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Original Article
Received:
Revised:
Accepted:
Nov/18/2022
Dec/23/2022
Dec/29/2022
Journal of
Drought and Climate change Research (JDCR)
Summer 2023, Vol. 1, No. 2, pp 27-40
10.22077/JDCR.2022.5797.1000
Copyright: © 2022 by the authors. Licensee Journal of Drought and Climate change Research (JDCR). This article is an open access article diributed
under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
How to cite this article:
Khodabandeh Baygi, R., Ghezelsooo, A.A., Eftekhari, M., & Ragoo, M. (2023). Impacts of Climate Change on the Statiical Trends of
Monthly and Annual Flood Discharges in the Kashafrood River Basin Iran. Journal of Drought and Climate change Research (JDCR), 1(2),
27- 40. 10.22077/JDCR.2022.5797.1000.
Abs tract
Global warming has caused the climate of the planet to lose its equilibrium in recent
decades, so climate change has attracted a great deal of attention in the s tudy of climate
change. Long-term s tatis tics can demons trate dramatic shifts in climate. The purpose
of this s tudy is to examine the s tatis tical trends of monthly and annual series, ood
discharge of the Kashafrood river basin in Iran. Therefore, the s tatis tical data of the
las t 65 years related to the monthly and annual series of the s tation were examined.
Mann-Kendall tes t was used to check whether it was random or non-random, for the
trend, type and time of change, which was observed in 5 months of an increasing trend
and in 7 months of sudden jump. Mann-Whitney and Kruskal-Wallis tes ts were used
to evaluate the signicance of jump points, which were signicant in mos t months
except November and June.
Keywords:
Climate Change, Flood,
Kashafrood, Mann-Kendall,
Mann-Whitney, Trend.
Impacts of Climate Change on the Statis tical Trends of Monthly and
Annual Flood Discharges in the Kashafrood River Basin Iran
Reza Khodabandeh Baygi1, Abbas Ali Ghezelsooo2, Mobin Eftekhari 3*, Melika Ras tgoo4
1. M.Sc., Civil Engineering, Water and Hydraulic Structures,Mashhad Branch, Islamic Azad University, Mashhad, Iran.
2. Associate Professor, Civil Engineering, Water and Hydraulic Structures, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
3. M.Sc., Civil Engineering, Water and Hydraulic Structures. Young Reearchers and Elite Club, Mashhad Branch, Islamic Azad
University, Mashhad, Iran.
4. MSc. Student of Engineering and Water Resources Management, Faculty of Civil and Environmental Engineering, Tarbiat Madras
University, Tehran, Iran.
*Corresponding Author: [email protected]
28
Journal of Drought and Climate change Research (JDCR)
Summer 2023, Vol. 1, No. 2, pp 27-40
Khodabandeh Baygi et al.,
Introduction
Climate is the prevailing weather
conditions over a long period of time. In
fact, the climate of a region is made up of a
set of elements and factors that result from
the changes in each element due to climatic
factors. This creates special conditions in
terms of weather that are unique for each
region (Doulabian et al., 2021).
Although the ow regime shows seasonal
variations in river ow, it does not provide
accurate information on the magnitude
and frequency of oods and droughts. The
s tudy of ood behavior is very important
because they are able to carry signicant
amounts of sediment and play an inuential
role in canal formation.
Climate change has led to changes in the
worldwide hydrological regime in recent
decades, so that the likelihood of exposure
to maximum climatic events such as
oods has increased. Since increasing this
probability for future periods could have
detrimental eects on human societies,
in recent years, research on this issue has
been considered for various catchments
around the world (HassanMohammed et
al., 2021; Eslaminezhad et al., 2022).
Hydrological hazards occur as a result of
changes in the frequency and intensity of
rainfall, rising temperatures and changes
in land use. Global warming has led
to increasing rainfall and decreasing
snowfall in winter, rising the river levels
and increasing river discharges. This
increase causes life and property losses
and increases the likelihood of ooding.
Therefore, by examining the factors
aecting the change in ood intensity
and appropriate management practices
such as the development of watershed
management, fores try, etc., the volume of
ood damage can be reduced in the basin.
Changes in climatic parameters such as
precipitation aect the river ow regime.
Given the impact of climate change on
human life, an eort to unders tand more
about climate change events is essential.
The metropolis of Mashhad has a wide
geographical location in the alluvial plain.
Being located between the sedimentary
heights of Kopehdagh and Hezarmasjed
has led to the creation of a special
morphology for the alluvial plain of
Mashhad. The Kashafrood River drains
all the rivers of Mashhad plain and the
drainage rivers of the Binalood heights
pass through Mashhad city and discharge
into the Kashafrood River. The physical
development of the city over the years
has led to changes in the morphology of
the city’s rivers, and in some areas on
the riverbeds, high-rise s tructures have
been built with high importance and
have increased the likelihood of hazards
(Azamizade et al., 2021).
The s tructures that have been created due
to the beautiful landscape in the highlands
of the metropolis of Mashhad are built
exactly on waterways. As a result of
the reduction in water inltration, the
runo volume created is generally high,
which in turn increases the likelihood of
ooding and its severity in residential and
commercial areas. These issues make the
city vulnerable to increases periodic oods
(Eslaminezhad et al., 2020).
Ghorbani (2002) used 40 years of
meteorological s tatis tics at Gorgan S tation
to s tudy climate change. The rate of change
in temperature and precipitation was
inves tigated by the simple linear regression
method. According to the results, no
signicant changes in temperature were
observed but rainfall decreased.
In our country Azizi et al. (2004) s tudied
the presence or absence of temperature and
precipitation trends.
Asgari et al. (2006) s tudied the trend of
eleven precipitation indices across the
country and found that in about two-thirds
of countries, the annual precipitation index
had a negative and positive trend in wet
days and heavy rainfall days, respectively.
Hosseinzadeh et al. (2007) inves tigated the
issue of oods and ooding in Mashhad
29 Impacts of Climate Change on the Statiical ...
Journal of Drought and Climate change Research (JDCR)
Summer 2023, Vol. 1, No. 2, pp 27-40
and expressed the indirect eects of urban
expansion within catchment areas and the
possibility of ood intensication in the
urban context.
Avand et al. (2011) Inves tigated the eects
of climate change and land use on ood
prone areas in the Tajan watershed of Iran.
The results showed that elevation (21.55),
dis tance from the river (15.28), land use
(11.1), slope (10.58), and rainfall (6.8)
are the mos t important factors aecting
ooding in this basin. The factors were
modied according to land use changes
and climate changes and the models were
revised. Landuse and climate forecas ting
in this region indicate that land use change,
like decreased fores t cover (−77.19 km2)
and reduced rangeland (−218.83 km2) near
rivers and downs tream, can be expected
and rainfall is projected to increase (under
both scenarios). These changes would
result in increased probabilities of ooding
in the downs tream portion of the watershed
and near the sea.
Darand et al. (2013) inves tigated the
temperature and precipitation behavior at
Kermanshah s tation. The results showed
that cold extreme indices in Kermanshah
are decreasing while extreme warm indices
are increasing.
Sayemuzzaman et al. (2014) examined the
trend of seasonal and annual rainfall in
North Carolina, USA. In order to analyze
the annual and seasonal temporal and spatial
trends, they used the Mann- Kendall tes t for
uniform dis tribution of 249 precipitation
data in North Carolina during the s tatis tical
period (1950–2009), respectively, to
determine its trend and signicance. The
local trend of precipitation (mountainous,
foothills and coas tal) was also determined
by the tes ts mentioned above. Before using
s tatis tical tes t, pre-bleaching method was
used to eliminate the correlation eect of
precipitation data series. The results show a
signicant increasing and decreasing trend
in winter and autumn rainfall, respectively.
For annual, spring and summer rainfall, a
combined (increasing/decreasing) trend
has been identied. Signicant trends were
detected in only 8, 7, 4 and 10 s tations out
of 249 s tations in winter, spring, summer
and autumn, respectively. The amount
of annual precipitation varies between
5.5 to 9 mm per year. In spatial trend
analysis, increased precipitation has been
recorded in mountainous and coas tal areas
throughout the period except in winter. In
the foothills area, the trend is rising in the
summer and autumn, but decreasing in the
winter and spring. Modarres et al. (2016)
s tudied the great changes in the ood and
the severity of drought in Iran for the years
1950 to 2010, with changes in the time
interval in some s tations. Both increasing
and decreasing trends were observed for
drought severity and ood magnitude in
dierent climate regions and major basins
of Iran using these tes ts. The increase in
ood magnitude and drought severity can
be attributed partly to land use changes, an
annual rainfall negative trend, a maximum
rainfall increasing trend, and inappropriate
water resources management policies.
Waikar (2014) has calculated that the rate
of water discharge in a drainage basin
has an inverse relationship with drainage
density. In this case, factors such as walling
the banks of oodways and removing
the mazes change the characteris tics and
morphology of the river.
Ahmad et al. (2015) inves tigated the
precipitation changes at 15 s tations in
Pakis tan’s Swat River basin over a 51-
year period (1961-2011). They used
nonparametric Mann-Kendall and
Spearman s tatis tical tes ts to detect monthly,
seasonal, and annual precipitation trends.
The results of monthly, seasonal, and
annual precipitation show a combination
of positive (incremental) and negative
(detractive) trends. One s tation in
particular, Sayed Sharif s tation, recorded
the mos t signicant monthly rainfall. On a
seasonal time scale, the precipitation trend
has changed from summer to autumn.
30
Journal of Drought and Climate change Research (JDCR)
Summer 2023, Vol. 1, No. 2, pp 27-40
Khodabandeh Baygi et al.,
Sayed Sharif s tation showed the highes t
positive trend (7.48 mm/year) in annual
precipitation. Across the Swat River
basin, a s tatis tically signicant trend was
found for the annual rainfall series. The
lower Swat basin, however, showed the
maximum increase in precipitation (2.18
mm/year). Also, the performance of Mann-
Kendall and Spearman tes ts was cons tant
at the signicantly conrmed level.
Zhou et al. (2019) examined the eects of
recent urban development on hydrological
runo and urban ood volume in a large
city in northern China, and compare
the eects of urbanization with the
eects of climate change under two
representative focus paths (RCPs 2.6 and
8.5). They then map the urban drainage
sys tem to reduce ood volume for future
adaptation s trategies. The results show that
urbanization has led to an annual increase
in surface runo of 208 to 413%, However,
changes in urban ood volume can vary
greatly depending on the performance of
the drainage sys tem during development.
In particular, urbanization changes in the
expected annual ood volume range from
194 to 942 percent, which is much greater
than the eects of climate change under
the RCP 2.6 scenario (64 to 200 percent).
Sun et al. (2021) examined an urban
rains torm model and a scenario simulation
method in downtown Shanghai. Firs t,
the ood risks in the s tudy area under
the inuence of future climate change
were inves tigated using a simulation with
dierent rainfall return periods. They
then evaluated the benets of traditional
drainage sys tem adaptation measures and
low-impact development (LID) practices
in reducing urban ood risks. The results
show that the volume of urban oods
increases non-linearly with increasing
rainfall intensity under climate change.
The maximum ood zone increases
accordingly and is much more sensitive
to smaller rainfall events. Both traditional
drainage adaptation measures and LID
practices can eectively reduce oods.
Pal et al. (2022) explore future oods
in India due to climate change and land
use. Human activities and related carbon
emissions are a major cause of land
use and climate change, which has a
signicant impact on oods. They provide
ood sensitivity maps for various future
periods (up to 2100) using a combination
of remote sensing data and GIS modeling.
To quantify future ood susceptibility
to various ooding factors, the Global
Circulation Model (GCM) of precipitation
and land use and land cover (LULC)
data are predicted. They evaluated the
current ood sensitivity model through the
receiver performance characteris tic curve
(ROC), in which the area under the curve
(AUC) shows 91.57% accuracy of this
ood sensitivity model and can be used
to model future ood sensitivity. Based
on the predicted LULC, rainfall and ood
sensitivity, the results of the s tudy indicate
that the maximum monthly rainfall in
2100 will increase by approximately 40
to 50 mm, while the conversion of natural
vegetation into agricultural land and is
about 0.071 million m2 and the area of
severe ooding will now increase to 122%
(0.15 million km2).
A ood is considered as one of the
Natural disas ters and the limiting factor of
development, especially along coas tlines
and river banks. Therefore, s tudies
of ooding and ood prevention are
important in water resources management.
It is benecial to identify ood-prone areas
in watersheds when planning infras tructure
programs for rural, agricultural and
indus trial development.
Due to the fact that the Kashafrud watershed
has critical and supercritical ood potential
in some areas, especially in Mashhad and
Chenaran, the importance of s tudying
changes in the intensity and duration of
oods increases. Kashafrud river basin is a
part of Qaraqoom catchment in Iran. This
catchment is located in the northeas t of the
31 Impacts of Climate Change on the Statiical ...
Journal of Drought and Climate change Research (JDCR)
Summer 2023, Vol. 1, No. 2, pp 27-40
country and in the north of Khorasan Razavi
province. It has an area of 15,650 km2.
In the Kashfrud watershed, several authors
conducted s tudies on ooding. BaniWaheb
et al. (2006) inves tigated regional
ooding in the Wes tern Kashafrud river
basin. The results showed that among the
factors aecting the ood, the average
of maximum 24-h precipitation and the
percentage of area covered by vegetation
had a more signicant eect on the
maximum ins tantaneous discharge values
than other factors.
Barati et al. (2011) analyzed the regional
ood frequency in the Kashafrud catchment
using the linear moment method. Based on
the linear moment diagram and the Zdis t
s tatis tic, the dis tribution of generalized
limit values has been identied as an
appropriate dis tribution for the s tudy area.
Sayari et al. (2011) compared two models
of general atmospheric circulation in
predicting climatic parameters and water
needs of plants under climate change in the
Kashafrud River basin. The results showed
that the average annual rainfall with the
model (CGCM2) and two scenarios (A2,
B2) decreased by 13 and 16%, respectively.
But for the model (HADCM3), the average
annual rainfall was increased by 2 and 8%.
The model (CCCM2) and two scenarios
(A2 and B2) decreased by 13 and 16
percent.
Azamizadeh et al. (2019) inves tigated
the ooding potential of Kashafrud river
basin of Mashhad by SCS method in CIS
environment. The results showed that
68.25% of total basin area has normal
ood potential, 25.5% is critical, 6.25%
has supercritical ood potential.
Helmi and Shahidi inves tigated the impact
of drought on the water quality of Kashfroud
river using precipitation, temperature,
and quality data from six s tations over a
30-year period. They found that during
drought, water quality parameters such as
TDS, EC, Ca, Mg, Na, SO4-, HCO3-, and
Cl- increased signicantly compared to
the long-term average. The concentration
of Cl- reached a maximum of 7.66 mg/l
at Olang Asadi s tation. Additionally, the
increase in temperature during drought
led to the highes t water quality changes
at Olang Asadi s tation. Overall, the
s tudy concludes that drought, coupled
with reduced rainfall and increased
temperature, results in decreased water
quality, particularly downs tream.
The purpose of this s tudy was to examine
the s tatis tical trend of monthly and annual
series, ood discharges of the Kashafrud
river basin. Therefore, s tatis tical data of
the las t 65 years related to the monthly and
annual series of the s tation were examined
using Excel and SPSS software.
Materials and Methods
Case s tudy
This s tudy area is part of the Qaraqoom
catchment in Iran, located in the northeas t
of the country and in the north of the
Khorasan Razavi province, with an area of
15,650 km2.
It is bounded to the north by the ridge of
the Hezarmasjed Kopehdagh mountains,
to the south by the Binalood mountains,
to the wes t by the Khajeh Ali, Poshteh
Par and Shah Jahan mountains, and to the
eas t by the Harirod River. The river that
drains this basin is called the Kashafrud
River. A major tributary originates from
Khajeh Ali, Poshteh Par and Shah Jahan
Mountain, located in the Hezar Masjed
and Binalood Mountains in the eas t of
Quchan city, before owing from wes t to
eas t. After crossing the Mashhad plain, the
Kashafrood River crosses the Mashhad-
Sarakhs road and enters the narrow valley
(AqDarband) in the south of the village of
Mozdoran. After leaving it, it joins Harirod
in a place called Pol Khatun on the border
of Turkmenis tan and forms the Tajan River.
In order to detect climate change from
a s tatis tical point of view, special
methods are used. This method provides
a comprehensive overview in addition
32
Journal of Drought and Climate change Research (JDCR)
Summer 2023, Vol. 1, No. 2, pp 27-40
Khodabandeh Baygi et al.,
to providing many facts, and it is more
valuable to s tudy these methods together.
In this research, the s tatis tical analysis of
monthly and annual oods in Cosford river
basin was inves tigated.
In climate change s tudies, long-term
s tatis tics can show change, how, and its
characteris tics to a large extent. Monthly
and annual ood discharge data for the
las t 65 years were entered into Excel
software and the Mann-Kendall tes t was
used to determine the randomness or non-
randomness of the series and
then to determine the direction, type
and time of change, the Mann- Kendall
graphical tes t was used.
Mann-Kendall tes t
In this tes t, rs t the time series, arranged in
ascending order of the year, were entered
into Excel software and the ood discharge
data related to each series were ranked and
Mann- Kendall’s s tatis tical equation (1)
was entered in Excel software (Shaikh et
al., 2022).
= 4
( − 1)
(1)
(1)
T is the Mann-Kendall s tatis tic and p is the
sum of the ranks greater than T=4p/(n(n-1)),
and n is the total number of S tatis tical years.
The following equation is used to assess
the signicance of T-s tatis tic
(2)
=∓tan√4+10
−1
(2)
Where tg is for the critical value of the
normal or s tandard score (z) with the
tes t probability level and with the 95%
probability level, it is equal to1.96. If this
value is applied Tt, will be obtained.
If+(T)t>T>-(T)t, no trend is observed
in the series and the Serie is random. If
T<-(T)t represents a negative trend in the
series and if T<(T)t, the positive trend in
the series will prevail (Azizi and Roshani,
2008).
Mann-Kendall graphical tes t
It is necessary to use the Mann-Kendall
graphical tes t to determine the direction
and type of change. For this purpose, a
special table is used. In this table, rs t,
the s tatis tical data are entered in the order
of the year, and in the second column,
the data are given a row number. Then,
in the third column, the values of the
desired parameter are entered and ranked
(to identify the s tarting points of the time
series trend, the time series diagram in
terms of the values u(t) and u`(t) is used.
There are 12 computational s teps for
calculating this tes t, which 6 s teps are
required for calculating u(t) and six s teps
are required to calculating u`(t).
S tep one: Determine rankings for variables.
S tep two: the number of ranks smaller than
each row ti that is placed before it.
S tep three: Use this formula to calculate
this s tep Ei. In terms of the null hypothesis,
its dis tribution function is asymptotically
equal to the mean and variance.
ni: Row number of each variable
Figure 1: Geographical location of the region in (A) Iran, (B) province and (C) Qaraqoom catchment
Figure 1. Geographical location of the region in (A) Iran, (B) province and (C) Qaraqoom catchment
33 Impacts of Climate Change on the Statiical ...
Journal of Drought and Climate change Research (JDCR)
Summer 2023, Vol. 1, No. 2, pp 27-40
=(− 1)
4
(3)
(3)
S tep four: To calculate the variance ⅴi, we
will use the following equation.
ni: Row number.
=((− 1).(2 × + 5)
72
(4)
(4)
S tep ve: Calculate the cumulative density
of Ti, add each number to the previous
number. This is called zti
S tep Six: The following equation is used
to obtain u(t).
()=(− )
0.5
(5)
(5)
These six s teps are jus t calculation of u (t).
In order to determine the time of change,
in addition to u (t), the component of u
(t) mus t be calculated. To calculate u (t),
simply reverse the rating of the data and
perform the above six s teps to obtain u (t).
The successive values of ui and u i obtained
from the Mann-Kendall tes t are displayed
graphically (Excel). If the values of ui and
u i overlap several times, there is no trend
or change, but where the curves intersect
each other’s, the trend or changes are seen.
If the curves intersect each other’s within
the critical range, they indicate the time
of onset of sudden change, and if they
intersect each other’s outside the critical
range, they indicate a trend in the time
series.
Extent and signicance level of changes
Regression analysis is a s tatis tical process
for es timating the relationships between
variables. This method includes many
techniques for modeling and analyzing
specic and unique variables.
When determining the amount of changes
in trends and sudden jumps, the linear
regression method was used for the series
that showed changes in the tes t to examine
the correlation between the independent
and dependent variables. The s tatis tical
method of regression analysis is used to
inves tigate the trend of meteorological
parameters. The purpose of regression
analysis is to predict the dependent
variable through the independent variable.
Therefore, the application of regression
analysis in climate s tudies is measurement,
analysis and prediction.
In the measuring s tage, the data should be
evaluated for regression analysis. Then,
the conditions in each s tation should be
evaluated. Finally, the relationship and the
degree of change of the variables should be
predicted. The coecient of determination
(R to the power of 2) shows the percentage
of changes in the dependent variable by the
independent variables and is explained by
this regression model.
The level of signicance (type 1 error)
is a measure known as the signicance
level. If the relationship between the
variables is less than 0.05, the probability
of the relationship being by chance is very
low and the relationship is signicant,
However, if it is more than 0.05, the
relationship is likely to be random and not
signicant. Sig is the amount of error we
commit in rejecting the initial assumption.
The condence level shows the frequency
of the condence interval that includes
the desired parameter. In this research, the
calculations were done at a signicance
level of 5% and a condence level of
95%. The signicance of jump points was
evaluated by Mann-Whitney and Kruskal-
Alice tes ts.
Mann-Whitney tes t for one jump and
Kruskal-Wallis tes t for multiple jumps
were used to evaluate the signicance of
jump points. These tes ts are available in
SPSS software and performed with SPSS.
The Mann-Whitney tes t is one of the
hypothetical s tatis tical tes ts between
two independent groups. This tes t is a
nonparametric and is not suitable for data
with a normal dis tribution. The Kruskal-
Wallis tes t is a nonparametric tes t used to
compare three or more independent groups,
which are measured at three rating levels.
34
Journal of Drought and Climate change Research (JDCR)
Summer 2023, Vol. 1, No. 2, pp 27-40
Khodabandeh Baygi et al.,
Results and Discussion
Results of series randomness
In all the monthly and annual series for the
las t 65 years, the Mann-Kendall s tatis tical
value is greater than the critical value at
the AqDarband S tation, which shows
trends and changes.
Table 1. Mann-Kendall statistics values for AqDarband station
month
AgDarband station
January
0.539
February
0.469
March
0.704
April
0.749
May
0.779
June
0.807
July
0.882
August
1.035
September
1.117
October
1.17
November
0.981
December
0.684
Annual
0.691
critical
0.166
Mann- Kendall Graphical tes t
The results of the Mann- Kendall Graphical
Tes t are presented in Table 2 and Figures
2-14. If the UV prime lines intersect within
the critical range, it indicates a sudden
jump, and if they intersect outside the
critical range, it indicates a trend.
Graphical Mann- Kendall Tes t
In October 1954, the diagrams of u and
u intersect each other out of the critical
range, which indicates an increasing trend.
In November 1960 and 1971, the diagrams
of u and u were overlapped each other
which shows that there is no trend. In
November 1981, the diagrams of u and u
intersected each other outside the critical
range, which shows an increasing trend.
In December 1966, the diagrams of u and
u intersect each other within the critical
range, which shows a sudden jump and
change. In February 1954 and 1960 the
diagrams of u and u intersect within the
critical range, indicating a sudden jump
and change. In March 1960, the diagrams
of u and u intersect each other within the
critical range, which shows a sudden jump
and change. In April 1964, 1970, 1974 and
1981, the diagrams of u and u intersect
each other within the critical range, which
shows a sudden jump with oscillation. In
May 1956, 1960 and 1974, the diagrams
of u and u intersect each other within the
critical range, which shows a sudden jump.
In June1961 and 1971, the diagrams of
u and u intersect each other within the
critical range, which shows a sudden jump
and change. In July1955, the diagrams of
u and u intersect each other each other
outside the critical range, which shows
an increasing trend. In Augus t 1956, the
diagrams of u and u intersected each other
outside the critical range, which shows
an increasing trend. In September 1956,
the diagrams of and u intersect each other
outside the critical range, which shows an
35 Impacts of Climate Change on the Statiical ...
Journal of Drought and Climate change Research (JDCR)
Summer 2023, Vol. 1, No. 2, pp 27-40
0
1
2
3
4
5
6
1950 1970 1990 2010
flood discharge (m3/s)
year
october
Ui
UI*
-0/5
0
0/5
1
1/5
2
2/5
3
3/5
1950 1970 1990 2010
flood discharge (m3/s)
year
November
ui
ui*
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
1950 1970 1990 2010
flood discharge (m3/s)
year
December
ui
ui*
-8
-6
-4
-2
0
2
4
6
8
1950 1970 1990 2010
flood discharge (m3/s)
year
January
ui
ui*
-8
-6
-4
-2
0
2
4
6
8
1950 1970 1990 2010
flood discharge (m3/s)
year
February
Ui
ui*
-5
-4
-3
-2
-1
0
1
2
3
4
1950 1970 1990 2010
flood discharge (m3/s)
year
March
Ui
ui*
Figure 2. flood discharge (graphic Mann-Kendall test) in all months
-4
-3
-2
-1
0
1
2
3
4
1950 1970 1990 2010
flood discharge (m3/s)
year
April
Ui
ui*
-4
-3
-2
-1
0
1
2
3
1950 1970 1990 2010
flood discharge (m3/s)
year
May
Ui
ui*
-4
-3
-2
-1
0
1
2
3
1950 1970 1990 2010
flood discharge (m3/s)
year
June
Ui
ui*
-2
-1
0
1
2
3
4
5
6
7
1950 1970 1990 2010
flood discharge (m3/s)
year
July
Ui
ui*
0
1
2
3
4
5
6
7
1950 1970 1990 2010
flood discharge (m3/s)
year
August
Ui
ui*
0
1
2
3
4
5
6
7
8
1950 1970 1990 2010
flood discharge (m3/s)
year
September
Ui
ui*
36
Journal of Drought and Climate change Research (JDCR)
Summer 2023, Vol. 1, No. 2, pp 27-40
Khodabandeh Baygi et al.,
Figure 2. flood discharge (graphic Mann-Kendall test) in all months
-4
-3
-2
-1
0
1
2
3
4
1950 1970 1990 2010
flood discharge (m3/s)
year
April
Ui
ui*
-4
-3
-2
-1
0
1
2
3
1950 1970 1990 2010
flood discharge (m3/s)
year
May
Ui
ui*
-4
-3
-2
-1
0
1
2
3
1950 1970 1990 2010
flood discharge (m3/s)
year
June
Ui
ui*
-2
-1
0
1
2
3
4
5
6
7
1950 1970 1990 2010
flood discharge (m3/s)
year
July
Ui
ui*
0
1
2
3
4
5
6
7
1950 1970 1990 2010
flood discharge (m3/s)
year
August
Ui
ui*
0
1
2
3
4
5
6
7
8
1950 1970 1990 2010
flood discharge (m3/s)
year
September
Ui
ui*
Figure 2. ood discharge (graphical Mann-Kendall tes t) in all months
Figure 3. Annual chart of flood discharge (graphic Mann-Kendall test)
-5
-4
-3
-2
-1
0
1
2
3
4
5
1950 1970 1990 2010
flood discharge (m3/s)
year
annual
Ui
Ui*
Figure 3. Annual chart of ood discharge (graphical Mann-Kendall tes t)
37 Impacts of Climate Change on the Statiical ...
Journal of Drought and Climate change Research (JDCR)
Summer 2023, Vol. 1, No. 2, pp 27-40
increasing trend.
In the annual series, the diagrams u and
u in 1960 and 1968 intersect each other
within the critical range, which shows a
sudden jump and change.
Table 2. Summary of the Mann- Kendall test results
Annual
September
August
July
June
May
April
March
February
January
December
November
October
month
CD
1960
TI
1956
TI
1956
TI
1955
CD
1961
CD
1956
CD
1960
CD
1960
CI
1954
CI
1954
CI
1966
TI
1981
TI
1954
Aqdarband
station
*T indicates the trend, C indicates its sudden jump, I and D are symbols that shown increase and decrease of each
The extent and signicance level of
variation
The results obtained from the regression
method are presented in Table 3. The value
of r refers to the simple correlation between
the two variables and in other words
indicates the intensity of the correlation
between the two variables. The value of R
to the power of 2 indicates how much of
the dependent variable by the independent
variable. In mos t months, there is a
moderate to weak correlation between the
independent and dependent variables. The
value of sig of the regression model shows
that if the value obtained is less than 0.05,
we conclude that the model used was a
good predictor, In the monthly series of
December, January, February and March
and the annual series of the used model,
it has been a good prediction. The results
of Kruskal-Wallis and Mann-Whitney
tes ts are presented in Table 4. Kruskal-
Wallis and Mann-Whitney tes ts are used
to evaluate the signicance of the jump
points. The value of sig of the Kruskal-
Wallis and Mann-Whitney tes ts shows that
if the value obtained is less than 0.05, the
jump is signicant. In mos t months, except
November and June, the jump points are
signicant which shows that the jump
points are not random and the eect of
climate change is conrmed.
Table 3. Summary Results of Linear Regression Method
month
Sig
October
0.007
0.506
November
0.006
0.54
December
0.247
0.000
January
0.267
0.000
February
0.233
0.000
March
0.122
0.04
April
0.031
0.162
May
0.039
0.115
June
0.00
0.990
July
0.16
0.309
August
0.037
0.127
September
0.049
0.077
Annual
0.136
0.003
38
Journal of Drought and Climate change Research (JDCR)
Summer 2023, Vol. 1, No. 2, pp 27-40
Khodabandeh Baygi et al.,
Conclusion
The planet’s climate has los t its balance
in recent decades, so the issue of climate
change has attracted a lot of attention.
In the s tudy of climate change, long-
term s tatis tics can show changes to a
large extent. The purpose of this s tudy
was to inves tigate the s tatis tical trend of
monthly and annual series, ood discharge
of Kashafrood river basin . Therefore,
s tatis tical data of the las t 65 years related
to the monthly and annual series of the
Table 4. The Summary of Mann-Whitney and Kruskal-Wallis Tests Results
month
Mann-Whitney test
Kruskal-Wallis Test
October
0.03
-
November
-
0.107
December
0.012
-
January
-
0.000
February
-
0.000
March
0.04
-
April
-
0.029
May
-
0.02
June
-
0.147
July
0.004
-
August
0.005
-
September
0.017
-
Annual
-
0.007
s tation were examined.
Increasing urban population along with
climate change has led to an increased
risk of ooding in cities. Therefore,
assessing the impact of future climate
change on oods and taking eective
ood management measures is becoming
increasingly important.
Mann-Kendall tes t was used to determine
if the trend, type, and time of change
were random or not. It was found that in
5 months of the year the upward trend
and in 7 months of the year a sudden
jump occurred. Mann-Whitney and
Kruskal-Wallis tes ts were used to evaluate
the signicance of jump points, which
were signicant in mos t months except
November and June.
Acknowledgment
We would like to thank the Islamic Azad
University of Mashhad for supporting this
project, as well as the Khorasan Razavi
Regional Water Organization for assis ting
with the collection of information.
Conict of Interes t
The authors declare that they have no
conict of interes ts.
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