Probabilistic flood inundation mapping through copula Bayesian multi-modeling of precipitation products

Accurate prediction and assessment of extreme flood events are crucial for effective disaster preparedness, response, and mitigation strategies. One crucial factor influencing the intensity and magnitude of extreme flood events is precipitation. Precipitation patterns, particularly during intense weather phenomena such as hurricanes, can play a significant role in triggering widespread flooding over densely populated areas. Traditional flood prediction models typically rely on single-source precipitation data, which may not adequately capture the inherent variability and uncertainty associated with extreme events due to certain limitations in the precipitation generation framework, availability, or both spatial and temporal resolutions. Moreover, in coastal regions, the complex interaction between local precipitation, river flows, and coastal processes (i.e., storm tide) can result in compound flooding and amplify the overall impact and complexity of flooding patterns. This study presents an implementation of the global copula-embedded Bayesian model averaging (BMA) (Global Cop-BMA) framework for improving the accuracy and reliability of extreme flood modeling. The proposed framework integrates a collection of precipitation products with different spatiotemporal resolutions to account for uncertainty in forcing data for hydrodynamic modeling and generating probabilistic flood inundation maps. The methodology is evaluated with respect to Hurricane Harvey, which was a catastrophic weather event characterized by intense precipitation and compound flooding processes over the city of Houston in the state of Texas in 2017. The results show a significant improvement in predictive accuracy compared to those based on a single precipitation product (e.g., the Nash–Sutcliffe efficiency (NSE) performance of a single quantitative precipitation estimation (QPE) is in the range of 0.695 to 0.846, while the Cop-BMA yields an NSE of 0.858), demonstrating the merits of the Global Cop-BMA approach. Furthermore, this research extends its impact by generating probabilistic flood extension maps that account not only for the primary influence of precipitation as a flood driver but also for the intricate nature of compound flooding processes in coastal environments.</p

Evolution of microstructure and crystallographic texture during dissimilar friction stir welding of duplex stainless steel to low carbon-manganese structural steel

Electron backscattered diffraction (EBSD) was used to analyze the evolution of microstructure and crystallographic texture during friction stir welding of dissimilar type 2205 duplex stainless steel (DSS) to type S275 low carbon-manganese structural steel. The results of microstructural analyses show that the temperature in the center of stirred zone reached temperatures between Ac 1 and Ac 3 during welding, resulting in a minor ferrite-to-austenite phase transformation in the S275 steel, and no changes in the fractions of ferrite and austenite in the DSS. Temperatures in the thermomechanically affected and shoulder-affected zones of both materials, in particular toward the root of the weld, did not exceed the Ac 1 of S275 steel. The shear generated by the friction between the material and the rotating probe occurred in austenitic/ferritic phase field of the S275 and DSS. In the former, the transformed austenite regions of the microstructure were transformed to acicular ferrite, on cooling, while the dual-phase austenitic/ferritic structure of the latter was retained. Studying the development of crystallographic textures with regard to shear flow lines generated by the probe tool showed the dominance of simple shear components across the whole weld in both materials. The ferrite texture in S275 steel was dominated by D 1, D 2, E, E¯ , and F, where the fraction of acicular ferrite formed on cooling showed a negligible deviation from the texture for the ideal shear texture components of bcc metals. The ferrite texture in DSS was dominated by D 1, D 2, I, I¯ , and F, and that of austenite was dominated by the A, A¯ , B, and B¯ of the ideal shear texture components for bcc and fcc metals, respectively. While D 1, D 2, and F components of the ideal shear texture are common between the ferrite in S275 steel and that of dual-phase DSS, the preferential partitioning of strain into the ferrite phase of DSS led to the development of I and I¯ components in DSS, as opposed to E and E¯ in the S275 steel. The formations of fine and ultrafine equiaxed grains were observed in different regions of both materials that are believed to be due to strain-induced continuous dynamic recrystallization (CDRX) in ferrite of both DSS and S275 steel, and discontinuous dynamic recrystallization (DDRX) in austenite phase of DSS