ABSTRACT
This study aims to investigate variability in precipitation and air temperature data of all provinces in Turkey. The Innovative Trend Pivot Analysis Method (ITPAM) was used to analyze the trend in precipitation (mm) and air temperature (oC) data of Turkey in this study. Analyzing the country as a whole using this method is one of the strengths of the study. 30-year data sets between 1991 and 2020 were analyzed. As a result of the study, trend analysis results of precipitation and air temperature data of 81 provinces in Turkey were obtained. The significance level in trends was determined as 5%. Forty-one percent of monthly total precipitation data of Turkey showed an increasing trend, 41% showed a decreasing trend, while 18% showed no trend in this study. In the standard deviation of precipitation data, 44% of the data showed an increasing trend, 42% showed a decreasing trend and 14% showed no trend. Moreover, it was concluded that there was an increasing trend of 67% in monthly average air temperature data and an increasing trend of 46% in the standard deviation data. According to these results, it is concluded that monthly average air temperature data has a largely increasing trend.
HIGHLIGHTS
Meteorological data of Turkey were analyzed for the first time with ITPAM.
The effect of climate change was investigated in all provinces of Turkey.
Forty-one percent of precipitation data of Turkey showed an increasing trend, 41% showed a decreasing trend, while 18% showed no trend.
Sixty-seven percent of temperature data of Turkey showed an increasing trend, 27% showed a decreasing trend, while 6% showed no trend.
INTRODUCTION
The effects of climate change on hydrological and meteorological data sometimes reach disaster proportions. These disastrous effects cause floods in some places and droughts in others. It is observed that there is an increasing trend in the frequency of these events in recent years. It is sometimes insufficient to define extreme disasters such as droughts and floods only through average values. Therefore, it is important to analyze and classify the measured meteorological and hydrological data within themselves (Şen 2010; Dabanlı 2017). In order to prevent or mitigate the impacts of climate change, hydro-meteorological data such as runoff, precipitation, and temperature should be adopted with regional-scale approaches in degree to predict possible future changes (Aktaş 2020). Trend analysis methods are one of the methodological approaches that estimate and describe the possible impacts of climate change at small scales (Şen 2021).
The purpose of trend analysis is to detect a change in observed time series data such as precipitation and temperature. Trend identification, interpretation, and forecasting help data processing to detect and address visible and hidden problems in environmental changes, such as the effects of climate change on the water cycle. Trend analysis determines whether the general trend of time series data is increasing (upward), decreasing (downward,) or trendless (neutral). In addition, a linear or nonlinear temporal trend is a continuous and systematic increase or decrease of data along the time axis. Innovative trend analysis (ITA) techniques are used objectively to solve sustainable management issues logically and quantitatively (Şen 2017a, 2017b). Trend analysis of natural, social, and artificial phenomena is performed to identify systematic changes in a dataset of at least 30 years. In the last 30 years, it has been observed that the number of trend studies and applications related to hydro-meteorological time series as climate change has accelerated after the studies by Mann (1945) and Kendall 1975, known as Mann–Kendall (MK) (Mann 1945; Kendall 1975; Şen 2017a, 2017b).
There are many studies in the literature on time series trend components and trend analysis. The pioneer of these and one of the most widely used until today is the Mann method, which was introduced by Mann in his publication ‘Nonparametric Tests Against Trend’ in 1945 (Mann 1945). In his study, Şen (1968) argued that Kendall's (Kendall 1955) regression coefficient β is error prone and sensitive to normal distribution. He also examined its correlation based on Tau with a simple and robust (point and interval) β estimator. Yu et al. (1993) analyzed 17 components of water quality data from 15 stations of 4 river basins in Kensas State using nonparametric trend methods. As a result of their study, they presented that the concentrations of calcium, specific conductance, total hardness, total dissolved solids, potassium, sulfate, sodium, chloride, amanioc, total phosphorus, suspended sediment, and organic nitrogen showed a decreasing trend. In their homogeneity tests, they concluded that both station and basin-wide trends were not homogeneous. Reihan et al. (2012) analyzed flood data (maximum flow, maximum flow height, and timing) for the spring season of the Baltic countries for the periods 1922–2008, 1941–2008, 1961–2008, and 1991–2008. The data from 70 stations were analyzed by MK and Sen methods. As a result of the study, they concluded that the maximum flow and flow height of the spring season showed a decreasing trend throughout the long period, and there were trends to be considered only in the maximum flow data during the 1991–2008 period. Şen (2012) introduced the ITA method for the first time in his study. In this study, Sen documented the validity of MK and Spearman's Rho tests and the possibility of the ITA method to overcome these limitations in the case of the independent nature of the time series, the long structure, and the normality of the distribution by using Monte Carlo simulations. Sen's new approach avoids all the limitations of MK and Spearman's Rho methods and also shows that it is possible to calculate the trend magnitude from squared area plots. He gave an application of this method for a series of rainfall and runoff time series from different parts of the world. Many other works in this area have been reviewed in the literature (Sayemuzzaman & Jha 2014; Hussain et al. 2015; Birara et al. 2018; Chang et al. 2018; Güçlü 2018; Kabanda 2018; Malik & Kumar 2020; Hussain et al. 2021; Buyukyildiz 2023; Koycegiz & Buyukyildiz 2023a, 2023b; Köyceğiz & Büyükyıldız 2023c). Şen (2021) proposed the trend polygon star concept method. Ceribasi et al. (2021b) analyzed 22 years (1996–2017) of monthly average temperature data of six stations in Susurluk Basin of Turkey by using innovative polygonal trend analysis (IPTA) and trend polygon star concept methods. Polygon graphs were created for each station. The literature review shows that the most commonly used methods for identifying a monotonic trend and statistical quantification of its slope for a given time series are Sen's ITA methods and classical MK trend analysis methods. However, disadvantages of the MK test are sample size, normality of data, serial independence of the given time series, pre-whitening, and lack of serial comparison between different parts of the same record (Şen et al. 2019). Sen's innovative assumptions are calculated by a regression approach of the trend slope magnitude. In the past, hydro-meteorological time series have been considered stationary stochastic processes for the purposes of climate change and trend detection (Şen et al. 2019). Sen argued that this assumption is not valid due to dynamic anthropogenic impacts on climate, basins, and atmosphere (Şen 2017b).
Trend analysis is constantly on the research and application agenda, and scholarly research has shown that innovative methodologies for the trend analysis are being developed and even existing approaches are being modified. Şen's innovation method, ITA, innovative triangular trend analysis, and IPTA are trend analysis methods commonly used in academic studies (Şen 2012, 2014; Ceribasi et al. 2021a). ITA easily interprets the visual inspection of the trend type (increasing, decreasing, or no trend) and then numerically calculates the trend slope. The ITA method uses the Cartesian coordinate system, and data on a 1:1 (45°) straight line correspond to no trend, and any deviation from the 1:1 line represents the presence of a trend. This nonparametric ITA approach has been used by many researchers in many scientific studies worldwide (Elouissi et al. 2016; Wu & Qian 2017; Dabanlı & Şen 2018; Almazroui et al. 2019). Şen et al. (2019) introduced IPTA to explore trend probabilities in monthly hydro-meteorological time series. IPTA is a nonparametric approach to identify trends and trend transitions between successive segments of two equal parts from the original hydro-meteorological time series and is analyzed by the formation of an irregular 12-sided trend polygon. Numerical interpretations and inferences are obtained from a given time series. This method is independent of assumptions and can be applied directly. There are few studies in the literature that use the IPTA method for the analysis of hydro-meteorological time series data (Şen et al. 2019; Achite et al. 2021; Ahmed et al. 2021; Ceribasi & Ceyhunlu 2021; Ceribasi et al. 2021b; Akçay et al. 2022; Hırca et al. 2022). The Innovative Trend Pivot Analysis Method (ITPAM) is a nonparametric method that is independent of the sample size and the normality of the dataset, is the first in trend analysis methods, and determines risk classes by establishing the relationship between data.
Therefore, this study aims to investigate variability in monthly total precipitation and monthly average air temperature data of all provinces in Turkey. Turkey is among the countries affected by climate change. Therefore, it is extremely important to investigate in which province or basin of the country climate change is more effective because it would be more beneficial to make location improvements to reduce the impact of climate change. This study includes all provinces of the country and analyzes both precipitation and air temperature data with the ITPAM (it is applied for the first time to a study across the country) and reveals the results of climate change. Therefore, this study will both make a great contribution to the literature and direct the studies on climate change in the country. The study has great innovation in this aspect. Analyses were made by taking the average and standard deviation of the 30-year monthly total precipitation and monthly average air temperature data between 1991 and 2020.
MATERIALS AND METHODS
Study area
Meteorological data
The data used in the study are monthly total precipitation and monthly average air temperature data. These data were taken from the General Directorate of Meteorology of Turkey, and since there are no deficiencies in the data, it has a quality structure and the data used in the study is homogeneous. The length of these data, which covers all provinces of Turkey, is 30 years and covers the period 1991–2020. These data were taken on a daily basis, rainfall was processed as a monthly total, and air temperatures were treatment as a monthly average for analysis. Station information of the data is given in Table 1.
General information of meteorological stations
No . | Station name . | Station No . | Altitude (m) . | No . | Station name . | Station No . | Altitude (m) . |
---|---|---|---|---|---|---|---|
01 | Adana | 17351 | 23 | 42 | Konya | 17244 | 1,026 |
02 | Adiyaman | 17265 | 669 | 43 | Kütahya | 17155 | 969 |
03 | Afyonkarahisar | 17190 | 1,013 | 44 | Malatya | 17199 | 972 |
04 | Ağri | 17099 | 1,640 | 45 | Manisa | 17186 | 42 |
05 | Amasya | 17085 | 392 | 46 | Kahramanmaraş | 17255 | 568 |
06 | Ankara | 17130 | 870 | 47 | Mardin | 17275 | 1,150 |
07 | Antalya | 17300 | 43 | 48 | Muğla | 17292 | 646 |
08 | Artvin | 17045 | 597 | 49 | Muş | 17204 | 1,300 |
09 | Aydin | 17234 | 92 | 50 | Nevşehir | 17193 | 1,250 |
10 | Balikesir | 17150 | 101 | 51 | Niğde | 17250 | 1,208 |
11 | Bilecik | 17120 | 526 | 52 | Ordu | 17033 | 10 |
12 | Bingöl | 17203 | 1,177 | 53 | Rize | 17040 | 4 |
13 | Bitlis | 17205 | 1,545 | 54 | Sakarya | 17069 | 31 |
14 | Bolu | 17070 | 732 | 55 | Samsun | 17030 | 10 |
15 | Burdur | 17238 | 1,025 | 56 | Siirt | 17210 | 895 |
16 | Bursa | 17116 | 100 | 57 | Sinop | 17026 | 32 |
17 | Çanakkale | 17112 | 3 | 58 | Sivas | 17090 | 1,285 |
18 | Çankiri | 17080 | 730 | 59 | Tekirdağ | 17056 | 3 |
19 | Çorum | 17084 | 798 | 60 | Tokat | 17086 | 623 |
20 | Denizli | 17237 | 450 | 61 | Trabzon | 17038 | 37 |
21 | Diyarbakir | 17280 | 674 | 62 | Tunceli | 17165 | 914 |
22 | Edirne | 17050 | 48 | 63 | Şanliurfa | 17270 | 547 |
23 | Elaziğ | 17201 | 1,015 | 64 | Uşak | 17188 | 921 |
24 | Erzincan | 17094 | 1,214 | 65 | Van | 17172 | 1,661 |
25 | Erzurum | 17096 | 1,893 | 66 | Yozgat | 17140 | 1,317 |
26 | Eskişehir | 17124 | 732 | 67 | Zonguldak | 17022 | 136 |
27 | Gaziantep | 17261 | 838 | 68 | Aksaray | 17192 | 900 |
28 | Giresun | 17034 | 84 | 69 | Bayburt | 17089 | 1,550 |
29 | Gümüşhane | 17088 | 1,210 | 70 | Karaman | 17246 | 1,250 |
30 | Hakkari | 17285 | 1,720 | 71 | Kirikkale | 17135 | 700 |
31 | Hatay | 17372 | 85 | 72 | Batman | 17282 | 550 |
32 | Isparta | 17240 | 1,043 | 73 | Şirnak | 17950 | 377 |
33 | Mersin | 17340 | 10 | 74 | Bartin | 17020 | 25 |
34 | Istanbul | 17061 | 30 | 75 | Ardahan | 17046 | 1,800 |
35 | Izmir | 17220 | 10 | 76 | Iğdir | 17100 | 858 |
36 | Kars | 17097 | 1,750 | 77 | Yalova | 17119 | 2 |
37 | Kastamonu | 17074 | 800 | 78 | Karabük | 17078 | 278 |
38 | Kayseri | 17196 | 1,071 | 79 | Kilis | 17262 | 640 |
39 | Kirklareli | 17052 | 203 | 80 | Osmaniye | 17355 | 120 |
40 | Kirşehir | 17160 | 985 | 81 | Düzce | 17072 | 149 |
41 | Kocaeli (Izmit) | 17066 | 76 |
No . | Station name . | Station No . | Altitude (m) . | No . | Station name . | Station No . | Altitude (m) . |
---|---|---|---|---|---|---|---|
01 | Adana | 17351 | 23 | 42 | Konya | 17244 | 1,026 |
02 | Adiyaman | 17265 | 669 | 43 | Kütahya | 17155 | 969 |
03 | Afyonkarahisar | 17190 | 1,013 | 44 | Malatya | 17199 | 972 |
04 | Ağri | 17099 | 1,640 | 45 | Manisa | 17186 | 42 |
05 | Amasya | 17085 | 392 | 46 | Kahramanmaraş | 17255 | 568 |
06 | Ankara | 17130 | 870 | 47 | Mardin | 17275 | 1,150 |
07 | Antalya | 17300 | 43 | 48 | Muğla | 17292 | 646 |
08 | Artvin | 17045 | 597 | 49 | Muş | 17204 | 1,300 |
09 | Aydin | 17234 | 92 | 50 | Nevşehir | 17193 | 1,250 |
10 | Balikesir | 17150 | 101 | 51 | Niğde | 17250 | 1,208 |
11 | Bilecik | 17120 | 526 | 52 | Ordu | 17033 | 10 |
12 | Bingöl | 17203 | 1,177 | 53 | Rize | 17040 | 4 |
13 | Bitlis | 17205 | 1,545 | 54 | Sakarya | 17069 | 31 |
14 | Bolu | 17070 | 732 | 55 | Samsun | 17030 | 10 |
15 | Burdur | 17238 | 1,025 | 56 | Siirt | 17210 | 895 |
16 | Bursa | 17116 | 100 | 57 | Sinop | 17026 | 32 |
17 | Çanakkale | 17112 | 3 | 58 | Sivas | 17090 | 1,285 |
18 | Çankiri | 17080 | 730 | 59 | Tekirdağ | 17056 | 3 |
19 | Çorum | 17084 | 798 | 60 | Tokat | 17086 | 623 |
20 | Denizli | 17237 | 450 | 61 | Trabzon | 17038 | 37 |
21 | Diyarbakir | 17280 | 674 | 62 | Tunceli | 17165 | 914 |
22 | Edirne | 17050 | 48 | 63 | Şanliurfa | 17270 | 547 |
23 | Elaziğ | 17201 | 1,015 | 64 | Uşak | 17188 | 921 |
24 | Erzincan | 17094 | 1,214 | 65 | Van | 17172 | 1,661 |
25 | Erzurum | 17096 | 1,893 | 66 | Yozgat | 17140 | 1,317 |
26 | Eskişehir | 17124 | 732 | 67 | Zonguldak | 17022 | 136 |
27 | Gaziantep | 17261 | 838 | 68 | Aksaray | 17192 | 900 |
28 | Giresun | 17034 | 84 | 69 | Bayburt | 17089 | 1,550 |
29 | Gümüşhane | 17088 | 1,210 | 70 | Karaman | 17246 | 1,250 |
30 | Hakkari | 17285 | 1,720 | 71 | Kirikkale | 17135 | 700 |
31 | Hatay | 17372 | 85 | 72 | Batman | 17282 | 550 |
32 | Isparta | 17240 | 1,043 | 73 | Şirnak | 17950 | 377 |
33 | Mersin | 17340 | 10 | 74 | Bartin | 17020 | 25 |
34 | Istanbul | 17061 | 30 | 75 | Ardahan | 17046 | 1,800 |
35 | Izmir | 17220 | 10 | 76 | Iğdir | 17100 | 858 |
36 | Kars | 17097 | 1,750 | 77 | Yalova | 17119 | 2 |
37 | Kastamonu | 17074 | 800 | 78 | Karabük | 17078 | 278 |
38 | Kayseri | 17196 | 1,071 | 79 | Kilis | 17262 | 640 |
39 | Kirklareli | 17052 | 203 | 80 | Osmaniye | 17355 | 120 |
40 | Kirşehir | 17160 | 985 | 81 | Düzce | 17072 | 149 |
41 | Kocaeli (Izmit) | 17066 | 76 |
Innovative trend pivot analysis method
ITPAM was presented in 2021. This method avoids some of the shortcomings of the IPTA method. ITPAM is a nonparametric analysis method that is independent of the sample size and normality of the dataset. ITPAM is the first trend analysis method that determines the risk classes of the trend by establishing the relationship between the data. The biggest feature that distinguishes this method from other methods is the division of trend regions into five different regions. It also provides the determination of risk classes by correlating the connection between the data. This method can be used in many different fields such as engineering, meteorology, economy, and water resources. It can analyze data on a daily, monthly, annual, and seasonal basis. Considering that ITPAM will be applied to monthly data, the data are written as a matrix. When creating the matrix, the row data consists of monthly data in a year. Let X1n, X2n, ……, Xin be the monthly meteorological data, where i represents the number of months and n represents the number of years. The written matrix is divided into two equal series, upper and lower, in the middle and becomes a matrix format. After the data series are divided into two equal parts, the mean and standard deviation of each series are calculated separately. The averages of the upper series are placed on the X-axis in the Cartesian system and the lower series on the Y-axis. The application and analysis steps of ITPAM are as follows:
2. Statistical calculations are completed in each series (such as mean, standard deviation, min or max) according to the data period (monthly, annual, seasonal…).
3. The first series of data is placed on the horizontal axis and the second series on the vertical axis.
4. Risk and trend classes are identified in the graphs.
5. The points where the data intersect are drawn as polygons.
Figure 2 shows the ITPAM analysis on monthly data. The graph on the left is the actual ITPAM graph. As seen in the figure, risk classes were determined by dividing the horizontal and vertical axes into five equal parts. The graph on the right is the revised graph of the IPTA method. This graph is again divided into five regions according to the degree of trend. Thus, the risk status of any point on the right graph is read from the left graph. For example, while the September data are in a high-degree downtrend, it is in the first degree risky class in the left graph. In this case, it can be concluded that there is a large variation among the September datasets. In addition, the following information is obtained from the aforementioned graph.
1. The variation between two consecutive months is expressed by the length of the straight line between the 2 months.
2. If the horizontal and vertical slopes of the straight line connecting each 2 consecutive months are close to each other, it means that the monthly data contribute significantly to the mean hydro-meteorological variation.
3. The variation between each 2 months is assumed to be a linear variation. Since this assumption is smaller than the 1-year assumption, the result of the trend analysis is more realistic.
4. In ITPAM, if the polygons are narrow and around a single direction, it means that the data are homogeneous and isotropic and consist of uniform variation, while if the polygon is wide and complex, it means that the data are heterogeneous.
5. The small size of the polygon indicates the consistency and stability of the hydro-meteorological data.
- 6. ITPAM can also calculate the length and slope of the trend between the data. The slope and length of the trend between two consecutive points are calculated as shown in Equations (1) and (2):where S is the trend slope, AB is the trend length, X1 and X2 are two consecutive points on the horizontal axis on the Cartesian system, and Y1 and Y2 are two consecutive points on the vertical axis.
ANALYSIS RESULT
Changes in monthly precipitation
When January and February are examined from analysis results of monthly total precipitation given in Figure 4, it is seen that there is an increasing trend with the first and second degree risk in Turkey precipitation data throughout January, but Antalya, Eskişehir, Karabük, Kahramanmaraş, Diyarbakır, Şanlıurfa, Hakkari, Şırnak, and Artvin provinces are in the decreasing trend regions with the first and second degree risk. Ardahan, Kars, Iğdır, Adana, and Kastamonu provinces are in the fifth degree risky increasing trend regions.
When precipitation data for February are analyzed, it is concluded that there is a first and second degree risky increasing trend in the Marmara region, Balıkesir and Tekirdağ provinces are in the fifth degree risky increasing trend region, and İzmit province has a second degree risky decreasing trend. While a decreasing trend with first degree risk was observed in the Eastern Black Sea, an increasing trend with fourth degree risk was observed in Western Black Sea. However, it is concluded that there is a decreasing trend with a third degree risk in Karabük and Sinop provinces. In Eastern and Southeastern Anatolia regions, it is concluded that there is a decreasing trend with first and second degree risk, Kars and Erzincan provinces are in the decreasing trend region with the fifth degree risk, but Van, Batman, Iğdır, Malatya, and Elazığ provinces are in the increasing trend region with first and second degree risk. In the Mediterranean region, Hatay, Kahramanmaraş, and Antalya provinces are in the decreasing trend region with second and third degree risk, while Burdur province is in the decreasing trend region with the fifth degree risk. Isparta province has an increasing trend with third degree risk, while Osmaniye, Mersin, and Adana provinces have an increasing trend with first and second degree risk. It is concluded that there is a third, fourth, and fifth degree decreasing trend in the Aegean region, but Muğla and Aydın provinces remain in the second degree risky increasing trend region. It is concluded that there is an increasing trend with the second degree risk throughout the Central Anatolia region, while Yozgat and Nevşehir provinces remain in the decreasing trend region with first and second degree risk.
Standard deviation of monthly precipitation
Standard deviation of monthly precipitation of Adana Province of Turkey.
Standard deviation of monthly precipitation of all provinces of Turkey.
When January and February are examined from analysis results of standard deviation of monthly total precipitation given in Figure 6, it is seen that there is an increasing trend with first and second degree risk in standard deviation of Turkey's precipitation data throughout January, but Antalya, Isparta, Burdur, Karaman, Adana, Yozgat, Gaziantep, Kilis, Şanlıurfa, Diyarbakır, Elazığ, Sivas, Şırnak, Sakarya, Kocaeli, Yalova, Bursa Çanakkale, Tekirdağ, Kırklareli, Edirne, and the Western Black Sea region are in the second and third degree risk decreasing trend regions.
When the analysis of the standard deviation precipitation data for February is analyzed, it is observed that there is an increasing trend with first and second degree risk throughout the Marmara region, Balıkesir province is in the decreasing trend region with the second degree risk, a decreasing trend with the second and third degree risk is observed in the Eastern Black Sea, while an increasing trend with the fourth degree risk is observed in the Western Black Sea. However, it is concluded that Düzce and Sinop provinces have a decreasing trend with the fourth degree risk. In the Eastern and Southeastern Anatolia regions, there is a decreasing trend with first and second degree risk, but Iğdır and Van provinces are in the region of increasing trend with the fourth degree risk, while Adıyaman, Gaziantep, and Kilis provinces are in the region of increasing trend with the second degree risk. In the Mediterranean region, Hatay, Adana, Burdur, and Isparta provinces are in the fourth and fifth degree risk, Kahramanmaraş and Osmaniye provinces are in the first degree risk increasing trend region, while Antalya province is in the fourth degree risk decreasing trend region. It is concluded that there is a decreasing trend of second and third degree in the Aegean region, but Uşak and Afyon provinces remain in the increasing trend region with second and third degree risk. It is concluded that there is a second degree risky increasing trend in the Central Anatolia region, while Çankırı province remains in the third degree risky decreasing trend region.
Changes in monthly air temperature
When January and February are examined from analysis results of monthly average air temperature given in Figure 8, it is seen that there is a fifth degree risky increasing trend in Turkey's air temperature data throughout January, but Çanakkale, Balıkesir, Çankırı, Trabzon, Diyarbakır, Mardin, Batman, Şırnak, Siirt, Muş, Hatay provinces, and the coastal parts of the Aegean region are in the fifth degree risky decreasing trend regions. It is concluded that İzmir province has a decreasing trend with a third degree risk, while Kars and Kayseri provinces are in the increasing trend regions with a fourth degree risk. The analysis of February average air temperature data concluded that there is an increasing trend tendency with a fourth degree risk throughout Turkey, but there is an increasing trend tendency with a fifth degree risk in Şırnak, Rize, Trabzon, Bayburt, and Eskişehir provinces.
Standard deviation of monthly air temperature
Standard deviation of monthly air temperature of Adana Province of Turkey.
Standard deviation of monthly air temperature of all provinces of Turkey.
When January and February are examined from analysis results of standard deviation of monthly average air temperature given in Figure 10, it is concluded that there is a decreasing trend with first, second, and third degree risk in air temperature data of Turkey in January, but there is an increasing trend with first degree risk in some parts of the Marmara region and in Zonguldak, Sinop, Artvin, Kars, Iğdır, Van, Şırnak, Batman, Diyarbakır, Bingöl, Malatya, Hatay, Adana, and Kayseri provinces. When the standard deviation air temperature data for February are analyzed, it is concluded that there is a decreasing trend tendency with first, second, and third degree risk in Marmara, Southeastern Anatolia, Aegean, and Mediterranean regions, but there is an increasing trend tendency with first and second degree risk in Black Sea, Eastern Anatolia, and Central Anatolia regions.
DISCUSSION
When the analysis results of monthly total precipitation data of Turkey are examined, it is detected that 41% of Turkey has a decreasing trend, 41% has an increasing trend, and 18% has no trend. In addition, it is concluded that 9% of the regions with increasing trend tendency are in the fifth degree risk, 12% in the fourth degree risk, 9% in the third degree risk, 7% in the second degree risk, and 4% in the first degree risk increasing trend region. It is concluded that 8% of the regions with decreasing trend tendency are in the fifth degree risk, 12% in the fourth degree risk, 8% in the third degree risk, 7% in the second degree risk, and 5% in the first degree risk decreasing trend tendency. Demirgül et al. determined that a decrease of 0.6 mm (0.1%) (from 574 to 573.4 mm) of the normal rainfall throughout the country. They observed that annual air temperature values tend to increase with a change of 0.46 °C (3.51%) (from 13.09 to 13.55 °C). These results coincide with the analysis results of the study (Demirgül et al. 2022). Touhedi et al. investigated the variability of standard annual maximum precipitation in all provinces of Turkey in their study. As a result of the research, they observed an increasing trend in 45% of all provinces. As a result of the analysis of this study, this value was found to be 41%. Therefore, it was observed that the analysis results overlapped (Touhedi et al. 2023). When standard deviation analysis results of monthly total precipitation data are examined, it is detected that 42% of Turkey has a decreasing trend, 44% has an increasing trend, and 14% has no trend. In addition, 8% of the regions with increasing trend tendency are in the fifth degree risk, 10% in the fourth degree risk, 14% in the third degree risk, 9% in the second degree risk, and 3% in the first degree risk increasing trend region. It is concluded that 6% of the regions with decreasing trend tendency are in the fifth degree risk, 10% in the fourth degree risk, 13% in the third degree risk, 9% in the second degree risk, and 3% in the first degree risk decreasing trend tendency.
When the analysis results of monthly average air temperature data of Turkey are examined, it is detected that 6% of Turkey has a decreasing trend, 67% has an increasing trend, and 27% has no trend. In addition, 22% of the regions with increasing trend tendency were found to be in the fifth degree risk, 12% in the fourth degree risk, 8% in the third degree risk, 17% in the second degree risk, and 9% in the first degree risk increasing trend region. It was concluded that 1% of the regions with a decreasing trend tendency were in the decreasing trend tendency with a fifth degree risk, 1% with a fourth degree risk, 1% with a third degree risk, 1% with a second degree risk, and 1% with a first degree risk. Ciftci and Sahin stated in their study that the most striking change in Turkey's average air temperature occurred in the summer months, with an increase of 1.56 °C. In addition, extreme air temperature indices indicate that summer is the season with the highest temperature change (Ciftci & Sahin 2023). Therefore, these results gave similar results to the analysis results of air temperature parameter of this study. Bahadır stated that the increase in temperature in the Black Sea region of Turkey was 0.3 °C, and the increase in temperature in the Mediterranean region was 0.5–0.6 °C. These results showed that similar results were obtained in the analysis results of these regions of the study, especially in the GIS maps in November and December (Bahadır 2011). Serkendiz et al. evaluated Turkey's drought in their study. As a result of the analysis using both precipitation and evaporation data, they stated that drought has generally increased in most parts of Turkey according to the MK method. In the analysis results of this study, an increasing trend in precipitation was observed in some regions and a decreasing trend in some regions. However, increasing trends in temperatures have been observed across the country. Therefore, it can be seen that the analysis results are similar (Serkendiz et al. 2024). When standard deviation analysis results of monthly average air temperature data are examined, it is detected that 43% of Turkey has a decreasing trend, 46% has an increasing trend, and 11% has no trend. In addition, 8% of the regions with increasing trend tendency are in the fifth degree risk, 11% in the fourth degree risk, 14% in the third degree risk, 11% in the second degree risk, and 2% in the first degree risk increasing trend region. It was concluded that 10% of the regions with a decreasing trend tendency were in the fifth degree risk, 10% in the fourth degree risk, 11% in the third degree risk, 11% in the second degree risk, and 1% in the first degree risk decreasing trend tendency.
CONCLUSION
This study aims to investigate variability in monthly total precipitation and monthly average air temperature data of all provinces in Turkey. The strongest aspect of the study is that it analyzes the entire country and applies the ITPAM method to meteorological data across the country for the first time. However, the use of precipitation and air temperature parameters in the study and the lack of use of other hydro-meteorological parameters (such as lakes, streams, etc.) are stated as a weakness. In this study, both average and standard deviation analyses of 30-year precipitation and air temperature data between 1991 and 2020 were carried out with the ITPAM method, one of the most up-to-date trend methods. As a result of the study, trend analysis results of precipitation and air temperature data of 81 provinces in Turkey were obtained, interpreted separately, and Turkey precipitation-air temperature trend maps were obtained. As a result, it is concluded that monthly average air temperature data of Turkey has a largely increasing trend. It is also recommended that all future climate change studies and action plans to be prepared on these issues should take into account the recommendations listed below.
Water management should be planned within the framework of a holistic approach and developed in line with this principle.
In degree to monitor water resources, systems that track the use of water resources should be made widespread.
Awareness projects should be prepared and supported to ensure and promote water conservation.
Incentives should be provided for rainwater collection systems to ensure efficient use of rainwater by the public.
Water waste in agriculture should be prevented by applying new technologies in irrigation systems.
New designs of water structures should be applied to minimize this situation by taking into account the decreases in surface water through evaporation and evaporation with the decreases in precipitation and increases in air temperature levels.
Increasing these meteorological parameters and investigating the changes in water resources (such as lakes, streams, etc.) across the country are recommended for future studies.
ACKNOWLEDGEMENTS
We are sincerely thankful to the General Directorate of Meteorology in Turkey for providing related data for this study.
AUTHOR CONTRIBUTION
The analysis in this article was made by A.I.C. A.I.C. and G.C. created the graphics. The article was written by A.I.C. and G.C. A.I.C. and G.C. reviewed and approved the article.
FUNDING
No funding was received from any organization for this study.
ETHICS APPROVAL
All authors have read, understood, and have complied as applicable with the statement on ‘Ethical responsibilities of Authors’ as found in the Instructions for Authors and are aware.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.