İran'ın karun üst havzasındaki düşük akımların analizi
Low flow analysis of Iran?s Karun upper basin
Yazar: FARROKH MAHNAMFAR
Danışman: PROF. DR. BİHRAT ÖNÖZ
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / İnşaat Mühendisliği Ana Bilim Dalı / Hidrolik ve Su Kaynakları Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2012
Konu: İnşaat Mühendisliği = Civil Engineering

Özet:
Bu çalışmada, İran'ın Karun üst Havzası'nda en az 15 yılık kayıt süresi olan ve barajlardan etkilenmeyen akım ölçme istasyonlarının günlük değerleri kullanılmıştır. Ayrıca, seçilen tüm istasyonlarda günlük veri ve sadece bir istasyonda (21-233) 2 su yılı (1987,1988) veri eksikliği vardır. Sonuç olarak 12 istasyon seçilmiştir.Çalışmada, 21-215, 21-223, 21-225, 21-231, 21-233, 21-497 istasyonları için günlük akım değerleri doğrusal regresyon ile tamamlanmış ve düşük akım değerleri (7 günlük minimum akım) hesaplanmıştır.İstasyonlarda günlük akım değerleri üzerine süreklilik çizgileri (debi süreklilik, yıl içi debi frekans) elde edilmiştir. Debi süreklilik çizgisi gözlenmiş verilere göre akarsularda belli bir debinin aşıldığı zaman yüzdesini gösterir. Burada 39 yıl (21-205 istasyonu örnek olarak) için günlük verileri (14245 günük veri) kullanarak bu çizgiler elde edilmiştir. Yıl içi debi frekans çizgisi 7 günlük minimum akımlar için elde edilmiştir. Burada her yıl için dönüş aralığını bulunmuş ve 7 günlük minimum akımlarla noktalanmıştır. Bu noktalara bir çizgi uydurarak, yıl içi debi frekans çizgisi elde edilmiştir.Her istasyon için çeşitli dönüş aralıklarında, düşük akım değerini tahmin etmek için iki parametreli (Lognormal, Weibul, Üstel) ve üç parametreli (Lognormal, Weibul, Pearson Tip 3, Logpearson Tip 3, Genel Ekstreme, Genel Lojistik) dağılımlar kullanılmıştır. İstasyonların verilerine Kolmogorov-Smirnov ve Olasılık Çizgisi Korelasyon katsayısı (PPCC) testini uygulayarak verilere en uygun dağılım seçilmiştir ve çeşitli dönüş aralıklarında düşük debiler tahmin edilmiştir.Verilerin zaman değişimi ile artış ve azalışlarını araştırmak için Mann-Kendall trend analizi yapılmıştır.Sonuç olarak Karun havzasında yürütülen düşük akımların analizi çalışmasının bulguları mevcut yapıların işletmesi ve havza da yapılabilecek su kaynakları çalışmaları açısından da yorumlanmıştır.

Abstract:
Drought is a normal, recurrent feature of climate, although many erroneously consider it a rare and random event. It occurs in virtually all areas, whatever their normal climate may be, and the characteristics of a drought may be very different from one region to another. Technically, drought is a ?temporary? condition, even though it may last for long periods of time.Drought is an insidious hazard of nature. Unlike many disasters which are sudden, droughts result when there is less than normal precipitation over an extended period of time, usually a season or more. The decreased water input results in a water shortage for some activity, group, or environmental sector. Drought can also occur when the temperature is higher than normal for a sustained period of time; this causes more water to be drawn off by evaporation. Other possible causes are delays in the start of the rainy season or timing of rains in relation to principal crop growth stages (rain at the ?wrong? time). High winds and low relative humidity can make matters much worse.Drought is not a disaster for nature itself, the disaster occurs when we consider the demand people place on their water supply. Human beings often increase the impact of drought because of high use of water which cannot be supported when the natural supply decreases. Droughts occur in both developing and developed countries and can result in economic and environmentalimpacts and personal hardships. All societies are vulnerable to this "natural" hazard.Drought is difficult to define precisely, but operational definitions often help define the onset, severity, and end of droughts. No single operational definition of drought works in all circumstances, and this is a big part of why policy makers, resource planners and others have more trouble recognizing and planning for drought than for other natural disasters. In fact, most drought planners now rely on mathematic indices to decide when to start implementing water conservation or measures in response to drought.Meteorological drought is usually measured by how far from normal the precipitation has been over some period of time. These definitions are usually region-specific, and presumably based on a thorough understanding of regional climates.Agricultural drought occurs when there isn't enough soil moisture to meet the needs of a particular crop at a particular time. Agricultural drought happens after meteorological drought but before hydrological drought. Agriculture is usually the first economic sector to be affected by drought.Hydrological drought refers to deficiencies in surface and subsurface water supplies. It is measured as streamflow, and as lake, reservoir and ground water levels. There is a time lag between lack of rain and less water in streams, rivers, lakes and reservoirs, so hydrological measurements are not the earliest indicators of drought. When precipitation is reduced or deficient over an extended period of time,this shortage will be reflected in declining surface and subsurface water levels.Socioeconomic drought is what happens when physical water shortage starts to affect people, individually and collectively. Or, in more abstract terms, most socioeconomic definitions of drought associate it with the supply and demand of an economic good.The largest river by discharge in Iran, the Karun River's watershed covers 65,230 square kilometres in parts of two Iranian provinces. The river is around 950 kilometres long and has an average discharge of 575 cubic metres per second. There are a number of dams on the Karun River, mainly built to generatehydroelectric power and provide flood control.In this study, iran?s karun upper basin, at least 15 years of recording time and not affected by dams, daily value stations were used. Furthermore, all selected stations have daily missing data and only 21-233 station has 2 years value missing data. As a result, 12 stations were selected.In this study, 21-215 ,21-223, 21-225, 21-231, 21-233, 21-497 stations were completed by linear regression for daily flow values and low flow values (7 day minimum flow) were calculated.Duration curves on the daily flow values (flow duration curve, flow frequency in year) were obtained. Flow duration curve based on the observed data shows the flow rate exceeded a certain percentage of the time in streams.Here, 21-205 station as an example, flow duration curves were obtained using daily values for 39 years (14245 daily values). Flow frequency in year was obtained for 7 day minimum flow values. Return period was found for each year and this value punctuated with 7 day minimum flow values. Passing a line through these points, flow frequency in year was obtained.Each station for various return periods (2,5,10,25), low flow value to was estimated by two-parameter and three-parameter distributions.The probability distributions are classified according to the number of parameters into two types.2-parameters probability distributions which include:LognormalWeibullExponential3-parameters probability distributions which include:LognormalWeibullPearson type IIILogpearson type IIIGeneralized extreme valueGeneralized logisticThe parameters of 7 day minimum flow values were estimated for each station. These parameters are the mean x ?, standard deviation Sx, coefficient of variation Cvx, coefficient of skew Csx and coefficient of kurtosis ks. Using probability weighted moments (PWMs) L- moments are easily computed, L-moment ratios are defıned as L-coefficient of variation, L- skewness and L- kturtosis.Various methods were used for estimation of the parameters of probability distributions. The parameters of the N distribution are the mean x ? aid standard deviation Sx. Nonnormal distributed variables can be adjusted to the normal distribution by means of a suitable distribution. One of these transformation methods is computing the logarithms (y=lnx), In this case logarithmic mean y ? andstandard deviation Sy, will be the parameters of the LN2 distribution.For the EVl distribution, ?, u scale and location parameters were estimated by PWMs and L-moments.Just as the Lognormal distribution represents the Normal distribution of the logarithms of the variable x, so the 3-parameter Log-normal distribution represents the Normal distibution of the Logarithms of the variable (x-xo) where xo is the third parameter corresponding to a lower boundary.The parameters of the GEV distribution is estimated by PWMs and L-moments, ?, u and k are the scale, location and shape parameters, respectively. The parameters of the P3 and LP3 distributions can be estimated by the method of moments.By applying L-Moment test, Kolmogorov-Smirnov test and probability plot correlation coefficient test (PPCC) for stations? data, the most appropriate distribution was chosen. Low flows were estimated for various return periods.In the Kolmogoror-Smirnov (K-S) test the test statistic D İs based on the maximum absolute difference between the theoretical and sample cumulative distribution fiınctions. D values will be compared by critical values at the %5 level of significance which depend on the sample size for each distribution. The probability distribution will be accepted if the value of D/D0.05, is smaller than one.The probability plot correlation coefficient (PPCC) test is known to be more powerful than X2 and K-S tests. This test statistic is the correlation coefficient r which measures and evaluates the linearity between the ordered observations xi and the İnverse values of the hypothesized cumulative distribution function Mi (statistic medians). r values is compared with the critical values taken from various references. If the values of r0.05/r is smaller than one the hypothesized probability distribution is accepted at the %5 level of significance.The linearity between the ordered observations and the statistic median values is more powerful if the value of the correlation coefficient r is closed to one.Trend is a change (decrease/increase) of the values of a random variable. It is very important to determine the trend of the amount of water in the rivers in different periods of time for suitable planning and management of the water resources. There are different works to determine this change.For the determination of stream flow trends, parametric and nonparametric tests have been used. If data fit to normal distribution, parametric tests give good results. Nonparametric tests are independent of distribution and parameters of a random variable. These tests are related to the ranks in the arranged sample of the data. Generally, the distributions are not normal. So the use of nonparametric tests give good results.Investigation the increasement or decreasement of data exchange in time series, Mann-Kendall trend analysis test was conducted. For each station data, plotted non- dimensional graphic with divided by average of data. In addition to this, keeping up with the linear Trend line graphs that visually checked regardless the trend.As a consequence of this study, GEV distribution for 9 stations, W3 for 2 stations and LN2 for 1station has been estimated minimum value to different return intervals.In the L-moment diagrams the GEV distribution was acceptable to the 25% of the stations, the P3 distribution was acceptable to the 25% of the stations, GL distribution was acceptable to the 41% of the stations, LN3 distribution was acceptable to the 9% of the stations, W2 distribution was acceptable to the 58% of the stations and LN2 distribution was acceptable to the 42% of the stations.As a result of K-S test, it is seen that W2 andGL distributions, is the only one that accepted at all stations, and has the best-fit at 12 stations. All the distributions were acceptable at stations 21-497 and 21-931.By applying PPCC tests to the upper Karun basin, P3 and LP3 have been the most appropriate distributions. P3 and LP3 distributions accepted at the all stations. All the distributions were acceptable at 7 stations.As a result of Mann-Kendall trend analysis at 8 stations, not increased and decreased in the time series at the 0.05 significant level. Despite this, at 3 stations decrease trend and 1 station increase trend was observed.consequently, knowledge obtained about Karun basin low flows analysis can be used for current structures management and water resource activities in basin.