Emerging Directions in Socio-Hydrology: Digital, Big Data & AI

This Research Topic is part of a series of article collections published within Frontiers in Water including:
- Emerging Directions in Socio-Hydrology: Digital, Big Data & AI
- Emerging Directions in Socio-Hydrology: Policy, Practice & Communication
- Emerging Directions in Socio-Hydrology: Climate Adaptation and Transformation


Socio-hydrology has emerged as an interdisciplinary field that seeks to understand the dynamic interactions and feedbacks between human societies and water systems. Traditionally, socio-hydrological research has relied on conceptual models, limited datasets, and case-specific analyses to explore how human behavior, governance, and hydrological processes co-evolve. However, rapid advances in digital technologies are transforming the way human–water interactions can be observed, modeled, and understood. The increasing availability of satellite-based Earth observation, environmental sensors, social media data, and large-scale socio-economic datasets provides unprecedented opportunities to analyze socio-hydrological dynamics across multiple spatial and temporal scales. At the same time, artificial intelligence, machine learning, and digital platforms are enabling new approaches to integrate diverse data sources and develop predictive models of human–water systems. These developments open new frontiers for socio-hydrology by enabling scalable, data-driven analyses of complex socio-environmental feedbacks.

The aim of this Research Topic is to explore how emerging digital technologies and data-driven approaches can advance the understanding and management of socio-hydrological systems. While socio-hydrology has provided important insights into the co-evolution of water and society, many challenges remain in capturing the complexity, heterogeneity, and scale of human–water interactions. Digital tools such as remote sensing, artificial intelligence, big data analytics, and digital twins offer powerful opportunities to address these challenges by integrating diverse data sources and enabling new forms of modeling and analysis.

This Research Topic seeks to highlight innovative approaches that leverage digital and computational methods to investigate socio-hydrological processes, improve decision-support systems, and strengthen our capacity to anticipate and manage water-related risks. By bringing together contributions from hydrologists, data scientists, social scientists, and practitioners, the topic aims to foster interdisciplinary collaboration and promote the development of scalable, transparent, and open data-driven approaches to socio-hydrology. Ultimately, this initiative seeks to advance the digital transformation of socio-hydrology and support more informed and adaptive management of human–water systems.

This Research Topic welcomes contributions that apply digital technologies, large datasets, and computational approaches to the study of socio-hydrological systems. Topics of interest include, but are not limited to: integrating remote sensing with socio-economic or behavioral datasets; machine-learning approaches for socio-hydrological modeling; development of open-source socio-hydrological datasets and platforms; digital twins for human–water systems; and the use of big data to scale local socio-hydrological insights to regional or global analyses. Contributions that combine Earth observation, citizen-generated data, and behavioral information for improved decision-support systems are also encouraged.

We welcome Original Research Articles, Systematic Review, Methods papers, and Perspectives that advance theoretical understanding, methodological innovation, or practical applications of digital and data-driven socio-hydrology. Interdisciplinary studies bridging hydrology, data science, social sciences, and water management are particularly encouraged.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Socio-hydrological Modelling, Artificial Intelligence in Water Systems, Remote Sensing and Earth Observation, Big Data Analytics, Digital Twins for Water Systems

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.