TY - JOUR
T1 - Model inter-comparison design for large-scale water quality models
AU - van Vliet, Michelle TH
AU - Flörke, Martina
AU - Harrison, John A.
AU - Hofstra, Nynke
AU - Keller, Virginie
AU - Ludwig, Fulco
AU - Spanier, J. Emiel
AU - Strokal, Maryna
AU - Wada, Yoshihide
AU - Wen, Yingrong
AU - Williams, Richard J.
PY - 2019/2/1
Y1 - 2019/2/1
N2 - Several model inter-comparison projects (MIPs) have been carried out recently by the climate, hydrological, agricultural and other modelling communities to quantify modelling uncertainties and improve modelling systems. Here we focus on MIP design for large-scale water quality models. Water quality MIPs can be useful to improve our understanding of pollution problems and facilitate the development of harmonized estimates of current and future water quality. This can provide new opportunities for assessing robustness in estimates of water quality hotspots and trends, improve understanding of processes, pollution sources, water quality model uncertainties, and to identify priorities for water quality data collection and monitoring. Water quality MIP design should harmonize relevant model input datasets, use consistent spatial/temporal domains and resolutions, and similar output variables to improve understanding of water quality modelling uncertainties and provide harmonized water quality data that suit the needs of decision makers and other users.
AB - Several model inter-comparison projects (MIPs) have been carried out recently by the climate, hydrological, agricultural and other modelling communities to quantify modelling uncertainties and improve modelling systems. Here we focus on MIP design for large-scale water quality models. Water quality MIPs can be useful to improve our understanding of pollution problems and facilitate the development of harmonized estimates of current and future water quality. This can provide new opportunities for assessing robustness in estimates of water quality hotspots and trends, improve understanding of processes, pollution sources, water quality model uncertainties, and to identify priorities for water quality data collection and monitoring. Water quality MIP design should harmonize relevant model input datasets, use consistent spatial/temporal domains and resolutions, and similar output variables to improve understanding of water quality modelling uncertainties and provide harmonized water quality data that suit the needs of decision makers and other users.
UR - http://www.scopus.com/inward/record.url?scp=85056202034&partnerID=8YFLogxK
U2 - 10.1016/j.cosust.2018.10.013
DO - 10.1016/j.cosust.2018.10.013
M3 - Review article
AN - SCOPUS:85056202034
SN - 1877-3435
VL - 36
SP - 59
EP - 67
JO - Current Opinion in Environmental Sustainability
JF - Current Opinion in Environmental Sustainability
ER -