Model inter-comparison design for large-scale water quality models
Model inter-comparison design for large-scale water quality models
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DOI：
10.1016/j.cosust.2018.10.013
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发表时间：
2019-02
影响因子：
7.2
通讯作者：
M. V. van Vliet;M. Flörke;J. Harrison;N. Hofstra;V. Keller;F. Ludwig;J. Spanier;M. Strokal;Y. Wada;Y. Wen;Richard J. Williams
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文献类型：
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作者：
M. V. van Vliet;M. Flörke;J. Harrison;N. Hofstra;V. Keller;F. Ludwig;J. Spanier;M. Strokal;Y. Wada;Y. Wen;Richard J. Williams
模型间比较项目（MIPs）可以确定对水质热点和趋势估计的稳健性。水质MIPs可以提高对污染原因和模型不确定性的理解。MIP设计应侧重于使用一致的输入数据集，协调输出变量和空间/时间分辨率。集总模型的MIPs应关注流域出水口的污染物负荷。基于网格模型的MIPs可以比较流域内的空间水质异质性。最近，气候、水文、农业和其他模拟界开展了若干模式间比较项目（MIPs），以量化模拟的不确定性并改进模拟系统。本文主要研究大规模水质模型的MIP设计。水质MIPs有助于提高我们对污染问题的理解，并促进对当前和未来水质的统一估计的发展。这可以为评估水质热点和趋势估计的稳健性，提高对过程、污染源、水质模型不确定性的理解，以及确定水质数据收集和监测的优先事项提供新的机会。水质MIP设计应协调相关模型输入数据集，使用一致的空间/时间域和分辨率，以及类似的输出变量，以提高对水质建模不确定性的理解，并提供符合决策者和其他用户需求的协调水质数据。
HighlightsModel inter-comparison projects (MIPs) can identify robustness in estimates of water quality hotspots and trends.Water quality MIPs can improve understanding of pollution causes and model uncertainties.MIP design should focus on using consistent input datasets and harmonize output variables and spatial/temporal resolutions.MIPs of lumped models should focus on pollutant loadings at river basin outlets.MIPs of grid-based models can compare spatial water quality heterogeneity within basins.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.