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A roadmap for identifying and interpreting physical processes and national water model prediction bias associated with baseflow index regimes across the contiguous United States

Understanding how groundwater–surface water interactions shape streamflow variability is critical for diagnosing low flow behavior and prediction bias in continental scale hydrologic models. We present a process informed framework that links observed baseflow (BF) dynamics, watershed attributes, and National Water Model (NWM) performance across the contiguous United States. Using daily observed streamflow from 797 reference quality streamgages, we developed monthly baseflow index (BFI) signatures using a streamgage specific, calibrated digital filter. Hierarchical clustering of these signatures identified seven distinct BFI regimes capturing regional and seasonal variability. We evaluated NWM v3.0 retrospective streamflow performance within each regime using multiple hydrograph and flow duration curve-based metrics. Model skill varied systematically across regimes: mixed flow systems were simulated most accurately, while predominantly BF dominated and quickflow dominated regimes exhibited substantially poorer performance. Across nearly all regimes, the NWM underestimated observed BFI magnitude and frequently failed to reproduce seasonal BF patterns, indicating systematic biases in simulated low flow contributions. To relate these regimes to potential process controls, we trained a Random Forest classifier using static watershed attributes and applied Shapley Additive Explanations to identify features most strongly associated with each regime. Results highlight regionally varying influences, including the dominant role of snow fraction and seasonal runoff timing in snow dominated basins and the importance of evapotranspiration and aridity in quickflow dominated systems. Collectively, these findings demonstrate how hydrologic signatures combined with interpretable machine learning can diagnose regime specific model biases and generate process-based hypotheses about limitations in large scale hydrologic prediction systems.

contiguous United States

Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach

Ciénegas are rare wetlands in arid landscapes of the North American Southwest, historically providing critical ecological and hydrological functions but increasingly threatened by changing climate and land use pressures. This study quantifies changes in ciénega condition and floodplain dynamics using a state-and-transition model (STM) informed by expert knowledge and remote sensing. Key factors include woody plant encroachment, water availability, and soil aggradation. We mapped 31 ciénegas with high-resolution imagery and analyzed Landsat data (1985–2023) to assess vegetation health and moisture using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII). Results show substantial interannual variability in phenology, water stress, and soil moisture, with regional drying and elevation strongly influencing ciénega resilience. We classified ciénegas into three functional states—healthy, desiccated, and dormant—and mapped their 2023 condition. Trend analyses indicate most ciénegas exhibit greening despite drought, though localized variability underscores the need for site-specific management. None are in a stable climax (reference) state; rather, they transition among states in response to external drivers. Increasing woody plant cover and surface drying, likely linked to declining regional water tables, favor deep-rooted species over wetland grasses—a pattern mirrored in adjacent control plots. Spatially explicit analysis revealed intra-ciénega variability often masked by aggregated data, highlighting the importance of high-resolution monitoring. Seasonal and long-term trends provide context for understanding ciénega dynamics, including degradation and restoration pathways. This study emphasizes the importance of groundwater conservation and demonstrates how remote sensing supports long-term monitoring. The STM framework offers a practical tool for adaptive management to sustain freshwater resources in arid environments.

Arizona, New Mexico

Predictable seismic cycles result from structural rupture barriers on oceanic transform faults

Earthquakes of magnitude ( M ) >5.5 on oceanic transform faults (OTFs) repeatedly rupture the same locked patches, sometimes quasiperiodically. These patches are separated by “barriers” that halt earthquake propagation and slip mostly aseismically. However, the physical processes governing this systematic behavior remain unclear. We analyzed two barriers along the Gofar transform fault that have arrested ~15 M 6 earthquakes over the past three decades. Ocean bottom seismometer data indicate that the barriers hosted intense microseismicity before the mainshocks and comprise multistrand faults and transtensional stepovers with 100- to 400-m lateral offset. These characteristics contradict earthquake rupture termination models invoking velocity-strengthening friction or large geometric steps and instead point to damage-enhanced porosity and dilatancy-strengthening mechanisms. By isolating rupture segments, the barriers regulate the quasiperiodic recurrence of OTF earthquakes.

Science

Water table rise sustains carbon release from soils in wetland-dominated landscapes: An intact soil core study

Wetland-dominated landscapes influence carbon cycling through their potential to act as both carbon sinks and sources. Wetlands in low-relief landscapes have dynamic terrestrial-aquatic interfaces that change seasonally with variable surface water and groundwater levels. However, few studies have directly quantified dissolved organic matter (DOM) release and greenhouse gas (CO 2 , CH 4 ) fluxes from wetland soils along terrestrial-aquatic interfaces as they are seasonally re-saturated by groundwater. To estimate groundwater-mediated soil DOM and gas fluxes, we performed laboratory simulations of vertical groundwater rise on intact soil cores collected from four Delmarva bay wetlands located in the Mid-Atlantic United States. At each wetland, one core was collected from within the wetland basin and the other from the transitional zone at the basin edge. Cores were re-saturated with groundwater over 15 days and then kept fully saturated for an additional 25 days. Source groundwater, soil porewater, and exfiltrated surface water samples were collected and analyzed for pH, ORP, DOM concentration, and DOM optical indices. In both the wetland and transition zone cores, porewater DOM concentrations increased over the wet up and were sustained during prolonged saturation. Optical indices shifted from recently produced, microbial-like signatures towards aromatic, terrestrial-like signatures. Fluxes of CO 2 decreased as the duration of soil saturation increased and soil cores switched from CH 4 sinks to sources upon full soil core saturation. Results indicate that groundwater rise sustains carbon mobilization from soils in wetland-dominated landscapes, emphasizing the need to understand how climate-driven changes to groundwater dynamics may affect carbon fluxes along terrestrial-aquatic interfaces.

Delaware, Maryland

Asynchronous landslide seasonality across the United States

Mid-range landslide outlooks can facilitate weather-related landslide preparedness and disaster response planning, but seasonal landslide activity remains poorly quantified at continental scales. Leveraging >55,000 reported landslides from across the United States (U.S.), we used circular statistics to quantify landslide seasonality in 67 National Weather Service County Warning Areas (CWAs). We found regional differences in landslide season timing and duration, with transitions between domains variably corresponding to climate class or river basin. We assessed differences in seasonality by movement type for slides, flows, and falls, detecting apparent, but uncertain, differences between slide and fall seasonalities in 27 of 35 (77%) of CWAs with both types reported. In the Pacific Northwest, where long records exist, we found a credible shift toward a later mean landslide season in western Washington from 1990 to 2020, but no trend in western Oregon. Our results can provide emergency planners a resource to assess seasonal landslide probability nationwide.

Geophysical Research Letters

Critical minerals memory match game

An educational information packet about the 2025 List of Critical Minerals, which includes a memory match game about select critical minerals and how they are used.

General Information Product

Observing northern high-latitude river systems to understand changes in a warming Arctic

Purpose of Review Streams and rivers are undergoing rapid change as the Arctic warms and thaws. We review recent observations in Arctic stream systems to identify ubiquitous changes and the most useful tools for observing change and exploring the underlying processes. Recent Findings Recent literature indicates increasingly significant trends in river hydrology and chemistry due to persistent warming in the Arctic and longer observational records for analysis. However, regional differences in the magnitude and direction of these trends persist. We also observe thresholds in ground thaw and surface–groundwater interactions that can impact river hydrology and chemistry. Summary Warming and thaw are occurring rapidly at high latitudes, resulting in increasing, yet variable responses in stream systems across regions and scales. These differences highlight the need for long-term records and an interdisciplinary approach to explain trends and predict future states. Stream systems respond to multiple landscape changes related to hydrology (changing precipitation and subsurface flow), geology (ground thaw dynamics), and ecology (vegetation change).

Current Climate Change Reports

Temperate wetlands lose climate-cooling capacity under warming

Temperate inland wetlands have long been viewed as climate regulators, with sustained CO₂ sequestration offsetting continuous CH₄ emissions over centennial–millennial timescales. However, the net climatic effect of these opposing fluxes remains uncertain under anthropogenic warming as they differ in both magnitude and atmospheric lifetime. We combine 19,000 CH₄ chamber measurements from 202 temperate inland wetlands with carbon burial rates from 16 dated sediment cores in North America’s Prairie Pothole Region to estimate greenhouse gas dynamics over two centuries. Here we show that CH₄ emissions rise by ~1.39 kg CH₄ ha⁻¹ yr⁻¹ over the historical period and are projected to approach 950 kg CH₄ ha⁻¹ yr⁻¹ under high-forcing scenarios by 2100, while CO₂ sequestration is expected to decline by ~0.3 kg CO₂ ha⁻¹ yr⁻¹, driving a shift toward persistent net CO₂-equivalent source behaviour and a weakening of the long-term cooling effect that wetlands have provided under natural conditions.

Prairie Pothole Region

Bayesian belief network model to predict human-wildlife conflict in protected areas

Human-wildlife conflict (HWC) poses a pervasive global challenge, affecting livelihoods and threatening biodiversity. To better anticipate and mitigate HWC risk, we developed a large-scale predictive model using a Bayesian Belief Network (BBN). We surveyed 1,011 park rangers across 135 terrestrial protected areas in three Andean countries, documenting recent HWC incidents involving wildlife persecution or killing, livestock depredation, crop damage, or threats to human safety and property. We identified key drivers of HWC risk, including governance, wildlife acceptance, participation, and habitat quality. A sensitivity analysis revealed that enhancing governance and improving wildlife acceptance could reduce HWC risk by > 85%. The BBN model demonstrated scalability, effectively identifying strategies to reduce HWC risk at multiple scales, from individual protected areas to national networks. Our findings highlight the importance of strengthening governance, increasing wildlife acceptance, and enhancing community participation in conservation efforts. BBNs provide a flexible, cost-effective, and data-driven tool to guide protected areas and wildlife managers in monitoring, anticipating, and making informed decisions to mitigate conflict and promote coexistence.

Scientific Reports

A 481 m-high landslide-tsunami in a cruise ship-frequented Alaska fjord

Early in the morning of 10 August 2025, a >64 × 10 6 –cubic meter landslide struck Tracy Arm fjord in Alaska. The landslide was preconditioned by glacial retreat caused by climate change. The resulting 481-meter runup megatsunami followed an initial 100-meter-high breaking wave traveling at >70 meters per second. The landslide was preceded by several days of microseismicity, which increased in rate and magnitude until ~1 hour before failure. The landslide produced globally observed long-period seismic waves equivalent in size to a moment magnitude 5.4 earthquake. A long-period (~66 second) global seismic signal, produced by a landslide-induced seiche trapped within the fjord, persisted for up to 36 hours, the second time a days-long seiche had thus been observed. With fjord regions increasingly visited by cruise ships, and climate change making similar events more likely, this unanticipated, near-miss event highlights the growing risk from landslides and tsunamis in coastal environments.

Alaska

Unprecedented burning in tropical peatlands during the 20th century compared to the previous two millennia

Tropical peatland wildfire incidence has risen in recent decades, driven by drainage for land use and intensified by severe droughts with global climate change. These disturbances have altered vegetation structure, disrupted ecosystem functioning, and increased carbon emissions, particularly in Southeast Asia. However, the long-term history and characteristics of wildfires in tropical peatlands remain largely unknown. Here, we compiled fifty-eight macro-charcoal records from peatlands across the tropics, ranging from lowland forested to montane peatlands, to assess millennia-scale changes and controlling factors of tropical peatland burning. We divided the datasets into four main sub-regions: Neotropical, Afrotropical, Indomalayan and Australasian ecoregions to explore regional variability. Tropical peatlands had high burning levels between 0 and 850 ce , followed by a relatively low and stable period until a marked increase during the 20th century. The general trend in tropical peatland burning follows changes in global temperature, and climate variables that control the length and severity of drought events have a notable influence on peat burning before 1900 ce . During the 20th century, regional differences were observed, with declining fire trends in the Neotropical and Afrotropical regions and increasing fire trends in the Indomalayan and Australasian regions. This difference is likely attributable to human activities, and such intervention is also evident in palm swamps and hardwood swamps under similar wet, weakly seasonal climates. With the increase in anthropogenic pressures on peatlands and greater climate variability, future wildfires in peatlands are likely to become more frequent and widespread across all tropical ecoregions. Conservation and sustainable land-use practices could be used to mitigate and control peatland burning and protect these carbon-rich sinks.

Global Change Biology

Rapid earthquake magnitude classification via P-wave strains from borehole strainmeters and Distributed Acoustic Sensing

Distributed Acoustic Sensing (DAS) offers a promising approach for earthquake early warning (EEW) in settings where seismic networks are costly to maintain. By repurposing fiber-optic cables as dense strainmeter arrays, DAS enables real-time earthquake detection wherever those fibers are accessible. However, poor azimuthal coverage and challenges in estimating magnitude from strain measurements remain key hurdles in applying for earthquake monitoring. Here, we develop a machine learning method to distinguish large (M≥5.4) earthquakes from smaller ones within the first 4 seconds of a strain waveform after a P-wave arrival without determining location. Using ensemble decision tree models trained on borehole strainmeter data (3.5≤M≤7.1) and tested on onshore DAS waveforms (including the 2024 M7 Offshore Cape Mendocino earthquake), we find that low-frequency (0.2–0.5 Hz) continuous wavelet transform coefficients are the strongest predictors of magnitude, in addition to strain amplitude. Both DAS and borehole strainmeters effectively capture long-period strain signals, making these findings valuable for EEW systems. Our method shows high precision compared to the real-time EEW system, ShakeAlert®, supporting the position that DAS is a viable technology for earthquake monitoring and magnitude classification.

California

High-pass corner frequency selection and review tool for use in ground-motion processing

Raw seismological waveform data contain noise from the instrument’s surroundings and the instrument itself that can dominate recordings at low and high frequencies. To use these data in ground‐motion modeling, the effects of noise on the signals must be reduced and the signals’ usable frequency range identified. We present automated procedures to efficiently reduce low‐frequency noise that are implemented in the software package gmprocess. These procedures check for, and as needed remove, low‐frequency artifacts in the displacement record using polynomial fits, which can be used in combination with existing signal‐to‐noise ratio (SNR)‐based corner‐frequency selection procedures. The automated selections are then efficiently verified and refined using a graphical user interface (GUI) that plots relevant ground‐motion time series and spectra and tracks modifications to signal processing parameters. We demonstrate these procedures using recordings from the 2020 M 5.1 Sparta, North Carolina, and the 2013 M 4.7 southern Ontario earthquakes. Data processed with the SNR‐only and polynomial criteria for these events contain displacement artifacts in 37% and 23% of processed traces, respectively. Records with remaining artifacts are corrected manually using the GUI. These processing steps illustrate the workflow for efficient data processing with quality control.

Seismological Research Letters

Integrating habitat suitability and climate constraints to predict nonnative fish invasion risk for U.S. streams and inland lakes

Introduction: Nonnative fishes in streams and inland lakes can substantially alter aquatic ecosystems by degrading habitat conditions, competing with native species, and restructuring food webs. Identifying invasion hotspots can inform proactive management and prevention of nonnative species establishment. Methods: In this study, we identified streams and lakes across the conterminous United States that are vulnerable to invasion by 18 nonnative stream and 28 nonnative lake fish species. Habitat suitability models were developed separately for stream and lake species across nine ecoregions using natural and anthropogenic predictors, and species occurrences were predicted by integrating model results with climate match scores based on comparisons between environmental conditions in species’ native and potential nonnative ranges. We classified areas with both high habitat suitability and strong climate similarity as having high invasion potential and generated hotspot maps by stacking species-level predictions. Results: Florida exhibits the highest invasion risk for both habitat types. Stream hotspots were concentrated near major metropolitan areas, whereas lake hotspots were more widely distributed across the Great Lakes basin and the northeastern U.S. Twelve species were predicted to invade both habitat types, with goldfish ( Carassius auratus ) and pirapitinga ( Piaractus brachypomus ) showing particularly high invasion potential. Discussion: These findings provide a comprehensive assessment of nonnative freshwater fish invasion risk and support targeted monitoring and management strategies.

conterminous United States

Where will the cat cross the road? Comparing camera and GPS-based models for identifying wildlife corridors

Designing effective wildlife corridors is a critical conservation challenge in fragmented landscapes. GPS-based step selection functions strongly predict dispersal corridors and connectivity, but GPS collaring can be expensive and invasive. Camera-based occupancy models are widely used for connectivity analyses but may involve trade-offs in data resolution. Despite widespread use of both approaches, few studies have directly compared them using concurrent datasets. We developed a stacked single-species, single-season occupancy model and a Circuitscape connectivity surface for mountain lions (Puma concolor) on Washington’s Olympic Peninsula, USA, and compared them with a connectivity surface from an existing integrated step selection function. Both models predicted mountain lion GPS locations well, with binned Spearman rank correlations of 1 for Circuitscape and 0.96 for the step selection function, though step selection better identified habitat use by dispersers. Connectivity predictions were moderately correlated across the landscape ( r = 0.26), but agreement was strongest in human-dominated areas most critical for corridor planning. We conclude that GPS-based approaches are advantageous when data collection is feasible and the focus is on dispersal or fine-scale movement. However, camera-based approaches may be preferable for multi-species monitoring, large spatial and temporal scales, noninvasive sampling, when resources are limited, or when fine-scale or dispersal-specific inference is not required.

Washington

Submarine avalanche deposits hold clues to past earthquakes

Earthquakes and other natural events sometimes shake the seafloor near coastlines severely enough to cause underwater avalanches that rush down steep slopes, scouring the seabed and carrying sediment to greater depths. These fast-moving sediment-laden flows, called turbidity currents , have at times damaged underwater infrastructure like pipelines and communications cables, as they did, for example, in snapping transatlantic cables off the coast of Newfoundland after the 1929 Grand Banks earthquake.

EOS Transactions

Interplay between tectonics and submarine mass transport deposits in Cortes Basin: New high-resolution geophysics in the Outer California Borderland

The Outer California Borderland (OCB) is an active transform plate boundary offshore Southern California, where the relationship between faulting and submarine mass transport deposits (MTDs) remains poorly understood. Onshore paleoseismic data provide high-resolution earthquake records, whereas marine geophysical data capture longer-term histories. Offshore fault systems pose hazards to infrastructure and dense coastal populations, particularly when linked to submarine landslides. We present new high-resolution geophysical data set (cruise SR2303), including bathymetric and CHIRP sub-bottom data integrated with legacy seismic reflection data and chronostratigraphic constraints from ODP Site 1012 to examine Quaternary MTD recurrence and tectonic controls in the Cortes Basin, OCB. Bathymetry shows deformational features, including slide scarps and previously unmapped fault segments with evidence of Holocene activity. CHIRP profiles reveal 10 stacked MTDs in the East Cortes Basin and 8 in the West Cortes Basin, spanning ∼752 ka with an average recurrence of ∼83.6 ± 1 ka. Acoustic imaging shows 7 MTD intervals coinciding with fault offset increments and fault growth suggesting earthquake-triggered mass wasting. A strong association between MTD occurrences and sea-level extremes also supports glacio-eustatic contribution to slope failure. Stratigraphic correlations suggest quasi-synchronous MTDs across the eastern and western areas, likely triggered by larger eathquakes in the Quaternary. Although the identified MTDs occur relatively far from the Southern California coast, they still pose a potential tsunamigenic hazard requiring further assessment. Moreover, if linked to earthquakes along major strike-slip faults, for example, the Ferrelo fault, the MTDs may provide valuable proxies to constrain rupture scenarios and fault connectivity within the understudied OCB.

California

Metallurgical coal—Deposits, production, resources, market dynamics, and supply chain risks

Plain Language Summary Metallurgical coal (met coal; consumed to produce coke for steelmaking) must meet specific chemical and physical specifications. In 2023, the conterminous United States produced 66 million short tons (mst) of met coal, consumed 15.85 mst domestically, exported 51.1 mst, and imported 0.7 mst. Most met coal was produced in the Appalachian Basin, but there are also resources that meet the specifications for met coal in the Western United States.

conterminous United States