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Characterizing annual flood patterns variation using information and complexity indices.

Mohamad Basel Al Sawaf1, Kiyosi Kawanisi1, Cong Xiao1

  • 1Department of Civil and Environmental Engineering, Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima 739-8527, Japan.

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Summary
This summary is machine-generated.

This study introduces a novel method using information and complexity principles to analyze annual flood patterns in a Japanese river catchment. The approach effectively identifies flood triggers and characterizes event complexity, aiding watershed management.

Keywords:
ComplexityEast Asian monsoonInformationPatternTorrential floodsTropical cyclones

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Area of Science:

  • Hydrology
  • Environmental Science
  • Data Analysis

Background:

  • Increasing frequency of torrential rain and complex flood patterns necessitates improved watershed management.
  • Understanding flood dynamics is crucial due to their changeable and unpredictable nature.

Purpose of the Study:

  • To investigate and characterize annual flood patterns in a Japanese river catchment.
  • To propose and validate a method based on information and complexity principles for flood pattern analysis.

Main Methods:

  • Utilized a two-pillar approach: (1) word pattern analysis for flood event duration and (2) information-complexity indices (Mean Information Gain - MIG, Effective Measure Complexity - EMC, Fluctuation Complexity - FC) to quantify pattern frequency and randomness.
  • Applied the method to annual flood data from a riverine system in west Japan.

Main Results:

  • The proposed method successfully detected hidden flood patterns and identified stations with similar flood characteristics.
  • Flood events were primarily linked to precipitation from the East Asian monsoon and tropical cyclones.
  • Information-complexity indices effectively captured internal flood pattern structures: MIG indicated randomness, EMC reflected flood event duration, and FC quantified the number of separated events.

Conclusions:

  • The developed approach is powerful for uncovering patterns in hydrological data.
  • The findings provide insights into flood event triggers and characteristics, enhancing watershed management strategies.
  • The methodology has potential for broader applications in system behavior analysis.