Dr. Elisa Ragno
I joined TU Delft as a postdoctoral researcher in September 2018 with the project: "A Generalized Framework for Quantifying Coastal Flood Risk in Areas with Multiple Flood Drivers" funded by the Marie Skłodowska-Curie cofund scheme LEaDing Fellows Postdocs Programme (http://leadingfellows.eu/). I obtained my PhD at the University of California, Irvine in June 2018 with the thesis “Advancing Process-Based Nonstationary Analysis of Climate Extremes: Modeling, Uncertainty Assessment, and Multivariate Attribution”. I obtained my bachelor degree (2009) and master degree (2012) in Structural Civil Engineering at Politecnico di Milano (Italy).
The project: "A Generalized Framework for Quantifying Coastal Flood Risk in Areas with Multiple Flood Drivers" aims to better characterize and predict the risk of compound flooding by further investigating the interplay between flood drivers, i.e., river discharge and storm surge, and the reliability of man-made infrastructures to cope with extreme events. Traditionally, the risk of a flood event has been quantitatively defined based on the probability of occurrence of one single physical driver, e.g., river discharge, or storm surge. However, the single-driver assumption can lead to substantial underestimation of the risk of flooding in coastal areas prone to multi-driver extreme events (also known as compound events). The theoretical framework developed will be used to generate coastal flood risk maps at regional scale. Insights for mitigation and adaptation strategies can be derived from the interaction between the hazard (i.e., compound flood) and the vulnerability (i.e., flood protection systems) and exposure (i.e., people and environment at risk) of the flood-prone area. Great attention will be given on estimating river discharge at the catchment level using statistical-based models.
My research interests include statistical methods for modelling hydroclimatic extremes and natural hazard definition for improving design, risk awareness and mitigation strategies.
Ragno, E., AghaKouchak, A., Love, C. A., Cheng, L., Vahedifard, F., & Lima, C. H. R. (2018). Quantifying changes in future Intensity‐Duration‐Frequency curves using multimodel ensemble simulations. Water Resources Research, 54, 1751–1764. https://doi.org/10.1002/2017WR021975
Sadegh, M., E. Ragno, and A. AghaKouchak (2017), Multivariate Copula Analysis Toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework, Water Resour. Res., 53, 5166–5183, https://doi.org/10.1002/2016WR020242
Sadegh, M., Moftakhari, H., Gupta, H. V., Ragno, E., Mazdiyasni, O., Sanders, B., Matthew, R., & AghaKouchak, A. (2018). Multihazard scenarios for analysis of compound extreme events. Geophysical Research Letters, 45, 5470–5480. https://doi.org/10.1029/2018GL077317
Vahedifard, F., AghaKouchak, A., Ragno, E., Shahrokhabadi, S., & Mallakpour, I. (2017). Lessons from the Oroville dam. Science, 355(6330), 1139-1140. https://doi.org/10.1126/science.aan0171