Abstract
Mountainous regions present numerous obstacles to agriculture. These include the terrain, which is associated with surface erosion, as well as surface runoff, which washes away plant nutrients and weak soil. Spatial analysis is currently used in the study of various stochastic variables, especially those of high priority for soil water properties. Small watershed and basin-scale models were used to simulate the quantity of surface run-off, groundwater and predict the environmental impact of land use and land management practices. A new generation of the distributed hydrological models has greatly broadened simulation fields to soil and water diversified situations. The study also measured declines in slope and grain size distribution, factors impacting surface erosion and surface runoff. Multivariate statistics (canonical analysis) showed that soil moisture was most correlated both with agricultural land and forests, which is why it was used to create the model of spatial distribution. The model showed that salinity has the smallest forecast error in modeling, and thus best corresponds with the soil moisture. It is important to make a correct diagnosis of soil properties, and the degree of degradation. The assessment of the physiographic parameters of a basin will contribute to the development of proper usage and determine the quality of the water in the soil, which will be essential for forest resources and agricultural land in mountain areas exposed to surface erosion.
Highlights
The salt’s direction of migration depends on the amount of water contained in the soil, as well as weather and climate conditions. This was the reason for the designation of the spatial variability of electrical conductivity (EC; salinity), EC was selected for spatial analysis because salt reduces the number of nutrients and temperature, which inhibits the growth of plant roots
The results clearly showed that the larger catchment area is characterized by useful hydrological parameters for soil water retention
Meadows were more closely tied to forests, as evidenced by the positive correlation between the measured parameters of Multivariate analysis can be used to identify factors affecting the amount of land use
Summary
It is important to determine the spatial variability (diversity) and spatial continuity (similarities). A geographic information system (GIS) technique is commonly used in assessing the dynamics of rivers and the physiographic characteristics of a basin [2]. The evaluation of the quality of river basins is important in environmental monitoring [3]. The continuous spatial data of soil are difficult to present uniformly, and new techniques and models are constantly being sought out, while results are verified using calibration [4]. Multivariate statistics are used to assess the quality of river basins [5]. The soil should be examined as well as other physical and chemical parameters
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