Metalearning approach coupled with CMIP6 multi-GCM for future monthly streamflow forecasting
Spatial and temporal variability of streamflow due to climate change affects hydrological processes and irrigation demands at a basin scale. This study investigated the impacts of climate change on the Kurau River in Malaysia using metalearning, an ensemble machine learning technique using support v...
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| Main Authors: | , |
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| Format: | Article |
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American Society of Civil Engineers (ASCE)
2022
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| Online Access: | http://eprints.utm.my/103255/ http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0002176 |
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| Summary: | Spatial and temporal variability of streamflow due to climate change affects hydrological processes and irrigation demands at a basin scale. This study investigated the impacts of climate change on the Kurau River in Malaysia using metalearning, an ensemble machine learning technique using support vector regression (SVR) and random forest (RF) coupled with the Coupled Model Intercomparison Project CMIP6 multi-Global Climate Model (GCM). Five global climate models and three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were used. The climate sequences generated by the delta change factor method were applied as input to the metalearning model to predict the streamflow changes in the Kurau River from 2021 to 2080. The model fitted reasonably well, with Kling-Gupta efficiency (KGE), Nash-Sutcliffe efficiency (NSE), percent bias (PBias), and RMS Error (RMSE) of 0.79, 0.83, 2.52, and 4.51, respectively, for the training period (1976-1995) and 0.72, 0.72, 5.85, and 6.90, respectively, for the testing period (1995-2005). Future projections of multi-GCM over the 2021-2080 period under three SSPs predicted an increase in rainfall for all months except April-June during the dry period (off-season), with a higher increase occurring during the wet period (main season). Temperature projections indicated an increase in maximum and minimum temperatures under all SSP scenarios, with a higher increase of approximately 2.0°C under SSP5-8.5 predicted during the 2051-2080 period relative to the baseline period of 1976-2005. The model predicted that the seasonal changes in streamflow of two planting periods range between -7.5% and 7.1% and between 1.2% and 5.9% during the off-season and the main season, respectively. A significant streamflow decrease was predicted in April and May for all SSP scenarios due to high temperatures during the off-season, with SSP5-8.5 being the worst. The impact assessment of climate variabilities on the availability of water resources is vital to identify appropriate adaptation strategies to deal with an expected increase in irrigation demand due to global warming in the future. The predicted future streamflow under the potential climate change impacts is crucial for the Bukit Merah Reservoir to establish suitable operational policies for irrigation release. |
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