Chapter 5 Coastal Engineering: Sea Level Change

Chapter 5 Coastal Engineering: Sea Level Change#

  1. Introduction: Sea Level Change

  2. Simulation: Sea Level Change

  3. Self-Assessment

1. Introduction#

Descriptive alt text for accessibility

Fig. 32 **Figure 5.13 **: Sealevel Change and Coastal Structures.#

🌊 Sea Level Rise: Definition, Data, Methods, and U.S. Coastal Impacts#

📌 What Is Sea Level Rise?#

Sea level rise refers to the long-term increase in the average height of the ocean’s surface, primarily driven by:

  • 🌡️ Thermal expansion: Warmer water expands

  • 🧊 Melting glaciers and ice sheets: Adds freshwater to oceans

  • 🚰 Groundwater extraction: Transfers water from land to sea

Global sea level has risen by 21–24 cm (8–9.5 inches) since 1880, with the rate accelerating to 3.4–4.6 mm/year in recent decades.

🏗️ Coastal Projects That Require Sea Level Rise Information#

Sea level rise data is essential for:

  • Harbor and port design

  • Floodplain mapping and zoning

  • Wetland restoration and marsh migration planning

  • Storm surge barrier and seawall engineering

  • Infrastructure siting (roads, wastewater, power plants)

  • Managed retreat and relocation strategies

  • Climate adaptation and resilience planning

Agencies like NOAA, FEMA, USACE, and state coastal commissions use sea level projections to guide permitting, hazard mitigation, and long-term investment.

🛰️ What the Data Tell Us About Future Sea Level Rise#

🔭 Observational Data#

  • Tide gauges: Track local relative sea level changes over decades

  • Satellite altimetry: Measures global sea surface height since 1993

  • Ice mass loss: Monitored via GRACE and ICESat missions

📡 Satellite-Based Insights#

  • NASA’s Sentinel-6 and Jason missions show record-high sea levels in 2024, exceeding expectations

  • Copernicus and ESA data reveal regional variations and acceleration trends

🧮 Model-Based Projections#

  • IPCC AR6 and NOAA scenarios project:

    • 0.3–1.0 m rise by 2100 under low emissions

    • 0.5–1.9 m rise by 2100 under high emissions

  • Models incorporate ice sheet dynamics, thermal expansion, and land subsidence

🛠️ Typical Methods for Measuring and Projecting Sea Level Rise#

Method

Description

Tide gauges

Long-term local measurements

Satellite altimetry

Global sea surface height tracking

InSAR

Detects land subsidence and vertical motion

Extensometers

Measure aquifer compaction and subsidence

Climate models

Simulate ocean warming and ice melt

Semi-empirical models

Use historical trends to project future rise

Structured expert judgment

Combines expert opinions with model outputs

🇺🇸 Sea Level Rise in U.S. Coastal Regions#

  • East Coast: Cities like New York, Boston, and Norfolk have seen 11–18 inches of rise over the past century

  • Gulf Coast: Galveston and New Orleans face subsidence + sea level rise, with up to 25 inches observed

  • West Coast: Slower rise, but vulnerable to king tides and erosion

  • Alaska: Some areas experience relative sea level fall due to land uplift

🔍 Implications#

  • Increased high-tide flooding (sunny-day floods)

  • Loss of wetlands and barrier islands

  • Threats to infrastructure, housing, and drinking water

  • Displacement of coastal communities

  • Need for resilient design, managed retreat, and nature-based solutions

By 2050, 48,000+ U.S. properties could be below the high tide line.

References#

[Intergovernmental Panel on Climate Change, 2021] provides a detailed and authoritative review of sea level rise projections in response to various anthropogenic forcings, including greenhouse gas emissions and land ice melt. Recent studies show sea levels along the U.S. Gulf Coast have risen at ~12–14 mm/year since 2010, nearly three times the global average

Sea level rise is not just a future problem—it’s already reshaping coastlines, ecosystems, and economies. Planning with robust data and adaptive strategies is essential.

2. Simulation#

🌍 Interactive Sea Level Rise (SLR) Projection Dashboard#

This Jupyter Notebook tool visualizes projected sea level rise from 2020 to 2100 across different locations and climate scenarios using a simplified numerical model.

🧠 What It Does#

  • Computes SLR projections based on selected climate scenarios:

    • SSP1-2.6: low emissions

    • SSP2-4.5: intermediate emissions

    • SSP5-8.5: high emissions

  • Uses a linear model to estimate cumulative SLR (in meters)

  • Plots SLR over time for the selected location and scenario

  • Updates in real time using interactive dropdowns and radio buttons

🎛️ Inputs#

Control

Description

Location

Select from New Orleans, Galveston, Mobile, Tampa

Scenario

SSP1-2.6, SSP2-4.5, or SSP5-8.5

📊 Output#

  • 📈 Line chart showing projected SLR from 2020 to 2100

  • 🟦 Blue curve shows SLR trajectory per scenario

  • Title reflects selected location

  • Y-axis shows SLR in meters

  • 📦 Real-time plot updates upon interaction

🧭 How to Interpret#

  • Steeper curve = faster SLR under more severe climate scenarios

  • Use visualization to assess local vulnerability and planning urgency

  • Helps compare expected impacts between regions and emissions pathways

A compact dashboard for visualizing long-term climate-driven sea level changes under multiple futures.

import numpy as np
import matplotlib.pyplot as plt
import ipywidgets as widgets
from IPython.display import display, clear_output

# Projection model
def slr_projection(years, scenario):
    if scenario == 'SSP1-2.6':
        return 0.25 + 0.003 * (years - 2020)
    elif scenario == 'SSP2-4.5':
        return 0.3 + 0.005 * (years - 2020)
    else:  # SSP5-8.5
        return 0.4 + 0.007 * (years - 2020)

years = np.arange(2020, 2101)

# Widgets
location_selector = widgets.Dropdown(
    options=['New Orleans', 'Galveston', 'Mobile', 'Tampa'],
    value='New Orleans',
    description='Location:'
)

scenario_selector = widgets.RadioButtons(
    options=['SSP1-2.6', 'SSP2-4.5', 'SSP5-8.5'],
    value='SSP2-4.5',
    description='Scenario:'
)

output = widgets.Output()

# Update logic using matplotlib
def update_plot(change=None):
    with output:
        clear_output(wait=True)
        location = location_selector.value
        scenario = scenario_selector.value
        slr = slr_projection(years, scenario)

        plt.figure(figsize=(10, 5))
        plt.plot(years, slr, label='Projected SLR', color='royalblue')
        plt.title(f"Projected Sea Level Rise – {location}")
        plt.xlabel("Year")
        plt.ylabel("Sea Level Rise (m)")
        plt.ylim([0, max(slr) + 0.1])
        plt.grid(True, linestyle='--', alpha=0.5)
        plt.legend()
        plt.tight_layout()
        plt.show()

# Attach observers
location_selector.observe(update_plot, names='value')
scenario_selector.observe(update_plot, names='value')

# Display UI
display(widgets.VBox([location_selector, scenario_selector, output]))
update_plot()

3. Self-Assessment#

❓ Sea Level Rise Projection — Quiz Block#

Multiple Choice Questions

  1. Which SSP scenario results in the highest rate of sea level rise in the model?

  • A. SSP1-2.6

  • B. SSP2-4.5

  • C. SSP5-8.5

  1. What is the projected sea level rise in meters for SSP2-4.5 in the year 2100?

  • A. 0.5 m

  • B. 0.7 m

  • C. 0.9 m Explanation: \(text{SLR} = 0.3 + 0.005 \times (2100 - 2020) = 0.3 + 0.4 = 0.7 \), \text{m}

  1. Which Python library is used to create the interactive dropdown and radio button widgets?

  • A. NumPy

  • B. Matplotlib

  • C. ipywidgets

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