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JDR Vol.9 No.1 pp. 69-77
(2014)
doi: 10.20965/jdr.2014.p0069

Paper:

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Uncertainty Estimation During the Process of Flood Risk Assessment in Developing Countries – Case Study in the Pampanga River Basin –

Toshio Okazumi, Mamoru Miyamoto, Badri Bhakta Shrestha,
and Maksym Gusyev

International Centre for Water Hazard and Risk Management, Public Works Research Institute, 1-6 Minamihara, Tsukuba, Ibaraki 305-8516, Japan

Received:
November 28, 2013
Accepted:
January 16, 2014
Published:
February 1, 2014
Creative Commons License
Keywords:
flood disaster, hydrological data, damage data, vulnerability, damage curve
Abstract
Flood risk assessment should be one of the basic methods for disaster damage mitigation to identify and estimate potential damage before disasters and to provide appropriate information for countermeasures. Existing methods usually do not account for uncertainty in risk assessment results. The concept of uncertainty is especially important for developing countries where risk assessment results may often be unreliable due to inadequate and poor quality data. We focus on three questions concerning risk assessment results in this study: a) How much does lack of data in developing countries influence flood risk assessment results? b) Which datamost influence the results? and c) Which data should be prioritized in data collection to improve risk assessment effectiveness? We found the largest uncertainty in the damage data among observation, model, and agricultural damage calculations. We conclude that reliable disaster damage data collection must be emphasized to obtain reliable flood risk assessment results and prevent uncertainty where possible. We propose actions to improve assessment task efficiency and investment effectiveness for developing countries.
Cite this article as:
T. Okazumi, M. Miyamoto, B. Shrestha, and M. Gusyev, “Uncertainty Estimation During the Process of Flood Risk Assessment in Developing Countries – Case Study in the Pampanga River Basin –,” J. Disaster Res., Vol.9 No.1, pp. 69-77, 2014.
Data files:
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