Innovations in Integrated Surface and Groundwater Model Development and Socio-Environmental Applications

Dear Colleagues,

Over the last decade, the concept of integrated hydrological modeling where surface and groundwater systems are simulated as one system has become accepted as a good modeling practice. Addressing problems arising from the complex interconnected environmental, agricultural and other land-based ecosystems requires a deep understanding of the interconnected processes governing water flow, solute transport, and energy exchange within these systems. Relatively recent advancements in high-performance computing (HPC) and big data infrastructure have been catalysts enhancing the predictive power and fidelity of existing integrated hydrological models that are no longer restricted to simplified, regional systems but are now capable of simulating catchment-to-continental-scale processes with unprecedented spatial and temporal resolution. The ability to ingest and process vast datasets, including high-resolution LiDAR, satellite-derived soil moisture and evapotranspiration data, and large-scale hydrogeologic databases have been key to these innovations. The long-recognized, critical issue of data scarcity in hydrologic modeling, that provide the fine-scale inputs necessary for comprehensive, physically based model parameterization and calibration are being addressed, in part, through the adoption and application of novel artificial Intelligence (AI) and machine learning (ML) techniques. These data-driven approaches can help to address some of inherent challenges of traditional, purely physically based models, such as high computational costs and the difficulty in accurately representing complex, surface and subsurface processes. The integration of deterministic and stochastic modeling techniques with these new tools will invariably lead to faster model execution times and uncertainty quantification, making integrated models more useful for water and water quality management and for model-based forecasting under dynamic conditions.

Another important integrative element that has hitherto been neglected is that of socio-hydrological systems analysis that takes account of social processes, with goals, rules, norms, and practices that are adapting to a changing world and that are often drivers of the integrated hydrologic models we seek to enhance. Again, the advent of novel artificial Intelligence (AI) and machine learning (ML) techniques and their application to the simulation of human decision making offers, perhaps for the first time, more integrative and collaborative modeling capability that accounts for social, ecological, economic, physical, and human dimensions for better decision making at a range of system scales. The last decade has seen the introduction of new journals focused on enhancing effective decision making on water-related socio-environmental issues built upon existing and innovative new integrated modeling platforms. Improved stakeholder engagement through the use of interactive narrative, and visualization techniques informed by AI is one of the outcomes that we hope to realize through these new technologies.

The broad aim of this Special Issue of the MDPI journal Water is to recognize innovative thinking and accomplishment in better integration of surface and groundwater models and the embrace of socio-environmental applications where possible. One major area of interest is water quality which presents challenges for both surface and groundwater modeling given the issues of physical and temporal scaling and for the communities impacted by a given problem as well as identifying and managing model uncertainty while juggling challenging issues of model transparency and stakeholder acceptance. Papers should address how innovative scientific thinking, that may include the integration of AI and ML applications, can lead to workable, long-term problem solutions.

Dr. Nigel W. T. Quinn
Guest Editor

Manuscript Submission Information

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• decision support
• model integration
• artificial intelligence/machine learning
• water resources
• water quality
• socio-environmental applications