Yunnan province in China has rich forest resources but high forest fire frequency. Therefore, a better understanding of the relationship between climate change and forest fires in this region is important for forest fire prevention. This study used the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage change (TWSC) data to analyze the influence of climate change on forest fires in the region during 2003–2016. To improve the accuracy and reliability of GRACE TWSC data, we used the generalized three-cornered hat (GTCH) and the least square method to fuse TWSC data from six GRACE solutions. The spatiotemporal variation of forest fires during 2003–2016 was investigated using burned area data. Then, the relationship between burned area and hydrological and climatic factors was analyzed. The results indicate that more than 90% of burned areas are located in northwestern and southern Yunnan (NW and S). On the seasonal scale, forest fires are mainly concentrated in January–April (dry season) and the burned area is negatively correlated with precipitation (correlation coefficient r = −0.83 (NW) and −0.51 (S)), relative humidity (r = −0.79 (NW) and −0.92 (S)), GRACE TWSC (r = −0.57 (NW) and −0.73 (S)) and evapotranspiration (r = −0.90 (NW) and −0.35 (S)). However, the burned area has no significant correlations with the above four factors on the interannual scale. The composite analysis suggests that the extreme climate affects precipitation, evapotranspiration and TWSC in this region, thereby changing water storage of the air in this region, leading to the formation of an environment prone to forest fires. Such conditions have led to an increase in the burned area in the above region. We also found that the difference between TWSC in high- and low-fire years is much greater than the precipitation in the same period. The above results show that GRACE satellites can detect the influence of climate change on forest fires in Yunnan province.

In the summer of 2022, Pakistan experienced a severe flood event that brought great destruction to the local people and ecosystem. However, there is no comprehensive study on the process, spread and causes of this flood. Therefore, we combined multiple satellite gravity data, meteorological data, hydrological data, and satellite remote sensing data to conduct a thorough investigation and study of this flood. The results show that a 20-year time series of the terrestrial water storage change based multiple gravity data has the high accuracy and reliability, which is used for detecting the flood. The flood propagated through meteorological system (three months), agricultural system (six months) and terrestrial ecosystems (five months), respectively, and the two southern provinces (Balochistan and Sindh) are the most affected by the flood, whose flood severity is 6.955 and 9.557, respectively. The center of the severe flood is located at the border region between the above two province. The severe flood is attributed primarily to the global extreme climate events (La Niña and negative Indian Ocean Dipole events) that altered the transport path of water vapor in the Indian Ocean, causing large amounts of water vapor to converge over Pakistan, resulting in heavy precipitation, and secondarily to the melting of extensive glacier in the mountainous of northern Pakistan as a result of the high temperature in March-May 2022. The above results contribute to the understanding of the mechanism of the impact of extreme climate events on the regional climate, and provide some references for the study of severe floods.

This paper deals with the interpretation of Bouguer gravity anomalies measured along a 250 km long Suhaitu-Etuokeqi gravity profile located at the transitional zone of the Alxa and Ordos blocks where geophysical characteristics are very complex. The analysis is carried out in terms of the ratio of elevation and Bouguer gravity anomaly, the normalized full gradient of a section of the Bouguer gravity anomaly (G^h) and the crustal density structure reveal that (1) the ratio of highs and lows of elevation and Bouguer gravity anomaly is large between Zhengyiguan fault (F4) and Helandonglu fault (F6), which can be explained due to crustal inhomogeneities related to the uplift of the Qinghai-Tibet block in the northeast; (2) the main active faults correspond to the G^h contour strip or cut the local region, and generally show strong deformation characteristics, for example the Bayanwulashan mountain front fault (F1) or the southeast boundary of Alxa block is in accord with the western change belt of G^h, a belt about 10 km wide that extends to about 30 km; (3) Yinchuan-Pingluo fault (F8) is the seismogenic structure of the Pingluo M earthquake, and its focal depth is about 15 km; (4) the Moho depth trend and Bouguer gravity anomaly variation indicates that the regional gravity field is strongly correlated with the Moho discontinuity.

The deep learning-based gravity anomaly inversion method addresses the complex challenge of deriving subsurface density variation models from surface gravity anomaly data. In order to generate various geological environments and their corresponding surface gravity anomaly datasets, three-dimensional density models considering different spatial locations and density variations are created in this paper. At the same time, the residual module and spatial attention mechanism are introduced into the U-Net architecture to improve the learning ability and inversion accuracy of complex geological structures. Experimental results demonstrate that the proposed method achieves the high-precision reconstruction of density variation models in complex anomaly environments, with a model residual error lower than 3%. Additionally, the inversion results of the density change and the gravity change in the Longshoushan fault zone show that the 2022 Menyuan MS6.9 earthquake is in the middle of the positive and negative density changes, which verifies the applicability of the U-Net network in the field of gravity change data, highlighting the method’s value in the real-world environment.

The Sichuan-Yunnan block, characterized by complex geological formations and persistent high-intensity seismicity, remains a focal domain for contemporary geodynamic research. To investigate the fracture depth of the northern Sichuan-Yunnan block and the southeastern part of the Qiangtang block, CRUST1.0 was employed to fit the trend items in the study area and to refine the gravity field model with measured data as control points. A two dimensional discrete wavelet transform analysis method is applied to separate the field sources from the obtained high-precision grid data. Additionally, radial logarithmic power spectrum analysis is utilized to estimate the average equivalent source depth of the abnormal geological bodies corresponding to each order wavelet, thereby extracting both transverse and longitudinal tectonic characteristics of the crust in the region. The results reveal that the Bouguer gravity anomaly within the study area exhibits a general pattern of low values in the northern region and high values in the southern areas. Major faults, including the Bianba-Luolong fault, Nujiang fault, Lancangjiang fault, Batang fault, Jinshajiang fault, Yushu-Ganzi fault and Ganzi-Litang fault are all deep and large faults, with respective depths of 38.9 km, 52.9 km, 52.9 km, 52.9 km, 38.9 km, 52.9 km, 52.9 km. This study provides physical insights into the crustal structure distribution of the northern Sichuan-Yunnan rhombohedral block.

The geological structure in the Red River fault zone (RRF) and adjacent areas is complex. Due to the lack of high-precision gravity data in the study area, it is difficult to obtain the distribution of materials within the Earth’s crust. In this study, a gravity data-fused method is proposed. The Moho depth model data are utilized to construct the gravity anomaly trend, and the mapping relation between the gravity field model data and the measured gravity data is established. Using 934 high-precision measured gravity data as control points, the bilinear interpolation method is used to calculate high-precision grid data of the RRF. Finally, the apparent density inversion method is used to obtain clear crustal density images across the RRF. The experimental results show that the fuses data not only reflect the regional anomaly trend but also maintain the local anomaly information; the root-mean-square error of the fused data is less than 5% and the correlation coefficient is greater than 90%. Through an in-depth comparative analysis of density images, it is found that the low-density anomalous zones, with depths of ~20 km in the northern and southern sections of the RRF, are shallower than those in the middle. The data-fused method provides a new way to process geophysical data more efficiently.