Exploratory studies into seasonal flow forecasting potential for large lakes

• Published in: Hydrology and earth system sciences Vol. 22; no. 1; pp. 127 - 141
• Main Authors: Sene, Kevin, Tych, Wlodek, Beven, Keith
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 09.01.2018; Copernicus Publications
• Subjects: Approximation; Climatic indexes; Data assimilation; Data collection; Estimates; Forecasting; Hydraulic flow; Hydroelectric power; Hydrologic models; Hydrology; Lakes; Mathematical models; Measurement; Modelling; Natural history; Rain; Rainfall; Regression analysis; Response time; Statistical methods; Stochasticity; Storage; Stream flow; Transfer functions; Water balance; Water balance models; Water shortages; Water supply; Lake Malawi; Siam; Lake Victoria; Africa
• ISSN: 1607-7938; 1027-5606; 1607-7938
• DOI: 10.5194/hess-22-127-2018

In seasonal flow forecasting applications, one factor which can help predictability is a significant hydrological response time between rainfall and flows. On account of storage influences, large lakes therefore provide a useful test case although, due to the spatial scales involved, there are a number of modelling challenges related to data availability and understanding the individual components in the water balance. Here some possible model structures are investigated using a range of stochastic regression and 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 – Lake Malawi and Lake Victoria – with forecast skill demonstrated several months ahead using water balance models formulated in terms of net inflows. In both cases slight improvements were obtained for lead times up to 4–5 months from including climate indices in the data assimilation component. The paper concludes with a discussion of the relevance of the results to operational flow forecasting systems for other large lakes.