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

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1 Geomatics, Natural Hazards and Risk ISSN: (Print) (Online) Journal homepage: Ganga floods of 2010 in Uttar Pradesh, north India: a perspective analysis using satellite remote sensing data C.M. Bhatt & G.S. Rao To cite this article: C.M. Bhatt & G.S. Rao (2016) Ganga floods of 2010 in Uttar Pradesh, north India: a perspective analysis using satellite remote sensing data, Geomatics, Natural Hazards and Risk, 7:2, , DOI: / To link to this article: Taylor & Francis Published online: 27 Aug Submit your article to this journal Article views: 1881 View related articles View Crossmark data Citing articles: 4 View citing articles Full Terms & Conditions of access and use can be found at Download by: [ ] Date: 12 January 2018, At: 12:37

2 Geomatics, Natural Hazards and Risk, 2016 Vol. 7, No. 2, , Ganga floods of 2010 in Uttar Pradesh, north India: a perspective analysis using satellite remote sensing data C.M. BHATT* and G.S. RAO Disaster Management Support Division, National Remote Sensing Centre, Indian Space Research Organization (ISRO), Balanagar, Hyderabad , India (Received 3 February 2014; accepted 27 July 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 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. 1. Introduction Floods are among the most frequent and catastrophic natural disasters around the globe (Berz et al. 2001; ISDR2004) impacting human lives and infrastructure. Due to flood disaster, about 100,000 persons are reported to lose their life and about 1.4 billion people are estimated to be affected in the last decade of the twentieth century (Jonkman 2005). India is one of the most flood-prone countries in the world. Floods occur often in the region triggered by heavy monsoon precipitation, which cause extensive damage to lives, property, crops and infrastructure. High-magnitude floods during the monsoon season are considered to be India s recurring and leading natural disaster (Kale et al. 1994). During 2010, the country witnessed local to regional-scale catastrophic hydrological events during the monsoon season (Sharma 2012) like the Leh (Jammu and Kashmir) flash floods (August 2010), severe Ghaggar River floods in Punjab and Haryana (July 2010), floods in Yamuna River, Delhi (August 2010) and the unprecedented flood situation in Uttar Pradesh along the Ganga River (September 2010). The town of Leh and its adjoining areas were affected by severe flash floods and mudslides triggered by a cloud burst over the Leh region on *Corresponding author. cmbhatt@nrsc.gov.in Ó 2014 Taylor & Francis

3 748 C.M. Bhatt and G.S. Rao 6 August 2010 (Bhatt et al. 2011). Severe flooding was observed in parts of Punjab and Haryana due to heavy rains and overflowing of Ghaggar River. Due to heavy rains in the upper catchment areas, Yamuna River crossed the danger mark causing flooding in low-lying areas along the Yamuna banks in parts of Delhi. In Uttar Pradesh, at three gauge stations (i.e. Kannauj, Ankinghat and Kanpur) located along the Ganga River, water level exceeded the previous high-flood water level ever recorded at these sites. The flood situation was described to be unprecedented by CWC (Central Water Commission), which is the nodal organization for carrying out the flood-forecasting activity on the major rivers and has fixed warning level (WL) and danger level (DL) at important sites in India. Such severe flood disaster situations if captured, analysed and correlated with other hydrological parameters can provide a window of opportunity for holistic understanding of the event. This can also be useful to generate permanent record of the event which can be used for better flood disaster management. During severe flood disaster events, obtaining information on the severity extent by conventional means is a challenging task particularly under unfavourable weather conditions, collapse of communication systems and damage to transportation systems. Under such catastrophic conditions, the gauge stations located along the river to record the water level also may be swept away or damaged. Satellite remote sensing technology has emerged as one of the most important means of capturing such disastrous flood events, providing information in time and space and also a permanent record of such events (Sharma et al. 1996; Jain et al. 2005; Schumann et al. 2007). Recent advances in remote sensing and space technology provides surface, meteorological and atmospheric information to detect, monitor and assess flood hazard (Prasad et al. 2006). Earth observation satellites provide the basic support in predisaster preparedness programmes, in-disaster response and monitoring activities, and post-disaster reconstruction (Jayaraman et al. 1994). Satellite images provide near real-time, comprehensive, synoptic and multi-temporal coverage of inaccessible areas at frequent intervals, which is required for quick response and planning of emergency operations. Multi-spectral remotely sensed estimates provide timely and cost-effective hydrologic monitoring in sparsely gauged basins, irrespective of the political boundaries and other geophysical barriers (Khan et al. 2011, 2014). Techniques utilizing satellite remote sensing data can provide objective information which help to detect floods and to monitor their spatiotemporal evolution (Smith 1997; Brakenridge et al. 2003, 2007). Meteorological satellites (National Oceanic Atmospheric Administration (NOAA), Advanced Very High Resolution Radiometer (AVHRR), Geostationary Meteorological Satellite (METEOSAT) and Indian National Satellite (INSAT)) have been widely used for monitoring flood-affected areas (Islam & Sado 2000). Microwave measurements from space can be used for flood monitoring even when cloud cover is present and hence have become invaluable in mapping flood inundation extent (Imhoff et al. 1987; Horritt 2000; Martinez & Toan 2007; Schumann et al. 2007). Brightness temperature measurements from passive microwave remote sensing sensors (Tropical Rainfall Measuring Mission Microwave Imager (TMI), Advanced Microwave Scanning Radiometer (AMSR-E) and Special Sensor Microwave Imager (SSM/I)) have allowed to study the variability of soil wetness and its relation with floods (Singh et al. 2009). Owing to their merits, satellite images have become an integral part of disaster management and are being extensively used globally for mapping, monitoring and damage assessment of extreme disaster events (Brivio et al. 2002; Henry et al. 2006; Bhatt et al. 2010; Schumann & Di Baldassarre 2010; Bhatt et al. 2013).

4 Geomatics, Natural Hazards and Risk 749 The present study focuses on the unprecedented flood situation captured by satellite images witnessed by the Ganga River stretch during September 2010, wherein at three gauge stations, the river attained water level that exceeded the previous highflood level (HFL) attained by the river more than a decade ago. The flood situation observed is explained with the aid of pre- and post-flood satellite images, coupled with hydrological (river water level) and meteorological (rainfall) data. 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. 2. Study area The present study focuses on the Ganga River stretch of 325 km, flowing through the districts of Kannauj, Unnao, Kanpur, Fatehpur and Allahabad located in the state of Uttar Pradesh, northern part of India. Along this particular stretch during 2010, Ganga River attained water level that exceeded the previous HFL attained by the river more than a decade ago at three of the five gauge stations located along its course. The 2525-km long Ganga River (also known as Ganges) rises in the western Himalayas in the Indian state of Uttarakhand and flows through the Gangetic Plain of north India traversing through the states of Uttar Pradesh, Bihar, Jharkhand and West Bengal before entering into Bangladesh and draining into the Bay of Bengal. Among the various states lying in the Ganga basin, Uttar Pradesh, Bihar and West Bengal are the worst flood affected. Uttar Pradesh, which is one of the most populous states of India, experiences severe flooding problem during the monsoon season, almost every year due to heavy inflows in the Ganga River and its tributaries. Uttar Pradesh has experienced severe flooding in 1998, 2000, 2001 and 2008 and about 30 districts of this state are seriously prone to flooding (Singh & Awasthi 2010). Figure 1 shows the location map of the selected study area (a) the Ganga basin and (b) the IRS satellite image overlaid with district s boundaries (blue colour) and location of gauge stations (white colour circle) located along the Ganga River. 3. Data used and methodology Daily water level data obtained from CWC from 5 September 2010 to 5 October 2010 is used to generate flood hydrograph for the five gauge stations (Kannauj in Kannauj district, Ankinghat in Kanpur district, Kanpur in Kanpur district, Dalamau in Rae Bareilly district and Phaphamau in Allahabad district) located along the Ganga River. Rainfall information is extracted from CPC (climate prediction centre) data available freely (ftp://ftp.cpc.ncep.noaa.gov/fews/s.asia/) at 0.1 by 0.1 degree. Observations made from flood hydrograph, rainfall analysis are correlated with pre- and post-flood event satellite-based observations to explain about the changes in river course due to the new HFL attained during 2010 by the Ganga River. Pre-flood and during flood event changes in the water spread are explained with the aid of Resourcesat-2, AWiFS (Advanced Wide Field Sensor) satellite images. SRTM (Shuttle Radar Topographic Mission) digital elevation model (DEM) is used for analysing the river valley profile and the lateral spreading of inundation. For identification of villages affected, village boundaries are used. For demonstrating the concept for development of a library of flood inundation layers by geotagging water level to

5 750 C.M. Bhatt and G.S. Rao Figure 1. (a) Location map of the study area and (b) IRS satellite image overlaid with district s boundaries (blue colour) and location of gauge stations (white colour circle) located along Ganga River. To view this figure in colour, please see the online version of the journal. inundation layer, multi-temporal satellite data from Radarsat-2 (Scan SAR wide beam, horizontal-horizontal (HH) polarization) of 18 and 25 September 2010 and Resourcesat-2, AWiFS (Advanced Wide Field Sensor) data of 29 September 2010 is used. Raster-based analysis is carried out in ERDAS Imagine (ver. 13.0) and for vector-based GIS analysis, Arc GIS (ver. 9.3) software is used. Method for inundation layer extraction based on satellite data analysis is explained in detail below.

6 Geomatics, Natural Hazards and Risk 751 For delineation of inundated area, synthetic aperture radar (SAR) images were pre-processed which involves the generation of beta naught (db) image and then computation of sigma naught (db) and incidence angle (degrees) images. It is pertinent to mention here that the SAR data in HH polarization and with shallow incidence angles (20 49 ) is used because it is less sensitive to small-scale roughness of waves on the water surface than VV-like polarization or cross-polarization for better discrimination of inundated areas (Henry et al. 2006). SAR images were filtered to suppress speckle using median filter. The speckle-suppressed images were then geometrically corrected with master reference images to a defined projection system for positional accuracies. Variable incidence angle threshold technique is used for the extraction of inundation layer, which is based on the principle that the radar backscatter from a feature decreases with increase in its incidence angle (Ulaby & Dobson 1989; Baghdadi et al. 2001). Backscatter response at river cross-sections and areas adjacent to the river in near and far ranges are evaluated by drawing the transect lines and an average backscatter range is chosen. Intensities ( 18 to 25) within these ranges are regarded as water, whereas pixels with intensities above the threshold are regarded as non-flooded. In case of IRS optical data, Resourcesat-2, AWiFS data of 29 September 2010, having a swath of 740 km and moderate resolution 56 m. Inundation layer was extracted using unsupervised iterative ISODATA clustering algorithm. By taking a convergence threshold of 0.95 and maximum iterations of 10, about 20 spectral clusters were chosen. The different obtained classes were merged finally to obtain the single class. The final inundation layer extracted was converted to one bit format and used further for spatial analysis. 4. Results and discussion This section discusses about the 2010 Ganga unprecedented flood situation in terms of satellite-based observations, coupled with hydrological and meteorological observations. Method towards development of a library of modelled flood inundation extents by geotagging water levels and spatial characterization of river water level is also explained Hydrological observations CWC is the nodal organization which carries out the flood forecasting activity on the major rivers and has fixed WL and DL at important sites. A river is said to be in flood when its water level touches or exceeds DL at that particular site and the flood situation is described to be unprecedented when the water level of the river crosses the HFL recorded at the forecasting site earlier and accordingly the agency issues a special red bulletin. The Ganga River was in unprecedented flood situation during September 2010 at three gauge stations, i.e. Kannauj, Ankinghat and Kanpur. Figure 2 shows the flood hydrograph prepared for the three gauge stations from 5 September 2010 to 5 October A perusal of figure 2 shows that at Kannauj gauge station, the Ganga River water from 5 September 2010 started rising and crossed the DL ( m) on 21 September 2010 and thereafter crossed the previous HFL ( m) on 24 September 2010 attained by the river about 12 years back on 29 August After crossing the HFL, the Ganga River was flowing in the unprecedented flood situation (above the

7 752 C.M. Bhatt and G.S. Rao Figure 2. Flood hydrograph for the Ganga River at Kannauj, Ankinghat and Kanpur gauge stations from 5 September 2010 to 5 October previous HFL) and attained a new HFL of m on 27 September 2010, which was about 0.54 m above the previous HFL and 0.81 m above the DL. The Ganga River remained above DL for about 13 days (19 September 2010 to 1 October 2010); of these 13 days, it was observed to be flowing in unprecedented flood situation continuously for seven days (24 30 September 2010). From figure 2, it can be observed that at Ankinghat gauge station which is located about 32 km downstream of Kannauj site, the Ganga River water from 9 September 2010 started rising continuously and crossed the DL (124.0 m) on 25 September The rising river water crossed the previous HFL ( m), acquiring a new HFL of m on 28 September 2010 attained by the river about 32 years ago on 9 September The new HFL was recorded to be about 0.18 m above the previous HFL. At this site, the river water was flowing above DL for nine days (23 September 2010 to 1 October 2010), including three days (26 September 2010 to 28 September 2010) when it remained in unprecedented flood situation before the river water started receding from 2 October At Kanpur gauge station located about 65 km downstream of Ankinghat, the Ganga River started registering a continuous increase in water level since 16 September 2010 and went on to surpass the previous HFL of m (9 September 1967) by 0.61 m, after a gap of about 43 years (figure 2) to attain a new HFL value of m on 29 September The river water was flowing in unprecedented flood situation continuously for eight days (25 September 2010 to 2 October 2010) at this started before it started receding.

8 Geomatics, Natural Hazards and Risk 753 At Dalamau and Phaphamau stations located downstream of Ankinghat, the Ganga River did not cross the HFL. However, at Dalamau River crossed the DL towards the end of September and was flowing above the DL for four days (29 September 2010 to 2 October 2010). From the hydrological observations it is clear that from 19 September 2010 to 2 October 2010, flood situation was very severe along this stretch, when the water level attained by the Ganga River at Kannauj about 12 years back, at Ankinghat about 32 years back and at Kanpur about 42 years back was surpassed, attaining a new HFL Meteorological observations Daily rainfall information was extracted from CPC data during June October months. Resolution of rainfall estimate data is 0.1 by 0.1 degree and inputs include GTS station data, as well as geostationary infrared cloud top temperature fields and polar orbiting satellite precipitation estimates data from SSM/I (Special Sensor Microwave Imager) and AMSU-B (Advanced Microwave Sounding Unit) microwave sensors. The data-sets downloaded were converted into raster (img) format using generic binary option and affine transformation was applied to the images. All images were reprojected to make the data compatible with the base layer data projection. These images were converted from continuous format to thematic format and integrated with basin boundary for extracting rainfall estimates on a monthly basis during monsoon season (June October), using zonal statistics. The monthly spatial rainfall distribution shows that during August and September months, the basin experienced heavy rainfall of about 733 and 689 mm, respectively (figure 3(a)). The daily rainfall distribution shows that during June and July months, rainfall was scattered and generally less than 10 mm, except few peaks (figure 3(b)). However, during August and by third week of September, there were about 25 days when more than 10 mm of rainfall was observed. During the start of September month, continuous rainfall peaks of more than 20 mm are observed. This is the period when the Ganga River water levels have recorded the unprecedented flood situation. Plot of seven days cumulative rainfall (figure 3(c)) shows the presence of three rainfall peaks, with more than 100-mm rainfall. The first spell of rainfall is seen between 22 and 27 August 2010 and with a gap of seven days, another spell from 5 to 10 September 2010 is observed and then the third spell again took place with a week gap from 17 to 23 September A perusal of figure 3(b) shows that two continuous spells of more than seven days cumulative rainfall, with rainfall of more than 100 mm and with just a week gap, have led the river to cross the DL and also the continuous third spell taking place with just a week gap has resulted in the river crossing the HFL and leading to unprecedented flood situation. This kind of graphical relation observed during 2010, between rainfall and crossing of DL, can be used as one of the proxy indicators towards flood alert during monsoon season Satellite-based pre- and post-flood event observation IRS AWiFS image of 29 September 2010 acquired under peak flood period when the river at three (Kannauj, Ankinghat and Kanpur) gauge stations was observed to be

9 754 C.M. Bhatt and G.S. Rao Figure 3. (a) Monthly rainfall distribution in the Ganga sub-basin during June September (b) Spatial distribution of rainfall and (c) relation between rainfall and rivers crossing danger level. In (b), the dotted brown line indicates 10-mm rainfall mark and in (c), numbers 1, 2 and 3 in between the brown dotted line represent the three rainfall spell of more than 100 mm and the blue line indicates the rainfall spell duration between 22 August and 23 September Yellow arrow indicates the crossing of danger level by the river. To view this figure in colour, please see the online version of the journal. flowing in unprecedented flood situation is compared with the pre-flood IRS AWiFS satellite image acquired during 2010 for observing the changes. Figure 4(a and b) shows the comparison between the pre-irs AWiFS image of 2010 and during flood IRS AWiFS image of 29 September 2010 increase in the spatial spread of inundation extent of the Ganga River near Kannauj and Kanpur. The analysis of the river valley margin profiles generated from SRTM DEM at Kannauj, Ankinghat, Kanpur and

10 Geomatics, Natural Hazards and Risk 755 Figure 4. (a and b) Pre-IRS AWiFS, FCC image of 2010 and during-flood IRS AWiFS, FCC image of 29 September 2010 showing changes in course of the Ganga River near Kannauj and Kanpur. Circles in white colour represent gauge stations and yellow colour major settlements, respectively. Vegetated areas in image appear in shades of red colour, habitated area appears in shades of bluish-grey, while water appears in light to dark bluish black tone and sand appears in bluishwhitetone.toviewthisfigureincolour,pleaseseetheonlineversionofthejournal. Dalamau (figure 5(a e)) together with the visual interpretation of pre- and duringflood IRS AWiFS images suggests that the subsurface topography influences the flood inundation pattern and flow dynamics of the Ganga River. The subsurface morphology of the basin and the tectonics plays an important role in the

11 756 C.M. Bhatt and G.S. Rao Figure 5 (a d) Valley margin cross-section profiles at Gumatiya, Ankinghat, Kanpur and Dalamau. (e) Valley margin spatial profile with help of DEM. Gauge stations are shown in yellow circle encircled with red colour, valley margins in black dotted line and river with blue line. To view this figure in colour, please see the online version of the journal.

12 Geomatics, Natural Hazards and Risk 757 Figure 5 (Continued) sedimentation pattern along the Ganga River (Srivastava & Singh 1999). Tectonics has been a major factor affecting the sedimentation in the Indo-Gangetic basin (Raiverman et al. 1983). The Ganga River first migrated in an eastern direction and then started its westward movement due partly to changes in the stress direction associated with the plate motions and the emplacement of the Bundelkhand Faizabad ridge in Neogene period (Srivastava & Singh 1999). The presence of many fluvial geomorphological features like ox-bow lakes, meander cut-off, straightened river course and sudden narrowing down of the flood plain can be observed in the satellite data which indicate towards possible influence of tectonic control in the region. Many workers in the past have confirmed through their research the role of active tectonics in this region responsible for the river course shifting initially towards eastern direction and then subsequently towards western direction, evidence of preferential alignment, asymmetrical terrace building and other fluvial features like the meander cut-off observed near Bithoor (figure 4(b)) attributed to the lineaments produced in a Himalayan compressional regime (Singh & Rastogi 1973; Khan et al.

13 758 C.M. Bhatt and G.S. Rao 1996; Srivastava & Singh 1999). From the comparison of the pre- and during-flood satellite images, it is observed that the Ganga River flow during the lean period is towards the western margin of the river valley, and during the monsoon season, with high discharges, the flood water spreads towards the eastern margin. The western margin of the river appears to be incised having a high cliff line, whereas the river has a wide flood plain along the eastern margin, thus causing more inundation towards the eastern side. From the satellite image acquired during peak flood period, it is observed that the Ganga River widened its course from 2 to 12 km at Kannauj. At Ankinghat, the river course is observed to be broadened to 14 km, which is only 2 km during normal flow. At Kanpur, the river widening was observed to be about 6 km, which in lean period is about 1 km. Further, downstream of Kanpur, the Ganga River, lateral spread in flood water is not so much significant due to the narrowing down of the floodplain margin near Dalamau and Phaphamau. The Ganga Plain is one of the most densely populated regions of the world due to its fertile soil and availability of water. The high-density pressure of increasing population on land in Uttar Pradesh can be sensed from the fact that the average population density of India is 382 (2011 census), whereas for Uttar Pradesh state, it is 829 in 2011 when compared to 689 in Due to high population growth and its increasing pressure on land, the flood plains and low-lying areas adjoining the river bank are densely populated and being extensively utilized for agricultural activities. During high river discharges, these settlements are vulnerable to flooding. Figure 5(e) shows the low-lying margins (indicated with black dotted line) along the Ganga River between Kannauj and Dalamau, delineated with the help of DEM. In standard false colour composite (FCC) satellite image (figure 4(a and b)), vegetated areas appear in shades of red colour and habitated area appears in shades of bluish-grey and shows checkered pattern (Prakash & Gupta 1998). During the 2010 unprecedented flood event, about 1135 villages, located in nine districts along the river bank, were observed to be affected. Maximum number of about 425 villages in Unnao district, followed by 155 villages in Hardoi district, 135 villages in Raei Bareilly district, 119 villages in Kanpur, 78 villages in Kaushambi district, 64 villages in Allahabad district, 73 villages in Fatehpur district, 50 villages in Kannauj district and 36 villages in Pratapgarh district, were observed to be affected by flood inundation. 5. Development of library of inundation extents Conventional method of identifying areas to be inundated for issuing flood alert requires inputs like discharge data, fine resolution DEM, software for modelling and technically trained manpower to interpret the results meaningfully. However, in developing countries due to poor availability of high resolution DEMs and good network of hydrological observations (Sanyal & Lu 2005, 2006), flood early warning becomes a difficult task. Further, for hydrological modelling, discharge data is an important input, which is not available for most of the Indo-Gangetic Rivers due to being a classified document. During flood season, CWC provides information on daily river water level and on forecasted water level for all major river systems and also issues special bulletins when the river is in moderate (yellow bulletin), high (orange bulletin) and unprecedented (red bulletin) flood situation. However, these bulletins provide information about the observed or forecasted water level which is only an elevation value, non-spatial in nature and does not help in understanding the

14 Geomatics, Natural Hazards and Risk 759 inundation (spatial dimension) which may be caused at various water levels. Historical satellite images analysis can be very useful for building a library consisting of inundation maps that have been created in advance of a flood for predetermined stream stage, by which decision-makers can quickly access the map corresponding to the forecasted or real-time stage data. A series of flood-inundation map libraries can be generated by tagging the inundation layers derived from satellite data analysis with the corresponding water level recorded by the gauge station on a particular date. Figure 6 demonstrates the methodology for the development of library of inundation extents. This can be one of quick and cost-effective methods for building a library of flood inundation extents, which can be utilized during flood disaster for alerting population and taking the relief and rescue operations. Figure 6. Concept for development of library of inundation extents.

15 760 C.M. Bhatt and G.S. Rao The above-explained concept is practically demonstrated with multi-temporal data acquired during 2010 September Ganga floods from Radarsat (18 and 25 September 2010) and IRS AWiFS (29 September 2010). The satellite images are processed and analysed according to the procedures explained under methodology section to extract the water spread layer. The water spread obtained from the analysis of data is tagged with the water level observed on that particular date. Figure 7 shows the changes in spatial inundation extent at different water levels at Ankinghat gauge station located along the Ganga River based on the multi-temporal satellite data analysis of 18, 25 and 29 September About 168 villages are observed to be inundated when the river water level at Ankinghat is about m (on 18 September 2010) which represents a situation when river water is flowing 0.57 m above the WL (123.0 m). The number of villages submerged increases to 196 as observed from satellite data analysis of 25 September 2010, when river water level at Ankinghat reaches to m which is 0.20 m above the DL (124.0 m). When the river water level attains a height of m, on 29 September 2010 which is 0.49 m above DL and 0.18 m above previous HFL, about 320 villages are observed to be marooned. The approach explained of geotagging water spread obtained from historical satellite data analysis and tagging it with river water level can be attempted for rivers prone to flooding to develop a library consisting of a series of water spread layers Figure 7. Changes in spatial inundation extent at different water levels at Ankinghat gauge station located along Ganga River.

16 Geomatics, Natural Hazards and Risk 761 and maps representing water spread pertaining to different water levels. During a flood disaster based on the forecasted river water level, the library can be accessed to fetch the spatial inundation layer corresponding to that forecasted water level. This layer can be visualized from a spatial dimension together with other spatial information like administrative boundaries, transport network, land use and land cover, digital elevation data and satellite images for better understanding and visualization of areas to be inundated spatially on free web-based earth visualization portals like ISRO s Bhuvan portal ( This can help decision-makers in taking quick appropriate measures for warning, planning relief and rescue operations for the population to get affected under that river stage. 6. Conclusions In this paper, the contribution of space technology to capture the water spread spatial extent under unprecedented flood situation like that experienced along the Ganga River during September 2010 is highlighted. From the hydrological observations, it is clear that from 19 September 2010 to 2 October 2010, flood situation was very severe along the Ganga River in Uttar Pradesh, when the river water levels attained by the Ganga River at Kannauj about 12 years back, at Ankinghat gauge station about 32 years back and at Kanpur gauge station about 42 years back were surpassed, attaining a new HFL. The hydrological, meteorological and satellite-based observations made for the unprecedented flood event can be a good input for researchers to investigate the changes in climate regime which caused the river water levels to exceed after a decade. The spatial extent of inundation and villages identified to be affected under new HFLs experienced after a gap of more than a decade will be useful for updating the existing information on flood-prone area inventory and list of flood affected villages. The seven-day cumulative rainfall analysis shows that the two continuous spells of rainfall more than 100 mm with a week gap led to the crossing of DL by river, and if followed by a third spell, could lead to an unprecedented flood situation. This relationship observed between rainfall and river water level can be used as one of the proxy indicators towards flood alert during monsoon season. The concept of development of a library hosting a series of inundation layers representing inundation at different water levels, created by geotagging of water spread area observed from historical satellite data analysis with corresponding water level observed for a gauge station, can be used as a quick and cost-effective method for alerting the habitation at risk during flood season. This method can be helpful in anticipating the areas to be affected in situations where satellite images are available, but due to cloud cover cannot be effectively utilized and also when the area of interest is partially covered in satellite data. Though simulation of inundation extents through hydrological modelling remains the best means, but keeping into consideration most of the Himalayan Rivers discharge data being classified, non-availability of fine resolution DEM and real-time hydrological data, this approach can be of help especially for decision-makers in times of crisis and making disaster management plans for flood season. Acknowledgements The authors gratefully acknowledge the support and cooperation given by Director, National Remote Sensing Centre (NRSC), Hyderabad; Deputy Director, Remote Sensing Applications

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