High Nitrate Concentrations in Some Midwest United States Streams in 2013 after the 2012 Drought

Metre, Peter C. Van; Frey, Jeffrey W.; Musgrove, MaryLynn; Nakagaki, Naomi; Qi, Sharon L.; Mahler, Barbara J.; Wieczorek, Michael E.; Button, Daniel T.

doi:10.2134/jeq2015.12.0591

Cited by 60 publications

(42 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Peak N fluxes vary from year to year, but 2010 was similar to the years 2011, 2014 and 2015. In 2012, a drought caused unusually low N discharge (Figure 4; Van Metre et al, 2016), which explains why we did not observe high fluxes in 2012, and why Yan et al ( 2016) deduced that the September 2010 flux was unusually high. This example illustrates how critical long-term monitoring is to fully understand export mechanisms and flux drivers.…”

Section: Was the Flux Event In September 2010 After Dwh Unusual?mentioning

confidence: 71%

The ecosystem baseline for particle flux in the Northern Gulf of Mexico

Giering¹,

Yan

Sweet

et al. 2018

Elementa: Science of the Anthropocene

View full text Add to dashboard Cite

Response management and damage assessment during and after environmental disasters such as the Deepwater Horizon (DWH) oil spill require an ecological baseline and a solid understanding of the main drivers of the ecosystem. During the DWH event, a large fraction of the spilled oil was transported to depth via sinking marine snow, a routing of spilled oil unexpected to emergency response planners. Because baseline knowledge of particle export in the Northern Gulf of Mexico and how it varies spatially and temporally was limited, we conducted a detailed assessment of the potential drivers of deep (~1400 m depth) particle fluxes during 2012-2016 using sediment traps at three contrasting sites in the Northern Gulf of Mexico: near the DWH site, at an active natural oil seep site, and at a site considered typical for background conditions. The DWH site, located ~70 km from the Mississippi River Delta, showed flux patterns that were strongly linked to the Mississippi nitrogen discharge and an annual subsequent surface bloom. Fluxes carried clear signals of combustion products, which likely originated from pyrogenic sources that were transported offshore via the Mississippi plume. The seep and reference sites were more strongly influenced by the open Gulf of Mexico, did not show a clear seasonal flux pattern, and their overall sedimentation rates were lower than those at the DWH site. At the seep site, based on polycyclic aromatic hydrocarbon data, we observed indications of three different pathways for "natural" oiled-snow sedimentation: scavenging by sinking particles at depth, weathering at the surface before incorporation into sinking particles, and entry into the food web and subsequent sinking in form of detritus. Overall, sedimentation rates at the three sites were markedly different in quality and quantity owing to varying degrees of riverine and oceanic influences, including natural seepage and contamination by combustion products.

show abstract

Section: Was the Flux Event In September 2010 After Dwh Unusual?mentioning

confidence: 71%

The ecosystem baseline for particle flux in the Northern Gulf of Mexico

Giering¹,

Yan

Sweet

et al. 2018

Elementa: Science of the Anthropocene

View full text Add to dashboard Cite

show abstract

“…The regional differences in NO3‐N shared environmental correlations may be related to the dominant source of NO3‐N to lakes in these two regions. In the Midwest subregion, the dominant source of NO3‐N is from agricultural land use practices (Van Metre et al ), which is also a significant source of P. This may result in similar shared environmental correlations among nutrients and biological responses (e.g., CHL concentrations). In contrast, the dominant source of NO3‐N in the forested Northeast subregion is from atmospheric deposition (Aber et al ), which may reduce the shared environmental correlations of NO3‐N with landscape‐derived nutrients (e.g., P).…”

Section: Discussionmentioning

confidence: 99%

Combining nutrient, productivity, and landscape‐based regressions improves predictions of lake nutrients and provides insight into nutrient coupling at macroscales

Wagner

Schliep

2018

Limnology & Oceanography

View full text Add to dashboard Cite

Empirical nutrient models that describe lake nutrient, productivity, and water clarity relationships among lakes play a prominent role in limnology. Landscape‐based regressions are also used to understand macroscale variability of lake nutrients, clarity, and productivity (hereafter referred to as nutrient‐productivity). Predictions from both models are used to inform eutrophication management globally. To date, these two classes of models are generally conducted separately, which ignores the known dependencies among nutrient‐productivity variables. We present a statistical model that integrates nutrient‐productivity and landscape‐based regressions—where lake nutrients, productivity, and clarity variables are modeled jointly. We fitted a joint nutrient‐productivity model to over 7000 lakes with three nutrients (total phosphorus, total nitrogen, nitrate concentrations), chlorophyll a concentrations, and Secchi disk depth as response variables and landscape features as predictor variables. Because lakes in different regions respond to landscape features differently, we focused our analysis on two subregions with different dominant land uses, the agricultural Midwest and the forested Northeast U.S. Predictive performance was enhanced by modeling nutrient‐productivity variables jointly. We also found strong evidence that nutrient‐productivity variables were coupled, and that only nitrate may be decoupled from other nutrient‐productivity variables in the forested region. We speculate that these regional differences may be related to differences in the strength of biogeochemical cycles and stoichiometric controls between these regions. Jointly modeling nutrient‐productivity variables in lakes effectively integrates the two dominant approaches for studying lakes nutrient‐productivity relationships and provides novel insight into macroscale patterns of the coupling of nutrients, chlorophyll, and water clarity in lakes.

show abstract

“…Nitrogen (N) fertilizer inputs are crucial for achieving high crop yields, but the loss of reactive N from agricultural systems leads to atmospheric, surface water, and groundwater pollution [1][2][3], ultimately diminishing environmental quality and human well-being [4,5]. Despite the potential environmental consequences to society, the pressure on producers to increase productivity oftentimes leads to N fertilizer applications in excess of crop requirement [6,7].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery

et al. 2020

View full text Add to dashboard Cite

The ability to predict spatially explicit nitrogen uptake (NUP) in maize (Zea mays L.) during the early development stages provides clear value for making in-season nitrogen fertilizer applications that can improve NUP efficiency and reduce the risk of nitrogen loss to the environment. Aerial hyperspectral imaging is an attractive agronomic research tool for its ability to capture spectral data over relatively large areas, enabling its use for predicting NUP at the field scale. The overarching goal of this work was to use supervised learning regression algorithms—Lasso, support vector regression (SVR), random forest, and partial least squares regression (PLSR)—to predict early season (i.e., V6–V14) maize NUP at three experimental sites in Minnesota using high-resolution hyperspectral imagery. In addition to the spectral features offered by hyperspectral imaging, the 10th percentile Modified Chlorophyll Absorption Ratio Index Improved (MCARI2) was made available to the learning models as an auxiliary feature to assess its ability to improve NUP prediction accuracy. The trained models demonstrated robustness by maintaining satisfactory prediction accuracy across locations, pixel sizes, development stages, and a broad range of NUP values (4.8 to 182 kg ha−1). Using the four most informative spectral features in addition to the auxiliary feature, the mean absolute error (MAE) of Lasso, SVR, and PLSR models (9.4, 9.7, and 9.5 kg ha−1, respectively) was lower than that of random forest (11.2 kg ha−1). The relative MAE for the Lasso, SVR, PLSR, and random forest models was 16.5%, 17.0%, 16.6%, and 19.6%, respectively. The inclusion of the auxiliary feature not only improved overall prediction accuracy by 1.6 kg ha−1 (14%) across all models, but it also reduced the number of input features required to reach optimal performance. The variance of predicted NUP increased as the measured NUP increased (MAE of the Lasso model increased from 4.0 to 12.1 kg ha−1 for measured NUP less than 25 kg ha−1 and greater than 100 kg ha−1, respectively). The most influential spectral features were oftentimes adjacent to each other (i.e., within approximately 6 nm), indicating the importance of both spectral precision and derivative spectra around key wavelengths for explaining NUP. Finally, several challenges and opportunities are discussed regarding the use of these results in the context of improving nitrogen fertilizer management.

show abstract

High Nitrate Concentrations in Some Midwest United States Streams in 2013 after the 2012 Drought

Cited by 60 publications

References 30 publications

The ecosystem baseline for particle flux in the Northern Gulf of Mexico

The ecosystem baseline for particle flux in the Northern Gulf of Mexico

Combining nutrient, productivity, and landscape‐based regressions improves predictions of lake nutrients and provides insight into nutrient coupling at macroscales

Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery

Contact Info

Product

Resources

About