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GIS-based SWMM model for simulating the catchment response to flood events
Author(s) -
Pawan Kumar,
B. R. Chahar,
C. T. Dhanya
Publication year - 2016
Publication title -
hydrology research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 48
eISSN - 1996-9694
pISSN - 0029-1277
DOI - 10.2166/nh.2016.260
Subject(s) - shuttle radar topography mission , environmental science , hydrology (agriculture) , streamflow , digital elevation model , storm water management model , land cover , flood myth , calibration , hydrological modelling , elevation (ballistics) , flood forecasting , meteorology , geographic information system , hydrograph , drainage basin , surface runoff , remote sensing , land use , climatology , stormwater , geology , geography , cartography , statistics , civil engineering , mathematics , engineering , ecology , archaeology , biology , geometry , geotechnical engineering
The Storm Water Management Model (SWMM) has been an effective tool for simulating floods in urban areas, but has been seldom applied for river systems. In this study, a geographic information systems-based SWMM model was developed to authenticate the model's viability as a streamflow simulator for modeling floods in the Brahmani river delta. The model was set up using a Shuttle Radar Topography Mission-Digital Elevation Model (SRTM-DEM), National Remote Sensing Centre Landuse/Land Cover (NRSC LU/LC), soil from National Bureau of Soil Survey (NBSS), Indian Meteorological Department (IMD) meteorological forcings, and tuned using India-Water Resource Information System (India-WRIS) streamflow data. The calibration and validation of the model was carried out on a monthly time-scale from 1980 to 2012, using a Monte Carlo based auto-calibration technique. In addition, a daily basis calibration-validation was also carried out. The Nash–Sutcliffe efficiency and Percent Bias values were found to lie between 0.616–0.899 and 0.09–14.1%, respectively. Moreover, the root mean square error-observations standard deviation ratio (RSR) values were almost close to zero indicating reasonably good model performance. Subsequently, the model reasonably predicted the maximum flow that should be regulated to prevent any possible inundation in the downstream areas. The developed model can thus be employed as an effective flood modeling tool.

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