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This dataset consists of groundwater levels measured within wells distributed across Watershed 3 at Hubbard Brook Experimental Forest from 2007-2020. Water levels are expressed as a depth (cm) from the soil surface. This dataset is a part of a larger project aimed at explaining the spatial and temporal variation in stream water chemistry at the headwater catchment scale using a framework based on the combined study of hydrology and soil development – hydropedology. The project will demonstrate how hydrology strongly influences soil development and soil chemistry, and in turn, controls stream water quality in headwater catchments. Understanding the linkages between hydrology and soil development can provide valuable information for managing forests and stream water quality. Feedbacks between soils and hydrology that lead to predictable landscape patterns of soil chemistry have implications for understanding spatial gradients in site productivity and suitability for species with differing habitat requirements or chemical sensitivity. Tools are needed that identify and predict these gradients that can ultimately provide guidance for land management and silvicultural decision making. Better integration between soil science, hydrology, and biogeochemistry will provide the conceptual leap needed by the hydrologic community to be able to better predict and explain temporal and spatial variability of stream water quality and understand water sources contributing to streamflow. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. 

Nitrogen (N) is a critical element in many ecological and biogeochemical processes in forest ecosystems. Cycling of N is sensitive to changes in climate, atmospheric carbon dioxide (CO2) concentrations, and air pollution. Streamwater nitrate draining a forested ecosystem can indicate how an ecosystem is responding to these changes. We observed a pulse in streamwater nitrate concentration and export at a long-term forest research site in eastern North America that resulted in a 10-fold increase in nitrate export compared to observations over the prior decade. The pulse in streamwater nitrate occurred in a reference catchment in the 2013 water year, but was not associated with a distinct disturbance event. We analyzed a suite of environmental variables to explore possible causes. The correlation between each environmental variable and streamwater nitrate concentration was consistently higher when we accounted for the antecedent conditions of the variable prior to a given streamwater observation. In most cases, the optimal antecedent period exceeded two years. We assessed the most important variables for predicting streamwater nitrate concentration by training a machine learning model to predict streamwater nitrate concentration in the years preceding and during the streamwater nitrate pulse. The results of the correlation and machine learning analyses suggest that the pulsed increase in streamwater nitrate resulted from both (1) decreased plant uptake due to lower terrestrial gross primary production, possibly due to increased soil frost or reduced solar radiation or both; and (2) increased net N mineralization and nitrification due to warm temperatures from 2010 to 2013. Additionally, variables associated with hydrological transport of nitrate, such as maximum stream discharge, emerged as important, suggesting that hydrology played a role in the pulse. Overall, our analyses indicate that the streamwater nitrate pulse was caused by a combination of factors that occurred in the years prior to the pulse, not a single disturbance event. 

Statistical confidence in estimates of timber volume, carbon storage, and other forest attributes depends, in part, on the uncertainty in field measurements. Surprisingly, measurement uncertainty is rarely reported, even though national forest inventories routinely repeat field measurements for quality assurance. We compared measurements made by field crews and quality assurance crews in the Forest Inventory and Analysis program of the U.S. Forest Service, using data from 2790 plots and 51 740 trees and saplings across the 24 states of the Northern Region. We characterized uncertainty in 12 national core tree-level variables; seven tree crown variables used in forest health monitoring; three variables describing seedlings; and 11 variables describing the site, such as elevation, slope, and distance from a road. Discrepancies in measurement were generally small but were higher for some variables requiring judgment, such as tree class, decay class, and cause of mortality. When scaled up to states, forest types, or the region, uncertainties in basal area, timber volume, and aboveground biomass were negligible. Understanding all sources of uncertainty is important to designing forest monitoring systems, managing the conduct of the inventory, and assessing the uncertainty of forest attributes required for making regional and national forest policy decisions. 

Soil temperature and soil moisture have been measured at multiple locations at the Hubbard Brook Experimental Forest (HBEF), as part of a study of the relationships between snow depth, soil freezing and nutrient cycling (http://www.ecostudies.org/people_sci_groffman_snow_summary.html). In October 2010, we established 6, 20 x 20-m plots (intensive plots) and 14 10 x 10-m plots (extensive plots) along an elevation gradient, with eight of the plots on north-facing slopes and twelve on south-facing slopes. Soil temperature and soil moisture were measured at hourly intervals on these plots beginning in November 2010. Six locations were discontinued in September 2012 (E04, E05, E06, E11-B, E13, and E14). Previous versions of this dataset included both temperature and moisture. These data are now available as temperature (this dataset) and moisture (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=137). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. 

Soil temperature and soil moisture have been measured at multiple locations at the Hubbard Brook Experimental Forest (HBEF), as part of a study of the relationships between snow depth, soil freezing and nutrient cycling (http://www.ecostudies.org/people_sci_groffman_snow_summary.html). In October 2010, we established 6, 20 x 20-m plots (intensive plots) and 14 10 x 10-m plots (extensive plots) along an elevation gradient, with eight of the plots on north-facing slopes and twelve on south-facing slopes. Soil temperature and soil moisture were measured at hourly intervals on these plots beginning in November 2010. Six locations were discontinued in September 2012 (E04, E05, E06, E11-B, E13, and E14). Previous versions of this dataset included both temperature and moisture. These data are now available as moisture(this dataset) and temperature (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=315]. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. 

Stream fluxes are commonly reported without a complete accounting for uncertainty in the estimates, which makes it difficult to evaluate the significance of findings or to identify where to direct efforts to improve monitoring programs. At the Hubbard Brook Experimental Forest in the White Mountains of New Hampshire, USA, stream flow has been monitored continuously and solute concentrations have been sampled approximately weekly in small, gaged headwater streams since 1963, yet comprehensive uncertainty analyses have not been reported. We propagated uncertainty in the stage height–discharge relationship, watershed area, analytical chemistry, the concentration–discharge relationship used to interpolate solute concentrations, and the streamflow gap‐filling procedure to estimate uncertainty for both streamflow and solute fluxes for a recent 6‐year period (2013–2018) using a Monte Carlo approach. As a percentage of solute fluxes, uncertainty was highest for NH₄⁺(34%), total dissolved nitrogen (8.8%), NO₃⁻(8.1%), and K⁺(7.4%), and lowest for dissolved organic carbon (3.7%), SO₄²⁻(4.0%), and Mg²⁺(4.4%). In units of flux, uncertainties were highest for solutes in highest concentration (Si, DOC, SO₄²⁻, and Na⁺) and lowest for those lowest in concentration (H⁺and NH₄⁺). Laboratory analysis of solute concentration was a greater source of uncertainty than streamflow for solute flux, with the exception of DOC. Our results suggest that uncertainty in solute fluxes could be reduced with more precise measurements of solute concentrations. Additionally, more discharge measurements during high flows are needed to better characterize the stage‐discharge relationship. Quantifying uncertainty in streamflow and element export is important because it allows for determination of significance of differences in fluxes, which can be used to assess watershed response to disturbance and environmental change.

 

This data set documents the temporal and spatial variation of soil and deadwood moisture, and nearby microclimate, for the a four-month period from June to October 2018. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and main tained by the USDA Forest Service, Northern Research Station.