Bruno Adriano

Accurate exposure data are widely regarded as a prerequisite for reliable disaster damage modelling, yet empirical evidence on how accurate exposure data actually need to be remains limited. This study investigates the sensitivity of tsunami-induced building damage modelling with respect to exposure data accuracy using the 2011 Tōhoku tsunami as a large-scale empirical case study. Focusing on three urban contexts in Miyagi Prefecture, i.e., Kesennuma, Ishinomaki, and Sendai, we systematically degrade exposure data, i.e., building-type accuracy, in a high-quality post-event dataset and analyze the resulting changes in predicted damage patterns. Tsunami fragility functions based on inundation depth are computed to estimate building damage probabilities, which are subsequently aggregated into spatial damage probability surfaces. Model performance is evaluated using a combination of mean absolute error, root mean square error and structural similarity index metrics. Results reveal a non-linear sensitivity of model performance to exposure data accuracy, with the largest marginal losses concentrated at the high-accuracy end of the scale. At the same time, models retain substantive predictive capability even under substantially reduced accuracy conditions, consistently outperforming randomized baselines. Thus, this study provides empirical guidance for balancing exposure data acquisition effort and model performance in disaster risk assessment procedures.

Accurate flood extent and depth information are essential to emergency response, yet most existing studies treat these tasks separately. This work introduces an integrated SAR-to-depth framework that combines water body semantic segmentation with DEM-based geometric depth estimation to generate both flood-extent maps and pixel-wise depth products from Sentinel-1 imagery. For flood extent mapping, we propose a cross-scale Mamba with selective state-space blocks, which achieves a mean IoU of 79.8% across ten European flood events from the KuroSiwo benchmark, outperforming RSMamba by 7.4% and surpassing common CNN baselines. The experimental results demonstrate that the proposed model also generalizes well to unseen events, with test performance exceeding validation scores. When both CS-Mamba predictions and KuroSiwo reference masks are input to FLEXTH, the resulting depth estimates agree within ±2% across four global DEMs. Initial validation against ICESat-2 altimetry using MERIT DEM (19 matched points) shows RMSE of 4.60 m and Bias of -1.88 m, providing preliminary validation evidence with systematic underestimation. Systematic DEM comparison shows FLEXTH is robust across all four DEMs, with Copernicus and MERIT showing closest agreement with reference mask estimates. The framework produces three-class flood masks and pixel-wise depth maps, combining extent mapping with quantitative depth information for operational flood monitoring.

Robust tsunami casualty estimation is vital for Peru’s central coast. While static maps ignore evacuation dynamics, precise agent-based models (ABMs) are often too computationally demanding for rapid screening. To bridge this gap, we propose an efficient geospatial workflow coupling TUNAMI-N2 simulations with shortest-path routing. Evaluating four subduction scenarios across Chorrillos and Villa El Salvador, the model tracks census-block evacuation progress. By intersecting evacuation trajectories with tsunami arrival times, casualties are calculated using empirical depth-dependent fragility functions. Results highlight that delayed reaction times significantly increase mortality. Furthermore, a counterintuitive dynamic emerges in spatially constrained corridors lacking vertical evacuation: higher walking speeds can paradoxically increase fatalities by advancing evacuees into deeper inundation zones before being overtaken. This highlights that behavioral preparedness must be coupled with structural urban interventions. Ultimately, our scalable approach enables DRR (Disaster Risk Reduction) managers to rapidly map mortality hotspots and prioritize critical infrastructure improvements in highly exposed coastal zones.

Owing to recent extreme weather events, flood risk has been rising annually, increasing the demand for fast and accurate flood mapping. Synthetic aperture radar imagery has received considerable attention for flood-mapping applications owing to its all-weather, day-and-night imaging capabilities. Although previous studies have achieved accurate mapping in non-urban areas, challenges remain for urban regions. This study focuses on flood events in Japan by employing a deep learning model and PALSAR-2 imagery to classify non-flooded areas, floods in open areas, and floods in urban areas. To understand the complex spectral characteristics specific to urban areas, this study investigates the integration of geographical features, such as slope and building footprints, into the segmentation process. The experimental results suggest that the inclusion of these supplementary data improves the prediction performance of the trained models.

Peru faces a significant tsunami hazard due to its location along the Pacific Ring of Fire. Historical megathrust earthquakes and their resulting tsunamis have caused severe damage, highlighting the need for improved warning systems. This study investigates potential tsunami impacts along the central Peruvian coast—including the regions of Ica, Callao, Lima, and Ancash—using numerical simulations. To enable rapid and efficient simulations, we developed gWave-CPU, a parallelized version of the TUNAMI-N2 model created at Tohoku University. Using 90-m-resolution topographic and bathymetric data in combination with census data, we assessed population exposure under two seismic scenarios: a plausible event based on interseismic coupling and a historical scenario simulating the 1746 tsunami. Under the historical scenario, the exposed population was estimated at 320,128, with the highest concentrations in Callao and Lima. Numerical simulations of four hours of tsunami propagation and inundation were conducted using our parallelized implementation, reducing computation time to 68 minutes—a 26.3-fold speedup compared to the conventional model. The results demonstrate that tsunami inundation and population exposure in this region can be efficiently estimated using the proposed approach, providing a valuable contribution to tsunami hazard assessment, management, and emergency preparedness along Peru’s central coast.

Early damage estimation in the immediate aftermath of a catastrophic earthquake is critical for effective disaster response and informed decision-making, especially in densely populated urban areas with high seismic activity, such as the Lima Metropolitan Area, Peru. This research focuses on designing and developing a web-based geographic information system for early earthquake damage estimation, featuring a four-stage workflow. Stage 0 generates pre-event damage estimations using earthquake scenarios, infrastructure datasets, and vulnerability models. Stage 1, initiated immediately after an earthquake, selects the closest pre-calculated scenario by comparing the peak ground acceleration values recorded by accelerometers with pre-estimated intensity measures. Stage 2 creates ShakeMaps by interpolating observed ground motion indices to produce the updated damage estimates based on real-time intensity distributions. A future Stage 3 will refine these estimates using post-event observed data. Data acquisition relies on open geospatial services and Peruvian governmental platforms, integrating datasets related to general buildings, essential buildings (schools and hospitals), transportation networks (bridges and roads), and lifeline systems (water supply and sewage). The system was implemented using open-source geospatial tools, providing a cost-effective, scalable solution for early-time disaster management in earthquake regions.