Estimation of Uncertainty Contribution of Multiple Sources of GCMs in Hydrological Prediction.
- Indian Institute of Technology Bombay, Department of Civil Engineering, Mumbai, India (firstname.lastname@example.org)
The estimation of the impacts of climate change on hydrology at the local level comprises various sources of uncertainty. Especially, global climate models (GCMs) are found to be one of the major sources of uncertainty at the local level and it is important to identify for robust water resource planning and management. Therefore, this study demonstrates the separate and multi-model ensemble GCMs uncertainty for the surface runoff projections for near, mid, and far future under representative concentration pathway (RCP) 4.5 (present condition) and RCP 8.5 (worst condition) at medium level river sub-basins scale in the Western Ghats region of India. The results indicate that considered GCMs are not appropriate for use to prediction of peak surface runoff in the wet season. In addition, uncertainty from ensemble GCMs is closer to actual data than individual GCM because of closely associated with ensemble rainfall data which is maximum influencing the peak surface runoff for the near mid, and far future. Furthermore, findings also suggest that the selection of appropriate GCMs for the study of peak flow analysis at the local level is important for the projection of future surface runoff. Therefore, it is also important to make attention to rainfall data while projecting surface runoff for future time periods in the humid tropic regions.
How to cite: Sinha, R. K. and Eldho, T. I.: Estimation of Uncertainty Contribution of Multiple Sources of GCMs in Hydrological Prediction., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12896, https://doi.org/10.5194/egusphere-egu21-12896, 2021.