At a global scale, deltas are vital economic hubs, in part due to the combination of their access to inland regions via river systems with their proximity to sea. However, with the sea in close vicinity also comes the threat of freshwater contamination by saline seawater, especially during droughts. This study explores the potential of a mitigation measure to estuarine salt intrusion, namely the construction of a (temporary) earthen sill—a measure implemented in the Lower Mississippi River near New Orleans (LA, USA). This study suggests design guidelines on how a sill can be used to mitigate estuarine salt intrusion: the design should focus on the longitudinal placement and the height of the sill, and the mitigating efficiency of the sill reduces with increasing tidal range. Overall, a (temporary) sill has great potential to reduce salt intrusion in salt wedge estuaries if there is sufficient water depth available.

Estuaries are among the most densely populated and heavily utilised regions in the world, where crucial functions – e.g., freshwater availability and water safety – strongly relate to the natural dynamics of the system. When developing nature-based solutions to safeguard these essential functions, a thorough understanding of estuarine dynamics is required. This study describes an elaborate sensitivity analysis on the salt intrusion length using an idealised estuary, which is parametrically designed using key estuary-scale parameters – e.g., river discharge and tidal flats – to cover a wide range of estuary classes. We were able to systematically investigate such a wide range of estuary classes due to the combination of (1) state-of-the-art hydrodynamic modelling software, (2) high performance computing, and (3) reduction and analysis techniques using machine learning. The results show that the extent of the estuarine salt intrusion length is largely determined by four estuarine features: (1) river discharge; (2) cross-sectional area (especially water depth); (3) tidal damping/amplification; and (4) tidal asymmetry. In general, the salt intrusion length shows clear correlations with (a combination of) estuary-scale parameters, which all put an upper limit on the salt intrusion length. These relations provide crucial insights for successful development of nature-based solutions to mitigate salt intrusion in estuarine environments.

In recent years, coastal management has been facing new challenges: socio-economic growth and consequent climate change impose new boundary conditions pushing coastal systems towards unseen states. For adaptation and mitigation strategies as well as risk management, the resilience of systems to these projected changes must be tested and quantified using predictive tools, given the scarcity of observations. Process-based models, which limit the number of assumptions, are the preferred tools. However, these models are computationally expensive and therefore unattractive for global sensitivity and uncertainty analyses. Input and model reduction techniques, as well as behavioural empirical models, have been widely used to overcome these computational difficulties. In this paper, we propose a process-based hybrid workflow—that combines statistical and machine learning with a process-based numerical model—to provide sensitivity analyses on complex systems. As an example we explore salt intrusion in estuaries. The novelty of the method presented is the implementation of an adaptive sampling technique of numerical experiments with a process-based hydrodynamic model, and the training of a neural network to augment the set of numerical runs executed. The first uses predictive uncertainty to automatically explore the response of the complex system to varying environmental boundary conditions and geomorphological configurations. The second is trained to provide system responses around the sampled points. This exploration is closed by simulating the extremes in the output space as found by a genetic algorithm. This scheme is shown to be highly efficient in non-linear, heteroscedastic, and highly non-stationary systems.

The increasing pressure on Earth's ecosystems due to climate change is becoming more and more evident and the impacts of climate change are especially visible on coral reefs. Understanding how climate change interacts with the physical environment of reefs to impact coral growth and reef development is critically important to predicting the persistence of reefs into the future. In this study, a biophysical model was developed including four environmental factors in a feedback loop with the coral's biology: (1) light; (2) hydrodynamics; (3) temperature; and (4) pH. The submodels are online coupled, i.e. regularly exchanging information and feedbacks while the model runs. This ensures computational efficiency despite the widely-ranged timescales. The composed biophysical model provides a significant step forward in understanding the processes that modulate the evolution of coral reefs, as it is the first construction of a model in which the hydrodynamics are included in the feedback loop.