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Poster Session
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Sediment yield prediction & prioritisation of watershed using Remote Sensing data
II. Case Study in Doon Valley, India.
II. 1. Study Area.
Doon Valley is located between Himalaya foot-hills in the north, Siwalik hill ranges in the south, river Ganga in the east and river Yamuna in the west with a longitudinal stretch of about 80 km & 20 km wide. Asan river watershed (730 sq. km.) drains part of Doon Valley into Yamuna river. Eco-development of this valley has assumed
Presented at the 12th Asian Conference on Remote Sensing, Singapore, October 30 – November 5, 1991.
Significance in view of increasing human pressure in the form of hill-terrace cultivation, deforestation, mining activities, tourism and human settlements etc.
II. 2. Meteorology
Resources information of landuse-cover & stream network of Asan river watershed are extracted from Landsat TM FCC image 146-039 dated 19 January 1989 on 1:50,000 scale, following systematic image interpretation techniques (Figure 1 & 2). Other two thematic maps are area distribution of rainfall using Thiessen Polygon method and topographic contour information from topographic map of 1:50,000 scale. For sediment yield estimation & watershed management priority determination, Asan watershed is segmented into 24 sub-watersheds. Sub-watershed-wise lumps parameters of Pi, Ai, Ddi & Si are arrived from thematic map of area mean rainfall, stream network & topographic contour, whereas parameters Fc is determined be regrouping satellite derived landuse-cover classes into one of Fi parameters in relation (2) as shown in Table 1.
Figure 1: Stream Network Map of Asan River Watershed, Doon Valley, India Interpreted form Landsat TM FCC Image 146-039, Date 19 January 1989.
Figure 2: Landuse-cover Map of Asan River Watershed, Doon Valley, India Interpreted form Landsat TM FCC Image 146-039, Dated 19 January 1989.
Table 1
| Fi |
Model parameter, FiParameter description |
Satellite derived regrouped land use – cover classes in terms of erosion
potential |
F1
F2
F3
F4
F5 |
Area under reserved & protected forest
Unclassified forest area
Cultivated land
Grass & posture land
Wasteland |
YSF, MSF, MF
SF, SC, HO, TG
HTC, RTC, CF
UB, OS, FB, B
E, SQ, SD |
Table 3
| Sub-watershed |
Sediment yield rate ha.m/100 sq.km/year |
Priority class for conservation planning |
H
K
A, B
C, D, E, F, I, J,L
G |
> 9.0
6.4 - 9.0
3.8 - 6.4
1.2 – 3.8
<1.2 |
Very High
High
Medium
Low
Very low |
Conclusion
Sediment yield model, adopted here, has the advantage of accepting significant physical parameters ALui, Ai, Ddi of watershed derived from satellite data. a schemes for priority class of watersheds is outlines based on these yield data ranges. In absence of measured sediment yield in small watersheds, this method will be immensely useful.
for conservation planning proposes. A more global approach would be to build up sufficient data base using the methodology enunciated here to arrive at optimum threshold for prioritization of watersheds.
Acknowledgement
Author is thankful to Prof. B.L. Deekshatulu, Director, NRSA & Prof. S.K.Bhan, Head IIRS for good encouragement to carry out this R&D work and for permission to present the paper in this conference.
References
Bali, Y.P.Karale, R.L.1977. ‘Sediment Yield Index as a Criterion for Choosing Priority Basins’, IAHS-AISH Publication No.122, Paris, P. 180-188.
Kumar, S. 1985, ’Reservoir Sedimentation’ in Proc. Short Term Course on Planning, Design & Operation of Reservoirs. Patna University, India, 8p.
Rao, H.S.S. Mahabaleswara, H.1990. ’Prediction of Rate of Sedimentation of Tungabhadra Reservoir’ Proc. Sym. On Erosion, Sedimentation & Resource Conservation, Dehradun, India, Vol. I, P-12-20.
Table 2 Estimation of sediment yield using sediment yield model & TM FCC image in Asan river watershed, doon valley, India.
| Sub watershed |
Ai |
Li |
Ddi |
Si |
Fci |
Pi |
Vsi |
SYRi |
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
X
Y |
49.355
36.465
7.021
23.165
24.550
12.292
17.010
49.682
21.144
21.171
61.389
61.044
95.421
13.116
22.857
5.320
4.781
4.967
34.106
6.784
4.817
15.649
15.023
7.887 |
137.16
119.74
14.18
64.39
59.09
13.19
12.85
102.53
31.42
32.76
99.66
18.68
111.52
18.84
33.15
8.75
11.50
8.00
50.64
10.37
5.85
31.55
21.32
17.56 |
2,78
3.28
2.01
2.78
2.40
1.07
0.76
2.06
1.48
1.54
1.62
0.30
1.168
1.436
1.450
1.644
2.400
1.610
1.484
1.528
1.210
2.010
1.420
2.220 |
0.157
0.170
0.070
0.130
0.102
0.068
0.030
0.152
0.041
0.027
0.102
0.029
0.039
0.066
0.091
0.043
0.108
0.101
0.075
0.062
0.020
0.053
0.131
0.092 |
0.365
0.341
0.306
0.303
0.292
0.367
0.340
0.593
0.452
0.405
0.548
0.214
0.352
0.408
0.240
0.259
0.250
0.282
0.290
0.243
0.273
0.235
0.219
0.257 |
196.62
194.40
184.72
186.71
184.15
161.26
156.57
146.33
138.51
127.32
144.53
156.63
136.14
153.44
136.65
116.12
116.12
116.12
166.12
126.63
149.89
144.76
148.64
158.64 |
23.0
15.4
0.87
4.957
4.368
1.817
1.717
46.33
5.30
3.475
42.054
10.00
17.62
2.334
1.300
0.182
0.252
0.228
2.754
0.244
0.208
0.868
0.767
0.568
|
4.66
4.22
1.24
2.14
1.78
1.49
1.01
9.34
2.51
1.64
6.85
1.64
1.85
1.78
0.57
0.34
0.53
0.46
0.81
0.36
0.43
0.55
0.51
0.72 |
| 24 |
615.016 |
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