A Bayesian Approach to Hydrological Modeling of Groundwater/Surface Water Interaction
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrogeology".
Deadline for manuscript submissions: closed (25 November 2023) | Viewed by 996
Special Issue Editors
Interests: groundwater modeling; geostatistics; hydroinformatics; hydrogeophysics; remote sensing
Special Issue Information
Dear Colleagues,
Groundwater/surface water interactions have been widely studied in recent decades, and computational modeling has enhanced our understanding of the physical and chemical processes that occur at the interface between a stream bed and groundwater aquifer. With the dramatic increase in computing speed and memory, computation of complex hydrologic systems has become more comprehensive, involving large-scale simulations and more dynamic settings. However, these hydrologic models remain uncertain due to the heterogeneity of hydrologic systems and non-linear dynamics of hydrological processes. The Bayesian approach has been actively adopted for hydrological modeling, especially for groundwater, providing cost-effective data collection, optimal parameterization, reducing model uncertainty, and allowing reliable decision making for remediation and water resource management. Recent advances in high-resolution remote sensing and machine learning/artificial intelligence have increased the effectiveness of the Bayesian approach to hydrological modeling.
This Special Issue addresses the recent advances of Bayesian methods in hydrological modeling, with particular emphasis on surface and groundwater interactions. Potential topics include (but are not limited to):
- Bayesian statistics in hydrologic modeling.
- Bayesian updating of model parameters and uncertainty.
- Reliability analysis and Bayesian in remedial design performance and water resource management.
- Integration of in situ and remote sensing data using Bayesian statistics.
- Bayesian machine learning and artificial intelligence for hydrological processes.
Both original research articles and review articles are welcome.
Prof. Dr. Jejung Lee
Dr. Howard Reeves
Guest Editors
Manuscript Submission Information
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Keywords
- bayesian statistics
- groundwater and surface water interaction
- uncertainty analysis
- reliability
- remedial design
