Articles | Volume 28, issue 2
https://doi.org/10.5194/npg-28-213-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-28-213-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 May 2021
Research article |
| 06 May 2021
| 06 May 2021
Recurrence analysis of extreme event-like data
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, 14412 Potsdam, Germany
Institute of Environmental Science and Geography, University of Potsdam, 14476 Potsdam, Germany
Bedartha Goswami
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, 14412 Potsdam, Germany
Cluster of Excellence “Machine Learning”, University of Tübingen, 72074 Tübingen, Germany
Yoshito Hirata
Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Deniz Eroglu
Department of Bioinformatics and Genetics, Kadir Has University, 34083 Istanbul, Turkey
Bruno Merz
Institute of Environmental Science and Geography, University of Potsdam, 14476 Potsdam, Germany
Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany
Jürgen Kurths
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, 14412 Potsdam, Germany
Institute of Physics, Humboldt Universität zu Berlin, Berlin, Germany
Norbert Marwan
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, 14412 Potsdam, Germany
Institute of Geoscience, University of Potsdam, 14476 Potsdam, Germany
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This study explored how droughts have evolved over time in northern Benin, a region that relies heavily on rainfall for farming. By analyzing rainfall and river flow patterns, the research found that while conditions improved after major droughts in the 1970s and 1980s, recent years have seen longer dry spells and worsening water shortages. These results can inform better planning for farming and water use in a changing climate.
Tobias Braun, Sara M. Vallejo-Bernal, Norbert Marwan, Juergen Kurths, Johannes Quaas, Albert Díaz-Guilera, Luis Gimeno, and Miguel D. Mahecha
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Marc Lennartz, Benjamin Poschlod, and Bruno Merz
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Predicting hourly rainfall extremes under climate change is crucial yet highly uncertain. Using convection-permitting climate model data over Germany, we compare stationary and non-stationary GEV and sMEV methods. Results show that the sMEV approach exhibits lower uncertainty across return periods. Moreover, the non-stationary sMEV better captures climate-change-induced changes, though care is needed when projecting future extremes.
Tarun Sadana, Jeroen C. J. H. Aerts, Dirk Eilander, Bruno Merz, Hans de Moel, Tim Busker, Veerle Bril, and Jens de Bruijn
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Xiaoxiang Guan, Viet Dung Nguyen, Paul Voit, Bruno Merz, Maik Heistermann, and Sergiy Vorogushyn
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Sergiy Vorogushyn, Li Han, Heiko Apel, Viet Dung Nguyen, Björn Guse, Xiaoxiang Guan, Oldrich Rakovec, Husain Najafi, Luis Samaniego, and Bruno Merz
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The July 2021 flood in central Europe was one of the deadliest floods in Europe in the recent decades and the most expensive flood in Germany. In this paper, we show that the hydrological impact of this event in the Ahr valley could have been even worse if the rainfall footprint trajectory had been only slightly different. The presented methodology of spatial counterfactuals generates plausible unprecedented events and helps to better prepare for future extreme floods.
Adarsh Jojo Thomas, Jürgen Kurths, and Daniel Schertzer
Nonlin. Processes Geophys., 32, 131–138, https://doi.org/10.5194/npg-32-131-2025, https://doi.org/10.5194/npg-32-131-2025, 2025
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We have developed a systematic approach to study the climate system at multiple scales using climate networks, which have been previously used to study correlations between time series in space at only a single scale. This new approach is used to upscale precipitation climate networks to study the Indian summer monsoon and to analyze strong dependencies between spatial regions, which change with changing scales.
Xiaoxiang Guan, Baoying Shan, Viet Dung Nguyen, and Bruno Merz
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Understanding and predicting extreme floods is crucial for reducing disaster risks, yet existing models struggle with unprecedented events. We tested multiple modeling approaches across 400+ river catchments in Central Europe and found that deep learning models outperform traditional methods but still underestimate extreme floods. Our findings suggest that combining data-driven models with physical knowledge can improve flood predictions, helping communities better prepare for future extremes.
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
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Bruno Merz, Günter Blöschl, Robert Jüpner, Heidi Kreibich, Kai Schröter, and Sergiy Vorogushyn
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Viet Dung Nguyen, Sergiy Vorogushyn, Katrin Nissen, Lukas Brunner, and Bruno Merz
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Alberto Montanari, Bruno Merz, and Günter Blöschl
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Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024, https://doi.org/10.5194/hess-28-833-2024, 2024
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In some rivers, the occurrence of extreme flood events is more likely than in other rivers – they have heavy-tailed distributions. We find that threshold processes in the runoff generation lead to such a relatively high occurrence probability of extremes. Further, we find that beyond a certain return period, i.e. for rare events, rainfall is often the dominant control compared to runoff generation. Our results can help to improve the estimation of the occurrence probability of extreme floods.
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Domenico Giaquinto, Warner Marzocchi, and Jürgen Kurths
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Despite being among the most severe climate extremes, it is still challenging to assess droughts’ features for specific regions. In this paper we study meteorological droughts in Europe using concepts derived from climate network theory. By exploring the synchronization in droughts occurrences across the continent we unveil regional clusters which are individually examined to identify droughts’ geographical propagation and source–sink systems, which could potentially support droughts’ forecast.
Renee van Dongen, Dirk Scherler, Dadiyorto Wendi, Eric Deal, Luca Mao, Norbert Marwan, and Claudio I. Meier
EGUsphere, https://doi.org/10.5194/egusphere-2022-1234, https://doi.org/10.5194/egusphere-2022-1234, 2022
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El Niño Southern Oscillation (ENSO) is a climatic phenomenon that causes abnormal climatic conditions in Chile. We investigated how ENSO affects catchment hydrology and found strong seasonal and spatial differences in the hydrological response to ENSO which was caused by different hydrological processes in catchments that are dominated by snowmelt-generated runoff or rainfall-generated runoff. These results are relevant for water resources management and ENSO mitigation in Chile.
Heiko Apel, Sergiy Vorogushyn, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 22, 3005–3014, https://doi.org/10.5194/nhess-22-3005-2022, https://doi.org/10.5194/nhess-22-3005-2022, 2022
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The paper presents a fast 2D hydraulic simulation model for flood propagation that enables operational forecasts of spatially distributed inundation depths, flood extent, flow velocities, and other flood impacts. The detailed spatial forecast of floods and flood impacts is a large step forward from the currently operational forecasts of discharges at selected gauges, thus enabling a more targeted flood management and early warning.
Yoshito Hirata and Yoshinori Yamada
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2022-9, https://doi.org/10.5194/npg-2022-9, 2022
Revised manuscript not accepted
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We show that there exists some complicated days-of-week effects in precipitation at Tokyo. By using the effects, we could improve short-term precipitation forecasts up to 2 hours ahead.
Cinthya Esther Nava Fernandez, Tobias Braun, Bethany Fox, Adam Hartland, Ola Kwiecien, Chelsea Pederson, Sebastian Hoepker, Stefano Bernasconi, Madalina Jaggi, John Hellstrom, Fernando Gázquez, Amanda French, Norbert Marwan, Adrian Immenhauser, and Sebastian Franz Martin Breitenbach
Clim. Past Discuss., https://doi.org/10.5194/cp-2021-172, https://doi.org/10.5194/cp-2021-172, 2022
Manuscript not accepted for further review
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We provide a ca. 1000 year long (6.4–5.4 ka BP) stalagmite-based reconstruction of mid-Holocene rainfall variability in the tropical western Pacific. The annually laminated multi-proxy (δ13C, δ18O, X/Ca, gray values) record comes from Niue island and informs on El Nino-Southern Oscillation and South Pacific Convergence Zone dynamics. Our data suggest that ENSO was active and influenced rainfall seasonality over the covered time interval. Rainfall seasonality was subdued during active ENSO phases
Nico Wunderling, Jonathan F. Donges, Jürgen Kurths, and Ricarda Winkelmann
Earth Syst. Dynam., 12, 601–619, https://doi.org/10.5194/esd-12-601-2021, https://doi.org/10.5194/esd-12-601-2021, 2021
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In the Earth system, climate tipping elements exist that can undergo qualitative changes in response to environmental perturbations. If triggered, this would result in severe consequences for the biosphere and human societies. We quantify the risk of tipping cascades using a conceptual but fully dynamic network approach. We uncover that the risk of tipping cascades under global warming scenarios is enormous and find that the continental ice sheets are most likely to initiate these failures.
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