at SciVerse ScienceDirect

Environmental Modelling & Software 46 (2013) 155e162

Contents lists available

Environmental Modelling & Software

journal homepage: www.elsevier .com/locate/envsoft

Application of the HEC-HMS model for runoff simulation in a tropicalcatchment

D. Halwatura, M.M.M. Najim*

Environmental Conservation and Management Degree Programme, Department of Zoology, Faculty of Science, University of Kelaniya, Kelaniya, Sri Lanka

a r t i c l e i n f o

Article history:Received 19 September 2012Received in revised form7 March 2013Accepted 10 March 2013Available online 6 April 2013

Keywords:HEC-HMSCalibrationValidationSCS Curve NumberSnyder unit hydrographClark unit hydrograph

* Corresponding author. Tel.: þ94 777412089, þ92914479.

E-mail address: mnajim@kln.ac.lk (M.M.M. Najim)

1364-8152/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.envsoft.2013.03.006

a b s t r a c t

Hydrologic simulation employing computer models has advanced rapidly and computerized modelshave become essential tools for understanding human influences on river flows and designing ecolog-ically sustainable water management approaches. The HEC-HMS is a reliable model developed by the USArmy Corps of Engineers that could be used for many hydrological simulations. This model is not cali-brated and validated for Sri Lankan watersheds and need reliable data inputs to check the suitability ofthe model for the study location and purpose. Therefore, this study employed three different approachesto calibrate and validate the HEC-HMS 3.4 model to Attanagalu Oya (River) catchment and generate longterm flow data for the Oya and the tributaries.

Twenty year daily rainfall data from five rain gauging stations scattered within the Attanagalu Oyacatchment and monthly evaporation data for the same years for the agro meteorological stationHenarathgoda together with daily flow data at Dunamale from 2005 to 2010 were used in the study. GISlayers that were needed as input data for the flow simulation were prepared using Arc GIS 9.2 and usedin the HEC-HMS 3.4 calibration of the Dunamale sub catchment using daily flow data from 2005 to 2007.The model was calibrated adjusting three different methods. The model parameters were changed andthe model calibration was performed separately for the three selected methods, the Soil ConservationService Curve Number loss method, the deficit constant loss method (the Snyder unit hydrographmethod and the Clark unit hydrograph method) in order to determine the most suitable simulationmethod to the study catchment. The calibrated model was validated with a new set of rainfall and flowdata (2008e2010). The flows simulated from each methods were tested statistically employing the co-efficient of performance, the relative error and the residual method. The Snyder unit hydrograph methodsimulates flows more reliably than the Clark unit hydrograph method. As the loss method, the SCS CurveNumber method does not perform well.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

1.1. Hydrological simulation modeling

There is a need for supporting environmental planning choiceswith simulation and prediction models, due to the development ofregulatory and planning tools, such as the river basin master plan,which involve a direct link between the description of physicalphenomena (such as floods) and the attribution of land planningconstraints. The need of such a modeling system is stimulated, andsometimes even enforced, by the many activities required by riverbasin planning andmanagement, ranging from timely flood alert to

4 11 2903402; fax: þ94 11

.

All rights reserved.

the demarcation of areas at risk of flooding, to the programming ofwater budget at the basin scale, according to the national andregional regulations in the field (Razi et al., 2010). With limited orno data, the quantitative understanding and prediction of theprocesses of runoff generation and its transmission to the outletrepresent one of the most challenging areas of hydrology. Tradi-tional techniques for design flood estimation include the rationalmethod, empirical methods, flood frequency method, unit hydro-graph techniques, and watershed models. The unit hydrographtechniques and watershed models can be used to estimate thedesign flood hydrograph in addition to the magnitude of the designflood peak (Kalita, 2008). Hydrological modeling is a commonlyused tool to estimate the basin’s hydrological response due toprecipitation. The selection of the model depends on the basin andthe objective of the hydrological prediction in the basin(Hunukumbura et al., 2008).

D. Halwatura, M.M.M. Najim / Environmental Modelling & Software 46 (2013) 155e162156

1.2. HEC-HMS model for flow simulation

HEC-HMS (Hydrologic Engineering Center e HydrologicModeling System) model was developed by the US Army Corps ofEngineers (Feldman, 2000) that could be used formanyhydrologicalsimulations. The HEC-HMS model can be applied to analyze urbanflooding, flood frequency, flood warning system planning, reservoirspillway capacity, stream restoration, etc. (U.S. Army Corps ofEngineers, 2008). The proliferation of personal computers and thedevelopmentof theHEC-1model of theU.S. ArmyCorpsof Engineersin 1998 to a GUI (graphical user interface) based user-friendly HEC-HMSmodel is available in the public domain, have come as anotheruseful tool to the field hydrologists. Unfortunately, the HEC-HMSmodel, or any of the many watershed models for that matter, hasnot found many takers due to the uncertainty involved in the esti-mation of parameters of themodels. But, parameter estimation on aregional scale at least may be possible to switch over to watershedmodels like the HEC-HMS and take advantage of the high speedcomputer programs than spreadsheet exercises (Kalita, 2008).

The HEC-HMS contains four main components. 1) An analyticalmodel to calculate overland flow runoff as well as channel routing,2) an advanced graphical user interface illustrating hydrologicsystem components with interactive features, 3) a system forstoring and managing data, specifically large, time variable datasets, and 4) a means for displaying and reporting model outputs(Bajwa and Tim, 2002). This model is not calibrated and validatedfor the Sri Lankan watersheds and need reliable data inputs tocheck the suitability of the model for the study location and pur-pose. Calibration of rainfall-runoff models with respect to localobservational data is used to improve model predictability. Whenmodel results match observed values from stream-flow measure-ment, users have greater confidence in the reliability of the model(Muthukrishnan et al., 2006).

A total of nine different loss methods are provided in HEC-HMSand some of these methods are designed primarily for simulatingevents, while others are intended for continuous simulation.Gridded Loss Methods and Soil Moisture Accounting Loss Methodsare not preferred for the simulation studies because they require ahigh number of parameters. Among the remaining loss methods,the simplest one “Initial and Constant Loss” method is selected forthe event based simulation studies. The method is simple andpractical because it requires only three input parameters such asinitial loss (mm), constant rate (mm/h) and impervious area (%). Atotal of seven different transformation methods are provided inHEC-HMS. Some of these methods are complicated which requestmore inputs which are not available for most of the ungaugedcatchments. Snyder unit hydrograph (Yilma and Moges, 2007;Hunukumbura et al., 2008; Fang et al., 2005) and Clark unithydrograph (Cunderlik and Simonovic, 2010; Straub et al., 2000;Banitt, 2010) methods have been applied successfully to simulatelong term stream flows elsewhere. Therefore a study was doneemploying the three different methods of HEC-HMS 3.4 to calibrateand validate it to Attanagalu Oya (River) catchment and generatelong term flow data for the Oya and tributaries. The methods usedin the study were Soil Conservation Service Curve Number lossmethod, deficit constant loss Snyder unit hydrograph method andClark unit hydrograph method.

2. Methodology

2.1. Study area

Attanagalu Oya catchment which is located between 7� 6.600e7� 7.880 N latitudeand 80� 7.020e80� 5.220 E longitude was selected for this study. The river and itstributaries are shown in Fig. 1.The studied catchment area is composed of theAttanagalu Oya and Dee Eli Oya and it covers approximately 337.06 km2.

Considering the topography of the catchment area, it can generally be divided into three areas, hills (200 me400 m), plains (20 me40 m) and in between hills andplains there is a middle elevation area (40 me200m). In view of the landuse types ofthe entire catchment, it can be generally divided in to agricultural areas, urbanizedareas, natural forest patches and scrublands. The basin lies almost entirely withinthe ‘low country wet zone’ agro ecological zone; characterized by 75% expectancy ofannual rainfall of 1700 mme3200 mm.

2.2. Data collection

Daily rainfall was collected from five stations, Vincit, Chesterford, Kirindiwela,Nittambuwa and Pasyala for the past twenty years (1991e2010). Monthly evapo-ration data for the same years for the agro meteorological station HenarathgodaeGampaha was used in the study. The rainfall data and the evaporation data wereobtained from, the Rainfall Division of the Department of Meteorology, Colombo.Daily river flow data for the past six years (2005e2010) at Dunamale gauging stationof the Attanagalu Oya was obtained from the Department of Irrigation, Colombo.Monthly minimum flows were considered as the base flow accordingly at theDunamale gauging station.

2.3. Data map preparation

Spatial data preparation was done using Arc GIS 9.2 software package. Datamaps that were needed as input data for the flow estimation were prepared. Areacovered by each landuse type, the total area of the catchment and the stream lengthswere measured based on the digitized maps. Within the main catchment, three subcatchments (Pasyala, Nittambuwa, and Yakkala) were digitized according to thedistribution of rain gauge stations (Fig. 1). For the model calibration and validationprocess, another sub catchment was digitized based on the Dunamale river flowgauging station (Fig. 1) (latitudes e 7� 6.500 N, longitudes e 80� 4.450 E).

2.4. Model application

The daily stream flows were computed using the HEC-HMS 3.4 model and theprepared data maps were used in the model. Watershed and meteorology infor-mation were combined to simulate the hydrologic responses. Data that are requiredfor the hydrological modeling of the catchment are; area of the catchment and thesub catchments, landuse patterns of the catchment areas, daily rainfall data, dailyriver flow data, monthly evaporation data, base flow, peaking coefficient, impervi-ousness, standard lag, initial deficit, constant rate, time of concentration, storagecoefficient and curve number. These values were taken considering the prominentsoil type in the catchment area. The main geological formations in the basin area arelaterite, unconsolidated sand, alluvium, peat deposits and crystalline basementrocks (Wijesekara and Kudahetty, 2010).

2.5. Model calibration

Dunamale sub catchment was used to calibrate the model. Daily rainfall data forthree years (2005e2007), monthly base flows of the river, monthly evaporation dataof the catchment and the catchment areawere inserted to themodel. Themodel wascalibrated employing three different approaches in order to determine the mostsuitable method for the study catchment. The flows simulated from each of themethods were tested statistically.

2.5.1. Soil Conservation Service (SCS) Curve Number loss methodFor the calibration and validation process, Soil Conservation Service (SCS) Curve

Number (CN) loss method was used. The SCS CN method implements the curvenumber methodology for incremental losses. Originally, the methodology wasintended to calculate total infiltration during a storm. The program computes in-cremental precipitation during a storm by recalculating the infiltration volume atthe end of each time interval. Infiltration during each time interval is the differencein volume at the end of two adjacent time intervals. The SCS CN method requirespercentage landuse pattern of the catchment and the sub catchments, total length ofthe river and the elevation of the catchment area. SCS CN model estimates precip-itation excess as a function of cumulative precipitation, soil cover, landuse andantecedent moisture content (Feldman, 2000). The maximum retention andwatershed characteristics are related through an intermediate parameter, the curvenumber. The CN values range from 100 (for water bodies) to approximately 30 forpermeable soils with high infiltration rates.

2.5.1.1. SCS Curve Number. The CN were taken as a weighted value based ondifferent landuses in the study area. Calculation of weighted curve number (WCN) isshown by Equation (1),

WCN ¼Pi¼n

i¼1 CNi$AiPi¼ni¼1 Ai

(1)

where, WCN is weighted curve number, Ai is area for ith landuse type and CNi iscurve number for ith landuse type. Curve numbers were taken from standard curve

Fig. 1. The drainage network, four sub-catchments and the location of the flow gauging station in the study catchment.

D. Halwatura, M.M.M. Najim / Environmental Modelling & Software 46 (2013) 155e162 157

number tables (Schwab et al., 2005). Calculated weighted curve number was used inthe calibration of the model and was changed consecutively. The model simulationwas performed for each curve number separately in order to find the most suitablecurve number for the study area.

The area of the sub-basin which is impervious (%) needs to be specified as aportion of total area. No loss calculations are carried out on the impervious areaswhere all the precipitation on such portions become excess precipitation and sub-jected to direct runoff.

2.5.2. Deficit constant loss methodThe deficit constant loss method uses a single soil layer to account for contin-

uous changes in the moisture content. It should be used in combination with ameteorological model that computes evapotranspiration. The potential evapo-transpiration computed by the meteorological model is used to dry out the soil layerbetween precipitation events.

2.5.2.1. Transform method. While a sub-basin element conceptually representsinfiltration, surface runoff and subsurface processes interact together with the actualsurface runoff. The calculation of the surface runoff is performed by a transformmethod contained within the sub-basin. A total of seven different transformmethods are provided by the model. The deficit constant loss method was per-formed in two different transform methods, namely the Clark unit hydrograph andthe Snyder unit hydrograph.

2.5.2.2. Clark unit hydrograph. The Clark unit hydrograph is a synthetic unithydrograph method. That is, the user is not required to develop a unit hydrographthrough the analysis of the past observations. Instead a time versus area curve builtinto the program is used to develop the translation hydrograph resulting from aburst of precipitation. The resulting translation hydrograph is routed through alinear reservoir to account for the storage attenuation across the sub-basin.

2.5.2.3. Snyder unit hydrograph. The Snyder unit hydrograph is also a synthetic unithydrograph method. It was originally developed to compute the peak flow as a unitof precipitation. Additionally, empirical methods have been developed for esti-mating the time base of the hydrograph and the width at 50% of the peak flow. Theimplementation used in the program utilizes a unit hydrograph generated with theClark methodology so that the empirical Snyder relationships are maintained.

The unit hydrograph technique is used in the runoff component of a rain eventto transform rainfall excess to out flow and it represents direct runoff at the outlet ofa basin resulting from one unit of precipitation excess over the basin. The excessoccurs at constant intensity over a specified duration. The deficit constant lossmethod is designed as a simple, one-layer model for continuous soil moisturesimulation. The soil is assumed to have a fixed water holding capacity, fixed infil-tration rate and the full potential amount is removed from the soil without ac-counting for reductions due to increasing tension at lowwater contents. Simplifyingassumptions are made regarding soil dynamics so that infiltration only occurs whenthe soil is saturated. Water is removed from the soil to simulate evapotranspiration.

Table 1Three year means (2005e2007) of the CPA0 for different CN values.

Month Mean CPA0

CN 71 CN 65 CN 60 CN 55 CN 50 CN 45 CN 40 CN 20

January 35.6 16.2 9.5 7.4 7.1 7.1 7.1 7.1February 20.9 12.8 7.8 4.2 2.0 0.9 0.6 0.5March 1167.8 996.6 848.3 699.5 554.3 416.9 292.0 11.4April 847.4 798.0 747.0 686.5 616.4 536.8 448.6 75.4May 232.6 223.9 214.4 202.7 188.2 170.8 150.1 39.3June 627.0 613.5 598.2 578.3 552.9 520.6 479.9 202.5July 138.4 136.4 134.0 130.8 126.6 121.1 113.9 56.9August 257.7 254.9 251.7 247.4 241.6 233.8 223.4 132.5September 335.8 332.8 329.3 324.5 318.0 309.3 297.4 187.1October 21.5 21.4 21.2 21.0 20.7 20.3 19.7 13.8November 68.2 68.1 67.8 67.5 67.1 66.5 65.6 54.5December 7.6 7.6 7.6 7.5 7.5 7.5 7.4 6.4Mean 313.4 290.2 269.7 248.1 225.2 201.0 175.5 165.6

D. Halwatura, M.M.M. Najim / Environmental Modelling & Software 46 (2013) 155e162158

Potential evapotranspiration is computed by any of the methods available in theprogram. While a sub-basin element conceptually represents infiltration, surfacerunoff, and subsurface processes interacting together, the actual infiltration calcu-lations are performed by a loss method contained within the sub-basin. The lossmodels describe the precipitation loss as a result of interception, depression,evaporation, etc. The SCS CN loss model calculates the rainfall runoff based onprecipitation, land use/land cover, and antecedent moisture. The initial abstractionrepresents the precipitation depth before precipitation excess can occur. The CN ineach sub-basin represents the combination of the different land use/land cover andsoil groups in this sub-basin. The hydrologic characteristics of soils within awatershed are a primary factors influencing the runoff potential (U.S. Army Corps ofEngineers, 2008; Feldman, 2000; Cunderlik and Simonovic, 2010; Duan, 2011;Fleming and Scharffenberg, 2012).

2.6. Statistical evaluation

James and Burgess (1982), Perrone and Madramootoo (1997), Babel et al. (2004)and Najim et al. (2006) suggested a common method for the evaluation of timeseries agreement by examining the sum of the squared differences. They havesuggested “coefficient of performance for the error series A” (CPA) which is used inthe studies related to hydrologic simulations (Equation (2)). They have furthersuggested dividing the above term by the length of series to obtain a measure of theerror individual values within the series known as coefficient of performance (CPA0),which is shown in Equation (3). The coefficient of performance approaches to zero asthe observed and the predicted values get closer. The equations to calculate the CPAand the CPA0 are shown below.

CPA ¼XNi¼1

½Si � Oi�2 (2)

CPA0 ¼ CPAPN

i¼1�OðiÞ � Oavg

�2 (3)

Si ¼ ith simulated parameter; Oi ¼ ith observed parameter; Oavg ¼ mean of theobserved parameter; and N ¼ total number of events.

In addition, the model performance was evaluated by comparing the simulatedand observed parameters in term of the relative error (RE) (Babel et al., 2004; Najimet al., 2006). Relative errors, error/measured value, weigh the metric toward smallervalues since larger ones may only have small relative error. The majority of metricsalready defined can be calculated on relative errors (Bennett et al., 2013). The per-centage RE is defined in the Equation (4).

RE% ¼�Simulated� Observed

Observed� 100

�(4)

The percentage RE is negative for under prediction and positive for over pre-diction. Following the simulation process of three different methods, simulatedflows were statistically analyzed by using the CP method. Generated CP values weregraphically compared to find the coefficient of performance values that approachesvirtually zero. In addition the validated model was evaluated by residual method(Bennett et al., 2013; Pauly, 1980). Bennett et al. (2013) stated that the most prev-alent methods for model evaluation are residual methods, which calculate the dif-ference between observed and modeled data points. The residual plot is a simplegraphical method to analyze model residuals. Of the many possible numerical cal-culations on model residuals, by far the most common are bias and Mean SquareError. Bias is simply the mean of the residuals, indicating whether the model tendsto under- or over-estimate the measured data, with an ideal value zero.

The calibrated model was used in the validation process with a new set ofrainfall data for the next three years (2008e2010). The parameters that were foundfrom the calibration and the validation processes were used in the model simulationfor the whole catchment. The calibrated and the validated model was applied to thefour sub catchments for daily rainfall values of fifty years. Daily flows were gener-ated in cumecs (m3 s�1).

3. Results and discussion

3.1. Runoff by SCS CN method

The Natural Resources Conservation Service (NRCS) CN devel-oped by the U.S. Department of Agriculture and NRCS, formerlyknown as the SCS, are used to estimate the runoff of an area or sub-areawith a given type of cover, over a given soil, for a given depth ofprecipitation. A higher CN means more runoff where a CN of 100means that all the rain will flow as runoff. CN’s are no greater than98, even for conventional pavements, since some small amount ofrainfall will be held by the surface. The CNmethod provides a more

flexible and site specific method for selecting appropriate designvalues for estimating runoff.

The calibration process was continued by adjusting the SCS CN.The model was initially run with the weighted CN (WCN) and theCPA0 was calculated. The CN was changed by a certain percentageuntil the results of the statistical evaluation gives a CPA0 value closerto zero. The obtained CPA0 values observed for different CN valuesare given in Table 1. The CPA0 values corresponding to the per-centage reduction of CN for the upper Attanagalu Oya catchmentare shown in Fig. 2.

The results show the means of obtained CPA0 values (313.4) forthe three years of the calibration process (2005e2007) for theWeighted Curve Number (WCN). The CPA0 values are far from zero(Table 2). The CPA0 values gained from the calibration process arehaving a negative correlation with the percentage reduction in theCN. When the percentage reduction of the CN increases, the ob-tained CPA0 values show a continuous decreasing trend (Fig. 2a).When applying the SCS CN method for the Attanagalu Oya catch-ment for the model calibration, calculated CN value was 70.9 andthe obtained CPA0 value was 313.4 which was extremely far fromzero. Most consistent CPA0 value gained from the calibration pro-cess, which is closer to the zero, was 65.64 but the correspondingCN value was 20. Valid CN values for the model range from 100 (forwater bodies) to approximately 30 for permeable soils with highinfiltration rates (Feldman, 2000). Therefore, the CN value of 20cannot be taken for the calibration process.

The CN method was initially developed for agricultural andnatural watersheds, and extending it to rural urbanwatersheds, forwhich the existing CN are not representative, can cause the modelto predict approximate runoff. Secondly, in the CNmethod, runoff isdirectly proportional to precipitation with an assumption thatrunoff is produced after the initial abstraction of 20 percent of thepotential maximum storage (Heshmatpoor, 2009). Moreover, theCN method may not be valid for urban watersheds, where evensmall rainfall events produce significant direct runoff because ofincreased efficiency of surface drainage through storm-drainagesystems. The storage factor presumably becomes less and less sig-nificant as more and more surface area is paved (Muthukrishnanet al., 2006). A naturally occurring problem in applying thismethod is the effect of basin wetness on the CN. Values of the CNare expected to vary with the soil and site moisture. Anotherproblemwith the CN method that could arise is the variable rate ofrainfall in time (Kovar, 1990).

Since the CN method was developed by the U.S. Department ofAgriculture and NRCS, there is a quandary when applying it to atropical region as it is originated in a temperate regime. Not onlystudies in tropical regionsbut also in temperate regionswithvaryingclimatic conditions experiencedproblemswhen theCNmethodwas

Fig. 2. Percentage reduction in the parameters and the corresponding CPA0 for (b) theSnyder unit hydrograph method and the Clark unit hydrograph method and (a) thecurve number method for the upper Attanagalu Oya catchment.

D. Halwatura, M.M.M. Najim / Environmental Modelling & Software 46 (2013) 155e162 159

used. Normally the CN for different landuse patterns are taken fromstandard CN tables (Sonbol et al., 2005), which occasionallymay notprovide accurate results due to the range of climatic conditions.Atkinson (2001) stressed the need to use accurate predictions of theCN in order to predict runoff fromwatersheds.

The standard SCS method used to find the average CN for thebasins failed to estimate excess rainfalls correctly. This resulted inunacceptably large deviations of predicted peak discharges fromthe observed ones. It is concluded that the use of standard SCStables of runoff CN in tropical climate may lead to large errors inrunoff estimates. Prior to the application of the standard SCSmethod, suitability of this method should be verified or altogetherreplaced by a method deriving CN values from local rainfall runoffdata (Muzik,1993). Knebl et al. (2005) stated that decreasing the CNincreased the amount of recharge into the watershed system in SanAntonio river basin in the United States and therefore reducedoverestimation of runoff in the model.

Table 2Mean values of the observed flows, the simulated flows, employing the Snyder unithydrograph method, the CPA0 values and the relative errors of validation results forthe next three years (2008e2010).

Month Simulated flows(m3 s�1)

Observed flows(m3 s�1)

CPA0 Relativeerror (%)

January 2.06 1.59 1.14 þ0.30February 0.87 0.85 0.48 þ0.02March 3.71 3.97 0.68 �0.07April 6.97 7.41 0.71 �0.06May 14.34 11.09 0.94 þ0.29June 10.13 9.29 1.33 þ0.09July 6.66 6.27 0.67 þ0.06August 2.17 1.91 0.94 þ0.14September 5.08 4.82 0.66 þ0.05October 15.88 12.94 0.98 þ0.23November 16.25 11.78 2.33 þ0.38December 9.92 9.19 0.94 þ0.08Mean 0.98 þ0.12

Descheemaeker et al. (2008) stated that in steep hill slopes withnatural vegetation in semi-arid tropical highlands of NorthernEthiopia, the landuse type was an important explanatory factor forthe variation in curve numbers, whereas the hydrologic soil groupwas not. The curve numbers were negatively correlated with thevegetation cover. Taking antecedent soil moisture conditions intoaccount did not improve runoff prediction using the curve numbermethod. Runoff predictionwas less accurate in areaswith lowcurvenumbers. Senay and Verdin (2004) stated that the application of theSCS CN method at larger watershed areas may result in an over-estimation of the runoff when the substantial transmission lossesare not considered. Babel et al. (2004) and Najim et al. (2006) hasused the SCS CN method for runoff estimation in a mixed forestedwatershed in Thailand and the runoff volumes calculated by themodel were within the acceptable limits but the SCS CN methodover estimated the peak flows. The Antecedent Moisture Content ofthe Attanagalu OyaWatershed varies from time to time due towidespread rainfall pattern. This change does not correspond with theweighted curve numbers used in the continuous simulations.

When considering the landuse types of the study catchment, theupper catchment mainly consists of rubber plantations and can beconsidered as a forested watershed. Therefore, greater fraction ofprecipitation does not directly fall on the soil but remain as inter-ception storage therefore there is an issue in calculated weightedCN. Rubber is a deciduous plant inwhich leaves fall annually. In thatcondition the calculated weighted CN may not be accurate forcertain periods on the year. Hawkins (1984) stated that the CNprocedure does not work well in karsts topography areas. This isbecause a large portion of the flow is subsurface rather than directrunoff. In general, the CN method seems to work the best in agri-cultural watersheds, next best for range lands and the worst forforested watersheds. The above reasons could be attributed to poorprediction of flow by the SCS CN method in the Attanagalu Oyacatchment. Under the SCS CNmethod, once the soil moisture deficitis reach, no more rainfall is available for infiltration and the wholerainfall contributes to runoff generation. As the soils in the Atta-nagalu Oya basin are permeable, the runoff generated by the modelis over predicted.

3.2. Runoff by deficit constant loss method

Due to the uncertainty in the SCS Curve Number method, deficitconstant loss method was performed with two different transformmethods (Clark unit hydrograph and Snyder unit hydrograph). Thedeficit constant loss method requires similar values for initialdeficit (mm), maximum storage (mm), constant rate (mm/h) andimperviousness (%) for both the Clark unit hydrograph and theSnyder unit hydrograph transform methods. The Clark unithydrograph method requires time of concentration (h) and storagecoefficient (h) whereas the Snyder unit hydrograph requires stan-dard lag (h) and peaking coefficient.

The model was initially run with the calculated model param-eters and the coefficient of performance (CPA0) was calculated. Tofind the most suitable values for the best performance of the modelthat gives most reliable CPA0 value, the constant rate (mm/h) of theSnyder unit hydrograph method and the Clark unit hydrographmethod were changed by a certain percentage until the results ofthe statistical evaluation give a CPA0 value closer to zero.

The results showed that the model is giving a best simulationresult with a CPA0 reaching to 1.15 for the Snyder unit hydrographmethod when the percentage reduction of the initial constant rate(ICR) is 50% (Fig. 2b). For the Clark unit hydrograph model, the bestsimulation result is given with a CPA0 reaching to 1.02 when thepercentage increase of the initial constant rate (ICR) is 33% (Fig. 2b).

Fig. 3. Frequency distribution of the 2192 residuals; suggesting a normal distribution.

D. Halwatura, M.M.M. Najim / Environmental Modelling & Software 46 (2013) 155e162160

The obtained CPA0 decreased to aminimum from the initial valueand increased exponentially with the increase of percentagereduction in the constant rate in both the Snyder unit hydrographand the Clark unit hydrograph methods. Considering the meanvalues of the two transform methods, the Snyder unit hydrographmethod gives a CPA0 value closer to zero (1.33) while the CPA0 of theClark unit hydrograph differs much from zero (10.16).

According to the obtained results, the most reliable CPA0 valuegained from the calibration process, which is closer to the zero(Clark unit hydrograph e 10.16, Snyder unit hydrograph e 1.33),was achieved from the Snyder unit hydrograph method. The Clarkunit hydrograph is used for modeling direct runoff (Cunderlik andSimonovic, 2005). In the Clark method, overland flow translationis based on a synthetic timeearea histogram and the time of con-centration (Cunderlik and Simonovic, 2010). Straub et al. (2000)noted that the Clark unit hydrograph method is commonlyapplied for hydrologic designs. Banitt (2010) has used the HEC-HMSwith the Clark unit hydrograph method to transform the rainfallinto runoff to generate 100-year simulations of natural, existingand alternative operation plans for the Salt River watershed, in theMississippi River basin. The results from the HEC-HMS model filledthe actual stream gauge data gaps. Schoener (2010) modeled all thesub-catchments in Rio Rancho area in Central NewMexico by usingthe HEC-HMS model that, performed by using the Clark unithydrograph method. With the low imperviousness, the peak flowrates were significantly higher. The Clark unit hydrograph methodalso could be used to estimate runoff in tropical watersheds as itprovides reasonable output compared to the SCS-CN method.

3.3. Model validation

Means of the observed flows, the simulated flows, the CPA0

values and the relative errors of validation results employing theSnyder unit hydrograph of the deficit constant loss method for thenext three years (2008e2010) were shown in the Table 2. Observedand simulated flows for three years of validation period 2008e2010are shown in Fig. 4. The CPA0 value of the flow generated by thevalidation process (0.98) is much smaller than the CPA0 for thecalibration process (1.33). The CPA0 generated for the validationprocess is also satisfactory as it is closer to zero. The results from thevalidation show that most of the events simulated the flowwithin amean of �0.12% of relative error thus these results confirm thecalibration process. The calculated model parameters for the cali-brated and the validated model employing the Snyder unithydrograph of the deficit constant loss method were standard lag41.1 h and peaking coefficient 0.2, respectively.

According to the frequency analysis of regression ANOVA, 68% ofthe model predictions are within �1 SD rage and 95% of the modelpredictions are within �2 SD range. According to Bennett et al.(2013) and Pauly (1980), the coefficient of the model output ofthe regression line at the a level of confidence shows the F statisticswhich suggest that the residuals are normally distributed atP < 0.000. The relative error of �0.12% generated for this analysisthus confirms the satisfactory performance of the calibrated andthe validated model (Fig. 3). Bennett et al. (2013) stated that theresidual plot reveals unmodelled behavior when there is systematicdivergence from zero. For instance, high density of negative valuesindicates that the model tends to underestimate correct values.

If there is a suddenheavy rainfall after 2e3weeks of dry spell, themodel tends to over predict the runoff. Daily rainfall that are morethan 40 mm receiving continuously more than one week also tendsto over predict runoff. These over predictions are shown by the re-siduals that are more than þ1 SD. The runoff is under predicted bythemodel when a high rainfall event is receivedwithin a longer dryspellwhich is shownby the residuals that are less than�1SD (Fig. 3).

Fig. 4 compares the observed and the simulated flows throughout the validation years and show a similar pattern of observedflows throughout the year.

Fang et al. (2005) stated that in Snyder’s method, for developinga synthetic unit hydrograph, it is assumed that the lag time isconstant for the particular watershed and is not influenced by thevariation in the rainfall intensity. Hunukumbura et al. (2008) hasused the Snyder unit hydrograph method as the transformationmethod to estimate the runoff from the Upper Kotmale basin.Though the model predicts reasonable flows, the model cannotpredict flow accurately to the variations in landuse in the UpperKotmale basin.

Yilma and Moges (2007) applied the HEC-HMS model for bothlong term and short term runoff simulations in Ethiopian Nile riverbasin. The Snyder unit hydrograph and method of base flow esti-mation was found as the best model for short period flood fore-casting. In accordance with the same criteria, model combinationcontaining the deficit and constant loss, the Snyder unit hydro-graph as a flood forecasting model, has given satisfactory results forlong term simulations for the study area. Kalita (2008) has used theHEC-HMS program reliably for design flood estimation in SouthBrahmaputra by using the Snyder unit hydrograph method.

Properly calibrated and validated HEC-HMS model can be usedin many hydrological applications. Gichamo et al. (2012) applied ahydraulic model (HEC-RAS) that models unsteady state flowthrough the river channel network based on the HEC-HMS-derivedhydrographs to simulate flooding on a part of Tisza River, Hungaryand showed that this approach could be successfully used in areaswith topographic data scarcity. Castronova and Goodall (2013)successfully employed the HEC-HMS to test the performance ofthe Open Modeling Interface (OpenMI) Software Development Kit(SDK) where infiltration, surface runoff, and channel routing pro-cesses are each implemented as independent model components.

The HEC-HMS, a one dimensional model which is not appro-priate to simulate flood hydrographs because of its inability tosimulate the lateral diffusion and inaccuracies due to cross-sectional discretization (Bates and De Roo, 2000). Instead,Kalyanapu et al. (2011) showed Graphics Processing Unit (GPU)enabled two dimensional flood models can be more accuratelyused in large domain floodmodeling studies such as dam breaks. Qiand Altinakar (2011) applied widely used and accepted HEC-RASdam break simulation and HEC-FDA to compare the resultsgenerated from a two dimensional CCHE2D-Flood and DSS system.In addition the HEC-HMS can be used to simulate continuous river/stream flows that can be used to evaluate hydrologic flow regimesand environmental flows.

Fig. 4. Observed and simulated flows of year 2008e2010.

D. Halwatura, M.M.M. Najim / Environmental Modelling & Software 46 (2013) 155e162 161

4. Conclusion

The HEC-HMS 3.4 computer model can be reliably used tosimulate Attanagalu Oya flows with calibration and validation. Asthe transformation method in the model, the Snyder unit hydro-graph method simulates flows more reliably in the study catch-ment than the Clark unit hydrograph method. As the loss method,the SCS CN method does not perform well but the deficit andconstant method is a good option. Therefore, the Snyder unithydrograph method could be recommended as the best trans-formationmethod for the Attanagalu Oya basinwith the deficit andconstant method as the loss method. As there are plenty of un-gauged rivers located in the wet zone in Sri Lanka, this approachcan reliably be applied in order to simulate river flows in thecountry and also same approach of calibration and validation canbe applied in other parts of the tropics.

Acknowledgments

Our heartfelt thanks go to Director General, Department ofMeteorology and Director General, Department of Irrigation forproviding meteorological data and flow data, respectively. We arealso thankful to the General Manager, Water Supply and Drainage

Board, Ratmalana, for providing the past and future water extrac-tion data of the Attanagalu Oya basin.

References

Atkinson, E.L., 2001. Natural Resources Conservation Service Curve Number Analysisfor Texas. Unpublished thesis in Civil Engineering. Graduate Faculty of TexasTech University, USA.

Babel, M.S., Najim, M.M.M., Loof, R., 2004. Assessment of agricultural nonpointsource model for a watershed in tropical environment. Journal of EnvironmentEngineering 130 (9), 1032e1041.

Bajwa, H.S., Tim, U.S., 2002. Toward immersive virtual environments for GIS-basedfloodplain modeling and visualization. In: Proceedings of 22nd ESRI UserConference, San Diego, TX, USA.

Banitt, A.M.M., 2010. Simulating a century of hydrographs e Mark Twain reservoir.In: 2nd Joint Federal Interagency Conference, Las Vegas, USA, June 2010.

Bates, P.D., De Roo, A.P.J., 2000. A simple raster-based model for flood inundationsimulation. Journal of Hydrology 236 (1e2), 54e77.

Bennett, N.D., Croke, B.F.W., Guariso, G., Guillaume, J.H.A., Hamilton, S.H.,Jakeman, A.J., Marsili-Libelli, S., Newham, L.T.H., Norton, J.P., Perrin, C.,Pierce, S.A., Robson, B., Seppelt, R., Voinov, A.A., Fathi, B.D., Andreassian, V.,2013. Characterising performance of environmental models. EnvironmentalModelling & Software 40, 1e20.

Castronova, M.A., Goodall, J.L., 2013. Simulating watersheds using loosely integratedmodel components: evaluation of computational scaling using open MI. Envi-ronmental Modelling & Software 39, 304e313.

Cunderlik, J.M., Simonovic, S.P., 2005. Hydrological extremes in a southwesternOntario river basin under future climate conditions. Hydrological Sciences 50(4), 631e654.

D. Halwatura, M.M.M. Najim / Environmental Modelling & Software 46 (2013) 155e162162

Cunderlik, J.M., Simonovic, S.P., 2010. Hydrologic models for inverse climate changeimpact modeling. In: 18th Canadian Hydro-technical Conference, Manitoba,August 2007.

Descheemaeker, K., Poesen, J., Borselli, L., Nyssen, J., Raes, D., Haile, M., Muys, B.,Deckers, J., 2008. Runoff curve numbers for steep hillslopes with naturalvegetation in semi-arid tropical highlands, northern Ethiopia. HydrologicalProcesses 22 (20), 4097e4105.

Duan, Z., 2011. Optimum simulation of flood flow rate: comparing combinations ofprecipitation loss and rainfall excess-runoff transform models. Bechtel Tech-nology Journal, 1e10.

Fang, X., Cleveland, T., Garcia, C.A., Thompson, D., Malla, R., 2005. Literature Reviewon Timing Parameters for Hydrographs. Department of Civil Engineering, LamarUniversity, Beaumont, Texas, p. 77.

Feldman, A.D., 2000. Hydrologic Modeling System HEC-HMS, Technical ReferenceManual. U.S. Army Corps of Engineers, Hydrologic Engineering Center, HEC,Davis, CA, USA.

Fleming, M., Scharffenberg, W., 2012. Hydrologic Modeling System (HEC-HMS):New Features for Urban Hydrology. Hydraulic Engineer, USACE HydrologicEngineering Center, Davis, CA.

Gichamo, T.Z., Popescu, I., Jonoski, A., Solomatine, D., 2012. River cross-sectionextraction from the ASTER global DEM for flood modeling. EnvironmentalModelling & Software 31, 37e46.

Hawkins, R.H., 1984. A comparison of predicted and observed runoff curve umbers.In: Proceeding of Water Today and Tomorrow, Flagstaff Arizona. American So-ciety of Civil Engineers, pp. 702e709.

Heshmatpoor, A., 2009. Identification runoff source area in tropical watershed. In:Proceedings of Postgraduate Qolloquium Semester. Department of Environ-mental Engineering, University Putra Malaysia, Malaysia, pp. 30e39.

Hunukumbura, P.B., Weerakoon, S.B., Herath, S., 2008. Runoff modeling in the upperKotmale Basin. In: Hennayake, N., Rekha, N., Nawfhal, M., Alagan, R., Daskon, C.(Eds.), Traversing No Man’s Land, Interdisciplinary Essays in Honor of ProfessorMadduma Bandara. University of Peradeniya, Sri Lanka, pp. 169e184.

James, L.D., Burgess, S.J.,1982. Selections, calibrationand testingofhydrologicmodels.In: Haan, C.T., Brakensiek, D.L. (Eds.), HydrologicModelling of Small Watersheds.American Society of Agricultural Engineers, Michigan, pp. 437e472.

Kalita, D.N., August 2008. A study of basin response using HEC-HMS and subzonereports of CWC. In: Proceedings of the 13th National Symposium on Hydrology.National Institute of Hydrology, Roorkee, New Delhi.

Kalyanapu, A.J., Shankar, S., Pardyjak, E.R., Judi, D.R., Burian, S.J., 2011. Assessment ofGPU computational enhancement to a 2D flood model. Environmental Model-ling & Software 26, 1009e1016.

Knebl, M.R., Yanga, Z.L., Hutchisonb, K., Maidment, D.R., 2005. Regional scale floodmodeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for theSan Antonio River Basin Summer 2002 storm event. Journal of EnvironmentalManagement 75, 325e336.

Kovar, P., 1990. Hydrology of mountainous areas. In: Proceedings of the StrbskéPleso Workshop, Czechoslovakia, pp. 391e401.

Muthukrishnan, S., Harbor, J., Lim, K.J., Bernard, A.E., 2006. Calibration of a simplerainfall-runoff model for long-term hydrological impact evaluation. Urban andRegional Information Systems Association (URISA) Journal 18 (2), 35e42.

Muzik, I., 1993. Applicability of the modified SCS runoff prediction method to smallcatchments in Thailand. In: Proceedings of the Yokohama Symposium. Hy-drology of Warm Humid Regions, Japan, pp. 195e201.

Najim, M.M.M., Babel, M.S., Loof, R., 2006. AGNPS model assessment for a mixedforested watershed in Thailand. Science Asia 32, 53e61.

Pauly, D., 1980. On the interrelationship between natural mortality, growth pa-rameters, and mean environmental temperature in 175 fish stocks. Journal ofInternational Exploration 39 (2), 175e192.

Perrone, J., Madramootoo, C.A., 1997. Use of AGNPS for watershed modelling inQuebec. Transactions of the American Society of Agricultural Engineers (ASAE)40 (5), 1349e1354.

Qi, H., Altinakar, M.S., 2011. A GIS-based decision support system for integratedflood management under uncertainty with two dimensional numerical simu-lations. Environmental Modelling & Software 26, 817e821.

Razi, M.A.M., Ariffin, J., Tahir, T., Arish, A.M., 2010. Flood estimation studies usinghydrologic modeling system (HEC-HMS) for Johor River, Malaysia. Journal ofApplied Sciences 10, 930e939.

Schoener, G., 2010. Comparison of AHYMO and HEC-HMS for Runoff Modeling inNew Mexico Urban Watersheds. Unpublished Project Report for the Degree ofMaster of Water Resources Hydro-science. University of New Mexico, Albu-querque, New Mexico.

Schwab, G.O., Fangmeier, D.D., Elliot, W.J., Frevert, K.R., 2005. Soil and Water Con-servation Engineering, fourth ed. John Wiley and Sons, New York, p. 508.

Senay, G.B., Verdin, J.P., 2004. Developing maps for water-harvest potential in Africa.American Society of Agricultural Engineers 20 (6), 789e799.

Sonbol,M.A.,Mtalo, F., El-bihery,M., Abdel-motteleb,M., 2005.WatershedModellingof Wadi Sudr and Wadi Al-Arbain in Sinai, Egypt. Natural Sciences HydrologyProgramme. UNESCO Cairo Office, Egypt.

Straub, T.D., Melching, C.S., Kocher, K.E., 2000. Equations for estimating Clark unit-hydrograph parameters for small rural watersheds in Illinois. In: Water-re-sources Investigations Report 00-4184. Illinois Department of Natural Re-sources, Office of Water Resources U.S. Geological Survey, Urbana, Illinois,pp. 4e6.

U.S. Army Corps of Engineers, 2008. Hydrologic Modeling System (HEC-HMS) Ap-plications Guide: Version 3.1.0. Institute for Water Resources, Hydrologic En-gineering Center, Davis, CA.

Wijesekara, R.S., Kudahetty, C., 2010. Preliminary groundwater assessment andwater quality study in the shallow aquifer system in the Attanagalu Oya Basin.In: Evans, A., Jinapala, K. (Eds.), Proceedings of National Conference on Water,Food Security and Climate Change in Sri Lanka. Water Quality, Environment andClimate Change, vol. 2. BMICH, Colombo, Sri Lanka, pp. 77e87.

Yilma, H., Moges, S.A., 2007. Application of semi-distributed conceptual hydrolog-ical model for flow forecasting on upland catchments of Blue Nile River Basin, acase study of Gilgel Abbay catchment. Catchment and Lake Research, 200.