ESTIMATION OF NRCS CURVE NUMBER FROM WATERSHED MORPHOMETRIC PARAMETERS: A CASE STUDY OF YIBA WATERSHED IN SAUDI ARABIA

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 7, Issue 2, March-April 2016, pp , Article ID: IJCIET_07_02_022 Available online at Journal Impact Factor (2016): (Calculated by GISI) ISSN Print: and ISSN Online: IAEME Publication ESTIMATION OF NRCS CURVE NUMBER FROM WATERSHED MORPHOMETRIC PARAMETERS: A CASE STUDY OF YIBA WATERSHED IN SAUDI ARABIA Mohammad O. Alagha, Saud A. Gutub Faculty of Civil Engineering, King Abdulaziz University, Jeddah, Saudi Arabia Amro M. Elfeki Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University Jeddah, Saudi Arabia ABSTRACT The NRCS-CN equation for flood predictions relies on the value of the Curve Number and the amount of rainfall event to determine the corresponding runoff. Usually, the curve number value (CN value) is extracted from the tables that follow United State land features classification which might not be applicable to the land features in Saudi Arabia. This research paper doesn t use NRCS-CN table values form of the US for estimating the curve number value, rather, the CN values have been estimated from the data of rainfall and runoff events of some gauged watersheds in the western region of Saudi Arabia (Yiba watershed and its sub-basins). The observed CN values are in the range of 61 and 99. For the rainfall events, the CN behavior follows the standard regime with an approached value, of 52. It has also been shown that there is a relatively good agreement between the observed CN and the theoretical NRCS-CN curves with the factor of initial abstraction (λ = 0.2). The watershed morphometric characteristics have an effect on the value of the curve number. Some parameters give a strong relation with the average CN such as basin average elevation, shape factor, basin slope, basin Length, and watershed area where R 2 (i.e., R-Square which known as the coefficient of determination) is 0.99, 0.81, 0.87, 0.78 and 0.56 respectively within some range of the specified parameter given in each equation. These relationships could be used to estimate average curve numbers for similar basins without relying on NRCS-CN tables editor@iaeme.com

2 Mohammad O. Alagha Saud A. Gutub and Amro M. Elfeki Keywords: Curve number, runoff calculation, Saudi Arabia, Yiba watershed, floods, NRCS method, and regression analysis. Cite this Article: Mohammad O. Alagha, Saud A. Gutub and Amro M. Elfeki, Estimation of NRCS curve number from watershed morphometric parameters: A Case Study of Yiba Watershed in Saudi Arabia, International Journal of Civil Engineering and Technology, 7(2), 2016, pp INTRODUCTION The estimation of the expected runoff resulting from rainfall is very important for flood mitigation studies. The direct runoff estimation can not only be used for flood analysis but it is also useful for agricultural irrigation, electricity generation in many countries. Many methods are used to predict runoff. One of these methods which, is widely used, is the Soil Conservation Service Curve Number (SCS-CN) method and its name was changed in 1994 to Natural Resources Conservation Service (NRCS) method (Ferguson, 1988). The NRCS-CN method estimates runoff depth by the following equation, (1b) where P is the rainfall depth (mm), Q is the Runoff depth (mm), and S is the potential maximum storage which is given by, where CN is the curve number parameter. Its value is extracted from the tables formulated by the United States Department of Agriculture (see e.g. Technical Release 55, 1986). The CN ranges from 30 to 100; the minimum values indicate low runoff, however, the values of curve number close to 100 express high runoff. The curve number is dependent on multi factors, for example, land use, soil type, and moisture condition. This method has been established by the United States Department of Agriculture and it depends on the type of land use and land cover of agricultural watersheds in the United States which could be different from the type of land use and land cover characteristics in Kingdom of Saudi Arabia (KSA). The land cover in Saudi Arabia is mainly mountainous with ephemeral streams, sand dunes, fine bed sediments in the stream networks, and has sparse vegetation in the stream course. There is a discussion in the scientific community of water resources concerned with a capability of using the NRCS-CN method in hilly watersheds because the method is developed for flat farmland, so it doesn't take into account hilly features such as in KSA. The major factors affecting runoff generation are: soil type, land use, surface condition, antecedent moisture condition, and treatment. These factors are incorporated in a single CN parameter. The NRCS-CN shortcomings are the following: (1) it does not take into account the effect of spatial scale, (2) it does not take into account the effect of rainfall intensity and its temporal distribution, (3) it is deeply sensitive when incorporating multi factors (e.g. soil type, land use, surface condition, antecedent moisture condition and treatment) in a single parameter; and (4) it does not take the effect of the adjacent moisture condition into consideration (1a) (2) editor@iaeme.com

3 Estimation of NRCS curve number from watershed morphometric parameters: A Case Study of Yiba Watershed in Saudi Arabia (Hawkins, 1993; Ponce and Hawkins, 1996; Michel et al., 2005). The Common technique of estimation of NRCS-CN is as follows (Naseela, et al., 2015): 1. Select and map the outlines of the drainage basin for which curve number needs to be calculated. 2. Determine the area of the drainage basin. 3. Map the soil types and land use for the drainage basin of interest. 4. Convert the soil types to hydrologic soil groups. 5. Overlay the land use and hydrologic soil group maps, identify each unique land use soil group polygon and select the area of each polygon. 6. Assign a CN to each singular polygon, based on standard NRCS-CN table. 7. Calculate an overall CN for the drainage basin by area-weighting the land use soil group polygons within the drainage basin outlines. Ebrahimian et al. (2012) studied the estimation of runoff using standard and slopeadjusted NRCS-CN method in the Kardeh watershed, northeastern Iran. The effects of slope on CN values and runoff depth were determined using a slope-adjusted CN equation. The correlation between estimated and observed runoff depths has r = 0.56, at a rainfall depth P < The results showed that the slope-adjusted CN equation appeared to be inappropriate for runoff estimation in steep slope watersheds, the standard CN method can be used with 55% accuracy in such watersheds for watershed management but not for flood estimation. Shrestha, et al. (2013) investigated the effect of slope on curve number through experimental plots that contain maize crop. They used measured rainfall and runoff data. The investigated watershed slopes in their study are 1%, 3%, and 5%. Applying the infiltration test using double ring infiltrometer to the soil resulted in a soil type of the type of a Hydrologic Soil Group C. These soils have a low rate of water transmission ( mm/hr). The results showed positive correlations between CN and slope. Tedela N. et al. (2012) studied the accuracy and consistency of the NRCS-CN method using rainfall-runoff series from 10 small forested-mountainous watersheds in the eastern United States, they used eight annual maximum series, and these series are the basis to compare tabulated curve numbers with values estimated using five methods. The results show that the Runoff estimates using tabulated CN are unreliable to estimate runoff for 9 of the 10 forested mountainous watersheds. CN chosen for the forests of the Appalachian Highlands requires independent calibration to watersheds delegate of the regional landscape. In this research paper, the authors have not used NRCS-CN tables to estimate runoff based on the aforementioned method; however, the CN values are obtained from rainfall and runoff data of a gauged watershed in the western region of Saudi Arabia. In the current study, an attempt is made to relate the parameter to the morphology of the basin. To the best of the authors knowledge, this is the first attempt to investigate such a relation. Therefore, a black box approach is followed using regression analysis between the observed CN from rainfall-runoff events and watershed morphometric parameters. The values of CN obtained from such study reflect the common values that are expected to be prevailing in the Kingdom of Saudi Arabia (KSA), especially for the south western part of KSA. Yiba watershed is utilized for such study. A complete description of such an area is given in the next section. The proposed approach helps researchers and engineers to obtain CN values from morphometric parameters rather than the classical method of estimation of CN editor@iaeme.com

4 Mohammad O. Alagha Saud A. Gutub and Amro M. Elfeki from Tables of NRCS-CN. The morphometric characteristics that have been used in the study are focused on five parameters: the watershed area, the basin length, the basin slope, the shape factor and the drainage density. Figure 1 shows a sketch for these parameters. Figure1 Morphometric parameters used in the current study 2. THE STUDY AREA The study area is located in the south western part of Saudi Arabia. Figure 2 a, shows the location of the study area. It is in Asier district which is about 380 km away from Jeddah city, lying between & longitudes and & latitudes with an area of 2830 km 2. In this area, there is an experimental watershed called Yiba watersheds, of whose rainfall and runoff depths were measured during the period of 1984 up to 1987 (Dames and Moore, 1984). 3. DATA COLLECTION The data in this study consists of rainfall and runoff measurements. The number of rainfall events used for this study is around 82 events in the period ( ). Figure 3 shows a sample of rainfall-runoff event at station SA401 which has happened on 14 May The location of the runoff station is shown in Figure editor@iaeme.com

5 Estimation of NRCS curve number from watershed morphometric parameters: A Case Study of Yiba Watershed in Saudi Arabia Figure 1 a) Location of Yiba watershed, western region of Saudi Arabia b) Digital elevation model (Modified from Elbishi, 2015) editor@iaeme.com

6 Mohammad O. Alagha Saud A. Gutub and Amro M. Elfeki (a) a) b) c) Clock Time(hr) Rainfall (mm) Time (hr) Q (m3/s) Figure 2 a) Sample of rainfall - runoff event (14 May 1985) at station SA422. b) Table of recorded rainfall data, c) Table of recorded runoff data (Dames and Moore, 1984) editor@iaeme.com

7 Estimation of NRCS curve number from watershed morphometric parameters: A Case Study of Yiba Watershed in Saudi Arabia Figure 3 Yiba watershed, their sub-basins and runoff stations (Modified from Elbishi, 2015). 4. METHODOLOGY The NRCS-CN general equation (Chen, 1982) is given by: where P is the rainfall depth (mm), Q is the Runoff depth (mm), λ is the coefficient for initial abstraction, I a and S is the potential maximum storage. In the current study, the estimation of CN is made through the application of the following equations for rainfall and runoff events in the study area. Taking into account λ = 0.2, Equation (3) reads, (3) and CN is estimated from S as, (4) (5) editor@iaeme.com

8 Mohammad O. Alagha Saud A. Gutub and Amro M. Elfeki The S value is obtained by substituting rainfall and runoff depths in Equation (4) and the value of the curve number is obtained by substituting S in Equation (5). The next flowchart explains the methodology used in the current study. 4.1 Estimation of CN values from rainfall-runoff data The CN values have been calculated using Equation (4) to obtain S, which expresses the maximum potential storage, then substituting S in Equation (5) to get the CN value, Table 1 shows the estimated CN values from rainfall-runoff events in the study area in the period Average rainfall values of curve number are obtained for each sub-basin and regression analysis is applied on these average values since these values heavily depend on rainfall data. Table 2 shows the average values of CN and some statistical measures of CN. The upper and lower limits of 68% confidence of the average CN values are represented in Figure 6. The results show considerable variation in CN for stations SA401 and SA422. However, the variation is relatively less for stations SA423 and SA424. Table 1Estimated CN values from rainfall-runoff events. Station SA401 SA422 Event Date 14-May May Sep Apr Sep Apr-85 CN Event Date 23-Apr Apr May May Apr May-85 CN Event Date 12-May May May Jun May May-85 CN Event Date 15-Aug Mar May May-85 CN SA423 SA May May May Aug May May May May Nov Apr Apr Apr Aug Sep Sep Apr Apr Jun Sep Sep Apr Aug Aug Sep Apr Apr Apr Jun Mar Mar Apr Jun Mar editor@iaeme.com

9 Estimation of NRCS curve number from watershed morphometric parameters: A Case Study of Yiba Watershed in Saudi Arabia Figure 4 Flow-chart illustrates the application of the proposed methodology editor@iaeme.com

10 Curve Number Mohammad O. Alagha Saud A. Gutub and Amro M. Elfeki Table 2 Some statistical measures of curve number calculated from rainfall and runoff data. Station Standard Upper and Number of Average Curve deviation, σ, of lower limit for rainfall (event) Number ( ) (CN) 68% confident +σ -σ Station SA Station SA Station SA Station SA SA401 SA422 SA423 SA424 Yiba Watershed sub-basin at the specified satations Figure 5 The average CN values and its upper and lower limits with 68% confidence. 4.2 Extraction of Yiba watershed and its sub-basins. Firstly, to start the delineation process for a specific watershed, it is required to know the location of the basins outlet and the digital elevation model (DEM) of the study area. The available DEM has multiple resolutions such as 30 m and 90 m. The available DEM used for this study is 90 m, which was obtained from King Abdelaziz City for Science and Technology (KACST). The delineation process has been done by Watershed modeling system (WMS7.1). Figure 6 shows Yiba watershed and its subbasins at the runoff station indicated in the figure. 4.3 Estimation of morphometric parameters of the basins. The morphometric parameters considered in this research are explained in Table 3. These parameters are: the area of the basin, the shape factor, the basin average elevation, the basin slope and the length of the basin editor@iaeme.com

11 Estimation of NRCS curve number from watershed morphometric parameters: A Case Study of Yiba Watershed in Saudi Arabia Morphometric parameter Area Shape Factor Max stream Slope Basin Average Elevation Basin Length Table 3 Morphometric parameters considered in the study. Symbol A SF BS BAE BL The area of the basin Definition Basin shape factor (SF) Where, L c = The distance along the main channel from the basin outlet to the point on the main channel opposite the center of area (centroid). L w = The length of the watershed. (α = 0.3 for length measurements in kilometer) The slope of the basin The Basin Average Elevation of the watershed, calculated from the digital elevation model (DEM). The distance between the outlet and the most distance vertex on the boundary of the basin. The estimated values of these parameters are extracted from WMS software and are summarized in Table 4. These values are later plotted against the estimated values for further analysis as given in the next section. Stations SA401 Table 4 Morphometric parameters for all sub-basins of Yiba watershed. Area (km 2 ) 785 Basin Slope Shape Factor Basin average elevation(m) Basin Length (km) SA SA SA Regression analysis A regression analysis approach is followed to find out relationships between and morphometric parameters given in the previous section. The analysis is based on the least square method and a fitting equation is obtained for each parameter. The criterion of the best fit is made through the evaluation of coefficient of determination, R 2 (Andale, 2016). 5. DISCUSSIONS OF THE RESULTS 5.1 Relationship between CN and Rainfall Depth Hawkins (1993), classified rainfall- runoff system behavior into three categories. These categories are complacent, standard and violent variations. In the complacent behavior, the CN declines steadily with increasing rainfall depth. In the standard behavior, the CN declines with increasing storm size as in the complacent situation, however in the standard behavior, the CN approaches a near-constant value with increasingly large storms. The violent behavior has a different pattern where CN rises suddenly and asymptotically approaches an apparent constant value. Complacent behavior often appears at lower rainfall. The CN is plotted against the rainfall depth editor@iaeme.com

12 Mohammad O. Alagha Saud A. Gutub and Amro M. Elfeki as shown in Figure 7. The observed CN declines with increasing storm rainfall depth. The equation of the standard behavior is in the form of Equation (6) Hawkins (1993), (6) where is a constant approached as ; and b is a fitted constant. The least square method is used to estimate the fitting parameters. The final form of the equation is given as, (7) It has been observed that the CN are highly dependent on the value of the rainfall as confirmed by others researches such as Hawkins (1993) and Kazimierz (2010). The equation that describes the complacent behavior is given by, The equation gives the threshold of runoff at rainfall depth, or where P = 0.2S, the CN value from complacent behavior cannot be used safely for design purposes because no constant value has been clearly approached. (8) Figure 6 CN behavior with the size of the storm 5.2 Comparison between theoretical CN and observed CN. In Figure 8, the observed runoff depths are plotted against the total rainfall depths. Each data point represents one storm and it is depicted with different symbols depending on the range of CN values according to the upper and lower limits as given in the table below, Runoff predictions of the NRCS-CN method for the CN values are estimated using Equations (1a) and (2). The observed CN has been plotted on CN theoretical curves editor@iaeme.com

13 Estimation of NRCS curve number from watershed morphometric parameters: A Case Study of Yiba Watershed in Saudi Arabia Table 5 Limits of categorization of the CN values: A: is the lower limit and B: is the upper limit for each category CN Theory Mean Values Limits of CN values for storm categorization of storm data A CN < B CN < CN < CN < CN < 100 (USDA, 1986). The initial abstraction (I a ) used has the standard value of 0.2. It is obvious, that there is an agreement between the observed CN and the theoretical CN curves. Figure 7 Rainfall-Runoff scatters diagram showing the comparison between theoretical CN mean value, upper-lower limits, and Observed CN. 5.3 Relationship between and Basin Average Elevation (BAE). Figure 9 graph (a) shows a relationship between average CN for the storms at the watershed and BAE in meters. An equation in form of ; 770 BAE 1200 m (9) is fitted to the data under the condition given in the equation. The R 2 for the fit is 0.99 which shows a very good relationship. 5.4 Relationship between and Basin Shape Factor (SF). Figure 9 graph (b) displays a relationship between average CN and SF. An equation in the form of editor@iaeme.com

14 Mohammad O. Alagha Saud A. Gutub and Amro M. Elfeki ; 1.60 SF 2.00 (10) is fitted to the data under the condition given in the equation. The R 2 for the fit is 0.81 which shows a relatively very good relationship. 5.5 Relationship between and Basin Slope (BS) Figure 9 graph (c) shows a relationship between average CN and BS. An equation in the form of ; 0.20 BS 0.30 (11) is fitted under the condition given in the equation. The R 2 for the fit is 0.87 which shows a relatively very good relationship. 5.6 Relationship between and Basin Length (BL) Figure 9 graph (d) shows a relationship between CN and BL in kilometers. An equation in the form of ; 20 BL 50 km (12) is fitted to the data under the condition given in the equation. The R 2 for the fit is 0.78 which shows a good relationship. 5.7 Relationship between and Watershed Area Figure 9 graph (e) shows a relationship between CN and Area in square kilometers. An equation in form of ; 320 Area 2300 (km 2 ) (13) is fitted to the data under the condition given in the equation. The R 2 for this fit is 0.56 which shows a reltively moderate relationship editor@iaeme.com

15 Estimation of NRCS curve number from watershed morphometric parameters: A Case Study of Yiba Watershed in Saudi Arabia Figure 8 Relationship between and watershed parameters. Table 6 summarizes the empirical equations derived in the current study and the range of CN obtained under the limits of applicability of these equations. The equations produce almost the same order of magnitude of upper and lower limits. The average lower limit of is 85.2 and the upper limit is The coefficient of variation, CV, for both minimum and maximum values is very small (CV<<1). These results lead to a conclusion that any of these equations can be used for the estimation of with relatively very small error editor@iaeme.com

16 Mohammad O. Alagha Saud A. Gutub and Amro M. Elfeki Table 6 Comparison of the upper and lower limits of the CN from various equations Parameters Equation Validity of the equation Min CN Max CN R 2 Basin average elevation Shape factor = BAE 770 BAE 1200 (m) = SF 1.65 SF Basin slope = BS 0.20 BS Basin length = BL 20 BL 50 km Area = Area 320 Area 2300 km Average = Standard deviation = Coefficient of variation = VALIDATION The validation process has been performed for the runoff volume and peak discharges by comparing both the observed runoff volumes and peak discharges with the computed runoff volume and peak discharges. The validation is based on the developed formulas of CN in the current study. Equation (9) is adopted for the validation process since it gives the highest R 2 of Validation by Runoff Volume Figure 10 shows a scatter plot between the observed and estimated runoff volumes. A 45 degrees line is added to the figure to test the model performance. The obvious inspection of the results shows relatively reasonable agreement editor@iaeme.com

17 Estimation of NRCS curve number from watershed morphometric parameters: A Case Study of Yiba Watershed in Saudi Arabia Figure 9 Scatter plot between the observed and the computed runoff volumes. 6.2 The Peak Runoff Discharge Validation The peak discharge validation has been performed through the NRCS for TR-55 (USDA, 1986) model given by the formulae, where Q peak is the peak discharge (m 3 /s), A is the area (km 2 ), and Q is the depth of runoff (mm). Q is calculated from the curve number NRCS equation and the CN value has been obtained from Equation (9), and q u is the unit peak discharge (m 3 /s/km 2 /mm), which is calculated from, q u = (15) where, is the conversion parameter ( in metric units), t c is the time of concentration, and C 0, C 1 and C 2 are constants based on the storm type (Type II). The time of concentration, t c, has been calculated by the equation, (16-a) (14) (16-b) where, is the lag time (hr), L is the watershed length (m), CN is the curve number, and H is the average watershed land slope (%). The values of C 0, C 1 and C 2 are constant factors which have been obtained from Table (7). In Figure 11, the observed Q peak are plotted against the computed Q peak, with an additional virtual line of 45 degrees. It should be mentioned that a correction factor of 4.5 has been used to get a reasonable agreement between the observed Q peak and the computed Q peak. The justification of this correction factor has two sides: first is that the storm pattern in Saudi Arabia does not match the NRCS-Type II storms (Elfeki, et al., 2013), and second is that the NRCS equation for peak discharge is derived in editor@iaeme.com

18 Mohammad O. Alagha Saud A. Gutub and Amro M. Elfeki temperate areas that have different characteristics distinct from Saudi Arabia (Elbishi, et al., 2016). Table 7 The values of C 0, C 1 and C 2 for Type II storms (Source: TR-55, USDA) Rainfall Type Ia/P C 0 C 1 C Type (II) Figure 10 Scatter plot between the observed runoff and computed peak discharge. 7. CONCLUSIONS The results show that the curve number, CN, depends on the rainfall event. For Yiba watershed and under the rainfall events during , the CN behavior follows the standard regime with an approached value of 52. It has also been shown that there is a relatively good agreement between the observed CN and the theoretical NRCS- CN curves with the factor of initial abstraction (λ = 0.2). The watershed parameters have an effect on the value of the curve number. Some parameters give a strong relation with the average CN such as the basin average elevation, shape factor, basin slope, basin Length, and watershed area where R 2 is 0.99, 0.81, 0.87, 0.78 and 0.56 respectively within some range of the specified parameter given in each equation editor@iaeme.com

19 Estimation of NRCS curve number from watershed morphometric parameters: A Case Study of Yiba Watershed in Saudi Arabia (Table 6 summarizes the results). Through the validation process, it has been shown that the empirical equation that relates CN to the basin average elevations is relatively good in representing the volume. However, the validation for the peak runoff required some correction factor of 4.5 to get a reasonable fit. The reason for that has two folds: first is that the storm pattern in Saudi Arabia does not match the NRCS-Type II storms and second is that the NRCS equation for peak discharge is derived in temperate areas that have different characteristics that is different distinct from Saudi Arabia. REFERENCES [1] Elbishi M. (2015). Unit Hydrograph of Watersheds in Arid Zones: Case Study in South Western Saudi Arabia, MSc Thesis, King Abdulaziz University, Saudi Arabia. [2] Elbishi, M., Bahrawi, J., and Elfeki, A.M. M. (2016). Empirical Equations for Flood Analysis in Arid Zones, Poster presentation at IWC 2016 International Water Conference 2016 on Water Resources in Arid Areas: the Way Forward. [3] Dames and Moore. (1988). Representative basins study for Wadi: Yiba, Habwnah, Tabalah, Liyyah and Al-Lith (Main Report) Kingdom of Saudi Arabia, Ministry of Agriculture and Water, Water Resource Development Department. [4] Elfeki, A. M. M., Ewea, H. A. and Al-Amri, N. S, (2013). Development of storm hyetographs for flood forecasting in the Kingdom of Saudi Arabia, Arabian Journal of Geosciences, Vol.7, No.10, pp [5] Ebrahimian M., Nuruddin A., Mohd Soom M., Mohd Sood A., and Neng L. (2012). Runoff Estimation in Steep Slope Watershed with Standard and Slope Adjusted Curve Number Methods, Pol.J. Environ Stud, Vol.21, No.5, pp [6] Gajbhiye S. (2015). Morphometric Analysis of a Shakkar River Catchment Using RS and GIS. International Journal of u- and e- Service, Science and Technology Vol.8, No.2, pp [7] Hawkins, R.H. (1993). Asymptotic determination of runoff curve numbers from data, Journal of Irrigation and Drainage Engineering, Amer Soc Civ Eng, Vol.119, No.2, pp [8] Shrestha R. and Mishra S. (2013). Curve number affected by slope of experimental plot having maize crop, Journal of Indian water resources, Vol. 33, No. 2, pp [9] Tedela, N.H., McCutcheon, S.C., Rasmussen, T.C. Hawkins, R.H., Swank, W.T., Campbell, J.L., Adams, M.B., Jackson, C.R. and Tollner, E.W.(2012). Runoff curve numbers for 10 small forested watersheds in the mountains of the eastern United States, Journal of Hydrologic Engineering, Vol.17, No.11, pp [10] USDA. (1986). Urban hydrology for small watershed (Tr-55), second edition, 42 P. [11] Ferguson, B. (1988). Introduction to storm water, John Wiley and Sons, INC., Canada [12] Ponce, V. M. and Hawkins, R. H. (1996). Runoff curve number: Has it reached maturity?, J. Hydrol. E.-ASCE, Vol.1, No.1, pp [13] Michel, C., Andréassian, V. and Perrin, C. (2005) Soil Conservation Service Curve Number Method: How to Mend a Wrong Soil Moisture Accounting Procedure? Water Resources Research, Vol.41, No.2, pp editor@iaeme.com