Preprints
https://doi.org/10.5194/hess-2024-168
https://doi.org/10.5194/hess-2024-168
19 Jun 2024
 | 19 Jun 2024
Status: this preprint is currently under review for the journal HESS.

Processes and controls of regional floods over eastern China

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

Abstract. Mounting evidence points to elevated regional flood hazards under a changing climate, but existing knowledge about their processes and controls is albeit limited. This is partially attributed to inadequate characterizations of the spatial extent and potential drivers of these floods. Here we develop a machine-learning based framework (mainly including the density-based clustering algorithm DBSCAN and conditional random forest model) to examine the processes and controls of regional floods over eastern China. Our empirical analyses are based on a dense network of stream gauging stations with continuous observations of annual maximum flood peak (i.e., magnitude and timing) during the period 1980–2017. A comprehensive catalog of 318 regional floods is developed. We reveal a pronounced clustering of regional floods in both space and time over eastern China. This is dictated by cyclonic precipitating systems and/or their interactions with topography. We highlight contrasting behaviors of regional floods, in terms of their spatial extents and intensities. These contrasts are determined by fine-scale structures of flood-producing storms and anomalous soil moisture. While land surface properties might play a role in basin-scale flood processes, it is more critical to capture spatial-temporal rainfall variabilities and soil moisture anomalies for reliable large-scale flood hazard modeling and impact assessments. Our analyses contribute to flood science by better characterizing the spatial dimension of flood hazards and can serve as basis for collaborative flood risk management under a changing climate.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Yixin Yang, Long Yang, Jinghan Zhang, and Qiang Wang

Status: open (until 14 Aug 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Yixin Yang, Long Yang, Jinghan Zhang, and Qiang Wang
Yixin Yang, Long Yang, Jinghan Zhang, and Qiang Wang

Viewed

Total article views: 174 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
127 36 11 174 20 7 7
  • HTML: 127
  • PDF: 36
  • XML: 11
  • Total: 174
  • Supplement: 20
  • BibTeX: 7
  • EndNote: 7
Views and downloads (calculated since 19 Jun 2024)
Cumulative views and downloads (calculated since 19 Jun 2024)

Viewed (geographical distribution)

Total article views: 176 (including HTML, PDF, and XML) Thereof 176 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 Jul 2024
Download
Short summary
We introduce a machine-learning framework to study flood spatial characteristics and drivers in eastern China, using 37 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.