Groundwater resources are connected with social, economic, ecological, and Earth systems. We introduce the framing of groundwater‐connected systems to better represent the nature and complexity of these connections in data collection, scientific investigations, governance and management approaches, and groundwater education. Groundwater‐connected systems are social, economic, ecological, and Earth systems that interact with groundwater, such as irrigated agriculture, groundwater‐dependent ecosystems, and cultural relationships to groundwater expressions such as springs and rivers. Groundwater‐connected systems form social‐ecological systems with complex behaviors such as feedbacks, nonlinear processes, multiple stable system states, and path dependency. These complex behaviors are only visible through this integrated system framing and are not endogenous properties of physical groundwater systems. The framing is syncretic as it aims to provide a common conceptual foundation for the growing disciplines of socio‐hydrogeology, eco‐hydrogeology, groundwater governance, and hydro‐social groundwater analysis. The framing also facilitates greater alignment between the groundwater sustainability discourse and emerging sustainability concepts and principles. Aligning with these concepts and principles presents groundwater sustainability as more than a physical state to be reached; and argues that place‐based and multifaceted goals, values, justice, knowledge systems, governance, and management must continually be integrated to maintain groundwater's social, ecological, and Earth system functions. The groundwater‐connected systems framing can underpin a broad, methodologically pluralistic, and community‐driven new wave of data collection and analysis, research, governance, management, and education. These developments, together, can invigorate efforts to foster sustainable groundwater futures in the complex systems groundwater is embedded within.

Effective communication of disaster risks is crucial to provoking appropriate responses from citizens and emergency operators. With recent advancement in Artificial Intelligence (AI), several researchers have begun exploring machine learning techniques in improving disaster risk communication. This paper adopts a systematic literature approach to report on the various research activities involving the application of AI in disaster risk communication. The study found that research activities focus on two broad areas: (1) prediction and monitoring for early warning, and (2) information extraction and classification for situational awareness. These broad areas are discussed, including background information to help establish future applications of AI in disaster risk communication. The paper concludes with recommendations of several ways in which AI applications can have a broader role in disaster risk communication.