Natural stochasticity can pose challenges in managing the quality of the environment, or hinder understanding of the system structure. It is problematic because unfavourable stochastic event cancels the costly management effort and because favourable stochastic event overestimates success of the management effort. This paper presents a variance-based modelling method that can be used to quantify the extent to which the natural stochasticity can affect the target environment. We use a case study of a lake water quality assessment in a Norwegian lake of Årungen, together with a lake model MyLake, in order to present the method, and how this method could assist in answering scientific and managerial questions. Specifically, the case study's goal was to disentangle the respective significance of nutrient loading (management) and weather (the confounding natural stochasticity). Many scientifically and managerially relevant understandings have been revealed. For example, variation in runoff volume was most prevalent during autumn and winter, while variation in phosphorus inflow was most extensive from late winter to early spring. Thermal related properties in the lake were mostly determined by weather conditions, whereas loading was the most important factor for phytoplankton biomass and water transparency. Mild winters and greater inputs of suspended matter and phosphorus were followed by increased phytoplankton biomass and light attenuation. These findings suggest also that future changes in the global climate may have important implications for local water management decision-making. The present method of disentangling mutually confounding factors is not limited to lake water quality studies and therefore should provide certain utility in other application field of modelling.
This discussion paper is/has been under review for the journal Hydrology and Earth System Sciences (HESS). Please refer to the corresponding final paper in HESS if available.