JWSS - Journal of Water and Soil Science

The annual soil erosion in different regions of the world has been estimated using various empirical and numerical methods whose accuracy is very dependent on their utilized parameters. One of the most common methods in the evaluation of the mean annual soil erosion especially in sheet and furrow regions is the USLE method. In this relationship, almost all factors that normally affect the soil loss process such as land cover, slope, precipitation, soil type, and support practice parameter of soil have been employed but, in this research, it was shown that the accuracy of this method in mountainous areas covered by rock and snow is somewhat low. To do this, a part of the Tibet plateau in China, where observation soil loss data were available, was selected for investigation. To implement the numerical and analytical analysis, many maps including DEM, NDVI, orientation, soil type, mean monthly and annual precipitation for 30 years were collected. For increasing the accuracy of the model, the cover management parameter was extracted from high accuracy NDVI maps and all USLE parameters were calculated in ArcGIS. The final results were shown that the amount of annual soil loss which was estimated by the USLE method is more than the observed data which were collected by Chinese researchers. This is because the large areas of the study area are covered by lichen and snow where soil loss due to the erosion process is very low but these regions cannot be recognized from NDVI maps. Also, the analysis of the NDVI maps was shown that the relationships of Fu, Patil, and Sharma were not suitable for soil loss estimation in elevated mountainous areas. If the other relationships such as Lin, Zhu, and Durigon are used for the regions with a height of more than 5500 m, a new correction coefficient needs to be used for the C factor which was calculated as 0.2 for the study area.

Snow cover is one of the important climatic elements based on which climate change may have a special effect. In general, climate change may be reflected in different climatic elements. Therefore, it is very important to study and measure changes in snow level as one of the important sources of water supply. Ardebil and Sarein cities are located at 48° 18׳ east longitude and 38° 15׳ north latitude. In this study, Sentinel-2 optical satellite was used to monitor the snow cover surface in 2018, and NDVI, S3, NWDI, NDSI, Cloud mask indices were applied to detect snow-covered surfaces using ArcGIS and Snap software. Next, to validate the snow maps extracted from the images, it was compared with the snow data in terrestrial stations using linear regression in MATLAB software and to evaluate the accuracy of the model statistical indices including RMSE, MSE, BIAS, CORR were used. The present study showed that according to Ardabil city climatic conditions, maximum-snow covered area in January with an area of 356.52 km2 and minimum snow-covered area in March with an area of 96.10 km2. The highest snow cover is observed in the high slope areas in the western slopes (Sabalan Mountain Heights) and the lowest snow cover is observed in the lower eastern slopes. The results of linear regression with generalization coefficient are 85% and the results of statistical indices of error are equal to MSE: 0.086, BASAS: 0.165, CORR: 0.924, and RMSE: 0.03. Correlation relationships between terrestrial data and estimated snow maps showed a high degree of correlation. This result is statistically significant at the 99% level. The use of optical images in estimating snow levels is very cost-effective due to the size of the areas and the high cost of installing snowmobiles. The results obtained in the present study indicated that traditional radar images with high spatial resolution and good correlation with terrestrial data can be a good alternative to snowmobiling ground stations at high altitudes or in passable areas.