Urban agglomeration has become a crucial topic in order for the Chinese government to promote new-type urbanization in China, and its urbanization will greatly affect China’s eco-environment. Existing literature on bidirectional influence between urbanization and eco-environment from the perspective of urban agglomeration is, however, limited. This study establishes a conceptual framework to identify bidirectional relationships between urbanization and eco-environment in urban agglomerations. After evaluating urbanization level and eco-environment quality for each city in an urban agglomeration, this framework determines key interaction factors, and employs a global regression approach to quantify the coercing effects of urbanization on eco-environment and constraining effects of eco-environment on urbanization. Spatial heterogeneity of bidirectional interactions is then examined using local regression, represented by geographically weighted regression. The case study in the urban agglomeration in the middle reaches of the Yangtze River from 2000 to 2015 indicated the existence of bidirectional interactions and coercing threat that was stronger than constraining pressure in this region. The coercion that urbanization posed on the eco-environment began to vary in space significantly from 2010, whereas the constraint of eco-environment on urbanization was spatially stationary. This study will help policy-makers to develop sustainable policies to balance urban development and eco-environment conservation.

Temperature forecasting is a crucial part of climate change research. It can provide a valuable reference, as well as practical significance, for understanding the macroscopic evolutionary processes of regional temperature and for promoting sustainable development. This study presents a new integrated model, called the Variational Mode Decomposition-Autoregressive Integrated Moving Average (VMD-ARIMA) model, which reduces the required data input and improves the accuracy of predictions, based on the deficiencies of data dependence and the complicated mechanisms associated with current temperature forecasting. In this model, the variational mode decomposition (VMD) was used for mining the trend features and detailed features contained in a time series, as well as denoising. Moreover, the corresponding autoregressive integrated moving average (ARIMA) models were derived to reflect the different features of the components. The final forecasted values were then obtained using VMD reconstruction. The annual temperature time series from the Wuhan Meteorological Station were investigated using the VMD-ARIMA model, ARIMA model, and Grey Model (1, 1) based on three statistical performance metrics (mean relative error, mean absolute error, and root mean square error). The results indicate that the VMD-ARIMA model can effectively enhance the accuracy of temperature forecasting.

The study of industrial spatial linkages of urban agglomerations is crucial to recognizing spatial structure and optimizing regional division and cooperation. The existing studies often focus on external spatial interaction at the inter-city level, but few have considered complex internal economic linkages at the inter-sector level. In this study, we established an integrated framework by combining the wave effect gradient field with the gravity model. The wave effect gradient field was used to analyze the inter-sector relation, while the gravity model was adopted to explore the spatial interactions of industry at the inter-city level. The Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR) was taken as a case study, which demonstrates the applicability of the proposed framework. The results indicate that there exists an imbalanced development in the network of industrial linkages in the study region. Each subgroup has presented a self-organized spatial linkage network, but the linkages between subgroups are immature. Compared with other sectors, the high-tech and internet industrial sectors contribute most to economic linkages among cities. Thus, policymakers should take actions to strengthen the inter-subgroup spatial linkages and give priority to the high-tech industries, which is necessary for the integrated and sustainable development of UAMRYR.

The spatiotemporal variability of droughts over a region can often be characterized by combining orthogonal climate modes with corresponding temporal coefficients. The temporal characteristics of climate modes have been extensively addressed, while their spatial development remains largely unexplored. Hence, this study investigated the spatiotemporal evolution of meteorological droughts from the perspective of climate modes. Nearly 50 years of monthly precipitation records (1960–2007) from 73 meteorological stations in the Poyang Lake basin, China, were used. The standardized precipitation index at a 1-month scale was applied to quantify meteorological droughts. The dominant modes of the droughts over the basin were identified using principal component analysis, K-means cluster analysis, and analysis of variance. Based on the trajectory migration identification method, the role of the climate modes in spatiotemporal evolution was analyzed. The results showed that four spatial modes of the droughts in the basin were identified. The spatial extents, centroids, and severity of the drought clusters based on the identified modes were significantly consistent with those based on the meteorological stations (R² > 0.8, p < 0.05), which indicated that these modes could mostly match the large areas where serious dry/wet conditions occurred. Moreover, their performances in characterizing the spatiotemporal evolutional features (severity, migration distance, and pattern) of drought events were valid, which indicated that they might be considered as the elementary constituents of the historical meteorological drought events across the basin. The findings might offer some implications to understand drought development and causes through possible connections between the dominant modes and climate variability.

Drought is a common and greatly influential natural disaster, yet its reliable estimation and prediction remain a challenge. The object of this paper is to investigate the spatiotemporal evolution of drought in the Yangtze River basin. The multi-time scale drought characteristics were analyzed based on 19 models and 3 emission scenarios of CMIP6. The results show that the CMIP6 model generally has moisture deviation in the Yangtze River basin, but the accuracy has been improved after correction and ensemble. The drought conditions in the near future (2030–2059) of the Yangtze River basin will be more severe than those in the historical period (1981–2010), with the drought intensity increasing by 7.47%, 18.24%, 18.34%, and 41.48% in the order of 1-month, 3-month, 6-month, and 12-month scales, but it will be alleviated in the far future (2070–2099) to 5.97%, 11.86%, −4.09%, and −8.97% of the historical period, respectively. The 1-month scale drought events are few, and the spatial heterogeneity is strong under different scenarios; areas of high frequency of the 3-month, 6-month, and 12-month scale drought events shift from the upper and middle reaches, middle and lower reaches in the historical period to the southwestern part of the entire basin in the future, and the harm of drought in these regions is also higher. The Yangtze River basin will get wetter, and the variability will increase in the future. The larger the time scale is, the more intense the change will be, with the 12-month scale varying about three times as much as the 1-month scale.