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Comparative Analysis of Spatial Rainfall-Runoff Estimation Using Advance Geospatial Tools, ANN & Empirical approach

Hydrologic parameters such as rainfall, runoff etc vary both in space and time. For most of the hydrological models, one of the main elements involved is rainfall-runoff process. Determination of the amount of average rainfall or runoff that a given storm event will produce on each subbasin is of prime importance as it is major cause of error in the applied hydrology. To take care of the spatial variability, the watershed is divided into various sub-basins with hydro meteorologically homogeneous characteristics like rainfall pattern to arrive at representative spatial runoff. Larger the number of sub-basins, better is the accuracy. Using modern tools of geospatial technology(SRS,GIS) as well as Artificial Neural Networks (ANN), it is possible to achieve the objectives of representative as well as more accurate spatial rainfall and concurrent runoff. This paper discusses Digital Rainfall Modellingan innovative tool for estimation of spatial rainfall for the storm event with the objective of more accurate assessment of the spatial rainfall distribution. For spatial runoff estimation, hydrologic models such as AGricultural Non Point Source [AGNPS] model, HEC-1, HEC GEOHMS, ILWIS, SWAT, as well as Arc-CN Runoff tools of ESRI at Arc GIS are reported. Estimation of spatially varied runoff depths using Arc-CN Runoff tools -Digital Runoff modeling, is discussed. Land use and soil map of the catchment are used as inputs. The digital runoff thus obtained for the event is compared with the same obtained using ANN and rational formula with respect to the observed runoff for the events occurred in Panshet reservoir catchment within Krishna river basin in India.