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This study will explore the use of a nonlinear relationship between dew point temperature (DPT) and precipitation to apply a method to downscale global circulation models (GCMs) information. Recently, there have been attempts to characterize dew point temperature (DPT) and precipitation intensity. The DPT has shown to be an easier variable to predict, and therefore forecast or prediction on GCM could provide a more reliable estimation. DPT using Clausius-Clapeyron (CC) relationship is one of the methods. This relationship has approximated a non-linear relation into a simple slope coefficient. In a recent study in the Netherlands, DPT was processed by the Advanced Delta Change Method (ADCM) and then converted to precipitation using CC or super CC relation observing better results. In this research work this approach is compared with a non-linear (neural network) model of the relationship. The main contribution of the overall project is to explore the improvement of a better relationship description and how much this could impact in our engineering intensity duration frequency curves. The problem posed is the analysis of the implications of climate change information for civil engineering design in the Metropolitan Area of Monterrey (Santa Catarina River Basin in Mexico).

epic series in engineering

Building a Nonlinear Relationship Between Dew Point Temperature and Precipitation to Apply a Method to Downscale GCMs Information: Case study in Santa Catarina River Basin, Monterrey