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  1. Abstract. Large sample datasets are transforming the catchment sciences, but there are few off-the-shelf stream water chemistry datasets with complementary atmospheric deposition, streamflow, meteorology, and catchment physiographic attributes. The existing CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) dataset includes data on topography, climate, streamflow, land cover, soil, and geology across the continental US. With CAMELS-Chem, we pair these existing attribute data for 516 catchments with atmospheric deposition data from the National Atmospheric Deposition Program and water chemistry and instantaneous discharge data from the US Geological Survey over the period from 1980 through 2018 in a relational database and corresponding dataset. The data include 18 common stream water chemistry constituents: Al, Ca, Cl, dissolved organic carbon, total organic carbon, HCO3, K, Mg, Na, total dissolved N, total organic N, NO3, dissolved oxygen, pH (field and lab), Si, SO4, and water temperature. Annual deposition loads and concentrations include hydrogen, NH4, NO3, total inorganic N, Cl, SO4, Ca, K, Mg, and Na. We demonstrate that CAMELS-Chem water chemistry data are sampled effectively across climates, seasons, and discharges for trend analysis and highlight the coincident sampling of stream constituents for process-based understanding. To motivate their use by the larger scientific community across a variety of disciplines, we show examples of how these publicly available datasets can be applied to trend detection and attribution, biogeochemical process understanding, and new hypothesis generation via data-driven techniques.

     
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    Free, publicly-accessible full text available January 1, 2025
  2. Key Points We re‐evaluate equations proposed by Francis Hall to assess concentration‐discharge ( C ‐ Q ) relationships using newly available long‐term and high‐frequency data sets Across time steps we find that log‐log and log‐linear models perform equally well to describe C ‐ Q relationships Parametrization of storage‐discharge relationships via recession analyses provides additional insight to C ‐ Q relationships 
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    Free, publicly-accessible full text available August 1, 2024
  3. The concurrent reduction in acid deposition and increase in precipitation impact stream solute dynamics in complex ways that make predictions of future water quality difficult. To understand how changes in acid deposition and precipitation have influenced dissolved organic carbon (DOC) and nitrogen (N) loading to streams, we investigated trends from 1991 to 2018 in stream concentrations (DOC, ~3,800 measurements), dissolved organic nitrogen (DON, ~1,160 measurements), and dissolved inorganic N (DIN, ~2,130 measurements) in a forested watershed in Vermont, USA. Our analysis included concentration-discharge (C-Q) relationships and Seasonal Mann-Kendall tests on long-term, flow-adjusted concentrations. To understand whether hydrologic flushing and changes in acid deposition influenced long-term patterns by liberating DOC and dissolved N from watershed soils, we measured their concentrations in the leachate of 108 topsoil cores of 5 cm diameter that we flushed with solutions simulating high and low acid deposition during four different seasons. Our results indicate that DOC and DON often co-varied in both the long-term stream dataset and the soil core experiment. Additionally, leachate from winter soil cores produced especially high concentrations of all three solutes. This seasonal signal was consistent with C-Q relation showing that organic materials (e.g., DOC and DON), which accumulate during winter, are flushed into streams during spring snowmelt. Acid deposition had opposite effects on DOC and DON compared to DIN in the soil core experiment. Low acid deposition solutions, which mimic present day precipitation, produced the highest DOC and DON leachate concentrations. Conversely, high acid deposition solutions generally produced the highest DIN leachate concentrations. These results are consistent with the increasing trend in stream DOC concentrations and generally decreasing trend in stream DIN we observed in the long-term data. These results suggest that the impact of acid deposition on the liberation of soil carbon (C) and N differed for DOC and DON vs. DIN, and these impacts were reflected in long-term stream chemistry patterns. As watersheds continue to recover from acid deposition, stream C:N ratios will likely continue to increase, with important consequences for stream metabolism and biogeochemical processes. 
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  4. Abstract

    Oxygen (O2) regulates soil reduction‐oxidation processes and therefore modulates biogeochemical cycles. The difficulties associated with accurately characterizing soil O2variability have prompted the use of soil moisture as a proxy for O2, as O2diffusion into soil water is much slower than in soil air. The use of soil moisture alone as a proxy measurement for O2could result in inaccurate O2estimations. For example, O2may remain high during cool months when soil respiration rates are low. We analyzed high‐frequency sensor data (e.g., soil moisture, CO2, gas‐phase soil pore O2) with a machine learning technique, the Self‐Organizing Map, to pinpoint suites of soil conditions associated with contrasting O2regimes. At two riparian sites in northern Vermont, we found that O2levels varied seasonally, and with soil moisture. For example, 47% of low O2levels were associated with wet and cool soil conditions, whereas 32% were associated with dry and warm conditions. Contrastingly, the majority (62%) of high O2conditions occurred under dry and warm conditions. High soil moisture levels did not always lead to low O2, as 38% of high O2values occurred under wet and cool conditions. Our results highlight challenges with predicting soil O2solely based on water content, as variable combinations of soil and hydrologic conditions can complicate the relationship between water content and O2. This indicates that process‐based ecosystem and denitrification models that rely solely on soil moisture to estimate O2may need to incorporate other site and climate‐specific drivers to accurately predict soil O2.

     
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  5. Abstract

    The capacity for floodplains to capture sediment and filter pollutants is spatially variable and depends on the complex interactions of geomorphic, geologic, and hydrologic variables that operate at multiple scales. In this study, we integrated watershed‐scale and local assessments to improve our understanding of floodplain depositional patterns. We developed a dataset of event‐scale observations of sediment and phosphorus deposition rates distributed at 129 plots across large environmental gradients of floodplain topography, valley geometry, and watershed characteristics in the Lake Champlain Basin, Vermont. Plot‐scale observations were used to evaluate the cross‐scale influence of environmental factors and were summarized into site‐scale averages to explore regional trends. Consistent with other studies, floodplain deposition generally scaled with drainage area, but trends were longitudinally discontinuous and depended on variations in valley width and slope. While variability in deposition patterns at the watershed‐scale was large (average of 2.0 (0.2–9.8) kg sediment m−2 yr−1; average of 1.4 (0.2–6.5) g phosphorus m−2 yr−1), the range in deposition rates locally across a floodplain was greater (average of 4.6 (0.06–21.7) kg sediment m−2 yr−1; average of 6.4 (0.1–41.1) g phosphorus m−2 yr−1). Local variables that described the proximity to water and sediment sources, and frequency with which the plot was activated by a flood, had the greatest relative contribution to boosted regression tree models of phosphorus deposition rates, highlighting the importance of river–floodplain connectivity for floodplain functioning and the profound impact of human activities that limit such connectivity. Patterns identified in our study may guide prioritization of restoration and conservation practices designed to capture sediment and phosphorus on floodplains.

     
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  6. null (Ed.)
    Understanding and predicting catchment responses to a regional disturbance is difficult because catchments are spatially heterogeneous systems that exhibit unique moderating characteristics. Changes in precipitation composition in the Northeastern U.S. is one prominent example, where reduction in wet and dry deposition is hypothesized to have caused increased dissolved organic carbon (DOC) export from many northern hemisphere forested catchments; however, findings from different locations contradict each other. Using shifts in acid deposition as a test case, we illustrate an iterative “process and pattern” approach to investigate the role of catchment characteristics in modulating the steam DOC response. We use a novel dataset that integrates regional and catchment-scale atmospheric deposition data, catchment characteristics and co-located stream Q and stream chemistry data. We use these data to investigate opportunities and limitations of a pattern-to-process approach where we explore regional patterns of reduced acid deposition, catchment characteristics and stream DOC response and specific soil processes at select locations. For pattern investigation, we quantify long-term trends of flow-adjusted DOC concentrations in stream water, along with wet deposition trends in sulfate, for USGS headwater catchments using Seasonal Kendall tests and then compare trend results to catchment attributes. Our investigation of climatic, topographic, and hydrologic catchment attributes vs. directionality of DOC trends suggests soil depth and catchment connectivity as possible modulating factors for DOC concentrations. This informed our process-to-pattern investigation, in which we experimentally simulated increased and decreased acid deposition on soil cores from catchments of contrasting long-term DOC response [Sleepers River Research Watershed (SRRW) for long-term increases in DOC and the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO) for long-term decreases in DOC]. SRRW soils generally released more DOC than SSHCZO soils and losses into recovery solutions were higher. Scanning electron microscope imaging indicates a significant DOC contribution from destabilizing soil aggregates mostly from hydrologically disconnected landscape positions. Results from this work illustrate the value of an iterative process and pattern approach to understand catchment-scale response to regional disturbance and suggest opportunities for further investigations. 
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  7. Abstract

    Centuries of human development have altered the connectivity of rivers, adversely impacting ecosystems and the services they provide. Significant investments in natural resource projects are made annually with the goal of restoring function to degraded rivers and floodplains and protecting freshwater resources. Yet restoration projects often fall short of their objectives, in part due to the lack of systems‐based strategic planning. To evaluate channel‐floodplain (dis)connectivity and erosion/incision hazard at the basin scale, we calculate Specific Stream Power (SSP), an estimate of the energy of a river, using a topographically based, low‐complexity hydraulic model. Other basin‐wide SSP modeling approaches neglect reach‐specific geometric information embedded in Digital Elevation Models. Our approach leverages this information to generate reach‐specific SSP‐flow curves. We extract measures from these curves that describe (dis)connected floodwater storage capacity and erosion hazard at individual design storm flood stages and demonstrate how these measures may be used to identify watershed‐scale patterns in connectivity. We show proof‐of‐concept using 25 reaches in the Mad River watershed in central Vermont and demonstrate that the SSP results have acceptable agreement with a well‐calibrated process‐based model (2D Hydraulic Engineering Center's River Analysis System) across a broad range of design events. While systems‐based planning of regional restoration and conservation activities has been limited, largely due to computational and human resource requirements, measures derived from low‐complexity models can provide an overview of reach‐scale conditions at the regional level and aid planners in identifying areas for further restoration and/or conservation assessments.

     
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  8. Abstract

    Research at long‐term catchment monitoring sites has generated a great volume, variety, and velocity of data for analysis of stream water chemistry dynamics. To harness the potential of these big data and extract patterns that are indicative of underlying functional relationships, machine learning tools have advantages over traditional statistical methods, and are increasingly being applied for dimension reduction, feature extraction, and trend identification. Still, as examples of complex systems, catchments are characterized by multivariate factor interactions and equifinality that are not easily identified by most machine‐learning methods. Using dissolved organic carbon (DOC) dynamics as an illustration, we applied a new evolutionary algorithm (EA) to extract geologic, topographic, meteorologic, hydrologic, and land use attributes that were correlated to mean stream DOC concentration in forested catchments distributed across the continental United States. The EA reduced dimensionality of our attribute dataset to identify the combination of factors, and their specific value ranges, that interacted to drive membership in High or Low mean DOC clusters. High mean DOC concentrations were associated with two distinct geographic locations of variable climatic and vegetative conditions, indicating equifinality. Our findings underscore the importance of critical zone structure in mediating hydrological and biogeochemical processes to govern DOC dynamics at the catchment scale. This multi‐scale, pattern‐to‐process approach is being applied to refine hypotheses for process‐based modeling of DOC dynamics in forested headwater streams at catchment to site scales.

     
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