Storm events dominate sediment delivery to stream corridors, but the effects of anthropogenic disturbances on altering the sources, pathways, and timing of delivery remain uncertain. To address this knowledge gap, we analyzed 849 events from over a decade of high-frequency turbidity data across five watersheds in an urbanization gradient. Sensing results suggested that hysteresis patterns evolved with land use from clockwise (low-rural) to figure-eight (high-rural) to counter-clockwise (high-urban), indicating a disturbance-driven shift of sediment provenance from proximal to distal. Sediment loading pathways in the lowest-disturbance rural watershed were dominated by a single hysteresis shape (>90% of export by clockwise events), whereas the mostdisturbed urban basin had the greatest variability in loading pathways (∼25% of export by clockwise, counter-clockwise, figure-eight, and complex events, respectively). Finally, wastewater treatment facilities modulated the release of "hungry-water" baseflow, causing more-rapid periods of high streamflow variance in catchments with a treatment facility (∼4 h period) than in those without (∼6 h period). Together, our results indicate that anthropogenic disturbances, including tile drainage, impervious surfaces, and roadway density, increase the connectivity of distally located sediment that wouldin undisturbed basinsdeposit along the sediment cascade. This information is important to watershed managers as they mitigate erosion in developing basins.

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Understanding the physics of nitrate contamination in surface and subsurface water is vital for mitigating downstream water quality impairment. Though high frequency sensor data have become readily available and computational models more accessible, the integration of these two methods for improved prediction is underdeveloped. The objective of this study was to utilize high‐frequency data to advance our understanding and model representation of nitrate transport for an agricultural karst spring in Kentucky, USA. We collected 2‐years of 15‐min nitrate and specific conductance data and analyzed source‐timing dynamics across dozens of events to develop a conceptual model for nitrate hysteresis in karst. Thereafter, we used the sensing data, specifically discharge‐concentration indices, to constrain modeled nitrate prediction bounds as well as the uncertainty of hydrologic and nitrogen processes, such as soil percolation and biogeochemical transformation. Observed nitrate hysteresis behavior at the spring was complex and included clockwise (n = 11), counterclockwise (n = 13), and figure‐eight (n = 10) shapes, which contrasts with surface systems that are often dominated by a single hysteresis shape. Sensing results highlight the importance of antecedent connectivity to nitrate‐rich storages in determining the timing of nitrate delivery to the spring. After integrating hysteresis analysis into our numerical model evaluation, simulated nitrate prediction bounds were reduced by 43 ± 12% and parameter uncertainty by 36 ± 20%. Taken together, this study suggests that discharge‐concentration indices derived from high‐frequency sensor data can be successfully integrated into numerical models to improve process representation and reduce modeled uncertainty.

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