Using the past to predict the future: lake sediments and the modelling of limnological disturbance

Abstract

Most lakes have been disturbed to varying degrees but for an individual lake the timescale of these disturbances is rarely known. Lake sediments, however, can be used as natural archives of perturbation histories, e.g. acidification and eutrophication. At present the use of simple weighted averaging models permits the reconstruction of a variety of water chemical variables from diatom and other microfossils preserved in lake sediments (pH, total phosphorus, salinity and lakewater temperature). Sediment records can, therefore, provide lake-specific background data for lake management as well as information about their ecological histories. The common models used in palaeolimnology (dating, transfer-functions) are reviewed and their role in environmental monitoring discussed. Predictions of future lake water quality following lake restoration methods tend to be made from dynamic mathematical models, but they are also used for hindcasting (e.g. the MAGIC model of catchment acidification). A problem with using dynamic models is that they are often site-specific and require calibration for a given lake. Combined with reliable dating, chemical reconstructions from microfossil-based transfer functions offer the possibility of testing hindcast predictions derived from dynamic mathematical models, e.g. for salinity, TP and pH. In this way, sediment microfossil-based models can assist with the parameterization of more complex, dynamic models of contemporary processes. In this review, comparisons between the two approaches (sediment-based and dynamic models) are given and possible future interactions outlined. Validation of mathematical models by palaeolimnological data might enhance their predictive ability when used for forecasting lake recovery. There is clearly, however, a need for a more rigorous approach to palaeolimnology, i.e. critical hypothesis generation. Multidisciplinary studies of lake disturbance, that combine palaeolimnology, dynamic modelling and contemporary process studies, would also be beneficial.