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In this study, we explore observational, experimental, methodological, and practical aspects of the flux quantification of greenhouse gases from local point sources by using in situ airborne observations, and suggest a series of conceptual changes to improve flux estimates. We address the major sources of uncertainty reported in previous studies by modifying (1) the shape of the typical flight path, (2) the modeling of covariance and anisotropy, and (3) the type of interpolation tools used. We show that a cylindrical flight profile offers considerable advantages compared to traditional profiles collected as curtains, although this new approach brings with it the need for a more comprehensive subsequent analysis. The proposed flight pattern design does not require prior knowledge of wind direction and allows for the derivation of an ad hoc empirical correction factor to partially alleviate errors resulting from interpolation and measurement inaccuracies. The modified approach is applied to a use-case for quantifying CH4 emission from an oil field south of San Ardo, CA, and compared to a bottom-up CH4 emission estimate. [ABSTRACT FROM AUTHOR]

Excessive nitrogen loading to waterways leads to increased eutrophication and associated water quality impacts. An understanding of the regional and interannual variability in nitrogen loading and associated drivers is necessary for the design of effective management strategies. Here we develop a parsimonious empirical model based on net anthropogenic nitrogen input, precipitation, and land use that explains 68% of the observed variability in annual total nitrogen flux (QTN) (76% of ln(QTN)) across 242 catchment years. We use this model to present the first spatially and temporally resolved estimates of QTN for all eight-digit hydrologic unit (HUC8) watersheds within the continental United States (CONUS), focusing on the period 1987-2007. Results reveal high spatial and temporal variability in loading, with spatial variability primarily driven by nitrogen inputs, but with interannual variability and the occurrence of extremes dominated by precipitation across over three-quarters of the CONUS. High interannual variability and its correlation with precipitation persist at large aggregated scales. These findings point to a fundamental challenge in managing regions with high nutrient loading, because these regions also exhibit the strongest interannual variability and because the impact of changes in management practices will be modulated by meteorological variability and climatic trends. [ABSTRACT FROM AUTHOR]

Hypoxia has been observed in the central basin of Lake Erie for decades. To understand the impact of various controlling factors, we analyze a record of hypoxic extents for Lake Erie for 1985-2012 and develop a parsimonious model of their interannual variability. We find that the 2012 North American drought and accompanying low tributary discharge was associated with a record-breaking hypoxic event in Lake Erie, whereas a record-setting harmful algal bloom in 2011 was likely associated with only mild hypoxia. River discharge and the timing of nutrient input therefore impact western basin bloom growth and central basin oxygen demand in distinct ways that merit further investigation. Overall, April to June tributary discharge, May to July soluble reactive phosphorus loading, July wind stress, and June northwesterly wind duration explain 82% of the interannual variability of hypoxia, and discharge alone explains 39%, indicating that meteorological factors need to be considered in the development of nutrient management strategies, especially as both extreme precipitation events and droughts become more frequent under a changing climate. [ABSTRACT FROM AUTHOR]

Stratification and nutrient loading are two primary factors leading to hypoxia in coastal systems. However, where these factors are temporally correlated, it can be difficult to isolate and quantify their individual impacts. This study provides a novel solution to this problem by determining the effect of stratification based on its spatial relationship with bottom-water dissolved oxygen (BWDO) concentration using a geostatistical regression. Ten years (1998-2007) of midsummer Gulf of Mexico BWDO measurements are modeled using stratification metrics along with trends based on spatial coordinates and bathymetry, which together explain 27-61% of the spatial variability in BWDO for individual years. Because stratification effects explain only a portion of the year-to-year variability in mean BWDO; the remaining variability is explained by other factors, with May nitrate plus nitrite river concentration the most important. Overall, 82% of the year-to-year variability in mean BWDO is explained. The results suggest that while both stratification and nutrients play important roles in determining the annual extent of midsummer hypoxia, reducing nutrient inputs alone will substantially reduce the average extent. [ABSTRACT FROM AUTHOR]

Information about attributes such as contaminant concentrations or hydraulic properties in benthic sediments is typically obtained in core sections of varying lengths, and only the average value is measured in each section. However, an estimate of the attribute distribution at a uniform spatial resolution is often required for site characterization and the design of appropriate risk-based remediation alternatives. Because attributes exhibit spatial autocorrelation, geostatistical methods have become an essential tool for estimating their spatial distribution. The purpose of this paper is to optimally infer the spatial distribution of sampled attributes at a uniform resolution from fluvial core sampling data, using a downscaling technique formulated as a geostatistical inverse problem. We compare geostatistical downscaling to the more traditional approach of point-to-point ordinary kriging for a hypothetical case study, and for total organic carbon observations from the Passaic River, New Jersey. Although frequently used to interpolate measurements, ordinary kriging is shown not to be able to estimate the spatial distribution of attributes accurately, because this approach assumes that data are sampled at a uniform resolution. Geostatistical downscaling, on the other hand, is shown to resolve this problem by explicitly accounting for the relationship between the known average measurements and the unknown fine-resolution attribute distribution to be estimated. [ABSTRACT FROM AUTHOR]

Hypoxic conditions, defined as dissolved oxygen (DO) concentrations below 2 mg/L, are a regular summertime occurrence in Lake Erie, but the spatial extent has been poorly understood due to sparse sampling. We use geostatistical kriging and conditional realizations to provide quantitative estimates of the extent of hypoxia in the central basin of Lake Erie for August and September of 1987 to 2007, along with their associated uncertainties. The applied geostatistical approach combines the limited in situ DO measurements with auxiliary data selected using the Bayesian Information Criterion. Bathymetry and longitude are found to be highly significant in explaining the spatial distribution of DO, while satellite observations of sea surface temperature and satellite chlorophyll are not. The hypoxic extent was generally lowest in the mid-1990s, with the late 1980s (1987, 1988) and the 2000s (2003, 2005) experiencing the largest hypoxic zones. A simple exponential relationship based on the squared average measured bottom DO explains 97% of the estimated variability in the hypoxic extent. The change in the observed maximum extent between August and September is found to be sensitive to the corresponding variability in the hypolimnion thickness.

Information about attributes such as contaminant concentrations or hydraulic properties in benthic sediments is typically obtained in core sections of varying lengths, and only the average value is measured in each section. However, an estimate of the attribute distribution at a uniform spatial resolution is often required for site characterization and the design of appropriate risk-based remediation alternatives. Because attributes exhibit spatial autocorrelation, geostatistical methods have become an essential tool for estimating their spatial distribution. The purpose of this paper is to optimally infer the spatial distribution of sampled attributes at a uniform resolution from fluvial core sampling data, using a downscaling technique formulated as a geostatistical inverse problem. We compare geostatistical downscaling to the more traditional approach of point-to-point ordinary kriging for a hypothetical case study, and for total organic carbon observations from the Passaic River, New Jersey. Although frequently used to interpolate measurements, ordinary kriging is shown not to be able to estimate the spatial distribution of attributes accurately, because this approach assumes that data are sampled at a uniform resolution. Geostatistical downscaling, on the other hand, is shown to resolve this problem by explicitly accounting for the relationship between the known average measurements and the unknown fine-resolution attribute distribution to be estimated.