Abstract
This research focuses on improving the estimations of key dam breach parameters (peak discharge, breach formation time, and average breach width). These parameters are crucial for understanding and mitigating the impact of dam failures. The study acknowledges the difficulty in estimating these parameters due to limited historical data and existing empirical models’ uncertainties. To overcome these challenges, the research follows a systematic approach: (1) Data Compilation and Cleaning: multiple datasets of historical dam break events were compiled and cleaned. Missing data were filled in, and anomalous outliers were removed to ascertain the data quality. (2) Data Fusion approach was then considered for (2-a) Identifying the most suitable empirical equations for each breach parameter. (2-b) Developing regression-based models based on the selected empirical equations. (2-c) Conducting uncertainty analyses to assess the reliability and confidence of the models’ results. The newly developed data fusion-based models were claimed to outperform existing empirical equations, reducing uncertainty across all breach parameters. (3) The research finally applied the proposed models to four real-world dam cases via a two-dimensional hydraulic model. This application demonstrated how the models can be used in practical engineering scenarios, aiming to enhance our ability to predict and consequently mitigate the impact of dam failures, ultimately contributing to improved dam safety and risk management.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdulrahman KZ (2022) Peak discharge from breached embankment dams, analysis, and prediction. Arab J Geosci 15(10):947. https://doi.org/10.1007/s12517-022-10226-y
Abdulrazzaq ID, Jalut QH, Abbas JM (2021) Sensitivity analysis for dam breach parameters using different approaches for earth-fill dam. Diyala J Eng Sci 14(4):90–97. https://doi.org/10.24237/djes.2021.14408
Abrahart RJ, See L (2002) Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments. Hydrol Earth Syst Sci 6(4):655–670. https://doi.org/10.5194/hess-6-655-2002
ANCOLD (2000) Guidelines on selection of acceptable flood capacity for dams
ANCOLD (2002) ANCOLD guidelines on risk assessment
ANCOLD (2003) Guidelines on dam safety management
ANCOLD (2023) Dams information. australian national committee on large dams. https://www.ancold.org.au/?page_id=24. Accessed 8 Mar 2023
Araghinejad S, Azmi M, Kholghi M (2011) Application of artificial neural network ensembles in probabilistic hydrological forecasting. J Hydrol (amst). https://doi.org/10.1016/j.jhydrol.2011.07.011
ASCE, EWRI (Task Committee on Dam, Levee Breaching) (2011) Earthen embankment breaching. J Hydraul Eng. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000498
ASDSO (2023) Dam failures and incidents. Association of State Dam Safety Officials. https://www.damsafety.org/incidents. Accessed 8 Mar 2023
Aureli F, Maranzoni A, Petacia G (1968) Review of historical dam-break events and laboratory tests on real topography for the validation of numerical models. Water 2021:13. https://doi.org/10.3390/w13141968
Azimi R, Vatankhah A, Kouchakzadeh S (2015) Predicting peak discharge from breached embankment dams. In: 36th IAHR world congress. New York
Azmi M, Araghinejad S, Kholghi M (2010) Multi model data fusion for hydrological forecasting using K-nearest neighbor method. Iran J Sci Technol Trans B Eng 34(February):81–92
Azmi M, Rüdiger C (2018) Validating the data fusion-based drought index across Queensland, Australia, and investigating interdependencies with remote drivers. Int J Climatol. https://doi.org/10.1002/joc.5555
Azmi M, Rüdiger C, Walker JP (2016) A data fusion-based drought index. Water Resour Res. https://doi.org/10.1002/2015WR017834
Azmi M, Sarmadi F (2016a) Improving the accuracy of K-nearest neighbour method in long-lead hydrological forecasting. Sci Iran. https://doi.org/10.24200/sci.2016.2164
Azmi M, Sarmadi F (2016b) Comparative evaluations of multivariate methods in spatial clustering of precipitation using GPCC V7 gridded data set: application to the Northern Territory of Australia. Arab J Geosci. https://doi.org/10.1007/s12517-015-2269-6
Bellos V, Tsakiris VK, Kopsiaftis G, Tsakiris G (2020) Propagating dam breach parametric uncertainty in a river reach using the HEC-RAS software. Hydrology 7(4):1–14. https://doi.org/10.3390/hydrology7040072
Bones EJ (2014) Predicting critical shear stress and soil erodibility classes using soil properties. Georgia Institute of Technology, Atlanta
Cattell R (1966) The scree test for the number of factors. Multivar Behav Res 1:245–276
Chen S, Zhong Q, Shen G (2019) Numerical modeling of earthen dam breach due to piping failure. Water Sci Eng 12(3):169–178. https://doi.org/10.1016/j.wse.2019.08.001
Chou SY, Dewabharata A, Zulvia FE, Fadil M (2022) Forecasting building energy consumption using ensemble empirical mode decomposition, wavelet transformation, and long short-term memory algorithms. Energies (basel). https://doi.org/10.3390/en15031035
Curtis SM, Ghosh SK (2011) A Bayesian approach to multicollinearity and the simultaneous selection and clustering of predictors in linear regression. J Stat Theory Pract 5(4):715–735. https://doi.org/10.1080/15598608.2011.10483741
Deb C, Zhang F, Yang J, Lee SE, Shah KW (2017) A review on time series forecasting techniques for building energy consumption. Renew Sustain Energy Rev 74(C):902–924. https://doi.org/10.1016/j.rser.2017.02.085
Department of Natural Resources Queensland (2018) Guideline for failure impact assessment of water dams
Department of Natural Resources Queensland (2019) Guidelines on acceptable flood capacity for water dams
DSMC (2018) Dam breach register book of the national reservoirs. Nanjing, China. (In Chinese)
Eghbali A, Behzadian K, Farhad Hooshyaripor F, Farmani R, Duncan A (2017) Improving prediction of dam failure peak outflow using neuroevolution combined with K-means clustering. J Hydrol Eng. https://doi.org/10.1061/(ASCE)HE.1943
Froehlich DC (1995) Embankment dam breach parameters revisited. Water Resources Engineering
Froehlich DC (2008) Embankment dam breach parameters and their uncertainties. J Hydraul Eng. https://doi.org/10.1061/ASCE0733-94292008134:121708
Froehlich DC (2016a) Empirical model of embankment dam breaching. River flow 2016, 1821–1826. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487–2742: CRC Press
Froehlich DC (2016b) Predicting peak discharge from gradually breached embankment dam. J Hydrol Eng. https://doi.org/10.1061/(asce)he.1943-5584.0001424
Gaagai A, Aouissi HA, Krauklis AE, Burlakovs J, Athamena A, Zekker I, Boudoukha A, Benaabidate L, Chenchouni H (2022) Modeling and risk analysis of dam-break flooding in a semi-arid montane watershed: a case study of the yabous dam, Northeastern Algeria. Water (switzerland). https://doi.org/10.3390/w14050767
Garoosi F, Nicole Mellado-Cusicahua A, Shademani M, Shakibaeinia A (2022) Experimental and numerical investigations of dam break flow over dry and wet beds. Int J Mech Sci. https://doi.org/10.1016/j.ijmecsci.2021.106946
Gavin H (2019) The Levenberg-Marquardt algorithm for nonlinear least squares curve-fitting problems, Vol. 19. Department of Civil and Environmental Engineering, Duke University
Gee DM (2009) Comparison of dam breach parameter estimators. In: World environmental and water resources congress
Goodell C, DeWillie G (2019) Probabilistic dam breach analysis - the future of dam safety is here (178):1–45
Gudeta SW (2022) Dam break modelling by using HEC-RAS and HEC-GEO RAS: a case study of dire embankment dam on legadad river. Int J Eng Technol Sci Innov. https://doi.org/10.51193/IJETSI.2021.7102
Harman H (1967) Modern factor analysis. University of Chicago Press, Chicago
Hawkins DM (2004) The problem of overfitting. J Chem Inf Comput Sci 44(1):1–12. https://doi.org/10.1021/ci0342472
HEC RAS (2016) HEC-RAS river analysis system hydraulic reference manual. Hydrologic Engineering Center 547
Hooshyaripor F, Tahershamsi A, Behzadian K (2015) Estimation of peak outflow in dam failure using neural network approach under uncertainty analysis. Water Resour 42(5):721–734. https://doi.org/10.1134/S0097807815050085
Hooshyaripor F, Tahershamsi A, Golian S (2014) Application of copula method and neural networks for predicting peak outflow from breached embankments. J Hydro-Environ Res 8(3):292–303. https://doi.org/10.1016/j.jher.2013.11.004
Jalili Kashtiban Y, Saeidi A, Farinas M-I, Quirion M (2021) A review on existing methods to assess hydraulic erodibility downstream of dam spillways. Water (basel) 13(22):3205. https://doi.org/10.3390/w13223205
Kaiser H (1960) The application of electronic computers to factor analysis. Educ Psychol Meas 20:141–151
Kocaman S, Ozmen-Cagatay H (2015) Investigation of dam-break induced shock waves impact on a vertical wall. J Hydrol (amst) 525:1–12. https://doi.org/10.1016/j.jhydrol.2015.03.040
Krogh A, Vedelsby J (1995) Neural network ensembles, cross validation and active learning. Adv Neural Inf Process Syst 7:231–238
Lee SO, Yoon KS, Lee JS, Hong SH (2019) Estimates of discharge coefficient in levee breach under two different approach flow types. Sustainability 11(8):2374. https://doi.org/10.3390/su11082374
Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Q Appl Math 2(2):164–168
MacDonald TC, Langridge-Monopolis J (1984) Breaching charateristics of dam failures. J Hydraul Eng 110(5):567–586. https://doi.org/10.1061/(ASCE)0733-9429(1984)110:5(567)
Marquardt D (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2):431–441
Mei S, Zhong Q, Yang M, Chen S, Shan Y, Zhang L (2023) Overtopping-Induced breaching process of concrete-faced rockfill dam: a case study of Upper Taum Sauk dam. Eng Fail Anal. https://doi.org/10.1016/j.engfailanal.2022.106982
Morris M, Hassan M, Kortenhaus A, Geisenhainer P, Visser P, Zhu Y (2009) Modelling breach initiation and growth. FLOODsite
Morris MW, Hassan MAAM, Vaskinn KA (2007) Breach formation: field test and laboratory experiments. J Hydraul Res 45(SPEC. ISS.):9–17. https://doi.org/10.1080/00221686.2007.9521828
Najar M, Gül A (2022) Investigating the Influence of Dam-Breach Parameters on Dam-Break Connected Flood Hydrograph. Teknik Dergi/techn J Turk Chamb Civ Eng 33(5):12501–12524. https://doi.org/10.18400/tekderg.796334
Nobarinia M, Kalateh F, Nourani V, Amini AB (2021) Dam failure peak outflow prediction through GEP-SVM meta models and uncertainty analysis. Water Supply 21(7):3387–3401. https://doi.org/10.2166/ws.2021.100
Nbs NWR (2013) Bulletin of first national census for water. China Water and Power Press, Beijing
Pektas AO, Erdik T (2014) Peak discharge prediction due to embankment dam break by using sensitivity analysis based ANN. KSCE J Civ Eng 18(6):1868–1876. https://doi.org/10.1007/s12205-014-0047-8
Peter SJ, Siviglia A, Nagel J, Marelli S, Boes RM, Vetsch D, Sudret B (2018) Development of probabilistic dam breach model using Bayesian inference. Water Resour Res 54(7):4376–4400. https://doi.org/10.1029/2017WR021176
Pierce M, Thornton C, Abt S (2010) Predicting peak outflow from breached embankment dams. J Hydrol Eng 15(5):338. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000197
Sammen SS, Mohamed TA, Ghazali AH, Sidek LM, El-Shafie A (2017) An evaluation of existent methods for estimation of embankment dam breach parameters. Nat Hazards 87(1):545–566. https://doi.org/10.1007/s11069-017-2764-z
Sarmadi F, Huang Y, Siems ST, Manton MJ (2017) Characteristics of wintertime daily precipitation over the australian snowy mountains. J Hydrometeorol 18(10):2849–2867. https://doi.org/10.1175/JHM-D-17-0072.1
Sattar AMA (2014) Gene expression models for prediction of dam breach parameters. J. Hydroinformatics 16(3):550–571. https://doi.org/10.2166/hydro.2013.084
See L, Abrahart RJ (2001) Multi-model data fusion for hydrological forecasting. Comput Geosci 27(8):987–994. https://doi.org/10.1016/S0098-3004(00)00136-9
Shafii I, Briaud J, Chen H, Shidlovskaya A (2016) Relationship between soil erodibility and engineering properties. Scour and Erosion, 1055–1060. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487–2742, CRC Press
Shu C, Burn DH (2004) Artificial neural network ensembles and their application in pooled flood frequency analysis. Water Resour Res. https://doi.org/10.1029/2003WR002816
Singh KP, Snorrason A (1984) Sensitivity analysis of outflow peaks and flood stages to the selection of dam breach parameters and simulation models. J Hydrol (amst) 68:295–310
Soares-Frazo S et al (2012) Dam-break flows over mobile beds: experiments and benchmark tests for numerical models. J Hydraul Res 50(4):364–375. https://doi.org/10.1080/00221686.2012.689682
Von Thun J, Gillette A (1990) Guidance on breach parameters. Denver
Tian S, Dai X, Wang G, Lu Y, Chen J (2021) Formation and evolution characteristics of dam breach and tailings flow from dam failure: an experimental study. Nat Hazards 107(2):1621–1638. https://doi.org/10.1007/s11069-021-04649-1
Tracy H (1957) Discharge characteristics of broad-crested weirs. Washington DC
USACE (2013) National inventory of dams trifold brochure
USACE (2014) Using HEC-RAS for dam break studies. U.S. Army Corps of Engineers Institute for Water Resources Hydrologic Engineering Center, p. 74
USBR (United States Bureau of Reclamation) (1988) Downstream hazard classification guidelines
Wahl TL (1998) Prediction of Embankment Dam Breach Parameters - A Literature Review and Needs Assessment.
Wahl TL (2004) Uncertainty of predictions of embankment dam breach parameters. J Hydraul Eng 130(5):389–397. https://doi.org/10.1061/ASCE0733-94292004130:5389
Wan CF, Fell R (2004) Investigation of rate of erosion of soils in embankment dams. J Geotech Geoenviron Eng 130(4):373–380. https://doi.org/10.1061/(ASCE)1090-0241(2004)130:4(373)
Wang B, Liu W, Wang W, Zhang J, Chen Y, Peng Y, Liu X, Yang S (2020) Experimental and numerical investigations of similarity for dam-break flows on wet bed. J Hydrol (amst). https://doi.org/10.1016/j.jhydrol.2020.124598
Webby MG (1996) Discussion of ‘peak outflow from breached embankment dam’ by David C. Froehlich J Water Resour Plan Manag 122(4):316–317. https://doi.org/10.1061/(ASCE)0733-9496(1996)122:4(316)
Xu Y, Zhang LM (2009) Breaching parameters for earth and rockfill dams. J Geotech Geoenviron Eng 135(12):1957–1970. https://doi.org/10.1061/(asce)gt.1943-5606.0000162
Yang Y, Zhou X, Chen X, Xie C (2022) Numerical simulation of tailings flow from dam failure over complex terrain. Materials. https://doi.org/10.3390/ma15062288
Zhang G, Zha R, Wan D (2022) MPS–FEM coupled method for 3D dam-break flows with elastic gate structures. Eur J Mech B/fluids 94:171–189. https://doi.org/10.1016/j.euromechflu.2022.02.014
Zhong Q, Chen S, Fu Z, Shan Y (2020) New empirical model for breaching of earth-rock dams. Nat Hazards Rev. https://doi.org/10.1061/(asce)nh.1527-6996.0000374
Zuo J, Xu T, Zhu DZ, Gu H (2022) Impact pressure of dam-break waves on a vertical wall with various downstream conditions by an explicit mesh-free method. Ocean Eng. https://doi.org/10.1016/j.oceaneng.2022.111569
Acknowledgements
We appreciate the constructive comments of reviews that led to improving the quality of this paper. We also like to extend our appreciation to David Froehlich and Tim Rhodes for their valuable detailed feedback on the technical and practical sides of this study.
Funding
This research received no grant from any funding agency in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interests
The authors have no conflict of interest to declare.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix 1: Historical dam failure cases
See Table 13
.
Appendix 2: Performance criteria histograms for groups #2 to #4
See Figs. 7, 8, 9, 10, 11, and 12.
Histograms and cumulative probability of performance criteria for POG2 (Breach peak outflow Group #2) estimates for 100,000 sampling by different models at the training stage
Histograms and cumulative probability of performance criteria for POG2 (Breach peak outflow Group #2) estimates for 100,000 sampling by data fusion-based nonlinear regression (DFM-NL) at the test stage
Histograms and cumulative probability of performance criteria for FT estimates for 100,000 sampling by different models at the training stage
Histograms and cumulative probability of performance criteria for FT estimates for 100,000 sampling by data fusion-based nonlinear regression (DFM-NL) at the test stage
Histograms and cumulative probability of performance criteria for FBWA estimates for 100,000 sampling by different models at the training stage
Histograms and cumulative probability of performance criteria for FBWA estimates for 100,000 sampling by data fusion-based nonlinear regression (DFM-NL) at the test stage
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Azmi, M., Thomson, K. Dam breach parameters: from data-driven-based estimates to 2-dimensional modeling. Nat Hazards 120, 4423–4461 (2024). https://doi.org/10.1007/s11069-023-06382-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-023-06382-3