Journal of Water Resource and Protection, 2021, 13, 254-270

https://www.scirp.org/journal/jwarp
ISSN Online: 1945-3108
ISSN Print: 1945-3094

Hydrological Modeling: A Better Alternative

to Empirical Methods for Monthly Flow
Estimation in Ungauged Basins

Suresh Marahatta1*, Laxmi Devkota2,3, Deepak Aryal1

1
Central Department of Hydrology and Meteorology, Tribhuvan University, Kathmandu, Nepal
2
Nepal Academy of Science and Technology (NAST), Kathmandu, Nepal
3
Water Modeling Solutions Pvt. Ltd. (WMS), Lalitpur, Nepal

How to cite this paper: Marahatta, S., Abstract

Devkota, L. and Aryal, D. (2021) Hydro-
logical Modeling: A Better Alternative to Water resource is required for agricultural, industrial, and domestic activities
Empirical Methods for Monthly Flow Es- and for environmental preservation. However, with the increase in popula-
timation in Ungauged Basins. Journal of
tion and growth of urbanization, industrialization, and commercial activities,
Water Resource and Protection, 13, 254-
270.
planning and management of water resources have become a challenging task
https://doi.org/10.4236/jwarp.2021.133015 to meet various water demands globally. Information and data on streamflow
hydrology are, thus, crucial for this purpose. However, availability of meas-
Received: February 18, 2021
ured flow data in many cases is either inadequate or not available at all. When
Accepted: March 22, 2021
Published: March 25, 2021
there is no gauging station available at the site of interest, various empirical
methods are generally used to estimate the flow there and the best estimation
Copyright © 2021 by author(s) and is chosen. This study is focused on the estimation of monthly average flows
Scientific Research Publishing Inc.
by such methods popular in Nepal and assessment of how they compare with
This work is licensed under the Creative
Commons Attribution International
the results of hydrological simulation. Performance evaluation of those me-
License (CC BY 4.0). thods was made with a newly introduced index, Global Performance Index
http://creativecommons.org/licenses/by/4.0/ (GPI) utilizing six commonly used goodness-of-fit parameters viz. coefficient
Open Access of determination, mean absolute error, root mean square error, percentage of
volume bias, Nash Sutcliff Efficiency and Kling-Gupta Efficiency. This study
showed that hydrological modeling is the best among the considered methods
of flow estimation for ungauged catchments.

Keywords
Ungauged Basins, Modeling, Monthly Flows, Global Performance Index

1. Introduction
Water resources is required to perform agricultural, industrial, and domestic ac-

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 S. Marahatta et al.

tivities and for environmental preservation [1]. With the increase in population
and accelerated growth of urbanization, industrialization, and commercial de-
velopment, demand for water resources of sufficient quantity and quality will
continue to increase [2] [3] [4]. The design of all water related structures such as
dams, highway bridges, embankments, among others, consists of three basic
components: hydrologic design, hydraulic design and structural design. Hydro-
logic design deals with the estimation of the quantities of water to be handled at
the site of the structure in terms of time distribution, time of occurrence and
frequency of occurrence [5]. Streamflow time series is, therefore, one of the most
important data required for the effective water resource planning and manage-
ment at both local and national scales [6]. However, availability of measured
flow data in many cases is either inadequate or not available at all [7] [8]. Such
situations create challenges not only for the optimal use of water resources in
ungauged river basins for various development works like domestic water supply
and sanitation, irrigation, hydropower etc. but also in flood control works [9]
[10]. Underestimation of the flows could lead to rejection of attractive projects
whereas overestimation could have huge implications on the physical infra-
structure and overall economic feasibility of the projects [4] [11]. Accurate flow
estimates are, therefore, necessary at these basins where water resources projects
are developed.
Although the global scientific community has put substantial efforts to resolve
the issue of flow estimation in ungauged basins/sites, a universal solution me-
thod is not available till date [12]. Various methods are found in use in different
parts of the world to deal with this issue. One of the oldest methods of generat-
ing flow data is the use of regression equation/s developed at the regional level
[7] [13] [14] [15]. Razavi and Coulibaly [6] reviewed regional methods and hig-
hlighted that those methods making use of different combinations of physio-
graphic information and meteorological attributes, among others, were found to
predict streamflows in ungagged basins/sites better. They listed catchment area,
elevation, slope of basin, rainfall and temperature as the main parameters used
in those methods. Another popular method is transposition of gauged stream-
flow data to ungauged sites. One of them is the Drainage Area Ratio (DAR) me-
thod [16] [17]. It is based on the assumption that the streamflow at the un-
gauged site can be estimated by multiplying the ratio of the drainage area for this
site and the drainage area for the gauging site by the streamflow of the gauging
site [17]. As it needs only catchments areas and the observed streamflow of the
gauged station, it is considered one of the easiest methods of flow prediction and
therefore popularly used in the past [16]. One of the variants of the DAR method
is MDAR (Multiple gauging stations Drainage Area Ratio). In the MDAR me-
thod, the weighted sum of more than one streamflow gauging stations is used to
estimate the flow at the site of interest [18]. Incorporating the basin rainfall ratio
of the ungauged basin to the gauged one as a multiplier to the DAR method has
been considered as an improved version of the DAR method [17] [19]. This me-
thod can be called as a General Transposition (GT) method.

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S. Marahatta et al.

Hydrological simulation method is a numerical method in which a hydrologic

model, a simplified software representation of the natural rainfall-runoff process
within a catchment boundary, is used to generate streamflow data at the site of
interest with known meteorological data. The hydrological model is first cali-
brated and validated at a gauged basin and then the model parameters are used
appropriately at other ungauged sites within the modeling domain to simulate
the flows using the calibrated model [8] [20]. Usually, several statistical indica-
tors as well as visual inspection of the results (hydrographs and the water bal-
ance distribution in particular) are relied upon to determine the performance
capacity and robustness of the model.
Since the simulation of the entire hydrologic cycle became a reality by Stan-
ford Watershed Model as reported by Crawford and Linsley in 1966, modeling at
large spatial scales and at small temporal scales [21] became possible with the
recent development in hardware and software capabilities at an exponential rate
in the last few decades [22]. Being able to use precise satellite data such as preci-
pitation in hydrological models has further improved the performance and thus
the overall applicability of hydrological models considerably around the globe.
In recent years, application of hydrological models is becoming popular in Nep-
al, for assessment of water availability, planning purposes and to examine the
impact of climate change in river hydrology [10] [23]-[28]. However, they are
confined mainly to academic research studies. When the world is utilizing artifi-
cial intelligence as part of a data-driven approach to assist watershed modeling
for stream flow generation [29], most of the project level studies in Nepal are
still using coarse conventional methods in ungauged sites in Nepal, especially in
the study of hydropower projects of different scales [19] [30] [31] [32]. As an
awakening step, SWAT (Soil and Water Assessment Tool), a public domain hy-
drological model and capable for hydrological modeling in Nepalese catchments
[23] [26] [27] [33] was used to estimate the flow at ungauged sites in this study
and compared with other commonly used methods viz.WECS/DHM1990,
NEA1997, DHM2004, DAR and its variant GT methods.
The Budhigandaki River Basin (BRB) of Nepal was chosen for the study. Six
popular performance evaluation parameters viz. coefficient of determination (R2),
Mean Absolute Error (MAR), Root Mean Square Error (RMSE), Percentage of
Volume Bias (PBIAS), Nash Sutcliff Efficiency (NSE) and Kling-Gupta Efficien-
cy (KGE) [16] [34] were used to evaluate the considered flow estimation me-
thods in this study. Global Performance Index (GPI) was introduced for overall
evaluation of flow estimation methods. The assessed flow estimation methods in
Nepal were ranked based on the GPI value. It was found that hydrological simu-
lation ranked the best among the considered methods.

2. Study Area
The Budhidgandaki River Basin (BRB) is situated in the central part of Nepal,
between 27˚50' and 29˚00'N latitudes and 84˚30' and 85˚10'E longitudes (Figure

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 S. Marahatta et al.

1). It has an elongated shape with its main axis oriented north-south. Its length
is about 113 km while the width is in the range of 15 and 30 km. The basin ele-
vations range from 315 masl at Budhigandaki-Trishuli confluence to 8163 me-
ters above sea level (masl) at Mount Manaslu (8th highest peak) of the world
[35] with a mean basin elevation of 3723 m. The basin area, thus, falls in two
physiographic regions; Middle Mountains and the Himalaya [36]. It is a part of
the Narayani drainage system, bordered in the north by the vast Tibetan Plateau,
in the south and east by the Trishuli River basin and in the west by the Mar-
syangdi River basin.
The reference flow gauging station is at Arughat (Department of Hydrology
and Meteorology, DHM station #445) which is at an elevation of 485 masl. The
catchment area of the BRB at this station is 3863 km2 while it is 4985 km2 for
Budhigandaki-Dam site (Figure 1).

3. Theoretical Background
When any water resources development project is planned and implemented in
an ungauged catchment, different methods are generally used to estimate the
flow at the project sites. Among them, one set of values are chosen for the design
purpose based on the prevailing site conditions and judgment of the hydrologist.
The most popular methods used in the estimation of mean monthly flow at un-
gauged sites are given below.

Figure 1. Location Map of the Budhigandki River Basin

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S. Marahatta et al.

3.1. Hydrological Simulation Method

Hydrological models have been broadly categorized depending on their spatial
discretization (lumped, semi-distributed, fully-distributed), period of simulation
(event-based or long term) and other complexities associated with the data re-
quirement, governing equations and licensing issues. There is no doubt that they
are gaining popularity in recent times. HEC-HMS, SWAT, MIKE SHE, MIKE
NAM and VIC, among others, are some popular hydrological models used global-
ly for assessing flows [29]. SWAT (Soil and Water Assessment Tool) model capa-
ble of simulating the hydrological process satisfactorily in Nepalese catchments
[23] [26] [27] [33] was used for simulating the flows of the BRB in this study.
SWAT is a process-based semi-distributed hydrological model that is capable
of simulating the impact of land management practices on flow, sediment and
agricultural chemical yields in basins with varying soils, land use and manage-
ment conditions [37]. Conceptually, SWAT divides a basin into sub-basins and
further into Hydrological Response Units (HRUs). An HRU is a unique combi-
nation of land use, topographical and soil characteristics in a sub-watershed.
SWAT simulates hydrology, vegetation growth and management practices at the
HRU level [26]. SWAT simulates the hydrologic cycle based on the water bal-
ance equation as expressed in Equation (1).

SWt = SW0 + ∑ i =1  Rday − Qsur − Ea − wseep − Qgw 

t
(1)

where:
SWt is final soil water content (mm); SW0 is initial soil water content on day i
(mm); t is time (day); Rday is amount of precipitation on day i (mm); Qsur is
amount of surface runoff on day i (mm); Ea is amount of evapotranspiration on
day i (mm); wseep is amount of water entering into the vadose zone from the soil
profile on day i (mm) and Qgw is amount of return flow (from groundwater) on
day i (mm).

3.2. WECS/DHM 1990 Method

The Water and Energy Commission Secretariat (WECS) and Department of
Hydrology and Meteorology (DHM), Government of Nepal (GoN) [13] pro-
posed a regression equation to estimate the long term mean monthly flow at an
un-gauged site given in Equation (2).
α
Qmean = CAtotal ⋅ A<β5 k ⋅ MWIγ (2)

where, Qmean is the mean monthly flow (m3/s); Atotal is the total catchment area
(km2); A<5k is catchment area below 5000 masl elevation (km2); MWI is monsoon
wetness index (total rainfall of the catchment from June to September in mm); C
is a regression constant; and α, β and γ are constants derived from the regression
analysis for each month (supplementary, S-1).

3.3. NEA 1997 Method

Nepal Electricity Authority (NEA), GoN proposed another regression based

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 S. Marahatta et al.

method to estimate the mean monthly flow for an un-gauged site [30]. It is given
in Equation (3)
α
Qmean = CAtotal ⋅ MWIγ (3)

where, constants C, α and γ for this method are given in S-2

3.4. DHM 2004 Method

The DHM developed Equations (4a and 4b) for the estimation of monthly flow
[14]. For some months logarithmic transformation gave a better estimate while
in the other months square root transformation performed better. Monthly flow
estimation equation with logarithmic transformation takes the following form:
= exp C + ε ⋅ ln ( AvgElev ) + ρ ⋅ ln ( AWI ) + µ ⋅ ln ( A<3k ) 
Qmean (4a)

Monthly flow estimation equation with square root transformation takes the
following form:
2
Qmean = C + δ ⋅ A<5 k  (4b)

where, AvgElev is average elevation of the catchment (masl); AWI is annual

wetness index (mm); A<3k is catchment area below 3000 masl elevation (km2);
and ε, ρ, μ and δ are the constants derived from regression analysis; their values
are given in S-3. For March, April and May, square root transformation is better
while for the other months, logarithmic transformation gives better estimates
[14].

3.5. Drainage Area Ratio (DAR) Method

The DAR method is a simple method based on the assumption that the specific
discharge calculated using the data from a flow gauging station remains constant
within the basin [16] [17]. It is expressed as in Equation (5).
Qgs
Qe -site = Asite (5)
Ags

where Qe-site is the estimated flow at the site of interest (m3/s); Qgs is the observed
flow at gauging station (m3/s); Ags and Asite are the catchment areas (km2) at the
gauging station and site of interest respectively.

3.6. General Transposition (GT) Method

The GT method can be considered as an improved version of the DAR method,
as it accounts the rainfall in addition to the drainage area. Although different
variations of this method are found in application [19] [31], a simple form given
in Equation (6) has been used in this study.
Qgs Pavg -site
Qe -site = Asite (6)
Ags Pavg -gs

where Pavg-site and Pavg-gs are the annual average precipitation values (mm) of the
basin up to the site of interest and the gauging station respectively.

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S. Marahatta et al.

4. Data Collection and Analysis

Spatial data (digital elevation model, land use land cover map and soil map) and
hydro-meteorological time series data (temperature, rainfall and discharge) are
required for this study. The collected data types and their use are given in Table
1. The Digital Elevation Model (DEM) and soil map were downloaded from
Shuttle Radar Topography Mission (SRTM) and SOTER soil map site respec-
tively while Land Use and Land Cover (LULC) Map was obtained from Interna-
tional Center for Integrated Mountain Development (ICIMOD), Nepal, De-
partment of Water Resources and Irrigation (DoWRI) and district soil map of
Nepal Agriculture Research Council (NARC). Precipitation, maximum and min-
imum temperature and discharge of Budhigandaki river at Arughat (#445) was
collected from the Department of Hydrology and Meteorology (DHM), GoN
while discharge at Budhigandaki Hydroelectric Project (BGHEP) dam site was
collected from BGHEP project office. Data collected by the BGHEP for two years
during the feasibility study was only available at this site.
Total catchments area, area below 3000 masl and 5000 masl of Arughat gaug-
ing station and Budhigandaki Hydro-Electric Project (BGHEP) dam site were
calculated using GIS. Annual wetness indexes, monsoon wetness index for the
basin area were calculated from the available daily rainfall data. Mean monthly
values of the observed flows of Arughat gauging station and BGHEP dam site
were calculated from the available daily flow data.
The hydrological model was setup and calibrated using ArcSWAT and used to
simulate the flow in the BRB. Model development was carried out by generating
the river networks and sub-basins using the 30 m × 30 m SRTM DEM. Hydro-
logical response units were generated from land use land cover, soil maps, and
by providing slope ranges. The model was calibrated (1983-2002) and validated
(2003-2012) at Arughat using 30 years flow data. However, simulation was done
up to 2014 to see how the model performed at BGHEP dam site lying down-
stream of Arughat station (Figure 2). The model simulated flows were extracted
at Arughat (1983-2012) and BGHEP dam site (2013-2014) and compared with
the respective observed data.

Figure 2. Hydrological simulation results at BGHEP dam site.

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 S. Marahatta et al.

Table 1. Data used in the study.

Accessed
SN Data Type Source Application
Data/Available Period

1. Spatial Data

DEM Shuttle Radar Topography Mission (SRTM) Hydrological modeling/regression

1.1 2019
(30 m resolution) DEM data [38] equations

1.2 LULC Map 2019 [39] Hydrological modeling

Soil and Terrain database system (SOTER)

1.3 Soil Map 2019 Hydrological modeling
soil map of China and Nepal [38]

2. Time Series Data

Temperature and DHM (daily) and Third Pole Environment Hydrological modeling and use in
2.1 1983-2014
Rainfall (TPE)—3-hourly [40] flow estimation methods

Daily discharge at Comparison with estimated value and

2.2 1983-2014 DHM
Arughat #445 to estimate discharge at BGHEP site

Mean monthly flows at Arughat and BGHEP dam site from WECS/DHM
1990, NEA 1997 and DHM 2004 methods were calculated using the equation
given in Sections 3.2 to 3.4. Flows were transposed by DAR and GT methods to
BGHEP dam site using observed monthly average flow data of Arughat station
and vice versa.

5. Performance Evaluation
Performance of the various flow estimation methods explained above was eva-
luated objectively using goodness-of-fit measures by comparing the estimated
and observed monthly flows. Performance evaluation of considered methods of
the study at Arughat and BGHEP dam site were made using the following statis-
tical parameters:

5.1. Coefficient of Determination (R2)

Coefficient of Determination measures both the strength of the linear relation-
ship between observed and estimated values. It is calculated by Equation (8).
2
 

R =
2
(
∑ i =1 Q0i − Qo Qei − Qe
n
)( ) 
(8)

( ) ( )
2 2
 ∑ n Q0i − Qo ∑ i 1 Qei − Qe
n

=  i 1= 
where, Qo = Observed Annual Average Flow (m3/s)
Qe = Estimated Annual Average Flow (m3/s)
Q0i = Observed monthly average flow of month i
Qei = Estimated monthly average flow of month i
n = number of data. As number of months are 12, n = 12 in this case.
The coefficient of determination (R2) is the square of the coefficient of corre-
lation.
Criteria: Larger the value of R2, better the performance.

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S. Marahatta et al.

5.2. Mean Absolute Error (MAE)

The MAE measures the average of the deviation of the estimated values with re-
spect to the observed ones. It is calculated using Equation (9).
1 n
=
MAE ∑ Q0i − Qei
n i =1
(9)

Criteria: Smaller the value of MAE, better the performance.

5.3. Root Mean Square Error (RMSE)

The RMSE measures the differences between the estimated and observed values.
It is given as Equation (10).
1 12
∑ Q0i − Qo ( )
2
=
RMSE (10)
n i =1
Criteria: Smaller the value of RMSE, better the performance.

5.4. Percentage Volume Bias (PBIAS)

The PBIAS measures the degree of volume biasness between the observed and
estimated values. It is given by Equation (11).

∑ V −∑ V
n n
=i
PBIAS = 1=
o i 1 e
% (11)
∑
n
i =1 o
V

where Vo = Observed total volume

Ve = Estimated total volume
Criteria: Smaller the absolute value of PBIAS, better the performance. The
sign of the PBIAS value shows the direction towards which the estimated result
is biased: +ve value is an indication of underestimation while −ve value shows
overestimation.

5.5. Nash Sutcliff Efficiency (NSE)

The NSE is a normalized statistic that determines the relative magnitude of the
residual variance compared to the observed value variance. It is calculated with
Equation (12).
 12
∑ ( Q0i − Qei ) 
2

NSE = 1 −  i =1
(12)
 12 2 
(
 ∑ i =1 Q0i − Qo  )
Criteria: Larger the value of NSE, better the performance.

5.6. Kling-Gupta Efficiency (KGE)

The KGE is considered as an improvement over the widely used NSE which con-
siders different types of model/estimation errors, namely the error in the mean,
the variability and the dynamics. The KGE is calculated using Equation (13).
2
 Q 
2
Q
KGE =− ∑ i =1 ( r − 1) +  Qe-sd − 1 +  Qe − 1 (13)
n 2
1
 0-sd   o 

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 S. Marahatta et al.

where, r = correlation coefficient

Q0-sd = Standard deviation of observed flow
Qe-sd = Standard deviation of estimated flow
Criteria: Larger the value of KGE, better the performance.

5.7. Global Performance Index

A total of six performance evaluation criteria of the flow estimation methods are
discussed above. However, assessment and comparison of the individual evalua-
tion criteria and thus establishing the preference of one criterion over another is
beyond the scope of this paper. Therefore, all the criteria are treated with equal
weights while evaluating the overall performance of the flow estimation methods.
To find the best method, the lowest performing method to the highest perform-
ing method with respect to a given parameter were assigned values from 1 to 6 in
increments of one. If two/three values are equal, average of the two/three values
are assigned for both/all of them. For example, if the NSE values calculated for
Hydro-Sim, WECS1990, NEA1997, DHM2004, DAR and GT methods are 0.97,
0.67,0.95, 0.67, 0.74 and 0.92 respectively, then the respective numerical values
these methods get are 6, 1.5, 5, 1.5, 3 and 4. The WECS1990 and DHM2004 me-
thods have equal values of NSE i.e., 0.67, and therefore both these methods are
assigned 1.5 (average of 1 and 2). The mean value of performance of each me-
thod was then calculated as Global Performance Index (GPI) as given by Equa-
tion (14).
R 2j + MAE j + RMSE j + PBIAS j + NSE j + KGE j
GPI j = (14)
6
where j represents the estimating method, say, for hydrological simulation me-
thod j = 1 while for DAR method, j = 5.
Based on the GPI value, the six considered methods are ranked from first to
sixth such that higher the GPI value, better the method for monthly flow estima-
tion.

6. Results and Discussion

6.1. Comparison of Average Flows at Arughat
The observed and estimated average monthly flows at Arughat gauging station
are given in supplementary material (S-5) and depicted in Figure 3. Numerical
figures of Goodness-of-fit of different flow estimation methods are presented in
Table 2. It is to be noted here that performance evaluation of simulation results,
WECS/DHM1990, NEA1997 and DHM2004 methods are made with respect to
long term averages of observed flow (1983-2012: Obs-A) at Arughat. However,
DAR and GT estimates are compared with the average of two years data
(2013-2014: Obs-B) at BGHEP Dam site. This limitation is because measured
data at BGHEP dam site is not available for the other years. It is assumed that
such difference will have minimum impact on performance parameters.
Considering the monthly values, R2 is almost the same for all the methods. All

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S. Marahatta et al.

the other calculated performance parameters except MAE show that the simu-
lated flows obtained through SWAT hydrological modeling are found closer to
the observed values. Even for MAE, the calculated value is very close to the NEA
1997 method. Thus, from the table, the overall performance ranking indicates
that that hydrological simulation is the best among the methods considered in
the study to estimate the flows for Arughat at monthly time steps. Further, the
NEA 1997 and GT methods ranked second and third in terms of performance
ranking.

600

Obs-A Sim
500

WECS1990 NEA1997

400
Flow (m3/s)

DHM2004 DAR

GT Obs-B
300

200

100

0
Months
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 3. Comparison of monthly flows at arughat.

Table 2. Performance parameters of estimated methods at arughat.

Obs-A: 30 years Obs-B: 2 years

Data Length
(1983-2012) (2013-2014)

Parameters Hydro Sim WECS1990 NEA1997 DHM2004 DAR GT Parameters

R2 0.98 0.97 0.97 0.96 0.99 0.99 R2

MAE 21.6 60.6 20.9 61.0 52.4 30.2 MAE

RMSE 36.6 209.6 65.1 211.4 181.5 104.5 RMSE

PBIAS −6.4 37.3 11.6 37.6 −39.5 −22.7 PBIAS

NSE 0.97 0.67 0.95 0.67 0.74 0.92 NSE

KGE 0.87 0.45 0.81 0.45 0.49 0.72 KGE

GPI 5.5 2.1 4.8 1.3 3.1 4.3 GPI

Performance Performance
I V II VI IV III
Rank Rank

Obs-A: Observed flow data from DHM; Hydro Sim: Simulated flow using SWAT; WECS1990: Flow calcu-
lated using the WECS/DHM-1990 method; NEA1997: Flow calculated using the NEA-1997 method;
DHM2004: Flow calculated using the DHM-2004 method; DAR: Flow calculated using drainage area ratio
method; GT: Flow calculated using general transposition method; Obs-B: Observed flow data from BGHP
at the dam site.

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 S. Marahatta et al.

From the viewpoint of availability of flow for electricity production and de-
mand of the electrical energy, three distinct seasons can be seen in Nepal [19]:
Dry (December to May), Monsoon (June to September) and Post Monsoon
(October and November). Seasonal evaluation at Arughat gauging site was also
done following the methods discussed above to see whether the performance of
each method differed from the monthly time steps. GPI based ranking in dry,
monsoon and post-monsoon seasons are presented in Table 3. For dry and post-
monsoon seasons, the GT and NEA 1997 methods respectively showed the best
performance while hydrological simulation is next to these methods in both cas-
es. However, its performance is better than the other methods in the monsoon
season. This is particularly important in most Nepalese catchments where the
runoff is largely rainfall driven. Based on weighted average GPI, the GT method
ranks first in overall. Hydrological simulation and NEA 1997 methods rank
second and third respectively. The remaining three methods are not found satis-
factory in terms of seasonal performance.

6.2. Comparison of Average Flows at Budhigandaki Dam Site

The monthly observed and estimated flows by different methods at Budhiganda-
ki Dam site are given in S-6 and shown in Figure 4. Values of the different per-
formance parameters of those methods are presented in Table 4. They clearly
indicate that the hydrological simulation method is the best by all criteria. The
GT method ranked second as shown by the respective values while the DAR
method ranked the last.
Seasonal performance of these methods at Budhigandaki dam site was also
analyzed to see if it is consistent with the monthly and annual performance. The
calculated seasonal GPI of the six performance parameters and its weightage av-
erage are given in Table 5. Although the dry season performance is found better
for the GT method, hydrological simulation is found better for the other two
seasons. The weighted GPI is the highest for hydrological modeling which is
similar to the results of the whole year at this site (Table 4). Based on the overall
GPI value, the hydrological simulation ranked first, GT the second and NEA
1997 the last.

Table 3. Seasonal Performance of flow estimation methods at Arughat.

GPI and Rank Hydro Sim WECS1990 NEA1997 DHM2004 DAR GT

GPI-Dry Season 3.83 1.50 3.50 3.83 2.92 5.42

GPI-Monsoon 4.92 2.33 4.58 1.25 3.42 4.50

GPI Post Monsoon 4.67 3.08 5.58 2.42 1.92 3.33

GPI-Weighted
4.33 2.04 4.21 2.74 2.92 4.76
Average

Rank II VI III V IV I

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S. Marahatta et al.

Table 4. Performance parameters of estimated methods at the budhigandaki dam site.

Data length 2 years (2013-2014)

Parameters Hydro Sim WECS1990 NEA1997 Q_DHM2004 DAR GT

R2 0.99 0.97 0.96 0.95 0.99 0.99

MAE 38 52 54 51 68 44

RMSE 69 181 186 176 234 153

PBIAS 8.39 21.88 22.62 21.26 28.30 18.51

NSE 0.95 0.89 0.88 0.88 0.85 0.94

KGE 0.78 0.69 0.67 0.69 0.63 0.76

GPI 5.83 3.25 2.08 3.17 1.67 5.00

Performance Rank I III V IV VI II

Table 5. Seasonal performance of flow estimation methods at budhigandaki dam site.

GPI and Rank Hydro Sim WECS1990 NEA1997 DHM2004 DAR GT

GPI-Dry Season 4.67 2.42 2.00 3.42 2.92 5.58

GPI-Monsoon 5.50 3.25 2.08 2.75 2.17 5.25

GPI Post Monsoon 5.50 4.83 3.17 3.83 1.42 2.25

GPI-Weighted Average 5.08 3.10 2.22 3.26 2.42 4.92

Rank I IV VI III V II

800

700
Obs Sim WECS1990

600
NEA1997 DHM2004 DAR

500
GT
Flow (m3/s)

400

300

200

100

0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Months

Figure 4. Comparison of monthly flow at budhigandaki dam site.

Based on the results presented above, it can be inferred that hydrological si-
mulation method is the best among the other considered methods of flow esti-
mation in the BRB. It is to be noted here that the WECS 1990, NEA 1997 and
DHM 2004 are regional methods and their coefficients are average values which

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 S. Marahatta et al.

have been established by regression analysis. Thus, these methods may perform
better in some catchments while poorer in the others depending upon how well
the coefficients represent the catchment characteristics. Since the DAR method
does not account the rainfall variation, it might be better suited for in regions
where rainfall variation is small. The GT method takes into account the rainfall,
and therefore, it performs better than the regional and the DAR methods. How-
ever, it does not take into consideration the spatial variation in soil type and land
use/land cover. Hydrological modeling takes all these factors into account and
the flow estimated by this method is better than that by all the other considered
regional methods for ungauged basins. Another advantage of the hydrological
simulation method over others is that it provides continuous data at the site of
interest which could be extremely useful for hydrological analysis required for
any water resources project development works. However, it is extremely im-
portant that quality (length, accuracy and reliability) of the input data for model
setup as well as calibration and validation is mandatory for the hydrological
model to perform its best.

7. Conclusion
This study was carried out to evaluate the performance of different flow genera-
tion methods namely, DAR, GT, DHM/WECS 1990, NEA 1997, DHM 2004 and
hydrological modeling using SWAT. The estimated flows from each method
were compared with the observed flows at Arughat and BGHEP dam site of the
Budhigandaki River Basin. Six performance parameters viz. R2, MAE, RMSE,
PBIAS, NSE and KGE were used to evaluate the considered flow estimation me-
thods. For overall evaluation of these flow estimation methods, Global Perfor-
mance Index (GPI) was introduced. Results show that hydrological modeling is
the best among all considered methods for estimating flows at monthly time-
scales. Carrying out hydrological analyses using suitable hydrological model(s)
for Nepalese river basins is recommended as a policy prescription to the Gov-
ernment of Nepal so that flow at the site of interest can be obtained when re-
quired for any water resources development project.

Conflicts of Interest
The authors declare no conflicts of interest regarding the publication of this pa-
per.

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