Search Results (3)

Terrestrial gross primary productivity (GPP) is the major carbon input to the terrestrial ecosystem. The Yangtze River Basin (YRB) holds a key role in shaping China’s economic and social progress, as well as in ecological and environmental protection. However, how the GPP in the YRB responds to the climate factors remain unclear. In this research, we applied the Vegetation Photosynthesis Model (VPM) GPP data to explore the spatial and temporal variations of GPP in the YRB during 2000–2018. Based on the China Meteorological Forcing Dataset (CMFD), the partial least squares regression (PLSR) method was employed to identify the GPP responses to changes in precipitation, temperature, and shortwave radiation between 2000 and 2018. The findings showed that the long-term average of GPP in the YRB was 1153.5 ± 472.4 g C m⁻² yr⁻¹ between 2000 and 2018. The GPP of the Han River Basin, the Yibin-Yichang section of the Yangtze River mainstream, and the Poyang Lake Basin were relatively high, while the GPP of the Jinsha River Basin above Shigu and the Taihu Lake Basin were relatively low. A significant upward trend in GPP was observed over the 19-year period, with an annual increase rate of 8.86 g C m⁻² yr⁻¹ per year. The GPP of the Poyang Lake Basin and Jialing River Basin grew much faster than other water resource regions. Savannas and forests also had relatively higher GPP rate of increase compared to other vegetation types. The relative contributions of precipitation, temperature, and shortwave radiation to GPP variations in the YRB were 13.85 ± 13.86%, 58.87 ± 9.79%, and 27.07 ± 15.92%, respectively. Our results indicated that temperature was the main climatic driver on the changes of GPP in the YRB. This study contributes to an in-depth understanding of the variations and climate-impacting factors of vegetation productivity in the YRB. Full article

Landslide susceptibility evaluation (LSE) refers to the probability of landslide occurrence in a region under a specific geological environment and trigger conditions, which is crucial to preventing and controlling landslide risk. The mainstream of the Yangtze River in Yichang City belongs to the largest basin in the Three Gorges Reservoir area and is prone to landslides. Affected by global climate change, seismic activity, and accelerated urbanization, geological disasters such as landslide collapses and debris flows in the study area have increased significantly. Therefore, it is urgent to carry out the LSE in the Yichang section of the Yangtze River Basin. The main results are as follows: (1) Based on historical landslide catalog, geological data, geographic data, hydrological data, remote sensing data, and other multi-source spatial-temporal big data, we construct the LSE index system; (2) In this paper, unsupervised Deep Embedding Clustering (DEC) algorithm and deep integration network (Capsule Neural Network based on SENet: SE-CapNet) are used for the first time to participate in non-landslide sample selection, and LSE in the study area and the accuracy of the algorithm is 96.29; (3) Based on the constructed sensitivity model and rainfall forecast data, the main driving mechanisms of landslides in the Yangtze River Basin were revealed. In this paper, the study area’s mid-long term LSE prediction and trend analysis are carried out. (4) The complete results show that the method has good performance and high precision, providing a reference for subsequent LSE, landslide susceptibility prediction (LSP), and change rule research, and providing a scientific basis for landslide disaster prevention. Full article

The upstream Yangtze River is located in the southwest of central China, where it flows through several ecosystems and densely populated regions that constitute a unique complex coupled system. To determine how the characteristics of supply and demand in a water-coupled system will vary under the influence of climate change and human activity in this area in the next 85 years, the upper Yangtze basin was considered as the study area and was divided into seven sub-basins according to seven main control sections: Shigu, Panzhihua, Xiluodu, Xiangjiaba, Zhutuo, Cuntan, and Yichang; a method for water supply and demand research considering climate change was proposed. Based on simulated runoff in the study area under changing environmental conditions, this study analyzed the available water supply and constructed a long-term water demand forecasting model using the classified water use index method under macro regulation in the study area from 2016 to 2100. The results show that the total water demand in the upstream Yangtze River appears to first increase and then decrease in 2016–2100 and will reach its peak around 2028. The ecological pressure in the upstream Yangtze River increases gradually from upstream to downstream but will not reach the surface water utilization stress threshold (hereinafter referred to as stress threshold) for the next 85 years. The contradiction between monthly supply and demand is more prominent under ecological restrictions. Under the RCP4.5 scenario, water demand exceeds the stress threshold in each sub-basin across several months (mainly March, April, and May), and the water demand nearly reaches the damage threshold in May as the basin extends below the Zhutuo section. Full article