Hydrological extremes—particularly floods and droughts—represent some of the most consequential natural hazards affecting societies worldwide. Intensified by climate variability, land-use transformations, and increasing anthropogenic pressures, these events disrupt water resources, damage ecosystems and infrastructure, and threaten human livelihoods. Traditional monitoring systems often fall short in providing comprehensive and continuous observations, especially in regions lacking dense hydrometeorological networks. The advent of remote sensing technologies has transformed hydrological analysis by offering multi-scale, high-resolution, and real-time monitoring capabilities for essential parameters such as precipitation, soil moisture, evapotranspiration, and surface water dynamics. Concurrently, advances in geospatial analytics, machine learning, and artificial intelligence (AI) have introduced new pathways to process and interpret complex, high-volume hydrological datasets. Yet, integrating these diverse data sources into cohesive systems for efficient monitoring and accurate forecasting remains an ongoing challenge. Bridging these gaps requires the development of novel frameworks that couple physical, statistical, and data-driven methods to capture nonlinear processes governing hydrological extremes.
This Research Topic aims to advance the understanding, monitoring, and prediction of hydrological extremes through the synergy of remote sensing, geospatial technologies, and AI. The overarching goal is to improve the early detection and forecasting capabilities of floods and droughts by integrating multi-source satellite observations, advanced analytics, and hydrological modelling. Remote sensing observations provide critical information on precipitation, soil moisture, surface water dynamics, topography, and land-use conditions that can be assimilated into hydrological and hydraulic models, as well as AI-driven frameworks, to enhance forecasting accuracy and support early warning systems. Specifically, it seeks to foster research that addresses uncertainties in model predictions, enhances real-time decision support, and facilitates risk-informed water and disaster management strategies. The topic also aspires to build stronger links between Earth observation science, data-driven hydrology, and policy-relevant applications in climate adaptation and resilience planning. Through interdisciplinary collaboration across hydrology, geoinformatics, and computational science, this Research Topic will contribute to the design of robust predictive systems capable of anticipating hydrological extremes under changing environmental conditions.
To gather further insights into the global challenge of monitoring and predicting hydrological extremes, we welcome studies that integrate innovative methods, models, and technologies. The scope covers theoretical, methodological, and applied research across spatial and temporal scales, emphasizing both the scientific and operational dimensions of hydrological hazard prediction. To gather further insights in this area, we welcome articles addressing, but not limited to, the following themes:
• Remote sensing-based approaches for flood and drought detection, mapping, and monitoring • Geospatial analysis and risk assessment of hydrological hazards at regional to global scales • Synergistic use of optical, SAR, LiDAR, and thermal data for hydrological applications • Integration of remote sensing data with hydrological, hydraulic, and climate models • Machine learning and deep learning techniques for forecasting and event classification • Multi-source data fusion, assimilation, and downscaling for enhanced prediction accuracy • Development of early warning systems and real-time monitoring platforms • Uncertainty quantification and performance evaluation in hydrological prediction • Applications of GeoAI in data-sparse or ungauged basins • Case studies demonstrating the operational use of satellite-derived hydrological information for disaster management and resilience planning
Appendix: We invite Original Research, Reviews, Methods, Data Reports, and Case Studies that explore theoretical advances, methodological frameworks, and practical applications of remote sensing, geospatial analytics, and AI for monitoring and prediction of hydrological extremes.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Data Report
Editorial
FAIR² Data
Hypothesis and Theory
Methods
Mini Review
Opinion
Original Research
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Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
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.
