A temporally continuous, probabilistic framework for observed multi-decadal flood susceptibility evolution in Canadian watersheds

Abstract. This study advances flood susceptibility analysis by introducing a temporally continuous, uncertainty-aware framework that moves beyond static or snapshot-based mapping. We leverage outputs from a machine learning model trained on a multi-decadal record of historic flood events which generated 24 annual flood susceptibility (FS) maps spanning 2000–2023. Annual watershed scores are derived from normalized pixel proportions and thresholds. Generalized Extreme Value (GEV) distributions fitted to these score series define watershed-specific tails of wetness and dryness, with uncertainty quantified via moving-block-bootstrap. Extreme years are refined using neighbour-year expansion to capture short-term hydroclimatic regimes and validated through change-point detection and Mann–Kendall trend analysis. Pixelwise envelopes are generated by aggregating FS values across selected extreme years and spatial smoothing for coherence. National-scale analysis reveals a clear increase in flood susceptibility in the 2020s across many watersheds, with notable clusters of extreme wet years from 2017–2023 in Atlantic Canada and the St. Lawrence River basin. The 2000s serve as a baseline period, the 2010s represent a transition decade with rising FS, and the 2020s demonstrate the strongest increase in wet extremes and spatial clustering. By explicitly treating flood susceptibility as a temporally evolving, stochastic process, this framework provides probabilistic bounds and diagnostic insights that extend beyond conventional static mapping, offering a robust basis for adaptive flood susceptibility assessment and long-term planning under changing hydroclimatic conditions.