Regional and Local Sea Level Change

1. Regional and Local Sea Level Change#

../_images/845aff7865f63a069e74ecdf1c2803a4dd2bd46b2d3f4aa348469a5b1358a940.png

In this notebook, we’ll be creating a table, a map, and a time series plot of absolute and regional sea level change at the Malakal, Palau tide gauge station from 1993-2022. Absolute sea level, typically measured by satellite altimetry, refers to the height of the sea surface relative to a reference ellipsoid. Here, we’ll use the global ocean gridded L4 Sea Level Anomalies (SLA) available from Copernicus, which is the sea surface height (SSH) minus the mean sea surface (MSS), where the MSS is the temporal mean of SSH over a given period. Relative sea level is measured by a tide gauge, and is the sea level relative to land at that location. Differences between the two measurements can arise from vertical land motion, regional oceanographic conditions like currents, and changes to the gravitational field (affecting the geoid).

Download Files: Map | Time Series Plot | Table

1.1. Setup#

First, we need to import all the necessary libraries.

# import necessary libraries
import numpy as np
import xarray as xr
import datetime as dt
from pathlib import Path
import pandas as pd
import os

# data retrieval libraries
import requests
from urllib.request import urlretrieve #used for downloading files
import json
import copernicus_marine_client as copernicusmarine

# data processing libraries
from scipy import stats
import seaborn as sns
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt

from myst_nb import glue #used for figure numbering when exporting to LaTeX

/Users/juliafiedler/anaconda3/envs/SLI39/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Next, we’ll establish our directory for saving and reading data, and our output files.

data_dir = Path('../data')
output_dir = Path('../output')  

Then we’ll establish some basic plotting rules for this notebook to keep everything looking uniform.

1.4. Retrieve the Tide Station Data#

1.4.1. Assess Station Data Quality#

for the POR (1993-2022 …consult w/Ayesha and John)

1.4.2. Plot the hourly time series#

For a quick glance at the data

# Set the style of the plot
sns.set_style("whitegrid")

# Set the style of the plot
sns.set_style("whitegrid")

# Create a color palette
palette = sns.color_palette("Paired")

# Create the figure and the axes
fig, ax = plt.subplots()

# Plot the data

# plot tide gauge data
ax.scatter(rsl_daily['time'],100*rsl_daily, label='RSL', 
           color=palette[1], alpha=1, s= 5)

# Set the title, labels, and limits
ax.set(
    title=f'Tide Gauge ({station_name}) ({start_date_str} to {end_date_str})',
    xlabel='Year',
    ylabel='Water Level (cm)',
    ylim=[-50, 50],
    xlim=[start_date, end_date]
);

../_images/7498583d2ef8601a40a8ecf5ce8af34954803b7fb002c66d60a95d32bc386a94.png

1.4.3. Calculate rate and magnitude of change#

Calculate values for both the Trend (rate of change) and Magnitude of Change

# calculate the rate of change for the tide gauge
trend_mag_rsl, trend_line_rsl, trend_rate_rsl = process_trend_with_nan(rsl_daily)

print(f'The trend magnitude for the tide gauge is {100*trend_mag_rsl:.2f} cm.')
print(f'The trend rate for the tide gauge is {100*trend_rate_rsl:.2f} cm/year.')

The trend magnitude for the tide gauge is 12.26 cm.
The trend rate for the tide gauge is 0.41 cm/year.

1.4.4. Plot time series#

Now we’ll combine our daily relative sea level time series, the monthly mean, and a trend line into the same plot. We will label it with the Relative Sea Level Sea Level Change (magnitude in cm) and rate of change (cm/yr) at the Tide Station. The “zero” line is referenced to the [1993,2012] period, following the altimetry.

# make an rsl monthly mean for plotting
rsl_monthly = rsl_daily.resample(time='1M').mean().squeeze()

# Set the style of the plot
sns.set_style("whitegrid")

# Create a color palette
palette = sns.color_palette("Set1")

# Plot the timeseries
# Create the figure and the axes
fig, ax = plt.subplots()
# plot altimetry data
ax.scatter(rsl_daily['time'], 100*rsl_daily, 
           label='Tide Gauge', color=palette[1], alpha=0.2, s= 5)
ax.plot(rsl_daily['time'], 100*trend_line_rsl, 
        label='Tide Gauge Trend', color=palette[1], linestyle='--')
# plot the monthly mean sea level
ax.plot(rsl_monthly['time'], 100*rsl_monthly, 
        label='Tide Gauge', color=palette[3])


# Set the title and labels
ax.set_title(f'Tide Gauge ({station_name}) ({start_date_str} to {end_date_str})')
ax.set_xlabel('Year')
ax.set_ylabel('Height (cm)')

# Set the y limits
ax.set_ylim([-50, 50])

# Set the x limits
ax.set_xlim([start_date, end_date])

# Add a legend
trendmag_str = f'Δ Sea Level: {100*trend_mag_rsl:.2f} cm, Trend: {100*trend_rate_rsl:.2f} cm/year'
# Add text in a white box to bottom right of plot
ax.text(0.95, 0.05, trendmag_str, transform=ax.transAxes, 
        fontsize=14, verticalalignment='bottom', horizontalalignment='right',
        bbox=dict(facecolor='white', alpha=0.5))

Text(0.95, 0.05, 'Δ Sea Level: 12.26 cm, Trend: 0.41 cm/year')

../_images/43147d14cc9e1060744606493810c58e45552d1bfd3d1847791799ed41a21733.png

1.5. Combining both sources#

1.5.1. Create a Table#

That compares the results of sections Section 1.3.5 and Section 1.4.4– TABLE

# Constants
DATA_SOURCE_ALTIMETRY = 'CMEMS SSH L4 0.25 deg (SLA)'
DATA_SOURCE_TIDE_GAUGE = 'UHSLC RQDS'
TIME_PERIOD = f'{start_date_str} to {end_date_str}'

# Calculated values 
trend_mmyr_altimetry = 1000 * trend_rate_asl.values
trend_mmyr_tide_gauge = 1000 * trend_rate_rsl.values
delta_sea_level_altimetry = 100 * trend_mag_asl.values
delta_sea_level_tide_gauge = 100 * trend_mag_rsl.values


# Create DataFrame
SL_magnitude_results = pd.DataFrame({
    'Trend (mm/yr)': [trend_mmyr_altimetry, trend_mmyr_tide_gauge],
    'Δ Sea Level (cm)': [delta_sea_level_altimetry, delta_sea_level_tide_gauge],
    'Latitude': [sla_nearest_lat, rsl['lat'].values[0]],
    'Longitude': [sla_nearest_lon, rsl['lon'].values[0]],
    'Time_Period': [TIME_PERIOD, TIME_PERIOD],
    'Data_Source': [DATA_SOURCE_ALTIMETRY, DATA_SOURCE_TIDE_GAUGE],
}, index=['Altimetry', 'Tide Gauge'])

# Save to CSV
output_file_path = output_dir / 'SL_magnitude_results.csv'

# Use the path for operations, e.g., saving a DataFrame
SL_magnitude_results.to_csv(output_file_path)

# glue trend_rates to the notebook
glue("trend_rate_rsl", trend_rate_rsl, display=False)
glue("trend_rate_asl", trend_rate_asl, display=False)

SL_magnitude_results

	Trend (mm/yr)	Δ Sea Level (cm)	Latitude	Longitude	Time_Period	Data_Source
Altimetry	5.790198	17.368217	7.375	134.375	1993-01-01 to 2022-12-31	CMEMS SSH L4 0.25 deg (SLA)
Tide Gauge	4.086637	12.258233	7.33	134.462997	1993-01-01 to 2022-12-31	UHSLC RQDS

1.5.2. Create a Map#

Now we’ll combine a both the tide gauge and altimetry sources into a map that includes the absolute change with the addition of an icon depicting the magnitude of relative change at the tide station.

fig, ax, crs,cmap = plot_map(vmin,vmax,xlims,ylims)

# plot the trend*100 for centimeters
trend_mag_cmems_cm = trend_mag_cmems * 100

# plot a map of the magnitude of SL change in centimeters
trend_mag_cmems_cm.plot(ax=ax, transform=crs,
                         vmin=vmin, vmax=vmax, cmap=cmap, add_colorbar=True, 
                         cbar_kwargs={'label': 'Δ Sea Level (cm, 1993-2022)'},
)

# add the EEZ
ax.plot(palau_eez[:, 0], palau_eez[:, 1], transform=crs, color='black', linewidth=2)
# add the tide gauge location with black outlined dot, colored by the sea level value
ax.scatter(rsl['lon'], rsl['lat'], transform=crs, s=200, 
           c=100*trend_mag_rsl, vmin=vmin, vmax=vmax, cmap=cmap,
           linewidth=0.5, edgecolor='black')

plot_zebra_frame(ax, lw=5, segment_length=2, crs=crs)

glue("mag_fig", fig, display=False)

# save the figure
output_file_path = output_dir / 'SL_magnitude_map.png'
fig.savefig(output_file_path, dpi=300, bbox_inches='tight')

../_images/18102f90bf44babf240b4b886385594c61aa6738a8462e72b3736dbe0dde67ce.png — Fig. 1.1 Map of absolute and relative sea level change from the altimetry and tide gauge record near Malakal, Palau station from 1993-01-01 to 2022-12-31.#

1.5.3. Create a Time series plot#

Finally we will combine both tide gauge and altimetry sources into a time series plot that includes both Absolute and Relative Time Series.

# Set the style of the plot
sns.set_style("whitegrid")

# Create a color palette
palette = sns.color_palette("Set1")

# Create the figure and the axes
fig, ax = plt.subplots()

# plot altimetry data
ax.scatter(sla_nearest['time'], 100*sla_nearest, 
           label='Altimetry', color=palette[0], alpha=0.2, s=5)
ax.plot(sla_nearest['time'], 100*trend_line_asl, 
        label='Altimetry Trend', color=palette[0], linestyle='--')

# plot tide gauge data
ax.scatter(rsl_daily['time'], 100*rsl_daily, 
           label='Tide Gauge', color=palette[1], alpha=0.2, s=10)
ax.plot(rsl_daily['time'], 100*trend_line_rsl, 
        label='Tide Gauge Trend', color=palette[1], linestyle='--')

# Set the title and labels
title = (
    f'Altimetry ({lat_str}, {lon_str}) vs \n'
    f'Tide Gauge ({station_name}) ({start_date_str} to {end_date_str})'
)
ax.set_title(title)
ax.set_xlabel('Year')
ax.set_ylabel('Height (cm, MHHW)')

# Set the y and x limits
ax.set_ylim([-40, 40])
ax.set_xlim([start_date, end_date])

# Add a legend below the plot
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=2)

glue("trend_fig", fig, display=False)

# save the figure
output_file_path = output_dir / 'SL_magnitude_timeseries.png'
fig.savefig(output_file_path, dpi=300, bbox_inches='tight')

Regional and Local Sea Level Change

Contents

1. Regional and Local Sea Level Change#

1.1. Setup#

1.2. Retrieve Data Sources#

1.2.1. Retrieve the location of the Malakal, Palau tide gauge#

1.2.2. Retrieve the EEZ boundary for Palau#

1.2.3. Retrieve altimetry data#

1.2.4. Retrieve Tide Gauge Data#

1.3. Process the data#

1.3.1. Clip#

1.3.2. Plot the timeseries#

1.3.3. Calculate change#

1.3.4. Plot a map#

1.3.5. Plot time series#