发布时间：2026-02-11

文章标题：【学术成果】一篇通讯作者文章在国际学术期刊 Geomorphology 上发表

内容：

我们首次将经典的 Gray-Scott 反应扩散模型引入风沙地貌学领域，成功构建了数学模型与现实地理驱动力之间的理论桥梁，为全球穹状沙丘场的自组织演化规律提供了全新的图灵斑图解释范式。

 

Cited as: Yan, M., He, N., Zhang, Y., & Lin, Y. (2026). Characterization of the self-organization of dome dune via the Turing pattern paradigm. Geomorphology, 110227.

https://doi.org/10.1016/j.geomorph.2026.110227

 

Abstract: Dome dunes, which are widely distributed on the surfaces of Earth and Mars, provide an ideal natural laboratory for testing self-organization theories. This study applies the Gray-Scott reaction-diffusion model to an aeolian dune system, aiming to establish a conceptual bridge between the model's abstract parameters and real-world geomorphological drivers to explain the self-organization of dome dune fields. Here, we conceptualize the dune system as an activator-inhibitor system composed of highly mobile sand (as a long-range inhibitor) and quasi-static deposited sand (as a local activator). This model is validated across both temporal and spatial scales: its internal timescale is linearly coupled with the timescale of physical models, and the model-generated Turing patterns exhibit resemblance to patterns observed in aeolian dune fields. Parameter space analysis further reveals that sediment supply intensity and wind intensity collectively drive a transition from isolated dunes to labyrinthine networks. The study finds that the packing density characterizing the dune field pattern increases with the sediment supply intensity; this trend is modulated by wind intensity, which promotes greater uniformity and regularity in the dune patterns. The geomorphological characteristics of eight real-world dune fields were successfully mapped onto the model's parameter space, and the results are consistent with their respective environmental conditions. This research demonstrates that a simple reaction-diffusion model can effectively capture the self-organizing behavior of a complex aeolian system, thereby offering a new, Turing-based paradigm for modeling large-scale aeolian landforms.

 

FIGURE. Representative dome dune fields selected for model validation. (a) Qualitative mapping of natural dome dune fields onto the Gray-Scott parameter space. The background contours illustrate the predicted transition of the spatial parameter ρ across the k-f plane, with ρ normalized to [0.7, 0.9] to match natural scales. The x-axis (Sand supply) and y-axis (Wind erosion) correspond to the dimensionless parameters f and k, respectively. Circles represent the eight validation sites: white circles denote mini-dome fields (D1: 38◦49′2.79″ N, 94◦34′9.07″ E; D2: 12◦54′53.88″ N, 41◦8′48.84″ E; D3: 19◦12′10.29″ N, 13◦54′33.68″ W; quasi-circular), and gray circles denote mega-dome fields (D4: 18◦34′05.5″ N, 53◦09′19.4″ E; D5: 25◦46′08.0″ N, 44◦28′12.0″ E; D6: 31◦56′39.8″ N, 7◦47′49.9″ E; D7: 18◦ 31′52.3″ N, 53◦ 33′ 45.4″ E; D8: 25◦20′10.3″ N, 12◦11′14.6″ E; morphologically diverse). Circle sizes are proportional to each site's calculated ρ. The positions of D1-D8 are determined by the relative rankings of their environmental variables (EST and mean annual wind speed). Insets illustrate the circularization method applied to complex dunes: blue contours represent the convex hull, and white dashed circles denote the effective circular boundary defined by the maximum chord length and centroid. (b) Geographic distribution of the eight dome dune fields (D1-D8) on a global map, highlighting their locations across North Africa, the Arabian Peninsula, and northwestern China.