Droughts are destructive climatic extreme events, which may cause significant damages both in nat... more Droughts are destructive climatic extreme events, which may cause significant damages both in natural environments and human lives. Drought forecasting plays an important role in the control and management of water resources systems. In this study, a conjunction model is presented to forecast droughts. The proposed conjunction model is based on dyadic wavelet transforms and neural networks. Neural networks have shown great ability in modeling and forecasting nonlinear and nonstationary time series in a water resources engineering, and wavelet transforms provide useful decompositions of an original time series. The wavelettransformed data aids in improving the model performance by capturing helpful information on various resolution levels. Neural networks were used to forecast decomposed sub-signals in various resolution levels and reconstruct forecasted sub-signals. The performance of the conjunction model was measured using various forecast skill criteria. The model was applied to forecast droughts in the Conchos River Basin in Mexico, which is the most important tributary of the Lower Rio Grande/Bravo. The results indicate that the conjunction model significantly
El análisis de la infiltración en el ciclo hidrológico es de importancia básica en la relación en... more El análisis de la infiltración en el ciclo hidrológico es de importancia básica en la relación entre la precipitación y el escurrimiento, por lo que a continuación se introducen los conceptos que la definen, los factores que la afectan, los métodos que se usan para medirla y el cálculo de dicha componente en grandes cuencas.
Droughts are destructive climatic extreme events, which may cause significant damages both in nat... more Droughts are destructive climatic extreme events, which may cause significant damages both in natural environments and human lives. Drought forecasting plays an important role in the control and management of water resources systems. In this study, a conjunction model is presented to forecast droughts. The proposed conjunction model is based on dyadic wavelet transforms and neural networks. Neural networks have shown great ability in modeling and forecasting nonlinear and nonstationary time series in a water resources engineering, and wavelet transforms provide useful decompositions of an original time series. The wavelettransformed data aids in improving the model performance by capturing helpful information on various resolution levels. Neural networks were used to forecast decomposed sub-signals in various resolution levels and reconstruct forecasted sub-signals. The performance of the conjunction model was measured using various forecast skill criteria. The model was applied to forecast droughts in the Conchos River Basin in Mexico, which is the most important tributary of the Lower Rio Grande/Bravo. The results indicate that the conjunction model significantly
El análisis de la infiltración en el ciclo hidrológico es de importancia básica en la relación en... more El análisis de la infiltración en el ciclo hidrológico es de importancia básica en la relación entre la precipitación y el escurrimiento, por lo que a continuación se introducen los conceptos que la definen, los factores que la afectan, los métodos que se usan para medirla y el cálculo de dicha componente en grandes cuencas.
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