The most recurring type of disaster in the world these days is flood because of the spread and extent of its effect on people, among all-natural disasters of the world. Human activities have paved the way for many of these flood behavior to change as they used to be in the past. Pakistan experienced one of the most devastating natural disasters in its history all across the country in 2010, but Thatta district in southern part got severely affected during this flood. For the research, a simple yet efficient methodology Normalized Difference Vegetation Index (NDVI) by using remote sensing images for identifying flood hazard areas was utilized. Geographic Information Systems (GIS) helps in finding shelter areas with a minimum effect of floods. It is essential to realize the importance of mapped results in consideration of manual flood management in future. The method used in this study is robust enough to explain the flood hazard for suggesting suitable shelter sites in case of flooding events. This would help disaster management bodies and other related agencies to formulate the development plans while keeping the hazard areas, which are unsuitable for development due to flood risk in the future.

While the economic impact of natural disasters has been studied extensively, there are rather few studies that have addressed their impact on household income. This research tries to fill this gap by analyzing their actual effect on household income caused by the 2010 floods. We look at the impact of natural disasters on rural households in Pakistan after a massive flooding event in 2010. We used the difference-in-difference (DID) approach, which showed statistical significance at 1 percent. We also calculated the household distance from the rivers to see how vulnerable household income is to such kinds of shock-like floods. The results showed that the sample households living near had more impact as compared to the ones living far. Stata and Geographical Information System (GIS) software packages have been used for regression analysis and calculation of distance, respectively. This research will not only give insight in order to understand household income losses but will allow government, policymakers, and International Aid agencies to plan, make countermeasure strategies before designing post-disaster projects. After taking into account the effect of floods, which tend to have far more impact on the households, which are located near the source of the flooding. In this case, they need to focus more in terms of reconstruction of infrastructure, particularly for the households which are near these flooded areas. Firstly, this finding can give policymakers insight in terms of strategies to develop agriculture and non-agriculture employment opportunities. Secondly, it is essential to reduce income vulnerability and improve rural household finance economic conditions.

The most recurring type of disaster in the world these days is flood because of the spread and extent of its effect on people, among all-natural disasters of the world. Human activities have paved the way for many of these flood behavior to change as they used to be in the past. Pakistan experienced one of the most devastating natural disasters in its history all across the country in 2010, but Thatta district in southern part got severely affected during this flood. For the research, a simple yet efficient methodology Normalized Difference Vegetation Index (NDVI) by using remote sensing images for identifying flood hazard areas was utilized. Geographic Information Systems (GIS) helps in finding shelter areas with a minimum effect of floods. It is essential to realize the importance of mapped results in consideration of manual flood management in future. The method used in this study is robust enough to explain the flood hazard for suggesting suitable shelter sites in case of flooding events. This would help disaster management bodies and other related agencies to formulate the development plans while keeping the hazard areas, which are unsuitable for development due to flood risk in the future.