Runoff Estimation of KCAET Campus by Curve Number Methods Adopting Remote Sensing and GIS Teachniques
By: Anjana S R.
Contributor(s): Jinu A(Guide).
Material type:
BookPublisher: Tavanur Department of Soil and Water Conservation Engineering , college of Agricultural Engineering and Technology 2019Description: 82p.Subject(s): Department of Soil and Water Conservation EngineeringDDC classification: 631.3 Online resources: Click here to access online Dissertation note: MSc Abstract: This study mainly focused to estimate the runoff of KCAET Campus using the 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.
| Item type | Current location | Collection | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|
Theses
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KAU Central Library, Thrissur Theses | Reference Book | 631.3 ANJ/RU (Browse shelf) | Not For Loan | 174722 |
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MSc
This study mainly focused to estimate the runoff of KCAET Campus using the 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.
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