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ScienceDirect Aquatic Procedia 4 (2015) 1322 – 1330

INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE 2015)

Prioritization of Sub Watershed Based on Morphometric Characteristics Using Fuzzy Analytical Hierarchy Process and Geographical Information System – A Study of Kallar Watershed, Tamil Nadu S. Abdul Rahamana* S. Abdul Ajeezb S.Aruchamyc R.Jegankumard a

Department of Geography, School of Geosciences, Bharathidasan University, Tiruchirappalli, 620024, India b Research and Development, Lumina Datamatics, Chennai, 600034, India c Department of Geography, School of Geosciences, Bharathidasan University, Tiruchirappalli, 620024, India d Department of Geography, School of Geosciences, Bharathidasan University, Tiruchirappalli, 620024, India

Abstract Watershed prioritization has gained importance in natural resources management, especially in the context of watershed management. Delineation of watersheds within a large drainage basin and their prioritization is required for proper planning and management of natural resources for sustainable development. Delineation of potential zones for implementation of conservation measures above the entire watershed at similar occurrence is inaccessible as well as uneconomical; therefore it is a prerequisite to apply viable technique for prioritization of sub watersheds (SW). The present research attempted to study various morphological characteristics and to implement Geographical Information System (GIS) and Multi Criteria Decision Making (MCDM) through Fuzzy Analytical Hierarchy Process (FAHP) techniques for identification of critical sub watersheds situated in transaction zone between mountainous and water scarcity region of kallar watershed, Tamil Nadu. The morphometric characterization was obtained through the measurement of three distinct linear, areal and relief aspects over the eleven sub-watersheds. The morphometric characterization showed vital role in distinguishing the topographical and hydrological behavior of the watershed. Each morphometric parameters were ranked with respect to the value and weightings obtained by deriving the relationships between the morphometric parameters obtained through classification of the SW by associating the strength of Fuzzy Analytical Hierarchy Processes (FAHP). Based on FAHP approach, sub watersheds were evaluated and divided into five prioritization zones: very less, less, medium, high and very high classes. The FAHP techniques is a practical approach for identification of the

* Corresponding author. Tel.: 09787914144; fax: +0-000-000-0000 E-mail address: [email protected]

2214-241X © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of ICWRCOE 2015 doi:10.1016/j.aqpro.2015.02.172

S. Abdul Rahaman et al. / Aquatic Procedia 4 (2015) 1322 – 1330

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sensitive priority zones and is useful for better management practices such as implementation of land and water resource management, conservation and sustainable agricultural development. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

© 2015 The Authors. Published by Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of ICWRCOE Peer-review under responsibility of organizing committee of ICWRCOE 2015 2015.

Keywords: Prioritization, Morphometric, Fuzzy Analytical Hierarchy Process, Geographic Information System

1. Introduction A drainage basin or watershed is an extent or an area of land where surface water from rain, melting snow, or ice converges to a single point at a lower elevation, usually the exit of the basin, where the waters join another water body, such as a river, lake, reservoir, estuary, wetland, sea, or ocean. The water shed plays a dominant role in the development of landforms and therefore, the study of drainage basin has a great significance in geomorphic studies. A watershed is an ideal unit for management of Natural resources like land and water and for mitigation of the impact of natural disasters for achieving sustainable development. The watershed management concept recognizes the interrelationships among the linkages between uplands, low lands, land use, geomorphology, slope and soil. Morphometry is the measurement and mathematical analysis of the configuration of the earth's surface, shape and dimension of its landforms (Agarwal, 1998; Obi Reddy et al., 2002). A major emphasis in geomorphology over the past several decades has been on the development of quantitative physiographic methods to describe the evolution and behaviour of surface drainage networks (Horton, 1945; Leopold & Maddock, 1953). Morphometric analysis of a watershed provides a quantitative description of the drainage system, which is an important aspect of the characterization of watersheds (Strahler, 1964). Pioneering work on the drainage basin morphometry has been carried out by Horton (1932, 1945), Miller (1953), Smith (1950), Strahler (1964) and others. In India, some of the recent studies on morphometric analysis using remote sensing technique were carried out by Natuiay (1994), Srivastava (1997), Nag (1998) and Srinivasa et al (2004). In the earlier researches, prioritization of watershed was accomplished through different approaches to instance soil erosion or sediment yield indexing (SYI), morphological characterization, socio-economic aspects, etc. some other studies focused on soil erosion and SYI modelling aspects by classifying the erosion affected priority areas (Suresh et al., 2004; Ratnam et al., 2005; Kalin and Hantush, 2009; Pandey et al., 2009; Niraula et al., 2011; Pai et al., 2011). Land deterioration as well as land use change impacts were also measured for evaluation of prospective zones of watersheds (Adinarayana, 2003; Deb and Talukdar, 2010; Kanth and Hassan, 2010; Javed et al., 2011; Sarma and Saikia, 2011). The remote sensing and GIS technique is a convenient method for morphometric analysis as the satellite images provides a synoptic view of a large area and is very useful in the analysis of drainage basin morphometry. GIS and remote sensing (RS) techniques are proved to be proficient tools for morphometric characterization of subwatersheds (Singh, 1994; Grohmann, 2004; Sreedevi et al., 2009; Aher et al., 2010; Rao et al., 2011). Mishra et al. (2007) carried out prioritization of sub-watersheds through morphological characteristics by using Soil and Water Assessment Tool (SWAT) model in the small multi-vegetated watershed of a sub-humid subtropical region in India. In recent research on prioritization of watersheds using multi-criteria evaluation through fuzzy analytical hierarchy process (Aher, P. D., 2013). Assessment of different vulnerability producing factors is the decision making process associated with formation of system knowledge database which involves multiple criteria and alternatives, which results in great degree of complexity. Therefore, in this research an attempt has been made for prioritization of sub-watersheds through analysis of the natural drainage system that implements a novel approach by investigating the fuzzy analytical hierarchy process (FAHP) to circumvent the complex information associated with various morphological characteristics by accomplishing better accuracy in identification and prioritization of sub-watersheds with earlier approaches.

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2. Study Area and Data products The Kallar watershed situated in Eastern slope of Western Ghats stretching from West to the East. It is located between 11˚17’0” to 11˚31’0” N Latitude and 76˚ 39’ 0” to 77˚ 8’ 45” E Longitude and covers an area of 1281.24 Sq.Km. It comprises of three districts The Nilgiris, Coimbatore and Erode, covering 6 taluks (Coonoor, Kothagiri, Udhagamandalam, Mettupalayam, Coimbatore north, and Sathyamangalam); it consists of 89 Revenue villages (Fig. 1. (a). The maximum and minimum elevation encountered in the watershed about 177m and 2615m above MSL (Fig. 1. (b). About 50% of areas are mountains covered with diverse plant communities that form various types of forest and other agricultural activities, especially Tea, coffee plantation, vegetables and orchards, which are normally cultivated in the upper and the lower area. The climate of this area is temperate and salubrious for more than half of the year. The average day temperature of the sub watershed is 16.15° C and the average rainfall is about 901.65mm. The winter is relatively cool. The maximum rainfall is received during the month of October and November. The Kallar streams flow from Southwest to Northeast and it connects the Bhavani River, which finally joins with Bhavanisagar Dam, it was built in the Northeastern part of the watershed. Which is primarily serves as source of irrigation. The area represent by clay soil, loam soil and rock out creep on steep to narrow sloping landform. Geologically, the area represented by charnockite and fissile hornblende-biotite gneiss. The present study area falls under the Survey of India toposheets (1:50,000 scale) 58 A/11, 15, 16 and 58E/3 & 4. The watershed and sub watershed boundary has been demarcated using this drainage network.

Fig. 1. (a) Study area map, (b) Elevation Map

3. Methodology 3.1. Morphometric Analysis Watershed is a natural hydrological unit, topographically delineated area drained by a stream system, from which runoff resulting from precipitation flow past from a point into single stream. The drainage network was derived from Survey of India toposheet, and the sub watershed boundaries were demarcated on the basis of contour and drainage flow direction. The Kallar watershed was divided into ten sub watersheds named as SW1 to SW10 (Fig. 2. (a) and (b)). The smallest (SW9) and the largest (SW1) sub watersheds measures 40.9 and 210 Sq.km, respectively. The morphometric parameters broadly classified into three aspects, viz... linear, areal, and relief, the sub parameters such as stream order, number of streams, stream length, bifurcation ratio, drainage density, stream frequency, drainage texture, relief ratio, basin shape, form factor, circularity ratio, elongation ratio, and length of overland flow. The naming of stream order is the first step in morphometric analysis of drainage basin, based on the hierarchic of stream proposed by straheler (1964). In the study area SW9 and SW10 are of forth order, SW4, SW5, SW6, and SW8 are of fifth order, SW1, SW2, and SW3 are of sixth order, whereas , SW7th have seventh order. The entire Kallar watershed is falls under seventh order. The morphometric characterization in the form of linear, areal and relif aspects for the delineated sub watershed was calculated based on given formula (Table 1 & 2). Table 1. Formula for computation of morphometric parameters.

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Sl. No. 1. 2.

Morphometric Parameters Linear aspect: Stream order Stream Length (Lu)

3.

Mean Stream Length (Lsm)

4.

Stream length Ratio(Rl)

5.

Bifurcation Ratio (Rb)

Formula/ Definition/Methods

Reference

Hierarchical order Length of the Stream (km) Lsm = Lu / Nu, Where, Lu=Total Stream length of order ‘U’, Nu= Stream length of next higher stream order. Rl=Lu/Lu-1; where, Lu= Total number of stream segment of order ‘U’, Lu-1=Stream length of next lower order. Rb = Nu / Nu+1 Where, Nu=Total Number of stream segments of order ‘U’, Nu+1= Number of segments of the next higher order.

Strahler 1964 Horton 1945 Horton 1945 Horton 1945

Schumm 1956

Arial Aspect: 6.

Drainage Density (Dd)

7.

Stream Frequency (Fs)

8.

Texture ratio (T)

9.

Form Factor (Rf)

10.

Circularity Ratio (Rc)

11.

Elongation Ratio (Re)

12. 13.

Length of Over Land Flow (Lof) Constant channel maintenance Relief Aspects:

14.

Basin Relief(Bh)

15.

Relief Ratio (Rh)

16.

Ruggedness number(Rn)

Dd =L/A Where, L=Total length of Stream, A=Area of the Watershed. Fs = N /A. Where, N= Total number of Stream, A= Area of the Watershed. T=N1/P. Where N1= Total number of first order stream, p= perimeter of watershed. Rf = A / (Lb) 2. Where, A=Area of the Watershed, Lb=Maximum Basin length. Rc = 4ðA/ P2. Where, A=Area of the Watershed, P= Perimeter of the basin, Ð = 3.14 Re= 2√ (A/ð) / Lb. Where, A=Area of the Watershed, Lb=Maximum Basin length, Ð = 3.14. Lof = 1/ 2Dd. Where, Dd = Drainage density. 1/dd. Where, Dd=Drainage Density. Vertical Distance between the lowest and highest point of water shade. Rh=Bh/Lb; where, Bh=Basin Relief, Lb= Basin length. Rn=Bh*Dd; Where, Bh=Basin Relief, Dd= Drainage Density.

Horton 1945 Horton 1945 Horton 1945 Horton 1932 Miller 1953 Schumm 1956 Horton 1945 Horton 1945 Schumm 1956 Schumm 1956 Schumm 1956

Table 2. Results of the morphometric analysis of the sub watershed Morphometric Parameters

SW1

SW2

SW3

SW4

SW5

SW6

SW7

SW8

SW9

No. of Stream Stream Length (Lu) m Bifurcation Ratio (Rb) I/ II Bifurcation Ratio (Rb) II/ III Bifurcation Ratio (Rb) III/ IV Bifurcation Ratio (Rb) IV/ V Bifurcation Ratio (Rb) V/ VI Bifurcation Ratio (Rb) VI/ VII Mean Bifurcation Ratio (Rbm) Stream length Ratio (Rl) II/ I Stream length Ratio (Rl) III/ II Stream length Ratio (Rl) IV/ III Stream length Ratio (Rl) V/ IV Stream length Ratio (Rl) VI/ V Stream length Ratio (Rl) VII/ VI Perimeter (P) (Km) Area (A) (Sq.Km) Stream Frequency (Fs) Drainage Texture (Dt) Drainage Density (Dd) Compactness Coefficient (Cc) Length of Basin (Lb) (Km) Basin shape (Bs) Length of overland flow (Lof) (Km)

1073 568 2.17 2.39 2.24 0.96 1.76 1.91 3.3 2.2 2.29 0.85 1.87 74.6 210 5.1 14.4 2.7 1.46 27.4 0.28 0.18

1103 544 2.4 1.84 1.75 1.04 14.4 4.29 3.97 1.67 1.92 1.27 5.27 78.1 172 6.41 14.1 3.16 1.69 24.4 0.29 0.16

400 221 2.04 1.85 2.75 2 2 2.13 3.11 1.67 1.81 1.59 1.94 50.4 85.1 4.7 7.94 2.59 1.55 16.4 0.32 0.19

823 423 1.85 2.34 3.23 1.24 2.16 2.7 1.9 2.68 1 61.4 122 6.73 13.4 3.46 1.58 20.1 0.3 0.14

500 314 1.78 3.25 1.26 1.52 1.95 2.51 2.93 1.13 0.9 61.9 120 4.17 8.07 2.62 1.61 19.9 0.3 0.19

355 239 1.91 2.77 1.21 3.22 -

458 309 1.83 2.35 5.18 2.59 2.49 1.76 4.29 0.42 54.2 126 3.63 8.45 2.45 1.37 20.5 0.3 0.2

500 314 1.78 3.25 1.26 1.52 -

191 102 2.04 2.78 0.86 -

2.28 1.95 3.04 1.2 2.1 49.5 112 3.18 7.17 2.14 1.33 19.1 0.31 0.23

1.59 2.51 2.93 1.13 0.9 61.9 120 4.17 8.07 2.62 1.61 19.9 0.3 0.19

1.89 2.95 1.8 0.84 32.5 40.3 4.73 5.87 2.53 1.46 10.7 0.35 0.2

SW10 657.00 414.67 1.97 2.20 3.65 23.00 7.71 2.44 1.82 2.25 0.88 94.94 194.58 3.38 6.92 2.13 1.93 26.19 0.28 0.23

Kallar Watershed 6098 3438.89 2.05 2.32 2.08 1.47 5.69 3.55 2.85 2.85 2.04 1.97 1.51 5.32 0.46 212.48 1293.15 4.72 28.70 2.66 1.68 76.80 0.22 0.19

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Constant of Channel Maintenance(C) Lemniscate’s (k) Circularity Ratio (Rc) Form Factor Ratio (Rf) Elongation Ratio (Re) Shape Factor Ratio (Rs) Min Elevation (h) (M) Max Elevation (H) (M) Total Basin Relief (R) (m ) Relief Ratio (Rr) Relative Relief (RhP) Ruggedness number (Rn) Gradient Ratio (Rg) Melton Ruggedness Number (MRn) Texture ratio (T)

S. Abdul Rahaman et al. / Aquatic Procedia 4 (2015) 1322 – 1330

0.37 3.56 0.47 0.28 0.29 3.56 293 2614 2321 84.8 31.1 6274 84.8 160 7.65

0.32 3.47 0.36 0.29 0.32 3.47 291 2242 1951 79.9 25 6159 79.9 149 7.42

0.39 3.15 0.42 0.32 0.48 3.15 302 812 510 31.2 10.1 1323 31.2 55.3 4.13

0.29 3.31 0.41 0.3 0.39 3.31 327 1578 1251 62.2 20.4 4329 62.2 113 7.05

0.38 3.3 0.39 0.3 0.4 3.3 310 1499 1189 59.8 19.2 3115 59.8 109 4.12

0.47 3.27 0.57 0.31 0.41 3.27 273 450 177 9.26 3.58 378 9.26 16.7 3.74

0.41 3.32 0.54 0.3 0.39 3.32 247 1369 1122 54.8 20.7 2747 54.8 99.9 4.52

0.38 3.3 0.39 0.3 0.4 3.3 258 2116 1858 93.4 30 4868 93.4 170 4.12

0.4 2.85 0.48 0.35 0.74 2.85 251 581 330 30.8 10.1 834 30.8 52 3.14

0.47 3.53 0.27 0.28 0.30 3.53 177 1887 1710.00 65.29 18.01 3644.19 65.29 122.59 3.83

0.38 4.56 0.36 0.22 0.10 4.56 177 2615 2438 31.75 11.47 6483.40 31.75 67.80 15.25

Fig. 2. (a) Drainage map, (b) Sub watershed

4. Prioritization of Sub Watershed using FAHP In order to prepare the prioritize watershed, various methods such as quantitative, fuzzy logic, statistic methods and AHP where used by several researches. The present study of Fuzzy Analytical Hierarchical Process (FAHP) with extent analysis method was used to prioritize the watersheds. The Analytical Hierarchy Process (AHP), which is a multiple criteria decision making tools, especially in the problems with spatial nature or GIS-based. In addition AHP, its manner to apply and its weaknesses and strengths and ultimately the fuzzy modified Analytical Hierarchy Process (FAHP) which is proposed after that the concepts like of fuzziness, uncertainty and vagueness was broadly posed in expert's decision making. Fuzzy AHP is described to obtain a crisp priority vector from a triangular fuzzy comparison matrix. In spite of popularity of AHP, this method is often criticized for its inability to adequately handle the inherent uncertainty and imprecision associated with the mapping of the decision maker’s perception to exact numbers (Deng, 1999). To apply the process depending on this hierarchy, according to the method of extent analysis, each criterion is taken and extent analysis for each criterion, gi; is performed on, respectively. Therefore, m extent analysis values for each criterion can be obtained by using following notation. M 1gi , M gi2 , M gi3 , M gi4 , M gi5 ,}}, M gin Where gi is the goal set (i = 1, 2, 3, 4, 5 … n) and all the M gij (j = 1, 2, 3, 4, 5…m) are Triangular Fuzzy Numbers (TFNs). The steps of Chang’s analysis can be given as in the following: Step 1: The fuzzy synthetic extent value (Si) with respect to the ith criterion as follows:

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ªn … M «¦ ¦ j 1 ¬i 1 m

Si

j gi

º M » ¦ j 1 ¼ m

1

j gi

(1)

This involves computation of m

¦M

j gi

(2)

j 1

Perform the “fuzzy addition operation” of m extent analysis values for a particular matrix given in (3) below, at the end step of calculation, new (l, m, u) set is obtained and used for the next: m

¦M

j gi

j 1

m

m

m

j 1

j 1

j 1

(¦ l j , ¦ m j , ¦ u j )

(3)

Where “l” is the lower limit value, “m” is the most promising value and “u” is the upper limit value and to obtain (4); this involves computation of

ª n m º j «¦¦ M gi » ¬i 1 j 1 ¼

1

(4)

Perform the “fuzzy addition operation” of M gij (j = 1, 2, 3, 4, 5…m) values given n

m

¦¦ M

j gi

i 1 j 1

m

m

m

j 1

j 1

j 1

(¦ li , ¦ mi , ¦ ui )

(5)

and then compute the inverse of the vector in (5) . (6) Is then obtained such that:

ª º j «¦¦ M gi » ¬i 1 j 1 ¼ n

m

1

º ª « 1 1 1 » « n , n , n » » « u i ¦ mi ¦ li » «¬ ¦ i 1 i 1 i 1 ¼

Step 2: The degree of possibility of M 2

V (M 2 t M1 )

(l 2 , m2 , n2 , ) t M 1

sup>min( P M 1 ( x), ( P M 2 ( y))@

(6)

(l1 , m1 , n1 , ) is defined as equation (7):

(7)

yt x

and x and y are the values on the axis of membership function of each criterion. This expression can be equivalently written as given in equation (8) below:

V (M 2 t M 1 )

­ ° 1, if m2 t m1 , ° ° ® 0, if m2 t m1, ° l1  u 2 °  ° ( m u 2)  ( m1  l1) ¯ 2

(8)

Step 3. The degree possibility for a convex fuzzy number to be greater than k convex fuzzy numbers Mi (i = 1, 2, 3, 4, 5 … k) can be defined by V ( M t M 1 , M 2 , M 3 ,}} M k ) V [( M t M 1 ) and ( M t M 2 ) and ( M t M 3 ) and }} and ( M t M k ) min V ( M t M i ), i

Assume that equation (9) is:

1,2,3,4,} k

(9)

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d ( Ai ) minV ( S i t S k ) For k = 1, 2, 3, 4, 5…n; k≠ i. Then the weight vector is given by equation (10):

W1

(10)

(d ( A1 ), d ( A2 ), d ( A3 ), d ( A4 ), d ( A5 )}d ( An ))T

Where Ai (i = 1, 2, 3, 4, 5, 6… n) are n elements. Step 4. Via normalization, the normalized weight vectors are given in (11):

W

(11)

(d ( A1 ), d ( A2 ), d ( A3 ), d ( A4 ), d ( A5 )}d ( An ))T

Where W is non-fuzzy numbers. 5. Result and Discussion The morphometric parameters i.e., bifurcation ratio (Rb), drainage density (Dd), stream frequency (Fs), drainage texture (Dt), circularity ratio (Rc), relief ratio (Rr), ruggedness number (Rn) basin relief (Rh) and basin shape (Bs) are also termed as erosion risk assessment parameters and have been used for prioritizing watersheds. The linear parameters such as drainage density, stream frequency, bifurcation ratio, drainage texture, have a direct relationship with erodibility, higher the value, more is the erodibility. Hence the prioritization of sub watershed, the highest value was rated as score or rank based on Triangular Fuzzy Numbers (TFN) as very strong importance (VSI 2,5/2,3), second highest value was rated as strongly importance (SI – 3/2,2,5/2), and so on, the least value was rated as weakly more importance (WMI-1,3/2,2), and both parameters meet an same influence was rated as just equal (JE – 1,1,1). Shape parameter such as circularity ratio, basin shape and compactness coefficient have an inverse relationship with erodibility, lower the value of shape parameters was rated as very strong importance (VSI), nest lower value was rated as ranked strongly importance (SI – 3/2,2,5/2), the highest value was rated as weakly more importance (WMI-1,3/2,2), and both parameters meet an same influence was rated as just equal (JE – 1,1,1), sub watershed wise morphometric variables used for pair-wise comparison matrix in fuzzy analytical hierarchy process to generate the morphometric criterion weight. Same way each morphometric parameters compare with every sub watershed (Table.3 & 4). Table 3. Morphometric properties matrix of Kallar watershed Mean bifurcation Ratio(Br)

Stream Frequency (Fs)

Drainage Texture (Dt)

Drainage Density (Dd)

Circularity Ratio (Rc)

Relief Ratio (Rr)

Ruggedness number (Rn)

Basin Relief (R) m

Basin Shape (Bs)

SW1

1.91

5.10

14.38

2.70

0.47

0.08

6.27

2321.00

0.28

SW2

4.29

6.41

14.13

3.16

0.36

0.08

6.16

1951.00

0.29

SW3

2.13

4.70

7.94

2.59

0.42

0.03

1.32

510.00

0.32

SW4

2.16

6.73

13.40

3.46

0.41

0.06

4.33

1251.00

0.30

SW5

1.95

4.17

8.07

2.62

0.39

0.06

3.12

1189.00

0.30

SW6

2.28

3.18

7.17

2.14

0.57

0.01

0.38

177.00

0.31

SW7

2.59

3.63

8.45

2.45

0.54

0.05

2.75

1122.00

0.30

SW8

1.59

4.17

8.07

2.62

0.39

0.09

4.87

1858.00

0.30

SW9

1.89

4.73

5.87

2.53

0.48

0.03

0.83

330.00

0.35

SW10

7.71

3.38

6.92

2.13

0.27

0.07

3.64

1710.00

0.28

Table 4. Pair-Wise matrix of morphometric properties of Kallar watershed Br

Sf (2.5, 3, 3.5)

Sf

(1,1,1) (0.28, 0.33, 0.4)

Dd (0.5, 1, 1.5)

(1, 1, 1)

(1, 1, 1)

Dd

(0.67, 1, 2)

(1,1,1)

(1, 1, 1)

Br

Dt (0.28, 0.33, 0.4) (0.33, 0.4, 0.5) (0.33, 0.4, 0.5)

Cr (0.5, 1, 1.5) (0.5, 0.67, 1) (0.67, 1, 2)

Rn (0.28, 0.33, 0.4) (0.33, 0.4, 0.5) (0.33, 0.4, 0.5)

Rh (0.28, 0.33, 0.4) (0.4, 0.5, 0.67)

Rr (0.28, 0.33, 0.4)

(1, 1, 1)

(0.67, 1, 2)

(2.5, 3, 3.5)

Bs

Wc 0.06

(1, 1, 1) (0.33, 0.4, 0.5) (0.33, 0.4, 0.5)

0.04 0.07

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Dt

(2.5, 3, 3.5)

(2, 2.5, 3)

Cr

(0.67, 1, 2)

Rn

(2.5, 3, 3.5)

(1, 1.5, 2) (0.4, 0.5, 0.67)

Rh

(2.5, 3, 3.5)

Rr Bs

(2, 2.5, 3) (0.5, 1, 1.5)

(1, 1, 1)

(2, 2.5, 3)

(0.5, 1, 1.5) (0.33, 0.4, 0.5)

(2.5, 3, 3.5)

(1.5, 2, 2.5) (0.28, 0.33, 0.4)

(1, 1, 1) (0.5, 1, 1.5)

(1,1,1)

(2, 2.5, 3)

(2, 2.5, 3)

(0.67, 1, 2)

(0.5, 1, 1.5)

(1, 1, 1) (0.5, 1, 1.5) (0.5, 1, 1.5) (1.5, 2, 2.5) (0.5, 1, 1.5)

(2, 2.5, 3) (1,1,1)

(0.67, 1, 2)

(2, 2.5, 3)

(0.67, 1, 2)

(0.67, 1, 2)

(1, 1, 1)

(0.67, 1, 2)

(0.33, 0.4, 0.5) (0.4, 0.5, 0.67) (0.33, 0.4, 0.5)

0.17

(0.5, 1, 1.5)

(1, 1, 1)

(0.67, 1, 2)

(2.5, 3, 3.5)

(2, 2.5, 3)

(0.5, 1, 1.5) (0.28, 0.33, 0.4)

(1, 1, 1) (0.33, 0.4, 0.5)

(2, 2.5, 3)

(1, 1, 1) 0.11 (0.67, 1, 2) 0.11 (0.67, 1, 2) 0.15 0.20

(0.5, 1, 1.5)

0.11 (1, 1, 1)

Finally we obtained criterion weight and alternative weight, for prioritization of sub watershed criterion weights were multiply with alternative weight, and overlaid all sub watershed criterion in GIS environment to prepare a prioritization of sub watershed. The final score ranges in Fuzzy Analytical Process is 0.074 to 0.167, where highest rank assigned to the sub watershed having the highest analysis value, the same way ranks were assigned to each sub watershed (Fig. 3. (a)) In this study SW10 was allotted for highest priority having FAHP value of 0.167 followed by SW4, SW1, SW7, SW9, SW6, SW3, SW5, SW8, SW2 is the least priority in this watershed (Table. 5). Table 5. Prioritization ranking of the sub watershed Sub Watershed

Sw1

Sw2

Sw3

Sw4

Sw5

Sw6

Sw7

Sw8

Sw9

Sw10

FAHP Score

0.109

0.074

0.084

0.113

0.082

0.100

0.107

0.080

0.104

0.167

Priority Rank

3

10

7

2

8

6

4

9

5

1

The prioritizes FAHP score was divided in to five priority classes from very less to very high based on the overall weights(Table.6). The Kallar watershed categorized priority classes, very less priority is SW2, it covers an area of 13.15%, and very high priority class is SW4, SW10, it covers an area of 24.50%. (Fig. 3. (b)). The SW10 falls under very high priority category, which is located near to the reservoir, it contain high amount of erosion. Hence the SW10 need immediate concern to protect and control the erosion. The SW1 is falls under the high priority category, which is located on high altitude area with huge amount of population and this sub watershed falls under the highly landslide hazard zone, this sub watershed also need immediate action to sustain the environmental condition. Table 6. FHAP score with different priority class S.No

Priority Score

Priority Class

Sub Watershed

Area in (%)

1

< 0.079

Very Less

SW2

13.32

2

0.0791 - 0.085

Less

SW3, SW5, SW8

24.39

3

0.0851 - 0.108

Medium

SW6, SW7, SW9

21.53

4

0.109 - 0.111

High

SW1

16.26

5

> 0.112

Very High

SW10, SW4

24.51

Fig. 3. (a) Priority Rank map, (b) Prioritization of sub watershed

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6. Conclusion The present study demonstrate the utility of Geographical Information System (GIS) and Multi Criteria Decision Making (MCDM) techniques in prioritizing sub watershed based on morphometric as well as integration of Fuzzy Analytical Hieratical Process (FAHP) with extent analysis. In this study, a new and consistent approach of MCDM processes i.e. FAHP analysis based prioritization was formulated successfully which plays a very important role in illustrating the problem through integration of risk assessment factors causing natural resources. This may be one of the feasible and efficient techniques, particularly in conventional watershed prioritization approaches for designing and developing the efficient sustainable development and management practices. 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