Assessing spatiotemporal variability in the concentration and composition of dissolved organic matter and its impact on iron solubility in tropical freshwater systems through a machine learning approach.

Dissolved organic matter (DOM) plays important roles not only in maintaining the productivity and functioning of aquatic ecosystems but also in the global carbon cycle, although the sources and biogeochemical functions of terrestrially derived DOM have not been fully elucidated, particularly in the tropics and subtropics. This study aimed to evaluate the factors influencing spatiotemporal variability in (i) the concentration and composition of DOM, including dissolved organic carbon (DOC), ultraviolet absorption coefficient at 254-nm wavelength (a), and components identified by fluorescence excitation-emission matrix coupled with parallel factor analysis (EEM-PARAFAC), and (ii) the concentration of dissolved iron (DFe) across freshwater systems (rivers, forested streams, and dam reservoirs) on a tropical island (Ishigaki Island, Japan) based on the results of water quality monitoring at 2-month intervals over a 2-year period. Random forests (RF) machine learning algorithm was employed, with the catchment characteristics (land use, soil type) and water temperature as the predictor variables for DOM and the composition of DOM (EEM-PARAFAC components) and hydrochemistry (water temperature, pH, and concentrations of divalent cations) as the predictor variables for DFe.

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.