13 citations as recorded by crossref.

1. Quantifying the Role of Occurrence Losses in Catastrophe Excess of Loss Reinsurance Pricing S. Khare & K. Roy https://doi.org/10.3390/risks9030052
2. Unravelling wind-driven impact of storm clusters, a case study for the insurer Generali France L. Hasbini et al. https://doi.org/10.5194/nhess-26-2817-2026
3. Detecting Flood‐Rich and Flood‐Poor Periods in Annual Peak Discharges Across Europe D. Lun et al. https://doi.org/10.1029/2019WR026575
4. A Big Data Framework to Address Building Sum Insured Misestimation C. Roberts et al. https://doi.org/10.1016/j.bdr.2023.100396
5. An EPIC model-based wheat drought risk assessment using new climate scenarios in China Y. Yue et al. https://doi.org/10.1007/s10584-018-2150-1
6. Epistemic uncertainties and natural hazard risk assessment – Part 1: A review of different natural hazard areas K. Beven et al. https://doi.org/10.5194/nhess-18-2741-2018
7. Adjusting catastrophe model ensembles using importance sampling, with application to damage estimation for varying levels of hurricane activity S. Jewson et al. https://doi.org/10.1002/met.1839
8. Clustering Simultaneous Occurrences of the Extreme Floods in the Neckar Catchment E. Modiri & A. Bárdossy https://doi.org/10.3390/w13040399
9. Statistical spatiotemporal analysis of hydro-morphological processes in China during 1950–2015 N. Wang et al. https://doi.org/10.1007/s00477-021-02007-y
10. Mapping the Global-Scale Maize Drought Risk Under Climate Change Based on the GEPIC-Vulnerability-Risk Model Y. Yin et al. https://doi.org/10.1007/s13753-021-00349-3
11. Application of uncertain hurricane climate change projections to catastrophe risk models S. Jewson https://doi.org/10.1007/s00477-022-02198-y
12. Weather persistence on sub-seasonal to seasonal timescales: a methodological review A. Tuel & O. Martius https://doi.org/10.5194/esd-14-955-2023
13. Temporal clustering of floods in Germany: Do flood-rich and flood-poor periods exist? B. Merz et al. https://doi.org/10.1016/j.jhydrol.2016.07.041