Xiangyang Zhang

Climate change and urbanization are altering urban flood pressure, raising questions about whether urban adaptive capacity can keep pace with these changes. This study examined flood pressure, adaptive capacity, and their coordination across 14 cities in the core development area of the Central Plains Urban Agglomeration, China, from 2016 to 2023. The entropy weight method, coupling coordination degree model, and four-quadrant classification were integrated to assess spatiotemporal patterns and transitions in pressure–adaptation combinations. The regional flood pressure index increased overall from 0.2549 in 2016 to 0.3028 in 2023, with fluctuations, while the adaptive capacity index rose steadily from 0.2730 to 0.5293. The regional mean coupling coordination degree increased from 0.5917 to 0.7626, and intercity differences narrowed. Pressure–adaptation combinations shifted from a predominance of low-adaptation types toward high-adaptation types: 13 of 14 cities were classified as low-adaptation types in 2016, whereas all 14 were in high-adaptation categories by 2023, including nine high-pressure–high-adaptation and five low-pressure–high-adaptation cities. These findings provide a scientific basis for differentiated urban flood management and adaptive capacity planning.

Introduction: Climatic change and anthropogenic activities have substantially influenced vegetation distribution in recent decades. However, identifying the dominant driving factors of vegetation variation remains challenging. Methods: This study investigated vegetation dynamics and quantified their responses to climatic factors (effective precipitation (EFPR) and active accumulated temperature ≥10 °C (ACTE)) and anthropogenic activities (urbanization, afforestation, and cultivation) for the Yellow River Basin (YRB). Results: The results showed that the YRB experienced vegetation greening in NDVI (as measured by normalized difference vegetation index, NDVI) during the period from 2001 to 2020. More than 90% of the regions in the YRB showed an increasing NDVI trend, with mean rates of 0.055 per decade. In regions with significant ( p < 0.05) variations in NDVI, ERPE, ACTE, and anthropogenic activities contributed to vegetation dynamics at rates of 0.012 per decade, 0.007 per decade, and 0.036 per decade, respectively. Contributions of climatic and anthropogenic factors accounted for 35% and 65% of the total NDVI variations, respectively. Discussion: Both climatic and anthropogenic factors drove the vegetation growth. In the alpine source regions of the Yellow river, climatic factors were the primary drivers of significant NDVI change. In the MB_YRB (middle basin) and the LB_YRB (lower basin), human activities were the main factors driving vegetation greening. Only in areas with urban agglomeration, such as Xi’an, Zhengzhou and Xi’ning cities, were anthropogenic activities associated with vegetation browning.

Deep learning models are widely used for urban flood prediction, but current research lacks a clear explanation of how indicator weight changes affect model accuracy. This study incorporated the attention mechanism between the convolutional and fully connected layers of the convolutional neural network (CNN) to enable the model to focus on critical flood-inducing factors, and employed the particle swarm optimization (PSO) to optimize the key hyperparameters (for example, the number of filters and learning rate). Furthermore, we employed Shapley additive explanation (SHAP) to analyze how flood-inducing indicator weight changes affect prediction accuracy. The model was tested on Haidian Island, China. The Nash-Sutcliffe efficiency (NSE) coefficient of the CNN model is 0.9287. After incorporating the attention mechanism into the CNN and optimizing the hyperparameters using PSO, the NSE is improved to 0.9503. The model demonstrates higher accuracy in predicting larger inundations, with the NSE for the 100-year return-period flood reaching 0.9535, compared to 0.8341 for the 5-year return period. Interpretability analysis shows that elevation is the most important flood-inducing factor, accounting for 44% of the total importance, followed by tidal levels, which account for 33%. The attention mechanism increases the weights of important flood-inducing factors (for example, elevation, tide level); after hyperparameter optimization, the model achieves more comprehensive learning, increasing the weights of the rainfall indicators that are neglected by the unoptimized model, and these weight changes improve the accuracy of the model. The research revealed the impacts of different flood-inducing factors on flooding and the influence of indicator weight changes on model accuracy.

With accelerating climate change, droughts have increased in frequency and exerted a substantial influence on socioeconomic factors. Under conditions of insufficient precipitation and high temperatures, meteorological droughts have the potential to develop into more intense hydrological droughts, and the independent impact of temperature factors on drought propagation has not been considered separately. This study constructed a Standardized Temperature Index (STI) and, combined with time-series datasets of standardized indices of precipitation and runoff (SPI and SRI), based on Bayesian network principles, analyzed the probabilistic characteristics of drought propagation from meteorology to hydrology due to the influence of single or dual factors in the Yiluo River Basin (1961–2020). It also explored the transmission mechanisms of temperature and precipitation that drive and affect meteorological and hydrological drought. The results showed that propagation of meteorological to hydrological droughts increased with rising temperatures, and the propagation probability to severe and extreme hydrological drought increased by approximately 5%. Under the most adverse circumstances (high temperature and precipitation shortage scenarios), the likelihood of meteorological droughts progressing into intense hydrological drought events rose to 80%. Increasing temperature is expected to lead to more severe hydrological droughts. This study offers a theoretical foundation for drought prevention and mitigation.