• Carson, M., A. Köhl, D. Stammer, A. B. A. Slangen, C. A. Katsman, R. S. W. van de Wal, J. Church, and N. White, 2016: Coastal sea level changes, observed and projected during the 20th and 21st century. Climatic Change, 134, 269281, doi:10.1007/s10584-015-1520-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, H.-P., J.-Q. Sun, and C. Xiao-Li, 2013a: Future changes of drought and flood events in China under a global warming scenario. Atmos. Ocean. Sci. Lett., 6, 813.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Q., C. Wu, G. Huang, and S. Wu, 2013b: Study on characteristics of short duration rainstorm in Guangzhou City (in Chinese). Water Res. Power, 31, 46.

    • Search Google Scholar
    • Export Citation
  • Cuo, L., T. K. Beyene, N. Voisin, F. Su, D. P. Lettenmaier, M. Alberti, and J. E. Richey, 2011: Effects of mid-twenty-first century climate and land cover change on the hydrology of the Puget Sound basin, Washington. Hydrol. Processes, 25, 17291753, doi:10.1002/hyp.7932.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elshamy, M. E., I. A. Seierstad, and A. Sorteberg, 2009: Impacts of climate change on Blue Nile flows using bias-corrected GCM scenarios. Hydrol. Earth Syst. Sci., 13, 551565, doi:10.5194/hess-13-551-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Favre, A.-C., S. El Adlouni, L. Perreault, N. Thiémonge, and B. Bobée, 2004: Multivariate hydrological frequency analysis using copulas. Water Resour. Res., 40, W01101, doi:10.1029/2003WR002456.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fischer, T., M. Gemmer, L. Liu, and B. Su, 2010: Trends in monthly temperature and precipitation extremes in the Zhujiang River basin, south China (1961–2007). Adv. Clim. Change Res., 1, 6370, doi:10.3724/SP.J.1248.2010.00063.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fischer, T., B. Su, Y. Luo, and T. Scholten, 2012: Probability distribution of precipitation extremes for weather index–based insurance in the Zhujiang River basin, south China. J. Hydrometeor., 13, 10231037, doi:10.1175/JHM-D-11-041.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gemmer, M., T. Fischer, T. Jiang, B. Su, and L. L. Liu, 2011: Trends in precipitation extremes in the Zhujiang River basin, south China. J. Climate, 24, 750761, doi:10.1175/2010JCLI3717.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harding, B. L., A. W. Wood, and J. R. Prairie, 2012: The implications of climate change scenario selection for future streamflow projection in the upper Colorado River basin. Hydrol. Earth Syst. Sci., 16, 39894007, doi:10.5194/hess-16-3989-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hay, L. E., R. L. Wilby, and G. H. Leavesley, 2000: A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States. J. Amer. Water Resour. Assoc., 36, 387397, doi:10.1111/j.1752-1688.2000.tb04276.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hosking, J. R. M., and J. R. Wallis, 2005: Regional Frequency Analysis: An Approach Based on L-Moments. Cambridge University Press, 244 pp.

  • IPCC, 2007: Summary for policymakers. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 1–18.

    • Search Google Scholar
    • Export Citation
  • IPCC, 2013: Summary for policymakers. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 3–29.

  • Kay, A. L., and D. A. Jones, 2012: Transient changes in flood frequency and timing in Britain under potential projections of climate change. Int. J. Climatol., 32, 489502, doi:10.1002/joc.2288.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kendall, M. G., 1975. Rank Correlation Methods. 4th ed. Charles Griffin, 272 pp.

  • Kendon, E. J., D. P. Rowell, R. G. Jones, and E. Buonomo, 2008: Robustness of future changes in local precipitation extremes. J. Climate, 21, 42804297, doi:10.1175/2008JCLI2082.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, J., Y. D. Chen, L. Zhang, Q. Zhang, and F. H. S. Chiew, 2016: Future changes in floods and water availability across China: Linkage with changing climate and uncertainties. J. Hydrometeor., 17, 12951314, doi:10.1175/JHM-D-15-0074.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X., J. L. Zhang, and Z. G. Gao, 2011: Discussion on semiempirical prediction method for sea level change of China. Mar. Sci. Bull., 30, 540543.

    • Search Google Scholar
    • Export Citation
  • Liu, L.-L., T. Jiang, J.-G. Xu, J. Q. Zhai, and Y. Luo, 2012: Responses of hydrological processes to climate change in the Zhujiang River basin in the 21st century. Adv. Climate Change Res., 3, 8491, doi:10.3724/SP.J.1248.2012.00084.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lutz, A. F., W. W. Immerzeel, A. Gobiet, F. Pellicciotti, and M. F. P. Bierkens, 2013: Comparison of climate change signals in CMIP3 and CMIP5 multi-model ensembles and implications for central Asian glaciers. Hydrol. Earth Syst. Sci., 17, 36613677, doi:10.5194/hess-17-3661-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, M., S. Song, L. Ren, S. Jiang, and J. Song, 2013: Multivariate drought characteristics using trivariate Gaussian and Student t copulas. Hydrol. Processes, 27, 11751190, doi:10.1002/hyp.8432.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mann, H. B., 1945: Non-parametric tests against trend. Econometrica, 13, 245259, doi:10.2307/1907187.

  • McIntosh, P. C., J. A. Church, E. R. Miles, K. Ridgway, and C. M. Spillman, 2015: Seasonal coastal sea level prediction using a dynamical model. Geophys. Res. Lett., 42, 67476753, doi:10.1002/2015GL065091.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., C. Covey, K. E. Taylor, T. Delworth, R. J. Stouffer, M. Latif, B. McAvaney, and J. B. Mitchell, 2007: The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc., 88, 13831394, doi:10.1175/BAMS-88-9-1383.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Min, S.-K., X. Zhang, F. Zwiers, H. Shiogama, Y.-S. Tung, and M. Wehner, 2013: Multimodel detection and attribution of extreme temperature changes. J. Climate, 26, 74307451, doi:10.1175/JCLI-D-12-00551.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mladjic, B., L. Sushama, M. N. Khaliq, R. Laprise, D. Caya, and R. Roy, 2011: Canadian RCM projected changes to extreme precipitation characteristics over Canada. J. Climate, 24, 25652584, doi:10.1175/2010JCLI3937.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nelsen, R. B., 2006: An Introduction to Copulas. 2nd ed. Springer, 272 pp.

  • O’Gorman, P. A., and T. Schneider, 2009: Scaling of precipitation extremes over a wide range of climates simulated with an idealized GCM. J. Climate, 22, 56765685, doi:10.1175/2009JCLI2701.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Panday, P. K., J. Thibeault, and K. E. Frey, 2015: Changing temperature and precipitation extremes in the Hindu Kush–Himalayan region: An analysis of CMIP3 and CMIP5 simulations and projections. Int. J. Climatol., 35, 30583077, doi:10.1002/joc.4192.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petrow, T., and B. Merz, 2009: Trends in flood magnitude, frequency and seasonality in Germany in the period 1951–2002. J. Hydrol., 371, 129141, doi:10.1016/j.jhydrol.2009.03.024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raff, D. A., T. Pruitt, and L. D. Brekke, 2009: A framework for assessing flood frequency based on climate projection information. Hydrol. Earth Syst. Sci., 13, 21192136, doi:10.5194/hess-13-2119-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rahmstorf, S., 2007: A semi-empirical approach to projecting future sea-level rise. Science, 315, 368370, doi:10.1126/science.1135456.

  • Sansom, J., and J. A. Renwick, 2007: Climate change scenarios for New Zealand rainfall. J. Appl. Meteor. Climatol., 46, 573590, doi:10.1175/JAM2491.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shiau, J.-T., S. Feng, and S. Nadarajah, 2007: Assessment of hydrological droughts for the Yellow River, China, using copulas. Hydrol. Processes, 21, 21572163, doi:10.1002/hyp.6400.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sklar, A., 1959: Fonctions de répartition à n dimensions et leurs marges. Publ. Inst. Stat. Univ. Paris, 8, 229231.

  • Slangen, A. B. A., J. A. Church, X. Zhang, and D. P. Monselesan, 2015: The sea level response to external forcings in historical simulations of CMIP5 climate models. J. Climate, 28, 85218539, doi:10.1175/JCLI-D-15-0376.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, A., P. Bates, J. Freer, and F. Wetterhall, 2014: Investigating the application of climate models in flood projection across the UK. Hydrol. Processes, 28, 28102823, doi:10.1002/hyp.9815.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, I., J. Syktus, C. McAlpine, and K. Wong, 2013: Squeezing information from regional climate change projections—Results from a synthesis of CMIP5 results for south-east Queensland, Australia. Climatic Change, 121, 609619, doi:10.1007/s10584-013-0956-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, doi:10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vermeer, M., and S. Rahmstorf, 2009: Global sea level linked to global temperature. Proc. Natl. Acad. Sci. USA, 106, 21 52721 532, doi:10.1073/pnas.0907765106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, C., G. Huang, and S. Wu, 2014a: Risk analysis of combinations of short duration rainstorm and tidal level in Guangzhou based on Copula function (in Chinese). J. Hydraul. Eng., 33, 3340.

    • Search Google Scholar
    • Export Citation
  • Wu, C., G. Huang, H. Yu, Z. Chen, and J. Ma, 2014b: Impact of climate change on reservoir flood control in the upstream area of the Beijiang River basin, south China. J. Hydrometeor., 15, 22032218, doi:10.1175/JHM-D-13-0181.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, C., G. Huang, and H. Yu, 2015: Prediction of extreme floods based on CMIP5 climate models: A case study in the Beijiang River basin, south China. Hydrol. Earth Syst. Sci., 19, 13851399, doi:10.5194/hess-19-1385-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, C., Z. Xian, and G. Huang, 2016: Meteorological drought in the Beijiang River basin, south China: Current observations and future projections. Stochastic Environ. Res. Risk Assess., 30, 18211834, doi:10.1007/s00477-015-1157-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wuebbles, D., and Coauthors, 2014: CMIP5 climate model analyses: Climate extremes in the United States. Bull. Amer. Meteor. Soc., 95, 571583, doi:10.1175/BAMS-D-12-00172.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, T., Q. Shao, Z.-C. Hao, X. Chen, Z. Zhang, C.-Y. Xu, and L. Sun, 2010: Regional frequency analysis and spatio-temporal pattern characterization of rainfall extremes in the Pearl River basin, China. J. Hydrol., 380, 386405, doi:10.1016/j.jhydrol.2009.11.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yao, Y., Y. Luo, J. Huang, and Z. Zhao, 2013: Comparison of monthly temperature extremes simulated by CMIP3 and CMIP5 models. J. Climate, 26, 76927707, doi:10.1175/JCLI-D-12-00560.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, D.-L., Y. Lin, P. Zhao, X. Yu, S. Wang, H. Kang, and Y. Ding, 2013: The Beijing extreme rainfall of 21 July 2012: “Right results” but for wrong reasons. Geophys. Res. Lett., 40, 14261431, doi:10.1002/grl.50304.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, H., K. Fraedrich, R. Blender, and X. Zhu, 2013: Precipitation extremes in CMIP5 simulations on different time scales. J. Hydrometeor., 14, 923928, doi:10.1175/JHM-D-12-0181.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, Y., X. Zou, L. Cao, and X. Xu, 2014: Changes in precipitation extremes over the Pearl River basin, southern China, during 1960–2012. Quat. Int., 333, 2639, doi:10.1016/j.quaint.2014.03.060.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Assessing the Impact of Climate Change on the Waterlogging Risk in Coastal Cities: A Case Study of Guangzhou, South China

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  • 1 School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China
  • | 2 School of Civil Engineering and Transportation, and State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, China
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Abstract

Climate warming is expected to occur with an increased magnitude of extreme precipitation and sea level rise, which leads to an increased probability of waterlogging in coastal cities. In this paper, a combined probability model is developed to evaluate the impact of climate change on waterlogging in Guangzhou by using eight climate models with four emissions scenarios [Special Report on Emissions Scenarios (SRES) scenario A1B and representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5, and RCP8.5]. The copula method was applied to derive the bivariate distributions of extreme rainfall and tidal level. The uncertainty in the projected future temperature, extreme rainfall, sea level, and the combined extreme rainfall and tidal level probability were discussed. The results show that although there is a large uncertainty driven by both climate models and emissions scenarios in the projection of climate change, most modeling results predict an increase in temperature and extreme precipitation in Guangzhou during the future period of 2020–50, relative to the historical period of 1970–2000. Moreover, greater increases are projected for higher emissions scenarios. The sea level is projected to increase in the range of 11.40–23.37 cm during the period 2020–50, consistent with climate warming. Both simultaneous probability and waterlogging probability are projected to show an upward trend in the future period 2020–50, with the largest and smallest increases in the RCP4.5 and RCP2.6 scenarios, respectively. The results of this paper provide a new scientific reference for waterlogging control in Guangzhou under climate change conditions.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Guoru Huang, huanggr@scut.edu.cn

Abstract

Climate warming is expected to occur with an increased magnitude of extreme precipitation and sea level rise, which leads to an increased probability of waterlogging in coastal cities. In this paper, a combined probability model is developed to evaluate the impact of climate change on waterlogging in Guangzhou by using eight climate models with four emissions scenarios [Special Report on Emissions Scenarios (SRES) scenario A1B and representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5, and RCP8.5]. The copula method was applied to derive the bivariate distributions of extreme rainfall and tidal level. The uncertainty in the projected future temperature, extreme rainfall, sea level, and the combined extreme rainfall and tidal level probability were discussed. The results show that although there is a large uncertainty driven by both climate models and emissions scenarios in the projection of climate change, most modeling results predict an increase in temperature and extreme precipitation in Guangzhou during the future period of 2020–50, relative to the historical period of 1970–2000. Moreover, greater increases are projected for higher emissions scenarios. The sea level is projected to increase in the range of 11.40–23.37 cm during the period 2020–50, consistent with climate warming. Both simultaneous probability and waterlogging probability are projected to show an upward trend in the future period 2020–50, with the largest and smallest increases in the RCP4.5 and RCP2.6 scenarios, respectively. The results of this paper provide a new scientific reference for waterlogging control in Guangzhou under climate change conditions.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Guoru Huang, huanggr@scut.edu.cn
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