Estimating nitrogen and phosphorus concentrations in streams and rivers across the Contiguous United States, supplement to: Shen, Longzhu; Amatulli, Giuseppe; Sethi, Tushar; Raymond, Peter; Domisch, Sami (accepted): Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework. Scientific Data

• Main Authors: Shen, Longzhu, Amatulli, Giuseppe, Sethi, Tushar, Raymond, Peter, Domisch, Sami
• Format: Dataset
• Language: English
• Published: PANGAEA - Data Publisher for Earth & Environmental Science 2019
• Subjects: freshwater nutrients; machine learning; nitrogen; phosphorus; stream network; water quality; File content; File name; File format; File size; Uniform resource locator/link to file; sami
• Online Access: https://dx.doi.org/10.1594/pangaea.899168; https://doi.pangaea.de/10.1594/PANGAEA.899168

Nitrogen (N) and Phosphorus (P) are essential nutritional elements for life processes in water bodies. However, in excessive quantities, they may represent a significant source of aquatic pollution. Eutrophication has become a widespread issue rising from a chemical nutrient imbalance and is largely attributed to anthropogenic activities. In view of this phenomenon, we present a new geo-dataset to estimate and map the concentrations of N and P in their various chemical forms at a spatial resolution of 30 arc-second (~1 km) for the conterminous US. The models were built using Random Forest (RF), a machine learning algorithm that regressed the seasonally measured N and P concentrations collected at 62,495 stations across the US streams for the period of 1994-2018 onto a set of 47 in-house built environmental variables that are available at a near-global extent. The seasonal models were validated through internal and external validation procedures and the predictive powers measured by Pearson Coefficients reached approximately 0.66 on average. : Updated version, 2020-03-04.