TY - DATA ID - shen2019enap T1 - Estimating nitrogen and phosphorus concentrations in streams and rivers across the Contiguous United States AU - Shen, Longzhu AU - Amatulli, Giuseppe AU - Sethi, Tushar AU - Raymond, Peter AU - Domisch, Sami PY - 2019/03/07/ T2 - Supplement to: Shen, L et al. (accepted): Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework. Scientific Data PB - PANGAEA DO - 10.1594/PANGAEA.899168 UR - https://doi.org/10.1594/PANGAEA.899168 N2 - 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. KW - freshwater nutrients KW - machine learning KW - nitrogen KW - Phosphorus KW - stream network KW - water quality ER -