Geology topics

Jeffrey D. Walker

Publications and source records attributed to Jeffrey D. Walker.

6 recordsLinked to original sources

USGS2026

An integrated drought early warning system to support coldwater fisheries management

Low streamflow resulting from drought and human water use is a threat to freshwater fisheries throughout western North America, creating a need for effective tools to aid management and fisheries use. We developed a drought early warning framework that forecasts river conditions that could result in temporary fishery closures and restrictions across Montana’s renowned trout fisheries. This tool provides reach-specific, probabilistic decision support for proactive management under drought conditions. We calibrated statistical models using historical streamflow and stream temperature data from U.S. Geological Survey (USGS) gauges to predict the likelihood of exceeding the low-flow and high-temperature thresholds for fisheries closure within 1- to 4-week outlooks during the warm season (May–October). These forecasts demonstrated consistently high model performance in predicting the criteria for low-flow fishing closure across all rivers (4-week outlook: mean R 2 = 0.96, IQR [interquartile range]: 0.94–0.97) and variable but often high model performance for temperature-based restriction criteria (mean R 2 = 0.80, IQR: 0.55–0.86), particularly at sites that frequently experience higher stream temperature conditions. This drought early warning system—available via the web-based tool TroutCast ( https://www.usgs.gov/apps/troutcast/ )—provides daily updated seasonal forecasts to help anticipate effects to recreational fishing opportunities. By quantifying forecast uncertainty and providing reach-specific probabilities, the system supports proactive management, improves coordination between fisheries and water managers, and provides stakeholders with actionable information on probable future effects on fishing access within a framework that is broadly applicable to other freshwater systems and water resource management challenges.

Montana↗

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

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↗

USGS2020

An interactive data visualization framework for exploring geospatial environmental datasets and model predictions

With the rise of large-scale environmental models comes new challenges for how we best utilize this information in research, management and decision making. Interactive data visualizations can make large and complex datasets easier to access and explore, which can lead to knowledge discovery, hypothesis formation and improved understanding. Here, we present a web-based interactive data visualization framework, the Interactive Catchment Explorer (ICE), for exploring environmental datasets and model outputs. Using a client-based architecture, the ICE framework provides a highly interactive user experience for discovering spatial patterns, evaluating relationships between variables and identifying specific locations using multivariate criteria. Through a series of case studies, we demonstrate the application of the ICE framework to datasets and models associated with three separate research projects covering different regions in North America. From these case studies, we provide specific examples of the broader impacts that tools like these can have, including fostering discussion and collaboration among stakeholders and playing a central role in the iterative process of data collection, analysis and decision making. Overall, the ICE framework demonstrates the potential benefits and impacts of using web-based interactive data visualization tools to place environmental datasets and model outputs directly into the hands of stakeholders, managers, decision makers and other researchers.

Water↗

USGS2018

Three visualization approaches for communicating and exploring PIT tag data

As the number, size and complexity of ecological datasets has increased, narrative and interactive raw data visualizations have emerged as important tools for exploring and understanding these large datasets. As a demonstration, we developed three visualizations to communicate and explore passive integrated transponder tag data from two long-term field studies. We created three independent visualizations for the same dataset, allowing separate entry points for users with different goals and experience levels. The first visualization uses a narrative approach to introduce users to the study. The second visualization provides interactive cross-filters that allow users to explore multi-variate relationships in the dataset. The last visualization allows users to visualize the movement histories of individual fish within the stream network. This suite of visualization tools allows a progressive discovery of more detailed information and should make the data accessible to users with a wide variety of backgrounds and interests.

Maine, Massachusetts↗

USGS2013

Metabolism of a nitrogen-enriched coastal marine lagoon during the summertime

We measured metabolism rates in a shallow, nitrogen-enriched coastal marine ecosystem on Cape Cod (MA, USA) during seven summers using an open-water diel oxygen method. We compared two basins, one directly receiving most of the nitrogen (N) load (“Snug Harbor”) and another further removed from the N load and better flushed (“Outer Harbor”). Both dissolved oxygen and pH varied greatly over the day, increasing in daylight and decreasing at night. The more N-enriched basin frequently went hypoxic during the night, and the pH in both basins was low (compared to standard seawater) when the oxygen levels were low, due to elevated carbon dioxide. Day-to-day variation in gross primary production (GPP) was high and linked in part to variation in light. Whole-ecosystem respiration tended to track this short-term variation in GPP, suggesting that respiration by the primary producers often dominated whole-system respiration. GPP was higher in the more N-loaded Snug Harbor. Seagrasses covered over 60 % of the area of the better-flushed, Outer Harbor throughout our study and were the major contributors to GPP there. Seagrasses covered 20 % of the area in Snug Harbor for the first 5 years of our study, and their contribution to GPP was relatively small. The seagrasses in Snug Harbor died off completely in the 6th year, but GPP remained high then and in the subsequent year. Overall, rates of phytoplankton GPP were relatively low, suggesting that benthic micro- and macro-algae may be the dominant primary producers in Snug Harbor in most years. Net ecosystem production in both Snug Harbor and the Outer Harbor was variable from year to year, showing net heterotrophy in some years and net autotrophy in others, with a trend towards increasing autotrophy over the 7 years reported here.

Massachusetts↗