Quantification of Spatial Variability of Soil Physical Properties in a Lesser Himalayan Sub-Basin of India
The quantification of spatial variability of soil properties is indispensable for evaluating the effectiveness of provisions undertaken to tackle various land surface processes like soil erosion, diffuse water pollution, etc. and is also essential for suggesting precise land management strategies. The present study was carried out in an urban sub-basin of lesser Himalayas, India, to appraise the spatial variability of soil textural fractions (sand, silt, and clay), bulk density (ρb), porosity (η) and saturated hydraulic conductivity (Ks). Using the Global positioning system (GPS) soil samples from 100 randomly selected locations were collected at an average depth of 25 cm under different land covers. Laboratory analysis was conducted to determine the soil physical properties. This was followed by statistical and geostatistical analysis for characterizing physical properties of soil and their spatial variability. The Coefficient of variation (CV) displayed moderate variability (0.14-0.36) for measured soil properties except for ρb which has low variability (CV = 0.08). The variability of soil physical properties under distinct land covers was determined by creating Box plots, and it was revealed that in general, shrubland has significantly lower Ks, ρb and sand content and higher η, silt and clay content than farmland and built-up land covers (P < 0.05) mainly due to lesser anthropogenic activities and continuous vegetal cover in shrub lands. The nugget to sill ratios for all the soil properties showed moderate to strong degree of spatial dependence. For mapping spatial variability of soil parameters, Ordinary Kriging (OK) approach of Geostatistical analysis was utilized revealing Spherical (Sand, ρb and η); Exponential (clay and Ks) and Gaussian (sand and silt) as the best fit models. Analysis by Moran's I signified that about 1850 m sampling interval would be sufficient to detect spatial structure of soil physical properties. The generated spatial interpolation maps could serve as efficient tool for identifying land degradation spots in the study region and suggesting precise land management strategies.