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Flood risk zone mapping of Dikrong sub basin in Assam
P. Sarma
A.E.E, Brahmaputra Board, Assam, India
P. Sarma
A.E.E, Brahmaputra Board, Assam, India
Geographic information system (GIS) provides a broad range of tools for determining area affected by floods and for forecasting areas that are likely to be flooded due to high water level in a river. When spatial data are used in an information system, one tends to speak of a spatial information system. Spatial data has a physical dimension and geographic location.
Spatial data stored in the digital data base of the GIS, such as a digital elevation model (DEM), can be used to predict the future flood events. The GIS data base may also contain agriculture, socio-economic, communication, population and infrastructural data. This can be used, in conjunction with the flooding data to adopt an evacuation strategy, rehabilitation planning and damage assessment in case of a critical flood situation.
Geographic information system have three important components - Computer hardware, set of application modules and a proper organisational network. These three components need to be in balance if the system is to function satisfactorily.
The advantage of using GIS are :
- Fast accessibility
- Data manipulation without disturbing original data.
- Quick retrieval of information
- Sharing/using of data by many users.
- Security and safety of data.
- Easy updating of information.
- Good and accurate representation of result.
General Description of the Area :
Dikrong river is one of the important north bank tributaries of Brahmaputra river in Assam. The catchment area of Dikrong is 1528 sq.km. which covers a part of the lower hills of Arunachal Pradesh and a part of Lakhimpur district of Assam between latitude 26o55’ N and 27o20’ N and longitude 93o15’ E and 94o00’ E. The river originates at an elevation of about 2580 metre near the border of Lower Subansiri district and East Kameng district of Arunachal Pradesh. Out of the total catchment area of 1528 sq.km., 270 sq.km. lies in Assam and remaining 1258 sq.km. lies in Lower Subansiri district of Arunachal Pradesh.
Flood in this sub basin, particularly in the plain areas which lies in Assam, is a regular annual phenomenon which causes inundation to vast areas on both banks and as a result cultivable lands and human dwellings of these areas are adversely affected. Since flood water from the river Dikrong generally inundates flood plains in the Lakhimpur district of Assam, we have selected this part of the basin in Assam as our study area which is of 270 sq.km. This portion of Dikrong basin lies between latitude 26o55 N and 27o11 N and longitude 93o48 and 94o00 E.
Population:
Population of this basin area in Assam as per 1991 census report was 83,172 nos with a population density 308 nos. per sq.km. People of this region depends mainly on agriculture. The cultivation is subjected to frequent floods which adversely affects economic condition of the people in this region.
Climate:
This area of the basin experiences a hot moist summer with cool dry winter. The cold season starts from mid November and ends in the beginning of March followed by pre monsoon rains. The monsoon set in early June and continues upto the end of September. The maximum temperature recorded was 36.4o C in 1990 and the average monthly temperature varies from 17.9o C to 29.7o C. The average annual rainfall recorded at Sisathar (1982 - 1994) was 2588 mm and the relative humidity varies from 69 - 90 %.
Land Use:
Land use pattern of this area of the sub basin has been classified into six categories - such as agricultural land, forest land, grass land, built up area (village etc.), tea gardens and water bodies. Agricultural and forest land occupy most of the areas in the sub basin.
Objectives
The main objective is to use GIS as tools for assessment of flood risk zones in the study area at different flood levels calculated by frequency analysis methods.
The specific objectives area as below :
- To create a digital elevation model (DEM) of the study area.
- To find out flood levels (Gauge) for different return periods by frequency analysis of available gauge data.
- To create area inundation map of the study area with levels computed from frequency analysis and using the DEM, in case of embankment failure.
- To calculate the area that will be flooded by a particular return period flood.
- To assess flood damage.
Data Used :
- Survey of India toposheet on 1:50,000 scale 83 E/16, 83 F/13, 83 I/4 & 83 J/I.
- IRS 1B data (LISS II) of 14 Feb. 1995.
- Landuse map of Lakhimpur district, Assam prepared by Assam Remote Sensing Application Centre.
- Hydrologic data from Brahmaputra Board, Ministry of Water Resources, Govt. of India.
- Literature and Maps on various themes of the area from Brahmaputra Board.
- ILWIS (Integrated Land and Water Information System) for digitization, calculation and all analysis with spatial and attribute data.
- MS Excel for flood frequency analysis.
Frequency Analysis :
The flood frequency analysis is one of the important studies of river hydrology. It is essential to interpret the past record of flood events in order to evaluate future possibilities of such occurrences. The estimation of the frequencies of flood is essential for the quantitative assessment of the flood problem. The knowledge of magnitude and probable frequency of such recurrence is also required for proper design and location of hydraulic structures and for other allied studies. The gauge data which are random variable, follow the law of statistical distribution. After a detailed study of the distribution of the random variables and its parameters such as standard deviation, skewness etc. and applying probability theory, one can reasonably predict the probability of occurrence of any major flood events in terms of discharge or water level for a specified return period.
Flood frequency analysis are done in our study by selecting annual maximum gauge levels at Sisapathar gauge site located in the study area. Two methods of statistical distribution i.e. Gumbel’s extreme value distribution and Log Pearson type III distribution are attempted by selecting peak gauge level data for 21 years (1974-1994) at Sisapathar.
Gumbel’s Method :
This extreme value distribution was introduced by Gumbel (1941) and is commonly known as Gumbel’s distribution. It is one of the most widely used probability analysis for extreme values in hydrologic and meteorological studies for prediction of flood, rainfall etc.
Gumbel defined a flood as the largest of the 365 daily flows and the annul series of flood flows constitute a series of largest values of flows. In our attempt to find out water levels at different return period, we have used the Gumbel’s equation
xT = Value of variate with a return period ‘T’
x = Mean of the variate
SDV = Standard deviation of the sample
k = Frequency factor expressed as
yT=Reduced variate expressed by
T =Return period
Yn =Reduced mean from table
Sn =Reduced standard deviation from table.
Log Pearson Type - III Method :
This method is extensively used in USA for project sponsored by US Government. In this method, the variate is first transformed into logarithmic form (base10) and the transformed data is then analysed. If ‘X’ is the variate of a random hydrologic series, then the series of ‘Z’ variates where z = log x are first obtained. For this ‘z’ series, for any recurrence interval "T’, the equation is
Kz = Frequency factor taken from table with values of coefficient of skew "Cs" and recurrence interval ‘T’.
SDV = Standard deviation of the ‘Z’ variate sample.
Cs = Co-efficient of skew of variate ‘Z’
za = Mean of the ‘z’ values
N = Sample size = Number of years of record
After finding ‘zT’ with the equation above, the corresponding value of ‘xT’ is obtained by
xT=Antilog (zT)
After obtaining gauge levels by above two method for different return period flood, Chi Square test is carried out for "goodness of fit".
Preparation of DEM and flooded area maps:
The preparation of DEM started with digitizing the existing contour lines from topographic maps. The vector map with the contour lines is converted to raster format and then interpolated. To create the flooded area map in event of embankment failure at Sisapathar site, an outlet point map is created at Sisapathar and different levels calculated by flood frequency method are converted to the outlet map. Then all neighbourhood pixels which has an elevation lower or equal to the specified flood levels are marked by interative process through "Mapcalculator" in ILWIS 1.4 software. If the embankment fails at Sisapathar location, inundation will occur in the areas that have an elevation lower than the gauge level and that are connected with the location of the embankment failure. The process is applied for 10, 25, 50 and 100 year return period floods and area demarcated. These floods maps are then crossesed with land use map and area affected of each landuse pattern is also calculated. Attempts have also been made to assess the inhabitants affected.
Result and Discussion
By using Gumbel’s Method, gauge levels obtained for 10, 25, 50 and 100 year return period flood are 88.806 M, 89.58 M, 90.176 M and 90.740 M respectively. The same gauge data are then analysed by Log Pearson Type III Method and gauge levels obtained for 10, 25, 50 and 100 year return period flood are 88.523, 88.943, 89.215 and 89.460 respectively.
The Chi square test comparing computed values with observed values is carried out to find the best fit method and Gumbel’s method is found to be the best fit. All these results are shown in table No1, 2 and 3.
The analysis undertaken through interactive process to mark the neighbourhood area which has elevation lower or equal to a particular gauge level calculated by Gumbel’s method shows the probable extent of flood water in case of failure of embankment on both sides at Sisapathar. The analysis for various return period flood leads us to the following conclusions.
- For a 10 year return period flood which gauge level is 88.806 M (&) the total area inundated by flood will be of 155.95 sq.km. The details of area affected in each landuse class is given in the table No 4. The population that will be affected by a 10 year return period flood is assessed to be 48033 nos. on basis of population density figure.
- For a 25 year return period flood with a gauge level of 89.58 metre, the total area affected will be 160.49 sq.km. The area affected in each landuse class is shown in the table No. 4. Population affected will be 49431 nos.
- For a 50 year return period flood, the area inundated will be of 161.40 sq.km. Area affected in each category of landuse class is shown in the table No. 4. The population that will be affected is in the order of 49712 nos.
- For a 100 year return period flood with a gauge level of 90.74 metres, the area flooded will be of 162.94 sq.km. Area affected in each category of landuse class is shown in the table No. 4. The nos. of population affected will be 50186 nos.
The river Dikrong is embanked along the left bank from Harmoti to Dahgharia and in the right bank, two portions are embanked near Bihpuria and Madhupur village. These embankments have rendered reasonable protection to the flood affected areas. However, the effectiveness is decreasing due to the rising of the river bed gradually. This is due to the fact that the river carries heavy silt annually. Cuts and breaches in the embankments occur due to overtopping, seepage and erosion. The materials used for construction of the embankments are mostly sand and silt which are susceptible to erosion. Due to lack of proper maintenance of existing inadequate section of embankment and meandering nature of the river, the erosion problem leading to breach of embankments is increasing year after year.
Although at Sisapathar both banks of Dikrong is embanked, considering the frequent phenomenon of breach in the embankments, this study has been undertaken at Sisapathar gauge discharge sites where hydrologic data for a long period is available. Moreover, river Dikrong is not embanked throughout its entire length on its right bank, for which flood water will definitely inundate a huge portion of land. The extend of flood can be calculated in any outlet point if gauge discharge data are available at that point.
Overtopping of embankment are also frequent in this region, but details could not be studied for lack of elevation data of embankments. But in general, it can be said that the embankments should be raised and strengthened for a 25 year return period flood level.
The basic idea of flood risk mapping as undertaken in this study is to regulate land use by flood plain zoning in order to restrict the damages. The Rashtriya Brah Ayog in their report of 1980 has recommended that flood plain management measures should be under-taken and suitable legislation enacted whenever necessary. In the light of above discussion, it can be said that flood risk mapping, being an important non-structural flood management technique, will go long way in reducing flood damages in areas frequented by flood.
References:
- Introduction to the use of Geographic Information System for Practical Hydrology I.T.C. Publication No. 23, Netherland / Flood study in the Mahgna-Dhonagoda Polder, Bangladesh, Allard M.J. Meijerink, Hans A.K. de Brouwer, Chris M. Mannaerts, Carles R. Valenzuela.
- K. Subramanya, Engineering Hydrology
- ILWIS user’s Manual Version 1.41, International Institute of Aerospace Survey and Earth Sciences (ITC), The Netherlands.
- ILWIS user’s Manual Version 2.00, ITC, Netherlands.
- Master Plan of Dikrong sub basin, 1996, Brahmaputra Board.
| YEAR | PEAK(M) | RANK | Rearg flood level | X-Xav | (X-Xav)*2 | REC.INT |
| 1974 | 89.44 | 1 | 89.440 | 2.049 | 4.198401 | 22.000 |
| 1975 | 84.54 | 2 | 88.490 | 1.099 | 1.207801 | 11.000 |
| 1976 | 86.74 | 3 | 88.200 | 0.809 | 0.654481 | 7.333 |
| 1977 | 87.24 | 4 | 87.840 | 0.449 | 0.201601 | 5.500 |
| 1978 | 87.24 | 5 | 87.640 | 0.249 | 0.062001 | 4.400 |
| 1979 | 87.64 | 6 | 87.550 | 0.159 | 0.025281 | 3.667 |
| 1980 | 87.21 | 7 | 87.510 | 0.119 | 0.014161 | 3.143 |
| 1981 | 87.84 | 8 | 87.490 | 0.099 | 0.009801 | 2.750 |
| 1982 | 87.37 | 9 | 87.470 | 0.079 | 0.006241 | 2.444 |
| 1983 | 87.42 | 10 | 87.420 | 0.029 | 0.000841 | 2.200 |
| 1984 | 87.51 | 11 | 87.370 | -0.021 | 0.000441 | 2.000 |
| 1985 | 87.1 | 12 | 87.370 | -0.021 | 0.000441 | 1.833 |
| 1986 | 86.9 | 13 | 87.240 | -0.151 | 0.022801 | 1.692 |
| 1987 | 87.24 | 14 | 87.240 | -0.151 | 0.022801 | 1.571 |
| 1988 | 87.49 | 15 | 87.240 | -0.151 | 0.022801 | 1.467 |
| 1989 | 88.2 | 16 | 87.220 | -0.171 | 0.029241 | 1.375 |
| 1990 | 88.49 | 17 | 87.210 | -0.181 | 0.032761 | 1.294 |
| 1991 | 87.22 | 18 | 87.100 | -0.291 | 0.084681 | 1.222 |
| 1992 | 87.55 | 19 | 86.900 | -0.491 | 0.241081 | 1.158 |
| 1993 | 87.47 | 20 | 86.740 | -0.651 | 0.423801 | 1.100 |
| 1994 | 87.37 | 21 | 84.540 | -2.851 | 8.128201 | 1.048 |
| SUM | 1835.220 | 15.389661 | ||||
| SD | 0.877 | |||||
| AVE | 87.391 |
Flood Calculations using Gumbel's Distribution
| REC.INT | T/(T-1) | Yt=-LNLN(T/T-1) | Yn | Sn | K | GAUGE |
| 2 | 2.000 | 0.3665 | 0.5252 | 1.0696 | -0.1483639 | 87.2609 |
| 5 | 1.250 | 1.4999 | 0.91127524 | 88.1902 | ||
| 10 | 1.111 | 2.2513 | 1.61378085 | 88.8063 | ||
| 20 | 1.053 | 2.9633 | 2.27945026 | 89.3901 | ||
| 25 | 1.042 | 3.1907 | 2.4920531 | 89.5765 | ||
| 30 | 1.034 | 3.3980 | 2.68586387 | 89.7465 | ||
| 40 | 1.026 | 3.6625 | 2.93315258 | 89.9634 | ||
| 50 | 1.020 | 3.9219 | 3.17567315 | 90.1761 | ||
| 100 | 1.010 | 4.6100 | 3.81899776 | 90.7403 |
Table-2 Frequency Analysis by Log Pearson Type-III Method
| YEAR | PEAK(M) | REARG PEAK | RANK | Z=LOGX | Z-Zavg | (Z-Zavg)*3 | SKEW COEF(Cs) |
| 1974 | 89.44 | 89.44 | 1 | 1.951531 | 0.0101318 | 1.04E-06 | 1.935E-12 |
| 1975 | 84.54 | 88.49 | 2 | 1.946894 | 0.0054942 | 1.66E-07 | 3.085E-13 |
| 1976 | 86.74 | 88.20 | 3 | 1.945468 | 0.0040686 | 6.73E-08 | 1.253E-13 |
| 1977 | 87.24 | 87.84 | 4 | 1.943692 | 0.0022923 | 1.2E-08 | 2.240E-14 |
| 1978 | 87.24 | 87.64 | 5 | 1.942702 | 0.0013024 | 2.21E-09 | 4.109E-15 |
| 1979 | 87.64 | 87.55 | 6 | 1.942256 | 0.0008562 | 6.28E-10 | 1.167E-15 |
| 1980 | 87.21 | 87.51 | 7 | 1.942057 | 0.0006577 | 2.84E-10 | 5.291E-16 |
| 1981 | 87.84 | 87.49 | 8 | 1.941958 | 0.0005584 | 1.74E-10 | 3.239E-16 |
| 1982 | 87.37 | 87.47 | 9 | 1.941859 | 0.0004591 | 9.68E-11 | 1.800E-16 |
| 1983 | 87.42 | 87.42 | 10 | 1.941610 | 0.0002108 | 9.37E-12 | 1.742E-17 |
| 1984 | 87.51 | 87.37 | 11 | 1.941362 | -3.766E-05 | -5.3E-14 | -9.938E-20 |
| 1985 | 87.10 | 87.37 | 12 | 1.941362 | -3.766E-05 | -5.3E-14 | -9.938E-20 |
| 1986 | 86.90 | 87.24 | 13 | 1.940715 | -0.0006843 | -3.2E-10 | -5.961E-16 |
| 1987 | 87.24 | 87.24 | 14 | 1.940715 | -0.0006843 | -3.2E-10 | -5.961E-16 |
| 1988 | 87.49 | 87.24 | 15 | 1.940715 | -0.0006843 | -3.2E-10 | -5.961E-16 |
| 1989 | 88.20 | 87.22 | 16 | 1.940616 | -0.0007839 | -4.8E-10 | -8.960E-16 |
| 1990 | 88.49 | 87.21 | 17 | 1.940566 | -0.0008337 | -5.8E-10 | -1.078E-15 |
| 1991 | 87.22 | 87.10 | 18 | 1.940018 | -0.0013818 | -2.6E-09 | -4.908E-15 |
| 1992 | 87.55 | 86.90 | 19 | 1.939019 | -0.0023802 | -1.3E-08 | -2.508E-14 |
| 1993 | 87.47 | 86.74 | 20 | 1.938219 | -0.0031806 | -3.2E-08 | -5.985E-14 |
| 1994 | 87.37 | 84.54 | 21 | 1.927062 | -0.0143378 | -2.9E-06 | -5.482E-12 |
| AVG= | 1.941447 | SUM= | -1.7E-06 | ||||
| STDV | 0.0043818 |
Flood Calculated using Log Person Type-III
| RT | Cs | Kz | Zt=Zav+Ks*STV | Xt=AntlogZt |
| 2 | 0 | 0 | 1.941440 | 87.3856 |
| 10 | 0 | 1.282 | 1.947058 | 88.523 |
| 25 | 0 | 1.751 | 1.949113 | 88.943 |
| 50 | 0 | 2.054 | 1.950441 | 89.2156 |
| 100 | 0 | 2.326 | 1.951633 | 89.4608 |
Table 3 CHI Square Test for Gumbel's Method
| Rank | Rt | Flood(o) | t/(t-1) | Yt | Yn | Sn | K | Flood(c) | Diff | Diffsqr | Diffsqr/f(c) |
| 1.000 | 22.000 | 89.440 | 1.048 | 3.060 | 0.525 | 1.0696 | 2.370 | 89.4695 | -0.030 | 0.00087 | 9.75E-06 |
| 2.000 | 11.000 | 88.490 | 1.100 | 2.351 | 1.707 | 88.8879 | -0.398 | 0.15830 | 0.001781 | ||
| 3.000 | 7.333 | 88.200 | 1.158 | 1.919 | 1.303 | 88.534 | -0.334 | 0.11155 | 0.00126 | ||
| 4.000 | 5.500 | 87.840 | 1.222 | 1.606 | 1.011 | 88.2773 | -0.437 | 0.19127 | 0.002167 | ||
| 5.000 | 4.400 | 87.640 | 1.294 | 1.355 | 0.776 | 88.0715 | -0.432 | 0.18623 | 0.002115 | ||
| 6.000 | 3.667 | 87.550 | 1.375 | 1.144 | 0.579 | 87.8985 | -0.349 | 0.12148 | 0.001382 | ||
| CHI.SQUAR | 0.008714 |
CHI Square Test for Log-Person Type-III Method
| Rank | Rt | Flood(o) | Cs | Kz | Zt | Antilog Zt | Difference Diff. sqr Diffsqr/fl(c) | ||
| 1.000 | 22.000 | 89.440 | 0 | 1.66325417 | 1.94869 | 88.856 | 0.584 | 0.34106 | 0.00384 |
| 2.000 | 11.000 | 88.490 | 0 | 1.348634664 | 1.94731 | 88.574 | -0.084 | 0.00706 | 8E-05 |
| 3.000 | 7.333 | 88.200 | 0 | 1.164573419 | 1.94650 | 88.409 | -0.209 | 0.04368 | 0.00049 |
| 4.000 | 5.500 | 87.840 | 0 | 1.034015159 | 1.94593 | 88.293 | -0.453 | 0.20521 | 0.00232 |
| 5.000 | 4.400 | 87.640 | 0 | 0.932730301 | 1.94549 | 88.202 | -0.562 | 0.31584 | 0.00358 |
| 6.000 | 3.667 | 87.550 | 0 | 0.850015808 | 1.94512 | 88.13 | -0.580 | 0.3364 | 0.00382 |
| 0.01413 | |||||||||
Table 4 Area Inundation at Different Return Period Flood
| Sl. | Return period | Gauge level(m) | Area affected (sqkm) |
| 1 | 10 | 88.806 | 155.95 |
| 2 | 25 | 89.576 | 160.49 |
| 3 | 50 | 90.176 | 161.4 |
| 4 | 100 | 90.74 | 162.94 |
Inundated Landuse at Different Return Period Flood(Sqkm)
| ID Code | Landuse | Total Area | Area Inundated at Different Return Period Flood | |||
| By 10yr RPF | By 25yr RPF | By 50yr RPF | By 100yr RPF | |||
| 1 | Agriculture | 199.45 | 120.697 | 125.013 | 125.91 | 127.412 |
| 2 | Forest | 20.44 | 0 | 0 | 0 | 0 |
| 3 | Grass land | 8.899 | 8.899 | 8.899 | 8.899 | 8.899 |
| 4 | River | 12.24 | 8.139 | 8.164 | 8.164 | 8.206 |
| 5 | Tea garden | 4.37 | 0.133 | 0.133 | 0.133 | 0.133 |
| 6 | Village | 24.06 | 17.795 | 17.996 | 18.003 | 18.004 |
| 7 | Water body | 0.44 | 0.281 | 0.281 | 0.281 | 0.281 |
| TOTAL | 269.899 | 155.944 | 160.486 | 161.39 | 162.935 | |
Table 5 Gauge and Discharge Data considered for Rating Curve at Sisapathar
| Serial No | Discharge(Cumec) | Gauge(m) |
| 1 | 2501.15 | 89.44 |
| 2 | 431.33 | 86.74 |
| 3 | 323.56 | 87.24 |
| 4 | 392.28 | 87.21 |
| 5 | 564.3 | 87.37 |
| 6 | 1184.5 | 87.42 |
| 7 | 1159.9 | 87.51 |
| 8 | 300.36 | 87.49 |
| 9 | 525.7 | 88.2 |
| 10 | 695.97 | 88.49 |
| 11 | 735.96 | 87.55 |
| 12 | 832.48 | 87.47 |
| 13 | 639.82 | 87.37 |
Figure 1
Figure 2
