Statistical modeling of sediment supply in torrent catchments of the northern French Alps - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Journal Articles Natural Hazards and Earth System Sciences Year : 2023

Statistical modeling of sediment supply in torrent catchments of the northern French Alps

Abstract

The ability to understand and predict coarse-sediment transport in torrent catchments is a key element for the protection against and prevention of the associated hazards. In this study, we collected data describing sediment supply at 99 torrential catchments in the northern French Alps. The sample covers a wide range of geomorphic activity: from torrents experiencing debris flows every few years to fully forested catchments exporting small bed load volumes every decade. These catchments have long records of past events and sediment supply to debris basins. The mean annual, the 10-year return period and the reference volume (i.e., the 100-year return level or the largest observed volume) of sediment supply were derived for the studied torrents. We examined the relationships between specific sediment supply volumes and many explanatory variables using linear regression and random forest approaches. Results showed that the ratio of sediment-contributing area (bare soil or rock) to catchment area is the most important predictor of the specific sediment production volumes (m3 km−2). Other variables such as the Melton index or the indices of sediment connectivity also have an influence. Several predictive models were developed in order to estimate the sediment supply in torrents that are not equipped with debris basins.
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hal-04179943 , version 1 (17-08-2023)

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Maxime Morel, Guillaume Piton, Damien Kuss, Guillaume Evin, Caroline Le Bouteiller. Statistical modeling of sediment supply in torrent catchments of the northern French Alps. Natural Hazards and Earth System Sciences, 2023, 23 (5), pp.1769-1787. ⟨10.5194/nhess-23-1769-2023⟩. ⟨hal-04179943⟩
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