In the scenario of climate change, it is very important to forecast the future values of watershed constituents for the better management of water resources. For that, several general circulation models (GCM) were made in many countries. In this study, BNU-ESM, CCC-ma, CNRM, MPI-ESM-LR, MPI-ESM-MR climate models inputs were used to find surface runoff, actual evapotranspiration (AET), potential evapotranspiration (PET) and sediment yield using soil and water assessment tool (SWAT) in Middle Tapi sub-basin, India. Efforts have been made in this study to evaluate which model’s inputs are reliable to find watershed constituents. The performance of all the climate models was evaluated using three indices, i.e. coefficient of determination (R2) Nash–Sutcliffe model efficiency coefficient (NSE) and per cent bias (PBIAS), by comparing the results with base model developed using IMD data. The results show that all the models are better predicting the AET based on their PBIAS values and the best PBIAS value (−3.84) was given by the CNRM model for AET among all the models. The precipitation, surface runoff, sediment yield, lateral flow, total water yield and PET values were underestimated by all climate models. Deep aquifer recharge, total aquifer recharge, percolation out of soil were overestimated by BNU-ESM, CCC-ma, CNRM, MPI-ESM-LR models and underestimated by MPI-ESM-MR model. The present study also shows the impact of climate change on the hydrological process of watershed of Middle Tapi sub-basin in the near future (2011–2040), mid-future (2041–2070) and far future (2071–2100) using the representative concentration pathways (RCP) of 4.5 and 8.5 scenario.
Comparison of Different Climate Models Projections for Watershed Using Soil and Water Assessment Tool: A Case Study of Middle Tapi Sub-basin
Lecture Notes in Civil Engineering
International Conference on Hydraulics, Water Resources and Coastal Engineering ; 2021 December 23, 2021 - December 25, 2021
2023-05-01
22 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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