Faculty Publications
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Item Streamflow response to land use-land cover change over the Nethravathi River Basin, India(American Society of Civil Engineers (ASCE), 2015) Babar, S.; Ramesh, H.Land use-land cover change (LULC) has considerable impacts on hydrologic response at the watershed level. Quantitative assessment of LULC impacts on runoff generations is vital for water resources development. The soil and water assessment tool (SWAT) model was used to study the effect of LULC change on streamflows. In addition to this, the present study proposed a newly developed flow-routing model called runoff coefficient routing model (RCRM). This new model is simple and requires limited data, such as precipitation, LULC and streamflows as compared to other models, which require meteorological and many more input data. The Nethravathi River basin was selected for testing the RCRM model with the SWAT model to study land use-land cover change on streamflows. The SWAT model and RCRM model have been calibrated for 2001-2005 and validated for 2006-2009 daily data. Results have shown that the simulated streams are well correlated with observed streamflows with a coefficient of correlation (R2) equal to 0.82 in calibration and 0.68 in validation period. Whereas, the RCRM model results have shown R2 of 0.81 and 0.66 in the calibration and validation period. Finally, the SWAT and RCRM results were compared. It is observed that the results of the RCRM model have shown a good agreement with SWAT model results of R2 equal to 0.99 and 0.98, respectively, in the calibration and validation period. The sensitivity analysis was also carried out based on Latin hypercube one factor-at-a-time (LH-OAT) method using the SWAT model and found 11 sensitive parameters out of 28 parameters. Model performance was carried out using the Nash-Sutcliffe model efficiency coefficient (NSE) and found 0.81 for calibration and 0.62 for the validation period in the SWAT model. RCRM has NSE of 0.79 and 0.63. The response of the streamflows for the year 2013 was simulated from the calibrated model. The results showed that the observed streamflows have shown good correlation with simulated streamflows with R2 values of 0.86 and NSE of 0.81. From the results, it is concluded that the runoff shows early response in the year 2013 compared to the year 2003. This is mainly due to changes in LULC, which shows the conversion of forest to agricultural area and increase in built-up area from 2003 to 2013. The effect of LULC change on the hydrological model parameters were calculated and observed a decrease in evapotranspiration (ET) of about 4.5%, an increase in runoff of about 0.9%, and an increase in groundwater of about 1.12%. In conclusion, the proposed RCRM in the present study simulates streamflows at par with the SWAT model with only few input data. Hence, the newly developed RCRM model would be used to study streamflows responses to LULC changes. © 2015 American Society of Civil Engineers.Item Modelling stream flow and soil erosion response considering varied land practices in a cascading river basin(Academic Press, 2020) Venkatesh, K.; Ramesh, H.; Das, P.[No abstract available]Item Monitoring water level fluctuations of reservoirs in the krishna river basin using sentinel-3 and icesat-2 altimetry data(Institute of Electrical and Electronics Engineers Inc., 2024) Nalluri, A.; Ramesh, H.; Dhote, P.R.The traditional approach to water level monitoring, using sensor devices to automatically measure levels in tanks or reservoirs, is costly and requires regular maintenance. This makes satellite altimetry crucial for reducing monitoring expenses and enhancing the observation of inland water bodies. This study employs Sentinel-3 and ICESat-2 sensors to examine six major reservoirs in the Krishna River basin in India, each intersected by at least two altimetry tracks. A two-way retrieval method for Sentinel analysis, including individual wet tropospheric correction and back-substitution, determines water levels. A novel hydro-flatness test removes outliers, increasing efficiency and saving time, though it is limited when fewer than ten high-distortion footprints are present. The 2018-2022 time series was effectively built for all reservoirs after assessing sensor performance against Central Water Commission data. The study successfully generates water level time series for the reservoirs, with correlation coefficients above 0.96 and Mean Absolute Percentage Errors below 1%. The Root Mean Square Error (RMSE) remains under 0.45m for all reservoirs except Ujjani, monitored by Sentinel-3A, which has an RMSE of 0.53 and a correlation of 0.96. These results can be applied to flood forecasting, reservoir operations, bathymetry retrieval, river modeling, and long-Term water level-discharge curves. © 2024 The Authors.