Assessment of Flood Damage on Early, Medium and Late Rice Crop

Abstract:

Rice crops are mainly categorized into early, medium and late variety, and they are differently damaged by flood depending on its growth characteristic, productivity, and value. However, the estimation method of flood damage based on rice crop types remains unexamined in previous studies due to shortage of past damage data and intensive field survey with local farmers, especially with those who experienced the past flood events. Hence, this study developed three types of flood depth- duration and damage curves (3-Type FDCs) based on field survey with 95 farmers in the flood-affected area, and method to create the paddy area map based on MODIS satellite imagery for assessing flood damage on early, medium and late rice crop. The accuracy of damage assessment was validated and well agreed with the damage reported by the local government. This method was used to assess rice crop damage under the past and present cropping patterns, different flood magnitudes and impact of climate change in Pursat province of Cambodia. The assessment showed that flood damage under present cropping pattern was overall higher than that under past cropping pattern; noticeably, almost double higher in terms of damage cost due to recent increase in the production area of early rice crop and its high farm gate price. Due to climate change impact, the overall damage in the study area may increase by 14% in the future, and early rice crop would be the most affected under present cropping pattern. This study enriches more understanding of how early, medium and late rice crops are affected by floodwater with the function of flood depth and flood duration, which can be useful for selecting suitable crop to be cultivated in the area of risk based on flood damage estimation and climate change impact, establishing a flood mitigation policy, and implementing the mitigation actions for climate change adaptation. The developed method can also be applied in other areas; mainly, in the area with scarce data.