@article{fdi:010097549,
  title = {{R}egional process-based analysis of trends in flood peaks and volumes using hourly data},
  author = {{T}ramblay, {Y}ves and {A}rnaud, {P}. and {J}avelle, {P}.},
  editor = {},
  language = {{ENG}},
  abstract = {{M}ost studies on flood trends rely on daily discharge data at the station scale, limiting their ability to disentangle contrasting trends in different flood-generation mechanisms. {T}his is particularly true for capturing short-duration processes such as flash floods. {H}ere, we propose a process-based regional framework to investigate trends across different flood types using hourly data. {W}e analyze 829 small to medium-sized catchments (<500 km(2)) across {F}rance using hourly discharge, radar rainfall, and reanalysis-based soil moisture data. {F}lood events are extracted using a peaks-over-threshold approach and classified into four categories based on response time (flash vs. slow) and antecedent soil moisture conditions (saturated vs. non-saturated). {C}atchments are grouped into four homogeneous hydro-climatic regions, and a spatio-temporal declustering procedure is applied to account for spatial and temporal dependence between concurrent events. {T}rends in flood peaks and direct runoff volumes are then assessed using regional quantile regression. {R}esults reveal strong contrasts between regions and flood types. {O}nly a few trends were detected for flood peaks, whereas flood volumes exhibit more frequent significant increasing trends. {T}he strongest increases are found for flash flood volumes in mountainous and {M}editerranean regions. {F}or slow-onset floods, significant positive volume trends are observed in basins located in temperate regions, in line with increases in both rainfall totals and duration. {O}verall, flood volumes show a systematically higher sensitivity to change than peak flows. {T}hese findings demonstrate that flood trend detection critically depends on temporal resolution, flood type, and regional context, and that volume-based metrics provide complementary and often stronger signals than peak-based indicators for assessing evolving flood hazards.},
  keywords = {{F}lood ; {T}rends ; {F}lash floods ; {R}egionalization ; {Q}uantile regression ; {FRANCE}},
  booktitle = {},
  journal = {{J}ournal of {H}ydrology},
  volume = {677},
  numero = {{B}},
  pages = {135949 [11 p.]},
  ISSN = {0022-1694},
  year = {2026},
  DOI = {10.1016/j.jhydrol.2026.135949},
  URL = {https://www.documentation.ird.fr/hor/fdi:010097549},
}