River discharge monitoring is an important component of the hydrology objectives of Surface Water and Ocean Topography mission (SWOT). River discharge can be estimated Solely using river widths and At Many-stations Hydraulic Geometry (AMHG), but the accuracy is low due to the parameters of At a-station Hydraulic Geometry (AHG) given by AMHG deviate from the truth. In view of this, a Constrained At-Many-Stations Hydraulic Geometry (CAMHG) is proposed to optimize AHG parameters. The performance of CAMHG is verified in three reaches of the Yangtze River using river widths derived from SWOT. After using CAMHG, the relative root mean square error (RRMSE) of estimated discharge reduce 100.1% to 24.4%, 1137.1% to 49.9% and 48.6% to 45.5% for Hankou, Shashi and Luoshan respectively. In addition, CAMHG can also weaken the accuracy difference of estimated discharge in dry and wet seasons benefited from its more reliable AHG parameters. Thus, the proposed CAMHG can dramatically improves the accuracy of discharge estimations and it is meaningful for the discharge calculation after SWOT data release.

Ground-based time-variable gravimetry with high accuracy is an important approach in monitoring geodynamic processes. The uncertainty of instruments including scale factor (SF) and drift rate are the primary factors affect the quality of observation data. Differing from the conventional gravity adjustment procedure, this study adopted the modified Bayesian gravity adjustment (MBGA) method, which accounts for the nonlinear drift rate, and where the SF is considered as one of the hyperparameters estimated using Akaike’s Bayesian information criterion. Based on the terrestrial time-variable gravity datasets (2018–2020) from the southeastern Tibetan Plateau, errors caused by nonlinear drift rate and SF were processed quantitatively through analysis of the gravity difference (GD) residuals and the mutual difference of the GD. Additionally, cross validation from absolute gravity (AG) values was also applied. Results suggest that: (1) the drift rate of relavive instruments show nonlinear characteristics, and owing to their different spring features, the drift rate of CG-5 is much larger than that of LCR-G gravimeters; (2) the average bias between the original and optimized SF of the CG-5 gravimeters is approximately 169 ppm, while that of the LCR-G is no more than 63 ppm; (3) comparison of the differences in gravity values (GV) suggests that the uncertainty caused by the nonlinear drift rate is smaller than that attributable to SF. Overall, the novel approach adopted in this study was found effective in removing errors, and shown to be adaptive and robust for large-scale hybrid surface gravity campaign which providing high accuracy gravity data for the geoscience research.

The annual variation trend of the gravity and lithospheric magnetic field for adjacent periods are analyzed by using the observation of rover gravity and geomagnetic fields in Yunnan from 2011 to 2021, which tend to be consistent every year during the seismogenic process of a strong earthquake. Thus, this study normalizes the annual value of the adjacent periods for the gravity and lithospheric magnetic field. The normalized values are converted into two classifications that can be compared within [−1,1]. In Yunnan Province, a grid of 0.1° × 0.1° was used to compare the data correlation between the variation of gravity and the variation in the lithospheric magnetic field at the same location. The results are as follows. First, the variation trend of the gravity field and total magnetic field tend to be synchronous year to year in strong earthquake years. The range of consistency increases gradually with the approach of the earthquake year reaching its maximum one year before the earthquake. Throughout the region, the overlap number of normalized annual variations in gravity and magnetic field reaches its maximum, and the peak difference of kernel density curve reaches its minimum. Second, the correlation coefficient of the annual variation in the gravity and magnetic field increases year to year during the development of a strong earthquake within a smaller region surrounding the event. The maximum appears one year before the earthquake, and after the earthquake, the correlation decreases. The analysis of gravity and magnetic fusion characteristics can be employed for the prediction of strong earthquakes.

This article has been retracted at the authors request, for the following reasons: •The work was submitted without the approval of the one of the co-authors, Linhai Wang. • The original gravity data used in the paper has been found incorrect through repeated verification recently, which is leading to erroneous conclusions. •The theoretical method used needs further development before it is ready for publication. All co-authors agree to this retraction. Retraction published: 10 June 2021