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1. Molecular-level exploration of spatiotemporal dynamics of fluvial particulate organic carbon sources during storm events: Using a high-temporal resolution multi-biomarker approach

   https://doi.org/10.1016/j.scitotenv.2025.178447  

   Kim, Jieun; Blair, Neal E; Papanicolaou, AN Thanos (February 2025, Science of The Total Environment)

   Identifying the origins of storm fluvial particulate organic carbon (POC) provides information about the hydrological connectivity within the river corridor and the roles of the land-stream interface in the carbon cycle. However, current understanding of storm-induced POC source dynamics is constrained by observations limited in space and time. This study presents a unique approach integrating higher spatial and temporal resolution sampling with a multi-biomarker analysis to better understand POC source dynamics across scales. Storm POC samples were collected at ~2 h intervals at three locations along the flow trajectory of an agricultural stream during six storm events with varied storm characteristics and seasonality, and characterized for their concentrations, C and N contents, stable C isotopes, and biomarker contents. Our results showed a source transition from in-stream algal production during early storm stages to surface soils with vascular plant signatures during peak precipitation and discharge across events and stations. Biomarkers further resolved the terrestrial signature into one likely from bank vegetation and another from row crop soils. This additional separation appeared conditionally, with the magnitude and sequence influenced by environmental factors such as storm trajectory, antecedent conditions, and management/vegetation cover. Source transitions were less distinctive in the lower reaches due to the greater integration of inputs, although one storm with localized precipitation showed the opposite pattern. Both scenarios align with the expected lower hydrological connectivity downstream. With the employed approach, the evolution of the storm pulse POC as it responds to river corridor processes could be visualized both temporally and spatially. 

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2. Season and Antecedent Conditions Impact Concentration‐Discharge Relationships for Dissolved Organic Carbon and Alkalinity in a Southeast Alaskan Watershed

   https://doi.org/10.1029/2025JG009090  

   Delbecq, Claire; Fellman, Jason B; Bellmore, J Ryan; Whitney, Emily J; Fitzgerald, Kevin; Falke, Jeffrey A (January 2026, Journal of Geophysical Research: Biogeosciences)

   Abstract Fluvial export of dissolved carbon plays an important role in watershed‐scale biogeochemistry. Predicted changes in climate are expected to impact watershed hydrologic regimes, and in turn, the sources and export of dissolved carbon from watersheds. Here, we utilize high resolution measurements of discharge and dissolved carbon concentration to examine how concentration‐discharge (CQ) relationships vary seasonally and during high flow events over the main runoff season (May–October) in a temperate forested watershed in Southeast Alaska. Concentration‐discharge relationships for dissolved organic carbon (DOC) and alkalinity demonstrated strong seasonal patterns, with more linear relationships in May and June versus other months. Changing power law model slopes (bvalues; the exponent in a power law regression between runoff and carbon yields) indicated potentially shifting watershed sources (biogenic vs. geologic) and contrasting dominant flowpaths (shallow vs. deeper groundwater) for DOC and alkalinity over the sampling period. During the largest storm event of the study, DOC and alkalinitybvalues shifted from an overall pattern of transport (meanb = 1.58 values >1.0 indicate transport limitation) and source limitation (meanb = 0.48, values <1.0 indicate source limitation) to chemostatic (DOC,b = 0.99; alkalinity,b = 1.019). In June through August, patterns in hysteresis index suggest that CQ relationships were altered when storms followed in close succession to each other. Together, these findings indicate that seasonal and antecedent flow conditions play a role in dissolved carbon export from forested watersheds. Understanding these dynamics, particularly during winter months, will become increasingly important as changes to hydroclimate impact riverine carbon export. 

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3. Changes in Dominant Streamflow Drivers as Network‐Scale Flow Regime Shifts From Intermittent to Ephemeral Across a Multi‐Year Drought

   https://doi.org/10.1002/hyp.70413  

   Giggy, Lauren; Price, Adam N; Zimmer, Margaret (February 2026, Hydrological Processes)

   ABSTRACT Dynamic surface water wetting and drying are characteristic of most headwater stream networks, influencing ecological function and downstream water quality. While topography and subsurface properties have been shown to influence spatial surface water persistence, it is unclear how sensitive non‐perennial stream network flow regimes are to persistent drought conditions. To address this unknown, we monitored surface water presence and absence for three consecutive drought years at 31 locations across a 0.25 km²non‐perennial headwater stream network in central coastal California. We coupled these observations with landscape characteristics and climatic conditions to examine both physical and climatic drivers of spatiotemporal flow activation and persistence. We observed non‐stationarity in flow regimes throughout the stream network; reaches that were characterised by seasonal flow in 1 year became ephemeral as the drought conditions progressed. We observed declining spatial variability in surface water persistence and declining correlations between persistence and landscape attributes with ongoing drought. From year one to year three, as drought conditions intensified, correlation coefficients between surface water persistence and topographic wetness index declined fromr = 0.60 tor = 0.17, stream channel slope declined fromr = 0.47 tor = 0.21, profile curvature declined fromr = −0.51 tor = −0.21, and elevation declined fromr = −0.33 tor = −0.06. A principal component analysis suggests that, unlike prior years, flow activation events after several years of drought were more closely associated with precipitation event characteristics than antecedent storage states. This work suggests semi‐arid landscapes such as central coastal California may see shifts in the flow regimes of aquatic systems as landscape aridity intensifies with climate change. As predictive indicators of flow may shift from physical to climatic factors, the timing, intensity and frequency of individual storm events may play increasingly larger roles in driving annual‐scale flow conditions. 

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4. Improving Stream Solute Predictions With a Modified LSTM Model Incorporating Solute Interdependences and Hysteresis Patterns

   https://doi.org/10.1029/2024JH000383  

   Agrawal, Tarun; Goodwell, Allison; Kumar, Praveen (March 2025, Journal of Geophysical Research: Machine Learning and Computation)

   NA (Ed.)

   Abstract Surface runoff and infiltrated water en route to the stream interact with dynamic landscape properties, ranging from vegetation and microbial activities to soil and geological attributes. Stream solute concentrations are highly variable and interconnected due to these interactions, flow paths, and residence times, and often exhibit hysteresis with flow. Significant unknowns remain about how point measurements of stream solute chemistry reflect interdependent hydrobiogeochemical and physical processes, and how signatures are encapsulated as nonlinear dynamical relationships between variables. We take a Machine Learning (ML) approach to understand and capture these dynamical relationships and improve predictions of solutes at short and long time scales. We introduce a physical process‐based “flow‐gate” into an Long Short‐Term Memory (LSTM) model, which enables the model to learn hysteresis behaviors if they exist. Further, we use information‐theoretic metrics to detect how solutes are interdependent and iteratively select source solutes that best predict a given target solute concentration. The “flow‐gate LSTM” model improves model predictions (1%–32% decreases in RMSE) relative to the standard LSTM model for all nine solutes included in the study. The predictive improvements from the flow‐gate LSTM model highlight the importance of lagged concentration and discharge relationships for certain solutes. It also indicates a potential limitation in the traditional LSTM model approach since flow rates are always provided as input sources, but this information is not fully utilized. This work provides a starting point for a predictive understanding of geochemical interdependencies using machine‐learning approaches and highlights potential improvements in model architecture. 

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5. Non-perennial streamflow and sediment mobilization in a semi-arid rangeland slated for development

   https://doi.org/10.1371/journal.pwat.0000466  

   Ramírez-Núñez, Jorge Santiago; Poteet, Dixie L; Bhaskar, Aditi S; Wilson, Stacy; Morrison, Ryan R; Zivkovich, Brik; Johnson, Sara; Kampf, Stephanie K; Grigg, Neil; Nelson, Peter A (November 2025, PLOS Water)

   Urbanization has profound impacts on stream morphology and streamflow patterns. Predicting these impacts in semi-arid areas is limited by the lack of information on pre-development streamflow, as non-perennial streams are not well monitored in semi-arid areas. This research focuses on the ephemeral stream West Stroh Gulch, located south of Denver, Colorado, U.S. This grassland watershed was used for grazing during the pre-development study period of June 2020 – August 2023. During this time, time-lapse photography at five locations along the stream network along with radar-rainfall data were used to determine which storm events did, or did not, lead to a streamflow response. Out of 115 recorded storm events, 32 led to streamflow. A classification tree indicated that streamflow was due to storm events with higher rainfall intensities (maximum 30-minute intensity above 4.3 mm/hr and 60-minute intensity above 6.2 mm/hr) and 7-day antecedent rainfall above 20.3 mm. A two-dimensional hydrodynamic model (SRH-2D) enabled comparison of the impacts of predicted flows through a reach of interest. The model used a digital elevation model developed using structure-from-motion techniques and drone aerial imagery. Storm Water Management Model (SWMM) generated peak flows were used to simulate impacts of different sized storms (Water Quality Event (WQE), 2-, 5-, 10-, 50-, and 100-year storms) and development and stormwater management scenarios (pre-development, developed undetained, and developed with distributed detention). Results predicted that the smallest but most frequent storms (WQE and 2-year storms) would cause increased flow and boundary shear stress post-development. For larger storms, distributed detention facilities reduced post-development flows below pre-development and were well below the undetained development, which consistently had the largest flow and potential for sediment mobilization. The findings contribute to understanding of ephemeral streamflow in rangelands and streamflow regime alterations as drivers of sediment mobilization with urban development. 

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