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Tree-derived dissolved organic matter (DOM) comprises a significant carbon flux within forested watersheds. Few studies have assessed the optical properties of tree-derived DOM. To increase understanding of the factors controlling tree-derived DOM quality, we measured DOM optical properties, dissolved organic carbon (DOC) and calcium concentrations in throughfall and stemflow for 17 individual rain events during summer and fall in a temperate deciduous forest in Vermont, United States. DOC and calcium fluxes in throughfall and stemflow were enriched on average 4 to 70 times incident fluxes in rain. A multiway model was developed using absorbance and fluorescence spectroscopy to further characterize DOM optical properties. Throughfall contained a higher percentage of protein-like DOM fluorescence than stemflow while stemflow was characterized by a higher percentage of humic-like DOM fluorescence. DOM absorbance spectral slopes in yellow birch (Betula alleghaniensis) stemflow were significantly higher than in sugar maple (Acer saccharum) stemflow. DOM optical metrics were not influenced by rainfall volume, but percent protein-like fluorescence increased in throughfall during autumn when leaves senesced. Given the potential influence of tree-derived DOM fluxes on receiving soils and downstream ecosystems, future modeling of DOM transport and soil biogeochemistry should represent the influence of differing DOM quality in throughfall and stemflow across tree species and seasons.

 

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. 

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.

 

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.

 

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.