Real-time forecasting of algal blooms in reservoirs

Lead Research Organisation: Lancaster University
Department Name: Lancaster Environment Centre


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;, 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.


10 25 50
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