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Results for “JGR - Atmospheres”

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113 records · Page 7Linked to original sources
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

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↗