International Journal of Research Studies in Agricultural Sciences (IJRSAS)
Volume 3, Issue 10, 2017, PP 17-36
ISSN No. (Online) 2454–6224
DOI: http://dx.doi.org/10.20431/2454-6224.0310003
www.arcjournals.org
Sensitivity Analysis of Runoff Model by SWAT to Meteorological
Parameters: A Case Study of Kasillian Watershed, Mazandaran,
Iran
Mohsen Ghane1, Sayed Reza Alvankar1, Saeid Eslamian2, Mahboubeh Amoushahi-Khouzani3,
Amir Gandomkar4,Elahe Zamani5, Maryam Marani-Barzani6, Masoud Kazemi7,Morteza
Soltani8,Shahide Dehghan4, Vijay P. Singh9, Kaveh Ostad-Ali-Askari10*, Majedeh HaeriHamedani2, Hamid-Reza Shirvani-Dastgerdi10, Zahra Majidifar11, Nicolas R. Dalezios12,
Bahareh Soltani2
1
Civil Engineering Department, South Tehran Branch, Islamic Azad University,Tehran,Iran.
2
3
Department of Water Engineering, Isfahan University of Technology, Isfahan, Iran.
Water Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran.
4
5
Department of Geography, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
Department of Physiologyand plant improvement,Ardakan University, Ardakan, Yazd, Iran.
6
Department of Geography, University of Malaya (UM) ,50603 Kuala Lumpur, Malaysia.
7
Civil Engineering Department, Najafabad Branch,Islamic Azad University,Najafabad Iran.
8
Department of Artichetctural Engineering, Shahinshahr Branch, Islamic Azad University, Shahinshahr,
Isfahan, Iran.
9
Department of Biological and Agricultural Engineering &Zachry Department of Civil Engineering, Texas A
and M University, 321 Scoates Hall, 2117 TAMU, College Station, Texas 77843-2117, U.S.A.
10
Department of Civil Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.
11
Department of Agronomy and Plant Breeding, Lorestan University, Lorestan, Iran
12
Laboratory of Hydrology, Department of Civil Engineering, University of Thessaly, Volos, Greece &
Department of Natural Resources Development and Agricultural Engineering, Agricultural University of
Athens, Athens, Greece.
*Corresponding Author: Dr. Kaveh Ostad-Ali-Askari, Department of Civil Engineering, Isfahan
(Khorasgan) Branch, Islamic Azad University, University Blvd, Arqavanieh, Jey Street, P.O.Box: 81595158 Isfahan, Iran.Email: Koa.askari@khuisf.ac.ir
Abstract: To design and construct mosthydraulic structures, e.g. dams, it is essential to determine watershed
runoff. If a watershed lacks any gaging station, thenhydrologic models can be utilized to estimate runoff.
The Soil Water Assessment Tool (SWAT) is one of the most widelyusedcomputerwatershed models. In this
model, we need to input meteorological data, such as precipitation, temperature, wind speed, solar radiation,
and relative humidity;as well as watershed data, including curvenumberandroughness coefficient, to
calculate the watershed runoff. Some watershedshave weather stations, but there is a risk that the
recordeddataof a station do not represent the whole watershed and the use of such data may cause error.
Consequently, the error of estimated runoff error needs to be determined. This study deals with the sensitivity
of runoff estimatedusing the SWAT model to the variations in meteorological parameters, such as
precipitation, solar radiation, wind, humidity, and temperature. Results indicate that with a 30% decrease in
the average monthly precipitation, sunshine, relative humidity, wind and temperature, we
witness,respectively,a 64.27% decrease, 114.67% increase, 45.93% decrease, 126.12% increase, and
39.21% increase in the estimated runoff.. Runoff estimation is found to be most sensitive to wind speed and
solar radiation,and least sensitive to temperature.
Keywords: meteorological parameters, rainfall runoff, sensitivity analysis, SWAT, watershed yield
International Journal of Research Studies in Agricultural Sciences (IJRSAS)
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Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
Watershed, Mazandaran, Iran
1. INTRODUCTION
In order to build a dam, it is vital to determinemonthly and annauwatershedyieldssothe volume of
storage and the height of dam can be evaluated.. A gage station can measure the input of water to the
dam. In the absence of the gage station, a computermodel, e.g. SWAT, can be used to estimate current
and historical watershed runoff. However, the model requires meteorological data, such as
precipitation, temperature, wind speed, solar radiation and relative humidity on one hand, and
watershed charactersitics, such as curve number and roughness coefficient on the other hand. Because
of the limited number of weather stations in some watersheds, the measured values of a station may
not represent the whole watershed. There is therefore a need to calculate the error in runoff
estimation.Thisstudy is based on, aims to investigate the sensitivity of watershed runoff estimated by
SWAT to variations in meteorological parameters, such as precipitation, solar radiation, wind,
humidity, and temperature.
2. LITERATURE REVIEW
Behtarinejad (2012) employed SWAT to investigate sedimentation and the waste of nutrients east of
Gorganrood watershed.The was verified from 1999 to 2006.Datafrom 2007 to 2010 was used to
validate the results which were found satisfactory. The SWAT model possesses the capability to
produce diverse scenariosfordifferent management options. Gholami (2004) used SWAT to stimulate
the average monthly runoff ofEmameh watershed (a sub-basin of Jajrood watershed). Results
exhibited a higher sensitivity of the model to overland roughness coefficient [1-12]. Omani et al.
(2007) used SWAT to stimulaterunoffin the Gharesar sub-basin northwest of the Karkheh River. They
found a higher sensitivity to curve number [3-15].
Saadati (2003) stimulated daily discharge and water balance in Kasillianwatershed.Resultsshowed that
the model was sensitive to the annual and monthly periods and yielded more reasonable results in
comparison with the daily period (Saadati 2003).Behtarinejad (2012) and Alavinia&Nasiri-saleh
(2011)employed the SWAT model to estimate the discharge and advocated its efficiency. Applying
to Ghareh-sarwatershed,Omani et al. (2008 concluded that the SWAT model was capable of
stimulating hydrologic components [6-7].
Simulating runoff from Behestabad watershed (one of the sub-basins of Northern Karoon), Rostamian
(2006) concluded that the SWAT model was not able to stimulatemaximumvalues [8-9].
Poorabdollah and Tajrishi (2009) employed SWAT in Emameh (a sub-basin of Latian Dam
watershed) and fpimdthe model to be efficient for runoff estimation [10].
Chu and Shirmohammade (2004) used SWAT to estimate overlandflow from a 33.4 square kilometer
watershed located in Maryland. Results showed that themodel was notaccurateduring very wet years.
When wet years were omitted, monthly runoff was estimated satisfactorily Behtarinejad 2012, Chu
&Shirmohammade 2004) [11].
Hatou et al. (2004) concluded that the runoff values estimated by SWAT agreed with the values
measured in Lershi watershed [12]. Schuol et al. (2006) argued that SWAT was capable of stimulating
the hydrological balance [13-18].
Santhi et al. (2001) satisfactorily forecasted the Bask River watershed runoff by SWAT [18-34].
2.1. Material
The case study is limited to Kasillian watershed (located in Northern forests of Alborzmountain in
Iran) that included Sangdeh, Darzikela, Sootkela, Valikchal and Valikbon villages. The area of
Kasillian watershed is approximately 66.81 square kilometers and the main river stretches for 16.8
kilometers. The geographical coordinates of the watershed are: latitude from 36˚-02’ to 36˚-11’ N,
and longitude from 53˚-10’ to 53˚-26’ E. There is a gage station on KasillianRiver at Valikbon. The
station, built in 1970, is located at longitude of 53˚-17’ and latitude 36˚-10’ to measure river
discharge. Fig. 1 shows the location of Kasillian watershed.
International Journal of Research Studies in Agricultural Sciences (IJRSAS)
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Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
Watershed, Mazandaran, Iran
The SWAT model uses precipitation, temperature, solar radiation, wind speed and relative
humiditydata which was available from January 1978 till January 1989. The statistical parameters
were retrieved from Pol-e-sefidcineoptic, Sangdeh and Darzikelaclimatology, Valikchalprecipitationgauge, and Valik hydrometer stations.
Fig1. Location of Kasilian Watershed until Valik hydrometer Station.
2.2. SWAT Model
SWAT was developed by the Grassland Water and Soil Research Laboratory,Temple, Texas, of the
U.S. Department of Agriculture, Agricultural Research Service. This model stimulates watershed
runoff and requires climatic data such as precipitation, temperature, solar radiation, wind speed and
relative humidity. At least temperature and precipitation data needed to be specified and the model is
able to stimulateotherdata. It also needs land map, land application, and the digital elevation model.
Arc GIS software runs the SWAT model [35-66].
2.3. Formulas and Tables
The Soil Conservation Service (SCS) curve number is a function of soil permeability, land
use,andantecedent soil moisture. Different curve numbers were considered for antecedent soil
moisturecondition II in diverse types of land use from 67 to 76 based on the SWAT formulas and the
optimum number for the region was obtained as 67 [67-82].
SCS runoff equation estimates runoff for different land uses and different types of soil [83-89].Equ.1
shows the runoff equation as (SCS 1972):
�
=
���
���
−��
2
(1)
−�� +
Where Qsurf is the accumulated runoff or the excess of precipitation (mm),Rday is the amount of
precipitation per day (mm),Ia is the initial abstraction of the surface reserve, the diffusion before
runoff (mm), and S is the maximum soil moisture retention (mm). A changeinthe S parameter results
from change in the soiltype, land use, management, slope and soil content. The Sparameteris defined
in Equ.2 (SWAT Theoretical Documentation Version 2009):
= 25.4
1000
��
− 10
(2)
where CN is the curve number for day. I a is approximately estimated as 0.2 and fed into Equ.1.to
obtain Equ.3 (SWAT Theoretical Documentation Version 2009):
�
=
���
���
−0.2
2
+ 0.8
(3)
Runoff occurs onlyif ��� > �� . The graphical solutions for Equ.3 with the numerical values of
different curves are presented in Fig.2 (SWAT Theoretical Documentation Version 2009). As evident
International Journal of Research Studies in Agricultural Sciences (IJRSAS)
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Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
Watershed, Mazandaran, Iran
in Fig.2, the higher the curve number, the more precipitationbecomesrunoff. The runoff resulting from
precipitationvarieswith a curve according to the curvenumber.
Runoff (mm)
The SCS curve defines three antecedent moisture conditions: 1- dry (wilting point), 2- average
antecedent moisture, and 3- wet (soil capacity). The humidity condition 1 (dry) possesses the lowest
value in the daily curve number. The curve numbers for antecedent moisture conditions 1 & 2 are
calculated based on equations 4& 5 (SWAT Theoretical Documentation Version 2009):
Fig2. Relationship of runoff to rainfall in SCS Curve number method.(SWAT Theoretical Documentation
Version 2009
.
−�� +��� .
�� = �� −
�� = �� . ��� .
.
−��
− .
.
− ��
(4)
−��
(5)
Where CN1, CN2 and CN3 are the curvenumbers for antecedent soil moisture conditions 1, 2 and 3,
respectively.
Williams (1995) developed a curve number equation for different slopes as Equ.6 (SWAT Theoretical
Documentation Version 2009):
��
�
=
�� −��
.
− . ��� −
.
. ��� + ��
(6)
Where CN2s (for the antecdedent soil moisture condition II) is set for the slope, CN3 (for the
antecedent soil moisture condition III) is for a 5% slope, CN2 (for the antecedent soil moisture
condition II) is for a 5% slope and SLP is the average slope of sub-basins. SWAT does not set the
curve numbers for the slope. Setting is done before entering the curve number and through the input
file management. SWAT input variables, utilizing the curve number method, affects the overland
runoff calculation as in table1 (SWAT Theoretical Documentation Version 2009):
Table1. SWAT input variables that pertain to surface runoff calculated with the SCS curve number
method.(SWAT Theoretical Documentation Version, 2009)
Variable Name
IEVENT
ICN
CNCOEF
PERCIPITATION
CN2
CNOP
Definition
Rainfall, runoff , routing option.
Daily curve number calculation method:0 caculate daily CN
value as a function of soil moisture ؛1 calculate daily CN
value as a function of plant evapotranspiration
Cncoef: Weihghting coefficient used to calculate the
retention coefficient for daily curve number calculations
dependent on plant evapotranspiration
R day: Daily precipitation (mm H2O)
CN2: Moisture condition II curve number
CN2: Moisture condition II curve number
International Journal of Research Studies in Agricultural Sciences (IJRSAS)
Input File
.bsn
.bsn
.bsn
.pcp
.mgt
.mgt
Page | 20
Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
Watershed, Mazandaran, Iran
The Manning overland roughness coefficient values for the intended watershed region and related
SWAT tables are in the range of .05 to .2. The optimum value for this region was calculated as 1 [90114].
The overland concentration timetov was calculated by Equ.7 (SWAT Theoretical Documentation
Version 2009):
=
��
(7)
3600 .
Where Lslp is the length of sub-basin slope,vov is the velocity of overland flow (m/s), and 3600 is the
unit conversion factor. The velocity of overland flow was estimated based on Equ.8 or Manning
equation (SWAT Theoretical Documentation Version 2009):
0.4
=
. � 0.3
0.6
(8)
Where qov is the average of the land current (cubic meter per second),slp is the mean slope of subbasin, and is the Manning roughness coefficient for the sub-basin. The rate of current is assumed as
6.35 mm/h and unit conversion was done through Equs. 9 and 10 (SWAT Theoretical Documentation
Version 2009).
=
0.3
0.005.�0.4
� . �
=
0.6
�0.6
� .
0.6
18. � 0.3
(9)
(10)
2.4. Soil Type
In this study, we used the optimum curve number and overland roughness coefficient. The
precipitation data was chosen from different meteorological stations to obtain the optimum curve
number and overland roughness coefficient. SWAT was initially run with the curve number
CN2=67and the overlandroughness coefficient of 0.1. Results are presented in fig. 3.
To optimize parameters, different values of the curve numberand roughness coefficient were utilized
and the correlation of discharge variation with each parameter introduced in tables 2 and 3is
represented in figures 4 to 7. By comparisonof runoff amounts registered at the hydrometer station
with the calculated amount, the most optimum curve number was found to be 67 and the roughness
coefficient as .1. Subsequently, based on thesevalues, variations in the SWAT input parameters were
used to simulatethewatershed runoff. The effect of variation in each of meteorological parameters on
runoff was calculated and compared with the observed runoff. It should be mentioned that in this
stage of calculations only precipitation data were fed into the model [115-127].
Fig3. Comparison ofMonthly Simulated Discharge of the SWAT with Measured Discharge.
International Journal of Research Studies in Agricultural Sciences (IJRSAS)
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Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
Watershed, Mazandaran, Iran
Fig4. Simulated discharge for different values of CN2
Fig5. Simulated discharge for difference Manning overland roughness coefficient values
Fig6.Comparison of simulated discharge river with different CN values with measured discharge
Fig7. Comparison of simulated discharge with different Manning overland eoughness coefficient values with
maesured discharge
International Journal of Research Studies in Agricultural Sciences (IJRSAS)
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Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
Watershed, Mazandaran, Iran
Table2. Effect of CN on averagesimulated discharge
CN
Average Simulated Discharge (m3/s)
Average Measured Discharge (m3/s)
Error (m3/s)
Percent change or variable
67
0.375787
0.498953
0.123166
0
69
0.377227
0.498953
0.121726
0.3992%
72
0.38084
0.498953
0.118113
1.3574%
76
0.388203
0.498953
0.11075
3.3271%
2.5. Sensitivity Analysis of Watershed Runoff to Meteorological Parameters
Required meteorological parameters, including temperature, relative humidity, wind speed,solar
radiation, and precipitation were fed into SWAT and average runoff, as is shown in the third row of
table 4, was calculated as 0.5704 cubic meters per second.
Table4. Simulated discharge for varing preiciptation
0.7863
0.2951
0.0715
Percent
variable
Simulated
Discharge
0.49895304
0.49895304
0.49895304
Difference
Average
Maesured
Discharge
and
Simulated
Discharge
(m3/s)
1.285224074
0.203889444
0.5704225
Average
Measured
Discharge
(m3/s)
Average
Simulated
Discharge
(m3/s)
Precipitati
on(mm)
PCP×1.5=3.11181
PCP×0.7=1.452178
PCP=2.07454
125.31%
-64.27%
0
2.6. Effect of Precipitation
In order to evaluate the sensitivity of runoff estimated by the SWAT model to precipitation, initially
all precipitation values were multipliedby 1.5 and runoff was calculated. The real amount of
precipitation was used to obtain the average long-term runoff (.570). With a 50% increase in
precipitation, runoff increased to 1.285 (a 125% increase). With a 30% decrease in precipitation, the
average runoff decreased by 64% (.204 cubic meters per second). Consequently, we obtained a .7148
increase and a .3666 decrease in monthly runoff. As evident in Fig.8, the monthly runoff trend was
ascending based on precipitation. With a 50% increase and a 30% decrease in input precipitation, the
stimulated runoff was .79 and .29 whichwere higher and lower than the average observed monthly
runoff, respectively [128-143].
Fig8. Simulated Discharge for varying precipitation
2.7. Effect of Solar Radiation
For a 20% increase and a 30% decrease in solar radiation, the simulated runoff varied from 0.57 cubic
meters per second to 0.59 and 1.22 cubic meters per second, respectively. The monthly variations are
presented in table 5 and figures 10 and 11, with a 20% increase and a 30% decrease in solar radiation,
the simulated runoff increased by 0.09 and 0.73 cubic meters per second, repectively [144-159].
International Journal of Research Studies in Agricultural Sciences (IJRSAS)
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Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
Watershed, Mazandaran, Iran
Table5. Simulated Discharge for different solar radiation values
Average
Measured
Discharge
(m3/s)
0.498653704
0.498653704
0.498653704
Percent
variable
Simulated
Discharge
Average
Simulated
Discharge
(m3/s)
0.59279263
1.224596
0.5704225
Difference
Average
Measured
Discharge
and
Simulated
Discharge
(m3/s)
Average
Solar
Radiation
(
MJ/(m^2/
)Day)
solar ×1.2= 22.16
solar ×0.7= 12.901
solar= 18.43
0.0938
0.7256
0.0715
3.9095%
114.67%
0
Fig10. Simulated Discharge for different solar radiation
Fig11. Average monthly simulated discharge for different solar radiation values
2.8. Effect of Relative
With a 20% increase and a 30% decrease in relative humidity, the average monthly runoff changed
from .5704 to .6947 and .3084, respectively. The 21.79% increase and 45% decrease are presented in
table 6and figures 12 and 13. With a 20% increase and 30% decrease in relative humidity, the
simulated runoff was 39.25% higher, and 38.18% lower than average measured monthly runoff,
respectively [160-178].
Fig12. Average monthly simulated discharge with varying relative humidity
International Journal of Research Studies in Agricultural Sciences (IJRSAS)
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Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
Watershed, Mazandaran, Iran
Fig13. Simulated discharge for varying relative humidity
Table6. Simulated Discharge with varying relative humidity
0.498953
0.498953
0.498953
0.0715
0.1958
0.1905
Percent
Difference
Simulated
Discharge
Average
Measured
Discharge (m3/s)
0.5704225
0.694742037
0.308425093
Difference
Average
Measured
Dsicharge and
Simulated
Dsicharge (m3/s)
Average
Simulated
Discharge (m3/s)
Average
Humidity(%)
Rh =0.4591
Rh×1.2=0.5509
0.7Rh×=0.3213
0
21.79%
-45.93%
2.9. Effect of Wind Speed
With a 50% increase and a 30% decrease in wind speed, the average monthly runoff was 1.23 and
1.28 cubic meters per second. The simulated values were .74 and .79 higher than the observed average
monthly runoff (figures 14 & 15, table 7).
Fig14. Simulated discharge for varying wind speed
Fig15. Simulated discharge for varying wind speed
International Journal of Research Studies in Agricultural Sciences (IJRSAS)
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Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
Watershed, Mazandaran, Iran
Table7. Measureddischarge and simulateddischargefor different wind speed
0.498953704
0.498953704
0.498953704
0.7909
0.7404
0.0715
Percent
variable
Simulated
Discharge
Average
Measured
Discharge
(m3/s)
1.2898388
1.23933713
0.5704225
Difference
Average
Maesured
Discharge
and
Simulated
Discharge(
m3/s)
Average
Simulated
Discharge
(m3/s)
Average
Wind
Speed(m/s)
0.7wind×=1.764
1.5 wind× =3.78
Wind=2.52
126.12%
117.26%
0
2.10. Effect of Temperature
With a 50% increase and a 30% decrease in temperature, the average monthly runoff varied from .570
to .242 and .794, that is, a 57.56% increase and a 39.21% decrease in monthly runoff. The Simulated
results were 51% lower and 61.22% higher than the measuredaveragemonthly runoff (figures 16 &
17, table 8).
Fig16. Simulated discharge for varying temperature
Fig17. Simulated discharge for varyingtemperature
Table8. Simulated discharge for varying temperature
0.498953704
0.498953704
0.498953704
0.2952
0.2569
0.0715
Percent
variable
Simulated
Discharge
0.79410463
0.242062685
0.5704225
Difference
Average
Measured
Discharge
and
Simulated
Discharge
(m3/s)
Average
Measured
Discharge
(m3/s)
T×0.7 =7.7627395
T×1.5 =16.635587
T= 11.08963
Average
Simulated
Discharge
(m3/s)
Temperature(C)
39.21%
-57.57%
0
3. RESULTS
1. With a 13.43% increase in the curve number, the simulated average monthly runoff would be
2.51% close to the measured average runoff. With a 1.5% increase in the roughness coefficient of
watershed, the simulated runoff would come .01% closer to the measured discharge.
International Journal of Research Studies in Agricultural Sciences (IJRSAS)
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Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
Watershed, Mazandaran, Iran
2. A 30% decrease in average monthly precipitation, solar radiation, relative humidity, wind and
temperature would cause a 64.27% decrease, 114.67% increase, 45.93% decrease, 126.12%
increase and 39.21% increase in average monthly runoff, respectively. It is evident that
precipitation and relative humidity produce the most decreases. The most increase in runoff was a
function of wind, then solar radiation and finally temperature.
3. With a 50% increase in the average monthly precipitation, a 20% increase in radiation and relative
humidity and a 50% increase in wind and temperature, the runoff amount would experience a
125.31% increase, 3.9095% increase, 21.79% increase, 117.26% increase and 57.57% decrease,
respectively. Precipitation then wind and relative humidity cause the most increases. Runoff is
least sensitive to solar radiation.
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Soil Conservation Service Engineering Division.1986.Urban hydrology for small watersheds.U.S.
Department of Agriculture, Technical Release 55.U
Talebizadeh.M,( Winter 2009).Daily Sediment Load Estimation Using The SWAT Model and Artificial
Neural Network;CaseStudy:Kasilian Watershed.; TarbiatModares University Natural Science Faculty.
USDA Soil Conservation Service.1972. National Engineering Handbook Section 4 Hydrology , Chapter 410.
USDA Soil Conservation Service.1983. National Engineering Handbook Section 4 Hydrology , Chapter
19.
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Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
Watershed, Mazandaran, Iran
[20] Ostad-Ali-Askari, K., Shayannejad, M. 2015, Study of sensitivity of Autumnal wheat to under irrigation in
Shahrekord, Shahrekord City, Iran. International Journal of Agriculture and Crop Sciences, 8 (4), 602-605.
[21] Shayannejad, M., Akbari, N., Ostad-Ali-Askari, K. 2015, Study of modifications of the river physical
specifications on muskingum coefficients, through employment of genetic algorithm. International Journal
of Development Research, 5(3), 3782-3785.
[22] Ostad-Ali-Askari, K., Shayannejad, M. 2015, The Reviews of Einstein's Equation of Logarithmic
Distribution Platform and the Process of Changes in the Speed Range of the Karkheh River, Khuzestan
province, Iran. International Journal of Development Research, 5(3), 3786-3790.
[23] Ostad-Ali-Askari, K., Shayannejad, M., Ghorbanizadee-Kharazi, H. 2015, Assessment of artificial neural
network performance and exponential regression in prediction of effective rainfall, International Journal of
Development Research, 5(3),3791-3794.
[24] Shayannejad, M. Akbari, N. and Ostad-Ali-Askari, K. 2015, Determination of the nonlinear Muskingum
model coefficients using genetic algorithm and numerical solution of the continuity. Int. J. of Science:
Basic and Applied Research, 21(1),1-14.
[25] Ostad-Ali-Askari, K., Shayannejad, M. 2015, The Study of Mixture Design for Foam Bitumen and the
Polymeric and Oil Materials Function in Loose Soils Consolidation. Journal of Civil Engineering
Research, 5(2), 39-44. DOI: 10.5923/j.jce.20150502.04
[26] Sayedipour, M., Ostad-Ali-Askari, K., Shayannejad, M. 2015, Recovery of Run off of the Sewage
Refinery, a Factor for Balancing the Isfahan-Borkhar Plain Water Table in Drought Crisis Situation in
Isfahan Province-Iran. American Journal of Environmental Engineering, 5(2): 43-46. DOI:
10.5923/j.ajee.20150502.02
[27] Ostad-Ali-Askari, K., Shayannejad, M. 2015, Developing an Optimal Design Model of Furrow Irrigation
Based on the Minimum Cost and Maximum Irrigation Efficiency. International Bulletin of Water
Resources & Development, 3(2), 18-23.
[28] Ostad-Ali-Askari, K., Shayannejad, M. 2015, Presenting a Mathematical Model for Estimating the Deep
Percolation Due to Irrigation. International Journal of Hydraulic Engineering, 4(1), 17-21. DOI:
10.5923/j.ijhe.20150401.03.
[29] Ostad-Ali-Askari, K., Shayannejad, M. 2015, Usage of rockfill dams in the HEC-RAS software for the
purpose of controlling floods. American Journal of Fluid Dynamics, 5(1), 23-29. DOI:
10.5923/j.ajfd.20150501.03.
[30] Soltani, A. R., Ostad-Ali- Askari, K., Shayannejad, M. 2015, The effect of heterogeneity due to
inappropriate tillage on water advance and recession in furrow irrigation. Journal of Agricultural Science,
7(6), 127-136.
[31] oltani, A. R., Shayannejad, M., Ostad-Ali-Askari, K. 2015, Effects of magnetized municipal effluent on
some chemical properties of soil in furrow irrigation. International Journal of Agriculture and Crop
Sciences, 8(3), 482-489.
[32] Ostad-Ali-Askari, K., Shayannejad, M. 2015, Optimal design of pressurized irrigation laterals installed on
sloping land. International Journal of Agriculture and Crop Sciences, ISSN 2227-670X. 8(5), 792-797.
[33] Ostad-Ali-Askari K, Shayannejad M, Eslamian S, Navab-Pour B. 2016, Comparison of solution of SaintVenant equations by characteristics and finite difference methods for unsteady flow analyzing in open
channel. International Journal of Hydrology Science and Technology, 6(3), 9-18.
[34] Ostad-Ali-Askari K, Shayannejad M, Eslamian S, et al. 2017, Deficit Irrigation: Optimization Models.
Management of Drought and Water Scarcity.Handbook of Drought and Water Scarcity, Taylor & Francis
Publisher, USA.Vol. 3.1th Edition, pp: 373-389.
[35] Eskandari S, Hoodaji M, Tahmourespour A, Abdollahi A, Mohammadian-Baghi T, Eslamian S, Ostad-AliAskari K. 2017, Bioremediation of Polycyclic Aromatic Hydrocarbons by Bacillus Licheniformis ATHE9
and Bacillus Mojavensis ATHE13 as Newly Strains Isolated from Oil-Contaminated Soil. Journal of
Geography, Environment and Earth Science International, 11(2): 1-11.
[36] Shayannejad M, Soltani A.R, Ostad-Ali-Askari K, Eslamian S, et al. 2017, Development of a new method
for determination of infiltration coefficients in furrow irrigation with natural non-uniformity of slope.
Sustain. Water Resour. Manag., 3(2): 163-169.
[37] Shojaei N, Shafaei-Bejestan M, Eslamian S, Marani-Barzani M, P. Singh V, Kazemi M, Ostad-Ali-Askari
K. 2017, Assessment of Drainage Slope on the Manning Coarseness Coefficient in Mountain Area.
International Journal of Constructive Research in Civil Engineering (IJCRCE), 3(1): 33-40.
[38] Bahmanpour H, Awhadi S, EnjiliJ, Hosseini S.M, Eslamian S, Ostad-Ali-Askari K. 2017, Optimizing
Absorbent Bentonite and Evaluation of Contaminants Removal from Petrochemical Industries Wastewater.
International Journal of Constructive Research in Civil Engineering (IJCRCE), 3(2): 34-42.
International Journal of Research Studies in Agricultural Sciences (IJRSAS)
Page | 28
Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
Watershed, Mazandaran, Iran
[39] [39] Shayannejad M, Eslamian S, Gandomkar A, Marani-Barzani M, Amoushahi-Khouzani M, Majidifar
Z, Rajaei-Rizi F, Kazemi M, P. Singh V, Dehghan SH, Shirvani-Dastgerdi H.R, Norouzi H, Ostad-AliAskari K. 2017, A Proper Way to Install Trapezoidal Flumes for Measurements in Furrow Irrigation
Systems. International Journal of Research Studies in Agricultural Sciences (IJRSAS), 3(7): 1-5.
[40] DehghanSh, Kamaneh S.A.A., Eslamian S, Gandomkar A, Marani-Barzani M, Amoushahi-Khouzani M,
Singh V.P., Ostad-Ali-Askari K. 2017, Changes in Temperature and Precipitation with the Analysis of
Geomorphic Basin Chaos in Shiraz, Iran. International Journal of Constructive Research in Civil
Engineering (IJCRCE), 3(2): 50-57.
[41] Ostad-Ali-Askari K, Shayannejad M. 2016, FLOOD ROUTING IN RIVERS BY MUSKINGUM’S
METHOD WITH NEW ADJUSTED COEFFICIENTS. International Water Technology Journal, IWTJ,
6(3): 189-194.
[42] Ostad-Ali-Askari K, Shayannejad M, Ghorbanizadeh-Kharazi H. 2017, Artificial Neural Network for
Modeling Nitrate Pollution of Groundwater in Marginal Area of Zayandeh-rood River, Isfahan, Iran.
KSCE Journal of Civil Engineering, 21(1):134-140. Korean Society of Civil Engineers.DOI
10.1007/s12205-016-0572-8.
[43] Soltani A.R, Shayannejad M, Ostad-Ali-Askari K, Ramesh A, Singh V.P., Eslamian S. 2017, Wastewater
and Magnetized Wastewater Effects on Soil Erosion in Furrow Irrigation. International Journal of
Research Studies in Agricultural Sciences (IJRSAS), 3(8): 1-14. http://dx.doi.org/10.20431/24546224.0308001.
[44] Akhavan S., Abedi-Koupai, J, Mousavi, S, F., Afyuni, M., Eslamian, S. S. and K. C. Abbaspour, 2010,
Application of SWAT model to investigate nitrate leaching in Hamadan–Bahar Watershed, Iran,
Agriculture, Ecosystems and Environment, Vol. 139, 675-688.
[45] Bazrkar, M. H., Zamani, N., Eslamian, S. S., 2014, Investigation of Landuse Impacts on Sediment Yield
using a SWAT (Case Study: Chamgodalan Reservoir Watershed, Iran), Proceeding of 3rdScienceOne
International Conference on Environmental Sciences, UAE.
[46] Bazrkar, M.H., Sarang, A. and Eslamian, S.S., 2013, Application of swat for sediment load estimation in
Ghamgordlan reservoir watershed, 28-30 March, Perm, Russia.
[47] Fakhri, M., Eslamian, S. S., Rostamian, R., and Fazeli, I, 2012, A Review on Erosion and Sediment
Transfer Models with Emphasis on Sediment Modeling of Beheshtabad Sub-basin, North Karoon, using
SWAT Model, Ninth International Conference on River Engineering, Ahvaz.
[48] Akhavan, S., Abedi-Koupai, J., Mousavi, S. F., Abbaspour, K., Afyuni, M. and S. S. Eslamian, 2010,
Estimation of Blue Water and Green Water Using SWAT Model in Hamadan-Bahar Watershed, The
Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Science, Vol.
14, No. 53, 9-23.
[49] Shayannejad M, Soltani A.R, Arab M.A, Eslamian S, Amoushahi-Khouzani M, Marani-Barzani M, OstadAli-Askari K. 2017, A Simple Method for Land Grading Computations and its Comparison with Genetic
Algorithm (GA) Method. International Journal of Research Studies in Agricultural Sciences (IJRSAS),
3(8): 26-38.
[50] Mohieyimen P, Eslamian S, Ostad-Ali-Askari K, Soltani M. 2017, Climate Variability: Integration of
Renewable Energy into Present and Future Energy Systems in Designing Residential Buildings.
International journal of Rural Development, Environment and Health Research(IJREH), 1(2): 18-30.
[51] Eslamian, S. and F. Eslamian, 2017, Handbook of Drought and Water Scarcity, Vol. 1: Principles of
Drought and Water Scarcity, Francis and Taylor, CRC Group, USA, 660 Pages.
[52] Eslamian, S. and F. Eslamian, 2017, Handbook of Drought and Water Scarcity, Vol. 2: Environmental
Impacts and Analysis of Drought and Water Scarcity, Francis and Taylor, CRC Group, USA, 680 Pages.
[53] Eslamian, S. and F. Eslamian, 2017, Handbook of Drought and Water Scarcity, Vol. 3: Management of
Drought and Water Scarcity, Francis and Taylor, CRC Group, USA, 645. Pages.
[54] Angelakis, A. N., Chiotis, E., Eslamian, S., Weingartner, H., 2017, Underground Aqueducts Handbook,
Taylor and Francis Group, CRC Press, USA, 511 Pages.
[55] Zalewski, M., McClain, M. E., and Eslamian, S., 2016, New Challenges and Dimensions of Ecohydrology,
Part II Ecohydrology and Hydrobiology, Special Issue, Volume 16, Issue 2, Pages 71-124, Elsevier.
[56] Zalewski, M., McClain, M. E., and Eslamian, S., 2016, New Challenges and Dimensions of Ecohydrology,
Part I, Ecohydrology and Hydrobiology, Special Issue, Volume 16, Issue 1, Pages 1-70, Elsevier.
[57] Godarzi, A., Eslamian, S., Ostad-Ali-Askari, K., 2016, Water in Literature Aspects: Social and Cultural
Aspects, Nashreshahr, 135 Pages.
International Journal of Research Studies in Agricultural Sciences (IJRSAS)
Page | 29
Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
Watershed, Mazandaran, Iran
[58] Eslamian, S., Ostad-Ali-Askari, K., Salehi, M., Agha-Esmaeli, M., Sadeghi, M., Navabpour, B., MohriEsfahani, E., Mousavi-Madani, M., Zad-Bagher-Seighalani, E., Sadri, A., Shirvani-Dastgerdi, H. R., 2016,
Engineering Operations Research: Linear Planning, Optimization and Genetic Algorithm, Kankash, 126
Pages.
[59] Eslamian, S., Ostad-Ali-Askari, K., Shayannejad, M., Ghasemi-Zeniani, M., Marzi-Nohadani, M.,
Heidari, F., Mohri-Esfahani, E., Haeri-Hamadani, M., 2016., Groundwater Hydrodynamic, Horoufchin,
193 Pages.
[60] Ostad-Ali-Askari, K., Shayannejad, M., Eslamian, S., Jahangiri, A. A., Shabani, A. H., 2016,
Environmental Hydraulics of Open Channel Flows, Kankash, 332 Pages.
[61] Eslamian, S. S. and R. Mirabbasi, 2017, Application of Statistical Methods in Water Sciences, Aeij
Publishing, Tehran, Iran, Under Press.
[62] Eslamian, S, 2015, (ed.) Urban Water Reuse Handbook, Francis and Taylor, CRC Group, USA, 1141
Pages.
[63] Eslamian, S., 2014, (ed.) Handbook of Engineering Hydrology, Vol. 1: Fundamentals and Applications,
Taylor and Francis, CRC Group, USA, 636 Pages.
[64] Eslamian, S., 2014, (ed.) Handbook of Engineering Hydrology, Vol. 2: Modeling, Climate Change and
Variability, Taylor and Francis, CRC Group, USA, 646 Pages.
[65] Eslamian, S., 2014, (ed.) Handbook of Engineering Hydrology, Vol. 3: Environmental Hydrology and
Water Management, Taylor and Francis, CRC Group, USA, 606 Pages.
[66] Eslamian, S. S., 2013, Groundwater and Surface Water Interaction (GSWI): 3: Unconvenntional
Groundwater, International Journal of Water, Special Issue Volume, Indersciences, Vol. 7, No. 1/2, 1-141.
[67] Eslamian, S. S., 2011, Groundwater and Surface Water Interaction (GSWI): 2. Case Studies, International
Journal of Water, Special Issue Volume, Indersciences, Vol. 6, No. 1, 1-136.
[68] Eslamian, S. S., and S. TarkeshEsfahani, 2011, Water Reuse (Urban Waste Water Application),
ArkanDanesh Publishing, Isfahan, Iran, 327 Pages.
[69] Sharifani, M. M. and S. S. Eslamian, 2010, Humid Region Fruit Trees, Aeij Publishing, Tehran, Iran.
[70] Eslamian, S. S., 2009, Basin Ecology and Environment (BEE), International Journal of Ecological
Economic & Statistics, Ed., Special Issue Volume, CESER, Vol. 13, No. W09, 1-99.
[71] Eslamian, S. S., 2009, Groundwater and Surface Water Interaction (GSWI): 1. Quality, International
Journal of Water, Special Issue Volume, Indersciences, Vol. 5, No. 2, 81-204.
[72] Eslamian, S. S., 2009, Wind Modeling and Frequency Analysis (WMFA), International Journal of Global
Energy Issues, Special Issue Volume, Indersciences. Vol. 32, No. 3, 175-304.
[73] Eslamian, S. S., 2008, Stream Ecology and Low Flows (SELF), International Journal of Ecological
Economic & Statistics, Ed., Special Issue Volume, CESER, Vol. 12, No. F08, 1-97.
[74] Eslamian, S. S., Soltani S. and A. Zarei, 2005, Application of Statistical Methods in Environmental
Sciences, Arkan Publishing, Isfahan, Iran, 408 p.
[75] Eslamian, S. S. and S. Soltani, 2002, Flood Frequency Analysis, Arkan Publishing, Isfahan, Iran, 332 p.
[76] Eslamian, S. S., 1995, Regional Flood Frequency Analysis Using a New Region of Influence Approach,
Ph.D. Thesis, Univ. of New South Wales, School of Civil Engineering, Dept. of Water Engineering,
Sydney, NSW, Australia, 1995, Supervised by: Professor David H. Pilgrim, 380 P.
[77] Coles, N. A. and Eslamian, S., 2017, Definition of Drought, Ch. 1 in Handbook of Drought and Water
Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis
and Taylor, CRC Press, USA, 1-12.
[78] Dalezios, N. R., Dunkel, Z., Eslamian, S., 2017, Meteorological Drought Indices: Definitions, Ch. 3 in
Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by
Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 24-44.
[79] Goyal, M. K. Gupta, V., Eslamian, S., 2017, Hydrological Drought: Water Surface and Duration Curve
Indices, Ch. 4 in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water
Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 45-72.
[80] Dalezios, N. R., Gobin, A., Tarquis Alfonso, A. M., and Eslamian, S., 2017, Agricultural Drought Indices:
Combining Crop, Climate, and Soil Factors, Ch. 5 in Handbook of Drought and Water Scarcity, Vol. 1:
Principles of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC
Press, USA, 73-90.
International Journal of Research Studies in Agricultural Sciences (IJRSAS)
Page | 30
Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
Watershed, Mazandaran, Iran
[81] TishehZan, P. and Eslamian, S., 2017, Agricultural Drought: Organizational Perspectives, Ch. 6 in
Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by
Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 91-108.
[82] Bazrkar, M. H., Eslamian, S., 2017, Ocean Oscillation and Drought Indices: Application, Ch. 8 in
Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by
Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 127-136.
[83] Basu, R., Singh, C. K., Eslamian, S., 2017, Cause and Occurrence of Drought, Ch. 9 in Handbook of
Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by Eslamian S. and
Eslamian F., Francis and Taylor, CRC Press, USA, 137-148.
[84] Bazrafshan, J., Hejabi, S., Eslamian, S., 2017, Drought Modeling Examples, Ch. 11 in Handbook of
Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by Eslamian S. and
Eslamian F., Francis and Taylor, CRC Press, USA, 167-188.
[85] Jonathan Peter Cox, Sara ShaeriKarimi, Eslamian, S., 2017, Real-Time Drought Management, Ch. 13 in
Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by
Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 209-216.
[86] Garg, V. and Eslamian, S., 2017, Monitoring, Assessment, and Forecasting of Drought Using
Remote Sensing and the Geographical Information System. Ch. 14 in Handbook of Drought and Water
Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis
and Taylor, CRC Press, USA, 217-252.
[87] Dalezios, N. R., Tarquis Alfonso, A. M., and Eslamian, S., 2017, Drought Assessment and Risk Analysis,
Ch. 18 in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed.
by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 323-344.
[88] Dalezios, N. R., Spyropoulosand, N. V., Eslamian, S., 2017, Remote Sensing in Drought Quantification
and Assessment, Ch. 21 in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and
Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 377-396.
[89] Araghinejad, S., Hosseini-Moghari, S. M., Eslamian, S., 2017, Application of Data-Driven Models in
Drought Forecasting, Ch. 23 in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought
and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 423-440.
[90] Vafakhah, M., and Eslamian, S., 2017, Application of Intelligent Technology in Rainfall Analysis, Ch. 24
in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by
Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 441-460.
[91] Vafakhah, M., AkbariMajdar, H. and Eslamian, S., 2017, Rainfall Prediction Using Time Series Analysis,
Ch. 28 in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed.
by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 517-540.
[92] González, M. H., Garbarini, E. M., Rolla, A. L., and Eslamian, S., 2017, Meteorological Drought Indices:
Rainfall Prediction in Argentina, Ch. 29 in Handbook of Drought and Water Scarcity, Vol. 1: Principles of
Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA,
541-570.
[93] Hadizadeh, R. and Eslamian, S., 2017, Modeling Hydrological Process by ARIMA–GARCH Time Series,
Ch. 30 in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed.
by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 571-590.
[94] Mujere, N., Yang, X. and Eslamian, S., 2017, Gradation of Drought-Prone Area, Ch. 31 in Handbook of
Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by Eslamian S. and
Eslamian F., Francis and Taylor, CRC Press, USA, 591-606.
[95] MahmudulHaque, M., Amir Ahmed, A., Rahman, A., Eslamian, S., 2017, Drought Losses to Local
Economy, Ch. 33 in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water
Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 627-642.
[96] Fakhruddin, B. S. H. M., Eslamian, S., 2017, Analysis of Drought Factors Affecting the Economy, Ch. 34
in Handbook of Drought and Water Scarcity, Vol. 1: Principles of Drought and Water Scarcity, Ed. by
Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 643-656.
[97] Dalezios, N. R., Eslamian, S., 2017, Environmental Impacts of Drought on Desertification Classification,
Ch. 3 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and Analysis of
Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA,
45-64.
[98] Nazif, S. and Tavakolifar, H., Eslamian, S., 2017, Climate Change Impact on Urban Water Deficit, Ch. 5
in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and Analysis of Drought and
Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 81-106.
International Journal of Research Studies in Agricultural Sciences (IJRSAS)
Page | 31
Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
Watershed, Mazandaran, Iran
[99] Shahid, S., Alamgir, M., Wang, X.-J., Eslamian, S., 2017, Climate Change Impacts on and Adaptation to
Groundwater, Ch. 6 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and
Analysis of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC
Press, USA, 107-124.
[100] Orimoogunje, O. O. I., Eslamian, S., 2017, Minimizing the Impacts of Drought, Ch. 8 in Handbook of
Drought and Water Scarcity, Vol. 2: Environmental Impacts and Analysis of Drought and Water Scarcity,
Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 143-162.
[101] Maleksaeidi, H., Keshavarz, M., Karami, E., Eslamian, S., 2017, Climate Change and Drought: Building
Resilience for an Unpredictable Future, Ch. 9 in Handbook of Drought and Water Scarcity, Vol. 2:
Environmental Impacts and Analysis of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F.,
Francis and Taylor, CRC Press, USA, 163-186.
[102] Reyhani, M. N., Eslamian, S., Davari, A., 2017, Sustainable Agriculture: Building Social-Ecological
Resilience, Ch. 10 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and
Analysis of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC
Press, USA, 187 -204.
[103] Crusberg, T. C., Eslamian, S., 2017, Drought and Water Quality, Ch. 11 in Handbook of Drought and
Water Scarcity, Vol. 2: Environmental Impacts and Analysis of Drought and Water Scarcity, Ed. by
Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 205-218.
[104] Gaaloul, N., Eslamian, S., and Laignel, B., 2017, Contamination of Groundwater in Arid and Semiarid
Lands, Ch. 16 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and Analysis
of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA,
291-314.
[105] Banjoko, B., Eslamian, S., 2017, Sanitation in Drought, Ch. 17 in Handbook of Drought and Water
Scarcity, Vol. 2: Environmental Impacts and Analysis of Drought and Water Scarcity, Ed. by Eslamian S.
and Eslamian F., Francis and Taylor, CRC Press, USA, 315-330.
[106] Davari, A., Bagheri, A., Reyhani, M. N., Eslamian, S., 2017, Environmental Flows Assessment in Scarce
Water Resources, Ch. 18 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and
Analysis of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC
Press, USA, 331-352.
[107] Qian, Q., Eslamian, S., 2017, Streamflow Quality in Low-Flow Conditions, Ch. 20 in Handbook of
Drought and Water Scarcity, Vol. 2: Environmental Impacts and Analysis of Drought and Water Scarcity,
Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 375-386.
[108] MohammadzadeMiyab, N., Eslamian, S., Dalezios, N. R., 2017, River Sediment in Low Flow Condition,
Ch. 21 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and Analysis of
Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA,
387-408.
[109] Pérez-Blanco, C. D., Delacámara., G., Gómez., C. M., Eslamian, S., 2017, Crop Insurance in Drought
Conditions, Ch. 23 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and
Analysis of Drought and Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC
Press, USA, 423-444.
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[112] Rahman, A., Hajani, E., Eslamian, S., 2017, Rainwater Harvesting in Arid Regions of Australia, Ch. 26 in
Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and Analysis of Drought and
Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 489-500.
[113] Mukherjee, S., Yadav, K., Eslamian, S., 2017, Soil Contaminations in Arid and Semiarid Land, Ch. 29 in
Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and Analysis of Drought and
Water Scarcity, Ed. by Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 547-556.
[114] Dayani, S., Sabzalian, M. R., Hadipour, M. Eslamian, S., 2017, Water Scarcity and Sustainable Urban
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Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
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River Basin, Ch. 32 in Handbook of Drought and Water Scarcity, Vol. 2: Environmental Impacts and
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[117] Abbasova, D., Eslamian, S., Nazari, R., 2017, Paleo-Drought: Measurements and Analysis, Ch. 34 in
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[118] Yihdego, Y., Eslamian, S., 2017, Drought Management: Initiatives and Objectives, Ch. 1 in Handbook of
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[119] Tuncok, I. K., Eslamian, S., 2017, Drought Management Strategies in Water-Stressed/Water-Scarce
Regions, Ch. 5 in Handbook of Drought and Water Scarcity, Vol. 3: Management of Drought and Water
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[120] Reinstädtler, S., Islam, S. N., Eslamian, S., 2017, Drought Management for Landscape and Rural Security,
Ch. 8 in Handbook of Drought and Water Scarcity, Vol. 3: Management of Drought and Water Scarcity,
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[121] Dalezios, N. R., Eslamian, S., 2017, Drought Assessment and Management for Heat Waves Monitoring,
Ch. 9 in Handbook of Drought and Water Scarcity, Vol. 3: Management of Drought and Water Scarcity,
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[122] Kruse, E., Eslamian, S., 2017, Groundwater Management in Drought Conditions, Ch. 11 in Handbook of
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[123] Araghinejad, S., Hosseini-Moghari, S.-M., Eslamian, S., 2017, Reservoir Operation during Drought, Ch.
12 in Handbook of Drought and Water Scarcity, Vol. 3: Management of Drought and Water Scarcity, Ed.
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[124] Eslamian, S., Khosravi, B., Sayahi, M., Haeri-Hamedani, M. 2017, Crises Management Planning and
Drought Management Plans, Ch. 13 in Handbook of Drought and Water Scarcity, Vol. 3: Management of
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293-304.
[125] Halbac-Cotoara-Zamfir, R., Eslamian, S., 2017, Functional Analysis of Regional Drought Management,
Ch. 14 in Handbook of Drought and Water Scarcity, Vol. 3: Management of Drought and Water Scarcity,
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[126] Zahraei, A., Saadati, S., Eslamian, S., 2017, Irrigation Deficit: Farmlands, Ch. 16 in Handbook of
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[127] Amiri, M. J., Eslamian, S., Bahrami, M., Yousefi, N. 2017, Deficit Irrigation: Greenhouse, Ch. 17 in
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Eslamian S. and Eslamian F., Francis and Taylor, CRC Press, USA, 359-372.
[128] Ostad-Ali-Askari, K., Shayanejad, M., Eslamian, S., Zamani, F., Shojaei, N., Navabpour, B., Majidifard,
Z., Sadri, A., Ghasemi-Siani, Z., Nourozi, H., Vafaei, O., Homayouni. S.-M.-A., 2017, Deficit Irrigation:
Optimization Models, Ch. 18 in Handbook of Drought and Water Scarcity, Vol. 3: Management of
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[129] Eludoyin, A. O., Eludoyin, O. M., Eslamian, S., 2017, Drought Mitigation Practices, Ch. 19 in Handbook
of Drought and Water Scarcity, Vol. 3: Management of Drought and Water Scarcity, Ed. by Eslamian S.
and Eslamian F., Francis and Taylor, CRC Press, USA, 391-402
[130] Irshad, S. M., Eslamian, S., 2017, Politics of Drought Management and Water Control in India, Ch. 22 in
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Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters: A Case Study of Kasillian
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[133] Sedaei, L., Sedaei, N., Cox, J. P., Dalezios N. R., Eslamian, S., 2017, Forest Fire Mitigation under Water
Shortage, Ch. 26 in Handbook of Drought and Water Scarcity, Vol. 3: Management of Drought and Water
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Citation: M. Ghane et al., “Sensitivity Analysis of Runoff Model by SWAT to Meteorological Parameters:
A Case Study of Kasillian Watershed, Mazandaran, Iran", International Journal of Research Studies in
Agricultural Sciences (IJRSAS), vol. 3, no. 10, p.20, 2017. http://dx.doi.or g/10.20431/2454-6224.0310003
Copyright: © 2017 Authors. This is an open-access article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium,
provided the original author and source are credited.
International Journal of Research Studies in Agricultural Sciences (IJRSAS)
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