Rainfall variability and Spatio temporal dynamics of flood inundation during the 18th August 2008 Kosi Flood in Bihar, India (original) (raw)

A decadal historical satellite data analysis for flood hazard evaluation: A case study of Bihar (North India)

Singapore Journal of Tropical Geography, 2015

Flood is one of the major recurrent natural disasters faced by the state of Bihar in north India. In the present study the authors assess the severity of flood hazard in Bihar, using 128 decadal historical satellite datasets acquired during different flood magnitudes during 1998 to 2010. The satellite-based observations have been analysed in conjunction with the hydrological data, for assessing the frequency of inundation, severity of flood hazard and cropped land under flood hazard. This study assesses the spatial distribution of flooding and creation of systematic flood hazard database, which can be analysed from a spatial dimension in GIS. It is observed that about 24.56 lakh ha of the state's area and about 15.85 lakh ha of the cropped area are vulnerable to flood hazard. North Bihar is more vulnerable to flooding; 8 of the 10 areas identified as worst floodaffected districts lie in this region.

Flood Hazard and Risk Zonation in North Bihar Using Satellite-Derived Historical Flood Events and Socio-Economic Data

Sustainability, 2022

North Bihar is one of the most flood-affected regions of India. Frequent flooding caused significant loss of life and severe economic damages. In this study, hydroclimatic conditions and historical flood events during the period of 2001 to 2020 were coupled over different basins in North Bihar. The main objective of this study is to assess the severity of floods by estimating flood hazards, vulnerability and risk in North Bihar. The uniqueness of this study is to assess flood risk at the village level as no such study was performed earlier. Other thematic data, namely, land-use and drainage networks, were also utilised with flood maps to validate the severity of the event. MOD09A1 satellite data (during 2001–2020) derived indices were used to derive inundation extents and flood frequency. Socio-economic vulnerability (SEV) was derived based on seven census parameters (i.e., population density, house-hold density, literacy rate, agricultural labour, and cultivator, total male, and fe...

Geospatial analysis of September, 2019 floods in the lower gangetic plains of Bihar using multi-temporal satellites and river gauge data

Geomatics, Natural Hazards and Risk

During late September, 2019 Bihar was struggling with severe flooding problem, which otherwise is marked as a period of flood recession due to withdrawal of southeast monsoons. The present study assess the flood situation using Sentinel-1 SAR images and complements the understanding about the flood event using long term (2000-18) multi-temporal space based flood sensitive proxy indicators like precipitation (GPM), soil moisture (AMSR-2), vegetation condition (MODIS) together with ground based river gauge (CWC) data. The study reveals that in 2019 during the 39 th week of the year (late September) the central and eastern parts of Bihar witnessed heavy precipitation (176 percent higher than average), leading to enhanced soil moisture build up (19 percent higher than average) and consequently triggering severe flooding. River Ganga was observed to be flowing above danger level for almost two weeks. Due to the prolonged submergence by floodwaters a significant drop was observed in the NDVI and EVI values of about 13.7 and 11.1 percent respectively from the normal. About 8.36 lakh ha area was observed to be inundated, impacting about 9.26 million population. Patna followed by Bhagalpur were the two worst affected districts with almost 30% and 36% of districts geographical area being flooded.

A study on the occurrence of flood events over Jammu and Kashmir during September 2014 using satellite remote sensing

Natural Hazards, 2015

During the first week of September 2014, the Jammu and Kashmir region witnessed devastating floods across the majority of its districts, caused by multi-day heavy rainfall events. According to data provided by the Home Ministry of India, several thousand villages across the state were hit and 390 villages completely submerged. The preliminary assessment of property damage was estimated between INR 50,000 million to INR 60,000 million. Approximately 277 people died. In this study, an effort was made to analyze the heavy rainfall events over Jammu and Kashmir using hourly data at the fine spatial scale from satellite remote sensing. Data over Jammu and Kashmir reveal strong diurnal variation in rainfall over the severely affected districts. Most of these districts experienced continuous frequent heavy rainfall rates in the range of 15-22 mm/h during the first week of September 2014. The results show that the cumulative rainfall during 2-6 September 2014 may have contributed to the flood events.

Ganga floods of 2010 in Uttar Pradesh, north India: a perspective analysis using satellite remote sensing data

Geomatics, Natural Hazards and Risk, 2014

The present study focuses on the unprecedented flood situation captured through multi-temporal satellite images, witnessed along the Ganga River in Uttar Pradesh during September 2010. At three gauge stations (Kannauj, Ankinghat and Kanpur), river water level exceeded the previous high-flood level attained by river more than a decade ago. The present communication with the aid of preand post-flood satellite images, coupled with hydrological (river water level) and meteorological (rainfall) data, explains about the unprecedented flood situation. In the latter part of the study, a novel and cost-effective method for building a library of flood inundation extents based on historical satellite data analysis and tagging the inundation layer with observed water level is demonstrated. During flood season, based on the forecasted water level, the library can be accessed to fetch the spatial inundation layer corresponding to the forecasted stage and anticipate in advance, likely spatial inundation pattern and submergence of villages and hence in alerting the habitation at risk. This method can be helpful in anticipating the areas to be affected in situations where satellite images cannot be effectively utilized due to cloud cover and also for providing information about the areas being partially covered in satellite data.

Remote sensing and GIS‐based flood vulnerability assessment of human settlements: a case study of Gangetic West Bengal, India

Hydrological processes, 2005

Flooding due to excessive rainfall in a short period of time is a frequent hazard in the flood plains of monsoon Asia. In late September 2000, a devastating flood stuck Gangetic West Bengal, India. This particular event has been selected for this study. Instead of following the conventional approach of flooded area delineation and overall damage estimation, this paper seeks to identify the rural settlements that are vulnerable to floods of a given magnitude. Vulnerability of a rural settlement is perceived as a function of two factors: the presence of deep flood water in and around the settlement and its proximity to an elevated area for temporary shelter during an extreme hydrological event. Landsat ETM C images acquired on 30 September 2000 have been used to identify the non-flooded areas within the flooded zone. Particular effort has been made to differentiate land from water under cloud shadow. ASTER digital elevation data have been used to assess accuracy and rectify the classified image. The presence of large numbers of trees around rural settlements made it particularly difficult to extract the flooded areas from their spectral signatures in the visible and infrared bands. ERS-1 synthetic aperture radar data are found particularly useful for extracting the settlement areas surrounded by trees. Finally, all information extracted from satellite imageries are imported into ArcGIS, and spatial analysis is carried out to identify the settlements vulnerable to river inundation. 3700 J. SANYAL AND X. X. LU detailed spatial database for flood prevention and mitigation in the developing countries. In recent years, efforts have been made to use remote sensing and geographic information systems (GISs) for creating national-level flood hazard maps for Bangladesh . Population density and other socio-economic data have been integrated with hydrologic information to identify priority zones for implementing anti-flood measures . These studies were undertaken on a regional scale using coarse-resolution AVHRR imageries from NOAA satellites. The results of such investigations would only be useful for nationallevel macro planning.

Satellite-based assessment of the catastrophic Jhelum floods of September 2014, Jammu & Kashmir, India

Geomatics, Natural Hazards and Risk, 2016

The state of Jammu and Kashmir in North India experienced one of the worst floods in the past 60 years, during the first week of September 2014. In the present study, multi-temporal synthetic aperture radar (SAR) satellite images acquired from Indian Remote Sensing (IRS) satellite RISAT-1 and Canadian satellite Radarsat-2 during the peak flood period (08thÀ23rd September 2014) are used for extraction of flood disaster footprints, mapping spatial and temporal dynamics of flood inundation and assessing the disaster impact. With the aid of pre-and post-flood satellite images, coupled with hydro-meteorological data, the unprecedented flood situation is analyzed. It is estimated that about 557 km 2 of the Kashmir Valley's geographical area was inundated. Bandipora, Pulwama, Srinagar, Baramulla and Budgam were the worst flood affected districts, having more than 50 km 2 of their area affected by flood waters. Of the total inundated area, about 80% of the area under agricultural activity was submerged, followed by built-up areas constituting about 12% of geographical area. About 22 lakh people in 287 villages were affected by floods. The flood waters persisted in the northern and central part of the valley for more than two weeks.

Satellite images for extraction of flood disaster footprints and assessing the disaster impact: Brahmaputra floods of June–July 2012, Assam, India

Current science

Satellite images provide information on the flood disaster footprints, which is essential for assessing the disaster impact and taking up flood mitigation activities.The Brahmaputra floods that occurred during June–July 2012 devastated a large part of Assam. This article discusses the maximum spatial extent affected due to the flood event, villages marooned and population affected, with the aid of multi-temporal satellite images coupled with the hydrological observations and freely available gridded population data. The study shows that about 4.65 lakh ha area was submerged, 23 of the 27 districts in Assam had more than 5% of the total geographical area submerged, about 3829 villagesmarooned and 23.08 lakh people were affected.Identification of the spatial extent of areas most vulnerable to flooding, captured from the satellite images acquired during the peak flood period will be helpful for prioritizing appropriate flood control measures in the flood-affected regions.

Spatial and temporal variation in flooding of rural floodplain farming areas in the

2016

The Okavango Delta is subject to annual and inter-annual inundation of varying magnitude. The inundation impacts on livelihoods of communities reliant upon the Delta for subsistence. One such livelihood option is flood recession (molapo) farming. Flood recession farming contributes substantially to rural livelihoods, however communities are faced with many challenges, particularly the unpredictability and unreliability of flooding. The study sought to determine the spatial and temporal variation in flooding that has occurred in the floodplains. Nine Landsat images were analysed. The analysis covered the period 1989 to 2008, it focused on three flood seasons each 10 years apart. The study took place in three villages on the peripheries of the Delta. Results show that the three study villages not only have very different floodplain lands available, but they also show different flooding patterns. The implication of this is that for both farmers and planners, it is not possible to have ...