Abstract

Extreme hydrological events have been recorded around the world, confirming the changing hydroclimate and therefore threatening the reliability over a planning horizon of the existing and projected hydraulic infrastruc-ture. Unfortunately, there is a gap between climate science and engineering planning and design, which is partially filled in this study, whose aim is to evaluate the expected reliability over a planning horizon of a hy-draulic infrastructure and its uncertainty for hydroclimatic projections. To achieve this goal, we implemented hydrological deep learning models that are driven by the Coupled Model Intercomparison Project Phase 6 (CMIP6). After validating and testing the hydrological deep learning models, sample series of the annual maximum flood (AMF) were generated for different mountain rivers of Chile and used for a standard frequency analysis that updates the exceedance probability of the flood events used for the hydraulic design. We found that the mean and the standard deviation of the sample series of the AMF are the key parameters that enable quantifying temporal changes in the exceedance probability of the design flow. The cumulative effects of these changes are summarized by the projected reliability over the planning horizon of the infrastructure, and the age of the infrastructure at which this projected reliability equates to the design reliability, quantifies the impact of climate change on the expected security of the hydraulic project. We show that the infrastructure that was designed for the smaller exceedance probability is more vulnerable to climate change, where the updated planning horizon can be as small as 45% of the planning horizon used for the design.