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Flood vulnerability assessment in the light of rice cultivation characteristics in Mekong River flood plain in Cambodia
Authors:Okazumi  Toshio  Tanaka  Shigenobu  Kwak  Youngjoo  Shrestha  Badri Bhakta  Sugiura   Ai
Affiliation:1.International Centre for Water Hazard and Risk Management (ICHARM), Public Works Research Institute (PWRI), 1-6 Minamihara, Tsukuba, Ibaraki, 305-8516, Japan
;2.Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan
;
Abstract:

More than 2 million hectares of the lower Mekong River flood plain (Cambodian Plain), which extends from southern Cambodia to the border with Vietnam, is cultivated with rice; and more than 60 % of the population in the flood plain are farmers involved in rice cultivation. Rice production in this area is seriously affected by floods almost every year; on the other hand, the farmers need floodwater for their crop fields. Therefore, the farming in this area is about coping with floods, and flood countermeasures should be carefully planned based on good understanding of the livelihood of the farmers. Thus, this study aimed to identify flood-vulnerable rice-growing communities in the Mekong River flood plain in Cambodia. In this research, we proposed a useful methodology to make flood inundation maps by conducting a simple analysis by combining satellite-based digital elevation model (DEM) and river water level data based on the flood characteristics in this area. Then, rice-crop vulnerability maps were derived from previous maps and results from other past researches using the geographical information system (GIS). Comparison among those maps was also conducted to find out the relationship between DEM and people’s lifestyles in this area. This simple, inexpensive methodology was proven useful to understand major crop damage and vulnerability in relation to floods in this area, based on flood characteristics in the Cambodian flood plain. Because the method is a GIS-based approach, it can deliver more accurate results when provided with more accurate data.

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