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At least 19 records

Watersheds and stream networks viewed longitudinally: Example insights from novel spatial portrayals of watershed characteristics

Abstract Longitudinal depictions of watershed structure and characteristics, including topography, stream networks, wetlands, ground water levels, and land use, can provide watershed knowledge and understanding unavailable from standard plan view maps. Three case studies provide examples of knowledge gained by applying longitudinal views of stream networks, watershed hydrologic behavior, and land use distributions. Longitudinal views of mountain stream networks show extreme variability in the slope‐area relationships of low Strahler order streams, large discontinuities in drainage area (large parts of drainage area space are absent in networks), and large variations in network curvature. Longitudinal views of a groundwater‐dominated headwater watershed increase the inference available from limited groundwater observations and clearly reveal how groundwater connections affect the permanence of surface water features and the distribution of vadose zone storage in the landscape. Plotting land uses longitudinally illuminates and allows a quantitative analysis of how land uses are distributed relative to topographic position. Viewing watersheds and stream networks longitudinally can provide new insights into watershed forms and processes and motivate new questions and research.

60 APPLIED LIFE SCIENCES↗

The influence of burn severity on dissolved organic carbon concentrations across a stream network differs based on seasonal wetness conditions

Large, high-severity wildfires in many regions across the globe have increased concerns about their impacts on carbon cycling in watersheds. Altered sources of carbon and changes in catchment hydrology after wildfire can lead to shifts in dissolved organic carbon (DOC) concentrations in streams, which can have negative impacts on aquatic ecosystem health and downstream drinking-water treatment. Despite its importance, post-fire DOC responses remain relatively unconstrained in the literature, and we lack critical knowledge of how burn severity, landscape elements, and climate interact to affect DOC concentrations. To improve our understanding of the impact of burn severity on DOC concentrations, we measured DOC at 129 sites across a stream network extending upstream, within, and downstream of a large, high-severity wildfire in Oregon, USA. We collected samples across the study sub-basin during four distinct seasonal wetness conditions. We used our high-spatial-resolution data to develop spatial stream network (SSN) models to predict DOC across the stream network and to improve our understanding of the controls on DOC concentrations. Spatially, we found no obvious wildfire signal – instead, we observed a pattern of increasing DOC concentrations from the high-elevation headwaters to the sub-basin outlet, while the mainstem maintained consistently low DOC concentrations. This suggests that effects from large wildfires may be “averaged” out at higher stream orders and larger spatial scales. When we grouped DOC concentrations by burn severity group, we observed a significant decrease in the variability of DOC concentrations in the moderate and high burn severity sub-catchments. However, our SSN models were able to predict decreases in DOC concentrations with increases in burn severity across the stream network. Decreases in DOC concentrations were also highly variable across seasonal wetness conditions, with the greatest (–1.40 to –1.64 mg L –1 ) decrease occurring in the high-severity group during the wetting season. Additionally, our models indicated that in all seasons, baseflow index was more influential in predicting DOC concentrations than burn severity was, indicating that groundwater discharge can obscure the impacts of wildfire in a stream network. Overall, our results suggested that landscape characteristics can regulate the DOC response to wildfire. Moreover, our results also indicated that the seasonal timing of sampling can influence the observed response of DOC concentrations to wildfire.

54 ENVIRONMENTAL SCIENCES↗

Investigating the impacts of solid phase extraction on dissolved organic matter optical signatures and the pairing with high‐resolution mass spectrometry data across a freshwater stream network

Abstract Advancing our understanding of dissolved organic matter (DOM) chemistry in aquatic systems necessitates the integration of data streams from multiple analytical platforms. Some measurements require pretreatment with solid phase extraction (SPE), while others are performed directly on whole water samples. Evidence has suggested that SPE will be biased against select DOM fractions, leading to concerns over the ability to establish data linkages across platforms with variable needs for SPE pretreatment, such as those from optical measurements and those that provide high‐resolution molecular information. Here, we directly addressed this concern by assessing the impact of SPE on DOM optical properties through excitation–emission matrices with parallel factor analysis (PARAFAC) for 47 samples across a stream network within a single watershed reflective of variable DOM sources. PARAFAC data was further paired with molecular information obtained by Fourier transform ion cyclotron resonance mass spectrometry (FTICR‐MS). A comparison of PARAFAC models first revealed no systematic qualitative differences in major components between whole water DOM and DOM isolated by SPE (SPE‐DOM); however, quantitative biases against select components were observed. Further linkages with FTICR‐MS data revealed that the molecular fingerprint associated with each PARAFAC component was consistent between the whole water DOM and SPE‐DOM. Our results suggest that bulk scale linkages across these analytical platforms could be inferred irrespective of the observed quantitative biases resulting from SPE for samples within this example watershed. This work represents a key step toward the systematic evaluation of linkages between optical and high‐resolution mass spectrometry datasets in freshwater lotic environments.

59 BASIC BIOLOGICAL SCIENCES↗

Spatial and temporal variation in river corridor exchange across a 5th-order mountain stream network

Although most field and modeling studies of river corridor exchange have been conducted at scales ranging from tens to hundreds of meters, results of these studies are used to predict their ecological and hydrological influences at the scale of river networks. Further complicating prediction, exchanges are expected to vary with hydrologic forcing and the local geomorphic setting. While we desire predictive power, we lack a complete spatiotemporal relationship relating discharge to the variation in geologic setting and hydrologic forcing that is expected across a river basin. Indeed, the conceptual model of Wondzell (2011) predicts systematic variation in river corridor exchange as a function of (1) variation in baseflow over time at a fixed location, (2) variation in discharge with location in the river network, and (3) local geomorphic setting. To test this conceptual model we conducted more than 60 solute tracer studies including a synoptic campaign in the 5th-order river network of the H. J. Andrews Experimental Forest (Oregon, USA) and replicate-in-time experiments in four watersheds. We interpret the data using a series of metrics describing river corridor exchange and solute transport, testing for consistent direction and magnitude of relationships relating these metrics to discharge and local geomorphic setting. We confirmed systematic decrease in river corridor exchange space through the river networks, from headwaters to the larger main stem. However, we did not find systematic variation with changes in discharge through time or with local geomorphic setting. While interpretation of our results is complicated by problems with the analytical methods, the results are sufficiently robust for us to conclude that space-for-time and time-for-space substitutions are not appropriate in our study system. Finally, we suggest two strategies that will improve the interpretability of tracer test results and help the hyporheic community develop robust datasets that will enable comparisons across multiple sites and/or discharge conditions.

54 ENVIRONMENTAL SCIENCES↗

Dynamics of streamflow permanence in a headwater network: Insights from catchment-scale model simulations

The hillslope and channel dynamics that govern streamflow permanence in headwater systems have important implications for ecosystem functioning and downstream water quality. Recent advancements in process-based, semi-distributed hydrologic models that build upon empirical studies of streamflow permanence in well-monitored headwater catchments show promise for characterizing the dynamics of streamflow permanence in headwater systems. However, few process-based models consider the continuum of hillslope-stream network connectivity as a control on streamflow permanence in headwater systems. The objective of this study was to expand a process-based, catchment-scale hydrologic model to better understand the spatiotemporal dynamics of headwater streamflow permanence and to identify controls of streamflow expansion and contraction in a headwater network. Further, we aimed to develop an approach that enhanced the fidelity of model simulations, yet required little additional data, with the intent that the model might be later transferred to catchments with limited long-term and spatially explicit measurements. This approach facilitated network-scale estimates of the controls of streamflow expansion and contraction, albeit with higher degrees of uncertainty in individual reaches due to data constraints. Our model simulated that streamflow permanence was highly dynamic in first-order reaches with steep slopes and variable contributing areas. The simulated stream network length ranged from nearly 98±2% of the geomorphic channel extent during wet periods to nearly 50±10% during dry periods. The model identified a discharge threshold of approximately 1 mm d –1 , above which the rate of streamflow expansion decreases by nearly an order of magnitude, indicating a lack of sensitivity of streamflow expansion to hydrologic forcing during high-flow periods. Overall, we demonstrate that process-based, catchment-scale models offer important insights on the controls of streamflow permanence, despite uncertainties and limitations of the model. We encourage researchers to increase data collection efforts and develop benchmarks to better evaluate such models.

54 ENVIRONMENTAL SCIENCES↗

Hydrologic Model Data for the East Fork Poplar Creek Watershed Simulated with the Advanced Terrestrial Simulator (ATS): Streamflow and Network Expansion–Contraction Dynamics

This dataset supports hydrologic modeling and stream network expansion–contraction analysis for the East Fork Poplar Creek (EFPC) Watershed in Tennessee. It includes a Jupyter notebook for model setup, model configuration files, simulation outputs, and derived products used to evaluate model performance and investigate stream dynamics under varying hydrologic conditions. The dataset was generated using the Watershed Workflow Python package and the Advanced Terrestrial Simulator (ATS), enabling integrated surface–subsurface hydrologic simulations using a stream-aligned mesh. Outputs include high-resolution time series of streamflow, active network length, water table depth, and related hydrologic variables. Also included are spatially explicit stream persistency indices and classifications of reaches as perennial or non-perennial. These data facilitate reproducibility and support further research on stream intermittency and variability in network extent.The model data archive is organized in following directories:1) model_setup_inputsContains the Watershed Workflow Jupyter notebooks (accessed through any open source code editor), selected input datasets, and resulting ATS input files, including XML files (access through any open source code editor), computational mesh (.exo files can be viewed using Paraview), and meteorological forcing files (.h5 files can be accessed through h5py python package and HDFView open source software). 2) model_outputsIncludes ATS simulation outputs relevant to this study. Time series of spatially integrated or averaged variables (e.g., streamflow, water table depth) are provided as CSV files. Select spatial fields (e.g., ponded depth and water table depth) are saved as pickled Python objects to reduce file size, and can be accessed through pickle package in Python. Key geometry objects from Watershed Workflow—such as the surface mesh and river tree—are also included to support analysis of streamflow persistency and expansion–contraction dynamics. These files can also be accessed through Watershed Workflow Python package.3) model_evaluationProvides observed streamflow time series and field survey-based flow regime classifications used to evaluate model performance. Jupyter notebooks for processing ATS outputs and comparing model predictions with observations to build confidence in the model prior to scientific analysis are also included.4) Q_L_relationshipsContains workflows for generating time series of discharge, active network length, and related hydrologic variables used in the stream network expansion–contraction analysis. Includes routines for delineating baseflow-dominated periods. For each catchment, notebooks and processed data (as pickled DataFrames accessed through Pandas Python package) are provided. 5) figure_scriptsProvides the Jupyter notebooks used to generate the figures presented in the paper.

54 ENVIRONMENTAL SCIENCES↗

Riverine organic matter functional diversity increases with catchment size

A large amount of dissolved organic matter (DOM) is transported to the ocean from terrestrial inputs each year (~0.95 Pg C per year) and undergoes a series of abiotic and biotic reactions, causing a significant release of CO 2 . Combined, these reactions result in variable DOM characteristics (e.g., nominal oxidation state of carbon, double-bond equivalents, chemodiversity) which have demonstrated impacts on biogeochemistry and ecosystem function. Despite this importance, however, comparatively few studies focus on the drivers for DOM chemodiversity along a riverine continuum. Here, we characterized DOM within samples collected from a stream network in the Yakima River Basin using ultrahigh-resolution mass spectrometry (i.e., FTICR-MS). To link DOM chemistry to potential function, we identified putative biochemical transformations within each sample. We also used various molecular characteristics (e.g., thermodynamic favorability, degradability) to calculate a series of functional diversity metrics. We observed that the diversity of biochemical transformations increased with increasing upstream catchment area and landcover. This increase was also connected to expanding functional diversity of the molecular formula. This pattern suggests that as molecular formulas become more diverse in thermodynamics or degradability, there is increased opportunity for biochemical transformations, potentially creating a self-reinforcing cycle where transformations in turn increase diversity and diversity increase transformations. We also observed that these patterns are, in part, connected to landcover whereby the occurrence of many landcover types (e.g., agriculture, urban, forest, shrub) could expand DOM functional diversity. For example, we observed that a novel functional diversity metric measuring similarity to common freshwater molecular formulas (i.e., carboxyl-rich alicyclic molecules) was significantly related to urban coverage. These results show that DOM diversity does not decrease along stream networks, as predicted by a common conceptual model known as the River Continuum Concept, but rather are influenced by the thermodynamic and degradation potential of molecular formula within the DOM, as well as landcover patterns.

54 ENVIRONMENTAL SCIENCES↗

Reconstructing the Stripping History of the Sagittarius Stream with Neural Networks

The Sagittarius (Sgr) Stream is produced by the ongoing disruption of the Sgr dwarf spheroidal (dSph) galaxy and is thought to contain multiple wraps that were stripped during different pericentric passages. In this study, we introduce a neural-network–based method trained on N-body simulations to infer the stripping time of Sgr Stream stars directly from their phase-space coordinates. We combine spectroscopic data from SEGUE, APOGEE DR17, and LAMOST DR7 low-resolution spectroscopic (LRS) survey with Gaia EDR3 astrometry and distance estimates from the latest StarHorse catalog to identify high-quality Sgr Stream members. Applying our method to these stars, we measure a clear metallicity gradient with stripping time, well described by a linear relation with slope ∼0.3 dex Gyr −1 . We further predict the stripping times of globular clusters previously suggested to originate from the Sgr dSph. M 54, Terzan 7, Terzan 8, and Arp 2 exhibit stripping times consistent with being currently bound to the Sgr remnant. Pal 12, Whiting 1, and NGC 2419 are inferred to have been stripped 0.9 ± 0.1, 1.1 ± 0.2, and 2.1 ± 0.2 Gyr ago, respectively. For NGC 4147 and NGC 5634, whose membership in the Sgr system remains uncertain, our analysis suggests stripping times of 1.1 ± 0.4 and 1.1 ± 0.1 Gyr, respectively, if they are ultimately confirmed as genuine Sgr members. These results demonstrate that data-driven models of dynamical stripping histories offer a promising approach for reconstructing the formation and chemical evolution of the Sgr Stream.

79 ASTRONOMY AND ASTROPHYSICS↗

Riparian vegetation shade restoration and loss effects on recent and future stream temperatures

River temperatures are expected to increase this century harming species requiring cold-water habitat unless restoration activities protect or improve habitat availability. Local shading by riparian vegetation can cool water temperatures, but uncertainty exists over the scaling of this local effect to larger spatial extents. Here, we evaluate this issue using a regional spatial stream network temperature model with covariates representing shade effects to predict mean August stream temperatures across 78,195 km of tributaries flowing into the Columbia River in the northwestern United States. We evaluate nine scenarios predicting stream temperatures for three riparian shade conditions (current, restored, and no riparian vegetation) within three different climate periods (2000s, 2040s, and 2080s). Results suggest riparian shade restoration (2000s climate) could decrease mean August stream temperatures by 0.62°C across the study network. Under the same restored shade conditions, temperature predictions for tributaries at their confluence with the Columbia River range from 0.02 to 2.08°C cooler than under current shade conditions. The climate warming effect predicted for the 2040s and 2080s, however, is greater than the cooling effect from restoring riparian shade. Streams less than 10-m bankfull width cooled more frequently with riparian shade restoration. In Oregon, the proportion of fish habitat for salmon and trout rearing and migration that meet temperature numeric water quality criteria could be increased by 20% under restored shade conditions although net habitat declines may still occur in the future. We conclude riparian vegetation restoration could partially mitigate future warming and help maintain cold-water habitats that function as thermal refuges if implemented strategically.

54 ENVIRONMENTAL SCIENCES↗

ThunderSecure: deploying real-time intrusion detection for 100G research networks by leveraging stream-based features and one-class classification network

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks (40/100G) across distributed sites for processing, storage, and analysis. Like general purpose networks, research networks experience intrusions. However, monitoring anomalies in such high-speed network traffics is challenging given current cyber-infrastructure. Moreover, traditional network intrusion detection systems (NIDS) are signature based. However, anomaly patterns are difficult to define and that rulesets are often not updated frequently enough to reflect the changes of attack behaviors. We present ThunderSecure, a high-throughput, unsupervised learning-based intrusions detection system for 100G research networks. ThunderSecure implements an efficient packet processing and detection pipeline using multi-cores and GPUs. It extracts statistical and temporal features from real-time network data streams and feeds them to a one-class anomaly detection network. A baseline of normal distribution will be created based on the training observation. Testing traffic deviated from the learned profile will be marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets mirrored from two 100G network connections at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. Results show that ThunderSecure can recognize science data traffic captured long after the training and made nearly certain detection on the segment of the streams where anomalous flows were injected.

100G research network↗

Groundwater and Surface Water Flow (GSFLOW) model files to explore bedrock circulation depth and porosity in Copper Creek, Colorado

This data package contains integrated hydrological model input and output files for Copper Creek, Colorado (24 km2), a tributary of the East River located in the headwaters of the Upper Colorado River Basin. The model code is the U.S. Geological Survey (USGS) Groundwater and Surface Water Flow (GSFLOW) model. The model contains a 100-m grid resolution and a daily timestep. The land surface model is dynamically linked to a three-dimensional groundwater flow model that allows for streamflow gaining and losing conditions. The groundwater model contains 12 model layers and extends 400 m below land surface. The original Copper Creek model was modified to contain geologic layers representing saprolite, shallow bedrock, and deep bedrock. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. For the shallow case, median flow depths occur in the shallow saprolite at depths <8 m, while the deep case promotes a median groundwater flow depth of 100 m. With this modeling framework we compare streamflow response to a plausible worst-case drought lasting up to five years. Streamflow metrics of analysis include average streamflow, fraction of stream network that is dry, no-flow duration, average groundwater flow to streams and time to recovery following the drought. Results and implications are presented in a paper submitted to Geophysical Research Letters titled, "The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought" by Rosemary WH. Carroll, Andrew H. Manning and Kenneth H Williams. A Readme.txt file provides instructions on how to download all model files and execute each model scenario. In addition to the GSFLOW output/prms/copper_drought.csv file containing daily basin water stores and fluxes (refer to GSFLOW manual) and the output/prms/copper_drought_statvar.dat file with output defined in the gsflow3.control file (refer to GSFLOW Manual), output files also include spatially distributed daily values of total evapotranspiration, canopy evaporation, precipitation, snowfall, infiltration, snow water equivalent, potential evapotranspiration, recharge, sublimation, soil moisture, contributing interflow, water table elevations, changes in groundwater storage, groundwater evapotranspiration, interbasin groundwater flow (limited to the alluvium below the stream outlet), and surface-groundwater exchanges within the river system.

54 ENVIRONMENTAL SCIENCES↗

Quantifying Variability and Controls of Riverine Dissolved Organic Carbon Exported to Arctic Coastal Margins of North America (Final Report)

This project involved implementation and application of a coupled permafrost hydrology and dissolved organic carbon process models to investigate how spatial and seasonal variations in terrestrial hydrology and soil freeze/thaw dynamics influence the mobilization, loading, and export of organic carbon to the stream network for selected arctic basins across northern Alaska and northwest Canada. The model simulations were constrained by detailed observations of in- stream chemistry, soil active layer profile moisture and temperature dynamics, streamflow, soil carbon inventories and satellite microwave remote sensing based assessments of surface soil freeze-thaw dynamics. We developed and applied the numerical modeling and data analysis, incorporating observed data for calibration and validation, in order to investigate the terrestrial hydrology, permafrost dynamics, and associated DOC production and loading to rivers across a region encompassing watersheds draining to the coast. The project produced six publications and three datasets archived in public repositories.

54 ENVIRONMENTAL SCIENCES↗

Differential Trends in Iron Concentrations of Boreal Streams Linked to Catchment Characteristics

Increasing iron (Fe) concentrations have been reported for freshwaters across northern Europe over the last decades. This increase, together with elevated concentrations of dissolved organic carbon (DOC), leads to browning of freshwaters, which affects aquatic organisms, ecosystem functioning, biogeochemical cycles, and brings challenges to drinking water production. However, how such increasing trends in stream Fe concentrations reflect the contribution of different catchment sources remains poorly resolved. Here, we explored how catchment characteristics, that is, mires and coniferous soils, regulate spatial and temporal patterns of Fe in a boreal stream network. For this, we determined Fe speciation in riparian and mire soils, and studied temporal Fe dynamics in soil-water and stream-water over a span of 18 years. Positive Fe trends were found in the solution of the riparian soil, while no long-term trend was observed in the mire. These differences were reflected in stream-water, where three headwater streams dominated by coniferous cover also displayed positive Fe trends, whereas the mire dominated stream showed no trend. Surprisingly, the majority of higher order streams showed declining Fe trends, despite long-term increases in DOC. In addition, we found that an extreme drought event led to a prolonged release of Fe and DOC from the riparian soils, that could have long-term effects on stream Fe concentrations. Our results show that riparian forest soils can be major contributors to ongoing increases in freshwater Fe concentrations and that drought can further promote the release of Fe from organic soils.

54 ENVIRONMENTAL SCIENCES↗

A dendritic strontium river isoscape for fisheries applications in the Sacramento River basin, California, USA

Objective Understanding the origins and movements of fish is fundamental to effective conservation and fisheries management. Strontium isotope ratios ( 87 Sr/ 86 Sr) in otoliths provide a powerful tracer of natal origin and migratory pathways. However, existing 87 Sr/ 86 Sr isoscapes for the Sacramento River basin, an ecosystem that supports ecologically and economically important salmon populations, rely on discrete classification approaches that overlook unsampled habitats and do not incorporate spatial uncertainty. Our objective was to develop a continuous, network-explicit 87 Sr/ 86 Sr isoscape with quantified uncertainty to fill in data gaps and enable probabilistic assignments of fish origin and movement. Methods We used river water 87 Sr/ 86 Sr data from 106 sites (1997–2021) to develop spatial stream network models that use dendritic connectivity and watershed characteristics (lithology, bedrock age, and land cover) to predict river water 87 Sr/ 86 Sr throughout the basin. Models were fitted using maximum and restricted likelihood and were evaluated via Akaike’s information criterion and leave-one-out cross validation. We produced both historical (pre-dam) and present-day (below-dam) isoscapes, delineated uncertainty-informed isotopic ranges using k -means clustering, and applied a proof-of-concept Bayesian assignment to estimate natal origins and early rearing habitats for two endangered winter-run Chinook Salmon Oncorhynchus tshawytscha. Results Cross validation indicated strong performance of the 87 Sr/ 86 Sr model (leave-one-out cross validation: R 2 = 0.91; root mean square error = 0.0005). Uncertainty-informed clustering identified 19 isotopic “suites” (reaches with indistinguishable 87 Sr/ 86 Sr values) in present-day anadromous habitats and 25 suites in the historical network. Example natal and early rearing assignments included predictions that challenged expectations for juvenile salmon migration based on predicted river 87 Sr/ 86 Sr compositions. Conclusions This study developed a continuous, network-explicit 87 Sr/ 86 Sr isoscape that integrates existing river data to predict 87 Sr/ 86 Sr in unsampled reaches and the likely achievable range and resolution of otolith-based origin and life history inference. The resulting river isoscape provides a valuable tool to predict salmon movements and identify habitats supporting their survival and growth that otherwise might remain undetected. Coupling these predictions with complementary approaches that ground-truth juvenile presence (e.g., targeted fish surveys) represents an important step toward science-informed restoration and management of critical habitats throughout the Sacramento River basin.

Environmental sciences↗

Model Data Archive Associated with Manuscript "Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon"

This data package supports the publication “Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon” by Li et al. (2026). The package contains processed model inputs, configuration files, restart files, simulation outputs, scripts, and visualization products used to evaluate post-fire dissolved organic carbon (DOC) dynamics in the Naches River Watershed, Washington, USA, following the 2021 Schneider Springs Fire. The modeling workflow couples ELM-BGC, the biogeochemistry-enabled Energy Exascale Earth System Model Land Model; ATS, the Advanced Terrestrial Simulator for integrated surface-subsurface hydrology; and PFLOTRAN, a reactive transport model for multicomponent aqueous geochemistry. Together, these models simulate how wildfire-induced changes in vegetation, litter, coarse woody debris, and soil organic matter influence DOC production, transport, and reaction from burned hillslopes to stream networks. The archive includes preprocessed meteorological, geospatial, hydrologic, and biogeochemical forcing data; ELM-BGC-derived DOC source terms; ATS mesh files; PFLOTRAN reactive-transport inputs; model configuration files; spin-up and transient restart files; watershed-scale diagnostic outputs; stream concentration time series; and figures or visualization files used to inspect and reproduce key results. File types include Hierarchical Data Format 5 (HDF5) files for gridded forcing and model-coupling data, model input and configuration files for ELM-BGC, ATS, and PFLOTRAN, restart and simulation-output files generated by the modeling workflow, tabular or time-series diagnostic outputs, scripts for post-processing and figure generation, and image or visualization products associated with the manuscript. Use of the package depends on the intended task. Re-running the simulations requires the relevant modeling software, including ELM-BGC, ATS, and PFLOTRAN as ATS's geochemical engine. Inspecting outputs and reproducing figures requires Python with scientific plotting libraries such as Matplotlib, and three-dimensional model outputs may be viewed with ParaView. Geographic information system files or maps may be inspected with ArcGIS Pro or comparable GIS software. The data package is intended to enable traceability, reuse, and partial reproduction of the coupled land-to-watershed hydro-biogeochemical modeling workflow used to test how wildfire disturbance affects terrestrial carbon pools and downstream DOC dynamics.

ATS↗

WHONDRS 2016 Sediment Organic Matter Characterization Data from Streams across HJ Andrews Experimental Forest, Oregon

This dataset supports a broader synoptic effort to map morphological, hydrological, chemical, and biological conditions across a fifth-order mountain stream network. Samples were generated through a collaborative synoptic sampling effort in 2016. The dataset provides sediment Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) from 60 sites across the HJ Andrews Experimental Forest, Oregon (https://andrewsforest.oregonstate.edu). Related data were collected as part of the event and were published separately in collaboration with other team members. The data are available at http://www.hydroshare.org/resource/ea6c0832885a46c3939e7bb22e48e754 and are described within https://doi.org/10.5194/essd-11-1567-2019 (Ward et al., 2019). The hydroshare data package contains processed FTICR-MS data from the samples included in this data package. The data were processed via Formultitude (previously called Formularity; https://github.com/PNNL-Comp-Mass-Spec/Formultitude). However, we have re-processed the data using Core-MS and included it in this data package. Additional related data collected in 2025 from a similar effort can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3023310 and http://www.hydroshare.org/resource/b274c4a234bf4b12b7cb8a54a696c629. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of sample data; (2) data dictionary; (3) file-level metadata; (4); (5) coordinates; and (6) readme. The sample data subfolder contains 12 Tesla (12T) FTICR-MS data. This folder contains the processed data and three subfolders, one containing the .xml files, one containing the CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .Rmd, .py, .cal, or .json.

Biogeochemistry↗

The fluid definition of the ‘waters of the United States’: Non-uniform effects of regulation on US wetland protections

Recent revisions to the definition of the ‘waters of the United States’ (WOTUS) have considerably altered how wetlands are federally regulated under the Clean Water Act. The two most recent modifications to WOTUS, the Clean Water Rule (CWR) and the Navigable Waters Protection Rule (NWPR), represent two opposing approaches to the federal wetland policy. The impacts of these two rules on the regulation of wetlands have as of yet been poorly characterized at broad spatial scales. Using New York State (NYS) as a case study, we evaluated the jurisdictional statuses of more than 253 000 wetlands under the CWR and the NWPR to assess the landscape-scale effects of WOTUS re-definitions. Here, we found that statewide and within each of NYS's hydrologic regions, the NWPR protects fewer wetlands and less total wetland area than the CWR. The efficacy of the two regulations varied considerably in space across NYS, highlighting the need for comprehensive, nationwide assessments of wetland policy outcomes. We also observed that both rules produced non-uniform patterns in jurisdiction across a range of landscape positions and wetland sizes, preferentially protecting large wetlands close to the stream network. This effect was particularly pronounced under the NWPR, which excludes all geographically isolated wetlands from protection. Our findings in NYS emphasize the existence of unique patterns in protected wetlands across spatial scales, highlighting the value in applying geospatial analyses to evaluate environmental policy.

54 ENVIRONMENTAL SCIENCES↗