4.5 Article

Using Simple Dilution Models to Predict New Zealand Estuarine Water Quality

期刊

ESTUARIES AND COASTS
卷 41, 期 6, 页码 1643-1659

出版社

SPRINGER
DOI: 10.1007/s12237-018-0387-6

关键词

Estuaries; Water quality; Nutrient loads; Land-use change; Eutrophication; Susceptibility

资金

  1. NIWA SSIF Catchment to Estuaries programme [FWCE1703/FWCE1803]

向作者/读者索取更多资源

A tool based on simple dilution models is developed to predict potential nutrient concentrations and flushing times for New Zealand estuaries. Potential nutrient concentrations are the concentrations that would occur in the absence of nutrient uptake or losses through biogeochemical processes, and so represent the pressure on a system due to nutrient loading. The dilution modelling approach gives a single time- and space-averaged concentration as a function of flow and nutrient input, with the capability to include seasonal nutrient and flow differences. This tool is intended to be used to identify estuaries likely to be highly sensitive to current nutrient loads based on their physical attributes, or to quickly compare the effects of different land-use scenarios on estuaries. The dilution modelling approach is applied both to a case study of a single New Zealand estuary, and used in a New Zealand-wide assessment of 415 estuaries. For the NZ-wide assessment, annual nutrient loads to each estuary were obtained from a GIS-based land-use model. Comparison with measured data shows that the predicted potential nitrate concentrations are significantly correlated with, but higher than, measured nitrate values from water quality sampling time series. This is consistent with expectations given that the measured concentrations include the effects of nitrogen uptake and loss. The estuary dilution modelling approach is currently incorporated into the GIS-land use model, and is also available as a web-app for assessing eutrophication susceptibility of New Zealand estuaries.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据