Autonomous generation of alluvial fans in landscape evolution models

We develop a robust and simple rule‐based algorithm to autonomously simulate alluvial fan deposition and evolution under continuously developing landscape conditions without prescribing deposition locations or imposing topographic constraints. Augmented with this algorithm, landscape evolution models are capable of dynamically detecting locations of potential fan deposition by statistical measures of surface topography and fluvial dynamics, then depositing fan sediments where and when the developed conditions require. To assess the method's efficacy in depositing sediment at a mountain‐valley transition zone characterized by a transport surface that permits unobstructed exit of sediment and water, a hypothetical scenario is created that involves a frontal, normal fault. It is followed by a series of sensitivity analyses to ascertain the influence of parameters affecting fan deposition and secondary processes. Uplift (u) and precipitation significantly impact fan morphological characteristics, which are within the range of real‐world fans. Higher rates of each cause the notable expansion of the fan area except in cases of exceptionally high precipitation rates. Fan area has a power‐law relationship with most of the tested parameters, Af∝u0.94μ0.31lf−0.14Kmax−0.65Acβ, where μ is erodibility (lithology), lf and Kmax are fluvial parameters, and Ac is catchment area ( β~0.9). This study is the first showcasing fan power‐law relationships using numerical modelling. While fan area increases with precipitation, there exists a threshold beyond which fan area diminishes, and the formation of fans ceases altogether. The algorithm provides a basis for improving mechanistic understanding of fans by offering a robust platform for testing process dominance and scaling. The results demonstrate its applicability for landscape evolution simulation over a long time and broad spatial scales. We also investigate the hydrological significance of including autonomously generated alluvial fans in coupled landscape evolution—hydrology models that focus on groundwater as well as surface water hydrology. We unveil a novel rule‐based algorithm that dynamically simulates alluvial fan deposition and evolution. This research marks the inaugural computational representation of fan power‐law correlations. The algorithm, demonstrating its broad‐scale applicability, serves as a robust tool for investigating process dominance and scaling, becoming vital for extended landscape evolution simul