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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. Antecedent Conditions Control Thresholds of Tile‐Runoff Generation and Nitrogen Export in Intensively Managed Landscapes

   https://doi.org/10.1029/2021WR030507  

   Cain, Molly R.; Woo, Dong Kook; Kumar, Praveen; Keefer, Laura; Ward, Adam S. (February 2022, Water Resources Research)

   Abstract Threshold changes in rainfall‐runoff generation commonly represent shifts in runoff mechanisms and hydrologic connectivity controlling water and solute transport and transformation. In watersheds with limited human influence, threshold runoff responses reflect interaction between precipitation event and antecedent soil moisture. Similar analyses are lacking in intensively managed landscapes where installation of subsurface drainage tiles has altered connectivity between the land surface, groundwater, and streams, and where application of fertilizer has created significant stores of subsurface nitrogen. In this study, we identify threshold patterns of tile‐runoff generation for a drained agricultural field in Illinois and evaluate how antecedent conditions—including shallow soil moisture, groundwater table depth, and the presence or absence of crops—control tile response. We relate tile‐runoff thresholds to patterns of event nitrate load observed across multiple storm events and evaluate how antecedent conditions control within‐event nitrate concentration‐discharge relationships. Our results demonstrate that an event tile‐runoff threshold emerges relative to the sum of gross precipitation and indices of antecedent shallow soil moisture and antecedent below‐tile groundwater moisture deficit, indicating that both shallow soil and below‐tile storages must be filled to generate significant runoff. In turn, event nitrate load shows a linear dependence on runoff for most time periods, suggesting that subsurface nitrate export and storage can be estimated using runoff threshold relationships and long‐term average nitrate concentrations. Finally, within‐event nitrate concentration‐discharge relationships are controlled by event size and the antecedent tile flow state because these factors dictate the sequence of flow path activation and tile connectivity over a storm event. 

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3. The Drying Regimes of Non‐Perennial Rivers and Streams

   https://doi.org/10.1029/2021GL093298  

   Price, Adam_N; Jones, C_Nathan; Hammond, John_C; Zimmer, Margaret_A; Zipper, Samuel_C (July 2021, Geophysical Research Letters)

   Abstract The flow regime paradigm is central to the aquatic sciences, where flow drives critical functions in lotic systems. Non‐perennial streams comprise the majority of global river length, thus we extended this paradigm to stream drying. Using 894 USGS gages, we isolated 25,207 drying events from 1979 to 2018, represented by a streamflow peak followed by no flow. We calculated hydrologic signatures for each drying event and using multivariate statistics, grouped events into drying regimes characterized by: (a) fast drying, (b) long no‐flow duration, (c) prolonged drying following low antecedent flows, (d) drying without a distinctive hydrologic signature. 77% of gages had more than one drying regime at different times within the study period. Random forests revealed land cover/use are more important to how a river dries than climate or physiographic characteristics. Clustering stream drying behavior may allow practitioners to more systematically adapt water resource management practices to specific drying regimes or rivers. 

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4. Stream chemical response is mediated by hydrologic connectivity and fire severity in a Pacific Northwest forest

   https://doi.org/10.1002/hyp.15231  

   Bush, Sidney_A; Johnson, Sherri_L; Bladon, Kevin_D; Sullivan, Pamela_L (July 2024, Hydrological Processes)

   Abstract Large‐scale wildfires are becoming increasingly common in the wet forests of the Pacific Northwest (USA), with predicted increases in fire prevalence under future climate scenarios. Wildfires can alter streamflow response to precipitation and mobilize water quality constituents, which pose a risk to aquatic ecosystems and downstream drinking water treatment. Research often focuses on the impacts of high‐severity wildfires, with stream biogeochemical responses to low‐ and mixed‐severity fires often understudied, particularly during seasonal shifts in hydrologic connectivity between hillslopes and streams. We studied the impacts of the 2020 Holiday Farm Fire at the HJ Andrews Experimental Forest where rare pre‐fire stream discharge and chemistry data allowed us to evaluate the influence of mixed‐severity fire on stream water quantity and quality. Our research design focused on two well‐studied watersheds with low and low‐moderate burn severity where we examined long‐term data (pre‐ and post‐fire), and instantaneous grab samples collected during four rain events occurring immediately following wildfire and a prolonged dry summer. We analysed the impact of these rain events, which represent the transition from low‐to‐high hydrologic connectivity of the subsurface to the stream, on stream discharge and chemistry behaviour. Long‐term data revealed total annual flows and mean flows remained fairly consistent post‐fire, while small increases in baseflow were observed in the low‐moderately burned watershed. Stream water concentrations of nitrate, phosphate and sulfate significantly increased following fire, with variance in concentration increasing with fire severity. Our end member mixing models suggested that during rain events, the watershed with low‐moderate severity fire had greater streamflow inputs from soil water and groundwater during times of low connectivity compared to the watershed with low severity fire. Finally, differences in fire severity impacts on concentration‐discharge relationships of biogenic solutes were most expressed under low catchment connectivity conditions. Our study provides insights into post‐wildfire impacts to stream water quality, with the goal of informing future research on stream chemistry responses to low, moderate and mixed severity wildfire. 

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5. 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 (February 2025, Journal of Geophysical Research: Machine Learning and Computation)

   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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