Confidence Interval Estimation for Precipitation Quantiles Based on Principle of Maximum Entropy

Area of Science:

• Hydrology
• Statistical modeling
• Environmental science

Background:

• The principle of maximum entropy (POME) is widely applied in hydrology.
• POME has not been previously utilized for confidence interval estimation.
• Accurate confidence intervals are crucial for reliable hydrological predictions.

Purpose of the Study:

• To apply the principle of maximum entropy for confidence interval estimation of precipitation quantiles.
• To evaluate the performance of POME in comparison to existing methods.
• To assess the impact of POME on the uncertainty of hydrological quantile estimators.

Main Methods:

• Employed the principle of maximum entropy (POME) for confidence interval estimation.
• Fitted precipitation data using gamma, Pearson type 3 (P3), and extreme value type 1 (EV1) distributions.
• Utilized Monte Carlo simulations to compare POME with methods of moments (MOM) and maximum likelihood (ML).

Main Results:

• POME demonstrated superior performance in reducing the uncertainty of precipitation quantile estimators.
• Confidence intervals calculated using POME were more reliable than those from MOM and ML.
• The study confirmed POME's effectiveness for T-year design precipitation calculations.

Conclusions:

• The principle of maximum entropy is a valuable tool for improving confidence interval estimation in hydrology.
• POME offers a more robust alternative to MOM and ML for quantile uncertainty reduction.
• This research advances statistical methods in hydrological frequency analysis.