Global flows of reactive nitrogen (N) have increased significantly over the last century in response to agricultural intensification and elevated levels of atmospheric deposition. Despite widespread implementation conservation measures, N concentrations in surface waters are often remaining steady or continuing to increase. Although such lack of response has been attributed to time lags associated with legacy N stores in subsurface reservoirs, it is unclear what the magnitudes of such stores are and how they are partitioned between shallow soil and deeper groundwater reservoirs. Here we have synthesized data to develop a 214 year (1800-2014) trajectory of N inputs to the land surface of the continental U.S. We have concurrently developed a parsimonious, process-based model, Exploration of Long-tErM Nutrient Trajectories (ELEMeNT) that pairs this input trajectory with a travel time-based approach to simulate transport and retention along subsurface pathways. Using the model, we have reconstructed historic nitrate yields at the outlets of two major U.S. watersheds, the Mississippi River Basin (MRB) and Susquehanna River Basin (SRB). Our results show significant N loading above baseline levels in both watersheds before the widespread use of commercial N fertilizers, largely due to the conversion of forest and grassland to row crop agriculture. Model results also allow us to quantify the magnitudes of legacy N in soil and groundwater pools and to highlight the dominance of soil legacies in MRB and groundwater legacies in SRB. Approximately 55% and 18% of the current annual N loads in the MRB and SRB were found to be older than 10 years of age.

In August 2017, the Gulf of Mexico's hypoxic zone was declared to be the largest ever measured. It has been estimated that a 60% decrease in watershed nitrogen (N) loading may be necessary to adequately reduce eutrophication in the Gulf. However, to date there has been no rigorous assessment of the effect of N legacies on achieving water quality goals. In this study, we show that even if agricultural N use became 100% efficient, it would take decades to meet target N loads due to legacy N within the Mississippi River basin. Our results suggest that both long-term commitment and large-scale changes in agricultural management practices will be necessary to decrease Mississippi N loads and to meet current goals for reducing the size of the Gulf hypoxic zone.

Increased loading of nitrogen (N) and phosphorus (P) from agricultural and urban intensification has led to severe degradation of inland and coastal waters. Lakes, reservoirs, and wetlands (lentic systems) retain these nutrients, thus regulating their delivery to downstream waters. While the processes controlling N and P retention are relatively well-known, there is a lack of quantitative understanding of how these processes manifest across spatial scales. We synthesized data from 600 lentic systems around the world to gain insight into the relationship between hydrologic and biogeochemical controls on nutrient retention. Our results indicate that the first-order reaction rate constant, k [T 21 ], is inversely proportional to the hydraulic residence time, s [T], across 6 orders of magnitude in residence time for total N, total P, nitrate, and phosphate. We hypothesized that the consistency of the relationship points to a strong hydrologic control on biogeochemical processing, and validated our hypothesis using a sediment-water model that links major nutrient removal processes with system size. Finally, the k-s relationships were upscaled to the landscape scale using a wetland size-frequency distribution. Results suggest that small wetlands play a disproportionately large role in landscape-scale nutrient processing-50% of nitrogen removal occurs in wetlands smaller than 10 2.5 m 2 in our example. Thus, given the same loss in wetland area, the nutrient retention potential lost is greater when smaller wetlands are preferentially lost from the landscape. Our study highlights the need for a stronger focus on small lentic systems as major nutrient sinks in the landscape. Plain Language Summary Excess nutrient pollution from intensive fertilizer use and farming operations poses an increasing threat to water quality worldwide. Lakes, streams, and wetlands restrict the movement of nutrients, and thus protect downstream waters. We have a limited understanding, however, of how removal processes are affected by the size and type of the water body. Based on a synthesis of data from lakes, reservoirs, and wetlands worldwide, we found that smaller water bodies tend to have higher nutrient removal rates. We applied our findings to the landscape scale and found that for the same wetland area lost, the loss of small wetlands corresponds to a greater loss in wetland nutrient removal potential. Such findings are significant to wetland protection and restoration efforts, which have historically focused on maximizing total wetland area rather than on preserving a distribution of different wetlands sizes within a landscape.

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Land use change and agricultural intensification have increased food production but at the cost of polluting surface and groundwater. Best management practices implemented to improve water quality have met with limited success. Such lack of success is increasingly attributed to legacy nutrient stores in the subsurface that may act as sources after reduction of external inputs. However, current water‐quality models lack a framework to capture these legacy effects. Here we have modified the SWAT (Soil Water Assessment Tool) model to capture the effects of nitrogen (N) legacies on water quality under multiple land‐management scenarios. Our new SWAT‐LAG model includes (1) a modified carbon‐nitrogen cycling module to capture the dynamics of soil N accumulation, and (2) a groundwater travel time distribution module to capture a range of subsurface travel times. Using a 502‐km2 Iowa watershed as a case study, we found that between 1950 and 2016, 25% of the total watershed N surplus (N Deposition + Fertilizer + Manure + N Fixation − Crop N uptake) had accumulated within the root zone, 14% had accumulated in groundwater, while 27% was lost as riverine output, and 34% was denitrified. In future scenarios, a 100% reduction in fertilizer application led to a 79% reduction in stream N load, but the SWAT‐LAG results suggest that it would take 84 years to achieve this reduction, in contrast to the 2 years predicted in the original SWAT model. The framework proposed here constitutes a first step toward modifying a widely used modeling approach to assess the effects of legacy N on the time required to achieve water‐quality goals.

Reactive nitrogen (N) fluxes have increased tenfold over the last century, driven by increases in population, shifting diets, and increased use of commercial N fertilizers. Runoff of excess N from intensively managed landscapes threatens drinking water quality and disrupts aquatic ecosystems. Excess N is also a major source of greenhouse gas emissions from agricultural soils. While N emissions from agricultural landscapes are known to originate from not only current-year N input but also legacy N accumulation in soils and groundwater, there has been limited access to fine-scale, long-term data regarding N inputs and outputs over decades of intensive agricultural land use. In the present work, we synthesize population, agricultural, and atmospheric deposition data to develop a comprehensive, 88-year (1930-2017) data set of county-scale components of the N mass balance across the contiguous United States (Trajectories Nutrient Dataset for nitrogen [TREND-nitrogen]). Using a machine-learning algorithm, we also develop spatially explicit typologies for components of the N mass balance. Our results indicate a large range of N trajectory behaviors across the United States due to differences in land use and management and particularly due to the very different drivers of N dynamics in densely populated urban areas compared with intensively managed agricultural zones. Our analysis of N trajectories also demonstrates a widespread functional homogenization of agricultural landscapes. This newly developed typology of N trajectories improves our understanding of long-term N dynamics, and the underlying data set provides a powerful tool for modeling the impacts of legacy N on past, present, and future water quality. Plain Language Summary Over the last century, people have increasingly used nitrogen fertilizer to increase crop yields. The nitrogen not taken up by crops in agricultural areas runs off of the land and pollutes rivers, lakes, and coastal areas. This excess nitrogen also forms a powerful greenhouse gas that contributes to climate change. Excess nitrogen can build up in the environment over time and pollute our water for decades. It is therefore necessary for us to know how much extra nitrogen has been applied over many decades to better understand current risks to the environment. In our study, we have used multiple data sources to calculate how much nitrogen has been added to the landscape, how much nitrogen has been removed through crop production, and how much human waste is produced, for every county in the contiguous United States from 1930 to 2017. We show that the main sources of nitrogen can be different in different areas of the country. We also show that high levels of nitrogen use can make landscapes in very different climates look very similar. This new data set will be very important for creating models that can predict how decades of high nitrogen inputs impact water quality and future changes in climate.

Reported groundwater recovery in South India has been attributed to both increasing rainfall and political interventions. Findings of increasing groundwater levels, however, are at odds with reports of well failure and decreases in the land area irrigated from shallow wells. We argue that recently reported results are skewed by the problem of survivor bias, with dry or defunct wells being systematically excluded from trend analyses due to missing data. We hypothesize that these dry wells carry critical information about groundwater stress that is missed when data are filtered. Indeed, we find strong correlations between missing well data and metrics related to climate stress and groundwater development, indicative of a systemic bias. Using two alternative metrics, which take into account information from dry and defunct wells, our results demonstrate increasing groundwater stress in South India. Our refined approach for identifying groundwater depletion hot spots is critical for policy interventions and resource allocation.