The motion state of a particle is a crucial aspect in sediment transport problems. In this paper, we conceptualized three states: stillness, ‘transport’ and ‘non-transport’. Starting from a data set of bed-load particle tracks obtained from Particle-Tracking-Velocimetry, we removed the bias from experimental uncertainty and applied one-dimensional, instantaneous and non-parametric criteria for distinguishing the different states. We present the distributions of particle velocity for all the moving states and separating the transport and non-transport states, fitting a literature model to them. The transport state is related to isotropic particle vibrations and does not significantly contribute to the bed-load rate. Vice-versa, the choice of accounting or not accounting for the non-transport state has major quantitative impact on the mean number of moving particles and mean particle velocity. Finally, the non-transport state has non-negligible contribution to the total kinetic energy of the bed-load particles.

Flood risk assessment and the design of protection measures often require the estimation of high water levels of a given probability of exceedance, i.e. design flood levels. The common approach for the estimation of design flood levels has typically three main steps. First, direct measurements of annual maximum water levels in a river cross-section are converted into annual maximum flows by using a rating curve. Second, a probability distribution function is fitted to these annual maximum flows to derive discharges of a desired probability of exceedance, i.e. design peak flows. Third, a hydraulic model is applied to derive the corresponding design flood levels. Each of the three steps is associated with significant uncertainties, affecting the accuracy of estimated design flood levels. In this study, we compare this common approach with an alternative one based on the statistical analysis of time series of annual maximum water levels. The rationale behind this study is that high water levels are directly measured and often come along with higher precision and accuracy than peak flows. While the direct use of high water levels is typical in coastal flood hazard and risk assessment, the potential of this approach in the context of river flooding has not been sufficiently explored. In this study we compare the common approach with an alternative approach based on statistical analysis of annual maximum water levels.

Deep tectonic tremors occur in the Nankai subduction zone, defining a belt-like zone with a width of a few ten km located at depths between 30 and 55 km along upper surface of the subducting Philippine sea slab. We interpret the geometry of the tremor belt based on temperature calculations. Time evolution of temperature is calculated using a 3-D heat conduction assuming constant geometry of the slab and the present-day plate convergence velocity. The results show that the tremors occur along the 450°C isotherm along the slab surface where the mantle wedge can be non-convective and already well-serpentinized. Two gaps of the tremor belts are explained by different mechanisms. The Ise gap is where the hanging wall is not mantle wedge but crust. The Kii Gap is not a gap orthogonal to the plate convergence direction, but only appears to be a gap because the isotherm of the plate surface steps in the plate convergence direction. The tremors can be caused by high pore-fluid pressure conditions due to aqueous fluids released by dehydration reactions in blueschist-facies oceanic crust to form eclogite facies. The depth of the tremor belt corresponds to that of decoupled, non-convective mantle wedge. Since the temperature and pressure are within the serpentinite stable condition, the released fluid would normally be absorbed by serpentinization of the mantle wedge peridotite. However, since the non-convective mantle wedge is already well serpentinized from exposure to previously released fluids, the newly released fluid is not absorbed and increases the pore pressure.

Sorghum is an important cereal crop grown across the globe for its grain and biomass value. It can also efficiently use resources such as nitrogen, and multiple varieties that are nitrogen-use and light-capture efficient are constantly being developed. This study focuses on using the spectral signature of sorghum varieties to predict flowering days, which could be used as a proxy for plants’ growth/productivity and development trends, thus helping breeders make quick decisions about what varieties to move to the next stage. Multiple sorghum varieties from the sorghum association panel were planted in a replicate-design field experiment with the variable supply of nitrogen. The flowering days were monitored and recorded. The hyperspectral reflectance data were collected and used to build a sorghum flowering days predictive model. Although regression models such as partial least square have been used to predict plants’ phenotypes, the non-parametric ensemble machine learning model turned out to perform better on flowering days with an accurate model up to 5 days.

Ocean current observations from different seasons of 15 years of vessel-mounted Doppler Current Profiler, merely from the PIRATA program, and drifters-derived near-surface currents are used to describe the structure, the variability of the North Equatorial Countercurrent (NECC) at 38{degree sign}W in the tropical Atlantic. Then used to validate the GLORYS12V1 ocean reanalysis, showing that the inferred NECC’s characteristics present reliable realism. This allows further analysis of the NECC’s seasonal and interannual variabilities over the full reanalysis period (1993-2018). The NECC presents an annual cycle of northward migration driven by the wind field with a two-cores structure. It exhibits a single branch, the sNECC, from December to June. With the addition of a second branch, the nNECC, the rest of the year. The sNECC starts the seasonal cycle in June, then grows northward, driven by the northward migration of the ITCZ with a three-month time lag. The nNECC core appears in August, migrates northward, then vanishes in December/January around 12{degree sign}N. From January to May the sNECC decays, with northward displacement toward 7-9{degree sign}N, driven by the second zero of the wind stress curl. From year to year, the NECC shows significant variations in relation with the tropical Atlantic zonal and meridional climate modes. First, positive NECC transport anomalies appear with “warm” meridional mode and zonal mode “Atlantic Niña” phases. Second, positive anomalies appear with weak meridional “cold” phase, and “Atlantic Niño” increasing zonal mode. Third, negative NECC anomalies occur with “cold” meridional phase and a negative zonal mode.

Representing snow equi-temperature metamorphism (ETM) is key to model the evolution and properties of the snow cover. Recently, a phase-field model describing mean curvature flow evolution on 3-D microstructures was proposed (Bretin et al., 2019). In the present work, this model is used to simulate snow ETM at the pore scale, considering the only process of moving interfaces by sublimation-deposition driven by curvatures. We take 3-D micro-tomographic images of snow as input in the model and obtain a time series of simulated microstructures as output. Relating the numerical time, as defined in the model, to the real physical time involves the condensation coefficient, a poorly-constrained parameter in literature. A calibration was performed by fitting simulations to experimental data through the evolution of specific surface area (SSA) of snow under ETM at -2°C. A value of the condensation coefficient was obtained: (9.8 ± 0.7) 10-4 and was used in all the following simulations. We then show that the calibrated model enables to well reproduce an independent time series of ETM at -2°C in terms of SSA, covariance length, and mean curvature distribution. Finally, the calibrated model was used to investigate the effect of ETM on microstructure and effective transport properties (thermal conductivity, vapor diffusion, permeability), for four different samples. As an interesting preliminary result, simulations show an enhancement of the structural anisotropy of snow in the case of initially anisotropic microstructures such as depth hoar. Results highlight the potential of such micro-scale models for the development of snow property predictions for large-scale snowpack models.

Seamounts can be habitats and hazards to submarine navigation, and their distribution reveals the volcanic history of the oceans. As only a few percent of ocean floor has been sounded, seamount distribution must be mapped by satellite altimetry. Wessel (doi:10.1029/2000JB000083) looked at data from an earlier generation of altimeter technology and suggested that all seamounts 2 km and taller had been found, but there might be as many as 50,000 seamounts between 1–2 km tall that were not yet found. The AltiKa altimeter delivers more precise sea level measurements at a higher along-track sampling rate than previous altimeters. These data resolve small seamounts not previously resolvable (Smith, doi:10.1080/01490419.2015.1014950), particularly if repeat-track profiles are “stacked” and band-pass filtered (Marks and Smith, doi:10.1007/s11001-016-9293-0). These two studies looked at only a few isolated locations where multibeam acoustic depth sounding surveys were available for “ground truth” for tuning a band-pass filter to detect the small seamount geoid signal. In the new work we present here we have stacked 32 repeat cycles of SARAL AltiKa data world-wide, and band-pass filtered the stacks, to yield 75,208 potential seamount locations distributed between +/- 81.5 latitude throughout the global ocean. These locations are detected as local maxima in the filtered geoid at least 2 cm above background and with a full-width at half-maximum (FWHM) at least 4 km wide. Of these, 4824 detections were over multibeam surveys. We assign a proxy seamount height to each by subtracting the regional SRTM30 depths from the multibeam depths. These proxy heights follow a Poisson statistical distribution similar to that which fits acoustic bathymetry profiles over seamounts (Jordan et al., doi:10.1029/JB088iB12p10508). We are currently investigating how to derive proxy heights from anomaly amplitude and FWHM, optimizing the trade-off between false negative and false positive detections, and whether it is possible to identify potential seamounts that may pose hazards to submarine navigation.

Ozone in the troposphere is a pollutant and greenhouse gas, and it is crucial to better understand its transport from the ozone-rich stratosphere. Tropopause folding, wherein stratospheric air intrudes downward into the troposphere, enables stratosphere-to-troposphere ozone transport (STT). However, systematic analysis of the relationship between folding and tropospheric ozone, using data that can both capture folding’s spatial scales and accurately represent tropospheric chemistry, is lacking. Here, we compare folding in both high-resolution (0.25°) reanalysis ERA5 and low-resolution (0.75°) chemical reanalysis CAMSRA over one year. High-resolution folding is dramatically more frequent and significantly better-correlated with tropospheric ozone. In particular, folding of deep tropospheric extent is nearly 100% missing at low resolution, and folding–ozone correlations increase most with resolution along midlatitude storm tracks, where deep folding is most common. Our results imply that STT is more attributable to tropopause folding than implied by low-resolution analysis, likely associated with resolving filamentary, deep folding.

The Barents Sea attracts year-around human activity as the winter sea ice cover retreats, creating a need for short and long term prediction of environmental conditions in the region. Previous studies have shown that local ocean heat content and heat transport at the Barents Sea Opening provide interannual to decadal predictability of Barents Sea ice cover. Part of this predictability is suggested to originate from thermodynamic anomalies propagating along the Norwegian Atlantic Current. To better understand this source of predictability, and the relevant timescales, we use models (Coupled Model Intercomparison Project Phase 6; CMIP6) and satellite observations, to study the linear response of the monthly mean Barents Sea ice cover to downstream sea surface temperature anomalies. We show that in March the sea ice response is strongest on short lead times (<2 year), vanishing towards ~7 year timescale and that the linear sea ice response function can be reconstructed using an advective-diffusive ‘leaky-pipe’ model with multiple propagation timescales. The sea surface temperature based sea ice predictability is linked to decadal and longer timescale variability. Our results also show that sea surface temperatures close to the sea ice edge provide the best predictability at short timescales, but with a skill that approaches that of the sea surface temperatures further away at long timescales.

Fine sediment dynamics is an important issue in alpine rivers since it impacts both dam management (decrease of the reservoir capacity, flushing strategy) and river management (interaction between fine sediment and vegetation, gravel bar stabilisation, flood risk). Suspended Particle Matter (SPM) is generally assumed to be transported as washload although significant exchanges with the river bed have been detected in several cases. As a consequence but also due to the variable SPM sources and differences in the discharge and sediment wave velocities, one can often observe a hysteresis behaviour during hydrological events that evolves spatially along the system. In this study, we will present results from a network of hydro-sedimentary stations (measuring both water discharge and sediment concentration) along a reach of 120 km starting from the Arvan River at its confluence and the Arc and Isère rivers, that is part of the ZABR (or Rhône Basin Long Term Environmental Research Observatory). The SPM dynamics is analysed for different types of events (large flood, spring flood, flushing event, debris flow). In particular, we focus on the potential of erosion or deposition between each station by calculating the total SPM flux during each event at each station as well as the hysteresis behaviour using a hysteresis index. The evolution of these parameters is related to input parameters such as discharge and sources and grain size characteristics of the suspension. Indeed, most observed sediment exchanges (erosion or deposition) occur on the gravel bars when inundated, and depend on settling characteristics of the SPM. Also the experimental site provides some useful data for estimating advection and dispersion characteristics of various SPM events that could be used to validate numerical models.

Cold wakes generated by cyclones enhance productivity and impact the local air-sea interaction, paths and intensities of subsequent storms in the region. However, in-situ observations of the recovery across such wakes are rare. A cold wake in the Arabian Sea was surveyed using multiple ship-board instruments approximately 10 days after the passage of Cyclone Biparjoy in 2023. The wake, nearly 30 \unit{\km} wide, had a stronger (weaker) buoyancy gradient at its eastern (western) edge and assumed a upfront (downfront) orientation relative to the south-westerly monsoon winds. This resulted in notable asymmetry in vertical temperature, salinity and velocity structures at the edges of the wake. While the wake recovery following a cyclone is often attributed to one-dimensional diurnal heating and cooling process, these observations underscore the role of coupling of monsoon winds and the underlying three-dimensional submesoscale fronts in speeding the recovery of a slow-moving cyclone through various submesocale processes.

The Canada Basin has exhibited a significant trend toward a fresher surface layer and thus a more stratified upper ocean over the past three decades. Here, we explore the extent to which the Community Earth System Model (CESM) accurately simulates the observed surface freshening and seasonal processes that contribute to the freshening. We examine 30 simulations from CESM1 (used in the IPCC AR5), 3 simulations from CESM2 (IPCC AR6), and ocean observations from 1975 and 2006-2012. In contrast to the observations, the models simulate salinity profiles that show relatively little variation between 1975 and 2012. We demonstrate that this bias can be partly attributed to the model’s tendency to mix freshwater too deep, creating a surface layer that is saltier than observed. The results provide insight for climate model improvement that could have wide-reaching implications because upper-ocean stratification influences the vertical transport of heat and nutrients.

One of the goals of the InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission is to constrain the interior structure of Mars. We present a hierarchical transdimensional Bayesian approach to extract phase velocity dispersion and interior shear-wave velocity (VS) models from a single seismogram. This method was adapted to Mars from a technique recently developed for Earth (Xu & Beghein, 2019). Monte Carlo Markov Chains (MCMC) seek an ensemble of one dimensional (1-D) VS models between a source and a receiver that can explain the observed waveform. The models obtained are used to calculate the phase velocities of fundamental and higher modes at selected periods, and a subsequent analysis is performed to assess which modes were reliably measured. An advantage of our approach is that it can also fit unknown data noise, which reduces the risk of overfitting the data. In addition, uncertainties in the source parameters can be propagated, yielding more accurate model parameter uncertainties. In this paper, we first present our technique and discuss the challenges stemming from using a single station to characterize both structure and the source and from the absence of a Mars reference model. We then demonstrate the method feasibility using the MSS blind test data and our own synthetic data, which included realistic noise levels based on the noise recorded by InSight.

Streamflow is one of the most important variables in hydrology but is difficult to measure continuously. As a result, nearly all streamflow time series are estimated from rating curves that define a mathematical relationship between streamflow and some easy-to-measure surrogate like water-surface elevation (stage). Most ratings are still fit manually, which is time-consuming and subjective. To improve that process, the U.S. Geological Survey (USGS), among others, is evaluating algorithms to automate that fitting. Several automated methods already exist, and each parameterizes the rating curve slightly differently. Because of the nonconvex nature of the problem, those differences can greatly affect performance. After some trial and error, we settled on reparameterizing the classic segmented power law somewhat like a Bayesian physics-informed neural network. Being physics-informed and Bayesian, the algorithm requires minimal data and also estimates uncertainty. Its implementation is open source and easily modified so that others can contribute to improving the quality of USGS streamflow data.

Rock fragments (RF) influence soil hydraulic properties (SHP) and knowledge about the SHP of stony soils is important in vadose zone hydrology. However, experimental evidence on effective SHP of stony soils is still scarce and mostly restricted to water-saturated conditions and low volumetric contents of RF. We examined the influence of RF on SHP through a series of measurements. Stony soils were prepared by packing 250 cm3 cylinders with soils of two textures (sandy loam and silt loam) and with different volumes of RF (up to 50 % v/v) with a diameter of 8-16 mm. Samples were prepared in a way that the background soils (diameter smaller than 2 mm) had identical bulk density. The simplified evaporation method was used to determine the effective SHP of stony soils. We used the obtained SHP data to evaluate the performance of models, which predict the effective SHP of stony soils from SHP of the background soil. The results highlight the systematic dependency of SHP on volumetric content of RF. The difference between modeled and measured SHP was substantial for cases in which the soil contained a high amount of RF. Accounting for the moisture content of RF improved the prediction of the effective WRC of stony soils compared with a simple scaling that used only the content of RF. Among the evaluated models for the effective HCC, the model based on the general effective medium theory (GEM) showed the best performance, particularly for low RF contents.

The accurate determination of the Field Line Resonance (FLR) frequency of a resonating geomagnetic field line is necessary to remotely monitor the plasmaspheric mass density during geomagnetic storms and quiet times alike. Under certain assumptions the plasmaspheric mass density at the equator is inversely proportional to the square of the FLR frequency. The most common techniques to determine the FLR frequency from ground magnetometer measurements are the amplitude ratio and phase difference techniques, both based on geomagnetic field observations at two latitudinally separated ground stations along the same magnetic meridian. Previously developed automated techniques have used statistical methods to pinpoint the FLR frequency using the amplitude ratio and phase difference calculations. We now introduce a physics-based automated technique, using non-linear least square fitting of the ground magnetometer data to the analytical resonant wave equations, that reproduces the wave characteristics on the ground, and from those determine the FLR frequency. One of the advantages of the new technique is the estimation of physics-based errors of the FLR frequency, and as a result of the equatorial plasmaspheric mass density. We present analytical results of the new technique, and test it using data from the Inner-Magnetospheric Array for Geospace Science (iMAGS) ground magnetometer chain along the coast of Chile and the east coast of the United States. We compare the results with the results of previously published statistical automated techniques.

With temperatures rising in Arctic regions at double the global average, decomposition rates and subsequent greenhouse gas release into the atmosphere are expected to rise, potentially shifting these regions from carbon sinks to carbon sources. General circulation models predict the relationship between respiration and temperature using exponential functions, showing a temperature-independent rate of increase to decomposition rates with temperature. However, enzymes catalyzing carbon depolymerization reactions have demonstrated departures from this predicted increase between 10 and 15 °C. Organic tundra soils were incubated across this range (4 to 20 °C, 4 °C increments) and harvested consecutively after carbon losses equivalent to those after 2, 4, 8, and 12 days at 20 °C. During each harvest, β-glucosidase (BG) and β-xylosidase (BX) activities were measured at both the incubation temperature and at 20 °C to determine temperature limitations to enzyme activity and production. While enzyme activities in soils incubated at different temperatures diverged significantly when measured at different temperatures, these differences were overcome when measured at 20 °C, suggesting enzyme activities are more limited than enzyme production. Two exceptions to this trend were the first harvest of the 4 and 8 °C incubations, which had significantly higher BG and BX activities when assayed at 20 °C than soils incubated at higher temperatures, indicating larger enzyme pool sizes in soils incubated at lower temperatures. For BG activities, increased enzyme pool size offset low temperature limitations to enzyme activity, resulting in no difference in measured rates when the 4 and 8 °C soils were compared with the 12 and 16 °C soils, respectively. However, in subsequent harvests, BG and BX activities measured at 20 °C declined and activities at 4 and 8 °C were significantly lower than in assays at 12, 16, and 20 °C. Declining activities of these enzymes in the 4 and 8 °C incubations may indicate limited substrate accessibility or nutrient availability at lower temperatures due to changes in the relative rates of other decomposition reactions with temperature. Improving our understanding of the multifaceted impacts of temperature on decomposition reactions will be important to better model and predict future carbon release.

An ensemble of forecasts is necessary to identify the uncertainty in predicting a non-linear system like climate. While ensemble averages are often used to represent the mean state and diagnose physical mechanisms, they can lead to information loss and inaccurate assessment of the model’s characteristics. We highlight an intriguing case in the seasonal hindcasts of the Climate Forecast System version-2. While all ensemble members often agree on the sign of predicted El Nino Southern Oscillation (ENSO) for a particular season, non-ENSO climate forcings, although present in individual members, are disparate. As a result, an ensemble mean retains ENSO anomalies while diminishing non-ENSO signals. This difference between ENSO and non-ENSO predictions and a more decisive impact of ENSO on seasonal climate increases the ensemble mean ENSO-Indian Summer Monsoon Rainfall correlation. Thus, a model’s teleconnection skills, which often help interpret physical mechanisms, should be studied using individual members rather than ensemble averages.