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The interpretation of grain size trends within the stratigraphic record has a wide range of applications, including the identification of external forcing events. Within fluvial systems, it is not yet well constrained as to how autogenic processes, i.e. those internal to the basin, influence grain size signatures. Using a recently developed model, GravelScape (Wild et al., 2025b), that couples the self-similar fining model (Fedele and Paola, 2007) to a Landscape Evolution Model, we investigate what controls the importance of autogenic processes and, in turn, their influence on grain size fining. For this, we perform a large number of numerical experiments by varying (1) the ratio between the incoming sediment flux and integrated subsidence rate (F), which characterizes the degree of bypass of the system; (2) the ratio of the discharge leaving the mountain to the discharge generated within the subsiding basin (β), which controls the shape of the topography of the basin; (3) the erodibility (K), which impacts the steady state or transient nature of the basin; and (4) the transport coefficient (G), which determines the transport- vs. detachment-limited behaviour of the depositional system that also influences the topography. We demonstrate that there exist two differing regimes for long-term grain size fining: one dominated by autogenic processes and one dominated by underlying subsidence. The subsidence-dominated regime occurs when the mean deposition matches the underlying subsidence, which is typical of low-bypass (filling) and low-slope systems (i.e. low values of F, high values of β, and low values of G). The autogenic-dominated regime occurs mostly under high bypass with steep topography when local variability in deposition rate is important (i.e. high F, high G, and low β). We also show that there is a strong correlation between the intensity of autogenic processes and the surface slope and across-basin topographic variability (rugosity). We introduce a framework in which we map the different regimes for grain size fining as a function of bypass (F) and surface geometry (β). We finally illustrate its use for the proper interpretation of grain size fining trends by positioning a series of natural systems within this framework.
