This paper considers the impact of sea level rise and storm surge on the Red River delta region of Vietnam. Permanently inundated lands and temporary flood zones are analyzed by combining sea level rise scenarios for 2050 with simulated storm surge levels for the 100-year event. Our analysis finds that sea level rise through 2050 could increase the effective frequency of the current 100-year storm surge, which is associated with a storm surge of roughly five meters, to once every 49 years. Approximately 10% of the Hanoi region’s GDP is vulnerable to permanent inundation due to sea level rise, and more than 40% is vulnerable to periodic storm surge damage consistent with the current 100-year storm. We conclude that coastal adaptation measures, such as a planned retreat from the sea, and construction of a more substantial seawall and dike system, are needed to respond to these threats.

Flooding is a natural hazard that causes substantial loss of lives and livelihoods worldwide. Developing predictive models for flood-induced financial losses is crucial for applications such as insurance underwriting. This research uses the National Flood Insurance Program (NFIP) dataset between 2000 and 2020 to evaluate the predictive skill of past data in predicting near-future flood loss risk. Our approach applies neural networks (Conditional Generative Adversarial Networks), decision trees (Extreme Gradient Boosting), and kernel-based regressors (Gaussian Processes) to estimate pointwise losses. It aggregates them over intervals using a bias-corrected Burr-Pareto distribution to predict risk. The regression models help identify the most informative predictors and highlight crucial factors influencing flood-related financial losses. Applying our approach to quantify the county-level coastal flood loss risk in eight US Southern states results in an R²=0.807, substantially outperforming related work using stage-damage curves. More detailed testing on 11 counties with significant claims in the NFIP dataset reveals that Extreme Gradient Boosting yields the most favorable results, and bias correction significantly improves the similarity between the predicted and reference claim amount distributions. Our experiments also show that, despite the already experienced climate change, the difference in future short-term risk predictions of flood-loss amounts between historical shifting or expanding training data windows is insignificant.

Motivated by the mid-latitude atmospheric circulation, we develop a system that uses observations from a differentially heated rotating annulus experiment to constrain a numerical simulation in real-time. The coupled physical-numerical system provides a tool to rapidly prototype new methods for state and parameter estimation, and facilitates the study of prediction, predictability, and transport of geophysical fluids where observations or numerical simulations would not independently suffice. A computer vision system is used to extract measurements from the physical simulation, which constrain the model-state of the MIT general circulation model in a hybrid data assimilation approach. Using a combination of parallelism, domain decomposition and an efficient scheme to select ensembles of model-states, we show that estimates that effectively track the fluid-state can be produced. To the best of our knowledge, this is the first realtime coupled system for this laboratory analog of planetary circulation.

The research assesses soil salinity in the southwest coastal region of Bangladesh, collecting a total of 162 topsoil samples between March 1 and March 9, 2024, and processing them following the standard operating procedure for soil electrical conductivity (soil/water, 1:5). Electrical conductivity (EC) measurements obtained using a HI-6321 advanced conductivity benchtop meter were visualized spatially using bubble density mapping. The map demonstrates that soil salinity in the region of interest ranges from 0.05 to 9.09 mS/cm. This dataset provides a critical resource for soil salinity-related research in the region, offering valuable insights to support decision-makers in understanding and mitigating the impacts of soil salinity in Bangladesh’s coastal areas.