INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 3, No 1, 2012 Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0 Research article ISSN 0976 4380 Risk and vulnerability assessment of flood hazard in part of Ghaggar Basin: A case study of Guhla block, Surjit Singh Saini 1, Kaushik. S.P 2 1-Research Scholar, Department of Geography, Kurukshetra University Kurukshetra, Haryana-136119 2- Associate Professor, Department of Geography, Kurukshetra University Kurukshetra, Haryana-136119 saini.surjit@gmail.com ABSTRACT The Ghaggar River in Haryana-Punjab plains, northern India presents a challenge in terms of repeated flash flood hazard. Although a long history of flood control management in the basin for more than 2 decades, the river continues to bring a lot of gloom through extensive flooding. This paper reconsiders the flooding problem in the Ghaggar River basin and presents an in-depth analysis of flood hydrology. We integrate the hydrological analysis with a Geographic Information System (GIS) based flood risk mapping in the middle parts of the basin. Typical hydrological and environmental characteristics of the study area include drainage congestion, drainage confluence, very high discharge variability, gentle slope, and agricultural practices. Besides, proximity to high slope of upper catchment area and disturbance of natural drainage channels due to human intervention, are identified main factors increasing vulnerability to flood hazard. Annual peak discharges often exceed the mean annual flood and the low-lying areas of the alluvial plains are extensively inundated year after year. The main objective of the study is to assess the risk and vulnerability based on multi-criteria assessment. In this study Rank Sum method is used to calculate the weights of factors contribute to flood hazard. Present study limited to environmental factors such as hydrology, slope, soil type, drainage density, landform and land use/ land cover to propose a Flood Risk Index (FRI). GIS techniques shown efficient role in the process of derivation, integration, and analysis of spatial data. The approach resulted in three classes of flood risk mapping ranging between low to high vulnerable area. GIS produced flood risk map is validated with recent flood occurred in July 2010 inundation data collected from irrigation department of the concerned district. Present study find out that GIS based long-term inundation maps can offers a cost-effective solution for planning mitigation measures and preparedness in flood prone areas. Keywords: Flood Risk Analysis, Rank Sum Method, GIS 1. Introduction Flood occurs in the Middle and lower Ghaggar river basin in Haryana and Punjab region almost every year with variation in extent but the flood in September, 1988, July, 1993, July, 1995 and July, 2010 getting worse consecutively. These flood causes loss of lives, damages to public and private properties and destruction of normal cultivating cycle. According to daily flood situation report (provisional) produced by Disaster Management Division, Ministry of Home Affairs, due to recent flood occurred in July, 2010, in Punjab 3.25 lakh acres of Crop land damaged and about 3 lakh population of 763 villages mainly from district Patiala, Sangrur and Mansa were affected. In Haryana over 4 lakh population from over 600 Submitted on May 2012 published on July 2012 42
villages primarily from district Ambala, Kurukshetra, Kaithal, Fatehabad and Sirsa have been affected due to floods. Nearly 3 lakh hectares of agricultural land have been inundated with flood water.there were 51 persons died from both the states due to flood. Major impact of flood has been observed in the middle and lower part of the Ghaggar River Basin, at the foot of the steep slopes of the Shiwalik Hills of Himalaya. This situation, combined with the heavy rainfalls common in the areas along the Ghaggar River makes this area to be frequently affected by flash floods of the Ghaggar, Markanda, Tangri and some other small watercourses. Due to an intense erosion of the riverbanks breaches occurs at several places along the margins of the Ghaggar River and its tributaries. In the study area due to flood (July, 2010), Ghaggar Bund (embankment) which has been constructed parallel to Ghaggar River in the left side of Ghaggar has breached at various places which caused damage to the crops and village abaadi (settlement) area. Parray (2006) studied that Ghaggar basin is also characterized with number of palaeochannels which are the low-lying part of the alluvial plains within basin; hence they act as conduits and become instrumental in carrying the floodwaters due to incessant rains. Some other low-lying parts of the alluvial plains has also played role in bringing flashfloods. Human activities like agriculture practices, settlements, construction of roads with inadequate culvert and the most important drains and canals have changed the morphology of the natural drainage system of plains and palaeochannels. These changes have also accelerated the occurrence of floods. Moreover, the incompatible developmental activities or encroachment along these river courses has narrowed their channels resultant capacity of discharge has been drastically reduced. In consequence, the amount of rainfall that is necessary to cause a flood has been decreasing during the last decades and several serious floods have occurred, causing a lot of damage. IPCC (2007) studies revealed that, there is no evidence that the trend will discontinue, and the implications of global warming for increased hazard events fuel concern about future flood disasters. There are also concerns about continued growth and development in floodplains interfering with natural systems and ecological processes and highlighting that human behavior is a contributor to the problem of flooding. In general, encroachments in the form of unsustainable land uses and development practices may often make a sizeable contribution to increase risk and vulnerability to floods. Ideally, the natural drains should have been widened (similar to road widening for increased traffic) to accommodate the higher flows of storm water. But on the contrary, there have been large scale encroachments on the natural drains and the river flood plains. A significant part of this type of risk assessment is dependent upon the use of science and technology for improved monitoring, modeling/forecasting and decision-support systems. One way of improving the preparedness for massive flooding is by setting up a vulnerability-based geospatial framework to generate and analyses different scenarios. This will help in identifying and planning for the most appropriate actions in a dynamic way to incorporate periodical changes that take place within basin to mitigate the adverse impact of floods. Sinha et al. (2008) have conducted a study on flood risk assessment in the part of Kosi River basin, they have integrated hydrological with geomorphological, land cover, topographic and social (population density) parameters using GIS to propose a Flood Risk Index (FRI). An appropriate weightage or importance has been given to each parameters using Analytical Hierarchical Approach (AHP). Based on this analysis a flood risk map has been developed shown high, medium and low flood risk zones and developed flood risk map validated with 43
MODIS satellite imagery derived map. They have find out that with long-term inundation maps can offers a cost-effective solution for planning mitigation measures in flood prone areas Present study is an attempt to generate flood risk and vulnerability map based on hydrological, geomorphological and land use characteristics and validated with inundation data of flood occurred in July, 2010 using Geoinformatics tools and techniques. 2. Study area Guhla Administrative Block of Kaithal district have been taken as a study area, which comprised of 104 villages with an area of about 560 sq.km. The study area is located between 29 51 49-30 12 40 N latitude and 76 10 0-76 29 10 E longitude. (Figure 1). Study Area is a part of Ghaggar river basin and known as middle reach of Ghaggar River. Study area has confluence point of major tributaries of Ghaggar i.e. Markanda, Tangri, Patialewali, Para river in Haryana and Patialewali in Punjab which cause this zone comparatively high vulnerable to frequent flood problem. The normal annual rainfall is 563 mm and about 85% of annual rainfall contributed by south-west monsoon sets in from last week of June and withdraws in end of September. July and August are the wettest months. Rest 15% rainfall is received during non-monsoon period in the wake of western disturbances. Study area has discharge outlet at Reduceable Distance (RD) no. 140000 near Tatiana village and conveyance the surplus flood discharge (q) of Markanda, Tangri, Patialewali and Para river catchment which comprised 7975.335 sq.km(40%) area of the total 20,000 sq.km of the whole Ghaggar basin. Study Figure 1: Study Area 44
3. Database and methodology One of the non-structural measures for flood risk reduction is considered the flood risk zone mapping according to their vulnerability, it involves modeling the complex interaction of river flow hydraulics with topographical and land use features of the floodplains. Figure 2: Flowchart of methodology In the present study a combination of different data sets such as morphologic, topographic, satellite imagery, and census data (2001) obtained from concerned government agencies (Table.1) have been used to compute a composite index of flood risk based on multiparametric analysis. The Multi Criteria Evaluation (MCE) approach through Rank Sum 45
Method is used to identify the flood risk areas after assigned weights to each factor taken into consideration as per their sensitivity. While conducted the present study following methodology schematically shown in Figure 2 is adopted. GIS application is fully used for reproducing, analyzing and integrated spatial data to prepare a flood risk map which not only defines the susceptibility of each settlement to inundation but also provides means for assessment of flood risk in terms of loss of life, crop land and property. Sr. No Table: 1 Spatial Data and its Sources Type of Data Sources Descriptions 1 Topographic Map Survey of India Number 53C/4, 53C/5 Published Year 1972 Scale 1:50,000 2 Satellite Imagery http://glcfapp.glcf.umd.edu web site of Global Land Cover Facilities, Earth Science 3 Rainfall and Peak Discharge Data Tar Ghar, Irrigation Department, Canal Colony Kaithal 4 Geomorphology Published Report - 5 Soil Published Report - 6 Slope and Drainage Density 7 Digital Elevation Model Survey of India Toposheet Survey of India Toposheet Satellite Name- Landsat 7 TM, Acquired Date: March, 2009,Spatial Resolution- 30m Collected random data of rainfall discharge for 22 year from 1976-2010 Digitization of Drainage Network Digitization of Contours and spot heights 8 Population Data Census (2001) Village wise population and their characteristics 5. Result & discussion 5.1 Hydrological analysis For hydrological analysis, data obtained from Telegraph Section in the Department of Irrigation Kaithal, Haryana has been used corresponding to one gauge/discharge stations at RD 140000 near Titiana Village (Figure 1). For the purpose of study 22 years annual peak rainfall discharge data has been collected and analysed for the calculation of flood frequency and return period (5years, 10 years and 20 years) to estimate the past flood occurrence and their intensity. Further insight to flood frequency is provided by the return period analysis using The most efficient formula for computing plotting positions for unspecified distributions, now commonly used for most sample data, is the Weibull equation: p= m/ n+1, where m is ranked lowest to highest (Ascending), P is an estimate of the probability of values being equal to or less the ranked value. For the probabilities expresses in percentages, the value of P becomes: p= m/n+1x100. 46
Onni S. et.al (2007) compared Weibull, Gringorten and L-Moments formula for flood frequency distribution of Sarawak river, Malaysia and found that L-moment method always give the least ratio at some stations and gives unreasonable return period and reduced variate range. Therefore, the appropriateness of L-moments with Gumbel distribution had some limitations. They observed that between Weibull and Gringorten formula, Gumbel distribution by Weibull formula is better than Gumbel distribution by Gringorten formula. Weibull and Gringorten formula is still the best plotting position method to be used with Gumbel distribution for frequency analysis. Table: 2 Gauge vs Peak Discharge Year Gauge(Feet) Peak discharge (Cumecs)or (meter 3 /second) 1976 23.5 1585 1981 22.4 NA 1983 NA 1096 1984 28.5 1841 1985 NA 735 1986 NA 654 1987 NA NA 1988 26.3 1680 1989 25.1 1561 1990 NA 1286 1991 NA NA 1992 NA NA 1993 30.4 2831 1994 25.1 1561 1995 27.1 1757 1996 24 1467 1997 25.2 1576 1998 NA 1331 1999 NA 1357 2000 25.6 1605 2001 24.6 1521 2002 NA 1278 2003 NA 1304 2004 27 1748 2008 24 1464 2010 27.5 1796 Source: Irrigation Department Kaithal, Haryana Table 2 shows a typical pattern of annual peak discharge for the period 1976-2010 which generally starts to peak in the month of July with the maximum in the month of August/September. According to flood hydrology data of RD No 140000 (discharge/gauge location), the Ghaggar river discharge holding capacity or gauge level is 22.0 feet (danger mark) as water level crossed the danger mark water starts overflowing from the river banks and create situation of flood in the immediate areas. Depending on the gauge level recorded in the study area flood risk are classified according to intensity. The gauge level and flood 47
intensity is summarized in the Table3 & Table.4. Table no.5 reveals that the study area has been affected four time (year 1976, 1981, 1996 and 2008) by low intensity flood and six time ( year 1988, 1989, 1994, 1997, 2000 & 2001) flood occurred of moderate intensity and in year 1984, 1993, 1995, 2004 and year 2010 the study area challenged with high flood hazard. Table 3: River Gauge vs. intensity of flood Sr. no Gauge (feet) Frequency Intensity of Flood 1. 22.0-24.0 4 Low flood 2. 24.1-26.9 6 Moderate flood 3. Above 27 5 High Flood Discharge (Cumecs) Decending order Table 4: Flood frequency analysis Rank (m) P= m/(n+1) Probability in Per cent Return Period (1/P) 1585 2831 1 0.043 4.35 23.0 1096 1841 2 0.087 8.70 11.5 1841 1796 3 0.130 13.04 7.7 735 1757 4 0.174 17.39 5.8 654 1748 5 0.217 21.74 4.6 1680 1680 6 0.261 26.09 3.8 1561 1605 7 0.304 30.43 3.3 1286 1585 8 0.348 34.78 2.9 2831 1576 9 0.391 39.13 2.6 1561 1561 10 0.435 43.48 2.3 1757 1561 11 0.478 47.83 2.1 1467 1521 12 0.522 52.17 1.9 1576 1467 13 0.565 56.52 1.8 1331 1464 14 0.609 60.87 1.6 1357 1357 15 0.652 65.22 1.5 1605 1331 16 0.696 69.57 1.4 1521 1304 17 0.739 73.91 1.4 1278 1286 18 0.783 78.26 1.3 1304 1278 19 0.826 82.61 1.2 1748 1096 20 0.870 86.96 1.2 1464 735 21 0.913 91.30 1.1 1796 654 22 0.957 95.65 1.0 Table 5: Land use/ Land cover Pattern Sr. No Class Area in sq.km Per cent to total 1 Crop Land 494.23 88.40 2 Built-up 23.95 4.28 3 Forest 32.82 5.87 4 Palaeo Channel 0.07 0.01 5 Waterbodies 7.98 1.43 6 Waste Land 0.02 0.00 Total 559.06 100.00 Source: Landsat 7 TM imagery of year 2009 http://glcfapp.glcf.umd.edu 48
Figure 3: Frequency distribution and flood intensity Figure 4: TM Landsat Satellite imagery of year 2009, 30 m resolution (path 147 and row 039) highlighted water bodies with combination of band 5,4,3 49
5.2 Multi-criteria analysis for risk and vulnerability assessment Multi-criteria evaluation (MCE) methods have been applied in several studies. Since 80 per cent of data used by decision makers is related geographically (Malczewski, 1999), Geographical Information System (GIS) may provide more and better information about decision making situations. GIS allows the decision maker to identify a list meeting a predefined set of criteria with the overlay process (Heywood et al., 1993).In order to create flood risk maps ranking method is used and each factor is weighted according to the estimated significance for causing flooding. Slope Parameters Drainage Density Landform Land use /land cover Table: 6 Parameters and Weights Weight Variables Sub-class of Parameters Rating Level 5 0-3 0 1 3 0-6 0 2 6 0-9 0 3 9 0 12 0 4 > 12 0 5 5 584-730 meters/ sq.km 1 438-584 meters/sq.km 2 292-438 meters/sq.km 3 146-292 meters/sq.km 4 0-146 meter/sq.km 5 2 Flood Plain 1 Upland Alluvial Plain 2 6 Waterbodies 1 Palaeo Channel 2 Crop Land 3 Waste Land 4 Forest 5 Built-up 6 Soil Type 1 Loam 1 Table: 7 Composite Index Calculations Using Rank Sum Method Sr.n Parameter of Inverse Weight Normalized Criteria 1 Slope 1 5 0.33 33.33 2 Drainage 2 4 0.27 26.67 3 Geomorphology 3 3 0.20 20.00 4 Land use 4 2 0.13 13.33 5 Soil 5 1 0.07 6.67 Sum 15 100.00 The relative weight is applied to these factors. 1 is representing the most important (Sensitive) factor and 6 is the least significance. Similarly, relative weight to each main factor and their sub-class element is assigned and normalizing using rank sum method. Finally, a composite flood risk index (FRI) is computed using weighted overlay analysis and raster calculator in Arc GIS 9.3 software. The composite FRI values vary from Low100- to high 319.99 (Figure 5) thus obtained for the part of the Ghaggar river basin, the flood risk map is further classified into low, medium, and high risk zones (Figure 5). Computed flood risk 50
Risk and vulnerability assessment of flood hazard in part of Ghaggar Basin: A case study of Guhla block, analysis and produced map is validated with the list of villages inundated by recent flood occurred in July 2010 in the study area collected from canal/irrigation department of district headquarter. The resultant map of flood risk is matched about 95 per cent with inundated data. Where wj is the normalized weight for the jth criterion, n is the number of criteria under consideration (j=1, 2 n), rj is the rank position of the criterion. Each criterion is weighted (n-rj+1) and then normalized by the sum of weights, that is, Σ (n-rj+1). Figure 5: Weighted Overlay Analysis using Raster Calculator in Arc GIS 9.3 Software to developed Composite Flood Risk Map 51
Figure 6: Classified Map of Settlements Vulnerable to Flood Risk Table 11: Vulnerability to flood hazard Sr.No Vulnerabilit y Class Number of Villages Likely to be Affected Total Population Likely To be Affected Per cent of Total Population Total SC Population Likely to be Affected SC Population Per cent of Total Population 1 Low 16 38186 24.96 9392 6.14 2 Moderate 43 69239 45.26 17029 11.13 3 High 45 45567 29.78 13192 8.62 Total 104 152992 100 39613 25.89 Table 11 reveals that about 25 per cent population residing in 16 villages vulnerable to low, 45 Per cent population belongs to 43 villages vulnerable to moderate and about 30 per cent population part of 45 villages are vulnerable to high flood hazard risk.it is also revealed that about 26 percent population of the total belong to scheduled cast likely to be affected by low, moderate and high flood hazard in the study area. 5.3 Conclusion The Ghaggar River in study area shows extreme variability in terms of flood magnitude and frequency both spatially as well as temporally. Rainfall discharge data of one station, RD 140000 near Tatiana village, Guhla block, show that the river is extremely prone to flooding due to merging of various main tributaries in the study area and here river channel capacity is 52
not enough to accommodate surplus flood water of other tributaries, resultant immediate surrounding settlements flooded even sometime with normal rainfall condition. This study presents the methods and techniques to assess and mapping the vulnerable area of the flood hazard. This study has presented that hydrological data is not enough for the assessment of flood hazard. Assessing flood hazard is a multi-dimensional problem. Hydrological data can be meaningfully integrated with socioeconomic data to create a flood hazard database. Such a database, when related to a map adds an additional dimension to its functionality. This study has also shown how flood hazard related information can be extracted from satellite imageries and synthesized with census data at village level to identify the land use that are exposed to different degree of flood risk. Further integration of pre and post flood satellite imageries and local knowledge into mapping process can make local resident responsive and supportive. Thus, GIS mapping provide improved ways of presenting vulnerability and hazard risk that can be applied at local levels. The flood vulnerability analysis and mapping helps to planner, insurers and emergency services.it is a valuable tool for assessing flood risk and preparedness to mitigate the impact of flood. The study fully appraised the role of Geoinformatics in decision making process using GIS based flood hazard zoning maps. Acknowledgement Authors are sincerely acknowledging the hydrological data provided by the Irrigation & Canal Department Kaithal, Haryana and National Disaster Management Division Ministry of Home Affairs for this study. Authors also thanks for sporadically technical and conceptual suggestions provided by Dr. H.S. Mangat, Former Professor, Department of Geography, Punjabi University Patiala, Punjab and the Faculty, Department of Geography, Kurukshetra University Kurukshetra, Haryana. 6. References 1. Heywood. I, Oliver. J, and Tomlinson. S., (1993), Building an exploratory multi criteria modeling environment for spatial decision support, International journal of geographical information science, 7(4), pp 315-329. 2. India floods (1993), DHA-Geneva information report, no. 223. 3. IPCC, (2007), Climate change, synthesis report An assessment of the intergovernmental panel on climate change, IPCC secretariat, World meteorological organization, Geneva, Switzerland. 4. Malczewski, J., (1999), GIS and multiple-criteria decision analysis, New York, John Wiley & Sons. 5. NDMA (2011), Flood situation report 2011, National disaster management division ministry of home affairs, www.ndmindia.nic.in. 6. Onni S. Selaman, Salim Said and F.J. Putuhena., (2007), Flood frequency analysis for Sarawak using Weibull, Gringorten and L-moments formula, Journal - The institution of engineers, Malaysia, 68 (1), pp 1-9. 53
7. Parray, Khursheed Ahmad., (2006), Ground water studies with special reference to Palaeochannels in Sangrur and adjoining areas, Punjab state India, Ph.D Thesis. 8. Sinha, R., G.V. Bapalu, G.V., Singh, L.K., and Rath, B., (2008), Flood risk analysis in the Kosi River Basin, North Bihar using multi-parametric approach of AHP, Indian Journal of remote sensing, 36, pp 293-307. 54