This study by Geospatial World attempts to collect and analyse data concerning the catchment characteristics that affect direct runoff such as soil type, land cover and rainfall for the catchment of Lokapavani river in Karnataka using remote sensing and GIS techniques. An attempt has been made to determine the weighted average Curve Number (CN) for different land use, land cover and soil types in Lokapavani catchment in order to estimate the runoff.

The SCS curve number method has been used as a distributed model where the runoff for smaller catchment within the whole catchment is estimated. An attempt was made to use GIS technique to develop a digital database using a classified image for the application of Curve Number method for estimating runoff from the basin and parts thereof.

The work in the present analysis involves integration of remotely sensed data with spatial data in a GIS platform. The following steps were adopted to arrive at weighted average CN for different rain gauge stations -

• Topographical maps of scale 1:50,000 were used as a base map. It is geo-referenced in Arcview GIS to demarcate the catchment boundary.
• The different thematic layers, like drainage network, Thiesson Polygon and soil type were created.
• Since the CN method was used as a distributed model, the entire catchment was divided into 24 sub-catchments which form a separate layer.
• IRS 1D–LISS-3 satellite image was processed using ERDAS 8.4 imagine. 15 training sites containing forest type, paddy, sugarcane, water bodies, residence, barren and dry cropland were used.
• Supervised classification of IRS ID – LISS – 3 was done using the above training sites and finally classified into 6 categories viz., irrigated area, dry cropland, barren, water body, residence zone and forest.
• Soil map was prepared by collecting data from the field. The thematic layers like soil map, sub catchment map, Thiessen map and land cover map were created in Arc View GIS 3.2a.
• Curve numbers are assigned for different land cover and soil types. Weighted curve numbers were obtained after integrating the above layers and mapped for different sub-catchments in different Thiessen areas. These data were used to estimate runoff.

The task of data collection and interpretation was made much simpler as compared to conventional methods using the above technique. From the analysis, the weighted average CNs were evaluated for the entire basin, which is useful for the prediction of runoff from the basin.

View the paper here