Spatio-Temporal Rainfall Variability and Flood Prognosis Analysis Using Satellite Data over North Bihar during the August 2017 Flood Event

Gaurav Tripathi,Arvind Chandra Pandey,Bikash Ranjan Parida

doi:10.3390/hydrology6020038

Gaurav Tripathi, Arvind Chandra Pandey + Show 1 more

Open Access

https://doi.org/10.3390/hydrology6020038

Copy DOI

Journal: Hydrology	Publication Date: May 17, 2019
Citations: 21	License type: CC BY 4.0

Affiliation: Central University of Jharkhand

Abstract

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.

Highlights

Flooding is one of the most devastating and recurring events in the Indian subcontinent because of its geographical and riverine structure, which makes various parts of the nation prone to floods.The causative factors of frequent floods in implemented by the government of Bihar (India) are intense rainfall, dam breach, unplanned urbanization, and land use/land cover (LU/LC) changes, that typically leads to the loss of lives and properties.As flood events have been increasing over the last three decades [1], the development of flood mapping using satellite data and application of flood inundation models become crucial to monitor and assess flood impact
The availability of composite data, such as the Moderate Resolution Imaging Spectrometer (MODIS)-based Near Real Time (NRT) Flood product developed by NASA/DFO has proven beneficial to monitor and assess the flood-induced inundation extent over a larger geographical area
It has been a known fact that the availability of a meteorological and hydrological dataset is limited over the North Bihar region, but these datasets are used in playing a crucial role in assessing pre, during- and post- flooding conditions

Summary

Introduction

As flood events have been increasing over the last three decades [1], the development of flood mapping using satellite data and application of flood inundation models become crucial to monitor and assess flood impact. The advances in satellite data in respect of high spatial and temporal resolutions have led to the development of near real-time flood mapping algorithms [3,4]. This provides vital information during an emergency as satellite-derived information typically used to calibrate and validate hydrodynamic models, such as, Hydrologic Engineering Center-River Analysis System (HEC-RAS 2D (Two dimensional), MIKE3 3D (Three dimensional), TUFLOW (3D), etc.) [5,6,7,8]. Satellite data have been used to improve the predictive accuracy and have subsequently, increased the stakeholder’s understanding of flood dynamics and flood forecasting [9,10]

Objectives

Methods

Results

Discussion

Conclusion