Earth and Space Science最新文献

Volcanic eruptions cause large-scale topographic changes, through the emplacement of lava flows and lava domes, the formation of craters and calderas, and thick ash and pyroclastic deposits. Here we analyze the TanDEM-X Digital Change Map (DCM), which compares the DEM produced during 2010–2015 with satellite acquisitions collected in 2016–2022. The DCM covers 159 eruptions at 103 volcanoes; the data was good quality at 44 of these but not useable at 28. Topographic changes associated with volcanic activity was visible at 58 volcanoes including lava flows, domes, intrusions, pyroclastic flows, lahars, tephra fall, crater formation and landslides. We analyze five case studies in detail: Sierra Negra, Galápagos; Erta Ale, Ethiopia; Sangay, Ecuador; Ebeko, Russia; and Nabro, Eritrea. Our measurements of the lava flows at Sierra Negra and Nabro and crater formation at Ebeko agree to within 15% of previous measurements, confirming the accuracy of the TanDEM-X DCM in volcanic areas. At Erta Ale, we find maximum lava thickness of >40 m, greatly exceeding previous field-based estimates (<2.5 m); consequently, our total volume estimate is an order of magnitude higher. At Sangay, the patterns of height change are consistent with local reports, but our measurements have high uncertainties due to the prevalence of vegetative noise and steep topography. Overall, we demonstrate that the TanDEM-X DCM can measure topographic changes at volcanoes, and in many cases allows us to make new measurements. Finally, we discuss the lessons learned from the TanDEM-X DCM for planning future satellite missions, including the upcoming European Space Agency Harmony Mission.

Global climate change is significantly impacting dust emission patterns in arid and semi-arid regions, posing challenges to environmental quality and human health. However, the impacts of future climate change on dust emissions in China remain insufficiently understood. This study employed the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) model to project future dust emissions in China under two climate scenarios (SSP2-4.5 and SSP5-8.5) for the years 2030, 2060, and 2090. Results indicated that in the near term (2030 and 2060), dust emissions were projected to be higher under the high-emission SSP5-8.5 scenario compared to the moderate-emission SSP2-4.5 scenario. This suggests that increased greenhouse gas concentrations and associated climatic changes may enhance conditions favorable for dust generation, such as elevated temperatures and reduced soil moisture. By 2090, however, this trend may reverse, with SSP2-4.5 exhibiting higher dust emissions than SSP5-8.5. This reversal highlights the complex, non-linear interactions between long-term climate variables and dust emission processes, potentially due to changes in precipitation patterns, atmospheric circulation, and vegetation cover. The spatial distribution of dust emissions consistently remains concentrated in northwestern China and southern Mongolia across all scenarios and time periods, emphasizing the persistent role of major dust source regions like the Taklamakan Desert and the Gobi Desert. These findings underscore the need for targeted mitigation and adaptation strategies to manage the environmental and health impacts associated with dust emissions in the context of climate change.

Submesoscale dynamics can induce significant vertical fluxes of phytoplankton, nutrients, and carbon, resulting in biological and climatological impacts such as enhanced phytoplankton production, phytoplankton community shifts, and carbon export. However, resolving these dynamics is challenging due to their rapid evolution (hours to days) and small spatial scales (1–10 km) of variability. The Modular Aerial Sensing System (MASS), an airborne instrument package measuring concurrent ocean dynamics and hyperspectral ocean color, provides a powerful tool to study the influence of submesoscale dynamics on phytoplankton and carbon. In this study, we present the first airborne observations pairing snapshots of sub-kilometer ocean velocities and their derivatives (i.e., vorticity, divergence, and strain) with concurrent ocean color and sea surface temperature. We developed airborne proxies of chlorophyll-a and particulate organic carbon, which explained about 66.2% and 56.2% of in situ variability without atmospheric correction, suggesting that MASS can capture phytoplankton variability. We also explored relationships between concurrent vorticity, divergence, strain, sea surface temperature, chlorophyll-a, and hyperspectral variables to illuminate the submesoscale processes that alter phytoplankton distributions. This study demonstrates the value of merging bio-optical and physical airborne remote sensing data to better understand the influence of submesoscale dynamics on oceanic ecosystems and organic carbon. We highlight the potential for suborbital remote sensing for studying processes that impact phytoplankton ecosystems and carbon transport without the spatiotemporal aliasing affecting in situ sensors.

An accurate geoid has important consequences for many fields such as engineering applications, underground resource exploration, geophysical surveys, etc. Its precise determination relies on two key data sets: gravity measurements and high-resolution elevation data, both of which are critical for achieving reliable results. In particular, accurate elevation data is indispensable for geoid modeling, as it is required for various computational steps, including the prediction of the free-air gravity anomalies, terrain corrections, and the calculation of complete Bouguer gravity anomalies. In the absence of accurate regional elevation data, the digital elevation model (DEM) generated by the Shuttle Radar Topography Mission (SRTM) is commonly used as a reliable alternative. Additionally, researchers from Japan and the United States have released a new DEM generated from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which provides an alternative to the widely used SRTM DEM. This study explores the consequence of the ASTER DEM on estimating mean free-air gravity anomalies in geoid determination, focusing on the Colorado experiment area, which is characterized by mountainous and rugged terrain. Numerical results indicate that the ASTER DEM yields less favorable statistics compared to the SRTM DEM in terms of height accuracy. The use of ASTER DEM introduces discrepancies (compared to SRTM DEM) ranging from −2 to 4 mGal in the interpolation of free-air gravity anomalies. Furthermore, it is demonstrated that the geoid differences resulting from the use of ASTER DEM are within a few centimeters, remaining below the accuracy level of external GNSS-leveling data.

We present a revision and update to the high-precision relocated seismicity catalog presented by Matoza et al. (2021, https://doi.org/10.1029/2020ea001253) for the Island of Hawai'i from 1986 to 2018. The starting catalog of hypocenters (input data), on which the study by Matoza et al. (2021, https://doi.org/10.1029/2020ea001253) was based, contained an inconsistent depth datum for events before and after 00:00 UT, 29 December 2017. Here we present a recomputed version of the catalog using a consistent reference depth. We corrected the starting catalog to a common depth datum (all events now use the model depth reference datum) and re-ran the entire workflow as described in the paper by Matoza et al. (2021, https://doi.org/10.1029/2020ea001253). This included pairing, cross-correlating, and relocating all seismic events again based on the updated starting catalog. We consider 347,446 events representing 32 years of seismicity on and around the island from 1986 to 2018. We now successfully relocate 299,966 (86%) events using ∼2.53 billion differential times (P and S) from ∼194 million similar-event pairs, derived from cross-correlations between ∼887 million event pairs total, a significant increase from our original analysis. The resolution of fine-scale seismicity features is improved and the median depth of shallow events (<5 km) under Kaluapele (Kīlauea summit caldera) in 2018 is shifted 926 m deeper as a result of the change. The interpretations and other major conclusions in the paper by Matoza et al. (2021, https://doi.org/10.1029/2020ea001253) are unchanged.

We leverage ambient seismic noise to implement a novel geometric phase sensing method for investigating the effects of environmental conditions on near-surface ground properties. The geometric phase, derived from topological acoustics, characterizes the geometry of a wavefield by incorporating cross-correlation information between seismic sensors. Changes in geometric phase, $��\Delta �\eta �$, are expressed as changes in vectorial orientation, describing the wavefield evolution over time. To demonstrate the method, we designed an end-to-end workflow by applying an open access temporal high-resolution data from a seismic array in southwest Iceland and measured $��\Delta �\eta �$ over a 2-year period. We observe that the seasonal fluctuations of $��\Delta �\eta �$ are highly correlated with surface air temperature, reflecting changes in ground properties during the freeze-thaw cycle. We assess the seasonal stability of the noise source distribution and conduct a numerical test to verify that the seasonal pattern in $��\Delta �\eta �$ is minimally affected by shifts in noise source direction. Several advantages of geometric phase measurements, including the elimination of lag window selection and reduced computational costs, suggest their strong effectiveness in monitoring changes in ground properties with time. We suggest that the geometric phase can play a significant role in the future of environmental monitoring.

The simulation of discrete karst networks presents a significant challenge due to the complexity of the physicochemical processes at their origin, occurring within various geological and hydrogeological contexts over extended periods. This complex interplay leads to a wide variety of karst network patterns, each intricately linked to specific hydrogeological conditions. We explore a novel approach that represents karst networks as graphs and applies graph generative models (deep learning techniques) to capture the intricate nature of karst environments. In this representation, nodes retain spatial information and properties, while edges signify connections between nodes. Our generative process consists of two main steps. First, we utilize graph recurrent neural networks (GraphRNN) to learn the topological distribution of karst networks. GraphRNN decomposes the graph simulation into a sequential generation of nodes and edges, informed by previously generated structures. Second, we employ denoising diffusion probabilistic models on graphs (G-DDPM) to learn node features (spatial coordinates and other properties). G-DDPMs enable the generation of nodes features on the graphs produced by the GraphRNN that adhere to the learned statistical properties by sampling from the derived probability distribution, ensuring that the generated graphs are realistic and capture the essential features of the original data. We test our approach using real-world karst networks and compare generated subgraphs with actual subgraphs from the database, by using geometry and topology metrics. Our methodology allows stochastic simulation of discrete karst networks across various types of formations, a useful tool for studying the behavior of physical processes such as flow and transport.

NASA's ICESat-2 (Ice, Cloud and land Elevation Satellite-2) satellite launched in 2018, carrying a single instrument, the Advanced Topographic Laser Altimeter System (ATLAS). The Level 1 science objectives of the mission focus primarily on the cryosphere, with specific interest in monitoring changes in polar ice sheets, glaciers and sea ice. However, in addition to planned observations and data products for polar, land, vegetation, ocean and the atmosphere, ATLAS's photon-counting, green-wavelength instrumentation enables impressive bathymetric measurement capability. Most of the ICESat-2 along-track data products were developed during pre-launch studies, without a dedicated effort focused on bathymetry. The absence of a dedicated bathymetry product has required the scientific community to develop independent, individual algorithms for bathymetric signal extraction, most often tailored to local or regional studies. No existing approaches have been proven applicable to global application. Over the last 3 years, the ICESat-2 Project Science Office has sought to address the need for coastal and nearshore bathymetry through the development of a Level 3a, along-track data product for global shallow-water bathymetry (ATL24). The ATL24 workflow embraces several independent signal extraction algorithms in a machine learning ensemble to provide robust signal extraction of the sea floor and sea surface heights in variable environmental conditions and water quality. This paper explains the approach to the algorithms and an assessment of the algorithm performance to evaluate the usefulness for high-priority science and application use cases.

Vegetation restoration in arid and semi-arid regions holds significant promise for climate change mitigation and ecosystem enhancement. However, limited water availability poses fundamental constraints. Here, we assess restoration potential and its hydrological impacts across global drylands using three Eco-Evolutionary Optimality (EEO) models—the Eagleson model, Yang-Medlyn model, and P model—under both historical (2001–2020) and future (2021–2100) climate conditions. Restoration potential was evaluated by expanding existing vegetation types, with water availability impacts quantified using a modified Budyko framework incorporating atmospheric moisture feedback. Results show that restoration potential increases from 97.3 ± 3.4 million hectares historically to ∼603 ± 46 million hectares under the high-emissions SSP5-8.5 scenario by the end of the century. While increased vegetation cover significantly enhances evapotranspiration and reduces water availability—especially during dry seasons in the Northern Hemisphere—future water stress is partly mitigated by projected precipitation increases and enhanced plant water-use efficiency under elevated CO₂. These findings underscore the need for region-specific, adaptive restoration strategies that balance ecological gains with water sustainability in a changing climate.

The study of pre-seismic variations in the ionosphere due to 2021 Haiti earthquake (Mw = 7.2) is carried out using ground and space-based instruments. The day time ionospheric response is analyzed using Vertical Total Electron Content (VTEC) from IGS stations and electron density from Swarm satellite data. Results demonstrate that bandpass filtered VTEC reveals clear, pronounced, pre - seismic oscillations of peak magnitudes of ∼0.2 TECU on 05 August 2021, that is, 9 days before the earthquake for stations near and just outside the earthquake preparation zone. Similarly, enhanced wave-like oscillations are also evident in the filtered VTEC data near the conjugate stations on the same day. Another unique feature during 05 August 2021 is the anomalous enhancement of northern Equatorial Ionization Anomaly crest shown by Swarm electron density data. Such an enhancement is not observed for other days during August. This is also concurrent with the drop in Relative humidity occurred during the same day near the impending epicenter region. Hence the concomitant anomalies found in various atmospheric and ionospheric parameters suggest that the anomalies found on 05 August 2021 is plausibly related to the Haiti 2021 earthquake. This study also sheds some light into similarities with the Haiti 2010 event which occurred very close to the epicenter of 2021 event, hence emphasizing the need of detailed study of the Earthquake prone regions of Haiti using multiple precursor parameters.