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Disturbance creates structural legacies that are important drivers of functional and compositional stabilities in forested ecosystems. We used an experimental ice storm disturbance to evaluate effects of disturbance severity on structural legacies and their functional consequences. We evaluated canopy structural characteristics (height, density, openness, and complexity) before and after disturbance using data from terrestrial LiDAR. We compared trajectories of structural characteristics and functional outcomes (composition, mortality, and productivity) among treatments and relative to controls. We found significant post-disturbance change for all canopy structural characteristics especially at higher severity levels, with persistent legacy effects on mean canopy height and canopy complexity. There were limited changes in biomass, productivity, and composition, and mortality did not vary significantly among treatments. There was limited evidence for linkages between structural and functional responses, but plots that retained greater complexity had higher stability of net primary productivity. Our findings indicate persistent structural legacies associated with ice storm disturbance, but declining structural legacies over time may affect interactions with subsequent disturbance or stressors. Improved understanding of these trajectories could help with predicting outcomes of changing disturbance regimes associated with global change. 

Machine‐learning models have been surprisingly successful at predicting stream solute concentrations, even for solutes without dedicated sensors. It would be extremely valuable if these models could predict solute concentrations in streams beyond the one in which they were trained. We assessed the generalizability of random forest models by training them in one or more streams and testing them in another. Models were made using grab sample and sensor data from 10 New Hampshire streams and rivers. As observed in previous studies, models trained in one stream were capable of accurately predicting solute concentrations in that stream. However, models trained on one stream produced inaccurate predictions of solute concentrations in other streams, with the exception of solutes measured by dedicated sensors (i.e., nitrate and dissolved organic carbon). Using data from multiple watersheds improved model results, but model performance was still worse than using the mean of the training dataset (Nash–Sutcliffe Efficiency < 0). Our results demonstrate that machine‐learning models thus far reliably predict solute concentrations only where trained, as differences in solute concentration patterns and sensor‐solute relationships limit their broader applicability. 

Temporal patterns in chemistry of headwater streams reflect responses of water and elemental cycles to perturbations occurring at local to global scales. We evaluated multi-scale temporal patterns in up to 32 y of monthly observations of stream chemistry (ammonium, calcium, dissolved organic carbon, nitrate, total dissolved phosphorus, and sulfate) in 22 reference catchments within the northern temperate zone of North America. Multivariate autoregressive state-space (MARSS) models were applied to quantify patterns at multi-decadal, seasonal, and shorter intervals during a period that encompassed warming climate, seasonal changes in precipitation, and regional declines in atmospheric deposition. Significant long-term trends in solute concentrations within a subset of the catchments were consistent with recovery from atmospheric deposition (e.g., calcium, nitrate, sulfate) and increased precipitation (e.g., dissolved organic carbon). Lack of evidence for multi-decadal trends in most catchments suggests resilience of northern temperate ecosystems or that subtle net effects of simultaneous changes in climate and disturbance regimes do not result in directional trends. Synchronous seasonal oscillations of solute concentrations occurred across many catchments, reflecting shared climate and biotic drivers of seasonality within the northern temperate zone. Despite shared patterns among catchments at a seasonal scale, multi-scale temporal patterns were statistically distinct among even adjacent headwater catchments, implying that local attributes of headwater catchments modify the signals imparted by atmospheric phenomena and regional disturbances. To effectively characterize hydrologic and biogeochemical responses to changing climate and disturbance regimes, catchment monitoring programs could include multiple streams with contributing areas that encompass regional heterogeneity in vegetation, topography, and elevation. Overall, detection of long-term patterns and trends requires monitoring multiple catchments at a frequency that captures periodic variation (e.g., seasonality) and a duration encompassing the perturbations of interest. 

The oxidation of organic matter from fuel combustion or vegetation emissions into organic acids is a major source of dissolved organic carbon (DOC) in precipitation. Long‐term measurements of DOC in precipitation are rare, but the existing records mostly show decreases due to reduction in fuel combustion. Here, we show a recent, sudden increase in precipitation DOC concentration in a 27‐year record from the Hubbard Brook Experimental Forest (HBEF) in northern New Hampshire, USA Starting in 2010, where mean annual DOC concentration increased from about 80 to 130 μmol L⁻¹in 2022. No other solutes in precipitation showed a similar sudden change. The weekly DOC concentration was not clearly related to the 72‐hr air mass trajectory characteristics or changes in trajectories. We assessed the feasibility of multiple possible causes for the DOC increase, including an increase in biogenic volatile organic compound (BVOC) emissions from the forest or from forest fires, changes in oxidation processes in the troposphere, and changes in gas‐phase solubility due to increasing pH in precipitation. Further study of sudden changes in BVOC emissions in the region, possible causes, and air quality effects are warranted. 

We evaluated shoot nonstructural carbohydrate (NSC) concentrations, stem wound closure, and radial growth of sugar maple ( Acer saccharum Marsh.) and red maple ( Acer rubrum L.) trees in a novel ice storm experiment in which five storm treatments (0, 6.4, 12.7, and 19.1 mm of radial ice accretion in 1 year and 12.7 mm of ice in two consecutive years) were applied within a mature northern hardwood forest. We tested for changes in physiology at two levels: (1) associated with plot-level ice treatments and (2) with crown damage classes of individual trees. Few differences in NSC or wound closure associated with treatment were found. Growth decreased for red maple in the medium and high treatments and sugar maple in the high treatment but no other treatments. Changes in physiology were more evident when assessed using crown damage classes. Two NSC components were elevated in sugar and red maples with high (≥50%) crown damage. Wound closure was less for red maples with high damage, and separation among damage classes was even greater for sugar maple. Red maples with moderate (<50%) and high crown damage showed gradually declining growth, whereas sugar maples with high damage showed ∼80% reduction in growth the first year after injury. 

The accurate soil frost depth measurement is critical for understanding freeze–thaw cycles and their impact on infrastructure stability, agricultural productivity, and ecosystem dynamics. This study introduces a novel pressure-based sensor integrated with wireless sensor networks (WSNs) to monitor frost depth in real time, offering a significant advancement over traditional manual frost tube methods. By leveraging the expansion of water upon freezing, the sensor detects pressure changes and transmits high-resolution data through a low-power, long-range (LoRa) wireless network. Field experiments demonstrated a strong correlation between the pressure sensor and manual frost tube measurements, with Pearson and Spearman correlation coefficients of 0.80 and 0.78, respectively, validating the sensor’s accuracy. The high temporal resolution of the system, which captures data at 5-min intervals, enabled a detailed analysis of freeze–thaw cycles, revealing rapid changes in frost depth during the early thaw periods. Designed for resilience in harsh winter conditions, the sensor offers a scalable, low-maintenance solution for long-term remote frost monitoring. These results underscore the system’s potential to enhance environmental monitoring, optimize agricultural practices, and mitigate infrastructure risks in response to changing climate conditions. 

Abstract 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. 

Flood peak magnitudes and frequency estimates are key components of any effective nationwide flood risk management and flood damage abatement program. In this study, we evaluated normalized peak design discharges (Qp) for 1,387 hydrologic unit code 16 to 20 (HUC16-20) watersheds in the White Mountain National Forest (WMNF), New Hampshire and in five Experimental Forest (EF) regions across the United States managed by USDA Forest Service (USDA-FS). Nonstationary regional frequency analysis (RFA) and single site frequency analysis (FA) with long-term high-resolution observed streamflow data along with the deterministic Rational Method (RM) and semi-empirical United States Geological Survey regional regression equation (USGS-RRE) were used. Additionally, a hydrologic vulnerability assessment was performed for 194 road culverts as a result of extreme precipitation-induced flooding on gauged and ungauged watersheds in the Hubbard Brook EF (HBR) within the WMNF. The RM outperformed the USGS-RRE in predicting Qp in the gauged and ungauged HUC16-20 watersheds of WMNF and in three other small, high-relief forest headwater watersheds—Coweeta Hydrologic Lab EF’s watershed-14, and watershed-27 in North Carolina and HJ Andrews EF’s watershed 8 in Oregon. However, the USGS-RRE performed better for larger watersheds, such as the Fraser EF’s St. Louis watershed in Colorado and the Santee EF’s watershed 80 in South Carolina. About 31 %, 26 %, and 56 % of the culverts at the HBR site could not accommodate the 100-yr Qp estimated by RFA, RM and USGS-RRE, respectively. Based on the chosen RIs and techniques, it is determined that except for one culvert with diameter = 0.91 m (36 in.), none of the culverts with diameter of 0.75 m (30 in.) or larger are hydrologically vulnerable. Our results suggest that the observation based RFA works best where multiple gauges are available to extrapolate information for ungauged watersheds, otherwise, RM is best-suited for smaller headwater watersheds and USGS-RRE for larger watersheds. Results from the hydrologic vulnerability analysis revealed that replacing undersized culverts with new culverts of diameter ≥ 0.75-m will improve flood resiliency, provided that the structure is geomorphologically safe (with minimal effects of debris flow, erosion, and sedimentation) and allows for both bank-full discharge and necessary fish passage within that design limit. This study has implications in managing road culverts and crossings at Forest Service and other forested lands for their resiliency to extreme precipitation and flooding hazards induced by climate change. 

Abstract 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. 

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.