Faculty Publications

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Author: search.filters.author.Mahesha, M.Subject: search.filters.subject.precipitation assessment

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    Evaluation of satellite precipitation products in simulating streamflow in a humid tropical catchment of india using a semi-distributed hydrological model
    (MDPI, 2020) Sharannya, T.M.; Al-Ansari, N.; Deb Barma, S.; Mahesha, M.
    Precipitation obtained from rain gauges is an essential input for hydrological modelling. It is often sparse in highly topographically varying terrain, exhibiting a certain amount of uncertainty in hydrological modelling. Hence, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. In this study, an attempt was made to evaluate the Tropical Rainfall Measuring Mission (TRMM) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), employing a semi-distributed hydrological model, i.e., Soil and Water Assessment Tool (SWAT), for simulating streamflow and validating them against the flows generated by the India Meteorological Department (IMD) rainfall dataset in the Gurupura river catchment of India. Distinct testing scenarios for simulating streamflow were made to check the suitability of these satellite precipitation data. The TRMM was able to better estimate rainfall than CHIRPS after performing categorical and continuous statistical results with respect to IMD rainfall data. While comparing the performance of model simulations, the IMD rainfall-driven streamflow emerged as the best followed by the TRMM, CHIRPS-0.05, and CHIRPS-0.25. The coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) were in the range 0.63 to 0.86, 0.62 to 0.86, and ?14.98 to 0.87, respectively. Further, an attempt was made to examine the spatial distribution of key hydrological signature, i.e., flow duration curve (FDC) in the 30–95 percentile range of non-exceedance probability. It was observed that TRMM underestimated the flow for agricultural water availability corresponding to 30 percent, even though it showed a good performance compared to the other satellite rainfall-driven model outputs. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
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    Regional Trends and Spatiotemporal Analysis of Rainfall and Groundwater in the West Coast Basins of India
    (American Society of Civil Engineers (ASCE), 2022) Krishnan, C.; Mahesha, M.
    The present study investigates the spatiotemporal variabilities of long-term (1950-2016) rainfall and regional groundwater levels for annual and seasonal periods over the west coast of India. The study area is a narrow strip of land between Western Ghats (mountainous terrain) and the Arabian Sea, extending over 1,500 km from south to north. The Mann Kendall (MK) and Sen's slope estimator established the long-term trend and magnitude of rainfall and groundwater. The nature of trends in the time series of hydroclimatic variables was identified through singular spectrum analysis (SSA). The SSA extracted nonlinear trends along with the shape for both increasing and decreasing trends. Annual and southwest monsoon rainfall exhibited prominent decreasing trends. The percentage departure analysis of rainfall revealed that earlier decades (1950-1980) were the wettest, followed by the drier decades (1980-2016) for Periyar, Varrar, and Netravati and vice versa for Vasishti and Bhatsol. The wavelet spectra for rainfall indicated short- and long-term modulations. The long-term groundwater level trends of 725 wells on the entire west coast showed a significant decline in 13% of wells, and 6% of wells indicated increasing trends. The Monte Carlo-based numerical investigations on the modified MK (mMK) test power indicated the influence of parent distributions on trend detection. The field significance of trends at a 5% significance level was examined using the bootstrap test. The precipitation data were then compared with groundwater level variation at each site, and correlations were established. The declining southwest monsoon rains and their uneven spatial distribution could be attributed to a subsequent decline in the region's postmonsoon groundwater levels. © 2022 American Society of Civil Engineers.