Transdisciplinary research requires improved knowledge exchange between science and practice. Such improvements include diversifying and scaling up knowledge accessing and sharing through online platforms. We conducted twenty interviews informed by behavioral science methods to clarify the aim, components, and participants' perspectives on the usefulness of the proposed components for an envisioned platform. Participants were members of a Dutch community of practice for river studies and a research programme into integrated and collaborative management. The proposed concept included storylines, data repositories, user profiles, interactive visualisations, and collaborative sessions. Interview results include drivers and barriers from prospective users that we translated into requirements to increase the potential adoption and effective use of online platforms with similar components. From the experiences with implementing these requirements, we provide recommendations for enabling primary drivers: (i) Combining online and offline interactions to provide various options for knowledge exchange between disciplines and organisations. (ii) Sharing the content and application of the research with a non-scientific audience. (iii) Reusing existing online platforms as much as possible without restricting any to improve the reuse of research methods and results. We further provide recommendations to overcome the main barriers: (i) Partnering with various communities to extend knowledge exchange. (ii) Following a participatory approach to improve the design and content while considering the time and resources that such a process entails. (iii) Providing flexible options to contribute and tailor overviews of available knowledge in different ways according to prospective users' roles in practice. (iv) Purposefully facilitating online interactions according to the transdisciplinary process-intended attributes.

River deltas commonly have a heterogeneous substratum of alternating peat, clay and sand deposits. This has important consequences for the river bed development and in particular for scour hole formation. When the substratum consists of an erosion resistant top layer, erosion is retarded. Upon breaking through a resistant top layer and reaching an underlying layer with higher erodibilty, deep scour holes may form within a short amount of time. The unpredictability and fast development of these scour holes makes them difficult to manage, particularly where the stability of dikes and infrastructure is at stake. In this paper we determine how subsurface lithology controls the bed elevation in net incising river branches, particularly focusing on scour hole initiation, growth rate, and direction. For this, the Rhine-Meuse Estuary forms an ideal study site, as over 100 scour holes have been identified in this area, and over 40 years of bed level data and thousands of core descriptions are available. It is shown that the subsurface lithology plays a crucial role in the emergence, shape, and evolution of scour holes. Although most scour holes follow the characteristic exponential development of fast initial growth and slower final growth, strong temporal variations are observed, with sudden growth rates of several meters per year in depth and tens of meters in extent. In addition, we relate the characteristic build-up of the subsurface lithology to specific geometric characteristics of scour holes, like large elongated expanding scour holes or confined scour holes with steep slopes. As river deltas commonly have a heterogeneous substratum and often face channel bed erosion, the observations likely apply to many delta rivers. These findings call for thorough knowledge of the subsurface lithology, as without it, scour hole development is hard to predict and can lead to sudden failures of nearby infrastructure and flood defence works.

To guarantee port accessibility, navigation approach channels need to be well maintained. Annual dredging efforts to maintain navigable channels may well exceed tens of millions m³ of sediment per year, which results in high recurrent costs for port operators. Quantification of expected siltation rates with process-based numerical models helps to effectively design and optimise approach channels. The setup of such models requires several assumptions and parameter settings which introduce uncertainty in model output. However, traditional Monte Carlo methods to quantify that uncertainty in model output are often too resource-intensive with current standard computer resources to be feasibly applied in coastal engineering projects such as approach channel design. Here, we use an alternative multifidelity approach to estimate the probability density function of channel siltation, at lower computational costs compared to direct Monte Carlo simulation. The idea behind this method is to map the output uncertainty of a faster, but inaccurate model to a preferred high-detailed model. The key requirement is that the faster, low-fidelity model and the detailed high-fidelity model are correlated, and that his correlation can be modelled with a probabilistic function. Since linearity of the correlation is not a requirement, the coarse-grid model can be very inaccurate but still serve as an adequate predictor of the high-fidelity model. In this study we did observe a highly nonlinear correlation, which in our case is explained by underestimation of channel siltation near the surf zone by the coarse model. In the presented multifidelity approach we adopted a combination of quasi-random Monte Carlo simulation and a non-parametric Gaussian process transfer function to estimate the uncertainty of total siltation and spatial patterns of siltation in a port approach channel. We argue that the multifidelity approach is conceptually straightforward and found that it can be used to significantly decrease the costs of probabilistic analysis; in our case we found a 85% decrease compared to direct Monte Carlo simulation. An additional advantage is that the approach allows for a trade-off between precision and efficiency by varying the number of high-fidelity model runs. Therefore, we conclude that the multifidelity framework is a potential powerful alternative for cases in which direct Monte Carlo simulation is infeasible or undesirable.