Investigation of the usability of modified aSPI and eRDI indexes in Sanliurfa Province and determina

Abstract

Introduction

the location, severity and frequency of drought [2,3,13]. Numerous drought studies conducted in recent years have revealed that the frequency and severity of droughts have increased in many regions [9,14,15]. Drought is defined as rainfall that is less than normal or below the long-term average in the literature [10, 16]. In addition, four types of drought are defined in the literature. These drought types are meteorological, hydrological, agricultural, and socioeconomic drought. Meteorological drought is defined as receiving less rainfall than usual for at least 30 years and is the initial stage of drought. Agricultural drought is described as the inability of plants to meet their needs due to low soil moisture levels. It arises as a result of diminishing water supplies. Agricultural drought is the second stage of drought and occurs after meteorological drought. Hydrological drought is characterized by the reduction of underground and surface water resources due to lack of rainfall and is the third stage of drought. It occurs following an agricultural drought. It can continue the effect of hydrological drought for many years. Socio-economic drought examines the effect of drought on socio-economic systems and thus on people’s socio-economic status and behavior [17-19]. The causes of four types of drought and their consequences are summarized in Figure 1. The diagram emphasizes that the main reasons for the onset of drought are lack

The agriculture sector is most affected by drought and global warming because it is the sector that uses the most freshwater [1]. Therefore, in addition to combating these negativities, adaptation efforts must also be carried out. Recently, both academics and government officials have been making great efforts to cope with the effects of global warming, and countries are allocating large budgets for this [2-5]. These efforts include; switching from fossil fuels to renewable energy sources to reduce greenhouse gas emissions, switching to irrigation methods such as sprinkler and drip to save water and encouraging the cultivation of crops that consume less water, building underground dams to reduce evaporation, and conveying water through closed pipes. In addition, conducting drought analyses to take precautions against drought is also included in these efforts. [3,6-8]. Any disturbance in the agricultural sector or water resources jeopardizes the provision of basic requirements such as food and water, causing hunger and thirst. Therefore, it is critical that we give this issue more attention. Climate modeling and drought evaluations have been conducted often in recent years to avoid negative impacts on water acquisition, which is critical in the agriculture sector [3,9-12]. Drought can have devastating effects on the agricultural sector. Therefore, it is important to determine

Figure 1. Diagram showing the timing, causes, and effects of four drought types [20,21] [created by author]

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of rainfall and increased temperatures and that these effects negatively affect agricultural activities and living things [20,21]. The literature contains a wide variety of drought analysis techniques. Each method may obtain better results for different purposes or for different drought types in different regions. A method can be developed to overcome the shortcomings of another method, or a method can be improved by modification [22]. In literature, there are methods used for different drought indicators, such as the SPI, aSPI, RDI, eRDI, Percentage of Normal Index (PNI), Standardized Precipitation Evapotranspiration Index (SPEI), Aridity Index (AI), Streamflow Drought Index (SDI), Palmer Hydrological Drought Index (PHDI), Precipitation Deciles (PD), Normalized Difference Vegetation Index (NDVI), Erinç Drought Index (EDI), De Martonne-Gotmann Index (DMGI), Relative Soil Moisture (RSM), and Effective Drought Index (EDI), [3,21,23,24]. Some of these methods analyze the drought with only one data type. Some methods require many different data types for drought analysis [23,25]. Methods that require only one or two types of data are simple and practical. In addition, analysis can be performed in regions where sufficient data cannot be obtained with these methods [24]. Methods such as SPI, aSPI, PNI, DI, EDI, and SDI only analyze the drought with precipitation data. RDI, eRDI, DMGI, and AI methods require precipitation and temperature data for drought analysis. Methods such as PHDI, PMDI, and SPEI require more than two data types [13,21,23,26]. The SPI method is the most popular method in the literature and is a simple, useful method that can produce very good results. The RDI method, which has been widely used in scientific research for the last 15 years and has given good results, performs drought analyses using PET values ​​ and precipitation data. Meteorological and agricultural drought analyses can be performed with the SPI and the RDI methods [8,27,28]. In the literature, particular approaches have been created for the desired objectives, and new methods have been obtained [29]. For example, the DI method was created to solve the computational problems in the PNI approach [30]. Similarly, aSPI and eRDI methods were developed by using effective precipitation data instead of precipitation data in order to make more effective the SPI and RDI methods in agricultural drought analysis. The aSPI and the eRDI methods have been shown to give more accurate results in predicting agricultural droughts, especially in arid regions [12,29,31-33]. After the aSPI and eRDI methods emerged and their competence was proven, these methods can be used directly in agricultural drought analyses without any further research. In this way, the researcher saves time and labor. In addition, as seen in the results of this study, by determining drought classes more precisely, more accurate measures can be taken. This will also avoid economic loss.

The reason why aSPI and eRDI methods were used in this study in Türkiye is the idea that these methods can also give good results in Türkiye, especially since they give good results in a country under the influence of the Mediterranean climate such as Greece. For this reason, it would be beneficial to try these methods in a province of Turkey where agricultural activities are intensive. Sanlıurfa province, which is affected by drought and where crops with high water consumption such as cotton, pistachios and corn are grown, was suitable for applying these methods. In addition, the agricultural sector in Şanlıurfa province is very dependent on irrigation due to drought and high evaporation. Although large dams were built to meet the irrigation needs, this was not enough. Irrigation problems are experienced especially in the summer months. For this reason, it has become necessary to take some precautions. In this study, drought analyses with SPI, aSPI, RDI, and eRDI methods were performed for Sanlıurfa province. The DrinC (Drought index Calculator) program was used for the analyses. The results obtained from the methods used in the study were compared. The objective of this study is to ascertain more sensitive the agricultural drought situation of Sanlıurfa province. It was tried to determine whether the aSPI and eRDI methods, which are newly used in the literature can be used in determining agricultural drought in Şanlıurfa province. In addition, long-term temperature and precipitation trends were investigated using the MannKendall trend analysis method to determine the causes of drought in Sanlıurfa province. All results were visualized with graphics. When the studies in the literature are examined, it has been determined that aSPI and eRDI have not yet been used in Turkey. For this reason, this study is important in terms of showing the applicability of this modified methods in the drought-affected Sanlıurfa province. This study conducted for Şanlıurfa province aims to eliminate this deficiency in the literature.

Literature Review

In the above paragraph, it was stated that the use of aSPI and eRDI methods in the literature is limited, although they give good results. For this reason, drought analysis studies with these methods should be carried out as soon as possible, especially in regions or provinces where agricultural activities were intensively carried out in Turkey. In the limited number of studies in the literature, traditional methods and modified methods were generally compared. In some studies, drought analyses were conducted with traditional methods to determine the drought status of a region. These studies are given below. In a study conducted with the SPI method, monthly and annual drought analyses were conducted at Siirt, Gaziantep, and Siverek stations in the Southeastern Anatolia Region of Turkey. According to the annual analysis results, although there were differences between stations, it was determined that severe drought occurred between 12% and 20% [34].

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In another study conducted for the Southeastern Anatolia Region in Turkey, drought analyses were conducted for 18 stations using SPI and RDI methods. The results of the two methods were similar. According to the annual SPI analysis results, the driest station was Gölbası station with 55.5%, while according to the annual RDI analysis results, Kilis station was determined as the driest station with 53.5% [35]. In the drought analysis study conducted for Batman and Diyarbakır provinces, which are two provinces located in the Southeastern Anatolia Region, meteorological and hydrological drought analyzes were conducted for the period 1990-2020 with four different indexes including the SPI and the RDI. The analysis results showed that both provinces were affected by drought during the analyzed period [36]. As in this study, in a study conducted with the SPI method for the Sanlıurfa province, data from 5 stations in Sanlıurfa were used, and drought analyses were made for 2 different periods (1975-1997 and 1997-2019). According to the annual analysis results at all stations, there were increases in the severity, frequency, and duration of droughts in the second period compared to the first period [37]. In another study conducted for the Sanlıurfa province, drought analyses were conducted using 78 years of precipitation data covering the period 1937-2014 for different time scales using the SPI method. According to the analysis results, it was determined that the number of dry months in the 1986-2014 period was higher than the number of dry months in the 1937-1985 period [38]. When the studies conducted in Turkey are examined, it has been determined that some studies have been conducted with original methods, and drought analysis studies have not been conducted with modified aSPI and eRDI methods. Therefore, it is necessary and important to conduct drought analysis studies with modified methods in Turkey. Drought assessments were carried out using SPI and RDI methods on 43 years of temperature and precipitation data from the Banas River basin in India. According to the findings of both techniques, drought has grown in several areas in recent years. As a result, it was determined that temperature and precipitation variations have an impact on agricultural indicators [14]. Another study that used SPI and RDI methods was carried out in Ghana’s Tano River basin. Drought analyses were performed with the DrinC program on various time scales. The results of the two methods were compared with regression analysis. Based on the findings, the correlation values ​​for time periods of 1, 6, and 12 months were obtained as 0.98, 0.97, and 0.88, respectively. In other words, it was determined that there was a higher correlation between the two methods at shorter time periods [19]. In some drought analysis studies, future drought conditions have been estimated according to future climate scenarios [11]. In one of these studies, drought analyses were conducted with the DrinC program using historical data and future data estimated with the RCP4.5 scenario with

SPI, RDI, and SDI methods. According to the results, while severe and extreme droughts occurred once every 2 years in the past, it was determined that this occurrence frequency will be 5 years in the future [13]. In another a study on estimating future drought conditions, future drought conditions were determined using CMIP5 (Coupled Model Intercomparison Project Phase 5) climate data in the Tana Lake basin in Ethiopia with SPI, RDI, and SDI methods based on scenarios from RCP4.5 and RCP8.5. It was determined that droughts in the future periods will be seen every 2 years according to SPI and RDI methods. Although there are different percentages on various time periods, it was determined that the drought percentage in the future will increase between 17.24% and 20.69% [27]. When the studies on aSPI and eRDI indexes used to determine agricultural drought in the literature are examined, the first studies are on the comparison of modified aSPI and eRDI methods with the original SPI and RDI methods. One of these studies was conducted with RDI and eRDI methods in agricultural areas of Greece with Mediterranean climatic conditions. The connection between drought analysis findings and crop yields for the analyzed period was tried to be understood by determining the correlation coefficients. Based on the findings of the analysis, it was concluded that the eRDI method gave better results than original the RDI method [31]. In another a study conducted for Greece, drought analyses were conducted for four regions of Greece using aSPI and SPI methods. Drought analysis results were compared with annual crop yields and annual crop loss rates. In these comparisons, aSPI results were determined to have a higher correlation than SPI results [32]. In an investigation carried out in the central plateau basin of Iran, drought analyses were conducted using aSPI, eRDI, aSPEI, and TWSDI (Terrestrial Water Storage Deficit Index) methods. Based on the findings of the analysis, it was determined that the findings of the eRDI and aSPI approaches were compatible with a high correlation [39]. In a study conducted in Saudi Arabia, drought analyses were conducted with 6 drought indices, including aSPI and eRDI methods, in its region where 70% of Arabia is known to be affected by drought. With the analyses, dry years and the longest dry period were determined for the period 1985–2020 [40]. In a study conducted for the Balochistan region in Pakistan, drought analyses were conducted for the period 1992-2021 with SPI, SPEI, and aSPI methods. It has been determined that many cities in the Balochistan region have severe and extreme drought years [41]. In a drought analysis study conducted for Sindh region, another region of Pakistan with a significant history of drought, monthly precipitation data between 1961-2004 were used to conduct drought research for different time periods with the SPI index. The analysis results showed that clearer results were obtained for longer time scales of 24 and 48 months. Sindh

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region was affected by drought in the early 1960s and late 1970s [42] In an investigation carried out in Greece on the integration of aSPI and eRDI methods into the DrinC program, it was determined that aSPI and eRDI methods can be used to determine agricultural drought [33]. Again, in a study conducted in Greece, winter wheat yield values were compared with eRDI and RDI drought indices ​​in different periods. It was determined that the eRDI method gave better results over all time periods [24]. In another study comparing yield values ​​with drought indices, yield values ​​of rainfed maize plants in the Sinaloa region of Mexico were compared with indice values ​​of SPI, aSPI, RDI, and eRDI methods using Pearson and Spearman correlation methods. It was determined that modified methods gave better results [43]. Drought analyses were conducted for the period 19822018 with aSPI and SVSWI (Standardized Vegetation Supply Water Index) in 4 regions of China. Drought analysis results were compared with the yield values of winter wheat and summer corn crops during this period by the Pearson correlation method. The analysis’s findings demonstrated that extreme drought events increased after 2000. This had an impact on the winter wheat growing period of DecemberMay [44]. In some studies, the usability of the aSPI and eRDI methods has been investigated by using tree ring data instead of yield values. In comparisons performed to understand the influence of drought on the development of the Black Pine tree growing in the Mediterranean area, it was determined that the indice values ​​of the aSPI and eRDI methods showed a higher correlation with the ring data [45].

Some studies have also investigated the usability of modified methods in humid and arid areas. In one of these studies, drought analyses were conducted with eRDI and RDI methods at 22 synoptic stations in Iran. Considering the analysis’s conclusions, it was concluded that the results of the RDI method and eRDI method were very similar in humid regions, but the eRDI method gave better results in stations with arid and extremely arid conditions [46]. There are other modified methods developed in the literature as an alternative to SPI and RDI methods. One of these methods is the mnSPI modified method. This method employed time-varying parameters to assess the potential threat of drought events. In an investigation carried out in the Jinsha River Basin, it was found that the mnSPI method gave better results [29].

Materials And Methods

Information About the Study Area Sanlıurfa is located in the southeast of Turkey, between 370 49’ - 400 10’ east longitude and 360 41’ - 370 57’ north latitude [47]. Sanlıurfa province is the 7th largest province in Turkey, with a total area of 18765 km2 [48]. Sanlıurfa has a continental climate with quite hot and dry summers and cold winters [45]. There is a temperature difference, especially between day and night. Since it is far from the sea, the humidity is low compared to the coastal areas. It is shown among the hottest provinces of Türkiye [47]. As seen in Figure 2, Sanliurfa province has 10 districts [49]. The average annual temperature of Sanlıurfa province is 18.4°C, and the average precipitation height is 463.6 mm. The annual maximum temperature in the period 19292019 is 46.8°C [50]. Figure 3 displays the monthly average

Figure 2. Sanlıurfa province and its districts [49] [created by author].

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Figure 3. Average of monthly precipitation and temperature data of Sanlıurfa province for the period 2014-2023 (Source: Author).

temperature and total precipitation statistics for Sanlıurfa during the last decade (2014–2023). Sanlıurfa has a high level of agricultural activity. The agricultural area in the province corresponds to approximately 5% of the total agricultural area of Turkey and 36% of the agricultural area of the GAP region. Sanlıurfa province’s agricultural area of approximately 12,200,000 decares constitutes 59.3% of its total land area. Sanlıurfa’s agricultural area is the third largest in Turkey, after Konya and Ankara [51,52]. For this reason, agriculture has an important place in the economy of Sanlıurfa. The production of crops such as cotton, red pepper, pistachio and corn in Sanlıurfa province has an important share in Turkey’s production. The shares of these crops in Turkey’s production are 41.95%, 38.76%, 34% and 11.82%, respectively [53]. Most of these crops have high water consumption and high economic value. On the other hand, water losses are high in Sanlıurfa province. In periods when summer rainfall is insufficient, irrigation is inevitable to prevent a decrease in crop yield. Data Utilized in the Research In this investigation, SPI, aSPI, RDI, and eRDI methods were used for drought analyses in Sanlıurfa province. Drought study requires at least 30 years of data. For this reason, drought analysis was based on 31 years of monthly total precipitation, monthly maximum temperature, and monthly minimum temperature data obtained from the MGM from 1993 to 2023. The data and methods used are described in Table 1 below. Annual maximum and minimum temperature values, annual average temperature

values, and annual average total precipitation values were used as trend analysis data. There is no deficiency in temperature data from the data used. In the precipitation data, it was seen that in some months the precipitation was zero, that is, there was no precipitation. When these months were examined, it was seen that there was generally no precipitation in the summer months, so this was thought to be normal. In a drought-affected province like Sanlıurfa, it is normal to be no precipitation in the summer months. If there was a lack of data due to a measurement deficiency in other months, these missing data could be completed by taking the arithmetic average of the data of the three neighboring provinces in those months. Neighboring provinces are adjacent to Şanlıurfa province and have similar climate characteristics. Therefore, it is not thought that spatial change problems will be very effective in completing the missing data. In addition, the arithmetic method used in completing the missing data is a widely used method in the literature. Since PET data are calculated using temperature and precipitation data in the DrinC program, there is no missing data in the PET data. Of course, there may also be a possibility of measurement error in the data used. However, MGM has been making meteorological measurements with automatic precision devices for the last 15-20 years. This minimizes measurement errors. When conducting drought analyses with the DrinC program, long-term data should be arranged according to the hydrological year. For this reason, in all methods, the data were organized so that the data began in October, which is the first month of the hydrological year, and concluded in September, which is the last month [54].

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Table 1. Methods used in drought analysis, data types used, periods analyzed, and drought types determined by the methods

Method

Monthly total precipitation data and monthly minimum and maximum temperature data for PET calculation

Aiunep

Total annual precipitation and total annual evapotranspiration

Monthly total effective precipitation data and monthly minimum and maximum temperature data for PET calculation

Methods USED In Analysis

Standardized Precipitation Index (SPI) The SPI method created by McKee, Doesken, and Kleist in 1993 is among the most widely used methods [37]. Its use has become very widespread due to its simple structure, low data requirement, and good results. It is used in the assessment of meteorological and agricultural drought. In the SPI method, the gamma density function is applied to the total monthly precipitation data. As seen in Equation (1), SPI index values are found by deducted the long-term precipitation averages from the total monthly precipitation data and dividing by the standard deviation [38]. There is no drought if the SPI indices are positive; on the other hand, a drought exists if they are negative. In the classification table given in Table 4, drought categories are given according to the indice values. (1) In the formula, Xij is the monthly total precipitation at the jth observation at the ith precipitation station, Xim is the long-term average precipitation, and σ represents the standard deviation. Unıted Nations Environment Programme Aridity Index (UNEP-AI) This index was applied to select the method using for calculating the effective rainfall data to be used in the modified aSPI and eRDI methods. In the analysis made with

Table 2. UNEP aridity index drought classes [55] [created by author] UNEP Aridity Index AIUNEP < 0.05

this method, Sanlıurfa province was found to be semi-arid with an aridity index value of 0.22. The UNEP aridity index value (AIUNEP) is calculated as given in Equation (2) [55]: (2) P represents the annual total precipitation (mm), and PET represents annual total potential evapotranspiration (mm). Drought categories of the UNEP aridity index are given in five classes in Table 2. Agricultural Standardized Precipitation Index (aSPI) A modified version of the SPI method is called as the aSPI method and is accepted to be a more effective method for determining agricultural drought [32,33]. It is clear from the investigations in the literature that the aSPI method is more suitable for use in regions where drought is effective, data acquisition is limited, and comprehensive drought indexes cannot be used [32,39,44]. In the aSPI method, effective precipitation (Pe), which is the amount of precipitation utilized by plants, is used instead of the monthly total precipitation. Pe is characterized as the proportion of precipitation entering the plant root zone. There are four different methods to calculate Pe in the literature. These methods are the USBR (United States Bureau of Reclamation) method, which is an empirical method, the USDA-SCS (United State Department of Agriculture-Soil Conservation Services) method, the shortened form of the USDA-SCS method and the empirical method proposed by FAO (Food and Agriculture Organization). Among these methods, USBR and USDA-SCS methods are suitable for use in semi arid and arid areas [31,43]. Therefore, firstly the drought situation of Sanlıurfa province was determined with the UNEP aridity index. According to the results of this analysis, Sanlıurfa province was found to be semiarid with a indice value of 0.22. For this reason, the USBR method, an empirical method that gives more good results from USDA-SCS method in semi-arid and arid areas, was chosen for calculating Pe [32,56]. In the USBR method, total precipitation (P), actual evapotranspiration (ETc), and soil water storage factor (SF) are used in the calculations as shown in Equation (3) and Equation (4), [32].

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Figure 4. Change with monthly effective precipitation (Pe) of monthly total precipitation (P) according to the USBR method [31] [created by author]

Table 3. Percentages corresponding to monthly precipitation to estimate effective precipitation according to the USBR method [45] [created by author] Monthly precipitation (mm)

Effective precipitation (mm) (percentage of montly precipitation)

(3) (4) Here, D represents the usable soil water storage. It makes up 40%-60% of the available soil moisture in the plant root zone. The unit of the parameters in the equation is mm. Figure 4 shows the variation of P and Pe based on the USBR technique. According to the USBR method, Table 3 shows the determination of effective precipitation with percentages determined according to the amount of monthly total precipitation values. Reconnaissance Drought Index (RDI) Tsakiris and Vangelis developed the RDI method for observing the meteorological and agricultural drought.

[58]. The RDI method is frequently applied in semiarid and arid areas. The basic data required for the calculation of RDI are monthly total precipitation (P) and potential evapotranspiration (PET). PET is defined as the maximum amount of water lost through evaporation and transpiration when water is sufficient. For the PET computation, maximum, minimum, average temperature, and amount of space radiation data are used as shown in Equation (8). To find the RDI indice values, after calculating the initial value , RDInor (normalized) and RDIstd (standardized) values are calculated [58,59]. The equations used to calculate the , RDInor and RDIstd are given with Equation (5), Equation (6), and Equation (7). (5) Here, 𝑃𝑖𝑗 represents total precipitation for the jth month of the ith year, 𝑃𝐸𝑇𝑖𝑗 is the PET value of the same period.

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The hydrological year between October and September serves as the basis for the analysis (k=1 for October, k=12 for September), and N is the total number of years.

Table 4. Common drought classification table used for SPI, aSPI, RDI, and eRDI methods

Where, 𝑦ki is the natural logarithm of 𝑎k, ln(𝑎𝑘(𝑖)), µk is the arithmetic mean of 𝑎k, 𝜎 is the standard deviation of 𝑎k. PET values used to calculate RDI indice values can be calculated by four methods. These methods are the Hargreaves, Thornthwaite, Blaney-Criddle, and PenmanMonteith methods [22]. Among these methods, the Thornthwaite method, which uses the average temperature in analyses, is frequently used. However, when the temperature is zero or lower, the PET value is zero and the findings may be incorrect. In this research, the Hargreaves-Samani method was employed for PET calculation since there are temperature values of zero and lower. PET values were ​​ calculated using Equation (8) with the Hargreaves-Samani method [57-60]. (8) Here, PET is the calculated potential evapotranspiration (mm); the 0.408 is the coefficient used to convert to equivalent evaporation in mm/day the radiation expressed in MJ/m2/day. 𝑅𝑎 is the amount of space radiation (MJ/m2/ day); 𝑇𝑜𝑟𝑡 is the monthly mean temperature (°C); 𝑇max is the monthly maximum temperature (°C); 𝑇min is the monthly minimum temperature (°C). Effective Reconnaissance Drought Index (eRDI) In order to determine more accurately the agricultural drought, the eRDI technique is a modified form of the RDI method that takes into consideration parameters related to evapotranspiration and precipitation. In the eRDI method, the total effective precipitation parameter is taken into account instead of total precipitation as in the aSPI method. This method retains the simple structure and low data requirement as in the original RDI method [31,33]. To find the eRDI indice values in the this method, the initial value ( ) is calculated with Equation(9) [24]. (9) Where Pej is the effective precipitation of jth month, PETij is the potential evapotranspiration of jth month. In the eRDI method, eRDInor and eRDIstd are calculated with the same equations as in the original RDI method. has to be written instead of . Therefore, these Only equations were not given here again [31].

Results And Discussion

SPI Index Results For the Sanlıurfa province, 12-monthly drought analysis was conducted with the SPI method using the monthly total precipitation data obtained from MGM for the period between 1993 and 2023. The reason of performing 12-monthly analyses is that there is a wide variety of crops in Sanlıurfa province and the growing periods of many crops are different. Agricultural drought is related to the growth season of the crop rather than the hydrological year period. Since many crops are grown in Sanlıurfa, instead of determining a crop-based drought analysis period, the

Figure 5. SPI method drought analysis results for Sanlıurfa province in the period 1993-2023 (Source: Author).

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analysis was made for a 12-monthly period based on the entire hydrological year [31]. The SPI method is mostly used to determine meteorological and agricultural drought. For agricultural drought, aSPI and eRDI methods that are determined to provide better results in the literature have been developed [31,43]. In this research, the analysis with the SPI method was conducted in order to compare with aSPI and eRDI results. According to the common classification table in Table 4, the results of the SPI method analyses for Sanlıurfa province are as follows: 1 year is extremely arid, 2 years are severely arid, 1 year is moderately arid, 13 years are mildly arid, 2 years are extremely humid, 2 years are moderately humid, and 10 years are mildly humid. As seen in Figure 5, in Sanlıurfa province, drought effect was observed in 17 years, while precipitation effect was observed in 14 years in the analyzed period. It was found that the extremely arid year was 2021 with an indice value of -2.19, while the severely arid years were 2010 and 2017 with indices of -1.65 and -1.95, respectively. 1995 was a moderately arid year, with an indice value of -1.03. The linear trend line, which shows the SPI drought indice values is given in Figure 5. It is seen that the linear trend line is moving downwards in the direction of drought.

analyses with the aSPI method. However, the USDA-SCS method, which produces accurate results in the computation of effective precipitation in semiarid and arid locations, is also included for comparison in this study. For the reasons explained in Section 3.1, the analysis was conducted for a 12-monthly period based on the entire hydrological year [31]. The results of the aSPI method analyses for Sanlıurfa province are largely similar with the original SPI method results. However, there are differences in some years. It is understood from many studies in the literature that they give largely similar results. According to the common classification table in Table 4, the results of the aSPI (USBR) method analysis for Sanlıurfa province are as follows: 2 years are extremely arid, 1 year is severely arid, 1 year is moderately arid, 12 years are mildly arid, 1 year is extremely humid, 3 years are moderately humid, and 11 years is mildly humid. As seen in Figure 6, in Sanlıurfa province, drought effect was observed in 16 years, while precipitation effect was observed in 15 years in the analysed period. It was found that the extremely arid years were 2017 and 2021 with indice values of -2.26 and -2.56, respectively, and the severely arid year was 2010 with an indice value of -1.83. 1995 was a moderately arid year, with an indice value of -1.02. When the original SPI results are compared with the aSPI (USBR) results, it is seen that the drought class of five years are different. When the USDA-SCS method was taken into account in the calculation of effective precipitation, it was determined that the drought classes of three years are different. The aSPI (USDA-SCS) method was found to be more compatible with the original SPI method. According

aSPI Index Results For the Sanlıurfa province, 12-monthly drought analysis was conducted with the aSPI method using the monthly total precipitation data obtained from MGM for the period between 1993 and 2023. The USBR method was selected in the program for effective precipitation calculation in the

Figure 6. Comparison of drought analysis results of SPI-aSPI(USBR)-aSPI(USDA-SCS) methods in the period 1993-2023 for Sanlıurfa province (Source: Author).

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to the correlation analysis, a correlation of 0.99 was identified between the original SPI drought indice values and the aSPI(USDA-SCS) drought indice values, while a correlation of 0.96 was found between the original SPI drought indice values and the aSPI (USBR) drought indice values. The fact that the aSPI (USDA-SCS) method results are more compatible with the original SPI results shows that aSPI(USDA-SCS) does not provide very sensitive results in agricultural drought analyses. In fact, it has been stated in the literature that the aSPI (USBR) method results are more different from the original SPI method results and are more effective in determining agricultural drought. As seen in Figure 6, the linear trend line shows a downward trend in the drought direction for all of the SPI and aSPI methods.

while precipitation effect was observed in 14 years in the analyzed period. It was found that the extremely arid year was 2021 with an indice value of -2.19, while the severely arid years were 2010 and 2017 with indices of -1.69 and -1.93, respectively. 1995 was a moderately arid year, with an indice value of -1.02. When the original RDI results were compared with the original SPI results, it was observed that the drought classes were completely the same in all years, with only minor differences in the indice values. It is known from studies in the literature that the drought analysis results of the SPI and RDI methods are very compatible [14,19]. As seen in Figure 7, the linear trend line of the RDI results shows a downward trend in the direction of drought.

RDI Index Results For the Sanlıurfa province, 12-monthly (annually) drought analysis was conducted with the RDI method using the monthly total precipitation data obtained from MGM for the period between 1993 and 2023. For the reasons explained in Section 3.1, the analysis was conducted for a 12-month period based on the entire hydrological year [32]. The RDI approach is applied to ascertain meteorological and agricultural drought. In this study, the analyses with the SPI and RDI method was applied for comparison with aSPI and eRDI results. According to the common classification table in Table 4, the results of the RDI method analysis for Sanlıurfa province are as follows: 1 year is extremely arid, 2 years are severely arid, 1 year is moderately arid, 13 years are mildly arid, 2 years are extremely humid, 2 years are moderately humid, 10 years are mildly humid. As seen in Figure 7, in Sanlıurfa province, drought effect was observed in 17 years,

eRDI Index Results For the Sanlıurfa province, 12-monthly (annually) drought analysis was conducted with the eRDI method using the monthly total precipitation data obtained from MGM for the period between 1993 and 2023. The USBR method was selected in the program for effective precipitation calculation in the analysis with the eRDI method. However, the results of the USDA-SCS method, another method that gives correct results in the calculation of effective precipitation in semiarid and arid areas, are also given in this study for comparison. For the reasons explained in Section 3.1, the analysis was conducted for a 12-monthly period based on the entire hydrological year [32]. The eRDI method is a newly developed method for determining agricultural drought, like the aSPI method. eRDI results are compared with RDI results in Figure 8. Also in Figure 8, the drought analysis results in which effective precipitation is determined by the USBR method and the USDA-SCS method are shown separately.

Figure 7. RDI method drought analysis results for Sanlıurfa province in the period 1993-2023 (Source: Author).

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Figure 8. Comparison of drought analysis results of RDI-eRDI (USBR)-eRDI (USDA-SCS) methods in the period 19932023 for Sanlıurfa province (Source: Author).

The results of the eRDI method analyses for Sanlıurfa province are largely similar with the RDI method results. However, there are differences in some years. It is understood from many studies in the literature that they give largely similar results. According to the common classification table in Table 4, the results of the eRDI method analysis for Sanlıurfa province are as follows: 2 years are extremely arid, 1 year is severely arid, 1 year is moderately arid, 11 years are mildly arid, 1 year is extremely humid, 3 years are moderately humid, and 12 years are mildly humid. As seen in Figure 8, in Sanlıurfa province, drought effect was observed in 15 years, while precipitation effect was observed in 16 years in the analyzed period. It was found that the extremely arid years were 2017 and 2021, with indice values of -2.22 and -2.56, respectively, and the severely arid year was 2010 with an indice value of -1.89. It was concluded that 1995 was moderately arid, with an indice value of -1.00. When the original RDI results are compared with the eRDI(USBR) results it is seen that the drought class of six years are different. When the original RDI results are compared with the eRDI (USDASCS) method results, it is seen that the drought class of two years are different. The eRDI (USDA-SCS) method was found to be more compatible with the original RDI method. According to the correlation analysis, a correlation of 0.99 was identified between the original RDI and eRDI (USDA-SCS) drought indice values, while a correlation of

Results

In lots of research in the literature, drought analyses were conducted with the original SPI and RDI methods.

Figure 11. Trend analysis of annual maximum temperatures in the period 1993-2023 for Sanlıurfa province (Source: Author)

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Figure 12. Trend analysis of annual minimum temperatures in the period 1993-2023 for Sanlıurfa province (Source: Author).

while extremely wet years were less common. It can be said that these results are realistic for the province of Sanliurfa, which is affected by droughts, and agricultural activities are intensive. In this study, a climate diagram graphic was created using the temperature and precipitation data of the last 10 years for Sanliurfa province. According to this graphic, it was determined that there was a water shortage in Sanliurfa province in June, July, August, and September. When the drought classes of the last 10 years for Sanliurfa province are examined, drought was observed in 6 years according to the aSPI (USBR) method, while drought was observed in 5 years according to the eRDI (USBR) method. Therefore, it can be said that the droughts experienced in Sanliurfa province are caused by the change in climate parameters. According to the Mann-Kendall trend analyses conducted in this study, the increasing trend in minimum and maximum temperatures also confirms this. In Şanlıurfa province, conducting drought analyses with modified methods that have just begun to be used in the literature is important both for the agricultural activities of the province and because it will contribute to the literature. In the future, the drought analysis results for Sanliurfa province can be compared with the yield values ​​of rainfed agricultural products to determine the accuracy of the modified methods more realistically [32,33,44,46]. In some studies in the literature, the modified methods aSPI and eRDI were compared with some methods other than the original SPI and RDI methods to verify the usability of the modified methods. In these studies, it was determined that modified methods gave accurate results [39]. In this study, modified methods were compared with original

methods, and it was determined that they gave accurate results. In addition, it has been stated in the literature that drought conditions may change in a region due to climate change and that drought analyses should be carried out at regular intervals with up-to-date data [3]. In this study, drought analyses were conducted for the first time with modified methods in the Sanlıurfa province using current data. In previous studies, drought analysis results of aSPI and eRDI modified methods were compared, and it was determined that they were compatible with a high correlation percentage [39]. In this study, it was found that the aSPI results were compatible with the eRDI results with a correlation percentage of 0.99. In this research, the compatibility of the results of the original method with the results of the modified methods was also examined. According to these results, when the effective precipitation values ​​were calculated with the USBR method, the correlation was 0.96, while when calculated with the USDA-SCS method, the correlation was 0.99. Since it is stated in the literature that the USBR method gives more accurate results in determining effective precipitation, the results of the USBR method were taken into consideration in this study [32,57]. In addition, future drought predictions have been made in some regions using future climate data obtained according to climate scenarios in the literature [13,27,68]. Drought analyses can be conducted for Sanliurfa province with future climate data to investigate how successful modified methods are in predicting future agricultural drought. Many studies in the literature have determined that droughts have increased in recent years [14,27,44]. When

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we look at the graphs of the drought analysis results obtained in this study, it is seen that the trend line tends downwards, in the direction of drought. Likewise, in this research, analogous to research in the literature, it was determined that there has been an increase in droughts in recent years.

noticeable differences between the outcomes of the original methods and the modified methods. Some years’ drought classes turned out to be different. While 2017 was severely arid in the original methods, it was extremely arid in the modified methods. In the last few years (2014, 2015, and 2023), mildly arid years have changed. Similar differences have been found in previous studies in the literature. For this reason, agricultural drought analyses in semiarid and arid areas should be carried out with modified methods, and measures should be taken according to these results. As can be seen, the original methods could not detect an extremely dry year, and differences from modified methods were observed in the drought classes of some years. If this change in drought classes of these years is not taken into account, the necessary precautions cannot be taken fully. As a result, agricultural activities will be disrupted, and economic losses may be experienced. For example, if more water is not stored in water reservoirs in 2017, which is determined as an extremely dry year by modified methods, there will be a water shortage in the summer, and agricultural activities will be negatively affected. Some crops will experience yield loss or will be completely destroyed. As a result of these negativities, farmers will suffer economic losses and some socio-economic problems will arise. From this perspective, the importance of performing drought analyses with modified methods that provide more precise results can be better understood. In addition, SPI and RDI analysis results were compared, and it was determined that they were compatible with a correlation percentage of 0.99. In this study, it was determined that the outcomes of the original methods were also compatible with the results of the modified methods. According to these results, when the effective precipitation values ​​were calculated with the USBR method, the modified methods were compatible with the original methods with a correlation percentage of 0.96, while when calculated with the USDA-SCS method, they were compatible with a correlation percentage of 0.99. In other words, when effective precipitation values ​​are calculated with the USBR method, modified methods can better determine the details that the original methods could not determine. These results showed that aSPI and eRDI modified methods can be utilized in agricultural drought analysis in arid and semiarid regions. Because, in order to take measures in agricultural activities in semiarid and arid areas, it is important to conduct agricultural drought analyzes with modified methods such as aSPI and eRDI instead of original methods in order to get more precise outcomes. In regions where drought analysis has been done previously, drought analyses should be done with current data and methods due to climate change. In regions where drought analysis has never been done previously, agricultural drought analyses should be done with modified methods. In addition, it would be useful to obtain future climate data by conducting climate modeling with climate scenarios widely used in the literature in these regions and

Limitations Although the data used in the article are obtained reliably from measurement stations established by national institutions, measurement errors may occur on some days due to environmental factors. PET values ​​were not obtained by on-site measurements, but were calculated using an equation that is frequently used in the literature and provides reliable results. There may be small errors in such calculations. In this study, these errors were ignored and analyses were conducted by relying on the data.

Conclusion

In this study, drought analyses were conducted in the drought-affected Sanlıurfa province using the aSPI and eRDI methods, which will be applied for the first time in Turkey, and the original SPI and RDI methods. Monthly total precipitation data, and maximum and minimum temperature data obtained from the Regional Directorate of Meteorology for the period 1993-2023 were used for the analyses. The average temperature data used for PET calculation in RDI and eRDI methods were calculated using the observational maximum and minimum temperature data entered into the DrinC program. The effective precipitation data used in the aSPI and eRDI methods were calculated by using the monthly total precipitation data entered into the DrinC program. USBR and USDA-SCS modules were selected for effective precipitation calculation in the program. In this research, the USBR method was used to calculate effective precipitation. However, in order to make a comparison, effective precipitation was also calculated by the USDA-SCS method, and drought analyzes were made. According to the analysis results, the aSPI and eRDI drought analysis results for Sanlıurfa province are analogous to the original SPI and RDI analysis results as similar to the research in the literature. However, the drought classes of some years were different. According to the aSPI results; it was determined that 2 years were extremely arid, 1 year was severely arid, 1 year was moderately arid and 12 years were mildly arid. According to the eRDI results; it was determined that 2 years were extremely arid, 1 year was severely arid, 1 year was moderately arid, and 11 years were mildly arid. When the original SPI and RDI results of Sanlıurfa were compared, although there are some differences in the drought indice values, the drought classes are completely the same. In both methods, it was determined that 1 year was extremely arid, 2 years were severely arid, 1 year was moderately arid, and 13 years were mildly arid. As can be seen, the original methods and the modified methods gave similar results among themselves. However, there were

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to make future drought predictions with modified methods. In future studies, the efficiency of modified methods can be better understood by comparing the yield values ​​of rainfed crops in drought-affected regions of Turkey with the drought indices obtained with modified and original methods. As stated in the limitations section of the study, although PET values a​​ re calculated with high accuracy, there may be some errors. After all, they are not data obtained by measurement. In future studies, evapotranspiration values​​ can be determined as a result of measurement and more reliable results can be achieved. There may be errors in the past data obtained from the measurement stations used in the study due to environmental conditions. However, the measurements of MGM stations, which make more precise measurements compared to the past, should be trusted. According to the results of the trend analysis, although precipitation data, which show a slightly decreasing trend, partially affect the droughts in Sanlıurfa province, the primary cause is the rise in minimum and maximum annual temperatures according to the 0.05 significance level. The p-values for the minimum and maximum temperature trends are 0.00 and 0.007, respectively. In other words, the p-value is less than α=0.05 in both cases. The Z values for minimum and maximum temperature trends are 247 and 210, respectively. In both cases, Z is higher than the limit value of 1.96. In other words, there is a noticeable increasing trend with 95% confidence in the annual minimum and maximum temperature data. This change in climate parameters requires the carrying out of agricultural drought analyses at regular intervals with updated data and modified methods in arid and semi-arid regions. In this study, according to the drought assessment made according to the Aridity Index, it was determined that Sanliurfa province is semi-arid. This research is the first drought analysis study conducted in Sanlıurfa province and Turkey with updated data using modified aSPI and eRDI methods. These features show that the study is new and original. The idea of ​​determining the cause of drought by looking at the trend in climate parameters also partially differs from the studies in the literature. The increasing trend in temperatures increases the evaporation especially in summer months, and causes drought. For this reason, irrigation should be carried out with water-saving systems like drip and sprinkler irrigation. Irrigation water must be transmitted through closed pipes to reduce evaporation, and underground dams should be built. Water reservoirs should be covered with shade covers. Crops that consume less water should be preferred. Plant adaptation strategies should be developed and crops that can easily adapt to drought should be grown. Additionally,people should be made aware of water saving.

N Number of years P Annual total precipitation, mm PET Annual total potential evapotranspiration, mm Pe Effective precipitation, mm 𝑅𝑎 Amount of space radiation, MJ/m2/day S Mann-Kendall method statistic SF Soil water storage factor Monthly maximum temperature, °C 𝑇max Monthly maximum temperature, °C 𝑇min Monthly mean temperature, °C 𝑇𝑜𝑟𝑡 Monthly total precipitation, mm Xij Xim Long-term average precipitation, mm Natural logarithm 𝑦ki Z Standart normal variable Zα/2 Standart normal critical variable Greek symbols σ Standard deviation Initial value αk i µk Arithmetic mean τ Kendall correlation coefficient Subscripts k Number of hydrological year

Data Availability Statement

The data supporting this study’s findings are available on request from the corresponding author.

Conflict Of Interest

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics

There are no ethical issues with the publication of this manuscript.

Statement On The Use Of Artificial Intelligence

Artificial intelligence was not used in the preparation of the article.

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