Using models to bridge the gap between land use and algal blooms: An example from the Loweswater catchment, UK

The goods and services that lakes provide result from complex interactions between meteorology, hydrology, nutrient loads and in-lake processes. Hydrology and nutrient loads are, in turn, influenced by socio-economic factors such as human habitation, water abstraction and land-management, within their catchments. Models provide a means of linking these different domains and also of forecasting and evaluating the effects of different management scenarios on lakes. This paper describes the application of such models to Loweswater, a well-studied lake with water quality problems in the English Lake District, where a community-based approach to catchment management is being undertaken.Three models were linked. Firstly, PLANET (Planning Land Applications of Nutrients for Efficiency and the environmenT), an ‘off the shelf’ farm nutrient budgeting model, was supplemented by local information on septic tanks and used to produce an annual nutrient load to the lake. Secondly, GWLF (Generalized Watershed Loading Function), a generic nutrient runoff model, was used to generate daily nutrient runoff values using input from PLANET plus additional information on land-cover, air temperature and rainfall within the catchment. Thirdly PROTECH (Phytoplankton RespOnses To Environmental CHange), driven by input from GWLF and locally measured meteorology, was used to forecast the abundance of different algal types within the lake. The linked models were used to describe the current impact of catchment management on lake water quality, validated by in situ measurements, and to explore the potential impact of a number of alternative catchment management scenarios. Issues surrounding the use of generic modelling applications for catchment management and relevance for stakeholders living in and/or managing land within the catchment are discussed.

► Linked models forecast impacts of land management scenarios on lake water quality.
► Local inputs and expert knowledge were incorporated into the models.
► Water quality and potentially-toxic algal blooms were forecast for the scenarios.
► Feedback to the catchment community provided evidence to drive changes in practices.