A Scientometric Review for Uncertainties in Integrated Simulation-Optimization Modeling System

Highlights Conducted a comprehensive review analysis of over 2000 articles in integrated simulation-optimization modeling systems, revealing significant advancements in hydrologic modeling and water resource management. Unveiled the knowledge structure, frontiers, influential regions, scholars, and publications in the field using advanced visualization techniques. Integrated GIS, environmental science, and data science to present a multidimensional perspective on water resource management. Highlighted the impact of climate change on water resource management, offering adaptive management methods and contributing to policy making, guiding future research directions and practical applications.Abstract Water resources management is a challenging task caused by huge uncertainties and complexities in hydrological processes and human activities. Over the last three decades, various scholars have carried out the study on hydrological simulation under complex conditions and quantitatively characterized the associated uncertainties for water resources systems. To keep abreast of the development of the collective knowledge in this field, a scientometric review and metasynthesis of the existing uncertainty analysis research for supporting hydrological modeling and water resources management has been conducted. A total of 2020 publications from 1991 to 2018 were acquired from the Web of Science. The scientific structure, cooperation, and frontiers of the related domain were explored using the science mapping software CiteSpace V5.4.R3. Through co-citation, collaboration, and co-occurrence network study, the results present the leading contributors among all countries and hotspots in the research domain. In addition, synthetical uncertainty management for hydrological models and water resource systems under climatic and land use change will continue to be focused on. This study comprehensively evaluates various aspects of uncertainty analysis in hydrologic simulation-optimization systems, showcasing advanced data analysis and artificial intelligence technologies. It focuses on current research frontiers, aiding decision-makers in better understanding and managing the complexity and uncertainties of water resource systems, thereby enhancing the sustainability and efficiency of responses to environmental changes.