Understanding the impacts of climate change on coastal dynamics is an essential precondition to anticipate and prepare for future hazards such as flooding. Despite ongoing research into coastal hydrodynamic and morphodynamic processes, existing engineering tools continue to struggle with predictions of morphological evolution in the presence of engineering interventions, such as nature-based solutions, that aim at enabling coastal zone development under climate change. Accurately predicting morphological changes, which under normal conditions occur over much longer timescales than hydrodynamic processes, represents a major challenge due to difficulties in modeling complex processes over large timescales, like sediment transport, particularly in the presence of other phenomena like flow-vegetation interactions and various human interventions. This challenge is further compounded by uncertainties associated with projected changes in climate on time scales of decades. Engineering tools providing confident predictions are essential to effectively plan for short- and long-term mitigation strategies against coastal erosion.
This Research Topic seeks to advance the existing coastal engineering toolbox to enable reliable predictions of coastal morphological evolution in the context of climate change. Current trends suggest that next-generation coastal engineering solutions may be versatile and multidisciplinary in nature, addressing various challenges simultaneously. For example, the pioneering Sand Engine project in The Netherlands combats coastal erosion while also fulfilling social and ecological functions. However, state-of-the-art nature-based solutions like this are still in their infancy, and questions remain about both their whole potential as well as their limitations at both local and regional scales. Answers to these questions are partially hindered by existing, limited models for coastal morphodynamics. In the context of ever evolving computational tools (perhaps best exemplified by the continuous advances in Artificial Intelligence), this Research Topic invites contributions proposing innovative tools for coastal morphological evolution at multiple spatial and temporal scales and in the presence of multi-biophysics phenomena, such as sediment transport and flow-vegetation interaction, and including reduced-order models as well as high-fidelity approaches.
We encourage submissions encompassing any areas related to the aforementioned goal; these may include but are not limited to: theoretical advancements in morphodynamic modelling considering the influence of coastal ecosystems, progress on computational models for short- and long-term predictions of coastal morphological evolution (either exploiting high-fidelity simulations or focusing on computational efficiency), and studies advancing our understanding of epistemic uncertainty in long-term predictions in particular. Explorations focusing on models enhanced by Artificial Intelligence are especially encouraged. While experimental studies are welcome, especially when aimed at improved parameterisations for numerical models, it is important for these studies to clearly articulate how their findings can be generalised beyond the specific parameter space being explored.
Understanding the impacts of climate change on coastal dynamics is an essential precondition to anticipate and prepare for future hazards such as flooding. Despite ongoing research into coastal hydrodynamic and morphodynamic processes, existing engineering tools continue to struggle with predictions of morphological evolution in the presence of engineering interventions, such as nature-based solutions, that aim at enabling coastal zone development under climate change. Accurately predicting morphological changes, which under normal conditions occur over much longer timescales than hydrodynamic processes, represents a major challenge due to difficulties in modeling complex processes over large timescales, like sediment transport, particularly in the presence of other phenomena like flow-vegetation interactions and various human interventions. This challenge is further compounded by uncertainties associated with projected changes in climate on time scales of decades. Engineering tools providing confident predictions are essential to effectively plan for short- and long-term mitigation strategies against coastal erosion.
This Research Topic seeks to advance the existing coastal engineering toolbox to enable reliable predictions of coastal morphological evolution in the context of climate change. Current trends suggest that next-generation coastal engineering solutions may be versatile and multidisciplinary in nature, addressing various challenges simultaneously. For example, the pioneering Sand Engine project in The Netherlands combats coastal erosion while also fulfilling social and ecological functions. However, state-of-the-art nature-based solutions like this are still in their infancy, and questions remain about both their whole potential as well as their limitations at both local and regional scales. Answers to these questions are partially hindered by existing, limited models for coastal morphodynamics. In the context of ever evolving computational tools (perhaps best exemplified by the continuous advances in Artificial Intelligence), this Research Topic invites contributions proposing innovative tools for coastal morphological evolution at multiple spatial and temporal scales and in the presence of multi-biophysics phenomena, such as sediment transport and flow-vegetation interaction, and including reduced-order models as well as high-fidelity approaches.
We encourage submissions encompassing any areas related to the aforementioned goal; these may include but are not limited to: theoretical advancements in morphodynamic modelling considering the influence of coastal ecosystems, progress on computational models for short- and long-term predictions of coastal morphological evolution (either exploiting high-fidelity simulations or focusing on computational efficiency), and studies advancing our understanding of epistemic uncertainty in long-term predictions in particular. Explorations focusing on models enhanced by Artificial Intelligence are especially encouraged. While experimental studies are welcome, especially when aimed at improved parameterisations for numerical models, it is important for these studies to clearly articulate how their findings can be generalised beyond the specific parameter space being explored.