Univrsity Of Tehran Press Water and Irrigation Management 2251-6298 14 1 2024 03 20 Estimation and comparison of the probable maximum flood entering the Latian dam using bayesian theory and HEC-HMS model Estimation and comparison of the probable maximum flood entering the Latian dam using bayesian theory and HEC-HMS model 234 248 96129 10.22059/jwim.2024.368218.1115 FA Ahmad Agheshloui Department of Water Resources Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran. Mojtaba Shourian Department of Water Resources Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran. Amir Alizadeh Fard Department of Water Resources Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran. Journal Article 2023 11 16 In the pursuit of efficient drainage basin management, examining the extreme event of flooding due to its high frequency and resulting life and financial damages holds particular significance. Considering the water crisis and flood management in agricultural usage, investigating floods becomes imperative. Therefore, this research focuses on estimating the probable maximum flood (PMF) of the Latyan Dam drainage basin using Bayesian theory and HEC-HMS models. Initially, the probability maximum precipitation (PMP) of the drainage basin was calculated using a 70-year statistical dataset. Subsequently, employing the Bayesian theory as a stochastic model, the maximum drainage basin flood was determined by examining five discrete values of coefficient of variation and four flood scenarios of annual maximum, daily maximum, annual and instantaneous maximum, daily and instantaneous maximum. In the second phase of the study, an HEC-HMS model for the Latyan Dam drainage basin was developed, and by applying the PMP, the drainage basin's peak flow was determined. Considering error values, a coefficient of variation of 0.4 was adopted. Two scenarios emerged as selected options: daily maximum and daily and instantaneous maximum flooding, with their respective posterior estimates averaging at 1532 and 1577 m³/s. The HEC-HMS model results indicated a drainage basin peak flow of 1025 m³/s, 34 percent lower than the Bayesian theory's calculated value. Based on these outcomes and the available regional data, the Bayesian theory demonstrates superior results in this particular study area.  In the pursuit of efficient drainage basin management, examining the extreme event of flooding due to its high frequency and resulting life and financial damages holds particular significance. Considering the water crisis and flood management in agricultural usage, investigating floods becomes imperative. Therefore, this research focuses on estimating the probable maximum flood (PMF) of the Latyan Dam drainage basin using Bayesian theory and HEC-HMS models. Initially, the probability maximum precipitation (PMP) of the drainage basin was calculated using a 70-year statistical dataset. Subsequently, employing the Bayesian theory as a stochastic model, the maximum drainage basin flood was determined by examining five discrete values of coefficient of variation and four flood scenarios of annual maximum, daily maximum, annual and instantaneous maximum, daily and instantaneous maximum. In the second phase of the study, an HEC-HMS model for the Latyan Dam drainage basin was developed, and by applying the PMP, the drainage basin's peak flow was determined. Considering error values, a coefficient of variation of 0.4 was adopted. Two scenarios emerged as selected options: daily maximum and daily and instantaneous maximum flooding, with their respective posterior estimates averaging at 1532 and 1577 m³/s. The HEC-HMS model results indicated a drainage basin peak flow of 1025 m³/s, 34 percent lower than the Bayesian theory's calculated value. Based on these outcomes and the available regional data, the Bayesian theory demonstrates superior results in this particular study area.  https://jwim.ut.ac.ir/article_96129_3be5b6b6067df44dfef74a07e08bd7c9.pdf