Temperature forecasting has been one of the most important factors considered in climate impact studies on sectors of agriculture, vegetation, water resources and tourism. The main purpose of this study is to forecast daily mean, maximum and minimum temperature time series employing three different artificial neural network (ANN) methods and provide the best-fit prediction with the observed actual data using ANN algorithms.The geographical location considered is one of Turkey's most important areas of agricultural production, the Geyve and Sakarya basin, located in the south-east of the Marmara region (40°N and 30°E). The methods chosen in this study are:(1) feed-forward back propagation (FFBP), (2) radial basis function (RBF) and, (3) generalized regression neural network (GRNN). Additionally, predictions with a multiple linear regression (MLR) model were compared to those of the ANN methods. All three different ANN methods provide satisfactory predictions in terms of the selected performance criteria; correlation coefficient (R), root mean square error (RMSE), index of agreement (IA) and the results compared well with the conventional MLR method.

The aim of this study is to research natural land cover change caused by the permanent effects of human activities in Duzce plain and its surroundings, and to determine the current status of the land cover. For this purpose, two Landsat TM images were used in the study for the years 1987 and 2010. These images are analysed by using data image processing techniques in ERDAS Imagine©10.0 and ArcGIS©10.0 software. Land cover change nomenclature is classified according to the Coordination of Information on the Environment Level 2 Classification (1--urban fabric, 2--industrial, commercial and transport units, 3--heterogeneous agricultural areas, 4--forests, and 5--inland wetlands). Furthermore, the image analysis results are confirmed by the field research. According to the results, a decrease of 33.5 % was recorded in forest areas from 24,840.7 to 16,529.0 ha; an increase of 11.2 % was recorded in heterogeneous agricultural areas from 47,702.7 to 53,051.7 ha. Natural vegetation, which is the large part of land cover in the research area, has been changing rapidly because of rapid urbanisation and agricultural activities. As a result, it is concluded that significant changes have occurred on the natural land cover between the years 1987 and 2010 in the Duzce plain and its surroundings.

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Bodrum Peninsula is one of the most important tourism centers of Turkey with its geographical location, coastal and marine tourism, natural and cultural features. It has been determined that the winter population has also increased in Bodrum in recent years, and it is thought that this may cause an increasing permanent resident population and urbanization. The objective of this study is to determine the changes in land cover due to the rapid increase in urbanization in Bodrum Peninsula. For this purpose, object-based classification analysis was applied to Landsat 4-5 TM 1990, 2000, 2010 and Landsat 8 OLI 2021 multispectral satellite images. Within the scope of the analysis, the objects were created by applying the segmentation process to satellite images. Secondly, land cover classes were determined according to the Corine land cover classification with levels 1-2-3. Thirdly, the classification process based on a decision tree was carried out with the classes defined using the threshold values determined for spectral and texture properties of the objects using multiresolution segmentation. In the last stage, accuracy assessment analysis was applied to the classification results. According to the results, it is obtained that while Urban Fabric and Burnt Areas are increased in 32 years, Forest and semi-natural areas are decreased. As a result of population pressure due to tourism, Urban Fabric areas have moved closer to Forests and Semi-Natural Areas. Wildfires with the effect of heatwaves were increased, biodiversity has been endangered in the study area located in the Mediterranean basin, where human-related climate change is most clearly detected. Significantly, there has been a wildfire in Bodrum in August 2021, which lasted for days and caused severe degradation on the land cover. For this, sustainable land cover management is recommended to protect the natural ecosystem by minimizing the risks that cause land degradation in the Bodrum peninsula.

Ustaoglu et al. : The effects of climate change on spatiotemporal changes of hazelnut (Corylus Avellana) Abstract. Turkey ranks the first among the hazelnut producers in the world. The purpose of this study is to question whether or not the hazelnut plant that grows under natural climate conditions will be affected by climate change. Spatial and temporal change simulations have been done in order to define the actual and the future status of hazelnut cultivation areas. The Marmara and the Black Sea regions have been chosen as study areas of hazelnut production in Turkey. The possible evolution of the current climate conditions to affect hazelnut cultivation in the upcoming 90 years and the estimated changes to occur in hazelnut areas have been asserted in the study. In order to determine the future climate conditions, the set of temperature and rainfall data of the upcoming 90 year period (2011 -2100) obtained from the A2 scenario of RegCM3 regional climate model has been used and by taking the averages of each 10 year period, it has been simulated with the MATLAB software. While an increase of up to 6 ºC in temperature for the upcoming 90 years can be expected to have negative effects on hazelnut cultivation depending on the A2 scenario (the worst), no change has been observed in the rainfall scale that may negatively affect hazelnuts. In particular, it has been observed that this temperature change may cause vertical and horizontal changes in hazelnut areas. Accordingly, it has been anticipated that hazelnut cultivation on the coast line between 0 -250 m may get affected in a negative way and the areas exceeding 1500 m that are not currently suitable for hazelnut cultivation may become arable lands due to vertical change.

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In the last century climate change has been a major threat to biodiversity, ecosystem services, and human well‐being. Atmospheric oscillations that occur at the regional oceanic flow pattern may affect significantly the climate of the Earth. In this study, we investigate the effects of ENSO (El Nino Southern Oscillation) and NAO (North Atlantic Oscillation) on the Mediterranean crop yield using the Nino 3, Nino3.4, Nino 4, ONI and NAO indices. Olive, which is a bioindicator type in the Mediterranean, and cotton and grapes with high yield and economic value crops were examined. According to the average production amounts in the Mediterranean Region between 1991 and 2020, 39% of cotton production is in Adana (205319 tone), 43% of grape production is in Mersin (228471 tone) and 37% of olive production is in Hatay (103854 tone). As a method, firstly, Mann Kendall rank correlation test was applied to the yield values of the crops. After the 2000s, it has been determined that the trend of yield has changed and was obtained an increasing trend. Secondly, the correlation between the yields and Nino 3, Nino3.4, Nino 4, and NAO indices were determined with the Spearman correlation coefficient. Accordingly, a high correlation of 50% and 80% was found at the p ≤ 0.05 and p ≤ 0.00 significance level in the phenological periods of the crops. The highest correlations were determined especially during the flowering period (April, May, June) for olive and grape yield with El Nino indices. The frequency of the correlation detected with the NAO index is weak. The effect on the efficiency of the phases when El Nino indices are strong was examined graphically. Accordingly, in the 1997 and 2015-2016 periods, when the El Nino phenomen was very strong, there were sharp decreases in the crop yields. This variability affects the countries whose economic activity is based on agriculture in the Mediterranean Basin, and it is likely to affect the food industry in the future.