@article{fdi:010089672,
  title = {{C}hanges in the climate system dominate inter-annual variability in flooding across the globe},
  author = {{K}im, {H}. and {V}illarini, {G}. and {W}asko, {C}. and {T}ramblay, {Y}ves},
  editor = {},
  language = {{ENG}},
  abstract = {{E}xtreme flood events have regional differences in their generating mechanisms due to the complex interaction of different climate and catchment processes. {T}his study aims to examine the capability of climate drivers to capture year-to-year variability in global flood extremes. {H}ere, we use a statistical attribution approach to model seasonal and annual maximum daily discharge for 7,886 stations worldwide, using season- and basin-averaged precipitation and temperature as predictors. {T}he results show robust performance of our seasonal climate-informed models in describing the inter-annual variability in seasonal and annual maximum discharges regardless of the geographical region, climate type, basin size, degree of regulation, and impervious area. {T}he developed models enable the assessment of the sensitivity of flood discharge to precipitation and temperature changes, indicating their potential to reliably project changes in the magnitude of flood extremes. {S}uccessfully modeling the changes in magnitudes of flood events is challenging due to the complexity of flood-driving processes, which vary depending on region and season. {H}ere, we examine how seasonal precipitation and temperature can describe changes in the flood magnitudes. {A}lthough we only consider these basic climate factors as predictors, our climate-informed models consistently show a good performance in capturing the year-to-year variability in the magnitude of seasonal and annual flooding events across 7,886 stations worldwide. {T}hese results show the suitability of seasonal precipitation and temperature as proxies for different climate and catchment processes leading to flooding. {F}or 7,886 stations across the globe, we develop statistical attribution models for seasonal and annual maximum daily discharge series {U}sing only climate predictors, our models perform well across climate type, basin size, degree of regulation, and impervious area {S}easonal climate predictors are suitable to capture complex interplays of different climate and catchment processes leading to peak flows},
  keywords = {},
  booktitle = {},
  journal = {{G}eophysical {R}esearch {L}etters},
  volume = {51},
  numero = {6},
  pages = {e2023{GL}107480 [10 p.]},
  ISSN = {0094-8276},
  year = {2024},
  DOI = {10.1029/2023gl107480},
  URL = {https://www.documentation.ird.fr/hor/fdi:010089672},
}