Projection of Hydrologic Intensity Duration Frequency Parameters and Their Uncertainties Based on Climate Projections for 21st Century in the State of Texas

Abstract Submission: There is a growing scientific and public consensus about the impact of changing climatic patterns on the intensification of extreme rainfall events. Existing stormwater infrastructure systems fail to adequately respond to the changing extreme storm events since they are designed with older manuals based on historical rainfall records. Thus, there needs to be a re-examination of the frequency and intensity of rainfall projections. Precipitation projections are generally conducted under Global Climate Models (GCMs) or Regional Climate Models (RCMs). To determine the frequency and intensity patterns for different rainfall events and update their parameters for Intensity-Duration- Frequency (IDF) curves, precipitation projections from GCMs and RCMs need to go through several stages: spatial downscaling, bias-correction, temporal disaggregation, extreme value analysis, and uncertainty quantification. Current projection frameworks already incorporate some, but not all, of the processes. Therefore, infrastructure design and management need a comprehensive, reproducible, and consistent methodology to process the projections and develop updated IDFs. The state of Texas is among the areas that require such an insight into the IDF projections for the future, considering the climatic variability within the State and the pressure of urbanization leading to a more urgent need to serve the growing population. This project proposes a comprehensive framework to apply all mentioned approaches to the precipitation projection for the State. The study will apply an array of traditional statistical methods and machine learning (ML) models to be trained using climate model outputs and historical precipitation records and to be applied to projective climate model generated precipitation and to update the IDF curves for Texas for the next 80 years. The method will be scalable and transferable to other locations, thus enabling the scientific community and engineers to better adapt to future rainfall patterns.

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