Advancing Compound Flood Modeling To Evaluate Coastal Protection An interdisciplinary team of scientists have completed a comprehensive review of published numerical modeling efforts to assess the use of natural infrastructure for coastal compound flood mitigation. In response to these findings, the team discussed a conceptual framework for developing holistic, coupled models that can better represent the flood mitigation performance of natural infrastructure.
Compound Flood Potential From Storm Surge And Heavy Precipitation In Modeling flood inundation at the street level at a fine scale, specifically focusing on the compounding effects of multi driver floods, is critical for quantifying flood risk in densely urbanized coastal cities. Here, we quantify potential coastal compound flood risk at 0.1∘ resolution by integrating flood hazard, population exposure, and empirical vulnerability. Together, the elements of this graphical abstract convey a sophisticated, interdisciplinary approach to cf risk evaluation, focusing on integrating hydrodynamic with data driven geoai models to better manage the complex challenges of systemic cf risks in urban coastal zones. As the model setup and coupling is automated, reproducible, and globally applicable, the presented framework is a promising step forward towards large scale compound flood hazard modeling.
Schematics Illustrating Various Approaches To Compound Coastal Flood Together, the elements of this graphical abstract convey a sophisticated, interdisciplinary approach to cf risk evaluation, focusing on integrating hydrodynamic with data driven geoai models to better manage the complex challenges of systemic cf risks in urban coastal zones. As the model setup and coupling is automated, reproducible, and globally applicable, the presented framework is a promising step forward towards large scale compound flood hazard modeling. Figure 1. schematic diagram of flood drivers showing (a) fluvial (river discharge), (b) pluvial (rainfall runoff), and (c) coastal (surge, tide, waves, and total sea level) components, as well as their (d) compound flood interactions. This study presents a one dimensional (1 d), reduced order physics compound inundation model tested over an idealized coastal watershed transect under various forcing conditions (e.g., coastal and pluvial) that varied in magnitude, time, and space. This research focuses on advancing robust and computationally efficient approaches to resolving the coastal compound flooding components for complex, estuary environments and their application to the puget sound region of washington state (usa) and the greater salish sea. This study develops a hybrid statistical–dynamical framework for accurate and efficient compound coastal flooding analysis by linking a stochastic generator of compound flooding drivers, a physics based hydrodynamic model, and machine learning based surrogate models.
Coastal Compound Flood Simulation Through Coupled Multidimensional Figure 1. schematic diagram of flood drivers showing (a) fluvial (river discharge), (b) pluvial (rainfall runoff), and (c) coastal (surge, tide, waves, and total sea level) components, as well as their (d) compound flood interactions. This study presents a one dimensional (1 d), reduced order physics compound inundation model tested over an idealized coastal watershed transect under various forcing conditions (e.g., coastal and pluvial) that varied in magnitude, time, and space. This research focuses on advancing robust and computationally efficient approaches to resolving the coastal compound flooding components for complex, estuary environments and their application to the puget sound region of washington state (usa) and the greater salish sea. This study develops a hybrid statistical–dynamical framework for accurate and efficient compound coastal flooding analysis by linking a stochastic generator of compound flooding drivers, a physics based hydrodynamic model, and machine learning based surrogate models.
Addressing Compound Flood Risks Integrating Pluvial Fluvial And This research focuses on advancing robust and computationally efficient approaches to resolving the coastal compound flooding components for complex, estuary environments and their application to the puget sound region of washington state (usa) and the greater salish sea. This study develops a hybrid statistical–dynamical framework for accurate and efficient compound coastal flooding analysis by linking a stochastic generator of compound flooding drivers, a physics based hydrodynamic model, and machine learning based surrogate models.
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