Current Science

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The capability of open-data sources in cloud computing was explored in rainfall–run-off modelling through the Soil Conservation Services curve number (SCS CN) model. The Google Earth Engine (GEE) has a peta­bytes catalogue of global remote sensing and GIS data­sets, numerous functions and algorithms to manipulate and visualize datasets rapidly. In this study, an algorithm has been developed to prepare dynamic CN maps in GEE using OpenLandMap Soil Texture and MODIS land use/land cover (LULC) data through ternary function and climate hazards group infrared precipitation rainfall collection for input rainfall and creation of antecedent moisture condition. The capabilities of the developed algorithm were demonstrated for Shipra, Kuttiyadi and Bah river catchments in India. However, it can be used with different satellite data for estimating the run-off and impact of LULC change on run-off for any part of the world and any desired period. The developed algorithm not only utilizes GEF and the public archive database for estimating the run-off at basin/subbasin scales for the planning of water resources, but also provides a quick evaluation of the impact of LULC change on run-off over time

Keywords

Cloud Computing Platform, Curve Number, River Basin, Rainfall–Run-Off Modelling, Water Resources.
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