The weather reanalysis datasets are very advantageous data types worldwide that fill the gaps of missed measuring data and are alternatives that compensate for the scarcity of observed climate data. The main purpose of this study was to evaluate the effect of horizontal distance, altitude, and climatic regions compared to sea level on NASA POWER reanalysis data for daily temperature variables, relative humidity, and wind speed observed in meteorology stations in the Mediterranean and Continental regions of Turkey. For this purpose, three different meteorology stations (Antalya airport, Elmalı, Teffenni) from the Mediterranean region with different distances and elevations compared to sea level and one station (Ankara) far from the Mediterranean region with continental climate were selected. The statistical approach used to compare observed and estimated values in this study was determination coefficient (R2), Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and Mean Bias Error (MBE). The results showed a high relation between the POWER reanalysis dataset and observed data for all parameters except wind speed. For daily maximum, minimum and mean temperature, the R2 and NSE achieved higher than 0.91 and 0.88 respectively, while the mean bias error MBE ranged between -3 °C up to +2 °C and the RMSE was less than 4 °C in all stations. Additionally, POWER estimated data correlation accuracy for temperature variables increased toward higher altitudes in the study area. Similarly, this performance was followed by relative humidity, increasing relation accuracy toward higher elevated regions. The R2 was higher than 0.69 in higher altitudes and less than 0.4 in lower elevations. The MBE for relative humidity ranges -2% in Antalya to +9% in Ankara, and the RMSE attained less than 13.81% in all regions. The POWER daily wind speed did not show relation with observed data without adjusting for elevation and seasonal bias correction. Overall, it was concluded that the NASA POWER dataset could predict temperature and relative humidity over study area and give a promising result if used in research, water, and agricultural decision-making where observation data are not available.

Plant tolerance to salinity stress is vital for irrigation scheduling, decision-making, planning and operation, and water resource management. This study is aimed to investigate the effects of irrigation water salinity on water use, growth, physiology, and yield parameters of drip-irrigated tomatoes in two different growing seasons. In addition to control (0.7 dS m⁻¹), three irrigation water salinity levels: 2.5 (low), 5.0 (moderate), and 7.5 (high) dS m⁻¹ were used. In both seasons, autumn 2021 and spring 2022, increased water salinities caused an increase in soil salinity, while a decrease in seasonal crop evapotranspiration. Plant heights did not show significant differences under different salinity levels in autumn 2021, while a meaningful difference among treatments was found in spring 2022. Marketable and total tomato yields, and the leaf area index showed significant decreases under increased salinities in both seasons. Stomatal conductance was not affected by salinity levels. The salt tolerance models for marketable and total fruit yields showed a low threshold and slope value in autumn 2021, contrasting with a high threshold and slope value determined in spring 2022. The results suggest that the salinity slope value should be considered, as well as the salinity threshold value, for decision making in tomato production at different growing seasons.