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Innovative approaches are needed to manage chronic and emerging water quality challenges in communities that rely on treated wastewater and urban stormwater as sources of raw water for drinking water treatment, or “One Water” systems. When amended to account explicitly for upstream versus distributed inflow to the reservoir, we show that unsteady transit time theory links pollution sources to water quality in the Occoquan Reservoir (Virginia, USA), one of the largest and oldest One Water systems in the United States. Using 11 years of hydrologic and water quality data, the model identified distinct sources and transformation rates for reactive (nitrate) and relatively non-reactive (sodium, chloride) solutes. High predictive skill was achieved with a strikingly small number of parameters: two for sodium and chloride (one for the upstream storage selection function, one for solute input from distributed sources; Nash–Sutcliffe Efficiency (NSE) = 0.65 and 0.76) and two additional for nitrate (capturing seasonal denitrification linked to summer stratification and hypolimnetic processes; NSE = 0.55). The simplicity of unsteady transit time theory supports rigorous parameter estimation (Bayesian Markov Chain Monte Carlo) and model structure evaluation (Bayesian Information Criterion). It also opens the door to real-time interactive simulations with stakeholders, supporting collaborative solutions to cascading water quality challenges. 

Abstract In this paper we demonstrate that several ubiquitous hyporheic exchange mechanisms can be represented simply as a one‐dimensional diffusion process, where the diffusivity decays exponentially with depth into the streambed. Based on a meta‐analysis of 106 previously published laboratory measurements of hyporheic exchange (capturing a range of bed morphologies, hydraulic conditions, streambed properties, and experimental approaches) we find that the reference diffusivity and mixing length‐scale are functions of the permeability Reynolds Number and Schmidt Number. These dimensionless numbers, in turn, can be estimated for a particular stream from the median grain size of the streambed and the stream's depth, slope, and temperature. Application of these results to a seminal study of nitrate removal in 72 headwater streams across the United States, reveals: (a) streams draining urban and agricultural landscapes have a diminished capacity for in‐stream and in‐bed mixing along with smaller subsurface storage zones compared to streams draining reference landscapes; (b) under steady‐state conditions nitrate uptake in the streambed is primarily biologically controlled; and (c) median reaction timescales for nitrate removal in the hyporheic zone are 0.5 and 20 hr for uptake by assimilation and denitrification, respectively. While further research is needed, the simplicity and extensibility of the framework described here should facilitate cross‐disciplinary discussions and inform reach‐scale studies of pollutant fate and transport and their scale‐up to watersheds and beyond. 

Abstract Headwater wetlands are important sites for carbon storage and emissions. While local- and landscape-scale factors are known to influence wetland carbon biogeochemistry, the spatial and temporal heterogeneity of these factors limits our predictive understanding of wetland carbon dynamics. To address this issue, we examined relationships between carbon dioxide (CO₂) and methane (CH₄) concentrations with wetland hydrogeomorphology, water level, and biogeochemical conditions. We sampled water chemistry and dissolved gases (CO₂and CH₄) and monitored continuous water level at 20 wetlands and co-located upland wells in the Delmarva Peninsula, Maryland, every 1–3 months for 2 years. We also obtained wetland hydrogeomorphologic metrics at maximum inundation (area, perimeter, and volume). Wetlands in our study were supersaturated with CO₂(mean = 315 μM) and CH₄(mean = 15 μM), highlighting their potential role as carbon sources to the atmosphere. Spatial and temporal variability in CO₂and CH₄concentrations was high, particularly for CH₄, and both gases were more spatially variable than temporally. We found that groundwater is a potential source of CO₂in wetlands and CO₂decreases with increased water level. In contrast, CH₄concentrations appear to be related to substrate and nutrient availability and to drying patterns over a longer temporal scale. At the landscape scale, wetlands with higher perimeter:area ratios and wetlands with higher height above the nearest drainage had higher CO₂and CH₄concentrations. Understanding the variability of CO₂and CH₄in wetlands, and how these might change with changing environmental conditions and across different wetland types, is critical to understanding the current and future role of wetlands in the global carbon cycle. 

Abstract Headwater stream networks contribute substantially to the global carbon dioxide terrestrial flux because of high turbulence and coupling with terrestrial environments. Heterogeneity within headwater stream networks, both spatially and temporally, makes measuring and upscaling these emissions challenging because measurements of carbon dioxide in streams are often limited to a few monitoring points. We modified a stream network model to reflect real measurements made under base flow and high flow conditions at Martha Creek in Stabler, WA in the US Pacific Northwest. We found that under high flow conditions, the stream network had much greater total carbon emissions than during low flow conditions (1.22 Mg C day⁻¹vs. 0.034 Mg C day⁻¹). We attribute this increase to a larger overall stream network area (0.04 vs. 0.01 km²) and discharge (1.9 m³ s⁻¹vs. 0.005 m³ s⁻¹) in November versus August. Our results demonstrate the need to understand the nonperennial stream reaches when calculating carbon emissions. We compared the stream network emissions with the terrestrial net ecosystem exchange (NEE) estimated by local eddy covariance measurements per watershed area (−5.5 Mg C day⁻¹in August and −2.2 Mg C day⁻¹in November). Daily stream emissions in November accounted for a much larger percentage of NEE than in August (54% vs. 0.62%). We concluded that the stream network can emit a large percentage of the forest NEE in the winter months, and annual estimates of stream network emissions must consider the flow regime throughout the year. 

Abstract River metabolism and, thus, carbon cycling are governed by gross primary production and ecosystem respiration. Traditionally river metabolism is derived from diel dissolved oxygen concentrations, which cannot resolve diel changes in ecosystem respiration. Here, we compare river metabolism derived from oxygen concentrations with estimates from stable oxygen isotope signatures (δ 18 O 2 ) from 14 sites in rivers across three biomes using Bayesian inverse modeling. We find isotopically derived ecosystem respiration was greater in the day than night for all rivers (maximum change of 113 g O 2  m −2  d −1 , minimum of 1 g O 2  m −2  d −1 ). Temperature (20 °C) normalized rates of ecosystem respiration and gross primary production were 1.1 to 87 and 1.5 to 22-fold higher when derived from oxygen isotope data compared to concentration data. Through accounting for diel variation in ecosystem respiration, our isotopically-derived rates suggest that ecosystem respiration and microbial carbon cycling in rivers is more rapid than predicted by traditional methods.