Browsing by Author "Ganasri, B.P."

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Assessment of soil erosion by RUSLE model using remote sensing and GIS - A case study of Nethravathi Basin
(Elsevier B.V., 2016) Ganasri, B.P.; Ramesh, H.
Soil erosion is a serious problem arising from agricultural intensification, land degradation and other anthropogenic activities. Assessment of soil erosion is useful in planning and conservation works in a watershed or basin. Modelling can provide a quantitative and consistent approach to estimate soil erosion and sediment yield under a wide range of conditions. In the present study, the soil loss model, Revised Universal Soil Loss Equation (RUSLE) integrated with GIS has been used to estimate soil loss in the Nethravathi Basin located in the southwestern part of India. The Nethravathi Basin is a tropical coastal humid area having a drainage area of 3128 km2 up to the gauging station. The parameters of RUSLE model were estimated using remote sensing data and the erosion probability zones were determined using GIS. The estimated rainfall erosivity, soil erodibility, topographic and crop management factors range from 2948.16 to 4711.4 MJ/mm·ha? 1hr? 1/year, 0.10 to 0.44 t ha? 1·MJ? 1·mm? 1, 0 to 92,774 and 0 to 0.63 respectively. The results indicate that the estimated total annual potential soil loss of about 473,339 t/yr is comparable with the measured sediment of 441,870 t/yr during the water year 2002–2003. The predicted soil erosion rate due to increase in agricultural area is about 14,673.5 t/yr. The probability zone map has been derived by the weighted overlay index method indicate that the major portion of the study area comes under low probability zone and only a small portion comes under high and very high probability zone. The results can certainly aid in implementation of soil management and conservation practices to reduce the soil erosion in the Nethravathi Basin. © 2015 China University of Geosciences (Beijing) and Peking University
Assessment of spatial variability of soil physical and hydraulic properties in Netravati catchment, Karnataka State, India
(2015) Ganasri, B.P.; Dwarakish, G.S.
Site specific management and precision agriculture seeks to identify and analyze variability in soil properties. Runoff prediction and sediment yield estimation using distributed hydrologic modeling requires spatially distributed soil data especially saturated hydraulic conductivity, water content and particle size distribution. Therefore, soil data becomes one of the major inputs in any watershed management studies. The present study was conducted to analyse spatial variability in soil physical and hydraulic properties in Netravati river basin, Karnataka State, India. The spatial interpolation of soil properties was carried out by using three methods such as Ordinary Kriging (OK), Inverse Distance Weighting (IDW) and Radial Basis Function (RBF). Results indicate that soil properties such as %sand, %silt and %clay are highly variable in nature with standard deviation value of 16.08, 13.27 and 8.82. The OK method is efficient in predicting sand, silt, clay and saturated hydraulic conducted. IDW and RBF methods predicted organic matter content and bulk density with acceptable error of 1.084%, 1.09% and 0.05gm/cc, 0.04gm/cc respectively. � 2015 IEEE.
Assessment of spatial variability of soil physical and hydraulic properties in Netravati catchment, Karnataka State, India
(Institute of Electrical and Electronics Engineers Inc., 2015) Ganasri, B.P.; Dwarakish, G.S.
Site specific management and precision agriculture seeks to identify and analyze variability in soil properties. Runoff prediction and sediment yield estimation using distributed hydrologic modeling requires spatially distributed soil data especially saturated hydraulic conductivity, water content and particle size distribution. Therefore, soil data becomes one of the major inputs in any watershed management studies. The present study was conducted to analyse spatial variability in soil physical and hydraulic properties in Netravati river basin, Karnataka State, India. The spatial interpolation of soil properties was carried out by using three methods such as Ordinary Kriging (OK), Inverse Distance Weighting (IDW) and Radial Basis Function (RBF). Results indicate that soil properties such as %sand, %silt and %clay are highly variable in nature with standard deviation value of 16.08, 13.27 and 8.82. The OK method is efficient in predicting sand, silt, clay and saturated hydraulic conducted. IDW and RBF methods predicted organic matter content and bulk density with acceptable error of 1.084%, 1.09% and 0.05gm/cc, 0.04gm/cc respectively. Â© 2015 IEEE.