Soil erosion is an important environmental problem worldwide. Climate change can affect soil erosion by changing the rainfall pattern; hence, it is essential to assess climate change and its effect on soil erosion in different regions. This study aimed to predict the effect of climate change on soil erosion risk using the RUSLE model in Anar Sheitan forest and Tal Siah area in Jiroft region. For this purpose, meteorological station data, remote sensing images, and GIS techniques were used to prepare the necessary model inputs. Three climate change scenarios, RCP2.6, RCP4.5 and RCP8.5, were simulated in three 2006-2025, 2046-2065, and 2080-2099 periods. The rainfall erosivity factor was estimated for these periods in order to assess the impact of climate change on soil erosion risk using existing meteorological data. Other factors of the RUSLE model were considered constant, and soil erosion risk was calculated for each scenario in each period using the ArcGIS software. The analysis of results under RCP8.5 scenario during 2006-2025 period and RCP8.5 scenario during 2080-2099 period indicated that the average soil erosion risk dropped from 0.8 to 1.2 ton ha-1 year-1 in Anar Sheitan forest, reflecting an surge of 0.4 ton ha-1year-1. Furthermore, soil erosion risk spiked from 0.23 to 0.35 ton ha-1 year-1 in Tal Siah, suggesting a surge of 0.1 ton ha-1year-1. Overall, the results suggest that the soil loss will be higher in the future, which may be partly prevented by watershed management and soil conservation practices.

The present research was conducted to compare different functions of two artificial neural networks (ANNs) including the multilayer perceptron (MLP) and radial basis function (RBF) in order to forecast the Horizontal Visibility (HV<1km) in Zabol city under dry and humid weather conditions. For this purpose, hourly data of horizontal visibility (HV), wind speed, relative humidity, temperature, and atmospheric pressure were used. Before importing these data to the ANNs, they were normalized and multicollinearity impact between the climatic variables was calculated using the variance inflation factor. In this study, 70% of data were used for data training and 30% for data testing. Accuracy of the models was estimated using the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the correlation coefficient (R) between observed and predicted values of HV. The sensitivity of the output data was determined based on the most accurate model. The results showed that according to function MLP4, the prediction accuracy of HV was more than the accuracy of other functions of neural networks (ANNs) for both dry and humid climates. The mentioned error values were estimated at less than 0.5. Pearson correlation between observed and predicted values was estimated according to training data and testing data as 0.66 and 0.7, respectively. These coefficients were calculated 0.9 and 0.99 for humid and dry weather, respectively. Moreover, the wind speed and air temperature for dry and humid climate were identified as the most important factors effective on HV at the time of dust storm occurrence.

The effect of different temperatures on germination was assessed in a laboratory study in the framework of complete randomized design with five repetitions to determine the specific temperature of Moringa peregrina seeds. Evaluation of germination response was conducted at constant temperatures of 5, 10, 15, 20, 25, 30, 35, 40 and 45 °C. Cardinal germination temperatures were fitted using three models, including beta, segmented and dent-like. Optimum and maximum temperatures of germination were calculated 17, 25-30 and 47 °C, respectively, based on the dent-like model, which was identified as the best model using statistical indicators. Then, to investigate germination and seedling growth response of Moringa peregrina toward different levels of salinity and drought stress at an optimum temperature, another test was conducted. In this experiment, seed germination was assessed in four levels of salinity and drought with the osmotic potential of 0, -4, -8 and -12 bar. The results indicated that seed germination speed and percentage were decreased due to drought and salinity stress. Generally, seed germination of Moringa peregrina was more sensitive to drought stress than to salinity stress.