Estimation and comparison of the probable maximum flood entering the Latian dam using bayesian theory and HEC-HMS model

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.