Reference evapotranspiration (ΕΤo) is a major component of the hydrological cycle and its estimation is essential for the net irrigation requirement, planning and management of regional water resources. The objective was to evaluate the performance of different empirical methods to estimate the reference evapotranspiration and propose an alternative to estimate the ETo in cases of limitation of meteorological data at the Yauri weather station. The methodology consisted of comparing the results of different empirical methods with the evapotranspiration estimated using the standard method of FAO-56 Penman-Monteith. The performance of the methods was compared using a qualitative evaluation (scatter plots) and quantitative statistical indicators percentage error (PE), root of the mean square error (RMSE), concordance index (d), correlation coefficient (r) and confidence index (c). The resu...

Reference evapotranspiration (ΕΤo) is a major component of the hydrological cycle and its estimation is essential for the net irrigation requirement, planning and management of regional water resources. The objective was to evaluate the performance of different empirical methods to estimate the reference evapotranspiration and propose an alternative to estimate the ETo in cases of limitation of meteorological data at the Yauri weather station. The methodology consisted of comparing the results of different empirical methods with the evapotranspiration estimated using the standard method of FAO-56 Penman-Monteith. The performance of the methods was compared using a qualitative evaluation (scatter plots) and quantitative statistical indicators percentage error (PE), root of the mean square error (RMSE), concordance index (d), correlation coefficient (r) and confidence index (c). The resu...

Reference evapotranspiration (ΕΤo) is a major component of the hydrological cycle and its estimation is essential for the net irrigation requirement, planning and management of regional water resources. The objective was to evaluate the performance of different empirical methods to estimate the reference evapotranspiration and propose an alternative to estimate the ETo in cases of limitation of meteorological data at the Yauri weather station. The methodology consisted of comparing the results of different empirical methods with the evapotranspiration estimated using the standard method of FAO-56 Penman-Monteith. The performance of the methods was compared using a qualitative evaluation (scatter plots) and quantitative statistical indicators percentage error (PE), root of the mean square error (RMSE), concordance index (d), correlation coefficient (r) and confidence index (c). The resu...

Reference evapotranspiration (ΕΤo) is a major component of the hydrological cycle and its estimation is essential for the net irrigation requirement, planning and management of regional water resources. The objective was to evaluate the performance of different empirical methods to estimate the reference evapotranspiration and propose an alternative to estimate the ETo in cases of limitation of meteorological data at the Yauri weather station. The methodology consisted of comparing the results of different empirical methods with the evapotranspiration estimated using the standard method of FAO-56 Penman-Monteith. The performance of the methods was compared using a qualitative evaluation (scatter plots) and quantitative statistical indicators percentage error (PE), root of the mean square error (RMSE), concordance index (d), correlation coefficient (r) and confidence index (c). The resu...

The forecast of river stream flows is of significant importance for the development of early warning systems. Artificial intelligence algorithms have proven to be an effective tool in hydrological modeling data-driven, since they allow establishing relationships between input and output data of a watershed and thus make decisions data-driven. This article investigates the applicability of the k-nearest neighbor (KNN) algorithm for forecasting the mean daily flows of the Ramis river, at the Ramis hydrometric station. As input to the KNN machine learning algorithm, we used a data set of mean basin precipitation and mean daily flow from hydrometeorological stations with various lags. The performance of the KNN algorithm was quantitatively evaluated with hydrological ability metrics such as mean absolute percentage error (MAPE), anomaly correlation coefficient (ACC), Nash-Sutcliffe efficienc...