PHYSICS-INFORMED ARTIFICIAL INTELLIGENCE FOR EARTH SYSTEM SCIENCE: APPLICATIONS IN CLIMATE MODELING, NATURAL HAZARD PREDICTION, AND SUSTAINABLE RESOURCE MANAGEMENT

Abstract

The increasing complexity of Earth system processes and the growing impacts of climate change, extreme weather events, and environmental degradation have highlighted the need for advanced computational approaches capable of delivering accurate, efficient, and physically consistent predictions. Traditional numerical models, although grounded in established physical laws, are computationally expensive and often struggle to assimilate the rapidly increasing volume of observational data. Conversely, conventional artificial intelligence (AI) and deep learning models provide powerful data-driven predictive capabilities but frequently lack physical interpretability, generalizability, and compliance with governing scientific principles. Physics-Informed Artificial Intelligence (PIAI) has emerged as a transformative paradigm that integrates physical laws, mathematical equations, and domain knowledge directly into machine learning algorithms, thereby combining the strengths of physics-based modeling and data-driven intelligence. This hybrid framework enables robust predictions while ensuring consistency with fundamental conservation laws and known environmental processes. This work presents a comprehensive review of the principles, methodologies, and applications of Physics-Informed Artificial Intelligence in Earth System Science, with particular emphasis on climate modeling, natural hazard prediction, and sustainable resource management. The study examines recent advances in Physics-Informed Neural Networks (PINNs), Scientific Machine Learning (SciML), deep learning, remote sensing integration, data assimilation, and digital twin technologies. The review demonstrates how PIAI enhances the simulation of atmospheric dynamics, ocean circulation, hydrological processes, and land–atmosphere interactions by incorporating governing partial differential equations into learning frameworks. Furthermore, it highlights the capability of physics-informed models to improve forecasting accuracy for extreme events such as floods, hurricanes, earthquakes, landslides, droughts, and wildfires while reducing computational costs and improving model interpretability. This also explores the application of PIAI in sustainable resource management, including groundwater assessment, water resource optimization, renewable energy forecasting, agricultural monitoring, ecosystem conservation, and environmental risk assessment. By integrating satellite observations, sensor networks, and multi-source geospatial datasets with physical constraints, PIAI provides reliable decision-support tools for policymakers and resource managers. Despite these promising developments, several challenges remain, including data scarcity, uncertainty quantification, scalability to large-scale Earth system simulations, computational complexity, and the integration of heterogeneous data sources. Emerging research directions involving foundation models, explainable artificial intelligence, high-performance computing, and autonomous Earth system digital twins are discussed as potential solutions to these limitations. Overall, this review demonstrates that Physics-Informed Artificial Intelligence represents a significant advancement in scientific machine learning by bridging the gap between theoretical physics and modern artificial intelligence. Its ability to produce physically consistent, interpretable, and computationally efficient models positions PIAI as a critical enabling technology for next-generation Earth system science. The integration of physical knowledge with data-driven learning is expected to play a pivotal role in enhancing climate resilience, improving natural hazard preparedness, supporting sustainable resource management, and informing evidence-based environmental policy in an era of accelerating global environmental change.