Fundamental Analysis for Flood Risk Management in the Selected River Basins of Southeast Asia
Badri Bhakta Shrestha*1, Toshio Okazumi*2, Mamoru Miyamoto*1,
Seishi Nabesaka*3, Shigenobu Tanaka*4, and Ai Sugiura*1
*1International Centre for Water Hazard and Risk Management (ICHARM), Public Works Research Institute (PWRI), 1-6 Minamihara, Tsukuba, Ibaraki 305-8516, Japan
*2Ministry of Land, Infrastructure, Transport and Tourism, Chiyoda-ku, Tokyo, Japan
*3Asakura Integrated Office for Koishibaragawa Dam Constructing Project, Japan Water Agency, Asakura-shi, Fukuoka, Japan
*4Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji-shi, Kyoto, Japan
Flood features were analyzed and risk knowledge was examined in studies in selected river basins of Southeast Asia. Rainfall runoff features were analyzed in Indonesia’s Solo river basin and in the Philippines’ Pampanga and Cagayan river basins using ground-observed and satellite-based (GSMaP) rainfall data. Flood damage was assessed for risk management by considering physical damage to agricultural and household in the Cambodian flood plain of the Lower Mekong Basin and in the Philippines’s Pampanga river basin. A comparison of simulated and observed runoff hydrographs showed that the accuracy of GSMaP rainfall in the Solo and Cagayan river basins in studied flood events was lower than in the Pampanga river basin case. In the Pampanga and Cagayan river basins, the density of rainfall station networks was below the WMO recommendation, and GSMaP rainfall data would be more effective in getting supplementary information for existing flood-forecasting systems for these river basins. Physical damage to households including residential assets and agricultural damage were estimated quantitatively based on flood features. The estimated value of agricultural and house damage was fairly consistent with reported values. Reliable flood damage data are important for developing flood damage functions and for confirming such estimation. Uncertainties associated with input data, model parameters, and damage information strongly influence the damage estimated. These uncertainties must be considered carefully in flood risk assessment models.
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