Near the threshold of grain motion, sediment transport is “on-off” intermittent, characterized by large but rare bursts separated by long periods of low transport. Without models that can predict the presence of intermittency, measurements of average sediment flux can be in error by up to an order of magnitude. Despite its known presence and impact, it is not clear whether on-off intermittency arises from the grain activity (the number of moving grains) or grain velocities, which together determine the sediment flux. We use laboratory flume experiments to show that the on-off intermittency has its origins in the velocity distributions of grains that move by rolling along the bed, whereas grain activity is not on-off intermittent. Improved predictions of sediment flux require that the types of intermittency we identify be incorporated into stochastic models of sediment flux. Their recognition opens the door to physically based uncertainty estimates of time-averaged sediment flux.

Recently, many in the space weather community have taken up the cause to advocate for an orphan among our own. It’s an important fight – for ground-based sensor networks. Although ground-based sensors are used across all disciplines of space weather, in terms of long-term support, they have no single clear home in any United States agency or department. This has resulted in an ongoing struggle throughout the community to maintain important space weather sensors and networks.The Promoting Research and Observations of Space Weather to Improve the Forecasting of Tomorrow (PROSWIFT) Act of 2020 (Public Law 116-181) attempts to clarify Federal roles and responsibilities, stating that “… ground-based observations provide crucial data necessary to understand, forecast, and prepare for space weather phenomena”, which it defines as ”radars, lidars, magnetometers, neutron monitors, radio receivers, aurora and airglow imagers, spectrometers, interferometers, and solar observatories.”The data from this list of sensors and arrays support research across the space weather domains, including magnetospheric, ionospheric, and atmospheric science. Networks are run by governmental, academic, and commercial providers, and are used to support a range of end-users, from aviation to the power sector. Given the wide range of applications, it’s not surprising that no single entity has primary custody.In separate sections of PROSWIFT, sustainment of these instruments is assigned to “The Director of the National Science Foundation, the Director of the United States Geological Survey, the Secretary of the Air Force, and, as practicable in support of the Air Force, the Secretary of the Navy” who are directed to “maintain and improve ground-based observations of the Sun, as necessary and advisable”, and also to the National Oceanic and Atmospheric Administration (NOAA), as the civil operational space weather agency that is responsible for maintaining “ground-based… assets to provide observations needed for space weather forecasting, prediction, and warnings”.While PROSWIFT’s clarification of federal responsibilities is welcome, what is highlighted is a problem of the “ownership” of the issue of long-term sustainability of such varied instruments.We can start to unravel the ownership problem by understanding its history. One complication to an easy definition is that ground-based sensor networks support both space weather science and operations. The National Science Foundation (NSF) has a long history of supporting novel instrument development, small arrays of sensors placed for scientific research (fundamental research is the foundation of NSF’s mandate), and mid- and larger-scale facilities. But the needs of science do not necessarily intersect the needs of operations, and neither do their requirements in terms of engineering and support. Operational sensors, in many cases, are entirely different than scientific sensors.Like scientific arrays, operational sensors must provide the “right” data - accurate and relevant – but the delivery of those data must also be timely, consistent, and reliable. In other words, the data must be usable for space weather predictions, forecasts, and alerts. The United States Geological Survey (USGS) is one example of a federal provider of operational ground-based data. The commercial sector, by mandate of PROSWIFT, is another.Whether scientific or operational, ground-based networks need to be supported and maintained long-term to fulfill their missions. It is more expensive to shut down and rebuild an array than to keep it operating, and strategic planning is required to prioritize and balance needs across the space weather enterprise.Those taking up the initiative to support ground-based sensors span the space weather enterprise, reflecting the interdisciplinary and cross-sector need for these data. In addition to a myriad of white papers submitted to the Heliophysics Decadal Survey (e.g., Hartinger et al., and Bhatt et al.) and publications (see Engebretson and Zesta, 2017, and Bain et al., 2023), advisory groups such as the Space Weather Advisory Group (SWAG) and the National Academies Space Weather Roundtable, both put into place by the PROSWIFT Act itself, have taken up the cause. The SWAG, in a public meeting on March 20, 2023 (https://www.weather.gov/swag), called for a “paradigm shift”, agreeing upon a recommendation that there is a need “Provide long-term support for operational ground-based and airborne sensors and networks”.It’s clear that these data are crucial for space weather – both space weather research and operations. With the approach of solar maximum, and the associated rise in space weather hazard, what’s less clear is whether this problem will be solved in time. The community efforts have been effective in raising awareness about the dire situation facing many ground-based sensor networks. What is needed now is a mechanism to maintain these networks long-term, and advocacy for new Federal appropriations to support the organizations that take on the responsibility.

Extreme solar particle events (ESPEs) are rare and the most potent known processes of solar eruptive activity. During ESPEs, a vast amount of cosmogenic isotopes (CIs) 10Be, 36Cl and 14C can be produced in the Earth’s atmosphere. Accordingly, CI measurements in natural archives allow us to evaluate particle fluxes during ESPEs. In this work, we present a new method of ESPE fluence (integral flux) reconstruction based on state-of-the-art modeling advances, allowing to fit together different CI data within one model. We represent the ESPE fluence as an ensemble of scaled fluence reconstructions for ground-level enhancement (GLE) events registered by the neutron monitor network since 1956 coupled with satellite and ionospheric measurements data. Reconstructed ESPE fluences appear softer in its spectral shape than earlier estimates, leading to significantly higher estimates of the low-energy (E<100 MeV) fluence. This makes ESPEs even more dangerous for modern technological systems than previously believed. Reconstructed ESPE fluences are fitted with a modified Band function, which eases the use of obtained results in different applications.

Anthropogenic dust is an important constituent of airborne particles in the urban environment but its ice nucleation activity remains uninvestigated. Here, we studied the sources and ice nucleating properties of size-resolved particles in the urban atmosphere under mixed-phase cloud conditions. The heat-resistant ice nucleating particles (INPs) unexpectedly contributed ~70% of the INPs in coarse mode at temperatures below −15 oC. Detailed size-resolved particle chemical composition analysis showed that these INPs were contributed by anthropogenic dust, such as traffic-influenced road dust. A parameterization based on coarse particles was developed to predict the anthropogenic dust INP concentration, due to their correlations on concentration and similarity in chemical compositions. The parameterization can be used for further evaluating the anthropogenic dust contribution to INPs on a global scale. We suggest anthropogenic dust associated with rapid urbanization will become an important factor for urban climate change by altering the cloud microphysics.

My earth science focused PhD research included the analysis of annual fluorescent laminae in cave stalagmites, under the supervision of karst hydrologist Peter Smart (Bristol, UK). At the time, the source of this fluorescence was uncertain, opening new research opportunities characterizing what is now understood to be soil-derived, water-soluble fluorescent dissolved and colloidal organic matter that is transported to the cave by vadose zone percolation waters. Aquatic organic matter fluorescence research benefitted at this time from significant laboratory analytical advances that were commercially driven, including Sony’s Blu-ray technology and the use of fluorescent labelling in the biomedical sciences. Faster analyses at increasingly higher energy excitation energies opened opportunities for novel fingerprinting of organic matter in hydrological science, including landfill leachates and sewage contamination. Today, hand-held fluorescence sensors can instantaneously determine microbial water quality. And back in the field of speleothem (cave deposit) science, annual geochemical laminae are now recognized to be widely preserved in regions where there is a seasonality in recharge, providing a precise chronology for the stalagmite paleoenvironmental archive. Currently, we are utilizing this precise chronology to generate high-resolution fire history records, where the fire proxy is water-soluble ash-derived elements transported from soil to cave by vadose zone percolation waters.

Magnetohydrodynamic (MHD) models have been used for nearly four decades to study the dynamics of magnetospheric substorms. However, until recently no demonstration has been made that MHD models can consistently reproduce substorm onset times in a statistical sense. To test whether MHD can reproduce observed substorm onset times, we developed a procedure for identifying substorm onsets that can be applied both to observational data and to MHD output. Our substorm identification procedure aims to improve upon existing methods of substorm identification by using multiple types of observations to corroborate each identified substorm. Using this procedure, we identified over 100 substorms from the period 1-31 January 2005. Using this list of substorm onset times, we show that the MHD model has weak, but statistically significant skill in predicting substorm onset times. We explore paths to improving the ability of the MHD model to predict substorm dynamics by testing different configurations of the MHD model.

Identification of global methane (CH4) sources is critical for the quantification and mitigation of this potent greenhouse gas. In preparation for Carbon Mapper, a future spaceborne imaging spectrometer mission, our work so far has focused on developing a robust methane plume detection method with AVIRIS-NG Columnwise Matched Filter (CMF) data. While we have previously demonstrated robust classification of plume source presence in ~800m square tiles, a deployed Science Data System (SDS) pipeline will require heat map or mask products that highlight the location of methane plume sources in entire flightlines and scenes.We present two methods for implementing pixel-wise methane plume detection. First, we convert our existing GoogLeNet Convolutional Neural Network (CNN) classifier into a Fully Convolutional Network (FCN) segmentation model with a novel implementation of shift-and-stitch, in which the final fully connected layer is replaced by a one-by-one convolutional layer. This allows us to produce a saliency map of methane plumes in scenes of arbitrary sizes without re-training the existing classification model. While heatmaps produced by this method lack pixel-wise precision, they are sufficiently localized to direct attention for further review.Second, we propose a new hybrid segmentation model based on the popular U-Net architecture. Notably, we backpropagate both segmentation and classification losses during training, which significantly reduces the number of false positive plume detections due to false enhancements and artifacts. Additionally, we successfully use algorithm-generated weakly-labeled segmentation masks, mitigating the need for expensive human-generated segmentation annotations. We report and compare the scene-level detection performance of these two methods on previously curated datasets from “COVID” (2020 California), “CACH4” (2018 California), and “Permian” (2019 Texas) AVIRIS-NG Campaigns.

The interaction of Io with the co-rotating magnetosphere of Jupiter is known to produce Alfven wings that couple the moon to Jupiter's ionosphere. We present first results from a new numerical model to describe the propagation of these Alfven waves in this system. The model is cast in magnetic dipole coordinates and includes a dense plasma torus that is centered around the centrifugal equator. Results are presented for two density models, showing the dependence of the interaction on the magnetospheric density. Model results are presented for the case when Io is near the centrifugal and magnetic equators as well as when Io is at its northernmost magnetic latitude. The effect of the conductance of Jupiter's ionosphere is considered, showing that a long auroral footprint tail is favored by high Pedersen conductance in the ionosphere. The current patterns in these cases show a U-shaped footprint due to the generation of field-aligned current on the Jupiter-facing and Jupiter-opposed sides of Io, which may be related to the structure in the auroral footprint seen in the infrared by Juno. A model for the development of parallel electric fields is introduced, indicating that the main auroral footprints of Io can generate parallel potentials of up to 100 kV.

A document by Brandon G Reichl. Click on the document to view its contents.

Existing models for estimating hyporheic oxygen mass transfer often require numerous parameters related to flow, bed, and channel characteristics, which are frequently unavailable. We performed a meta-analysis on existing dataset, enhanced with high Reynolds number cases from a validated Computational Fluid Dynamics model, to identify key parameters influencing effective diffusivity at the sediment water interface. We applied multiple linear regression to generate empirical models for predicting eddy diffusivity. To simplify this, we developed two single-parameter models using either a roughness or permeability-based Reynolds number. These models were validated against existing models and literature data. The model using roughness Reynolds number is easy to use and can provide an estimate of the oxygen transfer coefficient, particularly in scenarios where detailed bed characteristics such as permeability might not be readily available.

Advances in automated image analysis using open-source computer vision tools, such as PlantCV, have greatly increased the throughput of aboveground phenotyping in a variety of crop species. However, PlantCV was largely optimized to analyze images collected under controlled laboratory conditions, and has seldom been used to analyze images collected under field conditions. Further, there are no known applications of PlantCV for analyzing images collected belowground, such as those obtained from minirhizotron imaging devices. In this study, we demonstrated applications of PlantCV for extracting plant trait information from aboveground and belowground images collected in two perennial crop mapping populations. The first population was composed of nearly 1,200 individuals of a potential perennial oilseed crop (Silphium Integrifolium x Perfoliatum ), and the second population was composed of nearly 1,700 individuals of a perennial cover crop (Trifolium ambiguum , Kura Clover). We designed and used a field-based imaging cart to collect overhead and profile images of individuals from both populations in August and October, which improved the efficiency of field-based image capture. Around the time of aboveground image collection, belowground images of root networks were collected using minirhizotron imaging devices. We then assessed the application of PlantCV for measuring aboveground traits (crop canopy area, height, leaf color and growth rates) and belowground traits (root length and growth rates), and we explored future directions of PlantCV for field-based image analysis of aboveground and belowground crop tissues.

Fluid inclusion water isotope measurements in speleothems have great potential for paleoclimate studies, as they can be used to provide reconstructions of precipitation dynamics and land temperature. Several previous observations, however, suggest that inclusion waters do not always reflect the isotopic composition of surface precipitation. In such cases, dripwaters are thought to be modified by evaporation in the cave environment that result in more positive d2H and d18O values and shallow d2H/d18O slopes. Although evaporation can occur in cave systems, water can also be lost to evaporation during analysis but before water extraction. Here, we examine the likelihood of this possibility with a stalagmite from Borneo. We demonstrate that many samples loose water, and that water loss is controlled by the type and size of inclusions. With multiple replicate measurements of coeval samples, we calculate an evaporative d2H/d18O slope of 1±0.6 (2SE). This value is consistent with model predictions of evaporative fractionation at high analytical temperature at low humidity. Finally, we provide a robust and physically based correction method. We find that fluid–calcite d18O paleotemperatures calculated with corrected d18O data show excellent agreement with recent microthermometry temperature estimates for Borneo during the last deglaciation, suggesting minimal variations in stalagmite d18O disequilibrium over time. Similarly, corrected fluid inclusion d18O and d2H values follow the expected hydroclimate response of Borneo to periods of reduced Atlantic Ocean meridional overturning circulation. Our results suggest that careful petrographic examination and multiple replicate measurements are necessary for reliable paleoclimate reconstructions with speleothem fluid inclusion water isotopes.

Data science is an interdisciplinary subject that uses scientific methodologies, data mining techniques, machine-learning algorithms, and large amounts of data to extract information and insights. The article presents an overview of the current state and future possibilities of Data Science in a variety of sectors, explores the benefits, outlines the frameworks and methodologies employed, explains the current obstacles, and offers feasible solutions.

Inland waters are hotspots of greenhouse gas (GHG) emissions, and small water bodies are now well known to be particularly active in the production and consumption of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). High variability in physical, chemical, and environmental parameters affect the production of these GHG, but currently the mechanistic underpinnings are unclear, leading to high uncertainty in scaling up these fluxes. Here, we compare the relative magnitudes and controls of emissions of all three major GHG in twenty pairs of natural wetland ponds and constructed reservoirs in Canada’s largest agricultural region. While gaseous fluxes of CO2 and CH4 were comparable between the two waterbody types, CH4 ebullition was greater in wetland ponds. Carbon dioxide levels were associated primarily with metabolic indicators in both water body types, with primary productivity paramount in agricultural reservoirs, and heterotrophic metabolism a stronger correlate in wetland ponds. Methane emissions were positively driven by eutrophication in the reservoirs, while competitive inhibition by sulfur-reducing bacteria may have limited CH4 in both waterbody types. Contrary to expectations, N2O was undersaturated in both water body types, with wetlands a significantly stronger and more widespread N2O sink than were reservoirs. These results support the need for natural and constructed water bodies for regional GHG budgets and identification of GHG processing hotspots.

Fire regimes are influenced by both exogenous drivers (e.g., increases in atmospheric CO2; and climate change) and endogenous drivers (e.g., vegetation and soil/litter moisture), which constrain fuel loads and fuel aridity. Herein, we identified how exogenous and endogenous drivers can interact to affect fuels and fire regimes in a semiarid watershed in the inland northwestern U.S. throughout the 21st century. We used a coupled ecohydrologic and fire regime model to examine how climate change and CO2 scenarios influence fire regimes over space and time. In this semiarid watershed we found that, in the mid-21st century (2040s), the CO2 fertilization effect on vegetation productivity outstripped the effects of climate change-induced fuel decreases, resulting in greater fuel loading and, thus, a net increase in fire size and burn probability; however, by the late-21st century (2070s), climatic warming dominated over CO2 fertilization, thus reducing fuel loading and fire activity. We also found that, under future climate change scenarios, fire regimes will shift progressively from being flammability to fuel-limited, and we identified a metric to quantify this shift: the ratio of the change in fuel loading to the change in its aridity. The threshold value for which this metric indicates a flammability versus fuel-limited regime differed between grasses and woody species but remained stationary over time. Our results suggest that identifying these thresholds in other systems requires narrowing uncertainty in exogenous drivers, such as future precipitation patterns and CO2 effects on vegetation.

Anthropogenic global warming caused by increased atmospheric carbon forcing is expected to cause a decrease in peak snow water equivalent (SWE), shift the timing of snowmelt to earlier in the year, and lead to slower melt rates in the mountains of the Western United States. High-elevation forests in mountainous terrain represent a critical carbon sink. Understanding the ecohydrology of subalpine forests is crucial for assessing the health of these sinks. The Niwot Ridge Long Term Ecological Research station, located at 3000 m amsl in the southern Rocky Mountains of Colorado, receives just over 1 m of annual precipitation mostly as snow, supporting a persistent seasonal snowpack in alpine and subalpine ecosystems. Previous studies show that longer growing season length is correlated with shallower snowpack, earlier spring onset and reduced net CO2 uptake. Co-located sensors provide over 20 years of continuous SWE and eddy covariance (EC) data, allowing for robust direct comparison of snow and carbon phenomena in a high-elevation catchment. Linear regression and time series analysis was performed on snowmelt, meteorological, phenological and ecosystem productivity variables. Peak productivity is correlated with peak SWE (R2=0.54) and further correlated with snowmelt disappearance (R2=0.38) and the timing of spring growth onset (R2=0.30). Timing of both peak productivity and spring growth onset are correlated with snowmelt and meteorological variables. A multivariable regression of meteorological variables, timing of spring growth onset, a temporal trend, and snowmelt rate and explains 94% of interannual variability in the timing of peak forest productivity. These results develop support and introduce new evidence for the existing studies of Niwot Ridge ecohydrology. Future work will investigate the meteorological and hydrological record extending back to 1979 and the long-term trends in snowmelt and forest productivity.

Models of fault slip development generally consider interfacial strength to be frictional and deformation of the bounding medium to be elastic. The frictional strength is usually considered as sliding rate- and state-dependent. Their combination, elastic deformation due to differential slip and rate-state frictional strength, leads to nonlinear partial differential equations (PDEs) that govern the spatio-temporal evolution of slip. Here, we investigate how data on fault slip rate and stress can directly discover the complex system (of PDEs) that governs aseismic slip development. We first prepare (synthetic) data sets by numerically solving the forward problem of slip rate and fault stress evolution with models, such as a thin laterally deformable layer over a thick substrate. We now identify the variables, for example, slip rate or friction state variable, and use nonlinearity identification algorithms to discover the governing PDE of the chosen variable.In particular, we use sparse identification of nonlinear dynamics algorithm (SINDy, Brunton et al., 2016) where we solve a regression problem, Ax=y. Here, y is the time derivative of the variable of interest, for example, slip rate. A is a large matrix (library) with all possible candidate functions that may appear in the slip rate evolution PDE. The entries in x, to be solved for, are coefficients corresponding to each library function in matrix A. We update A according to the solutions x so that A's column space can span the dynamics we seek to find. To find the suitable column space for A, we encourage sparse solutions for x, suggesting that only a few columns in matrix A are dominant, leading to a parsimonious representation of the governing PDE.We show that the algorithm successfully recovers the terms of the PDE governing fault slip and could also find the frictional parameter, for example, a/b, where a and b, respectively, are the magnitudes that control direct and evolution effects. Moreover, the algorithm can also determine whether the associated state variable evolves as aging- or slip-law types or their combination. Further, with the data set prepared from distinct initial conditions, we show that the SINDy can also determine the problem parameter’s spatial distribution (heterogeneities) from fault slip rate and stress data.

The evolution of coastal hazards in the context of climate change has been addressed at the regional scale by studying the height and frequency of extreme sea levels (ESL). However, sea level is not the only factor determining the hazard changes that can be used at this scale. Therefore, this article proposes an assessment method of coastal hazard changes integrating other determinants: geographical configurations (continental or island), tidal regimes and meteo-oceanic event types. This method, applied to the coasts of France (mainland and overseas), reveals significant differences in the evolution of coastal hazards: coasts subjected to high tidal ranges and storms (e.g., Atlantic, English Channel and North Sea) will experience a relatively moderate evolution of the hazard, thanks to «training» for the future conditions that present-day high variations constitute. Conversely, the microtidal shorelines of temperate latitudes (e.g., those of the Mediterranean) benefit from only a small variability generated mainly by storm surges, and are therefore poorly prepared for sea level rise. The situation of the small tropical islands is of particular concern: with the passage of cyclones these territories are subjected to very energetic sea states, but by their form, the surges remain moderate, which constitutes, as well as the low tidal ranges, a limiting factor for preparing for sea level rise. In addition to this approach at the regional level, geological, sedimentary and biological evolutions, as well as local hydraulic phenomena, should be considered to assess at a finer spatio-temporal scale the hazard changes.