The natural, built, and social environments shape drinking water quality supplied by private wells. However, the combined effects of these factors are not well understood. Using North Carolina as a case study, we (i) estimate the demographic characteristics of the private well population; (ii) evaluate representation in well testing records; and (iii) demonstrate how spatial scale influences knowledge of well-using household demographics and representation in testing. We leverage a statewide database of 117,960 well testing records collected over 20 years and a national model predicting well locations. An estimated 25% well-using households identify as Black, Indigenous, and Persons of Color (BIPOC) and 15% have incomes below the poverty threshold. While there is robust well sampling (an average of 4,269 wells tested annually), we observed that most testing records were from predominately White block groups (BGs). Well-using households that did not participate in state testing were 2.4 times more likely to be from predominately BIPOC BGs compared predominately White BGs. Due to the spatial heterogeneity of the well population, demographic differences in well populations were more evident using higher resolution data. Multifaceted testing approaches that couple government-driven efforts with localized studies that engage underrepresented communities are needed to facilitate evidence-based management.

Headwater streams comprise most of the global river length, and hydrologic processes occurring in headwaters affect the chemical, physical, and biological functions of downstream aquatic ecosystems. However, we do not have a clear understanding of the spatial scales that drive hydrologic processes across headwater systems, particularly in Coastal Plain landscapes. We address this gap by characterizing hydrologic connectivity in a small, forested watershed in the Coastal Plain of Alabama, USA. We collected data across three spatial scales: the watershed (0.9 km2), hydrogeomorphic feature (100-500 m), and hillslope (10-100 m) scales. We characterized stream network variability using seasonal surveys combined with water monitoring wells to characterize stream hydrologic state across 2021, paired with an Electrical Resistivity Tomography (ERT) and Time Domain Induced Polarization (TDIP) survey to characterize subsurface structure. Our results suggest that discretizing the river corridor into distinct hydrogeomorphic features provides a framework for understanding the dynamics of hydrologic connectivity within a watershed. Each hydrogeomorphic feature experienced consistent hydrologic states that differed along the network: incised channels gained water, intact riparian zones lost water, and wetland-stream complexes reflected no net water gain or loss from the river corridor. Subsurface structure observed with the ERT/TDIP survey indicate heterogeneous perched flowpaths, with saturation occurring variably throughout both space and time. Altogether, these results suggest that studying watersheds across a hierarchy of scales can reveal the dynamics of hydrologic connectivity, and that hydrogeomorphic features can provide a key intermediate scale for the integration of hydrologic processes across the river corridor.

The protection of headwater streams faces increasing challenges, exemplified by limited global recognition of headwater contributions to watershed resiliency and a recent US Supreme Court decision limiting federal safeguards. Despite accounting for 77

ABSTRACTFloodplains along low‐gradient, meandering river systems contain diverse hydrogeomorphic features, ranging from isolated depressions to hydrologically‐connected channels. These ephemerally‐flooded features inundate prior to river water overtopping all banks, enhancing river‐floodplain connectivity during moderately high flow stages. Predicting when and where ecological functions occur in floodplains requires understanding the dynamic hydrologic processes of hydrogeomorphic features, including inundation and exchange. In this study, we examined storm event‐scale inundation and exchange dynamics along a lowland, meandering river system in central Illinois (USA). We monitored surface water presence/absence, surface water level, and groundwater level across floodplain hydrogeomorphic feature types (i.e., isolated depression, backwater channel, and flow‐through channel). Using these data, we evaluated inundation onset and recession characteristics, drivers of groundwater‐surface water interactions, and direction of hydrologic exchange with the river channel. Surface water presence/absence patterns suggested inundation onset timescales were primarily controlled by microtopography and recession timescales were correlated with floodplain elevation. Employing a novel hysteresis approach for characterising groundwater‐surface water interactions, we observed distinct patterns indicating differences in water sources across hydrogeomorphic units and event characteristics. Finally, differences in hydraulic head along floodplain channels revealed that channels with multiple inlets/outlets (i.e., flow‐through channels) conveyed down‐valley flow and channels with single inlets primarily functioned as sinks of river‐derived water to the floodplain with short source periods. These results highlight the heterogeneity of hydrologic processes that occur along lowland, meandering river‐floodplains, and more specifically, point to the important role hydrogeomorphic features play in controlling dynamic connectivity within the river corridor.

Disaster recovery poses unique challenges for residents reliant on private wells. Flooding events are drivers of microbial contamination in well water, but the relationship observed between flooding and contamination varies substantially. Here, we investigate the performance of different flood boundaries-the FEMA 100 year flood hazard boundary, height above nearest drainage-derived inundation extents, and satellite-derived extents from the Dartmouth Flood Observatory-in their ability to identify well water contamination following Hurricane Florence. Using these flood boundaries, we estimated about 2600 wells to 108,400 private wells may have been inundated-over 2 orders of magnitude difference based on boundary used. Using state-generated routine and post-Florence testing data, we observed that microbial contamination rates were 7.1-10.5 times higher within the three flood boundaries compared to routine conditions. However, the ability of the flood boundaries to identify contaminated samples varied spatially depending on the type of flooding (e.g., riverine, overbank, coastal). While participation in testing increased after Florence, rates were overall still low. With <1% of wells tested, there is a critical need for enhanced well water testing efforts. This work provides an understanding of the strengths and limitations of inundation mapping techniques, which are critical for guiding postdisaster well water response and recovery.

Non-perennial streams, which lack year-round flow, are widespread globally. Identifying the sources of water that sustain flow in non-perennial streams is necessary to understand their potential impacts on downstream water resources, and guide water policy and management. Here, we used water isotopes (δ18O and δ2H) and two different modeling approaches to investigate the spatiotemporal dynamics of young water fractions (Fyw) in a non-perennial stream network at Konza Prairie (KS, USA) during the 2021 summer dry-down season, as well as over several years with varying hydrometeorological conditions. Using a Bayesian model, we found a substantial amount of young water (Fyw: 39.1 to 62.6%) sustained flows in the headwaters and at the catchment outlet during the 2021 water year, while 2015-2022 young water contributions estimated using sinusoidal models indicated smaller Fyw amounts (15.3% ± 5.7). Both modeling approaches indicate young water releases are highly sensitive to hydrological conditions, with streamwater shifting to older sources as the network dries. The shift in water age suggests a shift away from rapid fracture flow towards slower matrix flow that creates a sustained but localized surface water presence during late summer and is reflected in the annual dynamics of water age at the catchment outlet. The substantial proportion of young water highlights the vulnerability of non-perennial streams to short-term hydroclimatic change, while the late-summer shift to older water reveals a sensitivity to longer-term changes in groundwater dynamics. Combined, this suggests that local changes may propagate through non-perennial stream networks to influence downstream water availability and quality.

Biological assemblages in streams are influenced by hydrological dynamics, particularly in non-perennial systems. Although there has been increasing attention on how drying impacts stream organisms, few studies have investigated how specific characteristics of drying and subsequent wetting transitions influence biotic responses via resistance and resilience traits. Here, we characterized how hydrologic metrics, including those quantifying drying and wetting transitions as well as dry and wet phases, alter diversity and composition of three aquatic assemblages in non-perennial streams in southern California: benthic macroinvertebrates, soft-bodied algae and diatoms. We found that flow duration prior to sampling was correlated with variation in macroinvertebrate and soft-bodied algal assemblage composition. The composition and richness of diatom assemblages, however, were predominantly influenced by the drying start date prior to sampling. Contrary to other studies, the duration of the dry phase prior to sampling did not influence the composition or richness of any assemblage. Although our study was conducted within a region in which each assemblage experienced comparable environmental conditions, we found no single hydrologic metric that influenced all assemblages in the same way. The hot-summer Mediterranean climate of southern California likely acts as a strong environmental filter, with taxa in this region relying on resistance and resilience adaptations to survive and recolonize non-perennial streams following wetting. The different responses of algal and diatom assemblages to hydrologic metrics suggest greater resilience to drying and wetting events, particularly for primary producers. As drying and wetting patterns continue to change, understanding biodiversity responses to hydrologic metrics could inform management actions that enhance the ecological resilience of communities in non-perennial streams. In particular, the creation and enhancement of flow regimes in which natural timing and duration of dry and wet phases sustain refuges that support community persistence in a changing environment.

Private wells often lack centralized oversight, drinking water quality standards, and consistent testing methodologies. For lead in well water, the lack of standardized data collection methods can impact reported measurements, which can misinform health risks. Here, we conducted a targeted community science testing of 1143 wells across 17 counties in North Carolina (USA) and compared results to state testing data primarily associated with new well construction compiled in the NCWELL database. The goal of our study was to explore the impacts of sampling methodology and household representation on estimated lead exposures and subsequent health risks. At the household scale, we illustrated how sampling and analytical techniques impact lead measurements. The community science testing first draw samples (characterizing drinking water) had a 90th percentile lead value of 12.8 mu g l-1 while the NCWELL database flushed samples (characterizing groundwater) had a value below the reporting level of 5 mu g l-1. As lead was associated with the corrosion of premise plumbing, flushing prior to collection substantially reduced lead concentrations. At the community scale, we examined how the lack of representation based on household demographics and well construction characteristics impacted the knowledge of lead and blood lead level (BLL) occurrence. When simulating representative demographics of the well populations, we observed that the 90th percentile lead level could differ by up to 6 mu g l-1, resulting in communities being above the USEPA action level. This translated to a 1.0-1.3 mu g dl-1 difference in predicted geometric mean BLL among infants consuming reconstituted formula. Further, inclusion of less common well construction types also increased lead in water occurrence. Overall, under- and overestimations of lead concentrations associated with differences in sampling techniques and sample representation can misinform conclusions about risks of elevated BLLs associated with drinking water from private wells which may hinder investigations of waterborne lead exposure.

Headwater wetlands are important sites for carbon storage and emissions. While local- and landscape-scale factors are known to influence wetland carbon biogeochemistry, the spatial and temporal heterogeneity of these factors limits our predictive understanding of wetland carbon dynamics. To address this issue, we examined relationships between carbon dioxide (CO2) and methane (CH4) concentrations with wetland hydrogeomorphology, water level, and biogeochemical conditions. We sampled water chemistry and dissolved gases (CO2 and CH4) and monitored continuous water level at 20 wetlands and co-located upland wells in the Delmarva Peninsula, Maryland, every 1–3 months for 2 years. We also obtained wetland hydrogeomorphologic metrics at maximum inundation (area, perimeter, and volume). Wetlands in our study were supersaturated with CO2 (mean = 315 μM) and CH4 (mean = 15 μM), highlighting their potential role as carbon sources to the atmosphere. Spatial and temporal variability in CO2 and CH4 concentrations was high, particularly for CH4, and both gases were more spatially variable than temporally. We found that groundwater is a potential source of CO2 in wetlands and CO2 decreases with increased water level. In contrast, CH4 concentrations appear to be related to substrate and nutrient availability and to drying patterns over a longer temporal scale. At the landscape scale, wetlands with higher perimeter:area ratios and wetlands with higher height above the nearest drainage had higher CO2 and CH4 concentrations. Understanding the variability of CO2 and CH4 in wetlands, and how these might change with changing environmental conditions and across different wetland types, is critical to understanding the current and future role of wetlands in the global carbon cycle.

Low flows pose unique challenges for accurately quantifying streamflow. Current field methods are not optimized to measure these conditions, which in turn, limits research and management. In this essay, we argue that the lack of methods for measuring low streamflow is a fundamental challenge that must be addressed to ensure sustainable water management now and into the future, particularly as climate change shifts more streams to increasingly frequent low flows. We demonstrate the pervasive challenge of measuring low flows, present a decision support tool (DST) for navigating best practices in measuring low flows, and highlight important method developmental needs. Water resource management is facing mounting challenges associated with water scarcity, including interactive effects of a changing climate and increased water demand (Craig et al. 2017). Climate change is increasing drought severity in many regions (Cook et al. 2020), while demand for limited water supplies depletes water resources (de Graaf et al. 2019). Combined, these stressors result in lower and more variable flows in streams and rivers (Zipper et al. 2021), particularly in arid regions (Hammond et al. 2021). Despite challenges posed by low-flow conditions, the majority of resources (e.g., time, funding) for monitoring streamflow have historically focused on high-water concerns, such as ensuring navigation and predicting floods (Vörösmarty et al. 2001; Ruhi et al. 2018), in larger, perennially-flowing systems (Krabbenhoft et al. 2022). Low-flow conditions (Mauger et al. 2021), which we define as streams or rivers with little downstream surface water flow caused by small volumes or very low downstream velocities (i.e., slackwater), are increasingly prevalent and thus necessitate greater focus on quantification approaches. Streamflow is the underlying physical template structuring biotic and abiotic processes, biogeochemical cycling, and ecological communities in river systems; thus, inaccurate low-flow measurements can propagate to and hinder diverse analyses requiring accurate low-flow data, ranging from drought characterization (Hammond et al. 2022), environmental flow allocations (Neachell and Petts 2019), ecological function assessments (Leigh and Datry 2017), species conservation plans (Lopez et al. 2022), and streamflow forecasting (Forzieri et al. 2014). We posit that a lack of low-flow measurement techniques leaves monitoring networks ill-equipped to inform water management, which is a fundamental challenge that must be addressed to ensure sustainable water management in the future. Our objectives are to: (1) demonstrate the widespread challenges in low-flow measurement across an existing monitoring network in the United States, (2) discuss limitations of current streamflow measurement methods in low-flow conditions, (3) present a DST for choosing among existing measurement methods, and (4) highlight important methodological developments needed to improve low-flow measurement and monitoring. Such methodological progress is a prerequisite for understanding how low flows will respond to changing climate and human demands, thereby supporting management and policy actions seeking to avoid or minimize these impacts. Point measurements of streamflow are essential for short- and long-term studies and monitoring, and can be made using many different methods (Turnipseed and Sauer 2010). If conducted over a range of flow conditions, discrete streamflow measurements can be used to develop a rating curve which relates stage and discharge, allowing for long-term, continuous quantification of discharge via stage sensors (Turnipseed and Sauer 2010). We focus our analysis and discussion on methods for point measurements of streamflow, but emphasize that limitations in these approaches have implications for the accuracy of longer-term streamflow monitoring via rating curve development. To quantify the prevalence of substandard low-flow measurements, we examined manual point measurements of streamflow from 8008 U.S. Geological Survey (USGS) gages across the continental United States in the GAGES II dataset (Falcone 2011), which is a dataset of sites with either 20+ years of discharge since 1950 or that were operational as of 2009 (Appendix S1). For each manual streamflow measurement, we collected the quality code assigned by USGS hydrographers immediately after making the discharge measurement: “Poor” quality is assigned when uncertainty in the discharge measurement is estimated to be above 8%, “fair” when uncertainty is estimated to be less than 8%, good when uncertainty is estimated to be less than 5%, and excellent when uncertainty is estimated to be less than 2% (Turnipseed and Sauer 2010). These quality codes are a qualitative method for estimating the accuracy of individual discharge measurements based on suitability of the channel cross-section, flow state, and other flow conditions (Turnipseed and Sauer 2010). For each gage, we identified the minimum streamflow value associated with a “good” manual flow measurement and calculated the percent of each gage's daily streamflow record below the minimum “good” threshold. To ensure our results were not overly sensitive to the value of the minimum “good” threshold, we also compared the percentage of each gage's streamflow record below two additional thresholds: (1) streamflow value corresponding to the minimum “fair” measurement, and (2) average of minimum “fair” and minimum “good” thresholds (see Table S1 for details), and obtained comparable results. The “minimum good” metric provides a conservative estimate of the duration of flow measurements with high uncertainty for each site; it only considers uncertainty related to manual measurements and does not account for additional uncertainty in stage measurements stemming from low-flow conditions. We interrogated the USGS network because it represents a high standard that many individual investigators use as a benchmark, and because it provided a large dataset relating manual streamflow measurements with qualitative assessments of quality/uncertainty. We performed all analyses in R version 4.2.1 (R Core Team 2022) and obtained USGS data from the National Water Information System using the DataRetrieval Package (De Cicco et al. 2022). Across the GAGES II network, the average percentage of flow records below the minimum good measurement was 8.4%, indicating high overall quality of the streamflow measurements. However, we found that 393 gages (~ 5.5%) had at least 50% of flow records below the minimum good flow value, 68 of which had over 95% of flow records below the minimum good flow threshold (Fig. 1A). Sites with a high percentage of streamflow below the minimum “good” threshold are widely distributed across diverse climatic zones, land uses, and hydrologic settings, although the greatest density of high uncertainty records are concentrated in the arid southwestern United States where low flows and water management issues linked to scarcity are pervasive (Brown et al. 2019). To provide an example of the difficulties in making low-flow measurements, we focused on the gage for Kings Creek near Manhattan KS (USGS Gage 06879650), a well-studied, grassland stream with a long continuous record (1979–present). Only 73 of the 238 manual flow measurements (~ 31%) were considered “good” or “excellent” (Fig. 1B). The relatively low incidence of “good” manual flow measurements at Kings Creek resulted in over 58.6% of the daily flow record (from 1980 to 2021) being below the lowest “good” flow measurement, with the proportion below that threshold in a given water year ranging from 2.5% to 100%. This underscores that even for a given site, the relative importance of accurate low-flow measurements will vary from year-to-year, with greatest impact during dry years (Fig. 1C). Furthermore, uncertainties in low-flow measurements may propagate into subsequent estimates of nutrient export, which may lead to some annual load estimates to be much less certain than others. Systems with frequent low flows and flashy high flows may also face highly uncertain streamflow measurements at the high flow end of the rating curve, leading to additional sources of uncertainty. While a sensitivity analysis of uncertainty propagation in streamflow is beyond the scope of this paper, our analysis highlights many areas in the United States where current methods are poorly suited to capture low-flow conditions. Three general categories of methods comprise the toolbox available to most practitioners. These include: (1) velocity-area methods; (2) tracer-based methods using salt or dye; and (3) measuring stage at a known streambed geometry (e.g., flume or weir) or capturing flow at a channel constriction (WMO 2010). Most methods tend to be inaccurate or unusable under low-flow conditions (Hamilton 2008) for three reasons: (1) low water velocities and/or shallow water depths (Fig. 2A,B), (2) mobile streambeds and/or irregular channels (Fig. 2D,E), and (3) high proportions of flow in the subsurface (Fig. 2C–E). Many streams transition from visible surface water flow to very slow or imperceptible movement of water, which is sometimes spatially discontinuous or pooled. Low velocities can lead to poor tracer mixing and recovery when using dilution gaging methods (Fig. 2A). High channel width-to-depth ratios (i.e., very wide channels with shallow water) can also lead to poor tracer mixing and the inability to fully submerge velocimeters (Fig. 2E). Furthermore, highly variable bed elevations (e.g., rocks and boulders) or emergent vegetation can further reduce the accuracy of velocity measurements and even render them impossible (Fig. 2D). Finally, estimates of discharge based on velocity-area methods only measure surface-water flow and therefore are not directly comparable to tracer-based estimates, which capture some subsurface flow. This is particularly relevant in low-flow conditions which often exhibit a greater proportion of hyporheic flow. These general problems are not mutually exclusive; indeed, multiple issues can arise in low-flow settings, leaving practitioners unsure about which method to use and leading to considerable uncertainty in low-flow measurements. Given these challenges, we present a DST that reflects our collective experience working in low-flow systems, and describes how we approach applying existing discharge methods given the complicating factors that dominate low-flow systems (Fig. 3). The aim of the DST is to offer guidance on a systematic way to apply consistent methods to complex systems. This tool assumes the chosen location is the best available site (i.e., there are no better sites within a reasonable distance upstream or downstream) and highlights what conditions should be avoided in site selection. The DST is not intended to be a data-driven study on the optimal way to measure low flows, rather it is offering informed opinions on what methods tend to work best in specific contexts from experts who frequently attempt flow measurements under non-ideal conditions. In compiling the DST, we also highlight conditions where method development should be prioritized, which we hope catalyzes further discussion and method advances within the water resource community. The initial bifurcation in this DST separates sites by whether water is visibly flowing or not (Fig. 3). We define visible flow as whether material in the water (e.g., leaves) can be observed moving downstream. If there is no visible movement, fewer options exist to measure flow. If streamflow is visible, the DST prompts a series of questions regarding channel cross-section and water depth to help practitioners identify the most suitable flow measurement for their site (Fig. 3). We acknowledge that the pathways and nodes are not equally likely to be encountered. For example, very few locations have natural constriction points for which the bucket method is suitable (Fig. 2F), even though it appears twice (Fig. 3). Furthermore, three nodes terminate in “no widely used methods.” In our experience, the majority of sites where we work (numbering in the dozens, examples in Fig. 2) fall into nodes characterized by “no widely used method” for at least part of the year, leaving us unable to accurately measure hydrologic fluxes and limiting subsequent analyses like long-term nutrient flux estimates. While this DST can be used to help practitioners identify the best possible methods, we acknowledge that under many low-flow conditions, even a recommended method can lead to suboptimal discharge measurements with relatively high error. Selecting a method to measure discharge requires practitioners to identify the degree of precision needed for their study and consider trade-offs between precision and resource costs. For some studies, hydrologic parameters that are easier to measure—like depth, wetted width/area, or approximate flow state—may be sufficient (Jaeger et al. 2023). In contrast, biogeochemistry studies for which water movement is a key variable for calculating nutrient loads (Gómez-Gener et al. 2021) may require greater precision than studies focused on aquatic habitat. Other trade-offs, including personnel costs, measurement frequency, and available time to conduct a measurement may outweigh the scientific considerations given in Fig. 3. At low but visible flows, dilution gaging can be used but may take hours to days, rather than minutes to an hour required at moderate to high flow conditions. In addition, dilution gaging at low flows often results in non-optimal breakthrough curves from incomplete mixing that are not suitable for discharge estimates. Portable flumes/weirs are faster to implement but require modifying the channel, for example manually creating berms to concentrate flow through a flume (Fig. 2C), which may not be possible for many reasons. While the DST provides recommendations for general categories of measurement methods, further modifications of each method can help accommodate specific flow conditions (e.g., different variations on the application of dilution gaging; Table S1). We provide suggestions for situations where modifications of standard methods may be desirable, and further challenges in applying those modifications in Table S1. In streams and rivers, streamflow is the underlying physical template structuring biotic and abiotic processes, biogeochemical cycling, and ecological communities. Discharge is used to assess the degree of connectivity between tributaries and quantify movement of solutes through a stream network. Time series of discharge are key inputs for models of aquatic ecosystem function and biogeochemical cycling, and the desired output of hydrologic models identifying factors driving streamflow and predicting responses to anthropogenic change. All of these applications require accurate discharge measurements across the full range of flow variability. While there is no universal answer to the question of “what percent error is acceptable when measuring low flows,” we argue the general need for a high degree of accuracy is clear. Although absolute changes in streamflow in low-flow systems may be small (e.g., changes from 0.01 to 0.02 m3/s), this represents a large relative change within the system (100%). Small changes in discharge at low flows can have substantial consequences for habitat extent and suitability (Rolls et al. 2012). Detection of long-term trends is hampered by imprecise or uncertain data, which may cause trends in vulnerable low-flow systems to go unquantified (Whitfield and Hendrata 2006). Environmental flow regulations require precise data for enforcement (Neachell and Petts 2019), and uncertain low-flow data can complicate implementation and enforcement. Finally, there are many systems ranging from large, arid rivers to small streams where difficult-to-measure low-flow conditions are the norm and thus prevent accurate streamflow measurements across the flow-duration curve, leaving sites with minimal data for research and management purposes. Although low flows may represent a smaller component of annual water or solute fluxes than high flows in many systems, they are critical for understanding and predicting hydrological, ecological, and biogeochemical dynamics in river systems. This is not possible without robust low-flow discharge measurements. In addition to providing guidance for systematically deciding which methods to employ in determining low-flow discharge, our DST (Fig. 3) highlights areas of critical need for method development and uncertainty assessment. In some cases, further modification and optimization of existing methods may be sufficient (e.g., Table S1). However, there are conditions for which entirely new methods need to be developed or refined, such as: (1) slackwater pools (Fig. 2A); (2) wide, shallow, irregular, or threaded channels (Fig. 2E), particularly in locations with no opportunity for channel modification; (3) reaches with dense emergent vegetation; and (4) reaches where wind strongly affects water surface velocities. These conditions are commonly found in freshwaters but share similarities with coastal settings, opening up the potential for method transfer to/from coastal hydrology (e.g., Birgand et al. 2022). There are promising recent technological advances including micro velocity sensors (Osorno et al. 2018), time-lapse imagery analysis from trail cameras and videos (Birgand et al. 2022; Chapman et al. 2022; Dolcetti et al. 2022) or radar altimetry (Bandini et al. 2020), and presence/absence sensors for measuring water surface extent (Chapin et al. 2014;). Emerging tools like time-lapse imagery analysis and water presence/absence sensors may improve our understanding of the spatiotemporal variation in the hydrologic state of low-flow systems by providing an assessment of surface water presence at the time of streamflow measurements, or in the absence of suitable discharge measurement approaches. However, more work must be done to advance these methods because as of now they only estimate stage or water presence/absence, leaving the difficulties of estimating discharge unresolved. Finally, there may be settings in which modeling or mathematical relationship development may be the best option (Gao et al. 2021). We suggest a concentrated effort on uncertainty assessment and method development is urgently needed, as there are numerous settings for which there is no current viable method for measuring streamflow. Methods development for accurate low-flow measurements will be critical as environmental change accelerates, leading to increased hydrologic variability and shifts to low flows around the world. To better manage future trade-offs among water uses, managers will require accurate data on streamflow under low-flow conditions. To achieve this, we need methodological flexibility to capture extreme flow conditions, including at low flows. Without improvements, we will not be able to sustain existing long-term streamflow records that can help us predict the continuing trajectories of environmental change. Understanding and managing shifts in water resources will be critical for ensuring habitat integrity, promoting good water quality, and safeguarding sustainable water access. The first step is ensuring consistent high-quality flow measurements in these vulnerable systems. Data S1. Supplementary Information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. This work was supported by NSF-DEB Grant #1754389 to the Dry Rivers Research Coordination Network and NSF-IOA Grant #2019603 to the Aquatic Intermittency effects of Microbiomes in Streams (AIMS) project. We would like to thank the members in the Dry Rivers Research Coordination Network and the AIMS team for their thoughtful conversations and contributions that helped advance the ideas explored in this manuscript.

Wetlands’ morphometric or shape properties, such as their area and perimeter, impact a multitude of ecosystem functions and services. However, current models used to quantify these functions often only use area as an independent variable, as the static area and perimeter of different wetlands have been found to be closely related. The study uses monthly inundation maps, derived from remote sensing data, to assess the temporal covariation of geographically isolated wetlands’ perimeter and surface area. The results show that using static representations of wetlands to evaluate temporal dynamic perimeter–area relationships can introduce significant discrepancies and that these discrepancies can be reduced if evaluations using static data are performed separately for each wetlandscape. This study concludes that models that use implicit area–perimeter relationships based on static wetland representations, as is usually the case, should be applied with caution. Additionally, it suggests that incorporating perimeter–area relationships from temporally dynamic data can improve estimates of wetland functions.

Across watershed science, two key variables emerge–streamflow and solute concentration–which serve as the basis for efforts ranging from basic watershed biogeochemistry research to policy decisions surrounding watershed management. However, we rarely account for how error in discharge (Q) impacts estimates of downstream nutrient loading. Here, we examined the impact of uncertainty in streamflow measurements on estimates of downstream nitrate export using publicly available data from the U.S. Geological Survey (USGS). We characterized how uncertainty in stage-discharge relationships impacts annual flux estimates across 70 USGS gages. Our results indicate the interquartile range of relative error in Q was 33% across these USGS sites. We documented a wide range in mean error in annual nitrate loads; some sites were underestimated (−105%), while predicted loads at other sites vastly overestimated (500%). Overall, any error in estimating Q leads to significant unpredictability of annual nutrient loads, which are often used as critical success benchmarks for governmental nutrient reduction strategies. Moreover, error in annual nitrate loads (as mass, kg) increases with mean Q; thus, as high flows become more unpredictable and intense under future climate change, error in estimates of downstream nutrient loading may also increase. Together, this indicates that error in Q may drastically influence our measures of water quality success and decrease our ability to accurately quantify progress towards algal bloom and ‘dead zone’ reduction.

Drinking water supplied by private wells poses public health challenges and well users are increasingly concerned about emerging contaminants such as hexavalent chromium (Cr(VI)). In 2018, Hurricane Florence made landfall in North Carolina as a Category 1 hurricane and caused dam failures and flooding of coal ash disposal and reuse sites. Residents surrounding these sites were concerned about potential contamination of their well water with Cr(VI) from coal ash, but there is also widespread naturally-occurring Cr(VI) in the groundwater. To evaluate source attribution techniques we: (1) reviewed literature related to Cr(VI) source tracking and (2) applied these techniques to Cr(VI) data we collected from 1,265 private wells across 22 North Carolina counties to determine if there were differences in sources identified. Almost two thirds of private wells tested (62.0%) exceeded the Cr(VI) public health goal of 0.07 ppb, with concentrations ranging from <0.02-13.9 ppb (median=0.12 ppb). In the literature review, we identified 33 Cr(VI) groundwater tracking techniques from 51 publications and only 5 techniques were used in more than 12 papers. All papers used different combinations of techniques. We applied these techniques to our well sampling data, and inconclusive results were reported for 7 techniques, while three techniques reported potential geogenic and three techniques reported anthropogenic Cr(VI) sources. Specifically looking at coal ash, two techniques did not support coal ash as a primary source and three were inconclusive. Taken holistically, these techniques did not provide definitive insight into Cr(VI) source(s) in the region. This may be due to the fact that, these techniques primarily focused on regional scale identification, rather than household-level occurrence. Overall, this study demonstrates the difficulty and complexity in identifying and distinguishing the source(s) responsible for Cr(VI) in well water.

Knowing where and when rivers flow is paramount to managing freshwater ecosystems. Yet stream gauging stations are distributed sparsely across rivers globally and may not capture the diversity of fluvial network properties and anthropogenic influences. Here we evaluate the placement bias of a global stream gauge dataset on its representation of socioecological, hydrologic, climatic and physiographic diversity of rivers. We find that gauges are located disproportionally in large, perennial rivers draining more human-occupied watersheds. Gauges are sparsely distributed in protected areas and rivers characterized by non-perennial flow regimes, both of which are critical to freshwater conservation and water security concerns. Disparities between the geography of the global gauging network and the broad diversity of streams and rivers weakens our ability to understand critical hydrologic processes and make informed water-management and policy decisions. Our findings underscore the need to address current gauge placement biases by investing in and prioritizing the installation of new gauging stations, embracing alternative water-monitoring strategies, advancing innovation in hydrologic modelling, and increasing accessibility of local and regional gauging data to support human responses to water challenges, both today and in the future.

Wetlands store significant soil organic carbon (SOC) globally due to anoxic conditions that suppress SOC loss. However, stored SOC may become vulnerable to decomposition where climate and land use change alter wetland hydrology. Seasonally saturated wetlands experience fluctuating hydrologic conditions that could promote physicochemical mechanisms known to stabilize terrestrial SOC. These wetlands are therefore likely to be important for SOC storage at the landscape-scale. This study examined physicochemical stabilization of SOC within five seasonally saturated wetlands across a hydrologic gradient from the frequently saturated basin edge to the rarely saturated upland. At each wetland, we monitored water level and collected soil samples from the top two mineral horizons across five transect points to quantify physical protection of SOC in aggregates and organo-mineral associations between SOC and iron (Fe). As expected, both SOC concentrations and SOC stocks from 10–50 cm decreased across the transect from frequently saturated soils to rarely saturated soils. However, SOC stocks from 0–10 cm increased along this gradient, indicating diverging SOC dynamics throughout the soil profile. The majority of SOC was associated with macroaggregates across the transect, suggesting that macroaggregates are likely to physically protect wetland SOC during seasonal drying. By contrast, Fe-associated SOC was low across the transect, though modest accumulations of Fe (5 mg Fe g −1 soil) were observed in the transition zone where saturation was most dynamic throughout the year. Our results suggest that SOC stabilization occurs via physical protection within macroaggregates and, to a lesser extent, organo-mineral associations during dry periods in and around seasonally saturated wetlands. As climate scenarios predict intensified wet and dry cycles in many wetlands, understanding SOC stabilization is critical to predicting vulnerability to future change.

Hydrologic controls on carbon processing and export are a critical feature of wetland ecosystems. Hydrologic response to climate variability has important implications for carbon‐climate feedbacks, aquatic metabolism, and water quality. Little is known about how hydrologic processes along the terrestrial‐aquatic interface in low‐relief, depressional wetland catchments influence carbon dynamics, particularly regarding soil‐derived dissolved organic matter (DOM) transport and transformation. To understand the role of different soil horizons as potential sources of DOM to wetland systems, we measured water‐soluble organic matter (WSOM) concentration and composition in soils collected from upland to wetland transects at four Delmarva Bay wetlands in the eastern United States. Spectral metrics indicated that WSOM in shallow organic horizons had increased aromaticity, higher molecular weight, and plant‐like signatures. In contrast, WSOM from deeper, mineral horizons had lower aromaticity, lower molecular weights, and microbial‐like signatures. Organic soil horizons had the highest concentrations of WSOM, and WSOM decreased with increasing soil depth. WSOM concentrations also decreased from the upland to the wetland, suggesting that continuous soil saturation reduces WSOM concentrations. Despite wetland soils having lower WSOM, these horizons are thicker and continuously hydrologically connected to wetland surface and groundwater, leading to wetland soils representing the largest potential source of soil‐derived DOM to the Delmarva Bay wetland system. Knowledge of which soil horizons are most biogeochemically significant for DOM transport in wetland ecosystems will become increasingly important as climate change is expected to alter hydrologic regimes of wetland soils and their resulting carbon contributions from the landscape.

As the field of hydrologic sciences continues to advance, there is an increasing need to develop a workforce with tools to curate, manage, and analyze large datasets. As such, undergraduate and graduate curricula are beginning to regularly incorporate scientific programing in the classroom. However, there are several key challenges to successfully incorporating scientific programming into a hydrology course or curriculum, such as meeting disciplinary outcomes alongside teaching students to code, equity issues with access to computing power, and effective classroom management. While these challenges were exacerbated by the global pandemic, shifting to online and hybrid learning formats provided an opportunity to explore and re-evaluate the way we facilitated our hydrology courses and integrated coding exercises and learning. In this article, we reflect on these experiences in three very different hydrology courses (e.g., courses housed in geoscience/engineering, environmental science, and biology programs) with an eye toward identifying successes and opportunities for improvement. We explore this by presenting ten best management practices (BMPs), representing a series of recommendations we have for teaching a virtual, hybrid, or in-person hydrology course that incorporates coding. While all recommendations provided can be applied to many programming languages, the focus of the paper (given the expertise of the authors) is on R. Our BMPs focus on technological facilitation, managing the virtual classroom, and instructional resources, with lessons learned that are applicable to in-person instruction. We also summarize the ways that the authors of this article integrate coding into our coursework to serve as a framework for prepping new courses or those revising existing hydrologic coursework. Above all, we hope these series of recommendations will evolve as hydrology courses continue to emphasize computational skills alongside disciplinary learning.

There are increasing advocacy and policy efforts to consolidate private wells and other small systems into larger regulated systems to address water quality-related public health and climate resilience challenges. However, little is known about how trade-offs between water quality and affordability implications of these consolidation efforts are experienced and perceived by residents. Here, we analyzed changes in water quality, affordability, and perception associated with a groundwater contamination-motivated transition from private well to regulated municipal water. We conducted water quality samplings and household surveys pre-and post-municipal service extension in the Town of Orleans, NY. Our findings illustrate that water quality improved due the connection to municipal service, resulting in increased consumption of tap water, improved perception of water quality and reliability, and decreased use of household treatment, but with a much higher monthly expenditure. Residents reported general satisfaction with their new municipal water service; however, the cost of service was more than expected, and some were dissatisfied with the taste of chlorine in the municipal supply. Despite the positive public health implications, it is important to consider the trade-offs in regulated water transitions and the perceptions of residents to foster successful, sustainable solutions.

Methane emissions from small freshwater ecosystems represent one of the largest components of uncertainty in the global methane budget. While these systems are known to produce large amounts of methane relative to their size, quantifying the timing, magnitude, and spatial extent of their emissions remains challenging. We begin to address this challenge in seasonally inundated forested mineral soil wetlands by (1) measuring wetland methane fluxes and hydrologic regime across both inundated and non-inundated soils, (2) characterizing how wetland hydrologic regime impacts the spatial extent of methane emission source areas, and (3) modeling average daily wetland-scale flux rates using four different upscaling techniques. Our results show that inundation extent and duration, but not frequency or depth, were major drivers of wetland methane emissions. Moreover, we found that methane fluxes were best described by the direction of water level change (i.e. rising or falling), where emissions were generally higher when water levels were falling. Once soils were inundated, subsequent changes in water level did not explain observed variability of methane concentrations in standing water. Finally, our spatial modeling suggests that representing inundation and associated methane source areas is a critical step in estimating local to regional scale methane emissions. Intermittently inundated soils alternated between being sources and sinks of methane depending on water level, soil moisture, and the direction of water level change. These results demonstrate that quantifying the hydrologic regime of seasonally inundated forested freshwater wetlands enables a more accurate estimation of methane emissions.

Abstract A major source of uncertainty in the global methane budget arises from quantifying the area of wetlands and other inland waters. This study addresses how the dynamics of surface water extent in forested wetlands affect the calculation of methane emissions. We used fine resolution satellite imagery acquired at sub‐weekly intervals together with a semiempirical methane emissions model to estimate daily surface water extent and diffusive methane fluxes for a low‐relief wetland‐rich watershed. Comparisons of surface water model predictions to field measurements showed agreement with the magnitude of changes in water extent, including for wetlands with surface area less than 1,000 m2. Results of methane emission models showed that wetlands smaller than 1 hectare (10,000 m2) were responsible for a majority of emissions, and that considering dynamic inundation of forested wetlands resulted in 49%–62% lower emission totals compared to models using a single estimate for each wetland’s size.