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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES Volume 3, No 6, 2013

© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0

Research article ISSN 0976 – 4402

Received on March 2013 Published on June 2013 2019

Effects of Seasonal/Monthly Variation on Runoff Curve Number for

Selected Watersheds of Narmada Basin

Sarita Gajbhiye1, S.K. Mishra

1, Ashish Pandey

1

1- Department of Water Resources Development and Management, Indian Institute of

Technology Roorkee, Roorkee, U.K., India

[email protected]

doi: 10.6088/ijes.2013030600021

ABSTRACT

This study examined seasonal and monthly effects on the runoff Curve Number for four

watersheds of Narmada basin. The periods between April-June and October-December were

defined as the Pre-Monsoon and Post-Monsoon seasons, respectively. The Curve Number

method was used to determine Curve Numbers using these observed rainfall and runoff

values. Based on the date that rainfall and runoff volume were observed, the Curve Number

values were grouped to their respective seasons for statistical analysis. The results from all

watersheds showed monthly Curve number indicates a homogeneous pattern of variation in

all the four watersheds in the basin. The monthly curve number has peak (during July) and

valley (during August). However, at Shakkar watershed the peak is during August instead of

July in other watersheds. The maximum monthly curve number is recorded during the month

of September with the average value of 97.96 in Mohgaon watershed and the minimum

Curve Number is recorded during the month of September with the average value of 17.88 in

Bamhani watershed. Pre-monsoon contributes the major portion of the curve number with the

average value range 25.70-27.76% for all watersheds. However, Post-monsoon curve number

is almost negligible in Bamhani watershed (3.46 %). The average maximum and minimum

curve number is 97.43 and 95.74 for Manot and Bamhani watershed respectively. Because

the Cultivated lands to exhibit higher CN values than Forest land.

Keywords: Runoff Curve Number, CN variation, NEH-630, seasonal effect.

1. Introduction

The Soil Conservation Service (SCS) Curve Number (CN) method has gained general

acceptance in the engineering practice due to its simplicity in estimating storm water runoff

depth from rainfall depth and its endorsement by the United States Department of Agriculture

(USDA). Originally developed from daily rainfall data from small agricultural watersheds in

the Midwestern United States (Mockus 1949; Rallison 1980), this method was first

introduced 1954 (NRCS, 2001). the CN method continues to be updated and amended with

increasing data and research (Mishra and Singh 1999; Jain et al. 2006), as well as innovative

applications such as water quality modelling (Mishra and Singh 2003) and continuous

simulations (Williams and LaSeur 1976; Hawkins 1978; Arnold et al. 1990; Mishra and

Singh 2004; and others). Many researchers have demonstrated from rainfall and runoff data

that its key parameter CN has variable components and is not a constant for a watershed

(Hjelmfelt et al. 1982; McCuen 2002), and varies with rainfall. In many parts of the country

rainfall distribution is very uneven so this uneven distribution should also be considered

while determining CN. More than 80% of the annual rainfall occurs during the monsoon

Effects of Seasonal/Monthly Variation on Runoff Curve Number for Selected Watersheds of Narmada Basin

Sarita Gajbhiye, S.K. Mishra, Ashish Pandey

International Journal of Environmental Sciences Volume 3 No.6, 2013 2020

periods in Upper Narmada basin of Madhya Pradesh. Moreover, intensity of monsoon rainfall

is very uneven both in space and time, resulting in scarcity of availability of water in some

parts of the region during non monsoon periods. Thus, it is essential to analyse the variation

of curve number during various seasons.

In general, variation in CN between events can result from variations in storm

characteristics and surface conditions. Much of the variability in CN has been attributed to

antecedent runoff content (ARC) such that soils that are wetter have a higher curve number,

creating more runoff for a given amount of precipitation, than soils that are drier (Huang et

al., 2007; Shaw and Walter, 2009).A parameter that includes the effect of seasonal variation

on forecasting runoff volume has not been incorporated in the Curve Number method and as

a result ignores the impact of seasonal and monthly variation on evaporation, transpiration

and interception. Although the Curve Number method is well documented and widely used,

as Jacobs and Srinivasan (2005) pointed out, a need to use the method as a guideline and

interpret inputs on a more local and regional level combined with seasonal variation is

essential. Runoff simulation with annually consistent parameters has limited application

because watershed response varies remarkably from season to season (Paik et al. 2005). The

seasonal tank model developed by Paik et al. (2005) showed better performance compared to

the non seasonal tank model because it can successfully simulate runoff with little error.

Varying the Curve Number on a seasonal and monthly basis, therefore, may also

result in more accurate runoff estimation and improve the Curve Number performance. Some

of the previous works carried out in the seasonal variation of CN at various locations.

However, detailed study on the seasonal and monthly variation and distribution of CN in

Narmada sub-basin has not been included. So it is necessary to analyse the effect of seasonal

and monthly variation on the Curve Number for selected watersheds of Narmada basin based

on observed rainfall and runoff volume data.

THEORY: Estimation of runoff depth (Q) from rainfall depth (P) using the Curve Number

method is well established in hydrologic and environmental impact analyses for urban and

agricultural land use (Ponce and Hawkins, 1996; Schneider and McCuen, 2005; Garen and

Moore, 2005; and Michel et al. 2005; McCutcheon et al. 2006). The method can be based on

the water balance equation (1) and two other equations (2) and (3).

(1)

(2)

(3)

Where Ia is the initial abstraction (rainfall intercepted by vegetation, litter, and ground

surface expressions); F is the cumulative retention; λ is the initial abstraction ratio or

coefficient; and S is the maximum retention capacity.

As the method is practiced today, Q can be computed with the CN based on the land use and

hydrologic soil group, and rainfall depth by combining equations (1) and (2) as:




(4)

Equation (4) is valid for P > Ia and Q = 0 otherwise. With the initial abstraction included in

equation (4), the actual retention P – Q asymptotically approaches a constant value of S + Ia

as the rainfall increases unbounded. Using the value of λ = 0.2, equation (4) becomes

For P > 0.2S

For P > 0.2S (5)

Equation (5) is subject to P > 0.2S and Q = 0 otherwise. Equation (4) now contains only one

parameter (potential retention, S), which ranges between 0 to . For convenience in practical

applications, S is defined in terms of a dimensionless parameter, CN (Curve Number), which

varies in a more restricted range 0-100.

(6)

CN = 100 represents a condition of zero potential retention (S = 0), that is an impermeable

watershed. Conversely, CN = 0 a theoretical upper bound to the potential retention (S = ),

which is an infinitely abstracting watershed. If an event rainfall depth and the CN of a

watershed are known, the runoff volume can easily be determined using equation (5) and (6).

The potential maximum retention (S) for each of the maximum annual storm volume Q, and

the rainfall volume P will be computed using

(7)

This equation is an algebraic rearrangement of the runoff equation (5). For gauged

watersheds where both rainfall and runoff volumes are known the Curve Number values can

be determined using equations (6) and (7).

2. Methodology

The study watersheds include four watersheds of Narmada basin. Table (1) summarizes the

selected watersheds. Monthly rainfall and runoff data for the period of ten years (1999

2009) has been collected from the Central Water Commission (CWC, Bhopal). A pair of

annual maximum rainfall-runoff volume was selected for each year based on the (C = Q/P) <

1. Sort the remaining P-Q data in the descending order of P and assign the probability to P

using Weibull’s plotting position formula and calculate CN using Eq. 6 and 7, sort the CN in

descending order and Describe CN for 10%, 50%, and 90% probability of exceedances (PE)

as to correspond to wet, normal, and dry conditions, respectively. Since these values are

estimate from daily filtered P-Q data, the estimates CN-values correspond to 1-d rain duration.

The collected rainfall data has been categorized into four major seasons as listed in Table 1.

Finally, the data has been interpreted by preparing various charts and diagrams using

software packages.




Table - 1: Monsoon seasons and corresponding months

S.No Season Months

1 Pre-Monsoon April, May and June

2 Post- Monsoon October, November and December

3 Rainy (Monsoon) July, August, September and October

4 Summer March, April, May and June

5 Winter November, December, January and February

3. Study Area

Bamhani catchment- River Banjar is a tributary of River Narmada and its catchment is

geographically located in Mandla district between 21o65’55”N and 22

o29’00”N Latitudes and

80 o

22’00”E and 81o00’00”E longitudes. The watershed covers 2542 sq.km area. It is

situated in the eastern part of Madhya Pradesh. The climate of Mandla district is

characterized by a hot summer and general dryness except during the south-west monsoon

season. Climate of the basin can be classified as sub-tropical sub-humid with average annual

rainfall of 1,178 mm. About 90% of the annual rainfall is received during monsoon season

(June–October). The estimates of evapotranspiration vary from 4 mm/day in winter to 10

mm/day in summer. The area comprises of both flat and undulating lands covered with

timber, grasses, and cultivated land. Soils vary from black to mixed red soils. Nearly 65% of

the catchment area is covered with forest. Agricultural crops are grown in 29% area, and the

remaining area comes under degraded lands and water bodies. Soils of the area are

characterized by black grey, red, and yellow colours, often mixed with red and black

alluvium and ferruginous red ravel or lateritic soils. These soils are commonly known as

black soils.

Manot catchment-The Narmada catchment up to Manot lies between north latitudes 22°26′

to 23°18′ and east longitudes 80°24′ to 81°47′. The length of the river Narmada from its

origin up to Manot is about 269 km with the drainage area of 4884 km2. The catchment is

covered by forest and its topography is hilly. Its elevation ranges from 450 m near Manot site

to 1,110 m in the upper part of the catchment. It has continental type of climate classified as

sub-tropical and sub-humid with average annual rainfall of 1,596 mm. It is very hot in

summer and cold in winter. In the major part of the catchment, soils are red, yellow, and

medium black with shallow to very shallow depth. In some small pockets of plain land, soils

are moderately deep dark grayish clay. Approximately 52% of catchment area is under

cultivation, about 35% under forest, and 13% under wasteland.

Mohgaon catchment-The Burhner River rises in the Maikala range, south–east of Gwara

village in the Mandla district of Madhya Pradesh at an elevation of about 900 m at north

latitude 22° 32′ and east longitude 81° 22′. It flows in westerly direction for a total length of

177 km to join the Narmada near Manot. Its catchment area up to Mohegoan is about 3978

km2. The elevation at Mohegoan gauging site drops to 509 m. Climate of the basin can be

classified as sub-tropical and sub humid with average annual rainfall of 1,547 mm. The

evapotranspiration varies from 4 mm day in winter to 10 mm/day in summer. The catchment

area comprises both flat and undulating lands covered with forest and cultivated lands. Soils

are mainly red and yellow silty loam and silty clay loam. Forest and agricultural lands share

nearly 58 and 42% of the catchment area, respectively.




Shakkar catchment-The Shakkar river rises in the Satpura range, east of the Chhindi village,

Chhindwara district, Madhya Pradesh an elevation of about 600 m at latitude 22°23′ N

longitude 78°52′ E. The watershed covers 2220 km2 area. The climate of the basin is

generally dry except the southwest monsoon season. The southwest monsoon starts from

middle of June and lasts till end of September. October and middle of November constitute

the post monsoon or retreating monsoon season. The normal annual rainfall is 1192.1mm.

The normal maximum temperature received during the month of May is 42.50

C and

minimum during the month of January is 8.20

C. Soils are mainly clayey to loamy in texture

with calcareous concretions invariably present They are sticky and in summer, due to

shrinkage, develop deep cracks. They generally predominate in montmorillonite and

beidellite type of clays .In rest of alluvial areas , mixed clays , black to brown to reddish

brown, derived from sandstones and traps is observed which is sandy clay in nature with

calcareous concretions. Near the banks of the rivers and at the confluence, light yellow to

yellowish brown soils are noticed which were deposited during the recent past. These soils

are clayey to silt in nature.The location map of the study area is shown in Fig 1.

Fig. 1: Location map of the study area

4. Results and discussion

4.1 Monthly Variation of Curve Number

The average monthly Curve number of ten years period (1999 - 2008) calculated and

preparing variation diagrams (Fig. 2-5). It is inferred that a homogeneous pattern of Curve

number variation is found in all the watersheds with the runoff coefficient except Bamhani

watershed, increasing trend in the Curve number is noticed between June and July, and

declining trend is noticed between August and September. The runoff coefficient (C)

gradually increases from June to august and then decrease for all watersheds except Bamhani

watershed. In the Bamhani watershed curve number and runoff coefficient decreases from

July to September.




Fig. 2: Monthly variation of CN (Bamhani

Watershed)

Fig. 3: Monthly variation of CN (Manot

Watershed)

Fig. 4: Monthly variation of CN (Mohgaon

Watershed)

Fig. 5: Monthly variation of CN (Shakkar

Watershed)

4.2 Seasonal Variation of Curve number

Basic statistical attributes of annual and seasonal Curve Number for the period of 10 years

(1999-2008) of all watersheds were analysed, such as mean, standard deviation (SD), and

coefficient of variations (CV) (Table 2), and the percentage contribution during each season

has also been determined (Fig. 6-9). Mean and SD of the Seasonal Curve Number of all

watershed varied from 8.65 to 97.78 and 0.38 to 51.79, respectively (Table 3).

Fig. 6: Seasonal variation of CN and percentage distribution of CN at Bamhani watershed (ARC

II)




Fig. 7: Seasonal variation of CN and percentage distribution of CN at Manot watershed (ARC II)

Fig. 8: Seasonal variation of CN and percentage distribution of CN at Mohgaon watershed (ARC

II)

Fig. 9: Seasonal variation of CN and percentage distribution of CN at Shakkar watershed (ARC

II)

In general, monsoon (Rainy Season) contributes major portion of the total curve number,

which is maximum 0 – 50 % in all watersheds. Because in rainy season watershed receive

more rainfall compare to other season. Maximum Curve number of 99.33 is recorded at

Manot watershed in the year 2008 (rainy), Curve number during the post-monsoon and

Winter Season is Minimum at all the Watersheds. Maximum and average Curve number at all

watersheds during various seasons are presented in table 2.




Table - 2: Average and maximum Curve number during various seasons

Bamhani Watershed Manot Watershed

Month/Season/

Monsoon Average

Maximum Average

Maximum

CN Year CN Year

June 83.69 98.19 2002 86.56 98.90 2000

July 83.97 98.24 2004 97.08 99.15 2006

August 29.04 98.92 2000 94.97 97.73 2005

Sept. 17.88 94.21 2009 85.46 98.27 2002

Rainy 92.88 98.55 2004 96.90 99.33 2008

Summer 82.86 98.34 2001 87.18 99.46 2006

Winter 08.65 86.51 2003 57.15 98.46 2008

Pre-M 83.24 98.34 2001 95.07 99.03 2000

Post-M 09.41 94.10 2000 28.70 96.93 2001

Yearly 95.74 99.32 2001 97.43 98.32 2006

Mohgaon Watershed Shakkar Watershed

Month/Season/

Monsoon Average

Maximum Average

Maximum

CN Year CN Year

June 85.31 98.95 2006 86.18 98.20 2000

July 97.31 98.66 2002 95.78 97.19 2001

August 97.19 98.25 2002 97.16 99.19 2001

Sept. 97.96 98.86 2008 87.26 98.62 2000

Rainy 97.78 98.38 2002 96.56 98.69 2000

Summer 86.93 98.95 2006 47.78 99.62 2000

Winter 18.55 94.51 2005 59.40 99.83 1999

Pre-M 96.34 99.55 2000 96.11 99.55 2000

Post-M 69.00 99.48 2005 49.12 99.55 2001

Yearly 97.32 98.44 2002 97.40 99.18 2001

Table 3: Statistical summary of annual and seasonal CN of the study area

Season

Watershed

Bamhani Manot Mohgaon Shakkar

Mean

Annual 95.74 97.43 97.32 97.40

Rainy 92.88 96.90 97.78 96.56

Summer 82.86 95.18 86.93 47.78

Winter 8.65 57.15 18.55 59.40

Pre-M 83.24 95.07 96.34 96.11

Post-M 9.41 28.70 69.00 49.12

SD

Annual 2.95 0.67 0.64 1.15

Rainy 4.58 1.87 0.38 1.57

Summer 29.60 1.99 30.63 50.46

Winter 27.36 49.23 39.11 51.13

Pre- M 29.66 4.95 4.08 1.82

Post-M 29.76 46.21 47.62 51.79




CV

Annual 0.03 0.01 0.01 0.01

Rainy 0.05 0.02 0.01 0.02

Summer 0.36 0.02 0.35 1.06

Winter 3.16 0.86 2.11 0.86

Pre-M 0.36 0.05 0.04 0.02

Post-M 3.16 1.61 0.69 1.05

The shape of the basin affects the discharge characteristics of any watershed. The

hydrograph curve of the elongated basin is smoother which is expressed by the greater time

lag for the water from the upper catchment to reach the outlet (Verstappen, 1983). In case of

circular basin, water from the lower, middle and upper catchments reaches to the outlet in

less time and causes higher discharge during the shorter period (Verstappen, 1983) It can be

depicted from areal aspects (area, perimeter and length of basin) Mohgaon watershed has

somewhat circular shape. Therefore, its CN value is highest among all other comparison in

average month wise CN values. As this is evident from circularity of watershed, that it gives

the higher peaks of runoff hydrograph. However, Shakkar river watershed also has the less

circular shape so its CN values are also varying in same fashion as the Mohgaon watershed. It

is also evident from Table 4 that the Shakkar and Mohgaon watershed has the similar

watershed characteristics therefore their CN values are more or less similar. Further, Manot

watershed has elongated shape. Therefore, the CN values of this watershed is not going that

much higher as Shakkar and Manot watershed, which is a tendency of elongated watershed.

Moreover, the Bamhani watershed has elongated shape but in this watershed as comparison

to Manot the forest cover is more (59.03%) therefore it is affecting the CN values.The

average annual, seasonal and monthly variations in Curve number illustrated in the Figure. 10.

Table - 4: Watershed characteristics of the study area

Watershed Parameter Soil Type (%) Land Use (%)

Shakkar

Area(km2) 2220 Clay 25.04

Water body 1.46

Perimeter(km) 269 Agriculture land 54.77

Length of basin (km)

66.80

Loamy 74.96 Forest 39.41

Waste land 4.36

Manot


Water body 2.04



130.81


Waste land 0.00

Mohgaon


Water body 0.74



95.6


Waste land 15.47

Bamhani


Water body 1.47



92.2


Waste land 0.00




Fig. 10a : Average Monthly, Seasonal and Annual variation of CN of Bamhani and Manot

Fig. 10b : Average Monthly, Seasonal and Annual variation of CN of Mohgaon and Shakkar

5. Conclusions

Variability of annual and seasonal curve number were analyzed in all the watersheds.Monthly

Curve number analysis concludes that maximum Curve number is recorded during the month

of June at Bamhani and Manot and July at Mohgaon and Shakkar. The highest annual and

seasonal curve number variability values of the order of 0.03 and 1.05-3.16, respectively. The

average maximum and minimum curve number is 97.43 and 95.74 for Manot and Bamhani

watershed respectively. Cultivated lands to exhibit higher CN values than Forest land.

Analysis of seasonal Curve number concludes that the percentage contributions of Curve

number during various monsoon periods are in the following order:

Bamhani watershed:

Summer (31.07%)> Rainy (31.05%)>Pre-M (31.02%) > winter (3.37%)>Post-M (3.46%)

Manot watershed:

Pre-M (25.70%)> summer (25.69%)> Rainy (25.65%)> winter (15.32%) >Post-M (7.62%)

Mohgaon watershed:

Pre-M (26.32%) >Rainy (26.31%)> summer (23.68%)> Post-M (18.42%)> winter (5.24%)

Shakkar watershed:

Pre-M (27.76%) >Rainy (27.74%)> winter (16.71%)> Post-M (13.93%) > summer (13.84%)

6. References

1. Arnold J. G., Williams J. R., Nicks A. D., and Sammons N. B., (1990), SWRRB: A

basin scale simulation model for soil and water resources management, Texas A&M

University Press, College Station, pp142, 10 appendices.




2. Garen, David C., Daniel S. Moore, (2005), Curve Number Hydrology in Water Quality

Modelling: Uses, Abuses, and Future Directions. Journal of the American Water

Resources Association, 41(2), pp 377-388.

3. Hawkins R. H., (1973), Improved prediction of storm runoff from mountain watershed,

Irrigation and Drainage Division., ASCE, 99 (4), pp 519-523.

4. Hawkins R. H., (1978), Runoff curve numbers with varying site moisture, Journal of

Irrigation and Drainage Division, 104 (4), pp 389–398.

5. Hjelmfelt A. T., (1980), Empirical Investigation of curve number technique Journal of

Hydrologic Division, ASCE, 106(9), pp 1471-1477.

6. Hjelmfelt A. T., Jr., Kramer L. A., and Burwell R. E., (1982), Curve numbers as random

variables. Proc. Int. Symposium on Rainfall-Runoff Modeling, Water Resources

Publishers, Littleton, Colo., pp 365–373.

7. Jacobs J.H. and Srinivasan R., (2005), Effects of Curve Number modification on runoff

estimation using WSR-88D rainfall data in Texas watersheds, Journal of Soil and Water

Conservation, 60 (5), pp 274-278.

8. Jain M. K., Mishra S. K., Babu P. S., Venugopal K., and Singh V. P., (2006), “Enhanced

Runoff Curve Number Model Incorporating Storm Duration and a Nonlinear I a -S

Relation.” Journal of Hydrologic Engineering, 11(6), pp 631-635.

9. Mingbin Huang, Jacques Gallichand, Cuiyun Dong, Zhanli Wang, (2007), Use of soil

moisture data and curve number method for estimating runoff in the Loess Plateau of

China, Hydrological Processes, 21, pp 1471-1481.

10. McCutcheon S.C., Tedela N.H., Adams M.B., Swank W., Campbell J.L., Hawkins R.H.,

Dye C.R., (2006), Rainfall-Runoff Relationships for Selected Eastern U.S. Forested

Mountain Watersheds: Testing of the Curve Number Method for Flood Analysis, Report

prepared for the West Virginia Division of Forestry, Charleston, West Virginia.

11. Michel C., Andreessen V., Perrin C., (2005), Soil Conservation Service Curve Number

method: How to mend a wrong soil moisture accounting procedure? Water Resources

Research, 41(W02011): pp 1-6.

12. McCuen R. H., (2002), Approach to Confidence Interval Estimation for Curve Numbers,

Journal of Hydrologic Engineering, 7(1), pp 43-48.

13. Mishra S. K. and Singh V. P. (1999). “Another look at SCS-CN method.” Journal of

Hydrologic Engineering, ASCE 4(3), Paper 5120, 257-264. (July)

14. Mishra S. K., and Singh V. P., (2003), Soil conservation service curve number (SCS-CN)

methodology, Kluwer Academic, Dordrecht, The Netherlands, ISBN 1-4020-1132-6.

15. Mishra S. K., and Singh V. P., (2004), Long-term hydrologic simulation based on Soil

Conservation Service curve number.” Hydrologic Processes 18, pp 121 -131.




16. Mockus V., (1949), Estimation of total (peak rates of) surface runoff for individual

storms, Exhibit A of Appendix B, Interim Survey Rep. Grand (Neosho) River

Watershed, USDA, Washington, D.C.

17. NRCS, National Engineering Handbook, (2001), “Section-4 Hydrology” Soil

Conservation Service, USDA, Washington, DC.

18. Plummer A., and Woodward D. E., (2002), The origin and derivation of Ia /S in the

runoff curve number system.

19. Paik K, Kim J. H., Kim H. S., Lee D. R., (2005), A conceptual rainfall-runoff model

considering seasonal Variation, Hydrological Procesess, 19, pp 3837–3850.

20. Ponce V.M., Hawkins R.H., (1996), Runoff Curve Number: Has it Reached Maturity?

Journal of Hydrologic Engineering, 1(1), pp 11-19.

21. Rallison R. E., (1980), Origin and evolution of the SCS runoff equation, Proc., ASCE

Irrigation And Drainage Division Symposium On Watershed Mgmt., ASCE, New York,

N.Y., Vol. II, pp 912-924.

22. Rallison R. E., and Cronshey R. C., (1979), Discussion of runoff curve number with

varying site moisture, by Richard H. Hawkins Journal of Irrigation and Drainage

Engineering, 105(IR4), 439 – 441.

23. Schneider L.E., McCuen R. H., (2005), Statistical guideline for Curve Number

Generation, Journal of Irrigation and Drainage Engineering, 131(3), pp 282-290.

24. Shaw S.B. and Walter M.T., (2009), Estimating storm runoff risk using bivariate

frequency analyses of rainfall and antecedent watershed wetness, Water Resources

Research. 45: W03404 [doi: 10.1029/2008WR006900].

25. USDA, NRCS (2009), National Engineering Handbook, Part 630, Chapter 9, Hydrologic

Soil Cover Complexes, USDA, NRCS, Washington, D.C.

26. Verstappen H.Th., (1983), Applied Geomorphology- Geomorphology Surveys for

Environmental Development. Elsevier Science Publishers B.V. Amsterdam, the

Netherland, pp 66-71.

27. Williams J. D., and LaSeur W. V., (1976), Water yield model using SCS curve numbers,

Journal of the Hydraulics Division, Americans Society of Civil Engineers 102(HY9),

1241 -1253 (September).

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