Skip to main content

Advertisement

SpringerLink
Log in

Forecast and uncertainty analysis of extreme precipitation in China from ensemble of multiple climate models

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

Global climate change is expected to have a major impact on the hydrological cycle. Understanding potential changes in future extreme precipitation is important to the planning of industrial and agricultural water use, flood control, and ecological environment protection. In this paper, we study the statistical distribution of extreme precipitation based on historical observation and various global climate models (GCMs), and predict the expected change and the associated uncertainty. The empirical frequency, generalized extreme value (GEV) distribution, and L-moment estimator algorithms are used to establish the statistical distribution relationships and the multi-model ensemble predictions are established by the Bayesian model averaging (BMA) method. This ensemble forecast takes advantage of multi-model synthesis, which is an effective measure to reduce the uncertainty of model selection in extreme precipitation forecasting. We have analyzed the relationships among extreme precipitation, return period, and precipitation durations for 6 representative cities in China. More significantly, the approach allows for establishing the uncertainty of extreme precipitation predictions. The empirical frequency from the historical data is all within the 90% confidence interval of the BMA ensemble. For the future predictions, the extreme precipitation intensities of various durations tend to become larger compared to the historic results. The extreme precipitation under the RCP8.5 scenario is greater than that under the RCP2.6 scenario. The developed approach not only effectively gives the extreme precipitation predictions, but also can be used to any other extreme hydrological events in future climate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Availability of data and material

Not applicable.

Code availability

Not applicable.

References

  • Awange JL, Hu KX, Khaki M (2019) The newly merged satellite remotely sensed, gauge and reanalysis-based multi-source weighted-ensemble precipitation: evaluation over Australia and Africa (1981-2016). Sci Total Environ 670:448–465

    Article  Google Scholar 

  • Chen J, Brissette FP, Poulin A et al (2011) Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed. Water Resour Res 47:W12509

    Google Scholar 

  • Chen J, Brissette FP, Lucas-Picher P, Caya D (2017) Impacts of weighting climate models for hydro-meteorological climate change studies. J Hydrol 549:534–546

    Article  Google Scholar 

  • Christensen NS, Lettenmaier DP (2007) A multimodel ensemble approach to assessment of climate change impacts on the hydrology and water resources of the Colorado River Basin. Hydrol Earth Syst Sci 11:1417–1434

    Article  Google Scholar 

  • Du JB, Park K (2019) Estuarine salinity recovery from an extreme precipitation event: hurricane Harvey in Galveston Bay. Sci Total Environ 670:1049–1059

    Article  Google Scholar 

  • Duan Q, Ajami NK, Gao X, Sorooshian S (2007) Multi-model ensemble hydrologic prediction using Bayesian model averaging. Adv Water Resour 30(5):1371–1386

    Article  Google Scholar 

  • Fang J, Yang W, Luan Y, du J, Lin A, Zhao L (2019) Evaluation of the TRMM 3B42 and GPM IMERG products for extreme precipitation analysis over China. Atmos Res 223:24–38

    Article  Google Scholar 

  • Graham LP, Andreasson J, Carlsson B (2007) Assessing climate change impacts on hydrology from an ensemble of regional climate models, model scales and linking methods - a case study on the Lule River Basin. Clim Chang 81:293–307

    Article  Google Scholar 

  • Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90:1095–1107

    Article  Google Scholar 

  • Hawkins E, Sutton R (2011) The potential to narrow uncertainty in projections of regional precipitation change. Clim Dyn 37:407–418

    Article  Google Scholar 

  • Hay LE, Wilby RL, Leavesley GH (2000) A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States. J Am Water Resour Assoc 36(2):387–397

    Article  Google Scholar 

  • Hosking JM, Wallis JR (1997) Regional frequency analysis: an approach based on L-moments. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • IPCC (2013) In: Stocker TF, Qin D, Plattner GK et al (eds) Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

    Google Scholar 

  • Knutti R (2010) The end of model democracy? Clim Chang 102:395–404

    Article  Google Scholar 

  • Knutti R, Sedlacek J, Sanderson BJ et al (2017) A climate model weighting scheme accounting for performance and interdependence. Geophys Res Lett 44:1909–1918

    Google Scholar 

  • Krzysztofowicz R, Herr HD (2001) Hydrologic uncertainty processor for probabilistic river stage forecasting: precipitation-dependent model. J Hydrol 249(1-4):46–68

    Article  Google Scholar 

  • Maraun D, Widmann M (2018) Cross-validation of bias-corrected climate simulations is misleading. Hydrol Earth Syst Sci 22(9):4867–4873

    Article  Google Scholar 

  • Massoud EC, Espinoza V, Guan B, Waliser DE (2019) Global climate model ensemble approaches for future projections of atmospheric rivers. Earth’s Future 7:1136–1151

    Article  Google Scholar 

  • Minville M, Brissette F, Leconte R (2008) Uncertainty of the impact of climate change on the hydrology of a Nordic watershed. J Hydrol 358:70–83

    Article  Google Scholar 

  • Novoa V, Ahumada-Rudolph R, Rojas O (2019) Understanding agricultural water footprint variability to improve water management in Chile. Sci Total Environ 670:188–199

    Article  Google Scholar 

  • Qi LY, Huang JC, Yan RH, Gao JF, Wang SG, Guo YY (2017) Modeling the effects of the streamflow changes of Xinjiang Basin in future climate scenarios on the hydrodynamic conditions in Lake Poyang, China. Limnology 18(2):175–194

    Article  Google Scholar 

  • Raftery AE, Gneiting T, Balabdaoui F, Polakowski M (2005) Using Bayesian model averaging to calibrate forecast ensembles. Mon Weather Rev 113(5):1155–1174

    Article  Google Scholar 

  • Sanderson BJ, Knutti R (2012) On the interpretation of constrained climate model ensembles. Geophys Res Lett 39:L16708

    Article  Google Scholar 

  • Sanderson BJ, Knutti R, Caldwell P (2015a) Addressing interdependency in a multimodel ensemble by interpolation of model properties. J Clim 28:5150–5170

    Article  Google Scholar 

  • Sanderson BJ, Knutti R, Caldwell P (2015b) A representative democracy to reduce interdependency in a multimodel ensemble. J Clim 28:5171–5194

    Article  Google Scholar 

  • Shao J, Wang J, Lv SY, Bing J (2016) Spatial and temporal variability of seasonal precipitation in Poyang Lake basin and possible links with climate indices. Hydrol Res 47(S1):51–68

    Article  Google Scholar 

  • Sloughter JM, Raftery AE, Gneiting T, Fraley C (2007) Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Mon Weather Rev 135(9):3209–3220

    Article  Google Scholar 

  • Smith I, Chandler E (2010) Refining rainfall projections for the Murray Darling Basin of south-east Australia-the effect of sampling model results based on performance. Clim Chang 102(3-4):377–393

    Article  Google Scholar 

  • Song XM, Zhang JY, Zou XJ, Zhang C, AghaKouchak A, Kong F (2019) Changes in precipitation extremes in the Beijing metropolitan area during 1960-2012. Atmos Res 222:134–153

    Article  Google Scholar 

  • Wang L, Chen W (2014) Equiratio cumulative distribution function matching as an improvement to the equidistant approach in bias correction of precipitation. Atmos Sci Lett 15:1–6

    Article  Google Scholar 

  • Wang QJ, Robertson DE (2011) Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences. Water Resour Res 47:W02546

    Google Scholar 

  • Wang R, Zhang JQ, Guo EL, Zhao C, Cao T (2019) Spatial and temporal variations of precipitation concentration and their relationships with large-scale atmospheric circulations across Northeast China. Atmos Res 222:62–73

    Article  Google Scholar 

  • Wilby RL, Harris I (2006) A framework for assessing uncertainties in climate change impacts: low-flow scenarios for the River Thames, UK. Water Resour Res 42:W02419

    Article  Google Scholar 

  • Wootten A, Terando A, Reich BJ, Boyles RP, Semazzi F (2017) Characterizing sources of uncertainty from global climate models and downscaling techniques. J Appl Meteorol Climatol 56:3245–3262

    Article  Google Scholar 

  • Yang X, Yu X, Wang Y, Liu Y, Zhang M, Ren L, Yuan F, Jiang S (2019) Estimating the response of hydrological regimes to future projections of precipitation and temperature over the upper Yangtze River. Atmos Res 230:104627

    Article  Google Scholar 

  • Yuan Z, Xu J, Wang Y (2018) Projection of future extreme precipitation and flood changes of the Jinsha river basin in China based on CMIP5 climate models. Int J Environ Res Public Health 15(11):2491

    Article  Google Scholar 

  • Zhao HH, Pan XB, Wang ZW et al (2019a) What were the changing trends of the seasonal and annual aridity indexes in northwestern China during 1961-2015? Atmos Res 222:154–162

    Article  Google Scholar 

  • Zhao T, Wang QJ, Schepen A, Griffiths M (2019b) Ensemble forecasting of monthly and seasonal reference crop evapotranspiration based on global climate model outputs. Agric For Meteorol 264:114–124

    Article  Google Scholar 

  • Zhu J, Forsee W, Schumer R, Gautam M (2013) Future projections and uncertainty assessment of extreme rainfall intensity in the United States from an ensemble of climate models. Clim Chang 118:469–485

    Article  Google Scholar 

Download references

Funding

This work was partly supported by the Natural Science Foundation of Jiangsu Province of China (Grant No. BK20150922).

Author information

Authors and Affiliations

  1. School of Hydrology and Water Resources, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Peng Deng

  2. Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY, 82071, USA

    Jianting Zhu

Authors
  1. Peng Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianting Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Peng Deng designed and directed the project, wrote most of the article, proceeded the computation, and drew the figures; Jianting Zhu participated in writing and editing the manuscript.

Corresponding author

Correspondence to Peng Deng.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, P., Zhu, J. Forecast and uncertainty analysis of extreme precipitation in China from ensemble of multiple climate models. Theor Appl Climatol 145, 787–805 (2021). https://doi.org/10.1007/s00704-021-03660-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-021-03660-7

Search

Navigation