Abstract
<p><strong>Abstract.</strong> 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 events cancel management efforts and because a favourable stochastic event may overestimate perceived success. This paper presents a variance-based modelling method that can be used to quantify the extent to which natural stochasticity can affect the target environment. We use a case study of a eutrophication assessment of a Norwegian lake, Årungen, using a lake model, MyLake, in order to present the method, and to investigate how this method could assist in answering scientific and management questions. Here we contrasted two effects of nutrient loading in runoff (partially controllable by policies) and meteorology (purely natural stochastic events), illustrated in the case study, in order to achieve the season-by-season quantification of mutually confounding factors of stochastic events. The results indicate that, 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 well predicted by the model, and showed that the time of thermocline formation varied among years by more than 1 month, from mid-April to mid-May, 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 also suggest 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 may also provide utility in other types of aquatic system modelling.</p>
Highlights
Natural stochasticity sometimes presents challenges in maintaining the quality of the environment
The current study primarily aims to evaluate the relative importance of year-to-year variation in two major factors, namely meteorological forcing and nutrient loading, which contribute to the lake’s physical, chemical and biological conditions
Observed water temperature measurements were well predicted by simulation and the root mean square error (RMSE) was less than 2 ◦C at
Summary
Natural stochasticity sometimes presents challenges in maintaining the quality of the environment. Such is the case in the context of reducing nutrient loads for the purpose of improving water quality of downstream environments (Deelstra et al, 2010; Skarbøvik and Bechmann, 2010). For example, may confound costly abatement efforts by counteracting any positive benefits derived from the abatement itself. The weather may be directly consequential in lake processes such as algal growth. With these challenges, it is paramount to evaluate to what extent confounding variables can make significant differences in lake water quality. The present study illustrates how a variance-based modelling method is able to disentangle two major factors affect-
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
