Downscaling of Precipitation for Climate Change Projections Using Multiple Machine Learning Techniques: Case Study of Shenzhen City, China
Publication: Journal of Water Resources Planning and Management
Volume 148, Issue 11
Abstract
To examine the characteristics of future precipitation under climate change is of great significance to urban water security. In this paper, multiple machine learning techniques, i.e., statistical downscaling model (SDSM), support vector machine (SVM), and multilayer perceptron (MLP), were used to downscale large-scale climatic variables simulated by the General Circulation Models (GCMs) to precipitation on a local scale. It was demonstrated in Shenzhen city, China, through multisite downscaling schemes based on projections from the Max Planck Institute Earth System Model (MPI-ESM1.2-HR), Meteorological Research Institute Earth System Model Version 2.0 (MRI-ESM2.0), and Beijing Climate Center Climate System Model (BCC-CSM2-MR). The obtained results showed that the downscaled precipitation would provide good monthly simulations against observations at 10 discrete stations. Regardless of superior performance of SVM and MLP over SDSM, the daily precipitation simulations should be further improved, and downscaling of heavy daily precipitations would be promoted by quantile mapping corrections. Due to the relatively poor simulation performance of BCC-CSM2-MR, the other two climate models were considered under the Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios) for ensemble precipitation projections for 2015–2100. Under the SSP1-2.6 scenario, the amounts of annual average precipitation for 10 stations were estimated to be higher relative to the historical period (2.7%–17%), and 9 out of 10 stations presented an increasing trend. However, downward trends also existed at three stations when it comes to scenarios SSP2-4.5 and SSP5-8.5. Moreover, a significantly positive trend was found to dominate the trend changes of annual extreme daily precipitation during 2015–2050, but the detected trends at stations were greatly dependent on the downscaling techniques and climate models. Besides, the increase in daily extreme precipitations for various return periods as well as statistically different precipitation characteristics for discrete stations would further shed light on urgent demands on urban resilient strategies for climate change adaptation.
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Data Availability Statement
All of the observed data, models, or codes that support the findings of this study are available from the corresponding author on reasonable request.
Acknowledgments
The authors are grateful to the editors and two anonymous reviewers for their constructive comments that have improved the overall presentation of this paper. This work was financially supported by the Major Basic Research Development Program of the Science and Technology, Qinghai Province (2021-SF-A6 and 2019-SF-146), National Natural Science Foundation of China (No. 51809007), Open Research Fund Program of State Key Laboratory of Hydroscience and Engineering (sklhse-2021-A-02), Water Conservancy Science and Technology Innovation Project of the Guangdong Province (2017-03), and Fundamental Research Funds for the Shenzhen University (2110822).
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Journal of Water Resources Planning and Management
Volume 148 • Issue 11 • November 2022
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© 2022 American Society of Civil Engineers.
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Received: Dec 15, 2021
Accepted: Jul 1, 2022
Published online: Sep 10, 2022
Published in print: Nov 1, 2022
Discussion open until: Feb 10, 2023
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