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Quaternary International 226 (2010) 66e74

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Quaternary International

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Sensitivity analysis and impact quantification of the main factors affecting peakdischarge in the SCS curve number method: An analysis of Iranian watersheds

Mohammad Reza Kousari a,*, Hossein Malekinezhad b, Hossein Ahani a, Mohammad Amin Asadi Zarch b

a Fars Organization center of Jahad Agriculture, Shiraz, Iranb Faculty of Natural Resources, Yazd University, Yazd, Iran

a r t i c l e i n f o

Article history:Available online 4 June 2010

* Corresponding author. Fax: þ987112299029.E-mail address: mohammad_kousari@yahoo.com

1040-6182/$ e see front matter � 2010 Elsevier Ltd adoi:10.1016/j.quaint.2010.05.011

a b s t r a c t

The SCS curve number method is the most commonly used method for the estimation of peak dischargein a watershed. This method is used in numerous complex models such as SWAT, HEC-HMS, EPIC, andAGNPS, but has never been analyzed using the sensitivity analysis, to the best of the authors’ knowledge.The present study deals with the effects of the time of concentration time, watershed area, amount ofrainfall at different return periods and the a-coefficient on the nature of the SCS curve number method inthe estimation of peak discharge and its reaction to change in the input parameters (coefficient ofestimation for getting the time of effective rainfall, which is usually equal to 0.133). Results indicate theeffective role of CN on the input to the peak discharge model. The sensitivity of the model, during theestimation of peak discharge, increases following the increase in the return period. The sensitivityanalysis of SCS curve number method was performed via the MATLAB program. Due to the increasedapplication of MATLAB program for general basin conditions, it can be applicable in special watersheds tofind out the parameter(s) having a significant impact on peak discharge.

� 2010 Elsevier Ltd and INQUA. All rights reserved.

1. Introduction

Inwatershed analysis, the hydrological models are more diverserather than their other counterparts. A careful study shows thatother effective models such as sediment delivery and erosionestimation models are affected by hydrological models and playa basic role in the algorithm of other models. It is notable thatsimplemodels play the basic role for complexmodels such as SWAT(Soil & Water Assessment Tool) (Arnold et al., 1996), HEC-HMS(Hydrologic Modeling System), EPIC (Erosion Productivity ImpactCalculator), and AGNPS (Agricultural Non-Point Source PollutionModel), which were developed after the SCS curve number (SoilConservation Service; now Natural Resources Conservation Service{NRCS}) hydrograph method.

One of the most commonly used and widely applicablemethods is the SCS curve number method used in the estimationof watershed flood hydrograph ordinates. Easy usage and theavailability of the models’ inputs and numerous outputs such aspeak discharge of flood, time to peak, lag time and flood timemake the SCS method more applicable. Many exact hydraulic

(M.R. Kousari).

nd INQUA. All rights reserved.

models derive their input data (i.e., the peak discharge for steadyand uniform flows while flood hydrograph for unsteady flows)from the SCS curve number method. The SCS curve numbermethod is widely used for predicting the storm runoff volume(Zhan and Huang, 2004).

As for every other model, the SCS curve number model alsoneeds the input for getting output, where the amount ofprecipitation, time of concentration time, watershed area and CNare its main inputs. However, a question arises: which input inwhich conditions is effective for the main output (i.e. peakdischarge)? Sensitivity analysis can answer this question. Sensi-tivity analysis accurately compares the certainty and efficiency ofthe models and finds the sensitive conditions for the calibrationof these models. Sensitivity analysis is less time consuming,economical, and effective. It is important to evaluate howa model responds to changes in its inputs as part of the process ofmodel development, verification and evaluation. Furthermore,a sensitivity analysis of the models’ input parameters can serve asa guide to any further application of the model. Quantitativesensitivity analysis is being increasingly used for corroboration,quality assurance, and the defensibility of model-based analyses(Ascough et al., 2004).

Thus, a sensitivity analysis is usually the first step towardsmodel calibration because it answers several questions such as (a)

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where the data collection efforts should focus; (b) what degree ofcare should be taken for parameter estimation; and (c) the relativeimportance of various parameters (Cho and Lee, 2001). Sensitivityanalysis can be used as an aid in identifying the important uncer-tainties for the purpose of prioritizing the additional data collectionor research (Frey and Patil, 2002). In addition, the sensitivityanalysis can play an important role in model verification and vali-dation throughout the course of model development and refine-ment (Kleijnen and Sargent, 2000). Sensitivity analysis can also beused to provide insight into the robustness of the model resultswhen making decisions (Saltelli et al., 2000).

Different methods are available for carrying out sensitivityanalyses and expressing their results (Lenhart et al., 2002; VanGriensven et al., 2002; Van Griensven, 2006; Kannan et al.,2007). Some methods use a percent change in input and reporta corresponding change in output variables. This is not alwayssuitable for the parameters such as saturated hydraulic conduc-tivity and curve number (CN). Hydraulic conductivity can varyover several orders of magnitude, and a 10% variate of a CN valuein hydrologic soil group C can lead to a CN value in the soil group Bor D (Neitsch et al., 2001). Ascough et al. (2004) have reported themethods of sensitivity analysis especially in Natural ResourceManagement with the eligibility and limitations of each method.They concentrated on the qualitative evaluation of four sensitivityanalysis methods: 1) Fourier Amplitude Sensitivity Test (FAST), 2)Response Surface Method (RSM), 3) Mutual Information Index(MII), and 4) the methods of Sobol; and have mentioned that inNatural Resource Management the FAST and Sobol methods areparticularly attractive.

On the other hand, there are different ways of classifyingsensitivity analysis methods, which are broadly classified asmathematical, statistical (or probabilistic), and graphical (Frey andPatil, 2002). Alternatively, these methods can be classified asscreening, local, and global (Saltelli et al., 2000; Ascough et al.,2004). FAST, RSM, MII and Sobol methods are classified as statis-tical (Ascough et al., 2004).

In sensitivity analysis, the use of each method depends on thestructure of model, research condition and the level of accuracy.Some methods are good for complex models and their applicationto the simple model can consume extra time and energy.

The objective of this paper is to survey the effect of main factorson the peak discharge in the SCS curve number method. Themathematical-graphical method that determines the impact ofeach input on peak discharge in a step by stepmanner was used. Formore accuracy and also for saving time, the algorithm for peakdischarge estimation by SCS method and sensitivity analysis wererecorded with the help of MATLAB program.

Direct application of sensitivity analysis studies to the SCScurve number method cannot be found. Therefore, the resultsfrom complex models that use the SCS curve number method havebeen mentioned in these cases (Bhuyan et al., 2002). For the EPICmodel, sand, silt, coarse fragment contents, and CN were found tobe the most sensitive parameters (Brath and Montanari, 2003).Another study indicated that the infiltration is one of the impor-tant factors affecting peak discharge. Mohammed et al. (2004)showed that in the evaluation of AGNPS (Agricultural Non-PointSource Pollution) model, the CN is the most sensitive input for themodel. Holvoet et al. (2005) concluded that, in the sensitivityanalysis of SWAT model, the dominant hydrological parameterswere the curve number (CN), the surface runoff lag time, therecharge to deep aquifer and the threshold depth of water in theshallow aquifer. Cryer and Havens (1999) showed that the runoffcurve number was the most sensitive input parameter for theGLEAMS (Groundwater Loading Effects of Agricultural Manage-ment Systems) model.

2. Materials and methods

2.1. Determination of the SCS curve number method algorithm

The runoff curve number (also called a curve number or simplyCN) is an empirical parameter used in hydrology for predictingdirect runoff or infiltration from rainfall excess.

The curve number method was developed by the USDA NaturalResources Conservation Service, which was formerly called the SoilConservation Service or SCS, and the curve number is still popularlyknownasaSCS runoff curvenumber in the literature.The runoff curvenumber was developed from an empirical analysis of runoff fromsmall catchments and hillslope plots monitored by the USDA. It iswidely used and is an efficient method for determining the approxi-mate amount of direct runoff froma rainfall event in a particular area.

The runoff curve number is based on the area’s hydrologic soilgroup, land use, treatment and hydrologic condition.

The runoff equation is:

Q ¼ ðP � IaÞ2P � Ia þ S

Where:Q is runoff; mm, cm or inch)P is rainfall; mm, cm or inch)S is the potential maximum soil moisture retention after runoff

begins (mm, cm or inch) Ia is the initial abstraction (mm, cm orinch), or the amount of water before runoff, such as infiltration, orrainfall interception by vegetation; and it is generally assumed thatIa ¼ 0.2S.

So the above equation could be written as:

Qp ¼ ðP � 2SÞ2P þ :8S

The runoff curve number, CN, is then related:S ¼ 25400

CN � 254 in mm. lower numbers of CN indicate lowrunoff potential while larger numbers are for increasing runoffpotential.

Qp is used for computation of flood hydrograph (base ondimensionless hydrograph) corresponding to each precipitation incertain return period (Mahdavi, 2007).

To perform sensitivity analysis on the SCS curve numbermethod, its algorithm was determined and rewritten in MATLAB(Fig. 1). CN, time of concentration (Tc), watershed area (A), andamounts of rainfall in different return periods (2, 5, 10, 25, 50 and100 years) were the inputs for studying the sensitivity analysis ofthis program. The effect of a-coefficient was also evaluated alongwith the sensitivity of the above mentioned parameters.

2.2. Determination of the input parameters

The input parameters for the sensitivity analysis were deter-mined in artificial conditions. As the SCS model supports a widerange of inputs for driving significant and logical results, the inputswere divided into 4 homogenous combinations according to Iran’swatershed conditions. This makes the inputs more homogenousand can aid in analyzing the conditional sensitivity. For example,when the time of concentration is about 3 hours, a watershed areaof about 30 km2 is more logical for this value of time of concen-tration. The precipitation in different return periods must also belogical. For example, a 24 hourly precipitation of nearly 100 milli-meters having a 2-year return period is far from the normalweather conditions of Iran. CN values were determined as rangingbetween 60 and 90, as these conditions are usual for Iran’s

Fig. 1. The algorithm for peak discharge estimation for different return periods. qp ¼ peak discharge of unit hydrograph (m3.s�1), Tp ¼ Time to peak (h), Tc ¼ Time of concentration(h), S ¼ The potential maximum soil moisture retention after runoff begins (here is cm), CN ¼ curve number, PRT ¼ precipitation in related to its return period (cm), P ¼ precipitation(cm), QRT ¼ The runoff height in cm and different return period (cm), D ¼ duration time of effective precipitation (h), Qpn ¼ peak discharge in different return period (m3.s�1),a ¼ the coefficient for determination of duration time of effective precipitation from Tc usually equal to 0.133.

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watershed areas and the SCS output may be erroneous outside thisrange (Mahdavi, 2007). As shown in Table 1, out of the fourcombinations, the watershed area and the time concentration havea major effect on the determined homogeneous combinations.Table 2 shows the amount of 24 hour precipitations with returnperiods ranging from 2 to 100 y. These combinations are related tothe size of watersheds.

The range of each of the input parameter for each watershedmay be different from the others, but this program only derives theinputs and for each special condition. It can also carry out thesensitivity analysis within that specific condition.

Table 1Range of inputs, means and repetition steps of watershed area, time of concentration, cu

Combination Area (km2) Mean Time of Concentration TC (h) Mean

Repetition step Repeti

1 1e10 5.5 0.25e1.0 0.6250.1 0.1

2 10e30 20 1e3 20.1 0.1

3 30e60 45 3e6 4.50.1 0.1

4 60e100 80 6e10 80.1 0.1

2.3. Program runs and discharges

In this section, with attention to the determined conditionsaccording to Table 1 and 2 and the algorithm of Fig. 1, one param-eter in the iteration loop format changes in this range with itsrepetition step as shown in Tables 1 and 2. Other parameter valuesare fixed at their average values. For the parameter value corre-sponding to the final step of run in the iteration loop, 6 peakdischarges for six different return periods were obtained and savedin a matrix. A numerical example can be given here to understandthe above mentioned process. For example, to find out the effect of

rve number (CN) and a-coefficient for combinations 1e4.

Curve number CN Mean Coefficient a Mean

tion step Repetition step Repetition step

60e90 75 0.133e0.15 0.14150.1 0.001

60e90 75 0.133e0.15 0.14150.1 0.001

60e90 75 0.133e0.15 0.14150.1 0.001

60e90 75 0.133e0.15 0.14150.1 0.001

Table 2Return periods, means and repetition steps for 24 hour precipitation.

Returnperiod (y)

24ehprecipitation (cm)

Meanprecipitation (cm)

Repetitionstep

2 0.2e0.5 0.35 0.0015 0.5e1.0 0.75 0.110 1e2 1.5 0.125 2e4 3 0.150 3e6 4.5 0.1100 5e10 7.5 0.1

Table 3The systematic program runs used to estimate discharges. The CN is changed but theother input parameters are fixed at their averages.

Name of Inputparameters andalso output discharge

Range of inputs and outputs

CN 60 60.1 60.2 60.3 . 90Area (km2) 80 80 80 80 80 80concentration time (h) 8 8 8 8 8 8Alpha coefficients 0.14 0.14 0.14 0.14 0.14 0.14Precipitation in

different returnperiods (cm)

2 years 0.35 0.35 0.35 0.35 0.35 0.355 years 0.75 0.75 0.75 0.75 0.75 0.7510 years 1.5 1.5 1.5 1.5 1.5 1.525 years 3 3 3 3 3 350 years 4.5 4.5 4.5 4.5 4.5 4.5100 years 7.5 7.5 7.5 7.5 7.5 7.5

Discharge indifferent returnperiods (m3/2)

2 years 0 0 0 0 . 05 years 0 0 0 0 . 0.2710 years 0 0 0 0 . 5.5325 years 0 0 0 0 . 26.7850 years 1.63 1.68 1.72 1.77 . 54.41100 years 19.08 19.27 19.45 19.63 . 117.02

Fig. 2. The change in peak discharge due to the change in standard normal variates ofinput parameters for combination 1 given in Table 1.

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curve number on the peak discharge, in the first step, a CN value of60 (which is the lower limit of CN range) is made to enter theiteration loop and 6 peak discharge values having six differentreturn periods are obtained. Other parameters (i.e., precipitationfor 6 different return periods, Time of Concentration, Area, and a-coefficient) are fixed at their average values. In the next step, CNenters the loop with a value of 60 þ 0.1 (where 0.1 is the repetitionstep for CN), other parameters are fixed at their average values, andthe 6 peak discharge values related to 6 different return periods areobtained. Table 3 indicates the process.

The same procedurewas followed for other input parameters. Toperform these processes for 4 sets of parameters for more than1000 times, the iterating loop must be repeated.

2.4. Normalization of the inputs

Sensitivity analysis is a very cumbersome process, because it hasto be carried out differently among different sets of input param-eters. For example, in this case, the area of watershed is in squarekilometers, the time of concentration is in hours, amount ofprecipitation is in centimeters, and the CN and a-coefficient aredimensionless. This problem is seen when the effects of differentparameters on peak discharge are shown in the same graph. On theother hand, for comparing and determining the relative impact ofeach input parameters on the output (i.e., peak discharge), thesteepness of each curve is related to each parameter. Normalizationof all the input parameters must be carried out in such an experi-ment. The standardization of the input parameters makes themdimensionless. Equation (1) is used for this process wherein thestandard normal variable is computed.

ðnÞ ¼ ðXi � XÞst

Standard normal variable (1)Where Xi is the ith member of input set for each parameter, X

and st are the population mean and population standard deviationof each input parameter, respectively.

2.5. Effects of changes

After the above steps, the peak discharge values were comparedfrom each of the normalized input parameters. Figs. 2e5 displaythis processes for 4 combinations as indicated in Table 1. Each groupof 5 curves in these figures is related to a certain return period. Theupper group of 5 curves is related to the 100-year return period. Asp< 0.2 s condition in the SCSmethod algorithm (indicated in Fig.1),the peak discharge for low return period values of 2 and 5 y are 0,because the runoff amount is 0. Thus, no curve group is related toreturn period values of 2 and 5 y.

Figs. 2e5 are not sufficient to display the relative impact of theinput parameters on the peak discharge. They show a generalcomparison of effect of each parameter on the peak discharge.According to these figures, each curve with more steepness has

more effect on peak discharge, the relation between peak dischargeand the time of concentration or a-coefficient is an inverse rela-tionship, and the effect of time of concentration or CN is not linearbut exponential. This creates a problem in distinguishing the rela-tive impact of the input parameters on the peak discharge at thesame steepness of two or more input parameters. To solve thisproblem, each curve is divided into two regions to distinguishheight and slope, which were then compared. Mathematically, thedependence of a variable y on a parameter x is expressed by thepartial derivative Dy/Dx (Lenhart et al., 2002). Figs. 6e17 displaythis process only for the 1th and 4th combinations, respectively.Each figure is associated with a certain return period.

Figs. 2e5 present the general comparison among all inputparameters at 4 return periods from 10 to 100 y. For the time ofconcentration and a-coefficient, which have negative slopes andare inversely related to the peak discharge, the absolute amounts(abs function) must be applied to access the steepness andcomparison with other curves.

Fig. 3. The change in peak discharge due to the change in standard normal variates ofinput parameters for combination 2 given in Table 1.

Fig. 5. The change in peak discharge due to the change in standard normal variates ofinput parameters for combination 4 given in Table 1.

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3. Results

According to Figs. 2e5, a general concept of the effects of allparameters on peak discharge values for different return periodscan be noted. For example, it is understood that the time ofconcentration, especially where it has a low value, severely affectsthe amount of peak discharge, because the slope of the time ofconcentration is very steep as compared to other input parameters.On the other hand, this effect increases with the increase in thereturn period, because the distance among the group of 5 curvesincreases with the increase in the return period. The generaldistances between curve groups related to a 50-year return periodis lesser than for curves of 25-year return period. The model outputis sensitive to a shorter time of concentration and increases withthe increase in return period. In total, with an increase in the returnperiod, the sensitivity of the model to all of the input parametersincreases. The time of concentration and a-coefficient are inverselyrelated, whereas the watershed area, amount of precipitations indifferent return periods and CN are directly related to peak

Fig. 4. The change in peak discharge due to the change in standard normal variates ofinput parameters for combination 3 given in Table 1.

discharge. From Figs. 2e5, results could be achieved from thesteepness of the curve at distinct heights. For example, Fig. 11indicates the curve’s steepness for combination 1 for a 100-yearreturn period. The y axis shows curve steepness (slope change ofeach distinct height) and the x axis is related to the standard normalvariate of the input parameters. In this figure, the more effectiveparameter has a greater value. The contact point of each of twocurves indicates that the effect of a specific parameter on peakdischarge has increased or decreased.

From Fig. 11, the model is sensitive to low values of time ofconcentration (TC), because its slope change values in peakdischarge are higher. By increasing the amount of curve number(CN), TC, the slope change due to change in TC becomes lower thanthat compared to the change in CN. This shows that in this range ofstandard normal variate, CN is the more effective parameter espe-cially when CN is large.

Fig. 6. The slope change graph in peak discharge for 2-year return period for combi-nation 1 of Table 1 for different standard normal variate values of input parameters.

Fig. 7. The slope change graph in peak discharge for 5-year return period for combi-nation 1 of Table 1 for different standard normal variate values of input parameters.

Fig. 9. The slope change graph in peak discharge for 25-year return period forcombination 1 of Table 1 for different standard normal variate values of inputparameters.

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This experiment determines the comparatively increasedpriority of CN. In all figures and combinations, the effect of changesin a-coefficient is less significant than that of any other parameter.

Figs. 7 and 12 represent the slope changes in peak discharge dueto the standard normal variates of curve number (CN) for a 5-yearreturn period. A horizontal straight line passing through 0 is shownin both these figures, corresponding to p < 0.2 s condition. Thisindicates that no other input parameter has any impact on theoutput of peak discharge.

Fig. 17 shows slope change graphs for a 100-year return period.The effect of low amounts of precipitation is significant comparedto that of other parameters. By increasing the amount of CN, theimpact of CN will be greater.

The 4 combinations stated in the present study are related toIran’s general watershed conditions. Different results will appearwhen the sensitivity analysis is used for a specific watershed.

Fig. 8. The slope change graph in peak discharge for 10-year return period forcombination 1 of Table 1 for different standard normal variate values of inputparameters.

4. Discussion

The results derived from the present study are in agreementwith previous work (Cryer and Havens, 1999; Bhuyan et al., 2002;Brath and Montanari, 2003; Mohammed et al., 2004). When thetime of concentration is short, this indicates small watershed area,low potential for infiltration, low loss of precipitation, more slope,circularity of watershed, and short stream length. Any one of thesefactors or a combination of them causes increased peak dischargein the outlet. In other words, the evacuation of the flood-volume ismore that there is no storage routing in small and sloped water-sheds. From Fig. 2, by increasing the time of concentration, peakdischarge decreases as compared to CN or precipitation. Anincreasing concentration time is related to an increase in the streamlength, lesser slope of watershed, less slope of main stream,increased loss of rainfall, more watershed area, more watershed

Fig. 10. The slope change graph in peak discharge for 50-year return period forcombination 1 of Table 1 for different standard normal variate values of inputparameters.

Fig. 11. The slope change graph in peak discharge for 100-year return period forcombination 1 of Table 1 for different standard normal variate values of inputparameters.

Fig. 13. The slope change graph in peak discharge for 5-year return period forcombination 4 of Table 1 for different standard normal variate values of inputparameters.

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elongation, and the occurrence of storage routing, as it is effectivefor decreasing the peak discharge at the outlet. The inverse rela-tionship between peak discharge and time of concentration isrelated to this phenomenon.

The peak discharge increases by increasing the amount ofprecipitation, which is more significant at high return periods. Theoccurrence of huge floods with high return periods confirms thisphenomenon. The watershed area shows a linear variation withpeak discharge. There is an empirical equation in hydrologyaccording to which an increase in the watershed area leads to anincreasing peak discharge, but this relationship is not linear. Asshown in Fig. 1, the related equation comprising the area in thealgorithm is linear and the a-coefficient (i.e., the ratio of the unithydrograph duration and time of concentration) is same for all thewatershed areas, but other parameters do not show a linearequation relationship.

Fig. 12. The slope change graph in peak discharge for 2-year return period forcombination 4 of Table 1 for different standard normal variate values of inputparameters.

The parameter with the largest influence on the peak dischargeis the curve number (CN). By increasing the CN, the peak dischargeincreases exponentially and the slope of this curve is steeper thanthat due to other parameters. The effect of CN on peak dischargeincreases with the increasing return period and watershed area. Inthe presence of large amounts of CN, the potential of the watershedfor runoff production increases. Increasing CN causes an increasedvolume of flooding. Additionally, CN affects the time of concen-tration. An increase in CN causes the concentration time todecrease, producing more peak discharge in the watershed outlet.Therefore, increasing CN can affect in two ways: 1) increasing therunoff and 2) decreasing the time of concentration. On the otherhand, a decrease in CN causes a decrease in the peak discharge. Thiseffect is more significant for high return periods. It indicates therole of CN in watershed management and flood mitigation. Anincrease in CN causes an increase in the effect of precipitation,

Fig. 14. The slope change graph in peak discharge for 10-year return period forcombination 4 of Table 1 for different standard normal variate values of inputparameters.

Fig. 15. The slope change graph in peak discharge for 25-year return period forcombination 4 of Table 1 for different standard normal variate values of inputparameters.

Fig. 17. The slope change graph in peak discharge for 100-year return period forcombination 4 of Table 1 for different standard normal variate values of inputparameters.

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which to some extent that indicates the relation between CN andprecipitation. This effect can be seen in bare soils of semiarid andarid regions where the convectional precipitation causes largeflooding with very low infiltration.

An increase in the frequency and volume of flood leads to anincrease in the peak discharges in Iranian watershed conditionsalong with increased soil-degradation, and weather changesconfirm this effect. These factors cause serious degradation in theland cover consisting of plant and soil, which generally increasesthe amount of watershed CN. A number of studies have shown thatchanges in vegetation cover, i.e. deforestation or the lack of forestsleads to an increase or decrease in water yield. Such changes havebeen observed in catchments with different areas ranging from lessthan 1 to over 1000 km2 (Richey et al., 1989; Laurance, 1998; Fohreret al., 2001; Huang and Zhang, 2004; Brown et al., 2005). Very lowchanges in CN have an important effect on peak discharge. The

Fig. 16. The slope change graph in peak discharge for 50-year return period forcombination 4 of Table 1 for different standard normal variate values of inputparameters.

present study has shown the same effect of CN on the peakdischarge as shown by other researchers (Cryer and Havens, 1999;Bhuyan et al., 2002; Brath and Montanari, 2003; Mohammed et al.,2004). These models use CN method as a basic algorithm for theestimation of runoff and flood hydrograph.

It is obvious that the effect of CN ismore pronounced than that ofother parameters. Additionally, the present study has also observedthe effect of other input parameters on the peak discharge.

Many various combinations of basin conditions could beselected for the sensitivity analysis of SCS curve number method inIran. Therefore, in this study, more general conditions in fourcombinations have been considered. These combinations were setto cover the general condition. For example, CN and precipitation ismore variable rather than other physiographic parameters such asarea of basin or to some extent time of concentration. The amountof CN in each basin probably can change according to conditions(antecedent soil moisture, land use and vegetation canopy cover,and soil texture). In all combinations, the amounts of CN andprecipitation have been changed widely rather than time ofconcentration and basin area. CN changes over these ranges coverall conditions such as good or poor vegetation cover, and drier ormoist soil conditions.

Although this sensitivity analysis method has produced somevaluable results, this method does not indicate the interactionbetween the parameters. There is a need for the application ofstatistical methods in sensitivity analysis such as FAST, RSA, andGLUE. Future works aim to survey sensitivity analysis of SCS curvenumber method using these statistical methods.

5. Conclusion

The present study, with the help of mathematical and graphicalsensitivity analysis of the SCS curve number method, has clearlyestablished the nature of peak discharge to the various inputparameters. Within these parameters, CN and the amount ofprecipitation have been identified as the main factors in estimatingthe peak discharge and flood mitigation. In general, CN, Precipita-tion, TC, Area and a coefficient are more sensitive, respectively. Thisindicates the practical and research-based importance of the allo-cation of time, money and skill to estimate these sensitive param-eters required in the process of watershed management. Before

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estimating the peak discharge in the special watershed, it is betterto test the SCS curve numbermethodwith some accuratemodels toprecisely estimate the affecting parameters to give more preciseestimates of peak discharges. The sensitivity of models increaseswith an increase in the return period. Therefore, calibration of thismethod for high return periods is necessary. Keeping in mind theeffect of CN on peak discharge, a decrease in its value can lead toa reduction of flood, erosion and sediment delivery in watersheds.

Acknowledgments

The authors acknowledge the efforts of the Jahad -e- AgricultureOrganization of Fars province and University of Yazd for supportingthe present work.

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