United States Geological Survey
  • Reston, Colorado, United States
Recent publications
Most wildfires are started by humans, however, geographic variation of potential ignition sources is not often explicitly accounted for in wildfire simulation modelling or risk assessments. In this study, we investigated how patterns of human and lightning ignitions can influence modelled fire simulations and demonstrate how these data can be used to assess post-fire flooding and sediment transport.Weusedhistoricalignitiondata(1992–2015) to characterize ignition patterns for thirteen mountain ranges in southern Arizona, United States, and developed FlamMap burn probability (BP) models for three scenarios: human ignition, lightning ignition, and random ignition. We then developed a watershed-scale case study assessing the impacts of ignition scenarios on post-fire hydrology using the KINEROS2 model that simulates runoff and erosion. BP models illustrated considerable differences in landscape fire risk between the three ignition scenarios. Results from the watershed model indicate the greatest impacts from the post-fire human ignition scenario, with a 10-fold increase in sediment discharge and four-fold increase in peak flow compared to pre-fire conditions. Our results show that consideration of ignition source and location is important for assessing fire risk, and our modelling approach provides a planning mechanism to identify locations most at risk to fire-induced flood hazards, where prevention and mitigation activities can be focused.
Background China has committed to achieving peak CO2 emissions before 2030 and carbon neutrality before 2060; therefore, accelerated efforts are needed to better understand carbon accounting in industry and energy fields as well as terrestrial ecosystems. The carbon sink capacity of plantation forests contributes to the mitigation of climate change. Plantation forests throughout the world are intensively managed, and there is an urgent need to evaluate the effects of such management on long-term carbon dynamics. Methods We assessed the carbon cycling patterns of ecosystems characterized by three typical plantation species (Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), oak (Cyclobalanopsis glauca (Thunb.) Oerst.), and pine (Pinus massoniana Lamb.)) in Lishui, southern China, by using an integrated biosphere simulator (IBIS) tuned with localized parameters. Then, we used the state-and-transition simulation model (STSM) to study the effects of active forest management (AFM) on carbon storage by combining forest disturbance history and carbon cycle regimes. Results 1) The carbon stock of the oak plantation was lower at an early age (<50 years) but higher at an advanced age (>50 years) than that of the Chinese fir and pine plantations. 2) The carbon densities of the pine and Chinese fir plantations peaked at 70 years (223.36 Mg·ha‒1) and 64 years (232.04 Mg·ha‒1), respectively, while the carbon density in the oak plantation continued increasing (>100 years). 3) From 1989 to 2019, the total carbon pools of the three plantation ecosystems followed an upward trend (an annual increase of 0.16–0.22 Tg C), with the largest proportional increase in the aboveground biomass carbon pool. 4) AFM increased the recovery of carbon storage after 1996 and 2009 in the pine and Chinese fir plantations, respectively, but did not result in higher growth in the oak plantation. 5) The proposed harvest planning is reasonable and conducive to maximizing the carbon sequestration capacity of the forest. Conclusions This study provides an example of a carbon cycle coupling model that is potentially suitable for simulating China's plantation forest ecosystems and supporting carbon accounting to monitor peak CO2 emissions and reach carbon neutrality.
Background Deep-sea mussels in the subfamily Bathymodiolinae have unique adaptations to colonize hydrothermal-vent and cold-seep environments throughout the world ocean. These invertebrates function as important ecosystem engineers, creating heterogeneous habitat and promoting biodiversity in the deep sea. Despite their ecological significance, efforts to assess the diversity and connectivity of this group are extremely limited. Here, we present the first genomic-scale diversity assessments of the recently discovered bathymodioline cold-seep communities along the U.S. Atlantic margin, dominated by Gigantidas childressi and Bathymodiolus heckerae . Results A Restriction-site Associated DNA Sequencing (RADSeq) approach was used on 177 bathymodiolines to examine genetic diversity and population structure within and between seep sites. Assessments of genetic differentiation using single-nucleotide polymorphism (SNP) data revealed high gene flow among sites, with the shallower and more northern sites serving as source populations for deeper occurring G. childressi . No evidence was found for genetic diversification across depth in G. childressi , likely due to their high dispersal capabilities. Kinship analyses indicated a high degree of relatedness among individuals, and at least 10–20% of local recruits within a particular site. We also discovered candidate adaptive loci in G. childressi and B. heckerae that suggest differences in developmental processes and depth-related and metabolic adaptations to chemosynthetic environments. Conclusions These results highlight putative source communities for an important ecosystem engineer in the deep sea that may be considered in future conservation efforts. Our results also provide clues into species-specific adaptations that enable survival and potential speciation within chemosynthetic ecosystems.
Biological ocean science has a long history; it goes back millennia, whereas the related data services have emerged in the recent digital era of the past decades. To understand where we come from—and why data services are so important—we will start by taking you back to the rise in the study of marine biology—marine biodiversity—and its key players, before immersing ourselves in the data life cycle, past and present joint global initiatives, and systems that allow(ed) scientists to more easily access biological data, online services through some simple keyboard strokes, and the many challenges we still encounter on a daily basis when dealing with these types of data.
The transition towards renewable energy requires increasing quantities of nonfuel mineral commodities, including tellurium used in certain photovoltaics. While demand for tellurium may increase markedly, the potential to increase tellurium supply is not well-understood. In this analysis, we estimate the quantity of tellurium contained in anode slimes generated by electrolytic copper refining by country between 1986 and 2018, including uncertainties. For 2018, the results indicate that 1930 (1500-2700, 95% confidence interval) metric tons of tellurium were contained in anode slimes globally. This is nearly quadruple the reported tellurium production for that year. China has the greatest potential to increase tellurium supplies. However, most of the tellurium potentially recoverable by Chinese refineries appears to come from copper mined elsewhere. Further research into the business decisions associated with tellurium recovery may help translate the physical availability of tellurium into economic availability. The methodology presented here can be applied to other byproduct elements.
Algal blooms around the world are increasing in frequency and severity, often with the possibility of adverse effects on human and ecosystem health. The health and economic impacts associated with harmful algal blooms, or HABs, provide compelling rationale for developing new methods for monitoring these events via remote sensing. Although concentrations of chlorophyll-a and key pigments like phycocyanin are routinely estimated from satellite images and used to infer algal or cyanobacterial cell counts, current methods are unable to provide information on the taxonomic composition of a bloom. This study introduced a new approach capable of differentiating among genera based on their reflectance characteristics: Spectral Mixture Analysis for Surveillance of HABs, or SMASH. The foundation of SMASH is a multiple endmember spectral mixture analysis (MESMA) algorithm that takes a library of cyanobacteria endmembers and a hyperspectral image as input and estimates the fractional abundance of each genus, plus water, on a per-pixel basis. Importantly, we assume that the water column consists of only pure water and cyanobacteria, implying that our linear spectral unmixing models do not account for other optically active constituents such as suspended sediment and colored dissolved organic matter (CDOM). We used reflectance spectra for 12 genera measured under a microscope to populate an algal spectral library and applied the SMASH workflow to satellite images from four waterbodies across the United States. Normalized spectral separability scores indicated that the 12 genera were distinct from one another and the MESMA algorithm reproduced known input fractions for simulated mixtures that included all pairwise combinations of genera and water. We used Upper Klamath Lake as an example to illustrate data products generated via SMASH: maps of the normalized difference chlorophyll index and cyanobacterial index, a MESMA-based classification of algal genera, fraction images for each endmember, and a root mean square error (RMSE) image that summarizes uncertainty. For Upper Klamath Lake, these outputs highlighted a complex algal bloom featuring several genera, primarily Aphanizomenon, and intricate spatial patterns associated with gyres. The maximum RMSE constraint imposed on the MESMA algorithm provided a means of avoiding false positive detection of genera not present in a waterbody but must not be set so low as to leave much of an image unclassified in cases where genera included in the library are present. Comparison of endmember fractions with relative biovolumes calculated from field samples indicated that taxonomic information from SMASH was consistent with field observations. For example, the algorithm successfully identified Microcystis in Owasco Lake but avoided misclassifying Asterionella, a genus not yet included in our library, in Detroit Lake. This proof-of-concept investigation demonstrates the potential of SMASH to enhance our understanding of algal blooms, particularly with respect to their spatial and temporal dynamics.
Urban street trees are a key part of public green infrastructure in many cities, however, leaf litter on streets is a critical biogenic source of phosphorus (P) in urban stormwater runoff during Fall. This study identified mass of street leaf litter (Mleaf) and antecedent dry days (ADD) as the top two explanatory parameters that have significant predictive power of event end-of-pipe P concentrations through multiple linear regression (MLR) analysis. Mleaf and volume of runoff (Vol) were the top two key explanatory parameters of event end-of-pipe P loads. Two-predictor MLR models were developed with these explanatory parameters using a 40-storm dataset derived from six small urban residential watersheds in Wisconsin, USA, and evaluated using storms specific to each study basin. The MLR model validation results indicated sensitivity to storm composition in the datasets. Our analysis shows selected parameters can be used by environmental managers to facilitate end-of-pipe P prediction in urban areas. This information can be used to reduce the amount of P in stormwater runoff by adjusting the timing and frequency of municipal leaf collection and street cleaning programs in urban areas.
Regeneration and survival of forested wetlands are affected by environmental variables related to the hydrologic regime. Climate change, specifically alterations to precipitation patterns, may have outsized effects on these forests. In Tennessee, USA, precipitation has increased by 15% since 1960. The goal of our research was to assess the evidence for whether this change in precipitation patterns resulted in shorter growing seasons and recruitment failure in common canopy trees for a forest wetland. In 2001 and 2018, the density of Quercus lyrata (overcup oak), Liquidambar styraciflua (sweetgum), Quercus phellos (willow oak), and Betula nigra (river birch) seedling, sapling and adult density were mapped in an area of 2.3 ha within a seasonally flooded karst depression. Overall, the percentage of the growing season experiencing inundation was 26% greater in the deep than in shallow areas between 2001 and 2018. Saplings and small adults of all four species were restricted to shallow areas, and their abundance has declined substantially. Overcup oak and sweetgum individuals that were recruited into the adult life history stage were repelled from the deep zone. Overcup oak and sweetgum adults experienced lower mortality across the 2.3-ha study area (11% and 26%, respectively) relative to willow oak (56%) and river birch (64%) over the 17-year study. Growing-season inundation showed no relation to mortality in adult sweetgum and willow oak, a positive relation to mortality among adult river birch across size classes and among small adult overcup oak, and an inverse relation to mortality among large adult overcup oak. In shallow regions, overcup oak and sweetgum adults had greater basal area increment relative to the intermediate and deep regions of the pond. Results of hydrologic modeling for the study area, based on rainfall and temperature records covering 1855–2019, show ponding durations after 1970 considerably longer than the historical baseline, across ponding-depth classes. Our results strongly suggest that climate change is a driving factor suppressing tree regeneration since 1970 in this seasonally flooded karst depression.
Prescribed fire in dry coniferous forests of the western U.S. is used to reduce fire hazards. How large, old trees respond to these treatments is an important management consideration. Growth is a key indicator of residual tree condition, which can be predictive of mortality and response to future disturbance. Using a combination of long-term plot records and dendrochronological samples, we analyzed the effects of prescribed fire treatments from the early 1990 s on forest structure and individual tree growth in mixed-conifer forests of Lassen Volcanic National Park in northern California. Prescribed fire reduced stand live tree basal area and stem density at our sites up to 10 years following fire. Within two prescribed fire burn units and two adjacent unburned stands, we analyzed tree cores from 136 large (mean stem diameter > 70 cm) yellow pine (Pinus jeffreyi and P. ponderosa) and 136 large (mean stem diameter > 50 cm) white fir (Abies concolor). After accounting for annual precipitation, basal area increment for individual trees initially declined up to < 3 years post-fire for white fir and > 10 years post-fire for yellow pine, presumably in response to tree injuries. Growth improved for both species at a site that was burned twice, particularly for white fir. Recent average basal area increment was positively related to crown ratio and negatively associated with an index of local competition. Our findings suggest that forest management, such as prescribed fire and mechanical thinning, may be beneficial in terms of maintaining or improving tree growth among large residual trees. However, managers may want to balance the benefits of these treatments against inadvertent injury and mortality of large trees.
Grass carp, bighead carp, and silver carp spawn in flowing water. Their eggs, and then larvae, develop while drifting. Hydraulic conditions and water temperature control spawning locations, egg survival, and the downstream distance traveled before the hatched larvae can swim for low velocity nursery habitats. Existing egg drift models simulate the fluvial transport of carp eggs but have limitations in capturing the effect of localized turbulence on egg transport due to inadequate dimensions of hydrodynamics and/or empirical parameterization of river dispersion. We present a three-dimensional Lagrangian particle tracking model that uses fully resolved river hydrodynamics and a continuous random walk algorithm driven by local turbulent kinetic energy and its dissipation rate. We incorporate a new set of equations to compute evolving egg characteristics with fully resolved 3-D hydrodynamics. To demonstrate the performance of the model, we conducted a case study in an eight-kilometer reach of the Missouri River at the discharge of approximately 25% daily flow exceedance. Three-dimensional river hydrodynamics was modeled, calibrated, and evaluated with measurement data. Egg drift was modeled and compared using fully three-dimensional, depth-averaged two-dimensional, and zone-averaged one-dimensional hydrodynamics. The comparison shows a generally good agreement among models of downstream egg transport due to advection but a different dispersion pattern of eggs in the river, as a result of turbulent diffusion and shear induced dispersion.
Living shoreline projects have been built to preserve coastal ecosystems under future climate change and sea level rise. To quantify the wave power variation across living shorelines, the wave characteristics around the constructed oyster reefs (CORs) in upper Delaware Bay were investigated in this study. Wave parameters seaward and shoreward of CORs were recorded by wave gauges in early 2018. Four winter storms happened in this period and induced strong winds and coastal flooding at the study site. To estimate the wind wave characteristics across the CORs on a yearly basis, soft computing-based models combining fully connected neural networks and long short-term memory were developed to extend the two-month energetic wave measurements. The results show that when CORs were emergent or slightly submerged, the averaged wave height attenuation was about 39.8% from the offshore gauge to the nearshore gauge (behind CORs) during 2018–2020, owing to the combined effect of nearshore bathymetric changes and CORs. Furthermore, it was found that the annually averaged wave power reduction from offshore to nearshore at the study site was about 30.0% in 2018, 2019, and 2020. This study provides a novel framework to predict long-term wave characteristics based on short-term wave measurements using soft computing-based models.
Sediment deposition on floodplains is essential for the development and maintenance of riparian ecosystems. Upstream erosion is known to influence downstream floodplain construction, but linking these disparate processes is challenging, especially over large spatial and temporal scales. Sediment fingerprinting is thus a robust tool to establish process linkages between downstream floodplain development and sediment production in distal headwater basins. Here we use sediment geochemistry to connect historical erosion in several tributaries of the Yampa River in Colorado and Wyoming, USA, to the construction of downstream floodplains on which extensive cottonwood forests established. Using a combination of conventional techniques and the relatively novel machine-learning random forest algorithm, we build multiple fingerprints of diagnostic geochemical tracers that are then input into a Bayesian mixing model to apportion provenance of floodplain sediment. Sediment samples for provenance analysis were collected from an excavated floodplain in Deerlodge Park on the Yampa River at the rooting surface of the surrounding cottonwood forest and dominantly comprised of very fine (4Φ) sand. Fingerprinting analysis of the 4Φ fraction of collected floodplain sink (n = 38) and tributary source (n = 218) samples revealed floodplain sediment to be dominantly sourced from the tributaries of Muddy Creek (45 ± 4%) and Sand Wash (42 ± 6%). Dendrochronology results moreover indicate the Deerlodge floodplain sediment was deposited in ∼1912, which falls squarely within the time (1880–1940) these tributaries were actively eroding. Taken together, study results indicate a demonstrable link between historical tributary erosion and downstream floodplain construction and concomitant forest establishment. Our findings suggest processes operating in tributary watersheds play an important role in the dynamics of large rivers and emphasize both the need for holistic, collaborative management of sediment as an essential resource and the potential to utilize sediment fingerprinting to inform and direct river ecosystem management.
Coastal tidal wetlands are highly altered ecosystems exposed to substantial risk due to widespread and frequent land-use change coupled with sea-level rise, leading to disrupted hydrologic and ecologic functions and ultimately, significant reduction in climate resiliency. Knowing where and when the changes have occurred, and the nature of those changes, is important for coastal communities and natural resource management. Large-scale mapping of coastal tidal wetland changes is extremely difficult due to their inherent dynamic nature. To bridge this gap, we developed an automated algorithm for DEtection and Characterization of cOastal tiDal wEtlands change (DECODE) using dense Landsat time series. DECODE consists of three elements, including spectral break detection, land cover classification and change characterization. DECODE assembles all available Landsat observations and introduces a water level regressor for each pixel to flag the spectral breaks and estimate harmonic time-series models for the divided temporal segments. Each temporal segment is classified (e.g., vegetated wetlands, open water, and others – including unvegetated areas and uplands) based on the phenological characteristics and the synthetic surface reflectance values calculated from the harmonic model coefficients, as well as a generic rule-based classification system. This harmonic model-based approach has the advantage of not needing the acquisition of satellite images at optimal conditions (i.e., low tide status) to avoid underestimating coastal vegetation caused by the tidal fluctuation. At the same time, DECODE can also characterize different kinds of changes including land cover change and condition change (i.e., land cover modification without conversion). We used DECODE to track status of coastal tidal wetlands in the northeastern United States from 1986 to 2020. The overall accuracy of land cover classification and change detection is approximately 95.8% and 99.8%, respectively. The vegetated wetlands and open water were mapped with user's accuracy of 94.6% and 99.0%, and producer's accuracy of 98.1% and 93.5%, respectively. The cover change and condition change were mapped with user's accuracy of 68.0% and 80.0%, and producer's accuracy of 80.5% and 97.1%, respectively. Approximately 3283 km² of the coastal landscape within our study area in the northeastern United States changed at least once (12% of the study area), and condition changes were the dominant change type (84.3%). Vegetated coastal tidal wetland decreased consistently (~2.6 km² per year) in the past 35 years, largely due to conversion to open water in the context of sea-level rise.
Previous studies indicate that cyanobacterial harmful algal bloom (cyanoHAB) frequency, extent, and magnitude have increased globally over the past few decades. However, little quantitative capability is available to assess these metrics of cyanoHABs across broad geographic scales and at regular intervals. Here, the spatial extent was quantified from a cyanobacteria algorithm applied to two European Space Agency satellite platforms—the MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3. CyanoHAB spatial extent was defined for each geographic area as the percentage of valid satellite pixels that exhibited cyanobacteria above the detection limit of the satellite sensor. This study quantified cyanoHAB spatial extent for over 2,000 large lakes and reservoirs across the contiguous United States (CONUS) during two time periods: 2008–2011 via MERIS and 2017–2020 via OLCI when cloud-, ice-, and snow-free imagery was available. Approximately 56% of resolvable lakes were glaciated, 13% were headwater, isolated, or terminal lakes, and the rest were primarily drainage lakes. Results were summarized at national-, regional-, state-, and lake-scales, where regions were defined as nine climate regions which represent climatically consistent states. As measured by satellite, changes in national cyanoHAB extent did have a strong increase of 6.9% from 2017 to 2020 (|Kendall’s tau (τ)| = 0.56; gamma (γ) = 2.87 years), but had negligible change (|τ| = 0.03) from 2008 to 2011. Two of the nine regions had moderate (0.3 ≤ |τ| < 0.5) increases in spatial extent from 2017 to 2020, and eight of nine regions had negligible (|τ| < 0.2) change from 2008 to 2011. Twelve states had a strong or moderate increase from 2017 to 2020 (|τ| ≥ 0.3), while only one state had a moderate increase and two states had a moderate decrease from 2008 to 2011. A decrease, or no change, in cyanoHAB spatial extent did not indicate a lack of issues related to cyanoHABs. Sensitivity results of randomly omitted daily CONUS scenes confirm that even with reduced data availability during a short four-year temporal assessment, the direction and strength of the changes in spatial extent remained consistent. We present the first set of national maps of lake cyanoHAB spatial extent across CONUS and demonstrate an approach for quantifying past and future changes at multiple spatial scales. Results presented here provide water quality managers information regarding current cyanoHAB spatial extent and quantify rates of change.
Soil is the largest terrestrial reservoir of organic carbon and is central for climate change mitigation and carbon-climate feedbacks. Chemical and physical associations of soil carbon with minerals play a critical role in carbon storage, but the amount and global capacity for storage in this form remain unquantified. Here, we produce spatially-resolved global estimates of mineral-associated organic carbon stocks and carbon-storage capacity by analyzing 1144 globally-distributed soil profiles. We show that current stocks total 899 Pg C to a depth of 1 m in non-permafrost mineral soils. Although this constitutes 66% and 70% of soil carbon in surface and deeper layers, respectively, it is only 42% and 21% of the mineralogical capacity. Regions under agricultural management and deeper soil layers show the largest undersaturation of mineral-associated carbon. Critically, the degree of undersaturation indicates sequestration efficiency over years to decades. We show that, across 103 carbon-accrual measurements spanning management interventions globally, soils furthest from their mineralogical capacity are more effective at accruing carbon; sequestration rates average 3-times higher in soils at one tenth of their capacity compared to soils at one half of their capacity. Our findings provide insights into the world’s soils, their capacity to store carbon, and priority regions and actions for soil carbon management. Mineral-organic associations play a key role in soil carbon preservation. Here, Georgiou et al. produce global estimates of mineral-associated soil carbon, providing insight into the world’s soils and their capacity to store carbon
The distribution of groundwater age is useful for evaluating the susceptibility and sustainability of groundwater resources. Here, we compute the aquifer-scale cumulative distribution function to characterize the age distribution for 21 Principal Aquifers that account for ~80% of public-supply pumping in the United States. The aquifer-scale cumulative distribution function for each Principal Aquifer was derived from an ensemble of modeled age distributions (~60 samples per aquifer) based on multiple tracers: tritium, tritiogenic helium-3, sulfur hexafluoride, chlorofluorocarbons, carbon-14, and radiogenic helium-4. Nationally, the groundwater is 38% Anthropocene (since 1953), 34% Holocene (75 – 11,800 years ago), and 28% Pleistocene (>11,800 years ago). The Anthropocene fraction ranges from <5 to 100%, indicating a wide range in susceptibility to land-surface contamination. The Pleistocene fraction of groundwater exceeds 50% in 7 eastern aquifers that are predominately confined. The Holocene fraction of groundwater exceeds 50% in 5 western aquifers that are predominately unconfined. The sustainability of pumping from these Principal Aquifers depends on rates of recharge and release of groundwater stored in fine-grained layers. Principal aquifers in the US with a large fraction of Anthropocene groundwater are susceptible to land-surface contamination, according to the cumulative distribution function analysis of the groundwater age. We prefer: US drinking water aquifers with a large fraction of Anthropocene groundwater are susceptible to land-surface contamination, according to the analysis of groundwater age distributions at public-supply wells.
Understanding genetic structure and diversity within species can uncover associations with environmental and geographic attributes that highlight adaptive potential and inform conservation and management. The California gnatcatcher, Polioptila californica, is a small songbird found in desert and coastal scrub habitats from the southern end of Baja California Sur to Ventura County, California. Lack of congruence among morphological subspecies hypotheses and lack of measurable genetic structure found in a few genetic markers led to questions about the validity of subspecies within P. californica and the listing status of the coastal California gnatcatcher, P. c. californica. As a U.S. federally threatened subspecies, P. c. californica is recognized as a flagship for coastal sage scrub conservation throughout southern California. We used restriction site‐associated DNA sequencing to develop a genomic dataset for the California gnatcatcher. We sampled throughout the species' range, examined genetic structure, gene–environment associations, and demographic history, and tested for concordance between genetic structure and morphological subspecies groups. Our data support two distinct genetic groups with evidence of restricted movement and gene flow near the U.S.‐ Mexico international border. We found that climate‐associated outlier loci were more strongly differentiated than climate neutral loci, suggesting that local climate adaptation may have helped to drive differentiation after Holocene range expansions. Patterns of habitat loss and fragmentation are also concordant with genetic substructure throughout the southern California portion of the range. Finally, our genetic data supported the morphologically defined P. c. californica as a distinct group, but there was little evidence of genetic differentiation among other previously hypothesized subspecies in Baja California. Our data suggest that retaining and restoring connectivity, and protecting populations, particularly at the northern range edge, could help preserve existing adaptive potential to allow for future range expansion and long‐term persistence of the California gnatcatcher.
Plain Language Summary Incision in bedrock rivers sets the pace of landscape evolution by controlling the rate of geomorphic responses to climatic and tectonic signals, yet the processes driving incision occur at much finer scale than those captured by landscape evolution models. Local bedrock river incision is driven by flow structures that are not well understood. Rivers typically flow fastest near the surface and slowest near the bed, but many bedrock rivers have channel morphologies that cause this velocity/depth relation to invert. The fastest‐flows submerge toward the bed enhancing near‐bed velocities, sediment transport, and consequently the potential for bedrock incision by particle impacts. However, the first observations of these “plunging flows” were from relatively low discharges and it is not clear if they persist during floods. Here we show that plunging flows get stronger during floods, which clears sediment cover that protects the underlying bedrock and increases bedrock incision potential. The length of the plunging flows matches their coincident pools which are common features of bedrock rivers, explaining why these pools exist. Formation of deep scour pools by complex flow structures in bedrock‐confined rivers is the mechanism that drives incision, begging for a re‐examination of the models used to explore landscape evolution.
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2,881 members
Thomas J. Smith III
  • Wetland and Aquatic Research Center
Lillian Rose Ostrach
  • Astrogeology Science Center
Luke Iwanowicz
  • Eastern Ecological Science Center
Carole Mcivor
  • Wetland and Aquatic Research Center
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Denver Federal Center, 80225-0046, Reston, Colorado, United States
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