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Abstract Climate change is increasing sulfate export and changing wetland extent in mountain regions. These changes may increase microbially mediated production of the neurotoxic substance methylmercury due to enhanced sulfate metabolism in mountain environments. Here, we assess methylmercury concentrations and formation rates across high-elevation wetlands in the Colorado Rocky Mountains. We also investigate sulfate controls on methylmercury production within subalpine peatlands by amending soils with sulfate to mimic increased stream export of sulfate from the alpine zone and measuring methylmercury formation rates for different sulfate treatments. We found that subalpine peatlands have statistically significant higher methylmercury concentrations and formation rates compared to alpine, mineral-soil wetlands. Methylmercury production in subalpine peatlands also increased significantly (p < 0.05) following sulfate additions; the highest rates occurred in sediments with intermediate extractable sulfate concentrations (~0.60–1.4 mg sulfate g-1 dry soil). Our study is the first to identify soil sulfate-related thresholds for methylmercury production and sulfate-limitation of methylmercury production in subalpine peatlands. These findings highlight important linkages between climate-driven mineral weathering and mercury cycling in mountain regions globally. 

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. 

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.