Water and Hydrocomplexity aims to publish research that enhances our understanding, characterization and/or prediction of the hydrological cycle, and the cycles of energy, nutrients and pollutants linked to the water cycle. The specialty section places a special focus on the role of observational and data systems, and associated information to improve the understanding, characterization and prediction in the interdependent climate, environment, infrastructure, and social system, also a basis for the optimization of water resources management. The captured information related to the water cycle is increasing in resolution, variety, heterogeneity, and volume. Real-time information is becoming more of a reality and rapidly increasing in volume through the deployment of low-cost in situobservational networks, and emerging remote sensing technologies through drones and low-earth orbiting satellites. This is further enhanced through citizen-based observations in social networks, novel types of measurements through emerging sensing technologies, new standardized monitoring networks like the Integrated Carbon Observation System (ICOS), coordinated multivariate measurements in observatories and their networks, and output from simulation models at increasing spatial and temporal resolution. These data streams complement the traditional observational systems. This emerging frontier opens up a large potential to utilize this increasing amount of information to provide new insights into the complexity of dependence, interactions, thresholds and trends across traditional physical, chemical, biological and social dimensions in the hydrologic cycle.
We invite manuscripts that address the curation and use of diverse information sources through observations and simulation for exploring emerging frontiers in hydrological science. This includes a focus on the data products themselves including a better understanding and processing of measurements and their representation; increased understanding across scales and/or better prediction of hydrological processes through big data analytics including statistical, machine and deep learning; and model-data fusion methods like data assimilation and inverse modelling along with advanced visualization and interpretation of data and output from simulation models.
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