Articles | Volume 26, issue 3
https://doi.org/10.5194/npg-26-251-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/npg-26-251-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
15 Aug 2019
Research article | Highlight paper |
| 15 Aug 2019
| 15 Aug 2019
Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach
Jürgen Kurths
Potsdam Institute for Climate Impact Research (PIK), Member of the
Leibniz Association, Telegrafenberg, Potsdam, Germany
Institute of Earth and Environmental Science, University of Potsdam,
Potsdam, Germany
Institute of Physics, Humboldt Universität zu Berlin, Germany
Invited contribution by Jürgen Kurths, recipient of the EGU Lewis Fry Richardson Medal 2013.
Ankit Agarwal
CORRESPONDING AUTHOR
Potsdam Institute for Climate Impact Research (PIK), Member of the
Leibniz Association, Telegrafenberg, Potsdam, Germany
Institute of Earth and Environmental Science, University of Potsdam,
Potsdam, Germany
GFZ German Research Centre for Geosciences, Section 5.4: Hydrology,
Telegrafenberg, Potsdam, Germany
Roopam Shukla
Potsdam Institute for Climate Impact Research (PIK), Member of the
Leibniz Association, Telegrafenberg, Potsdam, Germany
Norbert Marwan
Potsdam Institute for Climate Impact Research (PIK), Member of the
Leibniz Association, Telegrafenberg, Potsdam, Germany
Maheswaran Rathinasamy
Dept. of Civil Engineering, MVGR College of Engineering, Vizianagaram,
India
Levke Caesar
Potsdam Institute for Climate Impact Research (PIK), Member of the
Leibniz Association, Telegrafenberg, Potsdam, Germany
Institute of Physics and Astronomy, University of Potsdam, Potsdam,
Germany
Raghavan Krishnan
Indian Institute of Tropical Meteorology, Pune, India
Bruno Merz
Institute of Earth and Environmental Science, University of Potsdam,
Potsdam, Germany
GFZ German Research Centre for Geosciences, Section 5.4: Hydrology,
Telegrafenberg, Potsdam, Germany
Related authors
Tobias Braun, Sara M. Vallejo-Bernal, Norbert Marwan, Juergen Kurths, Johannes Quaas, Albert Díaz-Guilera, Luis Gimeno, and Miguel D. Mahecha
Earth Syst. Dynam., 17, 695–716, https://doi.org/10.5194/esd-17-695-2026, https://doi.org/10.5194/esd-17-695-2026, 2026
Short summary
Short summary
Atmospheric rivers (ARs) move vast amounts of water through the atmosphere and often cause weather extremes, yet they are usually studied as regional events. Using 84 years of mapped AR trajectories, we reveal the global "roadmap" of ARs, a transport network of high-activity hubs, sparse atmospheric highways & hierarchical basins. Our approach shows how water vapor is systematically channelled through an atmospheric transport network, offering new ways to study changes in the global water cycle.
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
Short summary
Short summary
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.
Sara M. Vallejo-Bernal, Frederik Wolf, Niklas Boers, Dominik Traxl, Norbert Marwan, and Jürgen Kurths
Hydrol. Earth Syst. Sci., 27, 2645–2660, https://doi.org/10.5194/hess-27-2645-2023, https://doi.org/10.5194/hess-27-2645-2023, 2023
Short summary
Short summary
Employing event synchronization and complex networks analysis, we reveal a cascade of heavy rainfall events, related to intense atmospheric rivers (ARs): heavy precipitation events (HPEs) in western North America (NA) that occur in the aftermath of land-falling ARs are synchronized with HPEs in central and eastern Canada with a delay of up to 12 d. Understanding the effects of ARs in the rainfall over NA will lead to better anticipating the evolution of the climate dynamics in the region.
Domenico Giaquinto, Warner Marzocchi, and Jürgen Kurths
Nonlin. Processes Geophys., 30, 167–181, https://doi.org/10.5194/npg-30-167-2023, https://doi.org/10.5194/npg-30-167-2023, 2023
Short summary
Short summary
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.
Tobias Braun, Sara M. Vallejo-Bernal, Norbert Marwan, Juergen Kurths, Johannes Quaas, Albert Díaz-Guilera, Luis Gimeno, and Miguel D. Mahecha
Earth Syst. Dynam., 17, 695–716, https://doi.org/10.5194/esd-17-695-2026, https://doi.org/10.5194/esd-17-695-2026, 2026
Short summary
Short summary
Atmospheric rivers (ARs) move vast amounts of water through the atmosphere and often cause weather extremes, yet they are usually studied as regional events. Using 84 years of mapped AR trajectories, we reveal the global "roadmap" of ARs, a transport network of high-activity hubs, sparse atmospheric highways & hierarchical basins. Our approach shows how water vapor is systematically channelled through an atmospheric transport network, offering new ways to study changes in the global water cycle.
Piers M. Forster, Tristram Walsh, Chris Smith, William F. Lamb, Robin Lamboll, Christophe Cassou, Mathias Hauser, Zeke Hausfather, June-Yi Lee, Matthew D. Palmer, Karina von Schuckmann, Aimée B. A. Slangen, Sophie Szopa, Blair Trewin, Jeongeun Yun, Nathan P. Gillett, Stuart Jenkins, H. Damon Matthews, Krishnan Raghavan, Aurélien Ribes, Joeri Rogelj, Debbie Rosen, Xuebin Zhang, Myles Allen, Robbie M. Andrew, Chris Atkinson, Richard A. Betts, Antonio Bombelli, Samantha N. Burgess, Lijing Cheng, Helen E. Claxton, Pierre Friedlingstein, Thomas L. Frölicher, Catia M. Domingues, Thomas Gasser, Catherine H. Gregory, Rachel M. Hoesly, Daniel Huppmann, Masayoshi Ishii, Christopher Kadow, Alexia Karwat, John Kennedy, Rachel E. Killick, Mahesh V. M. Kovilakam, Paul B. Krummel, Xin Lan, Jean-François Lamarque, Aurélien Liné, Belén Martín-Míguez, Didier P. Monselesan, Colin Morice, Jens Mühle, Pino Mussak, Glen P. Peters, Anna Pirani, Julia Pongratz, Matthew Rigby, Robert Rohde, Abhishek Savita, Sonia I. Seneviratne, Steven J. Smith, Ghassan Taha, Caterina Tassone, Peter Thorne, Christopher Wells, Luke M. Western, Guido R. van der Werf, Susan E. Wijffels, Marco Zecchetto, Junting Zhong, Xiao-Ye Zhang, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 18, 3889–3933, https://doi.org/10.5194/essd-18-3889-2026, https://doi.org/10.5194/essd-18-3889-2026, 2026
Short summary
Short summary
We give our annual update of key climate indicators. Our work quantifies the human contribution to global warming and the pace of climate change. This represents a large effort by the international community akin to an Intergovernmental Panel on Climate Change (IPCC) report.
Orou Moctar Ganni Mampo, Kossi François Guedje, Ezéchiel Obada, Halissou Yarou, Bruno Merz, Ravi Kumar Guntu, Jean Hounkpe, and Adéchina Eric Alamou
Proc. IAHS, 388, 33–41, https://doi.org/10.5194/piahs-388-33-2026, https://doi.org/10.5194/piahs-388-33-2026, 2026
Short summary
Short summary
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.
Stefan Rahmstorf and Levke Caesar
EGUsphere, https://doi.org/10.5194/egusphere-2026-2110, https://doi.org/10.5194/egusphere-2026-2110, 2026
Short summary
Short summary
The Atlantic Ocean circulation has a big impact on climate, not only on European temperatures. There has been a longstanding concern that it could slow down or even shut down in response to global warming. While climate models predict that, it is debated whether a slowing is already underway. Here we discuss the available data and conclude that it is very likely that indeed the circulation is already slowing down.
Domenico De Santis, Silvia Barbetta, Sumit Sen, Viviana Maggioni, Farhad Bahmanpouri, Ashutosh Sharma, Ankit Agarwal, Sagar Gupta, Francesco Avanzi, and Christian Massari
Nat. Hazards Earth Syst. Sci., 26, 1075–1104, https://doi.org/10.5194/nhess-26-1075-2026, https://doi.org/10.5194/nhess-26-1075-2026, 2026
Short summary
Short summary
A conceptual, semi-distributed hydrological model was tailored to simulate high flows in monsoon-dominated, glacier-influenced and flood-prone Himalayan basins. Multi-data calibration using satellite-based glacier mass loss and evapotranspiration estimates improved process realism in data-scarce environments. The proposed modelling approach captured key streamflow features despite significant input uncertainties, proving to be a useful tool for exploring the local hydrological response dynamics.
Marc Lennartz, Benjamin Poschlod, and Bruno Merz
EGUsphere, https://doi.org/10.5194/egusphere-2025-6419, https://doi.org/10.5194/egusphere-2025-6419, 2026
Short summary
Short summary
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-4387, https://doi.org/10.5194/egusphere-2025-4387, 2025
Short summary
Short summary
We evaluated a global flood model using satellite data from 499 historical flood events across 96 countries. Our study shows that larger upstream river basins are modelled more accurately, while using observed river gauges and high-resolution elevation data can improve results. Our findings highlight the importance of large-scale validation and sensitivity analyses to enhance future global flood hazard assessments and prediction accuracy.
Xiaoxiang Guan, Viet Dung Nguyen, Paul Voit, Bruno Merz, Maik Heistermann, and Sergiy Vorogushyn
Nat. Hazards Earth Syst. Sci., 25, 3075–3086, https://doi.org/10.5194/nhess-25-3075-2025, https://doi.org/10.5194/nhess-25-3075-2025, 2025
Short summary
Short summary
We evaluated a multi-site stochastic regional weather generator (nsRWG) for its ability to capture the cross-scale extremity of heavy-precipitation events (HPEs) in Germany. We generated 100 realizations of 72 years of daily synthetic precipitation data. The performance was assessed using WEI and xWEI indices, which measure event extremity across spatiotemporal scales. The results show that nsRWG simulates the extremity patterns of HPEs well, although it overestimates short-duration small-extent events.
Irewola Aaron Oludehinwa, Andrei Velichko, Olasunkanmi Isaac Olusola, Olawale Segun Bolaji, Norbert Marwan, Babalola Olasupo Ogunsua, Abdullahi Ndzi Njah, and Timothy Oluwaseyi Ologun
Nonlin. Processes Geophys., 32, 225–242, https://doi.org/10.5194/npg-32-225-2025, https://doi.org/10.5194/npg-32-225-2025, 2025
Short summary
Short summary
The contributing influence of SSWs (sudden stratospheric warming) on regional ionosphere through chaos theory is examined. We find that ionospheric chaos is more pronounced in the European sector compared to the African sector during an SSW. Evidence of orderliness behavior in regional ionosphere of African sector is observed. Finally, we notice that after the peak phase of an SSW, ionospheric chaos is found to be more pronounced.
Sergiy Vorogushyn, Li Han, Heiko Apel, Viet Dung Nguyen, Björn Guse, Xiaoxiang Guan, Oldrich Rakovec, Husain Najafi, Luis Samaniego, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 25, 2007–2029, https://doi.org/10.5194/nhess-25-2007-2025, https://doi.org/10.5194/nhess-25-2007-2025, 2025
Short summary
Short summary
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.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Christophe Cassou, Mathias Hauser, Zeke Hausfather, June-Yi Lee, Matthew D. Palmer, Karina von Schuckmann, Aimée B. A. Slangen, Sophie Szopa, Blair Trewin, Jeongeun Yun, Nathan P. Gillett, Stuart Jenkins, H. Damon Matthews, Krishnan Raghavan, Aurélien Ribes, Joeri Rogelj, Debbie Rosen, Xuebin Zhang, Myles Allen, Lara Aleluia Reis, Robbie M. Andrew, Richard A. Betts, Alex Borger, Jiddu A. Broersma, Samantha N. Burgess, Lijing Cheng, Pierre Friedlingstein, Catia M. Domingues, Marco Gambarini, Thomas Gasser, Johannes Gütschow, Masayoshi Ishii, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Aurélien Liné, Didier P. Monselesan, Colin Morice, Jens Mühle, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Jan C. Minx, Matthew Rigby, Robert Rohde, Abhishek Savita, Sonia I. Seneviratne, Peter Thorne, Christopher Wells, Luke M. Western, Guido R. van der Werf, Susan E. Wijffels, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 17, 2641–2680, https://doi.org/10.5194/essd-17-2641-2025, https://doi.org/10.5194/essd-17-2641-2025, 2025
Short summary
Short summary
In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. Here we compile monitoring datasets to track real-world changes over time. To make our work relevant to policymakers, we follow methods from the Intergovernmental Panel on Climate Change (IPCC). Human activities are increasing the Earth's energy imbalance and driving faster sea-level rise compared to the IPCC assessment.
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
Short summary
Short summary
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-1509, https://doi.org/10.5194/egusphere-2025-1509, 2025
Preprint archived
Short summary
Short summary
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
Hydrol. Earth Syst. Sci., 29, 447–463, https://doi.org/10.5194/hess-29-447-2025, https://doi.org/10.5194/hess-29-447-2025, 2025
Short summary
Short summary
Flood peak distributions indicate how likely the occurrence of an extreme flood is at a certain river. If the distribution has a so-called heavy tail, extreme floods are more likely than might be anticipated. We find heavier tails in small catchments compared to large catchments, and spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show effects. The results can improve estimations of probabilities of extreme floods.
Bruno Merz, Günter Blöschl, Robert Jüpner, Heidi Kreibich, Kai Schröter, and Sergiy Vorogushyn
Nat. Hazards Earth Syst. Sci., 24, 4015–4030, https://doi.org/10.5194/nhess-24-4015-2024, https://doi.org/10.5194/nhess-24-4015-2024, 2024
Short summary
Short summary
Flood risk assessments help us decide how to reduce the risk of flooding. Since these assessments are based on probabilities, it is hard to check their accuracy by comparing them to past data. We suggest a new way to validate these assessments, making sure they are practical for real-life decisions. This approach looks at both the technical details and the real-world situations where decisions are made. We demonstrate its practicality by applying it to flood emergency planning.
Viet Dung Nguyen, Sergiy Vorogushyn, Katrin Nissen, Lukas Brunner, and Bruno Merz
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 195–216, https://doi.org/10.5194/ascmo-10-195-2024, https://doi.org/10.5194/ascmo-10-195-2024, 2024
Short summary
Short summary
We present a novel stochastic weather generator conditioned on circulation patterns and regional temperature, accounting for dynamic and thermodynamic atmospheric changes. We extensively evaluate the model for the central European region. It statistically downscales precipitation for future periods, generating long, spatially and temporally consistent series. Results suggest an increase in extreme precipitation over the region, offering key benefits for hydrological impact studies.
Wolfgang Schwanghart, Ankit Agarwal, Kristen Cook, Ugur Ozturk, Roopam Shukla, and Sven Fuchs
Nat. Hazards Earth Syst. Sci., 24, 3291–3297, https://doi.org/10.5194/nhess-24-3291-2024, https://doi.org/10.5194/nhess-24-3291-2024, 2024
Short summary
Short summary
The Himalayan landscape is particularly susceptible to extreme events, which interfere with increasing populations and the expansion of settlements and infrastructure. This preface introduces and summarizes the nine papers that are part of the special issue,
Estimating and predicting natural hazards and vulnerabilities in the Himalayan region.
Viet Dung Nguyen, Jeroen Aerts, Max Tesselaar, Wouter Botzen, Heidi Kreibich, Lorenzo Alfieri, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 24, 2923–2937, https://doi.org/10.5194/nhess-24-2923-2024, https://doi.org/10.5194/nhess-24-2923-2024, 2024
Short summary
Short summary
Our study explored how seasonal flood forecasts could enhance insurance premium accuracy. Insurers traditionally rely on historical data, yet climate fluctuations influence flood risk. We employed a method that predicts seasonal floods to adjust premiums accordingly. Our findings showed significant year-to-year variations in flood risk and premiums, underscoring the importance of adaptability. Despite limitations, this research aids insurers in preparing for evolving risks.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
Short summary
Short summary
Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Seth Bryant, Heidi Kreibich, and Bruno Merz
Proc. IAHS, 386, 181–187, https://doi.org/10.5194/piahs-386-181-2024, https://doi.org/10.5194/piahs-386-181-2024, 2024
Short summary
Short summary
Our study found that simplifying data in flood risk models can introduce errors. We tested 344 damage functions and found errors up to 40 % of the total asset value. This means large-scale flood risk assessments may have significant errors due to the modelling approach. Our research highlights the need for more attention to data aggregation in flood risk models.
Marina Batalini de Macedo, Nikunj K. Mangukiya, Maria Clara Fava, Ashutosh Sharma, Roberto Fray da Silva, Ankit Agarwal, Maria Tereza Razzolini, Eduardo Mario Mendiondo, Narendra K. Goel, Mathew Kurian, and Adelaide Cássia Nardocci
Proc. IAHS, 386, 41–46, https://doi.org/10.5194/piahs-386-41-2024, https://doi.org/10.5194/piahs-386-41-2024, 2024
Short summary
Short summary
More and more extreme rainfall causes flooding problems in cities and communities, affecting the health and well-being of the population, as well as causing damage to the economy. To help design actions aiming at reducing the impacts of these floods, computational models can be used to simulate their extent. However, there are different types of models currently available. In this study, we evaluated three different models, for a city in Brazil and a region in India, to guide the best use of it.
Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
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
Short summary
Short summary
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.
Seth Bryant, Guy Schumann, Heiko Apel, Heidi Kreibich, and Bruno Merz
Hydrol. Earth Syst. Sci., 28, 575–588, https://doi.org/10.5194/hess-28-575-2024, https://doi.org/10.5194/hess-28-575-2024, 2024
Short summary
Short summary
A new algorithm has been developed to quickly produce high-resolution flood maps. It is faster and more accurate than current methods and is available as open-source scripts. This can help communities better prepare for and mitigate flood damages without expensive modelling.
Sara M. Vallejo-Bernal, Frederik Wolf, Niklas Boers, Dominik Traxl, Norbert Marwan, and Jürgen Kurths
Hydrol. Earth Syst. Sci., 27, 2645–2660, https://doi.org/10.5194/hess-27-2645-2023, https://doi.org/10.5194/hess-27-2645-2023, 2023
Short summary
Short summary
Employing event synchronization and complex networks analysis, we reveal a cascade of heavy rainfall events, related to intense atmospheric rivers (ARs): heavy precipitation events (HPEs) in western North America (NA) that occur in the aftermath of land-falling ARs are synchronized with HPEs in central and eastern Canada with a delay of up to 12 d. Understanding the effects of ARs in the rainfall over NA will lead to better anticipating the evolution of the climate dynamics in the region.
Domenico Giaquinto, Warner Marzocchi, and Jürgen Kurths
Nonlin. Processes Geophys., 30, 167–181, https://doi.org/10.5194/npg-30-167-2023, https://doi.org/10.5194/npg-30-167-2023, 2023
Short summary
Short summary
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
Preprint archived
Short summary
Short summary
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
Short summary
Short summary
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.
Abhishek Kashyap, Mukunda Dev Behera, Anand Kumar Pandey, and Ankit Agarwal
EGUsphere, https://doi.org/10.5194/egusphere-2022-533, https://doi.org/10.5194/egusphere-2022-533, 2022
Preprint archived
Short summary
Short summary
Bedrock landslides are currently spatially dispersed over a process of landscape evolution in the NW Himalayan river catchments. Our analysis indicates that the zones with slope range between 24–32°, topographic relief ranges between 800–1200 m, and elevation range between 1200–2400 m, are compatible with precipitation intensity ranges between 1500–3000 mm/year in the NW Himalayan river catchments, have the highest probability of frequently occurring landslides.
Pankaj R. Dhote, Joshal K. Bansal, Vaibhav Garg, Praveen K. Thakur, and Ankit Agarwal
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-101, https://doi.org/10.5194/nhess-2022-101, 2022
Preprint withdrawn
Short summary
Short summary
In the present paper, we have developed framework to establish virtual stage-discharge gauging network in sparsely gauged basin using hydrodynamic modelling and satellite altimetry data. The publication of the work will provide more insights to hydraulic community dealing with flood hazard in sparsely gauged basins, on how to monitor extreme river flow events using remote sensing data at ungauged locations.
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
Short summary
Short summary
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
Cited articles
Abid, M. A., Almazroui, M., Kucharski, F., O'Brien, E., and Yousef, A. E.:
ENSO relationship to summer rainfall variability and its potential
predictability over Arabian Peninsula region, npj Climate and Atmospheric
Science, 1, 20171, https://doi.org/10.1038/s41612-017-0003-7, 2018.
Agarwal, A.: Unraveling spatio-temporal climatic patterns via multi-scale
complex networks, Universität Potsdam, 2019.
Agarwal, A., Maheswaran, R., Kurths, J., and Khosa, R.: Wavelet Spectrum and
Self-Organizing Maps-Based Approach for Hydrologic Regionalization – a Case
Study in the Western United States, Water Resour. Manag., 30,
4399–4413, https://doi.org/10.1007/s11269-016-1428-1, 2016.
Agarwal, A., Marwan, N., Rathinasamy, M., Merz, B., and Kurths, J.: Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach, Nonlin. Processes Geophys., 24, 599–611, https://doi.org/10.5194/npg-24-599-2017, 2017.
Agarwal, A., Marwan, N., Maheswaran, R., Merz, B., and Kurths, J.:
Quantifying the roles of single stations within homogeneous regions using
complex network analysis, J. Hydrol.,
563, 802–810, https://doi.org/10.1016/j.jhydrol.2018.06.050, 2018a.
Agarwal, A., Maheswaran, R., Marwan, N., Caesar, L., and Kurths, J.:
Wavelet-based multiscale similarity measure for complex networks,
Eur. Phys. J. B, 91, 296, https://doi.org/10.1140/epjb/e2018-90460-6, 2018b.
Agarwal, A., Caesar, L., Marwan, N., Maheswaran, R., Merz, B., and Kurths,
J.: Network-based identification and characterization of teleconnections on
different scales, Sci. Rep., 9, 8808, https://doi.org/10.1038/s41598-019-45423-5,
2019a.
Agarwal, A., Marwan, N., Ozturk, U., and Maheswaran, R.: Unfolding Community
Structure in Rainfall Network of Germany Using Complex Network-Based
Approach, in: Water Resources and Environmental Engineering II, edited by:
Rathinasamy, M., Chandramouli, S., Phanindra, K. B. V. N., and Mahesh, U.,
179–193, Springer Singapore, Singapore, 2019b.
Araghi, A., Mousavi-Baygi, M., Adamowski, J., and Martinez, C.: Association
between three prominent climatic teleconnections and precipitation in Iran
using wavelet coherence, Int. J. Climatol., 37,
2809–2830, https://doi.org/10.1002/joc.4881, 2017.
Ashok, K., Guan, Z., and Yamagata, T.: Impact of the Indian Ocean dipole on
the relationship between the Indian monsoon rainfall and ENSO, Geophys.
Res. Lett., 28, 4499–4502, https://doi.org/10.1029/2001GL013294, 2001.
Bansod, S. D.: Interannual variability of convective activity over the
tropical Indian Ocean during the El Niño/La Niña events,
Int. J. Remote Sens., 32, 5565–5582,
https://doi.org/10.1080/01431161.2010.506896, 2011.
Behera, S. K., Krishnan, R., and Yamagata, T.: Unusual ocean-atmosphere
conditions in the tropical Indian Ocean during 1994, Geophys. Res.
Lett., 26, 3001–3004, https://doi.org/10.1029/1999GL010434, 1999.
Bharath, R. and Srinivas, V. V.: Delineation of homogeneous
hydrometeorological regions using wavelet-based global fuzzy cluster
analysis, Int. J. Climatol., 35, 4707–4727,
https://doi.org/10.1002/joc.4318, 2015.
Bhatla, R., Singh, A. K., Mandal, B., Ghosh, S., Pandey, S. N., and Sarkar,
A.: Influence of North Atlantic Oscillation on Indian Summer Monsoon
Rainfall in Relation to Quasi-Binneal Oscillation, Pure Appl.
Geophys., 173, 2959–2970, https://doi.org/10.1007/s00024-016-1306-z, 2016.
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E.: Fast
unfolding of communities in large networks, J. Stat. Mech.-Theory E., 2008, P10008,
https://doi.org/10.1088/1742-5468/2008/10/P10008, 2008.
Boers, N., Goswami, B., Rheinwalt, A., Bookhagen, B., Hoskins, B., and
Kurths, J.: Complex networks reveal global pattern of extreme-rainfall
teleconnections, Nature, 566, 373–377, https://doi.org/10.1038/s41586-018-0872-x, 2019.
D'Arrigo, R.: On the variability of ENSO over the past six centuries,
Geophys. Res. Lett., 32, https://doi.org/10.1029/2004GL022055, 2005.
Ding, R., Kang, S., Vargas, R., Zhang, Y., and Hao, X.: Multiscale spectral analysis of temporal variability in evapotranspiration over irrigated cropland in an arid region, Agr. Water Manag., 130, 79–89, https://doi.org/10.1016/j.agwat.2013.08.019, 2013.
Dong, X.: Influences of the Pacific Decadal Oscillation on the East Asian
Summer Monsoon in non-ENSO years: Influences of the Pacific Decadal
Oscillation on the East Asian Summer Monsoon, Atmos. Sci. Lett.,
17, 115–120, https://doi.org/10.1002/asl.634, 2016.
Donges, J. F., Zou, Y., Marwan, N., and Kurths, J.: Complex networks in
climate dynamics: Comparing linear and nonlinear network construction
methods, The European Physical Journal Special Topics, 174, 157–179,
https://doi.org/10.1140/epjst/e2009-01098-2, 2009.
Ekhtiari, N., Agarwal, A., Marwan, N., and Donner, R. V.: Disentangling the
multi-scale effects of sea-surface temperatures on global precipitation: A
coupled networks approach, Chaos: An Interdisciplinary Journal of Nonlinear
Science, 29, 063116, https://doi.org/10.1063/1.5095565, 2019.
Feliks, Y., Groth, A., Robertson, A. W., and Ghil, M.: Oscillatory Climate
Modes in the Indian Monsoon, North Atlantic, and Tropical Pacific, J.
Climate, 26, 9528–9544, https://doi.org/10.1175/JCLI-D-13-00105.1, 2013.
Feng, Q. Y., Vasile, R., Segond, M., Gozolchiani, A., Wang, Y., Abel, M., Havlin, S., Bunde, A., and Dijkstra, H. A.: ClimateLearn: A machine-learning approach for climate prediction using network measures, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2015-273, 2016.
Fortunato, S.: Community detection in graphs, Phys. Rep., 486,
75–174, https://doi.org/10.1016/j.physrep.2009.11.002, 2010.
Goswami, B. N. and Krishnan, R.: Opportunities and challenges in monsoon
prediction in a changing climate, Clim. Dynam., 41, 1–1,
https://doi.org/10.1007/s00382-013-1835-4, 2013.
Goswami, B. N., Madhusoodanan, M. S., Neema, C. P., and Sengupta, D.: A
physical mechanism for North Atlantic SST influence on the Indian summer
monsoon, Geophys. Res. Lett., 33, https://doi.org/10.1029/2005GL024803,
2006.
Grinsted, A., Moore, J. C., and Jevrejeva, S.: Application of the cross wavelet transform and wavelet coherence to geophysical time series, Nonlin. Processes Geophys., 11, 561–566, https://doi.org/10.5194/npg-11-561-2004, 2004.
Guhathakurta, P., Menon, P., Inkane, P. M., Krishnan, U., and Sable, S. T.:
Trends and variability of meteorological drought over the districts of India
using standardized precipitation index, J. Earth Syst. Sci.,
126, 120, https://doi.org/10.1007/s12040-017-0896-x, 2017.
Guimerà, R. and Amaral, L. A. N.: Cartography of complex networks:
modules and universal roles, J. Stat. Mech.-Theory
E., 2005, P02001, https://doi.org/10.1088/1742-5468/2005/02/P02001, 2005.
Halverson, M. J. and Fleming, S. W.: Complex network theory, streamflow, and hydrometric monitoring system design, Hydrol. Earth Syst. Sci., 19, 3301–3318, https://doi.org/10.5194/hess-19-3301-2015, 2015.
Hannachi, A., Jolliffe, I. T., and Stephenson, D. B.: Empirical orthogonal
functions and related techniques in atmospheric science: A review,
Int. J. Climatol., 27, 1119–1152,
https://doi.org/10.1002/joc.1499, 2007.
Harenberg, S., Bello, G., Gjeltema, L., Ranshous, S., Harlalka, J., Seay,
R., Padmanabhan, K., and Samatova, N.: Community detection in large-scale
networks: a survey and empirical evaluation: Community detection in
large-scale networks, Wiley Interdisciplinary Reviews: Computational
Statistics, 6, 426–439, https://doi.org/10.1002/wics.1319, 2014.
Hu, Q. and Feng, S.: Interannual Rainfall Variations in the North American
Summer Monsoon Region: 1900–98*, J. Climate, 15, 1189–1202,
https://doi.org/10.1175/1520-0442(2002)015<1189:IRVITN>2.0.CO;2,
2002.
Hu, W. and Si, B. C.: Technical note: Multiple wavelet coherence for untangling scale-specific and localized multivariate relationships in geosciences, Hydrol. Earth Syst. Sci., 20, 3183–3191, https://doi.org/10.5194/hess-20-3183-2016, 2016.
Izumo, T., Vialard, J., Lengaigne, M., de Boyer Montegut, C., Behera, S.K., Luo, J.-J., Cravatte, S., Masson, S., and Yamagata, T.: Influence of the state of the Indian Ocean Dipole on the following year’s El Niño, Nat. Geosci., 3, 168–172, https://doi.org/10.1038/ngeo760, 2010.
Jiang, X. and Ting, M.: A Dipole Pattern of Summertime Rainfall across the
Indian Subcontinent and the Tibetan Plateau, J. Climate, 30,
9607–9620, https://doi.org/10.1175/JCLI-D-16-0914.1, 2017.
Katul, G., Lai, C.-T., Schäfer, K., Vidakovic, B., Albertson, J., Ellsworth, D., and Oren, R.: Multiscale analysis of vegetation surface fluxes: from seconds to years, Adv. Water Resour., 24, 1119–1132, https://doi.org/10.1016/S0309-1708(01)00029-X, 2011.
Krishnamurthy, L. and Krishnamurthy, V.: Teleconnections of Indian monsoon
rainfall with AMO and Atlantic tripole, Clim. Dynam., 46,
2269–2285, https://doi.org/10.1007/s00382-015-2701-3, 2016.
Krishnan, R. and Sugi, M.: Pacific decadal oscillation and variability of
the Indian summer monsoon rainfall, Clim. Dynam., 21, 233–242,
https://doi.org/10.1007/s00382-003-0330-8, 2003.
Krishnan, R. and Swapna, P.: Significant Influence of the Boreal Summer
Monsoon Flow on the Indian Ocean Response during Dipole Events, J.
Climate, 22, 5611–5634, https://doi.org/10.1175/2009JCLI2176.1, 2009.
Kumar, K. K., Rajagopalan, B., Hoerling, M., Bates, G., and Cane, M.:
Unraveling the Mystery of Indian Monsoon Failure During El Nino, Science,
314, 115–119, https://doi.org/10.1126/science.1131152, 2006.
Lancichinetti, A. and Fortunato, S.: Community detection algorithms: A
comparative analysis, Phys. Rev. E, 80,
056117, https://doi.org/10.1103/PhysRevE.80.056117, 2009.
Luo, J.-J., Zhang, R., Behera, S. K., Masumoto, Y., Jin, F.-F., Lukas, R.,
and Yamagata, T.: Interaction between El Niño and Extreme Indian Ocean
Dipole, J. Climate, 23, 726–742, https://doi.org/10.1175/2009JCLI3104.1,
2010.
Luterbacher, J., Xoplaki, E., Casty, C., Wanner, H., Pauling, A.,
Küttel, M., Rutishauser, T., Brönnimann, S., Fischer, E., Fleitmann,
D., Gonzalez-Rouco, F. J., García-Herrera, R., Barriendos, M., Rodrigo,
F., Gonzalez-Hidalgo, J. C., Saz, M. A., Gimeno, L., Ribera, P., Brunet, M.,
Paeth, H., Rimbu, N., Felis, T., Jacobeit, J., Dünkeloh, A., Zorita, E.,
Guiot, J., Türkes, M., Alcoforado, M. J., Trigo, R., Wheeler, D., Tett,
S., Mann, M. E., Touchan, R., Shindell, D. T., Silenzi, S., Montagna, P.,
Camuffo, D., Mariotti, A., Nanni, T., Brunetti, M., Maugeri, M., Zerefos,
C., Zolt, S. D., Lionello, P., Nunes, M. F., Rath, V., Beltrami, H.,
Garnier, E., and Ladurie, E. L. R.: Chapter 1 Mediterranean climate
variability over the last centuries: A review, in: Developments in Earth and
Environmental Sciences, 4, 27–148, Elsevier, 2006.
McGregor, S., Timmermann, A., England, M. H., Elison Timm, O., and Wittenberg, A. T.: Inferred changes in El Niño–Southern Oscillation variance over the past six centuries, Clim. Past, 9, 2269–2284, https://doi.org/10.5194/cp-9-2269-2013, 2013.
Miralles, D. G., Teuling, A. J., van Heerwaarden, C. C., and Vilà-Guerau
de Arellano, J.: Mega-heatwave temperatures due to combined soil desiccation
and atmospheric heat accumulation, Nat. Geosci., 7, 345–349,
https://doi.org/10.1038/ngeo2141, 2014.
Mishra, V., Smoliak, B. V., Lettenmaier, D. P., and Wallace, J. M.: A
prominent pattern of year-to-year variability in Indian Summer Monsoon
Rainfall, P. Natl. Acad. Sci. USA, 109,
7213–7217, https://doi.org/10.1073/pnas.1119150109, 2012.
Mitra, C., Kurths, J., and Donner, R. V.: Rewiring hierarchical scale-free
networks: Influence on synchronizability and topology, EPL-Europhys.
Lett., 119, 30002, https://doi.org/10.1209/0295-5075/119/30002, 2017.
Mokhov, I. I., Smirnov, D. A., Nakonechny, P. I., Kozlenko, S. S., and
Kurths, J.: Relationship between El-Niño/Southern Oscillation and the
Indian monsoon, Izvestiya, Atmos. Ocean. Phys., 48, 47–56,
https://doi.org/10.1134/S0001433812010082, 2012.
Newman, M. E. J.: Detecting community structure in networks, Eur.
Phys. J. B, 38, 321–330,
https://doi.org/10.1140/epjb/e2004-00124-y, 2004.
Newman, M. E. J.: Modularity and community structure in networks,
P. Natl. Acad. Sci. USA, 103, 8577–8582,
https://doi.org/10.1073/pnas.0601602103, 2006.
Okin, G. S., Parsons, A. J., Wainwright, J., Herrick, J. E., Bestelmeyer, B.
T., Peters, D. C., and Fredrickson, E. L.: Do Changes in Connectivity Explain
Desertification?, BioScience, 59, 237–244, https://doi.org/10.1525/bio.2009.59.3.8,
2009.
Ouachani, R., Bargaoui, Z., and Ouarda, T.: Power of teleconnection patterns
on precipitation and streamflow variability of upper Medjerda Basin,
Int. J. Climatol., 33, 58–76, https://doi.org/10.1002/joc.3407,
2013.
Ozturk, U., Marwan, N., Korup, O., Saito, H., Agarwal, A., Grossman, M. J.,
Zaiki, M., and Kurths, J.: Complex networks for tracking extreme rainfall
during typhoons, Chaos: An Interdisciplinary Journal of Nonlinear Science,
28, 075301, https://doi.org/10.1063/1.5004480, 2018.
Pai, D. S., Sridhar, L., Rajeevan, M., Sreejith, O. P., Satbhai, N. S., and
Mukhopadyay, B.: Development of a new high spatial resolution
(
Pai, D. S., Sridhar, L., Badwaik, M. R., and Rajeevan, M.: Analysis of the
daily rainfall events over India using a new long period (1901–2010) high
resolution (
Paluš, M.: Cross-Scale Interactions and Information Transfer, Entropy,
16, 5263–5289, https://doi.org/10.3390/e16105263, 2014.
Percival, D. B.: Analysis of Geophysical Time Series Using Discrete Wavelet
Transforms: An Overview, in: Nonlinear Time Series Analysis in the
Geosciences, vol. 112, edited by: Donner, R. V. and Barbosa, S. M., 61–79,
Springer Berlin Heidelberg, Berlin, Heidelberg, 2008.
Percival, D. B and Walden, A. T.: Wavelet methods for time series analysis, 4, Cambridge university press, 2000.
Peters, D. P. C., Pielke, R. A., Bestelmeyer, B. T., Allen, C. D.,
Munson-McGee, S., and Havstad, K. M.: Cross-scale interactions,
nonlinearities, and forecasting catastrophic events, P.
Natl. Acad. Sci. USA, 101, 15130–15135,
https://doi.org/10.1073/pnas.0403822101, 2004.
Peters, D. P. C., Bestelmeyer, B. T., and Turner, M. G.: Cross–Scale
Interactions and Changing Pattern–Process Relationships: Consequences for
System Dynamics, Ecosystems, 10, 790–796, https://doi.org/10.1007/s10021-007-9055-6,
2007.
Pillai, P. A. and Mohankumar, K.: Individual and combined influence of El
Niño-Southern Oscillation and Indian Ocean Dipole on the Tropospheric
Biennial Oscillation, Q. J. Roy. Meteor. Soc.,
136, 297–304, https://doi.org/10.1002/qj.579, 2010.
Quiroga, R. Q., Kreuz, T., and Grassberger, P.: Event synchronization: A
simple and fast method to measure synchronicity and time delay patterns,
Phys. Rev. E, 66, 041904-1, https://doi.org/10.1103/PhysRevE.66.041904, 2002.
Rai, P. and Dimri, A. P.: Effect of changing tropical easterly jet, low
level jet and quasi-biennial oscillation phases on Indian summer monsoon:
TEJ, LLJ and QBO phases and Indian summer monsoon, Atmos. Sci. Lett., 18, 52–59, https://doi.org/10.1002/asl.723, 2017.
Rajeevan, M. and Pai, D. S.: On the El Niño-Indian monsoon predictive
relationships, Geophys. Res. Lett., 34,
L04704, https://doi.org/10.1029/2006GL028916, 2007.
Rathinasamy, M., Khosa, R., Adamowski, J., ch, S., Partheepan, G., Anand, J.,
and Narsimlu, B.: Wavelet-based multiscale performance analysis: An approach
to assess and improve hydrological models, Water Resour. Res., 50,
9721–9737, https://doi.org/10.1002/2013WR014650, 2014.
Rubinov, M. and Sporns, O.: Weight-conserving characterization of complex
functional brain networks, NeuroImage, 56, 2068–2079,
https://doi.org/10.1016/j.neuroimage.2011.03.069, 2011.
Shukla, R., Agarwal, A., Sachdeva, K., Kurths, J., and Joshi, P. K.: Climate
change perception: an analysis of climate change and risk perceptions among
farmer types of Indian Western Himalayas, Climatic Change,
152, 103–119, https://doi.org/10.1007/s10584-018-2314-z, 2018.
Sivakumar, B., Singh, V. P., Berndtsson, R., and Khan, S. K.: Catchment
Classification Framework in Hydrology: Challenges and Directions, J.
Hydrol. Eng., 20, A4014002,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000837, 2015.
Steinhaeuser, K., Ganguly, A. R., and Chawla, N. V.: Multivariate and
multiscale dependence in the global climate system revealed through complex
networks, Clim. Dynam., 39, 889–895,
https://doi.org/10.1007/s00382-011-1135-9, 2012.
Tan, X., Gan, T. Y., and Shao, D.: Wavelet analysis of precipitation extremes
over Canadian ecoregions and teleconnections to large-scale climate
anomalies: Large Precipitation and Climate Anomalies, J. Geophys.
Res.-Atmos., 121, 14469–14486, https://doi.org/10.1002/2016JD025533,
2016.
Torrence, C. and Compo, G. P.: A Practical Guide to Wavelet Analysis,
B. Am. Meteorol. Soc., 79, 61–78,
https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2,
1998.
Tsonis, A. A., Swanson, K. L., and Roebber, P. J.: What Do Networks Have to
Do with Climate?, B. Am. Meteorol. Soc., 87,
585–595, https://doi.org/10.1175/BAMS-87-5-585, 2006.
Tsonis, A. A., Wang, G., Swanson, K. L., Rodrigues, F. A., and da Fontura Costa, L.: Community structure and dynamics in climate networks, Clim. Dynam.,
37, 933–940, https://doi.org/10.1007/s00382-010-0874-3, 2011.
Xoplaki, E., González-Rouco, J. F., Luterbacher, J., and Wanner, H.: Wet
season Mediterranean precipitation variability: influence of large-scale
dynamics and trends, Clim. Dynam., 23, 63–78, https://doi.org/10.1007/s00382-004-0422-0,
2004.
Zhang, R. and Delworth, T. L.: Simulated Tropical Response to a Substantial
Weakening of the Atlantic Thermohaline Circulation, J. Climate,
18, 1853–1860, https://doi.org/10.1175/JCLI3460.1, 2005.
Short summary
We examined the spatial diversity of Indian rainfall teleconnection at different timescales, first by identifying homogeneous communities and later by computing non-linear linkages between the identified communities (spatial regions) and dominant climatic patterns, represented by climatic indices such as El Nino–Southern Oscillation, Indian Ocean Dipole, North Atlantic Oscillation, Pacific Decadal Oscillation and Atlantic Multi-Decadal Oscillation.
We examined the spatial diversity of Indian rainfall teleconnection at different timescales,...
