Rapid urbanization and intensification of human activities increases the risk of disturbance of ecological systems via multiple sources, with consequences for regional ecological security and health. Landscape ecological risk assessment (LERA) is an effective way to identify and allocate risk to resources. We used the north and south Qinling Mountain area as a case study to analyze the spatial heterogeneity of landscape ecological risk using a potential- connectedness-resilience three-dimensional (PCR 3D) framework based on an integrated and dynamic risk assessment concept from adaptive cycle theory. We explored factors driving the risks with a spatial model GeoDetector. The results show that the comprehensive landscape ecological risk was north–south polarized and dominated by low and moderate risk levels (90.13% of total risk) across the whole study area. The high-risk area was centered on the Weihe plain north of the Qinling Mountains (NQL), while low-risk areas accounted for 86.87% of the total area and were prevalent across the south of the study area. The areas with high potential and connectedness risks were centered in the Xi’an–Xianyang urban agglomeration and those with high-resilience risk were in the upper reaches of the Hanjiang River. The vast majority of the area to the south of the Qinling Mountains (SQL) is at low risk. In terms of driving forces, population density and vegetation coverage (NDVI) are the primary factors affecting landscape ecological risk. Our findings suggest that anthropogenic activity is the primary cause of landscape ecological risks in the study area and regional socioeconomic exploitation and environmental conservation need to be rebalanced to achieve sustainability for the social ecosystem. The PCR 3D LERA framework employed in this study can be used to inform landscape ecological health and security and to optimize socioeconomic progress at regional scales.

Urban agglomeration is the core area of not only regional economic development and urbanization but also human–land contradiction. Based on the population–economy–society–spatial model and the pressure–state–response model, this study develops an evaluation index system for the urbanization and eco-environment of 13 urban agglomerations in China. The urbanization index and eco-environment index are determined using the coefficient of variation method. The coupled coordination of the two indices is measured with the coupled coordination model, and the influence of the indicator factors is calculated with a geographical detector. The results reveal the following: (1) The urbanization indices of the 13 urban agglomerations in China increase year by year, and the spatial distribution pattern is high in the east and low in the west. (2) The eco-environment index shows an “S-shaped” variation trend, and the spatial difference between urban agglomerations gradually decreases. (3) The coupled coordination is mainly characterized as a mild imbalance, and the spatial distribution pattern is “balance in the east and imbalance in the west”. (4) The coupled coordination degree between urbanization and eco-environment is affected by many factors. Among such factors, economic urbanization, social urbanization, spatial urbanization, and eco-environment response are the main controlling factors. The impact of population urbanization, eco-environment pressure, and eco-environment state presents a gradual increase.

In this study, we investigated the spatial and temporal characteristics of precipitation heterogeneity and meteorological drought/flood in China based on the precipitation-concentration degree (PCD) and the integrated meteorological drought index. We also studied the corresponding relationship between precipitation heterogeneity and meteorological drought/flood in China by using Spearman correlation analysis and canonical correlation analysis. The results show that: (1) The severity of meteorological drought/flood exhibited a spatial pattern of gradual change from Northwest to Southeast China. (2) With a higher PCD and a delayed precipitation-concentration period (PCP), the drought severity was higher but the flood severity was lower. In contrast, with a lower PCD and an early PCP, the drought severity was lower and the flood severity was higher. (3) The correlation between meteorological drought/flood and PCD was significant. The higher the PCD, the longer the duration of drought and more frequently, the droughts occurred, and vice versa. It is concluded that PCD and PCP were significantly correlated with meteorological drought/flood in China.

Climate change can have critical impacts on ecosystem services (ESs) and their inter-relationships, especially for water-related services. However, there has been little work done on characterizing the current and future changes in these services and their inter-relationships under a changing climate. Based on the revised universal soil loss equation (RUSLE), the soil conservation service curve number model (SCS-CN), and the improved stochastic weather-generator-based statistical downscaled global climate models (GCMs), we examined two important water-related services, namely, the soil conservation (SC) service and the flood mitigation (FM) service, and their inter-relationship under baseline and future climate scenarios (Representative Concentration Pathways (RCPs) 4.5 and 8.5). We took the Upper Hanjiang River Basin (UHRB), which is the core water source area of the China’s South-to-North Water Diversion Project (S–NWDP), as an illustration. The findings revealed that (1) the SC and FM services will both decrease under the two climate scenarios examined; (2) the SC and FM services showed a significant synergistic inter-relationship and the synergy will be improved by 16.48% and 2.95% under RCP 4.5 and RCP 8.5, respectively, which provides an opportunity for management optimization; (3) the ecological degradation in the UHRB will likely have serious consequences for the middle and lower reaches of the Hanjiang river basin, and therefore impact the actual economic benefits of the S–NWDP. This study points to the necessity for understanding the dynamic changes and inter-relationships of ecosystem services under future climate change and provides information regarding the consequences of climate change, which is useful for policy and infrastructure investment.

Strengthening research on urban tourism competitiveness is vital in evaluating the current situation and potential of urban tourism, maintaining the sustainable development of the tourism economy and assisting in the regional macro decision making. In this study, an index system evaluation of urban tourism competitiveness in city agglomerations across the Guanzhong Plain is established by collecting cross-section data from the years 2017 and 2010. The entropy value method is adopted to determine the index weight. Cluster analysis is performed and the spatial-temporal pattern and evolution laws of urban tourism competitiveness among city agglomerations in the Guanzhong Plain are analyzed and the geographic detector utilized to discuss the influencing factors. Results show that the spatial gradient difference of urban tourism competitiveness of agglomerations in the Guanzhong Plain is significant. In 2010, it presented the characteristic of ‘the high and middle levels having a zonal distribution from east to west, and the low level was distributed along the north and south wings’. In 2017, the characteristic of ‘polarization’ became highly prominent, that is, the scope of high-level and low-level cities expanded and the scope of medium-level cities decreased. Urban tourism competitiveness in city agglomerations across the Guanzhong Plain exhibited a trend of ‘strengthening in the east, weakening in the west’. The competitiveness of resources and management shifted aggressively and supporting factors competitiveness underwent a slight change. The urban tourism competitiveness of city agglomerations in the Guanzhong Plain is generally low, while the urban tourism competitiveness of Xi’an had an absolute advantage in city agglomerations of the Guanzhong Plain. According to the cluster analysis results, resources and management competitiveness, supporting factors competitiveness, demand conditions competitiveness, situational conditions competitiveness and urban tourism competitiveness of Xi’an in 2010 and 2017 were all at an extremely high level, which was relatively higher than the index values of other cities in the city agglomerations of the Guanzhong Plain. Tourism resources, service support capacity, infrastructure support capacity, tourism income scale, tourism reception scale and economic development power are the core influencing factors of urban tourism competitiveness among city agglomerations in the Guanzhong Plain. The single factor explanatory power of destination management indicates a downward trend while the single factor explanatory power of the ecological environment condition shows an upward trend. Tourism resources are the leading interactive factor of urban tourism competitiveness, and destination management and ecological environment condition are the most significant indicators for the collaborative effect.