Modeling Water Quality in Watersheds: From Here to the Next Generation. Issue 11 (18th November 2020)

• Record Type: Journal Article
• Authors: Fu, B.; Horsburgh, J. S.; Jakeman, A. J.; Gualtieri, C.; Arnold, T.; Marshall, L.; Green, T. R.; Quinn, N. W. T.; Volk, M.; Hunt, R. J.; Vezzaro, L.; Croke, B. F. W.; Jakeman, J. D.; Snow, V.; Rashleigh, B.
• Is Part Of: Water resources research. Volume 56:Issue 11(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-18
• Subjects: water quality -- water quality models -- watershed management -- sediments -- nutrients -- data; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2020WR027721
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in‐stream water quality and process interactions, soil health and land management, and (peri‐)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations. Key Points: We assess four potential improvements in water quality modeling: environmental interfaces, in‐stream processes, soil health, and urban areas Challenges include data quality control, model calibration, uncertainty management, scale mismatches, and model tool provision Modelers need to strengthen connections with experimentalists and stakeholders and cultivate procedural knowledge for modeling processes