## 1 Introduction  
Sediment transport is one of the most important processes for the mass movement of soil particles within a basin area. The driving force behind this process is primarily surface runoff in the land phase and flows in the channel network in the riverbed phase [1, 2]. In practice, this concept covers both transport and the erosion that initiates it [3], and the deposition that concludes it [4, 5]. This process is of great importance due to its social, ecological, and economic effects [6]. It is also highly sensitive to changes in land use, climate, river regulation, operation of hydrotechnical devices, and dam reservoirs [4, 5]. These are some of the main reasons why sediment transport is becoming more and more non-linear in both time and space. Their consequence is a decrease in the depth, fertility and productivity of soils, a decrease in the river's capacity, a shortened life of dam reservoirs, and thus, an increase in the risk of flooding [7]. Not surprisingly, in recent decades, this process has become an important topic on the agenda of local, national, and European policy makers, and is of scientific interest [8, 9, 10, 11, 12]. Sediment particle tracking is now one of the priority problems for water management, and environmental models were quickly identified as a possible solution [5, 13, 14, 15, 16]. This task was seemingly simple, but it quickly became clear that river basins are dynamic and complex systems, and the processes responsible for the transport of sediments in each of the phases are stochastic and variable [17]. In mountainous areas, the problem becomes even more complicated. Observational periods are usually short [17, 18, 19], and the system of channels and rivers is often cut off by anti-erosion structures and dam reservoirs, which retain almost all, especially coarse-grainedsediments transported by the riverbed, thus interrupting the sediment continuum and losing river connectivity [20]. Over the last 15 years, there has been a dynamic increase in the use of computer applications, as evidenced by the number of articles on qualitative modeling published in scientific journals during this period, which has exceeded more than 3000 [21]. A rapid development of mathematical tools occurred and with it, this field of knowledge also created new possibilities for research on the transport of sediments. Currently, a wide range of sediment models are available, which differ in complexity, accuracy, inputs and outputs, approaches, and their spatial and temporal scales, which can be broadly classified as empirical (e.g., USLE, RUSLE, TMDL), conceptual (e.g., HSPF, AGNPS, SWAT), physical (e.g., KINEROS, MIKE-SHE, WESP), and hybrid (e.g., IHACRES-WQ, SEDNET) [3; 22]. The achievements of these models in simulating sediment yield and sediment load are at least satisfactory (e.g., [23; 24; 25; 26; 27; 28]). Nevertheless, the possibilities of considering the high affinity of fine-grained sediment fractions (<63 \(\upmu\)m) with pollutants (e.g., heavy metals and nutrients) are still limited [29]. As a consequence, even the most advanced models often make it impossible to identify the sources of sediment within the basin [30], and to predict the risk of increased levels of pollution in rivers and water bodies [31; 32; 33; 34; 35]. Difficulties also exist in basins with a dam reservoir, which together constitute an integral dynamic system with deep interactions. These two elements of the basin area are usually studied using tools operating at different spatial and temporal scales, often treating them as separate units, or largely generalizing the relationships between them. The best solution to these problems seems to be the integration of both individual models as well as methods and databases. The primary goal of such integration is to combine fragmented knowledge to strengthen the ability to rigorously assess hypotheses and system responses in dynamic scenarios [36]. Model integration is usually difficult and limited by specific model code as well as formats and data exchange between models.