Browsing by Author "Moftakhari, Hamed"

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    Establishing flood thresholds for sea level rise impact communication
    Mahmoudi, Sadaf; Moftakhari, Hamed; Munoz, David F.; Sweet, William; Moradkhani, Hamid (Nature Portfolio, 2024-05-18)
    Sea level rise (SLR) affects coastal flood regimes and poses serious challenges to flood risk management, particularly on ungauged coasts. To address the challenge of monitoring SLR at local scales, we propose a high tide flood (HTF) thresholding system that leverages machine learning (ML) techniques to estimate SLR and HTF thresholds at a relatively fine spatial resolution (10 km) along the United States’ coastlines. The proposed system, complementing conventional linear- and point-based estimations of HTF thresholds and SLR rates, can estimate these values at ungauged stretches of the coast. Trained and validated against National Oceanic and Atmospheric Administration (NOAA) gauge data, our system demonstrates promising skills with an average Kling-Gupta Efficiency (KGE) of 0.77. The results can raise community awareness about SLR impacts by documenting the chronic signal of HTF and providing useful information for adaptation planning. The findings encourage further application of ML in achieving spatially distributed thresholds.
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    Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting
    Abbaszadeh, Peyman; Muñoz, David F.; Moftakhari, Hamed; Jafarzadegan, Keighobad; Moradkhani, Hamid (Elsevier, 2022-10)
    This perspective discusses the importance of characterizing, quantifying, and accounting for various sources of uncertainties involved in different layers of hydrometeorological and hydrodynamic model simulations as well as their complex interactions and cascading effects (e.g., uncertainty propagation) in forecasting compound flooding (CF). Over the past few decades, CF has come to attention across the globe as this natural hazard results from a combination of either concurrent or successive flood drivers with larger economic, societal, and environmental impacts than those from isolated drivers. A warming climate and increased urbanization in flood-prone areas are expected to contribute to an escalation in the risk of CF in the near future. Recent advances in remote sensing and data science can provide a wide range of possibilities to account for and reduce the predictive uncertainties; hence improving the predictability of CF events, enabling risk-informed decision-making, and ensuring a sustainable CF risk governance.