Sea Level Rise and Extreme Weather Events Analysis

Pod

Rajasaurus Baris, CMA 2023

Overview

This repository contains the code, data, and analysis for our CMA 2023 project. Here we aimed to investigate the relationship between sea level rise (SLR) and extreme weather events, particularly their impact on coastal communities. The project focuses on comparing sea level measurements from tidal gauges (TG) with data obtained from satellite-based models (ECCO) during severe storm events.

Table of Contents

• Scientific Background
• Research Question
• Hypotheses
• Data Collection
• Data Analysis
• Results
• Future Work
• Contributors

Scientific Background

Sea level rise is a critical environmental issue that exacerbates the impact of storms on coastal communities. The high amplitude of sea level changes during storm events poses challenges for accurate measurement and representation by climate models. Tidal gauge (TG) data provides higher-resolution in-situ measurements of sea level fluctuations, which can be compared with satellite-based models like ECCO.

Research Question

How does the resolution and reliability of sea surface height (SSH) data from ECCO compare to tidal gauge (TG) data during severe storm impact events?

Hypotheses

• H0: There is no significant difference in resolution/reliability between SSH data and TG data during severe storm events.
• H1: There is a significant difference in resolution/reliability between SSH data and TG data during severe storm events, favoring TG data.

Data Collection

The project focuses on analyzing specific severe storm events, including:

• Hurricane Sandy (October 2012)
• Hurricane Maria (September 2017)
• Typhoon Ketsana (September 2009)

For each event, TG data and ECCO SSH data were collected for various coastal locations. The data included sea level measurements and associated metadata.

Data Analysis

The collected data underwent analysis to calculate Pearson's correlation between TG and ECCO SSH time series. We also considered Bland-Altman plots to evaluate the consistency and agreement between datasets.

Results

The analysis yielded valuable insights:

• Strong correlation between TG and ECCO SSH data in some locations.
• Some TG data provided higher resolution and reliability during extreme weather events.
• ECCO data's lower resolution near the coast influenced the results.

Event	Location	Pearons Statistic	Pvalue	Min TG	Max TG	Min SAT	Max SAT	Percent_usable
Sandy	Atlantic City, NJ	-0.8307771964541347	0.001537424752063651	-0.34474992752075195	0.7571251392364502	-0.011457881579796492	0.011457881579796436	100.0
Sandy	The Battery, NY	-0.7424619647484809	0.008869248556628137	-0.4015833333333332	0.8669166666666668	-0.014469226201375252	0.014469226201375363	100.0
Sandy	Montauk, NY	-0.7531328301741064	0.0074559342297149805	-0.3132878787878788	0.7259204545454547	-0.01068242515126866	0.010682425151268604	100.0
Maria	Isabel Segunda, PR	-0.4611116383274443	0.06246702286955319	-0.09793877601623535	0.2222280502319336	-0.001560370127359989	0.0015603701273600445	100.0
Maria	Esperanza, PR	TOO SHORT	TOO SHORT	NO DATA	NO DATA	-0.001560370127359989	0.0015603701273600445	0.0
Maria	Arecibo, PR	TOO SHORT	TOO SHORT	NO DATA	NO DATA	-0.0016681849956512451	0.0016681849956512451	0.0
Maria	Mayaguez, PR	TOO SHORT	TOO SHORT	NO DATA	NO DATA	-0.0013145009676616137	0.0013145009676615027	0.0
Maria	Fajardo, PR	0.8002793342695729	0.10387470925223188	-0.07427835464477539	0.2175273895263672	-0.000390092531840025	0.0003900925318399695	100.0
Ketsana	Malakal, Palau	-0.47123345888475304	0.3454710918143166	-0.25140976905822754	0.11129868030548096	-0.0017577782273292542	0.0017577782273292542	100.0
Ketsana	Legaspi, Philippines	TOO SHORT	TOO SHORT	NO DATA	NO DATA	-0.0019367411732673645	0.0019367411732673645	0.0
Ketsana	Manila, Philippines	TOO SHORT	TOO SHORT	-0.11147904396057129	0.26039600372314453	NO DATA	NO DATA	0.0
Ketsana	Subic Bay, Philippines	0.6426570312975	0.16872571988471247	-0.107208251953125	0.2514582872390747	-0.0008405869205793115	0.0008405869205792005	100.0
Ketsana	Qui Nhon, Vietnam	0.9491202756007416	0.0038172621818771666	-0.20693743228912354	0.38843750953674316	-0.0023103132843971252	0.0023103132843971252	100.0

Future Work

The project opens avenues for further investigation:

• Explore additional storm events and locations.
• Incorporate more sophisticated statistical methods.
• Investigate the impact of SLR on storm intensity and frequency.

Contributors

This project was a collaborative effort involving the following contributors:

Pod members:

• Franck Porteous
• Hannah Lili
• James Milward
• Faith Hunja
• Ayman khaled Attah-Allah
• Ruben Espeleta
• Aizat Salam
• Denisse Córdova Carrizales

Pod TA: Grant Parajuli
Project TA: Gaby Mayorga Adame

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thanks for reading!
if you have any question you can email Franck