*Soumen Dey Department of Geography, Visva Bharati University *Author for Correspondence

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1 FLOOD INUNDATION STUDY OF COASTAL ODISHA DUE TO VERY SEVERE CYCLONIC STORM PHAILIN, USING MODIS SURFACE REFLECTANCE DATA *Soumen Dey Department of Geography, Visva Bharati University *Author for Correspondence ABSTRACT Remote sensing images can be effective and efficient tools to determine flood inundation areas. In the past, many studies have been conducted using remote sensing data to detect spatial and temporal changes of flood inundation areas, delineate wetlands and study its changes, flood damage assessments in urban areas, dynamics and behaviors of floods. In this context, detecting spatio-temporal extents flood of 11 districts of coastal Odisha due to very severe cyclone Phailin from 16th October to 8th November are studied using time-series 8 day composite of MODIS surface reflectance data (MOD09A1). Flood inundation maps are developed from Normalized Difference Water Index (NDWI) derived from surface reflectance. The inundation map developed from MODIS data has been compared with SRTM data to understand the flood area. The products derived from MODIS 500m imagery shows the ability to study flood dynamics considering that MODIS products have a great advantage in the high-frequent observation. The study concludes that this is a useful method to clarify the entire extent of the temporal floods of Odisha as well as India. Keywords: NDWI, Remote Sensing and GIS, Flood INTRODUCTION Odisha is vulnerable to multiple disasters. Due to its sub-tropical littoral location, the State is prone to tropical cyclones, storm surges and tsunamis. The river systems having densely populated coastal plains which are rich in alluvial deposits. The rivers in these areas with heavy load of silt have very little carrying capacity, resulting in frequent floods, only to be compounded by breached embankments. According to the Indian Meteorological Department from year 1891 to 2000, 98 nos. of cyclones/severe cyclones had crossed the Odisha coast. This is more than that of the coast of West Bengal, Andhra Pradesh, Tamil Nadu, Karnataka, Maharashtra, Goa, Gujrat and Kerala. Very Severe Cyclonic Storm Phailin originated from a remnant cyclonic circulation from the South China Sea. The cyclonic circulation lay as a low pressure are over Tenasserim coast on 6th October 2013 (CWD, 2013). It lay over north Andaman Sea as a well marked low pressure area on 7th October. It concentrated into a depression over the same region on 8th October near latitude 12.00N and longitude 96.00E. Moving westnorthwestwards, it intensified into a deep depression on 9th morning and further into cyclonic storm, Phailin in the same day evening. Moving northwestwards, it further intensified into a severe cyclonic storm in the morning and into a very severe cyclonic storm in the forenoon of 10th October over east central Bay of Bengal (Figure 2). Phailin hit Odisha coast on and the landfall point was Gopalpur in Ganjam District. The wind velocity was recorded to be kmph. After hitting the Gopalpur coast, the cyclonic storm with tidal waves of 3.0 to 3.5 meter height ravaged the coastal districts of Ganjam, Puri, Khordha, Jagatsinghpur and Kendrapada. The cyclone was accompanied with torrential rains for 3 days (Figure 3), leading to floods in a number of major rivers. Incessant rains and flood with high wind have also caused serious damage to other districts of the State. The application of satellite captured earth observation imagery for monitoring and mapping flood events has proven useful in numerous crisis situations. Right after its launching in December 1999, MODIS satellite with its moderate-resolution optical sensor of m becomes useful tools for scientific studies and research (Sun and Yu, 2010). Several studies were conducted to determine surface water Copyright 2014 Centre for Info Bio Technology (CIBTech) 96

2 content and valuable information on inundation extent in flood affected areas through MODIS as temporal resolution of this data is very high. Study Area The selected eleven districts of coastal Odisha namely Ganjam, Khordha, Puri, Jagatsinghpur, Baleswar, Bhadrak, Kendrapara, jajapur, Cuttack, Gajapati and Nayagarh are located between Latitude N to 21 0 N and Longitude E to E (Figure 1). The area is mainly floodplain served by river like Baitarani, Burhabalang, Rusikulya, Subarnarekha, Jalaka, Birupa, Salandi, Mahanadi and others which ultimately fall into the Bay of Bengal. Figure 1: Location of study area Copyright 2014 Centre for Info Bio Technology (CIBTech) 97

3 MATERIALS AND METHODS The Moderate Resolution Imaging Spectroradiometer (MODIS) data used in this study are the 8-day composite (the best quality daily reflectance data of the 8-day period), 500-meter surface reflectance data (MOD09A1) obtained from the Land Processes Distributed Active Archive Center (LP DAAC) and accessed from the Earth Observing System (EOS) Data Gateway. The 500-meter spatial resolution of MODIS makes it a natural tool to monitor soil moisture conditions. A data quality control process was applied to screen the cloud and fill value pixels obtained from the associated MODIS quality assurance (QA) data product. To identify the impact of flood after Phailin three images of post flood period have been selected (Figure 7, 8 & 9). Shuttle Radar Topography Mission (SRTM) data with 90 metres resolution used in this study to understand the topography and affected area (Table.1). Table 1: Satellite data used S.No Date Data Composition Resolution 1 23 April, 2013 MOD09A1 8 Day Meters MODIS Surface Reflectance Data 2 16 October, 2013 MOD09A1 8 Day Meters MODIS Surface Reflectance Data 3 24 October, 2013 MOD09A1 8 Day Meters MODIS Surface Reflectance Data 4 1 November, 2013 MOD09A1 8 Day Meters MODIS Surface Reflectance Data 5 February, 2000 Shuttle Radar Topography Mission (SRTM) NA 90 Meters Normalised Difference Water Index (NDWI) is widely used to identify water related surface (Cretaux et al., 2011). The main reason of using NDWI is that short-wave infrared (SWIR) is highly sensitive to moisture content in the soil and the vegetation canopy. A number of studies have been conducted in use of the spectroscopic characterization of SWIR to detect water content of an area (Gao, 1996; Jackson et al., 2004; McFeeters, 1996; Rogers and Kearney, 2004; Tong et al., 2004). Normalised Difference Water Index (NDWI) = Band RED Band SWIR Band RED + Band SWIR (where is the reflectance of Red nm, MODIS Band 1and SWIR is reflectance of short-wave infrared nm, MODIS Band 6 of the solar spectrum ) After identifying water-related pixel using NDWI it is essential to classify whether it is Flood pixel or Long term water. For that a pre-flood image of MODIS data has been used to outline long term water body (Figure 6). Using this proposed methodology, changes of spatial extent with time are analysed and three flood inundation maps are developed (Figure 10, 11 & 12) for 16 Oct 2013 to 8 Nov A DEM has developed of the study area using SRTM data (Figure 13). State of Odisha map is registered using WGS 1984 datum and digitisation has been done for district level map. All Image Processing work is done in ERDAS imagine 9.2 and Geoprocessing and output maps are prepared on ARC GIS 10.0 platform. The algorithm for processing of NDWI has been shown in figure 5. Copyright 2014 Centre for Info Bio Technology (CIBTech) 98

4 Figure 2: Observed track of VSCS Phailin during 8 th to 14 th October, 2013 (Source: IMD) Figure 3: Daily rainfall (9 th to 14 th October, 2013) (Source:IMD) Copyright 2014 Centre for Info Bio Technology (CIBTech) 99

5 Figure 4: Total rainfall (9 th to 14 th October, 2013) (Source:IMD) Figure 5: Methodology for NDWI calculation Copyright 2014 Centre for Info Bio Technology (CIBTech) 100

6 Figure 6: MODIS 8 Day composite, 23 rd to 30 th April Figure 7: MODIS 8 Day composite, 16 th to 23 rd October Figure 8: MODIS 8 Day composite, 24 th to 31 th October Figure 9: MODIS 8 Day composite, 1 st to 8 th November Figure 10: Flood extent map (16 th to 23 rd October) Figure 11: Flood extent map (24 th to 31 th October) Copyright 2014 Centre for Info Bio Technology (CIBTech) 101

7 Figure 12: Flood extent map (1 st to 8 th November) Figure 13: DEM of study area Figure 14: Flood affected area of coastal Odisha RESULTS AND DISCUSSION The rainfall data of 11 coastal districts show 219 mm of average rainfall during the cyclone period from 9 to 14 October where maximum rainfall occurs at Khordha (273.3mm) and Nayagarh (261.8mm) district. The maximum rainfall occurred the next day after the cyclone phailin hit the Odisha coast and average of 11 districts rainfall was mm of that day. (Figure 4) Maximum extent of the flooded area can be detected immediately after the occurrence of cyclone. 16 October, 2013 MODIS image depicts that maximum flood extent falls in the coastal districts of Puri ( sq.km) followed by Baleswar ( sq.km) and Kendrapara ( sq.km) (Figure 14). Copyright 2014 Centre for Info Bio Technology (CIBTech) 102

8 The northern part of Chilka Lake drained by Bhargavi, Daya, Nun, Makra river is the most affected part of Puri district. Whereas low lying parts of Subarnarekha and Burhabalang river of Baleswar district were under the flood water. Each of these flood areas falls under 0 to 5 metres zone from mean sea level. October 24th image shows that Jajapur (837.12sq.km) having the maximum extent of flood followed by Kendrapara ( sq.km) and Puri (740.6sq.km). The basin like shape of the area specially the junction area of three districts i.e. Bhadrak, Kendrapara and Jajapur is mostly affected in this period. No development can be observed in the south western part of Puri district in this period. Flood extent map of 1st November shows little part of inundation except northern part of Chilka Lake. Conclusion The study depicts that MODIS surface reflectance data from Land Processes Distributed Active Archive Center is one of the key component to identify macro scale flood hazards. This study modifies a methodology which was developed by Macharia (2010) in order to detect spatial extents and temporal changes of flood inundation. Using this modified methodology, MODIS satellite images were used to develop flood inundation maps in Odisha. This low resolution (500m) MODIS based maps area compared with subsequent flood inundation maps based on high resolution (50m) RADARSAT satellite images prepared by Decision Support Centre, NRSC. Such inundation maps will be useful for integrating water resources management and the maintenance of ecosystems of wetlands. REFERENCES Cretaux JF, Nguyen MB, Leblanc M, Rio RAD, Delclaux F, Mognard N, Lion C, Pandey RK, Tweed S, Calmant S and Maisongrande P (2011). Flood mapping inferred from remote sensing data. Fifteenth International Water Technology Conference, Alexandria, Egypt CWD (Cyclone Warning Division) (2013). A Report on Very Severe Cyclonic Storm, Phailin over the Bay of Bengal, India Meteorological Department, New Delhi Gao BC (1996). NDWI- A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 58(3) Gu Y, Brown JF, Verdin JP and Wardlow B (2007). A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysical Research Letters 34 L06407(1-6) Gu Y, Hunt E, Wardlow B, Basara JB, Brown JF and Verdin JP (2008). Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data. Geophysical Research Letters 35 L22401(1-5) Guerschman JP, Warren G, Byme G, Lymburner L, Mueller N and Dijk AV (2011). MODIS-based standing water detection for flood and large reservoir mapping: algorithm development and applications for the Australian continent. CSIRO: Water for a Healthy Country National Research Flagship Report, Canberra Islam AS, Bala SK, Haque A (2009). Flood inundation map of Bangladesh using MODIS surface reflectance data, 2nd International Conference on Water & Flood Management IWMI (International Water Management Institute) (2013). Mapping Floods in Northern India: States of Uttarakhand and Uttara Pradesh using NASA's Satellite images. Macharia D (2010). SERVIR-Africa: Operational Flood Prediction and Post-Event Flood Mapping. Workshop on Space Technology and Geo-information for Sustainable Development, Cairo, Egypt Martinis S, Twele A, Strobl C, Kersten J and Stein E (2013). A Multi-Scale flood monitoring system based on fully automatic MODIS and TerraSAR-X processing chain. Remote Sensing SRC (Special Relief Commissioner) (2013). Memorandum on Phailin and the subsequent flood. Revenue & Disaster Management Department, Government of Odisha Sun DL, Yu YY (2010). Deriving water fraction and flood map with the EOS/ MODIS data using regression tree approach. IAPRS 38(7b) Xu H (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing 27(14) Copyright 2014 Centre for Info Bio Technology (CIBTech) 103