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.