radiation, relative humidity, and/or wind speed as missing climatic data using guidelines given by the FAO. Our results presented that wind speed and actual vapor pressure do not affect ETo estimates as much as the other climatic variables; therefore, in the Cerrado’s conditions, wind speed and relative humidity measurements are less required than temperature and radiation data. When radiation data were missing, the computed ETo was overestimated compared to the benchmark. FAO procedures to estimate the net radiation presented good results during the wet season; however, during the dry season, their results were overestimated because the method could not estimate negative Rn. Our results indicate that radiation data have the highest impact on ETo for our study area and presumably for regions with similar climatic conditions. In addition, those FAO procedures for estimating radiation are not suitable when radiation data are missing.

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Evaluation of FAO-56 procedures for estimating reference evapotranspiration using missing climatic data for a Brazilian tropical savanna

10.22541/au.162074314.47282029/v1 ◽

2021 ◽

Author(s):

Luiz Claudio Valle Junior ◽

George Vourlitis ◽

Leone Francisco Curado ◽

Rafael Palacios ◽

José Nogueira ◽

...

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Reference Evapotranspiration ◽

Meteorological Data ◽

Climatic Data ◽

Wet Season ◽

Resources Management ◽

Radiation Data ◽

D Values ◽

Irrigation Agriculture

Since the Brazilian Cerrado has been heavily impacted by agricultural activities over the last four to five decades, reference evapotranspiration (ETo) plays a pivotal role in water resources management for irrigation agriculture. The Penman-Monteith (PM) is one of the most accepted models for ETo estimation, but it requires many inputs that are not commonly available. Therefore, assessing the FAO guidelines to compute ETo when meteorological data are missing could lead to a better understanding of how climatic variables are related to water requirements and atmospheric demands for a grass-mixed savanna region and which variable impacts the estimates the most. In this study, ETo was computed from April 2010 to August 2019. We tested twelve different scenarios considering radiation, relative humidity, and/or wind speed as missing climatic data using guidelines given by FAO. When wind speed and/or relative humidity data were the only missing data, the PM method showed the lowest errors in the ETo estimates and correlation coefficient (r) and Willmott’s index of agreement (d) values close to 1.0. When radiation data were missing, computed ETo was overestimated compared to the benchmark. FAO procedures to estimate the net radiation presented good results during the wet season; however, during the dry season, their results were overestimated, especially because the method could not estimate negative Rn. Therefore, we can infer that radiation data have the highest impact on ETo for our study area and also regions with similar conditions and FAO guidelines are not suitable when radiation data are missing.

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Evaluation of FAO-56 Procedures for Estimating Reference Evapotranspiration Using Missing Climatic Data for a Brazilian Tropical Savanna

10.21203/rs.3.rs-160543/v1 ◽

2021 ◽

Author(s):

Luiz Cláudio Galvão Valle Júnior ◽

George Louis Vourlitis ◽

Leone Francisco Amorim Curado ◽

Rafael da Silva Palácios ◽

José de Souza Nogueira ◽

...

Keyword(s):

Wind Speed ◽

Relative Humidity ◽

Reference Evapotranspiration ◽

Meteorological Data ◽

Climatic Data ◽

Wet Season ◽

Resources Management ◽

Radiation Data ◽

D Values ◽

Irrigation Agriculture

Abstract Since the Brazilian Cerrado has been heavily impacted by agricultural activities over the last four to five decades, reference evapotranspiration (ETo) plays a big role in water resources management for irrigation agriculture. The Penman-Monteith (PM) is one of the most accepted models for ETo estimation, but it requires many inputs that are not commonly available. Therefore, assessing the FAO guidelines to compute ETo when meteorological data are missing could lead to a better understanding of how climatic variables are related to water requirements and atmospheric demands for a grass-mixed savanna region and which variable impacts the estimates the most. ETo was computed from April 2010 to August 2019. We tested twelve different scenarios considering radiation, relative humidity, and/or wind speed as missing climatic data using guidelines given by FAO. When wind speed and/or relative humidity data were the only missing data, the PM method showed the lowest errors in the ETo estimates and correlation coefficient (r) and Willmott’s index of agreement (d) values close to 1.0. When radiation data were missing, computed ETo was overestimated compared to the benchmark. FAO procedures to estimate net radiation presented good results during the wet season; however, during the dry season, their results were overestimated, especially because the method could not estimate negative Rn. Therefore, we can infer that radiation data have the largest impact on ETo for our study area and regions with similar conditions and FAO guidelines are not suitable when radiation data are missing.

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Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN)

Hydrology Research ◽

10.2166/nh.2012.074 ◽

2012 ◽

Vol 44 (1) ◽

pp. 131-146 ◽

Cited By ~ 48

Author(s):

Ana Pour-Ali Baba ◽

Jalal Shiri ◽

Ozgur Kisi ◽

Ahmad Fakheri Fard ◽

Sungwon Kim ◽

...

Keyword(s):

Solar Radiation ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Reference Evapotranspiration ◽

Climatic Data ◽

Inference System ◽

Independent Variables ◽

Radiation Data ◽

Neuro Fuzzy ◽

Artificial Neural

Daily reference evapotranspiration (ET0), as a dependent variable, was estimated for two weather stations in South Korea, using 8 years (1985–1992) of measurements of independent variables of air temperature, sunshine hours, wind speed and relative humidity. The model uses the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) for estimating daily ET0. In the first part of the study, the applied models were trained, tested and validated using various combinations of the recorded independent variables, which corresponded to the Hargreaves–Samani, Priestly–Taylor and FAO56-PM equations. The goodness of fit for the models was evaluated in terms of the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and Nash–Sutcliffe coefficient (NS). In the second part of the study, the estimated solar radiation data were applied as input parameters (for the same input combinations, as the first part), instead of recorded sunshine values. The results indicated that the both applied ANFIS and ANN models performed quite well in ET processes from the available climatic data. The results also showed that the application of estimated solar radiation data instead of the recorded sunshine values decreases the models’ accuracy.

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