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Coastal regions face escalating challenges, including climate change, rapid urbanisation, ocean pollution, habitat degradation, and nutrient enrichment, which threaten coastal ecosystem health, biodiversity, and human livelihoods. A comprehensive understanding of coastal hydro-environmental processes, encompassing hydrodynamics, sediment transport driven by waves and currents, and biogeochemical dynamics influencing water quality, is essential for sustainable coastal management. This study presents a global systematic review of assessment methods for these processes, focusing on field data collection, laboratory experiments, numerical modelling, and artificial intelligence techniques. A bibliometric analysis was conducted on 165 peer-reviewed articles from Scopus and Web of Science, adhering to PRISMA 2020 guidelines. The findings reveal a significant shift from conventional standalone methods to integrated approaches, with 31.5% of studies combining field data with numerical models and 20% incorporating AI with field data, emphasising the need for real-time data integration and interdisciplinary strategies to enhance model reliability. This study also introduces a novel process–method–time classification framework that functionally aligns various assessment methods with specific coastal processes. However, challenges such as limited long-term datasets, high computational costs, and data resolution constraints persist. By synthesising global research trends and methodological advancements, this study offers critical insights to support more resilient, adaptive, and data-driven coastal management strategies.
Lee et al. (Sun,) studied this question.