Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 545541
Title Model inter-comparison design for large-scale water quality models
Author(s) Vliet, Michelle T.H. van; Flörke, Martina; Harrison, John A.; Hofstra, Nynke; Keller, Virginie; Ludwig, Fulco; Spanier, J.E.; Strokal, Maryna; Wada, Yoshihide; Wen, Yingrong; Williams, Richard J.
Source Current Opinion in Environmental Sustainability 36 (2019). - ISSN 1877-3435 - p. 59 - 67.
DOI https://doi.org/10.1016/j.cosust.2018.10.013
Department(s) WIMEK
Water Systems and Global Change
Environmental Systems Analysis Group
Publication type Refereed Article in a scientific journal
Publication year 2019
Abstract

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.

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