Real-time forecasting of algal blooms in reservoirs
Lead Research Organisation:
Lancaster University
Department Name: Lancaster Environment Centre
Abstract
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.
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.
Planned Impact
The economic benefits to the Water Utilities is an issue to be explored as part of this Pathfinder proposal. See also beneficiaries section.
Publications
Beven K
(2018)
On hypothesis testing in hydrology: Why falsification of models is still a really good idea
in WIREs Water
Page T
(2018)
Adaptive forecasting of phytoplankton communities.
in Water research
Page T
(2017)
Constraining uncertainty and process-representation in an algal community lake model using high frequency in-lake observations
in Ecological Modelling
| Description | The forecasting of phytoplankton populations is feasible and required by the Water Industry. A report of workshop and stakeholder research was produced |
| Exploitation Route | Submission of a follow-up implementation proposal to NERC or Industry. |
| Sectors | Environment |
| Description | A follow-up project with the Water Industry using the MyLake model to explore the impacts of solar power arrays on lakes and reservoirs. |
| First Year Of Impact | 2019 |
| Sector | Energy,Environment |
| Impact Types | Policy & public services |
| Title | Lake Phytoplankton Forecasting Method |
| Description | Implementation of forecasting methodology based on an improved version of the PROTECH lake phytoplankton model |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2018 |
| Provided To Others? | Yes |
| Impact | Publication in Water Research (Page et al., 2018) |
| Description | Contact with Water Industry, Market Research for Forecasting Method funded by NERC Pathfinder Grant |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Industry/Business |
| Results and Impact | Report provided for NERC Pathfinder Grant. Has potential to be developed into a marketable tool |
| Year(s) Of Engagement Activity | 2017 |