Analysis of human risks due to dam break floods—part 2: application to Tangjiashan landslide dam failure

• Published in: Natural hazards (Dordrecht) Vol. 64; no. 2; pp. 1899 - 1923
• Main Authors: Peng, M., Zhang, L. M.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-11-2012; Springer; Springer Nature B.V
• Subjects: Bayesian analysis; Civil Engineering; Dam failure; Dams; Downstream; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Earthquakes; Engineering and environment geology. Geothermics; Environmental Management; Environmental policy; Exact sciences and technology; Fatalities; Flood routing; Floods; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Landslides; Landslides & mudslides; Natural Hazards; Natural hazards: prediction, damages, etc; Natural resources; Numerical analysis; Original Paper; Population; Risk; Risk analysis; Risk assessment; Seismic activity; Dams; Dam break; Loss of life; Dam safety; Bayesian networks; Risk analysis; Risk management; Floods; risk assessment; floods; erodibility; simulation; global; case studies; networks; earthquakes; dams; rupture; safety; numerical analysis; uncertainties; models; probability; lead; management; landslides; populations
• ISSN: 0921-030X; 1573-0840
• DOI: 10.1007/s11069-012-0336-9

The Tangjiashan landslide dam was formed during the Ms8.0 Wenchuan earthquake in 2008 and posed high risks to 1.2 million people downstream the dam. A human risk analysis model (HURAM) reported in the companion paper is applied to evaluate the human risk in the Tangjiashan landslide dam failure. The characteristics of this landslide dam are introduced first. The breaching parameters in two cases (i.e., the actual case and a high erodibility case) are predicted with a physically based model, and the flood routing processes in these two cases are simulated using numerical analysis. The population at risk downstream of the landslide dam is then obtained based on the results of the flood routing simulations. Subsequently, the human risks are analyzed with HURAM using Bayesian networks. Fourteen influence parameters and their interrelationships are considered in a systematic structure in the case study. A change in anyone of them may affect the other parameters and leads to loss of life. HURAM allows not only cause-to-result inference, but also result-to-cause inference by updating the Bayesian network with specific information from the study case. The uncertainties of the parameters and their relationships are studied both at the global level using multiple sources of information and at the local level by updating the prior probabilities.