Earth and Space Science最新文献

As a particularly common mineral in granites, the presence of feldspar and feldspar-chlorite gouges at hydrothermal conditions has important implications in fault strength and reactivation. We present laboratory observations of frictional strength and stability of feldspar (K-feldspar and albite) and feldspar-chlorite gouges under conditions representative of deep geothermal reservoirs to evaluate the impact on fault stability. Velocity-stepping experiments are performed at a confining stress of 95 MPa, pore pressures of 35–90 MPa, and temperatures of 120–400°C representative of in situ conditions for such reservoirs. Our experiment results indicate that the feldspar gouge exhibits strong friction (μ ∼ 0.71) at all experimental temperatures (∼120–400°C) but when T > 120°C, the frictional response transitions from velocity-strengthening to slightly velocity-weakening. At constant confining pressure and temperature, increasing the pore pressure increases the friction coefficient (∼0.70–0.85) and the gouge remains slightly velocity weakening. The presence of alteration-sourced chlorite leads to a transition from velocity weakening to velocity strengthening in the mixed gouge at experimental temperatures and pore pressures. As a ubiquitous mineral in reservoir rocks, feldspar is shown to potentially contribute to unstable sliding over ranges in temperature and pressure typical in deep hydrothermal reservoirs. These findings emphasize that feldspar minerals may increase the potential for injection-induced seismicity on pre-existing faults if devoid of chloritization.

Bedload transport is a natural process that strongly affects the Earth's surface system. An important component of quantifying bedload transport flux and establishing early warning systems is the identification of the onset of bedload motion. Bedload transport can be monitored with high temporal resolution using passive acoustic methods, for example, hydrophones. Yet, an efficient method for identifying the onset of bedload transport from long-term continuous acoustic data is still lacking. Benford's Law defines a probability distribution of the first-digit of data sets and has been used to identify anomalies. Here, we apply Benford's law to continuous acoustic recordings from Baiyang hydrometric station, a tributary of Liwu River, Taroko National Park, Taiwan at the frequency of 32 kHz from stationary hydrophones deployed for 3 years since 2019. We construct a workflow to parse sound combinations of bedload transportation and analyze them in the context of hydrometric sensing constraining the onset, and recession of bedload transport. We identified three separate sound classes in the data related to the noise produced by the motion of pebbles, water flow, and air. We identify two bedload transport events that lasted 17 and 45 hr, respectively, covering about 0.35% of the total recorded time. The workflow could be transferred to other different catchments, events, or data sets. Due to the influence of instrument and background noise on the regularity of the residuals of the first-digit, we recommend identifying the first-digit distribution of the background noise and ruling it out before implementing this workflow.

The ICESat-2 (Ice, Cloud and Land Elevation Satellite-2) photon-counting laser altimeter technology required the design and development of very sophisticated onboard algorithms to collect, store and downlink the observations. These algorithms utilize both software and hardware solutions for meeting data volume requirements and optimizing the science achievable via ICESat-2 measurements. Careful planning and dedicated development were accomplished during the pre-launch phase of the mission in preparation for the 2018 launch. Once on-orbit all of the systems and subsystems were evaluated for performance, including the receiver algorithms, to ensure compliance with mission standards and satisfy the mission science objectives. As the mission has progressed and the instrument performance and data volumes were better understood, there have been several opportunities to enhance ICESat-2's contributions to Earth observation science initiated by NASA and the ICESat-2 science community. We highlight multiple updates to the flight receiver algorithms, the onboard software for signal processing, that have extended ICESat-2's data capabilities and allowed for advanced science applications beyond the original mission objectives.

Global Earth system models are often enlisted to assess the impacts of climate variability and change on marine ecosystems. In this study, we compare high frequency (daily) outputs of potential ecosystem stressors, such as sea surface temperature and surface pH, and associated variables from an Earth system model (GFDL ESM4.1) with high frequency time series from a global network of moorings to directly assess the capacity of the model to resolve local biogeochemical variability on time scales from daily to interannual. Our analysis indicates variability in surface temperature is most consistent between ESM4.1 and observations, with a Pearson correlation coefficient of 0.93 and bias of 0.40°C, followed by variability in surface salinity. Physical variability is reproduced with greater accuracy than biogeochemical variability, and variability on seasonal and longer time scales is more consistent between the model and observations than higher frequency variability. At the same time, the well-resolved seasonal and longer timescale variability is a reasonably good predictor, in many cases, of the likelihood of extreme events. Despite limited model representation of high frequency variability, model and observation-based assessments of the fraction of days experiencing surface T-pH and T-Ω_arag multistressor conditions show reasonable agreement, depending on the stressor combination and threshold definition. We also identify circumstances in which some errors could be reduced by accounting for model biases.

Observing System Simulation Experiments (OSSEs) provide an effective way to evaluate the impact of assimilating data from a specific observing system on hindcasting, nowcasting, and forecasting of environmental systems. The NSF NCAR's Data Assimilation (DA) Research Testbed/Thermosphere-Ionosphere-Electrodynamics General Circulation Model (DART/TIEGCM) tool, to be hosted at the NASA Community Coordinated Modeling Center, serves as a valuable and accessible community resource for quantitatively evaluating the impact of observations from both current and future ionosphere-thermosphere (IT) observing systems. This study demonstrates the utility of DART/TIEGCM as an IT OSSE tool, using synthetic observations simulated using a currently planned NASA Geospace Dynamics Constellation (GDC) observing system design. Five sets of OSSEs are carried out to compare the effects of assimilating various combinations of prospective GDC observations (e.g., neutral temperature, neutral wind, neutral composition, atomic oxygen ion density, and ion and electron temperature) during a major geomagnetic storm period of the St Patrick's Day Storm on 17 March 2013. The maximum error reduction in neutral temperature and atomic ion oxygen density is 24.6% and 43.3% compared to the control experiment. These OSSEs indicate the benefits of coupled IT DA approaches implemented in DART/TIEGCM to maximize the impact of multi-parameter IT observations, such as those expected from the GDC mission. Although more work is required to draw any definitive conclusion on the GDC data impact, the study provides an illustrative example of how the DART/TIEGCM community tool can be used to evaluate observational impacts of planned or existing missions for geospace research and applications.

In a warming climate, an intensifying fire regime and higher likelihood of extreme rain are expected to increase watershed sediment yield in many regions. Understanding regional variability in landscape response to fire and post-fire rainfall is essential for managing water resources and infrastructure. We measured sediment yield resulting from sequential wildfire and extreme rain and flooding in the upper Carmel River watershed (116 km²), on the central California coast, USA, using changes in sediment volume mapped in a reservoir. We determined that the sediment yield after fire and post-fire flooding was 854–1,100 t/km²/yr, a factor of 3.5–4.6 greater than the long-term yield from this watershed and more than an order of magnitude greater than during severe drought conditions. In this first large-scale field validation test of the WEPPcloud/wepppy framework for the Water Erosion Prediction Project (WEPP) model on a burned landscape, WEPP predicted 81%–106% of the measured sediment yield. These findings will facilitate assessing and predicting future fire effects in steep watersheds with a Mediterranean climate and indicate that the increasingly widespread use of WEPP is appropriate for evaluating post-fire hillslope erosion even across 100-km² scales under conditions without debris flows.

This study examines the accumulation, distribution, and mobility of Potentially Toxic Elements (PTEs) in the sediments of a low-lying coastal drainage network (Ravenna, Italy). The aim is to understand the geochemical processes occurring between drainage water and canal bed sediments and assess factors affecting and driving PTE distribution and enrichment in these environments. A geochemical database resulting from the analysis of 203 drainage sediment samples was analyzed using Principal Component Analysis and compared to undisturbed near-surface sediment samples from the same depth and depositional environment. The results reveal PTEs exceeding national regulation limits. Distance from the sea, electrical conductivity of drainage water, and fertilizer use were identified as the main driving factors. The primary mechanisms for PTE precipitation (As, Co, Mo) and subsequent enrichment in the sediments is attributed to the absorption on Fe- and Mn-oxyhydroxides (HFO and HMO), particularly in high salinity areas near the coast. While Cu, Zn, Pb, Cr, and V also have affinity for HFO and HMO, their adsorption efficiency decreases due to the competition with salt-derived cations during ongoing salinization processes. Anthropogenic sources, including agriculture, hunting activities, traffic dust, and railways, contribute to the local abundance of other elements (Cr, Ni, Cu, Zn, Pb, and Sn). This paper's significant progress lies in assessing the concurrent interactions of chemical and physical processes that drive PTE distribution and accumulation in reclaimed low-lying coastal plains. The findings are significant for assessing PTE accumulation risks and sediment toxicity in coastal areas affected by water salinization, drainage, and subsidence, providing valuable information to water management institutions globally.

As a key water quality parameter, dissolved oxygen (DO) concentration, and particularly changes in bottom water DO is fundamental for understanding the biogeochemical processes in lake ecosystems. Based on two machine learning (ML) models, Gradient Boost Regressor (GBR) and long-short-term-memory (LSTM) network, this study developed three ML model approaches: direct GBR; direct LSTM; and a 2-step mixed ML model workflow combining both GBR and LSTM. They were used to simulate multi-year surface and bottom DO concentrations in five lakes. All approaches were trained with readily available environmental data as predictors. Indices of lake thermal structure and mixing provided by a one-dimensional (1-D) hydrodynamic model were also included as predictors in the ML models. The advantages of each ML approach were not consistent for all the tested lakes, but the best one of them was defined that can estimate DO concentration with coefficient of determination (R²) up to 0.6–0.7 in each lake. All three approaches have normalized mean absolute error (NMAE) under 0.15. In a polymictic lake, the 2-step mixed model workflow showed better representation of bottom DO concentrations, with a highest true positive rate (TPR) of hypolimnetic hypoxia detection of over 90%, while the other workflows resulted in, TPRs are around 50%. In most of the tested lakes, the predicted surface DO concentrations and variables indicating stratified conditions (i.e., Wedderburn number and the temperature difference between surface and bottom water) are essential for simulating bottom DO. The ML approaches showed promising results and could be used to support short- and long-term water management plans.

Satellite observations of column-averaged water isotopes are relatively new retrieval products that are in need of further in situ evaluation. Such evaluation studies are generally difficult to perform due to the wide mismatch in temporal and spatial scales between the satellite observations based on instantaneous pixel averages during an overpass and airborne in situ measurements ranging up to several hours over a km-scale. In addition, topography, weather conditions and in particular cloudiness impose severe constraints on an exact collocation between satellite and airborne in situ measurement platforms. Here we present a new method that allows a comparison between in situ measurements and satellite observations of δD on a broader statistical basis. We use regional isotope-enabled model simulations as intermediate information to identify the area for best comparisons. Applying our methodology to TROPOMI total column δD retrievals for the L-WAIVE campaign in Annecy, France, during June 2019 increases the number of satellite pixels for comparison despite widespread cloudiness on average by a factor of 20. In addition, the comparison of simulated and observed δD revealed a dependency of the satellite evaluation on the structure of the middle and upper troposphere. We conclude that our method provides a more robust statistic basis for in situ evaluation of δD satellite retrievals. The method will thus be useful in planning and executing forthcoming validation and evaluation campaigns, and can potentially be used for the evaluation of other satellite products.

This study evaluates the coded aperture imaging method for pitch angle observations of magnetospheric energetic electrons in the solar, Earth, and planetary space environments. We present a review of key previous energetic electron instruments with pitch angle-resolved observations across a range of electron energies. We describe the coded aperture imaging method, typically used for high angular resolution X-ray and gamma ray observations, and evaluate design parameters in the context of energetic electron observations. We present the results of simulations of energetic electrons in Geant4 and evaluate the method's ability to resolve sources with high angular and temporal resolution. We also evaluate the impact of secondary radiation produced from electron interactions in the tungsten coded aperture, as well as the impact of artifacts from the decoding process. With these simulated results, we identify key areas in magnetospheric science that would benefit from high angular resolution observations of energetic electrons. We find that coded aperture imaging may be well-suited for high-resolution observations of intense localized structures, such as low energy (tens of eV to several keV) field-aligned electron beams or the electron strahl wind.