Evaluation of the uncertainty associated with precipitation projections considering climate change in the Turbio river basin of Guanajuato

Abstract

Climate change is the great challenge of the 21st century, each year the frequency and magnitude of weather increases. Therefore, it is of importance to forecast the variables associated to this phenomenon, such as precipitation. However, determining and incorporating the uncertainty associated with projections of meteorological variables is a problem that requires further investigation. For this reason, this research focuses on evaluating the uncertainty through Monte Carlo, including the precipitation projections of the general circulation models and downscaling with artificial neural networks. The results obtained show that downscaling with artificial neural networks significantly reduces the uncertainty to the projections of the general circulation models. Furthermore, there is a tendency to underestimate rainfall in most of the stations along with bias in the outputs related to the historical series.