In this paper, we discuss insights gained from previous studies using a novel combined water balance and data-driven approach for two of Africa’s largest lakes, Lake Victoria and Lake Malawi. Factors which increased predictability included the unusually long hydrological response times and statistically significant links to ocean-atmosphere processes such as the Indian Ocean Dipole. Other lessons learned concerned the benefits of data assimilation and the choice of performance metrics.

The theme of the paper is exploration of the potential for seasonal flow forecasting for large lakes using a range of stochastic transfer function techniques with additional insights gained from simple analytical approximations. The methods were evaluated using records for two of the largest lakes in the world. The paper concludes with a discussion of the relevance of the results to operational flow forecasting systems for other large lakes.

This paper describes simple models of phosphorus load which are identified for three catchments in the UK. The models use new hourly observations of phosphorus load, which capture the dynamics of phosphorus transfer in small catchments that are often missed by models with a longer time step. Unlike more complex, process-based models, very few parameters are required, leading to low parameter uncertainty. Interpretation of the dominant phosphorus transfer modes is made based solely on the data.