Predicting water quality at the catchment scale: learning from two decades of monitoring

预测流域范围内的水质：从二十年的监测中学习

• 批准号: LP140100495
• 负责人: Prof Andrew Western
• 金额: $ 22.2万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2015
• 资助国家: 澳大利亚
• 起止时间: 2015-06-17 至 2018-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP140100495
• 关键词: Predicting; water; quality; catchment; scale

Poor water quality affects many rivers and receiving waters such as the Great Barrier Reef and Gippsland Lakes. This project aims to use Bayesian hierarchical models of statewide water quality data to quantify the effects of a range of factors on stream water quality including climate, land use, river flow, vegetation cover, etcetera. The analysis intends to extract information from the entire data set rather than concentrating on individual sites. It intends to underpin a new predictive capacity including response to land use and management changes and climatic variations based on long-term data sets, as well as a water quality prediction capability. It is intended that the models developed will jointly model a range of inter-related water quality parameters.

水质差影响到许多河流和接收沃茨，如大堡礁和吉普斯兰湖。该项目旨在使用全州水质数据的贝叶斯层次模型来量化一系列因素对河流水质的影响，包括气候，土地利用，河流流量，植被覆盖等。分析的目的是从整个数据集中提取信息，而不是集中在个别网站。它打算加强新的预测能力，包括根据长期数据集对土地使用和管理变化以及气候变化作出反应，并加强水质预测能力。开发的模型将共同模拟一系列相互关联的水质参数。