TY - JOUR
T1 - Modeling Water Quality in Watersheds
T2 - From Here to the Next Generation
AU - Fu, B.
AU - Horsburgh, J. S.
AU - Jakeman, A. J.
AU - Gualtieri, C.
AU - Arnold, T.
AU - Marshall, L.
AU - Green, T. R.
AU - Quinn, N. W.T.
AU - Volk, M.
AU - Hunt, R. J.
AU - Vezzaro, L.
AU - Croke, B. F.W.
AU - Jakeman, J. D.
AU - Snow, V.
AU - Rashleigh, B.
N1 - Publisher Copyright:
©2020. The Authors.
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
KW - data
KW - nutrients
KW - sediments
KW - water quality
KW - water quality models
KW - watershed management
UR - http://www.scopus.com/inward/record.url?scp=85096465988&partnerID=8YFLogxK
U2 - 10.1029/2020WR027721
DO - 10.1029/2020WR027721
M3 - Review article
SN - 0043-1397
VL - 56
JO - Water Resources Research
JF - Water Resources Research
IS - 11
M1 - e2020WR027721
ER -