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Jennifer H. Fair

Publications and source records attributed to Jennifer H. Fair.

18 recordsLinked to original sources
USGS2026

Evaluation of submersible pressure transducers for streamflow monitoring in small streams

Compact streamgages requiring minimal infrastructure and equipped with submersible pressure transducers (PTs) are increasingly used to monitor small streams, yet disparate implementations obscure their accuracy under real-world conditions. This study isolated instrumentation-derived uncertainty in stage monitoring by co-locating various combinations of commercial vented and unvented PTs with seven U.S. Geological Survey (USGS) reference streamgages on small streams. Multi-year PT stage records from compact streamgages, collected and corrected following USGS protocols, were compared to concurrent reference observations. Vented PTs demonstrated an average measurement uncertainty of ±0.005 m and mean absolute percent error (MAPE) of ±0.2%. Unvented PTs exhibited higher uncertainty, averaging ±0.009 m and ±0.4% MAPE. Although both sensor types had stage errors up to ±0.3 m, 95% of vented PT and unvented PT observations were within 0.01 and 0.02 m of reference stage, respectively. Analysis of additional unvented PTs revealed stage errors of up to ±1.2% when using barometric sensors within 15 km of the in-water sensor. Propagation of stage error to discharge using reference rating models resulted in cumulative discharge MAPEs of ±4.5% for vented and ±5.5% for unvented PTs. These findings highlight PTs as practical alternatives to reference instrumentation when deployed with standardized procedures, potentially expanding access to reliable streamflow data.

conterminous United States↗
USGS2025

Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model

Despite their ubiquity and importance as freshwater habitat, small headwater streams are under-monitored by existing stream gage networks. To address this gap, we describe a low-cost, non-contact, and low-effort method that enables organizations to monitor relative streamflow dynamics in small headwater streams. The method uses a camera to capture repeat images of the stream from a fixed position. A person then annotates pairs of images, in each case indicating which image has more apparent streamflow or indicating equal flow if no difference is discernible. A deep learning modeling framework called streamflow rank estimation (SRE) is then trained on the annotated image pairs and applied to rank all images from highest to lowest apparent streamflow. From this result a relative hydrograph can be derived. We found that our modeled relative hydrograph dynamics matched the observed hydrograph dynamics well for 11 cameras at 8 streamflow sites in western Massachusetts. Higher performance was observed during the annotation period (median Kendall's Tau rank correlation of 0.75, with a range of 0.6–0.83) than after it (median Kendall's Tau of 0.59, with range 0.34–0.74). We found that annotation performance was generally consistent across the 11 camera sites and 2 individual annotators and was positively correlated with streamflow variability at a site. A scaling simulation determined that model performance improvements were limited after 1000 annotation pairs. Our model's estimates of relative flow, while not equivalent to absolute flow, may still be useful for many applications, such as ecological modeling and calculating event-based hydrological statistics (e.g., the number of out-of-bank floods). We anticipate that this method will be a valuable tool to extend existing stream monitoring networks and provide new insights on dynamic headwater systems.

Massachusetts↗
USGS2025

Methods for estimating selected low-flow statistics at gaged and ungaged stream sites in Massachusetts

The U.S. Geological Survey, in cooperation with the Massachusetts Department of Conservation and Recreation, Office of Water Resources, computed selected at-site streamflow statistics at U.S. Geological Survey streamgages in and near Massachusetts and developed regional regression equations for estimating selected streamflows at ungaged stream sites in Massachusetts. Two sets of regional regression equations were developed: (1) the “mainland” equations, for mainland Massachusetts excluding the area covered by the second set, and (2) the “southeastern” equations, for the Plymouth-Carver-Kingston-Duxbury aquifer area in southeastern Massachusetts and for Cape Cod. The regression equations and at-site statistics may be used by Federal, State, and local water managers in addressing water-resources issues relevant in Massachusetts. Regional regression analyses for the mainland equations were developed to estimate the following 27 streamflow statistics: 99-, 98-, 95-, 90-, 85-, 80-, 75-, 70-, 60-, and 50-percent flow durations; monthly June, July, August, and September 90- and 50-percent flow durations; February, June, and August median of the monthly means; harmonic mean; and medians of the following annual low-flow frequency statistics: 7-day; 7-day, 2-year; 7-day, 10-year; 30-day, 2-year; and 30-day, 10-year. The analyses used 81 streamgages with minimal to no regulations in and near Massachusetts. The regression analyses determined that four basin characteristics—drainage area, combined hydrologic soils A and B, streamflow variability index, and annual mean temperature—were the only significant explanatory variables for the different mainland equations. Regional regression equations were developed for the Plymouth-Carver-Kingston-Duxbury aquifer area in southeastern Massachusetts and Cape Cod, because surface-water drainage areas and groundwater contributing areas do not always coincide in this area of the State. The regression analyses to estimate 10 flow durations from the 99th to 50th percentiles used 18 streamflow sites with some occasional minor regulations—because there are few unregulated streams in southeastern Massachusetts. The analyses determined that groundwater contributing area and storage (combined water bodies and wetlands) were the only significant explanatory variables in the southeastern equations.

Massachusetts↗
USGS2025

A low-cost approach to monitoring streamflow dynamics in small, headwater streams using timelapse imagery and a deep learning model

Despite their ubiquity and importance as freshwater habitat, small headwater streams are under monitored by existing stream gage networks. To address this gap, we describe a low-cost, non-contact, and low-effort method that enables organizations to monitor streamflow dynamics in small headwater streams. The method uses a camera to capture repeat images of the stream from a fixed position. A person then annotates pairs of images, in each case indicating which image has more apparent streamflow or indicating equal flow if no difference is discernible. A deep learning modelling framework called Streamflow Rank Estimation (SRE) is then trained on the annotated image pairs and applied to rank all images from highest to lowest apparent streamflow. From this result a relative hydrograph can be derived. We found that our modelled relative hydrograph dynamics matched the observed hydrograph dynamics well for 11 cameras at 8 streamflow sites in western Massachusetts. Higher performance was observed during the annotation period (median Kendall’s Tau rank correlation 0.75 with range 0.6–0.83) than after it (median Kendall’s Tau 0.59 with range 0.34 – 0.74). We found that annotation performance was generally consistent across the eleven camera sites and two individual annotators and was positively correlated with streamflow variability at a site. A scaling simulation determined that model performance improvements were limited after 1,000 annotation pairs. Our model’s estimates of relative flow, while not equivalent to absolute flow, may still be useful for many applications, such as ecological modelling and calculating event-based hydrological statistics (e.g., the number of out-of-bank floods). We anticipate this method will be a valuable tool to extend existing stream monitoring networks and provide new insights on dynamic headwater systems.

EGUSphere↗
USGS2025

USGS Flow Photo Explorer is still going and growing!

The Flow Photo Explorer (FPE) platform continues to grow rapidly as a national resource for using imagery to monitor environmental conditions. As of early December 2025, FPE now supports more than 350 users , operating across more than 600 monitoring sites . The database has expanded to over 12 million images , 800,000 annotations , and approximately 160 trained models , reflecting accelerating engagement from federal, state, tribal, academic and nonprofit partners. Please see two critical updates below. Thank you for your continued support and contributions, we’re looking forward to many exciting improvements in the year to come!

Flow Photo Explorer↗
USGS2024

Utility of an instantaneous salt dilution method for measuring streamflow in headwater streams

Streamflow records are biased toward large streams and rivers, yet small headwater streams are often the focus of ecological research in response to climate change. Conventional flow measurement instruments such as acoustic Doppler velocimeters (ADVs) do not perform well during low-flow conditions in small streams, truncating the development of rating curves during critical baseflow conditions dominated by groundwater inflow. We revisited an instantaneous solute tracer injection method as an alternative to ADVs based on paired measurements to compare their precision, efficiency, and feasibility within headwater streams across a range of flow conditions. We show that the precision of discharge measurements using salt dilution by slug injection and ADV methods were comparable overall, but salt dilution was more precise during the lowest flows and required less time to implement. Often, headwater streams were at or below the depth threshold where ADV measurements could even be attempted and transects were complicated by coarse bed material and cobbles. We discuss the methodological benefits and limitations of salt dilution by slug injection and conclude that the method could facilitate a proliferation of streamflow observation across headwater stream networks that are highly undersampled compared to larger streams.

Virginia↗
USGS2024

Effects of episodic stream dewatering on brook trout spatial population structure

Stream dewatering is expected to become more prevalent due to climate change, and we explored the potential consequences for brook trout ( Salvelinus fontinalis) within a temperate forest ecosystem in eastern North America. We estimated fish density within stream pools ( n = 386) from electrofishing surveys over 10 years (2012–2021) to compare a stream that exhibits episodic dewatering (Paine Run) against a stream of similar size that remains flow-connected (Staunton River) within Shenandoah National Park, Virginia (U.S.A.). Annual surveys encompassed fluvial distances ranging from 2.6 to 4.4 km in each stream. Mean annual fish density (fish/pool m 2 ) was not different between streams for juvenile or adult age classes, but spatial variation in density was greater in Paine Run for both age classes of fish. Paine Run also included a greater proportion of unoccupied pools than Staunton River and exhibited stronger spatial autocorrelation in fish density among nearby pools, suggesting dispersal limitation due to surface flow fragmentation. Fish density in pools increased during years with low summer precipitation, and this effect was observed in both streams but was stronger in Paine Run than Staunton River, further indicating the importance of fish movement into pools in response to low-flow thresholds. Our results indicate the importance of pools as ecological refuges during low-flow conditions and that episodic dewatering may affect extirpation risks for brook trout by sequestering more fish into fewer areas. Our findings also highlight the importance of hydrological variation within stream networks because downstream river gages could not predict the observed spatial heterogeneity in fish density or pool occupancy.

Freshwater Biology↗
USGS2024

Diel temperature signals track seasonal shifts in localized groundwater contributions to headwater streamflow generation at network scale

Groundwater contributions to streamflow sustain aquatic ecosystem resilience; streams without significant groundwater inputs often have well-coupled air and water temperatures that degrade cold-water habitat during warm low flow periods. Widespread uncertainty in stream-groundwater connectivity across space and time has created disparate predictions of energy and nutrient fluxes across headwater networks, hindering predictions of cold-water habitat resilience under climate change scenarios. Recently, annual paired air and water temperature signals have been harnessed to indicate stream water thermal sensitivity and the dominance of deep versus shallow groundwater influence, although the utility of diel air–water temperature signal metrics for hydrologic inference has remained unexplored. Here we analyzed two consecutive years of locally paired, air–water temperature data from 47 headwater stream sites in the Catskill Mountains, New York, USA, and discovered characteristic seasonal patterns in diel temperature signal sinusoid metrics (amplitude ratio, phase lag, and mean ratio) driven by shifts in streamflow generation mechanisms and stream network position. Hydrologic interpretations of observed patterns were supported by stream heat budget model scenarios and additional analysis of paired air–water temperature data from two streams in Shenandoah National Park, Virginia, USA, with well characterized stream-groundwater connectivity. We found that within smaller tributaries, streamflow generation transitions from runoff to groundwater dominance were driven by hillslope drying during seasonal periods of lower precipitation. This was evidenced by significant correlations (p < 0.01) between daily water:air temperature signal amplitudes (non-linear decreases of ∼ 50 %) and derived base-flow index at 22 of the 28 sites, indicating enhanced local groundwater influence on streamflow promotes decoupling of diel air–water temperature signals. Additionally, ratios between daily water:air temperature signal means were lower in tributaries (∼0.68) when compared to main-stem (∼0.8) sites, increasing linearly throughout the observational period. In conceptual stream heat budget models, groundwater inflow had minimal effects on daily phase lags (∼0.2 hr), but increases in fractional groundwater discharge (0–50 %) depressed daily amplitude (∼20 % to 50 %) and mean ratios (∼15 %), supporting the sensitivity of daily metrics to interpreted changes in seasonal groundwater contributions to streamflow. During observational periods (i.e., April through October 2021 and 2022), significant differences (p < 0.01) between tributary and main-stem air–water metrics occurred when base-flow contributions were highest (∼0.93 vs. ∼ 0.68), as sites lower in the network had daily temperature metrics dominated by stream channel thermal inertia, rather than local groundwater connectivity, showing enhanced air–water diel signal coupling during warmer, drier periods. Divergent air temperature coupling across the network was interpreted as being driven by distance from local groundwater source zones, additional lateral groundwater inflows do not contribute a meaningful fraction to channel discharge lower in the network. Given the growing footprint of stream temperature observations, diel air–water temperature signals can provide distributed metrics sensitive to upstream groundwater discharge. Consequently, these metrics can support ongoing efforts by resource managers and researchers seeking to forecast the resilience of cold-water habitat to climate warming and changing precipitation regimes in mountain headwater streams.

New York, Virginia↗
USGS2023

Stabilising effects of karstic groundwater on stream fish communities

Although groundwater exchange processes are known to modulate atmospheric influences on stream temperature and flow, the implications for ecological stability are poorly understood. Here, we evaluated temporal change in stream fish communities across a gradient of groundwater influence defined by karst terrain (carbonate parent materials) within the Potomac River basin of eastern North America. We surveyed 12 sites in 2022 that had been sampled 29–30 years previously with similar methods. We also collected stream temperature data from each site and used the regression slope of the air-water temperature relationship to index stream thermal sensitivity and groundwater exchange processes. Sites in karst terrain exhibited strong groundwater controls on stream temperature, and fish communities were more stable over time in these locations than elsewhere. However, stream thermal sensitivity was a stronger predictor of species persistence than the spatial distribution of karst terrain in contributing areas, highlighting the ecological importance of local variation in groundwater discharge processes. The presence of calcium precipitates (marl) in stream substrates was associated with low thermal sensitivity and ecological stability over time, and we suggest such visible features may be a useful indicator of climate change refugia in stream ecosystems.

Ecology of Freshwater Fish↗
USGS2023

CGS: Coupled growth and survival model with cohort fairness

Fish modeling in complex environments is critical for understanding drivers of population dynamics in aquatic systems. This paper proposes a Bayesian network method for modeling fish survival and growth over multiple connected rivers. Traditional fish survival models capture the effect of multiple environmental drivers (e.g., stream temperature, stream flow) by adding different variables, which increases model complexity and results in very long and impractical run times (i.e., weeks). We propose a coupled survival-growth model that leverages the observations from both sources simultaneously. It also integrates the Bayesian process into the neural network model to efficiently capture complex variable relationships in the system while also conforming to known survival processes used in existing fish models. To further reduce the performance disparity of fish body length across cohorts, we propose two approaches for enforcing fairness by the adjustment of training priorities and data augmentation. The results based on a real-world fish dataset collected in Massachusetts, US demonstrate that the proposed method can greatly improve prediction accuracy in modeling survival and body length compared to independent models on survival and growth, and effectively reduce the performance disparity across cohorts. The fish growth and movement patterns discovered by the proposed model are also consistent with prior studies in the same region, while vastly reducing run times and memory requirements.

Conference Paper↗
USGS2022

Bedrock depth influences spatial patterns of summer baseflow, temperature and flow disconnection for mountainous headwater streams

In mountain headwater streams, the quality and resilience of summer cold-water habitat is generally regulated by stream discharge, longitudinal stream channel connectivity and groundwater exchange. These critical hydrologic processes are thought to be influenced by the stream corridor bedrock contact depth (sediment thickness), a parameter often inferred from sparse hillslope borehole information, piezometer refusal and remotely sensed data. To investigate how local bedrock depth might control summer stream temperature and channel disconnection (dewatering) patterns, we measured stream corridor bedrock depth by collecting and interpreting 191 passive seismic datasets along eight headwater streams in Shenandoah National Park (Virginia, USA). In addition, we used multi-year stream temperature and streamflow records to calculate several baseflow-related metrics along and among the study streams. Finally, comprehensive visual surveys of stream channel dewatering were conducted in 2016, 2019 and 2021 during summer low flow conditions (124 total km of stream length). We found that measured bedrock depths along the study streams were not well-characterized by soils maps or an existing global-scale geologic dataset where the latter overpredicted measured depths by 12.2 m (mean) or approximately four times the average bedrock depth of 2.9 m. Half of the eight study stream corridors had an average bedrock depth of less than 2 m. Of the eight study streams, Staunton River had the deepest average bedrock depth (3.4 m), the coldest summer temperature profiles and substantially higher summer baseflow indices compared to the other study steams. Staunton River also exhibited paired air and water annual temperature signals suggesting deeper groundwater influence, and the stream channel did not dewater in lower sections during any baseflow survey. In contrast, Paine Run and Piney River did show pronounced, patchy channel dewatering, with Paine Run having dozens of discrete dry channel sections ranging from 1 to greater than 300 m in length. Stream dewatering patterns were apparently influenced by a combination of discrete deep bedrock (20+ m) features and more subtle sediment thickness variation (1–4 m) depending on local stream valley hydrogeology. In combination, these unique datasets show the first large-scale empirical support for existing conceptual models of headwater stream disconnection based on spatially variable underflow capacity and shallow groundwater supply.

Virginia↗
USGS2022

VIMTS: Variational-based Imputation for Multi-modal Time Series

Multi-modal time series data in real applications often contain data of different dimensionalities, e.g., high-dimensional modality such as image data series, and low-dimensional univariate time series. Multi-modal time series data with missing high-dimensional modal values are ubiquitous in real-world classification and regression applications. To accurately predict the target labels, it is important to appropriately impute the high-dimensional modal missing values. However, most existing imputation methods focus on multivariate time series, fail to simultaneously consider temporal dependencies within each series and the correlations across the series, and also lack a probabilistic interpretation. In this paper, we propose a novel method, which uses a new structured variational approximation technique for the imputation of missing values in multi-modal time series. Instead of directly imputing high-dimensional modal missing values, we use the variational approximation technique to impute intermediate lower-dimensional feature representations of high-dimensional modal missing values from simple modalities related to high-dimensional modality and then feed them into a dynamical model. The dynamical model captures the temporal dependencies of the feature representations and finally predicts the target labels. In order to address the optimization difficulties caused by the lack of ground truth values of lower-dimensional feature representations, we also propose a two-stage isolated optimization strategy for better convergence. We evaluate our method on a real-world stream monitoring dataset. Our extensive experiments demonstrate that the proposed method outperforms several state-of-the-art methods in both data imputation and prediction performance.

Conference Paper↗
USGS2021

Evaluating streamwater dissolved organic carbon dynamics in context of variable flowpath contributions with a tracer-based mixing model

This study focuses on characterizing the contributions of key terrestrial pathways that deliver dissolved organic carbon (DOC) to streams during hydrological events and on elucidating factors governing variation in water and DOC fluxes from these pathways. We made high-frequency measurements of discharge, specific conductance (SC), and fluorescent dissolved organic matter (FDOM) during 221 events recorded over 2 years within four Vermont (USA) watersheds that range in area from 0.4 to 139 km 2 . Using the SC measurements, together with statistical information on discharge, we separated the event hydrographs into contributions from three terrestrial pathways, which we refer to as riparian quickflow, subsurface quickflow, and slow-flow groundwater. The pathway discharges were used as input to a mixing model that closely approximated sub-hourly streamwater DOC concentrations as measured with the FDOM sensors. Subsurface quickflow, comprised of pre-event water, was the leading contributor to streamwater DOC fluxes, while riparian quickflow, comprised of event water, was the second-leading contributor to streamwater DOC fluxes, despite comprising the smallest proportion of streamflow yield among the three end-member pathways. Fixed-effects regression analysis revealed that the relationship between DOC fluxes from the end-member pathways and event magnitude was consistent across the four watersheds. This analysis also showed that DOC fluxes from the quickflow pathways increased significantly with temperature and varied inversely, but weakly, with catchment antecedent wetness. We believe that our approach, which leverages in-stream sensors that enable high-frequency measurements over extended periods, may be applicable for evaluating controls on DOC export from other watersheds within and beyond our study region.

New Hampshire, Vermont↗
USGS2020

Source switching maintains dissolved organic matter chemostasis across discharge levels in a large temperate river network

Dissolved organic matter (DOM) helps regulate aquatic ecosystem structure and function. In small streams, DOM concentrations are controlled by transport of terrestrial materials to waterways, and are thus highly variable. As rivers become larger, the River Continuum Concept hypothesizes that internal primary production is an increasingly important DOM source, but direct evidence is limited. Recently, the Pulse-Shunt Concept postulated that terrestrial DOM concentrations in larger rivers increase with flow and temperature, which seemingly contradicts previously reported DOM chemostasis in large rivers. This study estimates daily gross primary production (GPP) in 13 streams and rivers across the Connecticut River watershed (watershed areas 0.4–25,019 km 2 ) from 2015 through 2017. Chemostasis of DOM concentrations is maintained by a switch from autochthonous sources of DOM at low flows to terrestrial sources of DOM at high flows in a large temperate river and to a lesser degree in smaller tributaries. At low flow, autochthonous DOM linked to aquatic GPP is the dominant fraction of the DOM pool in large rivers. This autochthonous DOM maintains chemostasis in the main stem and to a lesser extent upstream. Thus, in larger rivers, low-flow autochthonous production stabilizes DOM concentrations during the summer, a critical time for riverine ecology. Consistent with the Pulse-Shunt Concept, terrigenous DOM is the dominant fraction of DOM during higher flow periods and about 70% of annual DOM fluxes to the coast are terrestrial. This pattern of DOM switching is potentially widespread in temperate watersheds with implications to both inland waters and coastal ecosystems.

Ecosystems↗
USGS2019

Molecular hysteresis: Hydrologically driven changes in riverine dissolved organic matter chemistry during a storm event

Hydrological events, driven by rainfall, control the amount and composition of dissolved organic matter (DOM) mobilized through river networks. In forested watersheds, the concentration, composition, and reactivity of DOM exported changes as baseflow transitions to storm flow, with major implications to downstream biogeochemistry. Hysteresis describes an observed difference between in-stream solute concentration/signal and discharge. By studying the relationship between DOM and stream discharge, we refine our understanding of the environmental and hydrological factors that influence the quantity and quality of stream DOM. The main objective of this study was to track hysteretic changes in riverine DOM molecular composition during storm events. Samples were collected from nested sites within the Passumpsic River catchment (Vermont, USA), a tributary of the Connecticut River. High-resolution monitoring of fluorescent DOM (via in situ sensors) and automated collection of discrete samples captured short-term, hydrologically driven variations in DOM concentration and composition. Ultrahigh-resolution mass spectrometry revealed an enrichment in aliphatic compounds at storm onset, while aromatic and polyphenolic compounds were more enriched at peak discharge. Molecular hysteresis patterns were similar across stream orders, indicating that fresh, terrigenous DOM is quickly shunted downstream, through the river network, during pulses of high discharge.

JGR Biogeosciences↗