4.7 Review

Modeling Water Quality in Watersheds: From Here to the Next Generation

Journal

WATER RESOURCES RESEARCH
Volume 56, Issue 11, Pages -

Publisher

AMER GEOPHYSICAL UNION
DOI: 10.1029/2020WR027721

Keywords

water quality; water quality models; watershed management; sediments; nutrients; data

Funding

  1. U.S. Department of Energy's National Nuclear Security Administration [DE-NA-0003525]
  2. U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program
  3. Australian Government (Queensland Department of Natural Resources, Mines and Energy) through the Reef Plan
  4. Australian Government (Department of Environment and Science) through the Reef Plan
  5. Queensland Government (Queensland Department of Natural Resources, Mines and Energy) through the Reef Plan
  6. Queensland Government (Department of Environment and Science) through the Reef Plan
  7. Queensland Water Modeling Network
  8. U.S. Department of Agriculture, Agricultural Research Service
  9. New Zealand Ministry for Business, Innovation and Employment's Our Land and Water National Science Challenge [C10X1507]
  10. Hilda John Endowment

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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.

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