Articles | Volume 28, issue 22
https://doi.org/10.5194/hess-28-4883-2024
https://doi.org/10.5194/hess-28-4883-2024
Research article
 | 
15 Nov 2024
Research article |  | 15 Nov 2024

Processes and controls of regional floods over eastern China

Yixin Yang, Long Yang, Jinghan Zhang, and Qiang Wang

Related authors

Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023,https://doi.org/10.5194/essd-15-2009-2023, 2023
Short summary
A high-accuracy rainfall dataset by merging multiple satellites and dense gauges over the southern Tibetan Plateau for 2014–2019 warm seasons
Kunbiao Li, Fuqiang Tian, Mohd Yawar Ali Khan, Ran Xu, Zhihua He, Long Yang, Hui Lu, and Yingzhao Ma
Earth Syst. Sci. Data, 13, 5455–5467, https://doi.org/10.5194/essd-13-5455-2021,https://doi.org/10.5194/essd-13-5455-2021, 2021
Short summary
On the flood peak distributions over China
Long Yang, Lachun Wang, Xiang Li, and Jie Gao
Hydrol. Earth Syst. Sci., 23, 5133–5149, https://doi.org/10.5194/hess-23-5133-2019,https://doi.org/10.5194/hess-23-5133-2019, 2019
The role of storm scale, position and movement in controlling urban flood response
Marie-Claire ten Veldhuis, Zhengzheng Zhou, Long Yang, Shuguang Liu, and James Smith
Hydrol. Earth Syst. Sci., 22, 417–436, https://doi.org/10.5194/hess-22-417-2018,https://doi.org/10.5194/hess-22-417-2018, 2018
Short summary
Attribution of hydrologic forecast uncertainty within scalable forecast windows
L. Yang, F. Tian, Y. Sun, X. Yuan, and H. Hu
Hydrol. Earth Syst. Sci., 18, 775–786, https://doi.org/10.5194/hess-18-775-2014,https://doi.org/10.5194/hess-18-775-2014, 2014

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Mathematical applications
A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks
Jun Liu, Julian Koch, Simon Stisen, Lars Troldborg, and Raphael J. M. Schneider
Hydrol. Earth Syst. Sci., 28, 2871–2893, https://doi.org/10.5194/hess-28-2871-2024,https://doi.org/10.5194/hess-28-2871-2024, 2024
Short summary
Inferring heavy tails of flood distributions through hydrograph recession analysis
Hsing-Jui Wang, Ralf Merz, Soohyun Yang, and Stefano Basso
Hydrol. Earth Syst. Sci., 27, 4369–4384, https://doi.org/10.5194/hess-27-4369-2023,https://doi.org/10.5194/hess-27-4369-2023, 2023
Short summary
Landscape structures regulate the contrasting response of recession along rainfall amounts
Jun-Yi Lee, Ci-Jian Yang, Tsung-Ren Peng, Tsung-Yu Lee, and Jr-Chuan Huang
Hydrol. Earth Syst. Sci., 27, 4279–4294, https://doi.org/10.5194/hess-27-4279-2023,https://doi.org/10.5194/hess-27-4279-2023, 2023
Short summary
Hydrological objective functions and ensemble averaging with the Wasserstein distance
Jared C. Magyar and Malcolm Sambridge
Hydrol. Earth Syst. Sci., 27, 991–1010, https://doi.org/10.5194/hess-27-991-2023,https://doi.org/10.5194/hess-27-991-2023, 2023
Short summary
Spatial variability in Alpine reservoir regulation: deriving reservoir operations from streamflow using generalized additive models
Manuela Irene Brunner and Philippe Naveau
Hydrol. Earth Syst. Sci., 27, 673–687, https://doi.org/10.5194/hess-27-673-2023,https://doi.org/10.5194/hess-27-673-2023, 2023
Short summary

Cited articles

Berens, P.: Circular statistics toolbox (directional statistics), MATLAB Central File Exchange [code], https://www.mathworks.com/matlabcentral/fileexchange/10676-circular-statistics-toolbox-directional-statistics (last access: 9 April 2022), 2024. 
Berens, P.: CircStat: a MATLAB toolbox for circular statistics, J. Stat. Softw., 31, 1–21, http://www.jstatsoft.org/v31/i10 (last access: 25 August 2021), 2009. 
Berghuijs, W. R., Allen, S. T., Harrigan, S., and Kirchner, J. W.: Growing spatial scales of synchronous river flooding in Europe, Geophys. Res. Lett., 46, 1423–1428, https://doi.org/10.1029/2018gl081883, 2019. 
Blöschl, G.: Flood generation: process patterns from the raindrop to the ocean, Hydrol. Earth Syst. Sci., 26, 2469–2480, https://doi.org/10.5194/hess-26-2469-2022, 2022. 
Blöschl, G., Hall, J., Parajka, J., Perdigão, R. A., Merz, B., Arheimer, B., Aronica, G. T., Bilibashi, A., Bonacci, O., and Borga, M.: Changing climate shifts timing of European floods, Science, 357, 588–590, https://doi.org/10.1126/science.aan2506, 2017. 
Download
Short summary
We introduce a machine-learning framework to study spatial characteristics and drivers of regional floods in eastern China, using 38 years of flood peak data from a vast gauging network. Our analyses provide better understanding of contrasting flood behaviors by explicitly characterizing their spatial extents. This knowledge can help improve flood risk management.