International Journal of Disaster Risk Reduction 10 (2014) 48–58

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International Journal of Disaster Risk Reduction

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Review Article Digital elevation based flood hazard and vulnerability study at various return periods in Sadar ,

Sadequr Rahman Bhuiyan a,n, Abdullah Al Baky b a Institute of Water and Flood Management, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh b Department of Geography and Environment, Jagannath University, Dhaka 1204, Bangladesh article info abstract

Article history: The objectives of the study are flood hazard mapping and crops and settlement Received 13 November 2013 vulnerability assessment in a low laying riverine flood prone area of Bangladesh for Received in revised form different flood magnitudes. Flood hazard maps have been developed for different flood 20 June 2014 magnitudes integrating the Digital Elevation Model (DEM) data of Shuttle Radar Topo- Accepted 26 June 2014 graphic Mission (SRTM) and interpolation of water level height of different water stations. Available online 17 July 2014 Frequency analysis has been carried out to determine the water level of 2.33, 5, 10, 20, 50 Keywords: and 100-year return periods flood. Landuse or land cover map has been generated from Riverine the LANDSAT satellite images supervised classification. Vulnerability functions of risk Frequency analysis elements and flood hazard maps are analyzed in GIS environment to develop vulnerability Landuse maps. Most of the settlement vulnerable areas were found in low laying lands from the Inundation Risk element settlement and crops vulnerability maps. This flood hazard vulnerability map can be used Return period for selecting the type of crops and area for cultivation during the monsoon period on the basis of magnitudes of inundation of different flood zones. & 2014 Elsevier Ltd. All rights reserved.

Contents

1. Introduction ...... 49 2. Studyarea...... 49 3. Methods and data ...... 49 4. Results and discussion...... 53 4.1. Landuse of the study area ...... 53 4.2. Flood frequency analysis ...... 53 4.3. Inundation mapping ...... 55 4.4. Development of crops and settlement damage functions ...... 55 4.5. Crop and settlement vulnerability mapping...... 58 5. Conclusion...... 58 Acknowledgments ...... 58

n Corresponding author. E-mail address: [email protected] (S.R. Bhuiyan). http://dx.doi.org/10.1016/j.ijdrr.2014.06.001 2212-4209/& 2014 Elsevier Ltd. All rights reserved. S.R. Bhuiyan, A.A. Baky / International Journal of Disaster Risk Reduction 10 (2014) 48–58 49

Appendix A Supplementary material ...... 58 Reference...... 58

1. Introduction cross section locations for such models [12] and, in addition, bathymetric information with precise resolution, surface nat- Bangladesh is extremely vulnerable to flooding because ure (topography, vegetation coverage, land use etc.) are not of its geographical setting. It is a low-laying deltaic country explicitly available especially in Bangladesh context. with big inland water bodies, including some of the biggest Apart from hydrodynamic model application for devel- rivers in the world. Flooding is an annual recurring event oping inundation maps, Geographic Information System during monsoon and 80% of the annual rainfall occurs in (GIS) Software can also be used. GIS has widely been used monsoon. Due to intense rainfall during monsoon (June to to map and model surface water and flood hazard (Aziz September), about one-fifth to one-third of the country is et al., 1998 (as cited in [6])). Digital Elevation Model (DEM) annually flooded by overflowing rivers caused by heavy based flood extent with depth is an integral part of GIS can rainfall. Bangladesh is a flood prone country and very often be adopted for flood hazard study. To get flood map of a experiences devastating flood during monsoon that causes study area, flood elevation generated from water level data, damage to crops, settlement, fisheries, infrastructures and is subtracted from ground elevation data [6]. For obtaining properties. This study assessed the flood hazard vulner- flood extent it is necessary to have both interpolated water ability of crops and settlement for different flood magnitude level and land elevation surfaces as flooding is a continuous by integrating LANDSAT and SRTM digital elevation data phenomenon and interpolation is the procedure of estimat- with geographical information system (GIS) and remote ing the value of properties at unsampled points or areas sensing (RS). using a limited number of sampled observations. In order to resolve the methodological gap, interpola- 2. Study area tion technique at GIS system has been applied using water level data of different stations in order to generate inter- There are two types of floods that occur in Bangladesh: polated water level surface. There are number of inter- annual floods and low frequency floods of high magnitude. polation techniques, designed for particular purpose are Flood hazard assessment is carried out to identify the available in ArcGIS framework. One of is Kriging interpola- potential areas of a region for flood mitigation [15]. In this tion, which has been developed based on statistical connection, in this present study, Sirajganj Sadar Upazila models that include autocorrelation [7]. But for water under has been chosen for flood hazard level surface generation, the technique will not be appro- and vulnerability study at various return periods. Sirajganj priate as there is no statistical relation between the is located in north-western zone of Bangladesh and under different stations in real scenario. On other hand, another the district Sirajganj Sadar Upazila with an area of interpolation technique, Spline is used for land surfaces 314.77 sq km, located in between 24122 and 24137 north generation, as the technique estimates values using a latitudes and in between 89136 and 89147 east longitudes. mathematical function that minimizes overall surface It is bounded by Raipur Upazila on the north, Belkuchi curvature, resulting in a smooth surface that passes exactly on the south, Kalihati and Bhuapur Upazilas on through the input points [7]. But only Topo to Raster the east, Kamarkhanda, Raiganj and Dhunat Upazilas on method is suitable for interpolating a hydrologically cor- the west (Fig. 1). The area falls in a major Agro Ecological rect surface [7]. In the present study, Topo to raster Zones (AEZ), which is the Active Brahmaputra–Jamuna interpolation tool of ArcGIS has been applied for generat- Floodplain (AEZ-8) [1]. The main cause of flooding in the ing interpolated water level surface. The point feature area is the Tran boundary inflow from upstream catch- datasets can be converted to 4 m resolution ArcGIS grid ment carried by the Jamuna River. Others Major important format datasets using the Topo to Raster tool located in the rivers, Bangali, Jamuneswari, Karatoa, and Hurasagar are ArcGIS Toolbox [14]. The Topo to Raster tool in ArcGIS 3D flowed in and around the upazila. analyst results in a connected drainage structure and corrects representation of ridges and streams [4]. 3. Methods and data To analyze of how often particular flood intensity is likely to occur termed as Flood Frequency Analysis (FFA) is Satellite Images with the integration of Geographical an important concept in flood hazard vulnerability study. Information System (GIS) are used for historic flood hazard FFA is a technique of statistical examination of the fre- analysis. National Oceanographic and Atmospheric Adminis- quency – magnitude relationship [5].Itisanattemptto tration (NOAA) and Advanced Very High Resolution Radio- place a probability on the likelihood of a certain event meter (AVHRR) data were used to analyze Bangladesh's occurring [5].InFFA,returnperiod(T) is used which have a historical flood event of 1988, which sets a hundred-year statistical term meaning the chance of accidence once every record for the inundated areas, with severe damage occurring T years over a long period [5].Inundationmapsatdifferent throughout this region [9]. Several hydrodynamic models return periods along with adjacent land coverage could be a have also been developed such as HEC – RAS, MIKE44, SOBEK, useful analysis for flood hazard study. In this present study, ISIS, ONDA and FLUCOMP to study inundation at watershed yearly peak water level data of Brahmaputra–Jamuna, level. Considerable skill is required to determine appropriate Karatoya and Bangali River has been used for FFA. 50 S.R. Bhuiyan, A.A. Baky / International Journal of Disaster Risk Reduction 10 (2014) 48–58

Fig. 1. Study area.

Flood Hydrology Study and Chowdhary and Karim have type 3 (P3), Log Pearson type 3 (LP3), Extreme value type 4 used different probability distributions and suggested that (EV4) have been adopted in this study. After that, using the Log Pearson type 3 (LP3) distributions are suitable for Probability Plot Correlation Coefficient (PPCC) and Bangladesh for the frequency analysis of discharge [8].For goodness-of-fit test, the best frequency analysis method FFA, commonly used empirical distributions 2- Parameter has been selected for flood mapping in this study. In this Log normal (LN2), 3- Parameter Log normal (LN3) Pearson study, observed peak water level data collected from S.R. Bhuiyan, A.A. Baky / International Journal of Disaster Risk Reduction 10 (2014) 48–58 51

Reconnaissance survey

Secondary Data (DEM, Water Level, Data Collection Primary Data (Questionnaire Satellite Image) Survey)

Data Analysis Flood Frequency Analysis Field Survey and Existing Data Analysis etc. Flood Damage Estimation and Develop Vulnerability Function Vulnerability Scaling Supervised image Develop Land-use Map classification, Ground truthing etc.

Develop Flood Inundation and Flood Depth Maps

Identification of Vulnerable Crops & Settlement

Develop Vulnerability Maps for Crops & Settlement Regarding different Return Period

Fig. 2. Overall view of the methodology.

Bangladesh Water Development Board (BWDB) have been classes: cultivated land, rural settlement, water bodies and used for FFA and estimated water level data from the FFA others. Others include free land, bare soil, etc. which are have been used as height source in linear interpolation not important elements for finding vulnerability. (Topo to ras) with ArcGIS 9.3.4 to develop flood depth Vulnerability is the degree of loss to a given element at maps in different land use classification. risk, or a set of such elements, resulting from the occur- Land topography is an essential data, required for GIS rence of natural phenomena of a given magnitude, and interpolation of water surface generation. The NASA Shuttle expressed on a scale 0 (no damage) to 4(total loss) unit [2]. Radar Topographic Mission (SRTM), a 3″ (approx. 90 m Damage due to flooding depends on several factors, such resolution), digital elevation data (DEMs) were downloaded as flood depths, duration of flooding and flow velocity. In from the SRTM FTP server (ftp://e0srp04u.ecs.nasa.gov/ the present study, only damages due to flood depth would srtm/version2/) for the study area. Then to fill in the no- be considered. The generation of data sets at disaggregated data voids or cells, the DEM data were further processed levels of depths and durations is often not feasible using ArcGIS 9.3. The data were then projected to WGS84 mainly because of the lack of adequate variations of depths projection to spatial matching with others layers. and durations in a specific flood of a given area [10]. The Samanta, et al., 2014, developed land use or land cover basic methods to evaluate the crops and settlement dataset from the digital image classification of LANDSAT losses are dependent upon the development and use of satellite images [11]. In this study, landuse map has been stage-damage curves, alternatively called vulnerability generated from the LANDSAT satellite images by super- functions. vised classification algorithm. The supervised classification Several field surveys were conducted to gathering of process is divided into two phases: a training phase, where damage data from actual flood event such as 1988, 1998, the computer is ‘trained’, by assigning for a limited 2004, 2007 and 2010 the major flood in Sirajganj. Vulner- number of pixels to what classes they belong to in this ability function of crops was developed with respect to particular image, followed by the decision making phase, flood depth in agriculture land. where the classification algorithm assigns a class label to Valuation surveys were conducted for the settlement all (other) image pixels, by looking for each pixel to which vulnerability assessment. Settlement has been classified in of the trained classes this pixel is most similar. To have four types such as Brick (floor) Brick (wall) (BB), Brick training areas, ground truthing points have been collected (floor) CI sheet (wall) (BC), Mud (floor) CI sheet (wall) for each landuse class from the field survey. In the present (MC), Mud (floor) Mud (wall) (MM). For the selected study, LANDSAT satellite image has been used for land use properties the surveyor quantifies the damage of all items mapping with the help of Integrated Land and Water due to flood and their current value based on type, quality Information System (ILWIS 3.4) software. A supervised and degree of wear. The survey included information on classification was performed on false color composition the height above the floor of each item or the heights can of band 3, 2 and 1 into following land use and land cover be taken as standard from house to house. The information 52 S.R. Bhuiyan, A.A. Baky / International Journal of Disaster Risk Reduction 10 (2014) 48–58 for all samples of each element class is then averaged and to developed crops and settlement vulnerability maps in stage-damage curves constructed. Vulnerability functions different flood magnitudes. The overall view of the meth- and flood hazard maps were analyzed in GIS environment odology is shown in Fig. 2.

Fig. 3. Existing land use pattern of the study area. S.R. Bhuiyan, A.A. Baky / International Journal of Disaster Risk Reduction 10 (2014) 48–58 53

SW 11 4. Results and discussion 16 R2 = 0.1083 15 4.1. Landuse of the study area 14 13 A supervised classification was performed on false color 12 composition of band 3, 2 and 1 into following land use and Peak WL(m) land cover classes: cultivated land, rural settlement, urban 11 settlement, water bodies and others. Others include trees, 10 bare soil, etc. which are not important elements for finding 1980 1984 1988 1992 1996 2000 2004 2008 2012 year vulnerability. Information collection during field survey as ground truthing point was used to assess the accuracy of Fig. 4. Trend of yearly maximum water level at station Khanpur SW 11. classification. About 28% and 43% area is covered by agricultural and rural settlement area, respectively. Urban

SW49 percentage is negligible, amounted to 4.90% of the total 16 area (Fig. 3). R 2 = 0.0314 15 4.2. Flood frequency analysis 14

Peak WL(m) 13 In the present study, the regression test for linear trend has been carried out for the annual water level series from 12 1980 1984 1988 1992 1996 2000 2004 2008 2012 1983 to 2012 at Khanpur SW-11 station of Bangali River, year Sirajganj SW-49 station of Brahmaputra–Jamuna River and Ulapara Rail crossing SW-66 of Deonai River. The graphical Fig. 5. Trend of yearly maximum water level at station Sirajganj SW 49. trend lines of peak water level for all three stations have been drawn in Figs. 4,5 and 6. The result (Table 1) shows SW 66 increasing trend of water level in Ulapara Rail crossing 16 R2 = 0.2465 SW-66 station and no linear trend could be detected at the 14 5% significance level in Khanpur SW-11 and Sirajganj SW- 49 stations. 12 Then the hydrologic data have been used to determine designed flood levels for several return periods (2.33, 5, 10, 20,

peak WL(m) peak 10 50 and 100 year floods) through flood frequency analysis. 8 For selecting best fitted distribution, goodness-of-fit test 1980 1984 1988 1992 1996 2000 2004 2008 2012 has been conducted. In the goodness-of-fit test, Probability year Plot Correlation Coefficient (PPCC) had been applied. The Fig. 6. Trend of yearly maximum water level at station Ulapara Rail results of the test are shown in Tables 2, 3 and 4.Basedon crossing SW 66. the result of the goodness-of-fit test, Pearson type 3 (P3)

Table 1 Trend of yearly maximum water level.

Name of the River Station Correlation coefficient, RP-value F-value Critical value at 5% significance level, Existence of trend (P-value, F-valueo0.05)

Khanpur SW 11 0.32909 0.04048 0.07064 P-valueo0.05 No trend F-value40.05 Sirajganj SW 49 0.47706 0.43587 0.34924 P-value40.05 No trend F-value40.05 Ulapara SW 66 0.49647 0.04477 0.00526 P-valueo0.05 Increasing trend F-valueo0.05

Table 2 Goodness-of-fit test for selecting most appropriate distribution, SW11.

Station: SW11

PDF Return period PPCC Rank

2.33 yr 5 yr 10 yr 20 yr 50 yr 100 yr

LN2 13.60 m 14.53 m 15.18 m 15.75 m 16.42 m 16.87 m 0.98172 4 LN3 13.69 m 14.53 m 15.08 m 15.52 m 16.02 m 16.33 m 0.98862 2 P3 13.70 m 14.54 m 15.08 m 15.51 m 15.98 m 16.29 m 0.98885 1 LP3 13.68 m 14.54 m 15.11 m 15.56 m 16.06 m 16.38 m 0.98858 3 EV1 13.42 m 14.37 m 15.14 m 15.88 m 16.83 m 17.55 m 0.95179 5 54 S.R. Bhuiyan, A.A. Baky / International Journal of Disaster Risk Reduction 10 (2014) 48–58

Table 3 Goodness-of-fit test for selecting most appropriate distribution, SW49.

Station: SW49

PDF Return period PPCC Rank

2.33 yr 5 yr 10 yr 20 yr 50 yr 100 yr

LN2 14.12 m 14.51 m 14.77 m 14.99 m 15.25 m 15.42 m 0.995362 4 LN3 14.13 m 14.51 m 14.76 m 14.97 m 15.21 m 15.36 m 0.995908 2 P3 14.13 m 14.51 m 14.76 m 14.97 m 15.20 m 15.35 m 0.995937 1 LP3 14.13 m 14.51 m 14.76 m 14.97 m 15.21 m 15.37 m 0.995902 3 EV1 14.03 m 14.44 m 14.77 m 15.09 m 15.51 mm 15.82 m 0.97062 5

Table 4 Goodness-of-fit test for selecting most appropriate distribution, SW66.

Station: SW66

PDF Return period PPCC Rank

2.33 yr 5 yr 10 yr 20 yr 50 yr 100 yr

LN2 11.91 m 12.57 m 13.03 m 13.42 m 13.88 m 14.19 m 0.99109 4 LN3 11.96 m 12.58 m 12.98 m 13.31 m 13.68 m 13.92 m 0.99111 2 P3 11.94 m 12.57 m 13.00 m 13.36 m 13.76 m 14.03 m 0.99186 1 LP3 11.92 m 12.57 m 13.01 m 13.39 m 13.81 m 14.10 m 0.99178 3 EV1 11.78 m 12.46 m 13.02 m 13.55 m 14.25 m 14.76 m 0.97071 5

Fig. 7. 100-yr return period flood inundation map of study area. S.R. Bhuiyan, A.A. Baky / International Journal of Disaster Risk Reduction 10 (2014) 48–58 55 has been selected for SW 11, SW 49 and SW 66 station for 4.4. Development of crops and settlement damage functions flood frequency analysis. Vulnerability is the degree of loss to a given element at risk, or a set of such elements, resulting from the 4.3. Inundation mapping

The flood water levels obtained from flood frequency analysis for various return periods were used for topo to raster interpolation technique and overlain onto the land surface elevation of the study area. Difference between water level interpolation and land elevation surfaces was considered as depth of inundation for each return period (Fig. 7). In the study, inundated areas are defined into four qualitative hazard classes viz. Low Hazard (o2 m), Medium Hazard (2.1–5.0 m), High Hazard (5.1–10.0 m) and Very High ( 410 m) based on the inundation depth. The flood free zone decreases with the increase of return period. Study conducted by the IWM (2008) termed the 1998 flood is a return period of 75 to 100 years. The percentage of flooded area in Sirajganj Sadar upazila was 54 as on September 17 [3].Inthisstudy,for100yearreturn period, 46.10% (144.98 sq km) area has been found under flooding, which is quite close to the BWDB study. The flood affected area increases with the increase of return period and flood depth. It is also noticeable that, Fig. 9. Crop vulnerability function. inundated areas become doubled for land use classes of agriculture and rural settlement with the increase of return periods. Rural settlement areas are inundated much more than that of urban settlement with the increase of return period and flood depth. Water bodies become inundated much more than that of any other classes of land use, most of the area are located in medium to very high hazard area. It reflects the loss of capture fisheries during flooding. For agriculture which is the most dominant land use type, most of the low laying agriculture area are vulnerable to flooding. In case of high magnitude floods, inundated area for agricultural landuseremainsthesamewiththeincreaseofreturnperiod. Bare soils, mostly occupied at deltaic lands, are highly prone to floods particularly located in low and medium land types. Fig. 8 depicts the inundated area (%) for each land use classes at different return periods. Fig. 10. Settlement vulnerability function.

Fig. 8. Inundated area (%) for each land use classes at different return periods. 56 S.R. Bhuiyan, A.A. Baky / International Journal of Disaster Risk Reduction 10 (2014) 48–58

Fig. 11. 100 year return period crops vulnerability map of the study area. S.R. Bhuiyan, A.A. Baky / International Journal of Disaster Risk Reduction 10 (2014) 48–58 57

Fig. 12. 100 year return period settlement vulnerability map of the study area. 58 S.R. Bhuiyan, A.A. Baky / International Journal of Disaster Risk Reduction 10 (2014) 48–58 occurrence of natural phenomena of a given magnitude, 5. Conclusion and expressed on a scale 0 (no damage) to 1 (total loss) unit [2]. A stage–damage curve normally relates to a specific Flood hazard mapping and vulnerability assessment of class of buildings or crops and presents information on the different risk elements in a low laying area based on relationship of flood damage to depth of flooding (or stage) landuse map in conjunction with digital elevation model [13]. (DEM) analysis in the GIS atmosphere can be effectively On the approach of developing crops and settlement accomplished. The authorities responsible for flood pro- stage–damage curve several field surveys were conducted tection are provided with an excellent tool to manage to gathering the damage data from actual flood event such disasters well in advance. In addition, land use policy as 1988, 1998, 2004, 2007 and 2010 the major flood in might be adopted on basis of the study result. Sirajganj. Crops damage data was collected with respect to flood depth in agriculture land. Valuation surveys are conducted for the settlement Acknowledgments vulnerability assessment. Settlement has been classified in four type such as Brick (floor) Brick (wall) (BB), Brick I wish to express my deepest thanks to IWFM climate (floor) CI sheet (wall) (BC), Mud (floor) CI sheet (wall) change study cell, BWDB and LGED authorities for provid- (MC), Mud (floor) Mud (wall) (MM). For the selected ing me important papers, GIS data and other technical properties the surveyor quantifies the damage of all items matters related to my study. due to flood and their current value based on type, quality and degree of wear. The survey included information on Appendix A. Supplementary material the height above the homestead Floor level of each item or the heights can be taken as standard from house to house. Supplementary data associated with this article can be The information for all samples of each element class is found in the online version at http://dx.doi.org/10.1016/j. then averaged and stage–damage curves constructed. In ijdrr.2014.06.001. These data include Google maps of the the argiculture and settlement vulnerability function vul- most important areas described in this article. nerability has been scale 0–1. 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