Skip to main content

Hazard Vulnerability Exposure Risk

Key Concepts in Hazard Identification, Vulnerability Assessment, Exposure Assessment, and Risk Analysis

Hazard-Exposure-Vulnerability-Risk (HEVR) Framework:

  • Hazard: A potential event or phenomenon that can cause harm.
  • Exposure: People, assets, or environments in harm's way.
  • Vulnerability: Susceptibility to damage or harm from a hazard.
  • Risk: The potential for loss or damage resulting from the interaction of hazards, exposure, and vulnerability.

Risk as a Function:

  • Risk can be calculated using the formula: Risk = Hazard × Vulnerability × Exposure.
  • Reducing any of these factors can decrease overall risk.

Types of Hazards:

  • Natural hazards: Earthquakes, floods, tsunamis, landslides, hurricanes.
  • Anthropogenic hazards: Industrial accidents, pollution, infrastructure failure, climate change.
  • Technological hazards: Nuclear accidents, chemical spills.

Vulnerability Dimensions:

  • Physical: Infrastructure quality, building codes, location.
  • Social: Age, income, disability, gender, access to resources.
  • Economic: Dependence on hazard-sensitive sectors.
  • Environmental: Ecosystem vulnerability.

Resilience:

  • The ability to adapt, recover, and thrive after a disaster.
  • Includes absorptive, adaptive, and transformative capacity.

Dynamic Nature of Risk:

  • Risk evolves due to changes in environmental, social, and economic conditions.

Risk Perception:

  • How individuals and communities perceive risk influences their preparedness and response.

Disaster Risk Reduction (DRR):

  • A holistic approach to prevent, reduce, or mitigate disaster risks.
  • Includes prevention, mitigation, preparedness, response, and recovery.

Risk Transfer and Financing:

  • Shifting risk to others (e.g., insurance) and securing funds for losses.

Early Warning Systems (EWS):

  • Detect and forecast hazards early for alerts and warnings.
  • Essential components: hazard detection, risk knowledge, dissemination, preparedness.

Mitigation vs. Adaptation:

  • Mitigation: Reducing long-term risk (e.g., flood defenses).
  • Adaptation: Adjusting to expected changes (e.g., climate-resilient infrastructure).

Integrated Risk Management:

  • A holistic approach that integrates different sectors and disciplines.

Systemic Risk:

  • Interconnectedness of hazards and vulnerabilities, where an event in one area can affect others.

Critical Infrastructure:

  • Essential systems and assets (e.g., water, energy, transportation, communication).

Preparedness and Response:

  • Preparedness: Planning, training, and resource stockpiling.
  • Response: Immediate actions to deal with disaster effects.

Additional Considerations:

  • Uncertainty: Risk assessment often involves uncertainty due to limited data or unpredictable events.
  • Equity: Disaster risk is not evenly distributed; vulnerable populations may face disproportionate impacts.
  • Governance: Effective governance is essential for implementing DRR measures and ensuring equitable outcomes.

Key Facts Shaping Hazard Identification, Vulnerability Assessment, Exposure Assessment, and Risk Analysis

  1. Increasing Frequency and Intensity of Natural Disasters:

    • Climate change, urbanization, and environmental degradation are driving this trend.
    • Weather-related disasters have increased by 300% in 50 years (WMO).

  2. Urbanization Exacerbates Vulnerability and Exposure:

    • Rapid urbanization, especially in low- and middle-income countries, increases risk.
    • By 2050, 68% of the world's population will live in urban areas.

  3. Disproportionate Impacts on Vulnerable Groups:

    • Socially vulnerable groups, like the elderly, poor, and marginalized, are most affected.
    • Example: Women accounted for 70% of fatalities in some regions during the 2004 Indian Ocean tsunami.

  4. Climate Change as a Major Driver of Risk:

    • Climate change is increasing the frequency and severity of extreme weather events.
    • IPCC predicts more heatwaves, coastal flooding, and ecosystem disruptions.

  5. Rising Economic Losses from Disasters:

    • Economic losses from disasters are increasing, despite reduced fatalities.
    • Between 2000 and 2019, global economic losses totaled $2.97 trillion (UNDRR).

  6. Vulnerability of Critical Infrastructure:

    • Power grids, water supply, and transportation networks are often highly vulnerable.
    • 80% of cities with over 1 million people are in high-risk areas.

  7. Early Warning Systems Save Lives:

    • Effective early warning systems can significantly reduce disaster-related deaths and losses.
    • Example: Bangladesh's cyclone early warning systems have saved tens of thousands of lives.

  8. Cost-Effectiveness of Preparedness and Mitigation:

    • Investing in preparedness and mitigation is more cost-effective than post-disaster response and recovery.
    • UNDP estimates a 4:1 return on investment in disaster risk reduction.

  9. Global Hotspots for Risk:

    • Asia-Pacific is the most disaster-prone region, accounting for over 40% of global events.
    • Small Island Developing States (SIDS) and the African Sahel are particularly vulnerable.

  10. Interconnected Risks and Systemic Vulnerability:

  • Disasters in one area can have ripple effects across regions and sectors.
  • Example: COVID-19 pandemic highlighted systemic risk in healthcare systems.
  1. Inadequate Insurance Coverage:
  • Many communities and businesses lack sufficient insurance coverage for disaster risks.
  • Only 35% of global disaster-related losses are covered by insurance.
  1. The Role of Ecosystems in Risk Reduction:
  • Healthy ecosystems, like wetlands and forests, can reduce disaster risk.
  • Degradation of these ecosystems increases exposure to disasters.
  1. Uneven and Prolonged Disaster Recovery:
  • Recovery from disasters is often lengthy and uneven, especially for marginalized groups.
  • Example: Hurricane Katrina recovery took years for some communities.
  1. Technology Enhances Risk Monitoring and Mapping:
  • GIS, remote sensing, and big data analytics improve risk assessment, hazard mapping, and early warning.
  • Tools like Google Earth Engine and NASA's satellites enable real-time monitoring.
  1. Importance of Building Codes and Zoning Regulations:
  • Strong building codes and land-use regulations are crucial for reducing vulnerability.
  • However, weak enforcement remains a significant issue in many developing countries.


Comments

Post a Comment

Popular posts from this blog

RADIOMETRIC CORRECTION

  Radiometric correction is the process of removing sensor and environmental errors from satellite images so that the measured brightness values (Digital Numbers or DNs) truly represent the Earth's surface reflectance or radiance. In other words, it corrects for sensor defects, illumination differences, and atmospheric effects. 1. Detector Response Calibration Satellite sensors use multiple detectors to scan the Earth's surface. Sometimes, each detector responds slightly differently, causing distortions in the image. Calibration adjusts all detectors to respond uniformly. This includes: (a) De-Striping Problem: Sometimes images show light and dark vertical or horizontal stripes (banding). Caused by one or more detectors drifting away from their normal calibration — they record higher or lower values than others. Common in early Landsat MSS data. Effect: Every few lines (e.g., every 6th line) appear consistently brighter or darker. Soluti...
Post a Comment
Read more

Atmospheric Correction

It is the process of removing the influence of the atmosphere from remotely sensed images so that the data accurately represent the true reflectance of Earth's surface . When a satellite sensor captures an image, the radiation reaching the sensor is affected by gases, water vapor, aerosols, and dust in the atmosphere. These factors scatter and absorb light, changing the brightness and color of the features seen in the image. Although these atmospheric effects are part of the recorded signal, they can distort surface reflectance values , especially when images are compared across different dates or sensors . Therefore, corrections are necessary to make data consistent and physically meaningful. 🔹 Why Do We Need Atmospheric Correction? To retrieve true surface reflectance – It separates the surface signal from atmospheric influence. To ensure comparability – Enables comparing images from different times, seasons, or sensors. To improve visual quality – Remo...
Post a Comment
Read more

Geometric Correction

When satellite or aerial images are captured, they often contain distortions (errors in shape, scale, or position) caused by many factors — like Earth's curvature, satellite motion, terrain height (relief), or the Earth's rotation . These distortions make the image not properly aligned with real-world coordinates (latitude and longitude). 👉 Geometric correction is the process of removing these distortions so that every pixel in the image correctly represents its location on the Earth's surface. After geometric correction, the image becomes geographically referenced and can be used with maps and GIS data. Types  1. Systematic Correction Systematic errors are predictable and can be modeled mathematically. They occur due to the geometry and movement of the satellite sensor or the Earth. Common systematic distortions: Scan skew – due to the motion of the sensor as it scans the Earth. Mirror velocity variation – scanning mirror moves at a va...
Post a Comment
Read more

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...
Post a Comment
Read more

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...
Post a Comment
Read more