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117 records · Page 7Linked to original sources
USGS2025

Wetlands, groundwater and seasonality influence the spatial distribution of stream chemistry in a low-relief catchment

Evaluating stream water chemistry patterns provides insight into catchment ecosystem and hydrologic processes. Spatially distributed patterns and controls of stream solutes are well-established for high-relief catchments where solute flow paths align with surface topography. However, the controls on solute patterns are poorly constrained for low-relief catchments where hydrogeologic heterogeneities and river corridor features, like wetlands, may influence water and solute transport. Here, we provide a data set of solute patterns from 58 synoptic surveys across 28 sites and over 32 months in a low-relief wetland-rich catchment to determine the major surface and subsurface controls along with wetland influence across the catchment. In this low-relief catchment, the expected wetland storage, processing, and transport of solutes is only apparent in solute patterns of the smallest subcatchments. Meanwhile, downstream seasonal and wetland influence on observed chemistry can be masked by large groundwater contributions to the main stream channel. These findings highlight the importance of incorporating variable groundwater contributions into catchment-scale studies for low-relief catchments, and that understanding the overall influence of wetlands on stream chemistry requires sampling across various spatial and temporal scales. Therefore, in low-relief wetland-rich catchments, given the mosaic of above and below ground controls on stream solutes, modeling efforts may need to include both surface and subsurface hydrological data and processes.

Michigan↗
USGS2026

Season and antecedent conditions impact concentration-discharge relationships for dissolved organic carbon and alkalinity in southeast Alaskan watershed

Fluvial export of dissolved carbon plays an important role in watershed-scale biogeochemistry. Predicted changes in climate are expected to impact watershed hydrologic regimes, and in turn, the sources and export of dissolved carbon from watersheds. Here, we utilize high resolution measurements of discharge and dissolved carbon concentration to examine how concentration-discharge (CQ) relationships vary seasonally and during high flow events over the main runoff season (May–October) in a temperate forested watershed in Southeast Alaska. Concentration-discharge relationships for dissolved organic carbon (DOC) and alkalinity demonstrated strong seasonal patterns, with more linear relationships in May and June versus other months. Changing power law model slopes ( b values; the exponent in a power law regression between runoff and carbon yields) indicated potentially shifting watershed sources (biogenic vs. geologic) and contrasting dominant flowpaths (shallow vs. deeper groundwater) for DOC and alkalinity over the sampling period. During the largest storm event of the study, DOC and alkalinity b values shifted from an overall pattern of transport (mean b = 1.58 values >1.0 indicate transport limitation) and source limitation (mean b = 0.48, values <1.0 indicate source limitation) to chemostatic (DOC, b = 0.99; alkalinity, b = 1.019). In June through August, patterns in hysteresis index suggest that CQ relationships were altered when storms followed in close succession to each other. Together, these findings indicate that seasonal and antecedent flow conditions play a role in dissolved carbon export from forested watersheds. Understanding these dynamics, particularly during winter months, will become increasingly important as changes to hydroclimate impact riverine carbon export.

Alaska↗
USGS2026

Mercury cycling across a U.S. semi-arid mountain ecosystem elevation gradient

Mountains comprise ∼30% of the Earth's surface, but mercury (Hg) cycling in these regions remains understudied, particularly in the semi-arid western U.S. where strong climatic and ecological gradients in mountainous landscapes influence Hg deposition, retention, and bioaccumulation. In this study, we quantified growing season inputs, storage, and bioaccumulation of Hg along a ∼2,000 m elevation gradient in the Colorado Rocky Mountains, spanning the plains to the alpine. We measured Hg in atmospheric deposition, vegetation, soil, and 12-day-old chickadees. Accounting for percent canopy cover, open precipitation was the largest component of atmospheric deposition at all elevations, followed by throughfall and litterfall fluxes. Atmospheric Hg fluxes peaked at mid-elevations, likely due to cloud-cap dynamics and denser canopy cover. Total gaseous Hg and precipitation fluxes were highest at low elevations, likely reflecting local emissions and meteorological pooling. Surface soil Hg storage was more strongly predicted by organic matter content ( R 2 = 0.49; p < 0.01) and water retention ( R 2 = 0.45; p < 0.01) than by elevation ( R 2 = 0.21; p < 0.05). Alpine soils (66.3 ± 25.3 ng g −1 ) had significantly higher total Hg concentrations than lower elevations (<41.0 ± 12.7 ng g −1 ; p < 0.01), likely reflecting slower organic matter turnover. Soils on north-facing slopes also retained significantly higher pools of Hg in surface soils compared with south- and east-facing slopes. Vegetation Hg pools were greatest in the alpine region, likely due to long-lived plant species. Methylmercury (MeHg) concentrations in chickadee feathers peaked at mid-elevations (205 ± 155 ng g −1 ), corresponding to higher ecosystem Hg inputs via throughfall. Our results show that deposition, canopy cover, and meteorological conditions—not elevation alone—predict Hg retention and bioaccumulation.

Colorado↗
USGS2026

Effects of wildfire on soil hydraulic properties in the western Oregon Cascades

Wildfires can substantially impact the hydrology of forested watersheds, increasing the risk of hydrologic hazards such as flash floods and debris flows. Soil hydraulic properties related to infiltration are a key control in determining the timing and magnitude of these hydrogeomorphic events. In our study, we collected 445 soil cores from burned (216 cores) and unburned (229 cores) reference catchments and analyzed them for soil hydraulic properties 10 months after the 2022 Cedar Creek Fire in Oregon, USA. We observed significantly greater field-saturated hydraulic conductivity ( K fs ), sorptivity ( S ), and wetting front potential ( Ψ f ) in burned soils relative to unburned soils, with median ratios of 5.7, 4.4, and 5.0, respectively. Among low-, moderate-, and high burn severity groups, soil hydraulic properties were not statistically different. Reductions in median soil bulk density with increasing burn severity suggested an expansion of pore sizes, which may have been partially responsible for increasing K fs and S . Additionally, in some burned soil samples, the increase in soil hydraulic properties may have been partially related to a concurrent reduction in “natural background” water repellency that is characteristic of dry, unburned soils in the Western Cascades. We observed no evidence of spatial autocorrelation in K fs using semivariogram analysis. Principal component analysis paired with a k- means cluster analysis suggested that soil physical properties explained variations in soil hydraulic properties better than landscape attributes. Although there is a lack of regional results for comparison, our results trend in the opposite direction from drier, lower net primary productivity regions that are typically studied for post-wildfire soil hydraulic properties.

Oregon↗
USGS2026

SlideDetect: Spatio-temporal landslide detection using a three-dimensional convolutional neural network

Landslides pose a serious and ongoing threat to both human lives and infrastructure worldwide; therefore, it is of interest to predict where and when landslides are likely to occur. Advances in machine learning techniques have spurred numerous studies aimed at estimating relative landslide propensity, but are limited to spatial (as opposed to temporal) prediction due to the sparsity of landslide timing data. We address this data gap by training SlideDetect, a 3-dimensional convolutional neural network (3D CNN), to identify landslides based on their spatial and temporal occurrence within multitemporal image stacks. We use an inventory of landsides triggered by the 2018 Hokkaido earthquake and two years of monthly composite optical imagery spanning this event. The model can identify not only landslide location but also landslide date with an area under the precision-recall curve (PR-AUC) of 0.84. We further present a new standard for presenting PR curve results that explicitly compares model performance at different confidence thresholds, allowing for clearer model evaluation and comparison. Our new approach to constraining landslide timing paired with this more consistent and objective method for evaluating model performance shows considerable promise, and with further application and testing, SlideDetect could enhance the data availability and tools needed to advance landslide hazard and risk assessments.

JGR Machine Learning and Computation↗
USGS2026

Localization of spatiotemporally heterogeneous subsurface flows using autoencoder-based deep learning framework for time-lapse self-potential tomography

Self-potential (SP) monitoring has emerged as a valuable method for characterizing subsurface hydrogeological features and processes due to its sensitivity to fluid-induced electrokinetic effects. Despite advancements in SP inversion, challenges remain in imaging groundwater dynamics from SP activities due to complex hydrological settings and transient noise. In this study, a deep learning autoencoder (AE)-based framework is proposed for the spatiotemporal localization of subsurface fluid movement from time-lapse SP tomography. Temporal segments of time-lapse numerical inversions were first derived from long-term SP monitoring conducted from a floodplain site in Oak Ridge, Tennessee, known for active hyporheic exchange. Subsequently, AE models based on vision transformer (ViT), convolutional long short-term memory (ConvLSTM), convolutional neural network, and temporal convolutional network were individually trained and compared on the SP tomography segments for reconstruction performance. Finally, the reconstruction error over time serves as an anomaly score to identify moments of active SP variation, whereas spatial distributions of errors within these moments are analyzed to image and localize regions associated with anomalous subsurface fluid movement. The results demonstrate that ConvLSTM- and ViT-AE are most capable for the localization task with contrasting error distributions and consistent delineation of anomalies. Applying the method to both SP arrays parallel and perpendicular to the stream produced consistent anomaly zones near a fault or karst feature, validating the robustness and generalization of the approach. These results demonstrate the potential of the proposed framework as a scalable and interpretable tool for spatiotemporal analysis of subsurface flow dynamics in complex hydrogeological systems.

Tennessee↗
USGS2026

Near-Earth solar wind speed of fast coronal mass ejections

Using solar wind and ground magnetometer data for solar cycles 23–25, extreme-value power-law models are developed that quantify relationships between near-Earth solar wind speed and Sun-to-Earth transit times of interplanetary coronal mass ejections (ICMEs). The models of near-Earth solar wind speed, conditional on ICME transit time, are (a) approximately stable for transit times as short as 15 hr, (b) sublinear, consistent with hydrodynamic drag acting on ICMEs during transit, with linear (ballistic) models not supported by the data, (c) representative of most of the information content of the data, and (d) insensitive to the solar cycle and interplanetary preconditioning. Applying the models to the September 1859 Carrington event ( 𝑇 =17.6 hours), we estimate an ICME-average speed of 𝑉 𝑎 =102⁢1 1164 896 km/s and a 1-hr ICME-maximum speed of 𝑉 𝑚 =157⁢5 1795 1382 km/s. These estimates indicate that Carrington-class magnetic storms do not require exceptionally extreme solar wind speeds, with values lower than some previous estimates.

JGR Space Physics↗
USGS2026

Quantifying methane emissions from a rich fen with uncrewed aircraft systems in boreal Alaska

Thawing of permafrost in northern latitudes is accelerating, potentially releasing substantial amounts of methane (CH 4 ) as forested permafrost plateaus transition into wetlands. This ecosystem shift alters the carbon exchange between the soil and atmosphere, influencing the permafrost-carbon feedback. Monitoring these changes may require measurement platforms operating across varied spatial and temporal scales. Recent advancements in small uncrewed aircraft systems (sUAS) enable high resolution CH 4 flux quantification in remote, complex terrains; however, comparisons with established methods such as eddy covariance flux towers remain limited. We used a hexacopter sUAS to quantify CH 4 emissions from the Alaska Peatland Experiment, a wetland within the Bonanza Creek Experimental Forest. Using an ensemble of methods to define the background CH 4 concentration, along with near surface emissions from soil chambers, helped constrain our flux estimates. The sUAS method yielded an average flux of 0.0077 ± 0.0019 mol s −1 CH 4 , within a factor of two concurrent tower-derived total source flux estimates (0.0036 ± 0.00042 mol s −1 CH 4 ). To assess spatial drivers of observed fluxes, we conducted a 2D footprint analysis and overlaid the results with high-resolution hyperspectral land cover classification, quantifying vegetative contributions within each footprint. This revealed higher fen representation in sUAS measurements (73.8%) than in tower footprints (58.8%), and lower tussock meadow representation (15.6% and 30.3%, respectively). These differences were consistent with known variation in vegetation-specific CH 4 emissions. Our results highlight that combining footprint modeling with land cover characterization can enhance interpretations of CH 4 fluxes and guide cross-platform comparisons.

JGR Atmospheres↗
USGS2026

Dimensionality reduction techniques for analyzing tsunami simulations in hazard assessment and forecasting applications

A wide selection of linear and non-linear dimensionality reduction techniques is evaluated on synthetic tsunami data that is the basis for tsunami hazard assessment and computational forecasting. The data were computed from earthquake rupture forecasts (ERFs) supplying initial generation conditions and a linear long-wave Green's function approach to efficiently calculate tsunami propagation. The dimensionality reduction techniques include various forms of principal component analysis, manifold learning, and neural networks. Both external (e.g., mutual information) and internal (silhouette) metrics are used to evaluate clustering in the reduced dimension, focusing on peak-nearshore tsunami amplitudes (PNTA) along a nearshore isobath and time-series (marigrams) at specific nearshore locations. This evaluation is intentionally scoped to cluster-based downsampling to produce probabilistic inundation maps. For our test case along the Nankai subduction zone, manifold learning methods produced the highest clustering metric scores for PNTA profiles among the examined dimensionality reduction techniques. The marigram results are less consistent, owing primarily to uncertainty in defining appropriate clusters in the original high-dimensional (ambient) space to establish a “ground truth.” A novel application of using the integrative ERF-tsunami approach is that features extracted in the reduced domain, particularly from manifold learning, can be mapped back to rupture zones along the fault. This yields targeted fault information associated with unique PNTA profiles and marigram characteristics, such as late-arriving waves. Further testing would be needed for application-specific workflows and more complex fault systems, but results from this single subduction zone case study support the consideration of non-linear techniques (e.g., manifold learning) as alternatives to linear methods.

Nankai megathrust↗