Abstract
In many areas, storm surges caused by tropical or 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 models that quantify the relationship between the predictand (storm surge) and relevant predictors (wind speed, mean sea-level pressure, etc.). This study explores the potential of data-driven models to simulate storm surges globally. A multitude of predictors (obtained from remote sensing and climate reanalysis) along with predictands (from tide gauge observations and storm surge reanalysis) are utilized to train and validate data-driven models to simulate daily maximum surge for the global coastline. Data-driven models simulate daily maximum surge better in extratropical and sub-tropical regions (average correlation and Root Mean Square Error (RMSE) of 0.79 and 7.5 cm, respectively), than in the tropics (average correlation and RMSE of 0.45 and 5.3 cm, respectively). For extreme events, the average correlation decreases to 0.54 (0.33) and RMSE increases to 14.5 (13.1) cm for extratropical (tropical) regions. Models forced with remotely sensed predictors showed a slightly better performance (average correlation of 0.69) than models forced with predictors obtained from reanalysis products (average correlation of 0.68). Results also highlight a significant improvement (i.e., average correlation increases from 0.54 to 0.68; RMSE reduces from 11 cm to 7 cm) over the Global Tide and Surge Reanalysis (GTSR), derived from the only global hydrodynamic model. For approximately 70% of tide gauges, mean sea-level pressure is the most important predictor to model daily maximum surge. Our results highlight the added value of data-driven models in the context of simulating storm surges at the global scale, in addition to existing hydrodynamic numerical models.
Highlights
Storm surge is a rise in the coastal water level due to low atmospheric pressure and strong winds (Muis et al, 2016), which could be induced by tropical or extratropical cyclones (Salmun and Molod, 2015), and modulated by the coastal bathymetry (Pore, 1964)
RF-RS-lag represents the instance, LR-AR-lag represents the linear regression model Random Forest model trained with remotely sensed predictors that was trained with atmospheric reanalysis predictors that that are lagged up to 30 h
For any given tide gage, the model configuration that gives the best error statistics in terms of Pearson’s correlation, root-mean-square error (RMSE), and Nash-Sutcliffe efficiency (NSE) is selected for that specific tide gage
Summary
Storm surge is a rise in the coastal water level due to low atmospheric pressure and strong winds (Muis et al, 2016), which could be induced by tropical or extratropical cyclones (Salmun and Molod, 2015), and modulated by the coastal bathymetry (Pore, 1964). Our first objective is to train and validate two data-driven models (statistical and machine learning based) in order to simulate daily maximum storm surge at quasi-global scale This is achieved by using remotely sensed meteorological and oceanographic variables (as predictors) with observed storm surges (as predictand) from a large number of tide gages distributed along the global coastline. The data set (covering the period 1979–2014) is a near-coast global reanalysis of storm surges It is obtained by forcing a hydrodynamic model, the Global Tide and Surge Model (GTSM) based on the Delft3D modeling suite, with wind speed and atmospheric pressure from the ERA-Interim reanalysis. These values are compared with daily maximum surge derived from the data-driven models and observed daily maximum surge
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