Predictive Understanding of Stream Salinization in a Developed Watershed Using Machine Learning.

Stream salinization is a global issue, yet few models can provide reliable salinity estimates for unmonitored locations at the time scales required for ecological exposure assessments. Machine learning approaches are presented that use spatially limited high-frequency monitoring and spatially distributed discrete samples to estimate the daily stream-specific conductance across a watershed. We compare the predictive performance of space- and time-unaware Random Forest models and space- and time-aware Recurrent Graph Convolution Neural Network models (KGE: 0.

Light and flow regimes regulate the metabolism of rivers.

Mean annual temperature and mean annual precipitation drive much of the variation in productivity across Earth's terrestrial ecosystems but do not explain variation in gross primary productivity (GPP) or ecosystem respiration (ER) in flowing waters. We document substantial variation in the magnitude and seasonality of GPP and ER across 222 US rivers. In contrast to their terrestrial counterparts, most river ecosystems respire far more carbon than they fix and have less pronounced and consistent seasonality in their metabolic rates.

The metabolic regimes of 356 rivers in the United States.

A national-scale quantification of metabolic energy flow in streams and rivers can improve understanding of the temporal dynamics of in-stream activity, links between energy cycling and ecosystem services, and the effects of human activities on aquatic metabolism. The two dominant terms in aquatic metabolism, gross primary production (GPP) and aerobic respiration (ER), have recently become practical to estimate for many sites due to improved modeling approaches and the availability of requisite model inputs in public datasets. We assembled inputs from the U.

Nutrient limitation and physiology mediate the fine-scale (de)coupling of biogeochemical cycles.

Nutrients in the environment are coupled over broad timescales (days to seasons) when organisms add or withdraw multiple nutrients simultaneously and in ratios that are roughly constant. But at finer timescales (seconds to days), nutrients become decoupled if physiological traits such as nutrient storage limits, circadian rhythms, or enzyme kinetics cause one nutrient to be processed faster than another. To explore the interactions among these coupling and decoupling mechanisms, we introduce a model in which organisms process resources via uptake, excretion, growth, respiration, and mortality according to adjustable trait parameters.