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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.

 

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.

 

The shallow and deep hypothesis suggests that stream concentration‐discharge (CQ) relationships are shaped by distinct source waters from different depths. Under this hypothesis, baseflows are typically dominated by groundwater and mostly reflect groundwater chemistry, whereas high flows are typically dominated by shallow soil water and mostly reflect soil water chemistry. Aspects of this hypothesis draw on applications like end member mixing analyses and hydrograph separation, yet direct data support for the hypothesis remains scarce. This work tests the shallow and deep hypothesis using co‐located measurements of soil water, groundwater, and streamwater chemistry at two intensively monitored sites, the W‐9 catchment at Sleepers River (Vermont, United States) and the Hafren catchment at Plynlimon (Wales). At both sites, depth profiles of subsurface water chemistry and stream CQ relationships for the 10 solutes analyzed are broadly consistent with the hypothesis. Solutes that are more abundant at depth (e.g., calcium) exhibit dilution patterns (concentration decreases with increasing discharge). Conversely, solutes enriched in shallow soils (e.g., nitrate) generally exhibit flushing patterns (concentration increases with increasing discharge). The hypothesis may hold broadly true for catchments that share such biogeochemical stratifications in the subsurface. Soil water and groundwater chemistries were estimated from high‐ and low‐flow stream chemistries with average relative errors ranging from 24% to 82%. This indicates that streams mirror subsurface waters: stream chemistry can be used to infer scarcely measured subsurface water chemistry, especially where there are distinct shallow and deep end members.

 

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 

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. 

Abstract Winters in snow-covered regions have warmed, likely shifting the timing and magnitude of nutrient export, leading to unquantified changes in water quality. Intermittent, seasonal, and permanent snow covers more than half of the global land surface. Warming has reduced the cold conditions that limit winter runoff and nutrient transport, while cold season snowmelt, the amount of winter precipitation falling as rain, and rain-on-snow have increased. We used existing geospatial datasets (rain-on-snow frequency overlain on nitrogen and phosphorous inventories) to identify areas of the contiguous United States (US) where water quality could be threatened by this change. Next, to illustrate the potential export impacts of these events, we examined flow and turbidity data from a large regional rain-on-snow event in the United States’ largest river basin, the Mississippi River Basin. We show that rain-on-snow, a major flood-generating mechanism for large areas of the globe (Berghuijs et al 2019 Water Resour. Res. 55 4582–93; Berghuijs et al 2016 Geophys. Res. Lett. 43 4382–90), affects 53% of the contiguous US and puts 50% of US nitrogen and phosphorus pools (43% of the contiguous US) at risk of export to groundwater and surface water. Further, the 2019 rain-on-snow event in the Mississippi River Basin demonstrates that these events could have large, cascading impacts on winter nutrient transport. We suggest that the assumption of low wintertime discharge and nutrient transport in historically snow-covered regions no longer holds. Critically, however, we lack sufficient data to accurately measure and predict these episodic and potentially large wintertime nutrient export events at regional to continental scales.