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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
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Related Experiment Video

Updated: May 15, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Measuring vulnerability to natural hazards: a macro framework.

Jacquleen Joseph1

  • 1Jamsetji Tata Center for Disaster Management, Tata Institute of Social Sciences, India. jacquleenjoseph@tiss.edu

Disasters
|January 3, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework to measure macro vulnerability, crucial for disaster risk reduction. It quantizes vulnerability using hazard probability and disaster risk, aiding in identifying vulnerable regions.

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An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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Last Updated: May 15, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Disaster risk reduction and management
  • Environmental science
  • Geospatial analysis

Background:

  • Measuring disaster vulnerability is essential for effective risk reduction and resilience.
  • Conceptual and operational challenges hinder wider application of vulnerability assessments.
  • Existing methods often lack a feasible framework for macro-level analysis.

Purpose of the Study:

  • To present an operationally feasible framework for measuring macro vulnerability.
  • To define and quantify vulnerability as a function of disaster risk and hazard probability.
  • To apply the framework to a case study of floods in Assam, India.

Main Methods:

  • Developed a framework to measure macro vulnerability.
  • Utilized past data and probability distributions to determine hazard probabilities and perceived disaster risk.
  • Constructed an analytical framework using a case study approach.

Main Results:

  • Macro vulnerability was measured as a function of disaster risk and hazard probability.
  • The framework demonstrated operational feasibility for macro-level assessments.
  • Identified key factors contributing to vulnerability in the case study area.

Conclusions:

  • The proposed framework offers a practical approach to measuring macro vulnerability.
  • This indicator can guide policymakers in identifying vulnerable regions for targeted interventions.
  • Facilitates a more detailed micro-level disaster vulnerability assessment.