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This study aimed to analyze the trend in land surface temperature (LST) over time using the entire archive of the available cloud-free Landsat images from 1986 to 2022 for Jordan and its nine local climate zones (LCZs). Two primary datasets were used (i) Landsat-5; -8 imagery, and (ii) map of LCZs of Jordan. All LST images were clipped, preprocessed, and checked for cloud contamination and bad pixels using the quality control bands. Then, time-series of monthly LST images were generated through compositing and mosaicking processes using cloud computing functions and Java scripts in Google Earth Engine (GEE). The Mann–Kendall (MK) test and Sen's slope estimator (SSE) were used to detect and quantify the magnitude of LST trends. Results showed a warming trend in the maximum LST values for all LCZs while there was annual fluctuation in the trend line of the minimum LST values in the nine zones. The monthly average LST values showed a consistent upward trajectory, indicating a warming condition, but with variations in the magnitude. The annual rate of change in LST for the LCZs showed that the three Saharan zones are experiencing the highest rate of increase at 0.0184 K/year for Saharan Mediterranean Warm (SMW), 0.0185 K/year for Saharan Mediterranean Cool (SMC), and 0.0169 K/year for Saharan Mediterranean very Warm (SMvW), indicating rapid warming in these regions. The three arid zones came in the middle, with values of 0.0156 K/year for Arid Mediterranean Warm (AMW), 0.0151 for Arid Mediterranean very Warm (AMvW), and 0.0139 for Arid Mediterranean Cool (AMC), suggesting a slower warming trend. The two semi-arid zones and the sub-humid zone showed lower values at 0.0138, 0.0127, and 0.0117 K/year for the Semi-arid Mediterranean Cool (SaMC), Semi-arid Mediterranean Warm (SaMW) zones, and Semi-humid Mediterranean (ShM) zones, respectively, suggesting the lowest rate of change compared to other zones. These findings would provide an overall understanding of LST change and its impact in Jordan's LCZs for sustainable development and water resources demand and management. [ABSTRACT FROM AUTHOR]

Analyzing trends can provide insights into price behavior to predict future change. The primary objective was to examine prospective changes in monthly price over the entire period, between 2008 and 2021 using parametric and non-parametric techniques. Four domestic markets were chosen based on the maximum arrival of onions (Allium cepa L.) in Maharashtra and the Indian export market. Some markets fit the linear regression model; the errors do not conform to parametric assumptions, requiring application of distribution-free techniques. Using the mean of prices, Innovative Polygon Trend Analyses (IPTA) was applied. The IPTA plots for all domestic markets indicated upward trends in almost all months with regular polygons, with the exception of the export market. Plots indicated peak arrival months in markets exhibiting a narrower polygon structure, while lean arrival months showed a broader polygon. When results of trend analysis based on other tests were compared to the IPTA technique, there was good agreement across all approaches. The export market exhibited the least variability in prices, and the average monthly price ranges from 127.90 to 334.56 USD∙Mt−1. Post-monsoon and harvest months exhibited the highest average prices. Based on comparisons of trend analysis techniques, the IPTA methodology places a strong emphasis on identifying trends and offers insight through a visual examination of trend patterns. [ABSTRACT FROM AUTHOR]

The harmful effects of factors such as human-induced effects, greenhouse gases, fossil fuel consumption, urbanization and destruction of agricultural lands on the environment and climate change are increasing day by day. All these changes can create undesirable and permanent effects on human health, living species and the environment. The analysis of climate parameters on a regional basis is very important in understanding the effects of climate change and taking the necessary predictable steps to reduce its harmful effects. This study analyzes changes in various climate parameters of Ardahan province, such as temperature, precipitation amount, wind speed and sunshine duration. For this purpose, the changes in the three-year averages of the climate parameters measured by Ardahan Meteorological Station between 2001-2021 are analyzed. In addition, non-parametric Mann Kendall and Sen slope methods are applied to climate parameters, and monthly, seasonal and annual values of each parameter are evaluated according to the statistical significance level of 5%. As a result, increasing trends in the temperature values of the region and decreasing trends in the average precipitation, relative humidity and average wind speed values are determined. Thus, this study aims to understand the impacts of climate change and manage risks, as well as to provide the necessary climate data for thermal energy projects planned to be carried out in the region. [ABSTRACT FROM AUTHOR]