Abstract:
curve number method. The study was carried out in GIS environment using
remote sensing data. Also the curve number method was validated for selected
storm events in the study area. The analysis was done for the year 2004 to 2007,
2018 and 2019 upto June. The land use map was digitized from Google earth of
year 2006 and 2018. ArcGIS 10.2 was used for the analysis. About 28.5% of the
total area belongs to high runoff potential class, 33.7% have medium runoff
potential and 37.7% of the area has low runoff potential.
The runoff percentage from the annual rainfall varied from 16% to 23% for
the study period. The runoff percentage in 2007 and 2018 were almost similar but
the rainfall depths of both years were 3971.8 mm and 2919.8 mm respectively.
The rainfall amount in the study area is showing a decreasing trend and runoff is
showing increasing trend. Seasonal analysis showed that maximum rainfall depth
was observed in south west monsoon and thereby runoff yield.
The runoff
percentage was lower in the pre monsoon season as the major part of the rainfall
will infiltrates into the soil. Also the runoff depth was highly influenced by
antecedent moisture condition and potential maximum retention capacity. The
curve number values for normal conditions were 57.77 and 58.95 for the year
2006 and 2018 respectively.
The curve number value tends to increase as
antecedent moisture condition increases. The simulated runoff was compared
with observed runoff for selected storm events in the study area. The correlation
coefficient was found to be 0.928. The integration of remote sensing and GIS
along with NRCS curve number method was found to be a powerful tool in
estimating runoff.
