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Land Surface Temperature (LST), in particular for the urban environment, is a key indicator to characterize urban heat changes, urban climate, global environmental change, and human-environment interactions. However, due to differences in the local spatial variations of LST and the related influence factors, few studies have discussed the spatial non-stationarity and spatial scale effects within urban areas. Moreover, in cities such as Mashhad, which are located in a hot and dry climate, have been less studied of the relationship between LST and urban influencing factors on a neighborhood scale. In the present study, the spatial distribution of the mean LST was evaluated in association with the 16 explanatory indices at the neighborhood's level in Mashhad City, Iran, as a case study. To assess the main components contributing to the LST variations, Principal Components Analysis (PCA) was employed in this study. Additionally, Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models were used to explore the spatially varying relationships and identify the model's efficiency at the neighborhood's scale. Our findings showed the five most important components contributing to LST variances, explaining 86.2% of the variability. The most negative relationship was observed between LST and the morphological features of neighborhoods (PC3). In contrast, the landscape composition of the green patches (PC2) exhibited the lowest negative impacts on LST changes. Moreover, road and traffic density characteristics of the neighborhoods (PC4) were the only effective components to alert the average LST positively. With R2= 0.678, AIC c= 2125.6, and Moran's I= 0.018, the results revealed that the GWR model had better efficiency than the corresponding non-spatial OLS model in terms of the goodness of fits. It suggests that the GWR model has more ability than the OLS one to predict LST intensities and characterize spatial non-stationary. Therefore, it can be applied to adapt more effective strategies in planning and designing the urban neighborhoods for mitigation of the adverse heat effects.

Precipitation is an important factor in the management of a variety of agricultural and industrial projects. This study investigated the temporal-spatial change of inter-annual precipitation of Iran from 1977 to 2007 by using the APHRODITE precipitation database. Statistical methods were applied, such as spatial auto-correlation, Global Moran's index, Local Moran's I index, and hotspots to acquire the variations in precipitation. The highest spatial anomalies belong to September (75.26) and October (45.02), based on the Dispersion index. Also, the size of the largest cluster of Iran's precipitation clusters is developed during winter, cited by the index's outputs, which indicates the relative regularity of Iran's precipitation. The results of the spatial statistics showed that inter-annual precipitation changes in Iran have an upward cluster model. The results of the Global Moran statistics showed that September, with the lowest number (0.712114), has the highest spatial precipitation anomalies throughout the year in Iran. Meanwhile, precipitation has a positive spatial autocorrelation on the Caspian Sea shores and western and south-western parts of the country (mainly Zagros highlands) and a negative spatial autocorrelation in parts of the central and south-eastern areas based on the Local Moran index and hotspots. HIGHLIGHTS This is the first research into this subject in terms of methodology in Iran.; The method of geo statistics can be applied in other regions in the Middle East.; This paper investigates rainfall changes during the last half-century.; This research can contribute to the knowledge of climate in this part of the world.; This research also can provide information on rainfall and water as a major problem in arid regions.

Atmospheric humidity has an important role in the energy balance of the planet Earth, and it is effective in explaining climate change and consequently global warming, which has been a major topic of discussion among scientists in recent decades. Therefore, in this study, the maximum and minimum annual and seasonal trends of the relative humidity of 41 synoptic stations in the country during the statistical period of 1960–2019 were evaluated. Three statistical tests were used to analyze the data trend: the Mann–Kendall statistical test, Sen's slope estimator, and linear regression. In the study area, the maximum and minimum annual humidity trends were positive for 83.26% and negative for 17.73% of the stations, respectively. The highest increase in maximum and minimum humidity values, respectively, includes Chabahar in Southeast and Dezful in Southwest stations with a rate of + 2.55 and + 2.10 °C per decade. At the seasonal scale, the highest decrease in maximum and minimum humidity was identified in the summer at Tabas station in the central part of Iran. The highest number of stations with a decreasing or negative trend in the seasonal time series of maximum and minimum humidity occurred in winter with 37 stations. In contrast, most stations had a significant increasing or positive trend in winter and fall. Overall, the results showed that although the rate of decreasing trend of humidity for the study period was higher than the rate of an increasing trend of humidity, there was a similar decreasing trend in the studied variables.

Examining the land surface temperature (LST) and its mechanism is very significant for urban planning.The purpose of this study is to determine the factors affecting the surface temperature of urban areas of Shiraz. The OLS and GWR were used o determine the effective factors. Also, satellite data of Landsat 8 for summer 2019 were used to obtain the surface temperature of Shiraz. To this end, the Landsat 8 satellite images of Shiraz urban districts during Summer 2019, were prepared. First, the LST and vegetation was extracted from the images. Before performing the regression model between the LST as the dependent variable and geographical variables such as slope, slope gradient, elevation, distance to rivers, direct and indirect solar radiation, sunshine duration, and Total Solar Irradiance (TSI) as independent variables, the component analysis method was employed to eliminate collinearity and dependence relations among independent variables and reducing variables. Four significant components that had eigenvalues above one and about 86.75% of the variance of the initial variables were selected, which included the significance of (direct, indirect, general) solar radiation, the direction of slope, distance to rivers, vegetation, and slope gradient, respectively. In the next step, the regression analysis was performed between the components and the LST via the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR). The results showed that GWR has better performance in showing the spatial distribution of LSTs in Shiraz. Shiraz UHIs correspond to the airport and the dirt and barren districts around the city, which are often non-residential.

Water scarcity has always been one of the most important obstacles in the process of agricultural development in arid and low-rainfall climates. In farming, the water quality also plays an important role; as the poor quality of water can be one of the limiting factors in this sector that in addition to the agricultural problems has also caused soil problems. In this paper, we introduce and study an indigenous method of water conservation and soil protection in the east of Iran termed as “water-colouring” and the role of geomorphologic features of marl hills in one of the rivers located in the southern slopes of the Joghtay Mountain (Kalateh-Sadat Watershed). In this study, field observation, sampling and analysis of water and sediment, physicochemical experiments, SPSS software for statistical analysis and satellite images have been used to produce digital models. Finally, the effects of muddy water and clear water have been compared in terms of chemical quality and productivity of agricultural land. The results show that the method of water-colouring is the best indigenous-economical method for moisture retention and regeneration of the agricultural land soil. Given the statistical reasons and studying the other researches in this field, water-colouring causes change texture and soil composition, fertility of agricultural land and reduce the losses due to evaporation and infiltration.