Normalized different water indices (NDWIs) derived from satellite images are commonly and successfully utilized in surface water body detection and mapping. In this study, the water body detection capability of three NDWI models (NDWI_{(Green, NIR)}, NDWI_{(Green, SWIR1)} and NDWI_{(Green, SWIR2)}) generated using 28 multitemporal Landsat-8 OLI multispectral satellite images was analyzed for Atikhisar Dam Lake, the only water source of Çanakkalecity’s central district in Turkey between 2013 and 2017. This study focused on two important open research questions: (i) Which NDWI model produces the most superior results? and (ii) How much does accuracy change in the use of 15 m and 30 m spatial resolution satellite data? For the accuracy analysis, area values extracted from the NDWI models were compared with in-situ lake area values as measured by the General Directorate of State Hydraulic Works (DSI). The results of this study show that as the lake area grows, discrimination of water from other classes is better with NIR region, and that the performance of NDWI_{(Green, NIR)} is relatively better in terms of lake expansion effect. Results also indicate that hydrometeorological factors such as precipitation and evaporation and anthropogenic factors such as irrigation and daily consumption are decisive in lake area variations. The order of accuracy from high to low was found to be NDWI_{(Green, NIR)}, NDWI_{(Green, SWIR1)} and NDWI_{(Green, SWIR2)}, and 15 m spatial resolution data generated better results than 30 m resolution.

Accurate spatial interpolation (SI) of climate data is vital for the management and supervision of natural resources and agriculture. Owing to the lack of an adequate number of meteorological stations, meteorological-station-data-based SI methods may not always reflect the real climatic conditions of an interpolated point. Land surface temperature (LST) data obtained from satellite sensors enable the characterization of meteorological conditions of areas without meteorological stations. The aim of this article is to present a new modified inverse distance weighting (M-IDW) SI method for air temperature (T_a), total precipitation (P_t), and relative humidity (RH) by integrating Landsat LST data with meteorological station data for the interpolation process. The M-IDW approach is based on the correlation relationship between the climate data and LST at each meteorological station, which is incorporated into the traditional IDW to improve the estimation of the climate data at an interpolation location of interest. The proposed method, M-IDW, is applied for the interpolation of long years’ (i.e. long term) monthly average (LYMA) T_a, P_t, and RH climate data from meteorological stations in the Eastern Thrace region, which is 23,764 km², located in southeast Europe. The LYMA of the T_a, P_t, and RH has been constructed using data obtained from 27 meteorological stations that had functioned at least 10 years between 2000 and 2012 and from the corresponding satellite data. The outputs of the interpolation are in the form of LYMA, so are the analysed climate data. The spatial resolution of the predicted surface was taken as 30 m, similar to the original data presented by United States Geological Survey. The results were compared with those of the standard IDW, ordinary kriging (OK), and ordinary cokriging (OCK) methods to analyse the performance and accuracy of the proposed method. The results show that the proposed M-IDW method has the potential for SI of climate data, if enough number of images and cloudless pixels are incorporated in the LYMA LST computation. The proposed method, in general, yields better results than standard IDW and OK methods, especially during spring, summer, and partially in autumn for the interpolation of T_a (with 0.72°C, 0.53°C, and 0.66°C root mean square error (RMSE) values, respectively) and P_t (with 11.07 mm, 7.64 mm, and 4.85 mm RMSE values, respectively). OCK and M-IDW results were comparable in spring, summer, and autumn where M-IDW was slightly better for T_a in autumn and spring and was slightly better for P_t in summer. For the RH interpolation, although M-IDW results were found to be close to the results of IDW, OK, and OCK in spring, summer, and autumn, for the overall seasonal interpretation, the RMSE values of M-IDW were worse than the others. In general, M-IDW yields worse results for the winter months, which in turn is related to cloudiness and availability of satellite images.

This research presents a comprehensive drought analysis using climate data obtained from 219 homogeneously distributed meteorological stations in Türkiye between 1991 and 2022. In this context, Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Reconnaissance Drought Index (RDI) drought indices were used and comparative analysis was made. Türkiye. The study demonstrates that below-normal precipitation over extended periods and increasing temperatures have contributed to the increased frequency of meteorological drought events. Türkiye's topographic conditions, particularly its location in the Mediterranean basin, significantly influence drought occurrences. It is noted that over the past 20 years, Türkiye has been trending towards drier conditions, with rising temperatures reinforcing this trend. The study observes that the moderate drought class range is the most frequently recurring in the SPI, SPEI, and RDI methods utilized. Regarding atmospheric conditions affecting the climate in Türkiye, it is observed that increased drought severity stands out prominently in years when the North Atlantic Oscillation is positive. During these years, increased drought severity is evident in the SPI, SPEI, and RDI indices, particularly in winter and autumn, while a wide area experiences drought effects in the summer months. Long-term analyses emphasize that drought periods occur less frequently but have more prolonged impacts, attributed to variations in precipitation patterns from year to year and the influence of rising temperatures due to global climate change. The potential future increase in drought in the Mediterranean basin due to global climate change and Türkiye's vulnerability to this situation could have adverse effects on water resources, food security, energy sources, and ecosystems.

Spatial interpolation (SI) is currently one of the most common ways to estimate wind speed (Ws). However, classic SI models either ignore the complex geography [e.g. inverse distance weighting (IDW)], or demand high computational resources (e.g. cokriging). This study aimed to develop a simple yet effective SI model for estimating Ws in Eastern Thrace of Turkey. This new method, named MIDW_(Ws), is a modified IDW through the integration of IDW with wind profile model, power law (PL), representing the influence of land cover and topography on Ws. Terrain features and elevation data of PL were obtained using normalized difference vegetation index (NDVI) and digital elevation model (DEM), respectively. Results showed superior and comparable performance of MIDW_(Ws) to standard IDW and ordinary kriging (OK) across all months of year. Compared to ordinary cokriging (OCK) using DEM as covariate, MIDW_(Ws) generated better results in the arid–semiarid seasons (around summer). Local complex atmospheric conditions during rainy seasons (around winter) may have affected the performance of incorporating PL with MIDW_(Ws). Generally, the proposed MIDW_(Ws) is simpler and easier to implement compared to OCK. For landscape-scale projects, its high computational efficiency and relatively robust performance show potential to deal with large volumes of datasets.

Kuraklık meteorolojik kökenli bir doğal afettir ve günümüzün en önemli sorunlarından biridir. Temelinde yağış eksikliği olarak başlayan kuraklığın doğaya ve tüm canlı hayatına yıkıcı etkileri sürekli olarak artmaktadır. Bu çalışmada Çanakkale Merkez İlçe sınırlarında bulunan ve bölgenin tek su kaynağı olan Atikhisar Baraj Gölü su alanının 1984-2018 yılları arasındaki yıllık zamansal değişimi uydundan uzaktan algılama görüntüleri ile belirlenmiştir ve meteorolojik kuraklığın bu değişime etkisi araştırılmıştır. Bu kapsamda bölgedeki meteorolojik kuraklığın değişimi eklenik sapma eğrisi ve standart yağış indisi (SPI) ile incelenmiştir. Tüm meteorolojik değerlendirmeleri Ekim ayı başında başlayan 12 aylık su yılı takvimine göre yapılmıştır. Gölün alansal değişimi Landsat 8 Operational Land Imager (OLI) multispektral çok-zamanlı uydu görüntülerinden üretilen normalize edilmiş fark su indisi (NDWI) veri seti ile belirlenmiştir. Meteorolojik veriler ile uyumlu olması için, tüm görüntüler 1984-2018 yılları arasında kurak dönemin (su yılının) sonunda elde edilmiştir. Böylelikle 12 aylık su yılı sonundaki meteorolojik kuraklığın NDWI ile belirlenen su alanına etkisi araştırılmıştır. Sonuçlar baraj göl alanı değişiminde bir tek meteorolojik kuraklığın etkili olmadığını göstermektedir. SPI meteorolojik kuraklık indisi değerlerinin eklenik sapma değerlerine kıyasla yıl bazında göl alanı değişimine daha hassas olduğu ve NDWI ile belirlenen göl alanı değerleri ile çok daha yüksek ve anlamlı korelasyonlara sahip olduğu tespit edilmiştir.