In recent decades, drastic land use changes in Golestan province caused to reduce a substantial amount of Hyrcanian forest. To investigate the changes, land cover maps produced using Landsat satellite imagery classification of sensors TM from 1984, 2012 and 2016 respectively used as input data in Land Change Modeler (LCM) to predict land cover changes in 2030. In order to assess the accuracy of modeling, statistics of relative performance characteristic (ROC), ratio Hits/False Alarms and figure of merit was used. In continue to investigate the role of land use changes in water yield as one of ecosystem services was discussed. The results show the accuracy of artificial neural network with the ROC equal to 0. 949, the ratio Hits/False Alarms equal to 57 percent and the figure of merit is equal to 11 percent. Land use change modeling results showed that from 1984 to 2012, The most prominent changes were related to reduction of forest cover. This process modeling using artificial neural network showed, from 2016 to 2030 forest cover will be reduced about 30361 hectares. The results of water yield study showed that runoff in the area, particularly in the East and North East area has increased. This increase in the amount of runoff occurred as a result of land use change on forest ecosystems to agriculture. Results of this study improve our understanding of hydrological consequences of land-use changes, and provide needed knowledge for effectively developing and managing land-use for sustainability and productivity in the Gorgan-rood watershed.

IntroductionMore than 30% of the heat energy generated by the engine is transferred by the cooling system. If this heat transfer is not accomplished properly, then the engine heat will increase and it will wear the parts by removing oil film between the pieces. A cooling system is used to remove this heat. The radiator is an important component of this system. Increasing heat transfer in the car engine by the cooling system is possible by using two methods of changing the radiator geometry and optimizing it and using fluids with high thermal properties. In this research, we investigated the improvement of radiator thermal performance using nanofluids using a laboratory model. The effect of nanoparticle volume fraction and cooling flow rate on heat transfer rate, and heat transfer coefficient was investigated.Materials and MethodsIn this research, a laboratory model was designed and manufactured to evaluate the thermal performance of the MF 285 tractor radiator using nanofluid. In this laboratory model, water was combined and used as a base fluid with nanoparticles AL2O3. 20 nm nanoparticles with volume percentages of 1 to 4% were used. An electric stirrer and magnetic stirrer were used to prepare the nanofluid. For the produced fluid to be usable, add SDBS surfactant to it. The temperature of the inlet fluid to the radiator was 85 °C and the cooling fluid flow rate was 3.18 to 15.08 (lit. min-1 )) and the airflow rate was 3.2 to 6.4 (m s-1). Two T-type thermocouples are installed to measure the inlet and outlet temperature of the radiator and two other front and rear fans to measure the inlet and outlet air temperature and four more are installed on the radiator to measure the radiator body temperature.Results and DiscussionThe results show that in nanofluid with a 4% volume fraction compared to a 1% volume fraction, it can be seen an increase of 8.7% in density, 7.7% in viscosity, and 9.1% in thermal conductivity, and also a decrease of 8.8% in specific heat. The maximum temperature difference between the inlet and outlet sensors of the radiator when the thermostat is open and the cooling fluid flows through the radiator is 12 to 15 °C. By increasing the speed of the electromotor from 40 Hz to 50 Hz, the temperature of the water cooling fluid at the outlet part becomes 4.7 °C cooler and the air temperature at the outlet part becomes 7.3 °C warmer. As the speed of the electromotor increases, the rate of heat transfer increases. At the maximum value of airflow and cooling fluid, by adding 4% by volume of nanoparticles to the base fluid, the rate of heat transfer can be increased about 37% compared to the base fluid. Compared to water, nanofluid containing 4% by volume of AL2O3 at maximum speed has a 28% increase in heat transfer coefficient. Also, by increasing the electric motor speed from 20 Hz to 40 Hz, the heat transfer coefficient of pure water shows about 26% increase and the nanofluid shows an average of 29% increase.ConclusionIncreasing the volume fraction of nanoparticles suspended AL2O3 in the base fluid increases the density, viscosity, and thermal conductivity, which increases the heat transfer rate and reduces the outlet temperature of the radiator. The presence of nanofluid in the engine cooling system increases the heat transfer from the radiator, and despite this feature, the size and weight of the radiator can be reduced without affecting its heat transfer performance. It can also improve heat transfer performance by increasing the cooling flow rate and the airflow rate.

Water accounting frameworks apply as a tool for organizing water data and water resources assessment. Adopting SEEA – Water framework, the paper will go through integrated water resource assessment in the Ajabshir study area in 2006 and 2016. The assessment was carried out based on the indicators associated to the different dimensions of water security in the area such as water resources, economic and social. According to the indicators of water resources dimension, agriculture, with more than 96% of water consumption, has the most effect on water stress. According to the intensity of water consumption in 2006 and 2016, the local water resource is highly unsustainable. Per capita renewable water increased from 835 m3 per person in 2006 to 1179 m3 per person in 1395. That is mainly due to the decrease of 17% of the population in 2016 compared to that in 2006, decrease in outflow to Lake Urmia because of the GHALEH-CHAY dam, as well as the further transbasin water import. The marginal value changes in the economic productivity of water in the agricultural sector indicate that the economic drivers of agricultural sector were highly dominating the growth mechanism in the area, which can result in ignoring strategic water resources restrictions in favor of short-term individual economic gains. In spite of decline in water consumption in the service and constant water consumption in the industrial sectors, increase in services and industrial revenues has led to an increase in the economic productivity of water in those sectors.

Background and Objectives: Gorgan Bay, as one of the unique aquatic ecosystems in northern Iran, has faced serious environmental challenges in recent years. These challenges stem from various factors, including climate change, decreasing water levels of the Caspian Sea, increased human activities, and morphological changes in the region. Accurately identifying the factors leading to the degradation of this ecosystem is essential for sustainable water resource management and environmental protection. This study aims to analyze changes in water surface area and water quality in Gorgan Bay from 2000 to 2023 and identify the factors influencing these changes.Materials and Methods: This research analyzes changes in water surface area and water quality in Gorgan Bay from 2000 to 2023 using satellite data from Landsat, MODIS, Sentinel, and Jason. Indicators such as water temperature, turbidity, and CDOM (Colored Dissolved Organic Matter) were examined to assess water quality in the region. Additionally, water surface area maps of the bay were created using MNDWI (Modified Normalized Difference Water Index) and NDWI (Normalized Difference Water Index). Pearson correlation coefficients (r) were utilized to analyze the relationship between meteorological and satellite parameters and the water surface area of the bay, identifying linear correlations among these variables. Shapley diagrams were employed to analyze feature importance and clarify complex impact patterns on the bay's surface area. A linear regression model was also applied to evaluate the linear relationship between input variables and the bay's surface area. Finally, to analyze the impact of factors such as the water level of the Caspian Sea, the area of the Caspian Sea, precipitation, temperature, and inflow discharge on the bay's surface area, a random forest model was utilized.Results: The results indicate that from 2015 to 2023, the minimum water temperature in Gorgan Bay increased by an average of 2.3°C, primarily observed in the southern and western regions of the bay. Furthermore, there has been a continuous increase in water turbidity in recent years, particularly in 2020, 2022, and 2023, reaching unhealthy levels in the western and southern areas of the bay. The CODM index for Gorgan Bay in 2020, 2022, and 2023 remained in a suitable condition, with no significant pollution detected. In 2015, the water quality was higher than in recent periods but still did not reach unhealthy levels, with only small portions of the western areas approaching unhealthy conditions. The MNDWI and NDWI indices indicate that over 50% of the initial surface area of Gorgan Bay has been lost over these years. The analysis shows a significant reduction in the bay's surface area, particularly in 2020, 2022, and 2023, with large areas of the western, southern, and northern parts of the bay completely dried up. The study found that the decrease in the water level of the Caspian Sea is the most critical factor contributing to the drying of Gorgan Bay. The Pearson correlation coefficient between the bay's surface area and the Caspian Sea water level was calculated to be less than -0.90, with a coefficient of determination of 0.82, indicating a strong inverse relationship between these two variables. The relationships of other parameters, including the water area of the Caspian Sea, inflow discharge, precipitation, and temperature with the surface area of Gorgan Bay, were determined with coefficients of determination of 0.43, 0.40, 0.19, and 0.11, respectively, indicating that temperature has the least impact on the reduction of the bay's surface area. Additionally, the Shapley coefficient revealed that the water level of the Caspian Sea had the greatest variability across the horizontal axis, indicating its role in the surface area of Gorgan Bay. Other examined parameters, such as inflow discharge, the area of the Caspian Sea, temperature, and precipitation, also played significant roles in this process. Regression analysis to assess the role of the examined parameters on the reduction of Gorgan Bay's surface area based on coefficient values indicated that the water level of the Caspian Sea (coefficient of 0.54) had a more significant role in the drying of Gorgan Bay compared to other variables. In fact, the water level of the Caspian Sea had more than 50% greater influence on the drying process of Gorgan Bay than other features. The feature importance analysis using the random forest method showed that the water level of the Caspian Sea had a coefficient of 0.78, the area of the Caspian Sea had a coefficient of 0.14, while inflow discharge, temperature, and precipitation had coefficients of less than 0.1, indicating their lesser impact on the drying of Gorgan Bay. Moreover, the analysis of the water level of the Caspian Sea and the surface area of Gorgan Bay from 2000 to 2023 revealed a decrease in the bay's water surface area from 400 square kilometers in 2000 to 260 square kilometers in 2023, closely related to a decrease in the Caspian Sea water level, which has dropped by over 2 meters in the past 23 years. These changes are directly linked to the regional morphology of Gorgan Bay, particularly the reduction in depth and changes in the coastal shape, which have exacerbated the drying process and reduced water surface area.Conclusion: The findings of this study indicate that Gorgan Bay has faced serious challenges from 2000 to 2023 due to declining water levels and water quality. Correlation analyses and regression models demonstrated that the water level of the Caspian Sea plays a primary role in these changes, with a direct and significant relationship between the decrease in water levels and the reduction of the bay's surface area. The results highlight the necessity for sustainable water resource management and the protection of Gorgan Bay as a sensitive ecosystem facing declining water levels and changes in water quality.