Therefore, this study was to evaluate the ecological potential of Ecotourism development in order to study extensive and focused outing in Shahreza County. In this respect, the ecological model of tourism development assessment was fitted. At first, data of slope, aspect, soil order, bedrock, vegetation, temperature and sunshine hour parameters were obtained, and the appropriate areas maps, from the perspective of each parameter is drawn with due observance of the principles and conditions of the said model, and apply the its thresholds. Then, the final mapping of focused and extensive outing of tourism development was drawn by the integration of prone areas maps. Eventually, the final layer of outing was assessing accuracy through the encounter of the natural attractions and rural centers layers. The results showed that extent about 9.0198 and 3.9526 km² (0.32% and 0.14%) of the Shahreza County are appropriate for the development of one and two levels of focused outing, respectively. Also, extent about 263.1973 and 298.1843 km² (9.41% and 10.67%) are appropriate for the development of one and two levels of extensive outing, respectively. The high adaptation of the natural attractions and rural centers to the classes evaluated of ecotourism development maps has been showed the acceptable accuracy of the tourism development and the spatial and land use management maps is the Shahreza County.
 

In order to protect soil and water resources should be identified erodible areas of watersheds, to be able to prevent land degradation and to control erosion in the form of soil conservation planning or watershed management. Therefore, the present study aimed to classification and delineation of susceptible areas to water erosion in the Hervi watershed using Hjulstrom curve, has been conducted. Increasing of accuracy, speed and facility of spatial achievement, using GIS technology, are advantages of the present study. In this study, were separated 10 sub-watersheds based on the feeding levels of streams. Spatial variability of soil properties in sub-watersheds, such as texture and the mean diameter size of the particles using hydrometer method were measured; also the maximum potential velocity of outlet in sub-watersheds based on the characteristics of each sub-watershed were calculated. Then, status of erosion and sediment in watersheds and sub-watersheds was studied using Hjulstrom curve (based on the mean diameter size of sediment particles and the maximum potential velocity of outlet), and was comparatively classified in term of erodibility. Finally, delineation map of susceptible regions to water erosion in the study area were obtained. The results revealed that the upstream and side areas of the watershed including: Sub-watershed No.4 (8.94% of the land), has very high erodibility, and Sub-watersheds No.1 and No.9 (36.94% of the land) have high erodibility. These areas are steep and often devoid of vegetation or have poor vegetation (such as poor pastures and rain-fed farming); so, it is necessary a proper strategy to prevent further erosion.

 Assessing and predicting future climate change is of particular importance due to its adverse effects on water resources and the natural environment, as well as its environmental, economic and social effects. Meanwhile, rainfall is also an important climatic element that causes a lot of damage in excess conditions. West Azerbaijan Province is no exception. The aim of this study is to model and predict 30 years of rainfall in West Azerbaijan province. The statistical period studied is 32 years (2019-1987). Selected stations in the province include Urmia, Piranshahr, Takab, Khoy, Sardasht, Mahabad and Mako stations. Average slider time series models, Sarima (seasonal Arima), Health Winters were used for analysis and prediction and also linear regression and Mann-Kendall test were used to determine the data trend. The results show an increasing trend of precipitation in Urmia, Piranshahr, Khoy, Sardasht and Mako stations and a decreasing trend in Takab and Mahabad stations. According to the results of comparing the models used, the Health Winters model with the least error in the absolute mean of deviations, mean squared deviations and the percentage of absolute mean errors was introduced as the best precipitation forecasting model for West Azerbaijan province. province.                                     [A1] 

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The torrential rains that occurred in April 2017 in Lorestan Province exemplified severe precipitation that inflicted substantial damage on agricultural, urban, transportation, and communication infrastructures. This study aims to investigate and elucidate the relationship between the physical structure of clouds responsible for two waves of heavy rainfall in April 2017 within the Doroud catchment area of Boroujerd. In this context, the statistical characteristics of two precipitation events on March 25 and April 1, 2019, were analyzed. The microphysical properties of the clouds generating these two heavy rainfall events were examined utilizing the Madis superconductor product and MOD06. Four microphysical factors contributing to the formation of clouds during these two rainfall waves in the Doroud-Borujerd basin—including cloud top temperature (CTT), cloud top pressure (CTP), optical cloud thickness (OCT), and cloud cover ratio (CFR)—were analyzed. Statistical assessments indicated that the first wave of heavy rainfall, occurring on March 25, 2019 (5 April 1398), accounted for 15% of the total annual rainfall, while the second wave on April 1, 2019 (12 April 1398) contributed 20% of the region's average annual rainfall within these two days. The findings from the analysis of the microphysical structure of the clouds producing these two precipitation waves, based on data from the MODIS cloud sensor product, revealed a significant spatial correlation between the four microphysical factors and the recorded precipitation values of these two heavy rainfall events. Specifically, the cloud top temperature and pressure, indicative of vertical cloud expansion in the area, exhibited a significant inverse relationship with the precipitation amounts in the basin. Conversely, the cloud cover ratio and optical thickness demonstrated a direct and significant spatial correlation with the recorded rainfall values. The results of this study thus establish a significant and robust relationship between the microphysical structure of clouds and the precipitation amounts recorded in the region during these two heavy rainfall events.