Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address the possible threats posed by changing environments. On the basis of the kernel normalized difference vegetation index (kNDVI) and key climate drivers (precipitation (PRE), temperature (TEM), solar radiation (SR), and potential evapotranspiration (PET)) in the basin during the period from 1982 to 2022, we utilized the multivariate statistical approach to analyze the spatiotemporal patterns of vegetation dynamics, identified the key climate variables, and discerned the respective impacts of climate change (CC) and human activities (HA) on these variations. Our analysis revealed a widespread greening trend across 93.1% of the YRB, with 83.2% exhibiting significant increases in kNDVI (p < 0.05). Conversely, 6.9% of vegetated areas displayed a browning trend, particularly concentrated in the alpine and urban areas. With the Hurst index of kNDVI exceeding 0.5 in 97.5% of vegetated areas, the YRB tends to be extensively greened in the future. Climate variability emerges as a pivotal determinant shaping diverse spatial and temporal vegetation patterns, with PRE exerting dominance in 41.9% of vegetated areas, followed by TEM (35.4%), SR (13%), and PET (9.7%). Spatially, increased PRE significantly enhanced vegetation growth in arid zones, while TEM and SR controlled vegetation variations in alpine areas and non-water-limited areas such as irrigation zones. Vegetation dynamics in the YRB were driven by a combination of CC and HA, with relative contributions of 55.8% and 44.2%, respectively, suggesting that long-term CC is the dominant force. Specifically, climate change contributed to the vegetation greening seen in the alpine region and southeastern part of the basin, and human-induced factors benefited vegetation growth on the Loess Plateau (LP) while inhibiting growth in urban and alpine pastoral areas. These findings provide critical insights that inform the formulation and adaptation of ecological conservation strategies in the basin, thereby enhancing resilience to changing environmental conditions.

Most cities in the world have extensive underground facilities, including public transport and commercial facilities, such as shopping malls, subways, and parks. Safety of underground space has been an important aspect of urban safety. Due to the influences of global climate change and human activities, waterlogging disasters in cities are becoming increasingly serious and underground facilities easily suffer from waterlogging disasters. Moreover, waterlogging disasters in cities can cause different degrees of damage to construction period and operating period of urban subway stations. By using the optimal combination weighting method combining subjective and objective weighting, this study assigned weights to evaluation factors. Based on this, a fuzzy synthetic evaluation model for waterlogging risk in the construction and operating periods of urban subway stations was established. Furthermore, the model was applied and verified to be effective in Chengdu Metro Line 4, Sichuan province, China, and the evaluation results coincided with the actual situation. The model in the study provides a new idea for evaluating waterlogging risks in urban subway stations, and these results can offer important information for local government to strengthen management on waterlogging risks.

Accurate assessment of agricultural drought risk is of strategic significance to ensure future grain production security in the main grain production areas of China. Agricultural drought risk assessment is based on drought vulnerability characteristics. In this study, firstly the drought thresholds were redefined by correlation analysis of drought strength based on the Standardized Precipitation Evapotranspiration Index (SPEI) and drought damage rates, then the information distribution and the two-dimensional normal information diffusion method were employed to establish the vulnerability curve between drought strength and drought damage rates. Finally, provincial drought risks and the conditional probabilities at different drought damage stages were obtained. The results show that the drought vulnerability curve was nonlinear. With the increase of drought strength, drought damage rates increased rapidly at the beginning, and after a small fluctuation locally, they no longer increased significantly and tended to be relative stable. The occurrence probabilities of agricultural drought risk presented great spatial differences, with the characteristics of high in the northern, moderate in the central and southwestern part, and lower in the southeastern provinces in the main grain production areas of China. The analysis of conditional probability showed that Hubei, Henan, and Jiangxi were the provinces most prone to drought-affected risk under the drought-induced condition; while Liaoning, Hunan, and Inner Mongolia were the ones most prone to lost harvest risk under the drought-induced or the drought-affected condition. The results could be used to provide guidance for drought risk management and to formulate appropriate plans by the relevant departments.

Collapse accidents happen frequently during the construction of metro lines, of which the water environment is the most important cause. Therefore, it is necessary to effectively monitor and predict the soil settlement of subways under heavy rainfall conditions. Taking Caiqiao Station as an example, the article first obtains the surface settlement data of the subway before and after 24 h of rainstorm conditions through on‐site monitoring and then predicts the soil settlement of the subway after 24 h of rainstorm conditions through Flac3D. Sensitivity analysis was conducted on the key influencing factors, and it was concluded that the deviation between the simulated values and the monitored values falls within a reasonable uncertainty range. Combined with the Matlab corrcoef function to calculate the linear correlation between the two groups of settlement results, the similarity is 0.9786 in the station area and 0.9688 in the zone area, and the trends of settlement curves are basically the same. Comparison results prove that numerical simulation can predict the subway soil settlement under heavy rainfall and take corresponding measures to effectively reduce the incidence of subway collapse accidents.

Accurate prediction of barge impact force contributes greatly to the research of the soundness of the bridge under barge impact. Toward this end, the calculation method of barge impact force caused by ship-bridge collision is investigated in this study. Firstly, a comparative analysis for different energy conversion relationships is conducted via the high-precision finite element method (HPFEM) and the coupled vessel impact analysis (CVIA). A construction method of nonlinear spring stiffness (NSS) is proposed therefrom. Secondly, a high-precision finite element model is developed to simulate the barge-bridge collision under different barge masses and impact velocities. NSS under each condition is obtained, and the influences of barge mass and impact velocity on NSS are further discussed. An NSS model considering the effects of impact mass and impact velocity is proposed. Lastly, based on the NSS model, a prediction method of barge impact force is introduced. The results show that the impact mass has little influence on the peak value of NSS; however, the impact mass and impact velocity have a great influence on the tail of the NSS curve. According to the characteristics of NSS, four key control points can be extracted and NSS is simplified into a polyline model. The NSS model of the barge is obtained by nonlinear response surface fitting. The impact force can be predicted by combining the simplified NSS model and the proposed method in this study. The proposed method in this paper does not consider the pier stiffness; hence, it can be combined with NSS to quickly estimate the impact force, which can provide a design basis for the impact force in ship-bridge collision design. The rigid wall and a continuous rigid frame bridge impacted by the barge are studied; the results show that the proposed method can reasonably estimate the impact force.

The rapid and accurate acquisition of crop planting spatial location information using remote sensing is one of the important guarantees to maintain the sustainable development of agriculture. However, the accuracy of crop identification by remote sensing is currently limited by many factors, such as the influence of other ground objects and the lack of time-series data. To overcome the above problems, this paper proposes an algorithm named improved artificial immune network crop recognition algorithm based on dispersed vegetation index genetic chain (IaiNet). This algorithm can be combined with multi-spectral data from Sentinel-2 series satellites for crop identification. As a test case, we identified and evaluated 3 different crop recognition scenarios in Henan, China. The results show that IaiNet can accurately identify the spatial distribution of crop planting. In all identification results, the accuracy is higher than 90%, and the kappa coefficient is greater than 0.9. In addition, the crop recognition results of IaiNet are significantly better than the random forest algorithm and support vector machine algorithm.