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Excess nitrogen from intensive agricultural production, atmospheric N deposition, and urban point sources elevates stream nitrate concentrations, leading to problems of eutrophication and ecosystem degradation in coastal waters. A major emphasis of current US‐scale analysis of water quality is to better our understanding of the relationship between changes in anthropogenic N inputs within watersheds and subsequent changes in riverine N loads. While most water quality modeling assumes a positive linear correlation between watershed N inputs and riverine N, many efforts to reduce riverine N through improved nutrient management practices result in little or no short‐term improvements in water quality. Here, we use nitrate concentration and load data from 478 US watersheds, along with developed N input trajectories for these watersheds, to quantify time‐varying relationships between N inputs and riverine N export. Our results show substantial variations in watershed N import‐export relationships over time, with quantifiable hysteresis effects. Our results show that more population‐dense urban watersheds in the northeastern U.S. more frequently show clockwise hysteresis relationships between N imports and riverine N export, with accelerated improvements in water quality being achieved through the implementation of point‐source controls. In contrast, counterclockwise hysteresis dynamics are more common in agricultural watersheds, where time lags occur between the implementation of nutrient management practices and water‐quality improvements. Finally, we find higher tile‐drainage densities to be associated with more linear relationships between N inputs and riverine N. The empirical analysis in this study is bolstered by modeled simulations to reproduce and further explain drivers behind the hysteretic relationships commonly observed in the monitored watersheds. Plain Language Summary: For decades, efforts have been made to improve nutrient management, reduce riverine N loads, and improve water quality. While measurable improvements have been achieved in some watersheds, in others, elevated N loads have persisted. To better our understanding of the time‐varying relationships between anthropogenic N inputs to watersheds and riverine N export, we analyze long‐term monitoring data from more than 478 U.S. watersheds, together with watershed N input trajectories. Our analysis shows that in the 400 watersheds in which net N inputs have decreased or plateaued, 33% demonstrate lagged responses to these changes, and another 30% show no clear response. In contrast, approximately 24% of all watersheds exhibit accelerated improvements in water quality, with riverine N reductions actually outpacing reductions in N inputs. Bettering our understanding of the relationships between watershed N management and riverine N export across the U.S. allows us to identify water quality success stories and provides critical insight into effective management scenarios for impacted watersheds. Key Points: Relationships between watershed N inputs and riverine N export frequently change over time, with quantifiable hysteresis resultsAgricultural watersheds commonly exhibit lagged relationships between N inputs and riverine N export due to legacy N accumulation within the watershedMany urban watersheds in the northeastern U.S. are exhibiting accelerated improvements in water quality due to point‐source controls [ABSTRACT FROM AUTHOR]

Pulling the rabbit out of the hat: Unravelling hidden nitrogen legacies in catchment-scale water quality models Human activities have significantly perturbed the global cycle of nitrogen by excess inputs of reactive nitrogen (N) to the environment (Rockström et al., 2009). Both hydrological and biogeochemical processes can lead to substantial legacy effects that result in accumulation of N in the soil and subsurface, and time lags in the propagation of N input flux to N responses in water bodies (Ascott et al., 2021; Basu et al., 2022). However, as N fluxes from the soil pools propagate through the model compartments, we need to quantify and evaluate soil N legacy in water quality models to more accurately simulate N fluxes and concentrations in receiving water bodies. [Extracted from the article]

The Upper Mississippi River Basin is the largest source of reactive nitrogen (N) to the Gulf of Mexico. Concentration‐discharge (C‐Q) relationships offer a means to understand both the terrestrial sources that generate this reactive N and the in‐stream processes that transform it. Progress has been made on identifying land use controls on C‐Q dynamics. However, the impact of basin size and river network structure on C‐Q relationships is not well characterized. Here, we show, using high‐resolution nitrate concentration data, that tile drainage is a dominant control on C‐Q dynamics, with increasing drainage density contributing to more chemostatic C‐Q behavior. We further find that concentration variability increases, relative to discharge variability, with increasing basin size across six orders of magnitude, and this pattern is attributed to different spatial correlation structures for C and Q. Our results show how land use and river network structure jointly control riverine N export. Plain Language Summary: Nitrate is a major agricultural pollutant that can harm freshwater and marine ecosystems. Understanding the relationships between the concentration of nitrate in a river and the river's flow rate (discharge) can allow us to infer drivers of nitrate release from land into waterways and to better manage nutrient pollution. Here, we found that artificial (i.e. tile) drainage increases the stability of nitrate concentrations in the rivers of the Upper Mississippi basin across a wide range of flow rates. We also found that the variability of nitrate concentrations, relative to discharge, increased as rivers grew larger and had more tributaries contributing to flow. This shows that both on‐land and in‐river processes control the concentration‐discharge relationships of nitrate. Key Points: Nitrate concentrations in UMRB show a threshold response, chemodynamic at low flows but chemostatic at high flowsTile drainage increases the degree of nitrate chemostasis in the UMRBNitrate concentrations for rivers within Upper Mississippi River Basin (UMRB) are more chemodynamic with increasing basin size [ABSTRACT FROM AUTHOR]

Nutrient legacies in anthropogenic landscapes, accumulated over decades of fertilizer application, lead to time lags between implementation of conservation measures and improvements in water quality. Quantification of such time lags has remained difficult, however, due to an incomplete understanding of controls on nutrient depletion trajectories after changes in land-use or management practices. In this study, we have developed a parsimonious watershed model for quantifying catchment-scale time lags based on both soil nutrient accumulations (biogeochemical legacy) and groundwater travel time distributions (hydrologic legacy). The model accurately predicted the time lags observed in an Iowa watershed that had undergone a 41% conversion of area from row crop to native prairie. We explored the time scales of change for stream nutrient concentrations as a function of both natural and anthropogenic controls, from topography to spatial patterns of land-use change. Our results demonstrate that the existence of biogeochemical nutrient legacies increases time lags beyond those due to hydrologic legacy alone. In addition, we show that the maximum concentration reduction benefits vary according to the spatial pattern of intervention, with preferential conversion of land parcels having the shortest catchment-scale travel times providing proportionally greater concentration reductions as well as faster response times. In contrast, a random pattern of conversion results in a 1:1 relationship between percent land conversion and percent concentration reduction, irrespective of denitrification rates within the landscape. Our modeling framework allows for the quantification of tradeoffs between costs associated with implementation of conservation measures and the time needed to see the desired concentration reductions, making it of great value to decision makers regarding optimal implementation of watershed conservation measures. [ABSTRACT FROM AUTHOR]