Spatio-Temporal Rainfall Variability and Flood Prognosis Analysis Using Satellite Data over North Bihar During the August 2017 Flood Event
Flooding is one of the most common natural disasters in India. Typically, the Kosi and Gandak river basins are well-known for lingering flood affected basins in North Bihar every year, which lies in the eastern part of India. There were no such comprehensive studies available in North Bihar that discussed flood progression and regression at shorter time-scales like two day intervals. So in this study, we employed high temporal resolution data to capture inundation extent and further, the flood extent has been validated with high spatial resolution data. The specific objective of this study was to analyze the satellite-derived Near Real Time (NRT) MODIS flood product for spatiotemporal mapping of flood progression and regression over the North Bihar. The synthetic aperture RADAR (SAR) data were also used to validate the MODIS NRT Flood data. As a case study, we selected a recent flood event of August–September 2017 and captured the flood inundation spatial extent at two day intervals using the 2 day composite NRT flood data. The flood prognosis analysis has revealed that during the peak flooding period, 12% to 17% of the area was inundated and the most adversely affected districts were Darbhanga and Katihar in North Bihar. We estimated that in total nearly 6.5% area of the North Bihar was submerged. The method applied was simple, but it can still be suitable to be applied by the community involved in flood hazard management, not necessarily experts in hydrological modeling. It can be concluded that the NRT MODIS flood product was beneficial to monitor flood prognosis over a larger geographical area where observational data are limited. Nevertheless, it was noticed that the flood extent area derived from MODIS NRT data has overestimated areal extent, but preserved the spatial pattern of flood. Apparently, the present flood prognosis analysis can be improved by integrating microwave remote sensing data (SAR) and hydrological models.