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Reconciling disagreement on global river flood changes in a warming climate

Nature Climate Change volume 12, pages 1160–1167 (2022)Cite this article

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Abstract

An intensified hydrological cycle with global warming is expected to increase the intensity and frequency of extreme precipitation events. However, whether and to what extent the enhanced extreme precipitation translates into changes in river floods remains controversial. Here we demonstrate that previously reported unapparent or even negative responses of river flood discharge (defined as annual maximum discharge) to extreme precipitation increases are largely caused by mixing the signals of floods with different generating mechanisms. Stratifying by flood type, we show a positive response of rainstorm-induced floods to extreme precipitation increases. However, this response is almost entirely offset by concurrent decreases in snow-related floods, leading to an overall unapparent change in total global floods in both historical observations and future climate projections. Our findings highlight an increasing rainstorm-induced flood risk under warming and the importance of distinguishing flood-generating mechanisms in assessing flood changes and associated social-economic and environmental risks.

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Fig. 1: Classification of annual maximum flood events in the 7,239 catchments.
Fig. 2: Changes in annual maximum flood and precipitation in the 7,239 catchments from 1950–2017.
Fig. 3: Comparison between the peak point temperatures of the P~T and Q~T scaling curves.
Fig. 4: Changes in flood and extreme precipitation from the historical to the future period.
Fig. 5: Changes in peak points of the P~T and Q~T scaling curves from the historical to the future period.

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Data availability

The streamflow records were obtained from the Global Runoff Data Centre (http://www.bafg.de/GRDC), the United States Geological Survey GAGES-II database (https://www.sciencebase.gov), the Water Survey of Canada Hydrometric Data (HYDAT; https://www.canada.ca/en/environment-climate-change), the Catchment Characterisation and Modelling–Joint Research Centre database (https://ccm.jrc.ec.europa.eu/), the HidroWeb portal of the Brazilian Agência Nacional de Águas (http://www.snirh.gov.br/hidroweb), the Australian Bureau of Meteorology (http://www.bom.gov.au/waterdata) and the Chilean Center for Climate and Resilience Research (http://www.cr2.cl/datos-de-caudales/). The Global Reservoir and Dam database is available at https://sedac.ciesin.columbia.edu/data/set/grand-v1-dams-rev01. The GlobCover v2.3 map is available at http://due.esrin.esa.int/page_globcover.php. The Global forest change dataset is available at http://earthenginepartners.appspot.com/science-2013-global-forest. The ERA5-Land dataset is available at https://www.ecmwf.int/en/era5-land. The CMIP6 data can be accessed through the Earth System Grid Federation (ESGF) system (https://esgf-node.llnl.gov/search/cmip6/).

Code availability

The code61 used as the basis for this study is available at https://doi.org/10.5281/zenodo.7319421.

References

  1. Allan, R. P. & Soden, B. J. Atmospheric warming and the amplification of precipitation extremes. Science 321, 1481–1484 (2008).

    Article  CAS  Google Scholar 

  2. Hirabayashi, Y. et al. Global flood risk under climate change. Nat. Clim. Change 3, 816–821 (2013).

    Article  Google Scholar 

  3. Lenderink, G. & Van Meijgaard, E. Increase in hourly precipitation extremes beyond expectations from temperature changes. Nat. Geosci. 1, 511–514 (2008).

    Article  CAS  Google Scholar 

  4. Trenberth, K. E., Dai, A., Rasmussen, R. & Parsons, D. The changing character of precipitation. Bull. Am. Meteorol. Soc. 84, 1205–1217 (2003).

    Article  Google Scholar 

  5. Fischer, E. M. & Knutti, R. Observed heavy precipitation increase confirms theory and early models. Nat. Clim. Change 6, 986–991 (2016).

    Article  Google Scholar 

  6. Prein, A. F. et al. The future intensification of hourly precipitation extremes. Nat. Clim. Change 7, 48–52 (2017).

    Article  Google Scholar 

  7. Yin, J. et al. Large increase in global storm runoff extremes driven by climate and anthropogenic changes. Nat. Commun. 9, 4389 (2018).

    Article  CAS  Google Scholar 

  8. Ali, H., Modi, P. & Mishra, V. Increased flood risk in Indian sub-continent under the warming climate. Weather Clim. Extrem. 25, 100212 (2019).

    Article  Google Scholar 

  9. Mallakpour, I. & Villarini, G. The changing nature of flooding across the central United States. Nat. Clim. Change 5, 250–254 (2015).

    Article  Google Scholar 

  10. Slater, L. J. & Villarini, G. Recent trends in U.S. flood risk. Geophys. Res. Lett. 43, 12428–12436 (2016).

    Article  Google Scholar 

  11. Archfield, S. A., Hirsch, R. M., Viglione, A. & Bloschl, G. Fragmented patterns of flood change across the United States. Geophys. Res. Lett. 43, 10232–10239 (2016).

    Article  CAS  Google Scholar 

  12. Zhang, X. S. et al. How streamflow has changed across Australia since the 1950s: evidence from the network of hydrologic reference stations. Hydrol. Earth Syst. Sci. 20, 3947–3965 (2016).

    Article  Google Scholar 

  13. Blöschl, G. et al. Changing climate both increases and decreases European river floods. Nature 573, 108–111 (2019).

    Article  Google Scholar 

  14. Do, H. X., Westra, S. & Leonard, M. A global-scale investigation of trends in annual maximum streamflow. J. Hydrol. 552, 28–43 (2017).

    Article  Google Scholar 

  15. Mudelsee, M., Borngen, M., Tetzlaff, G. & Grunewald, U. No upward trends in the occurrence of extreme floods in central Europe. Nature 425, 166–169 (2003).

    Article  CAS  Google Scholar 

  16. Hartmann, D. L. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 2 (IPCC, Cambridge Univ. Press, 2013).

  17. Hirsch, R. M. & Archfield, S. A. Flood trends: not higher but more often. Nat. Clim. Change 5, 198–199 (2015).

    Article  Google Scholar 

  18. Sharma, A., Wasko, C. & Lettenmaier, D. P. If precipitation extremes are increasing, why aren’t floods? Water Resour. Res. 54, 8545–8551 (2018).

    Article  Google Scholar 

  19. Wasko, C. Can temperature be used to inform changes to flood extremes with global warming? Phil. Trans. R. Soc. A 379, 20190551 (2021).

    Article  Google Scholar 

  20. Peterson, T. C. et al. Monitoring and understanding changes in heat waves, cold waves, floods, and droughts in the United States: state of knowledge. Bull. Am. Meteorol. Soc. 94, 821–834 (2013).

    Article  Google Scholar 

  21. Merz, R. & Bloschl, G. A process typology of regional floods. Water Resour. Res. https://doi.org/10.1029/2002WR001952 (2003).

  22. Sikorska, A. E., Viviroli, D. & Seibert, J. Flood-type classification in mountainous catchments using crisp and fuzzy decision trees. Water Resour. Res. 51, 7959–7976 (2015).

    Article  Google Scholar 

  23. Berghuijs, W. R., Woods, R. A., Hutton, C. J. & Sivapalan, M. Dominant flood generating mechanisms across the United States. Geophys. Res. Lett. 43, 4382–4390 (2016).

    Article  Google Scholar 

  24. Stein, L., Clark, M. P., Knoben, W. J., Pianosi, F. & Woods, R. A. How do climate and catchment attributes influence flood generating processes? A large‐sample study for 671 catchments across the contiguous USA. Water Resour. Res. 57, e2020WR028300 (2021).

    Article  Google Scholar 

  25. Kemter, M., Merz, B., Marwan, N., Vorogushyn, S. & Blöschl, G. Joint trends in flood magnitudes and spatial extents across Europe. Geophys. Res. Lett. 47, e2020GL087464 (2020).

    Article  Google Scholar 

  26. Wang, G. et al. The peak structure and future changes of the relationships between extreme precipitation and temperature. Nat. Clim. Change 7, 268–274 (2017).

    Article  Google Scholar 

  27. Wasko, C., Sharma, A. & Lettenmaier, D. P. Increases in temperature do not translate to increased flooding. Nat. Commun. 10, 5676 (2019).

    Article  CAS  Google Scholar 

  28. Kapnick, S. & Hall, A. Causes of recent changes in western North American snowpack. Clim. Dyn. 38, 1885–1899 (2012).

    Article  Google Scholar 

  29. Wu, X., Che, T., Li, X., Wang, N. & Yang, X. Slower snowmelt in spring along with climate warming across the Northern Hemisphere. Geophys. Res. Lett. 45, 12331–12339 (2018).

    Article  Google Scholar 

  30. Arnell, N. W. & Gosling, S. N. The impacts of climate change on river flood risk at the global scale. Clim. Change 134, 387–401 (2016).

    Article  Google Scholar 

  31. Clow, D. W. Changes in the timing of snowmelt and streamflow in Colorado: a response to recent warming. J. Clim. 23, 2293–2306 (2010).

    Article  Google Scholar 

  32. De Michele, C. & Salvadori, G. On the derived flood frequency distribution: analytical formulation and the influence of antecedent soil moisture condition. J. Hydrol. 262, 245–258 (2002).

    Article  Google Scholar 

  33. Bennett, B., Leonard, M., Deng, Y. & Westra, S. An empirical investigation into the effect of antecedent precipitation on flood volume. J. Hydrol. 567, 435–445 (2018).

    Article  Google Scholar 

  34. Wasko, C. & Nathan, R. Influence of changes in rainfall and soil moisture on trends in flooding. J. Hydrol. 575, 432–441 (2019).

    Article  Google Scholar 

  35. Musselman, K. N. et al. Projected increases and shifts in rain-on-snow flood risk over western North America. Nat. Clim. Change 8, 808–812 (2018).

    Article  Google Scholar 

  36. Beck, H. E. et al. Bias correction of global high-resolution precipitation climatologies using streamflow observations from 9372 catchments. J. Clim. 33, 1299–1315 (2020).

    Article  Google Scholar 

  37. Lehner, B. Derivation of Watershed Boundaries for GRDC Gauging Stations Based on the Hydrosheds Drainage Network Tech. Rep. 41 (Global Runoff Data Centre in the Federal Institute of Hydrology (BfG), Germany, 2012).

  38. Falcone, J. A., Carlisle, D. M., Wolock, D. M. & Meador, M. R. GAGES: a stream gage database for evaluating natural and altered flow conditions in the conterminous United States. Ecology 91, 621 (2010).

    Article  Google Scholar 

  39. Vogt, J. V., Soille, P., Colombo, R., Paracchini, M. L. & de Jager, A. Digital Terrain Modelling: A Pan-European River and Catchment Database (European Communities, Italy, 2007).

    Google Scholar 

  40. Zhang, Y. et al. Collation of Australian Modeller’s Streamflow Dataset for 780 Unregulated Australian Catchments Water for a Healthy Country Flagship Report (CSIRO, 2013).

  41. Alvarez-Garreton, C. et al. The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset. Hydrol. Earth Syst. Sci. 22, 5817–5846 (2018).

    Article  Google Scholar 

  42. Lehner, B. et al. High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Front. Ecol. Environ. 9, 494–502 (2011).

    Article  Google Scholar 

  43. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    Article  CAS  Google Scholar 

  44. Muñoz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349–4383 (2021).

    Article  Google Scholar 

  45. Hock, R. Temperature index melt modelling in mountain areas. J. Hydrol. 282, 104–115 (2003).

    Article  Google Scholar 

  46. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  47. Jones, P. W. First- and second-order conservative remapping schemes for grids in spherical coordinates. Mon. Weather Rev. 127, 2204–2210 (1999).

    Article  Google Scholar 

  48. Lyne, V. & Hollick, M. Stochastic time-variable rainfall-runoff modelling. In Institute of Engineers Australia National Conference (ed. Ratcliffe, J. S.) 89–93 (Barton, Australia: Institute of Engineers, 1979).

  49. Brutsaert, W. & Nieber, J. L. Regionalized drought flow hydrographs from a mature glaciated plateau. Water Resour. Res. 13, 637–643 (1977).

    Article  Google Scholar 

  50. Cheng, L., Zhang, L. & Brutsaert, W. Automated selection of pure base flows from regular daily streamflow data: objective algorithm. J. Hydrol. Eng. 21, 06016008 (2016).

    Article  Google Scholar 

  51. Tarasova, L., Basso, S., Zink, M. & Merz, R. Exploring controls on rainfall-runoff events: 1. Time series-based event separation and temporal dynamics of event runoff response in Germany. Water Resour. Res. 54, 7711–7732 (2018).

    Article  Google Scholar 

  52. Tarasova, L. et al. A process‐based framework to characterize and classify runoff events: the event typology of Germany. Water Resour. Res. 56, e2019WR026951 (2020).

    Article  Google Scholar 

  53. Giani, G., Rico-Ramirez, M. A. & Woods, R. A. A practical, objective, and robust technique to directly estimate catchment response time. Water Resour. Res. 57, e2020WR028201 (2021).

    Article  Google Scholar 

  54. Tarasova, L. et al. Causative classification of river flood events. Wiley Interdiscip. Rev. Water 6, e1353 (2019).

    Article  Google Scholar 

  55. Tarasova, L., Basso, S., Poncelet, C. & Merz, R. Exploring controls on rainfall‐runoff events: 2. Regional patterns and spatial controls of event characteristics in Germany. Water Resour. Res. 54, 7688–7710 (2018).

    Article  Google Scholar 

  56. Turkington, T., Breinl, K., Ettema, J., Alkema, D. & Jetten, V. A new flood type classification method for use in climate change impact studies. Weather Clim. Extrem. 14, 1–16 (2016).

    Article  Google Scholar 

  57. Duan, Q., Sorooshian, S. & Gupta, V. K. Optimal use of the SCE-UA global optimization method for calibrating watershed models. J. Hydrol. 158, 265–284 (1994).

    Article  Google Scholar 

  58. Caliński, T. & Harabasz, J. A dendrite method for cluster analysis. Commun. Stat. 3, 1–27 (1974).

    Google Scholar 

  59. Cleveland, W. S. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74, 829–836 (1979).

    Article  Google Scholar 

  60. Visser, J. B., Wasko, C., Sharma, A. & Nathan, R. Eliminating the ‘hook’ in precipitation–temperature scaling. J. Clim. 34, 9535–9549 (2021).

    Google Scholar 

  61. Zhang, S. Code for "Reconciling disagreement on global river flood changes in a warming climate". Zenodo https://doi.org/10.5281/zenodo.7319421 (2022).

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Acknowledgements

This study was supported by the National Natural Science Foundation of China under grant agreement numbers 42088101 (S. Zhang and Y.D.) and 42175168 (S. Zhang) and the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) under grant agreement number 311021009 (S. Zhang, Y.D. and Z.W.). L. Zhou acknowledges the US National Science Foundation (NSF AGS-1952745 and AGS-1854486).

Author information

Authors and Affiliations

  1. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China

    Shulei Zhang, Zhongwang Wei & Yongjiu Dai

  2. Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, NY, USA

    Liming Zhou

  3. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China

    Lu Zhang

  4. CSIRO Land and Water, Black Mountain, Canberra, Australia

    Lu Zhang

  5. State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China

    Yuting Yang & Dawen Yang

  6. State Key Laboratory of Earth Surface Process and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Sha Zhou, Xiaofan Yang, Xiuchen Wu & Xiaoyan Li

  7. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Yongqiang Zhang

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Contributions

S. Zhang, Y.D. and L. Zhou designed the study. S. Zhang, Y.Y. and Z.W. collected the data. S. Zhang performed the analysis. All authors contributed to the interpretation of the results. S. Zhang wrote the initial manuscript with contributions from L. Zhou, L. Zhang, S. Zhou and Y.Y. All authors reviewed and approved the manuscript.

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Correspondence to Shulei Zhang or Yongjiu Dai.

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Nature Climate Change thanks Larisa Tarasova and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Global classifications of annual maximum flood events based on CMIP6 outputs.

a-d, Global proportions of four flood types, IR-MF (a), ES-MF (b), SM-MF (c), and RS-MF (d), based on the ensemble mean of classification outcomes for individual CMIP6 models using outputs from 1950 to 2014 under historical forcing. e, Regions dominated by different types of floods (that is, the flood type showing the highest proportion of occurrence for each region).

Extended Data Fig. 2 Changes in flood and extreme precipitation from the historical to the future period (SSP245 scenario).

a, Time series of annual maximum precipitation (P) and streamflow (Q) averaged over the global land area from the historical (1950-2014 under historical forcing) to the future (2015–2100 under SSP245 scenario) period based on the outputs of 11 CMIP6 models. Shaded bands represent the variation by individual models. The trends were estimated based on the ensemble mean of model outputs using linear regression with significance level (a two-tailed student’s t test) labeled in the panel. b-c, Global patterns of the trends of annual maximum P (b) and Q (c) from 1950 to 2100. The whitespace represents the dry lands with very limited runoff. d-g, Time series of annual maximum Q averaged over regions dominated by different flood types, IR-MF (d), ES-MF (e), SM-MF (f), and RS-MF (g). The shaded bands, solid lines and dotted lines are similarly defined as those in panel a.

Extended Data Fig. 3 Changes in peak point temperatures of the P~T and Q~T scaling curves from the historical to the future period (SSP585 scenario).

a, Comparison between the changes in peak point temperatures of the P~T and Q~T scaling curves (Tpeak-P and Tpeak-Q) averaged over regions dominated by different flood types based on the outputs of 11 CMIP6 models from the historical (1950-2014 under historical forcing) to the future (2015-2100 under SSP585 scenario) period. Error bars indicate the variations among 11 models (mean value ± one standard deviation). b-c, Global spatial patterns of the changes in Tpeak-P (b) and Tpeak-Q (c) from the historical to the future period.

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Supplementary Information

Supplementary Figs. 1–19, Tables 1 and 2, and Texts 1–3.

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Zhang, S., Zhou, L., Zhang, L. et al. Reconciling disagreement on global river flood changes in a warming climate. Nat. Clim. Chang. 12, 1160–1167 (2022). https://doi.org/10.1038/s41558-022-01539-7

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