Analyzing Extreme Water Volume Events in Khwae Noi Bamrung Daen Dam: A Statistical Approach

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N. Deetae, P. Khamrot, T. Gaketem

Abstract

This study focuses on the statistical modeling of extreme water volumes at the Kwae Noi Bamrung Daen Dam in Phitsanulok Province using extreme value theory (EVT). The objective is to predict high water levels that may pose risks to dam safety and operations. Historical monthly water volume data from 2011 to 2023 were analyzed using the generalized extreme value (GEV) distribution. The Jarque-Bera test confirmed a non-normal distribution (p = 0.02906), justifying the use of EVT. Maximum likelihood estimation yielded parameter estimates of µ = 446.58, σ = 228.68, and ξ = -0.13. Goodness-of-fit tests (K-S and A-D) confirmed the adequacy of the GEV model, with p-values of 0.3559 and 0.1124, respectively. The model estimated that extreme water volumes exceeding 950 million cubic meters are expected approximately once every 25 years. These findings contribute to more accurate hydrological forecasting, improved early warning systems, and enhanced water resource management policies. The study supports risk-informed decision-making in flood-prone regions and advances the application of EVT in dam safety and environmental planning.

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References

  1. R. Minkah, An Application of Extreme Value Theory to the Management of a Hydroelectric Dam, SpringerPlus 5 (2016), 96. https://doi.org/10.1186/s40064-016-1719-2.
  2. T. Milojevic, J. Blanchet, M. Lehning, Determining Return Levels of Extreme Daily Precipitation, Reservoir Inflow, and Dry Spells, Front. Water 5 (2023), 1141786. https://doi.org/10.3389/frwa.2023.1141786.
  3. H. Rourke, D. Luppnow, The Risks of Excess Water on Tailings Facilities and Its Application to Dam-Break Studies, in: Tailings and Mine Waste Management for the 21st Century, Sydney, 2015.
  4. Y. Chai, Y. Li, Y. Yang, S. Li, W. Zhang, et al., Water Level Variation Characteristics Under the Impacts of Extreme Drought and the Operation of the Three Gorges Dam, Front. Earth Sci. 13 (2019), 510–522. https://doi.org/10.1007/s11707-018-0739-3.
  5. D. Liu, B. Xie, H. Li, Design Flood Volume of the Three Gorges Dam Project, J. Hydrol. Eng. 16 (2011), 71–80. https://doi.org/10.1061/(asce)he.1943-5584.0000287.
  6. S. Nadarajah, C. Kwofie, An Extreme Value Analysis of Water Levels at the Akosombo Dam, Ghana, Heliyon 10 (2024), e34076. https://doi.org/10.1016/j.heliyon.2024.e34076.
  7. X. Mei, Z. Dai, J. Du, S.E. Darby, Mega Dam‐induced Riverbed Erosion Exacerbates Drought Effects on River Water Surface Elevation, Hydrol. Process. 37 (2023), e14917. https://doi.org/10.1002/hyp.14917.
  8. S. El Adlouni, T.B.M.J. Ouarda, X. Zhang, R. Roy, B. Bobée, Generalized Maximum Likelihood Estimators for the Nonstationary Generalized Extreme Value Model, Water Resour. Res. 43 (2007), 2005WR004545. https://doi.org/10.1029/2005wr004545.
  9. D. Chikobvu, R. Chifurira, Modelling of Extreme Minimum Rainfall Using Generalised Extreme Value Distribution for Zimbabwe, South Afr. J. Sci. 111 (2015), 8. https://doi.org/10.17159/sajs.2015/20140271.
  10. L. Gao, B. Tao, Y. Miao, L. Zhang, X. Song, et al., A Global Data Set for Economic Losses of Extreme Hydrological Events during 1960‐2014, Water Resour. Res. 55 (2019), 5165–5175. https://doi.org/10.1029/2019wr025135.
  11. E. Gilleland, M. Ribatet, A.G. Stephenson, A Software Review for Extreme Value Analysis, Extremes 16 (2012), 103–119. https://doi.org/10.1007/s10687-012-0155-0.
  12. C. Jones, D.E. Waliser, K.M. Lau, W. Stern, Global Occurrences of Extreme Precipitation and the Madden–Julian Oscillation: Observations and Predictability, J. Clim. 17 (2004), 4575–4589. https://doi.org/10.1175/3238.1.
  13. J. Martel, A. Mailhot, F. Brissette, Global and Regional Projected Changes in 100-Yr Subdaily, Daily, and Multiday Precipitation Extremes Estimated from Three Large Ensembles of Climate Simulations, J. Clim. 33 (2020), 1089–1103. https://doi.org/10.1175/jcli-d-18-0764.1.
  14. M. Monirul Qader Mirza, Global Warming and Changes in the Probability of Occurrence of Floods in Bangladesh and Implications, Glob. Environ. Chang. 12 (2002), 127–138. https://doi.org/10.1016/s0959-3780(02)00002-x.
  15. I.E. Augustine, A.T. Akinlolu, Flood Disaster: An Empirical Survey of Causative Factors and Preventive Measures in Kaduna, Nigeria, Int. J. Environ. Pollut. Res. 3 (2015), 53–66.
  16. W.E. Fuller, Flood Flows, Trans. Am. Soc. Civ. Eng. 77 (1914), 564–617. https://doi.org/10.1061/taceat.0002552.
  17. S. Nadarajah, D. Choi, Maximum Daily Rainfall in South Korea, J. Earth Syst. Sci. 116 (2007), 311–320. https://doi.org/10.1007/s12040-007-0028-0.
  18. P. Busababodhin, A. Kaewmun, Extreme Values Statistics, J. King Mongkut's Univ. Technol. NorthBangkok 25 (2015), 55–65.
  19. F.C. Onwuegbuche, A.B. Kenyatta, S.B. Affognon, E.P. Enock, M.O. Akinade, Application of Extreme Value Theory in Predicting Climate Change Induced Extreme Rainfall in Kenya, Int. J. Stat. Probab. 8 (2019), 85–94. https://doi.org/10.5539/ijsp.v8n4p85.
  20. , Modelling of Extreme Maximum Rainfall Using Extreme Value Theory for Tanzania, Int. J. Sci. Innov. Math. Res. 4 (2016), 34–45. https://doi.org/10.20431/2347-3142.0403005.
  21. N. Deetae, Analysis and Mathematical Modeling for Flood Surveillance from Rainfall by Extreme Value Theory for Agriculture in Phitsanulok Province, Thailand, Eur. J. Pure Appl. Math. 15 (2022), 1797–1807. https://doi.org/10.29020/nybg.ejpam.v15i4.4558.
  22. P. Khamrot, N. Deetae, A New Class of Generalized Extreme Value Distribution and Application Under Alpha Power Transformation Method, Eur. J. Pure Appl. Math. 16 (2023), 2461–2475. https://doi.org/10.29020/nybg.ejpam.v16i4.4882.
  23. P. Khamrot, N. Phankhieo, P. Wachirawongsakorn, S. Piros, N. Deetae, Analysis of Carbon Dioxide Value with Extreme Value Theory Using Generalized Extreme Value Distribution, IAENG Int. J. Appl. Math. 54 (2024), 2108–2117.
  24. P. Aryastana, L. Dewi, P.I. Wahyuni, A Study of Rainfall Thresholds for Landslides in Badung Regency Using Satellite-Derived Rainfall Grid Datasets, Int. J. Adv. Appl. Sci. 13 (2024), 197–208. https://doi.org/10.11591/ijaas.v13.i2.pp197-208.