Headwater streams in the White Mountains, NH, United States have been shown to have downstream gradients of increasing pH and concentrations of base cations coupled with decreasing concentrations of aluminum. A two-stage acid neutralization model involving shallow soil exchange processes in headwaters coupled with deeper mineral weathering downstream had been proposed to explain these gradients. We conducted synoptic sampling of three headwater catchments in this region that showed variations in this longitudinal pattern, ranging from streams that remain acidic throughout their length to streams with circumneutral pH beginning at their source. To explain these differences, we mapped soils using a hydropedologic approach that emphasizes the influence of groundwater saturation frequency and water table regime on soil formation processes. Stream segments with lower pH and base cation concentrations, coupled with higher concentrations of dissolved organic carbon (DOC), aluminum, and in one case iron, were in subcatchments mapped with shallow to bedrock soils where eluvial soil forming processes dominated. In contrast, stream segments with higher pH and base cation concentrations coupled with low concentrations of DOC and aluminum were associated with subcatchments with deeper soils where illuvial processes were more dominant. Concentrations of sodium and silicon were relatively uniform across these gradients. Coupled with the higher concentrations of dissolved aluminum and small pools of exchangeable aluminum in the areas of bedrock outcrops and shallow soils, these data suggest that primary mineral dissolution is an important process influencing upper stream reaches, not just along longer, deeper flowpaths in downslope areas. In contrast, some stream reaches with obvious groundwater springs show a more abrupt transition in pH and base cation concentrations higher along the stream, suggesting that neutralization along deeper flowpaths may play a role in upslope areas as well. These data suggest a new three stage model of stream chemistry evolution. First, organic acids are introduced by frequent flushing of organic soils on shallow bedrock along ridge areas. Second, upper acidic reaches are controlled by mineral dissolution coupled with eluvial soil development. Third, downstream reaches are influenced by illuviation as organometallic complexes precipitate in spodic soil horizons, removing organic acids, and acid-mobile metals from drainage waters. This new model highlights differences in critical zone evolution along hillslopes in glaciated catchments with implications for understanding gradients in water quality, soil fertility, and response and recovery from disturbances.

Understanding forest dynamics requires reliable inventories that assess tree- and stand-level characteristics. Traditionally, this has relied on field measurements such as diameter at breast height (DBH), height, and crown attributes, but these methods are labor-intensive and spatially limited. Remote sensing, particularly Light Detection and Ranging (LiDAR), has expanded forest inventory capacity by generating three-dimensional structural information. Mobile laser scanning (MLS), a recent adaptation, offers flexible, high-resolution data collection, though its performance across complex forests is still being evaluated. This study assessed the effectiveness of MLS in detecting individual trees and estimating DBH in mixed-species forests of the Northeastern United States. We also evaluated the influence of tree- and plot-level characteristics on detection accuracy and DBH estimation. Results showed an 85.2% tree detection rate, a 23.5% commission rate, and a DBH root mean square error (RMSE) of 1.98 cm (9.65%). Among the variables tested, tree DBH was the only significant predictor of detection probability; tree density and relative density had minimal effect. These findings demonstrate that MLS can achieve precise DBH estimation when trees are correctly identified, but false detections remain a limitation. Further methodological improvements are needed to enhance accuracy in structurally complex forests and advance MLS for operational forest monitoring.