Flash-flood susceptibility modelling in a data-scarce region using a novel hybrid approach and trend analysis of precipitation

ABSTRACT

Recently, the incidence of heavy rainfall events and associated flash floods have encouraged us to investigate long-term trends in extreme rainfall and flash flood vulnerability mapping. Thus, in this study, a hybrid model was designed by integrating weight of evidence and Naïve Bayes (WOE-NB) to identify areas in Uttarakhand prone to flash floods, and we compared its ability with that of AdaBoost. Furthermore, the significance of long-term rainfall trends was evaluated using Mann–Kendall, modified Mann-Kendall, and innovative trend analysis (ITA), and extreme rainfall events were examined for 51 years (1970–2020). Results showed the WOE-NB and AdaBoost had acceptable goodness of fit (area under the curve = 0.969 and 0.973, respectively). Moreover, ITA can identify some important patterns based on on-trend results that other tests cannot. The return period revealed about 97.54% of the flash floods were caused by normal rainfall, with 2.45% being caused by severely abnormal rainfall.

KEYWORDS:

• flash flood susceptibility mapping
• bivariate statistical model
• multivariate statistical model
• rainfall trend
• extreme rainfall

Editor A. Castellarin; Associate Editor A. Petroselli

Editor A. Castellarin; Associate Editor A. Petroselli

Acknowledgements

We thank the editor and reviewers for taking the time and effort to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us improve the manuscript’s quality.

Disclosure statement

No potential conflict of interest was reported by the authors.