PITS

Patterns In The nearShore (PITS). Coastlines have been environments of vital importance in human history and continue to play a primary role in today’s society. The vast majority of the human population lives in close proximity to the open coast or along more protected estuaries, to the point that the coastline landscape is often a sequence of large cities and industrial centres. Ultimately, coastlines play a major economic role at the global scale. But exactly for the reasons just outlined, coastlines are subject to a tremendous pressure driven by the interaction between natural processes (usually erosive in the long-term), climatic and human drivers. The problem is at the global scale but management efforts to limit hazards and risk require a much deeper, multi-scale understanding of coastline geophysics. PITS will address the future of our coastlines: how their morphology changes over time, how feedbacks and nonlinear interactions (including the ones that might arise as a result of the combination of physical and biological processes) can cause unexpected changes, from the development of mesmerizing patterns to altering hazards and risks related to storms and flooding. PITS will tackle a range of spatial and temporal scales and so address both the underlying fast- and small-scale physical processes (e.g., the physics of sediment resuspension) and the slow- and large-scale effects of climate change (e.g.. sea-level rise) and anthropogenic disturbances (e.g., land reclamation and/or restoration). PITS will benefit geosciences (e.g., sediment transport and morphodynamics), nearshore oceanography and ecology (e.g., role of disturbances on population dynamics) and will provide new theories on the formation of natural patterns at different spatial and temporal scales. The objective of PITS is to address a) how/why/when, in the face of the many nonlinear complex interactions, spatial and temporal patterns emerge in nearshore morphodynamics, and b) develop predictive tools using Machine Learning techniques. PITS aims at enhancing our knowledge of the processes that shape the form and function of nearshore environments. This involves addressing feedbacks between physical processes and sometimes even connecting physical and biological processes. PITS will integrate strengths between the PI and the host institution, and will adopt a variety of techniques (from traditional numerical modelling to machine learning) to test the hypothesis that natural environments are more than the (linear) sum of its components (e.g., physics, biology …), and that in fact the functioning and evolution is dominated by hydrodynamics-sediment transport-morphology feedbacks (the so-called morphodynamic loop) which can in turn result in pattern formation (the so-called system self-organization). The patterns studied in PITS have been chosen because they are representative of different spatial and temporal scales, their relevance range from scientific (e.g., beach cusps) to ecological, societal and economic (e.g., shoreline stability), they can be affected by episodic events (e.g., storm impacts on inner shelf bedforms) or slowly evolve over extremely long temporal scales (e.g., estuarine evolution), they deal with purely physical or with bio-physical feedbacks (e.g., estuaries). In terms of modelling and data analysis, PITS will use a variety of techniques that have recently been shown to be particularly successful in unfolding the nonlinear properties of the system. These techniques apply to both process-based models (e.g., cellular automata) and data-driven models (e.g., genetic programming). PITS will address how feedbacks and nonlinear interactions (including the ones resulting from the combination of physical and biological processes) shape natural environments over a variety of spatial and temporal scales.