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A comparison of the SCS-CN-based models for hydrological simulation of the

Aghanashini River, Karnataka, India

Article in Journal of Water Supply: Research and Technology - AQUA · April 2023
DOI: 10.2166/aqua.2023.213

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 © 2023 The Authors AQUA — Water Infrastructure, Ecosystems and Society Vol 72 No 4, 507 doi: 10.2166/aqua.2023.213

A comparison of the SCS-CN-based models for hydrological simulation of the

Aghanashini River, Karnataka, India

Harmandeep Singha, Mohammad Afaq Alama, Priyank J. Sharmab and Kuldeep Singh Rautelab, *
a
Department of Civil Engineering, Punjab Engineering College (Deemed to be University), Sector – 12, Chandigarh 160012, India
b
Department of Civil Engineering, Indian Institute of Technology Indore, Madhya Pradesh 453552, Indore, India
*Corresponding author. E-mail: kuldeeprautela007@gmail.com

ABSTRACT

This present study investigates different techniques for estimating the surface runoff using the Soil Conservation Service Curve Number (SCS-
CN) method for the Aghanashini River in Karnataka, India. The SCS-CN method is a simplified approach for runoff estimation, but it does not
take into account the actual moisture content in the soil. Consequently, insignificant moisture level changes could induce significant vari-
ations in the runoff. The study analyzes six different models based on the SCS-CN method, including the original SCS-CN model and
several variations with added features (SCS-CN with slope correction, SCS-CN with λ-optimization, Mishra and Singh, Michel-Vazken
-Perrin (MVP), Activation Soil Moisture Accounting SCS-CN). The accuracy of each model was compared using several goodness-of-fit stat-
istics. Furthermore, based on the flood frequency analysis, three large flood events were reported in 2005, 2013, and 2014. The results
showed that the MVP model was the best-performing method in simulating runoff. The outcomes of this study can provide valuable infor-
mation to the local authorities in making informed decisions about flood forecasting and water conservation.

Key words: Aghanashini River, flood frequency, mathematical models, runoff estimation, SCS-CN method

HIGHLIGHTS

• Six mathematical models have been prepared on the basis of SCS-CN for a coastal river basin.
• The long-term hydrological simulation of the Aghanashini River has been carried out by taking AMC changes.
• Seven statistical indices were used to judge the efficiency of the developed models.
• The developed models compute surface runoff with the desired accuracy.

INTRODUCTION
Water is essential for human survival, and rivers have been crucial in shaping human civilization throughout history (Sofi
et al. 2021). They serve as a vital source of freshwater, providing a crucial resource for various purposes such as water
supply, irrigation, and the generation of hydropower (Rautela et al. 2022a, 2022b). The climatic and streamflow extremes
caused by human and natural factors profoundly impact spring and rain-fed river basins, leading to the drying up of streams
and springs and a significant alteration of land use (Sharma et al. 2019; Galavi & Mirzaei 2020; Singh et al. 2023). These
alterations will have a profound impact on the surface and sub-surface water flow dynamics of the basins, significantly affect-
ing their hydrological response (Galavi et al. 2019; Sofi et al. 2021). The consequences of these changes are significant and
highlight the need for an increased understanding and management of our freshwater resources. Hydrologic simulation of
surface runoff is a computational approach to predicting the movement of water in catchments, from precipitation to surface
water bodies, including rivers. It is particularly useful in ungauged catchments where limited data are available, as it allows
for the estimation of flow based on the complex physical and meteorological processes that govern water movement (Kumar
& Bhattacharjya 2021).
The simulation process involves mathematical modelling of the physical processes involved in water movement, including
precipitation, evaporation, infiltration, and storage (Amini-Zad et al. 2018; Rautela et al. 2023). These models are based on
hydrology, meteorology, and soil science principles and incorporate data on precipitation, temperature, and land use (Kumar
& Bhattacharjya 2021). The simulation results provide valuable information on the hydrologic response of a catchment to

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and
redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).

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 AQUA — Water Infrastructure, Ecosystems and Society Vol 72 No 4, 508

changes in land use and management practices. With the rapid increase in population, there is a growing need for water con-
servation and identifying new water resources. Accurate estimates of water flow in a region are crucial for making informed
decisions about water management and flood control (Kumar & Bhattacharjya 2021). The results of the hydrologic simu-
lation of surface runoff can be used to estimate the impacts of land use changes on water resources, to identify the best
strategies for conserving water, and to reduce the risk of flash flooding, which can cause severe damage to properties near
riverbanks.
Empirical models are mathematical formulas that use a limited number of easily measurable variables, such as precipitation
and temperature, to estimate runoff. These models are simple and quick to implement and can provide useful estimates for
areas where data are limited (Rawat et al. 2021). However, empirical models have limitations as they often cannot account for
complex processes in a watershed, such as the effect of soil type and land use on runoff (Sen & Altunkaynak 2006). Hydro-
logical models, conversely, are more complex and detailed models that simulate the complete water cycle within a watershed,
including precipitation, infiltration, evapotranspiration, and runoff. These models can provide more accurate runoff estimates
as they consider a wider range of variables and processes (Bunganaen et al. 2021). Examples of hydrological models used for
runoff estimation include the Hydrological Modelling System (HEC-HMS) and the Soil and Water Assessment Tool (SWAT)
(Murmu & Murmu 2021). However, these models require more data and computational resources and may require more time
and expertise. The choice between the two methods depends on the desired accuracy, data availability, and computational
resources (Young et al. 2009).
In the Soil Conservation Service Curve Number (SCS-CN) method, the curve number is the only parameter that is used for
the computation of runoff, which depends on the Land Use Land Cover (LULC), hydrological soil group, and Antecedent
Moisture Condition (AMC) (SCS 1956). The RS and GIS techniques are used for LULC and soil classification of the catch-
ment area. The soils are classified into four Groups A, B, C, and D, depending on their runoff generation potential of soils.
The curve number is assigned to the hydrologic units formed on intersecting LULC and soil map, which compute the runoff.
The curve number depends on the soil’s AMC condition, which depends on the previous 5-day precipitation. Very few
studies (Mishra et.al. 2004; Verma et al. 2020; Rawat et al. 2021) are available in the literature that presents the comparative
application of several SCS-CN-based models for runoff estimation. The present study performs the hydrologic simulation
using six mathematical models based on the SCS-CN method. The SCS-CN with slope correction, λ optimization are
simple models, while Mishra and Singh (MS) (Mishra & Singh 2004), Michel-Vazken-Perrin (MVP) (Michel et al. 2005),
and Activation Soil Moisture Accounting SCS-CN (ASMA-SCS-CN) (Verma et al. 2020) consider the long-term effect are
used for simulation. The model parameters are optimized for the catchment area, and simulation is performed for the
period of 18 years (2001–2018) at a daily time scale. The performance of the models is compared with the help of good-
ness-of-fit statistics. The selected model can be utilized to predict runoff in physioclimatically similar ungauged
catchments. This will provide crucial baseline information to local authorities for developing effective water conservation
strategies and flood management plans.

Study area and data collection

The catchment of the River Aghanashini (up to the Santeguli station) in Uttara Kannada, Karnataka, is considered the study
domain (Figure 1). The Aghanashini River originates from Shankara Honda in the Gadihalli (Sirsi) at 676 m Above Mean Sea
Level (AMSL). It is one of the virgin rivers of the world because of its unobstructed flow through the Western Ghats and
travels 117 km to join the Arabian Sea near Kumta while draining an area of 998.02 km2 up to Santeguli. The study
region is located between the longitudes 74° 340 21″ E–74° 550 7″ E and the latitudes 14° 150 36″ N–14° 370 33″ N. The ever-
green trees in the forest create swamps in the area, which help maintain the perennial flow of the river. The Myristica swamps
act as a sponge by absorbing the water during the monsoon period and releasing the same in the dry period. This also reduces
the impact of floods since the area already has water in its soil pores.
The daily district-level precipitation data of Uttara Kannada district and daily streamflow data at Santeguli Station are
obtained from the India-WRIS web portal from 1 January 2001 to 31 December 2018. The 30 m resolution Digital Elevation
Model (DEM) is obtained from the CARTOSAT-1 satellite of the Indian Space Research Organization (ISRO). For the LULC
classification, the Web Map Service layer from ISRO with a resolution of 50 K prepared during 2015–16 is used. The soil map
used in this study is obtained from the National Bureau of Soil Survey and Land Use Planning (NBSS and LUP), Nagpur, in
cooperation with the Department of Agriculture, Karnataka, on a scale of 1:500000.

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Figure 1 | Location map of the Aghanashini River Basin.

METHODOLOGY
Baseflow separation
The Web-based Hydrograph Assessment Tool (WHAT) is used to carry out the baseflow separation from observed streamflow
to compute the surface runoff (Lim et al. 2005). In the WHAT programme, the recursive digital filter algorithm is used con-
sidering the values of the filter parameter and maximum baseflow index as 0.98 and 0.80, respectively.

LULC classification
The LULC is the main parameter affecting the curve number in the SCS-CN method for the simulation of runoff. This study
assumes that the LULC is invariant during the simulation period from 2014 to 2018. The catchment area is divided into eight

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LULC classes, viz., residential, cultivated, plantation, dense forest, scrub forest, pasture, wasteland, and water body
(Figure 2(a)). The dense forest is found to be the prominent land use in the catchment covering an area of 753.35 km2,
which is 75.98% of the total catchment area.

Hydrological soil classification

The soil map is geo-referenced and digitized using the QGIS tool. The hydrological soil classification is done depending on
the texture of the soil and the soil is divided into Groups B, C, and D, hydrological soil groups (Figure 2(b)). Group B soil
covers 27.22 km2, Group C soil covers 681.53 km2, and Group D soil covers 289.26 km2 of the catchment area. The largest
area is covered by Group C hydrological soil type with moderately high runoff-producing potential.

Slope classification
The slope of the land is also a significant factor affecting the runoff. The catchment is divided into six zones from slope 0 to
144% based on the slope class having an equal slope interval (Figure 2(c)). From the slope map, it is observed that about
705.10 km2 area has a slope ranging between 0 and 24%. The steep slopes of the order of 120–144% occupy a very small
area (i.e. 0.3 km2).

Mathematical models
The six mathematical models based on the SCS-CN method are used for hydrologic simulation. The SCS-CN is a simple
method that uses LULC, soil type, and AMC conditions to compute the curve number (Chow et al. 2010). In the SCS-CN
with slope correction, a slope factor is applied on the curve number, which takes the effect of the slope of the catchment
into account (Shi & Wang 2020). It is observed from the studies that the value of the coefficient for the initial abstraction
of 0.3 is substantial (Woodward et al. 2003). In the SCS-CN with λ (initial abstraction coefficient) optimization method,
the value of the initial abstraction coefficient is optimized for the catchment. The MS model modifies the existing SCS-CN
model by including the cumulative static infiltration component in the formulation and simulates runoff for the long-term
(Mishra & Singh 2004). The soil moisture accounting procedure is incorporated into the MVP method by using the initial
soil moisture (Michel et al. 2005). In the ASMA-SCS-CN method, the concept of activation soil moisture is used by combining
the soil moisture accounting with static infiltration for long-term runoff simulation (Verma et al. 2020).

Figure 2 | (a) LULC map; (b) hydrological soil map; and (c) slope map of the Aghanashini River Basin.

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In the above mathematical models (Table 1), Q is the daily surface runoff, P is the daily precipitation, Ia is the initial abstrac-
tion, S is the amount of potential maximum retention in soil, λ is initial abstraction coefficient, Fc is cumulative static
infiltration, Sa is the threshold soil moisture, Vo is the initial soil moisture, and Vet is the revised threshold soil moisture.
The parameters Vo, Sa, and Fc used in the above mathematical models are estimated using the expressions of Mishra et al.
(2006) and Singh et al. (2015) as in the following Equations (1)–(3).

pffiffiffiffiffiffiffiffi
Vo ¼ a P5 S (1)
Sa ¼ bS (2)
Fc ¼ f c T (3)

where α is the coefficient for initial soil moisture, β is the coefficient for threshold soil moisture, P5 is the cumulative precipi-
tation of the preceding 5 days, fc is the minimum infiltration rate, and T is the duration of precipitation.

Parameter optimization
In the mathematical models, the parameters need to be optimized for the given catchment area and give the maximum effi-
ciency in terms of goodness-of-fit statistics. The SOLVER tool is used for optimization, which uses the Generalized Reduced
Gradient (GRG) non-linear method of optimization (Hirpurkar & Ghare 2015). The initial values and optimized values of all
parameters are shown in Table 2.

Performance evaluation
The goodness-of-fit statistics such as Nash–Sutcliffe Efficiency (NSE) (Equation (4)), Root Mean Square Error (RMSE)
(Equation (5)), Normalized RMSE (nRMSE) (Equation (6)), Percent Bias (PBIAS) (Equation (7)), Mean Absolute Error
(MAE) (Equation (8)), Standard Error (SE) (Equation (9)), and RMSE to Standard Deviation Ratio (RSR) (Equation (10))

Table 1 | Mathematical models used in the study with method of formulation

S. No. Method name Case Method formulation

1. SCS-CN P  Ia (P  Ia )2
Q¼
(P  Ia þ S)
P , Ia Q ¼ 0
2. SCS-CN with slope correction P  Ia (P  Ia )2
Q¼
(P  Ia þ S)
P , Ia Q ¼ 0
3. SCS-CN with λ optimization P  lS (P  lS)2
Q¼
(P þ (1  l)S)
P , lS Q ¼ 0
4. Mishra and Singh P  (Ia þ Fc ) (P  Ia  Fc )2
Q¼
(P  Ia  Fc þ S)
P , (Ia þ Fc ) Q ¼ 0
5. MVP Vo  (Sa  P) Q ¼ 0
(Sa  P) , Vo , Sa (P þ Sa  Vo )2
Q¼
(P þ Vo  Sa þ S)
" #
(Sa  Vo  (Sa þ S) (S þ Sa  Vo )2
Q¼P 1 2
S þ (S þ Sa  Vo )P

6. Activation Soil Moisture ASMA-SCS-CN Vo  (Vet  P) Q¼0

(Vet  P) , Vo , Vet (P þ Vo  Vet )2
Q¼
(P þ Vo  Vet þ S)
" #
(Vet  Vo  (Vet þ S) (S þ Vet  Vo )2
Q¼P 1 2
S þ (S þ Vet  Vo )P

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Table 2 | Optimized parameters of different mathematical models

S. No. Method Parameter Initial value Optimized value

1. SCS-CN with λ optimization λ Initial abstraction ratio 0.2 0.037

2. MS fc Minimum infiltration rate 1 0.02
S Amount of potential maximum retention in soil 125 41.95
3. MVP α Coefficient for initial soil moisture 0.01 0.22
β Coefficient for threshold soil moisture 0.01 0.02
S Amount of potential maximum retention in soil 125 217.5
4. ASMA-SCS-CN α Coefficient for initial soil moisture 0.01 0.11
β Coefficient for threshold soil moisture 0.01 0.01
fc Minimum infiltration rate 1 0.1
S Amount of potential maximum retention in soil 125 109

are used to evaluate the performance of models.

2 3
P
N
2
6 (Qobs  Qcomp )i 7
6 7
NSE ¼ 61  i¼1 7 (4)
4 PN
 2 5
(Qobs  Qobs )i
i¼1
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
u N
u1 X
RMSE ¼ t (Qobs  Qcomp )2i (5)
N i¼1
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
u N
u1 X
t (Qobs  Qcomp )2i
N i¼1
nRMSE ¼  (6)
Q obs
2 3
P
N
6 (Q obs  Q comp )i7
6 7
PBIAS ¼ 6i¼1 N 7 (7)
4 P 5
(Qobs )i
i¼1

1 X
N
MAE ¼ jQobs  Qcomp ji (8)
N i¼1
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
u N
1 uX
SE ¼ t (Qobs  Qcomp )2i (9)
(N  m þ 1) i¼1
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
PN
(Qobs  Qcomp )2i
i¼1
RSR ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi (10)
PN
 )2
(Qobs  Q obs i
i¼1

 obs is the mean observed flow, m is the degree of freedom, and

where Qobs is the observed flow, Qcomp is the computed flow, Q
N is the total number of datasets.

Flood frequency analysis

Flood frequency analysis is a statistical method used to estimate the probability of flood events of various magnitudes occurring
at a particular location. The annual maximum discharge series was derived from the daily observed discharge to conduct flood
frequency analysis using the Gumbel’s method (Subramanya 2017). The distribution fitting tool in the ‘Statistics and Machine
Learning Toolbox’ of MATLAB has been adopted for assessing the goodness-of-fit of different probability distributions for the

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annual maximum discharge series. After fitting the distribution, the goodness-of-fit is evaluated using a quantile–quantile plot.
Sensitivity analysis is performed to assess the impact of different distribution functions and parameter estimation methods on the
results. Based on the criteria suggested by Kale & Hire (2004), the flood events can be classified based on the return period (T ) as:
small floods (T  2.33 years), moderate floods (2.33 , T , 6.33 years), and large floods (T  6.33 years).

RESULTS AND DISCUSSION

Baseflow
The baseflow of the Aghanashini River changes noticeably because of the diverse soil characteristics of the study area. The
higher baseflow was obtained due to soil moisture holding capacity. Generally, the Groups B and C type soil hold the higher
moisture and release it in the dry periods as baseflow, but during wet seasons the soil is fully saturated, and it gives the higher
baseflow and leads to higher discharge in the river. From our analysis, the baseflow index is obtained as 0.71, which gives the
total baseflow of 37,054 mm and total direct runoff of 15,387 mm (Figure 3).

Simulation results
The simulation of the surface runoff is performed for six different mathematical models in Microsoft Excel. In the present
study, the calibration is performed for the years 2001–2009, while the validation is carried out for the years 2010–2018.
In the SCS-CN method, the observed and simulated runoff show poor agreement (Figure 4(a)). The simulated runoff is less
than the observed runoff because of the high initial abstraction ratio of 0.3, which reduces the simulated runoff significantly. A
significant difference of 16.17 m3/s is observed for the average discharge, while a deviation of 425.89 m3/s is observed for
the peak discharge. Significant deviations between observed and simulated values are noticed in this method (Figure 4(a)).
The coefficient of determination (R 2) of the SCS-CN method was found to be 0.61 (Figure 5(a)).
The SCS-CN with slope correction method considers the catchment’s mild slope and shows slightly good results compared
to the SCS-CN method. The simulated curve lies below the observed curve showing a difference of 15.86 m3/s in the average
value and a deviation of 415.76 m3/s in the maximum value (Figure 4(b)). A slight rise in the simulated values is observed
compared to the SCS-CN method due to the consideration of the effect of the mild slope. The scatter plot shows a trend simi-
lar to the SCS-CN method, which shows an R 2 of 0.61 (Figure 5(b)).
A good fit between observed and simulated values is noticed for the SCS-CN with λ the optimization method as compared
to previous methods (Figure 4(c)). The optimized value of 0.017 of the initial abstraction ratio increases the simulated runoff
and decreases the deviation as compared to previous methods. In calibration, a difference of 6.23 m3/s is observed in the
average value and a deviation of 445.78 m3/s is observed for the maximum value of the runoff. While in validation, a differ-
ence of 3.97 m3/s (240.25 m3/s) is observed for the average (peak) runoff value (Figure 4(c)). From Figure 4(c), the runoff
values show good agreement for the calibration period, while some deviations are noticed for the validation period due to the
lower peak flows. The scatter plot shows the R 2 of 0.72 and 0.61 for the calibration and validation period, respectively
(Figure 5(c) and 5(d)).

Figure 3 | Baseflow separation using WHAT for the study area during 2001–2018.

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Figure 4 | Comparison of observed and computed runoffs through time series plots for (a) SCS-CN; (b) SCS-CN with slope correction;
(c) SCS-CN with λ optimization; (d) MS; (e) MVP; and (f) ASMA-SCS-CN. (continued.).

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Figure 4 | Continued.

In the Mishra and Singh method, the observed and simulated values exhibit better conformity compared to
previous methods (Figure 4(d)). In calibration, the simulated average and peak values exhibited deviations of 6.95
and 369.51 m3/s, respectively, with respect to the corresponding observed values. A similar result was derived for
the validation phase also. This method yielded an improvement in RMSE, NSE, PBIAS, and MAE compared to the
SCS-CN and SCS-CN with slope correction; however, it could not outperform the SCS-CN with λ-optimization
method. The scatter plot shows the R 2 of 0.69 and 0.58 for the calibration and validation periods, respectively
(Figures 5(e) and (f)).
The MVP method shows better statistical performance in terms of NSE, RMSE, PBIAS, MAE, and RSR than all previous
methods. The MVP method significantly improves the PBIAS than the previous methods, indicating superior model perform-
ance (Table 3). The best fit is observed in the MVP method compared to other methods between observed and simulated
values (Figure 4(e)). The MVP method improves the accuracy in estimating average runoff as the deviations from the observed
values during the calibration (0.7 m3/s) and validation (1.67 m3/s) periods were minimal. On the other hand, the estimation
of peak runoff exhibited marginally higher deviations as compared to the other methods. The scatter plot shows better agree-
ment of observed and simulated runoff with higher R 2 of 0.73 and 0.63 for the calibration and validation periods, respectively
(Figures 5(g) and (h)).
In the ASMA-SCS-CN method, the result shows slightly higher deviations from peak values than the MVP method.
(Figure 4(f)). During calibration, the mean simulated value exceeded the mean observed value by 1.37 m3/s, and a deviation
of 487.72 m3/s was observed between the maximum values of runoff. In the validation period, a difference of 0.93 m3/s was
observed between average values, and a deviation of 331.05 m3/s was observed between the maximum values of runoff. The
scatter plot shows similar results to the MVP method, as R 2 of 0.73 and 0.62 for the calibration and validation period, respect-
ively (Figures 5(i) and (j)). In general, all the methods used in the study produced satisfactory to good R2, NSE, RMSE,
nRMSE, PBIAS, SE, and RSR values, which means that the simulated discharge was able to closely follow the observed dis-
charge. In certain cases, the models are not able to accurately capture the peak discharge because of the significant
contribution of baseflow to the river discharge.

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Figure 5 | Comparison of observed and computed runoff using scatter plot for (a) SCS-CN; (b) SCS-CN with slope correction; (c) SCS-CN with
λ optimization (calibration); (d) SCS-CN with λ optimization (validation); (e) MS (calibration) (f) MS (validation); (g) MVP (calibration); (h) MVP
(validation); (i) ASMA-SCS-CN (calibration); and (j) ASMA-SCS-CN (validation).

Performance evaluation
The performance of these models is evaluated through goodness-of-fit statistics such as NSE, RMSE, nRMSE, PBIAS, MAE,
SE, and RSR, as shown in Table 3. As per Moriasi et al. (2007), all six models perform satisfactorily as their NSE values lie

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Table 3 | Performance evaluation of the mathematical models for runoff estimation

S. No. Mathematical model NSE RMSE (m3/s) nRMSE PBIAS (%) MAE (m3/s) SE (m3/s) RSR

1 SCS-CN 0.54 117.99 4.36 59.80 71.50 2.76 0.68

2 SCS-CN with slope correction 0.55 117.05 4.33 58.67 71.01 2.74 0.67
3 SCS-CN with λ-optimization calibration 0.72 72.02 2.33 20.20 30.98 1.69 0.53
4 SCS-CN with λ-optimization validation 0.61 68.17 2.94 17.11 29.50 1.60 0.63
5 MS calibration 0.68 77.15 2.50 22.53 33.49 1.81 0.57
6 MS validation 0.57 71.65 3.09 21.14 30.66 1.68 0.66
7 MVP calibration 0.73 70.34 2.28 2.28 30.26 1.65 0.52
8 MVP validation 0.63 66.42 2.86 7.19 29.88 1.55 0.61
9 ASMA-SCS-CN calibration 0.72 71.07 2.30 4.44 30.38 1.66 0.53
10 ASMA-SCS-CN validation 0.62 67.31 2.90 4.00 30.09 1.58 0.62

Table 4 | Summary of flood events in the Aghanashini River

Return period (T ) Type of flood No of events Years

2.33 Small 10 2001, 2002, 2004, 2008, 2009, 2010, 2012, 2015, 2017, 2018
2.33 , T , 6.33 Moderate 5 2003, 2006, 2007, 2011, 2016
6.33 Large 3 2005, 2013, 2014

between 0.5 and 0.65. The MVP method shows superior performance compared to other methods with the least RMSE and
nRMSE values. According to criteria suggested by Moriasi et al. (2007) and Durbude et al. (2011), the MVP and ASMA-SCS-
CN models give better performance with a PBIAS value of less than 10%, while the MS method and SCS-CN with λ optim-
ization reflected fair performance having a PBIAS less than 25%. All the other methods show unsatisfactory performance
having PBIAS greater than 25%. Table 3 shows the lowest values of MAE and SE for the MVP method. All the models’ per-
formances are satisfactory, with an RSR value between 0.6 and 0.7 (Moriasi et al. 2007; Durbude et al. 2011).

Flood frequency analysis

The distribution fitting tool in MATLAB 2018a was adopted for modelling the annual maximum discharge series. In this
study, the Gumbel’s Extreme Value, Generalized Extreme Value (GEV), Log Normal 2 Parameter (LN2P) and Weibull dis-
tribution are used to fit the annual maximum series. The results indicated that GEV and LN2P are the best-performing
distributions for the annual maximum series; however, we adopted LN2P for the flood frequency analysis due to parsimony
considerations. The location (μ) and scale (σ) parameters for the LN2P distribution are 6.1 and 0.89, respectively. From the
analysis, the computed magnitude of mean annual flood was 613.8 m3/s corresponding to a return period (T ) of 2.78 years.
Table 4 shows that three large flood events have been observed in the Aghanashini catchment in the years 2005, 2013, and
2014. The results of this study provide valuable information for designing flood protection measures. This information can be
used to set design standards for flood walls or determine the height of riverbanks, depending on the specific location and the
level of protection required.

CONCLUSIONS
The study assesses the magnitude and the temporal variations in the stream discharge using six mathematical models based
on the SCS-CN method for the Aghanashini River catchment for the period of 18 years. The baseflow contribution in the
Aghanashini River is considerable due to the predominance of dense forests, pasture land, and the presence of soils with mod-
erately low runoff potential, thus allowing considerable infiltration. The simulation results reflect that the MVP model,
exhibiting the highest predictive efficiency and lowest errors, is superior for long-term hydrologic simulations in the Aghana-
shini River catchment compared to other models. The flood frequency analysis reflects the frequent occurrence of moderate

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 AQUA — Water Infrastructure, Ecosystems and Society Vol 72 No 4, 518

and high floods in the catchment, which stresses the importance of serving flood mitigation and protection measures. The
Aghanashini River catchment, unaffected by human interventions, presents a valuable opportunity for developing small-
and medium-scale water resources. This study is helpful for the concerned policymakers and stakeholders to adopt a suitable
method to simulate the runoff for water resources and flood management in the catchment.

ACKNOWLEDGEMENTS
The authors express their heartfelt gratitude and appreciation to the Director of Punjab Engineering College (Deemed to be
University) and the PG coordinator of the Water Resources Engineering Department for providing the necessary facilities to
conduct the present study. Without their support, this research would not have been possible. Additionally, the authors would
like to express their sincere appreciation to the Indian Institute of Technology, Indore for their valuable support and assist-
ance throughout the course of the study. Their contributions have been indispensable in the success of this research. The
authors also extend their thanks to the Central Water Commission (CWC) India for providing the discharge data through
the India-WRIS portal. The authors are truly grateful for the support and encouragement received from all these institutions.

AUTHORS’ CONTRIBUTIONS
Harmandeep Singh and Kuldeep Singh Rautela developed and designed the manuscript and wrote it under the supervision of
Mohammad Afaq Alam and Priyank J. Sharma. All authors read and approved the final manuscript.

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

CONFLICT OF INTEREST
The authors declare there is no conflict.

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First received 30 November 2022; accepted in revised form 12 March 2023. Available online 29 March 2023

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