A 69-station, densely spaced rain gauge network was maintained over the period [1951][1952][1953][1954][1955][1956][1957][1958] in the Coweeta Hydrologic Laboratory, located in the southern Appalachians in western North Carolina, USA. This unique dataset was used to develop the first digital seasonal and annual precipitation maps for the Coweeta basin, using elevation regression functions and residual interpolation. It was found that a 10-m elevation grid filtered to an approximately 7-km effective wavelength explained the most variance in precipitation (R 2 = 0.82-0.95). A 'dump zone' of locally high precipitation a short distance downwind from the mountain crest marking the southern border of the basin was the main feature that was not explained well by the precipitation-elevation relationship.These data and maps provided a rare 'ground-truth' for estimating uncertainty in the national-scale Parameter-elevation Relationships on Independent Slopes Model (PRISM) precipitation grids for this location and time period. Differences between PRISM and ground-truth were compared with uncertainty estimates produced by the PRISM model and cross-validation errors. Potential sources of uncertainty in the national PRISM grids were evaluated, including the effects of coarse grid resolution, limited station data, and imprecise station locations.The PRISM national grids matched closely (within 5%) with the Coweeta dataset. The PRISM regression prediction interval, which includes the influence of stations in an area of tens of kilometres around a given location, overestimated the local error at Coweeta (12-20%). Offsetting biases and generally low error rates made it difficult to isolate major sources of uncertainty in the PRISM grids, but station density and selection, and mislocation of stations were identified as likely sources of error. The methods used in this study can be repeated in other areas where high-density data exist to gain a more comprehensive picture of the uncertainties in national-level datasets, and can be used in network optimization exercises.
The National Ecological Observatory Network (NEON) is a multidecadal and continental-scale observatory with sites across the United States. Having entered its operational phase in 2018, NEON data products, software, and services become available to facilitate research on the impacts of climate change, land-use change, and invasive species. An essential component of NEON are its 47 tower sites, where eddy-covariance (EC) sensors are operated to determine the surface–atmosphere exchange of momentum, heat, water, and CO2. EC tower networks such as AmeriFlux, the Integrated Carbon Observation System (ICOS), and NEON are vital for providing the distributed observations to address interactions at the soil–vegetation–atmosphere interface. NEON represents the largest single-provider EC network globally, with standardized observations and data processing explicitly designed for intersite comparability and analysis of feedbacks across multiple spatial and temporal scales. Furthermore, EC is tightly integrated with soil, meteorology, atmospheric chemistry, isotope, phenology, and rich contextual observations such as airborne remote sensing and in situ sampling bouts. Here, we present an overview of NEON’s observational design, field operation, and data processing that yield community resources for the study of surface–atmosphere interactions. Near-real-time data products become available from the NEON Data Portal, and EC and meteorological data are ingested into AmeriFlux and FLUXNET globally harmonized data releases. Open-source software for reproducible, extensible, and portable data analysis includes the eddy4R family of R packages underlying the EC data product generation. These resources strive to integrate with existing infrastructures and networks, to suggest novel systemic solutions, and to synergize ongoing research efforts across science communities.
Technological advances have allowed in situ monitoring of soil water content in an automated manner. These advances, along with an increase in large-scale networks monitoring soil water content, stress the need for a robust calibration framework that ensures that soil water content measurements are accurate and reliable. We have developed an approach to make consistent and comparable soil water content sensor calibrations across a continental-scale network in a production framework that incorporates a thorough accounting of uncertainties. More than 150 soil blocks of varying characteristics from 33 locations across the United States were used to generate soil-specific calibration coefficients for a capacitance sensor. We found that the manufacturer's nominal calibration coefficients poorly fit the data for nearly all soil types. This resulted in negative (91% of samples) and positive (5% of samples) biases and a mean root mean square error (RMSE) of 0.123 cm 3 cm −3 (1s) relative to reference standard measurements. We derived soil-specific coefficients, and when used with the manufacturer's nominal function, the biases were corrected and the mean RMSE dropped to ±0.017 cm 3 cm −3 (±1s). A logistic calibration function further reduced the mean RMSE to ±0.016 cm 3 cm −3 (±1s) and increased the range of soil moistures to which the calibration applied by 18% compared with the manufacturer's function. However, the uncertainty of the reference standard was notable (±0.022 cm 3 cm −3 ), and when propagated in quadrature with RMSE estimates, the combined uncertainty of the calibrated volumetric soil water content values increased to ±0.028 cm 3 cm −3 regardless of the calibration function used.Abbreviations: DPHP, dual-pulse heat probe; FDR, frequency-domain reflectometry; NEON, National Ecological Observatory Network; NMM, neutron moisture meter; NSF, National Science Foundation; PRT, platinum resistance thermometer; RMSE, root mean square error; TDR, time-domain reflectometry.Soil moisture is an important driver of numerous biogeophysical processes at scales ranging from the aggregate to the globe. The vertical and lateral flow of water through the soil determines patterns of eluviation and illuviation, making them central to soil pedogenesis, and control the flux of solutes within the soil profile and across the terrestrial aquatic interface, with implications for the transport of nutrients and pollutants (Kaiser et al., 2004) including dissolved organic matter (Burns et al., 2016;Kalbitz et al., 2000). Dissolved organic matter is a significant component of the global C budget, and the flux of dissolved organic matter within soils and into water bodies has implications for the global C cycle (Battin et al., 2008). Additionally, soil moisture status is important for the decomposition of soil organic matter and the form in which C is respired (e.g., CO 2 or CH 4 ) (Davidson et al. 2008). Soil moisture is also a determinant of ecosystem structure, sensible and latent heat fluxes, water balance, and local climate (Koster...
Over the last several decades dissolved organic carbon concentrations (DOC) in surface waters have increased throughout much of the northern hemisphere. Several hypotheses have been proposed regarding the drivers of this phenomenon including decreased sulfur (S) deposition working via an acidity- change mechanism. Using fluorescence spectroscopy and data from two long-term (24+ years at completion of this study) whole watershed acidification experiments, that is, the Bear Brook Watershed in Maine (BBWM) and Fernow Experimental Forest in West Virginia (FEF) allowed us to control for factors other than the acidity-change mechanism (e.g., differing vegetation, shifting climate), resulting in the first study we are aware of where the acidity change mechanism could be experimentally isolated at the whole ecosystem and decadal scales as the driver of shifts in DOM dynamics. The multidecadal record of stream chemistry at BBWM demonstrates a significantly lower DOC concentration in the treated compared to the reference watershed. Additionally, at both BBWM and FEF we found significant and sustained differences in stream fluorescence index (FI) between the treated and reference watersheds, with the reference watersheds demonstrating a stronger terrestrial DOM signature. These data, coupled with evidence of pH shifts in upper soil horizons support the hypotheses that declines in S deposition are driving changes in the solubility of soil organic matter and increased flux of terrestrial DOC to water bodies.
Abstract. Describing the quality of measurements is necessary to understand the level of confidence in any observation. Accuracy, precision, trueness, repeatability, reproducibility, and uncertainty are all used to describe quality of measurement, but the terms are inconsistently defined and measured and thus easily misunderstood. One purpose of quality parameters is for the comparison of observations, but when dissimilar methods for estimating quality terms are utilized, a comparison is misrepresented. A standardized approach to estimating uncertainty provides a basis for meeting measurement requirements and providing a level of confidence for observations. Here, we show the approach used by the National Ecological Observatory Network to estimate uncertainty of the calibration processes and measurements illustrated with an example of uncertainty assessment on a temperature sensor. Detailing the approach for uncertainty assessment provides the transparency necessary for network science and allows for the approach to be adopted in the scientific community. Reporting uncertainty with all measurements needs to become consistent and commonplace across disciplines.
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