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In this study, the effect of upstream control of two entrenched sub-basins on the short-term hydrological forecast, with the joint investigation of two aspects: variation of the area controlled and variation of the frequency of acquision of model inflows. The site chosen for this study RBRH – Revista Brasileira de Recursos Hídricos Volume 19 n.1 –Jan/Mar 2014, 87-99 99 was the portion of the Ijuí river basin with its outflow at the rivergaging station of Ponte Mística, and its entrenched sub-basins in Santo Angelo, Ponte Nova do Potiribu, Colônia Mousquer, Passo do Faxinal and Turcato. The flow data used were obtained from the National Water Agency (ANA-Agencia Nacional de Águas) and from the Potiribu Basin monitoring project, while the data on precipitation were obtained through a historical series of mean precipitations from a grid of interpolated rainfalls based on the data from 65 raingaging stations in the region. For this study, data from August 22, 1989 to June 1, 1994 (1,408 days) were used. This period was selected because it is the longest period with concomitant data at all rivergaging stations. The models chosen for this study were the multiplelayer artificial neural networks, using the retropropagation algorithm The inputs to the models were the mean precipitation data and the daily mean flows of the Ponte Mistica basin and their sub-basins, and the otuflows were the daily mean flows of Ponte Mística one day ahead. Nine alternatives of upstream physical control were presented. Also, for each of the alternatives, lagged values of the variables were applied using the flow data 24h and 48h in advance. The use of hourly flows from Turcato was compared to an alternative that covers the same station, but with daily data to investigate whether the use of data with greater details regarding time can produce better results. The Nash — Sutcliffe (NS)coefficient was applied as quality statistics to analyze the network performance. Statistical evaluation presented good results in the flow forecast for all alternatives of control, and the smallest NS was 0.91 and the biggest 0.97. The use of greater time details, applying hourly flows, caused a reduction in model performance, with the NS dropping from 0.91 to 0.89. It was also observed that the larger the controlled area of the basins, the better the flow forecasting results. Key-words: Hydrological forecasting , neural networks.

Efeito do Controle de Montante de Sub-bacias Embutidas na Previsão Hidrológica de Curto Prazo com Redes Neurais: Aplicação à Bacia de Ponte Mística