MARACAJÁ, J. R. A.; http://lattes.cnpq.br/5819081081392371; MARACAJÁ, José Rosenilton de Araújo.
Resumo:
Many river basins have discontinuous and/or small hydrological series, raising the
hydrometric data demand in these basins. There are lacks of information in the semiarid reservoirs
related to seasonal forecasts of outflow or drained volume. This dissertation contemplates Piranhas-
Acu and Apodi river basins, located in the northeastern Brazil with great importance for the region,
with the use of the seasonal precipitation forecast. The basins with hydrometric data were used in
the establishment of a regional model to estimate the seasonal daily average outflow in the basins
without hydrometric data. The precipitation forecast and the physiographic characteristics of the
basins (draining area, length of the main river, mean slope of the river, density of draining) were
used in this process. The Artificial Neural Nets (ANNs) technique was used and its results were
compared to a Multiple Regression Model developed in previous researches. The ANN showed a
good performance when compared to Multiple Regression Model. Relations between the model
adjustment quality and the physical characteristics of the sub-basins were noticed. This result shows
the need of a data evaluation study with the use of representative samples of the set of basins for the
Neural Net calibration. Both hydrological models showed good performances in the seasonal
outflow forecast, despite the fact that the precipitation forecasts still contain high level of
uncertainty. It was evidenced that the accumulated uncertainties, propagated to the outflow
forecasting stage, had been attenuated in the transformation process of rainfall in outflow through
the hydrological basins.