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1,229 records · Page 3Linked to original sources
USGS2024

Self-guided decision support groundwater modelling with Python

The GMDSI tutorial notebooks repository provides learners with a comprehensive set of tutorials for self-guided training on decision-support groundwater modelling using Python-based tools. Although targeted at groundwater modelling, they are based around model-agnostic tools and readily transferable to other environmental modelling workflows. The tutorials are divided into three parts. The first covers fundamental theoretical concepts. These are intended as background reading for reference on an as-needed basis. Tutorials in the second part introduce learners to some of the core concepts parameter estimation in a groundwater modelling context, as well as providing a gentle introduction to the PEST, PEST++ and pyEMU software. Lastly, the third part demonstrates how to implement highly-parameterized applied decision-support modelling workflows. The tutorials aim to provide examples of both “how to use” the software as well as “how to think” about using the software. A key advantage to using notebooks in this context is that the workflows described run the same code as practitioners would run on a large-scale real- world application. Using a small synthetic model facilitates rapid progression through the workflow.

Journal of Open Source Education↗
USGS2025

Using probability difference to compare streamflow information of alternatives for efficient operation of monitoring networks

Efficient operation of streamflow monitoring networks requires investments in technology and labor that provide the greatest benefits from available resources. Economic analyses comparing the costs and benefits from different types of alternatives for monitoring have not been practical to implement. Streamflow information provides a generic measure of benefits that can be incorporated into operational decisions as an objective for monitoring networks. A methodology for comparing how accuracy, monitoring period, and monitoring instead of modeling affects streamflow information is developed from information-theoretic approaches for network design but contributes three novel features: (1) a probability-difference model for conditional probability of monotonically paired variables, (2) explicit discounting of unverified information that may exceed the accuracy of streamflow records, and (3) run analysis to account for non-stationarity in streamflow probabilities. Application of the methodology to the U.S. Geological Survey streamflow monitoring network indicates the value of monitoring period to reduce the uncertainty of streamflow probabilities and, thus, increase streamflow information. The methodology has important limitations, particularly for sites with non-perennial streamflow, but demonstrates that probability difference could be used to evaluate operational alternatives to increase the efficiency of monitoring networks.

PLOS Water↗
USGS2026

Groundwater spatial variability within an atoll island: Assessing shallow aquifer heterogeneity with geophysical and physicochemical measurements

This study examines the spatial variability of shallow groundwater on Dhigelaabadhoo Island using electromagnetic induction surveys, groundwater monitoring, and sediment analyses. The research reveals how variations in island morphology—such as differences in elevation, reef flat width, and sediment composition—affect the spatial distribution of groundwater lenses and the overall aquifer dynamics. Saltwater intrusion is especially pronounced in low elevated areas, with narrow reef flat plate and areas where higher hydraulic conductivity—driven by the presence of coarser sediments—is observed, whereas regions characterized by finer sediments, higher elevation, and wider reef flat plates tend to support more symmetric and less saline groundwater lenses. The geophysical investigations reveal that tidal oscillations alter groundwater movement by markedly changing water levels and conductivity, thereby underscoring the critical need to account for temporal dynamics in atoll coastal aquifer systems and the importance of integrating tidal dynamics into the aquifer zone. The findings highlight the significant role of intrinsic morphological and external hydrodynamic factors in shaping groundwater distribution on atoll islands, offering critical insights for sustainable freshwater resource management.

Dhigelaabadhoo Island↗
USGS2025

Divide and conquer: Separating the two probabilities in seismic phase picking

There are two fundamental probabilities in the seismic phase picking process – the probability of the existence of a seismic phase (detection probability) and the probability of correctly identifying the phase arrival time (timing probability). The nearly ubiquitous approach in developing deep learning phase picking models is to use a kernel, such as a truncated Gaussian, to mask the labeled phase arrival time, and train a segmentation model. Once a model is trained, the times of the peaks in the output are taken as phase arrival times (picks) and the height of the peaks are taken as “probability” of the picks. Here, we show that this “probability” represents neither the detection nor the timing probabilty because this approach forces the output to follow the shape of the kernel. We introduce an approach using two models to estimate these two distinct probabilities. We use a binary classifier with a calibrated confidence to address the detection probability and a multi-class classifier to obtain a probability mass function to address the timing probability. This new approach makes the deep learning-based phase picking process more interpretable and gives us options to logically control seismic monitoring workflows.

Geophysical Journal International↗
USGS2025

Simulation of the impacts of projected climate change on groundwater resources in the urban, semiarid Yucaipa Valley watershed, southern California using an integrated hydrologic model

Managing water resources in semiarid watersheds is challenging due to limited supply and uncertain future climate conditions. This paper examines the impact of future climate changes on an urban watershed in southern California using an integrated hydrologic model. GSFLOW modeling software is used to simulate the nonlinear relationships between climate trends and precipitation partitioning into ET, runoff, and subsurface storage. Four global circulation models (GCMs), each with two greenhouse-gas scenarios, RCP45 and RCP85 are used to project future climate conditions. GCMs include the CanESM2, CNRM-CM5, HadGEM2-ES, and MIROC5 models. The model's simulated hydrologic conditions are compared with historical data to assess changes in water budgets and groundwater supply. Results indicate decreased groundwater storage in most scenarios due to increased natural evapotranspiration, vegetation consumptive use, and streamflow out of the watershed. Only scenarios with substantially increased future precipitation show increased groundwater storage. The study also highlights increased future aridity despite the rise in precipitation and large precipitation events forecast by GCMs, which increase the risk of urban floods and decrease stream leakage and water available to vegetation.

California↗
USGS2025

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 the reduced space for the maximum a posteriori (MAP) inverse solution. Then, we randomize the MAP objective function and obtain samples of the posterior distribution by minimizing different realizations of the objective function. We test the proposed framework by comparing it with the iterative ensemble smoother and deep ensembling methods for a nonlinear diffusion equation with an unknown space-dependent diffusion coefficient. Among other applications, this equation describes the flow of groundwater in an unconfined aquifer. Depending on the training dataset and ensemble sizes, the proposed method provides similar or more descriptive posteriors of the parameters and states than the iterative ensemble smoother method. Deep ensembling underestimates uncertainty and provides less-informative posteriors than the other two methods. Our results show that, despite inherent uncertainty, surrogate models can be used for parameter and state estimation as an alternative to the inverse methods relying on (more accurate) numerical PDE solvers.

Journal of Computational Physics↗
USGS2025

Wet meadow regeneration through restoration of biophysical feedbacks

Wet meadows are globally significant ecosystems that provide critical hydrological, ecological, and biogeochemical functions, yet their extent has declined dramatically due to land use changes and hydrologic alteration. These sedge-dominated wetlands exist at the drier end of the wetland gradient, maintained by shallow groundwater and periodic inundation. This paper is a global synthesis of the ecological, geomorphic, and hydrological dynamics of wet meadows, with an emphasis on alluvial systems, to inform effective restoration strategies. We compare wet meadows to other wetlands, classify them into palustrine, lacustrine, and alluvial types, then focus on alluvial wet meadows and discuss how their formation and persistence depend on ground and surface water interactions, sediment deposition and flow obstructions, all mediated by biological processes. In particular, we highlight the role of hydric graminoids in resisting erosion and maintaining soil cohesion, how beaver promote meadow persistence, and the significance of wet meadows as carbon sinks. We also present stratigraphic evidence demonstrating that incision, often triggered by anthropogenic activity or changing climate, is the primary mechanism of alluvial wet meadow degradation, resulting in water table decline and shifts in vegetation composition. Restoration requires reversing these incisional processes through techniques that elevate water tables, disperse flow and retain sediment—methods traditionally associated with either soil conservation or stream restoration. These include nature-based solutions that create obstructions such as beaver dams and their analogues, rock and wood-based obstructions and incision trench or gully filling and grading. Given their multifunctional value—including but not limited to flood attenuation, biodiversity support, and carbon sequestration—wet meadows warrant a focused restoration framework. This review advocates for a valley-floor scale restoration paradigm that integrates hydrological reconnection, sediment retention, and biological reinforcement to ensure long-term resilience of these systems in the face of changing climate and land use pressures.

Frontiers in Environmental Science↗
USGS2026

Detecting snow avalanche activity using infrasound: Hooker Valley, New Zealand

Snow avalanches pose considerable hazards to people and infrastructure in alpine environments. Traditional avalanche monitoring relies on meteorological data and visual observations, which can be limited in scope and timeliness. Infrasound offers a promising complementary monitoring tool by detecting the low-frequency sound waves generated by avalanches. Here, we present infrasound and camera observations during a 50-day field campaign in the Hooker Valley of Aoraki/Mount Cook National Park, New Zealand. Our study detected seven avalanches with the cameras, whereas the infrasound system identified only one of these events, which was the largest and occurred under conditions that likely favoured infrasound propagation. The infrasound system recorded numerous other events not captured by the cameras, indicating the benefit of further investigation to determine their sources. These findings highlight the potential of infrasound technology for detecting avalanches and providing broad spatial coverage, capturing events in areas not monitored by cameras, while also showcasing limitations in infrasound capabilities. The limited detection of smaller avalanches underscores the opportunity for further research to enhance detection capabilities and understand environmental influences such as snow cover and wind noise. Overall, this study emphasises the utility of multidisciplinary monitoring techniques to improve avalanche detection in alpine environments.

Hooker Valley↗
USGS2026

Identifying overlap between native fish movements and a possible seasonal Grass Carp deterrent in the Sandusky River, Ohio, USA

Riverine fishes are vulnerable to habitat fragmentation caused by interrupted access to vital habitats. Fragmentation using artificial structures can be used to limit the spread of invasive fishes by inhibiting movement between critical habitats. Thus, tradeoffs exist between maintaining connectivity for native species and restricting movement of invasive fishes. A nonphysical deterrent has been proposed to prevent invasive Grass Carp ( Ctenopharyngodon idella ) from reaching spawning habitats in the Sandusky River, a tributary to Lake Erie, during late spring–summer. However, this timing overlaps with spawning periods of a suite of native fishes. We used acoustic telemetry and ichthyoplankton surveys to examine movement and spawning activity of multiple native species and to assess potential impacts of deterrent operation on reproduction. All species used the proposed deterrent location during the Grass Carp spawning period, and their movements corresponded with larval fish collections, indicating upstream spawning. These findings suggest the deterrent could unintentionally restrict native species’ access to essential habitats. Balancing habitat connectivity and invasive species control will likely depend on species-specific responses and careful timing of deterrent operation.

Ohio↗
USGS2026

Computational electromagnetic geophysics for groundwater system studies: A review on established practices and recent advances

Identifying effective solutions for locating groundwater resources and ensuring the quality of drinking water is increasingly urgent, given the challenges posed by climate change and population growth. This review investigates electromagnetic geophysical imaging techniques, in both time- and frequency-domain, that can provide valuable insights for groundwater assessment. We explore computational electromagnetic methods used to evaluate electromagnetic data and several recent hydrogeophysical case studies. As open-source frameworks for modeling electromagnetic geophysical problems become available, a broader range of researchers can interpret their data with computationally advanced software. We provide an overview of documented open-source codes for evaluating electromagnetic data and analyze various hydrological targets in relation to their electromagnetic surveying technique and the computational method applied. Furthermore, we evaluate the potential of advanced computational techniques, including three-dimensional modeling, non-deterministic inversion and machine learning, to couple geophysical with numerical groundwater modeling and apply it in groundwater system studies. Despite obstacles such as complexity and resource demands, our findings indicate that the quantification and integration of predictive uncertainties from both electromagnetic and hydrological data and simulations would significantly improve the reliability of hydrogeophysical models. This can lead to a deeper understanding of groundwater systems and improved management practices.

Journal of Hydrology↗
USGS2026

Subsurface water ice mapping on Mars: A probabilistic approach

Subsurface water ice deposits on Mars are an important resource for potential future human exploration. They are also an indicator of the planet’s past climate. However, the distribution of subsurface water ice in Mars’s midlatitudes is uncertain because spacecraft imagery cannot directly observe subsurface ice in most cases. Various spacecraft remote sensing instruments are sensitive to subsurface water ice, including thermal imaging spectrometers, radar sounders, and neutron spectrometers. Geomorphic analyses of images can also implicate subsurface ice. Building upon the data products from the Mars Subsurface Water Ice Mapping project, we provide a probabilistic framework to jointly interpret existing data and estimate the likelihood of subsurface water ice in the Martian midlatitudes between 60 ∘ S and 60 ∘ N with uncertainty. Broadly, we find that near-surface ice is likely present poleward of ∼45 ∘ in both the northern and southern hemispheres. However, closer to the equator, existing remote sensing data cannot uniquely constrain the presence of subsurface water ice. Our probabilistic results provide a framework for quantifying the abundance of ice on Mars, and our uncertainty estimates allow future analysis and exploration to target regions of high uncertainty.

Planetary Science Journal↗
USGS2026

Opportunities for the U.S. Geological Survey’s National Seismic Hazard Model to improve seismic risk assessment of critical infrastructure.

As fragility and risk modeling techniques and computational capabilities evolve, complemented by moving toward more routine and systematic seismic risk assessment of all buildings and critical infrastructure, the authors pose a few critical questions to investigate how the U.S. Geological Survey (USGS) National Seismic Hazard Models (NSHMs) can be used and enhanced further to serve such issues. In this paper, we use three examples from multiple sectors to (1) identify the role of USGS NSHMs in evaluating seismic risks to critical infrastructure, (2) quantify potential impacts from NSHM enhancements (i.e., [i] hazard curves for the vertical component of ground motion, [ii] stochastic event sets, and [iii] maps of probabilistic ground failure hazards), and (3) clarify the feasibility of relevant NSHM improvements. We illuminate that NSHMs are commonly used in location-specific performance assessments, whereas earthquake effects on critical infrastructure can be widespread across large geospatial regions. Further, we found that without the NSHM extensions considered here, risk can be severely underestimated, e.g., neglecting ground failure hazards can underestimate regional loss by a factor of two or more. Although many challenges remain, we developed example prototypes to clarify the feasibility of the NSHM extensions, which can facilitate improved management of risks to critical infrastructure.

Earthquake Spectra Journal↗
USGS2026

Variability and consistency in wildfire susceptibility: Insights from a national compilation

Background Wildfire risk in the United States is rising and remains a land management priority. The quantitative wildfire risk assessment (QWRA) framework integrates fuels, topography, weather and values at risk to estimate the potential change in value from wildfire. Within this, response functions (RFs) represent how values respond to fire intensity. These are often based on expert judgment, but variation across assessments is unclear. Aims This study uses data from the US Geological Survey (USGS) Wildfire Hazard and Risk Assessment Clearinghouse to characterize consistency and variation across categories and contexts. Methods We applied descriptive statistics to summarize RFs, using tables, box-and-whisker plots and heat maps stratified by highly valued resource or asset (HVRA) category and spatial scale. Key results RFs and value definitions vary, especially for ecosystem-related resources. Some functions, such as for buildings in the wildland–urban interface (WUI), translate well across contexts, while others require more input. Conclusions Some functions are broadly transferable, while others need customization. This analysis provides references and starting points for improvement to RFs in QWRAs. Interpretations Expanding the clearinghouse and dataset and building more transparency in expert elicitation can build trust among communities, agencies and end-users, and can support efficient use of limited resources to mitigate wildfire risk.

International Journal of Wildland Fire↗
USGS2026

Seasonal and hydrologic variation influences habitat and functional structure of stream fish assemblages

Introduction: Hydrologic variability is a key driver of ecological structure in lotic systems, shaping habitat conditions, taxonomic diversity, and the functional traits that mediate species’ persistence and performance (e.g., reproductive success). While many studies examine taxonomic responses to variation in flows, few evaluate how spatiotemporal hydrologic variation influences the functional organization within stream fish communities. Methods: We quantified seasonal habitat structure and functional trait diversity of fish assemblages across six Ozark Plateau headwater streams representing two contrasting flow regimes: Groundwater Flashy and Runoff/Intermittent Flashy. Fish and habitat data were collected seasonally during a dry year (2002) and a wet year (2003). Functional space was constructed using PCoA of morphological, ecological, and life-history traits, and functional diversity was measured using community weighted means (CWMs), functional richness (FRic), functional evenness (FEve), and functional divergence (FDiv). Results: We found that habitat structure differed strongly by flow regime and season, with Runoff/Intermittent streams exhibiting pronounced reductions in depth, area, and velocity, while groundwater streams remained structurally stable. Functional identity of assemblages was similar across flow regimes, dominated by benthic, hydrodynamic taxa with opportunistic and periodic life-history strategies. However, functional structure differed significantly: FEve and FDiv were consistently lower in Runoff/Intermittent Flashy streams in both years, indicating assemblage dominance of species with similar trait combinations and reduced trait partitioning under variable flow. FRic and taxonomic richness remained stable across seasons and flow regimes, suggesting high functional redundancy despite species turnover. Discussion: Together, results show that flow regime mediates both habitat structural stability and functional organization. As climatic warming and extreme drought increase hydrologic instability in headwaters, functional trait approaches provide a sensitive tool for detecting losses of functional roles that may not be evident by using taxonomic metrics alone.

Arkansas, Kansas, Missouri, Oklahoma↗
USGS2026

Post-fire soil hydrologic response and recovery in northern California (USA)

Background Wildfires abruptly change landscapes by altering soil properties and vegetation cover. These changes are thought to reduce soil infiltration capacity, making landscapes susceptible to runoff and erosion. However, post-fire soil response is complex and likely varies across locations and time. Aims Here, we aim to understand regional post-fire soil response and recovery by tracking changes across different northern California (USA) lithology and vegetation types. Methods We conducted repeat in situ soil infiltration tests for 3 years post-fire at 31 burned and 10 unburned sites spanning the 2021 Dixie, 2020 LNU Lightning Complex, 2020 Walbridge and 2020 Glass fires. Key results Our two main findings are: (1) burned chaparral soils have increased hydraulic conductivity compared with unburned sites, and (2) infiltration rates return to pre-fire conditions within 3 years across most lithologies and vegetations. Conclusions Recovery might be generalizable by vegetation and lithology but differ regionally, making it important to identify meaningful hydrologic response units (HRUs). Multi-year studies with paired burned and unburned measurements can constrain the recovery timeline and provide information missed by observations solely of burned soils. Implications Understanding where, and for how long, soil remains susceptible to runoff and erosion can help prioritize areas and time periods most in need of mitigation.

California↗
USGS2025

3D viscoelastic models of slip-deficit rate along the Cascadia subduction zone

Interseismic deformation in the Pacific Northwest is constrained by the horizontal crustal velocity field derived from the Global Positioning System (GPS) in addition to vertical rates derived from GPS, leveling, and tide gauge measurements. Such measurements were folded in to deformation models of fault slip rates as part of the 2023 National Seismic Hazard Model (NSHM) update. Here I build upon one of the contributing models, the viscoelastic earthquake-cycle model of Pollitz [2022]. This model permits inclusion of effects of time-dependent viscoelastic relaxation within earthquake cycles (i.e., ‘ghost transients’) and laterally variable elastic and/or ductile material properties. I lever-age these capabilities to incorporate the Cascadia megathrust into Western U.S.-wide deformation models in which crustal fault slip rates are estimated simultaneously with slip deficit rates along the interplate boundary between the descending Juan de Fuca plate and North American plate. This effort includes construction of a margin-wide model of viscoelastic structure founded on the Slab 2.0 model and probes different models of the ductile properties of the surrounding oceanic asthenosphere, continental lower crust, and mantle asthenosphere. This results in new estimates of the distribution of slip deficit rate along the ∼ 1000 km long margin, highlights the importance of correcting for glacial-isostatic adjustment effects, and permits assessment of sensitivity of results to assumed ductile properties.

California, Oregon, Washington↗
USGS2000

Storm impact scale for barrier islands

A new scale is proposed that categorizes impacts to natural barrier islands resulting from tropical and extra-tropical storms. The proposed scale is fundamentally different than existing storm-related scales in that the coupling between forcing processes and the geometry of the coast is explicitly included. Four regimes, representing different levels of impact, are defined. Within each regime, patterns and relative magnitudes of net erosion and accretion are argued to be unique. The borders between regimes represent thresholds defining where processes and magnitudes of impacts change dramatically. Impact level 1 is the 'swash' regime describing a storm where runup is confined to the foreshore. The foreshore typically erodes during the storm and recovers following the storm; hence, there is no net change. Impact level 2 is the 'collision' regime describing a storm where the wave runup exceeds the threshold of the base of the foredune ridge. Swash impacts the dune forcing net erosion. Impact level 3 is the 'overwash' regime describing a storm where wave runup overtops the berm or, if present, the foredune ridge. The associated net landward sand transport contributes to net migration of the barrier landward. Impact level 4 is the 'inundation' regime describing a storm where the storm surge is sufficient to completely and continuously submerge the barrier island. Sand undergoes net landward transport over the barrier island; limited evidence suggests the quantities and distance of transport are much greater than what occurs during the 'overwash' regime.

Journal of Coastal Research↗
USGS2025

Borehole geophysical time-series logging to monitor passive ISCO treatment of residual chlorinated-ethenes in a confining bed, NAS Pensacola, Florida

In-situ chemical oxidation (ISCO) is a common method to remediate chlorinated ethene contaminants in groundwater. Monitoring the effectiveness of ISCO can be hindered because of insufficient observations to assess oxidant delivery. Advantageously, potassium permanganate, one type of oxidant, provides the opportunity to use its strong electrical signal as a surrogate to track oxidant delivery using time-series borehole geophysical methods, like electromagnetic (EM) induction logging. Here we report a passive ISCO (P-ISCO) experiment, using potassium permanganate cylinders emplaced in boreholes, at a chlorinated ethene contamination site, Naval Air Station Pensacola, Florida. The contaminants are found primarily at the base of a shallow sandy aquifer in contact with an underlying silty-clay confining bed. We used results of the time-series borehole logging collected between 2017 and 2022 in 4 monitoring wells to track oxidant delivery. The EM-induction logs from the monitoring wells showed an increase in EM response primarily along the contact, likely from pooling of the oxidant, during P-ISCO treatment in 2021. Interestingly, concurrent natural gamma-ray (NGR) logging showed a decrease in NGR response at 3 of the 4 wells possibly from the formation of manganese precipitates coating sediments. The coupling of time-series logging and well-chemistry data allowed for an improved assessment of passive ISCO treatment effectiveness.

Florida↗