Amatulli, Giuseppe (2019): Estimating nitrogen and phosphorus concentrations in streams and rivers across the Contiguous United States. PANGAEA, https://doi.org/10.1594/PANGAEA.899168, Supplement to: Shen, Longzhu; Amatulli, Giuseppe; Sethi, Tushar; Raymond, Peter; Domisch, Sami (in press): Estimating nitrogen and phosphorus concentrations in streams and rivers across the contiguous United States: a machine learning framework. PeerJ, https://peerj.com/preprints/27585/
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Nitrogen (N) and Phosphorus (P) are essential nutrients for life processes in water bodies but in excessive quantities, they are a significant source of aquatic pollution. Eutrophication has now become widespread due to such an imbalance, and is largely attributed to anthropogenic activity. In view of this phenomenon, we present a new dataset and statistical method for estimating and mapping elemental and compound concentrations of N and P at a resolution of 30 arc-seconds (~1 km) for the conterminous US. The model is based on a Random Forest (RF) machine learning algorithm that was fitted with environmental variables and seasonal N and P concentration observations from 230,000 stations spanning across US stream networks. Accounting for spatial and temporal variability offers improved accuracy in the analysis of N and P cycles. The algorithm has been validated with an internal and external validation procedure that is able to explain 70-83% of the variance in the model. The dataset is ready for use as input in a variety of environmental models and analyses, and the methodological framework can be applied to large-scale studies on N and P pollution, which include water quality, species distribution and water ecology research worldwide.
Median Latitude: 37.950000 * Median Longitude: -102.300000 * South-bound Latitude: 27.700000 * West-bound Longitude: -123.800000 * North-bound Latitude: 48.200000 * East-bound Longitude: -80.800000
100 data points