COASTAL FLOODING & SOLUTIONS, Workshop Case Studies
Climate-based statistical Modeling of Monthly Mean Sea Level
US west coast
Sara Ortega van Vloten
Universidad de Cantabria
Coastal flooding in nearshore areas is ever more subject to analysis due to an increasing risk of potential impacts specially in highly populated areas and coastal infrastructures. In terms of quantifying the damage capacity of flood events, the concurrent total water level is of great importance. The total coastal sea level is computed as the aggregation of several components: sea level rise, sea level anomalies, astronomical tide, storm surge, setup and swash. Mean sea level anomalies can be accountable for variations of sea water density, marine circulations, setup from sustained trade winds and non-period climatic fluctuations (ENSO), among others.
In this work a statistical tool has been developed to obtain an estimation of the Monthly Mean Sea Level series at the US west coast from long tide gauge station records. Two regional predictors are used: Sea Surface Temperature (SST), and Sea Level Pressure fields (SLP). Existing global datasets ERSST v4, and CFSR reanalysis over several decades have been used respectively.
The proposed methodology performs PCA to reduce dimensionality of both regional predictors, then a linear regression model is applied to the Monthly Mean Sea Level and the Principal Components of monthly anomalies of both predictors. Finally a K-fold cross validation procedure evaluates the skill of the model.
This methodology has been implemented with a Python library composed by different modules and several JupyterLab notebooks have been produced for each site location to present results in a user-friendly format.
Applicability of statistical prediction of monthly mean sea level
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