Streamflow simulation at different temporal scales under rating curve uncertainty conditions using machine learning models

ABSTRACT Reducing uncertainty in streamflow simulation is vital for effective water resource management. The impact of uncertainty in model calibration data (discharge), commonly derived from the rating curve, is often overlooked. This study applies the Monte Carlo simulation technique (MCST) to assess uncertainty in the rating curve. Advanced machine learning (ML) models, bidirectional long short-term memory (BiLSTM), and bidirectional gated recurrent units (BiGRUs) were used comparatively to evaluate the propagation of this uncertainty onto streamflow simulation on both daily and monthly temporal scales. Different sets of streamflow data, derived from the fitted curve and its lower and upper uncertainty bands, were utilized to train ML models independently. The results show the substantial impact of rating curve uncertainty in streamflow simulations, with the BiGRU model surpassing the BiLSTM model on both scales. As a result, the uncertainty in the rating curve results in an uncertainty of the streamflow of up to 30 and 25% on daily and monthly simulations, respectively. These findings underscore the importance of considering rating curve uncertainty in streamflow simulation to ensure accurate and reliable results. Therefore, streamflow should be treated as an uncertain variable and managed by incorporating rating curve uncertainty in decision-making.