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At least 505 records · Page 28Linked to original sources
USGS2020

Methods to quality assure, plot, summarize, interpolate, and extend groundwater-level information—Examples for the Mississippi River Valley alluvial aquifer

Large-scale computational investigations of groundwater levels are proposed to accelerate science delivery through a workflow spanning database assembly, statistics, and information synthesis and packaging. A water-availability study of the Mississippi River alluvial plain, and particularly the Mississippi River Valley alluvial aquifer (MRVA), is ongoing. Software (visGWDBmrva) has been released as part of the study that demonstrates groundwater informatics for the aquifer. Considerable water-level data collected by multiple agencies over a seven-state area exist (18,903 wells; 287,272 measurements [April 22, 2019]). Data and metadata quality assurance methods, basic statistics, hydrograph visualization, outlier identification, hypothesis testing, and time-series modeling are described. Two approaches (generalized additive models [GAMs] and support vector machines [SVMs]) are used for data interpolation and extension to monthly water-level estimates. Numerical congruence between GAM and SVM estimates will be useful to limit inclusion of monthly estimates from subsequent science activities.

Mississippi River valley↗
USGS2021

Informing wetland management with waterfowl movement and sanctuary use responses to human-induced disturbance

Long-term environmental management to prevent waterfowl population declines is informed by ecology, movement behavior and habitat use patterns. Extrinsic factors, such as human-induced disturbance, can cause behavioral changes which may influence movement and resource needs, driving variation that affects management efficacy. To better understand the relationship between human-based disturbance and animal movement and habitat use, and their potential effects on management, we GPS tracked 15 dabbling ducks in California over ~4-weeks before, during and after the start of a recreational hunting season in October/November 2018. We recorded locations at 2-min intervals across three separate 24-h tracking phases: Phase 1) two weeks before the start of the hunting season (control (undisturbed) movement); Phase 2) the hunting season opening weekend; and Phase 3) a hunting weekend two weeks after opening weekend. We used GLMM models to analyze variation in movement and habitat use under hunting pressure compared with ‘normal’ observed patterns prior to commencement of hunting. We also compared responses to differing levels of disturbance related to the time of day (high - shooting/~daytime); moderate - non-lethal (~crepuscular); and low - night). During opening weekend flight (% time and distance) more than doubled during moderate and low disturbance and increased by ~50% during high disturbance compared with the pre-season weekend. Sanctuary use tripled during moderate and low disturbance and increased ~50% during high disturbance. Two weeks later flight decreased in all disturbance levels but was only less than the pre-season levels during high disturbance. In contrast, sanctuary use only decreased at night, although not to pre-season levels, while daytime doubled from ~45% to >80%. Birds adjust rapidly to disturbance and our results have implications for energetics models that estimate population food requirements. Management would benefit from reassessing the juxtaposition of essential sanctuary and feeding habitats to optimize wetland management for waterfowl.

Journal of Environmental Management↗
USGS2017

Cooperative science to inform Lake Ontario management: Research from the 2013 Lake Ontario CSMI program

Since the mid-1970s, successful Lake Ontario management actions including nutrient load and pollution reductions, habitat restoration, and fish stocking have improved Lake Ontario. However, several new obstacles to maintenance and restoration have emerged. This special issue presents management-relevant research from multiple agency surveys in 2011 and 2012 and the 2013 Cooperative Science and Monitoring Initiative (CSMI), that span diverse lake habitats, species, and trophic levels. This research focused on themes of nutrient loading and fate; vertical dynamics of primary and secondary production; fish abundance and behavior; and food web structure. Together these papers identify the status of many of the key drivers of the Lake Ontario ecosystem and contribute to addressing lake-scale questions and management information needs in Lake Ontario and the other Great Lakes and connecting water bodies.

Lake Ontario↗
USGS2005

Estimating discharge in rivers using remotely sensed hydraulic information

A methodology to estimate in-bank river discharge exclusively from remotely sensed hydraulic data is developed. Water-surface width and maximum channel width measured from 26 aerial and digital orthophotos of 17 single channel rivers and 41 SAR images of three braided rivers were coupled with channel slope data obtained from topographic maps to estimate the discharge. The standard error of the discharge estimates were within a factor of 1.5-2 (50-100%) of the observed, with the mean estimate accuracy within 10%. This level of accuracy was achieved using calibration functions developed from observed discharge. The calibration functions use reach specific geomorphic variables, the maximum channel width and the channel slope, to predict a correction factor. The calibration functions are related to channel type. Surface velocity and width information, obtained from a single C-band image obtained by the Jet Propulsion Laboratory's (JPL's) AirSAR was also used to estimate discharge for a reach of the Missouri River. Without using a calibration function, the estimate accuracy was +72% of the observed discharge, which is within the expected range of uncertainty for the method. However, using the observed velocity to calibrate the initial estimate improved the estimate accuracy to within +10% of the observed. Remotely sensed discharge estimates with accuracies reported in this paper could be useful for regional or continental scale hydrologic studies, or in regions where ground-based data is lacking. ?? 2004 Elsevier B.V. All rights reserved.

Journal of Hydrology↗
USGS2017

Remote measurement of river discharge using thermal particle image velocimetry (PIV) and various sources of bathymetric information

Although river discharge is a fundamental hydrologic quantity, conventional methods of streamgaging are impractical, expensive, and potentially dangerous in remote locations. This study evaluated the potential for measuring discharge via various forms of remote sensing, primarily thermal imaging of flow velocities but also spectrally-based depth retrieval from passive optical image data. We acquired thermal image time series from bridges spanning five streams in Alaska and observed strong agreement between velocities measured in situ and those inferred by Particle Image Velocimetry (PIV), which quantified advection of thermal features by the flow. The resulting surface velocities were converted to depth-averaged velocities by applying site-specific, calibrated velocity indices. Field spectra from three clear-flowing streams provided strong relationships between depth and reflectance, suggesting that, under favorable conditions, spectrally-based bathymetric mapping could complement thermal PIV in a hybrid approach to remote sensing of river discharge; this strategy would not be applicable to larger, more turbid rivers, however. A more flexible and efficient alternative might involve inferring depth from thermal data based on relationships between depth and integral length scales of turbulent fluctuations in temperature, captured as variations in image brightness. We observed moderately strong correlations for a site-aggregated data set that reduced station-to-station variability but encompassed a broad range of depths. Discharges calculated using thermal PIV-derived velocities were within 15% of in situ measurements when combined with depths measured directly in the field or estimated from field spectra and within 40% when the depth information also was derived from thermal images. The results of this initial, proof-of-concept investigation suggest that remote sensing techniques could facilitate measurement of river discharge.

Journal of Hydrology↗
USGS2025

Estimating the probability of export restrictions to inform mineral criticality

To assess risks associated with advanced technologies’ supply chain disruptions, governmental agencies and others have developed mineral “criticality” assessments, with criticality described using the economic impact and probability of supply chain disruptions. Previous work developed subjective supply risk indicators to approximate this probability, typically combining several factors such as supply diversity and trading partners’ political stability, where indicator weightings can substantially impact results. This work explicitly quantifies export barrier probability using an ensemble of machine learning classifiers, with probability estimates informed by exogenous variables, including prior barrier implementation and global export dominance. Major differences in high-probability countries and commodities are observed across models, but the ensemble method highlights Indonesia, China, Tanzania, and the United States as particularly high risk. The Supplementary Data File provides export barrier probability estimates for each analyzed country-commodity pair, enabling a direct, quantitative, objective contribution to assessing mineral criticality, enhancing risk identification and prioritization for policymakers.

Resources, Conservation, and Recycling↗
USGS2022

Water availability drives instream conditions and life-history of an imperiled desert fish: A case study to inform water management

In arid ecosystems, available water is a critical, yet limited resource for human consumption, agricultural use, and ecosystem processes—highlighting the importance of developing management strategies to meet the needs of multiple users. Here, we evaluated how water availability influences stream thermal regimes and life-history expressions of Lahontan cutthroat trout ( Oncorhynchus clarkii henshawi ) in the arid Truckee River basin in the western United States. We integrated air temperature and stream discharge data to quantify how water availability drives stream temperature during annual spawning and rearing of Lahontan cutthroat trout. We then determined how in situ stream discharge and temperature affected adult spawning migrations, juvenile growth opportunities, and duration of suitable thermal conditions. Air temperatures had significant, large effects (+) on stream temperature across months; the effects of discharge varied across months, with significant effects (−) during May through August, suggesting increased discharge can help mitigate temperatures during seasonally warm months. Two models explained adult Lahontan cutthroat trout migration, and both models indicated that adult Lahontan cutthroat trout avoid migration when temperatures are warmer (~ > 12 °C) and discharge is higher (~ > 50 m 3 *s −1 ). Juvenile size was best explained by a quadratic relationship with cumulative degree days (CDD; days>4 °C) as size increased with increasing CDDs but decreased at higher CDDs. We also found an interaction between CDDs and discharge explaining juvenile size: when CDDs were low, higher discharge was associated with larger size, but when CDDs were high, higher discharge was associated with smaller size. Stream temperatures also determined the duration of juvenile rearing, as all juvenile emigration ceased at temperatures >24.4 °C. Together, our results illustrated how stream discharge and temperature shape the life-history of Lahontan cutthroat trout at multiple stages and can inform management actions to offset warming temperatures and facilitate life-history diversity and population resilience.

California, Nevada↗
USGS2024

Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease

To mitigate the negative effects of emerging wildlife diseases in biodiversity and public health it is critical to accurately forecast pathogen dissemination while incorporating relevant spatio-temporal covariates. Forecasting spatio-temporal processes can often be improved by incorporating scientific knowledge about the dynamics of the process using physical models. Ecological diffusion equations are often used to model epidemiological processes of wildlife diseases where environmental factors play a role in disease spread. Physics-informed neural networks (PINN) are deep learning algorithms that constrain neural network predictions based on physical laws and therefore are powerful forecasting models useful even in cases of limited and imperfect training data. In this paper, we develop a novel ecological modeling tool using PINNs, which fits a feedforward neural network and simultaneously performs parameter identification in a partial differential equation (PDE) with varying coefficients. We demonstrate the applicability of our model by comparing it with the commonly used Bayesian stochastic partial differential equation method and traditional machine learning approaches, showing that our proposed model exhibits superior prediction and forecasting performance when modeling chronic wasting disease in deer in Wisconsin. Furthermore, our model provides the opportunity to obtain scientific insights into spatiotemporal covariates affecting spread and growth of diseases. This work contributes to future machine learning and statistical methodology development by studying spatio-temporal processes enhanced by prior physical knowledge.

Spatial Statistics↗
USGS2002

A portal for the ocean biogeographic information system

Since its inception in 1999 the Ocean Biogeographic Information System (OBIS) has developed into an international science program as well as a globally distributed network of biogeographic databases. An OBIS portal at Rutgers University provides the links and functional interoperability among member database systems. Protocols and standards have been established to support effective communication between the portal and these functional units. The portal provides distributed data searching, a taxonomy name service, a GIS with access to relevant environmental data, biological modeling, and education modules for mariners, students, environmental managers, and scientists. The portal will integrate Census of Marine Life field projects, national data archives, and other functional modules, and provides for network-wide analyses and modeling tools.

Oceanologica Acta↗
USGS2001

Some simple guides to finding useful information in exploration geochemical data

Most regional geochemistry data reflect processes that can produce superfluous bits of noise and, perhaps, information about the mineralization process of interest. There are two end-member approaches to finding patterns in geochemical data—unsupervised learning and supervised learning. In unsupervised learning, data are processed and the geochemist is given the task of interpreting and identifying possible sources of any patterns. In supervised learning, data from known subgroups such as rock type, mineralized and nonmineralized, and types of mineralization are used to train the system which then is given unknown samples to classify into these subgroups. To locate patterns of interest, it is helpful to transform the data and to remove unwanted masking patterns. With trace elements use of a logarithmic transformation is recommended. In many situations, missing censored data can be estimated using multiple regression of other uncensored variables on the variable with censored values. In unsupervised learning, transformed values can be standardized, or normalized, to a Z-score by subtracting the subset's mean and dividing by its standard deviation. Subsets include any source of differences that might be related to processes unrelated to the target sought such as different laboratories, regional alteration, analytical procedures, or rock types. Normalization removes effects of different means and measurement scales as well as facilitates comparison of spatial patterns of elements. These adjustments remove effects of different subgroups and hopefully leave on the map the simple and uncluttered pattern(s) related to the mineralization only. Supervised learning methods, such as discriminant analysis and neural networks, offer the promise of consistent and, in certain situations, unbiased estimates of where mineralization might exist. These methods critically rely on being trained with data that encompasses all populations fairly and that can possibly fall into only the identified populations.

Natural Resources Research↗
USGS2023

Train, inform, borrow, or combine? Approaches to process-guided deep learning for groundwater-influenced stream temperature prediction

Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the Delaware River Basin (USA). We found lower accuracy (root mean square error [RMSE] of 1.71 versus 1.35°C) and stronger seasonal bias (absolute mean monthly bias of 1.06 vs. 0.68°C) for reaches primarily influenced by deep groundwater as compared to atmospheric conditions. We then tested four approaches for improving groundwater process representation: (a) a custom loss function leveraging the unique patterns of air and water temperature coupling characteristic of different temperature drivers, (b) inclusion of additional groundwater-relevant catchment attributes, (c) incorporation of additional process model outputs, and (d) a composite model. The custom loss function and the additional attributes significantly improved the predictive accuracy in groundwater-dominated reaches (RMSE of 1.37 and 1.26°C) and reduced the seasonal bias (absolute mean monthly bias of 0.44 and 0.48°C), but neither approach could identify holdout groundwater reaches. Variable importance analysis indicates the custom loss function nudges the model to use the existing inputs more efficiently, whereas with the added features the model relies on a broader suite of inputs. This analysis is a substantial step toward more accurately representing groundwater discharge processes in stream temperature models and will improve predictive accuracy and inform habitat management.

Delaware River Basin↗
USGS2025

Imaging hyporheic exchange by integrating deep learning and physics-informed inversion of time-lapse self-potential data

Self-potential (SP) monitoring is increasingly used for subsurface flow characterization due to its sensitivity to hydrogeological and geochemical processes. However, SP inversion remains challenging due to its ill-posed nature, sparse data coverage, and strong transient noise. This study proposes a hybrid framework to image hyporheic exchange using a time-lapse SP data set monitored from a streamflow site in Oak Ridge, Tennessee. Dipole moment tomography grids generated from the physics-informed numerical inversion is first used to train a Vision Transformer (ViT) model that maps surface SP sequences to 2D source distributions. While the numerical method is more responsive to transient signals, the ViT model better captures persistent spatial structures. Their complementary outputs are jointly analyzed in the spatiotemporal domain to isolate dynamic hyporheic exchange zones and distinguish transient from steady state subsurface flow features. This approach integrates physical inversion and deep learning to enhance interpretability, generalization, and temporal awareness in SP analysis.

Geophysical Research Letters↗
USGS2026

Subsurface vertical connectivity shapes solute transport to montane streams: Insights from watershed-scale geophysics-informed modeling

Headwater catchments provide essential water and nutrients to downstream ecosystems. The timing and evolution of their transport is shaped in part by the “invisible” subsurface structure but have been largely unexplored at the watershed scale. This raises a fundamental question: how and to what extent does subsurface structure influence streamflow and solute transport in montane watersheds? Here, we test controls of three-dimensionally resolved subsurface structure on solute transport, which reflects source waters and water residence time. We integrate the geophysically mapped architecture of Coal Creek, a headwater catchment in the Colorado River Basin, into numerical flow-transport models. Our results reveal greater vertical connectivity elongates flow paths and enhances deep groundwater contributions to streams, therefore substantially influencing the timing and magnitude of solute transport. In contrast, watershed-integrated outlet discharge can be predicted without high-resolution subsurface information. These findings demonstrate incorporating subsurface architecture is critical for predicting ecosystem health and contaminant transport.

Colorado↗
USGS2010

Exact Scheffé-type confidence intervals for output from groundwater flow models: 2. Combined use of hydrogeologic information and calibration data

Calibration data (observed values corresponding to model-computed values of dependent variables) are incorporated into a general method of computing exact Scheffé-type confidence intervals analogous to the confidence intervals developed in part 1 (Cooley, this issue) for a function of parameters derived from a groundwater flow model. Parameter uncertainty is specified by a distribution of parameters conditioned on the calibration data. This distribution was obtained as a posterior distribution by applying Bayes' theorem to the hydrogeologically derived prior distribution of parameters from part 1 and a distribution of differences between the calibration data and corresponding model-computed dependent variables. Tests show that the new confidence intervals can be much smaller than the intervals of part 1 because the prior parameter variance-covariance structure is altered so that combinations of parameters that give poor model fit to the data are unlikely. The confidence intervals of part 1 and the new confidence intervals can be effectively employed in a sequential method of model construction whereby new information is used to reduce confidence interval widths at each stage.

Water Resources Research↗
USGS2010

Economic basis of resource information systems: The case of streamflow network design

A general method for the economic design of natural resource information systems is presented for a certain class of natural phenomena. The system design is determined by the interaction of the technical input‐output relationship, i.e., the production function, the set of resource constraints, and an economic loss function defined in terms of parameter uncertainty. An application of the proposed method to streamflow network design is presented. Results of this analysis indicate that the method is fairly robust with respect to the assumptions. Observations are made which suggest extensions to flood measurement networks, long‐term precipitation networks, and seismic observation network design.

Water Resources Research↗
USGS2003

Assessment of forest fragmentation in southern New England using remote sensing and geographic information systems technology

Spatial patterns and rates of forest fragmentation were assessed using digital remote sensing data for a region in southern New England that included 157 townships in southern New Hampshire and northeastern Massachusetts. The study area has undergone marked population increases over the last several decades. Following classification of 1973 and 1988 Landsat Multispectral Scanner data into forest and nonforest classes, data were incorporated into a geographic information system. The natural logarithms of forest area to perimeter ratios, referred to as the forest continuity index, were used to assess patterns and trends of forest fragmentation across the region Forest continuity index values were extracted from each township for both data sets and compared with population data. Forest continuity index values were found to decrease with increasing population density until about 200 persons per square kilometer, after which the relationship stabilized. With slight population increases at low densities forest continuity index values declined sharply, implying abrupt increases in forest fragmentation. Results from the study indicated good negative correlations (r 2 values of 0.81 and 0.77) between the Multispectral Scanner-derived forest continuity index and natural logs of township population density. Socioeconomic indicators such as affluence and commuting patterns did not appear to correlate well with forest fragmentation estimates. Decreases in forest continuity index values occurred throughout much of the study region between 1973 and 1988, suggesting that forest fragmentation is occurring over large regions within the eastern United States. It is technologically feasible to assess patterns and rates of forest fragmentation across much larger areas than analyzed in this study; such analyses would provide useful overviews enabling objective assessment of the magnitude of forest fragmentation.

Massachusetts, New Hampshire↗
USGS2025

Ecologically informed solar enables a sustainable energy transition in U.S. croplands

United States (U.S.) croplands are ideal recipient environments for solar photovoltaic (PV) energy because they are flat and have a high solar resource. Perceived threats of solar to agriculture have led some stakeholders to suggest that croplands be exclusively used to produce food. However, 12 million hectares of U.S. croplands, an area about the size of New York State, are already dedicated to corn grown for ethanol (i.e., biofuel), an energy product that requires significantly more land than solar PV per unit energy. Ecosystem service benefits of an ecologically-informed approach to solar development (i.e., ecovoltaics), coupled with significant land-use advantages over corn ethanol, make solar an attractive solution for a sustainable energy transition in croplands. Here, we evaluated how the conversion of a small fraction of corn-ethanol croplands into ecovoltaic solar facilities might improve land-use efficiency of energy generation, enhance ecosystem services, and provide landscape diversification. Through spatial analyses, we determined that converting just 3.2% of land currently used for corn ethanol would increase the share of utility-scale solar energy in the U.S. from 3.9% to 13%. We also identified target locations where strategic conversion of corn ethanol to solar PV co-located with perennial vegetation could filter excess nutrients transported from adjacent farm runoff, diversify and connect agricultural landscapes, and provide local wildlife habitat. In contrast to the common perception of land-use competition and land scarcity in the energy transition, our findings highlight benefits of co-located energy landscapes that integrate fundamental principles of energy development and sustainable agroecosystems.

contiguous United States↗
USGS2007

Analysis of impacts of urban land use and land cover on air quality in the Las Vegas region using remote sensing information and ground observations

Urban development in the Las Vegas Valley of Nevada (USA) has expanded rapidly over the past 50 years. The air quality in the valley has suffered owing to increases from anthropogenic emissions of carbon monoxide, ozone and criteria pollutants of particular matter. Air quality observations show that pollutant concentrations have apparent heterogeneous characteristics in the urban area. Quantified urban land use and land cover information derived from satellite remote sensing data indicate an apparent local influence of urban development density on air pollutant distributions. Multi‐year observational data collected by a network of local air monitoring stations specify that ozone maximums develop in the May and June timeframe, whereas minimum concentrations generally occur from November to February. The fine particulate matter maximum occurs in July. Ozone concentrations are highest on the west and northwest sides of the valley. Night‐time ozone reduction contributes to the heterogeneous features of the spatial distribution for average ozone levels in the Las Vegas metropolitan area. Decreased ozone levels associated with increased urban development density suggest that the highest ozone and lowest nitrogen oxides concentrations are associated with medium to low density urban development in Las Vegas.

International Journal of Remote Sensing↗