Dr. Md Mamunur Rashid (UCF)

Title: An indicator for US coastal extreme sea level variability

Abstract:  In the United States (U.S.), 40 percent of the total population live in coastal counties and contribute approximately 45 percent to the national Gross Domestic Product (GDP). Impacts of coastal flooding to these highly developed and densely populated but low-lying coastal areas can be devastating. Coastal flood risk assessments often account only for long-term trends in mean sea level (MSL, regional or global) and storm surge climatology (SCC). But in addition to these lon-term trends, significant variations have been observed in MSL and SSC at interannual to multi-decadal time scales, with important implications for coastal flood risk. To reflect this variability, separate indicators were developed for MSL and SSC for the contiguous U.S. coastlines. Aggregated extreme sea level indicators were derived through additively combining the MSL and SSC indicators with long-period tidal predictions. Results indicate that the fluctuations of different sea level components play a vital role in exacerbating or reducing the impacts of long-term MSL rise. Therefore, projections of future fluctuations of the different sea level components contributing to extremes could be a useful tool for decision makers and planners. We develop and test various prediction models for the SSC indicators using climate indices, sea level pressure, and sea surface temperature as predictor variables. We anticipate to make our results publicly available through the National Oceanic and Atmospheric Administration’s (NOAA) climate indicator data base.

In this talk, I will discuss the formulation of a comprehensive framework where quasi non-stationary extreme value analysis, cross-correlation, and K-means clustering are employed to develop the different sea level indicators. I will shed light on spatial and temporal variability of different sea level components and discuss their relative contributions to variability in extreme sea levels. I will present an innovative modeling framework where useful information is extracted from predictor variables using discrete wavelet transforms (DWT) and then considered in regression models to predict SSC variability along the U.S. coastline.     

Biographical Sketch: Md Mamunur Rashid is a Post-Doctoral Research Associate at the Civil, Environmental, and Construction Engineering Department and National Center for Integrated Coastal Research of the University of Central Florida (UCF). Before joining UCF, he worked as a Post-Doctoral Researcher at the Water Research Center of the University of New South Wales, Australia. He was a visiting research scholar at the Center for Computational Sciences of the University of Tsukuba, Japan. He received his Ph.D. in Water Engineering from the University of South Australia. Dr. Rashid’s research targets climate resilient smart development by assessing impacts of meteo-hydrologic extremes (e.g. extreme rainfall, flood, drought, heat, sea level rise, storm surge, and wind) on built infrastructure and socio-ecological systems, including human health. He assesses historical changes of extremes and their relationships with large scale climate variability. He explores how various types of extremes may change in the future and how society can adapt through innovative engineering solutions. Dr. Rashid has developed a variety of tools useful for nonstationary, nonlinear, and multivariate analysis as well as performed climate data analytics employing state-of-the-art statistical, mathematical, and machine learning techniques.