Skip to main content

Geography of Tsunami


A tsunami is a series of large ocean waves caused by disturbances such as underwater earthquakes, volcanic eruptions, landslides, or meteorite impacts. These waves travel across ocean basins with immense speed and energy, affecting coastal regions worldwide. Understanding the geography of tsunamis involves analyzing their origin, propagation, impact zones, and mitigation strategies.


1. Causes and Geophysical Processes

A. Tectonic Plate Movements (Seismic Tsunamis)

  • The most common cause of tsunamis is underwater earthquakes occurring along subduction zones, where one tectonic plate is forced under another.
  • When stress is released, the seabed shifts vertically, displacing a large volume of water, generating tsunami waves.
  • Example: The 2004 Indian Ocean Tsunami was triggered by a 9.1-magnitude earthquake off the coast of Sumatra, Indonesia.

B. Volcanic Eruptions (Volcanogenic Tsunamis)

  • Underwater or coastal volcanoes can cause tsunamis when they erupt violently, collapse, or generate pyroclastic flows into the ocean.
  • Example: The 1883 Krakatoa eruption in Indonesia created a tsunami that reached over 40 meters, destroying coastal villages.

2. Propagation and Wave Dynamics

A. Deep-Ocean Characteristics

  • Tsunami waves can travel at speeds of 500-800 km/h in deep water with a small wave height (few centimeters to a meter).
  • Unlike wind-generated waves, tsunami waves have extremely long wavelengths (over 100 km) and low amplitude.

B. Coastal Amplification (Shoaling Effect)

  • As tsunamis approach shallow coastal waters, their speed decreases, but their height increases due to wave compression.
  • The process is called wave shoaling, where the wavelength shortens, and wave height can exceed 30 meters.

C. Wave Types

  1. Drawback Effect: In some tsunamis, the waterline recedes dramatically before the wave strikes.
  2. Multiple Waves: Tsunamis often arrive as a series of waves, with the second or third being the largest.

3. Geographic Impact and Vulnerability

A. High-Risk Regions (Tsunami-Prone Areas)

  • Pacific Ring of Fire: Subduction zones around the Pacific Ocean (Japan, Chile, Alaska, Indonesia).
  • Indian Ocean: Sunda Trench and Andaman-Sumatra region (2004 Tsunami).
  • Mediterranean and Caribbean: Due to tectonic activity and volcanic presence.

B. Coastal Geography and Risk Factors

  • Low-lying areas: Countries like Bangladesh, Maldives, and Florida are highly vulnerable due to their low elevation.
  • Narrow bays and inlets: These can focus tsunami energy, increasing wave height (e.g., Hilo Bay, Hawaii).

4. Tsunami Warning Systems and Mitigation

A. Early Warning Systems

  • Pacific Tsunami Warning Center (PTWC): Monitors seismic and ocean data.
  • Tsunameters (DART buoys): Measure pressure changes in the deep ocean to detect tsunamis.

B. Coastal Defenses and Preparedness

  • Mangrove forests and coral reefs: Reduce wave energy.
  • Sea walls and breakwaters: Help protect coastal cities.
  • Evacuation plans and drills: Countries like Japan have extensive tsunami drills.

Major Tsunamis

  1. 2004 Indian Ocean Tsunami

    • Magnitude: 9.1 earthquake
    • Countries affected: Indonesia, Sri Lanka, India, Thailand
    • Casualties: ~230,000 deaths
  2. 2011 TĹŤhoku Tsunami (Japan)

    • Magnitude: 9.0 earthquake
    • Wave height: 40 meters
    • Nuclear disaster: Fukushima Daiichi power plant affected
  3. 1960 Chile Tsunami

    • Magnitude: 9.5 earthquake (strongest ever recorded)
    • Waves traveled across the Pacific, reaching Japan and Hawaii.

Comments

Popular posts from this blog

History of GIS

1. 1832 - Early Spatial Analysis in Epidemiology:    - Charles Picquet creates a map in Paris detailing cholera deaths per 1,000 inhabitants.    - Utilizes halftone color gradients for visual representation. 2. 1854 - John Snow's Cholera Outbreak Analysis:    - Epidemiologist John Snow identifies cholera outbreak source in London using spatial analysis.    - Maps casualties' residences and nearby water sources to pinpoint the outbreak's origin. 3. Early 20th Century - Photozincography and Layered Mapping:    - Photozincography development allows maps to be split into layers for vegetation, water, etc.    - Introduction of layers, later a key feature in GIS, for separate printing plates. 4. Mid-20th Century - Computer Facilitation of Cartography:    - Waldo Tobler's 1959 publication details using computers for cartography.    - Computer hardware development, driven by nuclear weapon research, leads to broader mapping applications by early 1960s. 5. 1960 - Canada Geograph...

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to  1832 , when  Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report  Rapport sur la marche et les effets du cholĂ©ra dans Paris et le dĂ©partement de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in  1854 , when  John Snow  inv...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...