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Predicting elemental cycles and maintaining water quality under increasing anthropogenic influence requires understanding the spatial drivers of river microbiomes. However, the unifying microbial processes governing river biogeochemistry are hindered by a lack of genome-resolved functional insights and sampling across multiple rivers. Here we employed a community science effort to accelerate the sampling, sequencing, and genome-resolved analyses of river microbiomes to create the Genome Resolved Open Watersheds database (GROWdb). This resource profiled the identity, distribution, function, and expression of thousands of microbial genomes across rivers covering 90% of United States watersheds. Specifically, GROWdb encompasses 1,469 microbial species from 27 phyla, including novel lineages from 10 families and 128 genera, and defines the core river microbiome for the first time at genome level. GROWdb analyses coupled to extensive geospatial information revealed local and regional drivers of microbial community structuring, while also presenting a myriad of foundational hypotheses about ecosystem function. Building upon the previously conceived River Continuum Concept 1 , we layer on microbial functional trait expression, which suggests the structure and function of river microbiomes is predictable. We make GROWdb available through various collaborative cyberinfrastructures 2, 3 so that it can be widely accessed across disciplines for watershed predictive modeling and microbiome-based management practices.

Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats-instructions, templates, and tools for consistently formatting data within a discipline-can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable.

Physical samples are foundational entities for research across biological, Earth, and environmental sciences. Data generated from sample-based analyses are not only the basis of individual studies, but can also be integrated with other data to answer new and broader-scale questions. Ecosystem studies increasingly rely on multidisciplinary team-science to study climate and environmental changes. While there are widely adopted conventions within certain domains to describe sample data, these have gaps when applied in a multidisciplinary context. In this study, we reviewed existing practices for identifying, characterizing, and linking related environmental samples. We then tested practicalities of assigning persistent identifiers to samples, with standardized metadata, in a pilot field test involving eight United States Department of Energy projects. Participants collected a variety of sample types, with analyses conducted across multiple facilities. We address terminology gaps for multidisciplinary research and make recommendations for assigning identifiers and metadata that supports sample tracking, integration, and reuse. Our goal is to provide a practical approach to sample management, geared towards ecosystem scientists who contribute and reuse sample data. (IGSN); physical samples; soil; water; plant; leaf; microbial communities; related identifiers; persistent identifiers.

Streambed grain sizes control river hydro‐biogeochemical (HBGC) processes and functions. However, measuring their quantities, distributions, and uncertainties is challenging due to the diversity and heterogeneity of natural streams. This work presents a photo‐driven, artificial intelligence (AI)‐enabled, and theory‐based workflow for extracting the quantities, distributions, and uncertainties of streambed grain sizes from photos. Specifically, we first trained You Only Look Once, an object detection AI, using 11,977 grain labels from 36 photos collected from nine different stream environments. We demonstrated its accuracy with a coefficient of determination of 0.98, a Nash–Sutcliffe efficiency of 0.98, and a mean absolute relative error of 6.65% in predicting the median grain size of 20 ground‐truth photos representing nine typical stream environments. The AI is then used to extract the grain size distributions and determine their characteristic grain sizes, including the 10th, 50th, 60th, and 84th percentiles, for 1,999 photos taken at 66 sites within a watershed in the Northwest US. The results indicate that the 10th, median, 60th, and 84th percentiles of the grain sizes follow log‐normal distributions, with most likely values of 2.49, 6.62, 7.68, and 10.78 cm, respectively. The average uncertainties associated with these values are 9.70%, 7.33%, 9.27%, and 11.11%, respectively. These data allow for the computation of the quantities, distributions, and uncertainties of streambed HBGC parameters, including Manning's coefficient, Darcy‐Weisbach friction factor, top layer interstitial velocity magnitude, and nitrate uptake velocity. Additionally, major sources of uncertainty in grain sizes and their impact on HBGC parameters are examined. Plain Language Summary: Streambed grain sizes control river hydro‐biogeochemical function by modulating the resistance, speed of water exchange, and nutrient transport at water‐sediment interface. Consequently, quantifying grain sizes and size‐dependent hydro‐biogeochemical parameters is critical for predicting river's function. In natural streams, measuring these sizes and parameters, however, is challenging because grain sizes vary from millimeters to a few meters, change from small creeks to big streams, and could be concealed by complex non‐grain materials such as water, ice, mud, and grasses. All these factors make the size measurements a time‐consuming and high‐uncertain task. We address these challenges by demonstrating a workflow that combines computer vision artificial intelligence (AI), smartphone photos, and new uncertainty quantification theories. The AI performs well across various sizes, locations, and stream environments as indicated by an accuracy metric of 0.98. We apply the AI to extract the grain sizes and their characteristic percentiles for 1,999 photos. These characteristic grain sizes are then input into existing and our new theories to derive the quantities, distributions, and uncertainties of hydrobiogeochemical parameters. The high accuracy of the AI and the success of extracting grain sizes and hydro‐biogeochemical parameters demonstrate the potential to advance river science with computer vision AI and mobile devices. Key Points: Stream sediments bigger than 44 microns can be detected from smartphone photos by You Only Look Once with a Nash–Sutcliffe efficiency of 0.98Quantities, distributions, and uncertainties of streambed grain sizes can be determined from photosImpact of grain size uncertainty on hydrobiogeochemical parameters is examined [ABSTRACT FROM AUTHOR]

Aerobic respiration of organic matter is a key metabolic process influencing carbon (C) biogeochemistry in aquatic ecosystems. Anthropogenic and environmental perturbations to stream ecosystem metabolism can have deleterious effects on downstream water quality. Various environmental features of rivers also influence stream metabolism, including physical (e.g., discharge, light, flow regimes) and chemical factors (nutrients, organic matter) and watershed characteristics (e.g., stream size or drainage area, land use). The relative proportion of surface water contact with benthic sediments has been considered the primary driver of ecosystem processes, including ecosystem respiration (ER). While aquatic ecosystem respiration occurs in the water column (ERwc) and in benthic sediments--including surficial and subsurface sediments (ERsed)--ERsed has long been assumed to be the primary contributor to whole-river ecosystem respiration (ERtot). Recent studies show, however, that somewhere along the river continuum (e.g., 5th-9th order), rivers transition from being dominated by benthic processes to being dominated by water column processes. Yet few metabolism studies have parsed contributions from the water column (ERwc) to ERtot, making it difficult to evaluate the relative magnitude and importance of ERwc across the river continuum and cross biomes. In this study, we used the Yakima River basin, Washington, USA, to increase our understanding of basin-scale variation in ERwc. We collected ERwc data and water chemistry samples in triplicate at 47 sites in the Yakima River basin distributed across Strahler stream orders 2-7 and different hydrological and biophysical settings during summer baseflow conditions in 2021. We found that observed ERwc rates were consistently slow throughout the basin during baseflow conditions, ranging from -0.11-0.03 mg O2 L-1 d-1, and were generally at the very slow end of the range of published ERwc literature values. When compared to reach-scale ERtot rates predicted for rivers across the conterminous United States (CONUS), the very slow ERwc rates we observed throughout the Yakima River basin indicate that ERwc is likely a small component of ERtot in this basin. Despite these slow rates, ERwc nonetheless shows spatial variation across the Yakima River basin that was well explained by watershed characteristics and water chemistry. Multiple linear regression model results show that nitrate (NO3-N), dissolved organic carbon (DOC), and temperature together explained 41.5 % of the spatial variation in ERwc. Supporting the findings of other studies, we found that ERwc increased linearly with increasing NO3-N, increasing DOC, and increasing temperature. We hypothesize that low concentrations of nutrients, DOC, and low temperatures in the water column, coupled with low TSS concentrations, likely contribute to the slow ERwc rates observed throughout the Yakima River basin. Because ERtot measurements integrate contributions from water column respiration and sediment-associated respiration (ERsed), estimating ERtot in cold, clear, low nutrient rivers like those in the Yakima River basin with very slow ERwc will essentially measure contributions from ERsed. [ABSTRACT FROM AUTHOR]

River corridors are fundamental components of the Earth system, and their biogeochemistry can be heavily influenced by processes in subsurface zones immediately below the riverbed, referred to as the hyporheic zone. Within the hyporheic zone, organic matter (OM) fuels microbial respiration, and OM chemistry heavily influences aerobic and anaerobic biogeochemical processes. The link between OM chemistry and respiration has been hypothesized to be mediated by OM molecular diversity, whereby respiration is predicted to decrease with increasing diversity. Here we test the specific prediction that aerobic respiration rates will decrease with increases in the number of unique organic molecules (i.e., OM molecular richness, as a measure of diversity). We use publicly available data across the United States from crowdsourced samples taken by the Worldwide Hydrobiogeochemical Observation Network for Dynamic River Systems (WHONDRS) consortium. Our continental-scale analyses rejected the hypothesis of a direct limitation of respiration by OM molecular richness. In turn, we found that organic carbon (OC) concentration imposes a primary constraint over hyporheic zone respiration, with additional potential influences of OM richness. We specifically observed respiration rates to decrease nonlinearly with the ratio of OM richness to OC concentration. This relationship took the form of a constraint space with respiration rates in most systems falling below the constraint boundary. A similar, but slightly weaker, constraint boundary was observed when relating respiration rate to the inverse of OC concentration. These results indicate that maximum respiration rates may be governed primarily by OC concentration, with secondary influences from OM richness. Our results also show that other variables often suppress respiration rates below the maximum associated with the richness-to-concentration ratio. An important focus of future research efforts will identify factors that suppress hyporheic zone respiration below the constraint boundaries observed here. [ABSTRACT FROM AUTHOR]

Background. The Worldwide Hydrobiogeochemistry Observation Network for Dy- namic River Systems (WHONDRS) is a consortium that aims to understand com- plex hydrologic, biogeochemical, and microbial connections within river corridors experiencing perturbations such as dam operations, floods, and droughts. For one ongoing WHONDRS sampling campaign, surface water metabolite and microbiome samples are collected through a global survey to generate knowledge across diverse river corridors. Metabolomics analysis and a suite of geochemical analyses have been performed for collected samples through the Environmental Molecular Sciences Laboratory (EMSL). The obtained knowledge and data package inform mechanistic and data-driven models to enhance predictions of outcomes of hydrologic perturbations and watershed function, one of the most critical components in model-data integration. To support efforts of the multi-domain integration and make the ever-growing data package more accessible for researchers across the world, a Shiny/R Graphical User Interface (GUI) called WHONDRS-GUI was created. Results. The web application can be run on any modern web browser without any programming or operational system requirements, thus providing an open, well- structured, discoverable dataset for WHONDRS. Together with a context-aware dy- namic user interface, the WHONDRS-GUI has functionality for searching, compiling, integrating, visualizing and exporting different data types that can easily be used by the community. The web application and data package are available at https://data. ess-dive.lbl.gov/view/doi:10.15485/1484811, which enables users to simultaneously obtain access to the data and code and to subsequently run the web app locally. The WHONDRS-GUI is also available for online use at Shiny Server (https://xmlin. shinyapps.io/whondrs/). [ABSTRACT FROM AUTHOR]

Surface water quality along river corridors can be modulated by hyporheic zones (HZs) that are ubiquitous and biogeochemically active. Watershed management practices often ignore the potentially important role of HZs as a natural reactor. To investigate the effect of hydrological exchange and biogeochemical processes on the fate of nutrients in surface water and HZs, a novel model, SWAT-MRMT-R, was developed coupling the Soil and Water Assessment Tool (SWAT) watershed model and the reaction module from a flow and reactive transport code (PFLOTRAN). SWAT-MRMT-R simulates concurrent nonlinear multicomponent biogeochemical reactions in both the channel water and its surrounding HZs, connecting the channel water and HZs through hyporheic exchanges using multirate mass transfer (MRMT) representation. Within the model, HZs are conceptualized as transient storage zones with distinguished exchange rates and residence times. The biogeochemical processes within HZs are different from those in the channel water. Hyporheic exchanges are modeled as multiple first order mass transfers between the channel water and HZs. As a numerical example, SWAT-MRMT-R is applied to the Hanford Reach of the Columbia River, a large river in the United States, focusing on nitrate dynamics in the channel water. Major nitrate contaminants entering the Hanford Reach include those from the legacy waste, irrigation return flows (irrigation water that is not consumed by crops and runs off as point sources to the stream), and groundwater seepage resulted from irrigated agriculture. A two-step reactions for denitrification and an aerobic respiration reaction are assumed to represent the biogeochemical transformations taking place within the HZs. The spatially variable hyporheic exchange rates and residence times in this example are estimated with the basin-scale Networks with Exchange and Subsurface Storage (NEXSS) model. Our simulation results show that 1) as the commonly used transient storage model for stream-HZ exchange of solutes uses a single residence time to parameterize the exchange coefficient, it may overestimate the nitrate attenuation role of HZs ignoring the contribution from HZs with low residence times; and 2) source locations of nitrate have different impact on surface water quality due to the spatially variable hyporheic exchanges. [ABSTRACT FROM AUTHOR]

Hydrologic exchange is a crucial component of the water cycle. The strength of the exchange directly affects the biogeochemical and ecological processes that occur in the hyporheic zone and aquifer from micro to reach scales. Hydrologic exchange fluxes (HEFs) can be quantified using many field measurement approaches, however, in a relatively large river (scale > 103 m), these approaches are limited by site accessibility, the difficulty of performing representative sampling, and the complexity of geomorphologic features and subsurface properties. In rivers regulated by hydroelectric dams, quantifying HEF rates becomes more challenging because of frequent hydropeaking events, featuring hourly to daily variations in flow and river stages created by dam operations. In this study, we developed and validated a new approach based on field measurements to estimate shallow water HEF rates across the river bed along the shoreline of the Columbia River, USA. Vertical thermal profiles measured by self-recording thermistors were combined with time series of hydraulic gradients derived from river stages and inland water levels to estimate the HEF rates. The results suggest that the HEF rates had high spatial and temporal heterogeneities over the riverbed, with predicted flux rates varied from +1 x 10-6m s-1 to -1.5 x 10-6m s-1 under different flow conditions. [ABSTRACT FROM AUTHOR]

The parafluvial hyporheic zone combines the heightened biogeochemical and microbial interactions indicative of a hyporheic region with direct atmospheric/terrestrial inputs and the effects of wet/dry cycles. Therefore, understanding biogeochemical cycling and microbial interactions in this ecotone is fundamental to understanding carbon cycling at the aquatic-terrestrial interface and to creating robust hydrobiogeochemical models. We aimed to (i) characterize biogeochemical and microbial differences in the parafluvial hyporheic zone across a small spatial domain (6 lateral meters) that spans a breadth of inundation histories and (ii) examine how parafluvial hyporheic sediments respond to laboratory-simulated reinundation. Surface sediment for assays and forced inundation laboratory incubations (destructively sampled at 0.5 hours and 25 hours) was collected at four elevations along transects perpendicular to flow of the Columbia River, eastern WA, USA. The sampling elevations were inundated by the river 0 days, 13 days, 127 days, and 398 days prior to sampling. Spatial variation in environmental variables (organic matter, moisture, nitrate, glucose, % C, % N) and microbial communities (16S and ITS rRNA gene sequencing, qPCR) were driven by differences in elevation and thus inundation history. Microbial respiration did not differ significantly across elevations prior to inundation. Inundation suppressed microbial respiration relative to uninundated sediment across all elevations, but the degree of suppression was dramatically different between the elevations saturated and unsaturated during sampling, indicating a binary threshold response. We present a conceptual model in which irregular hydrologic fluctuations facilitate microbial communities adapted to local conditions and a relatively high flux of CO2. Upon re-wetting, microbial communities are initially suppressed metabolically, which results in lower CO2 flux rates primarily due to suppression of fungal respiration. Following prolonged inundation, the microbial community adapts via a shift in composition. Our results indicate that the time between inundation events can push the system into alternate states: we suggest that (i) above some threshold of inundation-interval, re-inundation suppresses respiration to a consistent, low rate, and (ii) that below some inundation-interval, re-inundation has a minor effect on respiration. Extending reactive transport models to capture processes that govern such dynamics will provide more robust predictions of river corridor biogeochemical function under altered surface water flow regimes in both managed and natural watersheds. [ABSTRACT FROM AUTHOR]

Lakes are important sources of methane (CH4), accounting for 6-16% of natural global CH4 emissions annually. Vertical spatial resolution of CH4 sampling in the water column of lakes is limited because of physical dimensions of sampling devices and sampling-induced mixing. To improve pool-size estimates of carbon, diffusive flux calculations for CH4, and detailed mechanistic understanding of CH4 dynamics in lakes, more precise vertical profiles of dissolved gases through the water column are vital. We investigate the use of commercially available passive diffusion bags (PDBs) to obtain highly resolved (0.5-m increments) vertical profiles of dissolved CH4 concentrations and isotopic composition ( δ13 ). Laboratory experiments determined PDB equilibration time was between 5 d and 9 d for both CH4 concentration and δ13 . During July 2013, PDBs were deployed at intervals of 0.5-m depth for 5 d in two lakes located < 5 km from the southwestern margin of the Greenland ice cap. Data from PDB samples were compared with samples collected using a submersible, electric pump. In both lakes, PDB and pump samples yielded vertical profiles for CH4 concentration and δ13 that were in close agreement (CH4 concentration R2 = 0.998, p < 0.001; δ13 R2 = 0.970, p < 0.001). Overall agreement between PDB and pump samples verifies the applicability of the PDB approach to dissolved CH4 sampling. High-vertical resolution sampling of dissolved gas concentrations and isotopic composition using PDBs can provide more accurate estimates of pool sizes and diffusive fluxes that advance understanding of production, consumption, and emission processes controlling trace gas dynamics in lacustrine environments under warming climate. [ABSTRACT FROM AUTHOR]