Trend analysis of precipitation and drought in the Aegean region, Turkey

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1 METEOROLOGICAL APPLICATIONS Meteorol. Appl. 24: (2017) Published online 2 March 2017 in Wiley Online Library (wileyonlinelibrary.com) DOI: /met.1622 Trend analysis of precipitation and drought in the Aegean region, Turkey Ülker Güner Bacanli* Department of Civil Engineering, Engineering Faculty, Pamukkale University, Denizli, Turkey ABSTRACT: Trend analysis of precipitation and drought will play a significant role in the future development and sustainable management of water resources. It is the main purpose of this paper to analyse meteorological droughts and trends. The variability of the standardized precipitation index (SPI) was investigated at 1, 3, 6, 9, 12 and 24 monthly intervals. The trends of 1, 3, 6, 9, 12 and 24 monthly SPI results were analysed by applying linear regression and the Mann Kendall and Spearman s rho tests at the 5% significance level. The linear slopes of the trends were calculated with a technique proposed by Sen. Meteorological data from eight meteorological stations in the Aegean region of Turkey were used for the period The monthly precipitation trend decreases in December, January, February and March in all regions according to the linear regression analysis results. Annual precipitation decreased at five stations. In drought analysis by the SPI, in a short time period (such as 3 months) drought is more frequent but shorter, and as the period increases the duration of drought also increases but frequency decreases. Winter droughts are beginning to occur in the class of severe in recent years. In this study carried out for different time periods according to SPI values in all stations, the highest ranges are in the normal and mild drought degrees. In addition, severe and extreme droughts are also seen intensely frequently. Trend analysis of precipitation and drought is compared. The trend analyses of precipitation are in parallel with the results of drought analyses. KEY WORDS drought; trend analysis; standardized precipitation index (SPI); precipitation; Mann Kendall test; Sen slope estimates; Turkey Received 27 January 2016; Revised 6 May 2016; Accepted 4 August Introduction Drought is a natural disaster. It can be classified as meteorological, agricultural, hydrological, socio-economic and groundwater drought (Dracup et al., 1980; Wilhite and Glantz, 1985; Mishra and Singh, 2010). Droughts have resulted in long term social, economic and environmental impacts. They are difficult to detect. Different drought indices such as the Palmer drought severity index (Palmer, 1965; Li et al., 2015), the vegetation drought response index (Brown et al., 2008), the multivariate standardized drought index (Hao and Aghakouchak, 2013), the drought severity index (Mu et al., 2013), the surface water supply index (Shafer and Dezman, 1982) and the standardized precipitation index (SPI) (McKee et al., 1993; Ganguli and Reddy, 2014) are the most common tools for detecting from the past to the present. Many scientists have analysed drought indices, drought trends and precipitation trends (Partal and Kahya, 2006; Liu et al., 2008; Gocic and Trajkovic, 2013; Zhang et al., 2015). Karabulut et al. (2008) evaluated precipitation and temperature trends for the timescale. They observed which temperature data for the summer months presented statistically significant trends. Shahid (2008) analysed spatial and temporal characteristics of droughts. The SPI method and a geographic information system were used. Turkes and Tatlı (2009) presented a new methodology for the SPI, including intensity and frequency and identifying spatial and temporal patterns. Tabari et al. (2012) analysed the rainfall and drought severity time series. The drought severity was computed using the SPI for a 12 month timescale. The trend * Correspondence: Ü. Güner Bacanli, Department of Civil Engineering, Engineering Faculty, Pamukkale University, Denizli, Turkey. ugbacanli@pau.edu.tr analyses of the data were also performed using the Kendall and Spearman tests. Spinoni et al. (2013) presented maps of global drought frequency, duration and severity. Gocic and Trajkovic (2013) analysed precipitation and SPI trends during the period in Serbia. Ganguli and Reddy (2014) evaluated trends and multivariate frequency analyses of droughts. Meteorological drought was modelled using the SPI at a timescale of 6 months. Trends in the SPI time series were investigated by using the non-parametric Mann Kendall trend test. Zhang et al. (2015) studied changes in precipitation and drought in northwest China. Degefu and Bewket (2014) evaluated trends and spatial patterns of drought incidence. Drought indices were generated using the SPI. The Mann Kendall trend test and the Sen slope estimator were employed. Dshtpagerdi et al. (2015) investigated the drought intensity and drought magnitude based on the SPI in Iran. They showed that the arid and semi-arid areas of Iran had a negative SPI trend. Mahajan and Dodamani (2015) evaluated the drought trend of the SPI. The statistical significance at 95% confidence level as per Mann Kendall and Sen s slope estimator were used for drought trend analysis. In the present study, the aim was to assess the temporal and spatial characteristics of meteorological droughts in the Aegean region of Turkey to generate a guide for sustainable water resource management. The main objectives of this study were: (1) to investigate the drought in the Aegean region between 1960 and 2013, (2) to investigate the variability in the 1, 3, 6, 9, 12 and 24 monthly SPI, (3) to investigate trend analysis with the Mann Kendall and Spearman s rho methods in the 1, 3, 6, 9, 12 and 24 monthly SPI and (4) to assess the variability of precipitation on an annual timescale through applying the Mann Kendall, Spearman s rho and linear regression methods Royal Meteorological Society

2 240 Ü. Güner Bacanli KÜTAHYA MANISA IZMIR USAK AEGEAN REGION DENIZLI AYDIN AFYON MUGLA Figure 1. Aegean region. [Colour figure can be viewed at wileyonlinelibrary.com]. 2. Study area and data Turkey s diverse regions have different climates because of irregular topography. The Aegean region occupies 11% of the total area of Turkey (Figure 1). The Aegean coastal plain has an exceptionally mild climate. The Aegean region has perpendicular mountains to its shores and many valleys between them, thus permitting the sea climate to reach inner parts of the region, although some of the provinces inland also show characteristics of a continental climate (Erinç, 1957; Bacanli, 2011). The observed monthly rainfall data records from eight meteorological stations located in the Aegean Anatolia, Turkey, were selected for this study. The length of available records at these stations is between 1960 and The evaluated monthly rainfall data were measured by the Turkish State Meteorological Service (DMI, 2015). The SPI drought indices for this study were calculated on the basis of these rainfall data records. Table 1 shows the geographical co-ordinates of the synoptic stations chosen in the Aegean Anatolia region. The precipitation and temperature data of these stations for the 54 years between 1960 and 2013 were obtained from the DMI. The monthly precipitation series statistical parameters at seven stations during the period are presented in Table 2. The mean monthly precipitation is limited from to mm. 3. Methods 3.1. Standard precipitation index (SPI) The SPI method is commonly accepted and used in research. The SPI was developed by McKee et al. (1993, 1995). SPI method is used precipitation data. The SPI is obtained by dividing the difference between precipitation and mean by the standard deviation in a specific duration (McKee et al., 1993): SPI = x i x i (1) σ It is produced by standardizing the probability of observed precipitation for any duration. The magnitude, length and duration of drought can be calculated with the SPI. Studies have shown that precipitation is subject to the law of gamma distribution (Ganguli and Reddy, 2014; Zhang et al., 2015). The calculation of the SPI is complex. SPI classes are shown in Table 3. The datasets are organized for a given period (3, 6, 9, 12, 24 or 48 months). It is assumed that x is the cumulated monthly precipitation in the time period of research which fits a gamma probability density function g(x) as follows: 1 g (x) = β α Γ (α) xα 1 e x β forx > 0 (2) where x is the amount of precipitation and Γ(α) is the gamma function. α and β are shape and scale parameters respectively: α = 1 4A ( ) A 3 β = x α A = ln ( x ) ln (x) (5) n In these equations n is the number of precipitation observations. The cumulative probability distribution function is defined as follows: x x 1 G (x) = g (x) dx = x α 1 e x β dx (6) 0 β α Γ (α) 0 The gamma function is undefined for x = 0 and the precipitation distribution can have zero values. When this is the case, the cumulative probability distribution is defined as follows: (3) (4) H (x) = q + (1 q) G (x) (7) In this equation, q represents the probability of zero value. If m is used for denoting the zero values in a precipitation series then the following definition can be made: q = m/n. The cumulative probability value H(x) is converted to a Z variable with a standard normal random value denoting the SPI value having zero mean value and variance equal to 1. H(x) is the value of SPI. Normalization of SPI values enables consideration of the variations of precipitation series of that station by both time and place (McKee et al., 1993; Guttman, 1999).

3 Trend analysis of precipitation and drought 241 Table 1. Rain gauge stations in the Aegean regions. Rain gauge stations Geographic co-ordinates Rain gauge stations Geographic co-ordinates Latitude Longitude Elevation (m) Latitude Longitude Elevation (m) Afyon Kütahya Aydın Manisa Denizli Muğla İzmir Uşak Table 2. Statistical parameters of monthly precipitation series at seven stations during the period. Station name Afyon Aydın Izmir Kutahya Manisa Mugla Uşak Mean (mm) Standard deviation (mm) Skewness Min (mm) Max (mm) Kurtosis Table 3. Classification according to standardized precipitation index (SPI) values (McKee et al., 1993). SPI Drought category SPI Drought category 2 Extremely wet ( 1.0) to ( 1.49) Moderately dry 1.99 to 1.5 Very wet ( 1.5) to ( 1.99) Severely dry 1.49 to 1.0 Moderately wet 2 Extremely dry 0.99 to ( 0.99) Near normal 3.2. Mann Kendall test The non-parametric Mann Kendall test (Mann, 1945; Kendall, 1975) has generally been used to determine the significance of a trend at a site. This test is based on the statistic S: S = n 1 n k=0 j=k+1 sgn ( x j x k ) sgn (x) = +1, x > 0 0, x = 0 1, x < 0 where n is the length of the data and x j and x k are the data values in time series k and j (j > k), respectively. In cases where the sample size n 10, the mean and variance are given by: (8) E (s) = 0 (9) r var (s) = σ ( s {n (n 1)(2n + 5) t i ti 1 )( 2t i + 5 ) } 18 1 (10) In Equation (10), n is the number of tied groups and t i denotes the number of ties of extent i. A tied group is a set of sample data having the same value. The standard normal test statistic Z is computed as: s 1, s > 0 σ s Z = 0, s = 0 (11) s+1, s < 0 σ S According to Equation (11) positive values of Z inform increasing trends while a negative Z indicates decreasing trends. The testing of trends is made at a specific significance level α. The significance level α = 0.05 was used in this study. At the 5% significance level, the null hypothesis of no trend is rejected if Z >1.96 (Mann, 1945; Kendall, 1975) Sen method In the non-parametric procedure developed by Sen (1968), the slope estimates of N pairs of data are predicted by Sen s estimator: Q i = x j x k i = 1,, N (12) j k In Equation (12), x j and x k are the data values in time j and k (j > k), respectively. The N values of Q i are ranked from smallest to largest and the median of the slope or Sen s slope estimator is computed as: { N is odd Q med = Q (N+1) ( QN 2 + Q (N+2) 2 ) N is even (13) Q med is computed with a two-sided test and then a true slope can be obtained by the non-parametric test Spearman s rho method Spearman s rho test is a non-parametric method commonly used to verify the absence of trends. Its statistic D and the standardized test statistic Z D are expressed as follows (Lehmann, 1975; Sneyers, 1990): D = 1 n { ( ) } 2 6 R Xi i i=1 n ( n 2 1 ) (14) n 2 Z D = D (15) 1 D 2 where R(X i ) is the rank of the ith observation X j in the time series and n is the length of the time series. Positive values of Z D indicate increasing trends while negative Z D shows decreasing trends. At the 5% significance level, the null hypothesis of no trend is rejected for Z D > 2.08.

4 242 Ü. Güner Bacanli Table 4. Annual trend analysis results of precipitation for the study area. Afyon Aydın Denizli İzmir Kütahya Manisa Muğla Uşak Z Mann Kendall Z Spearman s rho Sen slope b Linear regression method A linear regression method was used to check whether there is a significant relation between the variables under consideration. The regression line is used to estimate the slope. The slope indicates the mean temporal change of the studied variable. Positive slope values show increasing trends, while negative slope values indicate decreasing trends. A linear regression line is an equation of the form: y = a + bx (16) where x is the explanatory variable, y is the dependent variable, b is the slope of the line and a is the intercept (Gocic and Trajkovic, 2013). 4. Results and discussion 4.1. Precipitation analysis Annual and monthly precipitation were analysed for the period Trend analysis was done of the value of annual precipitation (Table 4). Statistically significant negative trends were found for annual precipitation in Kütahya and Manisa. Linear analysis was done of the value of total annual rainfall. A decrease in annual precipitation was observed at Aydın, Denizli, Kütahya, Manisa and Mugla stations. Linear regression analysis of monthly precipitation data was carried out. Table 5 gives the slope. Negative values in Table 5 indicated decreasing trend. As a result, it was found that the monthly precipitation trend is a decrease in winter (December, January, February, March) in all regions (Table 5). In particular a declining trend in March and December precipitation was observed Standard precipitation index analysis assessment The SPI values were calculated separately for all eight rain gauge stations in 3, 6, 9, 12 and 24 month timescale conditions. The graphs of two stations, Aydın and Uşak stations from the Aegean region with different altitudes, are plotted in Figures 2 and 3 as examples. As can be seen, there are differences between them. On a 24 month timescale, important droughts occurred in , , 2001 and in the Aegean region. An extreme drought was observed in On a 24 month timescale for Uşak and Aydın stations in the Aegean region, a drought began in October 1985 at Uşak station and the same drought began in April 1986 at Aydın station. As noted, there are 7 month differences between Uşak and Aydın stations. As can be seen from Figures 2(a) (f) and 3(a) (f), in a short time period (e.g. 3 months) drought is more frequent but shorter, and as the period increases the duration of drought also increases but frequency decreases. Winter droughts are beginning to occur in the class of severe in recent years. From Figures 2 and 3, the dry and wet periods generally continue for 3 4 months and then the graph goes in the opposite direction. In some years, fluctuations continue up to 9 10 months. So, it is sometimes better to select three to four oscillations for a better understanding of the pattern. In shorter periods, e.g. 3 6 months, the duration of wet or dry periods of SPI values is shorter (Figures 2 and 3). In longer periods the duration of wet or dry periods is longer (Figures 2 and 3). These situations can be expanded in different approaches according to different water users. For the Aegean region, a moderate drought distribution varies between 5.6 and 11.8% for periods of 3 24 months. Severe drought distribution varies between 2.5 and 7.5% for periods of 3 24 months. Extreme drought distribution varies between 1.4 and 4.2% for periods of 3 24 months Trend analysis assessment Trend analysis of droughts in a changing climate is vital to assess climate changes and suggest appropriate water resource management strategies for the future. The results of the applied Mann Kendall and Sen slope estimator statistical tests for 1, 3, 6, 9, 12 and 24 monthly SPI over the period are presented in Table 6. As shown, in the Aegean region stations trends at the 5% significance level in the 1 monthly SPI series were not significant. In the 3 monthly SPI series a significant decreasing trend was found only Table 5. Monthly linear regression analysis results of precipitation for the study area. January February March April May June July August September October November December Afyon Aydın Denizli İzmir Kutahya Manisa Mugla Uşak Negative values in Table 5 indicated decreasing trend.

5 243 Trend analysis of precipitation and drought (a) (d) (b) (e) (c) (f) Figure 2. (a) (f) The standardized precipitation index (SPI) values of Us ak city for 1, 3, 6, 9, 12 and 24 month periods. (a) (d) (b) (e) (c) (f) Figure 3. (a) (f) The standardized precipitation index (SPI) values of Aydın city for 1, 3, 6, 9, 12 and 24 month periods Royal Meteorological Society Meteorol. Appl. 24: (2017)

6 244 Ü. Güner Bacanli Table 6. Results of statistical tests for 1, 3, 6, 9, 12 and 24 monthly standardized precipitation index (SPI). Station Test 1 month 3 month 6 month 9 month 12 month 24 month Afyon Z Mann Kendall Z Spearman s rho Sen slope b Aydın Z Mann Kendall Z Spearman s rho Sen slope b Denizli Z Mann Kendall Z Spearman s rho Sen slope b İzmir Z Mann Kendall Z Spearman s rho Sen slope b Kütahya Z Mann Kendall Z Spearman s rho Sen slope b Manisa Z Mann Kendall Z Spearman s rho Sen slope b Muğla Z Mann Kendall Z Spearman s rho Sen slope b Uşak Z Mann Kendall Z Spearman s rho Sen slope b Table 7. The relative frequencies of Aydın city standardized precipitation index (SPI) values for 3, 6, 9, 12 and 24 month periods. SPI range Aydın Months 2 or more Extremely wet to 1.99 Very wet to 1.49 Moderately wet to 0.99 Near normal to 1.49 Moderately dry to 1.99 Severely dry and less Extremely dry at Kutahya and Manisa stations. In the 6 monthly SPI series a significant decreasing trend was found at Kutahya, Manisa and Mugla stations, but a significant increasing trend was found at İzmir station. In the 9, 12 and 24 monthly SPI series a significant decreasing trend was found only at Manisa and Mugla stations, but a significant increasing trend was found at Afyon station. With regard to the 3, 6, 9, 12 and 24 month timescales for each station, the relative frequencies of drought events were calculated according to the classification proposed by McKee et al. (1995). The percentage distributions of drought severities in different categories for the investigated stations in the Aegean regions were analysed. The analysis results for the Aegean region are given in Tables The trends at the 5% significance level in the Aegean region maps are shown in Figures 4 6. A decreasing trend is marked by a downward arrow and an increasing trend by an upward arrow. The 1, 3, 6, 9, 12 and 24 monthly SPI results were evaluated seasonally by the Mann Kendall and Spearman s rho methods. The average values for the winter, spring, summer and autumn months were calculated for each station. The Mann Kendall and Spearman s rho methods gave parallel results. As can be seen from the maps, the 3 monthly SPI result in the winter and the 24 monthly SPI result in the autumn showed a decreasing trend at the 5% significance level at Mugla station. All other SPIs showed no trend for all seasons.

7 Trend analysis of precipitation and drought 245 Table 8. The relative frequencies of Afyon city standardized precipitation index (SPI) values for 3, 6, 9, 12 and 24 month periods. SPI range Afyon Months 2 or more Extremely wet to 1.99 Very wet to 1.49 Moderately wet to 0.99 Near normal to 1.49 Moderately dry to 1.99 Severely dry and less Extremely dry Table 9. The relative frequencies of Denizli city standardized precipitation index (SPI) values for 3, 6, 9, 12 and 24 month periods. SPI range Denizli Months 2 or more Extremely wet to 1.99 Very wet to 1.49 Moderately wet to 0.99 Near normal to 1.49 Moderately dry to 1.99 Severely dry and less Extremely dry The 1, 3, 6, 9, 12 and 24 monthly SPI results at Manisa station in the winter showed decreasing trends. The 24 monthly SPI results in all seasons showed decreasing trends. The 6, 9, 12 and 24 monthly SPI results at Kutahya station in the summer showed decreasing trends. The 6 and 9 monthly SPI results in the spring showed increasing trends. The 12 and 24 monthly SPI results in the autumn showed decreasing trends. The 3, 6, 12 and 24 monthly SPI results in the winter showed decreasing trends. No trend was found at the 5% significance level at Izmir, Denizli, Aydın and Uşak stations for all seasons and months. The 24 monthly SPI results at Afyon station in the summer showed increasing trends. The 3 and 6 monthly SPI results in the winter showed increasing trends. The 6 and 9 monthly SPI results in the spring showed increasing trends. From the decreasing trend in Manisa and Kutahya stations, these stations show that there is a significant increase in drought. The results are consistent with precipitation analysis. The trends of SPI results in the winter months were observed to be decreasing, namely a drought in more than 1, 3, 6, 9, 12 and 24 monthly SPI results was observed. SPI time increased in some stations could be easily observed that decrease of trend in all seasons. 5. Conclusion The aim of this study was to analyse precipitation trends and monthly standardized precipitation index (SPI) trends in the Aegean region of Turkey for the years According to the results of the Mann Kendall test, Spearman s rho and the Sen method Kütahya and Manisa stations showed a negative trend for annual precipitation. The results of linear analysis showed a negative trend in Aydın, Denizli, Kütahya, Manisa and Mugla stations. According to these results, the monthly precipitation trend was found to be decreased in winter (January, February and March) in all stations. Generally, March precipitation was observed to have a negative trend. In this study, the SPI for meteorological drought analysis was considered as a short term or seasonal variation for 3 and 6 month periods, for drought with an average duration in the 9 and 12 month periods and as a long term drought index for the 24 month period. Assessments for each station were made according to the SPI classification in these periods. The total relative frequencies of the drought vary between 10.2 and 21.2% for periods of 3, 6, 9, 12 and 24 months in all cities. For different time periods according to SPI values in all stations, the highest ranges are in the normal and mild drought Table 10. The relative frequencies of İzmir city standardized precipitation index (SPI) values for 3, 6, 9, 12 and 24 month periods. SPI range İzmir Months 2 or more Extremely wet to 1.99 Very wet to 1.49 Moderately wet to 0.99 Near normal to 1.49 Moderately dry to 1.99 Severely dry and less Extremely dry

8 246 Ü. Güner Bacanli Table 11. The relative frequencies of Kütahya city standardized precipitation index (SPI) values for 3, 6, 9, 12 and 24 month periods. SPI range Kütahya Months 2 or more Extremely wet to 1.99 Very wet to 1.49 Moderately wet to 0.99 Near normal to 1.49 Moderately dry to 1.99 Severely dry and less Extremely dry Table 12. The relative frequencies of Manisa city standardized precipitation index (SPI) values for 3, 6, 9, 12 and 24 month periods. SPI range Manisa Months 2 or more Extremely wet to 1.99 Very wet to 1.49 Moderately wet to 0.99 Near normal to 1.49 Moderately dry to 1.99 Severely dry and less Extremely dry degrees. The investigation of SPI values shows that, when the period increases, drought occurs less frequently but has a longer duration. For example, drought is more frequent in the 3 month period but has a shorter duration and when the period increases the duration of drought increases but the frequency decreases. When the periods are short the shift between positive and negative values is seen more frequently, and when the period increases it is observed that the SPI values respond to the varying precipitation conditions more slowly. The Mann Kendall and Sen slope estimator statistical tests for the results of 1, 3, 6, 9, 12 and 24 month SPI over the period were applied. As shown, in the 3, 6, 9, 12 and 24 month SPI series a significant decreasing trend was found only at Mugla and Manisa stations. In the 3 and 6 monthly SPI series a significant decreasing trend was found at Kutahya station and a significant increasing trend was found at Izmir station. In the 9, 12 and 24 monthly SPI series a significant increasing trend was found only at Afyon station. Table 13. The relative frequencies of Mugla city standardized precipitation index (SPI) values for 3, 6, 9, 12 and 24 month periods. SPI range Mugla Months 2 or more Extremely wet to 1.99 Very wet to 1.49 Moderately wet to 0.99 Near normal to 1.49 Moderately dry to 1.99 Severely dry and less Extremely dry Table 14. The relative frequencies of Uşak city standardized precipitation index (SPI) values for 3, 6, 9, 12 and 24 month periods. SPI range Uşak Months 2 or more Extremely wet to 1.99 Very wet to 1.49 Moderately wet to 0.99 Near normal to 1.49 Moderately dry to 1.99 Severely dry and less Extremely dry

9 Trend analysis of precipitation and drought 247 (a) (c) (b) (d) Figure 4. Spatial distribution of significant 24 monthly standardized precipitation index (SPI) trends at the 5% level ((a) Autumn, (b) Winter, (c) Spring and (d) Summer). (a) (c) (b) (d) Figure 5. Spatial distribution of significant 12 monthly standardized precipitation index (SPI) trends at the 5% level ((a) Autumn, (b) Winter, (c) Spring and (d) Summer).

10 248 Ü. Güner Bacanli (a) (c) (b) (d) Figure 6. Spatial distribution of significant 9 monthly standardized precipitation index (SPI) trends at the 5% level ((a) Autumn, (b) Winter, (c) Spring and (d) Summer). The Mann Kendall and Spearman s rho methods showed 1, 3, 6, 9, 12 and 24 monthly SPI seasonal trends at a 5% significance level, and the two methods showed the same results. The 3 monthly SPI result in the winter and the 24 monthly SPI result in the autumn were also found to have a decreasing trend at the 5% significance level at Mugla station. The 3, 6, 9, 12 and 24 monthly SPI results at Manisa station in the winter showed decreasing trends. The 6, 9, 12 and 24 monthly SPI results in the summer, the 12 and 24 monthly SPI results in the autumn and the 3, 6, 12 and 24 monthly SPI results in the winter showed decreasing trends at Kutahya station. However, the 6 and 9 monthly SPI results in the spring at Kutahya station showed increasing trends. The 24 monthly SPI results in the summer, the 3 and 6 monthly SPI results in the winter and the 6 and 9 monthly SPI results in the spring showed increasing trends at Afyon station. No trend was found at the 5% significance level at Izmir, Denizli, Aydın and Uşak stations for all seasons and months. In this study, trend analysis of precipitation and drought is compared. The trend analyses of precipitation are in parallel with the results of drought analyses. A decrease of the monthly SPI series refers to an increase in drought. Monitoring drought needs different indicators or indices. Drought management plans which involve basin management and are agriculturally important should immediately be prepared for the region. Further, trend analysis of drought is necessary in the determination of the priorities for the planning, design and construction of water structures. References Bacanli UG Dryness characterization: a climatic water deficit approach in Turkey. Fresenius Environ. Bull. 20(3): ISSN: Brown JF, Wardlow BD, Tadesse T, Hayes MJ, Reed BC The Vegetation Drought Response Index (VegDRI): a new integrated approach for monitoring drought stress in vegetation. GISci. Remote Sens. 45(1): Degefu MA, Bewket W Variability and trends in rainfall amount and extreme event indices in the Omo-Ghibe River Basin, Ethiopia. Reg. Environ. Change 14: , DOI: /geoa DMI (State Meteorological Service) en-us/climateof turkey.pdf (accessed November 2009). Dracup JA, Lee KS, Paulson EG On the statistical characteristics of drought events. Water Resour. Res. 16: , DOI: /WR016i002p Dshtpagerdi MM, Kousari MR, Vagharfard H, Ghonchepour D, Hosseini ME, Ahani H An investigation of drought magnitude trend during in arid and semi-arid regions of Iran. Environ. Earth Sci. 73(3): , DOI: /s Erinç S Applied Climatology and the Climate of Turkey. İstanbul Technical University, Hydrogeology Institute: İstanbul (in Turkish). Ganguli P, Reddy MJ Evaluation of trends and multivariate frequency analysis of droughts in three meteorological subdivisions of western India. Int. J. Climatol. 34(3): , DOI: /joc Gocic M, Trajkovic S Analysis of precipitation and drought data in Serbia over the period J. Hydrol. 494: 32 42, DOI: /j.jhydrol Guttman NB Accepting the standardized precipitation index: a calculation algorithm. J. Am. Water Resour. Assoc. 35(2):

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