Global patterns and key drivers of stream nitrogen concentration: A machine learning approach

Razi Sheikholeslami^1,2,3 and Jim Hall^1,2

^{1 School of Geography and the Environment, University of Oxford, Oxford, United Kingdom}

^{2 Environmental Change Institute, University of Oxford, Oxford, United Kingdom}

^{3 Department of Civil Engineering, Sharif University of Technology, Tehran, Iran}

Anthropogenic loading of nitrogen to river systems can pose serious health hazards and create critical environmental threats. Quantification of the magnitude and impact of freshwater nitrogen requires identifying key controls of nitrogen dynamics and analyzing both the past and present patterns of nitrogen flows. To tackle this challenge, we adopted a machine learning (ML) approach and built an ML-driven representation that captures spatiotemporal variability in nitrogen concentrations at global scale. Our model uses random forests to regress a large sample of monthly measured stream nitrogen concentrations onto a set of 17 predictors with a spatial resolution of 0.5-degree over the 1990–2013, including observations within the pixel and upstream drivers. The model was validated with data from rivers outside the training dataset and was used to predict nitrogen concentrations in 520 major river basins of the world, including many with scarce or no observations.

Publication details

Sheikholeslami, R, Hall, J. 2023. Global patterns and key drivers of stream nitrogen concentration: A machine learning approach. Science of the Total Environment 868. DOI: https://doi.org/10.1016/j.scitotenv.2023.161623

Global patterns and key drivers of stream nitrogen concentration: A machine learning approach