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73 records · Page 3Linked to original sources
USGS2024

Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall

Communities downstream of burned steep lands face increases in debris-flow hazards due to fire effects on soil and vegetation. Rapid postfire hazard assessments have traditionally focused on quantifying spatial variations in debris-flow likelihood and volume in response to design rainstorms. However, a methodology that provides estimates of debris-flow inundation downstream of burned areas based on forecast rainfall would provide decision-makers with information that directly addresses the potential for downstream impacts. We introduce a framework that integrates a 24 h lead-time ensemble precipitation forecast with debris-flow likelihood, volume, and runout models to produce probabilistic maps of debris-flow inundation. We applied this framework to simulate debris-flow inundation associated with the 9 January 2018 debris-flow event in Montecito, California, USA. When the observed debris-flow volumes were used to drive the probabilistic forecast model, analysis of the simulated inundation probabilities demonstrates that the model is both reliable and sharp. In the fully predictive model, however, in which debris-flow likelihood and volume were computed from the atmospheric model ensemble's predictions of peak 15 min rainfall intensity, I 15 , the model generally under-forecasted the inundation area. The observed peak I 15 lies in the upper tail of the atmospheric model ensemble spread; thus a large fraction of ensemble members forecast lower I 15 than observed. Using these I 15 values as input to the inundation model resulted in lower-than-observed flow volumes which translated into under-forecasting of the inundation area. Even so, approximately 94 % of the observed inundated area was forecast to have an inundation probability greater than 1 %, demonstrating that the observed extent of inundation was generally captured within the range of outcomes predicted by the model. Sensitivity analyses indicate that debris-flow volume and two parameters associated with debris-flow mobility exert significant influence on inundation predictions, but reducing uncertainty in postfire debris-flow volume predictions will have the largest impact on reducing inundation outcome uncertainty. This study represents a first step toward a near-real-time hazard assessment product that includes probabilistic estimates of debris-flow inundation and provides guidance for future improvements to this and similar model frameworks by identifying key sources of uncertainty.

California↗
USGS2025

State shifts in the deep Critical Zone drive landscape evolution in volcanic terrains

Understanding the near-surface environment where atmospheric and solid earth processes interact, often termed the “Critical Zone,” is important for assessing resources and building resilient societies. Here, we examine a volcanic landscape in the Oregon Cascade Range, an understudied Critical Zone setting that is host to major regional water resources, pervasive silicate weathering, and significant geohazards. We leverage a bedrock age chronosequence to show that the volcanic Critical Zone undergoes a structural shift, from depth extents of >1 km to meters, over timescales of ~1 My. We map an active groundwater volume comparable to major continental lakes, stored at the Cascade Range crest. This state shift makes volcanic landscape evolution a unique probe of deep coupling between Earth systems.

PNAS↗
USGS2026

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↗
USGS2025

Bølling-Allerød productivity in the subarctic Pacific driven by seasonal upwelling

The Bølling-Allerød deglacial event is marked by high diatom productivity and opal deposition throughout the subarctic Pacific. This opal could either constitute a strengthened biological pump and thus carbon sequestration, or a weakened biological pump and release of marine-sequestered CO 2 to the atmosphere. We quantify silicic acid supply at IODP Site U1340 in the Bering Sea using biogenic opal and δ 30 Si of Coscinodiscus , a diatom genus. These records, along with diatom environmental indicators, suggest the Bølling-Allerød had high silicic acid availability related to a shift from stratification to seasonal upwelling dynamics. We thus propose the primary cause of the high productivity event was increased macronutrient supply from vertical exchange that injected old, nutrient-rich, CO 2 -rich waters into the surface. Enhanced CO 2 release from the subarctic Pacific may help explain critical intervals of CO 2 rise that occur at the onsets of the Bølling-Allerød and PreBoreal.

Geophysical Research Letters↗
USGS1999

Effects of snowmobile use on snowpack chemistry in Yellowstone National Park, 1998

Snowmobile use in Yellowstone National Park has increased substantially in the past three decades. In areas of greatest snowmobile use, elevated levels of by-products of gasoline combustion such as ammonium and benzene have been detected in snowpack samples. Annual snowpacks and snow-covered roadways trap deposition from local and regional atmospheric emissions. Snowpack samples representing most of the winter precipitation were collected at about the time of maximum annual snow accumulation at a variety of locations in the park to observe effects of a range of snowmobile traffic levels. Concentrations of organic and inorganic compounds in snow samples from pairs of sites located directly in and off snow-packed roadways used by snowmobiles were compared to concentrations in samples collected at nearby off-road sites. Concentrations of ammonium were 2 to 5 times higher for the in-road snow compared to off-road snow for each pair of sites. Thus, concentrations decreased rapidly with distance from roadways. In addition, concentrations of ammonium, nitrate, sulfate, benzene, and toluene in snow were positively correlated with snowmobile use.

Wyoming↗
USGS2025

Ambient field seismology in critical zone hydrological sciences

Passive ambient noise monitoring is an emerging tool in environmental seismology, leveraging the ambient seismic field to assess temporal variations in shallow subsurface properties. This review focuses on the potential and challenges of using scattered coda waves from noise correlation functions to monitor critical zone dynamics. The sensitivity of seismic velocities to various environmental factors, including precipitation, snowmelt, atmospheric pressure, and groundwater fluctuations, underscores the method’s versatility. While coda waves excel in detecting subtle changes due to their scattered nature, ballistic waves provide higher spatial resolution, albeit with challenges in source stability. Advances in seismic sensing, including distributed acoustic sensing and low-cost geophone networks, have enabled high-resolution monitoring of hydrological processes, subsurface deformation, and seismic hazards. Integrating seismic data with hydrological models provides insights into water storage, pore pressure changes, and soil moisture dynamics. However, limitations in spatial resolution, calibration with ground truth data, and coupled effects between environmental factors remain key challenges. This review emphasizes the importance of interdisciplinary approaches in refining methodologies, enhancing sensor deployments, and addressing data gaps. Passive seismic monitoring offers opportunities to understand critical zone processes and their broader impacts on seismic hazards and environmental sustainability.

Comptes Rendus. Géoscience↗
USGS2025

Triple-oxygen isotopic evidence of prolonged direct bioleaching of pyrite with O2

Sulfate is often touted as containing atmospheric oxygen whose isotopic signature can constrain redox, environmental conditions, and biological activity. Yet, the amount and isotopic fractionation associated with air-O 2 incorporation during sulfate formation is still debated, making its verification difficult. In this study, we identify a distinct, microbially dominated environment with the potential to preserve maximum signals of air-O 2 in sulfate. We report triple-oxygen isotope data for sulfate produced from pyrite oxidation in microbial and abiotic experiments, and from natural dissolved sulfate from the Rio Tinto, Spain, an acid mine drainage site. The oxygen isotope systematics of sulfate in these environments define a unique kinetic isotope effect associated with initial stage pyrite oxidation by Acidithiobacillus ferrooxidans that preserves >80 % oxygen from air-O 2 in sulfate. Unlike experiments, which evolve toward water-oxygen dominated sulfate on short time scales, Rio Tinto, Spain hosts a microbe rich environment with distinct geochemistry that maintains high O 2 -oxygen in sulfate. Therefore, in addition to containing isotopic records from water and air, sulfates can also contain a biosignature that is promising for understanding conditions on Mars and early Earth.

Earth and Planetary Science Letters↗
USGS2026

Increasing artificial light at night enhances salmonid predator foraging efficiency in an urbanized lake

Artificial light at night (ALAN) poses a threat to ecosystems globally. It includes both direct and indirect light, or skyglow, which occurs when ALAN scatters in the atmosphere, extending beyond its original source. We analyzed ALAN trends in Lake Washington, WA, from 2014 to 2023 using Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light measurements, evaluated the relationship between in situ and satellite measurements, and modeled predator search volumes for a juvenile salmon predator, cutthroat trout ( Oncorhynchus clarki ), as a function of ambient light and turbidity conditions. Open water regions experienced significant increases in ALAN, while nearshore areas primarily showed no or negative trends, revealing the role of skyglow in shaping open water light environments. Using a visual foraging model for cutthroat trout, we found that juvenile salmon at the shallow southern pelagic site experienced light 28 times brighter, resulting in a 168% greater predation vulnerability than those at the northern site. In the Ship Canal, a narrow corridor for outmigrating salmon, predator search volumes were 249% higher than at the southern site. These contrasts in predation vulnerability demonstrate how local conditions influence predator–prey dynamics and provide critical insight for targeting mitigation of both nearshore and distant light sources.

Washington↗
USGS2024

Joint pilot fish habitat framework

This story map will take you through the process of exploring and testing methods necessary for a higher resolution, seamless fish habitat assessment across both inland and estuarine waters through the lens of our joint pilot assessment Fish habitat assessments attempt to relate past, current, or future landscape conditions to the state of fish species occurrence, distribution, abundance, or community and habitat condition in streams, rivers, or estuaries. Previous fish habitat assessments, such as the National Fish Habitat Assessment, conducted separate and disconnected assessments for inland waters and estuaries. In this project, National Oceanic and Atmospheric Administration ( NOAA ) and U.S. Geological Survey ( USGS ) researchers created a seamless spatial framework to allow assessments that integrate influences on fish habitat from headwaters to the estuary. This effort began when the Chesapeake Bay Program Fish Habitat Action Team expressed interest in a Baywide fish habitat assessment spanning tidal salt, tidal fresh, warm non-tidal, and cold non-tidal waters. However, the complexity of the myriad of implementation details to consider when developing such an assessment necessitated the need for a tributary-specific pilot assessment. To conduct this pilot assessment, a NOAA/USGS joint partnership was formed with cooperation and support from the Chesapeake Bay Agreement and Chesapeake Bay Fish Habitat Action Team (FHAT).

Maryland↗
USGS2026

Groundwater dependency and hydroclimatic influences on riparian and upland vegetation productivity, Upper San Pedro, Arizona, United States

In arid and semi-arid regions, groundwater sustains vegetation through subsurface water access, yet the responses of groundwater-dependent ecosystems (GDEs) to changing hydroclimate and groundwater availability are relatively understudied. This study investigates seasonal and spatial patterns in vegetation greenness using Landsat Enhanced Vegetation Index (EVI) values across riparian and upland zones in the semi-arid Upper San Pedro (USP) watershed, southern Arizona, which experiences a bimodal precipitation regime. We paired 25 years (2000–2024) of EVI and depth to groundwater (DTG) data from 89 wells and climate metrics (precipitation and vapour pressure deficit) to quantify the sensitivity of vegetation to subsurface moisture as well as atmospheric moisture supply and demand. Vegetation at wells near the USP riparian area showed strong associations between EVI and DTG anomalies during the monsoon season, indicating sustained groundwater use even during this wet period when summer precipitation is abundant. In contrast, upland vegetation that lacked access to groundwater showed minimal sensitivity in EVI to DTG and was generally less responsive to vapour pressure deficit. Interestingly, the riparian GDEs were not decoupled from precipitation and climate variability. These results underscore the importance of groundwater for maintaining riparian productivity and highlight the utility of remote sensing in identifying vegetation-climate-groundwater linkages across heterogeneous dryland landscapes.

Arizona↗
USGS2025

Gaps in water quality modeling of hydrologic systems

This review assesses gaps in water quality modeling, emphasizing opportunities to improve next-generation models that are essential for managing water quality and are integral to meeting goals of scientific and management agencies. In particular, this paper identifies gaps in water quality modeling capabilities that, if addressed, could support assessments, projections, and evaluations of management alternatives to support ecosystem health and human beneficial use of water resources. It covers surface water and groundwater quality modeling, dealing with a broad suite of physical, biogeochemical, and anthropogenic drivers. Modeling capabilities for six constituents (or constituent categories) are explored: water temperature, salinity, nutrients, sediment, geogenic constituents, and contaminants of emerging concern. Each constituent was followed through the coupled atmospheric-hydrologic-human system, with prominent modeling gaps described for a diverse array of relevant inputs, processes, and human activities. Commonly identified modeling gaps primarily fall under three types: (1) model gaps, (2) data gaps, and (3) process understanding gaps. In addition to potential solutions for addressing specific individual modeling limitations, some broad approaches (e.g., enhanced data collection and compilation, machine learning, reduced-complexity modeling) are discussed as ways forward for tackling multiple gaps. This gap analysis establishes a framework of diverse approaches that may support improved process representation, scale, and accuracy of models for a wide range of water quality issues.

Water↗
USGS2026

Predictions of anthropogenic background PFAS concentrations in soil and relation to bedrock lithology and groundwater quality

Detectable concentrations of per- and polyfluoroalkyl substances (PFAS) have been observed in soils in remote areas and presumably originate from atmospheric deposition. These anthropogenic background concentrations may enable some PFAS to leach to groundwater at levels that exceed regulatory criteria for drinking water. However, anthropogenic background soil concentrations and their connection to groundwater are not well characterized. We developed a boosted regression tree model to predict perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) concentrations in shallow soils across northern New England. Low soil pH was the most important predictor of elevated anthropogenic PFOS and PFOA concentrations in background soils, rather than potential PFAS sources, land use, or population density. Total organic carbon (TOC) was also an important predictor for PFOS soil concentrations. Model predictions indicate that 73% of the shallow soils within Maine, Vermont, and New Hampshire exceed New Hampshire’s Soil Remediation Standard for PFOS (0.5 ng/g) and 41% exceed the PFOA standard (0.4 ng/g). Analysis of soil model results and groundwater data suggests that areas with high soil pH are associated with higher groundwater detection frequencies, illustrating how areas with less retention in soil are, conversely, also areas with potentially greater groundwater vulnerability. Further analysis indicates that groundwater may be more vulnerable in calcareous lithologies.

Maine, New Hampshire, Vermont↗
USGS2025

Speleothem evidence for Late Miocene extreme Arctic amplification – An analogue for near-future anthropogenic climate change?

The Miocene provides an excellent climatic analogue for near-future runaway anthropogenic warming, with atmospheric CO 2 concentrations and global average temperatures similar to those projected for the coming century under extreme-emissions scenarios. However, the magnitude of Miocene Arctic warming remains unclear due to the scarcity of reliable proxy data. Here we use stable oxygen isotope and trace element analyses, alongside clumped isotope and fluid inclusion palaeothermometry of speleothems to reconstruct palaeo-environmental conditions near the Siberian Arctic coast during the Tortonian (8.68 ± 0.09 Ma ). Stable oxygen isotope records suggest warmer-than-present temperatures. This is supported by temperature estimates based on clumped isotopes and fluid inclusions giving mean annual air temperatures between + 6.6 and + 11.1 °C , compared with − 12.3 °C today. Trace elements records reveal a highly seasonal hydrological environment. Our estimate of > 18 °C of Arctic warming supports the wider consensus of a warmer-than-present Miocene and provides a rare palaeo-analogue for future Arctic amplification under high-emissions scenarios. The reconstructed increase in mean surface temperature far exceeds temperatures projected in fully coupled global climate models, even under extreme-emissions scenarios. Given that climate models have consistently underestimated the extent of recent Arctic amplification, our proxy data suggest Arctic warming may exceed current projections.

Lena River delta↗
USGS2024

High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements

Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.

Colorado, Utah↗
USGS2026

Widespread terrestrial ecosystem disruption at the onset of the Paleocene–Eocene Thermal Maximum

The Paleocene–Eocene Thermal Maximum (PETM, ~56 Mya) interval was marked by massive 13 C-depleted carbon emissions into the ocean/atmosphere system, manifested as a negative carbon isotope excursion (CIE) in sedimentary components, and ~5 °C global average warming. Episodes of hydrological perturbations and soil-erosion have been widely documented for the PETM but their link with vegetation- and carbon cycle changes remain poorly constrained. Here, we present organic microfossil evidence showing a strong increase in fern-dominated pioneer vegetation that replaced coniferous forests on the margin of the Norwegian Sea during the first millennia of the CIE. With the present stratigraphic constraints, the “fern spike” occurred simultaneously in terrestrial settings along the North Sea, Arctic Ocean, the US east coast and in southern Australia, indicating that pioneer vegetation persisted for several millennia following a partial collapse of previously stable terrestrial ecosystems. Both the ferns and influx of microcharcoal imply recurrent physical disturbance, including soil destabilization and erosion, potentially linked to droughts, wildfires, and strong hydrological forcing resulting from extreme climate change. Together with evidence for reworked clay minerals and ancient organic matter (kerogen), these findings show that highly disturbed terrestrial ecosystems were widespread across mid- and high-latitude regions globally. Carbon cycle model simulations suggest that a substantial loss of standing and buried biomass, along with oxidation of soil organic matter, acted as important positive feedbacks during the onset of the CIE. Additionally, enhanced kerogen weathering likely contributed as another major positive feedback throughout both the onset and main phase of the CIE.

Proceedings of the National Academy of Sciences↗
USGS2000

Monthly variability and possible sources of nitrate in ground water beneath mixed agricultural land use, Suwannee and Lafayette Counties, Florida

In an area of mixed agricultural land use in Suwannee and Lafayette Counties of northern Florida, water samples were collected monthly from 14 wells tapping the Upper Floridan aquifer during July 1998 through June 1999 to assess hydrologic and land-use factors affecting the variability in nitrate concentrations in ground water. Unusually high amounts of rainfall in September and October 1998 (43.5 centimeters total for both months) resulted in an increase in water levels in all wells in October 1998. This was followed by unusually low amounts of rainfall during November 1998 through May 1999, when rainfall was 40.7 centimeters below 30-year mean monthly values. The presence of karst features (sinkholes, springs, solution conduits) and the highly permeable sands that overlie the Upper Floridan aquifer provide for rapid movement of water containing elevated nitrate concentrations to the aquifer. Nitrate was the dominant form of nitrogen in ground water collected at all sites and nitrate concentrations ranged from less than 0.02 to 22 milligrams per liter (mg/L), as nitrogen. Water samples from most wells showed substantial monthly or seasonal fluctuations in nitrate concentrations. Generally, water samples from wells with nitrate concentrations higher than 10 mg/L showed the greatest amount of monthly fluctuation. For example, water samples from six of eight wells had monthly nitrate concentrations that varied by at least 5 mg/L during the study period. Water from most wells with lower nitrate concentrations (less than 6 mg/L) also showed large monthly fluctuations. For instance, nitrate concentrations in water from four sites showed monthly variations of more than 50 percent. Large fluctuations in nitrate concentrations likely result from seasonal agricultural practices (fertilizer application and animal waste spreading) at a particular site. For example, an increase in nitrate concentrations observed in water samples from seven sites in February or March 1999 most likely results from application of synthetic fertilizers during the late winter months. Lower nitrate concentrations were detected in water samples from five of eight wells sampled during high-flow conditions for the Suwannee River in March 1998 compared to low-flow conditions in November 1998. Evidence for reduction of nitrate due to denitrification reactions was observed at one site (AC-1), as indicated by elevated concentrations of nitrogen gas and a corresponding increase in nitrogen isotope (d15N-NO3) values with a decrease in nitrate concentrations. Denitrification is unlikely at other sites based on the presence of dissolved oxygen concentrations greater than 2 mg/L in ground water and no observed trend between nitrate concentrations and values d15N-NO3 values. Nitrate was the dominant nitrogen species in most monthly rainfall samples; however, ammonium concentrations were similar or greater than nitrate during November and December 1998. During February through May 1999, both nitrate and ammonium concentrations were substantially higher in monthly rainfall samples collected at the study area compared to mean monthly concentrations at the Bradford Forest site located east of the study area, which is part of the National Atmospheric Deposition Program/National Trends Network. Also, higher nitrogen deposition rates in the study area compared to those at Bradford Forest could indicate that substantial amounts of ammonia are volatilized from fertilizers and animal wastes, released to the atmosphere, and incorporated as nitrate and ammonium in rainfall deposited in the middle Suwannee River Basin. Ground-water samples from most sites had d15N-NO3 values that indicated a mixture of inorganic and organic sources of nitrogen, which corresponded to multiple land uses where both synthetic fertilizers and manure are used on fields near these sites. Distinct d15N-NO3 signatures, however, were observed at some sites. For example, water samples from areas of row-crop farming as the dominant land use had d15N-NO3 values less than 4 per mil, indicating an inorganic nitrogen source such as synthetic fertilizer. In contrast, d15N-NO3 values greater than 9 per mil were found in water samples from three sites where manure from dairy and or poultry operations is spread on fields throughout the year.

Florida↗
USGS2025

Beyond optimality: Dryland ecosystems infrequently use water efficiently for carbon gain

Optimality theory assumes plants maximize carbon gain per unit water lost and is often implemented to scale leaf-level carbon gain and water use to regional and global scales. Optimality theory is often mathematically represented by assuming plant water-use efficiency (WUE) scales with VPD k , where k = ½ represents expected optimal behavior. It is unclear, however, if this relationship holds in arid and semi-arid ecosystems that are strongly impacted by soil and atmospheric moisture status. We used data from seven flux tower sites along an aridity gradient in New Mexico to answer: how does the relationship between WUE and VPD compare to expectations based on optimality theory? To address this question, we integrated the Dynamic Evapotranspiration Partitioning Approach for Rapid Timescales with a stochastic antecedent model to estimate ecosystem-level WUE (GPP/T) and the net sensitivity of WUE to VPD, or k Dynamic , which we compare to the theoretical optimal sensitivity of k = ½. Our results show that optimality theory is not always appropriate, and k Dynamic often deviates from ½, especially at some of the more arid sites or during periods of low soil moisture. At less arid, higher elevation sites, k Dynamic is most consistent with optimality theory at moderate VPD levels, but not at high VPD. In general, the sensitivity of WUE to VPD is highly variable such that k Dynamic exhibits notable daily and seasonal variability, suggesting highly dynamic stomatal behavior. These results emphasize that representing plant water-use strategies as dynamic in time and space is critical to improving large-scale estimates of plant water use.

New Mexico↗
USGS2025

Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning

Accurate, high-resolution maps of bedrock outcrops can be valuable for applications such as models of land–atmosphere interactions, mineral assessments, ecosystem mapping, and hazard mapping. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock outcrop maps. In the United States, the existing 30 m U.S. Geological Survey (USGS) National Land Cover Database (NLCD) tends to misestimate extents of barren land, which includes bedrock outcrops. This impacts many calculations beyond bedrock mapping, including soil carbon storage, hydrologic modeling, and erosion susceptibility. Here, we tested if a machine learning (ML) model could more accurately map exposed bedrock than NLCD across the entire Sierra Nevada Mountains (California, USA). The ML model was trained to identify pixels that are likely bedrock from 0.6 m imagery from the National Agriculture Imagery Program (NAIP). First, we labeled exposed bedrock at twenty sites covering more than 83 km 2 (0.13%) of the Sierra Nevada region. These labels were then used to train and test the model, which gave 83% precision and 78% recall, with a 90% overall accuracy of correctly predicting bedrock. We used the trained model to map bedrock outcrops across the entire Sierra Nevada region and compared the ML map with the NLCD map. At the twenty labeled sites, we found the NLCD barren land class, even though it includes more than just bedrock outcrops, accounted for only 41% and 40% of mapped bedrock from our labels and ML predictions, respectively. This substantial difference illustrates that ML bedrock models can have a role in improving land-cover maps, like NLCD, for a range of science applications.

California↗