In the face of increasing environmental and climatic pressures, understanding the variability and causality of water, energy, and carbon cycles across spatial and temporal scales is critical for effective resource management, ecosystem sustainability, extreme event mitigation, and climate resilience strategies. Our research emphasizes the integration of data and domain knowledge with hybrid and explainable machine learning methods to improve the understanding of the interactions of climate, water, and ecosystems. Our ultimate goal is to both develop new tools and advance the Earth science that guides sustainable development and risk adaptation strategies under changing conditions.