Interpolating and forecasting lake characteristics using long-term monitoring data

Abstract

It is virtually impossible to quantify the limnological characteristics of every aquatic ecosystem all the time. The goal of this study was to assess the capacity of lake-monitoring data to predict annually resolved characteristics in systems where measurements are not always made. To address this, we provide an analysis of interpolation (i.e., predicting a current lake characteristic based on current characteristics of other lakes) and forecasting (i.e., predicting a current lake characteristic based on historical trends and characteristics of a set of study lakes) in seven lakes over a 28-yr time frame. The most effective interpolations are generated using 12-15 yr of training data. Interpolation models are 29% more effective, on average, when historical trends (forecasting) are also incorporated into the models. Forecasting models that predict lake characteristics using long-term trends in the focal lake were improved by including historical observations from other lakes. Direct comparisons of different prediction models further demonstrated that it is sometimes more effective to generate predictions based on a set of previously measured conditions (forecasts) rather than a set of known regional conditions that have been recently quantified (interpolations). Basic monitoring data have the potential to be upscaled to generate predictions of lake characteristics, but the effectiveness of predictions depend on the training data characteristics and prediction approaches employed.