Preprints
https://doi.org/10.5194/egusphere-2024-2139
https://doi.org/10.5194/egusphere-2024-2139
09 Sep 2024
 | 09 Sep 2024

The Return Period Analysis of Heavy Rainfall Disasters Based on Copula Joint Statistical Modeling

Siyu Liu and Xuguang Dong

Abstract. In the last few years, with the frequent occurrence of extreme weather across the globe, it has become clear that a comprehensive understanding of the patterns and main characteristics of disaster occurrence is essential, and the willingness to study these variables has become more urgent than ever. This paper analyses the multivariate and spatial distribution characteristics of heavy precipitation disasters and proposes a method for estimating the degree of disaster-causing risk using a joint statistical model. This paper tests the model's validity with hourly precipitation data from 122 national meteorological stations in Shandong from 1990 to 2023. Based on heavy precipitation events in the past thirty years, different marginal distribution functions fit the duration of heavy precipitation and precipitation amount. The joint probability distribution model of two related variables is established based on the Copula joint distribution to analyze the change rule of heavy precipitation recurrence period in different periods and to analyze the characteristics of heavy precipitation causing disasters in Shandong Province on this basis. Compared with the disaster return period calculated by relying on univariate variables, the Copula function can more reasonably simulate the natural occurrence of the degree of disaster. The joint return period (JRP) estimated by the Copula function shows that the JPR of heavy rainfall with a duration of 1 hour is 89 % higher than that of 6 hours, indicating a significant increase in the risk of disasters caused by short-term heavy rainfall in Shandong region. This method can more scientifically describe the risk of disasters caused by heavy precipitation in different scenarios, especially the characteristics of disasters caused by short-term heavy precipitation, which can provide an adequate scientific basis for disaster prevention and mitigation planning and disaster risk management.

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.
Siyu Liu and Xuguang Dong

Status: closed (peer review stopped)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-2139', Anonymous Referee #1, 09 Oct 2024
    • AC1: 'Reply on RC1', Siyu Liu, 23 Oct 2024
  • RC2: 'Comment on egusphere-2024-2139', Anonymous Referee #2, 28 Oct 2024

Status: closed (peer review stopped)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-2139', Anonymous Referee #1, 09 Oct 2024
    • AC1: 'Reply on RC1', Siyu Liu, 23 Oct 2024
  • RC2: 'Comment on egusphere-2024-2139', Anonymous Referee #2, 28 Oct 2024
Siyu Liu and Xuguang Dong
Siyu Liu and Xuguang Dong

Viewed

Total article views: 313 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
188 67 58 313 3 9
  • HTML: 188
  • PDF: 67
  • XML: 58
  • Total: 313
  • BibTeX: 3
  • EndNote: 9
Views and downloads (calculated since 09 Sep 2024)
Cumulative views and downloads (calculated since 09 Sep 2024)

Viewed (geographical distribution)

Total article views: 295 (including HTML, PDF, and XML) Thereof 295 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Dec 2024
Download
Short summary
The current research and application of the Copula function is mainly limited to two-dimensional; with the advancement of computer technology and the complexity of the research problems, the application of three-dimensional and above Copula function and the accompanying problems of parameter estimation and the choice of the function type need to be further researched. Therefore, the Copula function has great potential in the risk assessment of natural disasters caused by multiple factors.