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Real-time forecasting of algal blooms in reservoirs

水库藻华实时预报

基本信息

批准号：
NE/N004817/1
负责人：
Keith Beven
金额：
$ 2.17万
依托单位：
Lancaster University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FN004817%2F1
关键词：
Real time forecasting algal blooms

项目摘要

Algal blooms are a significant problem for water management worldwide and are costly to manage (e.g. costing an estimated £50 million per year in the UK at 2003 rates). Water companies are faced with problems such as blocked filters, poor taste and odour and, in more extreme cases, high levels of algal-derived toxins. There are a number of management strategies that can be implemented, often in a reactive way. It is therefore advantageous to be able to predict when an algal bloom is likely to occur. In a previous NERC funded project (UKLEON - NE/I007407/1; http://www.ecn.ac.uk/what-we-do/science/projects/ukleon), forecasts of algal blooms in lakes have been made with acceptable accuracy. The forecasts are made using a computer model which describes the growth of algal communities given weather forecasts. For this to be achievable, adequate data is required to be able to run the model and to be able to inform us of when the model is providing a good representation of the lake system. Adequate data availability is a critical part of the forecasting system and can be costly, so there is a requirement to balance the costs of data collection and modelling against the costs of managing algal blooms. The proposed project has the overall objective of defining the water industry's requirement for an algal forecasting system for reservoirs and to determine the likely cost-effectiveness of such a system. Information on the costs associated with different management strategies will be assessed against the costs associated with data collection, model calibration and implementation. These costs will vary based upon: - The accuracy of the forecasts for the required forecast period (e.g. 3, 5 or 10 days ahead). - The characteristics of the reservoir and its catchment. - The level of historic data available for setting up the forecasting system.If such a system is proven to be cost effective the potential for positive impacts on water supply management within the UK, the EU and world wide are significant both in terms of water quality and cost savings.

藻华是全球水资源管理的一个重大问题，管理成本高昂（例如，按 2003 年的价格计算，英国每年的成本估计为 5000 万英镑）。自来水公司面临着过滤器堵塞、味道和气味不佳等问题，在更极端的情况下，还面临着高水平的藻类毒素。有许多管理策略可以实施，通常以被动的方式实施。因此，能够预测藻华何时可能发生是有利的。在之前的 NERC 资助项目（UKLEON - NE/I007407/1；http://www.ecn.ac.uk/what-we-do/science/projects/ukleon）中，对湖泊藻华的预测已经达到了可接受的准确度。这些预测是使用计算机模型进行的，该模型描述了天气预报中藻类群落的生长情况。为了实现这一目标，需要足够的数据来运行模型并能够通知我们模型何时提供了湖泊系统的良好表示。充足的数据可用性是预测系统的关键部分，并且成本可能很高，因此需要平衡数据收集和建模的成本与管理藻华的成本。拟议项目的总体目标是确定水工业对水库藻类预报系统的要求，并确定该系统可能的成本效益。将根据与数据收集、模型校准和实施相关的成本来评估与不同管理策略相关的成本信息。这些成本将根据以下因素而有所不同： - 所需预测期间（例如提前 3、5 或 10 天）的预测准确性。 - 水库及其流域的特征。 - 可用于建立预测系统的历史数据水平。如果这种系统被证明具有成本效益，则在水质和成本节约方面对英国、欧盟和世界范围内的供水管理产生积极影响的潜力是巨大的。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Constraining uncertainty and process-representation in an algal community lake model using high frequency in-lake observations

使用高频湖内观测限制藻类群落湖泊模型中的不确定性和过程表示

DOI：
10.1016/j.ecolmodel.2017.04.011
发表时间：
2017
期刊：
Ecological Modelling
影响因子：
3.1
作者：
Page T
通讯作者：
Page T

Adaptive forecasting of phytoplankton communities.

DOI：
10.1016/j.watres.2018.01.046
发表时间：
2018-05
期刊：
Water research
影响因子：
12.8
作者：
T. Page;Paul Smith;Paul Smith;K. Beven;I. D. Jones;J. Elliott;S. Maberly;E. Mackay;M. D. Ville;H. Feuchtmayr
通讯作者：
T. Page;Paul Smith;Paul Smith;K. Beven;I. D. Jones;J. Elliott;S. Maberly;E. Mackay;M. D. Ville;H. Feuchtmayr

On hypothesis testing in hydrology: Why falsification of models is still a really good idea