Publications
This list of Water Resources Mission Area publications includes both official USGS publications and journal articles authored by our scientists. A searchable database of all USGS publications can be accessed at the USGS Publications Warehouse.
Filter Total Items: 19081
Multidecadal change in pesticide concentrations relative to human health benchmarks in the Nation’s groundwater Multidecadal change in pesticide concentrations relative to human health benchmarks in the Nation’s groundwater
Groundwater-quality trend assessments identify aquifers that are responding to changes in pesticide use and the compounds that may pose a threat to water availability. The U.S. Geological Survey has been monitoring pesticide concentrations in groundwater for 25 principal aquifers across the conterminous United States since 1993. The groundwater well locations represent a range of soils...
Authors
Sarah M. Stackpoole, Bruce D. Lindsey, Cee S. Nell
Examining the compositional selectivity of hydrocarbon oxidation products using liquid–liquid extraction and solid-phase extraction techniques Examining the compositional selectivity of hydrocarbon oxidation products using liquid–liquid extraction and solid-phase extraction techniques
The effect of extraction methods on detecting hydrocarbon oxidation products (HOPs) in groundwater remains unclear. HOPs are polar, water-soluble byproducts of petroleum biodegradation. Our previous work showed that liquid–liquid extraction (LLE), a method commonly used in regulatory monitoring, has a significantly lower extraction efficiency for HOPs compared to solid-phase extraction...
Authors
Phoebe Zito, Rana Ghannam, Maxwell L. Harsha, Barbara Bekins, David C. Podgorski
Quantifying groundwater response and uncertainty in beaver-influenced mountainous floodplains using machine learning-based model calibration Quantifying groundwater response and uncertainty in beaver-influenced mountainous floodplains using machine learning-based model calibration
Beavers (Castor canadensis) alter river corridor hydrology by creating ponds and inundating floodplains, and thereby improving surface water storage. However, the impact of inundation on groundwater, particularly in mountainous alluvial floodplains with permeable gravel/cobble layers overlain by a soil layer, remains uncertain. Numerical modeling across various floodplain structures...
Authors
Lijing Wang, Tristan Babey, Zach Perzan, Samuel Pierce, Martin A. Briggs, Kristin Boye, Kate Maher
Discharge and nutrients interact to determine trophic structure in a wetland: Evidence from a landscape-scale manipulation Discharge and nutrients interact to determine trophic structure in a wetland: Evidence from a landscape-scale manipulation
Identifying drivers of consumer biomass patterns and community structure is complex for managed freshwater ecosystems that are sensitive to nutrients and drought. In the Florida Everglades, flow restoration is expected to reintroduce discharge across an expansive wetland, yet most research on consumers has focused on water depth and dry disturbances. Low-velocity flow can mediate...
Authors
Marco Fernandez, Joel C. Trexler, Colin J. Saunders, Judson Harvey, Nathan J. Dorn
PFAS sampling activities in the U.S. Geological Survey national networks PFAS sampling activities in the U.S. Geological Survey national networks
Per- and polyfluoroalkyl substances (PFAS), frequently called “forever chemicals,” are used for a wide variety of industrial purposes and are often found in common household and industrial items such as firefighting foams, non-stick cookware, and water-resistant materials. The contamination of water, air, and soil by PFAS is a national and global issue due to their widespread occurrence...
Authors
Melissa L. Riskin, Bruce D. Lindsey, Ryan Conner McCammon
Sundial: A method for inferring image acquisition time from shadow orientation Sundial: A method for inferring image acquisition time from shadow orientation
Aerial photography and satellite imagery can be used to characterize landscape change over time and help to understand how these changes are related to climate and hydrology. Publicly available optical imagery from sources such as the United States National Agricultural Imagery Program (NAIP) is particularly valuable in this context due to its high temporal and spatial resolution...
Authors
Inhyeok Bae, Carl J. Legleiter, Elowyn Yager
Hyperspectral imaging of river bathymetry using an ensemble of regression trees Hyperspectral imaging of river bathymetry using an ensemble of regression trees
Remote sensing has emerged as an effective tool for characterizing river systems, and machine learning (ML) techniques could make this approach even more powerful. To explore this possibility, we developed an ML-based workflow for hyperspectral imaging of river bathymetry using an ensemble of regression trees (HIRBERT). This approach involves using paired observations of depth and...
Authors
Carl J. Legleiter, Paul J. Kinzel, Brandon Overstreet, Lee R. Harrison
Toward a new framework to evaluate process-based model configurations and quantify data worth prior to calibration Toward a new framework to evaluate process-based model configurations and quantify data worth prior to calibration
Model criticism, discrimination, and selection methods often rely on calibrated model outputs. Because calibration can be computationally expensive, model criticism can first be undertaken by assessing model outputs obtained from limited prior parameter ensembles. However, such prior-based methods are often heuristic and do not formalize the notion of balancing model consistency with...
Authors
Mark Shannon Pleasants, Michael N. Fienen, Hedeff I. Essaid, Joel D. Blomquist, Jing Yang, Ming Ye
Total uncertainty quantification in inverse solutions with deep learning surrogate models Total uncertainty quantification in inverse solutions with deep learning surrogate models
We propose an approximate Bayesian method for quantifying the total uncertainty in inverse partial differential equation (PDE) solutions obtained with machine learning surrogate models, including operator learning models. The proposed method accounts for uncertainty in the observations, PDE, and surrogate models. First, we use the surrogate model to formulate a minimization problem in...
Authors
Yuanzhe Wang, James Lucian McCreight, Joseph D. Hughes, Alexandre Tartakovsky
The influence of scale in modeling social vulnerability and disaster assistance The influence of scale in modeling social vulnerability and disaster assistance
Understanding how social vulnerability relates to disaster impacts is critical for addressing social equity, yet the role of spatial scale in this relationship is often overlooked. Most studies use aggregated data, risking ecological fallacy—misinterpreting individual outcomes from group-level data. This study examines how spatial scale influences the relationship between social...
Authors
Sina Razzaghi Asl, Oronde Oliver Drakes, Eric Tate, Samuel D. Brody, Wesley Highfield, Kayode Atoba
Clarifying the trophic state concept to advance macroscale freshwater science and management Clarifying the trophic state concept to advance macroscale freshwater science and management
For over a century, ecologists have used the concept of trophic state (TS) to characterize an aquatic ecosystem's biological productivity. However, multiple TS classification schemes, each relying on a variety of measurable parameters as proxies for productivity, have emerged to meet use-specific needs. Frequently, chlorophyll a, phosphorus, and Secchi depth are used to classify TS based...
Authors
Michael Frederick Meyer, Benjamin M. Kraemer, Carolina C. Barbosa, Davi G.F. Cuhna, Walter Dodds, Stephanie E. Hampton, César Ordóñez, Rachel M. Pilla, Amina Pollard, Joshua A. Culpepper, Alexander K. Fremier, Tyler King, Robert Ladwig, Dina M. Leech, Shin-Ichiro S. Matsuzaki, Isabella Oleksy, Simon N. Topp, Richard Woolway, Ludmila S Brighenti, Kate Colleen Fickas, Brian P. Lanouette, Jianning Ren, Mortimer Werther, Xiao Yang
A spatiotemporal deep learning approach for predicting daily air-water temperature signal coupling and identification of key watershed physical parameters in a montane watershed A spatiotemporal deep learning approach for predicting daily air-water temperature signal coupling and identification of key watershed physical parameters in a montane watershed
Seasonal shifts from runoff to groundwater dominance influence daily headwater stream temperatures, especially where local groundwater input is strong. This input buffers temperature during hot periods, supporting cold-water habitats. Recent studies use air–water temperature signal metrics to identify zones of strong stream–groundwater connectivity. While Previous studies used air–water...
Authors
Mohammad Reza M. Behbahani, David M. Rey, Martin A. Briggs, Amvrossios Bagtzoglou