沉积源汇系统研究通过分析物质从源到汇的全过程，旨在揭示地表动态变化、物质循环机 制及其对环境变化的响应，有助于我们全面了解沉积物从母岩风化、剥蚀、搬运至最终沉积的复杂 过程。传统的源汇研究多依赖于野外地质观测和实验室分析，这些方法受限于数据的可获取性较 差、时空分辨率低和多解性，难以捕捉过程的动态变化和长期、大尺度的系统演化。随着计算机软 硬件的飞速发展，数值模拟已成为源汇研究的重要工具。数值模拟能够弥补传统方法的不足，定 量分析沉积物在不同环境条件下的侵蚀、搬运和沉积过程，从而提供更全面、更动态的源汇系统演 化视图。重点介绍5 种主流的源汇系统数值模拟工具：Dionisos、SEDSIM、Landlab、goSPL 和 Delft3D。这些工具各具特点，适用于不同的研究场景。Dionisos 擅长模拟大尺度、长时间跨度的 沉积盆地充填过程，但结果趋于平均化，不擅长小尺度的动态变化模拟；SEDSIM基于水动力方程 精准模拟沉积过程，结果更符合实际情况，但模拟速度较慢，更多集中于碎屑岩沉积模拟；Landlab 提供高度自定义和多过程耦合的模拟能力，适合多种研究需求，但需要用户有较强的编程能力； goSPL能够进行全球尺度的高分辨率源汇过程模拟，但处理局部地质现象时存在局限，同时对计算 资源要求较高；而Delft3D在小尺度精细模拟方面表现优越，广泛应用于海岸、河流和湖泊环境模 拟，但对于大尺度模拟存在一定的局限性。未来，随着算力的进一步增强和算法模型的优化，预计 将出现更多高精度、多过程耦合的模拟工具。同时，大数据和人工智能技术在源汇研究中的应用 将成为重要趋势，也将更好地助力源汇模拟，推动该领域的多学科融合和智能化发展。

Abstract：The study of source-to-sink systems is an important field of research focused on understanding the entire process of material transport from source areas, such as mountain ranges or other landforms, to sink areas like river basins, lakes, or the ocean. This process involves the weathering of parent rock, the erosion of materials, their transport via various agents (such as wind, water, or ice), and their eventual deposition at sink locations. Analyzing this system helps reveal dynamic surface changes, material cycling mechanisms, and how these processes respond to environmental shifts over time. Understanding these complex processes is crucial for a variety of scientific fields, including geomorphology, environmental science, and natural resource management. Traditional methods used to study these systems, such as field observations and laboratory analyses, often face several limitations. Data availability, low spatial-temporal resolution, and ambiguity in interpretation make it difficult to capture the rapid and dynamic changes occurring in natural systems. Furthermore, these methods are not well-suited for analyzing long-term evolutionary processes or large-scale systems. As a result, numerical modeling has become a critical tool in the study of source-to-sink systems, addressing these traditional limitations by simulating complex processes over varying spatial and temporal scales. These models provide more quantitative insights into the dynamics of erosion, transport, and deposition under different environmental conditions.This paper highlights five key numerical tools commonly used in source-to-sink research: Dionisos, SEDSIM, Landlab, goSPL, and Delft3D. Each tool has specific advantages that make them suitable for different research needs. Dionisos, for instance, is particularly effective for modeling large-scale, long-term basin filling processes, but it may not be as effective for simulating small-scale, dynamic changes. SEDSIM, which is based on hydrodynamic equations, offers highly accurate results, especially in clastic sedimentary processes, though it tends to be slower and more focused on specific types of sediments. Landlab is highly customizable and capable of multi-process simulations, but it requires advanced programming skills for its effective use. goSPL excels at handling global-scale, high-resolution simulations, though it struggles with localized phenomena and demands significant computational resources. Delft3D, meanwhile, is ideal for small-scale, fine-detail simulations, particularly in coastal, riverine, and lacustrine environments, although it faces challenges in large-scale applications.As computational power continues to grow and algorithms improve, future advancements in sourceto- sink modeling are expected. The integration of big data and AI will likely play a key role in driving further developments, enabling more accurate predictions, facilitating multidisciplinary integration, and fostering the intelligent evolution of the field.