Mixing along the salt-freshwater interface is critical for geochemical reactions, transport and transformation of nutrients and contaminants in coastal ecosystems. However, the mechanisms and controls of mixing are not well understood. We develop an analytical model, based on the coupling between flow deformation and dispersion, that predicts the mixing dynamics along the interface for steady state flow in coastal aquifers. The analytical predictions are compared with the results of detailed numerical simulations, which show that non-uniform flow fields, inherent to seawater intrusion in coastal aquifer, result in a non-monotonic evolution of mixing width and mixing rates along the interface. The analytical model accurately captures these dynamics over a range of freshwater flow rates and dispersivities. It predicts the evolution of the mixing width and mixing rates along the interface, offering a new framework for understanding and modeling mixing and reaction processes in coastal aquifers.

The accuracy of global tidal and mean sea surface (MSS) models degrades significantly in coastal and estuarine regions. These geophysical models are important for correcting measurements from satellite altimetry and are used in numerous scientific and engineering applications. The new Surface Water Ocean Topography (SWOT) mission is providing measurements at unprecedented horizontal resolution in these regions. This dataset provides both the opportunity and the need to quantify and correct the spatial variability of these errors. We develop a variational Bayesian framework for tidal harmonic analysis and MSS estimation which can be applied in the early stages of SWOT. The approach demonstrates superior robustness to different types of noise contamination in comparison to conventional least-squares approaches whilst providing full uncertainty estimation. By imposing a spatially coherent inductive bias on the model, we achieve superior harmonic constituent inference using extremely sparsely sampled data. Bayesian uncertainty estimation gives rise to statistical methods for outlier removal and constituent selection. Using our approach, we estimate a lower bound for the residual tidal variability for two SWOT Cal/Val passes (003 and 016) around the European Shelf to be at least 7% on average. We also show similar estimates cannot be produced using standard least-squares approaches. Tide gauge validation in the same region confirms the superiority of our empirical approach in coastal environments. Analysis of the estimated MSS error highlights the danger of interpolation and averaging in masking small-scale oceanographic processes. Empirical corrections for the SWOT data products are provided alongside an open-source Python package, VTide.

Long-range U.S. summer rainfall prediction skill is low. Monsoon variability, especially over the West North Pacific Monsoon (WNPM) and/or East Asian Monsoon (EAM) region, can influence U.S. Great Plains hydroclimate variability via a forced Rossby wave response. Here we explored subseasonal monsoon variability as a source of predictability for Great Plains rainfall. The boreal summer intraseasonal oscillation is related to Great Plains convection and Great Plains low-level jet (LLJ) anomalies as well as a cross-Pacific wave train. Using a causal effect network, we found that the time between BSISO-related geopotential height anomalies and Great Plains rainfall anomalies is about 2 weeks; therefore, BSISO convection may be a valuable forecast of opportunity for subseasonal prediction of Great Plains convection anomalies. More specifically, causal link patterns/maps revealed that the above-normal weekly EAM rainfall, rather than WNPM rainfall or general geopotential height activity over the East Asia, was causally linked to Great Plains LLJ strengthening and active Great Plains convection the following week.

The Cenomanian-Turonian period recorded one of the largest disruptions to the oxygen and carbon cycles, the Oceanic Anoxic Event 2 (OAE2, 94 Ma). This event is global, yet paleo-reconstructions document heterogeneous ocean oxygenation states and sedimentary carbon contents, both temporally and spatially, suggesting that several mechanisms are at play. To better understand the long-term controls on oceanic oxygen and the initial oxygenation conditions prevailing at the beginning of OAE2, we perform numerical simulations of the Cenomanian using the IPSCL-CM5A2 Earth System Model, which includes a marine biogeochemistry component. We examine the control of the biogeochemical states of the global and Central Atlantic oceans by the depth of the Central American Seaway (CAS). The simulations show that a vigorous ocean circulation existed during the Cenomanian and that dysoxia/anoxia was caused by paleogeography rather than by ocean stagnation. The existence of restricted basins, disconnected from the deep global circulation and supplied with oxygen-depleted waters from Oxygen Minimum Zones of the surrounding basins, played a key role in the development of dysoxic/anoxic regions. A comparison with redox-proxy data suggests that a deep connection existed between the Pacific and Central Atlantic prior to OAE2. A shallowing of the CAS may have contributed to the establishment of enhanced anoxia in the Central Atlantic during OAE2. The paleogeographic configuration and that of gateways and submarine topographic barriers appear as major long-term controllers of the oceanic circulation and oxygen distribution, leading to low-oxygen concentrations in extended parts of the ocean as prerequisite conditions for OAEs to occur.

The left-lateral Xianshuihe Fault is located at the eastern boundary of the Tibetan Plateau and is one of the most active faults in China. It is associated with substantial seismic potential, with more than 20 Mw>6 earthquakes since 1700. The fault has been documented to be creeping at the surface for decades; however, the spatial and temporal distributions of shallow creep along the fault are not well resolved. In this study, we obtain high-resolution interseismic velocity maps along the 350-km-long central Xianshuihe Fault and Interferometric Synthetic Aperture Radar (InSAR) timeseries along the Kangding segment using ascending and descending Sentinel-1 data. The InSAR data reveal multiple creeping sections and the estimated surface creep rates show high along-strike variability. A coupling model characterizes the distribution of creep with depth. The seismic potential of apparent rupture asperities along the Xianshuihe Fault is further refined by considering fault-crossing baseline data and the distribution of historical ruptures and microseismicity. Moreover, a stress-driven afterslip model of the observed accelerated creep around the Mw 5.9 2014 Kangding rupture indicates substantial shallow afterslip, which provides further constraints on the distribution of locked and creeping patches along this section of the Xianshuihe Fault.

High-throughput phenotyping (HTP) has the potential to revolutionize plant breeding by providing scientists with exponentially more data than was available through traditional observations. Even though data collection is rapidly increasing, the optimum use of this data and implementation in the breeding program has not been thoroughly explored. In an effort to apply HTP to the earliest stages of a plant breeding program, we extended field-based HTP pipelines to evaluate and extract data from spaced single plants. Using a panel of 340 winter wheat lines planted in full plots and grid-spaced single plants for two growing seasons, we evaluated relationships between single plants and full plot yields. Normalized difference vegetation index (NDVI) was collected multiple times through the growing season using an unmanned aerial vehicle. NDVI measurements during grain filling stage from both single plants and full plots were typically positively associated with their respective grain yield with correlation ranging from -0.22 to 0.74. The relationship between single plant NDVI and full plot yield, however, was variable between seasons ranging from -0.40 to 0.06. A genome wide association analysis (GWAS) identified the same significant markers for traits measured in both full plots and single plots, but also displayed variability between growing seasons. Strong genotype by environment interactions could impede selection on quantitative traits, yet these methods could provide an effective tool for plant breeding programs to quickly screen early-generation germplasm especially for qualitative traits. Effective use of early-generation, affordable HTP data could improve overall genetic gain in plant breeding.

Declaration (Stauning, 2022) on “invalid PCS index” is based on the following arguments: PCS index is calculated with use of incorrect “unified” PC derivation method; PCS index used in analyses is a preliminary index, which was not approved by IAGA and, therefore, it cannot be regarded as a correct index; PCN and PCS indices demonstrate, intermittently, large difference in value, which should be treated as evidence of the PCS index invalidity. The paper presents comments to these arguments. Conclusion is made that criticism of the PCS index, presented in Stauning (2022), is based on groundless arguments.

We develop a method to estimate relative seismic moments M0 and corner frequencies fc of acoustic emission events recorded in laboratory experiments from amplitude spectra of signal’s coda composed of reverberated and scattered waves. This approach has several advantages with respect to estimations from direct waves that are often clipped and also are difficult to separate in experiments performed on small samples. Also, inversion of the coda spectra does not require information about the source locations ans mechanisms. We use the developed method to analyze the data of two experiments: (1) on granite from the Voronezh crystal massif and (2) on Berea sandstone. The range of absolute corner frequencies estimated in both experiments is around 70-700 kHz. The range of relative seismic moments covers 103.5. The relation between fc and M0 observed on the first stages of both experiments, consisted of increasing isotropic confining pressure, approximately follow M0 ~ fc-3 scaling and the b-value of the Gutenberg-Richter distribution was found close to 1. This can be interpreted as rupturing of preexisting material defects with a nearly constant stress-drop and has a similarity with observations of ‘natural’ earthquakes. Deviations from this ‘earthquake-like’ behavior observed after applying axial loading and initiation of sample damaging can be interpreted as changes in stress-drop. Lower stress-drops prevail for sandstone and higher for granite sample respectively that can be related to the strength of corresponding material.

The Nile river basin is one of the global hotspots vulnerable to climate change impacts due to fast growing population and geopolitical tensions. Previous studies demonstrated that general circulation models (GCMs) frequently show disagreement in the sign of change in annual precipitation projections. Here, we first evaluate the performance of 20 GCMs from the 6 Coupled Model Intercomparison Project (CMIP6) benchmarked against a high spatial resolution precipitation dataset dating back to 1983 from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR). Next, a Bayesian Model Averaging (BMA) approach is adopted to derive probability distributions of precipitation projections in the Nile basin. Retrospective analysis reveals that most GCMs exhibit considerable (up to 64% of mean annual precipitation) and spatially heterogenous bias in simulating annual precipitation. Moreover, it is shown that all GCMs underestimate interannual variability; thus, the ensemble range is under-dispersive and a poor indicator of uncertainty. The projected changes from the BMA model show that the value and sign of change varies considerably across the Nile basin. Specifically, it is found that projected change in the two headwaters basins, namely Blue Nile and upper White Nile is 0.03% and -1.65% respectively; both statistically insignificant at 0.05. The uncertainty range estimated from the BMA model shows that the probability of a precipitation decrease is much higher in the upper White Nile basin whereas projected change in the Blue Nile is highly uncertain both in magnitude and sign of change.

We use machine learning methods to predict whether an active region (AR) which produces flares will lead to a solar energetic particle (SEP) event using Space-Weather Michelson Doppler Imager (MDI) Active Region Patches (SMARPs). This new data product is derived from maps of the solar surface magnetic field taken by the Michelson Doppler Imager (MDI) aboard the Solar and Heliospheric Observatory (SOHO). We survey the SMARP active regions associated with flares that appear on the solar disk between June 5, 1996 and August 14, 2010, label those that produced SEPs as positive and the rest as negative. The AR SMARP features that correspond to each flare are used to train two different types of machine learning methods, the support vector machines (SVMs) and the regression models. The results show that the SMARP data can predict whether a flare will lead to an SEP with accuracy (ACC) {less than or equal to}0.72{plus minus}0.12 while allowing for a competitive leading time of 55.3{plus minus}28.6 minutes for forecasting the SEP events.

Nonlinear wave-particle interactions contribute to the acceleration and precipitation of electrons in the outer radiation belt. Recent simulations and spacecraft observations suggest that oblique whistler-mode chorus can cause loss cone overfilling through nonlinear Landau resonance and thus break the strong diffusion limit of quasilinear theories. Here we show with test-particle simulations that a single element of parallel-propagating chorus can also break the diffusion limit through nonlinear cyclotron resonance, as long as its amplitude remains high. This is due to the strong scattering at low pitch angles caused by individual chorus subpackets. We further demonstrate that the subpacket modulations create a discernible pattern in the precipitating electron fluxes, with peaks correlated with the largest subpackets. Such flux patterns may be connected to weak micropulsations within diffuse auroras.

Pool-riffle sediment and flow dynamics have been studied for many years, but there has been relatively little investigation on how field observations might be influenced by the variability of pool-riffle morphologies. In this letter we present a database of quantitative and qualitative measurements from sites where pool-riffle morphologies have been studied and apply two morphologic classifications that compare a) discharge and slope, and b) specific stream power and a representative coarse bed particle size. The classifications show that pool-riffles appear in different positions relative to mobility and morphologic transitions, which indicates that they occur within different flow and sediment regimes. Patterns in the distribution of studied pool-riffles show useful trends as well as an important diversity, the appreciation of which may help to clarify the importance of different flow and sediment transport phenomenon that underlie pool-riffle mechanics.

We present a 2D model of magma body formation in granitic crust by injection of rhyolitic or basaltic dikes and sills. An elastic analytical solution enables computation of rock displacement in response to magma intrusion. Phase diagrams for magma and host rocks predict melting/crystallization. We combine this model with our zircon crystallization/dissolution software and compute zircon survival histories within individual batches of magma and country rocks. Incremental accumulation of intruded magma generates interconnected magma batches of eruptible melt with melt fractions >50 vol% that form in clusters. The rate of melt production is highly variable in space and time. The volume of eruptible melt strongly depends on the input rates of magma Q and the width W of the injection region of dikes and on eruptions. For example, dikes injection with Q=0.125 m3/s with W=5 km during 100 ka generates ~50 km3 of eruptible melt while no significant melt forms if W=10 km. Injection of basaltic dikes produces more melt for the same flux rate. Frequent and small eruptions led to smaller magma bodies that are located deeper in the system, while systems with rare but voluminous eruption forms large melt. Due to partial melting, most host rock zircons loose significant portion of their old cores and, thus, their average age is reduced. Magmatic zircons in the periphery of the intrusion form very quickly due to rapid dikes cooling while in its central part crystals contain old cores and young rims and can grow during several hundreds of ka.

Atmospheric methane mixing ratio rose by 15 ppbv between 2019 and 2020, the fastest growth rate on record. We conduct a global inverse analysis of 2019-2020 GOSAT satellite observations of atmospheric methane to analyze the combination of sources and sinks driving this surge. The atmospheric methane growth rate increased by 31 Tg a-1 from 2019 to 2020, representing a 36 Tg a-1 forcing on the methane budget away from steady state. 86% of the forcing in the base inversion is from increasing emissions (82 ± 18% in the 9-member inversion ensemble), and 14% is from decrease in tropospheric OH. Half of the increase in emissions is from Africa (15 Tg a-1) and appears to be driven by wetland inundation. There is also a large relative increase in emissions from Canada and Alaska (4.8 Tg a-1 , 24%) that could be driven by temperature sensitivity of boreal wetland emissions.

Long-range water planning is complicated by factors that are rapidly changing in the 21st century, including climate, population, and water use. Here, we analyze climate factors and drought projections for Texas as an example of a diverse society straddling an aridity gradient to examine how the projections can best serve water stakeholder needs. We find that climate models are robust in projecting drying of summer-season soil moisture and decreasing reservoir supplies for both the eastern and western portions of Texas during the 21st century. Further, projections indicate drier conditions during the latter half of the 21st century than even the most arid centuries of the last 1,000 years that included megadroughts. To illustrate how accounting for drought non-stationarity may increase water resiliency, we consider generalized case studies involving four key stakeholder groups: agricultural producers, large surface water suppliers, small groundwater management districts, and regional water planning districts. We also examine an example of customized climate information being used as input to long-range water planning. We find that while stakeholders value the quantitative capability of climate model outputs, more specific climate-related information better supports resilience planning across multiple stakeholder groups. New suites of tools could provide necessary capacity for both short and long-term, stakeholder-specific adaptive planning.

Soil samples (n = 45) and water samples (n = 111) were collected in the coastal zone of SW Bangladesh in wet (November) and dry (May) seasons in 2016 to identify the factors influencing soil arsenic concentrations. Soils are entisols formed from recently deposited, predominantly silt-sized sediments with low carbon concentrations typical of the local mangrove forests. Arsenic concentrations in bulk soil are higher in November than in May and vary little between sites. Arsenic concentrations in deionized H2O extracts are ~2 orders of magnitude lower, indicating only ~1% of As is soluble. Water samples show that As concentrations are highest in groundwater from tubewells. Bulk soil As is positively correlated with As concentration in irrigation water, suggesting that As from irrigation water is added to the soil. Unlike other water types, As in rice paddy water is much higher in the wet season, consistent with some fields being irrigated with tubewell water. Arsenic in rice paddy water increases by soil sulfide dissolution, and decreases by dilution during the monsoon. Water soluble As in rice paddy soils is positively correlated with S and DOC concentrations in rice paddy soil extracts due to sulfide dissolution and complexation with DOC. Thus, waterlogging of rice paddy soils leads to reducing conditions, the absence of ferric oxyhydroxides that could sorb As, and the presence of sulfides that incorporate As. As soil pH increases from 7 to 8, KD(soil/extract) increases, consistent with the observed positive correlation between irrigation water pH and bulk soil As concentration. Arsenic bioavailability could be decreased through soil aeration (draining and tilling) and by avoiding the use of groundwater for rice paddy irrigation.

Lakes account for about 10% of the boreal landscape and are responsible for approximately 30% of biogenic methane emissions. However, its quantification is still of large uncertainty under changing climate conditions. Finland has the densest lake system in the world with most lakes situated in the boreal zone. This study uses a large observational dataset of lake methane concentrations to constrain its methane emissions with an extant process-based lake biogeochemical model. We found that the total current diffusive emission from Finnish lakes is 0.12±0.03 Tg CH yr and will increase by 26-59% by the end of this century. We discovered that while warming lake water and sediment temperature played an important role, the climate impact on ice-on periods was a key indicator to the degree of emission increase in the future. We concluded that these boreal lakes remain as a significant methane source under warming climate in this century.

Understanding the falling velocity of cloud droplets and ice crystals in a nonuniform flow is fundamental to cloud physics and precipitation. In this study, the mean settling velocity of a hydrometeor falling into a non-stationary fluid is examined. To investigate the possible particle-fluid interaction, a homogeneous and isotropic turbulent flow fields is considered. The study covers a wide range of flow velocities and hydrometeor density and size. The results show initially reduction of hydrometeor settling velocity and then enhancement in strong turbulence. The mean settling velocity depends on a) mean flow and turbulent intensity i.e., standard deviation of flow and b) particle terminal velocity i.e., the shape and size of hydrometeor, its density, the dynamic viscosity and the density of the fluid, and the gravitational acceleration. The non-dimensional parameters that are important in characterization of hydrometeor settling velocities are estimated using dimensional analysis. These non-dimensional parameters are then used to formulate the mean settling velocity.