Abstract
Climate change can increase the frequency of extreme weather events and thus have a profound effect on the water flow, leading to occurrences such as floods and other natural disasters. Therefore, streamflow prediction under climate change is critical for regional risk management. This study introduces the Soil and Water Assessment Tool (SWAT), the Short and Long-term Memory (LSTM), the Gated Cycle Unit (GRU) and coupled Empirical Mode Decomposition (EMD) with LSTM/GRU, to investigate the feasibility of runoff forecast in the Dagu River basin, Jiaozhou Bay. The EMD technique breaks down the initial signal into multiple intrinsic modal functions that aid in capturing distinct data characteristics. These functions, when coupled with the robust mapping and learning capability of LSTM/GRU models, facilitate the prediction of runoff for time series data. In the prediction performance evaluation of the five models above, EMD-LSTM has the best performance with an R2 of 0.74 and RMSE 14.5 lower than the other models. Based on five GCMs in CMIP6, under three discharge scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), the best-performed EMD-LSTM model is used to predict runoff changes of the study area under future climate change. The forecast results show significant increases in average annual runoff. Whether in mid or late of the century, the extreme streamflow will decrease from November to January and increase from February to May with the maximum amplitude of 41.96%, which implies the probability of spring floods. To some extents, this study proposes a method for improving the accuracy of runoff prediction and provides an early warning for possible regional flood disasters.
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The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
The research work is supported by the National Natural Science Foundation of China (Grant No. 52209137), China Postdoctoral Science Foundation (Grant No. 2022M7119035).
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Haitao Yang: Methodology, Writing and Investigation. Hao Sun: Visualization and Investigation. Chao Jia: Data curation, Project administration, Resources. Xiao Yang and Tian Yang revised this paper. All authors have read and approved the final manuscript.
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Yang, H., Sun, H., Jia, C. et al. Future Climatic Projections and Hydrological Responses with a Data Driven Method: A Regional Climate Model Perspective. Water Resour Manage 38, 1693–1710 (2024). https://doi.org/10.1007/s11269-024-03753-8
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DOI: https://doi.org/10.1007/s11269-024-03753-8