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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. 

Urgency of Precipitation Intensity-Duration-Frequency (IDF) estimation using the most recent data has grown significantly due to recent intense precipitation and cloud burst circumstances impacting infrastructure caused by climate change. Given the continually available digitized up-to-date, long-term, and fine resolution precipitation dataset from the United States Department of Agriculture Forest Service’s (USDAFS) Experimental Forests and Ranges (EF) rain gauge stations, it is both important and relevant to develop precipitation IDF from onsite dataset (Onsite-IDF) that incorporates the most recent time period, aiding in the design, and planning of forest road-stream crossing structures (RSCS) in headwaters to maintain resilient forest ecosystems. Here we developed Onsite-IDFs for hourly and sub-hourly duration, and 25-yr, 50-yr, and 100-yr design return intervals (RIs) from annual maxima series (AMS) of precipitation intensities (PIs) modeled by applying Generalized Extreme Value (GEV) analysis and L-moment based parameter estimation methodology at six USDAFS EFs and compared them with precipitation IDFs obtained from the National Oceanic and Atmospheric Administration Atlas 14 (NOAA-Atlas14). A regional frequency analysis (RFA) was performed for EFs where data from multiple precipitation gauges are available. NOAA’s station-based precipitation IDFs were estimated for comparison using RFA (NOAA-RFA) at one of the EFs where NOAA-Atlas14 precipitation IDFs are unavailable. Onsite-IDFs were then evaluated against the PIs from NOAA-Atlas14 and NOAA-RFA by comparing their relative differences and storm frequencies. Results show considerable relative differences between the Onsite- and NOAA-Atlas14 (or NOAA-RFA) IDFs at these EFs, some of which are strongly dependent on the storm durations and elevation of precipitation gauges, particularly in steep, forested sites of H. J. Andrews (HJA) and Coweeta Hydrological Laboratory (CHL) EFs. At the higher elevation gauge of HJA EF, NOAA-RFA based precipitation IDFs underestimate PI of 25-yr, 50-yr, and 100-yr RIs by considerable amounts for 12-h and 24-h duration storm events relative to the Onsite-IDFs. At the low-gradient Santee (SAN) EF, the PIs of 3- to 24-h storm events with 100-yr frequency (or RI) from NOAA-Atlas14 gauges are found to be equivalent to PIs of more frequent storm events (25–50-yr RI) as estimated from the onsite dataset. Our results recommend use of the Onsite-IDF estimates for the estimation of design storm peak discharge rates at the higher elevation catchments of HJA, CHL, and SAN EF locations, particularly for longer duration events, where NOAA-based precipitation IDFs underestimate the PIs relative to the Onsite-IDFs. This underscores the importance of long-term high resolution EF data for new applications including ecological restorations and indicates that planning and design teams should use as much local data as possible or account for potential PI inconsistencies or underestimations if local data are unavailable.

 

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. 

Resilience is the ability of ecosystems to maintain function while experiencing perturbation. Globally, forests are experiencing disturbances of unprecedented quantity, type, and magnitude that may diminish resilience. Early warning signals are statistical properties of data whose increase over time may provide insights into decreasing resilience, but there have been few applications to forests. We quantified four early warning signals (standard deviation, lag-1 autocorrelation, skewness, and kurtosis) across detrended time series of multiple ecosystem state variables at the Hubbard Brook Experimental Forest, New Hampshire, USA and analyzed how these signals have changed over time. Variables were collected over periods from 25 to 55 years from both experimentally manipulated and reference areas and were aggregated to annual timesteps for analysis. Long-term (>50 year) increases in early warning signals of stream calcium, a key biogeochemical variable at the site, illustrated declining resilience after decades of acid deposition, but only in watersheds that had previously been harvested. Trends in early warning signals of stream nitrate, a critical nutrient and water pollutant, likewise exhibited symptoms of declining resilience but in all watersheds. Temporal trends in early warning signals of some of groups of trees, insects, and birds also indicated changing resilience, but this pattern differed among, and even within, groups. Overall, ∼60% of early warning signals analyzed indicated decreasing resilience. Most of these signals occurred in skewness and kurtosis, suggesting ‘flickering’ behavior that aligns with emerging evidence of the forest transitioning into an oligotrophic condition. The other ∼40% of early warning signals indicated increasing or unchanging resilience. Interpretation of early warning signals in the context of system specific knowledge is therefore essential. They can be useful indicators for some key ecosystem variables; however, uncertainties in other variables highlight the need for further development of these tools in well-studied, long-term research sites.

 

Abstract An ice storm simulation was performed at the Hubbard Brook Experimental Forest to evaluate impacts of these extreme weather events on northern hardwood forests. Water was pumped from the main branch of Hubbard Brook and sprayed above the forest canopy in subfreezing conditions so that it rained down and froze on contact with trees. The experiment consisted of five treatments, including a control (no ice) and three target levels of radial ice accretion: low (6.4 mm), mid (12.7 mm), and high (19.0 mm). Two of the mid-level treatment plots (midx2) were iced in back-to-back years to evaluate impacts of consecutive storms. This dataset consists of hemispherical photographs of the forest canopy with leaves on and off the trees before and after the various ice treatments. 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.

 

Abstract Forest and freshwater ecosystems are tightly linked and together provide important ecosystem services, but climate change is affecting their species composition, structure, and function. Research at nine US Long Term Ecological Research sites reveals complex interactions and cascading effects of climate change, some of which feed back into the climate system. Air temperature has increased at all sites, and those in the Northeast have become wetter, whereas sites in the Northwest and Alaska have become slightly drier. These changes have altered streamflow and affected ecosystem processes, including primary production, carbon storage, water and nutrient cycling, and community dynamics. At some sites, the direct effects of climate change are the dominant driver altering ecosystems, whereas at other sites indirect effects or disturbances and stressors unrelated to climate change are more important. Long-term studies are critical for understanding the impacts of climate change on forest and freshwater ecosystems. 

The ice storm experiment was a novel experimental approach creating a suite of ice storms in a mature hardwood forest in New Hampshire, USA. The experiment included five ice storm intensities (0, 6.4, 12.7, and 19.1 mm radial ice accretion) applied in a single year, and one ice storm intensity (12.7 mm) applied in two consecutive years. This dataset quantifies the coarse woody debris transferred from the forest canopy to the soil under the different icing conditions. In this forest, little damage occurred below 6.4 mm radial ice accretion, moderate damage occurred with up to 12.7 mm of accretion, and significant branch breakage and canopy damage occurred with 19.1 mm of ice. The icing in consecutive years demonstrated an interactive effect of ice storm frequency and severity such that some branches damaged in the first year of icing appeared to remain in the canopy and then fall to the ground in the second year of icing. These results have implications for National Weather Service ice storm warning levels, and they provide a quantitative assessment of ice-load related inputs of forest debris that will be useful to municipalities creating response plans for current and future ice storms. 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.