Carbon dioxide (CO₂) evasion from inland waters is an important component of the global carbon cycle. However, it remains unknown how global change affects CO₂ emissions over longer time scales. Here, we present seasonal and annual fluxes of CO₂ emissions from streams, rivers, lakes, and reservoirs throughout China and quantify their changes over the past three decades. We found that the CO₂ emissions declined from 138 ± 31 Tg C yr⁻¹ in the 1980s to 98 ± 19 Tg C yr⁻¹ in the 2010s. Our results suggest that this unexpected decrease was driven by a combination of environmental alterations, including massive conversion of free-flowing rivers to reservoirs and widespread implementation of reforestation programs. Meanwhile, we found increasing CO₂ emissions from the Tibetan Plateau inland waters, likely attributable to increased terrestrial deliveries of organic carbon and expanded surface area due to climate change. We suggest that the CO₂ emissions from Chinese inland waters have greatly offset the terrestrial carbon sink and are therefore a key component of China’s carbon budget.

Flood hazards result in enormous casualties and huge economic losses every year in the Pearl River Basin (PRB), China. It is, therefore, crucial to monitor floods in PRB for a better understanding of the flooding patterns and characteristics of the PRB. Previous studies, which utilized hydrological data were not successful in identifying flooding patterns in the rural and remote regions in PRB. Such regions are the key supplier of agricultural products and water resources for the entire PRB. Thus, an analysis of the impacts of floods could provide a useful tool to support mitigation strategies. Using 66 Sentinel-1 images, this study employed Otsu’s method to investigate floods and explore flood patterns across the PRB from 2017 to 2020. The results indicated that floods are mainly located in the central West River Basin (WRB), middle reaches of the North River (NR) and middle reaches of the East River (ER). WRB is more prone to flood hazards. In 2017, 94.0% flood-impacted croplands were located in WRB; 95.0% of inundated croplands (~9480 hectares) were also in WRB. The most vulnerable areas to flooding are sections of the Yijiang, Luoqingjiang, Qianjiang, and Xunjiang tributaries and the lower reaches of Liujiang. Our results highlight the severity of flood hazards in a rural region of the PRB and emphasize the need for policy overhaul to enhance flood control in rural regions in the PRB to ensure food safety.

Understanding the processes governing lateral terrestrial organic carbon transfer is confounded by the fact that organic carbon deposits on land have not yet been fully explored. Despite recent advances in understanding organic carbon deposition in aquatic ecosystems, the burial of organic carbon in dry depositional environments remains unclear. Here, combining large-scale field surveys and remote sensing techniques, we provide a robust estimate for sediment retention and organic carbon burial of check dams on the Chinese Loess Plateau. We find that the 50,226 active check dams have intercepted 10.2 ± 0.6 Pg eroded sediment during 1970-2020, which equals to 46% of the sediment load of Yellow River. Based on 86 deep sediment cores, we estimate that 21.6 ± 9.9 Tg of organic carbon was buried over the past 50 years by check dams with a burial rate of 468 ± 204 g C m⁻² yr⁻¹, approximately one order of magnitude higher than that of global lakes/reservoirs. We also find that the organic carbon burial efficiency of check dams (~80%) is significantly higher than in other depositional environments. We argue that organic carbon burial by check dams represents a significant terrestrial carbon sink and must be accounted for in global carbon budget.

With the influence of climate change and human activities, the frequency and scale of forest fires have been increasing continuously, posing a significant threat to the environment and human safety. Therefore, rapid and accurate forest fire detection has become essential for effective control of forest fires. This study proposes a Forest Fire Detection and Segmentation Model (FFDSM) based on unmanned aerial vehicle (UAV) infrared images to address the problems of forest fire occlusion and the poor adaptability of traditional forest fire detection methods. The FFDSM integrates the YOLO (You Only Look Once) v5s-seg, Efficient Channel Attention (ECA), and Spatial Pyramid Pooling Fast Cross-Stage Partial Channel (SPPFCSPC) to improve the detection accuracy of forest fires of different sizes. The FFDSM enhances the detection and extraction capabilities of forest fire features, enabling the accurate segmentation of forest fires of different sizes and shapes. Furthermore, we conducted ablation and controlled experiments on different attention mechanisms, spatial pyramid pooling (SPP) modules, and fire sizes to verify the effectiveness of the added modules and the adaptability of the FFDSM model. The results of the ablation experiment show that, compared to the original YOLOv5s-seg model, the models fused with the ECA and SPPFCSPC achieve an improved accuracy, with FFDSM showing the greatest improvement. FFDSM achieves a 2.1% increase in precision, a 2.7% increase in recall, a 2.3% increase in mAP@0.5, and a 4.2% increase in mAP@0.5:0.95. The results of the controlled experiments on different attention mechanisms and SPP modules demonstrate that the ECA+SPPFCSPC model (FFDSM) performs the best, with a precision, recall, mAP@0.5, and mAP@0.5:0.95 reaching 0.959, 0.870, 0.907, and 0.711, respectively. The results of the controlled experiment on different fire sizes show that FFDSM outperforms YOLOv5s-seg for all three fire sizes, and it performs the best for small fires, with a precision, recall, mAP@0.5, and mAP@0.5:0.95 reaching 0.989, 0.938, 0.964, and 0.769, respectively, indicating its good adaptability for early forest fire detection. The results indicate that the forest fire detection model based on UAV infrared images (FFDSM) proposed in this study exhibits a high detection accuracy. It is proficient in identifying obscured fires in optical images and demonstrates good adaptability in various fire scenarios. The model effectively enables real-time detection and provides early warning of forest fires, providing valuable support for forest fire prevention and scientific decision making.

Monitoring and quantifying suspended sediment concentrations (SSC) in estuaries such as the Pearl River Estuary (PRE) provide crucial information for environmental processes, hydrological infrastructure, and navigation. Traditional SSC mapping based on in situ investigations lacks the spatial coverage necessitated by detailed analysis. In this study, based on in situ spectral data and SSC measurements, we developed models to quantify SSC based on Landsat TM/OLI and Sentinel-2 imagery. The models were then used to map the SSC distribution in the PRE for the period 1986–2020. The results indicated that SSC in the entire PRE displayed an overall decreasing trend over the past 35 years, but an abnormal increase was also observed in shallow waters near Hengmen. Geographically, the concentration along the west coast is higher than on the east coast, and the central part of Lingding Bay underwent a slight increase. The decreased sediment output from the Pearl River Basin is the major cause of the decreasing changes in SSC. However, the natural factors and human activities such as channel dredging and the construction of artificial facilities can also disturb the spatial distribution of SSC. Regions with a significant decrease in SSC should have special concerns about retaining the PRE’s health and sustainability.