Articles | Volume 26, issue 24
https://doi.org/10.5194/hess-26-6339-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-6339-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Dec 2022
Research article |
| 16 Dec 2022
| 16 Dec 2022
River flooding mechanisms and their changes in Europe revealed by explainable machine learning
Department of Computational Hydrosystems, Helmholtz Centre for
Environmental Research, 04318 Leipzig, Germany
Emanuele Bevacqua
Department of Computational Hydrosystems, Helmholtz Centre for
Environmental Research, 04318 Leipzig, Germany
Jakob Zscheischler
Department of Computational Hydrosystems, Helmholtz Centre for
Environmental Research, 04318 Leipzig, Germany
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Cited articles
Alfieri, L., Burek, P., Feyen, L., and Forzieri, G.: Global warming increases the frequency of river floods in Europe, Hydrol. Earth Syst. Sci., 19, 2247–2260, https://doi.org/10.5194/hess-19-2247-2015, 2015.
Alfieri, L., Bisselink, B., Dottori, F., Naumann, G., de Roo, A., Salamon, P., Wyser, K., and Feyen, L.: Global projections of river flood risk in a
warmer world, Earth's Future, 5, 171–182, https://doi.org/10.1002/2016ef000485, 2017.
Barnes, E. A., Toms, B., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., and
Anderson, D.: Indicator patterns of forced change learned by an artificial
neural network, J. Adv. Model. Earth Syst., 12, e2020MS002195, https://doi.org/10.1029/2020ms002195, 2020.
Bengtsson, L., Hodges, K. I., and Roeckner, E.: Storm tracks and climate change, J. Climate, 19, 3518–3543, https://doi.org/10.1175/jcli3815.1, 2006.
Beniston, M. and Stoffel, M.: Rain-on-snow events, floods and climate change
in the Alps: Events may increase with warming up to 4 degrees C and decrease
thereafter, Sci. Total Environ., 571, 228–236, https://doi.org/10.1016/j.scitotenv.2016.07.146, 2016.
Bennett, B., Leonard, M., Deng, Y., and Westra, S.: An empirical investigation into the effect of antecedent precipitation on flood volume,
J. Hydrol., 567, 435–445, https://doi.org/10.1016/j.jhydrol.2018.10.025, 2018.
Berghuijs, W. R., Woods, R. A., Hutton, C. J., and Sivapalan, M.: Dominant
flood generating mechanisms across the United States, Geophysical Research
Letters, 43, 4382–4390, 10.1002/2016gl068070, 2016.
Berghuijs, W. R., Harrigan, S., Molnar, P., Slater, L. J., and Kirchner, J.
W.: The relative importance of different flood-generating mechanisms across
Europe, Water Resour. Res., 55, 4582–4593, https://doi.org/10.1029/2019wr024841, 2019.
Bertola, M., Viglione, A., Lun, D., Hall, J., and Blöschl, G.: Flood
trends in Europe: are changes in small and big floods different?, Hydrol.
Earth Syst. Sci., 24, 1805–1822, https://doi.org/10.5194/hess-24-1805-2020, 2020.
Bertola, M., Viglione, A., Vorogushyn, S., Lun, D., Merz, B., and Bloeschl,
G.: Do small and large floods have the same drivers of change? A regional
attribution analysis in Europe, Hydrol. Earth Syst. Sci., 25, 1347–1364, https://doi.org/10.5194/hess-25-1347-2021, 2021.
Bevacqua, E., De Michele, C., Manning, C., Couasnon, A., Ribeiro, A. F. S.,
Ramos, A. M., Vignotto, E., Bastos, A., Blesic, S., Durante, F., Hillier, J., Oliveira, S. C., Pinto, J. G., Ragno, E., Rivoire, P., Saunders, K., van der Wiel, K., Wu, W. Y., Zhang, T. Y., and Zscheischler, J.: Guidelines for
studying diverse types of compound weather and climate events, Earth's
Future, 9, e2021EF002340, https://doi.org/10.1029/2021ef002340, 2021.
Blanchet, J. and Creutin, J. D.: Co-occurrence of extreme daily rainfall in
the French mediterranean region, Water Resour. Res., 53, 9330–9349,
https://doi.org/10.1002/2017wr020717, 2017.
Blöschl, G., Hall, J., Parajka, J., Perdigao, R. A. P., Merz, B., Arheimer, B., Aronica, G. T., Bilibashi, A., Bonacci, O., Borga, M., Canjevac, I., Castellarin, A., Chirico, G. B., Claps, P., Fiala, K., Frolova, N., Gorbachova, L., Gul, A., Hannaford, J., Harrigan, S., Kireeva, M., Kiss, A., Kjeldsen, T. R., Kohnova, S., Koskela, J. J., Ledvinka, O., Macdonald, N., Mavrova-Guirguinova, M., Mediero, L., Merz, R., Molnar, P., Montanari, A., Murphy, C., Osuch, M., Ovcharuk, V., Radevski, I., Rogger, M., Salinas, J. L., Sauquet, E., Sraj, M., Szolgay, J., Viglione, A., Volpi, E., Wilson, D., Zaimi, K., and Zivkovic, N.: Changing climate shifts timing of European floods, Science, 357, 588–590, https://doi.org/10.1126/science.aan2506, 2017.
Blöschl, G., Hall, J., Viglione, A., Perdigao, R. A. P., Parajka, J.,
Merz, B., Lun, D., Arheimer, B., Aronica, G. T., Bilibashi, A., Bohac, M.,
Bonacci, O., Borga, M., Canjevac, I., Castellarin, A., Chirico, G. B., Claps, P., Frolova, N., Ganora, D., Gorbachova, L., Gul, A., Hannaford, J., Harrigan, S., Kireeva, M., Kiss, A., Kjeldsen, T. R., Kohnova, S., Koskela, J. J., Ledvinka, O., Macdonald, N., Mavrova-Guirguinova, M., Mediero, L., Merz, R., Molnar, P., Montanari, A., Murphy, C., Osuch, M., Ovcharuk, V., Radevski, I., Salinas, J. L., Sauquet, E., Sraj, M., Szolgay, J., Volpi, E.,
Wilson, D., Zaimi, K., and Zivkovic, N.: Changing climate both increases and
decreases European river floods, Nature, 573, 108–111,
https://doi.org/10.1038/s41586-019-1495-6, 2019.
Brunner, M. I., Swain, D. L., Wood, R. R., Willkofer, F., Done, J. M.,
Gilleland, E., and Ludwig, R.: An extremeness threshold determines the
regional response of floods to changes in rainfall extremes, Commun. Earth Environ., 2, 173, https://doi.org/10.1038/s43247-021-00248-x, 2021.
Cohen, J., Ye, H. C., and Jones, J.: Trends and variability in rain-on-snow
events, Geophys. Res. Lett., 42, 7115–7122, https://doi.org/10.1002/2015gl065320,
2015.
Davenport, F. V., Herrera-Estrada, J. E., Burke, M., and Diffenbaugh, N. S.:
Flood size increases nonlinearly across the western United States in response to lower snow-precipitation ratios, Water Resour. Res., 56, e2019WR025571, https://doi.org/10.1029/2019wr025571, 2020.
Do, H. X., Gudmundsson, L., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata, Earth Syst. Sci. Data, 10, 765–785, https://doi.org/10.5194/essd-10-765-2018, 2018a.
Do, H. X., Gudmundsson, L., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive – Part 1: Station catalog and Catchment boundary, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.887477, 2018b.
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J.,
Münkemüller, T., McClean, C., Osborne, P. E., Reineking, B., Schröder, B., Skidmore, A. K., Zurell, D., and Lautenbach, S.:
Collinearity: a review of methods to deal with it and a simulation study
evaluating their performance, Ecography, 36, 27–46,
https://doi.org/10.1111/j.1600-0587.2012.07348.x, 2013.
ECA & D: E-OBS gridded dataset, ECA & D [data set], https://www.ecad.eu/download/ensembles/download.php (last access: 1 November 2021), 2022.
EROS: Digital Elevation – Global 30 Arc-Second Elevation (GTOPO30), USGS [data set], https://doi.org/10.5066/F7DF6PQS, 2018.
Federal Institute of Hydrology: Watershed Boundaries of GRDC Stations, Global Runoff Data Centre [data set], https://www.bafg.de/GRDC/EN/02_srvcs/22_gslrs/222_WSB/watershedBoundaries.html (last access: 1 November 2021), 2011.
Federal Institute of Hydrology: Global Runoff Database, Global Runoff Data Centre [data set], https://portal.grdc.bafg.de/applications/public.html?publicuser=PublicUser (last access: 1 November 2021), 2022.
Fischer, E. M. and Knutti, R.: Observed heavy precipitation increase confirms theory and early models, Nat. Clim. Change, 6, 986–991, https://doi.org/10.1038/nclimate3110, 2016.
Fontrodona-Bach, A., van der Schrier, G., Melsen, L. A., Tank, A., and Teuling, A. J.: Widespread and accelerated decrease of observed mean and
extreme snow depth over Europe, Geophys. Res. Lett., 45, 12312–12319, https://doi.org/10.1029/2018gl079799, 2018.
Forsythe, W. C., Rykiel, E. J., Stahl, R. S., Wu, H. I., and Schoolfield, R.
M.: A model comparison for daylength as a function of latitude and day of
year, Ecological Modelling, 80, 87-95, 10.1016/0304-3800(94)00034-f, 1995.
Frame, J. M., Kratzert, F., Klotz, D., Gauch, M., Shalev, G., Gilon, O.,
Qualls, L. M., Gupta, H. V., and Nearing, G. S.: Deep learning rainfall–runoff predictions of extreme events, Hydrol. Earth Syst. Sci.,
26, 3377–3392, https://doi.org/10.5194/hess-26-3377-2022, 2022.
Gers, F. A., Schmidhuber, J., and Cummins, F.: Learning to forget: continual
prediction with LSTM, in: 1999 Ninth International Conference on Artificial
Neural Networks ICANN 99, Conf. Publ. No. 470,, 7–10 September 1999, Edinburgh, UK, https://doi.org/10.1049/cp:19991218, 1999.
Gobiet, A., Kotlarski, S., Beniston, M., Heinrich, G., Rajczak, J., and Stoffel, M.: 21st century climate change in the European Alps – A review,
Sci. Total Environ., 493, 1138–1151, https://doi.org/10.1016/j.scitotenv.2013.07.050, 2014.
Hall, J. and Blöschl, G.: Spatial patterns and characteristics of flood
seasonality in Europe, Hydrolo. Earth Syst. Sci., 22, 3883–3901,
https://doi.org/10.5194/hess-22-3883-2018, 2018.
Hall, J., Arheimer, B., Borga, M., Brazdil, R., Claps, P., Kiss, A., Kjeldsen, T. R., Kriauciuniene, J., Kundzewicz, Z. W., Lang, M., Llasat, M. C., Macdonald, N., McIntyre, N., Mediero, L., Merz, B., Merz, R., Molnar, P., Montanari, A., Neuhold, C., Parajka, J., Perdigao, R. A. P., Plavcova, L., Rogger, M., Salinas, J. L., Sauquet, E., Schar, C., Szolgay, J., Viglione, A., and Bloschl, G.: Understanding flood regime changes in Europe: a state-of-the-art assessment, Hydrol. Earth Syst. Sci., 18, 2735–2772, https://doi.org/10.5194/hess-18-2735-2014, 2014.
Hallema, D. W., Moussa, R., Sun, G., and McNulty, S. G.: Surface storm flow
prediction on hillslopes based on topography and hydrologic connectivity,
Ecol. Process., 5, 13, https://doi.org/10.1186/s13717-016-0057-1, 2016.
Hamed, K. H. and Rao, A. R.: A modified Mann–Kendall trend test for
autocorrelated data, J. Hydrol., 204, 182–196, https://doi.org/10.1016/s0022-1694(97)00125-x, 1998.
Hamon, W. R.: Estimating potential evapotranspiration, J. Hydraul. Div., 87, 107–102, https://doi.org/10.1061/JYCEAJ.0000599, 1961.
Hartono, N. T. P., Thapa, J., Tiihonen, A., Oviedo, F., Batali, C., Yoo, J.
J., Liu, Z., Li, R., Marrón, D. F., Bawendi, M. G., Buonassisi, T., and
Sun, S.: How machine learning can help select capping layers to suppress
perovskite degradation, Nat. Commun., 11, 4172, https://doi.org/10.1038/s41467-020-17945-4, 2020.
Haylock, M. R., Hofstra, N., Tank, A., Klok, E. J., Jones, P. D., and New,
M.: A European daily high-resolution gridded data set of surface temperature
and precipitation for 1950–2006, J. Geophys. Res.-Atmos., 113, D20119, https://doi.org/10.1029/2008jd010201, 2008.
Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D.,
Watanabe, S., Kim, H., and Kanae, S.: Global flood risk under climate change, Nat. Clim. Change, 3, 816–821, https://doi.org/10.1038/nclimate1911, 2013.
Hochreiter, S. and Schmidhuber, J.: Long short-term memory, Neural Comput., 9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997.
Isotta, F. A., Frei, C., Weilguni, V., Tadic, M. P., Lassegues, P., Rudolf,
B., Pavan, V., Cacciamani, C., Antolini, G., Ratto, S. M., Munari, M.,
Micheletti, S., Bonati, V., Lussana, C., Ronchi, C., Panettieri, E., Marigo,
G., and Vertacnik, G.: The climate of daily precipitation in the Alps:
development and analysis of a high-resolution grid dataset from pan-Alpine
rain-gauge data, Int. J. Climatol., 34, 1657–1675, https://doi.org/10.1002/joc.3794, 2014.
Jiang, S.: An interpretive deep learning framework for identifying flooding mechanisms, Zenodo [code], https://doi.org/10.5281/zenodo.4686106, 2022.
Jiang, S. J., Zheng, Y., Wang, C., and Babovic, V.: Uncovering flooding
mechanisms across the contiguous United States through interpretive deep
learning on representative catchments, Water Resour. Res., 58, e2021WR030185, https://doi.org/10.1029/2021wr030185, 2022.
Keller, L., Rossler, O., Martius, O., and Weingartner, R.: Delineation of
flood generating processes and their hydrological response, Hydrol. Process., 32, 228–240, https://doi.org/10.1002/hyp.11407, 2018.
Kemter, M., Merz, B., Marwan, N., Vorogushyn, S., and Bloeschl, G.: Joint
trends in flood magnitudes and spatial extents across Europe, Geophys. Res. Lett., 47, e2020GL087464, https://doi.org/10.1029/2020gl087464, 2020.
Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, in: 3rd International Conference on Learning Representations, 7–9 May 2015, San Diego, https://doi.org/10.48550/arXiv.1412.6980, 2015.
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.:
Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks,
Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018.
Kratzert, F., Herrnegger, M., Klotz, D., Hochreiter, S., and Klambauer, G.:
NeuralHydrology – Interpreting LSTMs in Hydrology, in: Explainable AI:
Interpreting, Explaining and Visualizing Deep Learning, edited by: Samek,
W., Montavon, G., Vedaldi, A., Hansen, L. K., and Müller, K.-R., Springer International Publishing, Cham, 347–362, https://doi.org/10.1007/978-3-030-28954-6_19, 2019a.
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and
Nearing, G.: Towards learning universal, regional, and local hydrological
behaviors via machine learning applied to large-sample datasets, Hydrol.
Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019b.
Kroll, C. N. and Song, P.: Impact of multicollinearity on small sample
hydrologic regression models, Water Resour. Res., 49, 3756-3769,
https://doi.org/10.1002/wrcr.20315, 2013.
Labe, Z. M. and Barnes, E. A.: Detecting climate signals using explainable AI With single-forcing large ensembles, J. Adv. Model. Earth Syst., 13, e2021MS002464, https://doi.org/10.1029/2021ms002464, 2021.
Lavers, D. A. and Villarini, G.: The nexus between atmospheric rivers and
extreme precipitation across Europe, Geophys. Res. Lett., 40, 3259–3264, https://doi.org/10.1002/grl.50636, 2013.
Ledingham, J., Archer, D., Lewis, E., Fowler, H., and Kilsby, C.: Contrasting seasonality of storm rainfall and flood runoff in the UK and some implications for rainfall-runoff methods of flood estimation, Hydrol. Res., 50, 1309–1323, https://doi.org/10.2166/nh.2019.040, 2019.
Lees, T., Buechel, M., Anderson, B., Slater, L., Reece, S., Coxon, G., and
Dadson, S. J.: Benchmarking data-driven rainfall-runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models, Hydrol. Earth Syst. Sci., 25, 5517–5534, https://doi.org/10.5194/hess-25-5517-2021, 2021.
Lees, T., Reece, S., Kratzert, F., Klotz, D., Gauch, M., De Bruijn, J.,
Kumar Sahu, R., Greve, P., Slater, L., and Dadson, S. J.: Hydrological concept formation inside long short-term memory (LSTM) networks, Hydrol.
Earth Syst. Sci., 26, 3079–3101, https://doi.org/10.5194/hess-26-3079-2022, 2022.
Lehner, B.: Derivation of watershed boundaries for GRDC gauging stations
based on the HydroSHEDS drainage network, Federal Institute of Hydrology (BfG), Koblenz, 18 pp., https://www.bafg.de/GRDC/EN/02_srvcs/24_rprtsrs/report_41 (last access: 1 November 2021), 2012.
Merz, B., Vorogushyn, S., Uhlemann, S., Delgado, J., and Hundecha, Y.: HESS
Opinions “More efforts and scientific rigour are needed to attribute trends
in flood time series”, Hydrol. Earth Syst. Sci., 16, 1379–1387,
https://doi.org/10.5194/hess-16-1379-2012, 2012.
Merz, B., Blöschl, G., Vorogushyn, S., Dottori, F., Aerts, J. C. J. H.,
Bates, P., Bertola, M., Kemter, M., Kreibich, H., Lall, U., and Macdonald, E.: Causes, impacts and patterns of disastrous river floods, Nat. Rev. Earth Environ., 2, 592–609, https://doi.org/10.1038/s43017-021-00195-3, 2021.
Merz, R. and Blöschl, G.: A process typology of regional floods, Water
Resour. Res., 39, 1340, https://doi.org/10.1029/2002wr001952, 2003.
Moriasi, D. N., Gitau, M. W., Pai, N., and Daggupati, P.: Hydrologic and
water quality models: Performance measures and evaluation criteria, T. ASABE, 58, 1763–1785, 2015.
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., and Yu, B.: Definitions, methods, and applications in interpretable machine learning, P. Natl. Acad. Sci. USA, 116, 22071–22080, https://doi.org/10.1073/pnas.1900654116, 2019.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D.,
Frame, J. M., Prieto, C., and Gupta, H. V.: What Role Does Hydrological
Science Play in the Age of Machine Learning?, Water Resour. Res., 57,
e2020WR028091, https://doi.org/10.1029/2020wr028091, 2021.
Nied, M., Pardowitz, T., Nissen, K., Ulbrich, U., Hundecha, Y., and Merz, B.: On the relationship between hydro-meteorological patterns and flood types, J. Hydrol., 519, 3249–3262, https://doi.org/10.1016/j.jhydrol.2014.09.089, 2014.
Pagano, T. and Garen, D.: A recent increase in western US streamflow variability and persistence, J. Hydrometeorol., 6, 173–179, https://doi.org/10.1175/jhm410.1, 2005.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204,
https://doi.org/10.1038/s41586-019-0912-1, 2019.
Rottler, E., Bronstert, A., Burger, G., and Rakovec, O.: Projected changes
in Rhine River flood seasonality under global warming, Hydrol. Earth Syst. Sci., 25, 2353–2371, https://doi.org/10.5194/hess-25-2353-2021, 2021.
Rousseeuw, P. J.: Silhouettes – A graphical aid to the interpretation and
validation of cluster-analysis, J. Comput. Appl. Math., 20, 53–65, https://doi.org/10.1016/0377-0427(87)90125-7, 1987.
Salvador, S. and Chan, P.: Toward accurate dynamic time warping in linear
time and space, Intell. Data Anal., 11, 561–580, https://doi.org/10.3233/IDA-2007-11508, 2007.
Schiemann, R., Vidale, P. L., Shaffrey, L. C., Johnson, S. J., Roberts, M.
J., Demory, M. E., Mizielinski, M. S., and Strachan, J.: Mean and extreme
precipitation over European river basins better simulated in a 25 km AGCM,
Hydrol. Earth Syst. Sci., 22, 3933–3950, https://doi.org/10.5194/hess-22-3933-2018, 2018.
Sharma, A., Wasko, C., and Lettenmaier, D. P.: If precipitation extremes are
increasing, why aren't floods?, Water Resour. Res., 54, 8545–8551,
https://doi.org/10.1029/2018wr023749, 2018.
Shen, C. P.: A transdisciplinary review of deep learning research and its
relevance for water resources scientists, Water Resour. Res., 54, 8558–8593, https://doi.org/10.1029/2018wr022643, 2018.
Sherstinsky, A.: Fundamentals of Recurrent Neural Network (RNN) and Long
Short-Term Memory (LSTM) network, Physica D, 404, 132306, https://doi.org/10.1016/j.physd.2019.132306, 2020.
Sikorska, A. E., Viviroli, D., and Seibert, J.: Flood-type classification in
mountainous catchments using crisp and fuzzy decision trees, Water Resour.
Res., 51, 7959–7976, https://doi.org/10.1002/2015wr017326, 2015.
Stein, L., Pianosi, F., and Woods, R.: Event-based classification for global
study of river flood generating processes, Hydrol. Process., 34, 1514–1529, https://doi.org/10.1002/hyp.13678, 2020.
Stein, L., Clark, M. P., Knoben, W. J. M., Pianosi, F., and 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, https://doi.org/10.1029/2020wr028300, 2021.
Su, Y. H. and Kuo, C. C. J.: On extended long short-term memory and dependent bidirectional recurrent neural network, Neurocomputing, 356, 151–161, https://doi.org/10.1016/j.neucom.2019.04.044, 2019.
Sundararajan, M., Taly, A., and Yan, Q.: Axiomatic attribution for deep
networks, in: Proceedings of the 34th International Conference on Machine
Learning, August 2017, Sydney, https://doi.org/10.48550/arXiv.1703.01365, 2017.
Tarasova, L., Merz, R., Kiss, A., Basso, S., Blöschl, G., Merz, B.,
Viglione, A., Plotner, S., Guse, B., Schumann, A., Fischer, S., Ahrens, B.,
Anwar, F., Bardossy, A., Buhler, P., Haberlandt, U., Kreibich, H., Krug, A.,
Lun, D., Muller-Thomy, H., Pidoto, R., Primo, C., Seidel, J., Vorogushyn, S., and Wietzke, L.: Causative classification of river flood events, Wiley Interdisciplin. Rev.-Water, 6, e1353, https://doi.org/10.1002/wat2.1353, 2019.
Tarasova, L., Basso, S., Wendi, D., Viglione, A., Kumar, R., and Merz, R.: A
Process-Based Framework to Characterize and Classify Runoff Events: The
Event Typology of Germany, Water Resour. Res., 56, e2019WR026951, https://doi.org/10.1029/2019wr026951, 2020.
Tavenard, R., Faouzi, J., Vandewiele, G., Divo, F., Androz , G., Holtz, C.,
Payne, M., Yurchak, R., Rußwurm, M., Kolar, K., and Woods, E.: Tslearn,
a machine learning toolkit for time series data, J. Mach. Learn. Res., 21, 1–6, 2020.
Tellman, B., Sullivan, J. A., Kuhn, C., Kettner, A. J., Doyle, C. S., Brakenridge, G. R., Erickson, T. A., and Slayback, D. A.: Satellite imaging
reveals increased proportion of population exposed to floods, Nature, 596,
80–86, https://doi.org/10.1038/s41586-021-03695-w, 2021.
Toms, B. A., Barnes, E. A., and Ebert-Uphoff, I.: Physically interpretable
neural networks for the geosciences: Applications to Earth system variability, J. Adv. Model. Earth Syst., 12, e2019MS002002, https://doi.org/10.1029/2019ms002002, 2020.
Trenberth, K. E.: Changes in precipitation with climate change, Clim. Res., 47, 123–138, https://doi.org/10.3354/cr00953, 2011.
Turkington, T., Breinl, K., Ettema, J., Alkema, D., and Jetten, V.: A new
flood type classification method for use in climate change impact studies,
Weather Clim. Ext., 14, 1–16, https://doi.org/10.1016/j.wace.2016.10.001, 2016.
Vormoor, K., Lawrence, D., Heistermann, M., and Bronstert, A.: Climate change impacts on the seasonality and generation processes of floods – projections and uncertainties for catchments with mixed snowmelt/rainfall regimes, Hydrol. Earth Syst. Sci., 19, 913–931, https://doi.org/10.5194/hess-19-913-2015, 2015.
Vormoor, K., Lawrence, D., Schlichting, L., Wilson, D., and Wong, W. K.:
Evidence for changes in the magnitude and frequency of observed rainfall vs. snowmelt driven floods in Norway, J. Hydrol., 538, 33–48,
https://doi.org/10.1016/j.jhydrol.2016.03.066, 2016.
Wasko, C. and Nathan, R.: Influence of changes in rainfall and soil moisture
on trends in flooding, J. Hydrol., 575, 432–441, https://doi.org/10.1016/j.jhydrol.2019.05.054, 2019.
Whan, K., Sillmann, J., Schaller, N., and Haarsma, R.: Future changes in
atmospheric rivers and extreme precipitation in Norway, Clim. Dynam., 54, 2071–2084, https://doi.org/10.1007/s00382-019-05099-z, 2020.
Yu, S. W. and Ma, J. W.: Deep learning for geophysics: Current and future
trends, Rev. Geophys., 59, e2021RG000742, https://doi.org/10.1029/2021rg000742, 2021.
Zscheischler, J., Westra, S., van den Hurk, B., Seneviratne, S. I., Ward, P.
J., Pitman, A., AghaKouchak, A., Bresch, D. N., Leonard, M., Wahl, T., and
Zhang, X. B.: Future climate risk from compound events, Nat. Clim. Change, 8, 469–477, https://doi.org/10.1038/s41558-018-0156-3, 2018.
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton, R. M., van den Hurk, B., AghaKouchak, A., Jezequel, A., Mahecha, M. D., Maraun, D., Ramos, A. M., Ridder, N. N., Thiery, W., and Vignotto, E.: A
typology of compound weather and climate events, Nat. Rev. Earth Environ., 1, 333–347, https://doi.org/10.1038/s43017-020-0060-z, 2020.
Short summary
Using a novel explainable machine learning approach, we investigated the contributions of precipitation, temperature, and day length to different peak discharges, thereby uncovering three primary flooding mechanisms widespread in European catchments. The results indicate that flooding mechanisms have changed in numerous catchments over the past 70 years. The study highlights the potential of artificial intelligence in revealing complex changes in extreme events related to climate change.
Using a novel explainable machine learning approach, we investigated the contributions of...
