Future Changes in Floods and Water Availability across China: Linkage with Changing Climate and Uncertainties

Jianfeng Li Department of Geography and Resource Management, The Chinese University of Hong Kong, and Department of Geography, Hong Kong Baptist University, Hong Kong, China, and CSIRO Land and Water, Canberra, Australian Capital Territory, Australia

Search for other papers by Jianfeng Li in
Current site
Google Scholar
PubMed
Close
,
Yongqin David Chen Department of Geography and Resource Management, and Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, China

Search for other papers by Yongqin David Chen in
Current site
Google Scholar
PubMed
Close
,
Lu Zhang CSIRO Land and Water, Canberra, Australian Capital Territory, Australia

Search for other papers by Lu Zhang in
Current site
Google Scholar
PubMed
Close
,
Qiang Zhang Department of Water Resources and Environment, and Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou, and School of Earth Sciences and Engineering, Suzhou University, Anhui, China

Search for other papers by Qiang Zhang in
Current site
Google Scholar
PubMed
Close
, and
Francis H. S. Chiew CSIRO Land and Water, Canberra, Australian Capital Territory, Australia

Search for other papers by Francis H. S. Chiew in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Future changes in floods and water availability across China under representative concentration pathway 2.6 (RCP2.6) and RCP8.5 are studied by analyzing discharge simulations from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) with the consideration of uncertainties among global climate models (GCMs) and hydrologic models. Floods and water availability derived from ISI-MIP simulations are compared against observations. The uncertainties among models are quantified by model agreement. Only model agreement >50% is considered to generate reliable projections of floods and water availability and their relationships with climate change. The results show five major points. First, ISI-MIP simulations have acceptable ability in modeling floods and water availability. The spatial patterns of changes in floods and water availability highly depend on the outputs of GCMs. Uncertainties from GCMs/hydrologic models predominate the uncertainties in the wet/dry areas in eastern/northwestern China. Second, the magnitudes of floods throughout China increase during 2070–99 under RCP8.5 relative to those with the same return periods during 1971–2000. The increase rates of larger floods are higher than those of the smaller ones. Third, water availability decreases/increases in southern/northern China under RCP8.5, but changes negligibly under RCP2.6. Fourth, more severe floods in the future are driven by more intense precipitation extremes over China. The negligible change in mean precipitation and the increase in actual evapotranspiration reduce the water availability in southern China. Fifth, model agreements are higher in simulated floods than water availability because increasing precipitation extremes are more consistent among different GCM outputs compared to mean precipitation.

Corresponding author address: Yongqin David Chen, Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China. E-mail: ydavidchen@cuhk.edu.hk

Abstract

Future changes in floods and water availability across China under representative concentration pathway 2.6 (RCP2.6) and RCP8.5 are studied by analyzing discharge simulations from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) with the consideration of uncertainties among global climate models (GCMs) and hydrologic models. Floods and water availability derived from ISI-MIP simulations are compared against observations. The uncertainties among models are quantified by model agreement. Only model agreement >50% is considered to generate reliable projections of floods and water availability and their relationships with climate change. The results show five major points. First, ISI-MIP simulations have acceptable ability in modeling floods and water availability. The spatial patterns of changes in floods and water availability highly depend on the outputs of GCMs. Uncertainties from GCMs/hydrologic models predominate the uncertainties in the wet/dry areas in eastern/northwestern China. Second, the magnitudes of floods throughout China increase during 2070–99 under RCP8.5 relative to those with the same return periods during 1971–2000. The increase rates of larger floods are higher than those of the smaller ones. Third, water availability decreases/increases in southern/northern China under RCP8.5, but changes negligibly under RCP2.6. Fourth, more severe floods in the future are driven by more intense precipitation extremes over China. The negligible change in mean precipitation and the increase in actual evapotranspiration reduce the water availability in southern China. Fifth, model agreements are higher in simulated floods than water availability because increasing precipitation extremes are more consistent among different GCM outputs compared to mean precipitation.

Corresponding author address: Yongqin David Chen, Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China. E-mail: ydavidchen@cuhk.edu.hk
Save
  • Bierkens, M. F. P., and van Beek L. P. H. , 2009: Seasonal predictability of European discharge: NAO and hydrological response time. J. Hydrometeor., 10, 953968, doi:10.1175/2009JHM1034.1.

    • Search Google Scholar
    • Export Citation
  • Chen, H., Sun J. , Chen X. , and Zhou W. , 2012: CGCM projections of heavy rainfall events in China. Int. J. Climatol., 32, 441450, doi:10.1002/joc.2278.

    • Search Google Scholar
    • Export Citation
  • Dankers, R., and Hiederer R. , 2008: Extreme temperatures and precipitation in Europe: Analysis of a high-resolution climate change scenario. JRC Scientific and Tech. Rep. EUR 23291, European Union, 66 pp. [Available online at http://esdac.jrc.ec.europa.eu/ESDB_Archive/eusoils_docs/other/EUR23291EN.pdf.]

  • Dankers, R., and Coauthors, 2014: First look at changes in flood hazard in the Inter-Sectoral Impact Model Intercomparison Project ensemble. Proc. Natl. Acad. Sci. USA, 111, 32573261, doi:10.1073/pnas.1302078110.

    • Search Google Scholar
    • Export Citation
  • Davie, J. C. S., and Coauthors, 2013: Comparing projections of future changes in runoff from hydrological and biome models in ISI-MIP. Earth Syst. Dyn., 4, 359374, doi:10.5194/esd-4-359-2013.

    • Search Google Scholar
    • Export Citation
  • Davie, T., 2008: Fundamentals of Hydrology. Routledge, 220 pp.

  • Elliott, J., Deryng D. , and Müller C. , 2014: Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proc. Natl. Acad. Sci. USA, 111, 32393244, doi:10.1073/pnas.1222474110.

    • Search Google Scholar
    • Export Citation
  • Embrechts, P., 1997: Modelling Extremal Events for Insurance and Finance. 645 pp.

  • Gao, G., Chen D. , Xu C.-Y. , and Simelton E. , 2007: Trend of estimated actual evapotranspiration over China during 1960–2002. J. Geophys. Res., 112, D11120, doi:10.1029/2006JD008010.

    • Search Google Scholar
    • Export Citation
  • Gosling, S. N., and Arnell N. W. , 2011: Simulating current global river runoff with a global hydrological model: Model revisions, validation, and sensitivity analysis. Hydrol. Processes, 25, 11291145, doi:10.1002/hyp.7727.

    • Search Google Scholar
    • Export Citation
  • Gosling, S. N., Bretherton D. , Haines K. , and Arnell N. W. , 2010: Global hydrology modelling and uncertainty: Running multiple ensembles with a campus grid. Philos. Trans. Roy. Soc. London, A368, 40054021, doi:10.1098/rsta.2010.0164.

    • Search Google Scholar
    • Export Citation
  • Haddeland, I., and Coauthors, 2011: Multimodel estimate of the global terrestrial water balance: Setup and first results. J. Hydrometeor., 12, 869884, doi:10.1175/2011JHM1324.1.

    • Search Google Scholar
    • Export Citation
  • Hagemann, S., and Gates L. D. , 2003: Improving a subgrid runoff parameterization scheme for climate models by the use of high resolution data derived from satellite observations. Climate Dyn., 21, 349359, doi:10.1007/s00382-003-0349-x.

    • Search Google Scholar
    • Export Citation
  • Hamlet, A. F., and Lettenmaier D. P. , 2007: Effects of 20th century warming and climate variability on flood risk in the western U.S. Water Resour. Res., 43, W06427, doi:10.1029/2006WR005099.

    • Search Google Scholar
    • Export Citation
  • Hanasaki, N., Kanae S. , Oki T. , Masuda K. , Motoya K. , Shirakawa N. , Shen Y. , and Tanaka K. , 2008a: An integrated model for the assessment of global water resources—Part 1: Model description and input meteorological forcing. Hydrol. Earth Syst. Sci., 12, 10071025, doi:10.5194/hess-12-1007-2008.

    • Search Google Scholar
    • Export Citation
  • Hanasaki, N., Kanae S. , and Oki T. , 2008b: An integrated model for the assessment of global water resources—Part 2: Applications and assessments. Hydrol. Earth Syst. Sci., 12, 10271037, doi:10.5194/hess-12-1027-2008.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., and Soden B. J. , 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699, doi:10.1175/JCLI3990.1.

    • Search Google Scholar
    • Export Citation
  • Hempel, S., Frieler K. , Warszawski L. , Schewe J. , and Piontek F. , 2013: A trend-preserving bias correction—The ISI-MIP approach. Earth Syst. Dyn., 4, 219236, doi:10.5194/esd-4-219-2013.

    • Search Google Scholar
    • Export Citation
  • Hosking, J. R. M., 1990: L-moments: Analysis and estimation of distributions using linear combinations of order statistics. J. Roy. Stat. Soc., 52, 105124.

    • Search Google Scholar
    • Export Citation
  • IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Cambridge University Press, 582 pp.

  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp.

  • Jonkman, S. N., 2005: Global perspectives on loss of human life caused by floods. Nat. Hazards, 34, 151175, doi:10.1007/s11069-004-8891-3.

    • Search Google Scholar
    • Export Citation
  • Katz, R. W., Pariange M. B. , and Naveau P. , 2002: Statistics of extremes in hydrology. Adv. Water Resour., 25, 12871304, doi:10.1016/S0309-1708(02)00056-8.

    • Search Google Scholar
    • Export Citation
  • Khan, M. S., Coulibaly P. , and Dibike Y. , 2006: Uncertainty analysis of statistical downscaling methods. J. Hydrol., 319, 357382, doi:10.1016/j.jhydrol.2005.06.035.

    • Search Google Scholar
    • Export Citation
  • Laine, A., Nakamura H. , Nishii K. , and Miyasaka T. , 2014: A diagnostic study of future evaporation changes projected in CMIP5 climate models. Climate Dyn., 42, 27452761, doi:10.1007/s00382-014-2087-7.

    • Search Google Scholar
    • Export Citation
  • Li, J., Zhang Q. , Chen X. , and Jiang T. , 2011: Study of ecological instream flow in Yellow River: Considering the hydrological change (in Chinese). Acta Geogr. Sin., 66 (1), 99110.

    • Search Google Scholar
    • Export Citation
  • Li, J., Zhang Q. , Chen Y. D. , and Singh V. P. , 2013a: GCMs-based spatiotemporal evolution of climate extremes during the 21st century in China. J. Geophys. Res. Atmos., 118, 11 01711 035, doi:10.1002/jgrd.50851.

    • Search Google Scholar
    • Export Citation
  • Li, J., Zhang Q. , Chen Y. D. , Xu C.-Y. , and Singh V. P. , 2013b: Changing spatiotemporal patterns of precipitation extremes in China during 2071–2100 based on Earth System Models. J. Geophys. Res. Atmos., 118, 12 53712 555, doi:10.1002/2013JD020300.

    • Search Google Scholar
    • Export Citation
  • Liang, X., Lettenmaier D. P. , Wood E. F. , and Burges S. J. , 1994: A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res., 99, 14 41514 428, doi:10.1029/94JD00483.

    • Search Google Scholar
    • Export Citation
  • Manabe, S., Wetherald R. T. , Milly P. C. D. , Delworth T. L. , and Stouffer R. J. , 2004: Century-scale change in water availability: CO2-quadrupling experiment. Climatic Change, 64, 5976, doi:10.1023/B:CLIM.0000024674.37725.ca.

    • Search Google Scholar
    • Export Citation
  • Milly, P. C. D., Dunne K. A. , and Vecchia A. V. , 2005: Global pattern of trends in streamflow and water availability in a changing climate. Nature, 438, 347350, doi:10.1038/nature04312.

    • Search Google Scholar
    • Export Citation
  • Moss, R. H., and Coauthors, 2010: The next generation of scenarios for climate change research and assessment. Nature, 463, 747756, doi:10.1038/nature08823.

    • Search Google Scholar
    • Export Citation
  • Oki, T., and Kanae S. , 2006: Global hydrological cycles and world water resources. Science, 313, 10681072, doi:10.1126/science.1128845.

    • Search Google Scholar
    • Export Citation
  • Pavelsky, T. M., and Smith L. C. , 2006: Intercomparison of four global precipitation data sets and their correlation with increased Eurasian river discharge to the Arctic Ocean. J. Geophys. Res., 111, D21112, doi:10.1029/2006JD007230.

    • Search Google Scholar
    • Export Citation
  • Pokhrel, Y., Hanasaki N. , Koirala S. , Cho J. , Yeh P. J.-F. , Kim H. , Kanae S. , and Oki T. , 2012: Incorporating anthropogenic water regulation modules into a land surface model. J. Hydrometeor., 13, 255269, doi:10.1175/JHM-D-11-013.1.

    • Search Google Scholar
    • Export Citation
  • Poveda, G., and Coauthors, 2007: Linking long-term water balances and statistical scaling to estimate river flows along the drainage network of Colombia. J. Hydrol. Eng., 12, 413, doi:10.1061/(ASCE)1084-0699(2007)12:1(4).

    • Search Google Scholar
    • Export Citation
  • Raff, D. A., Pruitt T. , and Brekke L. D. , 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.

    • Search Google Scholar
    • Export Citation
  • Ranger, N., and Coauthors, 2011: An assessment of the potential impact of climate change on flood risk in Mumbai. Climatic Change, 104, 139167, doi:10.1007/s10584-010-9979-2.

    • Search Google Scholar
    • Export Citation
  • Riahi, K., and Coauthors, 2011: RCP8.5—A scenario of comparatively high greenhouse gas emissions. Climatic Change, 109, 3357, doi:10.1007/s10584-011-0149-y.

    • Search Google Scholar
    • Export Citation
  • Rosenzweig, C., Lglesias A. , Yang X. B. , Epstein P. R. , and Chivian E. , 2001: Climate change and extreme weather events: Implications for food production, plant diseases, and pests. Global Change Hum. Health, 2, 90104, doi:10.1023/A:1015086831467.

    • Search Google Scholar
    • Export Citation
  • Schewe, J., and Coauthors, 2014: Multimodel assessment of water scarcity under climate change. Proc. Natl. Acad. Sci. USA, 111, 32453250, doi:10.1073/pnas.1222460110.

    • Search Google Scholar
    • Export Citation
  • Stacke, T., and Hagemann S. , 2012: Development and evaluation of a global dynamical wetlands extent scheme. Earth Syst. Sci., 16, 29152933, doi:10.5194/hess-16-2915-2012.

    • Search Google Scholar
    • Export Citation
  • Tang, Q., Oki T. , and Kanae S. , 2006: A distributed biosphere hydrological model (DBHM) for large river basin. Proc. Hydraul. Eng., 50, 3742, doi:10.2208/prohe.50.37.

    • Search Google Scholar
    • Export Citation
  • Tang, Q., Oki T. , Kanae S. , and Hu H. , 2007: The influence of precipitation variability and partial irrigation within grid cells on a hydrological simulation. J. Hydrometeor., 8, 499512, doi:10.1175/JHM589.1.

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

    • Search Google Scholar
    • Export Citation
  • Teng, J., Vaze J. , Chiew F. H. S. , Wang B. , and Perraud J.-M. , 2012: Estimating the relative uncertainties sourced from GCMs and hydrological models in modeling climate change impact on runoff. J. Hydrometeor., 13, 122139, doi:10.1175/JHM-D-11-058.1.

    • Search Google Scholar
    • Export Citation
  • van Vuuren, D. P., and Coauthors, 2011: RCP2.6: Exploring the possibility to keep global mean temperature increase below 2°C. Climatic Change, 109, 95116, doi:10.1007/s10584-011-0152-3.

    • Search Google Scholar
    • Export Citation
  • Vörösmarty, C. J., Federer C. A. , and Schloss A. L. , 1998: Potential evaporation functions compared on US watersheds: Possible implications for global-scale water balance and terrestrial ecosystem modeling. J. Hydrol., 207, 147169, doi:10.1016/S0022-1694(98)00109-7.

    • Search Google Scholar
    • Export Citation
  • Vörösmarty, C. J., Green P. , Salisbury J. , and Lammers R. B. , 2000: Global water resources: Vulnerability from climate change and population growth. Science, 289, 284288, doi:10.1126/science.289.5477.284.

    • Search Google Scholar
    • Export Citation
  • Wada, Y., van Beek L. P. H. , van Kempen C. M. , Reckman J. W. T. M. , Vasak S. , and Bierkens M. F. P. , 2010: Global depletion of groundwater resources. Geophys. Res. Lett., 37, L20402, doi:10.1029/2010GL044571.

    • Search Google Scholar
    • Export Citation
  • Wang, G. Q., and Coauthors, 2012: Assessing water resources in China using PRECIS projections and VIC model. Hydrol. Earth Syst. Sci., 16, 231240, doi:10.5194/hess-16-231-2012.

    • Search Google Scholar
    • Export Citation
  • Warszawski, L., Frielet K. , Huber V. , Pointek F. , Serdeczny O. , and Schewe J. , 2014: The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP): Project framework. Proc. Natl. Acad. Sci. USA, 111, 32283232, doi:10.1073/pnas.1312330110.

    • Search Google Scholar
    • Export Citation
  • Weedon, G. P., and Coauthors, 2011: Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. J. Hydrometeor., 12, 823848, doi:10.1175/2011JHM1369.1.

    • Search Google Scholar
    • Export Citation
  • Wilby, R. L., Beven K. J. , and Reynard N. S. , 2008: Climate change and fluvial flood risk in the UK: More of the same? Hydrol. Processes, 22, 25112523, doi:10.1002/hyp.6847.

    • Search Google Scholar
    • Export Citation
  • Wilk, M. B., and Gnanadesikan R. , 1968: Probability plotting methods for the analysis of data. Biometrika, 55, 117, doi:10.2307/2334448.

    • Search Google Scholar
    • Export Citation
  • Xu, C.-H., and Xu Y. , 2012: The projection of temperature and precipitation over China under RCP scenarios using a CMIP5 Multi-Model Ensemble. Atmos. Oceanic Sci. Lett., 5, 527533, doi:10.1080/16742834.2012.11447042.

    • Search Google Scholar
    • Export Citation
  • Yang, H., and Yang D. , 2012: Climatic factors influencing changing pan evaporation across China from 1961 to 2001. J. Hydrol., 414–415, 184193, doi:10.1016/j.jhydrol.2011.10.043.

    • Search Google Scholar
    • Export Citation
  • Yong, B., and Coauthors, 2013: Spatial–temporal changes of water resources in a typical semi-arid basin of north China over the past 50 years and assessment of possible natural and socioeconomic causes. J. Hydrometeor., 14, 10091034, doi:10.1175/JHM-D-12-0116.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, L., Zhao F. , Chen Y. , and Dixon R. N. M. , 2011: Estimating effects of plantation expansion and climate variability on streamflow for catchments in Australia. Water Resour. Res., 47, W12539, doi:10.1029/2011WR010711.

    • Search Google Scholar
    • Export Citation
  • Zhang, Q., Singh V. P. , Li J. , and Chen X. , 2011: Analysis of the periods of maximum consecutive wet days in China. J. Geophys. Res., 116, D23106, doi:10.1029/2011JD016088.

    • Search Google Scholar
    • Export Citation
  • Zhang, Q., Li J. , Singh V. P. , Xu C.-Y. , and Bai Y. , 2012: Changing structure of the precipitation process during 1960–2005 in the Xinjiang, China. Theor. Appl. Climatol., 107, 255264, doi:10.1007/s00704-011-0476-y.

    • Search Google Scholar
    • Export Citation
  • Zhang, Q., Li J. , Singh V. P. , and Xiao M. , 2013a: Spatio-temporal relations between temperature and precipitation regimes: Implications for temperature-induced changes in the hydrological cycle. Global Planet. Change, 111, 5776, doi:10.1016/j.gloplacha.2013.08.012.

    • Search Google Scholar
    • Export Citation
  • Zhang, Q., Singh V. P. , and Li J. , 2013b: Eco-hydrological requirements in arid and semiarid regions: Case study of the Yellow River in China. J. Hydrol. Eng., 18, 689697, doi:10.1061/(ASCE)HE.1943-5584.0000653.

    • Search Google Scholar
    • Export Citation
  • Zhang, Q., Singh V. P. , Li K. , and Li J. , 2014: Trend, periodicity and abrupt change in streamflow of the East River, the Pearl River basin. Hydrol. Processes, 28, 305314, doi:10.1002/hyp.9576.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1181 482 68
PDF Downloads 661 184 23