For the first time, contemporary trends in water discharge, suspended sediment load, and the intensity of overall erosion in the river basins of the North Caucasus region, as one of Russia’s most agriculturally developed geographic areas, were identified. The study was carried out using monitoring data of the Federal Service for Hydrometeorology and Environmental Monitoring of the country for 21 rivers by comparing two periods: 1963–1980 and 2008–2017. According to the study’s results, trends of an increase in the mean annual water discharge (by 2–97%) and the essential reduction in its intra-annual variability have been found in most of the studied rivers. On the contrary, the trends of reduction in annual suspended sediment load and the intensity of erosion in the river basins were identified in most of the study region. Their most essential and statistically significant decreases (by 47–94%) were recorded within the Stavropol Upland, which several decades ago was considered one of the most erosion-dangerous territories of the entire country, as well as in some river basins of the central part of the Greater Caucasus’s northern slope (by 17–94%). The changes in climate (reducing the depth of soil freezing and meltwater runoff on the soil) and land use/cover (reduction of acreage and load (pressure) of agricultural machinery on the soil, reducing livestock on pastures, and the transfer of water from the neighboring, more full-flowing rivers) are considered the leading causes of the aforementioned trends. The findings will contribute to solving some economic and environmental problems of both the region and adjacent territories and water areas.
Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.
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