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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

Submitted on May 2012 published on July 2012 219

SCS curve number method in Narmada basin Tejram Nayak

1, Verma M.K

2, Hema Bindu.S

2

1- National Institute of Hydrology Regional Centre, Manorama Colony, Sagar (M.P.)

2- Dept. of Civil Engg., National Institute of Technology, Raipur (C.G.)

[email protected]

ABSTRACT

The SCS-CN method is an event-based model developed by the USDA Soil Conservation

Service (SCS). The Curve Number (CN) is a land-cover index for a given land and soil type

to determine the amount of rainfall that infiltrates into the ground and the amount that

becomes runoff for a specific storm event (USDA, 1986). The hydrological response of

watershed is usually altered due to revolution in the watershed development. Thus it is

necessary to quantify the likely changes in the surface runoff in a watershed as an impact of

the planned or unplanned changes made in the land use. The Uri river watershed in Lower

Narmada basin in Central India has been chosen to investigate the effects of land-use change

on surface runoff. Satellite imageries pertaining to two different periods, i.e. year 2001 and

2007 have been interpreted in ILWIS GIS platform for preparation of land use/cover maps,

analysis of their spatial distribution and changes between the two periods. The weighted

average Curve Numbers (CN) for both the year calculated on the basis of land use/cover type

and hydrologic soil class in the catchment area. The direct surface runoff volume computed

by the SCS Curve Number method have been compared with the observed runoff calculated

from recorded hydrograph at G&D site for the selected rainfall events. It was shown from the

results that the agricultural area have been increased drastically and forest area has reduced

considerably resulting in 20-40 % increased surface runoff volume in recent years (i.e. 2007)

in comparison to those in 2001 for the similar rainfall events.

Key Words: SCS-Curve Number, landuse change, rainfall-runoff, GIS, Narmada.

1. Introduction

The change of landuse has had a considerable impact on the runoff characteristics and related

hydrological processes. For example, evapotranspiration and interception decrease after trees

and vegetations are removed in the process of urbanization. Also, an increase in impervious

area due to the construction of houses, streets and culverts reduce infiltration and shorten the

time of concentration. Generally, land development & urbanization cause increase in peak

discharge and runoff volume. Remote Sensing (RS) techniques have been applied

extensively and are recognized as powerful and effective tools for detecting land use changes.

Remote sensing collects multi-spectral, multi-resolution, multi-temporal data, and turns them

into useful information. GIS technology provides a flexible environment for entering,

analyzing, and displaying digital data from various sources, for identifying urban features,

detecting change, and developing databases. Many researchers have developed an integrated

approach to combine RS and GIS techniques to elucidate the effects of land-use change on

runoff using a simple Soil Conservation Service (SCS) model. Pandey and Sahu(2002)

pointed out that the land use/land cover is an important parameter input of the SCS-CN

model. Nayak and Jaiswal (2003) found that there was a good correlation between the

measured and estimated runoff depth using GIS and CN. They concluded that GIS is an

SCS curve number method in Narmada basin

Tejram Nayak, Verma M.K, Hema Bindu.S

International Journal of Geomatics and Geosciences

Volume 3 Issue 1, 2012 220

efficient tool for the preparation of most of the input data required by the SCS curve number

model. Zhan and Huang (2004) described the development and application of the ArcCN

Runoff tool, an extension of ESRI ArcGIS software which can be applied to determine curve

numbers and calculate runoff or infiltration for a storm event within a watershed. Zhan and

Huang also suggested that the implementation of a precipitation time series and the

consideration of factors such as dry and wet antecedent moisture conditions (for CN

parameters) would improve the predictions of the ArcCN Runoff tool. Some more attempts to

apply hydrological models to investigate the impact of land use change are reported in De

Roo et al. (2001), Burns et al. (2005), Siriwardena et al. (2006), Podwojewski et al. (2008),

Wehmeyer and Weirich (2010) and Nayak and Narulkar (2011). In the present study,

distributed SCS-CN model in ILWIS GIS platform has been applied to estimate variations in

runoff during significant rainfall events for two different periods, i.e. year 2001 and 2007 for

the Goi river catchment of Narmada river basin in Central India. Satellite remote sensing

imageries have been used to prepare land use/cover and soils map, which was used to

calculate the average Curve Number for the basin. Variations in surface runoff resulting

from the similar rainfall during two different periods have been quantified.

2. Study area

Narmada is the longest west flowing river of India. It rises from a spring at a height of 1057m

above MSL on the summit of Amarkantak Hill in Madhya Pradesh and flows westwards over

a length of 1,312 km and drains an area of 98796 sq.km before falling into the Arabian Sea.

Uri river is an important north side tributary of River Narmada. It originates from Vindhyan

Ranges near village Bhilkheri in Sardarpur district Madhya Pradesh and meets the River

Narmada near Nisarpur town at about 13 km downstream of Barwani city.

Figure 1: Index map showing Uri river watershed in Narmada basin and rain gauges





The Uri river catchment upto Dhulsar gauging site has been considered for this study. It has

an elliptical shaped catchment and having 787 sq.km geographical area lies between East

longitudes 74°47' to 75°03' and North latitudes 22°11' to 22°37'. The gauging at Dhulsar was

started by the Central Water Commission in February, 1999 and recently an automatic data

acquisition system was installed by the Narmada Control Authority (NCA) for hourly gauge

and discharge measurement. The drainage density in Uri watershed is high due to hilly terrain

and undulating lands. The temperature of Uri catchment records maximum in the month of

May and minimum in the month of December and it receives about 90 percent of the annual

rainfall during the monsoon months i.e. 15th

June to 15th

October. The time of concentration

of runoff is about 8 hours at Dhulsar gauging site, therefore the sub-basin experience

occasional floods for very short duration during monsoon months.

3. Methodology

The runoff curve number method is a procedure for hydrologic abstraction developed by the

USDA Soil Conservation Service. In this method, runoff depth (i.e. effective rainfall) is a

function of total rainfall depth and an abstraction parameter referred to as runoff curve

number or simply curve number and is usually represented by CN (Mishra and Singh, 2003).

The SCS-CN model calculates direct runoff depth (Q) using the following equation:

for P > Ia - 1

Where, P= total precipitation (mm), Ia = initial abstraction (mm), and S= potential maximum

retention (mm). Q=0, for P ≤ Ia. The initial abstraction is related to S by the equation:

Ia = λ . S - 2

Where, λ is an initial abstraction ratio. The values of λ varies in the range of 0.1 and 0.3, The

value of λ has been developed for black soil region for Indian conditions as 0.3 for AMC-I

and 0.1 for AMC-II & III (Hand book of Hydrology, Mini. of Agriculture, 1972). In practice,

the runoff Curve Number (CN) is used to compute S in mm as,

25425400

−=

CNS - 3

4. Data collection and processing

4.1 Procurement of land use maps

The Survey of India toposheet covering the Uri watershed was selected as base map and geo-

referencing of other maps to bring them in a single platform, ILWIS GIS. The Index map of

Uri river catchment is shown at Fig-1. The land use/land cover maps were prepared by visual

interpretation of satellite imageries IRS 1A LISS-II data for the year 2001 and IRS 1D LISS-

III data for the year 2007 by CE&AMD, SGSITS Indore and MPCOST, Bhopal respectively.

The maps were transferred in ILWIS GIS for further use as shown in Fig-2 and Fig.3. The

spatial information on landuse at level-1 Classification were extracted from these maps for

computation of the SCS Curve Number.

Changes in spatial distribution of the landuse between the year 2001 and 2007 have been

presented in Table-1. The results reveal that scrubs with cultivation and agriculture inside

forest have been mostly converted into agricultural area, which has doubled during the

reported period. Surprisingly, the area under dense canopy forest has also been increased

from 43sq.km to 75sq.km. However, the medium and low canopy forest area has reduced to





about half. This could be due to plantations in compensation to the forest area under

submergence in the major projects in Narmada basin. The scrub-pasture and barren area have

also reduced to the half, which have been developed as cultivable lands by the local farmers.

4.2 Soil information

The soils map procured from CE&AD, SGSITS, Indore which were prepared using IRS-1A

LISS-I satellite imageries under a State Govt. sponsored project. The map has been digitized

and stored in the ILWIS platform as shown in Fig-4. The different soil classes in the map

have been further assigned to suitable hydrologic soil group.

4.3 Computation of average curve number

Area weighted average curve number for Uri watershed has been calculated for the year 2001

and 2007. Appropriate CN values correspond to AMC-II have been assigned to each

polygons obtained from cross map between land use and soil maps in ILWIS GIS Software.

These were taken from reputed publications related to SCS Method. Finally, sum of the

products of area and CN value of total polygons has been divided by the catchment area to

get area weighted average CN value for the Uri watershed. The CN values correspond to

Figure 2: Landuse of Uri river sub-basin in 2001





Figure 3: Landuse of Uri river sub-basin in 2007

Figure 4: Soil map of Uri river sub basin





Table1: Changes in Landuse/Land cover in Uri river watershed

Old Landuse

(2001)

Recent Landuse

(2007) Landuse Changes

Landuse Class Area

(Sq.km.) Percent

Area

(Sq.km.) Percent Sq.km. Percent

Built Up-Built Up area (Rural) not

reported -- 8.625 1.1% 8.625 --

Agricultural Land-Crop Land 209.46 26.5% 459.16 58.3% 249.70 119.2

Agricultural Land-Current

Fellow

not

reported -- 64.19 -- 64.19 --

Scrub with Cultivation /

Agriculture inside Forest 129.49 16.5% 27.86 3.5% -101.63 -78.5

Sub Total 338.95 43.0% 551.20 70.0% 212.26 62.6

Dense Canopy / Forest-

Deciduous-Dense/Closed 42.78 5.4% 75.40 9.6% 32.63 76.3

Medium Canopy / Forest-

Deciduous-Open 104.15 13.2% 52.71 6.7% -51.44 -49.4

Low Canopy / Forest-Scrub

Forest 52.08 6.6% 33.80 4.3% -18.29 -35.1

Sub Total 199.01 25.3% 161.91 20.6% -37.10 -18.6

Scrub-Pasture / Scrub Land +

Forest Blank 96.54 12.3% 46.00 5.8% -50.54 -52.4

Ravinous and Gullied/Barren

Rocky/Stony waste 152.51 19.4% 5.65 0.7% -146.86 -96.3

Waterbodies-River/Stream not

reported -- 13.62 1.7% 13.62 --

Grand Total 787.00 100.0% 787.00 100.0%

other antecedent moisture conditions, i.e. for AMC-I and AMC-III have been computed by

the following formulae:

CNI for AMC-I = 0.39*CNII*EXP(0.009*CNII) - 4

CNIII for AMC-III = 1.95*CNII*EXP(-0.00663*CNII) - 5

Where, CNII = runoff curve number for AMC-II.

The computed values of average CN, S and Ia for the year 2001 and 2007 have been given in

Table-2. These values have been used in SCS model to get the direct runoff volume for given

rainfall for different AMC conditions and growing seasons.

Table 2: SCS-CN model parameters for the year 2001 & 2007

Year 2001 Year 2007 Antecedent Moisture

Condition AMC-I AMC-II AMC-

III AMC-I AMC-II

AMC-

III

Average curve number,CN 64.85 80.24 91.67 67.74 82.26 92.70

Potential retention,S 137.69 62.54 23.07 120.94 54.78 20.00

Dormant season, Ia=0.1S 13.77 6.25 2.31 12.09 5.48 2.00

Growing season, Ia=0.2S 27.54 12.51 4.61 24.19 10.96 4.00

Full growth season, Ia=0.3S 41.31 18.76 6.92 36.28 16.44 6.00





Table 3: Computation of direct runoff using SCS-CN method for the year 2001 & 2007

Month Date AMC condition Rainfall

P (mm)

Direct

Runoff

Q (mm)

Volume

(MCM)

Total

Volume

computed

Observed

volume

Percent

Variatio

n

Year 2001 Q=(P-Ia)

2

(P-Ia+S) V=Q*A (MCM) (MCM)

10 AMC-II, Ia=.1S 14.3 0.910 0.716

11 AMC-II, Ia=.1S 9.7 0.177 0.139

12 AMC-II, Ia=.1S 4.3 0.000 0.000

JU

LY

20

01

13 AMC-II, Ia=.1S 8.0 0.047 0.037

0.892 0.773 15.4 %

05 AMC-I, Ia=.1S 25.9 4.682 3.685

06 AMC-II, Ia=.1S 0.7 0.000 0.000

07 AMC-II, Ia=.1S 3.0 0.008 0.006

08 AMC-II, Ia=.1S 11.7 1.218 0.959

09 AMC-III, Ia=.1S 4.2 0.143 0.113

10 AMC-II, Ia=.1S 14.3 0.910 0.716

AU

GU

ST

20

01

11 AMC-II, Ia=.1S 14.1 0.882 0.694

6.172 5.992 3.0 %

16 AMC-II, Ia=.1S 10.0 0.212 0.167

A

UG

20

01

17 AMC-III, Ia=.1S 10.0 1.923 1.514 1.680 1.777 -5.4 %

10 AMC-I, Ia=.2S 25.5 1.126 0.886

11 AMC-III, Ia=.2S 17.7 4.716 3.711

OC

T 2

00

1

12 AMC-III, Ia=.2S 29.3 12.734 10.021

4.597 4.619 -0.5 %

Year 2007

June

‘07 30 AMC-I, Ia=0.1S 5.57 0.00 0.00

01 AMC-II, Ia=0.1S 16.90 0.18 0.14

02 AMC-III,Ia=0.1S 12.10 3.39 2.67

03 AMC-II, Ia=0.1S 24.81 1.21 0.95

JU

LY

20

07

04 AMC-II, Ia=0.1S 20.25 0.52 0.41

4.17 4.81 -

13.3 %

09 AMC-I, Ia=0.1S 4.67 0.00 0.00

10 AMC-II, Ia=0.1S 14.85 0.06 0.05

11 AMC-III, Ia=0.1S 12.50 3.61 2.84

JU

LY

20

07

12 AMC-III, Ia=0.1S 10.44 2.50 1.97

4.86 4.91 -0.9 %

03 AMC-II, Ia=0.2S 0.33 0.00 0.00

04 AMC-III, Ia=0.2S 8.66 2.51 1.97

05 AMC-III, Ia=0.2S 5.00 0.20 0.16

AU

G 2

00

7

06 AMC-III, Ia=0.2S 2.28 0.00 0.00

2.13 2.04 4.4 %

07 AMC-III, Ia=0.2S 0.30 0.41 0.32

08 AMC-III, Ia=0.2S 15.00 8.07 6.35

AU

G

20

07

09 AMC-III, Ia=0.2S 10.97 4.43 3.49

10.16 8.64 17.6 %





Table 4: Effect of land use/cover change on direct surface runoff

Month Date AMC condition Rainfall

P (mm)

Computed direct

runoff Q (mm)

=(P-Ia)2/(P-Ia+S)

Year 2001 Year 2001 Year

2007

Difference

in direct

runoff bet.

year 2001

and 2007

Percent

variation

( Increase

between

2001-

2007)

10 AMC-II, Ia=.1S 14.3 0.910 1.215

11 AMC-II, Ia=.1S 9.7 0.177 0.298

12 AMC-II, Ia=.1S 4.3 0.000 0.000

JU

LY

20

01

13 AMC-II, Ia=.1S 8.0 0.047 0.111

0.386 43.2 %

05 AMC-I, Ia=.1S 25.9 4.682 5.531

06 AMC-II, Ia=.1S 0.7 0.000 0.000

07 AMC-II, Ia=.1S 3.0 0.008 0.018

08 AMC-II, Ia=.1S 11.7 1.218 1.450

09 AMC-III, Ia=.1S 4.2 0.143 0.218

10 AMC-II, Ia=.1S 14.3 0.910 1.215

AU

GU

ST

20

01

11 AMC-II, Ia=.1S 14.1 0.882 1.181

1.393 22.6 %

16 AMC-II, Ia=.1S 10.0 0.212 0.345

A

UG

20

01

17 AMC-III, Ia=.1S 10.0 1.923 2.285 0.390 23.2 %

10 AMC-I, Ia=.2S 25.5 1.126 1.568

11 AMC-III, Ia=.2S 17.7 4.716 5.547

OC

T 2

00

1

12 AMC-III, Ia=.2S 29.3 12.734 14.102

1.003 21.8 %

Year 2007

Jun ‘07 30 AMC-I, Ia=0.1S 5.57 0.00 0.00

01 AMC-II, Ia=0.1S 16.90 0.07 0.18

02 AMC-III, Ia=0.1S 12.10 2.92 3.39

03 AMC-II, Ia=0.1S 24.81 0.82 1.21

JU

LY

20

07

04 AMC-II, Ia=0.1S 20.25 0.29 0.52

0.944 29.3 %

09 AMC-I, Ia=0.1S 4.67 0.00 0.00

10 AMC-II, Ia=0.1S 14.85 0.01 0.06

11 AMC-III, Ia=0.1S 12.50 3.12 3.61

JU

LY

20

07

12 AMC-III, Ia=0.1S 10.44 2.12 2.50

0.959 23.2 %

03 AMC-II, Ia=0.2S 0.33 0.00 0.00

04 AMC-III, Ia=0.2S 8.66 1.89 2.51

05 AMC-III, Ia=0.2S 5.00 0.03 0.20

AU

G 2

00

7

06 AMC-III, Ia=0.2S 2.28 0.00 0.00

0.620 41.1 %

07 AMC-III, Ia=0.2S 0.30 0.00 0.41

08 AMC-III, Ia=0.2S 15.00 7.19 8.07

AU

G

20

07

09 AMC-III, Ia=0.2S 10.97 3.68 4.43

1.616 19.0 %

4.4 Hydrological data

Daily rainfall data of four rainguage stations located in/around Uri basin were obtained from

Indian Meteorological department (IMD) and State Govt. raingauge stations for the year 2001

and 2007 at Dhulsar, Kukshi, Jobat and Sardarpur. Thiessen polygon created for the





catchment area showed that Dhulsar, Kukshi, Jobat and Sardarpur have weightages 0.23, 0.18,

0.10 and 0.49 respectively as per the spatial coverage. Daily average discharge observed at

Dhulsar G&D site have been used to draw flood hydrograph and subsequently direct runoff

volume was calculated by separating the base flow component.

5. Results and discussion

In the present study, an attempt has been made to quantify the impact of landuse change on

direct runoff volume resulted from the same rainfall occurred in Uri watershed. The direct

runoff volume have been computed using SCS equation by applying appropriate AMC

condition. The direct surface runoff calculated from observed hydrograph have been

compared with the computed data for the year 2001 to 2007. The computation of runoff using

SCS-CN model and comparison between the observed and computed direct surface runoff

have been given in Table-3. In general good correlation has been found between observed

and computed runoff, which shows that the SCS-CN model performed well in estimating the

runoff volume in the Uri catchment. The land use changes in the watershed can be evaluated

in terms of change in curve number between the year 2001 and 2007. The rainfall occurred in

the year 2007 have been used for computation of direct runoff using CN values obtained for

the year 2001 (represents the hydrologic conditions in the year 2001) to assess the changes in

the runoff between the year 2001 and 2007 resulting by the same rainfall, which is presented

in Table-4. In general, 20 – 40 percent increase in runoff have been obtained in the year 2007

than that in year 2001 from the same rainfall events. The variation is mainly due to reduction

in forest cover and increase in the agricultural fields.

5.1 Conclusion

The conventional hydrological data are inadequate for purpose of design and operation of

water resources systems. In such cases remote sensing data are of great use for the estimation

of relevant hydrological parameters, such as landuse/land cover, soils, geomorphology,

drainage etc. GIS offers the potential to increase the degree of definition of spatial sub-units,

in number and in descriptive detail. The conclusions that may be drawn are

1. The combination of remote sensing and SCS model makes the runoff estimate more

accurate and fast;

2. Geographical information system arises as an efficient tool for the preparation of most

of the input data required by the SCS curve number model;

3. The runoff estimated using SCS curve number model are comparable with the runoff

measured by the conventional method; and

4. The analysis can be extended further to assess the impact of landuse changes, after

developments in the watershed, on the rainfall-runoff relationship.

6. References

1. Burns, D., T. Vitvar, J. McDonnell, J. Hassett, J. Duncan, and Kendall C., (2005), Effects

of suburban development on runoff generation in the Croton River basin, New York,

USA, Journal of Hydrology, 311(1–4), pp 266–281.

2. De Roo, A., Odjik, M., Schmuck, G., Koster, E., Lucieer, A., (2001), Assessing the

effects of land use changes on floods in the Meuse and Oder catchment, Physics and

Chemistry of the Earth, Part B – Hydrology, Ocean and Atmosphere, 26, pp 593-599.





3. Handbook of Hydrology, (1972), Soil Conservation Department, Ministry of Agriculture,

New Delhi

4. Mishra, S.K., Singh, V.P., (2003), Soil Conservation Service Curve Number (SCS-CN)

Methodology. Kluwer Academic Publishers, Dordrecht.

5. Nayak, T.R. and Jaiswal, R.K., (2003), Rainfall-runoff modelling using satellite data and

GIS for Bebas river in Madhya Pradesh, IE (I) Journal, 84, pp 47-50.

6. Nayak, T.R. and Narulkar, S.M., (2011), Effects of land use and land cover changes on

water yield in Goi watershed of Narmada basin, Journal of Indian Water Resources

Society, 31 (1-2), pp 35-44.

7. Pandey, A. and Sahu, A.K. (2002), Generation of curve number using remote sensing

and Geographic Information System, h,ttp://www.GISdevelopment.net.

8. Podwojewski, P. et al., (2008), Land-use impacts on surface runoff and soil detachment

within agricultural sloping lands in Northern Vietnam. Catena, 74(2), pp 109-118.

9. Siriwardena, L., Finlayson, B.L., and McMahon, T.A. (2006), The impact of land use

change on catchment hydrology in large catchments: The Comet River, Central

Queensland, Australia, Journal of Hydrology, 326, pp 199–214

10. USDA Soil Conservation Service (1986), Urban hydrology for small watersheds.

Technical Release 55, U.S. Department of Agriculture, Washington, DC.

11. Wehmeyer, Loren L. and Weirich, Frank H. (2010), Effect of historic land cover change

on runoff curve number estimation in Iowa, Journal of Hydrologic Engineering, ASCE

15(9), pp 692-695.

12. Zhan, X. and Huang, M. (2004), ArcCN-runoff: an ArcGIS tool for generating curve

number and runoff maps, Environmental Modeling and Software, 19(10), pp 875-879.

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