Climate change has led to concerns about increasing river floods resulting from the greater water-holding capacity of a warmer atmosphere1. These concerns are reinforced by evidence of increasing economic losses associated with flooding in many parts of the world, including Europe2. Any changes in river floods would have lasting implications for the design of flood protection measures and flood risk zoning. However, existing studies have been unable to identify a consistent continental-scale climatic-change signal in flood discharge observations in Europe3, because of the limited spatial coverage and number of hydrometric stations. Here we demonstrate clear regional patterns of both increases and decreases in observed river flood discharges in the past five decades in Europe, which are manifestations of a changing climate. Our results—arising from the most complete database of European flooding so far—suggest that: increasing autumn and winter rainfall has resulted in increasing floods in northwestern Europe; decreasing precipitation and increasing evaporation have led to decreasing floods in medium and large catchments in southern Europe; and decreasing snow cover and snowmelt, resulting from warmer temperatures, have led to decreasing floods in eastern Europe. Regional flood discharge trends in Europe range from an increase of about 11 per cent per decade to a decrease of 23 per cent. Notwithstanding the spatial and temporal heterogeneity of the observational record, the flood changes identified here are broadly consistent with climate model projections for the next century4,5, suggesting that climate-driven changes are already happening and supporting calls for the consideration of climate change in flood risk management.
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The flood discharge data from the data holders/sources listed in Extended Data Table 1 that were used in this paper are available at https://github.com/tuwhydro/europe_floods. The precipitation and temperature data from the E-OBS dataset are available at www.ecad.eu/download/ensembles/ensembles.php. The CPC soil moisture data can be downloaded from www.esrl.noaa.gov/psd.
The code used for the trend estimation and the extreme value analysis can be downloaded from https://github.com/tuwhydro/europe_floods.
IPCC. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) (Cambridge Univ. Press, 2012).
European Academies’ Science Advisory Council. Extreme Weather Events in Europe. Report No. 22 https://easac.eu/publications/details/extreme-weather-events-in-europe/ (EASAC, 2018).
Hall, J. et al. Understanding flood regime changes in Europe: a state of the art assessment. Hydrol. Earth Syst. Sci. 18, 2735–2772 (2014).
Kundzewicz, Z. et al. Differences in flood hazard projections in Europe – their causes and consequences for decision making. Hydrol. Sci. J. 62, 1–14 (2017).
Thober, S. et al. Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degrees global warming. Environ. Res. Lett. 13, 014003 (2018).
Desai, B., Maskrey, A., Peduzzi, P., De Bono, A., & Herold, C. Making Development Sustainable: The Future of Disaster Risk Management. Global Assessment Report on Disaster Risk Reduction http://archive-ouverte.unige.ch/unige:78299 (UNISDR, 2015).
Winsemius, H. C. et al. Global drivers of future river flood risk. Nat. Clim. Change 6, 381–385 (2016).
Blöschl, G. et al. Changing climate shifts timing of European floods. Science 357, 588–590 (2017).
Mangini, W. et al. Detection of trends in magnitude and frequency of flood peaks across Europe. Hydrol. Sci. J. 63, 493–512 (2018).
Berghuijs, W., Aalbers, E., Larsen, J., Trancoso, R. & Woods, R. Recent changes in extreme floods across multiple continents. Environ. Res. Lett. 12, 114035 (2017).
Hodgkins, G. A. et al. Climate-driven variability in the occurrence of major floods across North America and Europe. J. Hydrol. 552, 704–717 (2017).
Hall, J. et al. A European Flood Database: facilitating comprehensive flood research beyond administrative boundaries. Proc. Int. Assoc. Hydrol. Sci. 370, 89–95 (2015).
Sivapalan, M., Blӧschl, G., Merz, R. & Gutknecht, D. Linking flood frequency to long-term water balance: incorporating effects of seasonality. Wat. Resour. Res. 41, W06012 (2005).
Bayliss, A. C. & Jones, R. C. Peaks-over-threshold Flood Database: Summary Statistics and Seasonality. Report No. 121 (Institute of Hydrology, 1993).
Schröter, K., Kunz, M., Elmer, F., Mühr, B. & Merz, B. What made the June 2013 flood in Germany an exceptional event? A hydro-meteorological evaluation. Hydrol. Earth Syst. Sci. 19, 309–327 (2015).
Mediero, L., Santillán, D., Garrote, L. & Granados, A. Detection and attribution of trends in magnitude, frequency and timing of floods in Spain. J. Hydrol. 517, 1072–1088 (2014).
Hall, J. & Blӧschl, G. Spatial patterns and characteristics of flood seasonality in Europe. Hydrol. Earth Syst. Sci. 22, 3883–3901 (2018).
IPCC. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).
Archer, C. L. & Caldeira, K. Historical trends in the jet streams. Geophys. Res. Lett. 35, 08803 (2008).
Kang, S. M. & Lu, J. Expansion of the Hadley cell under global warming: winter versus summer. J. Clim. 25, 8387–8393 (2012).
Amponsah, W. et al. Integrated high-resolution dataset of high-intensity European and Mediterranean flash floods. Earth Syst. Sci. Data 10, 1783–1794 (2018).
Ban, N., Schmidli, J. & Schär, C. Heavy precipitation in a changing climate: does short-term summer precipitation increase faster? Geophys. Res. Lett. 42, 1165–1172 (2015).
Rogger, M. et al. Land use change impacts on floods at the catchment scale: challenges and opportunities for future research. Wat. Resour. Res. 53, 5209–5219 (2017).
Perdigão, R. A. P., Pires, C. A. L. & Hall, J. Synergistic dynamic theory of complex coevolutionary systems: disentangling nonlinear spatiotemporal controls on precipitation. Preprint at https://arxiv.org/abs/1611.03403 (2016).
Estilow, T. W., Young, A. H. & Robinson, D. A. A long-term Northern Hemisphere snow cover extent data record for climate studies and monitoring. Earth Syst. Sci. Data 7, 137–142 (2015).
Frolova, N. L. et al. Hydrological hazards in Russia: origin, classification, changes and risk assessment. Nat. Hazards 88, 103–131 (2017).
Mediero, L. et al. Identification of coherent flood regions across Europe by using the longest streamflow records. J. Hydrol. (Amst.) 528, 341–360 (2015).
Salinas, J. L., Castellarin, A., Kohnova, S. & Kjeldsen, T. Regional parent flood frequency distributions in Europe-Part 2: climate and scale controls. Hydrol. Earth Syst. Sci. 18, 4391–4401 (2014).
Xoplaki, E., Gonzalez-Rouco, J. F., Luterbacher, J. & Wanner, H. Wet season Mediterranean precipitation variability: influence of large-scale dynamics and trends. Clim. Dyn. 23, 63–78 (2004).
Brooks, H. E. Severe thunderstorms and climate change. Atmos. Res. 123, 129–138 (2013).
Vogt, J. et al. A pan-European River and Catchment Database. Report No. EUR 22920 (Office for Official Publications of the European Communities, 2007).
Haylock, M. et al. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res. 113, D20119 (2008).
van den Dool, H., Huang, J. & Fan, Y. Performance and analysis of the constructed analogue method applied to US soil moisture over 1981–2001. J. Geophys. Res. 108, 8617 (2003).
Sen, P. K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).
Theil, H. A rank-invariant method of linear and polynomial regression analysis. Part 1. Proc. K. Ned. Akad. Wet. 53, 386–392 (1950).
Mann, H. B. Nonparametric tests against trend. Econometrica 13, 245–259 (1945).
Hiemstra, P. H., Pebesma, E. J., Twenhӧfel, C. J. & Heuvelink, G. B. Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network. Comput. Geosci. 35, 1711–1721 (2009).
Wilcox, R. A note on the Theil-Sen regression estimator when the regressor is random and the error term is heteroscedastic. Biometrical J. 40, 261–268 (1998).
Helsel, D. R. & Frans, L. M. Regional Kendall test for trend. Environ. Sci. Technol. 40, 4066–4073 (2006).
Renard, B., Lang, M. & Bois, P. Statistical analysis of extreme events in a non-stationary context via a Bayesian framework: case study with peak-over-threshold data. Stoch. Env. Res. Risk A. 21, 97–112 (2006).
Martins, E. S. & Stedinger, J. R. Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data. Wat. Resour. Res. 36, 737–744 (2000).
Watanabe, S. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594 (2010).
This work was supported by the ERC Advanced Grant ‘FloodChange’ project (number 291152), the Horizon 2020 ETN ‘System Risk’ project (number 676027), the DFG ‘SPATE’ project (FOR 2416), the FWF ‘SPATE’ project (I 3174) and a Russian Foundation for Basic Research (RFBR) project (number 17-05-41030 rgo_a). The data analysis was performed in R using the supporting packages automap, boot, lattice, maptools, ncdf4, plyr, raster, RColorBrewer, rgdal and rworldmap. The authors acknowledge the involvement in the data screening process of C. Álvaro Díaz, I. Borzì, E. Diamantini, K. Jeneiová, M. Kupfersberger, S. Mallucci and S. Persiano during their stays at the Vienna University of Technology. We thank L. Gaál and D. Rosbjerg for contacting Finnish and Danish data holders, respectively; B. Renard (France), W. Rigott (South Tyrol, Italy), G. Lindström (Sweden) and P. Burlando (Switzerland) for assistance in preparing and/or providing data or metadata from their respective regions. We acknowledge all flood data providers listed in Extended Data Table 1.
The authors declare no competing interests.
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Extended data figures and tables
a, Elevation (in metres above sea level), main rivers and lakes. b, Locations of the hydrometric stations analysed. Open and full circles indicate stations with more than 30 years (n = 3,738) and more than 40 years (n = 2,835) of flood discharge data, respectively.
a, Points show local trends (n = 2,370), with larger points indicating statistically significant trends (significance level α = 0.1). Background pattern represents regional trends. Blue indicates increasing flood discharges and red denotes decreasing flood discharges. Rectangles indicate hotspot areas as in Fig. 2, Extended Data Fig. 3, Extended Data Table 2c. b, Uncertainties of the trends in terms of standard deviation. Points show local uncertainties. The background pattern represents regional uncertainties at the scale of a block size of 200 × 200 km2. Units of both panels are per cent of mean per decade.
Extended Data Fig. 3 Flood trends as in Fig. 1 and Extended Data Fig. 2, but using fewer stations.
a, Only stations with significant trends are used (n = 664). b, Only stations with distances larger than 50 km from each other are used (n = 745).
Extended Data Fig. 4 Long-term temporal evolution of timing of floods and their drivers for seven hotspots in Europe.
a, Northern United Kingdom; b, western France; c, southern Germany and western Czechia; d, northern Iberia; e, central Balkans; f, southern Finland; g, western Russia. Shown are the timing of observed floods (green), the seven-day maximum precipitation (purple), the snowmelt index (orange) and the maximum monthly soil moisture (blue). Lines show the median timing and shaded bands indicate the variability of timing within the year (±0.5 circular standard deviations). All data were subjected to a circular ten-year moving-average filter. Vertical axes show month of the year (June to May).
a, Long-term mean (in millimetres per day). b, Trends in precipitation (per cent of mean per decade), for which larger points indicate statistically significant trends (α = 0.1). Blue indicates increasing precipitation and red denotes decreasing precipitation.
a, Long-term mean (in degrees Celsius); b, trends in temperatures (in degrees Celsius per decade), with larger points indicating statistically significant trends (α = 0.1). Red indicates increasing temperature and blue represents decreasing temperature. JFMA, January to April.
a, Long-term mean (in millimetres). b, Trends in maximum soil moisture (per cent of mean per decade), for which larger points indicate statistically significant trends (α = 0.1). Blue indicates increasing soil moisture and red denotes decreasing soil moisture.
Points show local return periods (n = 2,370), with larger points indicating agreement of the 5th and the 95th percentiles of the uncertainty distribution in the sign of change. The background pattern represents regional return periods. Blue indicates lower return periods, representing increasing flood discharges, and red indicates higher return periods, representing decreasing flood discharges. This figure provides a continental overview and does not replace national-scale and local studies, for which more detailed information may be available.
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Blöschl, G., Hall, J., Viglione, A. et al. Changing climate both increases and decreases European river floods. Nature 573, 108–111 (2019). https://doi.org/10.1038/s41586-019-1495-6
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