Citation
Abstract
The forecasting of future streamflow aids researchers and policymakers to understand how changes in climate affect hydrological systems. However, traditional computational approaches demand intensive data specifically for the basin, and it is costly. The shift towards more contemporary and data-driven approaches known as support vector regression (SVR) in hydrological modeling utilizing only the hydro-climate data from Coupled Model Intercomparison Project Phase 6 (CMIP6) provides rapid input–output data processing with accurate future projection. CMIP6 is an updated and improved Global Climate Models (GCMs) for the exploration of the specific impacts of changing streamflow patterns for improved water management in agricultural areas. The delta change factor method was used to generate climate sequences, fed into the SVR model to project streamflow from 2021 to 2080. The SVR model fitted reasonably well, demonstrated by several statistical indicators, including Kling-Gupta Efficiency (KGE), Nash–Sutcliffe Efficiency (NSE), Percent Bias (PBias), and Root Mean Squared Error (RMSE), with the training phase performance surpassing the testing phase. Future projections indicated increased rainfall during the dry season for most months, excluding April to June. The rise in precipitation was particularly pronounced during the wet season. Maximum and minimum temperature projections increased for all SSPs, with SSP5-8.5 predicted a substantial increase. The projection revealed that seasonal streamflow changes would range between – 19.1 to – 1.2 and – 7.5 to – 3.1 in the dry and wet seasons, respectively. A considerable streamflow reduction is anticipated for all SSPs in April and May due to increased temperatures, with the most pronounced impact in the SSP5-8.5. Assessing the effects of climate variations on water resource availability is crucial for identifying effective adaptation strategies to address the anticipated rise in irrigation demands due to global warming. The projected streamflow changes due to potential climate impacts are significant for Bukit Merah Reservoir, aiding the formulation of appropriate operational strategies for irrigation releases.
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Additional Metadata
Item Type: | Article |
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Divisions: | Universiti Putra Malaysia |
Publisher: | Springer Science and Business Media Deutschland GmbH |
Keywords: | Bukit Merah reservoir; Kurau river; Malaysia; Perak; West Malaysia; Data handling; Efficiency; Global warming; Irrigation; Mean square error; Regression analysis; Reservoir management; Reservoirs (water); Stream flow; Water management; Well testing; Climate scenarios; Coupled model intercomparison project; Coupled model intercomparison project phase 6; Future projections; Project phasis; Streamflow; Streamflow changes; Support vector regression; Support vector regression models; Support vector regressions; Climate change; Cmip; Data processing; Dry season; Hydrological modeling; Irrigation system; Resource availability; Streamflow; Support vector machine; Water management; Climate models |
Depositing User: | Mohamad Jefri Mohamed Fauzi |
Date Deposited: | 15 Oct 2024 07:46 |
Last Modified: | 15 Oct 2024 07:46 |
URI: | http://psasir.upm.edu.my/id/eprint/106168 |
Statistic Details: | View Download Statistic |
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