A unifying synthesis of the hydrologic response of a catchment to surface runoff is attempted by linking the instantaneous unit hydrograph (IUH) with the geomorphologic parameters of a basin. Equations of general character are derived which express the IUH as a function of Horton's numbers RA, RB, and RL; an internal scale parameter LΩ and a mean velocity of streamflow v. The IUH is time varying in character both throughout the storm and for different storms. This variability is accounted for by the variability in the mean streamflow velocity. The underlying unity in the nature of the geomorphologic structure is thus carried over to the great variety of hydrologic responses that occur in nature. An approach is initiated to the problem of hydrologic similarity.

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 improves the ability of neural networks for forecasting the indexed regional drought.

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