University of Tabriz Journal of Geography and Planning 2008-8078 Articles in Press 2025 09 19 Statistical Modeling and Analysis of Peak Flood Discharge in the Tang-e-Karzin Catchment Using Bootstrapping and Multiple Probability Distributions Statistical Modeling and Analysis of Peak Flood Discharge in the Tang-e-Karzin Catchment Using Bootstrapping and Multiple Probability Distributions 20374 10.22034/gp.2025.67538.3413 FA Heeva Elmizadeh Faculty of Khorramshahr University of Science and Technology Hamid Zeinali Khorramshahr University of Marine Science and Technology Journal Article 2025 05 27 Flood frequency analysis and statistical modeling of peak discharge are essential for water resource management, hydraulic structure design, and flood hazard mitigation in semi-arid regions. This study analyzes the peak flood discharge of the Qareh-Aghaj River in the Tang-e-Karzin catchment, employing multiple probability distributions, including Gumbel Type 1, 2-parameter Gamma, 2- and 3-parameter Log-Normal, Generalized Pareto, Generalized Extreme Value (GEV), and Pearson Type 3. Parameters were estimated using methods of moments (MOM), maximum likelihood (MML), probability-weighted moments (PWM), and maximum entropy (ENT). To assess uncertainty in peak discharge predictions for return periods of 2 to 1,000 years, non-parametric bootstrapping with 1,000 random samples was applied. Peak discharge data were evaluated using foundational statistical tests: the runs test (z=0.025), Grubbs-Beck test (indicating an extreme flood event), Wald-Wolfowitz test (u=0.896), and Mann-Whitney test (p-value=0.6625), confirming data randomness, independence, and homogeneity. The Kolmogorov-Smirnov (K-S) test assessed goodness-of-fit, with the 2-parameter Log-Normal (K-S=0.089) and GEV (K-S=0.063, peak discharge 11,044.6 m³/s for a 1,000-year return period) distributions demonstrating superior performance. Bootstrapping generated 95% confidence intervals, reducing prediction uncertainty, particularly for these distributions. Comparisons using K-S and Anderson-Darling (A-D) criteria further confirmed their superiority. The findings validate bootstrapping as an effective tool for enhancing hydrological predictions and provide recommendations for designing flood-resilient structures and optimizing water resource management in the Tang-e-Karzin catchment. These results offer a foundation for flood management, hydraulic structure design, and water resource planning in this catchment and similar semi-arid regions. Flood frequency analysis and statistical modeling of peak discharge are essential for water resource management, hydraulic structure design, and flood hazard mitigation in semi-arid regions. This study analyzes the peak flood discharge of the Qareh-Aghaj River in the Tang-e-Karzin catchment, employing multiple probability distributions, including Gumbel Type 1, 2-parameter Gamma, 2- and 3-parameter Log-Normal, Generalized Pareto, Generalized Extreme Value (GEV), and Pearson Type 3. Parameters were estimated using methods of moments (MOM), maximum likelihood (MML), probability-weighted moments (PWM), and maximum entropy (ENT). To assess uncertainty in peak discharge predictions for return periods of 2 to 1,000 years, non-parametric bootstrapping with 1,000 random samples was applied. Peak discharge data were evaluated using foundational statistical tests: the runs test (z=0.025), Grubbs-Beck test (indicating an extreme flood event), Wald-Wolfowitz test (u=0.896), and Mann-Whitney test (p-value=0.6625), confirming data randomness, independence, and homogeneity. The Kolmogorov-Smirnov (K-S) test assessed goodness-of-fit, with the 2-parameter Log-Normal (K-S=0.089) and GEV (K-S=0.063, peak discharge 11,044.6 m³/s for a 1,000-year return period) distributions demonstrating superior performance. Bootstrapping generated 95% confidence intervals, reducing prediction uncertainty, particularly for these distributions. Comparisons using K-S and Anderson-Darling (A-D) criteria further confirmed their superiority. The findings validate bootstrapping as an effective tool for enhancing hydrological predictions and provide recommendations for designing flood-resilient structures and optimizing water resource management in the Tang-e-Karzin catchment. These results offer a foundation for flood management, hydraulic structure design, and water resource planning in this catchment and similar semi-arid regions.