ALBERT

Beschreibung: The carbon balance of peatlands is predicted to shift from a sink to a source this century. However, peatland ecosystems are still omitted from the main Earth system models that are used for future climate change projections, and they are not considered in integrated assessment models that are used in impact and mitigation studies. By using evidence synthesized from the literature and an expert elicitation, we define and quantify the leading drivers of change that have impacted peatland carbon stocks during the Holocene and predict their effect during this century and in the far future. We also identify uncertainties and knowledge gaps in the scientific community and provide insight towards better integration of peatlands into modelling frameworks. Given the importance of the contribution by peatlands to the global carbon cycle, this study shows that peatland science is a critical research area and that we still have a long way to go to fully understand the peatland–carbon–climate nexus.

Beschreibung: Pore-scale numerical simulation plays an important role in comprehending water quality. Predicting the permeability of porous media via machine learning models is a crucial aspect of current research in pore-scale modelling. These models can create a mapping relationship between permeability and pore structure, serving as a regression task. However, directly applying 3D data of porous media to classical machine learning models is not possible, requiring the extraction of features from the pore structure. Deep learning models, as an advanced form of classical machine learning, have been successful in permeability prediction using 3D digital images, but come at a high computational cost. To address this, we propose an intuitive feature extraction method that extracts pore structure features from slices of 3D porous media and uses them as input for machine learning models. This not only reduces the amount of input data but also improves training efficiency, while maintaining excellent prediction accuracy. Moreover, due to the spatial continuity of the 2D pore structure features, we achieved improved prediction performance compared to classical machine learning models by using a long short-term memory (LSTM) neural network. The use of transfer learning further demonstrates the LSTM model's remarkable generalization ability.

Beschreibung: On May 22, 2021, an Mw7.4 earthquake occurred in Maduo, Qinghai, China. The epicenter was located at 34.59 degrees north latitude and 98.34 degrees east longitude. This paper uses ASF HyP3 and MintPy software to conduct time-series InSAR analysis by using the Sentinel-1 SAR images from 2018 to 2023, and then to study the temporal and spatial distribution of regional crustal deformation and the distribution of the seismic fault ruptures. The time-series InSAR analysis are taken in three groups: Pre-earthquake period from January 2018 to May 2021, Earthquake onset period from April 2021 to June 2021, and Post-earthquake period From June 2021 to January 2023. The LOS(Light of Sight) velocity results of the time series INSAR analysis in the three groups and three dimension velocity of GPS stations in this area are fused to obtain regional tectonic strain rates, co-seismic fault slips, and post-seismic displacements. The results show that crustal deformation before the earthquake is small, the range of the coseismic surface rupture of the Maduo earthquake overlaps with the abnormal area of the regional strain field obtained, and the post-seismic viscoelastic deformation is strongly correlated with Maduo earthquake fault.

Beschreibung: Although the impact of the Indian Ocean on the decaying pace of El Niño events has been documented previously, contrary to the consensus that the Indian Ocean Basin (IOB) mode favors the decay of El Niño via modulating the zonal wind anomalies in the tropical western Pacific, the contribution of the Indian Ocean Dipole (IOD) on the following year’s El Niño remains highly controversial. Through investigating the evolution of fast and slow decaying El Niño events, this study demonstrates that the positive IOD phase with a strong western pole prompts the termination of El Niño, whereas no significant effect of the IOD with a weak western pole. The responsible physical mechanism is that the strong western pole of a positive IOD can lead to a strong IOB pattern along with peaking in the late winter (earlier than normal), enhancing local convection and causing anomalous rising (sinking) motions over the tropical Indian Ocean (western Pacific Ocean). The surface easterly wind anomalies on the western flank of the sinking motions stimulate oceanic upwelling equatorial Kelvin waves, which shoal the thermocline in the equatorial eastern Pacific and rapidly terminate the equatorial warming during El Niño. However, a weak western pole of IOD induces a weak IOB mode that peaks in late spring, and the above cross-basin physical processes will not appear.

Beschreibung: Low earth orbit (LEO) satellites have a natural advantage in sensing geocenter motion due to their low altitude. However, the complicated low-altitude space environment poses notable challenges for LEO orbit modeling, which can eventually impact the quality of derived geocenter motion. In this study, we focus on the LEO-based geocenter estimation. The geocenter motion is directly estimated in the LEO dynamic POD without incorporating ground GNSS observations. We use dynamic models instead of onboard accelerometer measurements for the non-gravitational forces in order to investigate the impact of orbit modeling on the geocenter estimation. Three year (2019-2021) onboard GNSS observations from seven LEO satellites in different orbits are processed. Our results indicate a strong correlation between LEO solar radiation pressure (SRP) modeling and estimated geocenter motion. The imperfections of a priori box-wing model can result in severe distortions of geocenter estimates, especially for the Z components. But by introducing a SRP scale factor, most orbital artifacts signal in the geocenter coordinates can be eliminated, and the time resolution of SRP scale factor have a negligible influence on the geocenter estimates. Furthermore, we compare our result with the solution based on the accelerometer measurements, and observed a good consistency in the annual signal characteristic of both solutions. We also evaluate the contribution of multiple LEO combination to the geocenter estimation. The combination of multiple LEO can significantly reduce the corelation between geocenter coordinates and orbit dynamic parameters, thereby improving the reliability of the derived geocenter motion.

Beschreibung: In this paper, a point target analysis method is developed for measuring the slant range from SAR satellite to the ground corner reflector. By using the pre-measured coordinate of the corner reflector and the precise orbit of SAR satellite, the theoretical slant range can be calculated, and the residual of the corrected slant range measurement by point target analysis is then obtained. For repeated SAR images over corner reflectors, the time series of the residual of measured slant ranges can be formed and used to detect displacements of corner reflectors. This method has been used to analyze five corner reflectors in Shanghai by using tens of Sentinel-1A SAR images. All the five corner reflectors are in same shape and size, which is triangular trihedral with inner-leg of 1.2 meter. Two of the five corner reflectors are located on stable ground, and three of them are located on Shanghai Yangtze River Bridge. The results show that the average standard deviation of the residual is about 0.15m, and it is affected by the nearby scattering environment of the individual corner reflector. The vibration of the bridge leads to larger standard deviations of the residuals. In addition, re-setup experiments of the two ground corner reflectors are conducted, they are raised up by 0.10m intentionally, which can be detected successfully through analysis of the residual time series. This method can be used to monitor large displacement of ground movements, such as landslides and movements of mining dams, by using high resolution SAR images.

Beschreibung: The present day’s thermal lithospheric thickness is usually calculated by one or two-dimensional steady-state heat conduction equation. Using the latest measured heat flow data of the North China Craton(NCC) and the one-dimensional steady-state heat conduction equation, the calculated thermal lithospheric thickness of the NCC is between 34.8-190 km. There are some regions, whose lithosphere is very thin, and the lithosphere adjacent to them is very thick, so the bottom boundary of the lithosphere fluctuates greatly. The last thinning of the NCC stopped at~24 Ma and then entered the thermal subsidence period. To study whether the fluctuates of the bottom boundary of the lithosphere can be maintained during the long-term thermal subsidence process, a thermal subsidence model was established. The modeling results show that after the thermal subsidence of 24 Ma, the fluctuates of the bottom boundary of the lithosphere are difficult to maintain, and the present day’s thermal lithospheric thickness of the NCC should not exceed 73 km, and the maximum surface heat flow value of the NCC is 75.6 mW·m〈sup〉-2〈/sup〉 due to the deep thermal state. The high heat flows anomaly in the NCC is more caused by shallow activities and cannot reflect the deep thermal state. After correction, the current thermal lithospheric thickness of the North China craton is ~73-190 km, and the bottom boundary of the lithosphere changes gently.

Beschreibung: Precise satellite clock product is an indispensable prerequisite for the real-time precise positioning service. To meet the requirement of numerous time-critical applications, real-time satellite clock corrections need to be broadcast to users with an update rate of 5 s or higher. With the rapid development of global navigation satellite systems (GNSS) over the past decades, abundant GNSS tracking stations and modern constellations have emerged, and the computation for multi-GNSS real-time clock estimation has become rather time-consuming. In this contribution, an efficient strategy is proposed to achieve high processing efficiency for multi-GNSS real-time clock estimation, wherein undifferenced method based on sequential least square is adopted. In the proposed strategy, parallel data processing and high-performance matrix operations are introduced to accelerate the processing of multi-GNSS clock estimation. The former is based on OpenMP (Open Multi-Processing), while the latter is achieved by the implementation of the Schur complement and the open-source library OpenBLAS. Multi- GNSS observations from 85 globally distributed tracking stations are employed for the generation of real-time precise clock products. The average elapsed time per epoch with the proposed strategy is 0.35, 0.68, and 2.30 s for GPS-only, dual-system, and quad-system solutions, respectively. Compared to the traditional serial strategy, the computation efficiency is significantly improved by 76.0%, 77.3%, and 77.7%, respectively. The accuracy of the estimated clocks is evaluated with respect to IGS final GPS clock products and GFZ final multi-GNSS clock products (GBM0MGXRAP), and multi-GNSS real-time precise point positioning (PPP) experiments are further carried out. All the results indicate that the proposed strategy is efficient, accurate, and can promise high-rate multi-GNSS real-time clock estimation.