Assessing the impact of watershed characteristics and management on nutrient concentrations in tropical rivers using a machine learning method

Excessive loadings of terrestrial nitrogen and phosphorus, as well as their imbalances with silicon, have been recognized as one of the major causes of water quality and ecosystem deterioration in receiving waters. In this study, a periodic water quality monitoring was conducted in the rivers and streams of a tropical island (Ishigaki Island, Japan) to identify the factors controlling the concentrations of dissolved inorganic nitrogen (DIN), total phosphorus (TP) and dissolved silicon (DSi) with a special focus on the catchment characteristics (e.g., land use, surface geology, topography). Random Forest (RF) machine learning algorithm was employed to develop pre-dictive models for nutrient concentrations from the catchment properties. The developed models could predict nutrient concentrations with sufficient accuracy, demonstrating that the studied nutrients are strongly affected by catchment properties. Agricultural land uses (e.g., livestock barn, sugarcane field) were ranked as the most important parameters for DIN and TP, while broadleaf forest was the most influential factor for DSi. Using the RF models, the contributions of DIN originating from sugarcane fields (i.e., fertilizers) and barns (i.e., manure) to riverine DIN were estimated, which were up to 60% in total in the studied river basins. Furthermore, the yield of DIN from sugarcane fields, calculated as the concentration of DIN derived from sugarcane fields divided by the percent area of sugarcane fields, strongly positively correlated with the areal coverage of limestone, suggesting that fertilizer-derived DIN is more prone to leaching out from cropland soil to groundwater and rivers in catchments with a higher dominance of calcareous geology. These results, including the methodology employed, have implications for water quality assessment and management in inland and coastal waters not only at the study site but also other regions.

Automated summary

Excessive loadings of nitrogen and phosphorus, combined with imbalances in silicon, have a detrimental effect on water quality and ecosystems in receiving waters. This study conducted periodic water quality monitoring in rivers and streams on Ishigaki Island, Japan, to identify the factors influencing the concentrations of dissolved inorganic nitrogen (DIN), total phosphorus (TP), and dissolved silicon (DSi), with a focus on catchment characteristics. Using a machine learning algorithm called Random Forest (RF), predictive models were developed to estimate nutrient concentrations based on catchment properties. The results showed that agricultural land use significantly influenced DIN and TP concentrations, while broadleaf forests were the most important factor for DSi. Additionally, the study estimated the contributions of DIN from sugarcane fields and livestock barns to riverine DIN, which accounted for up to 60% of the total in the studied river basins. The findings also indicated that DIN from sugarcane fields is more likely to leach into groundwater and rivers in catchments dominated by calcareous geology. These results and methodology have implications for water quality assessment and management in both inland and coastal waters.