This direction paper explores the evolving landscape of physics-informed machine learning (PIML) methodologies in the field of geotechnical engineering, aiming to provide a comprehensive overview of current advancements and propose future research directions. Recognising the intrinsic connection between geophysical phenomena and geotechnical processes, we delve into the inter of physics-based models and machine learning techniques. The paper begins by elucidating the significance of incorporating physics-informed approaches, emphasising their potential to enhance the interpretability, accuracy and reliability of predictive models in geotechnical applications. We review recent applications of PIML in soil mechanics, hydrology, geotechnical site investigation, slope stability analysis and foundation engineering, showcasing successes and challenges. Furthermore, we identify promising avenues for future research in geotechnical engineering, including the integration of domain knowledge, model explainability, multiphysics and multiscale problems, complex constitutive models, as well as digital twins and large AI models within PIML frameworks. As geotechnical engineering embraces the paradigm shift towards data-driven methodologies, this direction paper offers valuable insights for researchers and practitioners, guiding the trajectory of PIML for sustainable and resilient infrastructure development.

During recent decades there has been an increase in extreme flood events and their intensity in most regions, mainly driven by climate change. Furthermore, these critical events are expected to intensify in the future. Therefore, the improvement of preparedness, mitigation, and adaptation counterparts is mandatory. Many scientific fields are involved in this task, but from a meteorological and hydrological perspective, one of the main tools that can contribute to mitigating the impact of floods is the development of Early Warning Systems. In this sense, this paper presents a scientific literature review of some of the most representative Flood Early Warning Systems worldwide, many of which are currently fully operational, with a special focus on the numerical modeling component when it is developed and integrated into the system. Thus, from basic to technically complex, and from basin or regional to continental or global scales of application, these systems have been reviewed. In this sense, a brief description of their main features, operational procedures, and implemented numerical models is also depicted. Additionally, a series of indications regarding the key aspects of the newly developed FEWSs, based on recent trends and advancements in FEWSs development found in the literature, are also summarized. Thus, this work aims to provide a literature review useful to scientists and engineers involved in flood analysis to improve and develop supporting tools to assist in the implementation of mitigation measures to reduce flood damage for people, goods, and ecosystems and to improve the community resilience.