Uncertainty analysis of strong earthquake hazard estimation based on the generalized Pareto distribution model: A case study of the northeastern Tibetan Plateau

A statistical method to analyze the uncertainties of strong earthquake hazard estimation is proposed from Generalized Pareto distribution (GPD) model using the northeastern Tibetan Plateau earthquake catalogue (1885–2017) data. For magnitude threshold of 5.5, the magnitude return levels in 20, 50, 100, 200, and 500 years are 7.19, 7.70, 7.99, 8.22, and 8.45, respectively. The corresponding 95% confidence intervals are [6.77, 7.60], [7.27, 8.12], [7.53, 8.44], [7.71, 8.72], and [7.84, 9.06], respectively. The upper magnitude limit obtained from this GPD model is 9.07 and its 80% confidence interval is [8.16, 9.98]. The sensitivity analysis by the Morris method indicates that the input magnitude threshold has a relatively large influence on the estimation results. Thus, threshold selection is important for the GPD model construction. The sensitivity characteristic ranking of input factors become increasingly stable with the increasing of return period, which implies that GPD model is more suitable for estimating strong earthquakes magnitude return levels and upper magnitude limit. The GPD modeling approach and qualitative uncertainty analysis methods for strong earthquake hazard estimations proposed in this paper can be applied to seismic hazard analysis elsewhere.

Breaking Wave Hazard Estimation Model for the U.S. Atlantic Coast

As offshore wind development is in its infancy along the U.S. Atlantic Coast challenges arise due to the effects of strong storms such as hurricanes. Breaking waves on offshore structures induced by hurricanes, are of particular concern to offshore structures due to high magnitude impulse loads caused by wave slamming. Prediction of breaking wave hazards are important in offshore design for load cases using long mean return periods of environmental conditions. A Breaking Wave Hazard Estimation Model (BWHEM) is introduced that provides a means for assessing breaking hazard at long mean return periods over a large domain along the U.S. Atlantic Coast. The BWHEM combines commonly used breaking criteria with the Inverse First Order Method of producing environmental contours, and is applied in a numerical study using a catalog of stochastic hurricanes. The result of the study shows that breaking wave hazard estimation is highly sensitive to the breaking criteria chosen. Criteria including wave steepness and seafloor slope were found to predict breaking conditions at shorter return periods than criteria with only wave height and water depth taken into consideration. Breaking hazard was found to be most important for locations closer to the coast, where breaking was predicted to occur at lower mean return periods than locations further offshore.

Flood Hazard Estimation under Nonstationarity Using the Particle Filter

The presence of the nonstationarity in flow datasets has challenged the flood hazard assessment. Nonstationary tools and evaluation metrics have been proposed to deal with the nonstationarity and guide the infrastructure design and mitigation measures. To date, the examination of how the flood hazards are affected by the nonstationarity is still very limited. This paper thus examined the association between the flood hazards and the nonstationary patterns and degrees of the underlying datasets. The Particle Filter, which allows for assessing the uncertainty of the point estimates, was adopted to conduct the nonstationary flood frequency analysis (NS-FFA) for subsequently estimating the flood hazards in three real study cases. The results suggested that the optimal and top NS-FFA models selected according to the fitting efficiency in general align with the pattern of nonstationarity, although they might not always be superior in terms of uncertainty. Moreover, the results demonstrated the association and the sensitivity of the flood hazards to the perceived patterns and degrees of nonstationarity. In particular, the variations of the flood hazards intensified with the increase in the degree of nonstationarity, which should be assessed in a more elaborate manner, i.e., considering multiple statistical moments. These advocate the potential of using the nonstationarity characteristics as a proxy for evaluating the evolutions of the flood hazards.