CC BY
3Hydrometeorology and ecology №4 2024
UDC 551.558.1
IRSTI 37.21.02
PRESENT TRENDS IN AIR TEMPERATURE IN THE EAST OF KAZAKHSTAN
M.M. Makhambetova1*, Onur Satir2 PhD, A.S. Nyssanbaeva1 Candidate of geographical sciences
al-Farabi Kazakh National university, Almaty, Kazakhstan
Yuzuncu Yil University, Department of Landscape Architecture, Van, Turkey
E-mail: [email protected]
1
2
According to world research, over the past decades there has been a tendency for air temperature
to rise and an increase in the frequency of extreme weather events. The study of the climatic
predisposition of individual regions to extreme events, in particular fires, is an urgent problem
of our time. The main purpose of this study was to define current trends in air temperature in
Eastern Kazakhstan on an annual, seasonal and monthly scale. The non-parametric MannKendall test and Sen’s slope estimation were used for the analysis, and the co-kriging method
was used to interpolate the data to obtain areal distribution. As a result of the study, it was
revealed that in most of Eastern Kazakhstan, there has been an increasing trend in average and
maximum temperatures significantly. It was also noted that the main statistically significant
changes are observed in the spring and summer seasons. Changes at some stations were reached
from 0,2 to 1,0 °C in ten years. The most consistent and significant trends in temperature
increase were recorded in March and April.
Keywords: Air temperature, Non-parametric Mann-Kendall test, Co-kriging.
Accepted:16.10.2024 y.
DOI: 10.54668/2789-6323-2024-115-4-39-49
INTRODUCTION
According to the world climate literature,
there has been a trend of rising air temperatures
and an increase in the frequency of extreme
weather events in recent decades (Change, 2023,
Perreault-Carranza, 2024, Valavanidis, 2023).
In the IPCC’s sixth assessment Report on
the physical basis of climate change, it was noted
that human activities, mainly due to greenhouse
gas emissions have caused global warming. The
report showed that the temperature of the earth’s
surface between 2011 and 2020 was 1,1°C, higher
than between 1850 and 1900. It was also noted
that the global temperature of the earth’s surface
has risen faster than in any other 50-year period
since 1970, according to the data of the last 2000
years (IPCC, 2021).
Climate risks are potential risks that may
arise as a result of climate change (physical risks)
or measures to minimize its effects (transitional
risks) (Bank of Russia, 2022). The key factors that
cause a high degree of vulnerability may be due to
a number of geographical factors – for example,
the presence of territories in different climatic
zones.
In global research on climate change,
especially changes in air temperature trends,
it includes various methods, depending on the
objectives of the study, the data used, and the time
and spatial scale. In various works (Ceyhunlu,
2024, Costa, 2024, Mudelse, 2018) changes in
air temperature were calculated using the linear
regression method, the trend reversal analysis
method, automatic and cross-correlation analysis,
etc. In this study, the Mann-Kendall test and the
Sen’s slope estimation were selected for analysis.
In the works of many researchers (Faquseh,
2024, Hossen, 2023, Rahdari, 2024), it was noted
that the Mann-Kendall test is especially effective
in assessing monotonous trends such as air
temperature, precipitation, snow cover height, etc.
This nonparametric test is used not only in
meteorology but also in various fields of science
and medicine. The Mann-Kendall test allows us to
calculate the trend toward an increase or decrease
in the values of meteorological parameters, as well
as to assess the statistical significance of these
changes (Neel, 2018).
The Mann-Kendall’s statistics depend on
the functions of the observation ranks rather than
their actual values, which mean the method does
not depend on the actual distribution of data and is
39
Scientific article
Makhambetova, Satir, Nyssanbaeva. Present trends in air temperature...
less sensitive to outliers. Nonparametric statistics are
usually much less affected by outliers and other forms
of abnormality and are an indicator of monotonic
linear dependence. A statistically significant trend
identified using a nonparametric model, such as the
Mann-Kendall test (MK), can be supplemented by
an estimate of the slope of the Sen to determine the
magnitude of the trend (Yadav, 2014).
One of the manifestations of climate change is
an increase in air temperature, which is continuously
associated with an increase in the incidence of extreme
weather events such as droughts and fires and other
dangerous phenomena. It should be noted that in the
east of Kazakhstan, which is the object of research,
there are forests important for the ecosystem, the area
of which decreases every year and suffers from forest
fires. The fire that occurred in 2023 in the territory of
the Abad region destroyed more than 60,000 hectares
of forest, which is a major disaster on a global scale
(Wikipedia contributors, 2023).
In this regard, the purpose of this study
was to analyse trends in changes in the average and
maximum air temperature in the Eastern Kazakhstan
associated with an increase in extreme weather events,
in particular wildfires.
MATERIALS AND METHODS
The study area. The climate of the east of
µ
55°0'0"N
85°0'0"E
55°0'0"N
80°0'0"E
Kazakhstan is very diverse and in this regard, the
climatic risks of different regions of Kazakhstan are
also not the same. The key factors that cause a high
degree of vulnerability may be due to a number of
geographical factors – for example, the presence of
territories in different climatic zones.
The study area is the East of Kazakhstan,
which includes the Abai and East-Kazakhstan regions.
It is located between latitudes 45,32 and 51,43 north
latitude and 76,47 and 87,19 east longitude, has a
maximum length of 760 km from east to west and
610 km from north to south, covers an area of
283,300 km2, altitude ranges from 121 meters to 4350
meters above sea level. On the territory of the east of
Kazakhstan there are both vast flat areas and highaltitude areas where the Altai and Saur-Tarbagatai
mountains are located (Egorina, 2014).
The state of Kazakhstan’s forest lands is of
particular concern. Occupying only 4 % of the country’s
territory, they are the habitat of the most valuable and
rare species of animals, 90 % of the species of higher
plants known in the republic. The forest cover of
the entire republic is mainly in the east and north of
Kazakhstan, for each region the total forest cover is
14 % (Makeeva, 2022). The most important forests for
the republic are located in the studied area, and climate
change can affect changes in the ecosystem and forest
cover in this area.
Legend
Eastern Kazakhstan
Kazakhstan
50°0'0"N
Almaty
Legend
50°0'0"E
60°0'0"E
0 225 450
70°0'0"E
900
1 350
40°0'0"N
Astana
1 800
Kilometers
80°0'0"E
30°0'0"N
30°0'0"N
45°0'0"N
80°0'0"E
Kazakhstan
Kazakhstan
Kazakhstan
45°0'0"N
70°0'0"E
60°0'0"N
60°0'0"E
60°0'0"N
50°0'0"N
40°0'0"N
Eastern Kazakhstan
50°0'0"N
50°0'0"N
50°0'0"E
1 cm = 80 km
0
80°0'0"E
125
250
500 Kilometers
85°0'0"E
Fig. 1. The map of object of the study – the east of Kazakhstan
40
Hydrometeorology and ecology №4 2024
To conduct this study, data were taken
from the archive of urgent observations of RSE
Kazhydromet for the period 1978...2023 from 24
meteorological stations (Kazhydromet, n.d.).
The non-parametric Mann-Kendall test was used
to analyze the data, which allows us to identify
trends and determine the statistical significance
of changes in time series. It is important to note
that these methods are resistant to emissions and
are used to identify significant climatic trends,
which makes them optimal for this type of data
and they are justified in the context of analyzing
temperature trends.
According to this test, the statistics of S
and Z, Kendall’s tau (τ), the p value, etc. were
evaluated. Also, the values of Sen’s slope were
determined to assess the trend. All calculations
were created using the Python programming
language (Python Software Foundation, n.d.) in
the Jupyter Notebook software (Project Jupyter,
n.d.).
The Mann-Kendall test. The MK test is
a nonparametric test for detecting trends in time
series data. The S statistics for the MK test are
calculated using the following formulas (1...2):
𝑛𝑛
𝑆𝑆 = ∑𝑛𝑛−1
𝑘𝑘=1 ∑𝑗𝑗=𝑘𝑘+1 𝑠𝑠𝑠𝑠𝑠𝑠(𝑋𝑋𝑗𝑗 − 𝑋𝑋𝑘𝑘 ),
(1)
(2)
sgn(Xj − Xk ) = 〈if (Xj − Xk ) = 0,0 〉,
where, n is the length of the dataset, Xj
and Xk are the data values at times j and k, and
sgn is a signed function that takes values -1, 0
and +1. The value of t from S shows uptrends or
downtrends in climate datasets:
Var(S) =
p
n(n−1)(2n+5)− ∑k=1 tk (tk −1)(2tk +5)
18
, (3)
where, p is the associated group, and tk is
the number of observations in the k group.
The standard Z statistic for the Mann-Kendall test
is calculated using the following formula:
Zs =
𝑆𝑆−1
√𝑉𝑉𝑉𝑉𝑉𝑉(𝑆𝑆)
, 𝑆𝑆 > 0
0, 𝑆𝑆 = 0
,
, 𝑆𝑆 < 0
𝑆𝑆+1
(4)
{ √𝑉𝑉𝑉𝑉𝑉𝑉(𝑆𝑆)
where, ZS shows the significance of the
trend. Then, standardized test statistics are applied
to test the null hypothesis, H0, if ZS > Zα/2 and α
shows the confidence level.
The Sen’s slope is calculated using the
following formula:
Q=
Xj − Xk
j−k
k<j,
(5)
where, Xj and Xk are the values of
meteorological parameters at time j and k
(Gulakhmadov, 2021).
Data visualization. The co-kriging method
was chosen to visualize and calculate the spatial
distribution of data. Co-kriging is an advanced
kriging technique that is used to interpolate spatial
data using multiple interconnected data. The cokriging method has been extensively studied
by various researchers and adapted to different
practical scenarios. The advantage of co-kriging
is that it shows more accurate results (Giraldo,
2020). In this paper, Mann-Kendall statistics and
the height of the earth’s surface were used for cokriging.
RESULTS AND DISCUSSION
In many studies (Bisai, 2014, Latrech,
2023, Liyew, 2024) that the non-parametric MannKendall test was used, calculations have shown
that the trends to increase of the air temperature
is statistically significant. For example, in studies
conducted in Morocco from 1970 to 2019, the test
results showed that air temperature has seasonal
trends and revealed a trend towards an increase in
temperature in spring and summer (Qadem, 2024).
In studies in India, the results were also
obtained, according to which there is an increase
in the number of warm days and nights and the
number of cold days and nights decreases. The
authors also noted that the observed growing
warming trends may lead to floods in India in the
future (Frimpong, 2022).
The Mann-Kendall test was also used to
study trends of changes in average temperature
values across the territory of Kazakhstan.
According to the results of this study, the authors
note that there is a tendency for a significant
increase in air temperature in Kazakhstan. The
average annual temperature over the past 50 years
has increased by an average of 0,034 °C per year.
In all four seasons of the year, there were trends
towards an increase in the corresponding average
temperatures, especially significant changes were
in spring, summer and autumn (Farooq, 2021).
41
Scientific article
Makhambetova, Satir, Nyssanbaeva. Present trends in air temperature...
The same results, i.e. significant trends in
temperature increases in spring, summer, autumn
and minor changes in winter, were obtained
according to the study over the Kyzylorda region
of Kazakhstan (Abdolla, 2024).
Mann-Kendall statistics in the east of
Kazakhstan. As mentioned above, their average
and maximum values were taken to study trends in
air temperature changes. The analysis of changes
in the temperature range in the east of Kazakhstan
over the past 45 years was carried out. Tables 1...3
provide Z-values calculated for the average and
maximum air temperature. Table 1 shows annual
and seasonal Mann-Kendall statistics on average
and maximum temperatures for 24 meteorological
stations.
Table 1
Seasonal Mann–Kendall statistics (Z-values) for average (a) and maximum (b) temperatures (in °C)
for the period 1978…2023 in Eastern Kazakhstan
МS
Ann.
Average air temperature
Spr.
Sum.
Fall
Wint.
Ann.
Maximum air temperature
Spr.
Sum.
Fall
Aksuat
1,82
1,18
3,97*
3,94
2,97
3,95
4,14
Aktogai
0,70 -0,06
2,49
3,15
2,33
2,61
2,98
Ayagoz
0,91
0,24
3,27
3,95
2,91
2,45
3,63
Bakty
1,34 -0,33
3,29
3,59
3,67
3,61
3,77
Barshatas
0,70
0,04
1,30
2,89
3,60
2,02
3,11
Dmitriyevka
1,77
0,20
-0,50
1,83
3,26
2,21
3,39
Kainar
0,66
-0,40
0,00
2,11
3,31
2,15
3,79
Karaaul
1,58
1,37
-0,30 -0,49
1,68
3,60
3,92
Katon-karagai
0,22 -0,56
2,41
3,49
3,69
1,96
3,11
Kokpekty
0,90
0,92
3,18
3,98
3,36
3,41
3,28
Kurshim
0,05
0,09
2,05
3,05
3,01
3,21
3,38
Leninogorsk
1,43
-0,41 -0,86
0,30
4,08
3,02
3,04
Zapovednik Markakol
1,08
0,97
3,70
4,36
4,24
4,47
4,94
Samarka
0,66
0,64
2,83
3,32
2,85
2,48
3,21
Semey
1,73
1,66
0,39 -0,34
3,54
2,42
4,17
Semiyarka
1,60
1,65
0,39
-0,26
3,57
2,67
3,96
Shalabai
1,36
1,59
-0,14 -0,64
1,37
3,30
3,38
Shar
1,72
1,87
0,09 -0,44
3,50
2,21
3,90
Shemonaiha
0,67
0,13
2,39
3,88
2,47
2,66
4,42
Terekty
1,57
-0,16 -0,73
1,87
3,28
2,47
3,05
Ulken Naryn
1,61
0,59
-0,49
2,61
2,45
2,19
2,33
Urzhar
0,30
1,44
-0,64 -2,55
0,37
2,49
2,13
Zaisan
0,40
0,17
1,20
2,63
3,56
3,13
2,92
Zhalgyztobe
1,76
0,22 -0,59
1,64
3,57
2,07
3,51
*In the table, values in bold represent Mann–Kendall results that are significant at p < 0,05
Calculations show (Table 1) an increase in
the annual tends of the average temperature at 13
stations out of 24 and the maximum temperature
at 15 stations, which was statistically significant
(p value < 0.05). At the same time, none of the
stations showed negative dynamics.
During the spring season, significant
positive temperature changes were observed
at all meteorological stations. There was also
a significant positive trend during the summer
period, with the exception of a few stations such
2,78
3,14
1,41
4,01
0,53
1,77
1,24
1,54
2,17
3,44
4,19
2,30
4,91
2,55
2,22
3,25
2,64
2,67
3,18
1,91
2,25
0,39
2,50
2,53
1,44
1,09
0,32
1,21
-0,27
0,15
-0,04
-0,08
-0,13
0,86
0,91
-1,41
1,35
0,26
0,59
0,84
0,31
0,63
0,92
-0,09
0,33
-0,01
-0,42
0,24
Wint.
1,26
-0,26
-0,42
-0,14
-0,44
-0,54
-0,71
-0,67
-0,49
0,93
0,65
-2,00
1,48
-0,10
-0,38
-0,38
-1,72
-0,57
-0,46
-0,06
0,62
-2,09
-0,91
-1,17
as Shar, Shalabai, Semiyarka, Karaaul and Kainar,
for which temperature trends were insignificant.
In autumn and winter, no significant trends were
detected at the stations.
The average annual and seasonal trend
of maximum temperature repeats the average
temperature.
Table 2 shows the results of the statistical
Z values of the Mann–Kendall test for the average
temperature by month.
42
Hydrometeorology and ecology №4 2024
Table 2
Mann–Kendall statistics (Z-values) by month (in °C) for the period 1978…2023 in the eastern
Kazakhstan for average temperature
Average air temperature
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2,60
0,78
1,89
1,14
2,55
0,65
2,32
1,67
0,44
2,72
2,42
0,93
1,64
1,39
1,34
0,27
1,44
0,32
-0,50
3,46
3,18
1,10
1,85
1,43
1,82
0,93
1,40
0,55
-0,67
1,12
2,94
2,94
0,88
3,05
2,09
2,09
1,41
1,84
0,12
-0,68
0,28
1,53
3,32
2,70
1,08
1,50
0,23
1,68
0,09
1,50
0,05
-0,42
-0,58
0,55
2,99
2,89
1,00
1,45
0,31
1,72
-0,09
1,09
0,15
-1,33
-0,36
1,17
3,13
2,25
0,99
0,82
0,13
1,16
-0,75
0,41
-0,24
-0,64
-0,83
0,98
3,20
2,56
0,76
0,98
-0,10
0,99
-0,72
0,57
-0,17
-1,64
Katon-karagai
-0,64
0,62
2,92
2,67
0,41
2,05
1,83
3,24
-0,02
1,08
0,05
-1,62
Kokpekty
-0,01
1,46
3,51
2,70
0,93
2,26
1,55
1,88
-0,03
1,14
0,90
-0,08
Kurshim
-0,40
1,09
2,41
2,83
-0,17
1,74
1,67
1,84
-0,40
0,25
0,42
-0,65
Leninogorsk
-1,25
0,70
3,13
2,79
0,76
1,55
1,13
2,25
-0,84
0,64
-0,40
-1,88
Zapovednik Markakol
0,30
1,81
2,61
3,35
2,17
3,21
2,09
3,06
0,71
1,46
0,26
0,07
Samarka
0,15
1,65
2,79
2,53
0,60
1,83
1,04
1,77
-0,63
1,28
0,85
-0,22
Semey
-0,55
0,80
2,85
2,69
1,15
1,07
-0,28
1,71
0,14
1,12
0,04
-1,37
Semiyarka
-0,56
0,73
2,99
2,44
1,17
0,83
0,11
1,79
-0,27
1,18
0,28
-0,92
Shalabai
-1,34
0,66
2,99
2,99
0,47
1,17
0,13
1,03
-0,04
0,49
0,08
-1,49
Shar
-0,70
0,87
2,96
3,12
0,72
1,39
0,20
1,15
-0,09
0,76
0,06
-1,42
Shemonaiha
-0,47
1,23
3,28
2,99
1,13
1,35
0,99
1,89
0,20
1,15
0,55
-0,63
Terekty
-0,80
0,71
3,17
2,61
0,04
1,90
0,66
1,47
-0,37
0,80
0,09
-1,44
Ulken Naryn
-1,28
0,14
2,77
1,89
-0,32
1,62
0,83
1,13
-0,99
0,64
1,07
-0,09
Urzhar
-1,49
-0,20
2,07
2,11
0,30
1,22
0,57
0,62
-1,16
-0,23
-0,09
-1,52
Zaisan
-0,37
1,12
2,76
2,96
0,58
2,04
1,72
2,13
-0,06
0,64
0,72
-0,46
Zhalgyztobe
-0,87
0,71
2,95
0,60
1,62
0,65
1,37
0,00
0,83
0,17
-1,56
Station
Jan
Feb
Mar
Aksuat
0,08
2,12*
3,22
Aktogai
0,15
1,18
Ayagoz
0,00
1,55
Bakty
-0,04
Barshatas
Dmitriyevka
Kainar
Karaaul
2,96
*In the table, values in bold represent Mann–Kendall results that are significant at p < 0,05
It was found that in the period from 1978
to 2023, there was a tendency for temperatures to
rise in most months. However, not all months have
equally significant trends in temperature increase.
During the cold period of the year (SeptemberFebruary), negative trends are also noted, but
they are not significant. The most consistent and
significant trends in temperature increase were
recorded in March and April. In summer, in
June and August, a significant positive trend was
recorded only at 5...6 stations.
Table 3 shows the results of the statistical
values of the Mann–Kendall test for the maximum
temperature by month.
43
Scientific article
Makhambetova, Satir, Nyssanbaeva. Present trends in air temperature...
Table 3
Mann–Kendall statistics (Z-values) by month (in °C) for the period 1978…2023 in the eastern
Kazakhstan for maximum temperature
Maximum air temperature
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
3,32
1,48
2,31
1,59
2,16
0,28
1,30
1,52
0,24
2,11
1,33
2,28
1,58
1,38
0,34
1,21
0,93
-0,56
3,11
2,68
0,88
1,59
0,68
0,62
-0,43
0,68
0,58
-1,27
1,36
3,15
2,54
1,53
2,94
2,16
2,53
0,72
1,27
0,80
-0,70
0,88
2,73
2,49
1,07
1,03
-0,80
0,47
-1,16
0,39
0,02
-1,20
-0,84
0,97
3,13
3,03
1,13
1,19
0,37
1,28
-0,03
1,15
-0,09
-1,77
-0,44
0,98
2,92
2,57
1,80
1,14
0,06
0,77
-0,72
0,52
-0,13
-1,55
Karaaul
-1,04
0,90
3,20
2,94
1,49
1,66
0,30
1,15
-0,53
0,72
-0,19
-1,94
Katon-karagai
-0,48
0,93
2,72
2,03
0,31
1,57
0,70
1,68
-0,35
1,06
-0,20
-1,65
Kokpekty
0,44
1,68
3,08
2,34
1,19
2,62
1,55
2,21
0,02
1,00
0,89
-0,08
Kurshim
0,09
1,46
2,38
2,66
1,13
2,50
2,86
3,21
0,88
1,07
0,67
-0,26
Leninogorsk
-1,56
0,25
2,69
2,04
0,65
1,93
1,11
1,95
-0,89
-0,05
-1,31
-3,01
Zapovednik Markakol
0,62
2,40
2,88
4,26
2,87
3,70
3,04
4,11
1,62
1,52
0,47
0,38
Samarka
-0,09
0,91
2,30
2,57
1,05
2,28
0,99
1,47
-0,09
0,74
0,59
-0,69
Semey
-0,62
1,17
3,08
3,49
1,54
1,37
0,31
1,97
0,20
1,47
0,09
-1,44
Semiyarka
-0,71
0,63
3,16
3,18
2,13
1,75
1,29
2,68
0,27
1,73
0,27
-0,92
Shalabai
-1,56
-0,19
2,06
3,17
1,18
1,84
1,24
1,75
0,42
1,06
-0,06
-2,21
Shar
-0,87
0,78
2,79
3,40
1,15
1,95
0,99
1,81
0,31
1,48
0,33
-1,47
Shemonaiha
-0,69
1,00
3,22
3,55
1,60
2,13
1,86
2,34
0,59
1,60
0,46
-1,18
Terekty
-0,33
1,29
2,85
2,52
0,69
1,81
-0,01
1,86
-0,05
0,82
-0,17
-0,95
Ulken Naryn
-0,30
1,06
2,27
1,80
0,47
1,82
0,35
1,21
-0,66
-0,15
1,02
0,27
Urzhar
-1,50
0,10
1,84
1,71
0,25
0,58
0,03
-0,15
-0,97
0,35
0,06
-1,43
Zaisan
-1,23
0,45
2,42
2,44
0,31
1,93
1,37
1,38
-0,45
0,03
0,08
-1,21
Zhalgyztobe
-1,11
0,57
2,78
3,22
0,74
1,94
0,98
1,42
-0,08
0,96
-0,01
-2,06
Jan
Feb
Mar
Aksuat
0,68
1,99*
3,20
Aktogai
-0,24
0,44
2,43
Ayagoz
-0,54
1,29
Bakty
0,17
Barshatas
-0,46
Dmitriyevka
Kainar
Station
*In the table, values in bold represent Mann–Kendall results that are significant at p < 0,05
The monthly statistical values of Mann–
Kendall for maximum temperature have a similar
pattern to the average temperature. Significant
changes were also detected in March and April, and
an increase in temperature is observed at all stations.
Visualization of the current trend in air
temperature in the East of Kazakhstan. According
to the statistical data of the Sen’s slope estimator,
interpolated data were calculated using the co-
Dec
kriging method and maps of the spatial distribution
of air temperature changes in Eastern Kazakhstan
were visualized using the ArcGIS desktop
environment. On a seasonal scale, statistical
values varied within different limits for each
season, which made it difficult to use a single scale
for data visualization. Because of this difference,
each season has its own specific scale (Fig. 2).
44
Hydrometeorology and ecology №4 2024
Fig. 2. Annual and seasonal changes in average temperature (in °C/year) in the eastern Kazakhstan
Figure 2 shows maps with interpolated
values of Sen’s slope (in °C/year) on an annual
and seasonal scale for the average temperature.
Trend calculations of annual mean temperatures
show an increase of 0,2...0,4 °C over the decade.
The map shows a significant increase in the plains
of eastern Kazakhstan. Minimal changes were
recorded in the highlands.
Among the four seasons of the year, the
greatest temperature increase is observed in
spring and summer, and the minimum change is
during the cold season.
The smallest change was recorded in
autumn, during this period the trend varies
between 0,1...0,2 °C per decade. In spring, the
temperature increase varies between 0,6...1,0 °C
over ten years, changes with maximum values
are localized in the high-altitude area. In summer,
you can also see a significant positive increase
in temperature, in the range of 0,2...0,4 °C per
decade.
The calculated data based on the maximum
temperature were shown in the following maps
(Fig. 3).
45
Scientific article
Makhambetova, Satir, Nyssanbaeva. Present trends in air temperature...
Fig. 3. Annual and seasonal changes in maximum temperature (in °C/year) in the eastern Kazakhstan
Trend calculations of annual maximum
temperatures show an increase of 0,2...0,5 °C
over the decade. The map shows that a significant
increase was recorded in the mountain area of
eastern Kazakhstan.
Among the four seasons of the year, the
maximum temperature has a similar pattern to
the average temperature. The greatest increase is
observed in spring and summer, and the minimum
in winter and autumn.
The smallest change was recorded
in autumn and winter, during this period the
trend varies between 0,1...0,2 °C per decade. In
spring, the temperature increase varies between
0,6...1,0 °C over ten years, changes with maximum
values are localized in flat terrain. In summer,
you can also see a significant positive increase
in temperature, in the range of 0,2...0,8 °C per
decade.
CONCLUSION
Climate change in the studied region has
a significant impact on the ecosystem, agriculture
and economic stability. In this work were calculated
an annual, seasonal and monthly air temperature
changes in the territory of eastern Kazakhstan and
the following conclusions were made:
1. There is an increase in the average and
maximum temperature in most of the territory of
eastern Kazakhstan. Calculations show an increase
in the annual tends of the average temperature at 13
stations out of 24 and the maximum temperature
at 15 stations, which was statistically significant
(p value < 0,05).
2. A significant increase in the average
air temperature was in the flat terrain of eastern
Kazakhstan and minimal changes were recorded
in the highlands.
3. The spatial distribution of changes in
the maximum temperature has a diametrically
opposite distribution of the average temperature,
a significant increase in it was recorded in
mountainous areas.
4. According to the Sen’s slope estimator,
annual increases in average air temperature were
in the range of 0,2...0,4 °C over ten years. For the
maximum temperature, the changes were in the
range of 0,2...0,5 °C over ten years.
5. In spring and summer, there was a
significant increase in the average and maximum
temperature at all stations, the changes ranged
from 0,6...1,0 °C over ten years in terms of average
terms of maximum air temperature.
46
Hydrometeorology and ecology №4 2024
In conclusion, it can be noted that
statistically significant temperature changes with
an upward trend have been observed in recent
decades. The observed changes may lead to
an increase in the frequency of natural hazards
associated with high temperatures, in particular
fires, droughts and floods in the studied region.
REFERENCES
1. Abdolla, N. S., Nyssanbaeva, A. S., & Abdіrazak, A.
K. (2024). Kyzylorda oblysindagy aua temperaturasynyn
1961...2020 zhyldar aralygynda ozgeruі. Gidrometeorologiya
i ekologiya, (1)(112).
2.BankRossii.(2022).Klimaticheskieriskivmenyayushchikhsya
ekonomicheskikh usloviyakh: doklad dlya obshchestvennykh
konsul’tatsii. Retrieved from https://www.cbr.ru/Content/
Document/File/143643/Consultation_Paper_21122022.pdf
3. Bisai, D., Chatterjee, S., Khan, A., & Barman, N. (2014).
Application of Sequential Mann-Kendall Test for Detection
of Approximate Significant Change Point in Surface Air
Temperature for Kolkata Weather Observatory, West Bengal,
India. International Journal of Current Research, 6, 5319–5324.
4. Ceyhunlu, A., & Ceribasi, G. (2024). Changes in precipitation
and air temperature over Turkey using innovative trend pivot
analysis method. Journal of Water and Climate Change, 15,
2446–2463. https://doi.org/10.2166/wcc.2024.041
5. Intergovernmental Panel on Climate Change. (2023). Climate
change information for regional impact and risk assessment.
https://doi.org/10.1017/9781009157896.014
6. Costa, M., Brito, A., Castro, A., Dias, R., & Zebende, G.
(2024). Trends in the air temperature: A practical approach for
auto- and cross-correlation analysis. Advances in Meteorology,
2024. https://doi.org/10.1155/2024/3098248
7. Egorina, A. V., & Loginovskaya, A. N. (2014). Rel’ef kak
faktor rekreatsionnoi deyatel’nosti (na primere Vostochnogo
Kazakhstana).Naukaiturizm:strategiivzaimodeistviya,1,47–50.
8. Faquseh, H., & Grossi, G. (2024). Trend analysis of
precipitation, temperature, and snow water equivalent in
Lombardy region, northern Italy. Sustainable Water Resources
Management, 10. https://doi.org/10.1007/s40899-023-00992-2
9. Farooq, I., Shah, A. R., & Salik, K. M. (2021). Annual,
seasonal, and monthly trend analysis of temperature in
Kazakhstan during 1970–2017 using non-parametric
statistical methods and GIS technologies. Earth Systems and
Environment, 5, 575–595. https://doi.org/10.1007/s41748021-00244-3
10. Frimpong, B. F., Koranteng, A., & Molkenthin, F. (2022).
Analysis of temperature variability utilizing Mann–Kendall
and Sen’s slope estimator tests in the Accra and Kumasi
metropolises in Ghana. Environmental Systems Research, 11,
24. https://doi.org/10.1186/s40068-022-00269-1
11. Gulakhmadov, A. A. (2021). Otsenka trendov i
magnitudnykh izmenenii osadkov za poslednie desyatiletiya
v basseine reki Kafirnigan. Vestnik pedagogicheskogo
universiteta (Estestvennykh nauk), 1–2(9–10).
12. Giraldo, R., Herrera, L., & Leiva, V. (2020). Cokriging
prediction using as secondary variable a functional random
field with application in environmental pollution. Mathematics,
8(8), 1305. https://doi.org/10.3390/math8081305
13. Hossen, M. S., & Evan, R. (2023).Assessing historical climate
trends in Dhaka City: A multivariate analysis using MannKendall and Sen’s slope method. International Journal of Climate
Research, 7(1), 24–45. https://doi.org/10.18488/112.v7i1.3452
14. IPCC. (2021). Summary for policymakers. In Climate
Change 2021: The Physical Science Basis (pp. 3–32). Cambridge
University Press. https://doi.org/10.1017/9781009157896.001
15. Latrech, B., Yacoubi, S., Hermassi, T., Slatni, A., Jarray,
F., & Pouget, L. (2023). Homogeneity and trend analysis of
climatic variables in Cap-Bon region of Tunisia. Applied
Sciences, 13, 10593. https://doi.org/10.3390/app131910593
16. Liyew, C., Di Nardo, E., Meo, R., & Ferraris, S. (2024).
Identifying time patterns of highland and lowland air
temperature trends in Italy and the UK across monthly
and annual scales. Advances in Statistical Climatology,
Meteorology and Oceanography, 10, 173–194. https://doi.
org/10.5194/ascmo-10-173-2024
17. Makeeva, L. A., Urazbaeva, S. E., & Mukanov, S. S. (2022).
Izuchenie ekologicheskikh problemy prirodookhrannogo
kharaktera lesov Respubliki Kazakhstan. Endless light in
science, (November), 161–165.
18. Mudelsee, M. (2018). Trend analysis of climate time series:
A review of methods. Earth-Science Reviews, 190. https://doi.
org/10.1016/j.earscirev.2018.12.005
19. Neel, K., & Pachauri, S. (2018). Mann-Kendall test – A
novel approach for statistical trend analysis. International
Journal of Computer Trends and Technology, 63(1), 18–21.
20. Perreault-Carranza, T., Ni, V., Savoie, J., Saucier, J.,
Frenette, J., & Jbilou, J. (2024). Core competencies of the
public health workforce in climate change and extreme weather
events preparedness, response, and recovery: A scoping review.
International Journal of Environmental Research and Public
Health, 21. https://doi.org/10.3390/ijerph21091233
21. Qadem, Z., & Tayfur, G. (2024). In-depth exploration of
temperature trends in Morocco: Combining traditional methods
of Mann-Kendall with innovative ITA and IPTA approaches.
Pure and Applied Geophysics, 181, 2717–2739. https://doi.
org/10.1007/s00024-024-03535-8
22. Rahdari, M., Kharazmi, R., Rodrigo-Comino, J., &
Rodríguez-Seijo, A. (2024). Spatial-temporal assessment
of dust events and trend analysis of sand drift potential in
northeastern Iran, Gonabad. Land, 13, 1906. https://doi.
org/10.3390/land13111906
23. Valavanidis, A. (2023). Extreme weather events exacerbated
by the global impact of climate change. A glimpse of the future,
if climate change continues unabated, 1, 1–40.
24. Yadav, R., Tripathi, S., Gogumalla, P., & Dubey, S. (2014).
Trend analysis by Mann-Kendall test for precipitation and
temperature for thirteen districts of Uttarakhand. Journal of
Agrometeorology, 16, 164. https://doi.org/10.54386/jam.
v16i2.1507
25. Wikipedia contributors. (2023). 2023 Kazakhstan
wildfires. Retrieved from https://en.wikipedia.org/wiki/2023_
Kazakhstan_wildfires
26. Kazhydromet. (n.d.). Climate kadastr. Retrieved from
https://meteo.kazhydromet.kz/climate_kadastr/
27. Python Software Foundation. (n.d.). Python. Retrieved
from https://www.python.org
28. Project Jupyter. (n.d.). Jupyter. Retrieved from https://
jupyter.org
47
Scientific article
Makhambetova, Satir, Nyssanbaeva. Present trends in air temperature...
ҚАЗАҚСТАННЫҢ ШЫҒЫСЫНДАҒЫ АУА ТЕМПЕРАТУРАСЫНЫҢ
АҒЫМДАҒЫ ҮРДІСТЕРІ
M.M. Махамбетова1*, О. Шатыр2 PhD, А.С. Нысанбаева1 г.ғ.к.
әл-Фараби атындағы Қазақ ұлттық университеті, Алматы, Қазақстан
Юзунчу Йил Университеті, Ван, Түркия
E-mail: [email protected]
1
2
Әлемдік зерттеулерге сәйкес, соңғы жылдары ауа температурасының жоғарылау тенденциясы және экстремалды ауа райы құбылыстарының жиілігінің артуы байқалуда. Өңірлердің экстремалды құбылыстарға, атап айтқанда өрттерге климаттық бейімділігін зерттеу қазіргі заманның өзекті мәселелерінің бірі болып табылады. Бұл зерттеудің мақсаты
Қазақстанның шығысындағы ауа температурасының жылдық, маусымдық және айлық
ауқымдағы қазіргі заманғы трендтерін зерттеу болып табылады. Талдау үшін Манн-Кендаллдың параметрлік емес сынағы және Сенның өзгеру трендің бағалау әдісі қолданылды, ал деректерді визуализациялау үшін ArcGIS-те ко-кригинг әдісі қолданылды. Осы
зерттеу нәтижесінде Қазақстанның шығысындағы үлкен аумақта орташа және ең жоғары температураның көтерілу үрдісі байқалғаны анықталды. Сондай-ақ, негізгі статистикалық маңызды өзгерістер көктем мен жаз мезгілдерінде байқалатыны атап өтілді. Қарастырылып отырған кезеңде кейбір станциялардағы өзгерістер он жыл ішінде 0,2-ден
1,0 °C-қа дейін жетеді. Температураның көтерілуінің ең дәйекті және маңызды тенденциялары наурыз және сәуір айларында тіркелді.
Түйін сөздер: ауа температурасы, параметрлік емес Манн-Кендалл тесті, ко-кригинг әдісі.
ТЕКУЩИЕ ТЕНДЕНЦИИ ТЕМПЕРАТУРЫ ВОЗДУХА НА ВОСТОКЕ КАЗАХСТАНА
M.M. Махамбетова1*, О. Шатыр2 PhD, А.С. Нысанбаева1 к.г.н.
Казахский национальный университет имени аль-Фараби, Алматы, Казахстан
Университет Юзунку Йил, Ван, Турция
E-mail: [email protected]
1
2
По данным мировых исследований, за последние десятилетия наблюдается тенденция
повышения температуры воздуха и увеличение частоты экстремальных погодных явлений. Изучение климатической предрасположенности отдельных регионов к экстремальным явлениям, в частности пожаров, является актуальной проблемой современности.
Целью данного исследования является изучение современных трендов температуры
воздуха на востоке Казахстана в годовом, сезонном и месячном масштабе. Для анализа
были использованы непараметрический тест Манн-Кендалла и оценка наклона Сена, а
для визуализации данных был использован метод ко-кригинг в ArcGIS. В результате данного исследования, были выявлено, что на большей территории востока Казахстана наблюдается значительная тенденция к повышению средней и максимальной температуры.
Так же было отмечено, что основные статистически значимые изменения наблюдаются
в весенние и летние сезоны. В этот период изменения на некоторых станциях достигают
от 0,2 до 1,0 °C за десять лет. Наиболее последовательные и значимые тенденции к повышению температуры были зафиксированы в марте и апреле.
Ключевые слова: температура воздуха, непараметрический тест Манн-Кендалла, метод ко-кригинга.
48
Hydrometeorology and ecology №4 2024
About the authors / Авторлар туралы мәліметтер / Сведения об авторах:
M.M. Makhambetova – PhD student of the department of meteorology and hydrology of al-Farabi Kazakh National
university, al-Farabi ave., 71, Almaty, [email protected]
Onur Satir – PhD, professor of Faculty of landscape architecture, Head of remote sensing center, Van, Turkey,
A.S. Nyssanbaeva – Candidate of geographical sciences, Head of the department of meteorology and hydrology of
al-Farabi Kazakh National university, al-Farabi ave., 71, Almaty, [email protected]
M.M. Махамбетова – әл-Фараби атындағы Қазақ ұлттық университетінің метеорология және гидрология кафедрасының PhD студенті, әл-Фараби даңғылы, 71, Алматы, [email protected]
О. Шатыр – PhD, ландшафтық сәулет факультетінің профессоры, қашықтықтан зондтау орталығының жетекшісі,
Ван, Түркия, [email protected]
А.С.Нысанбаева – география ғылымдарының кандидаты, әл-Фараби атындағы қазақ Ұлттық университетінің метеорология және гидрология кафедрасының меңгерушісі, әл-Фараби даңғылы., 71, Алматы,
M.M. Махамбетова – студент PhD кафедры метеорологии и гидрологии Казахского национального университета
имени аль-Фараби, пр. аль-Фараби, 71, Алматы, [email protected]
О. Шатыр – PhD , профессор факультета ландшафтной архитектуры, руководитель центра дистанционного зондирования, Ван, Турция, [email protected]
А.С.Нысанбаева – кандидат географических наук, заведующий кафедрой метеорологии и гидрологии Казахского национального университета имени аль-Фараби, пр. аль-Фараби, 71, Алматы, [email protected]
Authors’ contribution / Авторлардың қосқан үлесі / Вклад авторов:
M.M. Makhambetova - сoncept development, methodology development, creating software, conducting statistical
analysis, conducting a research, preparing and editing the text, visualization
Onur Satir - сoncept development, methodology development, creating software, preparing and editing the text,
visualization
A.S. Nyssanbaeva - сoncept development, methodology development, preparing and editing the text, visualization
M.M. Махамбетова - тұжырымдаманы әзірлеу, әістемені әзірлеу, бағдарламалық жасақтама жасау, статистикалық
талдау жүргізу, зерттеу жүргізу, мәтінді дайындау және өңдеу, көрнекілік
О. Шатыр - тұжырымдаманы әзірлеу, әістемені әзірлеу, бағдарламалық жасақтама жасау, мәтінді дайындау және
өңдеу, көрнекілік
А.С.Нысанбаева - тұжырымдаманы әзірлеу, әістемені әзірлеу, мәтінді дайындау және өңдеу, көрнекілік
M.M. Махамбетова – разработка концепции, разработка методологии, создание программного обеспечения, проведение статистического анализа, проведение исследования, подготовка и редактирование текста, визуализация
О. Шатыр – разработка концепции, разработка методологии, создание программного обеспечения, подготовка и
редактирование текста, визуализация
А.С.Нысанбаева – разработка концепции, разработка методологии, подготовка и редактирование текста, визуализация
49
