Regionalização hidrológica na região do alto rio Piranhas-PB utilizando dados simulados com modelo distribuído.

Abstract

The watersheds located in the semi-arid northeast of Brazil present, In general, small and / or discontinuous hydrological series. For To overcome this problem, well-calibrated and validated hydrological models can be used to generate long period series. On the other hand, regionalization techniques can be used in homogeneous regions to determine a given variable of interest (e.g., maximum flow rate, average, etc.) at sites without information. In this research, hydrological regionalization techniques were employed to establish of mathematical functions for the determination of maximum daily average flow rates, long-term averages and 95% guaranteed flow rates based on simulated data with NAVMO model in nine sub-basins of the homogeneous region of the upper Piranhas River - PB (-15000 km2). The model was calibrated for each sub-basin and the parameters used in the simulation of long period series. Parametric and nonparametric tests were employed to verify the consistency and homogeneity of the simulated series and probability distributions, indicated in the literature, were tested with the Kolmogorov-Smirnov (K-S test) Correlation and regression analyzes (single and multiple) between the variables of interest and the physical and climatic characteristics of the basins allowed establish functions for the calculation of hydrological variables anywhere in the region of the study. The results showed that the chosen model was reasonably calibrated. based on annual flows and volumes and simulated series with calibrated parameters presented reasonable consistency and homogeneity. K-S test results showed that the Pearson III distribution adjusted the frequencies of the flow data long-term maximums and average flows at all locations. The Gamma distributions of Gumbel and Normal had some restrictions. Correlation and regression analyzes with linear and potential functions showed that the two physical variables that best explained the hydrological variables investigated were the basin area (simple regression) and the basin area and the length of the main river (multiple regression). The inclusion of other physical and climatic variables (eg, slope of the drainage density basin and precipitation) improved the results, but not markedly. In general the Linear equations represented variables better than potential equations.