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Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.

The U.S. Department of Energy's (DOE) Colorado East River Community Observatory (ER) in the Upper Colorado River Basin was established in 2015 as a representative mountainous, snow-dominated watershed to study hydrobiogeochemical responses to hydrological perturbations in headwater systems. The ER is characterized by steep elevation, geologic, hydrologic and vegetation gradients along floodplain, montane, subalpine, and alpine life zones, which makes it an ideal location for researchers to understand how different mountain subsystems contribute to overall watershed behaviour. The ER has both long-term and spatially-extensive observations and experimental campaigns carried out by the Watershed Function Scientific Focus Area (SFA), led by Lawrence Berkeley National Laboratory, and researchers from over 30 organizations who conduct cross-disciplinary process-based investigations and modelling of watershed behaviour. The heterogeneous data generated at the ER include hydrological, genomic, biogeochemical, climate, vegetation, geological, and remote sensing data, which combined with model inputs and outputs comprise a collection of datasets and value-added products within a mountainous watershed that span multiple spatiotemporal scales, compartments, and life zones. Within 5 years of collection, these datasets have revealed insights into numerous aspects of watershed function such as factors influencing snow accumulation and melt timing, water balance partitioning, and impacts of floodplain biogeochemistry and hillslope ecohydrology on riverine geochemical exports. Data generated by the SFA are managed and curated through its Data Management Framework. The SFA has an open data policy, and over 70 ER datasets are publicly available through relevant data repositories. A public interactive map of data collection sites run by the SFA is available to inform the broader community about SFA field activities. Here, we describe the ER and the SFA measurement network, present the public data collection generated by the SFA and partner institutions, and highlight the value of collecting multidisciplinary multiscale measurements in representative catchment observatories.

Atmospheric nitrous oxide (N2O) contributes directly to global warming, yet current models1-5 overlook bedrock-contained nitrogen (rock-N), the largest terrestrial N pool6, as a N2O source. Although rock-N release rates are large6-9, incomplete understanding on the fate of released rock-N has obscured connections between rock-N and atmospheric N2O. This connection emerged through our field studies of a hillslope underlain by marine shale. Bedrock weathering within the zone of the seasonally fluctuating water table controls the weathering depth, hence the release of rock-N. At this site, rock-N weathering contributes 78% of the subsurface reactive-N, with ~22% derived from atmospheric deposition and biological nitrogen fixation, commonly regarded as the sole sources of reactive-N in pristine environments10,11. About 56% of reactive-N denitrifies, including 14% emitted as N2O into the atmosphere. The remaining reactive-N discharges in porewaters to a floodplain where additional denitrification likely occurs. Using global rock-N releases of 11–18 Tg y-1 8, our measurements extrapolate to a weathering driven efflux of 1.3–2.1 Tg N-N2O y-1, consistent with a flux of 1.0–1.7 Tg N- N2O y-1 solely derived from the literature. Thus, bedrock weathering contributes approximately 10-17 % of nitrous oxide to the current global estimate of ~10 Tg y-1.

Geophysical methods can be used to create images of the Earth's interior that constitute snapshots at the moment of data acquisition. In many applications, it is important to measure the temporal change in the subsurface, because the change is associated with deformation, fluid flow, temperature changes, or changes in material properties. We present an overview of how noninvasive geophysical methods can be used for this purpose. We focus on monitoring mechanical properties, fluid transport, and biogeochemical processes, and present case studies that illustrate the use of geophysical methods for detecting time-lapse changes in associated properties.

The potential for transmission of deadly viral diseases to health care workers exists when contaminated blood is inoculated through injury or when blood comes in contact with nonintact skin. Operating room personnel are at particularly high risk for injury and blood contamination, but data on the specifics of which personnel are at greater risk and which practices change risk in this environment are almost nonexistent. To define these risk factors, experienced operating room nurses were employed solely to observe and record the injuries and blood contaminations that occurred during 234 operations involving 1763 personnel. Overall 118 of the operations (50%) resulted in at least one person becoming contaminated with blood. Cuts or needlestick injuries occurred in 15% of the operations. Several factors were found to significantly alter the risk of blood contamination or injury: surgical specialty, role of each person, duration of the procedure, amount of blood loss, number of needles used, and volume of irrigation fluid used. Risk calculations that use average values to include all personnel in the operating room or all operations performed substantially underestimate risk for surgeons and first assistants, who accounted for 81% of all body contamination and 65% of the injuries. The area of the body contaminated also changed with the surgical specialty. These data should help define more appropriate protection for individuals in the operating room and should allow refinements of practices and techniques to decrease injury.

• Health care workers, particularly surgeons, understand the importance of preventing contamination from blood of patients infected with deadly viruses. One of the most common areas of contamination is the hands and fingers due to the failure of glove protection. There are varying opinions regarding the frequency of glove failure, the necessity of wearing two gloves for added protection, and the ability to operate when wearing two gloves. We performed a prospective, randomized, trial of 143 procedures involving 284 persons to answer these questions for surgeons and first assistants. Overall, the glove failure rate (blood contamination of the fingers) was 51% when one glove was worn and 7% when two gloves were worn. Acceptability was 88% in the group who agreed to wear two gloves, and 88% of these did not perceive that tactile sense was significantly impaired. We believe that double gloving should be, and can be, used routinely during major surgical procedures to protect surgeons from blood contamination. ( Arch Surg. 1992;127:213-217)