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The environmental fates and consequences of intensive sulfur (S) applications to croplands are largely unknown. In this study, we used S stable isotopes to identify and trace agricultural S from field-to-watershed scales, an initial and timely step toward constraining the modern S cycle. We conducted our research within the Napa River Watershed, California, US, where vineyards receive frequent fungicidal S sprays. We measured soil and surface water sulfate concentrations ([SO₄²⁻]) and stable isotopes (δ³⁴S–SO₄²⁻), which we refer to in combination as the ‘S fingerprint’. We compared samples collected from vineyards and surrounding forests/grasslands, which receive background atmospheric and geologic S sources. Vineyardδ³⁴S–SO₄²⁻values were 9.9 ± 5.9‰ (median ± interquartile range), enriched by ∼10‰ relative to forests/grasslands (−0.28 ± 5.7‰). Vineyards also had roughly three-fold higher [SO₄²⁻] than forests/grasslands (13.6 and 5.0 mg SO₄²⁻–S l⁻¹, respectively). Napa Riverδ³⁴S–SO₄²⁻values, reflecting the watershed scale, were similar to those from vineyards (10.5 ± 7.0‰), despite vineyard agriculture constituting only ∼11% of the watershed area. Combined, our results provide important evidence that agricultural S is traceable at field-to-watershed scales, a critical step toward determining the consequences of agricultural alterations to the modern S cycle.

Climate warming in alpine regions is changing patterns of water storage, a primary control on alpine plant ecology, biogeochemistry, and water supplies to lower elevations. There is an outstanding need to determine how the interacting drivers of precipitation and the critical zone (CZ) dictate the spatial pattern and time evolution of soil water storage. In this study, we developed an analytical framework that combines intensive hydrologic measurements and extensive remotely-sensed observations with statistical modeling to identify areas with similar temporal trends in soil water storage within, and predict their relationships across, a 0.26 km 2 alpine catchment in the Colorado Rocky Mountains, U.S.A. Repeat measurements of soil moisture were used to drive an unsupervised clustering algorithm, which identified six unique groups of locations ranging from predominantly dry to persistently very wet within the catchment. We then explored relationships between these hydrologic groups and multiple CZ-related indices, including snow depth, plant productivity, macro- (10 2 ->10 3 m) and microtopography (<10 0 -10 2 m), and hydrological flow paths. Finally, we used a supervised machine learning random forest algorithm to map each of the six hydrologic groups across the catchment based on distributed CZ properties and evaluated their aggregate relationships atmore »the catchment scale. Our analysis indicated that ~40–50% of the catchment is hydrologically connected to the stream channel, lending insight into the portions of the catchment that likely dominate stream water and solute fluxes. This research expands our understanding of patch-to-catchment-scale physical controls on hydrologic and biogeochemical processes, as well as their relationships across space and time, which will inform predictive models aimed at determining future changes to alpine ecosystems.« less