4. Conclusions

Blaney-Criddle, Hamon, and Linacre methods, with RMSE values of 1.63, 1.31, 1.15, and 1.12,

Figure 9. (a) MBE and (b) RMSE for evapotranspiration comparison between the Penman-Monteith method and four

method was optimal for estimating ET at the Tainan Weather Station, followed by the Hamon

According to relevant studies and literature, Fontenot [32] declared that for meteorological stations near the coast, the Linacre method overestimated ET by 18.46% compared to the Penman-Monteith method. It was also pointed out that this method could be greatly affected by the dew point temperature. Compared with the Penman-Monteith method, the results of Thornthwaite, Hamon, and Blaney-Criddle methods all suggest underestimation, as these three temperaturebased formulas all took daylight hours into consideration. In spite of the high temperature, the results would still be lower than the actual amount when daylight hours were insufficient, causing underestimation. Even if the daylight hours were insufficient, ET still occurred. The

, the Linacre

respectively. In summary, according to the statistical results of MBE, RMSE, and R2

method. The Blaney-Criddle method was the least fit.

temperature-based methods.

18 Current Perspective to Predict Actual Evapotranspiration

This study mainly aimed to estimate ET using a limited number of meteorological parameters. With the internationally accepted Penman-Monteith method as the standard, the estimation formulas of six radiation-based methods were compared with those of four temperature-based methods. The 53-year dataset recorded by Tainan Weather Station from 1961 to 2013 was used to discuss ET. Statistical indexes were used to analyze and discuss the differences in ET calculated by the Penman-Monteith method and other estimation formulas in the hope of discovering a simple estimation formula to solve the issue of lacking or missing meteorological data.

This study discussed situations in which meteorological data were insufficient or missing in the Penman-Monteith method. The results showed that using the average Taiwan wind speed of 1.83 m s<sup>1</sup> when wind speed data were insufficient or missing exerted little impact on ET estimation of the Penman-Monteith method. In the cases where empirical formulas were used for substitution because of the lack of relative humidity data, the estimated ET was higher than the actual data, causing overestimation. In addition, this study explored the impact on ET estimation by the Penman-Monteith method caused by insufficient or missing radiation, relative

humidity, or wind speed data. It was discovered that the impact of wind speed was minimal, and the impact of relative humidity was the highest.

The six radiation-based methods selected in this study all suggested overestimation. In particular, the Turc method was optimal, followed by the Doorenbos-Pruitt method; the method with the worst performance was Jensen-Haise. This study found that ET was overestimated by the Jensen-Haise method in humid areas. In addition, among the four chosen temperature-based methods in this study, the Thornthwaite method, Hamon method, and Blaney-Criddle method all underestimated ET compared with the Penman-Monteith method, as these three temperature-based formulas all take daylight hours into consideration. In the cases where the daylight hours were insufficient, no matter how high the temperature was, underestimation would still occur. Even though the daylight hours were insufficient, ET was still occurring. The performance of the Linacre method was the best among the four estimation methods. The results of this study indicate that radiation-based estimation methods are better than temperature-based methods, as temperature is most likely to be the only meteorological parameter required in empirical formulas of temperature-based methods, making it easily affected by the data of meteorological stations, thus resulting in inaccuracy.
