As wildfire regimes shift, resource managers are concerned about potential threats to aquatic ecosystems and the species they support, especially fishes. However, predicting fish responses can be challenging because wildfires affect aquatic ecosystems via multiple pathways. Application of whole-ecosystem approaches, such as food web modeling, can act as heuristic tools that offer valuable insights that account for these different mechanisms. We applied a dynamic food web simulation model that mechanistically linked stream trophic dynamics to the myriad effects that wildfires can have on aquatic and riparian ecosystems at a local stream reach-scale. We simulated how wildfires of different severity may influence short- (months to years) and long-term (years to decades) periphyton, aquatic invertebrate, and fish biomass dynamics in forested headwater streams of the western Pacific Northwest (USA). In many cases, wildfire increased modeled periphyton, invertebrate, and fish biomass over both short- and long-time periods. However, modeled responses varied extensively in their direction (that is, positive or negative), magnitude, and duration depending on fire severity, time since fire, and trophic level. The shapes of these response trajectories were especially sensitive to predicted wildfire effects on water temperature, canopy cover, riparian shading, and instream turbidity. Model simulations suggest a single fire could result in a wide range of aquatic ecosystem responses, especially in watersheds with mixed burn severity. Our analysis highlights the utility of whole-ecosystem approaches, like food web modeling, as heuristic tools for improving our understanding of the mechanisms linking fire, food webs, and fish and for identifying contexts where fires could have deleterious impacts on fishes.

Mountain lakes provide cultural, aesthetic, and recreational services across the globe. Despite their recognized importance, there is no consistent definition of a mountain lake, which hampers describing them individually as well as in aggregate. Additionally, it makes it difficult to study and manage this unique population. We develop a rules-based approach for classifying mountain lakes according to the topography of the area draining directly to the lake. We apply the approach to the data from the United States National Lakes Assessment, for which the population is defined as lakes that are at least 1 ha in surface area and at least 1 m deep in the conterminous US (CONUS). Leveraging this national assessment allows for evaluating the condition of mountain lakes relative to all lakes in the CONUS. There are an estimated 12,353 (95 % C.I. 10,529-14,177) mountain lakes that account for 6.4 % of the lake population in the CONUS. Mountain lakes are in better condition than non-mountain lakes for 11 of 12 physical, chemical, and biological indicators (acid neutralizing capacity was the one exception). Approximately 25 % of mountain lakes are classified as eutrophic or hypereutrophic, and nearly 50 % are in fair or poor condition with respect to riparian vegetation and lakeshore disturbance. Mountain lake watersheds have lower proportions of developed land cover (mean ± 95 % CI: 0.8 ± 0.1 %) compared to non-mountain lakes (6.7 ± 0.3 %); however, developed land cover is more concentrated closer to the lakeshore for mountain lakes compared to non-mountain lakes. Coupled with characteristics such as high runoff, low hydraulic conductivity, and shallow bedrock depths, mountain lakes may be more susceptible to the adverse effects of human development and climate change compared to non-mountain lakes. These findings underscore the need for targeted monitoring, conservation, and management strategies to protect these valuable and sensitive lake environments.

Excess nitrogen and phosphorus export from agricultural lands is a primary contributor to water quality degradation in the United States. To improve water quality, significant investments have been made to implement conservation practices on agricultural lands, including through mandated spending in the Farm Bill and the Inflation Reduction Act. Effectively guiding conservation implementation requires assessment of practice efficacy at regional and national scales, consistent with the scales of water quality goals. To evaluate whether existing resources are sufficient for such conservation efficacy assessments, we review prior efforts and publicly available data and tools to evaluate the effects of agricultural conservation on water quality outcomes. We find that practice records from programs that fund agricultural conservation have a unique and substantial potential for secondary use to generate insights about conservation effects from local to national scales, but modifications would help maximize the potential of these data for assessing conservation efficacy. Such assessments would benefit from improved consistency in reporting units and geographic scales across program datasets; quantification of the duration of water quality benefits from conservation practices; publication of practice data aggregated across programs, to increase the spatial resolution of conservation insights while maintaining legal protections of producer privacy; and collection of water quality and conservation practice data at similar temporal and spatial scales. Enhancing existing and future datasets could deliver high return on effort by generating valuable insights to improve the use of conservation practices for water quality management.

Wildfires can dramatically alter vegetation cover and soil properties across large scales, resulting in substantial shifts in runoff generation, streamflow, and water quality. In September 2020, extensive and high-severity wildfires burned more than 490,000 ha of forest land on the westside of the Cascade Mountain Range in the Pacific Northwest. Much of the area impacted by these fires is critical for the provision of water for downstream aquatic ecosystems, agriculture, hydropower, recreation, and municipal drinking water. We undertook a study to evaluate the effects of four of the large high severity wildfires from 2020 (Riverside, Beachie Creek, Lionshead, and Holiday Farm) on streamflow in nine burned catchments in western Oregon. We also included four unburned, reference catchments in our analysis to enable us to assess post-fire streamflow changes in the burned catchments. To quantify the effects of wildfire on the catchment water balance we used publicly available streamflow data and estimated precipitation, potential evapotranspiration (PET), and actual evapotranspiration (ET), using satellite-based meteorological data. We quantified catchment area burned and burn severity with the average differenced normalized burn ratio (dNBR). We compared hydrologic conditions for the pre-fire (2001-2020) and post-fire (2021-2022) periods by analyzing catchment runoff ratios, ET ratios (evaporative index: quotient of ET divided by precipitation, referred to as EI hereafter), and Budyko curves. We also used random forest models to explore factors influencing the variability in EI. During the post-fire period, we observed decreases in EI and increases in runoff ratio in the burned catchments. Post-fire declines in EI were positively related to burn severity (R2 = 0.70 in 2021; 0.76 in 2022) and area burned (R2 = 0.91 in 2021; 0.95 in 2022), and were primarily driven by decreases in ET. Declines in ET were highly variable, ranging from 10.7-40.2 % in the first year after the fires and 6.1-32.0 % in the second year after the fires, and were generally related to catchment burn severity and area burned. The greatest increases in runoff (16.1 % in 2021 and 19.8 % in 2022) occurred in the same catchment. These results were reinforced by the random forest analysis, which illustrated the importance of burn severity as a predictor of EI. Interestingly, the variability in changes in EI during the post-fire period was also associated with other geomorphic factors such as catchment slope, elevation, geology, aspect, and pre-fire vegetation type. Since the duration and seasonality of post-fire impacts on hydrology remain uncertain, our findings bring new insights and guide future studies into the post-fire responses on hydrology that are crucial for water and forest management.

AbstractExcess nutrient pollution contributes to the formation of harmful algal blooms (HABs) that compromise fisheries and recreation and that can directly endanger human and animal health via cyanotoxins. Efforts to quantify the occurrence, drivers, and severity of HABs across large areas is difficult due to the resource intensive nature of field monitoring of lake nutrient and chlorophyll‐a concentrations. To better characterize how nutrients interact with other environmental factors to produce algal blooms in freshwater systems, we used spatially explicit and temporally matched climate, landscape, in‐lake characteristic, and nutrient inventory data sets to predict nutrients and chlorophyll‐a across the conterminous US (CONUS). Using a nested modeling approach, three random forest (RF) models were trained to explain the spatiotemporal variation in total nitrogen (TN), total phosphorus (TP), and chlorophyll‐a concentrations across US EPA's National Lakes Assessment (n = 2,062). Concentrations of TN and TP were the most important predictors and, with other variables, the RF model accounted for 68% of variation in chlorophyll‐a. We then used these RF models to extrapolate lake TN and TP predictions to lakes without nutrient observations and predict chlorophyll‐a for ∼112,000 lakes across the CONUS. Risk for high chlorophyll‐a concentrations is highest in the agriculturally dominated Midwest, but other areas of risk emerge in nutrient pollution hot spots across the country. These catchment and lake‐specific results can help managers identify potential nutrient pollution and chlorophyll‐a hot spots that may fuel blooms, prioritize at‐risk lakes for additional monitoring, and optimize management to protect human health and other environmental end goals.

AbstractA substantial fraction of nitrogen (N) fertilizer applied in agricultural systems is not incorporated into crops and moves below the rooting zone as nitrate (NO3−). Understanding mechanisms for soil N retention below the rooting zone and leaching to groundwater is essential for our ability to track the fate of added N. We used dual stable isotopes of nitrate (δ15N–NO3− and δ18O–NO3−) and water (δ18O–H2O and δ2H–H2O) to understand the mechanisms driving nitrate leaching at three depths (0.8, 1.5, and 3.0 m) of an irrigated corn field sampled every 2 weeks from 2016 to 2020 in the southern Willamette Valley, Oregon, USA. Distinct periods of high nitrate concentrations with lower δ15N–NO3− values indicated that a portion of that nitrate was from recent fertilizer applications. We used a mixing model to quantify nitrate fluxes associated with recently added fertilizer N versus older, legacy soil N during these “fertilizer signal periods.” Nitrate leached below 3.0 m in these periods made up a larger proportion of the total N leached at that depth (∼52%) versus the two shallower depths (∼13%–16%), indicating preferential movement of recently applied fertilizer N through the deep soil into groundwater. Further, N associated with recent fertilizer additions leached more easily when compared to remobilized legacy N. A high volume of fall and winter precipitation may push residual fertilizer N to depth, potentially posing a larger threat to groundwater than legacy N. Optimizing fertilizer N additions could minimize fertilizer losses and reduce nitrate leaching to groundwater.

Decision makers need to know the drivers of surface water phosphorus (P) concentrations, the environmental factors that mediate P loading in freshwater systems, and where pollution sources and mediating factors are co-located to inform water quality restoration efforts. To provide this information, publicly available spatial data sets of P pollution sources and relevant environmental variables, like temperature, precipitation, and agricultural soil erodibility, were matched with >7,000 stream and lake total P observations throughout the conterminous United States. Using three statistical approaches, consisting of (a) correlation, (b) regression, and (c) machine learning techniques, we identified likely drivers of P concentrations. Surface water concentrations in streams were more strongly correlated and effectively predicted by annual fertilizer and manure input rates and agricultural legacy sources compared to that of lakes. This observation suggests that streams may be more immediately responsive to improvements in agricultural nutrient management. In contrast, lake concentrations, though still positively associated with agricultural input and surplus variables, may be more influenced by historic erosional inputs, internal lake recycling, and other environmental factors. Thus, lake TP concentrations may not be as immediately responsive as streams to improvements in phosphorus management. Both stream and lake P concentrations will potentially increase because of warming temperatures and forest recovering from past acidification, putting even further pressure on existing water quality restoration efforts to meet nutrient loading reduction targets. The identified spatial data sets and relationships elucidated in this effort can inform the placement and development of watershed restoration strategies to reduce excess P in aquatic systems.

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 finescaled 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

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.

Evaluating sustainability stewardship at higher educational institutions is essential to working towards improving our environment. Many institutions have used environmental footprint indicators as a way to evaluate, track, and improve their impact on the environment. In this article, we present the web-based Integrated Environmental Footprint Tool (IEFT), which allows users to test how changes in certain activities impact nitrogen (N), greenhouse gases (GHG), phosphorus (P), and water (W) footprints for a university campus. This study uses the University of Virginia (UVA) as a model to show the impacts of their existing sustainability plans on multiple footprint indicators. Strategies from the University of Virginia's (UVA) two exisiting action plans, the GHG Action Plan and the N Action Plan, are evaluated to determine their impact on each of the footprints (GHG, N, P, and W). Based on the 2025 goal year, the strategies in these action plans are estimated to reduce the GHG, N, P, and W footprints by -38%, 32%, 25%, and 2.7% respectively. The damage costs associated with GHG and N footprints are assessed and reveal a 38 percent reduction in damage costs for GHG and a 42 percent reduction in costs for N. Using the IEFT to evaluate the impact of these action plan strategies, UVA optimized environmental outcomes. The model shown here can be used at other institutions to evaluate the environmental impact of planned changes to an institutions' operations.

Harmful algal blooms caused by cyanobacteria are a threat to global water resources and human health. Satellite remote sensing has vastly expanded spatial and temporal data on lake cyanobacteria, yet there is still acute need for tools that identify which waterbodies are at-risk for toxic cyanobacterial blooms. Algal toxins cannot be directly detected through imagery but monitoring toxins associated with cyanobacterial blooms is critical for assessing risk to the environment, animals, and people. The objective of this study is to address this need by developing an approach relating satellite imagery on cyanobacteria with field surveys to model the risk of toxic blooms among lakes. The Medium Resolution Imaging Spectrometer (MERIS) and United States (US) National Lakes Assessments are leveraged to model the probability among lakes of exceeding lower and higher demonstration thresholds for microcystin toxin, cyanobacteria, and chlorophyll a. By leveraging the large spatial variation among lakes using two national-scale data sources, rather than focusing on temporal variability, this approach avoids many of the previous challenges in relating satellite imagery to cyanotoxins. For every satellite-derived lake-level Cyanobacteria Index (CI_cyano) increase of 0.01 CI_cyano/km2, the odds of exceeding six bloom thresholds increased by 23–54 %. When the models were applied to the 2192 satellite monitored lakes in the US, the number of lakes identified with ≥75 % probability of exceeding the thresholds included as many as 335 lakes for the lower thresholds and 70 lakes for the higher thresholds, respectively. For microcystin, the models identified 162 and 70 lakes with ≥75 % probability of exceeding the lower (0.2 μg/L) and higher (1.0 μg/L) thresholds, respectively. This approach represents a critical advancement in using satellite imagery and field data to identify lakes at risk for developing toxic cyanobacteria blooms. Such models can help translate satellite data to aid water quality monitoring and management.

Targeted conservation approaches seek to focus resources on areas where they can deliver the greatest benefits and are recognized as key to reducing nonpoint source nutrients from agricultural landscapes into sensitive receiving waters. Moreover, there is growing recognition of the importance and complementarity of in-field and edge-of-field conservation for reaching nutrient reduction goals. Here we provide a generic prioritization that can help with spatial targeting and applied it across the conterminous US (CONUS). The prioritization begins with identifying areas with high agricultural nutrient surplus, i.e., where the most nitrogen (N) and/or phosphorus (P) inputs are left on the landscape after crop harvest. Subwatersheds with high surplus included 52% and 50% of CONUS subwatersheds for N and P, respectively, and were located predominantly in the Midwest for N, in the South for P, and in California for both N and P. Then we identified the most suitable conservation strategies using a hierarchy of metrics including nutrient use efficiency (proportion of new nutrient inputs removed by crop harvest), tile drainage, existing buffers for agricultural run-off, and wetland restoration potential. In-field nutrient input reduction emerged as a priority because nutrient use efficiency fell below a high but achievable goal of 0.7 (30% of nutrients applied are not utilized) in 45% and 44% of CONUS subwatersheds for N and P, respectively. In many parts of the southern and western US, in-field conservation (i.e., reducing inputs + preventing nutrients from leaving fields) alone was likely the optimal strategy as agriculture was already wellbuffered. However, stacking in-field conservation with additional edge-of-field buffering would be important to conservation strategies in 35% and 29% of CONUS subwatersheds for N and P, respectively. Nutrient use efficiencies were often high enough in the Midwest that proposed strategies focused more on preventing nutrients from leaving fields, managing tile effluent, and buffering agricultural fields. Almost all major river basins would benefit from a variety of nutrient reduction conservation strategies, underscoring the potential of targeted approaches to help limit excess nutrients in surface and ground waters.

Eutrophication, harmful algal blooms, and human health impacts are critical environmental challenges resulting from excess nitrogen and phosphorus in surface waters. Yet we have limited information regarding how wetland characteristics mediate water quality across watershed scales. We developed a large, novel set of spatial variables characterizing hydrological flowpaths from wetlands to streams, that is, "wetland hydrological transport variables," to explore how wetlands statistically explain the variability in total nitrogen (TN) and total phosphorus (TP) concentrations across the Upper Mississippi River Basin (UMRB) in the United States. We found that wetland flowpath variables improved landscape-to-aquatic nutrient multilinear regression models (from R2 = 0.89 to 0.91 for TN; R2 = 0.53 to 0.84 for TP) and provided insights into potential processes governing how wetlands influence watershed-scale TN and TP concentrations. Specifically, flowpath variables describing flow-attenuating environments, for example, subsurface transport compared to overland flowpaths, were related to lower TN and TP concentrations. Frequent hydrological connections from wetlands to streams were also linked to low TP concentrations, which likely suggests a nutrient source limitation in some areas of the UMRB. Consideration of wetland flowpaths could inform management and conservation activities designed to reduce nutrient export to downstream waters.

Wetland hydrologic connections to downstream waters influence stream water quality. However, no systematic approach for characterizing this connectivity exists. Here using physical principles, we categorized conterminous US freshwater wetlands into four hydrologic connectivity classes based on stream contact and flowpath depth to the nearest stream: riparian, non-riparian shallow, non-riparian mid-depth and non-riparian deep. These classes were heterogeneously distributed over the conterminous United States; for example, riparian dominated the south-eastern and Gulf coasts, while non-riparian deep dominated the Upper Midwest and High Plains. Analysis of a national stream dataset indicated acidification and organic matter brownification increased with connectivity. Eutrophication and sedimentation decreased with wetland area but did not respond to connectivity. This classification advances our mechanistic understanding of wetland influences on water quality nationally and could be applied globally.

Wildfires are a concern for water quality in the United States, particularly in the wildland-urban interface of populous areas. Wildfires combust vegetation and surface soil organic matter, reduce plant nutrient uptake, and can alter the composition of runoff and receiving waters. At the wildland-urban interface, fires can also introduce contaminants from the combustion of man-made structures. We examine post-wildfire effects on drinking water quality by evaluating concentrations and maximum contaminant level (MCL) violations of selected contaminants regulated in the U.S. at public drinking water systems (PWSs) located downstream from wildfire events. Among contaminants regulated under the U.S. Safe Drinking Water Act, nitrate, arsenic, disinfection byproducts, and volatile organic compounds (VOCs) were analyzed in watersheds that experienced major wildfires. Surface water sourced drinking water (SWDW) nitrate violations increased by an average of 0.56 violations per PWS and concentrations increased by 0.044 mg-N/L post-wildfire. Groundwater sourced drinking water (GWDW) nitrate violations increased by 0.069 violations per PWS and concentrations increased by 0.12 mg-N/L post-wildfire. SWDW total trihalomethane (TTHM) violations increased by 0.58 violations per PWS and concentrations increased by 10.4 μg/L. SWDW total haloacetic acid (HAA5) violations increased by 0.82 violations per PWS and concentrations increased by 8.5 μg/L. Arsenic violations increased by 1.08 violations per PWS and concentrations increased by 0.92 μg/L. There was no significant effect of wildfires on average VOC violations. Nitrate violations increased in 75% of SWDW sites and 34% of GWDW sites post-wildfire, while about 71% and 50% of SWDW sites showed an increase in TTHM and HAA5 violations. Violations also increased for 35% of arsenic and 44% of VOC sites post-wildfire. These findings support the need for increased awareness about the impact of wildfires on drinking water treatment to help PWS operators adapt to the consequences of wildfires on source water quality, particularly in wildfire-prone regions.

Nitrogen (N) fertilizer applications are important for agricultural yield, yet not all the applied N is taken up by crops, leading to surplus N storage in soil or leaching to groundwater and surface water. Leaching loss of fertilizer N represents a cost for farmers and has consequences for human health and the environment, especially in the southern Willamette Valley, Oregon, USA, where groundwater nitrate contamination is prevalent. While improved nutrient management and conservation practices have been implemented to minimize leaching, nitrate levels in groundwater continue to increase in many long-term monitoring wells. To elucidate controls on leaching rates and N dynamics in agricultural soils across soil depths, and in response to seasonal and annual variation in management (e.g., fertilizer input amount and summer irrigation), we intensively monitored the transport of water and nitrate every two weeks for four years through the vadose zone at three depths (0.8, 1.5, and 3.0 m) in a sweet corn (maize) field. Though nitrate leaching was highly variable among lysimeters at the same depth and across years, a strong pattern emerged: annual nitrate leaching significantly decreased with depth across the study, averaging ~104 kg N ha-1 yr-1 near the surface (0.8 m) versus ~56 kg N ha-1 yr-1 in the deep soil (3.0 m), a 54% reduction in leaching between the soil layers. Even though crops were irrigated in summer, most leaching (~72% below 3.0 m) occurred during the wet fall and winter. Based on steady state assumptions, a net equivalent of ~29% of surface N inputs leached below 3.0 m into the deeper soil and groundwater, while ~44% was removed in crop harvest, indicating considerable N retention in the soil (~27% of inputs or approximately 58 kg N ha-1 yr-1). The accumulation and long-term dynamics of deep soil N is a legacy of agricultural management that should be further studied to better manage and reduce nitrate loss to groundwater.

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.

Nitrate contamination of groundwater is a concern globally, particularly in agricultural regions where decades of fertilizer nitrogen (N) use has led to a legacy of N accumulation in soils and groundwater. Linkages between current management practices and groundwater nitrate dynamics are often confounded by the legacy effect, and other processes unrelated to management. A coupled analysis of dual stable isotopes of water (δH2O = δ2H and δ18O) and nitrate (δNO3 - = δ15N and δ18O) can be a powerful approach to identify sources and processes responsible for groundwater pollution. To assess how management practices impact groundwater nitrate, we interpreted behavior of δH2O and δNO3 -, together with nitrate concentrations, in water samples collected from long-term monitoring wells in the Southern Willamette Valley (SWV), Oregon. The source(s) of nitrate and water varied among wells, suggesting that the nitrate concentration patterns were not uniform across the shallow aquifer of the valley. Analyzing the stability versus variability of a well's corresponding δH2O and δNO3 - values over time revealed the mechanisms controlling nitrate concentrations. Wells with stable δH2O and δNO3 - values and nitrate concentrations were influenced by one water source with a long residence time and one nitrate source. Variable nitrate concentrations of other wells were attributed to dilution with an alternate water source, mixing of two nitrate sources, or variances in the release of legacy N from overlying soils. Denitrification was not an important process influencing well nitrate dynamics. Understanding the drivers of nitrate dynamics and interaction with legacy N is crucial for managing water quality improvement. This case study illustrates when and where such coupled stable isotope approaches might provide key insights to management on groundwater nitrate contamination issues.