Modelling Global Storm Surges in the Past, Present, and Future and the Associated Socio-economic Impacts
Project Description.
The continuous growth of population and critical infrastructure development in low-elevation coastal zones compounded with rising sea levels exacerbates the risk of flooding in these areas. In order to mitigate this risk, drivers of coastal flooding need to be modeled with high accuracy and their long-term variations needs to be studied. In many areas, storm surges caused by tropical and extratropical cyclones are the main contributors to critical extreme sea level events.
Storm surges can be simulated using numerical models that are based on the underlying physical processes, or by using data-driven methods (statistical/machine learning/deep learning) that explain the relationship between the target variable (storm surge) and relevant predictors without the need to explicitly understand the underlying physical processes. This study explores the potential of data-driven models to reconstruct and project storm surges globally. A multitude of predictors obtained from remote sensing and climate reanalysis are utilized to model daily maximum surge for the global coastlines for 840 tide gauge locations to train and validate the models based on in-situ observations.
Storm surges can be simulated using numerical models that are based on the underlying physical processes, or by using data-driven methods (statistical/machine learning/deep learning) that explain the relationship between the target variable (storm surge) and relevant predictors without the need to explicitly understand the underlying physical processes. This study explores the potential of data-driven models to reconstruct and project storm surges globally. A multitude of predictors obtained from remote sensing and climate reanalysis are utilized to model daily maximum surge for the global coastlines for 840 tide gauge locations to train and validate the models based on in-situ observations.
Framework outlining the applied methodology to develop data driven storm surge models using different predictor data sets, and comparison with numerical model output. Key: wind speed1, zonal and meridional wind speed; SST2, sea surface temperature; GPCP3, Global Precipitation Climatology Project; SLP4, mean sea-level pressure; PCA5, principal component analysis.
Model results show that daily maximum surge is very well captured, especially in extratropical and sub-tropical regions, with weaker model performance in the tropical regions around the equator. Similar spatial characteristics of model performance are found for extreme events. Models forced with remotely sensed predictors showed a slightly better performance than models forced with predictors obtained from reanalysis products. Results also highlight a significant improvement when compared with the Global Tide and Surge Reanalysis (GTSR), which is based on a hydrodynamic numerical model. The study also shows that for the majority of tide gauges, the most important predictor to model daily maximum surge is sea-level pressure. Finally, when adding tides to the analysis, the resulting total still water levels are reproduced with high accuracy everywhere along the global coastlines.
Relevant Publications
Tadesse M., Wahl T. and Cid A. (2020). Data-Driven Modeling of Global Storm Surges. Frontiers in Marine Science, 7:260. https://doi.org/10.3389/fmars.2020.00260
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All Rights Reserved.
Website developed and maintained by Javed Ali.