Surface Run-off, Soil Erosion and Slope Instability - Semi-quantitative Approaches for Landslide Assessment and Prediction - page 122

continuous monitoring generates a database of temporal variation in run-off. The
gauge station at all target points are not possible to set and so, inspite of having some
limitations models are to be used for estimating run-off. Run-off from a drainage
basin is influenced by various climatic (type of precipitation, intensity of rainfall,
duration of rainfall, areal distribution of rainfall, direction of storm movement,
antecedent precipitation, evaporation and transpiration) as well as physiographic
factors (land use, type of soil, area of the basin, shape of the basin, elevation, slope,
orientation, type of drainage network, indirect drainage and arti

cial drainage). The

geology or soil materials contribute to a large degree of the in ltration rate, and thus

affect run-off, vegetation and the practices incident to agriculture and forestry
(Schwab et al. 2002 ). But, it is dif cult to quantify the amount of surface run-off from

a drainage basin on the basis of these factors. Run-off is estimated by various
methods such as (i) Empirical formula and tables (Binnie

' s run-off coef cients,

Barlow ' s percentage run-off coef cients-K, Strange ' s Tables, Inglis and De Souza ' s

formulae, Lacey ' s formula, A.N. Khosla ' s formula, U.P. Irrigation Department ' s

formula, Indian Council of Agricultural Research ' s formulae-1971, Parker ' s

formulae, C.C. Vermuel ' s formulae, I.G. Justin ' s formulae. J. Rodier ' s Coef cients-

'

1967 and F.V. Zaleskkii U.S.S.R S formula-1967); (ii) Estimating Losses (A.N.

' '

Khosla s relation, U.S. formula and David Lloyd s formula); (iii) In ltration

'

Method; (iv) Rational Methods (R.L. Gregory and C.E. Arnold cation-1932,

M. Bernard ' s Coefficient-1938, American Society of Civil Engineering Coefficients,
W.S. Kerby formula-1959, C.F. Izzard Formula-1944 and Ben Chie Yen, Yung
Yuan Shen and Ven Te Chow-USA); and (v) Unit hydrograph method and sys-
tematic unit graph method. U.P. Irrigation Research Institute Roorke (1960)
developed a statistical correlation between run-off (R) and precipitation (P) in cms
for Himalayan and Bundelkhand Region Rivers in Uttar Pradesh. Soil Conservation
Research Demonstration and Training Centre Dehradun of ICAR (1971) analysed
run-off and rainfall from 17 sub-watersheds in the Nilgiri hills and also a reliable
regression equations have been reported for the estimation of nine important physio-
graphic characters i.e. catchment perimeters in km-Lp, main stream length in km-Ls,
compactness co-ef

s modi

cient Ce, rotundity factor R F, form factor F F, shape index S I, total
watershed relief in m-RT, T, drainage density (km/km 2 )-D D, and time of concentration
in minutes T C for the catchments. A rainfall-runoff modeling is developed by Singh
( 1988 ). Chow et al. ( 1988 ) also estimated surface runoff. A geomorphology based
arti cial neural networks (GANNs) was put forward for the estimation direct runoff

over watersheds by Zhang and Govindaraju ( 2003 ). Mishra et al. (2003) presented a
modi

ed SCS-CN Method. Saragni et al. ( 2007 ) evaluated three unit hydrograph
models to predict the surface runoff from a Canadian watershed. Recently, an
integrated approach for estimating surface run-off using Remote Sensing and GIS in
the field of hydrological research is applied by Durbude et al. ( 2001 ), Jasrotia and

Singh ( 2006 ), Tripathi et al. ( 2002 ) and Zade et al. ( 2005 ).
In the present approach Remote Sensing data is used as the basic information
input for computing runoff using the Soil Conservation Service (SCS) Run-off
Curve Number (RCN) model proposed by United State Department of Agriculture
(USDA-1972). The SCS models are mostly used in hydrology and water resource