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Characteristics of extreme precipitation and runoff in the Xijiang River Basin at global warming of 1.5 °C and 2 °C

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Abstract

Ten models of NEX-GDDP CMIP5 were used to perform equal-weighted averaging under the RCP4.5 and RCP8.5 scenarios to obtain daily precipitation and temperature data under a multi-model ensemble. The CREST and VIC models were used to project the change characteristics of runoff and precipitation in the Xijiang River Basin under the background of a global warming by 1.5 °C and 2 °C, respectively. The results show that: (1) under the two warming target scenarios, there are obvious regional differences in the extreme precipitation in the Xijiang River Basin under the RCP4.5 and RCP8.5 scenarios. The precipitation increases on the whole and more so under the high-emission and greater-warming scenarios. In addition, extreme precipitation events in the Xijiang River Basin are significantly different at a temperature rise of additional 0.5 °C. (2) CREST and VIC have good feasibility in the Xijiang River Basin. The projected runoff increases under different combinations of scenarios and at various time scales compared to the baseline period. (3) There is no significant difference between the multi-annual average monthly runoff distribution percentage calculated by the multi-model and hydrological model ensemble average and the multi-annual average monthly runoff distribution percentage during the baseline period and the distributions under the RCP 4.5 and 8.5 scenarios are more uniform and uneven, respectively, than that in the baseline period.

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References

  • Bao Y, Wen X (2017) Projection of China’s near- and long-term climate in a new high-resolution daily downscaled dataset NEX-GDDP. J Meteorol Res 31(1):236–249

    Google Scholar 

  • Chen H, Sun J (2015) Changes in climate extreme events in China associated with warming. Int J Climatol 35(10):2735–2751

    Google Scholar 

  • Chen L, Chang J, Wang Y et al (2019) Assessing runoff sensitivities to precipitation and temperature changes under global climate-change scenarios. Hydrol Res 50(1):24–42

    Google Scholar 

  • Dams J, Nossent J, Senbeta TB, Willems P, Batelaan O (2015) Multi-model approach to assess the impact of climate change on runoff. J Hydrol 529:1601–1616

    Google Scholar 

  • Das J, Umamahesh NV (2018) Assessment of uncertainty in estimating future flood return levels under climate change. Nat Hazards 93(1):109–124

    Google Scholar 

  • Das J, Treesa A, Umamahesh NV (2018) Modelling impacts of climate change on a river basin: analysis of uncertainty using REA and possibilistic approach. Water Resour Manag 32(15):4833–4852

    Google Scholar 

  • Döll P, Trautmann T, Gerten D et al (2018) Risks for the global freshwater system at 1.5 C and 2 C global warming. Environ Res Lett 3(4):044038

    Google Scholar 

  • Dong N, Yu Z, Yang C, Yang M, Wang W (2019a) Hydrological impact of a reservoir network in the upper Gan River Basin, China. Hydrol Process 33(12):1709–1723

    Google Scholar 

  • Dong N, Yu Z, Gu H, Yang C, Yang M, Wei J, Wang H, Arnault J, Laux P, Kunstmann H (2019b) Climate-induced hydrological impact mitigated by a high-density reservoir network in the Poyang Lake Basin. J Hydrol 579:124148

    Google Scholar 

  • Donnelly C, Greuell W, Andersson J et al (2017) Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level. Clim Change 143(1–2):13–26

    Google Scholar 

  • Dottori F, Szewczyk W, Ciscar JC et al (2018) Increased human and economic losses from river flooding with anthropogenic warming. Nat Clim Change 8(9):781

    Google Scholar 

  • Filahi S, Tanarhte M, Mouhir L, El Morhit M, Tramblay Y (2016) Trends in indices of daily temperature and precipitations extremes in Morocco. Theor Appl Climatol 124(3–4):959–972

    Google Scholar 

  • Huang S, Kumar R, Rakovec O et al (2018) Multimodel assessment of flood characteristics in four large river basins at global warming of 1.5, 2.0 and 3.0 K above the pre-industrial level. Environ Res Lett 13(12):124005

    Google Scholar 

  • Hulme M (2016) 1.5 C and climate research after the Paris Agreement. Nat Clim Change 6(3):222

    Google Scholar 

  • IPCC (2013) Climate change 2013: the physical science basis. Contribution of working group 1 to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

    Google Scholar 

  • Karmalkar AV, Bradley RS (2017) Consequences of global warming of 1.5°C and 2°C for regional temperature and precipitation changes in the contiguous United States. PLoS ONE 12:e0168697

    Google Scholar 

  • Khan SI, Hong Y, Wang JH, Yilmaz KK, Gourley JJ, Adler RF (2011a) Satellite remote sensing and hydrologic modeling for flood inundation mapping in Lake Victoria basin: implications for hydrologic prediction in ungauged basins. IEEE Trans Geosci Remote Sens 49(1):85–95

    Google Scholar 

  • Khan SI, Adhikari P, Hong Y, Vergara H, Adler RF, Policelli F (2011b) Hydroclimatology of Lake Victoria region using hydrologic model and satellite remote sensing data. Hydrol Earth Syst Sci 15(1):107–117

    Google Scholar 

  • Lehner F, Coats S, Stocker TF et al (2017) Projected drought risk in 1.5 C and 2 C warmer climates. Geophys Res Lett 44(14):7419–7428

    Google Scholar 

  • Li W, Jiang Z, Zhang X, Li L, Sun Y (2018) Additional risk in extreme precipitation in China from 1.5 °C to 2.0 °C global warming levels. Sci Bull 63:228–234

    Google Scholar 

  • Li Z, Guo X, Yang Y et al (2019) Heatwave trends and the population exposure over China in the 21st century as well as under 1.5 C and 2.0 C global warmer future scenarios. Sustainability 11(12):3318

    Google Scholar 

  • Liang X, Lettenmaier DP, Wood EF et al (1994) A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J Geophys Res Atmos 99(D7):14415–14428

    Google Scholar 

  • Liu L, Xu H, Wang Y, Jiang T (2017) Impacts of 1.5 and 2 C global warming on water availability and extreme hydrological events in Yiluo and Beijiang River catchments in China. Clim Change 145(1–2):145–158

    Google Scholar 

  • Lohmann D, Raschke E, Nijssen B et al (1998) Regional scale hydrology: I. Formulation of the VIC-2L model coupled to a routing model. Hydrol Sci J 43(1):131–141

    Google Scholar 

  • Meng-Zi Z, Guang-Sheng Z, Xiao-Min L, Li Z, Yu-He JI (2018) CMIP5-based threshold-crossing times of 1.5°C and 2°C global warming above pre-industrial levels. Clim Change Res 14:221–227

    Google Scholar 

  • Mitchell D et al (2017) Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design. Geosci Model Dev 10:571–583

    Google Scholar 

  • Nilawar AP, Waikar ML (2018) Use of SWAT to determine the effects of climate and land use changes on streamflow and sediment concentration in the Purna River basin, India. Environ Earth Sci 77(23):783

    Google Scholar 

  • Paltan H, Allen M, Haustein K et al (2018) Global implications of 1.5 °C and 2 °C warmer worlds on extreme river flows. Environ Res Lett 13(9):094003

    Google Scholar 

  • Rogelj J, Den Elzen M, Höhne N et al (2016) Paris Agreement climate proposals need a boost to keep warming well below 2 C. Nature 534(7609):631

    Google Scholar 

  • Rosenbrock HH (1960) An automatic method for finding the greatest or least value of a function. Comput J 3(3):175–184

    Google Scholar 

  • Schellnhuber HJ, Rahmstorf S, Winkelmann R (2016) Why the right climate target was agreed in Paris. Nat Clim Change 6(7):649

    Google Scholar 

  • Shen Y, Xiong AY (2016) Validation and comparison of a new gauge-based precipitation analysis over mainland China. Int J Climatol 36:252–265

    Google Scholar 

  • Shen Y, Zhao P, Pan Y, Yu JJ (2014) A high spatiotemporal gauge-satellite merged precipitation analysis over China. J Geophys Res Atmos 119(6):3063–3075

    Google Scholar 

  • Silberstein RP, Aryal SK, Durrant J, Pearcey M, Braccia M, Charles SP (2012) Climate change and runoff in south-western Australia. J Hydrol 475:441–455

    Google Scholar 

  • Tang GQ, Zeng ZY, Long D, Guo XL, Yong B, Zhang WH (2016) Statistical and hydrological comparisons between TRMM and GPM level-3 products over a midlatitude basin: is day-1 IMERG a good successor for TMPA 3B42V7? J Hydrometeorol 17(1):121–137

    Google Scholar 

  • Thrasher B, Maurer EP, Duffy PB et al (2012) Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol Earth Syst Sci 16:3309–3314

    Google Scholar 

  • Wang J, Hong Y, Li L et al (2011) The coupled routing and excess storage (CREST) distributed hydrological model. Hydrol Sci J 56(1):84–98

    Google Scholar 

  • Xie Z, Su F, Liang X et al (2003) Applications of a surface runoff model with Horton and Dunne runoff for VIC. Adv Atmos Sci 20(2):165–172

    Google Scholar 

  • Xu Y, Xu CH (2012) Preliminary assessment of simulations of climate changes over China by CMIP5 multi-models. Atmos Ocean Sci Lett 5(6):489–494

    Google Scholar 

  • Yuan F, Xie Z, Liu Q et al (2004) An application of the VIC-3L land surface model and remote sensing data in simulating streamflow for the Hanjiang River basin. Can J Remote Sens 30(5):680–690

    Google Scholar 

  • Zhou M, Zhou G, Lv X et al (2019) Global warming from 1.5 to 2 °C will lead to increase in precipitation intensity in China. Int J Climatol 39(4):2351–2361

    Google Scholar 

  • Zhu DH, Samiran Das, Ren QW (2017) Hydrological appraisal of climate change impacts on the water resources of the Xijiang Basin, South China. Water 9(10):793

    Google Scholar 

Download references

Acknowledgements

We are very grateful to the editors and anonymous reviewers for their critical comments and thoughtful suggestions.

Funding

This study was funded by the National Key R&D Program of China (Grant No. 2018YFC0407701) and Natural Science Foundation of China (Grant No. 71461010701).

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Authors and Affiliations

  1. School of Civil Engineering, Tianjin University, Tianjin, 300072, China

    Yinmao Zhao & Hao Wang

  2. China Institute of Water Resources and Hydropower Research, Beijing, 100044, China

    Yinmao Zhao, Siyu Cai & Hao Wang

  3. Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China

    Zhansheng Li

  4. State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, 100084, China

    Zhansheng Li

Authors
  1. Yinmao Zhao
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  2. Zhansheng Li
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  3. Siyu Cai
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  4. Hao Wang
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Contributions

Conceptualization: YZ and ZL; Methodology: YZ and SC; Writing-original draft preparation: YZ; Writing-review and editing: YZ and HW. All authors read and approved the final manuscript.

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Correspondence to Yinmao Zhao.

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Appendix

Appendix

See Fig. 12.

Fig. 12
figure 12

Spatial distribution of percent change of extreme precipitation indicators relative to the baseline period (1985–2014) under each combination scenario

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Zhao, Y., Li, Z., Cai, S. et al. Characteristics of extreme precipitation and runoff in the Xijiang River Basin at global warming of 1.5 °C and 2 °C. Nat Hazards 101, 669–688 (2020). https://doi.org/10.1007/s11069-020-03889-x

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