Lignin fiber is a type of green reinforcing material that can effectively enhance the physical and mechanical properties of sandy soil when mixed into it. In this study, the changes in the dynamic elastic modulus and damping ratio of lignin-fiber-reinforced sandy soil were investigated through vibratory triaxial tests at different lignin fiber content (FC), perimeter pressures and consolidation ratios. The research results showed that FC has a stronger effect on the dynamic elastic modulus and damping ratio at the same cyclic dynamic stress ratio (CSR); with the increase in FC, the dynamic elastic modulus and damping ratio increase and then decrease, showing a pattern of change of the law. Moreover, perimeter pressure has a positive effect on the dynamic elastic modulus, which can be increased by 81.22-130.60 %, while the effect on the damping ratio is slight. The increase in consolidation ratio increases the dynamic elastic modulus by 10.89-30.86 % and the damping ratio by 38.24-100.44 %. Based on the Shen Zhujiang dynamic model, a modified model considering the effect of lignin fiber content FC was established, and the modified model was experimentally verified to have a broader application scope with a maximum error of 5.36 %. This study provides a theoretical basis for the dynamic analysis and engineering applications of lignin-fiber-reinforced sandy soil.

Economic and human losses from flooding have had a significant global impact. Undeveloped nations often require extended periods to recover from flood-related damage, exacerbating the climate poverty trap, specifically in flood-prone regions. To address this issue, early warning systems (EWS) provide lead time for preparedness and measures to reduce vulnerability. However, EWS are mainly empirical at large scales and often do not incorporate hydrodynamic behaviors in flood forecasting, at least in developing regions with a lack of information. This study presents an open-source system integrating a hydrodynamic model with satellite rainfall data (PERSIANN PDIR-Now) and weather prediction data (GFS). It functions as a near real-time Digital Twin (DT) and Early Warning System for high-resolution flood forecasting. Simulated data can be compared with gauge stations in real-time through the model monitoring interface. A proof-of-concept was made by assessing the model capabilities in two case studies. First, the system simulated two consecutive extreme events (hurricanes ETA and IOTA) over the Sula Valley, Honduras, showing fidelity in streamflow responses. Second, the system worked as a DT and EWS to monitor the current and future hydrological states for two periods in 2022 and 2023. Results indicate that satellite data coupled with DT can provide up-to-date system conditions for flood forecasts for regions of lack of data for extreme rainfall events. This tool offered insights to enhance civil protection and societal engagement through warning dissemination against extreme events to build resilience to cope with the increasing magnitude and frequency of disasters in regions with data scarcity.