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This study attempts to find out the best-fit probability distribution function to low flows using the up-to-date data of intermittent and non-intermittent rivers in four hydrological basins from different regions in Turkey. Frequency analysis of D = 1-, 7-, 14-, 30-, 90- and 273-day low flows calculated from the daily flow time series of each stream gauge was performed. Weibull (W2), Gamma (G2), Generalized Extreme Value (GEV) and Log-Normal (LN2) are selected among the 2-parameter probability distribution functions together with the Weibull (W3), Gamma (G3) and Log-Normal (LN3) from the 3-parameter probability distribution function family. Selected probability distribution functions are checked for their suitability to fit each D-day low flow sequence. LN3 mostly conforms to low flows by being the best-fit among the selected probability distribution functions in three out of four hydrological basins while W3 fits low flows in one basin. With the use of the best-fit probability distribution function, the low flow-duration-frequency curves are determined, which have the ability to provide the end-users with any D-day low flow discharge of any given return period.

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Climate change increases the odds of worsening drought in many parts of the World. Climate projections for the Mediterranean basin in which Turkey is located expresses alarming conclusions about severe droughts. Droughts are expected to prevail in different severities and periods throughout Turkey. Iğdır plain, which lies in eastern part of Turkey is convenient for cultivation of many agricultural products because of its fertile soils and micro-climatic properties. In this study, drought analysis were carried out for Iğdır by using Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI) and Streamflow Drought Index (SDI). The data (precipitation and flow) were obtained in monthly intervals from Turkish institutions, namely General Directorate of Meteorology and General Directorate of State Hydraulic Works. Study was aimed to examine the integrated effect of low precipitations and high temperatures on hydrological and meteorological drought. Annual SPI results show that four severe and three moderate drought events whereas RDI detected four severe and four moderate drought events for the study period (47 years, 1971-2018). SPI index detected severe category droughts in the water years of 1980, 1989 and 1997. RDI detected severe category droughts in the mentioned years together with one more event in 2000. SDI identified 2002 as extreme drought year, and identified 1982, 1984, 1986 and 2002 as moderate drought years. The output of the study is aimed to serve for better understanding of droughts in the Igdir Plain.

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Determination of cropping pattern is a very important factor in quantifying irrigation water requirements at a catchment scale. In this regard, remote sensing is a robust tool for generating spatial-temporal variation of crops. This study focuses on crop classification by using remotely sensed data coupled with ground truth data. Therefore, this study aimed at both classifying each crop type and calculating crop evapotranspiration (ETc) based on reference evapotranspiration (ETo) by using the Penman-Monteith evapotranspiration model and crop coefficient (Kc). ETo was estimated by using data from two meteorological stations located in the study area. To this end, this study was conducted in Akarsu Irrigation District (≈95 km2), a sub-catchment in the Lower Seyhan Plain (LSP), in the 2021 hydrological year. Ground truth data were collected in the two growing seasons. The ENVI program was used to classify crop types from Sentinel 2A-2B satellite images with 10-m by 10-m spatial resolution. Image analysis results demonstrated that bare soil and citrus made up more than half of the area in the winter season, while corn and citrus were preponderant in summer. In addition, the total reference evapotranspiration and crop evapotranspiration were about 1308 mm and 890 mm, respectively in the 2021 water year. ETc values for second crop soybean, first crop corn, wheat, and citrus showed agreement with previous studies of direct methods of evapotranspiration in the Cukurova region. Furthermore, research findings led us to conclude that using remotely sensed satellite data in cropping pattern determination is promising in identifying the crops grown in large agricultural lands. Moreover, remote sensing images can be used to classify accurately crops in the winter and summer seasons, and this study has expanded the application value of remotely sensed data in large-scale irrigation schemes.