Context is Everything: Interacting Inputs and Landscape Characteristics Control Stream Nitrogen

The changing nitrogen landscape of United States streams: Declining deposition and increasing organic nitrogen

Air quality regulations have led to decreased nitrogen (N) and sulfur deposition across the conterminous United States (CONUS) during the last several decades, particularly in the eastern parts. But it is unclear if declining deposition has altered stream N at large scales. We compared watershed N inputs with N chemistry from over 2,000 CONUS streams where deposition was the largest N input to the watershed. Weighted change analysis showed that deposition declined across most watersheds, especially in the Eastern CONUS. Nationally, declining N deposition was not associated with significant large-scale declines in stream nitrate concentration. Instead, significant increases in stream dissolved organic carbon (DOC) and total organic N (TON) were widespread across regions. Possible mechanisms behind these increases include declines in acidity and/or ionic strength drivers, changes in carbon availability, and/or climate variables. Our results also reveal a declining trend of DOC/TON ratio over the entire study period, primarily influenced by the trend in the Eastern region, suggesting the rate of increase in stream TON exceeded the rate of increase in DOC concentration during this period. Our results illustrate the complexity of nutrient cycling that links long-term atmospheric deposition to water quality. More research is needed to understand how increased dissolved organic N could affect aquatic ecosystems and downstream riverine nutrient export.

A random forest approach to improve estimates of tributary nutrient loading

Estimating constituent loads from discrete water quality samples coupled with stream discharge measurements is critical for management of freshwater resources. Nutrient loads calculated based on discharge-concentration relationships form the basis of government nutrient load targets and scientific studies of the response of receiving waters to external loads. In this study, a new model is developed using random forests and applied to estimate concentrations and loads of total phosphorus, dissolved phosphorus, total nitrogen, and chloride, using data from 17 tributaries to Lake Champlain monitored from 1992 to 2021. I benchmark this model against one of the most widespread models currently used to estimate nutrient loads, Weighted Regressions on Time, Discharge, and Season (WRTDS). The random forest model outperformed both the base WRTDS model and an extension of the WRTDS model using Kalman filtering in the great majority of cases, likely due to the inclusion of rate-of-change in discharge and antecedent discharge over different leading windows as predictors, and to the flexibility of the random forest to model predictor-response relationships. The random forest also had useful visualization capabilities which provided important process insights. WRTDS remains a useful model for many applications, but this study represents a promising new approach for load estimation which can be applied easily to existing datasets, and which is easy to customize for different applications.

Estimating Yangtze River basin's riverine N2O emissions through hybrid modeling of land-river-atmosphere nitrogen flows

Riverine ecosystems are a significant source of nitrous oxide (N2O) worldwide, but how they respond to human and natural changes remains unknown. In this study, we developed a compound model chain that integrates mechanism-based modeling and machine learning to understand N2O transfer patterns within land, rivers, and the atmosphere. The findings reveal a decrease in N2O emissions in the Yangtze River basin from 4.7 Gg yr-1 in 2000 to 2.8 Gg yr-1 in 2019, with riverine emissions accounting for 0.28% of anthropogenic nitrogen discharges from land. This unexpected reduction is primarily attributed to improved water quality from human-driven nitrogen control, while natural factors contributed to a 0.23 Gg yr-1 increase. Notably, urban rivers exhibited a more rapid N2O efflux ( [Formula: see text] ), with upstream levels nearly 3.1 times higher than rural areas. We also observed nonlinear increases in [Formula: see text] with nitrogen discharge intensity, with urban areas showing a gradual and broader range of increase compared to rural areas, which exhibited a sharper but narrower increase. These nonlinearities imply that nitrogen control measures in urban areas lead to stable reductions in N2O emissions, while rural areas require innovative nitrogen source management solutions for greater benefits. Our assessment offers fresh insights into interpreting riverine N2O emissions and the potential for driving regionally differentiated emission reductions.

Characterizing legacy nitrogen-induced time lags in riverine nitrogen reduction for the Songhuajiang River Basin: Source analysis, spatio-seasonal patterns, and impacts on future water quality improvement

Legacy nitrogen (N) originating from net N inputs (NNI) may pose ongoing threats to riverine water quality worldwide and even cause serious time-lags between water quality restoration and NNI declines. A better understanding of legacy N effects on riverine N pollutions in different seasons is essential to improve riverine water quality. Here, we investigated contributions of legacy N on riverine dissolved inorganic N (DIN) changes in different seasons and quantified spatio-seasonal time-lags in the Songhuajiang River basin (SRB), a hotspot of NNI with four distinct seasons, by exploring long-term (1978-2020) NNI-DIN relationships. Results firstly showed a significant seasonal difference in NNI, with the highest value observed in spring (average, 2184.1 kg/km2), 1.2, 5.0, and 4.6 times higher than that in summer, autumn, and winter, respectively. Cumulative legacy N had dominated riverine DIN changes, with a relative contribution of approximately 64% in 2011-2020, causing time-lags of 11-29 years across the SRB. The longest seasonal lags existed in spring (average, 23 years) owing to greater impacts of legacy N to riverine DIN changes in this season. Mulch film application, soil organic matter accumulation, N inputs, and snow cover were identified as the key factors that strengthened seasonal time-lags by collaboratively enhancing legacy N retentions in soils. Furthermore, a machine learning-based model system suggested that timescales for water quality improvement (DIN, ≤1.5 mg/L) varied considerably (from 0 to >29 years, Improved N Management-Combined scenario) across the SRB, with greater lag effects contributing to slower recovery. These findings can provide a more comprehensive insight into sustainable basin N management in the future.

Shifts in the composition of nitrogen deposition in the conterminous United States are discernable in stream chemistry

Across the conterminous United States (U.S.), the composition of atmospheric nitrogen (N) deposition is changing spatially and temporally. Previously, deposition was dominated by oxidized N, but now reduced N (ammonia [NH3] + ammonium [NH4+]) is equivalent to oxidized N when deposition is averaged across the entire nation and, in some areas, reduced N dominates deposition. To evaluate if there are effects of this change on stream chemistry at the national scale, estimates of N deposition form (oxidized or reduced) from the National Atmospheric Deposition Program Total Deposition data were coupled with stream measurements from the U.S. Environmental Protection Agency (EPA) National Rivers and Streams Assessments (three stream surveys between 2000 and 2014). A recent fine-scaled N input inventory was used to identify watersheds (<1000 km2) where atmospheric deposition is the largest N source (n = 1906). Within these more atmospherically-influenced watersheds there was a clear temporal shift from a greater proportion of sites dominated by oxidized N deposition to a greater proportion of sites dominated by reduced forms of N deposition. We found a significant positive correlation between oxidized N deposition and stream NO3- concentrations, whereas the correlation between reduced N deposition and stream NO3- concentrations were significant but weaker. Sites dominated by atmospheric inputs of reduced N forms had higher stream total organic N and total N despite lower total N deposition on average. This higher stream concentration of total N is mainly driven by the higher concentration of total organic N, suggesting an interaction between elevated reduced N in deposition and living components of the ecosystem or soil organic matter dynamics. Regardless of the proportion of reduced to oxidized N forms in deposition, stream NH4+ concentrations were generally low, suggesting that N deposited in a reduced form is rapidly immobilized, nitrified and/or assimilated by watershed processes.

Exploration of the nitrogen contamination from sewers exfiltration to the unsaturated zone by modeling

Numerous elements, such as the degree of sewer degeneration, hydraulics, and geological conditions, influence the extent to which sewage pollutes the unsaturated zones of urban. The present study discussed the influence of sewer exfiltration on the urban unsaturated zone, using nitrogen from domestic sewage as a representative contaminant in combination with experiments, literature studies, modeling and sensitivity analysis. The study shows that soils with high sand content exhibit high permeability and strong nitrification capacity, and groundwater is more susceptible to contamination with nitrate. In contrast, the nitrogen in the clay texture or wet soils has short migration distances and a weak nitrification capacity. However, under such conditions, the accumulation of nitrogen can last for more than 10 years, and there is a possible threat of groundwater contamination due to the detection difficulty. The presence of sewer exfiltration and the damage degree of a sewer can be determined by the ammonium concentration at 1-2 m near the pipe or nitrate above the water table. The sensitivity analysis revealed that all parameters impact the nitrogen concentration in the unsaturated zone to varying degrees, four of which are the primary parameters: defect area, exfiltration flux, saturated water content and first-order response constant. In addition, changes in environmental conditions significantly influence the boundaries of the pollution plume, especially the horizontal. The research data collected in this paper will not only allow for a rigorous assessment of the study scenarios but will also provide data support for other researchers.

Stream water quality optimized prediction based on human activity intensity and landscape metrics with regional heterogeneity in Taihu Basin, China

The driving effects of landscape metrics on water quality have been acknowledged widely, however, the guiding significance of human activity intensity and landscape metrics based on reference conditions for water environment management remains to be explored. Thus, we used the self-organized map, long- and short-term memory (LSTM) algorithm, and geographic detectors to simulate the response of human activity intensity and landscape metrics to water quality in Taihu Lake Basin, China. Fitting results of LSTM displayed that the accuracy was acceptable, and scenario 2 (regional heterogeneity) was more efficient than scenario 1 (regional consistent) in the improvement of water quality. In the driving analysis for the reference conditions, clusters I and II (urban agglomeration areas) were mainly affected by the amount of production wastewater per unit of developed land and the amount of livelihood wastewater per unit of developed land, respectively. Their optimal values were 0.09 × 10³ t/km² (reduction of 35.71%) and 0.2 × 10³ t/km² (reduction of 4.76%). Cluster III (agricultural production areas) was mainly affected by interference intensity, and the optimal value was 2.17 (increased 38.22%), and cluster IV (ecological forest areas) was mainly affected by the fragmentation of cropland, and the optimal value was 1.14 (reduction of 1.72%). The research provides a reference for the prediction of water quality response and presents an ecological and economic sustainability way for watershed governance.

Characterizing temporal variability in streams supports nutrient indicator development using diatom and bacterial DNA metabarcoding

Interest in developing periphytic diatom and bacterial indicators of nutrient effects continues to grow in support of the assessment and management of stream ecosystems and their watersheds. However, temporal variability could confound relationships between indicators and nutrients, subsequently affecting assessment outcomes. To document how temporal variability affects measures of diatom and bacterial assemblages obtained from DNA metabarcoding, we conducted weekly periphyton and nutrient sampling from July to October 2016 in 25 streams in a 1293 km² mixed land use watershed. Measures of both diatom and bacterial assemblages were strongly associated with the percent agriculture in upstream watersheds and total phosphorus (TP) and total nitrogen (TN) concentrations. Temporal variability in TP and TN concentrations increased with greater amounts of agriculture in watersheds, but overall diatom and bacterial assemblage variability within sites-measured as mean distance among samples to corresponding site centroids in ordination space-remained consistent. This consistency was due in part to offsets between decreasing variability in relative abundances of taxa typical of low nutrient conditions and increasing variability in those typical of high nutrient conditions as mean concentrations of TP and TN increased within sites. Weekly low and high nutrient diatom and bacterial metrics were more strongly correlated with site mean nutrient concentrations over the sampling period than with same day measurements and more strongly correlated with TP than with TN. Correlations with TP concentrations were consistently strong throughout the study except briefly following two major precipitation events. Following these events, biotic relationships with TP reestablished within one to three weeks. Collectively, these results can strengthen interpretations of survey results and inform monitoring strategies and decision making. These findings have direct applications for improving the use of diatoms and bacteria, and the use of DNA metabarcoding, in monitoring programs and stream site assessments.

Regional patterns and drivers of total nitrogen trends in the Chesapeake Bay watershed: Insights from machine learning approaches and management implications

Anthropogenic nutrient inputs have led to nutrient enrichment in many waterbodies worldwide, including Chesapeake Bay (USA). River water quality integrates the spatial and temporal changes of watersheds and forms the foundation for disentangling the effects of anthropogenic inputs. We demonstrate with the Chesapeake Bay Non-Tidal Monitoring Network that machine learning approaches - i.e., hierarchical clustering and random forest (RF) classification - can be combined to better understand the regional patterns and drivers of total nitrogen (TN) trends in large monitoring networks, resulting in information useful for watershed management. Cluster analysis revealed regional patterns of short-term TN trends (2007-2018) and categorized the stations into three distinct trend clusters, namely, V-shape (n = 23), monotonic decline (n = 35), and monotonic increase (n = 26). RF models identified regional drivers of TN trend clusters by quantifying the effects of watershed characteristics (land use, geology, physiography) and major N sources on the trend clusters. Results provide encouraging evidence that improved agricultural nutrient management has resulted in declines in agricultural nonpoint sources, which in turn contributed to water-quality improvement in our period of analysis. Moreover, water-quality improvements are more likely in watersheds underlain by carbonate rocks, reflecting the relatively quick groundwater transport of this terrain. By contrast, water-quality improvements are less likely in Coastal Plain watersheds, reflecting the effect of legacy N in groundwater. Notably, results show degrading trends in forested watersheds, suggesting new and/or remobilized sources that may compromise management efforts. Finally, the developed RF models were used to predict TN trend clusters for the entire Chesapeake Bay watershed at the fine scale of river segments (n = 979), providing fine spatial information that can facilitate targeted watershed management, including unmonitored areas. More broadly, this combined use of clustering and classification approaches can be applied to other regional monitoring networks to address similar water-quality questions.

δ15N of Chironomidae: An index of nitrogen sources and processing within watersheds for national aquatic monitoring programs

Nitrogen (N) removal along flowpaths to aquatic ecosystems is an important regulating ecosystem service that can help reduce N pollution in the nation's waterways, but can be challenging to measure at large spatial scales. Measurements that integrate N processing within watersheds would be particularly useful for assessing the magnitude of this vital service. Because most N removal processes cause isotopic fractionation, δ¹⁵N from basal food-chain organisms in aquatic ecosystems can provide information on both N sources and the degree of watershed N processing. As part of EPA's National Aquatic Resource Surveys (NARS), we measured δ¹⁵N of Chironomidae collected from over 2000 lakes, rivers and streams across the continental USA. Using information on N inputs to watersheds and summer total N concentrations ([TN]) in the water column, we assessed where elevated chironomid δ¹⁵N would indicate N removal rather than possible enriched sources of N. Chironomid δ¹⁵N values ranged from -4 to +20‰, and were higher in rivers and streams than in lakes, indicating that N in rivers and streams underwent more processing and cycling that preferentially removes ¹⁴N than N in lakes. Chironomid δ¹⁵N increased with watershed size, N inputs, and water chemical components, and decreased as precipitation increased. In rivers and streams with high watershed N inputs, we found lower [TN] in streams with higher chironomid δ¹⁵N values, suggesting high rates of gaseous N loss such as denitrification. At low watershed N inputs, the pattern reversed; streams with elevated chironomid δ¹⁵N had higher [TN] than streams with lower chironomid δ¹⁵N, possibly indicating unknown sources elevated in δ¹⁵N such as legacy N, or waste from animals or humans. Chironomid δ¹⁵N values can be a valuable tool to assess integrated watershed-level N sources, input rates, and processing for water quality monitoring and assessment at large scales.

Limited progress in nutrient pollution in the U.S. caused by spatially persistent nutrient sources

Human agriculture, wastewater, and use of fossil fuels have saturated ecosystems with nitrogen and phosphorus, threatening biodiversity and human water security at a global scale. Despite efforts to reduce nutrient pollution, carbon and nutrient concentrations have increased or remained high in many regions. Here, we applied a new ecohydrological framework to ~12,000 water samples collected by the U.S. Environmental Protection Agency from streams and lakes across the contiguous U.S. to identify spatial and temporal patterns in nutrient concentrations and leverage (an indicator of flux). For the contiguous U.S. and within ecoregions, we quantified trends for sites sampled repeatedly from 2000 to 2019, the persistence of spatial patterns over that period, and the patch size of nutrient sources and sinks. While we observed various temporal trends across ecoregions, the spatial patterns of nutrient and carbon concentrations in streams were persistent across and within ecoregions, potentially because of historical nutrient legacies, consistent nutrient sources, and inherent differences in nutrient removal capacity for various ecosystems. Watersheds showed strong critical source area dynamics in that 2-8% of the land area accounted for 75% of the estimated flux. Variability in nutrient contribution was greatest in catchments smaller than 250 km2 for most parameters. An ensemble of four machine learning models confirmed previously observed relationships between nutrient concentrations and a combination of land use and land cover, demonstrating how human activity and inherent nutrient removal capacity interactively determine nutrient balance. These findings suggest that targeted nutrient interventions in a small portion of the landscape could substantially improve water quality at continental scales. We recommend a dual approach of first prioritizing the reduction of nutrient inputs in catchments that exert disproportionate influence on downstream water chemistry, and second, enhancing nutrient removal capacity by restoring hydrological connectivity both laterally and vertically in stream networks.