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.
Get full access to this article
View all available purchase options and get full access to this article.
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).
References
Almazroui, M., M. N. Islam, S. Saeed, F. Saeed, and M. Ismail. 2020. “Future changes in climate over the Arabian Peninsula based on CMIP6 multimodel simulations.” Earth Syst. Environ. 4 (4): 611–630. https://doi.org/10.1007/s41748-020-00183-5.
Bi, E. G., P. Gachon, M. Vrac, and F. Monette. 2017. “Which downscaled rainfall data for climate change impact studies in urban areas? Review of current approaches and trends.” Theor. Appl. Climatol. 127 (3–4): 685–699. https://doi.org/10.1007/s00704-015-1656-y.
Chen, P., Z. Jiang, and D. Peng. 2017. “Multi-model statistical downscaling of spring precipitation simulation and projection in central Asia based on canonical correlation analysis.” ACTA Meteorol. Sin. 75 (2): 236–247. https://doi.org/10.11676/qxxb2017.017.
Chen, S.-T., P.-S. Yu, and Y.-H. Tang. 2010. “Statistical downscaling of daily precipitation using support vector machines and multivariate analysis.” J. Hydrol. 385 (1–4): 13–22. https://doi.org/10.1016/j.jhydrol.2010.01.021.
Cheng, A., Q. Feng, J. Zhang, Z. Li, and G. Wang. 2015. “A review of climate change scenario for impacts process study.” Sci. Geog. Sin. 35 (1): 84–90. https://doi.org/10.13249/.cnki.sgs.2015.01.84.
Dibike, Y. B., and P. Coulibaly. 2006. “Temporal neural networks for downscaling climate variability and extremes.” Neural Netw. 19 (2): 135–144. https://doi.org/10.1016/j.neunet.2006.01.003.
ECMWF (European Meteorological Center). 2019. “ERA-Interim.” Accessed April 10, 2020. https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim.
Gebrechorkos, S. H., S. Huelsmann, and C. Bernhofer. 2019. “Statistically downscaled climate dataset for East Africa.” Sci. Data 6 (1): 1–8. https://doi.org/10.1038/s41597-019-0038-1.
Gulacha, M. M., and D. M. M. Mulungu. 2017. “Generation of climate change scenarios for precipitation and temperature at local scales using SDSM in Wami-Ruvu River Basin Tanzania.” Phys. Chem. Earth 100 (10): 62–72. https://doi.org/10.1016/j.pce.2016.10.003.
Guo, J., H. Chen, C.-Y. Xu, S. Guo, and J. Guo. 2012. “Prediction of variability of precipitation in the Yangtze River Basin under the climate change conditions based on automated statistical downscaling.” Stochastic Environ. Res. Risk Assess. 26 (2): 157–176. https://doi.org/10.1007/s00477-011-0464-x.
Hallegatte, S., C. Green, R. J. Nicholls, and J. Corfee-Morlot. 2013. “Future flood losses in major coastal cities.” Nat. Clim. Change 3 (9): 802–806. https://doi.org/10.1038/nclimate1979.
Han, J.-C., G. Huang, Y. Huang, H. Zhang, Z. Li, and Q. Chen. 2015. “Chance-constrained overland flow modeling for improving conceptual distributed hydrologic simulations based on scaling representation of sub-daily rainfall variability.” Sci. Total Environ. 524 (Feb): 8–22. https://doi.org/10.1016/j.scitotenv.2015.02.107.
Han, J.-C., Y. Zhou, Y. Huang, X. Wu, Z. Liu, and Y. Wang. 2020. “Risk assessment through multivariate analysis on the magnitude and occurrence date of daily storm events in the Shenzhen bay area.” Stochastic Environ. Res. Risk Assess. 34 (5): 669–689. https://doi.org/10.1007/s00477-020-01793-1.
Hassan, Z., S. Shamsudin, and S. Harun. 2014. “Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature.” Theor. Appl. Climatol. 116 (1–2): 243–257. https://doi.org/10.1007/s00704-013-0951-8.
Jaramillo, P., and A. Nazemi. 2018. “Assessing urban water security under changing climate: Challenges and ways forward.” Sustainable Cities Soc. 41 (14): 907–918. https://doi.org/10.1016/j.scs.2017.04.005.
Jiang, Z., C. Wang, Y. Liu, Z. Feng, C. Ji, and H. Zhang. 2019. “Study on the raw water allocation and optimization in Shenzhen City, China.” Water 11 (7): 1426. https://doi.org/10.3390/w11071426.
Lai, Y. C., and D. A. Dzombak. 2021. “Use of integrated global climate model simulations and statistical time series forecasting to project regional temperature and precipitation.” J. Appl. Meteorol. Climatol. 60 (5): 695–710. https://doi.org/10.1175/JAMC-D-20-0204.1.
Lancia, M., C. Zheng, X. He, D. N. Lerner, and C. Andrews. 2020. “Groundwater complexity in urban catchments: Shenzhen, southern China.” Ground Water 58 (3): 470–481. https://doi.org/10.1111/gwat.12935.
Lei, H., J. Ma, H. Li, J. Wang, D. Shao, and H. Zhao. 2020. “Bias correction of climate model precipitation in the upper Heihe River Basin based on quantile mapping method.” Plateau Meteorol. 39 (2): 266–279. https://doi.org/10.7522/j.issn.1000-0534.2019.00104.
Misra, S., S. Sarkar, and P. Mitra. 2018. “Statistical downscaling of precipitation using long short-term memory recurrent neural networks.” Theor. Appl. Climatol. 134 (3–4): 1179–1196. https://doi.org/10.1007/s00704-017-2307-2.
Ning, L., M. E. Mann, R. Crane, T. Wagener, R. G. Najjar, and R. Singh. 2012. “Probabilistic projections of anthropogenic climate change impacts on precipitation for the Mid-Atlantic Region of the United States.” J. Clim. 25 (15): 5273–5291. https://doi.org/10.1175/JCLI-D-11-00565.1.
Pham, H. X., A. Y. Shamseldin, and B. W. Melville. 2021. “Projection of future extreme precipitation: A robust assessment of downscaled daily precipitation.” Nat. Hazard. 107 (1): 311–329. https://doi.org/10.1007/s11069-021-04584-1.
Qian, S. N., J. Chen, X. Q. Li, C. Y. Xu, S. L. Guo, H. Chen, and X. S. Wu. 2020. “Seasonal rainfall forecasting for the Yangtze River Basin using statistical and dynamical models.” Int. J. Climatol. 40 (1): 361–377. https://doi.org/10.1002/joc.6216.
Raghavendra, S. N., and P. C. Deka. 2014. “Support vector machine applications in the field of hydrology: A review.” Appl. Soft Comput. 19 (Feb): 372–386. https://doi.org/10.1016/j.asoc.2014.02.002.
Sachindra, D. A., K. Ahmed, M. M. Rashid, S. Shahid, and B. J. C. Perera. 2018. “Statistical downscaling of precipitation using machine learning techniques.” Atmos. Res. 212 (May): 240–258. https://doi.org/10.1016/j.atmosres.2018.05.022.
Shamshirband, S., S. Hashemi, H. Salimi, S. Samadianfard, E. Asadi, S. Shadkani, K. Kargar, A. Mosavi, N. Nabipour, and K.-W. Chau. 2020. “Predicting standardized streamflow index for hydrological drought using machine learning models.” Eng. Appl. Comput. Fluid Mech. 14 (1): 339–350. https://doi.org/10.1080/19942060.2020.1715844.
Shiru, M. S., E. S. Chung, S. Shahid, and X. J. Wang. 2022. “Comparison of precipitation projections of CMIP5 and CMIP6 global climate models over Yulin, China.” Theor. Appl. Climatol. 147 (1–2): 535–548. https://doi.org/10.1007/s00704-021-03823-6.
Tabari, H., S. M. Paz, D. Buekenhout, and P. Willems. 2021. “Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought.” Hydrol. Earth Syst. Sci. 25 (6): 3493–3517. https://doi.org/10.5194/hess-25-3493-2021.
Tong, Y., X. Gao, Z. Han, and Y. Xu. 2017. “Bias correction of daily precipitation simulated by RegCM4 model over China.” Chin. J. Atmos. Sci. 41 (6): 1156–1166. https://doi.org/10.3878/j.issn.1006-9895.1704.16275.
van Vuuren, D. P., K. Riahi, K. Calvin, R. Dellink, J. Emmerling, S. Fujimori, K. C. Samir, E. Kriegler, and B. O’Neill. 2017. “The shared socio-economic pathways: Trajectories for human development and global environmental change.” Global Environ. Change 42 (10): 148–152. https://doi.org/10.1016/j.gloenvcha.2016.10.009.
Wang, Y., H. Li, H. Wang, B. Sun, and H. Chen. 2021. “Evaluation of CMIP6 model simulations of extreme precipitation in China and comparison with CMIP5.” Acta Meteorol. Sin. 79 (3): 369–386. https://doi.org/10.11676/qxxb2021.031.
Wilby, R. L., L. E. Hay, and G. H. Leavesley. 1999. “A comparison of downscaled and raw GCM output: Implications for climate change scenarios in the San Juan River Basin, Colorado.” J. Hydrol. 225 (1–2): 67–91. https://doi.org/10.1016/S0022-1694(99)00136-5.
Xiao, H., G. Lu, Z. Wu, and Z. Liu. 2013. “Flood response to climate change in the Pearl River Basin for the next three decades.” J. Hydraul. Eng. 44 (12): 1409–1419. https://doi.org/10.13243/j.cnki.slxb.2013.12.001.
Xiong, L., L. Yan, L. Li, C. Jiang, and T. Du. 2017. “Advances in analysis of impacts of changing environments on extreme urban rainfall and drainage infrastructure.” Adv. Water Sci. 28 (6): 930–942. https://doi.org/10.14042/j.cnki.32.1309.2017.06.014.
Xu, C. Y. 1999. “From GCMs to river flow: A review of downscaling methods and hydrologic modelling approaches.” Prog. Phys. Geogr.: Earth Environ. 23 (2): 229–249. https://doi.org/10.1177/030913339902300204.
Zhou, T., L. Zou, and X. Chen. 2019. “Commentary on the Coupled Model Intercomparison Project Phase 6 (CMIP6).” Progressus Inquisitiones de Mutatione Climatis 15 (5): 445–456. https://doi.org/10.12006/j.issn.1673-1719.2019.193.
Zhu, H., Z. Jiang, J. Li, W. Li, C. Sun, and L. Li. 2020. “Does CMIP6 inspire more confidence in simulating climate extremes over China?” Adv. Atmos. Sci. 37 (10): 1119–1132. https://doi.org/10.1007/s00376-020-9289-1.
Information & Authors
Information
Published In
Copyright
© 2022 American Society of Civil Engineers.
History
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
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Guyuan Li, Xiaofeng Wu, Jing-Cheng Han, Bing Li, Yuefei Huang, Yongqiang Wang, Flood risk assessment by using an interpretative structural modeling based Bayesian network approach (ISM-BN): An urban-level analysis of Shenzhen, China, Journal of Environmental Management, 10.1016/j.jenvman.2022.117040, 329, (117040), (2023).