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ABSTRACT 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 generalisability 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. 

ABSTRACT The timescales over which soil carbon responds to global change are a major uncertainty in the terrestrial carbon cycle. Radiocarbon measurements on archived soil samples are an important tool for addressing this uncertainty. We present time series (1969–2023) of radiocarbon measurements for litter (Oi/Oe and Oa/A) and mineral (0–10 cm) soils from the Hubbard Brook Experimental Forest, a predominantly hardwood forest in the northeastern USA. To estimate soil carbon cycling rates, we built different autonomous linear compartmental models. We found that soil litter carbon cycles on decadal timescales (Oi/Oe: ~7 years), whereas carbon at the organic‐mineral interface (Oa/A), and mineral soil (0–10 cm) carbon cycles on centennial timescales (~104 and 302 years, respectively). At the watershed‐level, the soil system appears to be at steady‐state, with no observed changes in carbon stocks or cycling rates over the study period, despite increases in precipitation, temperature, and soil pH. However, at the site‐level, the Oi/Oe is losing carbon (−15 g C m⁻² year⁻¹since 1998). The observed decline in carbon stocks can be detected when the Oi and Oe layers are modeled separately. This pattern suggests that the rapidly cycling litter layer at the smaller scale is responding to recent environmental changes. Our results highlight the importance of litter carbon as an “early‐warning system” for soil responses to environmental change, as well as the challenges of detecting gradual environmental change across spatial scales in natural forest ecosystems. 

ABSTRACT Parameterisation of fully coupled integrated hydrological models is challenging. The state‐of‐the‐art hydrogeology models rely on solutions of coupled surface and subsurface partial differential equations. Calibration of these models with traditional optimisation methods are not yet viable due to the high computational costs. Prior knowledge of the range of the parameters can be helpful as a starting point, however, due to natural variations, abstractions and conceptualizations used in modelling, a systematic exploration of the variable space is needed. In this study, we utilise the natural clustering of the soils based on their saturated and unsaturated hydraulic behaviour derived from soil texture maps in conjunction with two level Latin hypercube sampling to effectively explore model parameter spaces. Soil texture maps are similar to USDA soil classifications; however, the objective is to classify the soil based on their unsaturated behaviour, rather than soil texture. The method has never been utilised in the modelling and the results show that it can be applied to larger watersheds. The area of study is Hubbard Brook Experimental Forest, a northern hardwood forest in the White Mountains of New Hampshire, USA. An average Nash–Sutcliffe value of 0.80 is achieved for hourly discharge for the eight streams in the catchment. The Nash–Sutcliffe measure shows a 7% improvement with the addition of the snow melt and evapotranspiration parameters in the second stage. Exchange flux patterns vary seasonally in the catchment with largest infiltration occurring in spring. 

The pace and trajectory of ecosystem development are governed by the availability and cycling of limiting nutrients, and anthropogenic disturbances such as acid rain and deforestation alter these trajectories by removing substantial quantities of nutrients via titration or harvest. Here, we use six decades of continuous chemical and hydrologic data from three adjacent headwater catchments in the Hubbard Brook Experimental Forest, New Hampshire—one deforested (W5), one CaSiO₃-enriched (W1), and one reference (W6)—to quantify long-term nutrient and mineral fluxes. Acid deposition since 1900 drove pronounced depletion and export of base cations, particularly calcium, across all watersheds. Experimental deforestation of W5 intensified loss of biomass and nutrient cations and triggered sustained increases in streamwater pH, Ca²⁺, and SiO₂exports over nearly four decades, greatly exceeding the effects of direct CaSiO₃enrichment in both duration and magnitude. We detect no long-term changes in water yield or water flow paths in the experimental watersheds, and we attribute this multidecadal increase in weathering rates following deforestation to biological responses to severe nutrient limitation. Our evidence suggests that in the regrowing forest, plants are investing photosynthate into belowground processes that amplify mineral weathering to access phosphorus and micronutrients, consequently elevating the export of less limiting elements present in silicate parent material. Throughout decades of forest regrowth, enhanced biotic weathering has continued to deplete the acid buffering capacity of the terrestrial ecosystem while the export of weathering products has elevated the pH of the receiving stream. 

The Hubbard Brook Watershed Stream and Precipitation Chemistry Record is a dataset of chemical concentration data for precipitation and streamwater samples that have been collected weekly since the summer of 1963 from streams and precipitation gauges throughout the Hubbard Brook Experimental Forest, a research forest in the White Mountains of New Hampshire. HBWatER currently collects weekly samples from nine gauged watersheds, the mainstem of the Hubbard Brook into which each small stream drains, and two long-term precipitation collection sites. HBWatER was begun in the 1960's by Gene E. Likens, F. Herbert Bormann, Robert S. Pierce, and Noye M. Johnson who began collecting and analyzing stream and precipitation (rain and snow). They had a simple idea, that by comparing watershed inputs in rain and snow to watershed outputs from streams, they could measure the behavior of entire ecosystems in response to atmospheric pollution or forestry practices. Stream and precipitation samples have been collected every week from 1963 to the present day. Insights gained from studying this long-term chemical record led to the discovery of acid rain in North America and have documented the effectiveness of federal clean air legislation in reducing coalfired power plant emissions. The collection and analysis of HBWatER samples is currently sustained by Tammy Wooster (Cary IES) and Jeff Merriam (USFS) and the dataset is curated and maintained by a team of researchers: Chris Solomon (Cary IES), Emily Bernhardt (Duke), Bill McDowell (UNH), Charley Driscoll (Syracuse U.), Keith Nislow (USFS), and Mark Green (Case Western). Current Financial Support for HBWatER is provided by NSF LTREB # 2401760 and the USDA Forest Service Northern Research Station. 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. 

This data set reports ice on and ice off dates for Mirror Lake beginning in 1968 and continuing through the present. Mirror Lake is located within the Hubbard Brook valley in the White Mountains of New Hampshire, and has been the subject of numerous limnological investigations since the early 1960s. These Mirror Lake data are part of the Hubbard Brook Watershed Ecosystem Record (HBWatER), a long-term record of weekly sampling of nine gaged watersheds at Hubbard Brook which includes the stream draining Mirror Lake. The collection and management of the long-term record was initiated in 1963 by Gene E. Likens, F. Herbert Bormann, Robert S. Pierce, and Noye M. Johnson. HBWatER is currently sustained by Tammy Wooster (Cary IES) and Jeff Merriam (USFS) and the dataset is curated and maintained by a team of researchers: Chris Solomon (Cary IES), Emily Bernhardt (Duke), Bill McDowell (UNH), Charley Driscoll (Syracuse U.), Keith Nislow (USFS), and Mark Green (Case Western). Current financial Support for HBWatER is provided by NSF LTREB # 2401760 and the USDA Forest Service Northern Research Station. 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.