Collaborative Research: Modeling, Variability and Predictability of North American Hydrologic Extremes

University Of California-San Diego Scripps Inst Of Oceanography, La Jolla CA

Investigators

Abstract

This research is intended to produce tools to assess potential regional effects of global anthropogenic climate change on the structure of North American daily hydrology in different seasons. The PIs will apply climate prediction methodology to forecast the probabilities of hydrological weather extremes. A new comprehensive statistical framework will be developed for the description of daily precipitation and streamflow data. Using this framework, the PIs will examine variability and predictability of daily hydrological extremes. They will apply statistical methods designed for data with heavy-tailed distributions (distributions with relatively large numbers of extreme events that can't be accounted for by exponentially decaying tails of traditional Probability Density Functions (PDFs). They will then examine the influence of climate forcing in space and time on the shape of seasonal distributions of daily hydrology. The parameters describing the PDF's shape will become predictands from which statistical information, e.g., frequencies of extreme events, can be derived. This research has broader impacts including graduate student education and research results applied to the important societal issue of extreme hydrologic events in the context of a changing climate.