Analysis of total monthly precipitation of Susurluk Basin in Turkey using innovative polygon trend analysis method

The effects of climate change caused by global warming can be seen in changes of climate variables such as precipitation, humidity, and temperatures. These effects of global climate change can be interpreted as a result of the examination of meteorological parameters. One of the most effective methods to investigate these effects is trend analysis. The Innovative Polygon Trend Analysis (IPTA) method is a trend analysis method that has emerged in recent years. The distinctive features of this method compared with other trend methods are that it depends on time series and can compare data series among themselves. Therefore, in this study, the IPTA method was applied to total monthly precipitation data of Susurluk Basin, one of Turkey’s important basins. Data from ten precipitation observation stations in Susurluk Basin were used. Data were provided by the General Directorate of State Meteorology Affairs. The length of this data series was 12 years (2006–2017). As a result of the study, since there is no regular polygon in IPTA graphics of each station, it is seen that precipitation data varies by years. While this change is seen increasingly at some stations, it is seen decreasingly at other stations.


INTRODUCTION
Climate change resulting from global warming shows its effect in almost every region of the world. The impact of this climate change occurs as water scarcity in some regions, and it occurs as floods in some regions. People experience great difficulties from both effects. Therefore, studies on the issue of climate change have been increasing recently. In particular, it is seen that studies performed as forward-prediction models are increasing. However, data used in these studies do not establish an approach in transition between days, weeks, months, and years. In this context, the Innovative Polygon Trend Analysis (IPTA) method establishes an approach in transition between days, weeks, months, and years. Hence, it is seen that the IPTA method will be used frequently in academic studies (Sen et al. ).
The mean and standard deviation changes in hydrometeorological variables are very important in different human activities such as water supply, hydroelectric power generation, agricultural activities, and irrigation practices.  precipitation data, focusing on extreme events. As a result, differences in temperature index distributions showed that they would be particularly prominent between the last two periods and for indices related to minimum temperature and that there would be a tendency towards more rainy conditions throughout the 20th century (Alexander et al. ).

Study area
Susurluk Basin is one of Turkey's most important basins.
The basin has a total precipitation area of 24,332 km 2 .
Important streams of the basin are Nilufer Stream,

Mustafakemalpasa Stream, Simav Stream, and Koca
Stream. Annual water potential is 6.08 × 10 9 m 3 . Uluabat data can be daily, monthly, or yearly. If the IPTA method is applied to monthly data written in a matrix format, row data will consist of monthly data in a year. Monthly meteorological data are X 1,n , X 2,n , ……, X i,n (i represents the number of months and n represents the number of years). The written matrix is divided into two equal series ing that this data set belongs to precipitation data, it is concluded that there will be an increase in precipitation in January, February, May, and June. In this way, the polygon cycle is completed. If data have a homogeneous structure, the result of analysis will consist of a single polygon. However, depending on the complexity of the data analyzed, more complex and multiple polygons may occur in the analysis.

RESULTS AND DISCUSSION
The IPTA method was applied to total monthly precipitation data of Susurluk Basin, one of Turkey's important basins.
General evaluation of arithmetic mean analysis results for each station in Figure 4 are given in Table 2.
When analysis results of Table 2  IPTA method graphics of standard deviation analysis results for each station are given in Figure 5.
General evaluation of standard deviation results for each station in Figure 5 are given in Table 3.
When the analysis results of Table 3 are examined, the fact that polygons in each station are irregular and complex arises because the arithmetic mean is not constant and data  Table 3 are analyzed for each station, upward arrows in months show that there is more precipitation than the  and Keles) are given in Table 4 and statistical values of arithmetic mean and standard deviation of the other five stations (Manyas, Dursunbey, Susurluk, Simav, and Mustafa Kemalpasa) are given in Table 5.
The results given in Table 4  were selected in Susurluk Basin. The length of precipitation data used in the study is 12 years (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). As a result of the study, IPTA graphics were created for each station. In addition, trend lengths and trend slopes of monthly total   precipitation data of each station were calculated. After these analyses, the following evaluations were made: • Since there is not a regular polygon in IPTA graphics for each station, it is seen that precipitation data varies by years.
• It is seen that this change increases in some stations and decreases in others.
• This increasing and decreasing variability emerges from climate change.
• Size of trend lengths and trend slopes shows how much variability there is between months. For example, for Bandirma Station, max. trend length is, respectively, 118.42 mm and 95.05 mm, and max. trend slope was calculated as À432 and 4.06. These values show that transition between two months is severe and it is seen that this violent transition is caused by climate change.
The following recommendations can be made to reduce this impact of climate change: • The carbon emission values of existing industrial factories in the study area should be checked regularly.
• To minimize use of fossil fuels, local people should be made conscious of the facts and be encouraged to reduce their usage.
• As a result of industrialization brought about by increasing population, green residential areas that will decrease greenhouse gas levels should be increased.
• Awareness should be raised among future generations on the protection of nature through education.
• Protecting water resources in the study area and informing the public about water consumption is important in terms of reducing the effects of climate change.

DATA AVAILABILITY STATEMENT
All relevant data are available from an online repository (https://www.mgm.gov.tr/).