Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers
Dissolved oxygen (DO) concentration in rivers was able to be fairly accurately modeled by training a deep learning model on temperature data alone. Light was a close second in terms of modeling importance, while flow imparted minimal influence on DO modeling.
The concentration of dissolved oxygen (DO) is an important measure of water quality, aquatic metabolism, and redox conditions, and regulates the emission of riverine greenhouse gases and the mobilization of toxic metals and nutrients. The model developed in this work showed declining DO in warming rivers, which has important implications for water security and ecosystem health in the future climate.
The concentration of dissolved oxygen (DO), an important measure of water quality and river metabolism, varies tremendously in time and space. Riverine DO is commonly perceived as regulated by interacting and competing drivers (light, temperature and flow) that define rivers’ climate. Its continental-scale drivers, however, have remained elusive, partly due to the scarcity and spatio-temporal inconsistency of water quality data. We showed, via a deep learning model (long short-term memory) trained using data from 580 rivers, that temperature predominantly drives daily DO dynamics in the contiguous United States. Light comes a close second, whereas flow imparts minimal influence. This work showcases the promise of using deep learning models for data filling that enables large-scale systematic analysis of patterns and drivers. Results show fairly accurate prediction of DO by temperature alone, and declining DO in warming rivers, which has important implications for water security and ecosystem health in the future climate.