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Abstract Understanding controls on solute export to streams is challenging because heterogeneous catchments can respond uniquely to drivers of environmental change. To understand general solute export patterns, we used a large‐scale inductive approach to evaluate concentration–discharge (C–Q) metrics across catchments spanning a broad range of catchment attributes and hydroclimatic drivers. We leveraged paired C–Q data for 11 solutes from CAMELS‐Chem, a database built upon an existing dataset of catchment and hydroclimatic attributes from relatively undisturbed catchments across the contiguous USA. Because C–Q relationships with Q thresholds reflect a shift in solute export dynamics and are poorly characterized across solutes and diverse catchments, we analysed C–Q relationships using Bayesian segmented regression to quantify Q thresholds in the C–Q relationship. Threshold responses were rare, representing only 12% of C–Q relationships, 56% of which occurred for solutes predominantly sourced from bedrock. Further, solutes were dominated by one or two C–Q patterns that reflected vertical solute–source distributions. Specifically, solutes predominantly sourced from bedrock had diluting C–Q responses in 43%–70% of catchments, and solutes predominantly sourced from soils had more enrichment responses in 35%–51% of catchments. We also linked C–Q relationships to catchment and hydroclimatic attributes to understand controls on export patterns. The relationships were generally weak despite the diversity of solutes and attribute types considered. However, catchment and hydroclimatic attributes in the central USA typically drove the most divergent export behaviour for solutes. Further, we illustrate how our inductive approach generated new hypotheses that can be tested at discrete, representative catchments using deductive approaches to better understand the processes underlying solute export patterns. Finally, given these long‐term C–Q relationships are from minimally disturbed catchments, our findings can be used as benchmarks for change in more disturbed catchments. 

Abstract Dissolved organic and inorganic carbon (DOC and DIC) influence water quality, ecosystem health, and carbon cycling. Dissolved carbon species are produced by biogeochemical reactions and laterally exported to streams via distinct shallow and deep subsurface flow paths. These processes are arduous to measure and challenge the quantification of global carbon cycles. Here we ask: when, where, and how much is dissolved carbon produced in and laterally exported from the subsurface to streams? We used a catchment‐scale reactive transport model, BioRT‐HBV, with hydrometeorology and stream carbon data to illuminate the “invisible” subsurface processes at Sleepers River, a carbonate‐based catchment in Vermont, United States. Results depict a conceptual model where DOC is produced mostly in shallow soils (3.7 ± 0.6 g/m²/yr) and in summer at peak root and microbial respiration. DOC is flushed from soils to the stream (1.0 ± 0.2 g/m²/yr) especially during snowmelt and storms. A large fraction of DOC (2.5 ± 0.2 g/m²/yr) percolates to the deeper subsurface, fueling deep respiration to generate DIC. DIC is exported predominantly from the deeper subsurface (7.1 ± 0.4 g/m²/yr, compared to 1.3 ± 0.3 g/m²/yr from shallow soils). Deep respiration reduces DOC and increases DIC concentrations at depth, leading to commonly observed DOC flushing (increasing concentrations with discharge) and DIC dilution patterns (decreasing concentrations with discharge). Surprisingly, respiration processes generate more DIC than weathering in this carbonate‐based catchment. These findings underscore the importance of vertical connectivity between the shallow and deep subsurface, highlighting the overlooked role of deep carbon processing and export. 

Abstract Processes that drive variability in catchment solute sourcing, transformation, and transport can be investigated using concentration–discharge (C–Q) relationships. These relationships reflect catchment and in‐stream processes operating across nested temporal scales, incorporating both short and long‐term patterns. Scientists can therefore leverage catchment‐scale C–Q datasets to identify and distinguish among the underlying meteorological, biological, and geological processes that drive solute export patterns from catchments and influence the shape of their respective C–Q relationships. We have synthesized current knowledge regarding the influence of biological, geological, and meteorological processes on C–Q patterns for various solute types across diel to decadal time scales. We identify cross‐scale linkages and tools researchers can use to explore these interactions across time scales. Finally, we identify knowledge gaps in our understanding of C–Q temporal dynamics as reflections of catchment and in‐stream processes. We also lay the foundation for developing an integrated approach to investigate cross‐scale linkages in the temporal dynamics of C–Q relationships, reflecting catchment biogeochemical processes and the effects of environmental change on water quality. This article is categorized under:Science of Water > Hydrological ProcessesScience of Water > Water QualityScience of Water > Water and Environmental Change 

Abstract The Sierra Nevada has experienced unprecedented wildfires and reduced snowmelt runoff in recent decades, due partially to anthropogenic climate change and over a century of fire suppression. To address these challenges, public land agencies are planning forest restoration treatments, which have the potential to both increase water availability and reduce the likelihood of uncontrollable wildfires. However, the impact of forest restoration on snowpack is site specific and not well understood across gradients of climate and topography. To improve our understanding of how forest restoration might impact snowpack across diverse conditions in the central Sierra Nevada, we run the high‐resolution (1 m) energy and mass balance Snow Physics and Lidar Mapping (SnowPALM) model across five 23–75 km²subdomains in the region where forest thinning is planned or recently completed. We conduct two virtual thinning experiments by removing all trees shorter than 10 or 20 m tall and rerunning SnowPALM to calculate the change in meltwater input. Our results indicate heterogeneous responses to thinning due to differences in climate and wind across our five central Sierra Nevada subdomains. We also predict the largest increases in snow retention when thinning forests with tall (7–20 m) and dense (40–70% canopy cover) trees, highlighting the importance of pre‐thinning vegetation structure. We develop a decision support tool using a random forests model to determine which regions would most benefit from thinning. In many locations, we expect major forest restoration to increase snow accumulation, while other areas with short and sparse canopies, as well as sunny and windy climates, are more likely to see decreased snowpack following thinning. Our decision support tool provides stand‐scale (30 m) information to land managers across the central Sierra Nevada region to best take advantage of climate and existing forest structure to obtain the greatest snowpack benefits from forest restoration. 

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. 

Abstract Understanding the severity and extent of near surface critical zone (CZ) disturbances and their ecosystem response is a pressing concern in the face of increasing human and natural disturbances. Predicting disturbance severity and recovery in a changing climate requires comprehensive understanding of ecosystem feedbacks among vegetation and the surrounding environment, including climate, hydrology, geomorphology, and biogeochemistry. Field surveys and satellite remote sensing have limited ability to effectively capture the spatial and temporal variability of disturbance and CZ properties. Technological advances in remote sensing using new sensors and new platforms have improved observations of changes in vegetation canopy structure and productivity; however, integrating measures of forest disturbance from various sensing platforms is complex. By connecting the potential for remote sensing technologies to observe different CZ disturbance vectors, we show that lower severity disturbance and slower vegetation recovery are more difficult to quantify. Case studies in montane forests from the western United States highlight new opportunities, including evaluating post‐disturbance forest recovery at multiple scales, shedding light on understory vegetation regrowth, detecting specific physiological responses, and refining ecohydrological modeling. Learning from regional CZ disturbance case studies, we propose future directions to synthesize fragmented findings with (a) new data analysis using new or existing sensors, (b) data fusion across multiple sensors and platforms, (c) increasing the value of ground‐based observations, (d) disturbance modeling, and (e) synthesis to improve understanding of disturbance. 

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. 

Abstract Aquatic fluxes of carbon and nutrients link terrestrial and aquatic ecosystems. Within forests, storm events drive both the delivery of carbon and nitrogen to the forest floor and the export of these solutes from the land via streams. To increase understanding of the relationships between hydrologic event character and the relative fluxes of carbon and nitrogen in throughfall, stemflow and streams, we measured dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) concentrations in each flow path for 23 events in a forested watershed in Vermont, USA. DOC and TDN concentrations increased with streamflow, indicating their export was limited by water transport of catchment stores. DOC and TDN concentrations in throughfall and stemflow decreased exponentially with increasing precipitation, suggesting that precipitation removed a portion of available sources from tree surfaces during the events. DOC and TDN fluxes were estimated for 76 events across a 2‐year period. For most events, throughfall and stemflow fluxes greatly exceeded stream fluxes, but the imbalance narrowed for larger storms (>30 mm). The largest 10 stream events exported 40% of all stream event DOC whereas those same 10 events contributed 14% of all throughfall export. Approximately 2–5 times more DOC and TDN was exported from trees during rain events than left the catchment via streams annually. The diverging influence of event size on tree versus stream fluxes has important implications for forested ecosystems as hydrological events increase in intensity and frequency due to climate change. 

Abstract 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.