Department of Water Resources Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran.
10.22059/jwim.2024.368218.1115
Abstract
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.
simulations using multiple climate models. Journal of Hydrology, 574, 1110-1128.
Biondi, D., & De Luca, D. L. (2012). A Bayesian approach for real-time flood forecasting. Physics and Chemistry of the Earth, Parts A/B/C, 42, 91-97.
Dawdy, D. R., & Lettenmaier, D. P. (1987). Initiative for risk-based flood design. Journal of Hydraulic Engineering, 113(8), 1041-1051.
Fernandes, W., Naghettini, M., & Loschi, R. (2010). A Bayesian approach for estimating extreme flood probabilities with upper-bounded distribution functions. Stochastic Environmental Research and Risk Assessment, 24, 1127-1143.
Geweke, J., & Tanizaki, H. (2001). Bayesian estimation of state-space models using the Metropolis–Hastings algorithm within Gibbs sampling. Computational statistics & data analysis, 37(2), 151-170.
Hashemyan, F., Khaleghi, M. R., & Kamyar, M. (2015). Combination of HEC-HMS and HEC-RAS models in GIS in order to Simulate Flood (Case study: Khoshke Rudan river in Fars province, Iran). Research Journal of Recent Sciences, 4(8), 122-127.
Janicka, E., & Kanclerz, J. (2023). Assessing the Effects of Urbanization on Water Flow and Flood Events Using the HEC-HMS Model in the Wirynka River Catchment, Poland. Water, 15(1), 86.
Kabeja, C., Li, R., Guo, J., Rwatangabo, D. E. R., Manyifika, M., Gao, Z., ... & Zhang, Y. (2020). The impact of reforestation induced land cover change (1990–2017) on flood peak discharge using hec-hms hydrological model and satellite observations: a study in two mountain basins, china. Water, 12(5), 1347.
Kavetski, D., Kuczera, G., & Franks, S. W. (2006). Bayesian analysis of input uncertainty in hydrological modeling: 2. Application. Water resources research, 42(3).
Kim, S. J., Kim, G. T., Jeong, J. H., & Han, S. O. (2013). Flood inundation scenario development and analysis using HEC-HMS/RAS and HEC-GeoRAS models. Journal of the Korean Society of Hazard Mitigation, 13(4), 199-206.
Kottegoda, N. T., & Rosso, R. (2008). Applied statistics for civil and environmental engineers.
Naghettini, M. (2017). Fundamentals of statistical hydrology.
Affairs of the country's technical and executive system of the office of standards and projects of water and water supply. (2015). Methods for calculating maximum probable precipitation (PMP) and depth, area, duration curves of precipitation (DAD). Regulation No. 716. (in persian).
Ossandón, Á., Brunner, M. I., Rajagopalan, B., & Kleiber, W. (2022). A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow. Hydrology and Earth System Sciences, 26(1), 149-166.
Pechlivanidis, I. G., Jackson, B. M., Mcintyre, N. R., & Wheater, H. S. (2011). Catchment scale hydrological modelling: a review of model types, calibration approaches and uncertainty analysis methods in the context of recent developments in technology and applications. Global NEST journal, 13(3), 193-214.
Pistocchi, A., & Mazzoli, P. (2002). Use of HEC-RAS and HEC-HMS models with ArcView for hydrologic risk management. 1st International Congress on Environmental Modelling and Software - Lugano, Switzerland - June 2002.
Ros, F. C., Sidek, L. M., Ibrahim, N. N. N., & Razad, A. A. (2008, October). Probable maximum flood (PMF) for the Kenyir Catchment, Malaysia. In International conference on construction and building technology, 31, 325-334.
Te Chow, V., Maidment, D. R., & Mays, L. W. (1988). Applied hydrology.
Thakur, B., Parajuli, R., Kalra, A., Ahmad, S., & Gupta, R. (2017, May). Coupling HEC-RAS and HEC-HMS in precipitation runoff modelling and evaluating flood plain inundation map. In World Environmental and Water Resources Congress, 240-251.
Agheshloui, A., Shourian, M., & Alizadeh Fard, A. (2024). Estimation and comparison of the probable maximum flood entering the Latian dam using bayesian theory and HEC-HMS model. Water and Irrigation Management, 14(1), 234-248. doi: 10.22059/jwim.2024.368218.1115
MLA
Ahmad Agheshloui; Mojtaba Shourian; Amir Alizadeh Fard. "Estimation and comparison of the probable maximum flood entering the Latian dam using bayesian theory and HEC-HMS model", Water and Irrigation Management, 14, 1, 2024, 234-248. doi: 10.22059/jwim.2024.368218.1115
HARVARD
Agheshloui, A., Shourian, M., Alizadeh Fard, A. (2024). 'Estimation and comparison of the probable maximum flood entering the Latian dam using bayesian theory and HEC-HMS model', Water and Irrigation Management, 14(1), pp. 234-248. doi: 10.22059/jwim.2024.368218.1115
VANCOUVER
Agheshloui, A., Shourian, M., Alizadeh Fard, A. Estimation and comparison of the probable maximum flood entering the Latian dam using bayesian theory and HEC-HMS model. Water and Irrigation Management, 2024; 14(1): 234-248. doi: 10.22059/jwim.2024.368218.1115