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Study region: The Khorasan Razavi Province represents a variety of climatic types in the northeastern region of Iran. It lies between 56° 19′ and 61° 16′ east longitude and 33° 52′–37° 42′ north latitude, which is one of the largest and most densely populated provinces in Iran. Study focus: The aim of this study is using the Weather Research and Forecasting (WRF) model to evaluate and simulate rainfall in the northeastern region of Iran based on the climatic conditions in order to rainfall forecasting, also warn about and control probable floods. New hydrological insights for the region: Following the climatic classification of the study area, the six WRF model schemas were employed to implement the model in this area: Purdue-Lin (Lin), WRF Single-Moment class 3, 5, 6 (WSM3, WSM5, WSM6), and WRF Double-Moment class 5, 6 (WDM5, WDM6). The results revealed that the Threat Score (TS) coefficients for the WDM5 and Lin schemas in the desert climate are 1 and 0.97, respectively; the TS coefficient for each one of the Lin, WSM3, and WDM6 have the value of 0.60 in the semi-desert climate. The Lin scheme performs the best in all climates of the study area. The results revealed that the WRF model accurately simulates rainfall in the climatic conditions of the region. Hydrologists and planners will be able to use this meteorological model as an input to hydrological models based on the output of the WRF model, which can provide early flood warnings within the next 24 h based on these findings.

In recent years, climate change has caused an increase in the air temperature and consequently the temperature of the earth's surface. The temperature of the earth's surface has had various effects on urban life and has caused the formation of thermal islands in cities. Thermal islands are one of the important criteria in regional planning. The city of Shiraz is considered to be a large population center in the southwest of Iran on the one hand and one of the important tourism centers of Iran on the other hand, so the purpose of this research is to monitor the spatial form of thermal islands in this city. The satellite images of Shiraz city were first collected and extracted in the hot periods of the year from 1985-to 2020 using the data of Landsat’s 4 and 5 (TM), 7 (ETM+), and 8 (OLI/TIRS). After required preprocessing, the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Normalized Difference Built-up Index (NDBI), and the urban heat dispersion index were determined by the Urban Thermal Field Variance Index (UTFVI). The regions with UHI, the variation of the temperature in the city, and the relationship between the LST and surface vegetation were evaluated based on the results of image processing to determine the UHIs of Shiraz. The temporal variations of the temperature pattern of Shiraz city indicated that about 12.76 km2 was added to the area of the fourth temperature class from 1985 to 2020. Results of NDVI evaluation in the studied period showed a decline in the vegetation from 22.28 km2 in 1985 to 17.54 km2 in 2020 due to alteration of the land use which can explain the formation and increase of the UHIs in the mentioned regions. At the beginning of the investigated period (1985), the temperature of the earth's surface was 17°Celsius and reached 21°Celsius at the end of the period (2020). The monitoring of the spatial form of the surface temperature of the earth shows that heat islands are moving from the outskirts of the city towards the city center. UTFVI also revealed that the awful regions (very hot temperatures) are mainly concentrated in the west and north-west to the south west of Shiraz (including some parts of districts 9 and 10), south east of district 7, and north of district 1.

Land Surface Temperature (LST), particularly for the urban environment, is a crucial indicator of urban heat emission, urban climate, global environmental change, and human-environment interactions. In the present study, the spatial distribution of the mean LST was evaluated in association with the 16 explanatory indices at the neighbourhood's level in Mashhad City, Iran. To identify the main variables contributing to the LST variations, Principal Components Analysis (PCA), Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models were employed 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 configuration of the green patches (PC5) 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 = 0.687, AICc = 1545.848, and Moran's I = 0.021, 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.;

This research was carried out to evaluate the effect of climate change on changes in cold stress on potato plants in tropical regions of Kerman province. In this study, changes in the occurrence of cold stress in the observation data of three stations of Jiroft, Kahnouj and Manoajan during the period 1981-2005 and the period 2011-2100 were considered as the future period. The future course data, using of the general circulation model CanESM2 under rcp26, rcp45 and rcp85scenarios, using were quantified SDSM software.the climatic indices used to evaluate the changes in cold stress in potatoes were determined by the tolerance thresholds of potatoes and based on the average of long-term climate statistics and was evaluated its probability of occurrence during the growth period. In the Jiroft station the probability of occurrence of premature and late temperature below 5℃ respectively than 83% and 63% decrease. At Kahnoj and Manoajan stations, the probability of occurrence of premature and late temperature below 5℃ in the future period decreased and increased, respectively.The beginning and the end of the incidence of cold stresses have been set at the beginning and end of the cold season.The statistical results indicate an increasing the days of cold stress (5≤T≤0) at the Jiroft station during the periods 2011-2040 and the Manojan Station in the period 2071-2100 compared to the previous period . At the Kahnouj station, decreases the frequency of days with cold stress. However, the days with freezing stress in Jiroft, Kahnoj and Manojan stations in the next climate periods are showing a significant increase.The results indicate that the average date of occurrence of early stages is 10 days earlier and the late stages are 14 days later than the average long-term climate in the study area.

In order to identify the changes of spatial autocorrelation between heat clusters in Fars province, the minimum and maximum temperatures have been recorded to form a network database in Fars province. Then a 33 year period was selected for this research from the afore-mentioned database, the selected daily period was from 1/01/1980 to 12/31/2012, and an area with the dimensions of 18×18 km was added to the region under study. In order to achieve the temperature changes of these heat clusters, the newest spatial statistical methods such as spatial autocorrelation (Global Moran’s I), Anselin Local Moran’s and hot spots by environment GIS were used. The results of this study showed that the pattern of spatial and temporal variations of heat clusters of Fars Province is high-cluster. However, based on local Moran and hot spots, heat clusters in the South, South West and South East have positive spatial autocorrelation pattern (heat clusters) and parts of North and North East have a negative spatial autocorrelation (cold clusters). In the study period, a large part of the province, in most cases, almost half of the total area had no pattern of spatial autocorrelation, significant or non-significant.