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Assessment of Kernel Regression Based Statistically Downscaled Rainfall Over Tapi River Basin, India
The downscaling of coarser scale general circulation model (GCM) variables, preferably the rainfall and temperature, to finer resolution followed by estimation of uncertainty/bias in the downscaled outcomes are essentially required prior to their application for hydrological assessments and decision-making. The kernel regression-based statistical downscaled (KRSD) rainfall data of five GCM models, from Coupled Model Intercomparison Project Phase-5 (CMIP-5), have been assessed to ascertain their ability to simulate the magnitude, variability, and extremes of Indian summer monsoon rainfall (ISMR) over the Tapi River basin (TRB) in India. The KRSD rainfall of GCMs is compared with gridded rainfall data obtained from India Meteorological Department-Pune (IMD) for the period 1951–2005 on annual and monsoon months (JJAS). The GCMs underestimate annual rainfall (PRCPTOT) on average by 21.7–28.4% over the TRB. Further, GCMs overestimate the number of rainy days (RD) and longest spell of consecutive rainy days (CWD) at an annual scale vis-à-vis IMD gridded rainfall dataset. The four-to seven-fold overestimation of CWD and RD is observed during September month compared to June, July, and August months. Also, one-day (Rx1D), five-day (Rx5D), and rainfall extremes above 95th percentile value (R95) from GCMs observed underestimation of the parameters ranging from 46.4 to 52.7%, 41.4 to 44.1%, and 45.0 to 48.8%, respectively. The present investigation concludes that the GCM fails to account for the seasonality of ISMR over TRB. Overall, KRSD rainfall underestimates the PRCPTOT and rainfall extremes while overestimating the RD and CWD over the TRB. Thus, rainfall intensities are significantly underestimated for the historical period over TRB.
Assessment of Kernel Regression Based Statistically Downscaled Rainfall Over Tapi River Basin, India
The downscaling of coarser scale general circulation model (GCM) variables, preferably the rainfall and temperature, to finer resolution followed by estimation of uncertainty/bias in the downscaled outcomes are essentially required prior to their application for hydrological assessments and decision-making. The kernel regression-based statistical downscaled (KRSD) rainfall data of five GCM models, from Coupled Model Intercomparison Project Phase-5 (CMIP-5), have been assessed to ascertain their ability to simulate the magnitude, variability, and extremes of Indian summer monsoon rainfall (ISMR) over the Tapi River basin (TRB) in India. The KRSD rainfall of GCMs is compared with gridded rainfall data obtained from India Meteorological Department-Pune (IMD) for the period 1951–2005 on annual and monsoon months (JJAS). The GCMs underestimate annual rainfall (PRCPTOT) on average by 21.7–28.4% over the TRB. Further, GCMs overestimate the number of rainy days (RD) and longest spell of consecutive rainy days (CWD) at an annual scale vis-à-vis IMD gridded rainfall dataset. The four-to seven-fold overestimation of CWD and RD is observed during September month compared to June, July, and August months. Also, one-day (Rx1D), five-day (Rx5D), and rainfall extremes above 95th percentile value (R95) from GCMs observed underestimation of the parameters ranging from 46.4 to 52.7%, 41.4 to 44.1%, and 45.0 to 48.8%, respectively. The present investigation concludes that the GCM fails to account for the seasonality of ISMR over TRB. Overall, KRSD rainfall underestimates the PRCPTOT and rainfall extremes while overestimating the RD and CWD over the TRB. Thus, rainfall intensities are significantly underestimated for the historical period over TRB.
Assessment of Kernel Regression Based Statistically Downscaled Rainfall Over Tapi River Basin, India
Lecture Notes in Civil Engineering
Timbadiya, P. V. (Herausgeber:in) / Singh, Vijay P. (Herausgeber:in) / Sharma, Priyank J. (Herausgeber:in) / Gehlot, Lalit Kumar (Autor:in) / Patel, P. L. (Autor:in) / Timbadiya, P. V. (Autor:in)
International Conference on Hydraulics, Water Resources and Coastal Engineering ; 2021
24.05.2023
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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