Drought Analysis of the Seyhan Basin by Using Standardized Precipitation Index (SPI) and L-Moments

Tarım Bilimleri Dergisi Journal of Agricultural Sciences Tar. Bil. Der. Dergi web sayfası: Journal homepage: www.agri.ankara.edu.tr/dergi www.agri.ankara.edu.tr/journal

Drought Analysis of the Seyhan Basin by Using Standardized Precipitation Index (SPI) and L-moments

Emre TOPÇUa , Neslihan SEÇKİNa aÇukurova University, Faculty of Engineering and Architecture, Department of Civil Engineering, 01330, Adana, TURKEY

ARTICLE INFO

22 (2016) 196-215 Research Article DOI: 10.1501/Tarimbil_0000001381 Corresponding Author: Neslihan SEÇKİN, E-mail: [email protected], Tel: +90 (322) 338 60 84 Received: 03 April 2014, Received in Revised Form: 21 January 2015, Accepted: 28 January 2015

ABSTRACT

The aim of this study is to monitor drought in the Seyhan Basin by using Standardized Precipitation Index (SPI) based on a long term monthly precipitation of 11 meteorological stations and secondly, to also carry out regional frequency analysis using the index flood procedure coupled with the L-moments method based on recorded precipitation data of the most drought month for each year acquired from Standardized Precipitation Index method for each station. The SPI values of each station for 3, 6, 9 and 12-month time scales were calculated. According to the results, all stations are on the boundary of drought. Research results show that the wettest station is Ulukışla and the most drought station is Karaisalı with respect to the average SPI values. According to the drought frequency values, however, the station having the highest drought occurrence frequency is the Karaisalı station, whereas the station having the lowest drought frequency is the Tufanbeyli station. Results show that the Seyhan Basin is on the boundary of drought and mildly wet. Drought occurrence frequency is 47.7%. L-moments method was used to define homogenous regions. Homogenous regions for 3 and 6-month time scales minimum precipitation series couldn’t be obtained while homogenous regions for 9-month time scale minimum precipitation series were obtained only by dividing the whole basin into two parts. The whole basin is homogenous for the 12-month time scale minimum precipitation series. The Pearson Type 3 distribution is found to be most suitable for two homogenous sub-basins regarding 9-month time scale minimum precipitation series, whereas the Generalized Normal distribution is found to be most suitable for the whole basin with respect to 12-month time scale minimum precipitation series. JOURNAL OF AGRICULTURAL SCIENCES JOURNAL OF AGRICULTURAL Keywords: The Seyhan Basin; Standardized precipitation index (SPI); L-moments; Climate change; Rainfall —

L-momentler ve Standart Yağış İndeksi (SYİ) Yardımıyla Seyhan Havzası Kuraklık Analizi

ESER BİLGİSİ Araştırma Makalesi Sorumlu Yazar: Neslihan SEÇKİN, E-posta: [email protected], Tel: +90 (322) 338 60 84 Geliş Tarihi: 03 Nisan 2014, Düzeltmelerin Gelişi: 21 Ocak 2015, Kabul: 28 Ocak 2015

ÖZET TARIM BİLİMLERİ DERGİSİ TARIM Bu çalışmanın amacı Seyhan Havzası’ndaki 11 meteorolojik istasyonun uzun dönem aylık yağış değerlerine dayalı Standart Yağış İndeksi’ni (SYİ) kullanarak kuraklık takibi yapmak ve ayrıca ikinci olarak her istasyonun her yılı için Drought Analysis of the Seyhan Basin by Using Standardized Precipitation Index (SPI) and L-moments, Topçu & Seçkin

Standart Yağış İndeksi yönteminden elde edilen en kurak ayının yağış değerlerine indeks taşkın yöntemine dayalı L-momentler metodu uygulanarak bölgesel frekans analizi yapmaktır. Her istasyonun 3, 6, 9 ve 12 ay süreli SYİ değerleri hesaplanmıştır. Sonuçlara göre bütün istasyonlar kuraklık sınırındadır. Ortalama SYİ değerlerine göre en ıslak istasyon Ulukışla iken en kurak istasyon Karaisalı’dır. Kuraklık frekans değerlerine göre ise kuraklık meydana gelme frekansı en yüksek Karaisalı istasyonu iken kuraklık meydana gelme frekansı en düşük istasyon Tufanbeyli’dir. Sonuçlar gösteriyor ki, Seyhan havzası kuraklık sınırında ve biraz ıslaktır. Kuraklık oluşma frekansı % 47.7’dir. L-momentler metodu homojen bölgeleri belirlemek için kullanılmıştır. 3 ve 6 aylık minimum yağış serilerinden homojen bölgeler elde edilemezken 9 aylık minimum yağış serisinde havza iki kısma ayrılarak homojen alt havzalar elde edilmiştir. 12 aylık minimum yağış serisinde ise tüm havza homojen çıkmıştır. 9 aylık minimum yağış serisi için homojen iki alt havzaya en iyi uyan dağılım Pearson Tip 3 dağılımı iken 12 aylık minimum yağış serisi için tüm havzaya uyan en iyi dağılım Genelleştirilmiş Normal dağılım olmuştur. Anahtar Kelimeler: Seyhan Havzası; Standart yağış indeksi (SYİ); L-momentler; İklim değişikliği; Yağış

1. Introduction adversely. No wonder, this engenders economic crisis and decrease financial support. In a long period Drought is defined elementarily as the deficiency of dry weather, we observed a drought of more than of the customary total precipitations and dry air for the water basins, in any piece of land of the world 2 weeks diminished the soil moisture quite slowly for a specific timeframe by both hydrologist and and if it lasts longer, it raises maximum evaporation those concerned. Drought is one of the hardest, rate then all water reservoirs even subsurface water imponderable and unstoppable climatic incidents may clear away. For this reason hydrologists name like floods, hurricanes etc. There are in general the drought as creeping phenomenon. three types of droughts taken into account, they The amount of precipitation shortage impinges are, meteorological, agricultural and hydrological upon nearly all aspects of the social, environmental drought (Wilhite & Glantz 1987). Meteorological and economic life of a society. In terms of social life, drought is wholly relevant to weather conditions; drought has gross impacts such as; augmentation of it is described as decrease in the normal amount of poverty, migration, politic clashes, decrease in life, precipitations relative to at least recorded 30 years physical and mental stress for human body. In terms of precipitation series. Meteorological drought of environmental life, shortage of drinking water influences the other two types of drought and begins and food, rising plant, animal and human diseases, before them. Agricultural drought is considered as increase in desertification and wind erosion rate, the lack of soil moisture which leads to wilting and extinction of animal and rare plant species. In terms plant death in farmlands, even if there are adequate of economic life, decrease in tourism, commercial precipitations and water resources, agricultural livestock raising, hydroelectric power and energy, drought could occur because of the excessive usage increase in food prices. At present, scientists have of water and may be unnecessary agricultural discovered that drought and other climatic incidents activities. If moisture free wind blows, the intensity are caused by not only prominent and notorious of the drought will be exacerbated. Hydrological global warming but also sun spots and sun flares, drought occurs when the precipitation and amount of scientists are pondering that sun spots and sun water in reservoirs are enough but either population flares will be increased at highest level in 2000s, to make use of the water is high or rural activities so this will raise the radiation level and global heat and irrigation activities are excessive. So when (Vardiman 2008). The change in the amount of hydrological drought happens, urban life definitely radiation and heat means that atmospheric pressure comes across problems and hydroelectric power will be influenced unfavorably and climatic fatal production and sprinkler systems will be affected affairs which already cannot be determined properly

Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 197 L-momentler ve Standart Yağış İndeksi (SYİ) Yardımıyla Seyhan Havzası Kuraklık Analizi, Topçu & Seçkin and occur unpredictably mostly come out. There are Drought has short or long periods; it depends on the some studies about impacts of large scale volcanic characteristics of the drought. In the world, principally eruptions and air pollution on occurring droughts. middle zones of the broad continents such as America, The other exotic factors have force on droughts Asia and Africa have the wildest drought events by are aerosol gases sprayed from colossal volcanic reason of being far from warm sea and sea moisture. eruptions and industrial exhaust gases. This gases The most unpredictable points of drought are the beginning and ending time. Intensity of forthcoming such as CO2 (carbon dioxide) and SO2 (sulfur dioxide) alter the patterns of precipitations and drought can never be measured but only predicted have greenhouse effect which constitute the global from extreme dry periods which happened before, warming. Heat and radiation coming from the sun scientist and hydrologist are interested particularly could not be reflected to the space because of the on this point. Nowadays technology may not detect greenhouse effect (NASA 2010a). characteristics of droughts and other intense climatic events flawlessly, especially when to start and end, The beginning pattern of precipitation but by utilizing various mathematic algorithms commences with the collision of little microscopic these problems could be clarified. Knowledge of the rain drops, they collide or bond, but the air is characteristics of the drought is vital for water running polluted and covered by greenhouse effected gases, and urban organizations. There have been a lot of then the rain drops could not become together and drought monitoring tools developed by hydrologists consist of precipitation, this gives rise to drought in literature such as Palmer Drought Severity Index (Fang et al 2013). A good example of this is the (PDSI), Erinc and De Martonne methods. In this Pinatubo volcano, which erupted in 1991 and is one paper Standardized Precipitation Index (SPI) drought of the largest volcanic eruption in the 20th century, monitoring tool developed by (McKee et al 1993) as there was decrease in the amount of precipitation is used. It is significant to scrutinize the data for for some seasons, sprayed gases moved with strong the frequency of occurrence of drought, extreme winds around the World (Trenberth & Dai 2007). precipitations cause floods, whereas minimum As known, northern hemisphere’s heat increases precipitations cause droughts. Frequency analysis rapidly rather than southern hemisphere, since there method is used prevalently to estimate the droughts are larger continents in the northern hemisphere than and floods, precipitation time series are a must for the southern hemisphere. In the south, oceans cover planning hydraulic structures, and researchers usually larger places than the mainlands. The mainland use the maximum precipitation data to estimate flash cools down and heats up more quickly than water floods. However, in this paper, minimum precipitation covered areas. During the period 1997-2006 depths instead of maximum precipitation depths were average temperature of the northern hemisphere had employed to monitor the drought. For frequency increased almost 0.53 °C (0.27 °C for the southern analysis, L-moments method developed by (Hosking hemisphere) in comparison with the period 1961- 1986; 1990) was used by utilizing precipitation data 1990 (WMO 2007). Separately, sea ice volume of the most drought months which are detected from affects the climate and then droughts. SPI results of each year for all stations. Not so far or above the “Planet Earth”, there is one more unfavorable driver which is called ‘heat island’. 2. Material and Methods Wide concrete roads, dark structures are deprived of green areas in metropolitan cities induced heat island. 2.1. Material Since energy and heat coming from the sun in daytime In this study, the Seyhan Basin, one of the biggest absorbed by this type of factors mentioned. So, the and fertile basins in Turkey, North Mediterranean and difference in temperatures between metropolitan Europe, is selected as a study area and comes after the cities and very near rural areas can be 10 °C or more, Nile Basin in terms of size. The Seyhan Basin is one this of course affects the rain pattern (NASA 2010b). of the 26 basins in Turkey and located in the south.

198 Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 Drought Analysis of the Seyhan Basin by Using Standardized Precipitation Index (SPI) and L-moments, Topçu & Seçkin

The Seyhan Basin’s area is 20450 km2 and constitutes In this study at least recorded 20 years of of the 2.82% of Turkey. The Seyhan Basin provides precipitation time series are used for analysis. both economic and agricultural resources to Turkey Totally 11 meteorological stations are employed and the world. This basin has innumerable biological for this dissertation, the main characteristics of the species. Therefore researchers focus on Seyhan Basin stations are shown in Table 1. Data of the stations for drought or other issues. For instance, Gurkan are obtained from Turkish State Meteorological (2005) simulated for both based on decrease in Service. Locations of stations are shown in Figure precipitation amount and increase in temperature and 1. Precipitation depths increase from the north to potential evapotranspiration. Fujihara et al (2008) the south unlike temperature due to high geographic used the inverse approach to model hydrological risk formations. and vulnerability to changing climate conditions in the Seyhan River basin. Sen et al (2008) aimed a research to predict future climate and its possible impact on water resources and agricultural water use in Seyhan basin for the period 2071-2100 by using climate model RegCM3, Topcu (2013) carried out a thesis to assess the drought in Seyhan Basin and analyzed monthly precipitation for SPI and used outputs for L-moments. Selek & Tuncok (2014) conducted a study to determine the basis for climate change adaptation of water resources management policies in Seyhan River basin. It was determined that even though there was no water stress in Seyhan basin in 2010, many parts of the basin were expected to suffer significant shortages over the coming years. Altın & Barak (2012) calculated the Erinc Drought Index by using precipitation series of 29 meteorological stations for the period of 1970-2009 in Seyhan Basin. Figure 1- Locations of the stations in the Seyhan Basin Climate type is determined and mapped. Şekil 1- İstasyonların Seyhan Havzası’ndaki konumları

Table 1- Main characteristics of stations Çizelge 1- İstasyonların temel karakteristikleri Station name Number Observation years Elevation (m) Latitude Longitude Size (year) Adana 17351 1970-2012 27 37° 00’ 30” N 35° 20’ 67” E 43 Çamardı 6893 1970-1994 1500 37° 49’ 54” N 34° 59’ 03” E 25 Feke 6902 1970-1994 620 37° 49’ 04” N 35° 54’ 46” E 25 Karaisalı 17936 1970-2012 241 37° 15’ 05” N 35° 03’ 47” E 43 Karataş 17981 1970-2012 22 36° 33’ 51” N 35° 22’ 96” E 43 Pınarbaşı 17802 1970-2010 1500 38° 43’ 17” N 36° 23’ 39” E 41 Pozantı 17934 1970-1993 750 37° 25’ 00” N 34° 53’ 00” E 24 Sarız 17840 1970-2011 1500 38° 28’ 47” N 36° 29’ 49” E 42 Tomarza 17837 1970-2010 1400 38° 26’ 00” N 35° 48’ 00” E 41 Tufanbeyli 6204 1986-2012 1350 38° 15’ 54” N 36° 13’ 13” E 27 Ulukışla 17906 1970-2012 1453 37° 33’ 00” N 34° 29’ 00” E 43

Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 199 L-momentler ve Standart Yağış İndeksi (SYİ) Yardımıyla Seyhan Havzası Kuraklık Analizi, Topçu & Seçkin

2.2. Method analysis is performed. Dutra et al (2013) assessed the predictive capabilities of an integrated drought 2.2.1. Standardized precipitation index (SPI) monitoring and seasonal forecasting system (up to 5 method months lead time) based on SPI. The forecasts were Droughts and floods are widespread subject of the evaluated over four basins in Africa. Jha et al (2013) hydraulic engineers and researchers. This method is computed the SPI with monthly precipitation dataset used commonly to monitor the drought because of for each of the 14 mainland agro climatic zones of the simpleStudyStudy algorithm results results andshow show rapid that that response.significant significant The increasing increasing SPI StudyIndia. trends trends results Showedfor for show reference reference thatthat onlysignificantevapotranspiration evapotranspiration six out increasing of 14 have mainlandshave trends been been for detected detected reference evapotranspiration have been detected was developedandand according accordingby (McKee toto et RDI,RDI, al 1993) mildmild for droughtdrought providing hashas and beenbeenhave according experiencedexperienced a significant to RDI, inin general. general.trendmild drought InduringIn addition,addition, has summer been therethere experienced havehavemonsoon. beenbeen inaa general. In addition, there have been a drought significant informationsignificant amount amount in Colorado. of of events events Seasonalwhere where moderate moderate (3, significant Blainand and severely severely (2012) amount droughts droughts carried of events outoccurred. occurred. where analyses moderateGuenang Guenang by evaluating and& & Kamga Kamgaseverely (2014) (2014) droughts occurred. Guenang & Kamga (2014) computed SPI using 55 years of precipitation data recorded 24 observation stations in Cameroon. Four 6-month computed timecomputed scale) SPISPI or using using long 5555 period yearsyears (9, ofof precipitation 12,precipitation 24, the datadata normality recordedrecorded assumption 2424 observation observation of the stations stationsSPI distributions. inin Cameroon.Cameroon. FourFour Studystatistical results distribution show that functions significant (gamma, increasing exponential, trends for Weibull reference and evapotranspirationlognormal) are fitted have to beendata. detectedDrought 48-monthstatistical timestatistical scale) distribution distribution drought can functions functionsbe analyzed, (gamma, (gamma, which exponential, exponential,Observed Weibull Weibull that Pearson and and lognormal) lognormal) III distribution are are fitted fitted was to to betterdata. data. Drought Drought andthresholds according determined. to RDI, mildMcRoberts drought & has Nielsen been -experiencedGammon (2012) in general. developed In addition, high-resolution there have drought been a- affects allthresholds thresholdstypes of water determined.determined. resources McRoberts McRobertseven subsurface && NielsenNielsen significantthan-Gammon- Gammonthe amount Gamma (2012) (2012)of 2-parameter events developeddeveloped where distribution. moderate highhigh-resolution-resolution andXie severelyet al droughtdrought droughts-- occurred. Guenang & Kamga (2014) monitoringmonitoring tool tool to to assess assess drought drought on on multiple multiplemonitoring time time scales scalestool tousing using assess the the droughtSPI. SPI. Regional Regional on multiple frequency frequency time scalesanalysis analysis using is is the SPI. Regional frequency analysis is water. Soil moisture is affected immediately from performed.computed(2013) investigatedSPI Dutra using et a 55l (2013)the years spatiotemporal assessedof precipitation the variability predictive data recordedof capabilities 24 observationof an integrated stations drought in Cameroon. monitoring Four and abrupt precipitationperformed.performed. changes.Dutra Dutra et et a Hence,al l(2013) (2013) 3 assessed orassessed 6-month the the statisticalpredictive predictivedrought distribution capabilities incidencecapabilities infunctions of ofPakistan an an integrated integrated (gamma, during drought drought1960-2007 exponential, monitoring monitoring by Weibull and and and lognormal) are fitted to data. Drought seasonalseasonal forecasting forecasting system system (up (up to to 5 5 months monthsseasonal lead lead time) time)forecasting based based on systemon SPI. SPI. (upThe The toforecasts forecasts 5 months were were lead evaluated evaluated time) based over over on SPI. The forecasts were evaluated over time scale monitoring is preferred. On the other thresholdsSPI for 3,determined. 6 and 12-month McRoberts scales. Analysis& Nielsen revealed-Gammon (2012) developed high-resolution drought- fourfour basins basins in in Africa. Africa. Jha Jha et et al al (2013) (2013) computed computedfour basins the the SPI SPI in with Africa.with month month Jha lyetly precipitational precipitation (2013) computed dataset dataset thefor for SPIeach each with of of the themonth ly precipitation dataset for each of the hand, underground water, rivers and reservoirs do monitoring tool to assess drought on multiple time scales using the SPI. Regional frequency analysis is 1414 mainland mainland agro agro climatic climatic zones zones of of India. India.14 Showed Showed thatmainland droughts that that agro only only are climatic six sixwide-spread out out zonesof of 14 14 andof mainlandsmainlands India. often Showedoccur have have overa a thatsignificant significant only six out of 14 mainlands have a significant not respond rapidly to swift precipitation changes. performed.trendlarge during areas. Dutra summer Simsek et a lmonsoon. (2013)& Cakmak assessed Blain (2010) (2012) the analyzed predictive carried the outcapabilities analyses ofby an evaluating integrated the drought normality monitoring assumption and trendtrend during during summer summer monsoon. monsoon. Blain Blain (2012) (2012)seasonal carried carried forecasting out out analyses analyses system by by evaluating evaluating (up to 5 monthsthe the normality normality lead time) assumption assumption based on SPI. The forecasts were evaluated over Therefore,ofof 12-month the the SPISPI distributions.distributions.or long period Observed Observed time scale that that (24, Pearson Pearson of thedrought III SPIIII distribution distribution distributions.experience was wasin Observed2007-2008 better better than than that Agricultural the thePearson Gamma Gamma III Year 2 2distribution-parameter-parameter was better than the Gamma 2-parameter 48, 72-month) monitoring is chosen. There are fourdistribution.of basins Turkey. in Xie TheAfrica. et drought al Jha(2013) et was al investigated (2013)evaluated computed by the using spatiotemporal the SPI, SPI with monthvariabilityly precipitation of drought datasetincidence for ineach Pakistan of the distribution.distribution. Xie Xie et et al al (2013) (2013) investigated investigated 14the the mainlandspatiotemporal spatiotemporal agro climatic variability variability zones of of droughtofdrought India. incidence incidence Showed in thatin Pakistan Pakistan only six out of 14 mainlands have a significant lots of studies about Standardized Precipitation duringPercent 1960 of-2007 Normal by SPI Index for 3, (PNI) 6 and and 12 - themonth analyses scales. Analysis revealed that droughts are wide-spread duringduring 1960 1960-2007-2007 by by SPI SPI for for 3, 3, 6 6 and and 12 12-trendmonth-month during scales. scales. summer An Analysisalysis monsoon. revealed revealed Blain that that droughts (2012)droughts carried are are wide wide out- spreadanalyses-spread by evaluating the normality assumption Index for monitoring drought all around the world and often occur over large areas. Simsek & Cakmak (2010) analyzed the drought experience in 2007- andand oftenoften occuroccur overover largelarge areas.areas. SimsekSimsek of && theof CakmakCakmak precipitationSPI distributions. (2010)(2010) and analyzedanalyzed temperature Observed thethe drought droughtthat analysis. Pearson experienceexperience The III SPI distribution inin 20072007-- was better than the Gamma 2-parameter like (McKee et al 1993; Edwards & McKee 1997; 2008 Agricultural Year of Turkey. The drought was evaluated by using SPI, Percent of Normal Index 20082008 AgriculturalAgricultural YearYear ofof Turkey.Turkey. TheThe droughtdroughtdistribution.is the waswas non-dimensional evaluatedXieevaluated et al (2013) byby using using drought investigated SPI,SPI, index. PercentPercent the This ofspatiotemporalof Normal indexNormal IndexIndex variability of drought incidence in Pakistan Guttman (PNI)(PNI) 1999; and and Türkes the the a analyses nalyses 1999; of of Kömüscü precipitation precipitation 2001; and and (PNI) temperature temperatureis computedand the analysis. aanalysis.nalyses for long Theof The timeprecipitation SPI SPI precipitationis is the the non nonand-dimensional- dimensionaltemperature series for droughtanalysis.drought The SPI is the non-dimensional drought index.during This1960 -index2007 isby computedSPI for 3, for6 and long 12 time-month precipitation scales. An seriesalysis revealedfor the preferred that droughts time scales.are wide The-spread SPI Lloyd-Hughesindex.index. & This ThisSaunders indexindex 2002; isis computedcomputed Tonkaz for for2006; longlong Li time timethe precipitationprecipitation preferred seriestimeseries scales. forfor thethe The preferredpreferred SPI calculated timetime scales.scales. by TheThe SPISPI andcalculat oftened occurby Eq uationover large 1. areas. Simsek & Cakmak (2010) analyzed the drought experience in 2007- et al 2008;calculatcalculat Abolverdieded by by Eq &Eq uation Khaliliuation 1. 1. 2010; Keskin & Equation 1. 2008 Agricultural Year of Turkey. The drought was evaluated by using SPI, Percent of Normal Index Sorman 2010). Anlı (2014) carried out a study to (PNI) and the analyses of precipitation and temperature (1) analysis. The SPI is the non - dimensional drought (1) analysis meteorological drought in provinces of (1) (1) index. This index is computed for long time precipitation series for the preferred time scales. The SPI Where;İ SPI, standardized precipitation index; South Eastern Anatoliaİ İ Region̅̅ by using temporal calculatSPI =ed (byX Eq− uationX̅)/ σ 1. WhereWhereSPISPI; ;=SPI =SPI((X,X ,s standardized−tandardized− X X))// σ σ p precipitationrecipitation i ndexindexWhere; ; ,; , ddata SPIdataata, p point;spointtandardizedoint; ; , , , m mean;meanean precipitation; ; , , , s standardstandardtandard index d deviationdeviationeviation; , d ataof of thep theoint d data;ata , mean; , standard deviation of the data variation of reference evapotranspiration (ET0) the data and RDI (Reconnaissance Drought Index). Study İ (1) TheThe gamma gamma distribution distribution is is convenient convenient forforThe precipitation precipitationİİ gamma distribution series series and and isthe the convenient distribution distribution for parameters parametersXprecipitation can can series be X̅be and the𝜎𝜎 distribution parameters can be results show that significant increasing trends for XX The gammaX̅X̅ distribution𝜎𝜎𝜎𝜎 is convenient for calculatedcalculated by by maximum maximum likelihood likelihood approximation approximationcalculatedSPI = of of ( byXThom Thomİ −maximum X̅ )(1958). (1958)./ σ likelihood If If precipitation precipitation approximation data data has has ofzero zero Thom values, values, (1958). If precipitation data has zero values, reference evapotranspiration have been detected Wheremixedprecipitation ; distributionSPI, standardized series by andThom p recipitationthe (1951)distribution canindex beparameters; used, data for p ointincorporation; , mean; of , zerostandard probability deviation and of thenon d-zeroata and accordingmixedmixed todistributiondistribution RDI, mild byby droughtThomThom (1951) (1951) has been cancan bebe usedused forfor incorporationincorporation ofof zerozero probabilityprobability andand nonnon-zero-zero probabilitiesprobabilities ( Equation(Equation 2). 2). probabilitiescan be (Equation calculated 2). by maximum likelihood experienced in general. In addition, there have been The gamma distribution is convenient for precipitationİ series̅ and the distribution parameters can be approximation of Thom (1958). If precipitationX data X 𝜎𝜎 a significant amount of events where moderate andcalculated hasH(x) zero = qby values, + maximum(1-q) mixed G(x) likelihood distribution approximation by Thom (1951) of Thom (1958). If precipitation data has zero values, (2) H(x)H(x) = = q q + + (1 (1-q)-q) G(x) G(x) mixed distribution by Thom (1951) can be used for incorporation (2) (2) of zero probability and non-zero severely droughts occurred. Guenang & Kamga can be used for incorporation of zero probability probabilities (Equation 2). (2014) computed SPI using 55 years of precipitation andWhere; non-zero q, the probabilities probability (Equationof zero precipitation 2). ; G(x), the gamma cumulative probability function. Where;Where; qq, , thethe probabilityprobability ofof zerozero precipitationprecipitation ; ; G(x)G(x), , thethe gammagamma cumulativecumulative probabilityprobability function.function. data recordedProbabilitiesProbabilities 24 observation calculatedcalculated stations byby in Equation EquationCameroon. 22 Probabilities areare transformedtransformed calculated intointo thetheby standardstandardEquation normalnormal2 are distributiontransformeddistribution forintofor the standard normal distribution for Four statistical distribution functions (gamma, calculationH(x) = q of + the(1- q)SPI G(x) values (Guttman 1998). (2) (2) calcalculationculation of of the the SPI SPI values values (Guttman (Guttman 1998). 1998). exponential, Weibull and lognormal) are fitted to Where; q, the probability of zero precipitation; Where; q, the probability of zero precipitation; G(x), the gamma cumulative probability function. data. DroughtSPISPI thresholds methodmethod determined. showsshows thethe droughtMcRobertsdrought categorycategory & G(x),SPI correspondscorresponds method the gamma shows toto output output cumulative the valuesdroughtvalues probability (Table (Tablecategory 2).2). function. correspondsIfIf thethe SPISPI gives givesto output values (Table 2). If the SPI gives Probabilitiesnegative values, calculated it means by there Equation is drought 2 areand transformedlack of precipitation; into the ifstandard the SPI normalgives positive distribution values, for it Nielsen-Gammonnegativenegative (2012) values,values, developed itit meansmeans therehigh-resolutionthere isis droughtdrought cal andandculationProbabilities lacklack ofofof precipitation;theprecipitation; SPI calculated values if (Guttmanif the bythe SPISPI Equation gives1998).gives positive positive 2 are values, values, itit drought-monitoringmeansmeans ther ther toolee is is tono no assess drought drought drought and and enough onenough multiple precipitation. precipitation. meanstransformed ther Yet, eYet, is nothe the into droughtnegative negative the standard and values values enough are normalare gettingprecipitation. getting distribution lower, lower, Yet,the the degree thedegree negative values are getting lower, the degree of the drought shifts from no drought to extremely drought and then catastrophe bursts. And same for the time scalesofof the the using drought drought the shifts shifts SPI. from Regionalfrom no no drought drought frequency to to extremely extremely forSPI calculation method drought drought shows and andof thethen then the SPI catastrophe catastrophedrought values (Guttmancategory bursts. bursts. And1998). correspondsAnd same same for for tothe the output values (Table 2). If the SPI gives positivepositive values, values, if if the the positive positive values values are are getting positivegetting hi hivalues,ghergher the the if degree thedegree positive of of the the values drought drought are shifts shiftsgetting from from higher no no drought droughtthe degree of the drought shifts from no drought negativeto extremely values, wet itand means flash there floods is droughtare seen. and The lack drought of precipitation; frequency of if athe station SPI givesis obtained positive by values, dividing it toto extremelyextremely wetwet andand flashflash floodsfloods areare seen.seen.means TheThe ther droughtdroughte is no frequencyfrequency drought and ofof aenougha stationstation precipitation. isis obtainedobtained by byYet, dividingdividing the negative values are getting lower, the degree 200 droughtdrought eventevent Tarım sizesize by byBilimleri totaltotal observationobservation Dergisi – size.Journalsize.drought FrequencyFrequency ofevent Agricultural size valuesvalues by showtotalshow Sciences observation usus howhow 22 oftenoften (2016) size. droughtdrought Frequency196-215 conditionsconditions values show us how often drought conditions ofhappen the drought in a station shifts for from a prespecified no drought time to extremely scale. drought and then catastrophe bursts. And same for the happenhappen in in a a station station for for a a prespecified prespecified time time scale.positive scale. values, if the positive values are getting higher the degree of the drought shifts from no drought

to extremely wet and flash floods are seen. The drought frequency of a station is obtained by dividing drought event size by total observation size. Frequency values show us how often drought conditions

happen in a station for a prespecified time scale.

55 5

Table 2- Standardized precipitation index categories Çizelge 2- Standart yağış indeksi kategorileri

SPI index values Drought category 2.00 or more Extreme Wet 1.50 - 1.99 Severe Wet 1.00 - 1.49 Moderate Wet 0.0 - 0.99 Mild Wet 0.0 - (-0.99) Mild Drought (-1.00) - (-1.49) Moderate Drought (-1.50) - (-1.99) Severe Drought (-2.00) or less Extreme Drought

2.2.2. Index-flood method

Index flood method is a reliable procedure as to pooling summary statistics from different data samples. Its early applications were based on flood data in hydrology (Dalrymple 1960). However, Hosking & Wallis (1997) pointed out that this method can be applicable to every kind of data.

Drought Analysis of the Seyhan Basin by Using Standardized PrecipitationAccording Index (SPI) and to L-moments, the index Topçu-flood & Seçkin method, the exceedance probability distribution of annual peak discharge is accepted as identical for hydrologically homogeneous regions except for a site-specific scaling factor called the index flood (Dalrymple 1960; Hosking & Wallis 1997). SPI method shows the drought category The important physiographic and meteorological corresponds to output values (Table 2). If the SPI characteristicsThe important ofphysiographic a basin are reflected and meteorological by this index characteristics of a basin are reflected by this index gives negative values, it means there is drought floodflood parameter. parameter. In thisIn this method, method, the the relationship relationship in inEquation 3 is recommended to estimate the flood and lack of precipitation; if the SPI gives positive quantileEquation QT. 3 is recommended to estimate the flood values, it means there is no drought and enough quantile QT. precipitation. Yet, the negative values are getting (3) (3) lower, the degree of the drought shifts from no Where; T, return period at site i; μ, the product drought to extremely drought and then catastrophe Where;T TT, returni period at site i; μ, the product of index flood (average likely flood); q, the regional ofQ index= q floodμ (average likely flood); q, the regional bursts. And same for the positive values, if the growth factor. μ is also the function basin area and slope. q is a dimensionless frequency distribution growth factor. μ is also the function basin area and positive values are getting higher the degree of the quantity common to all sites within a hydrologic homogeneous region. drought shifts from no drought to extremely wet and slope. q is a dimensionless frequency distribution flash floods are seen. The drought frequency ofa quantityA relationship common based to allon sitesavailable within information a hydrologic gathered from the gauged sites is recommended to station is obtained by dividing drought event size estimatehomogeneous index flood. region. by total observation size. Frequency values show us A relationship based on available information how often drought conditions happen in a station for gatheredOnce an fromappropriate the gauged frequency sites isdistribution recommended has beento found within a hydrologic region with N sites, a prespecified time scale. regionalestimate growth index curves flood. are determined to represent the relationship between qT and T.

Once an appropriate frequency distribution has Table 2- Standardized precipitation index categories It can be summarized that index flood method based on regional flood-frequency analysis has three steps:been hydrologic found within homogeneous a hydrologic regionregionalization, with N sites, selection of regional frequency distribution and Çizelge 2- Standart yağış indeksi kategorileri estimationregional of growth index curvesflood arerelationship. determined To to estimate represent design flood of any intermediate values of return the relationship between q and T. SPI index values Drought category periods (e.g. T= 2, 5, 10, 20, 50,T 100 …years), the index flood-based regional relationships can be used.

2.00 or more Extreme Wet It can be summarized that index flood method 2.2.3.based L-moments on regional method flood-frequency analysis has three 1.50 - 1.99 Severe Wet steps: hydrologic homogeneous regionalization, 1.00 - 1.49 Moderate Wet L-momentsselection method of regional was frequencydeveloped distributionby (Hosking and 198 6; 1990). This method is widely used for regionalization, estimation parameters and determine the quantiles. L-moments method is the linear 0.0 - 0.99 Mild Wet estimation of index flood relationship. To estimate functiondesign of flood the probability of any intermediateweighted moments values (PWM). of return There are really comprehensive studies in 0.0 - (-0.99) Mild Drought literature like Dalrymple (1960), Landwehr et al (1979a; 1979b; 1979c), Hosking (1986; 1990), periods (e.g. T= 2, 5, 10, 20, 50, 100 …years), the Gebeyehu (1989), Hosking & Wallis (1993;1997), Fowler & Kilsbyc (2003), Eslamian & Feizi (2006), (-1.00) - (-1.49) Moderate Drought index flood-based regional relationships can be Seckin & Yurtal (2007; 2008), Norbiato et al (2007), Parida & Moalafhi (2008), Seckin et al (2010a; (-1.50) - (-1.99) Severe Drought used. 2010b; 2011), Dodangeh et al (2011) and Tallaksen et al (2011). Anlı et al (2009) conducted a study to (-2.00) or less Extreme Drought determine2.2.3. L-moments regional analysis method of the annual maxima precipitation influenced on floods in Trabzon Province. Annual maxima precipitation series of 10-78 years of 10 precipitation gauging stations over Trabzon 2.2.2. Index-flood method L-moments method was developed by (Hosking 1986; 1990). This method is widely used for Index flood method is a reliable procedure asto regionalization, estimation parameters and pooling summary statistics from different data determine the quantiles. L-moments method is the 6 samples. Its early applications were based on flood linear function of the probability weighted moments data in hydrology (Dalrymple 1960). However, (PWM). There are really comprehensive studies Hosking & Wallis (1997) pointed out that this in literature like Dalrymple (1960), Landwehr et method can be applicable to every kind of data. al (1979a; 1979b; 1979c), Hosking (1986; 1990), According to the index-flood method, the Gebeyehu (1989), Hosking & Wallis (1993;1997), exceedance probability distribution of annual peak Fowler & Kilsbyc (2003), Eslamian & Feizi (2006), discharge is accepted as identical for hydrologically Seckin & Yurtal (2008), Norbiato et al (2007), homogeneous regions except for a site-specific Parida & Moalafhi (2008), Seckin et al (2010a; scaling factor called the index flood (Dalrymple 2010b; 2011), Dodangeh et al (2011) and Tallaksen 1960; Hosking & Wallis 1997). et al (2011). Anlı et al (2009) conducted a study to

Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 201 Province were used as a material. Probability parameter estimation and regional analysis were used based on L-moments statistics. Some quantile functions were obtained through Monte Carlo simulation techniques. For flood management and the design of urban drainage networks, useful precipitation values were estimated for some quantile probabilities of the at-site and regional analysis for the Generalized Logistics and Generalized Extreme Value distributions obtained from simulation. Shi et al (2010) screened the data from 12 stream flow gauging sites of the Wujiang water system in Guizhou Province by using the discordancy measure, and homogeneity of the region is then tested employing the L-moments- based heterogeneity measure. Eslamian et al (2012) used two indexes of cumulative precipitation deficit Province(CPD) and were maximum used as precipitation a material. Probability deficit (MPD) parameter for evaluating estimation the andseverity regional of drought analysis in were a certain used month based onand Lregional-moments frequency statistics. analysis Some hasquantile been functionscarried out were by Lobtained-moments. through Gingras Monte & Adamows Carlo simulationki (1994) conducttechniques. a simulation For flood managementstudy to compare and the parametric design of Lurban-moments drainage and networks, nonparametric useful approaches precipitation in values flood frequencywere estimated analysis. for Safsome (2009) quantile carried probabilities out a study of tothe determine at-site and regional regional probability analysis distributionsfor the Generalized for the annualLogistics maximum and Generalized flood data Extremeobserved Valueat 45 streamflowdistributions gauging obtained sites from in the simulation. Kucuk and ShiBuyuk et Menderesal (2010) Riverscreened Basins the datain Turkey from 12 using stream index flow flood gauging L moments. sites of Thethe Wujianggeneralized water normal system extreme in Guizhou value Provincedistribution by hasusing been the identifieddiscordancy as themeasure, best-fit and distribution homogeneity for theof theupper region- and is lower then- Menderestested employing subregions. the L Ayd-momentsogan et- albased (2014) heterogeneity performed ameasure. regional Eslamia flood frequencyn et al (2012) analysis used of twoÇoruh indexes Basin of with cumulative the L-moments precipitation method. deficit The L-momentler ve Standart Yağış İndeksi (SYİ) Yardımıyla Seyhan Havzasıflow(CPD) Kuraklık values and Analizi, maximum determined Topçu & Seçkinprecipitation from the quantiles deficit (MPD) estimated for evaluating with the Lthe-moments severity methodof drought were in acompared certain month with thoseand regional estimated frequency previously analysis with an has at -beensite freque carriedncy out analysis by L -(Gumbelmoments. distribution) Gingras & onAdamows the basinki master(1994) planconduct for foura simulation large dams study in the to Çoruhcompare Basin. parametric Dubey (2014)L-moments used theand L nonparametric-Moments for parameterapproaches estimation in flood determine regional analysis of the annual maxima offrequency Generalizedfor the analysis. chosen Extreme basinSaf (2009) Value is developed (GEV)carried distribution.out utilizing a study GEV toRegional determine flood regional frequency probability relationship distributions for the chosfor theen precipitation influenced on floods in Trabzonbasinannual distribution. is maximum developed flood utilizing data GEV observed distribution. at 45 streamflow gauging sites in the Kucuk and Buyuk Menderes Province. Annual maxima precipitation series of River BasinsL-moments in Turkey are defined using index in Equation flood L moments.4 (Hosking The generalized normal extreme value distribution 10-78 years of 10 precipitation gauging stations has been identified as the best-fit distribution for the upper- and lower-Menderes subregions. Aydogan et L-moments& Wallis are1997). defined in Equation 4 (Hosking & Wallis 1997). over Trabzon Province were used as a material. al (2014) performed a regional flood frequency analysis of Çoruh Basin with the L-moments method. The Probability parameter estimation and regional flow values1 M determined100 from the quantiles estimated with the L-moments method were compared with analysis were used based on L-moments statistics. those estimated previously with an at-site frequency analysis (Gumbel distribution) on the basin master 2 2M110 M100 (4) Some quantile functions were obtained through plan for four large dams in the Çoruh Basin. Dubey (4)(2014) used the L-Moments for parameter estimation 6M 6M M Monte Carlo simulation techniques. For floodof Generalized3 120 Extreme110 Value (GEV)100 distribution. Regional flood frequency relationship for the chosen management and the design of urban drainage basin 4is developed20M130 utilizing30M120 GEV12 distribution.M110 M100 networks, useful precipitation values were estimated The significance or meaning of this would be for some quantile probabilities of the at-site and L-momentsThe significance are defined or inmeaning Equation of 4this (Hosking would &be Wallis that: M1997).100 is the zeroth, M110 is the first, M120 is the that: M is the zeroth, M is the first, M is the regional analysis for the Generalized Logistics and second, and100 M130 is the third probability110 weighted120 moments. Followed by these definitions listed below. second, and M is the third probability weighted Generalized Extreme Value distributions obtained 1 M100 130 moments. Followed by these definitions listed from simulation. Shi et al (2010) screened the data - The2 L2-mean,M110 1,M is100 a measure of central tendency which is the same as the c onventional mean. (4) below. from 12 stream flow gauging sites of the Wujiang -The3 L6-Mstandard120 6 deviation,M110 M 1002, is a measure of dispersion, as 3 and 4 are the third and the fourth L- water system in Guizhou Province by using the -moments. The L-mean, λ1, is a measure of central tendency 4 20M130 30M120 12M110 M100 -M110which is the is theexpected same asvalue the conventionalof the random mean. variable, x, weighted by its probability of non-exceedance, discordancy measure, and homogeneity of the region is then tested employing the L-moments- -P nexThe. L-standard deviation, λ , is a measure of The significance or meaning of2 this would be that: M100 is the2 zeroth, 3M110 is the first, M120 is the based heterogeneity measure. Eslamian et al (2012) -M120dispersion, and M130 asare λ the and expected λ are valuesthe third of x andweighted the by (Pnex) and (Pnex) , respectively. second,-The and dimensionless M130 is the 3Lthird-moment probability4 ratios weightedare defined moments. by Hosking Followed (1990) by as these in Equation definitions 5. listed below. used two indexes of cumulative precipitation deficit fourth L-moments. (CPD) and maximum precipitation deficit (MPD) for -- TheM L- mean, is the 1, expected is a measure value of central of the tendency random which is the same as the conventional mean. evaluating the severity of drought in a certain month 2= 2110/ 1 (L-variation coefficient, L-Cv) -Thevariable, L-standard x, weighted deviation, by its2, isprobability a measure of of non-dispersion, as 3 and 4 are the third and the fourth L- and regional frequency analysis has been carried 3= 3/ 2 (L-skewness coefficient, L-Cs) (5) moments.exceedance, Pnex. out by L-moments. Gingras & Adamowski (1994) 4= 4/ 2 (L-kurtosis coefficient, L-Ck) -M110 is the expected value of the random variable, x, weighted by its probability of non-exceedance, conduct a simulation study to compare parametric - M120 and M130 are the expected values of x Pnex. 2 3 L-moments and nonparametric approaches in flood Stedingerweighted et byal (1993)(Pnex) anddeveloped (Pnex) , respectively.the relationships for the parameters2 and3 the L-moments for various -M120 and M130 are the expected values of x weighted by (Pnex) and (Pnex) , respectively. distributions. frequency analysis. Saf (2009) carried out a study -- TheThe dimensionless dimensionless L- momentL-moment ratios ratios are are defined defined by Hosking (1990) as in Equation 5. to determine regional probability distributions for by Hosking (1990) as in Equation 5. the annual maximum flood data observed at 45 For a Probability Distribution that takes on merely positive values, τ2 varies within the interval: 0 τ2 2= 2/ 1 (L-variation coefficient, L-Cv) streamflow gauging sites in the Kucuk and Buyuk< 1, and the other ratios are within: –1 < τ3 < +1, and –1 < τ4 < +1. These properties would be claimed to 3= 3/ 2 (L-skewness coefficient, L-Cs) (5) (5) Menderes River Basins in Turkey using index be an advantage over the Conventional Coefficients of the skew and the kurtosis, because as the latter flood L moments. The generalized normal extrememen tioned4= 4/ may2 (L- kurtosisassume verycoefficient, high magnitudes L-Ck) and former would constantly remain in the reasonable and value distribution has been identified as the best- confined interval of (-1, +1). StedingerStedinger et alet al(1993) (1993) developed developed the the relationships relationships for the parameters and the L-moments for various fit distribution for the upper- and lower-Menderes distributions.for the parameters and the L-moments for various subregions. Aydogan et al (2014) performed a The arithmetical average of a sample series is the estimate of 1. The estimates of the L-coefficients of distributions. regional flood frequency analysis of Çoruh Basinτ 2, τ3, and τ4, are calculated using the recorded sample series with the aid of a suitable plotting position For a Probability Distribution that takes on merely positive values, τ2 varies within the interval: 0 τ2 with the L-moments method. The flow values For a Probability Distribution that takes on < 1,merely and the positive other ratios values, are τ2 within: varies –within1 < τ3 the < +1,interval: and – 1 < τ4 < +1. These properties would be claimed to determined from the quantiles estimated with the be an advantage over the Conventional Coefficients of the skew and the kurtosis, because as the latter 0 ≤ τ2 < 1, and the other ratios are within: –1 < τ3 L-moments method were compared with those mentioned may assume very high magnitudes and former would constantly remain in the reasonable and < +1, and –1 < τ4 < +1. These properties would be estimated previously with an at-site frequency confined interval of (-1, +1). 7 analysis (Gumbel distribution) on the basin master claimed to be an advantage over the Conventional plan for four large dams in the Çoruh Basin. Coefficients of the skew and the kurtosis, because The arithmetical average of a sample series is the estimate of 1. The estimates of the L-coefficients of as the latter mentioned may assume very high Dubey (2014) used the L-Moments for parameter τ2, τ3, and τ4, are calculated using the recorded sample series with the aid of a suitable plotting position estimation of Generalized Extreme Value (GEV) magnitudes and former would constantly remain in distribution. Regional flood frequency relationship the reasonable and confined interval of (-1, +1).

202 Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 7

formula. For which the biased-Landwehr formula is preferred, as done in this study also, to estimate Pnex’s of the elements of the series. formula. For which the biased-Landwehr formula is preferred, as done in this study also, to estimate Pnex’s formula. For which the biased-Landwehr formula is preferred, as done in this study also, to estimate Pnex’s 2.2.4. Homogeneity tests of the elements of the series.

Hosking (1991; 1993) recommended the following three statistical measures for homogeneity tests as 2.2.4. Homogeneity tests discordancy measure (Di), heterogeneity measure (H) and goodness of fit measure (Z).

Hosking (1991; 1993) recommended the followingfollowing threethree statisticalstatistical measuresmeasures forfor homogeneityhomogeneity teststests asas 2.2.5. Discordancy measure (Di) discordancyiscordancy measure (Dii), hheterogeneity measuremeasure (H)(H) andand ggoodnessoodness ofof fitfit measuremeasure (Z) (Z). .

This test is based on the L-moments ratios and utilized to identify the sites of a given group that are 2.2.5. Discordancy measure (Dii)) discordant with the other sites of entire group. Discordancy measure depends on the number of sites.

Discordant sites should be discarded from the analyzed or evaluated in another region. Hosking & Wallis This test is based on the L-moments ratiosatios andand utilizedutilized toto identifyidentify thethe sitessites ofof aa givengiven groupgroup thatthat areare (1997) presented a table which shows critical values of discordancy statistic correspond to a number of discordant with the other sites of entire group.group. DiscordancyDiscordancy measuremeasure dependsdepends onon thethe numbernumber ofof sites.sites. sites (Table 3). The site is accepted to be harmonious if discordancy value of site is smaller than the Discordant sites should be discarded from thethe analyzedanalyzed oror evaluatedevaluated inin anotheranother region region. .Hosking Hosking & & Wallis Wallis critical value of the discordancy statistic. (1997) presented a table which showsshows criticalcritical valuesvalues ofof discordancydiscordancy statisticstatistic correspondcorrespond to to a a number number of of

sites (Table 3). The site is accepted toto bebe harmoniousharmonious ifif discordancydiscordancy valuevalue ofof sitesite isis smallersmaller thanthan thethe Table 3- Critical values of discordancy statistic, Di critical value of the discordancy statistic. Çizelge 3- Uyumsuzluk ölçüsünün kritik değerleri, Di

Table 3- Critical values of discordancy statistic, Di TableNo. of 3sites- Critical Critical values ofNo. discordancy of sites Critical statistic, Di Çizelge 3- Uyumsuzluk ölçüsünün kritik değerleri, Di Çizelgein region 3- Uyumsuzlukvalue ölçüsününin region kritik değerleri,value Di

No. of5 sites Critical1.333 No. of11 sites Critical2.632 inin region6 value1.648 in region12 valuevalue2.757 57 1.3331.917 1113 2.6322.6322.869 68 1.6482.14 1214 2.7572.7572.971 79 1.9172.329 13≥15 2.8692.8693 810 2.142.491 14 2.9712.971 9 2.329 ≥15 33 Discordancy measure (Di) is defined in Equation 6, 7 and 8. 10 2.491

1 T DiscordancyD measureu u S(D1ii) uis definedu inin EquationEquation 6,6, 77 andand 8.8. i i i (6) Drought Analysis of the Seyhan Basin by Using Standardized Precipitation Index 3(SPI) and L-moments, Topçu & Seçkin 1 T 1 (7) Di u i u S u ii u (6) (6) 3−1 N The arithmetical average of a sample series is the i=1 i u̅ = N ∑ u (8) (7) (8) estimate of λ . The estimates of the L-coefficients (7) 1 −N1 N T Where;−i=11 Nui , vectori of LCv, LCs, LCk for a site I; of τ , τ , and τ , are calculated using the recorded S = ∑ (ui=1i− ui̅)(u − u̅) 2 3 4 u̅W=hereN; ui∑, vectour of LCv, LCs, LCk for a site I; S, covariance matrix of ui; u , mean of vector u i (8) (8) sample series with the aid of a suitable plotting S, covariance matrix of ui; u , mean of vector ui N T position formula. For which the biased-Landwehr 2.2.6. Heterogeneityi=1 i measurei (H) WS =here∑; ui,( vectou −ru̅ of)( LCv,u − LCs,u̅LCs,) LCkLCk forfor aa sitesite I;I; S,S, covariancecovariance matrixmatrix of of u ui;i;uu, ,mean mean of of vector vector u ui i 2.2.6. Heterogeneity measure (H) formula is preferred, as done in this study also, to The heterogeneity measure (Hi) is proposed for identification of the degree of heterogeneity of group of estimate Pnex’s of the elements of the series. 2.2.6.The Heterogeneity heterogeneity measure measure (H) (Hi) is proposed for sites and Hi can be estimated by Equation 9 and 10. identification of the degree of heterogeneity of

2.2.4. Homogeneity tests The heterogeneity measure (Hi) is proposed for identification of the degree of heterogeneity of group of The groupheterogeneity of sites measure and Hi can(Hi) beis proposedestimated for by identification Equation of the degree of heterogeneity of group of sites and Hi can be estimated by Equation 9 and 10. Hosking (1991; 1993) recommended the followingsites 9 and and H 10.Vi can bev estimated by Equation 9 and 10. H (9) three statistical measures for homogeneity tests as v V v discordancy measure (Di), heterogeneity measure N v N (9) H R i (9) (H) and goodness of fit measure (Z). τs n i τ s n i (s 2,3,4 ) (10) (9) i 1v i 1 N N 2.2.5. Discordancy measure (D ) RR NN ii NN i τ R n τ (i ) n (s 2,3,4) (10) τWhere;ôss = V∑, nnweightediiτôss standard∑nnii deviation((ss =of2 2L,,-33coefficient,,44)) (10) of variation values; and , the mean (10)and formula. For which the biased-Landwehr formula is preferred, as dones ini this1 istudys also,i 1 toi estimate Pnex’s This test is based on the L-moments ratios and standard deviationii=11 of a numberii=11 of simulations of V. of the elements of the series. utilized to identify the sites of a given group that Where; V, weighted standard deviation of Where; V, weighted standard deviation ofof LL--coefficientcoefficient ofof variationvariation valuesvalues; ; and and , , the the mean mean and and 2.2.4.are Homogeneitydiscordant withtests the other sites of entire group. L-coefficient of variation values; µ and σ , the mean standard deviation of a number of simulationssimulationsυ ofof V.υV. Discordancy measure depends on the number of and standard deviation of a number of simulations Hoskingsites. Discordant(1991; 1993) sites recommended should be thediscarded following from three the statistical of V. measures for homogeneity tests as discordancyanalyzed measureor evaluated (Di), h ineterogeneity another region. measure Hosking (H) and &goodness of fit measure (Z). 8 Suppose that the proposed region has N sites, Wallis (1997) presented a table which shows critical 2.2.5. Discordancy measure (Di) with site i having record length n and sample values of discordancy statistic correspond to a i 8 L-moment ratios τ (i), τ (i), τ (i). Denote by τ (R), τ (R), 8 number of sites (Table 3). The site is accepted to be 2 3 4 2 3 This test is based on the L-moments ratios and utilized to identifyτ (R) thethe regionalsites of a averagegiven group L-Cv, that L-Skewnessare and discordantharmonious with theif discordancy other sites of value entire of group. site isDiscordancy smaller measure4 depends on the number of sites. L-Kurtosis. V , weighted standard deviation of Discordantthan the sites critical should value be discardedof the discordancy from the analyzed statistic. or evaluated in another region1 . Hosking & Wallis (1997) presented a table which shows critical values of discordancyL-coefficient statistic correspond of variation to a number values of can be expressed sites (Table 3). The site is accepted to be harmonious if discordancyas Equation value 11. of site is smaller than the Table 3- Critical values of discordancy statistic, Di critical value of the discordancy statistic. Çizelge 3- Uyumsuzluk ölçüsünün kritik değerleri, D i (11) TableNo. 3- ofCritical sites valuesCritical of discordancy No. of statistic,sites DCriticali Çizelge 3- Uyumsuzluk ölçüsünün kritik değerleri, Di in region value in region value It is possible to construct heterogeneity measures in No. of sites5 Critical 1.333No. of sites 11Critical 2.632 which V in equation (11) is replaced by other measures in region 6 value 1.648in region 12value 2.757 5 1.333 11 2.632 of between-site dispersion of sample L-moments. One 7 1.917 13 2.869 of the measures is based on L-Cv and L-Skewness (V ) 6 1.648 12 2.757 2 8 2.14 14 2.971 can be expressed as Equation 12. 7 1.917 13 2.869 9 2.329 ≥15 3 8 2.14 14 2.971 (12) 9 10 2.329 2.491≥15 3 10 2.491 and the other based on L-Skewness and L-Kurtosis Discordancy measure (D ) is defined in Equation i (V3) can be expressed as Equation 13. Discordancy6, 7 and 8. measure (Di) is defined in Equation 6, 7 and 8.

1 T −1 (13) Di = 1(u i − u) TS 1(u i − u) (6) Di 3 u i u S u i u (6) 3 If heterogeneity of a region H<1 then the region (7) is acceptably homogeneous. 1≤H<2, (7) then the region −1 N i=1 i u̅ = N ∑ u (8) Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 203 N T i=1 i i WS =here∑; ui,( vectou −ru̅ of)( LCv,u − LCs,u̅) LCk for a site I; S, covariance matrix of ui; u , mean of vector ui

2.2.6. Heterogeneity measure (H)

The heterogeneity measure (Hi) is proposed for identification of the degree of heterogeneity of group of sites and Hi can be estimated by Equation 9 and 10.

V v H (9) v

R N i N τs n i τs n i (s 2,3,4) (10) i 1 i 1

Where; V, weighted standard deviation of L-coefficient of variation values; and , the mean and standard deviation of a number of simulations of V.

Suppose that the proposed region has N sites, with site i having record length ni and sample L-moment ratios i i i Denote by R R R the regional average L-Cv, L-Skewness and L-Kurtosis. V1, weighted standard deviation of L-coefficient of variation values can be expressed as Equation 11. 1 2 N i R 2 N V1 n i τ 2 τ 2 n i (11) Supposei that1 the proposed regioni 1 has N sites, with site i having record length ni and sample L-moment i i i R R R ratios Denote by the regional average L-Cv, L-Skewness and L-Kurtosis. V1, Itweighted is possible standard to construct deviation heterogeneity of L-coefficient measures of variation in which values V incan equation be expressed (11) isas replacedEquation by 11. other 1 2 measures of Nbetween-isite dispersionR 2 N of sample L-moments. One of the measures is based on L-Cv and L- V1 n i τ 2 τ 2 n i (11) Skewness (Vi 21) can be expressed asi Equation1 12.

1 2 It is possibleN to iconstructR 2heterogeneityi Rmeasures2 Nin which V in equation (11) is replaced by other V2 n i τ 2 τ 2 τ3 τ3 n i (12) measures ofi 1 between-site dispersion of sample L-moments.i 1 One of the measures is based on L-Cv and L- Skewness (V2) can be expressed as Equation 12. and the other based on L-Skewness and L-Kurtosis (V3) can be expressed as Equation 13. L-momentler ve Standart Yağış İndeksi (SYİ) Yardımıyla Seyhan1 2 Havzası Kuraklık Analizi, Topçu & Seçkin N i R 2 i R 2 N V2 N n i τ 2 τ 2 2 τ3 τ3 2 1 2 N n i (12) i 1 i R i R i 1 isV 3possiblyni heterogeneous.τ3 τ3 H≥2,τ4 thenτ4 the region isn i called biased-Landwehr plotting position (13) formula, i 1 i 1 and definitelythe other based heterogeneous. on L-Skewness and L-Kurtosis (V3) canwhich be expressed is Equation as Equation18. 13. If heterogeneity of a region H<1 then the region is acceptably homogeneous. 1 H<2, then the region 2.2.7. Goodness of fit measure (Z) 1 2 (18) is possibly Nheterogeneous.i R H2 2, theni the regionR 2 is definitelyN heterogeneous. HoskingV3 n&i Wallisτ3 (1993)τ3 proposedτ4 aτ 4well-matchedn i (13) i 1 i 1 measure for goodness of fit dependent upon The above equation defines that: F is the 2.2.7. Goodness of fit measure (Z) ij L-kurtosis. The ZDIST goodness of fit test is used estimate of P of the j’th element in the j’th sample If heterogeneity of a region H<1 then the region is acceptably homogeneous.nex 1 H<2, then the region Hoskingfor selecting& Wallis the (1993) appropriate proposed regional a well - frequencymatched measure for goodness of fit dependent upon L- is possibly heterogeneous. H 2, then the region is definitelyseries heterogeneous. arranged in ascending order, and nj is the kurtosis.distribution The ZDIST function goodness for aof group fit test of is sites. used Goodnessfor selecting the appropriate regional frequency distribution number of elements in the j’th sample series. functionof fit for measure a group (Z) of compares sites. Goodness the difference of fit measurebetween (Z) compares the difference between sample L- 2.2.7. Goodness of fit measure (Z) kurtosis and population L-kurtosis. sample L-kurtosis and population L-kurtosis. 3. Results and Discussion HoskingThe & GoodnessWallis (1993) of fit proposedmeasure for a eachwell -distributionmatched measure for goodness of fit dependent upon L- The Goodness of fit measure for each distribution is defined by the ZDIST statistic, which is outlined kurtosis. The ZDIST goodnessDIST of fit test is used for selecting the appropriate regional frequency distribution belowis definedin Equation by 14. the Z statistic, which is outlined 3.1. SPI results function for a group of sites. Goodness of fit measure (Z) compares the difference between sample L- below in Equation 14. kurtosis and population L-kurtosis. Standardized Precipitation Index shows climatic DIST t 4 changes and drought conditions in 11 meteorological ZDIST 4 4 (14) stations accomplished DIST for preferred time scales. (14) The The Goodness of4 fit measure for each distribution is defined by the Z statistic, which is outlined below in Equation 14. precipitation-time series were used to procure the Continuing, t 4 is the average of L-Ck values computed from an observed sample series in a given Continuing, t 4 is the average of L-Ck values SPI values for 3, 6, 9 and 12-month time scales. Dist region,computed is fromDISTthe bias an observed of t4, sample is theseries average in a given L-Ck values computed from simulation for a fitted DIST4 4 t 4 4 4 Dist The results show that the most severe drought region,Z β 4 is the bias of t , τ 4 is the average (14) distribution, and 4 is the standard4 deviation of L-Ck valuesyears (from were simulation), 1973, 1988, and 1989, β4 and 2001 σ4. E andquation 2008 for L-Ck values computed4 from simulation for a fitted 15 and 16 presented for clarification. the most of the 11 stations. The wettest years were Continuing,distribution, t and4 is σthe average is the standard of L-Ck deviationvalues computed of from an observed sample series in a given 4 1979, 1981, 1994. The periods have the most severe Dist L-Ck valuesN (from simulation), and β4 and σ4. region, 4 is thesim bias of t4, 4 is the average L-Ck valuesdrought computed months are from seen simulation in Table 4.for One a fittedcan infer Equation1 15 and (16m) presented for clarification. 4 N sim t 4 t 4 (15) distribution, and 4 is the standard deviation of L-Ck valuesroughly (from from simulation), Table 4 that and drought β4 and haveσ4. E experiencedquation m 1 15 and 16 presented for clarification. quinquennial overall basin. (15)1 Nsim 2 1 (m) 2 2 4 NsimNsim1 t 4 t 4 N sim 4 (16) 1 (m) Table 4- The most severe drought periods N t m 1t 4 (15) 4 sim 4 Çizelge 4- En şiddetli kurak periyotlar m 1 (16) 1 The above equation Nexplainssim that Nsim is the number2 of Adanasimulated regional(1970-1975), data (1997-2005)sets that are generated 1 (m) 2 2 using a KappaN distribution1 andt m ist 4 the indexN denoting the simulated region. The fit is considered to(16) be 4 The aboveDISTsim equation explains4 that sim N 4 is the Çamardı (1989-1992) suitable if Z is sufficientlym 1 close to zero.sim A reasonable criterion example would be considered by Equationnumber 17. of simulated regional data sets that are Feke (1970-1975), (1984-1987), (1989-1991) generated using a Kappa distribution and m is the Karaisalı (1970-1975), (1989-1993), (2003-2009) Theindex above denoting equation the explains simulated that region.Nsim is the The number fit is of simulated regional data sets that are generated Z DIST 1.64 (17) usingconsidered a Kappa todistribution be suitable and if Z mDIST is is the sufficiently index denoting close theKarataş simulated (1982-1985),region. The (1989-1992), fit is considered (2003-2007) to be suitable if ZDIST is sufficiently close to zero. A reasonable criterion example would be considered by to zero. A reasonable criterion example would be Pınarbaşı (1984-1987), (1989-1990), (1993-1994) Equation 17. considered by Equation 17. Pozantı 1973, 1982, 1984, (1989-1993) Z DIST 1.64 (17) Sarız (1981-1985), (1999-2003) (17) 9 Tomarza (1973-1974), (1999-2001) A detailed description of the index flood procedure and L-moments is described by Hosking Tufanbeyli 1989, 1999, 2001, (2003-2008) (1973-1975), (1981-1985), (1989-1994), & Wallis (1997). The non-exceedance probability of Ulukışla an element in a sample series is estimated by the so- (2004-2007) 9

204 Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 Drought Analysis of the Seyhan Basin by Using Standardized Precipitation Index (SPI) and L-moments, Topçu & Seçkin

As shown in Table 5, most of the minimum and happens more often. Moderate drought with SPI values are seen in 6-month (seasonal) time frequency value 9.1% was observed in 12-month scale. The minimum SPI value of a month -3.76 period. Probability occurrence corresponds to (exceptionally drought) was experienced in Tomarza drought categories for Karaisalı station are seen in station for 6-months period in May 1989. One can Figure 4. see the SPI results and read from example following Table 5- The minimum SPI values of stations figures for the 12-month period of Karaisalı and Çizelge 5- İstasyonların minimum SPI değerleri Tufanbeyli stations, graphs for the other stations and explanations are not dwelled on because of Station Minimum SPI Period Month Year their similarities. With reference to average monthly Adana -3.18 6-month February 1973 SPI values, the most severe drought station is the Çamardı -3.68 6-month June 1989 Karaisalı station, the maximum and minimum SPI Feke -3.2 3-month January 1973 values were observed as 3.59 (extremely wet) and Karaisalı -3.6 3-month April 1989 -3.6 (extremely drought) respectively in 3-month period. Extreme SPI values for Karaisalı station Karataş -3.74 6-month July 1989 are seen in Table 6. We can see the monthly flash Pınarbaşı -3.75 6-month May 1989 drought and precipitation changes by reading SPI Sarız -3.39 9-month January 2001 graph (Figure 2). Period 2005 to 2009 was moderate Pozantı -3 3-month April 1989 drought and mild drought. Especially year 2008 was Tomarza -3.76 6-month May 1989 severe drought (Figure 3). The drought frequency Tufanbeyli -3.37 6-month June 1989 is 50.9% for this station. It means that drought incidents are experienced more than wet conditions Ulukışla -3.3 6-month October 1984

Table 6- Extreme SPI values of droughts and precipitations for Karaisalı station, presenting 3, 6, 9 and 12-month time scales Çizelge 6- Karaisalı istasyonunun 3, 6, 9 ve 12 aylık zaman serileri için ekstrem SPI değerleri

3-month period 6-month period 9-month period 12-month period Exceptional drought month April July October April Observed year 1989 1989 1989 2008 SPI value -3.6 -2.98 -2.42 -2.15 Exceptional wet month March June September December Observed year 1994 1994 1994 1994 SPI Value 3.59 3.42 3.45 3.31 The most severe year in respect to the 2007 2008 2008 2008 average SPI value SPI value -0.88 -1.36 -1.7 -1.84 The most wet year in respect to the 1988 1994 1994 1994 average SPI value SPI value 0.99 1.54 2.24 2.84

Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 205 L-momentler ve Standart Yağış İndeksi (SYİ) Yardımıyla Seyhan Havzası Kuraklık Analizi, Topçu & Seçkin

The Tufanbeyli station is the wettest station with respect to drought frequency. Frequency value is 45.9%. The most exceptional drought was observed as -3.37 (extremely drought) in 6-month period. Extreme SPI values for Tufanbeyli station are shown in Table 7. Instantaneous monthly drought changes are seen in Figure 5. The climate after the year 2002 to 2009 shifted from mild drought to severe drought. Severe drought observed in year 2011 is remarkable. A Figure 2- 12-month SPI analysis for Karaisalı little increment of amount of precipitation was station observed in the last 3 years (explicitly shown in Şekil 2- Karaisalı istasyonunun 12 aylık SPI değerleri Figure 6), severe drought with frequency value 5.1% was lived in 12-month period. Probability occurrence corresponds to drought categories for Tufanbeyli station are seen in Figure 7. The Adana station is a mild drought station. The drought frequency is 47.7% for this station. Exceptional droughts were seen in the 12-month period, the climate is prone to shift having less precipitation and more drought. For the Pozantı station the drought frequency is 49.2%, severe drought was observed especially in the 12-month period. Exceptional and severe precipitations occurred Figure 3- 12-month average annual SPI analysis for in the period from 1974 to 1978. Exceptional Karaisalı station precipitations shown in 12-months are prone to Şekil 3- Karaisalı istasyonunun yıllık ortalama 12 shift to mild precipitations for the concluding aylık SPI değerleri 10 years, exactly like the other stations. For the Pınarbaşı, Tufanbeyli, Sarız, Karataş, Tomarza, Çamardı, Feke, Ulukışla stations drought frequencies are consecutively 46.3%, 45.9%, 46.2%, 46.8%, 46.8%, 48.3%, 49.8%, 47%. We have to underline that comparing stations in terms of drought, indeed, entails meteorological stations to have same size data. Because, observation size and beginning values alter the results of SPI. All stations seriously must have Figure 4- Probability of drought occurrence for Karaisalı station, presenting 3, 6, 9 and 12-month same beginning and end time. Longer data show time scales and different drought categories the results realistically. In fact, Ulukışla station Şekil 4- Karaisalı istasyonunun 3, 6, 9 ve 12 aylık SPI is the wettest station concerning just average SPI değerleri ve farklı kuraklık kategorilerine göre oluşma values not the drought frequency by reason of olasılıkları frequency includes size.

206 Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 Drought Analysis of the Seyhan Basin by Using Standardized Precipitation Index (SPI) and L-moments, Topçu & Seçkin

Table 7- Extreme SPI values of droughts and precipitations for Tufanbeyli station, presenting 3, 6, 9 and 12-month time scales Çizelge 7- Tufanbeyli istasyonunun 3, 6, 9 ve 12 aylık zaman serileri için ekstrem SPI değerleri 3-month period 6-month period 9-month period 12-month period Exceptional drought month January June September October Observed year 2001 1989 1989 2001 SPI value -3.24 -3.37 -3.27 -2.6 Exceptional wet month September November November January Observed year 2002 1996 1988 1989 SPI Value 2.42 2.83 2.43 1.87 The most severe year in respect to the 1989 1989 2001 2001 average SPI value SPI value -1.35 -1.46 -1.78 -1.85 The most wet year in respect to the 1988 1988 1988 1988 average SPI value SPI value 1.09 1.26 1.4 1.47

Figure 7- Probability of drought occurrence for Figure 5- 12-month SPI analysis for Tufanbeyli station Tufanbeyli station, presenting 3, 6, 9 and 12-month Şekil 5- Tufanbeyli istasyonunun 12 aylık SPI değerleri time scales and different drought categories Şekil 7- Tufanbeyli istasyonunun 3, 6, 9 ve 12 aylık SPI değerleri ve farklı kuraklık kategorilerine göre oluşma olasılıkları

The results also indicate that high elevation located stations have lower amount of precipitation, yet, because of the low temperature and sun energy, they have lowest evaporation rate so this engenders less drought events. Generally, all of the stations are on the boundary of drought. According to results, we can claim that the Seyhan Basin, average frequency Figure 6- 12-month average annual SPI analysis for of drought of the 11 stations considered, is neither Tufanbeyli station drought nor wet but experiencing extreme event Şekil 6- Tufanbeyli istasyonunun yıllık ortalama 12 from time to time. Basin has the 47.7% frequency of aylık SPI değerleri occurrence of drought.

Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 207 L-momentler ve Standart Yağış İndeksi (SYİ) Yardımıyla Seyhan Havzası Kuraklık Analizi, Topçu & Seçkin

3.2. L-moments results 3.2.1. The results of the stations in terms of L-moments method was employed to perform 12-month period minimum precipitation series regional frequency analysis. The amount of All of the 11 stations are in accordance with precipitations which correspond to minimum each other when the 12-month period minimum monthly SPI values of the each year were utilized precipitation series are used in L-moments. All of as input data for L-moments method. After the discordancy values are smaller than the critical procuring input data, L-moments method was value ‘Di= 2.632’ which determined by Hosking employed to specify the homogenous regions. & Wallis (1997) for the number of 11 stations.

After all, the best distributions for the basins or Discordancy values (Di) and L-moments ratios the sub-basins were elected. Generalized Extreme (t, t3, t4, t5) of each station are seen in Table 8. Value, Generalized Logistic, Generalized Normal, Heterogeneity values (H1), (H2) and (H3) are Generalized Pareto, Pearson Type 3 and Wakeby smaller than the critical value ‘1’ (Table 9). And Z distributions were implemented to homogenous (goodness-of fit test) value 0.35≤ 1.64. Considering regions and sub-basins. Unfortunately, there this, the Generalized Normal distribution is the are no proper distributions for 3 and 6-month most proper for the entire basin as regards the periods minimum precipitation series, even tried 12-month time scale minimum precipitation series dividing whole basin into several parts to detect (Table 10). The values of dimensionless growth homogenous regions. Heterogeneity values (H1), factors (QT/Qave) for various return periods are (H2) and (H3) are enormously greater than the calculated by using regional parameters (Table critical value ‘1’. But, for the 9 and 12-month 11) and shown in Table 12. Growth curves for periods minimum precipitation series, achieved whole basin, growth factors (QT/Qave) of best fitted homogenous regions and distributions. Results distributions correspond to y (gumbel reduced are seen in the following. values), are drawn in Figure 8.

Table 8- L-moments ratios and discordancy values for 12-month minimum precipitation series Çizelge 8- 12 aylık minimum yağış serileri için L-moment oranları ve uyumsuzluk ölçüleri

Station name Observation size Average precipitation t t 3 t 4 t 5 Di Adana 42 52.4 0.4837 0.2637 0.125 0.078 0.62 Çamardı 24 35.4 0.4331 0.2441 0.119 -0.006 0.76 Feke 24 80.4 0.4053 0.1425 0.097 0.1201 0.57 Pınarbaşı 40 27.1 0.4666 0.327 0.228 0.1586 0.58 Tomarza 40 29.3 0.394 0.1414 0.105 0.078 0.61 Sarız 41 33.8 0.3947 0.0742 0.112 0.1377 1.88 Ulukışla 42 21.2 0.3951 0.2597 0.184 0.1257 1.43 Karaisalı 42 72.0 0.485 0.3248 0.253 0.1719 1.69 Karataş 42 81.5 0.5137 0.3523 0.153 0.0596 1.51 Pozantı 23 69.6 0.4666 0.3713 0.223 0.1072 0.94 Tufanbeyli 26 34.3 0.4917 0.2863 0.166 0.1096 0.42

208 Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 Drought Analysis of the Seyhan Basin by Using Standardized Precipitation Index (SPI) and L-moments, Topçu & Seçkin

Table 9- Heterogeneity (H) measures for whole Table 10- Values of the ZDIST Statistic of various basin distributions for whole basin Çizelge 9- Tüm havzanın heterojenlik ölçüleri Çizelge 10- Tüm havzanın çeşitli dağılımlar için ZDIST değerleri All stations Distribution Whole basin H1 -0.06* Gen. extreme value Z= 1.04 Heterogeneity H2 0.68 Gen. normal Z= 0.35* H3 0.06 Pearson type III Z= -0.88 *, H≤1 whole basin is definitely homogenous *, absolute Z-statistic value closest to zero and lower than 1.64

Table 11- Regional parameters for various frequency distributions for whole basin Çizelge 11- Tüm havzanın farklı frekans dağılımları için bölgesel parametreleri

XI ALPHA K Distributions (location parameter) (scale parameter) (shape parameter) Gen. extreme value 0.591 0.571 -0.125 Gen. normal 0.8 0.712 -0.525 Pearson type III 1 0.857 1.52 XI ALPHA BETA GAMMA DELTA Wakeby -0.06 1.243 1.607 0.485 0.167

Table 12- Values of dimensionless growth factors (QT/Qave) for various return periods for whole basin

Çizelge 12- Tüm havzanın farklı dönüş periyotları için boyutsuz büyüme faktörü (QT/Qave) değerleri

T return F y (Gumbel period (non-exceedance Observed values

reduced values) (year) probability) GEV GNO PE3 WAKEBY y* Q / Qave -0.834 1.111111 0.1 0.138 0.136 0.131 0.112 -1.224 0.017 -0.476 1.25 0.2 0.327 0.316 0.294 0.284 -0.726 0.158 0.367 2 0.5 0.805 0.8 0.792 0.816 -0.244 0.43 1.5 5 0.8 1.533 1.553 1.589 1.552 0.248 0.738 2.25 10 0.9 2.074 2.101 2.142 2.058 0.747 1.043 3.2 25 0.96 2.835 2.842 2.843 2.78 1.23 1.375 3.9 50 0.98 3.46 3.428 3.358 3.395 1.754 1.713 4.6 100 0.99 4.138 4.04 3.865 4.083 2.321 2.188 5.3 200 0.995 4.874 4.683 4.366 4.854 2.845 2.553 6.21 500 0.998 5.948 5.584 5.02 6.022 3.404 2.942 6.9 1000 0.999 6.845 6.307 5.51 7.032 4.571 3.644 y*, Gumbel reduced values using Hosking plotting position formula; y*; Hosking noktalama pozisyon formülü ile hesaplanan Gumbel azalan değerler

Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 209 L-momentler ve Standart Yağış İndeksi (SYİ) Yardımıyla Seyhan Havzası Kuraklık Analizi, Topçu & Seçkin

Region 2 for 9-month period minimum precipitation

series. Discordancy values (Di) and L-moments ratios (t, t3, t4, t5) of region 1 and region 2 are seen in Table 13 and Table 14. Heterogeneity values (H1), (H2) and (H3) are smaller than the critical value ‘1’ for all regions (Table 15). Also Z (goodness-of fit test) values -0.88≤1.64 and 0.57≤1.64 for region 1 and 2 respectively. The Pearson Type 3 distribution is the most proper for two homogenous sub-basins as regards the 9-month time scale minimum precipitation series (Table 16). Figure 8- Growth curves for the whole basin The values of dimensionless growth factors (QT/

Şekil 8- Tüm havza için büyüme eğrileri Qave) for various return periods are calculated by using regional parameters (Table 17) and shown 3.2.2. The results of region 1 and region 2 in terms in Table 18 and Table 19 for region 1 and region of 9-month period minimum precipitation series 2 respectively. Growth curves, growth factors (QT/

Two homogenous regions are procured by Qave) of best fitted distributions correspond to y dividing basin, by selecting stations with respect (gumbel reduced values), are drawn in Figure 9 for to approximate elevation values, as Region 1 and region 1 and Figure 10 for region 2.

Table 13- L-moments ratios and discordancy values for region 1 Çizelge 13- Bölge 1’in L-moment oranları ve uyumsuzluk değerleri

Region 1 Station name Observation size Average precipitation t t 3 t 4 t 5 Di Adana 42 47.3 0.477 0.2348 0.1099 0.0828 1.09 Feke 24 82.5 0.4594 0.2416 0.1041 0.0775 0.51 Karaisalı 42 63.9 0.4538 0.2627 0.203 0.1666 1.12 Karataş 42 41.6 0.522 0.301 0.1485 0.0882 1.18 Pozantı 23 58.1 0.3867 0.2211 0.1559 0.0911 1.1

Table 14- L-moments ratios and discordancy values for region 2 Çizelge 13- Bölge 1’in L-moment oranları ve uyumsuzluk değerleri

Region 2 Station name Observation size Average precipitation t t 3 t 4 t 5 Di Çamardı 24 33 0.3365 0.156 0.1864 0.0494 1.26 Pınarbaşı 40 22.2 0.4081 0.158 0.1212 0.1103 0.32 Tomarza 40 25.6 0.3259 0.211 0.1423 0.0338 1.27 Sarız 41 29.8 0.3908 0.083 0.0617 0.0635 1.54 Ulukışla 42 16.6 0.3838 0.224 0.1391 0.0421 0.42 Tufanbeyli 26 39.6 0.4765 0.197 0.0682 0.0764 1.2

210 Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 Drought Analysis of the Seyhan Basin by Using Standardized Precipitation Index (SPI) and L-moments, Topçu & Seçkin

Table 15- Heterogeneity (H) measures for region 1 Table 16- Values of the ZDIST Statistic of various and region 2 distributions for region 1 and region 2 Çizelge 15- Bölge 1 ve Bölge 2’nin heterojenlik ölçüleri Çizelge 16- Tüm havzanın çeşitli dağılımlar için ZDIST değerleri Region 1 Region 2 Distribution Region 1 Region 2 H1 -0.21* 0.59* Gen. extreme value Z= 1.25 Z= 1.33 Heterogeneity H2 -1.5 -0.23 Gen. normal Z= 0.77 Z= 1.13 H3 -1.45 -0.94 Pearson type III Z= -0.08* Z= 0.57* *, H≤1 sub-regions are definitely homogenous Gen. pareto Z= -1.12 *, absolute Z-statistic values closest to zero and lower than 1.64

Table 17- Regional parameters for various frequency distributions in sub-regions 1 and 2 Çizelge 17- Alt bölgeler 1 ve 2’nin farklı frekans dağılımları için bölgesel parametreleri XI ALPHA K (location (scale (shape Region Distributions parameter) parameter) parameter) Gen. extreme value 0.573 0.589 -0.131 Gen. normal 0.789 0.736 -0.534 Region 1 Pearson type III 1 0.893 1.545 Gen. pareto -0.021 1.208 0.183 XI ALPHA BETA GAMMA DELTA Wakeby -0.058 1.345 0.546 0.093 0.508 Gen. extreme value 0.679 0.554 -0.002 Gen. normal 0.882 0.648 -0.353 Region 2 Pearson type III 1 0.705 1.039 XI ALPHA BETA GAMMA DELTA Wakeby -0.021 1.467 2.94 0.71 -0.095

Table 18- Values of dimensionless growth factors (QT/Qave) for various return periods in Region 1

Çizelge 18- Bölge 1’in farklı dönüş periyotları için boyutsuz büyüme faktörü (QT/Qave) değerleri y (Gumbel reduced -0.834 -0.476 0.367 1.5 2.25 3.2 3.9 4.6 5.3 6.21 6.9 values) T return period 1.1111 1.25 2 5 10 25 50 100 200 500 1000 (year) F (non-exceedance 0.1 0.2 0.5 0.8 0.9 0.96 0.98 0.99 0.995 0.998 0.999 probability) GPA 0.105 0.243 0.765 1.663 2.249 2.918 3.355 3.739 4.078 4.465 4.718 GEV 0.108 0.301 0.794 1.549 2.114 2.912 3.572 4.289 5.072 6.22 7.184 GNO 0.105 0.29 0.789 1.571 2.143 2.921 3.538 4.185 4.865 5.82 6.589 PE3 0.101 0.267 0.78 1.61 2.188 2.922 3.463 3.996 4.522 5.211 5.727 WAKEBY 0.09 0.246 0.795 1.613 2.109 2.733 3.262 3.916 4.778 6.427 8.262 y* -1.219 -0.763 -0.281 0.242 0.743 1.21 1.737 2.325 2.844 3.421 4.55

Q / Qave 0.004 0.112 0.349 0.736 1.042 1.383 1.776 2.186 2.638 3.007 3.64 y*, Gumbel reduced values using Hosking plotting position formula; y*, Hosking noktalama pozisyon formülü ile hesaplanan Gumbel azalan değerler

Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 211 L-momentler ve Standart Yağış İndeksi (SYİ) Yardımıyla Seyhan Havzası Kuraklık Analizi, Topçu & Seçkin

Table 19- Values of dimensionless growth factors (QT/Qave) for various return periods in Region 2

Çizelge 19- Bölge 2’nin farklı dönüş periyotları için boyutsuz büyüme faktörü (QT/Qave) değerleri

T return F y (Gumbel reduced period (non-exceedance Observed values

values) (year) probability) GEV GNO PE3 WAKEBY y* Q / Qave -0.834 1.11111 0.1 0.217 0.215 0.21 0.187 -1.228 0.055 -0.476 1.25 0.2 0.415 0.411 0.4 0.376 -0.793 0.216 0.367 2 0.5 0.882 0.882 0.88 0.889 -0.289 0.494 1.5 5 0.8 1.511 1.517 1.531 1.533 0.254 0.8 2.25 10 0.9 1.929 1.931 1.946 1.946 0.75 1.129 3.2 25 0.96 2.457 2.451 2.447 2.447 1.245 1.398 3.9 50 0.98 2.85 2.835 2.805 2.798 1.77 1.702 4.6 100 0.99 3.24 3.217 3.15 3.127 2.318 1.966 5.3 200 0.995 3.63 3.601 3.485 3.435 2.846 2.206 6.21 500 0.998 4.144 4.114 3.916 3.812 3.39 2.319 6.9 1000 0.999 4.534 4.508 4.236 4.076 4.589 2.851 y*, Gumbel reduced values using Hosking plotting position formula; y*, Hosking noktalama pozisyon formülü ile hesaplanan Gumbel azalan değerler

4. Conclusions Turkey is in the temperate (semi-arid) zone of the world. Due to its geographical change in short distance, there are various climate types. Some parts (northeast) are exceptionally wet but some parts (central south) severe drought. These circumstances affect, no doubt, the whole country economically, agriculturally, culturally and socially. The Standardized Precipitation Index identified the Figure 9- Growth curves for region 1 drought conditions realistically for the specific 3, Şekil 9- Bölge 1 için büyüme eğrileri 6, 9 and 12-month time scales. Probing the drought for 4 different time scales provides us tracing the bad or good impacts of the drought or excessive precipitations on water resources, reservoirs and underground water in the Seyhan Basin. Considering the output data, severe and extreme drought mostly experienced for 6-month time scale. SPI showed the pattern of drought of 11 stations in the Seyhan Basin successfully and unfolded the years have flash climatic changes, keeping in view monthly precipitation alteration. L-moments method was exploited to have probable minimum precipitations Figure 10- Growth curves for region 2 which will create drought. We can readily attain Şekil 10- Bölge 2 için büyüme eğrileri the minimum amount of precipitation correspond

212 Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 Drought Analysis of the Seyhan Basin by Using Standardized Precipitation Index (SPI) and L-moments, Topçu & Seçkin to requested return periods by using growth factor değerlerindeki değişimler ve eğilimler. Türk Coğrafya values multiplied by average precipitation. These Dergisi 58: 21-34 data should be put into cognizance by farmers, Anlı S A, Apaydın H & Öztürk F (2009). Trabzon ilinde politicians and water resource planners even citizens, gözlenen yıllık maksimum yağışların bölgesel consequently, the data may be used for other basins frekans analizi. Tarım Bilimleri Dergisi-Journal of having the same climatologic characters throughout Agricultural Sciences 15(3): 240-248 the world. For further researches precipitation values Anlı S A (2014). Güneydoğu anadolu bölgesinde referans bitki su tüketiminin (et ) zamansal değişimi ve rdi should not singly be used for monitoring drought, 0 (keşif kuraklık indeksi) yöntemiyle meteorolojik because occurrence of drought cannot be explained kuraklık analizi. Tarım Bilimleri Dergisi-Journal of by only lack of precipitation. Urbanization and Agricultural Sciences 20(3): 248-260 housing, vegetation condition, reservoir storage, Aydogan D, Kankal M & Önsoy H (2014). Regional number of snowy days, number of days without flood frequency analysis for Çoruh Basin of Turkey rain, wind direction, solar radiation, day length and with L-moments approach. Journal of Flood Risk duration of insolation, air temperature and humidity Management doi: 10.1111/jfr3.12116 also affect occurrence of drought. For this reason Blain C G (2012). Monthly values of the standardized all these factors, at least some of them must be precipitation index in the State of Sao Paulo, Brazil: combined during analysis and calculation drought. Trends and spectral features under the normality The Mediterranean region, including the Seyhan assumption. Bragantia 71(1): 122-131 Basin, is detected as the most vulnerable to global CSB (2011). Turkey’s National Climate Change warming (IPCC 2007). Scientists on this point Adaptation Strategy and Action Plan, T.R. Ministry of Environment and Urbanization, November 2011. say that temperature in this basin would increase Ankara (1st edition) http://www.csb.gov.tr/db/iklim/ about 2-3.5 °C until the end of this century (CSB editordosya/Adaptation_Strategy.pdf (Access Date 2011). Rising temperature accelerates the rate of 03.03.2014) evaporation, principally, from the beginning of Dalrymple T (1960). Flood Frequency Methods. U. the 2000s till now, as results show, there was a S. Geol. Survey, Water Supply Paper 1453 A, considerable decline in the amount of precipitations. Washington, 11-51 The worst of all, the Seyhan Basin is going through Dodangeh S, Sattari M T & Seçkin N (2011). Minimum not only decrease in precipitations but also increase akımların L momentler yöntemi ile bölgesel in temperatures and more frequent drought events. If frekans analizi. Tarım Bilimleri Dergisi-Journal of these drivers come together too often, it could worsen Agricultural Sciences 17: 43-58 the climatic situation already irresolute. Whether it Dubey A (2014). Regional flood frequency analysis is human induced or complexity of the atmosphere utilizing L-moments: A case study of Narmada Basin. itself, unluckily global warming threatens the International Journal of Engineering Research and world, needless to say our study area ‘the Seyhan Applications 4(2): 155-161 Basin’. We can see the flash or creeping alteration of Dutra E, Giuseppe D F, Wetterhall F & Pappenberger precipitations from graphs. Serious precautions must F (2013). Seasonal forecasts of droughts in African be taken before colossal disasters emerge. Basins using the standardized precipitation index. Hydrology and Earth System Sciences 17: 2359-2373 Edwards D C & McKee T B (1997). Characteristics of References 20th century drought in the United States at multiple Abolverdi J & Khalili D (2010). Probabilistic analysis time scales. Climatology Report Number 97-2, of extreme regional meteorological droughts by Colorado State University, Fort Collins, Colorado L-Moments in a semi-arid environment. Theoretical Eslamian S S & Feizi H (2006). Maximum monthly and Applied Climatology 102: 351-366 rainfall analysis using L-moments for an arid Altın B T & Barak B (2012). Seyhan Havzasında region in isfahan province, Iran. Journal of Applied 1970-2009 yılları arasında yağış ve hava sıcaklığı Meteorology and Climatology 46: 494-503

Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 213 L-momentler ve Standart Yağış İndeksi (SYİ) Yardımıyla Seyhan Havzası Kuraklık Analizi, Topçu & Seçkin

Eslamian S, Hassanzadeh H, Abedi-Koupai J & Gheysari Hosking J R M & Wallis J R (1993). Some statistics useful M (2012). Application of L-moments for regional in regional frequency analysis. Water Resources frequency analysis of monthly drought indexes. Research 29(2): 271-281 Journal of Hydrologic Engineering 17(1): 32-42 Hosking J R M & Wallis J R (1997). Regional Frequency Fang Y, Mauzerall D L, Liu J, Fiore A M & Horowitz L Analysis: An Approach Based on L-Moments. W H (2013). Impacts of 21st century climate change Cambridge University Press, UK on global air pollution-related premature mortality. IPCC (2007). Intergovernmental Panel on Climate Change. Climatic Change 121: 239-253 http://www.ipcc.ch/ (Access Date 03.03.2014) Fowler H J & Kilsbyc G (2003). A regional frequency Jha S, Sehgal K V, Raghava R & Sinha M (2013). Trend analysis of United Kingdom extreme rainfall from of standardized precipitation index during Indian 1961 to 2000. International Journal of Climatology summer monsoon season in agro climatic zones of 23: 1313-1334 India. Earth System Dynamics Discussions 4: 429-449 Fujihara Y, Simonovic P S, Topaloğlu F, Tanaka K & Keskin F & Sorman A Ü (2010). Assessment of the Tsugihiro W (2008). An inverse modelling approach Drought Pattern Change in Çamlıdere Basin Using to assess the impacts of climate change in the Seyhan SPI. BALWOİS 2010, 25-29 May Ohrid, Republic of River Basin, Turkey. Hydrological Sciences Journal Macedonia 53(6): 1121-1136 Kömüscü A Ü (2001). An analysis of recent drought Gebeyehu A (1989). Regional Flood Frequency Analysis. conditions in Turkey in relation to circulation patterns. The Royal Institute of Technology, Stockholm, Drought Network News (1994-2001). Paper 22 Sweden, Bulletin No. TRIVA-VBI-148 Landwehr J M, Matalas N C & Wallis J R (1979a). Gingras D & Adamowski K (1994). Performance of Probability weighted moments compared with L-moments and nonparametric flood frequency some traditional techniques in estimating gumbel analysis. Canadian Journal of Civil Engineering parameters and quantiles. Water Resources Research 21(5): 856-862 15(5): 1055-1064 Guenang M G & Kamga M F (2014). Computation of Landwehr J M, Matalas N C & Wallis J R (1979b). the standardized precipitation index (spi) and its Estimation of parameters and quantiles of wakeby use to assess drought occurrences in cameroon over distributions. 1. Known lover bounds. Water recent decades. Journal of Applied Meteorology and Resources Research 15(6): 1361-1372 Climatology 53: 2310-2324 Landwehr J M, Matalas N C & Wallis J R (1979c). Gurkan D (2005). Assessment of Climate Change Impacts Estimation of parameters and quantiles of wakeby on Surface Water Resources in Seyhan River Basin. distributions. 1. Unknown lover bounds. Water MSc, Thesis, Hacettepe University (in Turkish, Resources Research 15(6): 1373-1379 unpublished) Li W, Fu R, Juarez R I N & Fernandes K (2008). Guttman N B (1998). Comparing the palmer drought Observed change of the standardized precipitation severity index and the standardized precipitation index, its potential cause and implications to future index. Journal of the American Water Resources climate change in the amazon region. Philosophical Association 34(1): 113-121 Transactions of the Royal Society 363: 1767-1772 Guttman N B (1999). Accepting the Standardized Lloyd-Hughes B & Saunders M A (2002). A drought Precipitation İndex: A Calculation Algorithm. Journal climatology for Europe, International Journal of of the American Water Resources Association 35: Climatology 22: 1571-1592 311-322 McKee T B, Doesken N J & Kleist J (1993). The Relationship of Drought Frequency and Duration to Hosking J R M (1986). The Theory of Probability Weighted Moments. Research Rep. RC 12210, 160 Time Scales. Reprints, 8th Conference on Applied pp. IBM Research Division, Yorktown Heights, NY Climatology, Anaheim, CA, USA, 179-184 pp Hosking J R M (1990). L-moments: Analysis and McRoberts B D & Nielsen-Gammon W J (2012). The use estimation of distributions using linear combinations of a high-resolution standardized precipitation index of order statistics. Journal of the Royal Statistical for drought monitoring and assessment. Journal of Society 52(2): 105-124 Applied Meteorology and Climatology 51: 68-83

214 Tarım Bilimleri Dergisi – Journal of Agricultural Sciences 22 (2016) 196-215 Drought Analysis of the Seyhan Basin by Using Standardized Precipitation Index (SPI) and L-moments, Topçu & Seçkin

NASA (2010a). Is Current Warming Natural? http:// Simsek O & Cakmak B (2010). Drought analysis for earthobservatory.nasa.gov/Features/ (Access Date: 2007-2008 agricultural year of Turkey. Journal of 03.03.2014) Tekirdag Agricultural Faculty 7(3): 99-109 NASA (2010b). Satellites Pinpoint Drivers of Urban Heat Stedinger J R, Vogel R M & Foufoula-Georgiou E (1993). Islands in the Northeast. http://www.nasa.gov/topics/ Frequency Analysis of Extreme Events. Handbook of earth/features/heat-island (Access Date: 03.03.2014) Hydrology D. R. Maidment, ed., McGraw-Hill Book Norbiato D, Borga M, Sangati M & Zanon F (2007). Co., Inc., New York, NY Regional frequency analysis of extreme precipitation Tallaksen C Y, Ngongondo C S, Xu L, Alemaw B & in the Eastern Italian Alps in the August 29, 2003 flash Chirwa T (2011). Regional frequency analysis of flood. Journal of Hydrology 345: 149-166 rainfall extremes in southern malawi using the index rainfall and l-moments approaches. Stochastic Parida B P & Moalafhi D B (2008). Regional rainfall Environmental Research and Risk Assessment 25(7): frequency analysis for botswana using l-moments and 939-955 radial basis function network. Physics and Chemistry of the Earth 33: 614-620 Thom H C S (1951). A Frequency Distribution for precipitation (abstract). Bulletin of the American Saf B (2009). Regional flood frequency analysis using l Meteorological Society 32(10): 397 moments for the Buyuk and Kucuk Menderes river basins of Turkey. Journal of Hydrologic Engineering Thom H C S (1958). A Note on the Gamma Distribution. Monthly Weather Review 86: 117-122 14(8): 783-794 Tonkaz T (2006). Spatio-temporal assessment of historical Seckin N & Yurtal R (2008). L-momentlere dayalı droughts using SPI with GIS in GAP Region, Turkey. göstergesel metodu ile bölgesel taşkın frekans analizi. Journal of Applied Sciences 6(12): 2565-2571 Ç.Ü. Fen Bilimleri Dergisi 19: 120-129 Topcu E (2013). L-Momentler ve Standart Yağış İndeksi Seckin N, Yurtal R, Haktanır T & Doğan A (2010a). (SYİ) yardımıyla Seyhan Havzası Kuraklık Analizi. Comparison of probability weighted moments and Yüksek lisans tezi, Çukurova Üniversitesi Fen maximum likelihood methods used in flood frequency Bilimleri Enstitüsü, Adana analysis for Ceyhan River Basin. The Arabian Journal Trenberth K E & Dai A (2007). Effects of Mount Pinatubo for Science and Engineering 35(1): 49-69 volcanic eruption on the hydrological cycle as an Seckin N, Yurtal R, Haktanır T & Topaloğlu F (2010b). analog of geoengineering. Geophysical Research Regional flood frequency analysis of Ceyhan River Letters 34: L15702 Basin in Turkey using L-moments method. Fresenius Türkes M (1999). Vulnerability of Turkey to desertification Environmental Bulletin 19(11a): 2616-2624 with respect to precipitation and aridity conditions. Seckin N, Haktanır T & Yurtal R (2011). Flood frequency Turkish Journal of Engineering and Environmental analysis of Turkey using L-moments method. Science 23: 363-380 Hydrological Processes 25: 3499 Vardiman L (2008). A new theory of climate change. Acts Selek B & Tuncok K I (2014). Effects of climate change & Facts 37(11): 10 on surface water management of Seyhan Basin, Wilhite D & Glantz M R (1987). Understanding the Turkey. Environmental and Ecological Statistics drought phenomenon. The role of definitions, in 21(3): 391-409 Wilhite, David, Easterling, William and Wood, David Sen B, Topcu S, Giorgi F, Bi X, Kanıt E.G & Dalkılıç eds, Planning for drought: Boulder, Colo. Westview T (2008). Seyhan Havzasında iklim değişikliğinin Press pp. 11-27 tarımsal su kullanımına etkileri. TMMOB 2. Su WMO (2007). Global Climate Highlights in 2006. http:// Politikaları Kongresi 20-22 Mart 2008, Ankara www.wmo.int/pages/ (Access Date: 03.03.2014) Shi P, Chen X, Qu S, Zhang Z & Ma J (2010). Regional Xie H, Ringler C, Zhu T & Waqas A (2013). Droughts frequency analysis of low flow based on l moments: in Pakistan: A spatiotemporal variability analysis case study in karst area, southwest china. Journal of using the standardized precipitation index. Water Hydrologic Engineering 15(5): 370-377 International 38(5): 620-631

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