[Hydrologic variability and sensitivity based on Hurst coefficient and Bartels statistic]

Area of Science:

• Hydrology
• Climate Science
• Statistical Analysis

Background:

• Global climate change and human activities increasingly alter hydrological sequences.
• Purely stochastic models are insufficient; variations like jumps, trends, periods, and dependencies must be accounted for.
• Quantifying hydrological variability requires appropriate statistical indices.

Purpose of the Study:

• To define and evaluate hydrological variability using the Hurst coefficient and Bartels statistic.
• To assess the sensitivity of these indices to different types of variations (jump, trend, period, dependency).
• To apply these methods to real-world temperature data from the Lancang River basin.

Main Methods:

• Defined hydrological variability based on the Hurst coefficient and Bartels statistic.
• Employed Monte Carlo statistical tests to analyze index sensitivity to variations.
• Analyzed temperature series from weather stations in the Lancang River basin.

Main Results:

• Both Hurst coefficient and Bartels statistic detect jump and trend variations; Hurst is more sensitive to weak changes.
• Bartels statistic uniquely detects periodicity, while both detect dependency, with Bartels sensitive to weaker dependencies.
• Hydrological variability, measured by both indices, increases with variation range, confirming their utility for measuring variation intensity.
• Lancang River basin temperature data show warming trends and higher variability in upper and lower reaches.

Conclusions:

• The Hurst coefficient and Bartels statistic are effective tools for quantifying hydrological variability under various influences.
• These methods provide practical insights into climate change impacts, as demonstrated by the Lancang River basin temperature analysis.
• The study validates the practicability of using these statistical measures for hydrological sequence analysis.