A new, simple, general technique to predict seasonal variability of river discharge and lake temperature for lake ecosystem models

The aim of this work is to present a new technique to predict seasonal variability in tributary water discharge, lake water retention rate and lake temperature (surface and deep water temperature and stratification) when empirical data are missing, and the basic objective is to model ecosystem rather than hydrological processes. The main features of the technique are the curves (called the norms) describing the maximum (monthly) variability and the smoothing functions (the moderators) to change the maximum variability of the norms. The smoothing functions are calculated from readily available ''map parameters'' (latitude, longitude, altitude, mean annual precipitation, catchment area and lake volume). The norms apply to all lakes of a given type. The aims of this work are to present: (1) the new technique to predict seasonal variability in tributary water discharge (Q); the basic aim is to give an account of the construction of a norm and a seasonal variability moderator; (2) a model based on the same technique to predict seasonal variabilities in surface and bottom water temperature; (3) a model for the retention rate of lake water; (4) sensitivity tests illustrating how these models work, (5) examples how to use these models within the framework of a larger lake ecosystem model; and (6) examples showing that by accounting for the seasonal variability in this way, one can improve the behaviour and predictive accuracy of dynamic lake models. The seasonal moderator technique gives dimensionless expressions that may be used in dynamic models wherever one would like to account for seasonal variability in rates and coefficients. This paper is a technical account of the method. It has, however, been outside the scope to provide thorough empirical calibrations.