ARTICLE

Evaluation of HEC-HMS and WEPP for simulating watershedrunoff using remote sensing and geographical information system

Arbind K. Verma • Madan K. Jha •

Rajesh K. Mahana

Received: 11 July 2009 / Accepted: 20 November 2009 / Published online: 3 December 2009

� Springer-Verlag 2009

Abstract Although a variety of rainfall-runoff models are

available, selection of a suitable rainfall-runoff model for a

given watershed is essential to ensure efficient planning

and management of watersheds. Such studies are relatively

limited in developing nations, including India. In this

study, rainfall-runoff modeling was carried out using HEC-

HMS and WEPP hydrologic models, and remote sensing

and GIS (geographical information system) techniques in

the Upper Baitarani River basin of Eastern India using

daily monsoon season (June–October) rainfall and the

corresponding streamflow data of 6 years (1999–2005).

Other input data such as soil map, land use/land cover map,

and slope map were prepared using remote sensing and

GIS techniques. The modeling results revealed that both

the models under predict streamflow for 1999, 2002, 2004,

and 2005 and over predict for 2001 and 2003, whereas

HEC-HMS under predicts and WEPP over predicts

streamflow for the year 2000. The percent deviation of total

runoff volume simulated by HEC-HMS ranges between

-2.55 and 31%, while it varies from -13.96 to 13.05% for

the WEPP model which suggests that the WEPP model

simulates annual flow volumes more accurately than the

HEC-HMS model for most years. However, the lower

values of root mean square error (RMSE) and RMSE-

observation standard deviation ratio coupled with the

higher values of Nash–Sutcliffe efficiency, percent devia-

tion and coefficient of determination for HEC-HMS during

calibration and validation periods indicated that the

streamflow simulated by HEC-HMS is more reliable than

that simulated by WEPP. Overall, it is concluded that the

HEC-HMS model is superior to the WEPP model for

simulating daily streamflow in the Baitarani River basin of

Eastern India.

Keywords Rainfall-runoff modeling � HEC-HMS �WEPP � Model evaluation � Remote sensing � GIS

Introduction

Conservation of land and water resources is of significant

social and environmental concern these days. The rapidly

increasing human population and changes of lifestyle have

put tremendous pressure on these natural resources causing

their degradation and posing a global threat (Lal 1999). Out

of total degraded area of 1965 Mha, over 300 Mha are

strongly degraded over the world scale (Oldeman 1994). In

India, about 187.8 Mha of land constituting about 57% of

the total geographical area (328.73 Mha) suffers from

deleterious effects of soil erosion and other forms of land

degradation (Sehgal and Abrol 1994). Surface runoff, one

of the main causes of soil erosion, leads to the sedimen-

tation of reservoirs, loss of plant nutrients (agricultural

watersheds), and deterioration of river water quality.

Therefore, a major challenge still remaining is the accurate

prediction of catchment runoff responses to rainfall events

(Burger et al. 2007). One viable answer and approach to

this challenge is the use of suitable hydrologic models for

the efficient management of watersheds and ecosystems.

Hydrologic models are simplified representations of

actual hydrologic systems that allow us to study the func-

tioning of watersheds and their response to various inputs,

and thereby gain a better understanding of hydrologic

processes. Hydrologic models also allow us to predict the

A. K. Verma � M. K. Jha (&) � R. K. Mahana

Agricultural and Food Engineering Department,

Indian Institute of Technology Kharagpur,

Kharagpur 721 302, West Bengal, India

e-mail: madan@agfe.iitkgp.ernet.in; madaniit@rediffmail.com

123

Paddy Water Environ (2010) 8:131–144

DOI 10.1007/s10333-009-0192-8

hydrologic response to various watersheds management

practices and to have a better understanding of the impacts

of these practices (e.g., Mostaghimi et al. 1997; Rao et al.

2000; Tripathi et al. 2003; Gosain and Rao 2004; Arabi

et al. 2008). The computer advances in the 1960s made

possible the integration of models of different components

of hydrologic cycle and the simulation of virtually entire

watershed with a development of Stanford Watershed

Model-SWM (presently known as HSPF) by Crawford and

Linsley in 1966 (Singh and Fervert 2006). At present, there

are well-established conceptual as well as physically based

modeling approaches which have been employed for the

simulation of rainfall-runoff processes in different water-

sheds of India (e.g., Putty and Prasad 2000; Jena 2002;

Pandey et al. 2008) and abroad (e.g., Schuman et al. 2000;

Anderson et al. 2002; Raclot and Albergel 2006; Jang et al.

2007, Santhi et al. 2008). There have been various studies

dealing with the evaluation of a specific rainfall-runoff

model for the simulation of runoff and soil loss using field-

scale models such as USLE, CREAMS, and GLEAMS,

event-based watershed-scale models such as AGNPS and

ANSWERS, continuous time step-lumped watershed

models such as HSPF, SWRRB, and SWMM, and con-

tinuous time step-distributed parameter models such as

SWAT, WEPP, MIKE SHE, ANSWERS, TOPMODEL,

and HEC-HMS, among others. In addition, data mining

technique like ANN has also been applied for forecasting

hydrologic and water quality responses of a watershed

system (e.g., Minns and Hall 1996; Sarangi et al. 2005).

From the wide range of models available, the choice of

most appropriate model for any specific task is difficult;

particularly because each modeler tends to promote the

merits of his or her approach. Therefore, comparative

evaluation-based studies are needed to assess the applica-

bility and limitations of watershed models and to provide a

basis for selecting a model that will perform adequately in

a specific application (Johnson et al. 2003). Realizing this

need, a comparative evaluation of hydrological models

started in the 1968 when the World Meteorological Orga-

nization initiated an international project on intercompari-

son of conceptual models which was completed in 1974

(WMO 1982). Recently, Duan et al. (2006) reported results

of the model parameter estimation experiment (MOPEX)

in which data from 12 basins in the southeastern USA were

distributed and used to compare the performance of some

selected hydrological models (Clarke 2008). Besides these,

some studies (e.g., Duru and Hjelmfelt 1994; Yang et al.

1999; Johnson et al. 2003; Kallin and Hantush 2006; Nasr

et al. 2007) have been carried out across the globe as far as

the intercomparison of two or more hydrologic models are

concerned. In India, the comparative evaluation of SWAT

and HSPF hydrologic models for simulating runoff and

sediment yield in the Banha watershed of Hazaribagh,

Jharkhand (India) has been reported by Mishra (2004),

whereas the evaluation of AGNPS and ANSWERS

hydrologic models for simulating runoff, peak flow and

sediment yield in the Upper Damodar Valley of Hazari-

bagh, Jharkhand (India) has been reported by Singh (2002).

Thus, it is evident from the extensive review of the lit-

erature that the studies on comparative assessment of

watershed models for hydrologic simulations are very

much limited in developing countries, including India.

Considering this fact and the necessity of scientific studies

in the Baitarani watershed of Upper Baitarani River basin,

Eastern India, the present study was undertaken to assess

the performance of HEC-HMS 3.2 and WEPP 2006.5

hydrologic models for simulating watershed runoff using

remote sensing and geographical information system

(GIS). The HEC-HMS and WEPP models are physically

based and linked with a GIS. Considering these technical

advantages as well as their easy availability, wide use and

better technical support by the developers, they were

selected in this study. Also, they have been developed by

reputed organizations like the US Army Corps of Engineers

and the US Department of Agriculture (USDA), respec-

tively. The Baitarani watershed was chosen as a study area

in this study because no scientific studies in this watershed

are reported to date. The present study is first of its kind in

the Baitarani watershed of Upper Baitarani River basin.

Overview of the hydrologic models used in the study

Both the HEC-HMS and WEPP hydrologic models are

physically based, but the HEC-HMS model is designed to

simulate rainfall-runoff processes of networked watershed

systems which include sub-basins, reaches, junctions, res-

ervoirs, diversions, sources, and sinks. On the other hand,

the model framework for WEPP consists of a single

watershed composed of a network of hillslopes and chan-

nels. A brief description about these two models is pre-

sented below.

HEC-HMS Model

HEC-HMS is hydrologic modeling software developed by

the US Army Corps of Engineers Hydrologic Engineering

Center (HEC). It is designed to simulate the precipitation-

runoff processes of watershed systems in a wide range of

geographic areas such as large river basins and small urban

or natural watersheds. The system encompasses losses,

runoff transform, open-channel routing, analysis of mete-

orological data, rainfall-runoff simulation, and parameter

estimation. HEC-HMS uses separate models to represent

each component of the runoff process, including models

that compute runoff volume, models of direct runoff, and

132 Paddy Water Environ (2010) 8:131–144

123

models of baseflow. Each model run combines a basin

model, meteorological model, and control specifications

with run options to obtain results. The system connectivity

and physical data describing the watershed are stored in the

basin model. The precipitation and evapotranspiration data

necessary to simulate watershed processes are stored in the

meteorological model. The details of model structures and

various processes involved are given in the Technical

Reference Manual (USACE-HEC 2000) and the User’s

Manual (USACE-HEC 2008) of HEC-HMS. A succinct

description of this model is provided here.

HEC-HMS includes models of infiltration from the land

surface but it does not model storage and movement of water

vertically within the soil layer. It implicitly combines the

near surface flow and overland flow and models this as direct

runoff. HEC-HMS considers that all land and water in a

watershed can be categorized as either directly connected

impervious surface or pervious surface. Directly connected

impervious surface in a watershed is that portion of the

watershed for which all contributing precipitation runs off,

with no infiltration, evaporation, or other volume losses.

Precipitation on the pervious surfaces is subject to losses. In

HEC-HMS, many well-known models such as initial and

constant-rate loss model, deficit and constant-rate model,

SCS-CN (Soil Conservation Service curve number) loss

model, and Green-Ampt loss model are included to estimate

cumulative losses. With each model, precipitation loss is

found for each computation time interval, and is subtracted

from the mean areal precipitation (MAP) depth for that

interval. The remaining depth is referred to as precipitation

excess. This depth is considered uniformly distributed over a

watershed, so it represents a volume of runoff. The SCS-CN

loss model was used in the present study. The SCS-CN

model estimates precipitation excess as a function of

cumulative precipitation, soil cover, land use, and anteced-

ent moisture using the following equation (Singh 1994):

Pe ¼P� Iað Þ2

P� Ia þ Sð1Þ

where, Pe = accumulated precipitation excess at time t,

P = accumulated rainfall depth at time t, Ia = the initial

abstraction (initial loss), and S = potential maximum

retention, a measure of the ability of a watershed to abstract

and retain storm precipitation.

Based on the analysis of results from many small

experimental watersheds, the SCS developed an empirical

relationship between Ia and S as Ia = 0.2S. Therefore, the

cumulative excess at time t is given as:

Pe ¼P� 0:2Sð Þ2

Pþ 0:8Sð2Þ

The maximum retention (S) is determined using the

following equation (SI system):

S ¼ 25; 400� 254 CN

CNð3Þ

where CN is the SCS curve number. It is an index that

represents the combination of hydrologic soil group, land

treatment classes, and antecedent moisture conditions. The

values of CN can be obtained for different land uses,

treatment, and hydrologic conditions from the standard

table provided by SCS-USA (McCuen 1998). For an

impervious area, the value of CN is 98. In HEC-HMS, the

minimum and maximum values of CN are used as 1 and

100, respectively.

HEC-HMS transforms the rainfall excess into direct

surface runoff through a unit hydrograph or by the kine-

matics wave transformation. Rainfall excess is computed

for each time interval by subtracting infiltration losses from

incoming precipitation. In order to compute direct runoff

hydrograph by unit hydrograph method, HEC-HMS uses a

discrete representation of excess precipitation, in which a

pulse of excess precipitation is known for each time

interval. It then solves the discrete convolution equation for

a linear system as follows (USACE-HEC 2000):

Qn ¼Xn � M

m ¼ 1

PmUn�mþ1 ð4Þ

where Qn = storm hydrograph ordinate at time nDt,

Pm = rainfall excess depth in time interval mDt to (m ? 1)

Dt, M = total number of discrete rainfall pulses and Un–

m?1 = UH ordinate at time (n – m ? 1) Dt. Qn and Pm are

expressed as flow rate and depth, respectively and Un–m?1

has dimensions of flow rate per unit depth.

In the present study, SCS unit hydrograph (SCS UH)

model has been applied for estimating direct runoff.

Research by the SCS suggests that the UH peak (UP) and

time of UH peak (TP) are related as:

UP ¼ CA

TP

ð5Þ

where, A = watershed area; and C = conversion constant

(2.08 in SI).

The time of peak (also known as the time of rise) is

related to the duration of the unit of excess precipitation as

follows:

TP ¼D t

2þ tlag ð6Þ

where, Dt = the excess precipitation duration (which is also

the computational interval in HEC-HMS); and tlag = the

basin lag, defined as the time difference between the center

of mass of rainfall excess and the peak of the UH. When the

lag time is specified, HEC-HMS solves Eq. 6 to find the time

of UH peak, and Eq. 5 to find the UH peak.

In HEC-HMS, the baseflow model is applied both at the

start of simulation of a storm event, and later in the event as

Paddy Water Environ (2010) 8:131–144 133

123

the delayed subsurface flow reaches the watershed channels.

Three alternative models of baseflow such as ‘‘constant

monthly varying value’’, ‘‘exponential recession model’’,

and ‘‘linear reservoir volume accounting model’’ are

included. The recession model explains the drainage from

natural storage in a watershed. It defines the relationship of

the baseflow Qt at any time t to an initial value Q0 as:

Qt ¼ Q0 kt ð7Þ

where, k is an exponential decay constant.

After the peak of the direct runoff, a user-specified

threshold flow defines the time at which the recession

model (Eq. 7) defines the total flow. This threshold flow

may be specified as a flow rate or as a ratio to the computed

peak flow. At the threshold flow, baseflow is defined by the

initial baseflow recession. Thereafter, baseflow is not

computed directly, but is defined as the recession flow

minus direct surface runoff. When the direct surface runoff

eventually reaches zero (i.e., all rainfall has run off the

watershed), the total flow and baseflow are identical.

WEPP model

WEPP is a process-based continuous hydrologic model that

simulates hydrologic and erosion processes that occur on

small watersheds or on slopes on hills within the water-

sheds (Flanagan and Nearing 1995). In watershed appli-

cations, the model allows linkage of hillslope profiles to

channels and impoundments. The WEPP model (ver.

2006.5) includes components for weather generation,

hydrology component, water balance and percolation,

irrigation, plant growth, residue decomposition, soil dis-

turbance by tillage, and erosion. The model includes

options for single storm, continuous simulation, single

crop, crop rotation, contour farming, and strip cropping.

The climate component generates mean daily precipitation,

daily maximum and minimum temperature, mean daily

solar radiation, and mean daily wind direction, and speed.

A disaggregation model has been included in the climate

component to generate time-rainfall intensity (break point)

data from daily rainfall amounts. That is, given rainfall

amount and rainfall duration, the disaggregation model

derives a rainfall intensity pattern with properties similar to

those obtained from analysis of break-point data. The

break-point rainfall data are required by the infiltration

component to compute rainfall excess rates and thus runoff.

The hydrology component of WEPP computes infiltra-

tion, runoff, soil evaporation, plant transpiration, soil water

percolation, and plant and residue interception of rainfall,

depression storage, and soil profile drainage. This compo-

nent provides the erosion component with the duration of

rainfall excess, the rainfall intensity during the period of

rainfall excess, the runoff volume, and the peak discharge

rate. It also provides the amount of water that infiltrates

into the soil for the water balance and crop residue calcu-

lation. The rainfall excess volume is computed in con-

junction with the infiltration calculations. Plant growth

component uses information from water balance compo-

nent, and provides information to erosion and decomposi-

tion and management component. The soil parameters that

influence hydrology are updated in the soil component.

Bulk density reflects the total pore volume of the soil and is

used to update several infiltration related variables. Effec-

tive hydraulic conductivity is a key parameter in the WEPP

model that controls the prediction of infiltration and runoff.

The inter-rill erodibility parameter is a measure of the soil

resistance to detachment by raindrop impact. The rill

erodibility parameter is a measure of the soil resistance to

detachment by concentrated rill flow. The detailed mathe-

matical representations of channel hydrological processes

are given in the Technical Manual of WEPP (Flanagan and

Nearing 1995). A brief description of the processes used in

this study is presented here.

Infiltration is calculated using the modified Green and

Ampt infiltration equation which is known as ‘‘Green-

Ampt Mein-Larson’’ (GAML) model. It is mathematically

expressed as follows (Stone et al. 1995):

fi ¼Fi � Fi�1

ti � ti�1

ð8Þ

where, fi = average infiltration rate at current time interval

(m/s), F = cumulative infiltration depth (m), i = current

time interval, i – 1 = previous time interval, and t = time

(s).

The time intensity distribution of rainfall excess is

transformed into a time intensity distribution of runoff (the

hydrograph) by the kinematic wave model or a value of

peak discharge by the approximate method. The rainfall

excess volume is computed in conjunction with the infil-

tration calculations as follows:

Vi ¼ Ri � Fi for ri [ fi and Fi\Sp ð9Þ

Vi ¼ Vi�1 for ri � fi and Fi\Sp ð10Þ

Vi ¼ Ri for Fi� Sp ð11Þ

where, V = cumulative rainfall excess depth (m),

R = cumulative rainfall depth (m), F = cumulative infil-

tration depth (m), i = current time interval, i - 1 = pre-

vious time interval, and Sp = upper limit of water storage

(m) in the top two soil layers.

The storage upper limit is computed as:

Sp ¼ KminDr þmax O;X2

j ¼ 1

ULj � STj

� �" #

ð12Þ

where, Kmin = minimum saturated hydraulic conductivity

of the two layers (m/s), Dr = duration of rainfall (s),

134 Paddy Water Environ (2010) 8:131–144

123

ULj = upper limit of soil moisture storage (m),

STj = current soil moisture storage (m).

The runoff transformation in the WEPP watershed

model is brought about by the kinematic wave transfor-

mation model. The kinematic equations for flow on a plane

are the continuity equation as follows:

oh

otþ oq

ox¼ v ð13Þ

and a depth-discharge relationship as:

q ¼ a hm ð14Þ

where, h = depth of flow (m), q = discharge per unit

width of the plane (m2/s), a = depth-discharge coefficient,

m = depth-discharge exponent, and x = distance from top

of plane (m).

The water balance and percolation components of

WEPP are designed to use input from the climate, infil-

tration, and crop growth components to estimate soil water

content in the root zone and evapotranspiration losses

throughout the simulation period (Savabi and Williams

1995). The hydrologic processes in WEPP include infil-

tration, runoff routing, soil evaporation, plant transpiration,

and seepage. The model maintains a continuous water

balance on a daily basis using the equation as follows:

h ¼ hin þ ðP� IÞ � Q� ET� D� Qd ð15Þ

where, h = soil water content in the root zone on a given

day (m), hin = initial soil water content in the root zone

(m), P = cumulative precipitation (m), I = precipitation

interception (m), Q = cumulative amount of surface runoff

(m), ET = cumulative amount of evapotranspiration (m),

D = cumulative amount of percolation loss (m), and

Qd = subsurface flow (m).

Materials and methods

Overview of study area

Considering the land and water problems and the avail-

ability of hydrological, meteorological, soil, and other

collateral data, the Baitarani watershed was selected as the

study area for the present study. The study area is located

between 85� 10 to 85� 750 E longitude and 21� 250 to 22�250 N latitude in the Upper Baitarani River basin of Eastern

India (Fig. 1). It has an area of 1,776 km2 encompassing

Keonjhar, Nuagarh, Dhenkanal, Anugul, Rayagada, and

Sundergarh districts of Orissa. For this study, Champua

gauzing station was taken as the outlet of the watershed

which is located at 85� 400 5600 E longitude and 22� 030 5700

N latitude and has an elevation of 367 m above mean sea

level. The Baitarani River rises in the hill ranges of

Keonjhar District of Orissa near Mankarancho village at an

elevation of about 900 m above MSL. The study area falls

in the Northern Central Plateau agro-climatic zone which

has hot and moist sub-humid climate with an average

annual rainfall of 1,534 mm, of which 80% occurs during

June to October. The temperature variation in the region is

between 10 and 45�C, with a mean maximum temperature

of about 37�C and a mean minimum temperature of 11�C.

The average annual runoff from the study area measured at

the Champua gaging station is about 1,002 MCM (Million

Cubic Meter) with a standard deviation of 414 MCM

(CWC 2006).

The watershed is sloping toward north in general with a

slope varying from 5.3 to 17.2%. A number of hillocks

with forest (45%) and agricultural lands (42%) including

wasteland (10%) constitute the main land use of the study

area. The study area mainly contains loamy textured soils

associated with clay. The study area has rich mineral

reserves of iron, manganese, and chromium.

Data acquisition

The data used in this study were: (a) survey of India to-

posheets with map numbers 73F/8, 73F/12, 73G/1, 73G/2,

73G/5, 73G/6, 73G/7, 73G/9, 73G/10, and 73G/11 on

1:50,000 scale covering the entire study area; (b) daily

rainfall data of the four raingage stations (Keonjhar,

Champua, Jhumpura, and Tensa) for the 7-year period

(1999–2007) from the Indian Meteorological Centre,

Bhubaneswar, Orissa; (c) daily discharge data of the

Champua gauzing station for the 7-year period (1999–

2007) from CWC, Bhubaneswar, Orissa; (d) soil map of the

study area on 1:500,000 scale from the National Bureau of

Soil Survey and Land Use Planning, Nagpur; (e) digital

Elevation Model (DEM) of the Upper Baitarani River basin

was acquired from the CGIAR Consortium for Spatial

Information (http://srtm.csi.cgiar.org); and (f) Three

Landsat images (ETM?) were obtained from the Global

Land Cover Facility, Institute for Advanced Computer

Studies, Maryland (http://glcf.umiacs.umd.edu/data).

Preparation of model inputs

Thiessen Polygon method was used to calculate areal

rainfall over the watershed from the point rainfall values

observed at the four raingage stations (Champua, Tensa,

Jhumpura, and Keonjhar) with the help of ‘‘ArcView Ar-

ealRain Extension’’ software. The weightage factor for the

rainfall stations Champua, Tensa, Jhumpura, and Keonjhar

were found to be 0.304, 0.354, 0.148, and 0.294,

respectively.

The Landsat satellite data were used for the generation

of land use/land cover map of the study area using ERDAS

Paddy Water Environ (2010) 8:131–144 135

123

IMAGINE 8.5 software. Unsupervised classification was

performed by specifying a convergence threshold of 0.95.

The convergence threshold is the maximum percentage of

pixels whose cluster assignments can go unchanged

between iterations. The overall classification accuracy and

Kappa Statistics Coefficient (k) were found to be 82.4%

and 0.79, respectively. The value of k equal to 1 indicates

perfect agreement between categories and a zero value

indicates a poor agreement between categories, while a

value of 0.4 to 0.80 indicates fair to good agreement

(Manseurad and Leemans 1992). The land use/land cover

types thus obtained are shown in Table 1 along with the

area under each land use/land cover. Besides, the soil maps

of the study area were scanned, exported to ERDAS

IMAGINE 8.5, and rectified using map to map registration

taking permanent ground control features from the regis-

tered topographic map. All the three rectified maps were

mosaiced and the study area was extracted by sub-setting it

from the full map. Boundaries of different soil textures

were digitized and various polygons were assigned to

represent different soil categories such as loam, fine loam,

clay, sandy loam, and clay loam. Finally, the polygons

were assigned to different hydrologic soil groups (A, B, C,

and D) based on the information provided in the soil map

about dominant and associated soil families within a par-

ticular map unit highlighting the soil depth, drainage,

texture, erosion, and salinity, etc. The land use/land cover

and soil maps of the study area were used to assign CN

values with the help of the standard table provided by SCS-

USA (McCuen 1998) and weighted CN values were cal-

culated for different land use/land covers present in the

study area. The values of weighted CN for individual land

use/land covers are presented in Table 1.

The topographic maps of the study area were scanned

and imported to ERDAS IMAGINE 8.5 software for

Fig. 1 Location map and stream network of the study area

Table 1 Land use/land covers in the study area and their CN values

Land use/Land cover Area (km2) Weighted CN

Forest 799.12 53

Agriculture 746.92 72

Wasteland 183.61 50

Built-up land (residential) 29.84 45

Water bodies 16.61 92

Wetland (non-forested) 0.15 49

136 Paddy Water Environ (2010) 8:131–144

123

geometric correction using WGS 84 as the type of spheroid

and the datum. Sixteen graticule intersections were taken

as control points to obtain a better accuracy. The geometric

precision was examined with the help of root mean square

error (RMSE) of the corresponding graticule intersections

compared against their theoretical coordinates, which had

been kept within 1 pixel. The coordinates of the graticule

intersections in spherical coordinates were then trans-

formed to the rectangular coordinates with UTM projection

taking zone number 45. In order to produce geometrically

rectified map, resampling was carried out using the

‘‘Nearest Neighborhood’’ technique. All the rectified

topographic maps were mosaiced to get a single image of

the study area. The stream network up to the chosen outlet

point of the watershed was digitized from the mosaiced

topographic maps for the purpose of comparison with that

delineated from SRTM (Shuttle Radar Topographic Mis-

sion) DEM of the watershed (Fig. 1). The downloaded

SRTM DEM (90 m 9 90 m spatial resolution) was pro-

cessed using terrain processing module of HEC-GeoHMS

software with ArcView 3.2 GIS interface for watershed

delineation including stream network generation. Basin

processing module of HEC-GeoHMS was used for the

generation of background map file of the study area which

in turn was used as an input to the HEC-HMS model.

Moreover, the climate, slope, and soil data files were

prepared for inputs to the WEPP model. The climate file

was built using the BPCDG program which uses observed

standard weather data sets. The slope file was built within

the interface slope file builder. The WEPP model requires

information about landscape geometry which was entered

by way of slope file. ArcView and ERDAS IMAGINE

software packages were used to generate slope and river

length from the DEM. The soil file was created through soil

file builder in the WEPP interface. Information on soil

properties like percentage of sand, silt, clay, organic

matter, rock fragment fraction, and cation exchange

capacity to a maximum depth of 1.8 m were input to

WEPP through the soil file. The WEPP model internally

creates a new soil layer based on the original values.

The inputs of the HEC-HMS and WEPP models pre-

pared using remote sensing and GIS techniques are sum-

marized in Table 2.

Calibration and validation of the models

The successful application of a hydrologic watershed

model depends on how well the model is calibrated, which

in turn depends on the technical capability of hydrological

model as well as the quality of input data. HEC-HMS and

WEPP watershed models were calibrated using daily

monsoon season (June to October) rainfall and streamflow

data of 5 years (1999–2003). The objective of the model

calibration was to match simulated volumes, peaks, and

timing of hydrographs with the observed ones. For simu-

lating streamflow by the HEC-HMS model, the SCS unit

hydrograph transform method was used to compute direct

surface runoff hydrographs, the SCS curve number loss

method to compute runoff volumes, and the exponential

recession method was used for baseflow separation. Initial

abstraction (Ia), SCS lag time, and recession constant (k)

were considered as HEC-HMS calibration parameters,

whereas the effective hydraulic conductivity was consid-

ered as a calibration parameter for the WEPP model. These

model parameters were estimated using trial and error

method until a reasonable match between observed and

simulated streamflow hydrographs was obtained. After

each parameter adjustment and corresponding simulation

run, the simulated and observed streamflow hydrographs

were visually compared and NSE (Nash–Sutcliffe effi-

ciency) was computed to examine the improvement in

simulation results. Several simulation runs were performed

Table 2 Inputs of HEC-HMS and WEPP models prepared by remote sensing and GIS techniques

Input data Source of data Software used

(a) HEC-HMS Model

Mean areal rainfall Field data ArcView ArealRain Extension

Curve number Land use/Land cover map Landsat imagery (Remote Sensing) ERDAS IMAGINE and ArcView

Soil map Conventional map ERDAS IMAGINE and ArcView

Boundary map and drainage networks of the study

area

SRTM DEM (Remote Sensing) HEC-GeoHMS and ArcView

(b) WEPP Model

Length, steepness, shape and orientation of hillslope SRTM DEM (Remote Sensing) HEC-GeoHMS, ArcView and ERDAS IMAGINE

Length, width, and slope of channels SRTM DEM (Remote Sensing) HEC-GeoHMS, ArcView and ERDAS IMAGINE

Land use/Land cover map Landsat imagery (Remote Sensing) ERDAS IMAGINE and ArcView

Soil texture Soil map Conventional map ERDAS IMAGINE and ArcView

Mean areal rainfall Field data ArcView ArealRain Extension

Paddy Water Environ (2010) 8:131–144 137

123

to obtain the best values of calibration parameters corre-

sponding to the simulation run yielding highest value of

NSE. After the model calibration, both the models were

validated using daily monsoon season streamflow data of

the years 2004 and 2005 and taking average value of the

calibrated parameters.

Performance evaluation and comparison of HEC-HMS

and WEPP

In this study, the performance of HEC-HMS and WEPP

were evaluated by using both statistical and graphical

model evaluation techniques. The model evaluation sta-

tistics such as Nash–Sutcliffe efficiency (NSE) and percent

deviation (Dv) recommended by ASCE (1993) as well as

index of agreement (d1) suggested by Legates and McCabe

(1999), and RMSE-observation standard deviation ratio

(RSR) recommended by Moriasi et al. (2007) were used. In

addition, the commonly used statistical indicators, viz.,

coefficient of determination (R2), mean error (ME), and

root mean square error (RMSE) were also used. It is worth

mentioning that a subset of these statistics has been and is

being used in the studies on model evaluation with the use

of NSE in most studies (McCuen et al. 2006). The values of

ME, RMSE, RSR, NSE, d1, Dv and R2 were calculated

using the following equations:

ME ¼ 1

n

Xn

i ¼ 1

Qo � Qsð Þ ð16Þ

RMSE ¼ 1

n

Xn

i¼1

Qo � Qsð Þ2i

" #0:5

ð17Þ

RSR ¼ RMSE

STDEVobs

ð18Þ

NSE ¼ 1�

Pn

i ¼ 1

Qo � Qsð Þ2iPn

i ¼ 1

Qo � Qo

�� �2

i

ð19Þ

d1 ¼ 1:0�

Pn

i ¼ 1

Qo � Qsð ÞiPn

i ¼ 1

Qs � Qo

� �

i

������þ Qo � Qo

� �

i

������

� � ð20Þ

Dv ¼Vo � Vs

Vo

� 100 ð21Þ

R2 ¼

Pn

i ¼ 1

Qo � Qo

�� �

iQs � Qs

�� �

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPn

i ¼ 1

Qo � Qo

�� �2ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPn

i¼1

Qs � Qs

�� �2

i

svuut

2

6666664

3

7777775

2

ð22Þ

where, Qo = observed streamflow, Qs = simulated

streamflow, n = total number of observed data, Vo = ob-

served total flow volume, Vs = simulated total flow volume,

and STDEVobs = standard deviation of observed

streamflow.

Moreover, combined hydrographs using daily observed

streamflow, and HEC-HMS and WEPP simulated stream-

flow for the calibration and validation periods were plotted

for a visual checking of model performance. Scatter plots

(along with 1:1 line) of observed versus simulated

streamflows were also prepared for both the models for

calibration and validation periods as well as for the entire

simulation period. Finally, the total flow volumes (i.e.,

annual flow volumes) simulated by the two models were

plotted using bar graphs along with the observed total flow

volumes for the entire simulation period to examine the

efficacy of the models in simulating total flow volumes.

Results and discussion

Calibration results of HEC-HMS and WEPP

Calibrated values of the HEC-HMS and WEPP parameters

for the calibration period are presented in Table 3. It is

clear from this table that the values of the calibrated

parameters for both the models vary from year to year. In

case of HEC-HMS, minimum and maximum values of the

calibrated parameters, viz., Ia, SCS lag time and recession

constant are 304.20 and 408.51, 1668.32 and 4498.20 min,

and 0.70 and 0.89, respectively. The variation in Ia values

is attributed to the variation in antecedent moisture con-

dition (AMC) over the years and the variation in SCS lag

time is attributed to the varying observed streamflow

characteristics over the years. The variation of baseflow

and initial value of runoff at a certain period of time are

probably responsible for the different values of recession

constant over the years. In fact, the value of recession

constant should be constant for a given watershed.

Furthermore, for the WEPP model, the minimum and

maximum values of effective hydraulic conductivity are 4.63

and 5.69 mm/h, respectively (Table 3). The annual variation

of effective hydraulic conductivity is due to the fact that the

antecedent moisture condition (AMC) varies over the years.

Relative performance of HEC-HMS and WEPP models

for simulating streamflow

Performance evaluation using statistical indicators

The values of various statistical indicators, viz., ME,

RMSE, R2, d1, NSE, Dv, and RSR for both the models are

138 Paddy Water Environ (2010) 8:131–144

123

presented in Table 4 for calibration and validation periods.

It is apparent from this table that in case of HEC-HMS, the

values of mean error are positive in most of the years

(except in 2001 and 2003) indicating under prediction of

streamflow by the model. In the case of WEPP-simulated

streamflows, the ME value for the years 2000, 2001, and

2003 is negative, which indicates that the model over

predicts streamflow in these years. However, during vali-

dation period both the models under predict streamflow.

Further, the RMSE values for HEC-HMS and WEPP range

from 10.64 to 65.82 m3/s to 15.08 to 72.03 m3/s with

significantly higher values in the years 1999, 2001, and

2005 for both the models. Relatively high RMSE values for

these years could be attributed to considerably high rainfall

in these years. Moreover, a comparison of RMSE values

for the HEC-HMS and WEPP reveals that the HEC-HMS

model has lower values of RMSE than WEPP in each year

of calibration and validation periods. This indicates better

performance of the HEC-HMS model compared to the

WEPP model. This finding is further confirmed by smaller

RSR values obtained for the HEC-HMS model throughout

the calibration and validation periods.

Considering NSE and d1 values (Table 4), it is evident

that both the models simulated streamflow within an

acceptable level of accuracy with the lowest and highest

values of NSE as 0.63 and 0.83, and those of d1 as 0.68 and

0.80, respectively. However, higher values of NSE and d1

in every year of calibration and validation periods for

HEC-HMS than the WEPP model suggest that the HEC-

HMS model simulates daily streamflow more efficiently

than the WEPP model.

It is also obvious from Table 4 that the WEPP model has

lower values of Dv than the HEC-HMS model in most

years. Further, the values of Dv ranges from -13.96 to

13.05% for the WEPP model, while they vary considerably

from -2.55 to 31.36% in the case of HEC-HMS. This

finding suggests that the WEPP model simulates total

runoff volumes more accurately than the HEC-HMS

model.

Performance evaluation using graphical indicators

Visual checking of observed and simulated streamflow

hydrographs A comparison of the observed streamflow

hydrograph with the simulated one by HEC-HMS as well

as WEPP is shown in Fig. 2a–g. It is apparent from these

figures that the HEC-HMS model slightly under predicts

the streamflow for the calibration years 1999, 2000, and

Table 3 Calibrated values of

the model parametersModel Parameters Calibration years

1999 2000 2001 2002 2003

HEC-HMS Initial abstraction (mm) 408.51 306.69 304.20 362.21 336.69

SCS lag (min) 2116.93 1668.32 1698.90 3799.60 4498.20

Recession constant (k) 0.70 0.71 0.73 0.89 0.72

WEPP Effective hydraulic conductivity (mm/h) 4.63 4.87 5.52 4.78 5.69

Table 4 Model performance statistics during calibration and validation periods

Statistical indicators Model Calibration period Validation period

1999 2000 2001 2002 2003 2004 2005

ME (m3/s) HEC-HMS 29.94 9.38 26.94 2.65 21.37 5.66 4.98

WEPP 12.32 26.67 28.05 2.59 24.78 5.25 3.65

RMSE (m3/s) HEC-HMS 65.82 26.51 42.33 10.64 20.06 30.15 45.92

WEPP 72.03 29.35 55.01 15.08 25.09 31.13 47.81

RSR HEC-HMS 0.49 0.55 0.43 0.42 0.41 0.50 0.52

WEPP 0.53 0.61 0.57 0.59 0.48 0.52 0.54

NSE HEC-HMS 0.76 0.70 0.81 0.83 0.83 0.75 0.73

WEPP 0.71 0.63 0.68 0.65 0.73 0.73 0.71

d1 HEC-HMS 0.72 0.74 0.78 0.80 0.80 0.74 0.76

WEPP 0.68 0.72 0.72 0.72 0.74 0.72 0.74

R2 HEC-HMS 0.84 0.80 0.83 0.84 0.84 0.77 0.78

WEPP 0.73 0.78 0.74 0.74 0.78 0.75 0.77

Dv (%) HEC-HMS 31.40 19.64 27.92 9.03 22.55 9.94 6.08

WEPP 13.05 213.96 29.23 8.91 28.96 9.29 4.48

Paddy Water Environ (2010) 8:131–144 139

123

2002, and for the validation years 2004 and 2005. In

contrast, the model over predicts streamflows for the years

2001 and 2003. It can also be seen that although there is a

similar trend between the observed and simulated stream-

flow hydrographs, the peaks of the two hydrographs do not

match reasonably well for most years. This discrepancy is

due to the continuous simulation of runoff using HEC-

HMS which has been confirmed by the model developer

Dr. M. Fleming (Personal Communication, January 2008).

On the other hand, it is apparent from Fig. 2a–g that WEPP

slightly under predicts streamflow for the years 1999, 2002,

2004, and 2005. However, it over predicts streamflow for

the years 2000, 2001, and 2003. The under prediction and

over prediction of streamflow by WEPP have also been

reported by earlier researchers (e.g., Pandey, 2005; Croke

and Nethary 2006; Pieri et al. 2007).

Fig. 2 a–g Observed and simulated streamflow hydrographs for the calibration (1999–2003) and validation (2004 and 2005) periods

140 Paddy Water Environ (2010) 8:131–144

123

Fig. 3 a–g Scatter plots of observed streamflows versus simulated streamflows by HEC-HMS and WEPP for the calibration (1999–2003) and

validation (2004–2005) periods

Paddy Water Environ (2010) 8:131–144 141

123

In general, neither model is able to replicate the entire

shape of streamflow hydrographs for the simulation period.

This discrepancy might be due to imprecise representation

of spatial distribution of rainfall within the watershed by

the estimated mean areal rainfall used as an input. Never-

theless, the HEC-HMS model simulates streamflow peaks

and recession more accurately than the WEPP model.

Scatter plots of observed and simulated streamflows The

scatter plot confirms the under prediction of streamflow by

HEC-HMS model for the years 1999, 2000, 2002, 2004,

and 2005 in which the simulated streamflow values are

mostly distributed on the lower side of the 1:1 line, while

the simulated streamflow values for the years 2001 and

2003 are mostly falling on the upper side of the 1:1 line

which indicates a trend of over prediction as shown in

Fig. 3a–g. The under prediction and over prediction of

streamflow by the HEC-HMS model have also been

reported by other researchers (e.g., Bingner et al. 1989;

Montas and Madramootoo 1991; Pandey 2005; Das et al.

2007). In addition, the scatter plots confirm the under

prediction of sreamflow by WEPP for the years 1999, 2002,

2004, and 2005 with the simulated runoff values mostly

falling on the lower side of the 1:1 line, while the simulated

streamflow values for the years 2000, 2001, and 2003 are

mostly falling on the upper side of the 1:1 line which

indicates over prediction. The value of R2 for the HEC-

HMS model was found to be higher than the WEPP model

for all the simulation years.

Moreover, a scatter plot with 1:1 line and a regression

analysis of the entire observed streamflow against the

simulated streamflow by HEC-HMS and WEPP models for

the simulation period are illustrated in Figs. 4 and 5,

respectively. These figures show that the performance of

HEC-HMS in simulating streamflow is better than WEPP

which is further confirmed by a relatively high R2 value for

the HEC-HMS model.

Bar plots of observed and simulated annual flow vol-

umes Figure 6 shows a comparison between observed

and simulated annual flow volumes for the HEC-HMS and

WEPP models. It is apparent from this figure that the

WEPP simulated annual flow volumes are closer to the

corresponding observed annual flow volumes for the years

1999, 2000, 2004, and 2005, whereas the HEC-HMS

simulated annual flow volumes are closer to the observed

annual flow volumes for the years 2001 and 2003 only.

Further, the WEPP model simulates annual flow volume at

par with the HEC-HMS model for the year 2002 (Fig. 6).

Thus, it can be inferred that the WEPP model simulates

annual flow volumes (total flow volumes) more accurately

than the HEC-HMS model for most years.

Conclusions

In the present study, rainfall-runoff modeling was carried

out using HEC-HMS and WEPP hydrologic models, and

remote sensing and GIS techniques in the Upper Baitarani

River basin of Eastern India. The required precipitation and

streamflow data were collected for 7 years (1999–2005),

together with the soil map, topographic maps, and DEM and

Landsat images of the study area. The input files for the two

hydrologic models were prepared using remote sensing and

GIS techniques. For simulating streamflow by the HEC-

HMS model, the SCS unit hydrograph transform method

was used to compute direct surface runoff hydrographs, the

R2 = 0.79

0

100

200

300

400

500

600

700

0 100 200 300 400 500 600 700

Observed Streamflow (m3/s)

Sim

ula

ted

Str

eam

flo

w (

m3 /s

)

Data Points1:1 LineLinear

Fig. 4 Scatter plot of observed streamflows versus simulated stream-

flows by HEC-HMS for the 1999–2005 period

R2 = 0.74

0

100

200

300

400

500

600

700

0 100 200 300 400 500 600 700

Observed Streamflow (m3/s)

Sim

ula

ted

Str

eam

flo

w (

m3 /s

) Data Points1:1 LineLinear

Fig. 5 Scatter plot of observed streamflows versus simulated stream-

flows by WEPP for the 1999–2005 period

142 Paddy Water Environ (2010) 8:131–144

123

SCS curve number loss method to compute runoff volumes,

and the exponential recession method was used for baseflow

separation. For the WEPP model, watershed parameters

such as slope and river length were extracted using the

DEM, slope map, and drainage map of the study area.

BPCDG (Break Point Climate Data Generator) was used for

making climate file for the WEPP model. These two

hydrologic models were calibrated using the rainfall-

streamflow data of 1999 to 2003, and were validated using

the rainfall-streamflow data of 2004 and 2005 years.

Finally, the performance of HEC-HMS and WEPP models

was assessed using various statistical and graphical indica-

tors. Based on the analysis of the results obtained in this

study, the following conclusions could be drawn:

• For the calibration period, both the HEC-HMS and

WEPP models under predict streamflow in some years

and over predict in other years. However, both the

models under predict streamflow for the validation

period.

• Both the HEC-HMS model and the WEPP model were

found to simulate streamflow with an acceptable level

of accuracy. The values of NSE, d1, and R2 for the two

models range from 0.63 to 0.83, 0.68 to 0.80, and 0.73

to 0.84, respectively during the simulation period.

• Based on the statistical and graphical indicators used in

this study, it was found that the HEC-HMS simulated

daily streamflow is more reliable than the WEPP-

simulated streamflow. In contrast, the WEPP model

simulates annual flow volumes (total flow volumes) more

accurately than the HEC-HMS model for most years.

• Although there is a reasonably good matching between

observed and simulated streamflow hydrographs for

both HEC-HMS and WEPP models, the peaks of the

hydrographs do not match well for most of the years.

Overall, it is concluded that the HEC-HMS model is

superior to the WEPP model for simulating daily stream-

flow in the Upper Baitarani River basin of Eastern India.

Therefore, the use of HEC-HMS model is recommended

for future studies on hydrological modeling in this basin.

Acknowledgements The authors are very thankful to the Central

Water Commission (CWC), Bhubneshwar, Orissa, India and the India

Meteorological Department (IMD), Bhubneshwar, Orissa, India for

providing necessary hydrological and meteorological data for this

study. They are also grateful to the two anonymous reviewers for their

constructive suggestions.

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