River reach-level machine learning estimation of nutrient concentrations in Great Britain

Tso, Chak-Hau Michael; Magee, Eugene; Huxley, David; Eastman, Michael; Fry, Matthew ORCID: https://orcid.org/0000-0003-1142-4039. 2023 River reach-level machine learning estimation of nutrient concentrations in Great Britain. Frontiers in Water, 5, 1244024. 18, pp. https://doi.org/10.3389/frwa.2023.1244024

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Abstract/Summary

Nitrogen (N) and phosphorus (P) are essential nutrients necessary for plant growth and support life in aquatic ecosystems. However, excessive N and P can lead to algal blooms that deplete oxygen and lead to fish death and the release of toxins that are harmful to humans. Estimates of N and P levels in rivers are typically calculated at station or grid (>1 km) scale; therefore, it is difficult to visualise the evolution of water quality as water travels downstream. Using a high-resolution reach-scale river network and associating each reach with land cover fractions and catchment descriptors, we trained random forest models on aggregated data (2010–2020) from the Environmental Agency Open Water Quality Data Archive for 2,343 stations to predict long-term nitrate and orthophosphate concentrations at each river reach in Great Britain (GB). We separated the model training and predictions for different seasons to investigate the potential difference in feature importance. Our model predicted concentrations with an average testing coefficient of determination (R2) of 0.71 for nitrate and 0.58 for orthophosphate using 5-fold cross-validation. Our model showed slightly better performance for higher Strahler stream orders, highlighting the challenges of making predictions in small streams. Our results revealed that arable and horticultural land use is the strongest and most reliable predictor for nitrate, while floodplain extents and standard percentage runoff are stronger predictors for orthophosphate. Nationally, higher orthophosphate concentrations were observed in urbanised areas. This study shows how combining a river network model with machine learning can easily provide a river network understanding of the spatial distribution of water quality levels.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.3389/frwa.2023.1244024
UKCEH and CEH Sections/Science Areas:	Pollution (Science Area 2017-) Water Resources (Science Area 2017-)
ISSN:	2624-9375
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	river network, machine learning, nutrients, water quality, random forest
NORA Subject Terms:	Hydrology Computer Science Data and Information
Related URLs:	Dataset
Date made live:	09 Nov 2023 14:48 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/536077

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