Determination of runoff and sediment yield from a small watershed in sub-humid subtropics using the...

HYDROLOGICAL PROCESSESHydrol. Process. 21, 3035–3045 (2007)Published online 23 January 2007 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/hyp.6514

Determination of runoff and sediment yield from a smallwatershed in sub-humid subtropics using the HSPF model

Ashok Mishra,1* S. Kar2 and V. P. Singh3

1 International Research Institute for Climate and Society (IRI), Columbia University, Palisades, NY 10964-8000, USA2 Emeritus Professor, Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur, WB 721 302, India

3 Department of Biological & Agricultural Engineering, Texas A & M University, Scoates Hall, 2117 TAMU, College Station, Texas 77843-2117, USA

Abstract:

The Hydrologic Simulation Programme-Fortran (HSPF), a hydrologic and water quality computer model, was employed forsimulating runoff and sediment yield during the monsoon months (June–October) from a small watershed situated in a sub-humid subtropical region of India. The model was calibrated using measured runoff and sediment yield data for the monsoonmonths of 1996 and was validated for the monsoon months of 2000 and 2001. During the calibration period, daily-calibratedrunoff had a Nash-Sutcliffe efficiency (ENS) value of 0Ð68 and during the validation period it ranged from 0Ð44 to 0Ð67.For daily sediment yield ENS was 0Ð71 for the calibration period and it ranged from 0Ð68 to 0Ð90 for the validation period.Sensitivity analysis was performed to assess the impact of important watershed characteristics. The model parameters obtainedin this study could serve as reference values for model application in similar climatic regions, with practical implications inwatershed planning and management and designing best management practices. Copyright 2007 John Wiley & Sons, Ltd.

KEY WORDS modelling; watershed; HSPF; calibration; validation; runoff; sediment yield

Received 12 December 2005; Accepted 12 July 2006

INTRODUCTION

Sustainable agricultural development depends on the con-servation and management of land and water resources.This is especially true for the humid and sub-humidregions of India with monsoon climate, where inten-sive runoff and concomitant sediment loss are rapidlydegrading land and water resources. In these regions,monsoon rains are concentrated in about 18–38 eventsof high magnitude (rainfall [RF] >10 mm) which areeffective in generating runoff during the monsoon monthsof June–October (Thapliyal, 1997). These high-intensitymonsoon rains are responsible for causing floods andsevere soil erosion, leading to soil degradation and pollu-tion of water bodies. The rate of sediment generation andtransport depends on several factors related to watershedtopographic, geomorphologic and land use/land cover(LU/LC) characteristics, and the time series of RF (Joneset al., 2001; Naef et al., 2002; Seeger et al., 2004). Sincethe hydrologic response of a watershed varies spatiallyand temporally, an intensive study of the watershed isnecessary for developing management scenarios, design-ing best management practices, and transforming theresults from one watershed to another having similarcharacteristics.

The watersheds dominated by coarser soils are vul-nerable to soil erosion in regions receiving monsoonrains (Mandal et al., 2005). The widespread occurrence

* Correspondence to: Ashok Mishra, International Research Institute forClimate and Society (IRI), Columbia University, Palisades, NY 10964-8000, USA. E-mail: [email protected]

of soil erosion makes its measurement difficult. Hence,there is a need for an effective technology to developstrategic watershed management for controlling sedimenttransport. Fundamental to watershed management is ahydrologic model coupled with Geographical Informa-tion System (GIS) and Remote Sensing (RS) techniques.Preferable among the many hydrologic models availablethese days would be a physically based distributed param-eter continuous time model. The principal advantage ofsuch a model is that it affords a realistic representation ofthe spatial variability of watershed characteristics. A dis-tributed parameter continuous time model, supported byRS and GIS databases, can also assist management agen-cies in both, identifying the most critical erosion proneareas, and selecting appropriate management practices.Using such a model, management scenarios can be devel-oped to minimize runoff and sediment yield from the crit-ical erosion-prone watershed areas. The Hydrologic Sim-ulation Programme-Fortran (HSPF) is one such model.It has the capability to simulate watershed hydrologyunder various land use and climatic conditions, as wellas hydraulics (HYDRs) of dams and reservoirs (Donigianet al., 1983, 1984).

The HSPF model has been found successful in han-dling hydrologic and water quality problems such asstream loadings of sediment, nutrients, and pesticidesfrom agricultural lands (Moore et al., 1988; Chew et al.,1991; Laroche et al., 1996; Engelmann et al., 1999). Sev-eral investigators have employed HSPF for evaluating theimpact of various watershed management practices (Bick-nell et al., 1985; Moore et al., 1992; Tsihrintzis et al.,

Copyright 2007 John Wiley & Sons, Ltd.

3036 A. MISHRA, S. KAR AND V. P. SINGH

1996). Srinivasan et al. (1998) applied HSPF to glaciatedwatersheds consisting of wetlands and poorly drainedsoils in northeastern Pennsylvania. The HSPF modelhas been used worldwide, mostly to simulate hydrolog-ical processes of large watersheds (Albek et al., 2004;Hayashi et al., 2004; Singh, 2004). However, informa-tion on its suitability for small, mixed type watersheds insub-humid and subtropical regions dominated by mon-soon climate is limited. In these regions, sedimentationhas become alarming for conserving water resources.Hence, the objective of this study was to evaluate theHSPF model based on its ability to simulate runoff andsediment yield from a small mixed land use watershedsituated in a sub-humid subtropical region of India. Theresults of this study have implications for watershed man-agement by decision makers and design of best man-agement practices. Well calibrated and validated modelresults on runoff, sediment transport and non-point source(NPS) pollutants can be used to identify the best manage-ment practices or engineering-based control managementoptions to decide long term options of watershed man-agement to reduce the extent of these problems.

HSPF APPLICATION FOR RUNOFF ANDSEDIMENT YIELD SIMULATION

HSPF is a deterministic, lumped-parameter continu-ous time model that has evolved out of the StanfordWatershed Model (Crawford and Linsley, 1966), theUSEPA Agricultural Runoff Management (ARM) model(Donigian and Davis, 1978), and NPS (Donigian andCrawford, 1976) model. It can also be used as a dis-tributed parameter model as it reproduces spatial variabil-ity by dividing the basin in hydrologically homogeneousland segments and simulating runoff for each land seg-ment independently. A detailed description of the modelis given by Bicknell et al., (1997). The model requiresinput information on LU/LC, soil properties, sources ofnitrogen (N) and phosphorus (P), stream reach character-istics, and time series of precipitation, temperature, solarradiation and potential evapotranspiration.

HSPF considers three types of modelling segments ina watershed: pervious land segment (PERLND) or sub-watershed, impervious land segment (IMPLND) or sub-watershed and stream/reach/reservoir (RCHRES). Thesemodelling segments/modules have several componentsdealing with hydrological and water quality processes.The results of simulation for each sub-watershed arehydrographs and pollutographs. The model predicts flowrates, sediment loads, and nutrient and pesticide concen-trations. The sub-watershed runoff characteristics are thenutilized by the model to simulate in-stream processes fordetermining hydrographs and pollutographs at all perti-nent locations in the watershed.

HSPF simulates three sediment types (sand, silt, andclay), in addition to single organic chemical and trans-formation products of that chemical. Re-suspension andsettling of silt and clay (cohesive solids) are defined

in terms of shear stress at the sediment-water interface.For sand, the capacity of the watershed or channel sys-tem to transport sand at a particular flow is calculatedand re-suspension or settling is defined by the differencebetween the sand in suspension and the capacity. Cali-bration of the model requires data for each of the threesolid types.

Study watershed

A small, mixed, land-use watershed named Banha,of the Damodar Valley Corporation (DVC) in Haz-aribagh, Jharkhand, India (Figure 1), was selected forthis study. The average elevation of the watershed is425 m from mean sea level. It is bounded by lati-tudes of 24°1303000N and 24°1700600N and longitudes of85°1305000E and 85°1601500E. The watershed is 1695 hain area of which about 50% is under shrubs and for-est, 10% under barren land and the remaining land undercrop cultivation. About 25% of the area having the high-est elevation is rocky and forested, while the remaining75% has a slope with a soil depth up to 100 cm and isclassified under forest, barren and crop cultivation. Theslope of the watershed area ranges from 1 to 18% with anaverage slope of 1Ð9%. The average annual RF is about1200 mm of which more than 80% occurs during themonsoon months from June to October and the remain-ing falls in the winter months from December to Februarywith a few scattered events in the remaining months. Thedaily temperature ranges from a maximum of 42Ð5 °C inJune to a minimum of 2Ð5 °C in January. The daily meanrelative humidity varies from a minimum of 21Ð72% inthe month of April to a maximum of 90Ð36% in the month

Figure 1. The map of the Banha watershed showing sampling pointlocations, digitized stream and watershed outlet

Copyright 2007 John Wiley & Sons, Ltd. Hydrol. Process. 21, 3035–3045 (2007)DOI: 10.1002/hyp

WATERSHED RUNOFF AND SEDIMENT YIELD USING THE HSPF MODEL 3037

of September. The overall climate of the watershed isclassified as sub-humid subtropical.

The soils of the watershed vary texturally from loamysand to loam with sandy loam as the most commontexture. Texturally the soils are uniform in depth. Thebulk density of soils varies around 1Ð5 g cc�1 and thesaturated HYDR conductivity is moderately low, varyingfrom 9Ð7 to 16Ð8 cm day�1. The monsoon rains arethe primary source of sediment generation, floods anddegradation of land and water resources. The LU/LC ofthe area during the study period (June–October: KharifSeason) comprises mainly the rice crop, although blackgram, maize, soya bean and vegetables are also grownon some upland patches. The forested area consists ofa mixed forest of mainly Sal (Shorea robusta), Mahua(Madhuca indica), Kend (Diospyros melanoxylon) andPalas (Butea frondosa) trees.

Modelling strategy

The Banha watershed has only pervious land segmentsor sub-watersheds and a RCHRES network. Therefore,the PERLND and free-flowing reach or mixed reser-voir (RCHRES) modules of the HSPF program wereemployed. As the watershed area is rural, there are negli-gible impervious land areas and thus the IMPLND mod-ule was not utilized. The PWATER section of PERLNDis a major component of the program, which simulateswater budget, including surface flow, interflow and activegroundwater. In the RCHRES module, the HYDR modulewas utilized to simulate the stream HYDRs.

The key steps in modelling a watershed with HSPF arethe mathematical representation of the watershed, arrang-ing meteorological and hydrological input time series,estimation of model parameters and model calibrationand validation. The Watershed Data Management (WDM)program, developed by EPA, was utilized to provide timeseries to the model. The watershed information as wellas model parameters were supplied to HSPF through aninput file called the user’s control input (UCI) file.

Database development

Meteorological and hydrological data. HydrologicSimulation Programme-Fortran requires meteorologicaltime series of air temperature (maximum and minimum),dew-point temperature, wind velocity, solar radiation,potential evapotranspiration and precipitation to simulatethe components of the watershed hydrology. The averagedaily time series data were collected from different obser-vatories located nearest to the study watershed. Daily RFdata were collected from the automatic weather stationinstalled at the watershed outlet. Daily maximum andminimum air temperature and relative humidity were col-lected from the meteorological observatory of DVC inHazaribagh, which is about 45 km away from the water-shed. The pan evaporation and wind speed data werecollected from the observatory of the Central RegionalUpland Rice Research Institute (CRURRI) in Hazarib-agh. The solar radiation and wind velocity data were

collected from the meteorological observatory in Ranchi,which is about 98 km away from the study area and usedafter correction for the watershed location. The dew-pointtemperature data were obtained from the relative humid-ity measurements using the formula presented by Linsleyet al. (1988).

Besides meteorological time series, HSPF also requireshydrological time series of runoff and sediment yield formodel calibration and validation. These data, on average,were measured on a daily basis at the watershed outletand were supplied to the program.

Land and stream segmentation. For routing of waterand sediment, the Banha watershed was divided into itsnatural sub-watersheds, preserving natural flow paths,boundaries and channels. The watershed subdivisionincluded five nested sub-watersheds: SWS1, SWS2,SWS3, SWS4 and SWS5, based on topography and land-use pattern of the watershed so that the lumped param-eters could be assigned to each sub-watershed to repre-sent its characteristics (Figure 2). Individual reaches wereconsidered according to the five sub-watersheds. Reacheswere identified by considering constructed and man-madeobstacles to the stream, such as check dams. The firstreach collected the water and sediment from the first sub-watershed and delivered to the second. The second, thirdand fourth streams end at the check dams constructed atthe outlets of second, third and fourth sub-watersheds.The last and fifth reach extends from the third checkdam to the watershed outlet. The physical characteristicsof sub-watersheds and reaches are given in Table I.

Parameter estimation

Proper representation of the watershed and subsequentdepiction of the hydrological cycle required estimation

Figure 2. Sub-watershed and reach segmentation map of the Banhawatershed



Table I. Physical characteristics of sub-watersheds and reaches

Parameter Sub-watershed

SWS1 SWS2 SWS3 SWS4 SWS5

Sub-watershed area (ha) 337Ð3 427Ð1 331Ð7 557Ð7 41Ð2Average land slope (%) 2Ð078 2Ð385 2Ð212 2Ð298 1Ð593Average elevation (m) 431 425 413 410 398Channel length (km) 2Ð15 2Ð20 2Ð00 2Ð40 0Ð85Average channel slope (%) 0Ð025 0Ð009 0Ð020 0Ð008 0Ð001

of a large array of parameter values. Some of the param-eters were estimated from observations, measurements,experiments and theoretical considerations. ArcInfo GISwas used to delineate, store, analyse and visualize thewatershed data. The physical and chemical properties ofsoils were measured at 12 selected locations distributedover the watershed (Figure 1). The watershed features,such as topography, soil type, texture, existing LU/LC,water resources, and drainage pattern, were obtained fromtopographic maps and remotely sensed imagery and wereused to provide input to HSPF—the hydrologic and waterquality model. Initially, the watershed was delineatedfrom the Survey of India topographic sheets and thenregistered in ArcInfo GIS. A digitized contour coverageof the watershed was prepared and then a 30 ð 30 mdepressionless Digital Elevation Model (DEM) was gen-erated and used to estimate the slope and the drainagepattern of the area. The satellite image (IRS-1C, Octo-ber 1996) was collected and supervised classificationwas performed. Nine LU/LC classes, including low-landpaddy (rice), upland crops, shallow water body, deepwater body, growing forest (new plantation), degradedforest, dense forest, fallow land, and eroded land, wereidentified over the watershed as shown in Figure 3. Acomposite map, comprising the watershed/sub-watershedboundaries, channels (primary, secondary), slope, andLU/LC, was produced. Then sub-watershed-wise statis-tics (lumped parameters) were generated (Table I) andused as input to the HSPF model.

Proper values of input parameters were determined bycalibration and their adequacy was assessed by modelvalidation. Besides input parameters, the model alsorequires initial conditions, such as the amount of soilmoisture at the beginning of simulation. The calibratedparameter values used for validation of the hydrologicand sediment component of HSPF are given in Tables IIand III.

Input parameters and initial conditions were providedto HSPF through the UCI file, which was also used toconnect the meteorological and hydrological time seriesstored in the WDM file. The connections between landsegments and the water bodies in the watershed wereestablished within the UCI file which also contained theinput information about shape and HYDR behaviour ofthe water body in question by establishing a relationshipamong the depth, surface area, volume and discharge ofthe reaches in the form of F-tables. These relationships

Figure 3. Land use/land cover map of the Banha watershed for 1996

were determined from depth, width and discharge capac-ity relationships established for each reach.

Calibration, sensitivity analysis and validation

The model was calibrated using daily run-off andsediment yield values measured at the watershed outletduring the monsoon months of 1996. All the values ofthe input parameters were chosen within the prescribedrange of the parameter values. Several simulations wereperformed with different values of input parametersto get an adequately calibrated model. The manualcalibration procedure based on trial and error adjustmentof parameters was used.

For calibration parameters, runoff and sediment yieldvalues were simulated and were compared graphicallyas well as by statistical analysis with measured values tojudge the adequacy of model performance. After satisfac-tory calibration, a sensitivity analysis of model param-eters was performed to examine the effect of differentparameters on the model-simulated runoff and sedimentyield. Initially base output variables were determinedusing the base data file and then parameters were variedone by one within the prescribed range, keeping othersconstant. The simulated output values were then com-pared with the base values of total runoff and sedimentyield.



Table II. Process and physical parameters used in calibration and validation of the HSPF model for run-off

Process Description Parametric valuesparameter


LZSN Lower zone nominal storage (mm) 2Ð2 2Ð0 2Ð4 1Ð9 2Ð0INFILT Index to soil infiltration capacity (mm/h) 5Ð15 9Ð15 25Ð15 13Ð15 19Ð15AGWRC Groundwater recession coefficient (day�1) 0Ð88 0Ð88 0Ð88 0Ð88 0Ð88UZSN Upper zone nominal storage (mm) 18Ð0 18Ð0 20Ð0 20Ð0 23Ð0INFEXP Exponent in the infiltration equation 2Ð0 2Ð0 2Ð0 2Ð05 2Ð05INFILD Ratio between the max. and mean infiltration capacities 2Ð0 2Ð0 2Ð0 2Ð0 2Ð0IRC Interflow recession parameter 0Ð4 0Ð5 0Ð6 0Ð6 0Ð65LZETP Lower zone ET parameter 0Ð95 0Ð9 0Ð7 0Ð7 0Ð65LSUR Length of overland flow plane (m) 25 35 30 30 35SLSUR Slope of overland flow plane 0Ð15 0Ð05 0Ð02 0Ð025 0Ð02DEEPFR Fraction of groundwater inflow which will enter deep

groundwater and be lost0Ð078 0Ð08 0Ð085 0Ð085 0Ð09

BASETP Fraction of potential ET satisfied from baseflow 0Ð05 0Ð045 0Ð08 0Ð08 0Ð05AGWETP Fraction of potential ET satisfied from active groundwater

storage if enough is available0Ð02 0Ð02 0Ð02 0Ð02 0Ð02

CEPSC Interception storage capacity (mm) 8Ð0 7Ð0 4Ð0 4Ð0 2Ð0PLS NSUR Manning’s n for the overland flow 0Ð134 0Ð083 0Ð072 0Ð074 0Ð072

Table III. Parameters used for calibration and validation of the HSPF model for sediment yield

Process Description Parametric valuesparameter


SMPF Supporting management practice factor 0Ð5 0Ð5 0Ð5 0Ð5 0Ð5KRER Coefficient in the soil detachment equation 0Ð4 0Ð4 0Ð35 0Ð35 0Ð35JRER Exponent in the soil detachment equation 2Ð2 2Ð2 2Ð2 2Ð2 2Ð2AFFIX Fraction by which detached sediment storage decreases each

day as a result of soil compaction0Ð2 0Ð2 0Ð002 0Ð002 0Ð002

NVSI Rate at which sediment enters detached storage from theatmosphere

1 1 2 2 2

KSER Coefficient in the detached sediment washoff equation 5 5 4 4 3JSER Exponent in the detached sediment washoff equation 2Ð2 2Ð2 2 2 2KGER Coefficient in the matrix soil scour equation, which simulates

gully erosion1 1 1 1 1

JGER Exponent in the matrix soil scour equation, which simulatesgully erosion

1 1 1 1 1

Using measured meteorological and hydrological data,the model was validated for the monsoon months of Junethrough September of 2000 and June through October of2001. Since measurements of RF, runoff and sedimentyield were not available for the month of October 2000,the model output was compared with the measured datafor the months of June, July, August, and September2000 only. The measured daily runoff and sediment yieldvalues for both years were compared with simulatedvalues for evaluating the model validation performance.

Model evaluation

The performance of the model for simulating runoffand sediment yield was evaluated graphically as well asby linear regression (coefficient of determination, R2),Student’s t-test of significance for difference (Haan et al.,1995), the Nash-Sutcliffe efficiency (ENS), root meansquare error (RMSE) and percent deviation (Dv). Thesestatistics were used on the basis of the recommendations

of ASCE (1993). ENS was expressed as

ENS D 1 �

N∑

iD1

�Oi � Si�2

N∑

iD1

�Oi � OAvg�2

where Oi is the ith observed value, OAvg is the mean ofthe observed values, Si is the ith simulated value, andN is the total number of events. ENS can range fromnegative infinity to 1, with 1 denoting a perfect modelagreement with observed data.

Dv was calculated as

Dv�%� D Xm � Xs

Xmð 100

where Xm is the measured total runoff volume or sed-iment yield, and Xs is the model-simulated total runoffvolume or sediment yield. The smaller the number of Dv,



the better the model result. For a perfect model, Dv wouldbe equal to zero.

The model prediction performance and the acceptancelimits were also considered, as suggested by Bingneret al. (1989), where the underprediction or overpredictionlimit for the model simulation was decided by the Dv

of the simulated value from the measured value. TheDv within or equal to š20% from the measured valuewas considered as an acceptable limit for accuracyof simulation. Underprediction or overprediction wasconsidered as low (slight), moderate and severe when thelimit was �10%, 10–20% and 20–30% of the measuredvalues, respectively.

MODEL APPLICATION, RESULTS ANDDISCUSSION

Calibration

Figure 4 compares graphically measured and predicteddaily runoff and sediment yield values for the calibrationperiod. In general, simulated runoff follows a trend simi-lar to that of measured runoff. However, in the beginning

of the monsoon season when RF was low, runoff wasnegligibly small. This happened because the soil wasinitially dry, resulting in surface retention of a majorportion of RF. During the middle and toward the endof the monsoon season when soil was sufficiently wet,even small RF events resulted in significant runoff. Themodel-simulated as well as measured high runoff dur-ing high RF events showed slow infiltration and highoverland flow in the study watershed. Although for mostof the RF events, model-simulated runoff peaks closelymatched measured runoff peaks in all months of the mon-soon season. However, discrepancies occurred owing toerratic monsoon rain characteristics (quantity and inten-sity). It also seemed that during the recession phase ofthe monsoon season (September onward) observed runoffreceded slowly, whereas due to low infiltration it reap-peared as stream flow, but the model-simulated runoffwas less. This is clearer from the simulated cumulativerunoff that is quite close to the measured values in Juneand July, but remains slightly higher from mid-July to theend of the monsoon season. When model-simulated andobserved values are plotted, it is found that the simulated

y = 0.6467x + 0.0241

R2 = 0.72

0.0

0.3

0.6

0.9

1.2

1.5

0.0 0.3 0.6 0.9 1.2 1.5

Measured sediment (t/ha)

Sim

ulat

ed s

edim

ent (

t/ha)

1:1 Line Regression line

y = 1.0086x + 0.1966

R2 = 0.76

0

10

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0 10 20 30 40 50

Measured runoff (mm/d)

Sim

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ed r

unof

f (m

m/d

)


0.0

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un

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un

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31-J

ul

15-A

ug

30-A

ug

14-S

ep

29-S

ep

14-O

ct

29-O

ct

Time (daily)

Sed

imen

t yi

eld

(t/h

a)

0.0

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12.5

Cum

ulat

ive

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(t)

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off

(mm

/d)

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ff (m

m)

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Rai

nfal

l (m

m/d

)

Measured SimulatedMeasured cumulative Simulated cumulative

Figure 4. Runoff and sediment yield calibration results at the watershed outlet during June–October 1996



values are comparably distributed along the 1 : 1 line andindicate the model capability of estimating runoff duringmonsoon RF events. Variations are ascribed to character-istics of monsoon rains as well as agricultural practicesin the watershed.

Sediment estimation by the model showed thatalthough the trends of simulated and measured sedimentyield were similar during the initial as well as recessionperiods of the monsoon season, in the mid-season thesediment yield was underpredicted for high RF events.High sediment discharge events were not well matchedand mostly underestimated. This underprediction couldbe due to the RF characteristics. For example, in practice,high-intensity and even short-duration RF can generatemore sediment than did the model based on daily RF.The cumulative sediment yield showed that the yielddifference started from the end of July and continuedtill the end of the calibration period. This differencewas mainly due to the underestimation of sediment dur-ing high-magnitude events of RF occurring in July andAugust. The scattergram plot shows that the low valuesof the daily sediment yield data-points are better whencompared to the high values, therefore, predictability ofthe low values by the model is better. The model simula-tion also shows bias, as there are more points below theline than above it.

The results of statistical tests performed on the agree-ment between measured and simulated daily runoff andsediment yield are presented in Table IV. The mean andstandard deviation values showed that the model pro-duced more variability in estimated runoff, whereas therewas less variability in the simulated sediment yield thanactually occurs. However, Student’s t-test indicated thatthe differences between the means of measured and simu-lated daily runoff and sediment yield were not significantat the 95% confidence level. The R2 values of 0Ð76 and0Ð72, ENS values of 0Ð68 and 0Ð71 and low values ofRMSE (3Ð63 mm and 0Ð09 t ha�1), respectively, of runoff

Table IV. Statistical test values of measured and HSPF simulateddaily runoff and sediment yield for model calibration

Statistical parameters Runoff(mm)

SedimentYield (t/ha)

Average daily(Observed/Model-simulated)

3Ð88/4Ð11 0Ð071/0Ð070

Standard deviation(Observed/Model-simulated)

6Ð42/7Ð43 0Ð17/0Ð13

t-calculated �0Ð28 0Ð06t-critical (two tail) 1Ð97 1Ð97Coefficient of determination, R2 0Ð76 0Ð72Nash-Sutcliffe simulation efficiency,

ENS

0Ð68 0Ð71

Root mean square error, RMSE 3Ð63 0Ð09Deviation, Dv (%) �5Ð93 1Ð48

and sediment yield indicated close relationships betweensimulated runoff and sediment yield with measured val-ues. The overall Dv (�5Ð93% for runoff and 1Ð48% forsediment yield) showed that the model slightly overpre-dicted daily runoff and underpredicted the sediment yieldbut did so well within the acceptable limits of accuracy(Bingner et al., 1989).

Sensitivity analysis

Sensitivity analysis was performed to evaluate theeffect of parameters on the performance of the HSPFmodel in predicting runoff and sediment yield. The cal-ibrated parameters, namely, initial upper zone storage(UZS), infiltration capacity index of the soil (INFILT),and Manning’s n, for the assumed overland flow plane(NSUR) were considered for sensitivity analysis. Sim-ulated annual output values were compared with thebase values of annual runoff and sediment yield, and theresults are presented in Table V.

For a decrease in the initial UZS up to its lower limit(1Ð0 mm), the annual runoff decreased by 0Ð54%, whereasthe sediment yield decreased significantly (Dv D 2Ð6%)

Table V. Results of sensitivity analysis of the calibrated HSPF model

S. No. Parameters Value Surfacerunoff (Q)

Sedimentyield (SY)

Q(mm)

%Dev.

SY(t ha�1)

%Dev.

Base variable (calibrated) 633Ð84 11Ð171 UZS (Initial upper zone storage—mm) 1Ð00 630Ð41 0Ð54 10Ð88 2Ð60

10Ð00 636Ð74 �0Ð46 11Ð80 �5Ð6450Ð00 632Ð28 0Ð25 11Ð58 �3Ð67

100Ð00 634Ð71 �0Ð14 12Ð40 �11Ð012 INFILT (An index to the infiltration capacity of the soil) 1Ð00 657Ð34 �3Ð71 11Ð61 �3Ð94

10Ð00 654Ð43 �3Ð25 11Ð55 �3Ð4050Ð00 642Ð77 �1Ð41 11Ð34 �1Ð52

100Ð00 630Ð87 0Ð47 11Ð12 0Ð453 NSUR (Manning’s n for the assumed overland flow plane) 0Ð01 633Ð84 0Ð00 11Ð17 0Ð00

0Ð05 633Ð84 0Ð00 11Ð17 0Ð000Ð10 633Ð84 0Ð00 11Ð17 0Ð000Ð20 633Ð84 0Ð00 11Ð17 0Ð001Ð00 633Ð70 0Ð02 11Ð17 0Ð00



from the base value. On increasing the initial UZS aboveits base value, runoff first increased (at 10-mm UZS)and then above a certain limit (at 50-mm UZS) runoffdecreased. Further, when the initial UZS was increasedto its maximum limit (100Ð0 mm), runoff increased by0Ð14%, whereas sediment yield increased to its maximum,yielding 11Ð01% more than its base value.

On decreasing INFILT to its lower limit (1Ð0), bothrunoff and sediment yield increased significantly andthe respective deviations from their base values were3Ð71 and 3Ð94%. On the other hand, when INFILT wasincreased up to its upper limit (100Ð0), both runoff andsediment yield decreased but their Dv (0Ð47 for runoffand 0Ð45 for sediment yield) from the base values wereless.

On decreasing NSUR to its lower limit (0Ð01), annualrunoff and sediment yield showed no change, whereason increasing NSUR up to its upper limit (1Ð0), runoffshowed a very low response with a decrease of only0Ð02%, whereas sediment yield remained unaffected forany change in NSUR.

On the basis of the sensitivity analysis performed,it was concluded that the sediment yield was highlysensitive to UZS and then to INFILT. On the other hand,runoff was more sensitive to INFILT than UZS. HighUZS reduced infiltration, resulting in more runoff andmore sediment yield. Low INFILT caused more water toflow out as runoff and therefore more sediment transport,and vice-versa for high INFILT.

Validation

The calibrated HSPF model was validated for themonsoon months from June to September of 2000 andfrom June to October of 2001. The idea behind theselection of these years for validation was to test themodel under a lower RF monsoon year than the average.The total RF during the monsoon months of these yearswas less than the mean minus one standard deviationof total RF during monsoon months over 11 years(1991–2001). The results of model validation for dailyrunoff and sediment yield are presented in Figures 5and 6. Although the year 2000 was a relatively dryyear, as compared to the calibration year 1996, themagnitude and temporal variation of simulated runoffshowed a good response to RF and was attained closeto the measured runoff pattern. However, the modelunderestimated runoff in the initial phase owing to driersoil and more infiltration. The model response changedwhen soil was wet at the beginning of September whencontinuous RF was observed and as a result runoffincreased, as compared to the measured values. In 2001,the trend of measured and simulated runoff also matchedwell. The simulated seasonal runoff was initially less,because of higher infiltration in initially dry soil, but laterexceeded measured runoff. In both years, the cumulativerunoff was higher than observed runoff from Septemberonward, and this could be attributed to erratic RF(continuous RFs for a few days) and soil water content,

and it could also be influenced by paddy cultivation inthe watershed, which was not captured accurately by themodel and resulted in higher simulated runoff than whatactually occurred.

The simulated runoff values are distributed almostevenly about the 1 : 1 line for almost all the events exceptfor a few points. Owing to a greater number of high RFevents in 2000, the runoff yield was higher in 2000 than2001. The differences between measured and simulatedrunoff in both the years may be ascribed to the topo-graphic and complex morphological heterogeneity of thewatershed. Small differences may also result from inaccu-racies involved in the representation of subtle differencesin a channel, soil and subsurface characteristics by themodel (Van Liew and Garbrecht, 2003).

Comparison of measured and simulated daily sedi-ment yields for 2000 (June–September) showed that themodel-simulated daily sediment yield was less than themeasured yield for high RF events but for medium RFevents the simulated sediment yield deviated less from themeasured sediment yield. These differences are attributedto variations in RF characteristics and land cover condi-tions during the validation period. The validation resultsfor 2001 show that the simulated daily sediment yieldcompared well in trend with measured yield. However,in mid-June, sediment yield for high initial RF eventswas severely underestimated, perhaps owing to the ini-tial dryness of the soil, resulting in less runoff and thusless sediment yield than the measured sediment values.From the end of July onward, the differences in the esti-mated sediment yield could be due to the long gaps inRF occurrence and relatively dry soil generating moresediment.

Comparisons of daily sediment yield (Figure 6) showsthat sediment yield values for the year 2000 wereunderpredicted for most of the events. It is also seen fromthe regression line being below the 1 : 1 line. In the year2001, however, the regression line exactly followed 1 : 1but had more variations. Sometimes the model-estimatedsediment yield was more than the observed sedimentyield and vice-versa.

Table VI shows the values of statistical tests formeasured and simulated daily runoff and sediment yieldduring validation periods. The values of mean andstandard deviation show that model-simulated valuesshowed variability but at the same time Student’s t-testsindicated that the difference between the mean valuesof measured and simulated values were not significantat the 95% confidence level. Statistical tests indicatedoverprediction and low simulation efficiency of the modelfor runoff prediction, particularly in the year 2001. In caseof sediment yield, reasonably high values of R2 (0Ð93 and0Ð76) and ENS (0Ð90 and 0Ð68) indicated better predictionof sediment yield by the model than runoff. A high valueof Dv in 2001 (�37Ð38%) for sediment yield reflected asevere overprediction by the model that was beyond thelevel of acceptance but, considering the overall statisticsit can be said that model predictions were satisfactory.



Validation (2000)

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Figure 5. Runoff and sediment yield validation results at the watershed outlet during June–September 2000 and June–October 2001

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Figure 6. Comparison of validation results

The calibration and validation results of HSPF showthat the model-estimated daily runoff and sediment yieldwere in reasonable agreement with measured values.

They also showed that the model was capable of sim-ulating the response of a small watershed in terms ofdaily runoff and sediment yield. However, in relatively



Table VI. Statistical test values of measured and HSPF simulated daily runoff and sediment yield for model validation

Statistical parameters Runoff Sediment yield

2000 2001 2000 2001

Average daily (Observed/Model-simulated) 2Ð40/2Ð62 2Ð35/2Ð54 0Ð05/0Ð04 0Ð03/0Ð04Standard deviation (Observed/Model-simulated) 6Ð30/6Ð96 3Ð66/4Ð62 0Ð14/0Ð11 0Ð06/0Ð07t-calculated �0Ð25 �0Ð42 0Ð21 �1Ð38t-critical (two tail) 1Ð97 1Ð97 1Ð97 1Ð97Coefficient of determination, R2 0Ð73 0Ð65 0Ð93 0Ð76Nash-Sutcliffe simulation efficiency, ENS 0Ð67 0Ð44 0Ð90 0Ð68Root mean square error, RMSE 3Ð62 2Ð74 0Ð05 0Ð04Deviation, Dv (%) �8Ð80 �8Ð36 7Ð79 �37Ð38

dry years (2000 and 2001) compared to the calibrationyear (1996), the model marginally overpredicted runoffand inconsistently overpredicted sediment yield in theyear 2001.

The ultimate use of any model parameterization isto set up the model to study the watershed hydrologicbehaviour under varied weather conditions so that man-agement options can be evaluated and adopted. The abovecalibration and validation results showed that the modelpredictions were within acceptable accuracy during bothcalibration and validation. It can, therefore, be inferredthat the HSPF model (Version 11Ð0) can be used for sim-ulating runoff and sediment yield from small, mixed,land-use watersheds situated in sub-humid subtropicalregions of India with monsoon RF.

CONCLUSIONS

The following conclusions are drawn from this study:(1) The HSPF model predictions compare closely withmeasurements and produce a working best-fit set ofmodel parameters within physically realistic ranges andacceptable approximations of runoff and sediment yieldfrom a small, mixed, land-use watershed in sub-humidsubtropics during the monsoon months. (2) Sensitivityanalysis of the HSPF parameters indicates that runoffis most sensitive to the INFILT, followed by the initialUZS of water; whereas sediment yield is more sensitiveto the initial UZS of water than the infiltration capacityindex of soil. This will help reduce the calibration timefor future applications of the model under similar typesof watershed studies. (3) Although the model slightlyoverpredicts runoff and the modelling efficiency for dailyrunoff is low, prediction is still within the acceptablelimits of accuracy. (4) The prediction for sediment yieldis better than for runoff. (5) Overall, the calibrated modelparameters for an individual sub-watershed represent thenature and behaviour of the watershed quite realisticallyas the estimated runoff and sediment yield comparedreasonably well to measured values. Thus, HSPF can besuccessfully used to obtain the information on hydrologicbehaviour of a small watershed under varied weatherconditions and the results can be utilized in developingwatershed management plans for conserving soil and

water resources in sub-humid subtropical areas in theIndian subcontinent.

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