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2. Altimetry for the future: Building on 25 years of progress

3. Quantifying the effects of nature-based solutions in reducing risks from hydrometeorological hazards: Examples from Europe

6. A first look at river discharge from SWOT satellite observations

7. A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations

8. Altimetry for the future: Building on 25 years of progress

11. Potential Legacy of SWOT Mission for the Estimation of Flow–Duration Curves.

12. A review of hydro-meteorological hazard, vulnerability, and risk assessment frameworks and indicators in the context of nature-based solutions

14. Po River Morphodynamics Modelled with the Open-source Code iRIC

16. Contributors

19. Qualitative flood risk assessment for road and railway infrastructures: the experience of the MOVIDA project.

20. Brief communication: On the environmental impacts of the 2023 floods in Emilia-Romagna (Italy).

21. Estimations of Crop Losses Due to Flood Using Multiple Sources of Information and Models: The Case Study of the Panaro River

23. Brief communication: On the environmental impacts of 2023 flood in Emilia-Romagna (Italy).

29. A short-cut methodology for the spatial characterization of rivers water quality

38. The Movida Project To Support The Update Of Flood Risk Maps In The Po River District: Methodology For Flood Damage Assessment

39. Innovative techniques for the hydraulic and hydrological variables assessment

40. On the Management of Nature-Based Solutions in Open-Air Laboratories: New Insights and Future Perspectives

46. OPEn-air laboRAtories for Nature baseD solUtions to Manage hydro-meteo risks: Vulnerability and risk assessments of socio-ecological systems (SES): conceptual framework, impact chains and indicators

47. Large-scale stochastic flood hazard analysis applied to the Po River

48. A comparative analysis of flood damage models: lessons learnt and future challenges

49. Are flood damage models converging to “reality”? Lessons learnt from a blind test

50. Bayesian Data-Driven approach enhances synthetic flood loss models

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