Stable isotopes of water are important tracers in hydrologic research for understanding water partitioning between vegetation, groundwater, and runoff, but are rarely applied to large watersheds with persistent snowpack and complex topography. We combined an extensive isotope dataset with a coupled hydrologic and snow isotope fractionation model to assess mechanisms of isotopic inputs into the soil zone and implications on recharge dynamics within a large, snow-dominated watershed of the Upper Colorado River Basin. Results indicate seasonal isotopic variability and isotope lapse rates of net precipitation are the dominant control on isotopic inputs to the basin. Snowpack fractionation processes account for <5% annual isotope influx variability. Isotopic fractionation processes are most important in the shrub-dominated upper montane. Effects of isotopic fractionation are less important in the low-density conifer forests of the upper subalpine due to vegetative shading, low aridity, and a deep, persistent snowpack that buffers small sublimation losses. Melt fractionation can have sub-seasonal effects on snowmelt isotope ratios with initial snowmelt depleted but later snowmelt relatively enriched in heavy isotopes through the isotopic mass balance of the remaining snowpack, with the efficiency of isotopic exchange between ice and liquid water declining as snow ablation progresses. Hydrologic analysis indicates maximum recharge in the upper subalpine with wet years producing more isotopically depleted snowmelt (1-2‰ reduction in d18O) through reduced aridity when energy-limited. The five-year volume-weighted d18O in this zone (18.2±0.4‰) matches groundwater observations from multiple deep wells, providing evidence that the upper subalpine is a preferential recharge zone in mountain systems.

NASA’s InSight mission records several high-frequency (>0.5 Hz) dispersive seismic signals. These signals are due to the acoustic-to-seismic coupling, where the infrasound is generated by meteorite entry and impacts. This dispersion property is due to infrasound propagating in a structured atmosphere, and we refer to this infrasound as guided infrasound. We propose to model the propagation of guided infrasound and the seismic coupling to the ground analytically; we use a 1D layered atmosphere on a three-layer solid subsurface medium. The synthetic ground movements fit the observed dispersive seismic signals well. We also examine and validate the previously-published subsurface models derived from the InSight ambient seismic vibration data.

Ice Floe Tracker is an open-source tool designed to retrieve floe-scale sea ice motion in the Arctic marginal ice zone during spring and summer.  Ice Floe Tracker enables observation of the floe size distribution, ice floe rotation rates, small-scale variation in floe velocity, and individual floe trajectories. Sea ice motion occurs on a wide range of scales, from the interaction of individual pieces of ice at sub-meter scales, the formation of linear kinematic features, and ice transport via basin-wide gyres. Most existing methods for tracking ice motion from remote sensing imagery rely on cross-correlation and are optimized for the winter season in the central Arctic. Cross-correlation-derived motion vectors estimate area-averaged motion and thus are well-suited for close-packed central Arctic ice; however, such estimates have high uncertainties in the dynamic, strongly deforming sea ice cover of the marginal ice zone. Our tool aims to fill this gap by using shape detection and feature tracking to observe floe-scale ice motion.The Ice Floe Tracker algorithm consists of a series of customizable modules. The code is structured as a modular package written in open-source languages. It includes parallel processing, unit testing, a command line interface, and thorough documentation (available on Github). Routines are provided to download imagery from the NASA Moderate Resolution Imaging Spectroradiometer. The satellite imagery is processed to enhance the contrast between liquid water and sea ice, sharpen floe boundaries, and remove atmospheric noise. The image is then segmented, and geometric features of ice floes are extracted. Finally, ice floe geometry and locations are compared to those in subsequent images and linked to form trajectories.  By making this tool open-source, we aim to encourage cross-disciplinary collaboration. Recent results from collaborations between observational oceanography and discrete-element sea ice model development will be highlighted. 

Tides are critical to coastal and oceanic processes. While tidal data are available readily, they are often corrupted by various sources of error. An automated, fast MATLAB toolbox is developed to clean-up tidal timeseries data from estuarine and oceanic locations corrupted by errors. This toolbox will immensely speed up delivery of quality-controlled tidal data. It will also reduce errors in quality control, which typically involves several manual tasks. The toolbox corrects poorly interpolated and noisy data, erroneous outliers, and instrumentation bias such as spurious jumps, drifts, spikes, and modulations in the true signal. Signal clean-up involves multiple stages. First, thresholds are imposed on higher order temporal derivatives of the signal to remove gross interpolations and noise saturated signal chunks, followed by a moving median threshold to remove outliers. Then the surviving signal is filtered into tidal, subtidal and long-period components, and the long-period component is subject to a maximal overlap discrete wavelet transformation, in which the transform coefficients corresponding to multi-scale edge features are removed. Subsequently, local information in the subtidal and tidal components is compared relative to the whole signal to correct spurious amplitude modulations and sudden biases. Consequently, these components are added to recover the uncorrupted signal, and large data gaps are filled with short term harmonic reconstruction. For estuarine locations, the correlation in the spectrogram between two nearby stations is initially used to quantify and remove river influence in the signal. Applications to datasets at multiple global locations demonstrate the value of the toolbox.

This study investigates the influence of oceanic and atmospheric processes in extratropical thermodynamic air-sea interactions resolved by satellite observations (OBS) and by two climate model simulations run with eddy-resolving high-resolution (HR) and eddy-parameterized low-resolution (LR) ocean components. Here, spectral methods are used to characterize the sea surface temperature (SST) and turbulent heat flux (THF) variability and co-variability over scales between 50-10000 km and 60 days-80 years in the Pacific Ocean. The relative roles of the ocean and atmosphere are interpreted using a stochastic upper-ocean temperature evolution model forced by noise terms representing intrinsic variability in each medium, defined using climate model data to produce realistic rather than white spectral power density distributions. The analysis of all datasets shows that the atmosphere dominates the SST and THF variability over zonal wavelengths larger than ~2000-2500 km. In HR and OBS, ocean processes dominate the variability of both quantities at scales smaller than the atmospheric first internal Rossby radius of deformation (R1, ~600-2000 km) due to a substantial ocean forcing coinciding with a weaker atmospheric modulation of THF (and consequently of SST) than at larger scales. The ocean-driven variability also shows a surprising temporal persistence, from intraseasonal to multidecadal, reflecting a red spectrum response to ocean forcing similar to that induced by atmospheric forcing. Such features are virtually absent in LR due to a weaker ocean forcing relative to HR.

Understanding the simple mechanism of variable solar irradiance ocean warming/cooling allows for effective attribution, detection, and prediction of ocean-related extreme weather events based on solar activity level and duration. Modern and historical solar and ocean data were used to find the ocean warms from rising and high solar activity over any duration, and vice-versa, such as cooling from low activity during the Dalton Minimum. Equatorial ocean heat content and sea surface temperature are sensitive to daily total solar irradiance (TSI) variation and level, and to the upwelled heat accumulation from prior days sub-surface absorbed solar energy penetration. Equatorial evaporation from the absorbed solar energy performs as the pump of Earth’s hydrological system, sourcing atmospheric rivers and associated extreme weather events, follows a solar cycle pattern. Decadal scale ocean warming and post-solar cycle maximum El Nino events were empirically found to occur after solar activity rises above a decadal average of 120 sfu F10.7cm, equivalent to 94 v2 SIDC sunspot number and 1361.25 W/m2 LASP SORCE TSI. HadSST3 was found to linearly vary with the annual change in TSI, nominally at 0.5°C/W/year, varying with rate of TSI change. An empirical F10.7-TSI-SST model was derived combining a F10.7cm-SORCE TSI correlation model and the HadSST3-TSI sensitivity factor, predicated on the SWPC Solar Cycle 24 panel 2016 F10.7cm flux forecast. The author used this model in December of 2015 to uniquely and successfully predict the 2016 HadSST3 temperature fall to within 0.03°C. Cross correlation analysis indicate solar minimum La Nina events result from insufficient TSI over time, producing less equatorial evaporation, less cloud cover and precipitation, causing drought in the US. The Solar Modern Maximum that peaked in late 2003 warmed the ocean creating 20th century climate change via higher than average solar activity, higher than the 1361.25 W/m2 decadal TSI warming threshold. The 2003 European heat wave was forced by cumulative high solar activity warming of the AMO, and from high TSI during the heat wave. The 2013/14 Cold Wave is partly attributable to periods of lower than solar minimum level irradiance from October 2013 through February 2014. Future extremes depend on the duration of future solar cycle activity extremes.

Western Jezero crater of Mars exposes smooth- and rough-surfaced plains that bound a steep-sided fan-shaped plateau (i.e., western Jezero delta) in the west. The two plain terrains have a gradational contact and their end-member occurrences can be defined by the absence (smooth-surfaced) and presence (rough-surfaced) of littered surficial boulders and depressions. Early researchers interpret the plain terrains as morphological expression of volcanic flow and aeolian, fluvial, or lacustrine deposition, but these hypotheses have not been tested rigorously via detailed mapping. Here we show results of HiRISE-based mapping that reveals four landform units hosted by both smooth- and rough-surfaced terrains: (1) polygonal networks of double-ridged grooves that are 2-4 m wide, up to ~1700 m long, and spaced > ~100 m, (2) reticulate networks of grooves spaced mostly < ~10 m, (3) undulating surfaces hosting variously shaped depressions, and (4) NE-trending boulder-bearing ridges (~10 m wide and 70-300 m long) locally displaying NE-pointing streamlined shapes. Our mapping shows that the NE-trending boulder ridges formed first followed sequentially by the formation of depressions, double-ridged grooves, and reticulate grooves. None of the above observations can be explained as a whole by volcanic emplacement and/or earlier proposed aeolian, fluvial, and lacustrine depositional processes. Guided by Earth analogues, we interpret the streamlined boulder ridges as subglacial flutes, depressions including most circular pits with raised rims as kettle holes and/or thermokarsts, polygonal networks of double-ridged grooves as crevasse-filled ice-pressed moraine ridges and the polygonal pattern was inherited from the fractured glacier, and reticulate grooves as desiccation cracks during the final drying of glacial deposits at the end of a martian ice age.

Enhanced water management systems depend on accurate estimation of hydraulic properties of subsurface formations. This is while hydraulic conductivity of geologic formations could vary significantly. Therefore, using information only from widely spaced boreholes will be insufficient in characterizing subsurface aquifer properties. Hence, there is a need for other sources of information to complement our hydro-geophysics understanding of a region of interest. This study presents a numerical framework where information from different measurement sources is combined to characterize the 3-dimensional random field representing the hydraulic conductivity of a watershed in a Multi-Fidelity estimation model. Coupled with this model, a Bayesian experimental design will also be presented that is used to select the best future sampling locations. This work draws upon unique capabilities of electrical resistivity tests as well as statistical inversion. It presents a Multi-Fidelity Gaussian Processes (Kriging) model to estimate the geological properties in Upper Sangamon Watershed in east central Illinois, using multi-source observation data, obtained from electrical resistivity and pumping tests. We demonstrate the accuracy of Co-Kriging that is dependent on the locations and the distribution of both the high- and low-fidelity data, and also discuss its comparison with Single-High-Fidelity Kriging results. The uncertainties and confidence in the measurements and parameter estimates are then quantified and are in turn used to design future cycles of data collection to further improve the confidence intervals.

The instrument package SEIS (Seismic Experiment for Internal Structure) with the three very broadband and three short-period seismic sensors is installed on the surface on Mars as part of NASA’s InSight Discovery mission. When compared to terrestrial installations, SEIS is deployed in a very harsh wind and temperature environment that leads to inevitable degradation of the quality of the recorded data. One ubiquitous artifact in the raw data is an abundance of transient one-sided pulses often accompanied by high-frequency spikes. These pulses, which we term “glitches”, can be modeled as the response of the instrument to a step in acceleration, while the spikes can be modeled as the response to a simultaneous step in displacement. We attribute the glitches primarily to SEIS-internal stress relaxations caused by the large temperature variations to which the instrument is exposed during a Martian day. Only a small fraction of glitches correspond to a motion of the SEIS package as a whole caused by minuscule tilts of either the instrument or the ground. In this study, we focus on the analysis of the glitch+spike phenomenon and present how these signals can be automatically detected and removed from SEIS’ raw data. As glitches affect many standard seismological analysis methods such as receiver functions, spectral decomposition and source inversions, we anticipate that studies of the Martian seismicity as well as studies of Mars’ internal structure should benefit from deglitched seismic data.

In this study, contaminant transport behaviour in the aquifer system (140 m × 180 m × 5 m) was analyzed using a 3-D groundwater flow and contaminant transport model viz. MODFLOW2005 and MT3DMS. The impacts of hydrodynamic dispersion parameters on the conservative contaminant plume dynamics were analyzed for homogeneous and heterogeneous aquifer systems with low permeability porous media (LPPM). The spatio-temporal distribution of contaminant concentration and breakthrough curves (BTCs) at 12 observation wells were used to analyze the transport dynamics due to conservative contaminant released from a single point source over a hypothetical study area for a period of 1 year (365 days). Results from MODPATH show a significant variation in the pathway of groundwater for homogeneous and heterogeneous aquifer systems. During the source loading period, a very low value of concentration of order 10-9 mg/m3 was observed in the LPPM region. The spatial distribution of contaminant plume for aquifer system with LPPM varied largely as compared to homogeneous aquifer system. The maximum value of concentration in the aquifer with LPPM was found to be ~40% higher than the homogeneous system after source removal. After the source removal, the maximum value of 1.98 mg/m3 was observed for the homogeneous system at a location away from pumping and extraction well after 730 days; however, for a heterogeneous system with LPPM, the maximum value of 2.57 mg/m3 was observed. An early breakthrough was observed for αL= 54 m as compared to αL= 9 m for homogeneous aquifer system, clearly depicting the effect of longitudinal dispersivity on BTC. However, effects of dispersivity on the rising and falling limbs of the BTC were negligible for heterogeneous aquifer system with LPPM. Further, an impact of LPPM and longitudinal dispersivity on the peak concentration value at observation well (OBS-7) was undistinguishable. The numerical simulations carried out in this study mimic the realistic heterogeneous aquifer conditions and highlighted the relevance of LPPM and associated transport processes on contaminant transport dynamics at field-scale, which was usually overlooked.

This study evaluates the performance of a global storm-resolving model (GSRM), the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM). We analyze marine boundary layer clouds in a cold air outbreak over the Norwegian Sea in a 40-day simulation, and compare them to observations from satellite and a field campaign of the Atmospheric Radiation Measurement program (ARM). SCREAM qualitatively captures the cold air outbreak cloud transition in terms of the boundary layer growth, cloud mesoscale structure, and phase partitioning. SCREAM also correctly locates the greatest ice and liquid in the mesoscale updraft. However, the study finds that SCREAM might underestimate cloud supercooled liquid water in the cumulus cloud regime. This study showcases the promise of employing high-resolution and high-frequency observations under similar large-scale conditions for evaluating GSRMs. This approach can help identify model features for future process-level studies before allocating extra resources for a time-matched model intercomparison of a specific case.

This work presents Bayesian network-informed conditional random forests (BNICRF), a novel multiresponse classification technique able to downscale the joint probability distribution of precipitation occurrence at a set of geographical locations of interest from large-scale predictors. BNICRFs combine a Bayesian network, which provides a description of the multisite/spatial dependence structure of the target locations; with an ensemble of conditional random forests, which extract the relevant information from the large-scale predictors to produce accurate probabilistic predictions. This work extends two previous articles that explored Bayesian networks (Legasa & Gutiérrez, 2020) and random forests (Legasa et al., 2022) in the context of statistical climate downscaling. Building on the experimental and validation framework proposed in the experiment 1c of the COST action VALUE (the largest, most exhaustive intercomparison study of statistical downscaling methods to date) and under the perfect prognosis approach, we thoroughly assess the performance of the proposed methodology focusing on its capability to reproduce several multisite and single-site statistics of the observed series. BNICRFs accurately capture the relevant spatial relationships while keeping the same single-site predictive performance than both single-site random forests and a Generalized Linear Model that exhibited a good performance in VALUE. Moreover, we compare BNICRFs against a robust multisite methodology proposed in Chandler (2020), obtaining better predictive capability while keeping similar spatial performance. Since the assessed models still underestimate autocorrelation, we also propose a straightforward extension of BNICRFs that incorporates the temporal structure at each location of interest able to produce both temporarily and spatially realistic precipitation occurrence fields

The relationship between erosion and biodiversity is reciprocal. Soil organisms can both reduce soil loss, by improving porosity, and increase it, by diminishing soil stability as a result of their mixing activities. Simultaneously, soil runoff has ecological impacts on belowground communities. Despite these obvious interactions, soil erosion models do not consider biodiversity in their estimates and soil ecology has poorly investigated the effects of erosion. In order to start filling in these knowledge gaps, a novel biological factor was developed and introduced, for the first time, into a well-known soil erosion model (the revised universal soil loss equation, RUSLE). Furthermore, insights to advance soil erosion ecology were proposed. Thanks to available data on both soil erosion (Panagos et al., 2015) and earthworm diversity (Rutgers et al., 2016), an “earthworm factor” was generated and applied to produce the first maps of a modified soil erodibility. The incorporation of “earthworm factor” reduces soil erodibility and as a consequence soil erosion. On the other hand, also the erosive events affect the soil-living communities. Potential consequences of soil erosion on soil life were also identified: migration, invasion, violence, defence, rebuilding and functional effects were described. The results showed how new estimates of soil loss can be produced by adding biological factors to soil erosion models. The earthworm factor represents the first step towards the inclusion of a wider biological factor, which takes into account the whole soil-living community, into erosion modelling. Fostering the development of soil erosion ecology for better understanding the reciprocal effects of erosion on soil life, will contribute towards this achievement. The increasing availability of large-scale data on soil biodiversity distribution opens up the possibility of incorporating a biological component into bio-physical models such as the soil erosion one. The model integration between the soil ecologists and soil erosion modellers is key point for identifying the interactions between soil organisms and soil loss

Understanding what causes weather-related stresses that lead to crop failures is a critical step towards stabilizing global food production. While there are many sources of weather-related stresses, the 30-60 day Madden-Julian Oscillation (MJO) is the dominant source of subseasonal climate variability in the tropics, making it a potential – but as of yet unexplored – source of crop failures. Here crop models and observational yield statistics are used to assess whether the MJO affects maize yields. We find that the influence of the MJO is widespread, affecting crop yields throughout the tropics. In dry, hot environments the MJO can lead to crop failures by reducing precipitation, decreasing soil moisture, and increasing extreme heat, while in wetter, cooler environments - where water stress is less common - MJO-forced decreases in rainfall bring increases in solar radiation that benefits crop yields. These results provide a pathway to develop actionable early warnings using subseasonal forecasts.

Accurately determining the seismic structure of the deep crust of continents is crucial for understanding the geological record and continental dynamics. However, traditional surface wave methods often face challenges in solving the trade-offs between elastic parameters and discontinuities. In this work, we present a new approach that combines two established inversion techniques, receiver function H-ᵰ5; stacking and joint inversion of surface wave dispersion and receiver function waveforms, within a Bayesian Monte Carlo (MC) framework to address these challenges. As demonstrated by the synthetic test, the new method greatly reduces trade-offs between critical parameters, such as the deep crustal Vs, Moho depth, and crustal Vp/Vs ratio. This eliminates the need for assumptions regarding crustal Vp/Vs ratios in joint inversion, leading to a more accurate outcome. Furthermore, it improves the precision of the upper mantle velocity structure by reducing its trade-off with Moho depth. Additional notes on the sources of bias in the results are also included. Application of the new approach to USArray stations in the Northwestern US reveals consistency with previous studies and also identifies new features. Notably, we find elevated Vp/Vs ratios in the crystalline crust of regions such as coastal Oregon, suggesting potential mafic composition or fluid presence. Shallower Moho depth in the Basin and Range indicates reduced crustal support to the topography. The uppermost mantle Vs, averaging 5 km below Moho, aligns well with the Pn-derived Moho temperature map, offering the potential of using Vs as an additional constraint to Moho temperature and crustal thermal properties.

Surface waves on Earth’s magnetopause have a controlling effect upon global magnetospheric dynamics. Since spacecraft provide sparse in situ observation points, remote sensing these modes using ground-based instruments in the polar regions is desirable. However, many open conceptual questions on the expected signatures remain. Therefore, we provide predictions of key qualitative features expected in auroral, ionospheric, and ground magnetic observations through both magnetohydrodynamic theory and a global coupled magnetosphere-ionosphere simulation of a magnetopause surface eigenmode. These show monochromatic oscillatory field-aligned currents, due to both the surface mode and its non-resonant Alfvén coupling, are present throughout the magnetosphere. The currents peak in amplitude at the equatorward edge of the magnetopause boundary layer, not the open-closed boundary as previously thought. They also exhibit slow poleward phase motion rather than being purely evanescent. We suggest the upward field-aligned current perturbations may result in periodic auroral brightenings. In the ionosphere, convection vortices circulate the poleward moving field-aligned current structures. Finally, surface mode signals are predicted in the ground magnetic field, with ionospheric Hall currents rotating perturbations by approximately (but not exactly) 90º compared to the magnetosphere. Thus typical dayside magnetopause surface modes should be strongest in the East-West ground magnetic field component. Overall, all ground-based signatures of the magnetopause surface mode are predicted to have the same frequency across L-shells, amplitudes that maximise near the magnetopause’s equatorward edge, and larger latitudinal scales than for field line resonance. Implications in terms of ionospheric Joule heating and geomagnetically induced currents are discussed.

Plate boundary deformation zones represent a challenge in terms of understanding their underlying geodynamic drivers. Active deformation is well constrained by GNSS observations in the SW Balkans, Greece and W Turkey, and is characterized by variable extension and strike slip in an overall context of slow convergence of the Nubia plate relative to stable Eurasia. Diverse, and all potentially viable, forces have been proposed as the cause of the observed surface deformation, e.g., asthenospheric flow, horizontal gravitational stresses (HGSs) from lateral variations in gravitational potential energy, and rollback of the Hellenic slab. We use Bayesian inference to constrain the relative contribution of the proposed driving and resistive regional forces. Our models are spherical 2D finite element models representing vertical lithospheric averages. In addition to regional plate boundaries, the models include well-constrained fault zones like north and south branches of the North Anatolian Fault, Gulf of Corinth and faults bounding the Menderes Massif. Boundary conditions represent geodynamic processes: (1) far-field relative plate motions (2) resistive fault tractions (3) HGSs mainly from lateral variations in topography and Moho topology (4) slab pull and trench suction at subduction zones. The magnitude of each of these is a parameter in a Bayesian analysis of the models in the context of horizontal GNSS velocities. The search yields a probability distribution over all parameters, allowing us to determine mean/median parameter values, robustly estimate parameter uncertainties, and identify tradeoffs. Significant trench suction forces from the Hellenic slab act on the overriding Aegean Sea, including along the Pliny-Strabo STEP Fault. Resistive tractions on most plate boundaries and faults are low. The best-fitting models compare well with paleomagnetic rotations and fault slip rates from previous studies.

Autonomous large-scale detection of whistler-ode chorus elements in the Van Allen radiation belts has been an open computational challenge. This is primarily due to: (i) Variability of the spectral morphology of chorus elements, and (ii) Structured background interference from hiss-like chorus that can make elements difficult to detect using traditional signal processing and pattern recognition techniques. We will present computational techniques drawing on pixel connectivity, signal-to-noise (SNR) considerations as well as supervised and unsupervised pattern recognition techniques. Specifically, we will explore the efficacy of popular machine learning techniques trained on unfiltered spectral images versus those trained on culled features generated by unsupervised feature extraction techniques. Representative results will be presented based on magnetic field measurements taken by the EMFISIS instrument suite in the Van Allen probes mission.