In this study, a flood damage assessment method was proposed for the assessment of flood risk in data-poor river basins by using a physical-based numerical model, satellite-based information and socio-economic factors. The Pampanga river basin of the Philippines was selected for the case study. For the flood damage assessment, hazard characteristics, such as flood depth and duration, were computed using the rainfall runoff inundation model. Agriculture and households, which are major exposures in flood-prone areas, were taken into account for the flood damage assessment. The potential damage to agriculture was estimated by using the damage functions of agriculture and flood characteristics. The damage functions of agriculture for each growing stage were defined as the function of flood depth and duration. Field investigations and questionnaire surveys were conducted at the selected barangays (villages) in the Pampanga river basin to develop a methodology for household damage estimation. The damage estimation approach for damage to household buildings including assets, was developed based on the flood and household characteristics. The potential damage to house building and assets was estimated. The estimated damage of agriculture and households was compared with the reported values.
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.
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.
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