• Journal of Climate Change Research
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• v.8 no.2
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• pp.125-143
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• 2017

This study is conducted to estimate potential evapotranspiration of 10 weather observing systems in Andong Dam watershed with FAO56 Penman-Monteith (FAO56 PM) methodology using the meteorological data from 2013 to 2014. Also, assuming that there is no solar radiation data, humidity data or wind speed data, the potential evapotranspiration was estimated by FAO56 PM and the results were evaluated to discuss whether the methodology is applicable when meteorological dataset is not available. Then, the potential evapotranspiration was estimated with Hargreaves method and compared with the potential evapotranspiration estimated by FAO56 PM only with the temperature dataset. As to compare the potential evapotranspiration estimated from the complete meteorological dataset and that estimated from limited dataset, statistical analysis was performed using the Root Mean Square Error (RMSE), the Mean Bias Error (MBE), the Mean Absolute Error (MAE) and the coefficient of determination ($R^2$). Also the Inverse Distance Weighted (IDW) method was performed to conduct spatial analysis. From the result, even when the meteorological data is limited, FAO56 PM showed relatively high accuracy in calculating potential evapotranspiration by estimating the meteorological data.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.137-149
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• 2019

Accurate estimation of reference evapotranspiration (RET) is important to quantify crop evapotranspiration for sustainable water resource management in hydrological, agricultural, and environmental fields. It is estimated by different methods from direct measurements with lysimeters, or by many empirical equations suggested by numerous modeling using local climatic variables. The potential to use some such equations depends on the availability of the necessary meteorological parameters for calculating the RET in specific climatic conditions. The objective of this study was to determine the proper RET equations using limited climatic data and to analyze the temporal and spatial trends of the RET in South Korea. We evaluated the FAO-56 Penman-Monteith equation (FAO-56 PM) by comparing several simple RET equations and observed small fan evaporation. In this study, the modified Penman equation, Hargreaves equation, and FAO Penman-Monteith equation with missing solar radiation (PM-Rs) data were tested to estimate the RET. Nine weather stations were considered with limited climatic data across South Korea from 1973 - 2017, and the RET equations were calculated for each weather station as well as the analysis of the mean error (ME), mean absolute error (MAE), and root mean square error (RMSE). The FAO-56 PM recommended by the Food Agriculture Organization (FAO) showed good performance even though missing solar radiation, relative humidity, and wind speed data and could still be adapted to the limited data conditions. As a result, the RET was increased, and the evapotranspiration rate was increased more in coastal areas than inland.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.111-121
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• 2019

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.