Faculty Publications

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Bias correction methods for hydrologic impact studies over India's Western Ghat basins
(American Society of Civil Engineers (ASCE) onlinejls@asce.org, 2018) Mudbhatkal, A.; Mahesha, M.
The regional climate models (RCMs) used in the analysis of the impact of climate variables on the hydrology of river basins needs appropriate preprocessing (bias correction) to represent and reproduce future climate with a fair degree of accuracy. The performance of bias corrections methods was assessed in this investigation on the basis of their ability to minimize error on climate variables and streamflow. This work compares the performance of five bias correction methods applied for precipitation and four methods for temperature in modeling the hydrology of the river catchments of theWestern Ghats of India. TheWestern Ghats are a mountainous forest range along the entire west coast of India that plays a major role in the distribution of Indian monsoon rains. Simulations were used to evaluate the performance of the bias correction methods. Using raw RCM, bias corrected precipitation and temperature time series, streamflows were estimated by the soil and water assessment tool (SWAT) hydrological model. The results indicated that the raw RCM-simulated precipitation was biased by 42% and the temperature was biased by 12% across the catchments investigated. Subsequently, a bias of 65% was found in the streamflow. The performance of the delta change correction method was consistently better for precipitation (with Nash-Sutcliffe efficiency, NSE > 0.75 for 5 catchments) and temperature (NSE = 1) compared with other methods. Good performance was observed between the observed and bias corrected streamflow (daily time scale) for the catchments Purna (NSE = 0.97), Ulhas (NSE = 0.64), Aghanashini (NSE = 0.82), Netravathi (NSE = 0.89), and Chaliyar (NSE = 0.90); low performance with an NSE of 0.3 was observed for the catchments Kajvi and Vamanapuram. The methods failed for Malaprabha and Tunga catchments. The results indicate that the delta change correction method performed best in analyzing the hydrological impact of climate variables on the windward side of Western Ghats of India. © 2017 American Society of Civil Engineers.
Regional climate trends and topographic influence over the Western Ghat catchments of India
(John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2018) Mudbhatkal, A.; Mahesha, M.
This study investigates the role of elevation stratification and climate change on the hydrology of Western Ghat catchments during the period from 1951 to 2013 using gridded data. The trend analysis of rainfall and temperature was conducted using the Mann–Kendall trend test, and the hydrological modelling of the rivers was conducted using the Soil and Water Assessment Tool (SWAT) model. To characterize the spatial distribution of rainfall and streamflow based on elevation stratification, contemporary rainfall zones were delineated and the response of each zone was evaluated. The results indicated that the maximum rainfall occurs at certain distance on the windward side from the crest of the Western Ghats. On the leeward side (eastern plateau), the rainfall is maximum at crest (Western Ghats) and decreases with distance. The rivers in the southern portion of the Western Ghats of India were highly vulnerable to changing climate followed by the central portion. The annual and monsoon rainfall in the southern river decreased at 0.43 and 0.30% decade?1 (1% significance level), respectively. The summer rainfall in the river of the central portion (Netravathi River) decreased at 0.44% decade?1. The annual air temperature of the southern river catchment (Vamanapuram) increased at the rate of 0.12 °C decade?1 (at 0.1% significance level), and the air temperature of the central rivers increased at the rate of 0.09, 0.08, and 0.07 °C (0.1% significance level), respectively. The streamflow response of the southern and central rivers was discernible as the monsoon flow decreased at 37% decade?1 (0.1% significance level) in the southern river and 10% decade?1 (5% significance level) in the central river. Interestingly, the pristine Aghanashini River demonstrated resilience to climate change with an increase in annual rainfall and streamflow at 115 mm decade?1 (5% significance level) and 0.71 Mm3 decade?1 (0.1% significance level), respectively. © 2017 Royal Meteorological Society
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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