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Groundwater discharge to streams is a nonpoint source of nitrogen (N) that confounds N mitigation efforts and represents a significant portion of the annual N loading to watersheds. However, we lack an understanding of where and how much groundwater N enters streams and watersheds. Nitrogen concentrations at the end of groundwater flowpaths are the culmination of biogeochemical and physical processes from the contributing land area where groundwater recharges, within the aquifer system, and in the near-stream riparian area where groundwater discharges to streams. Our research objectives were to quantify the spatial distribution of N concentrations at groundwater discharges throughout a mixed land-use watershed and to evaluate how relationships among contributing and riparian land cover, modeled aquifer characteristics, and groundwater discharge biogeochemistry explain the spatial variation in groundwater discharge N concentrations. We accomplished this by integrating high-resolution thermal infrared surveys to locate groundwater discharge, biogeochemical sampling of groundwater, and a particle tracking model that links groundwater discharge locations to their contributing area land cover. Groundwater N loading from groundwater discharges within the watershed varied substantially between and within streambank groundwater discharge features. Groundwater nitrate concentrations were spatially heterogeneous ranging from below 0.03–11.45 mg-N/L, varying up to 20-fold within meters. When combined with the particle tracking model results and land cover metrics, we found that groundwater discharge nitrate concentrations were best predicted by a linear mixed-effect model that explained over 60% of the variation in nitrate concentrations, including aquifer chemistry (dissolved oxygen, Cl⁻, SO₄²⁻), riparian area forested land cover, and modeled physical aquifer characteristics (discharge, Euclidean distance). Our work highlights the significant spatial variability in groundwater discharge nitrate concentrations within mixed land-use watersheds and the need to understand groundwater N processing across the many spatiotemporal scales within groundwater cycling.

 

Riverbank groundwater discharge faces are spatially extensive areas of preferential seepage that are exposed to air at low river flow. Some conceptual hydrologic models indicate discharge faces represent the spatial convergence of highly variable age and length groundwater flowpaths, while others indicate greater consistency in source groundwater characteristics. Our detailed field investigation of preferential discharge points nested across mainstem riverbank discharge faces was accomplished by: (1) leveraging new temperature‐based recursive estimation (extended Kalman Filter) modelling methodology to evaluate seasonal, diurnal, and event‐driven groundwater flux patterns, (2) developing a multi‐parameter toolkit based on readily measured attributes to classify the general source groundwater flowpath depth and flowpath length scale, and, (3) assessing whether preferential flow points across discharge faces tend to represent common or convergent groundwater sources. Five major groundwater discharge faces were mapped along the Farmington River, CT, United States using thermal infrared imagery. We then installed vertical temperature profilers directly into 39 preferential discharge points for 4.5 months to track vertical discharge flux patterns. Monthly water chemistry was also collected at the discharge points along with one spatial synoptic of stable isotopes of water and dissolved radon gas. We found pervasive evidence of shallow groundwater sources at the upstream discharge faces along a wide valley section with deep bedrock, as primarily evidenced by pronounced diurnal discharge flux patterns. Discharge flux seasonal trends and bank storage transitions during large river flow events provided further indication of shallow, local sources. In contrast, downstream discharge faces associated with near surface cross cutting bedrock exhibited deep and regional source flowpath characteristics such as more stable discharge patterns and temperatures. However, many neighbouring points across discharge faces had similar discharge flux patterns that differed in chloride and radon concentrations, indicating the additional effects of localized flowpath heterogeneity overprinting on larger scale flowpath characteristics.

 

We used spatial data from previously mapped preferential groundwater discharges throughout the Farmington River watershed in Connecticut and Massachusetts (https://doi.org/10.5066/P915E8JY) to guide water sample collection at known locations of groundwater discharging to surface water. In 2017 and 2019 - 2021, samples were collected during general river baseflow conditions (July ? November, less than 30.9 cms mean daily discharge (USGS gage 01189995, statistics 2010-2022) when the riverbank discharge points were exposed. We collected a suite of dissolved constituents and stable isotopes of water directly in the shallow saturated sediments of active points of discharge, and coincident stream chemical samples were also collected adjacent to locations of groundwater discharge. Data collected includes nutrients (NO3, NH4, Cl, SO4, PO4, dissolved organic carbon (DOC), and total nitrogen (TN)), greenhouse gases (CO2, CH4, and N2O), dissolved gases (N2, dissolved oxygen (DO)), conductivity, sediment characteristics, temperature, and spatial information. This dataset includes 2 main files: 1) Farmington_Chemistry_2017_2021.csv contains attribute information for each biogeochemical constituent collected at preferential groundwater discharges along the Farmington River network. 2)Farmington_Temporal_Cl_Rn_Iso_2020.csv contain attribute information for source characteristic data (Chloride, Radon, Isotope) collected at locations of repeat sampling at 5 groundwater seep faces along the Farmington River (Alsop and Rainbow Island). 

Groundwater discharge to rivers takes many forms, including preferential groundwater discharge points (PDPs) along riverbanks that are exposed at low flows, with multi-scale impacts on aquatic habitat and water quality. The physical controls on the spatial distribution of PDPs along riverbanks are not well-defined, rendering their prediction and representation in models challenging. To investigate the local riverbank sediment controls on PDP occurrence, we tested drone-based and handheld thermal infrared to efficiently map PDP locations along two mainstem rivers. Early in the study, we found drone imaging was better suited to locating tributary and stormwater inflows, which created relatively large water surface thermal anomalies in winter, compared to PDPs that often occurred at the sub-meter scale and beneath riparian tree canopy. Therefore, we primarily used handheld thermal infrared imaging from watercraft to map PDPs and larger seepage faces along 12-km of the fifth-order Housatonic River in Massachusetts, USA and 26-km of the Farmington River in Connecticut, USA. Overall, we mapped 31 riverbank PDPs along the Housatonic reach that meanders through lower permeability soils, and 104 PDPs along the Farmington reach that cuts through sandier sediments. Riverbank soil parameters extracted at PDP locations from the Soil Survey Geographic (SSURGO) database did not differ substantially from average bank soils along either reach, although the Farmington riverbank soils were on average 5× more permeable than Housatonic riverbank soils, likely contributing to the higher observed prevalence of PDPs. Dissolved oxygen measured in discharge water at these same PDPs varied widely, but showed no relation to measured sand, clay, or organic matter content in surficial soils indicating a lack of substantial near-surface aerobic reaction. The PDP locations were investigated for the presence of secondary bank structures, and commonly co-occurred with riparian tree root masses indicating the importance of localized physical controls on the spatial distribution of riverbank PDPs. 

River networks regulate carbon and nutrient exchange between continents, atmosphere, and oceans. However, contributions of riverine processing are poorly constrained at continental scales. Scaling relationships of cumulative biogeochemical function with watershed size (allometric scaling) provide an approach for quantifying the contributions of fluvial networks in the Earth system. Here we show that allometric scaling of cumulative riverine function with watershed area ranges from linear to superlinear, with scaling exponents constrained by network shape, hydrological conditions, and biogeochemical process rates. Allometric scaling is superlinear for processes that are largely independent of substrate concentration (e.g., gross primary production) due to superlinear scaling of river network surface area with watershed area. Allometric scaling for typically substrate-limited processes (e.g., denitrification) is linear in river networks with high biogeochemical activity or low river discharge but becomes increasingly superlinear under lower biogeochemical activity or high discharge, conditions that are widely prevalent in river networks. The frequent occurrence of superlinear scaling indicates that biogeochemical activity in large rivers contributes disproportionately to the function of river networks in the Earth system.

 

Groundwater discharge generates streamflow and influences stream thermal regimes. However, the water quality and thermal buffering capacity of groundwater depends on the aquifer source-depth. Here, we pair multi-year air and stream temperature signals to categorize 1729 sites across the continental United States as having major dam influence, shallow or deep groundwater signatures, or lack of pronounced groundwater (atmospheric) signatures. Approximately 40% of non-dam stream sites have substantial groundwater contributions as indicated by characteristic paired air and stream temperature signal metrics. Streams with shallow groundwater signatures account for half of all groundwater signature sites and show reduced baseflow and a higher proportion of warming trends compared to sites with deep groundwater signatures. These findings align with theory that shallow groundwater is more vulnerable to temperature increase and depletion. Streams with atmospheric signatures tend to drain watersheds with low slope and greater human disturbance, indicating reduced stream-groundwater connectivity in populated valley settings.

 

Mean annual temperature and mean annual precipitation drive much of the variation in productivity across Earth's terrestrial ecosystems but do not explain variation in gross primary productivity (GPP) or ecosystem respiration (ER) in flowing waters. We document substantial variation in the magnitude and seasonality of GPP and ER across 222 US rivers. In contrast to their terrestrial counterparts, most river ecosystems respire far more carbon than they fix and have less pronounced and consistent seasonality in their metabolic rates. We find that variation in annual solar energy inputs and stability of flows are the primary drivers of GPP and ER across rivers. A classification schema based on these drivers advances river science and informs management. 

Groundwater is critical for maintaining stream baseflow and thermal stability; however, the influence of groundwater on streamflow has been difficult to evaluate at broad spatial scales. Techniques such as baseflow separation necessitate streamflow records and do not directly indicate whether groundwater inflow may be sourced from more dynamic shallow flowpaths. We present a web tool applicationPASTA(Paired Air and Stream Temperature Analysis;https://cuahsi.shinyapps.io/pasta/) that capitalizes on increased public stream temperature data availability and large‐scale, gridded climate observations to provide new and efficient insights regarding relative groundwater influence on streams.PASTAanalyzes paired air and stream water temperature signals to evaluate spatiotemporal patterns in stream thermal sensitivity and relative groundwater influence, including inference regarding the dominant source groundwater depth (shallow or deep (i.e., approximately >6 m depth)). The tool is linked to publicly available stream temperature datasets and accepts user‐uploaded datasets. As local air temperature is not often monitored, PASTA pulls daily air temperature data from the comprehensive Daymet products when directly measured data are unavailable, allowing the repurposing of existing stream temperature data. After data are selected or uploaded, the tool (a) fits sinusoidal curves of daily stream and air temperatures by year (water or calendar) to indicate groundwater influence characteristics and (b) performs linear regressions for stream versus air temperatures to indicate stream thermal sensitivity. Results are exported in ASCII file format, creating an efficient and approachable analysis tool for the adoption of newly developed heat tracing analysis from stream reach to landscape scales.