Machine learning modeling of lake chlorophyll content in a data scarce region (Northern Patagonia, Chile): insights for environmental monitoring

Abstract

Among South America, Chile is highly susceptible to climate change impacts on water resources and ecosystems. Chilean lakes and rivers have been impacted by anthropogenic activities leading to chemical pollution and eutrophication. Concerns for conservation and management of water resources has led to the current development of secondary norms for environmental quality of Northern Patagonian lakes. In this context, we analyze historical limnological databases (1979-2022) for these lakes utilizing Random Forest (RF) models. After filtering, we retained data for 11 lakes including key variables of: dissolved oxygen, electric conductivity, transparency, temperature, pH, total nitrogen, total phosphorus and chlorophyll-a. This dataset yielded robust results, accurately predicting chlorophyll-a content. Furthermore, we added lake geomorphological parameters, enhancing the performance of the model. Our study demonstrates the need to improve long-term monitoring programs, optimizing environmental data recording and decreasing costs. We conclude that the studied lakes generally maintain their oligotrophic characteristics, however further analysis suggests that these lakes are more sensitive to nitrogen loading than phosphorus. Our results highlight the need to implement adaptative management plans at the watershed level to regulate anthropogenic nitrogen contamination (from agriculture, pisciculture and urbanization). The features selected by RF, coupled with the assessment of historical trophic state variation, allow the establishment of permissible concentration thresholds for major nutrients and other sentinel parameters, informing the development of regulations such as the secondary norms for environmental quality. Lastly, the enhanced performance of RF modeling when including geographical parameters unveils the need to standardize and integrate geographical data in monitoring practices.