estimation of rainfall runoff using scs-cn and gis … · 2019. 2. 5. · service - curve number...

http://www.iaeme.com/IJCIET/index.asp 1978 [email protected]

International Journal of Civil Engineering and Technology (IJCIET)

Volume 10, Issue 01, January 2019, pp. 1978-1998, Article ID: IJCIET_10_01_180

Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=10&IType=01

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

© IAEME Publication Scopus Indexed

ESTIMATION OF RAINFALL RUNOFF USING

SCS-CN AND GIS APPROACH IN PUZHAL

WATERSHED

S.Nandhakumar*

Assistant Professor, Department of Civil Engineering, Sathyabama Institute of Science and

Technology, Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai 600119

S.Arsheya

Department of Civil Engineering, Sathyabama Institute of Science and Technology,

Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai 600119.

V.K.Kirthika Sri

Department of Civil Engineering, Sathyabama Institute of Science and Technology,

Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai 600119

ABSTRACT

Rainfall runoff is one of the important hydrological variables in determining land

and water resources application. Curve Number method is widely used and efficient

method to estimate the infiltration characteristic of the watershed in accordance with

the land use/land cover property and soil property. In this study to estimate the

rainfall runoff modeling in this study area with an area of 152.81 sq km using Soil

Conservation Service Curve Number (SCS-CN) method and GIS. The estimated

amount average annual rainfall 1322.29mm from 1999 to 2013.The runoff varied

from 285 mm–4053mm, which is corresponds to 61.6% of annual average rainfall of

Thiruvalur district. These details are used for better watershed management and

conservation purpose.

Keywords: Rainfall, Runoff, Watershed, Curve number, Soil Conservation Service,

GIS.

Cite this Article: S.Nandhakumar, S.Arsheya and V.K.Kirthika Sri, Estimation of

Rainfall Runoff using Scs-Cn and Gis Approach in Puzhal Watershed, International

Journal of Civil Engineering and Technology, 10(1), 2019, pp. 1978-1998

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=01

S.Nandhakumar, S.Arsheya and V.K.Kirthika Sri


1. INTRODUCTION

The word watershed means an area of land which contains a common set of rivers and

streams that all drain into a single body of water, such as a lake, a large river or an ocean. The

rainfall and runoff modeling can be done in the simplest way using the Soil Conservation

Service - Curve Number (SCS-CN) method which was developed by United States

Department of Agriculture (USDA),since this method is accepted worldwide[Ashish bansode

& Patil 2014]. The SCS-CN method has been proved to be rapid, precise estimator of runoff

and land use/ land cover classes and are combined with the soil group. There are four soil

groups and they are as follows: A, B, C and D [Satheeshkumar 2017]. The Group A soil

group have high in-filtration rates, group B and group C soil groups have moderate in-

filtration rates and group D soil group have low infiltration rates. The SCS-CN method is

commonly used for calculating direct runoff volume for any given rainfall event. This method

is also suitable for red hill slope areas as per observations examined by the USDA.

The Geographical Information System (GIS) is used in this study. Numerous researchers

utilize the GIS and curve number that has proved to be quick, accurate estimator of runoff.

The Geographic Information Systems (GIS) has been applied extensively in hydrologic

modeling in new studies. The runoff estimated when compared with that of GIS tool

indicated that the GIS method is providing agreeable results and also as a substitute to the

manual method of computation. Based on SCS-CN method and using GIS data as inputs and

median of ordering data for all the three antecedent moisture conditions (AMC I, AMC II and

AMC III) is used. The Sahu model (2007) and Michel model (2005), generally on the basis of

the SCS–CN methods, with slight modifications are used [Sundara kumar et al.

2016].Watershed management for conservation has required the runoff information for better

understanding and results. Three-dimensional data have made it possible to precisely predict

the runoff which has led to significant increases in its use in hydrological applications. The

SCS–CN is a flexible and widely used for runoff estimation. This method is an important

property of the watershed, specifically soil permeability, land use and antecedent soil water

conditions [Jaimin Patel et al. 2017].

There are the few estimated details for the better watershed management and conservation

purposes and they are as follows: the amount average annual rainfall 1073mm from 1999 to

2013 and the runoff varied from 587mm to 1705mm.

2. DESCRIPTION OF THE STUDY AREA

The study area, Puzhal watershed is located in Ponneri Taluk, Thiruvalur District, Tamil

Nadu, India. It is one of the rain fed reservoirs from where the water is drawn for supply of

water in Chennai city. It is situated at 13˚10ʹ0ʺ N and 80˚5ʹ0ʺE. It covers an area of 151.82 sq.

km shows the location map of study area. as shown in this Figure (1).The study area attains

maximum elevation of 53m and minimum elevation of 6m. It has a tropical climate and the

average annual temperature is 28.6˚C.

Estimation of Rainfall Runoff using Scs-Cn and Gis Approach in Puzhal Watershed


Figure 1 Study Area - Puzhal Watershed

3. METHODS AND DATA COLLECTION

The methodology of the present study is shown in this Figure (2), were the flowchart for the

model development of runoff is shown. There are various steps involved is deriving the

model of development of the runoff and they are as follows: the major entity required is the

study area in this case our study area is the Puzhal watershed. Firstly we need the Rainfall

data of the study are and it is collected from the Indian metrological department in this case

we have the rainfall data of Puzhal watershed from 1999 – 2013. Secondly we need the land

use/land cover of 2018 can be obtained from the satellite images LISS III and the toposheet

map were collected from the survey of India and also the textures and soil types (black soil,

red soil and clay,) are collected from Survey of India, Rainfall Data collected 1999–2013

from Indian Metrological Department (IMD), Chennai. To find out curve number, the

boundary of the watershed and catchment area is defined [Amutha & Porchel 2009]. After

obtaining all the data required first the soil texture is tested and grouped, there are four

groups in the hydrological soil group and they are as follows: A, B, C and D. The Group A

soil group have high in-filtration rates, group B and group C soil groups have moderate in-

filtration rates and group D soil group have low infiltration rates. They study areas soil is

group D which has low infiltration rates, high runoff potential when thoroughly wet, the

movement of soil is restricted and the group D soil contains less than 50 percent sand, 40

percent clay and it has a clayey texture[Ningaraju et al. 2016]. After studying the satellite

images the LU/LC is determined is shown Figure (3).



Figure 1 Flowchart of Methodology for Rainfall-Runoff

Figure 2 Land use / Land cover of Puzhal Watershed

4. SCS-CN MODEL

The United Sates Department of Agriculture (USDA) established a very simple method

called Soil Conversion Service – Curve Number (SCS-CN) in the year 1954 [USDA 1986].

The Soil Conversion Services (SCS) is defined in the National Engineering Handbook (NEH-

4) in the Hydrology Section. The SCS-CN method is based on the two fundamental theories

and water balance calculation. The first theory states that the amount of early abstraction is a

fraction of the probable maximum retention and the second theory states that the ratio of the



real quantity of direct runoff to the maximum possible runoff is equal to the ratio of the

amount of real infiltration to the quantity of the potential maximum retention. To estimate the

direct runoff from the watershed in the study area, the SCS-CN method is used very

frequently. The infiltration losses are combined with surface storage by the relation of

[USDA 1974],

𝑄 =(𝑃−𝐼𝑎)2

𝑃−𝐼𝑎+𝑆 (1)

Where, Q is the gathered runoff in mm, P is the rainfall depth in mm, Ia is the initial

abstraction in mm and surface storage, interception, and infiltration prior to runoff in the

watershed.

The empirical relationship is given by [USDA 1974],

𝐼𝑎 = 0.2𝑆 (2)

For Indian condition the form S in the potential maximum retention and it is given by,

𝑆 =25400

𝐶𝑁− 254 (3)

Where, CN is known as the curve number which can be taken from SCS handbook of

Hydrology (NEH-4), section – 4.

Now the equation can be written as,

𝑄 =(𝑃−0.2𝑆)2

𝑃+0.8𝑆 (4)

Significant the value of CN, the runoff from the watershed was calculated from Eqs. 3

and 4.

The SCS-CN is a purpose of the ability of soils to allow infiltration of water with respect

to land use/land cover (LU/LC) and Antecedent Soil Moisture Condition (AMC) [Amutha &

Porchel 2009].

5. ANTECEDENT MOISTURE CONDITION (AMC)

The Antecedent Moisture Condition (AMC) refers to the amount of water content present in

the soil at a given time. It is determined by total rainfall in 5-day period preceding a rainfall

event (SCS, 1986) [Vinithra & Yeshodha 2014].There are three different AMC they are as

follows: AMC I, AMC II and AMC III, these are based on different soil conditions shown in

Table (1). Using runoff Curve Numbers (CN) from LU/LC and soil type taken for the dry

conditions (AMC I), average conditions (AMC II) and wet conditions (AMC III), we can

calculate the Curve Numbers (CN).

Table 1 Group of Antecedent Moisture Condition (AMC) Classes

AMC

Group Soil Characteristics

Five day antecedent

rainfall in mm

I Wet Condition <13

II Average Condition 13-28

III Heavy Condition >28

To calculate CN(I), CN(II) and CN(III) [Chow et al.1988],



𝐶𝑁(𝐼𝐼) =4.2𝐶𝑁(𝐼𝐼)

10−0.058 𝐶𝑁(𝐼𝐼) (5)

𝐶𝑁(𝐼𝐼𝐼) =23𝐶𝑁(𝐼𝐼)

10+0.13 𝐶𝑁(𝐼𝐼) (6)

𝐶𝑁(𝐼) =Σ𝐴.𝐶𝑁

Σ𝐴 (7)

6. HYDROLOGIC SOIL GROUP

The soils are classified by the natural resource conservation service into four hydrologic soil

groups based on the soils ,the groups are A, B, C and D shown in Table (2). Details of this

classification can be found in “Urban Hydrology for Small Watersheds” published by the

engineering division of the natural resource conservation service, USDA, TR-55[Chow et

al.1988]. The hydrologic soil groups classify soil texture, runoff potentials, water

transmission and final infiltration. All the subjects mentioned above will be tabulated below

for a better understanding is shown in Table (1). The Group A soil indicates low runoff

potential and high infiltration rate, the Group B soil indicates moderate infiltration rate and

moderately well drained to well drained, the Group C soil indicates moderately fine to

moderately rough textures and moderate rate of water transmission and the Group D soil

indicates slow infiltration and possible high runoff. The study area, Puzhal watershed belongs

to Group D soil,is shown in Figure (3).

Table 2 Soil Conversion Service Classification (USDA 1974) [USDA 1974]

Hydrologic Soil

Group (HSG) Soil Texture

Runoff

Potential

Water

Transmission

Final

Infiltration

Group A Deep, well drained sands

and gravels. Low High Rate >7.5

Group B Moderately deep, well

drained with Moderate. Moderate Moderate Rate 3.8-7.5

Group C

Clay loams, shallow

sandy loan with

moderate to fine texture.

Moderate Moderate Rate 1.3-3.8

Group D Clay soils that swell

significantly when wet. High Low Rate <1.3



Figure 3 Puzhal Watershed - Soil Map

Puzhal watershed comes under gentle to steep slope class shown in Table (3). (Medium to

high runoff) thus improving chance of improving infiltration and recharge in the study area

shown in Figure ( 5).

Table 3 Slope Classes of Puzhal watershed [IMSD 1995]

Sl. No. %Slope Area in Km2 Implication of Potential

1 Nearly Level 33.76 Km2 Low surface runoff

2 Gentle 31.76 Km2 Low surface runoff

3 Moderate 46.69 Km2 Medium surface runoff

4 Steep 38.22 Km2 High surface runoff

5 Very Steep 11.78 Km2 High surface runoff



Figure 4 Puzhal Watershed - Slope Map

6. RESULTS AND DISCUSSIONS

The calculated curve numbers (CN) for normal, average and wet conditions are 77.93, 89.37

and 95.08 in Figure (7). The rainfall varies from 630 mm – 2396 mm (1999 - 2013) as shown

in Figure (6). The runoff varies from 285 mm – 4053 mm (1999 - 2013) as shown in Figure

(7).The average annual runoff calculated come to be 1311.21 mm Table (4) and average

runoff volume for fourteen years is 164,107,796,924 m2. The rainfall runoff relationship is

shown in Figure (8) for Puzhal watershed. The rainfall and runoff are strongly correlated with

a correlation coefficient (r) value being 0.944 Figure (9). For this study area, the relation was

found to be strongest linear.



Table 4 Annual average runoff depth and volume

Year Rainfall (mm) Runoff (mm)

1999 1005.2 720.19

2000 1256 635.53

2001 1429.6 817.016

2002 1077 574.83

2003 671.8 279.86

2004 1169.4 654.31

2005 2397.5 1705

2006 1326 748.01

2007 1455.4 782.81

2008 1755 1109.14

2009 1277 781.89

2010 1469.6 827.36

2011 1486 925.02

2012 941.3 524.04

2013 1117.6 526.91

Figure 5 Rainfall varies in Puzhal watershed



Figure 6 Runoff varies in Puzhal watershed

Figure 7 Chart between rainfall and runoff

Figure 9 Scatter plot between rainfall and calculated Runoff

7. CONCLUSION

The Soil Conversion service (SCS) and Curve Number (CN) method is used in the present

work with the help of land use and soil maps described in Arc GIS, as input. The amount of



runoff represented is 61.6% of the total annual rainfall. Maximum rainfall and runoff

occurred in the year 2005 and Minimum in the year 2003. The monthly rainfall-runoff

simulation found good in the watershed. In SCN-CN method Antecedent Moisture Condition

(AMC) of the soil plays a very significant role because the CN number differs according to

the soil and that is considered while estimating runoff depth. For a given study area that is

puzhal watershed CN number is calculated equals to 77.93 for AMC - I, 89.37 – AMC-II and

95.08 for AMC-III. In conclusion, Soil Conversations Service –Curve Number (SCS-CN)

methodology is efficiently proven as a better method, which consumes a smaller amount of

time and facility to handle wide-ranging data set and a larger environmental area to find site

selection of artificial recharge structures.

REFERENCE

[1] Satheeshkumar, S., Venkateswaran, R., & Kannan, R. (2017), Rainfall–runoff estimation

using SCS–CN and GIS approach in the Pappiredipatti watershed of the Vaniyar sub

basin, South India. DOI: 10.1007/s40808-017-0301-4.

[2] Ashish Bansode, K., & Patil, A. (2014), Estimation of Runoff by using SCS Curve

Number Method and Arc GIS, International Journal of Scientific & Engineering

Research, Volume 5, Issue 7, July-2014.

[3] Sundara Kumar, P., Praveen, T.V., Prasad, M .A., (2016), Rainfall-Runoff Modeling

using Modified NRCS-CN,RS and GIS -A Case Study, Int. Journal of Engineering

Research and Applications, Vol. 6, Issue 3, (Part - 1) March 2016.

[4] Jaimin Patel, N., Singh, P .,Indra Prakash, P., Khalid Mehmood, (2017), Surface Runoff

Estimation Using SCSCN method- A Case Study on Bhadar Watershed, Gujarat, India,

Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-5, 2017.

[5] Vinithra, R., l. Yeshodha, I., (2014), Rainfall- Runoff Modeling Using SCS-CN Method:

A Case Study of Krishnagiri District, Tamil Nadu, International Journal of Science and

Research (IJSR).

[6] Ningaraju, H. J., Ganesh kumar,S. B., Surendra, H. J., (2016), Estimation of Runoff

Using SCS-CN and GIS method in ungauged watershed: A case study of Kharadya mill

watershed, India, International Journal of Advanced Engineering Research and Science

(IJAERS), Vol-3, Issue-5, May- 2016.

[7] Dipesh, B., Chavda, Jaydip, j. Makwana, J., Hitesh, V., Parmar, Arvind, N., kunapara and

Girish V., Prajapati, (2016), Estimation of Runoff for Ozat Catchment using RS and GIS

based SCS-CN method, Current World Environment, Vol. 11(1), 212-217 (2016).

[8] Chow, V. T., Maidment, D. R., and l. w. Mays, I.W., (1988). Applied Hydrology.

McGraw-Hill, New York city, New York, USA.

[9] USDA (1986) urban hydrology for small Watersheds, TR-55, United States Department

of Agriculture, 210-VI-TR-55, 2nd edn June 1986.

[10] USDA (1972) Soil Conservation Service, National Engineering Handbook. Hydrology

Section 4. Chapters 4-10. Washington, D.C: USDA.

[11] USDA-SCS (1974) Soil survey of Travis County, Texas. College Station, Tex.: Texas

Agricultural Experiment Station, and Washington, D.C.USDA Soil Conservation Service.

[12] [Taha, M., Taher, (2014) Integration of GIS Database and SCS-CN Method to Estimate

Runoff Volume of Wadis of Intermittent Flow, DOI 10.1007/s13369-014-1541-5.

[13] r. viji, p. rajesh prasanna, r. ilangovan, Gis Based SCS - CN Method For Estimating

Runoff In Kundahpalam Watershed, Nilgries District, Tamilnadu, (2015), Earth Sci. Res.

J. Vol. 19, No. 1 (June, 2015): 59- 64



[14] G. p. bharathi, k. balasubramani, Rainfall - Runoff Modeling using Soil Conservation

Service – Curve Number (SCS-CN) Method – A Case Study of Ungauged Andipatti

Watershed, Tamil Nadu, India, (2015).

[15] IMSD (1995) Technical guidelines, integrated mission for sustainable development,

national remote sensing center (NRSC) Department of Space, Government of India.

[16] amutha r, porchelvan P (2009) Estimation of surface runoff in Malattar sub-watershed

using Himanshu Bavishi and Bhagat N.K, Rainfall Runoff CoRelationship Using

Empirical Methods for Lower Mahi Basin, India. International Journal of Civil

Engineering and Technology, 8(3), 2017, pp. 575–581. SCS-CN method. J Soc

RemoteSens 37(2):291–304

[17] Eshanthini P, P. Vijayalakshmi, P.K. Raji, Rainfall Runoff Estimation Using SCS Model

and Arc Gis for Micro Watershed in Cuddalore District. International Journal of Civil

Engineering and Technology, 9(9), 2018, pp. 990-996.

[18] H.L. Tiwari, Ankit Balvanshi and Deepak Chouhan, Simulation of Rainfall Runoff of

Shipra River Basin. International Journal of Civil Engineering and Technology, 7(6),

2016, pp.364–370

estimation of rainfall runoff using scs-cn and gis … · 2019. 2. 5. · service - curve number...

Documents