The prolonged mei-yu/baiu system with anomalous precipitation in the year 2020 has swollen many rivers and lakes, caused flash flooding, urban flooding and landslides, and consistently wreaked havoc across large swathes of China, particularly in the Yangtze River basin. Significant precipitation and flooding anomalies have already been seen in magnitude and extension so far this year, which have been exerting much higher pressure on emergency responses in flood control and mitigation than in other years, even though a rainy season with multiple ongoing serious flood events in different provinces is not that uncommon in China. Instead of delving into the causes of the uniqueness of this year’s extreme precipitation-flooding situation, which certainly warrants in-depth exploration, in this article we provide a short view toward a more general hydrometeorological solution to this annual nationwide problem. A "glocal" (global to local) hydrometeorological solution for floods (GHS-F) is considered to be critical for better preparedness, mitigation, and management of different types of significant precipitation-caused flooding, which happen extensively almost every year in many countries such as China, India and the United States. Such a GHS-F model is necessary from both scientific and operational perspectives, with the strength in providing spatially consistent flood definitions and spatially distributed flood risk classification considering the heterogeneity in vulnerability and resilience across the entire domain. Priorities in the development of such a GHS-F are suggested, emphasizing the user’s requirements and needs according to practical experiences with various flood response agencies.
Alfieri, L., P. Burek, E. Dutra, B. Krzeminski, D. Muraro, J. Thielen, and F. Pappenberger, 2013: GloFAS-global ensemble streamflow forecasting and flood early warning. Hydrology and Earth System Sciences, 17, 1161–1175, https://doi.org/10.5194/hess-17-1161-2013.
Allan, R. P., and B. J. Soden, 2008: Atmospheric warming and the amplification of precipitation extremes. Science, 321, 1481–1484, https://doi.org/10.1126/science.1160787.
Beven, K. J., and H. L. Cloke, 2012: Comment on “Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth’s terrestrial water” by Eric F. Wood et al. Water Resour. Res., 48, W01801, https://doi.org/10.1029/2011WR010982.
Bierkens, M. F. P., and Coauthors, 2015: Hyper-resolution global hydrological modelling: What is next? Hydrological Processes, 29, 310–320, https://doi.org/10.1002/hyp.10391.
Chen, X. L., T. J. Zhou, P. L. Wu, Z. Guo, and M. H. Wang, 2020: Emergent constraints on future projections of the western North Pacific Subtropical High. Nature Communications, 11, 2802, https://doi.org/10.1038/s41467-020-16631-9.
Gochis, D. J., W. Yu, and D. N. Yates, 2013: The NCAR WRF-hydro technical description and user’s guide, Version 1.0. NCAR Technical Document, 120 pp.
Trenberth, K. E., 2011: Changes in precipitation with climate change. Climate Research, 47, 123–138, https://doi.org/10.3354/cr00953.
Wood, E. F., and Coauthors, 2011: Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth’s terrestrial water. Water Resour. Res., 47, W05301, https://doi.org/10.1029/2010WR010090.
Wu, H., J. S. Kimball, N. Mantua, and J. Stanford, 2011: Automated upscaling of river networks for macroscale hydrological modeling. Water Resour. Res., 47, W03517, https://doi.org/10.1029/2009WR008871.
Wu, H., J. S. Kimball, H. Y. Li, M. Y. Huang, L. R. Leung, and R. F. Adler, 2012: A new global river network database for macroscale hydrologic modeling. Water Resour. Res., 48, W09701, https://doi.org/10.1029/2012WR012313.
Wu, H., R. F. Adler, Y. D. Tian, G. J. Huffman, H. Y. Li, and J. J. Wang, 2014: Real-time global flood estimation using satellite-based precipitation and a coupled land surface and routing model. Water Resour. Res., 50, 2693–2717, https://doi.org/10.1002/2013WR014710.
Wu, H., R. F. Adler, Y. D. Tian, G. J. Gu, and G. J. Huffman, 2017: Evaluation of Quantitative Precipitation Estimations through Hydrological Modeling in IFloodS River Basins. Journal of Hydrometeorology, 18, 529–553, https://doi.org/10.1175/JHM-D-15-0149.1.
Wu, H., J. S. Kimball, N. J. Zhou, L. Alfieri, L. F. Luo, J. Y. Du, and Z. J. Huang, 2019: Evaluation of real-time global flood modeling with satellite surface inundation observations from SMAP. Remote Sens. Environ., 233, 111360, https://doi.org/10.1016/j.rse.2019.111360.
Yan, Y., and Coauthors, 2020: Climatology and interannual variability of floods during the TRMM era (1998–2013). J. Climate, 33, 3289–3305, https://doi.org/10.1175/JCLI-D-19-0415.1.
This study was supported by the National Key R&D Program of China (Grant No. 2017YFA0604300) and the National Natural Science Foundation of China (Grant Nos. 41861144014, 41775106 and U1811464), as well as partially by the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No. 2017ZT07X355) and the project of the Chinese Ministry of Emergency Management on “Catastrophe Evaluation Modeling Study”.
About this article
Cite this article
Wu, H., Li, X., Schumann, G.JP. et al. From China’s Heavy Precipitation in 2020 to a “Glocal” Hydrometeorological Solution for Flood Risk Prediction. Adv. Atmos. Sci. 38, 1–7 (2021). https://doi.org/10.1007/s00376-020-0260-y
- flood risk
- global to local
- hydrological model
- extreme precipitation