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As rural decline sweeps the world and draws public attention, it becomes necessary to consider the relationship between urbanization and rural development. The paper investigates this topic in China for the period 1989–2018. The research results find positive contribution of urbanization to rural economy in the research period but with a decreasing tendency. Compared to less developed areas, rural economy of developed regions has become less dependent on cities and can flourish in a larger context with the support of modern transportation and telecommunication technologies. However, those peripheral rural areas tend to face continuous and large‐scale rural–urban migration which has aggravated rural decline. Rural economic growth helps to decrease rural outmigration and people will stay in their hometown with available job opportunities to raise their families and maintain livelihood. The paper proposes balance‐friendly urbanization structure and diversified rural economy to improve rural resilience to meet the challenge of rural decline. [ABSTRACT FROM AUTHOR]

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This paper illustrates the advantages of a multilevel/hierarchical approach for predictive modeling, including flexibility of model formulation, explicitly accounting for hierarchical structure in the data, and the ability to predict the outcome of new cases. As a generalization of the classical approach, the multilevel modeling approach explicitly models the hierarchical structure in the data by considering both the within- and between-group variances leading to a partial pooling of data across all levels in the hierarchy. The modeling framework provides means for incorporating variables at different spatiotemporal scales. The examples used in this paper illustrate the iterative process of model fitting and evaluation, a process that can lead to improved understanding of the system being studied. [ABSTRACT FROM AUTHOR]

This study investigates the impact of four-dimensional data assimilation (FDDA) on urban climate analysis, which employs the NCAR (National Center for Atmospheric Research) WRF (the weather research and forecasting model) based on climate FDDA (CFDDA) technology to develop an urban-scale microclimatology database for the Shenzhen area, a rapidly developing metropolitan located along the southern coast of China, where uniquely high-density observations, including ultrahigh-resolution surface AWS (automatic weather station) network, radio sounding, wind profilers, radiometers, and other weather observation platforms, have been installed. CFDDA is an innovative dynamical downscaling regional climate analysis system that assimilates diverse regional observations; and has been employed to produce a 5 year multiscale high-resolution microclimate analysis by assimilating high-density observations at Shenzhen area. The CFDDA system was configured with four nested-grid domains at grid sizes of 27, 9, 3, and 1 km, respectively. This research evaluates the impact of assimilating high-resolution observation data on reproducing the refining features of urban-scale circulations. Two experiments were conducted with a 5 year run using CFSR (climate forecast system reanalysis) as boundary and initial conditions: one with CFDDA and the other without. The comparisons of these two experiments with observations indicate that CFDDA greatly reduces the model analysis error and is able to realistically analyze the microscale features such as urban-rural-coastal circulation, land/sea breezes, and local-hilly terrain thermal circulations. It is demonstrated that the urbanization can produce 2.5 k differences in 2 m temperatures, delays/speeds up the land/sea breeze development, and interacts with local mountain-valley circulations. [ABSTRACT FROM AUTHOR]

Knowledge of temporal change in ecological condition is important for the understanding and management of ecosystems. However, analyses of trends in biological condition have been rare, as there are usually too few data points at any single site to use many trend analysis techniques. We used a Bayesian hierarchical model to analyse temporal trends in stream ecological condition (as measured by the invertebrate-based index SIGNAL) across Melbourne, Australia. The Bayesian hierarchical approach assumes dependency amongst the sampling sites. Results for each site “borrow strength” from the other data because model parameter values are assumed to be drawn from a larger common distribution. This leads to robust inference despite the few data that exist at each site. Utilising the flexibility of the Bayesian approach, we also modelled change over time as a function of catchment urbanisation, allowed for potential temporal and spatial autocorrelation of the data and trend estimates, and used prior information to improve the estimate of data uncertainty. We found strong evidence of a widespread decline in SIGNAL scores for edge habitats (areas of little or no flow). The rate of decline was positively associated with catchment urbanisation. There was no evidence of such declines for riffle habitats (areas with rapid and turbulent flow). Melbourne has experienced a decline in rainfall, indicative of either drought and/or longer-term climate change. The results are consistent with the expected coupled effects of these rainfall changes and increasing urbanisation, but more research is needed to isolate a causal mechanism. More immediately, however, the Bayesian hierarchical approach has allowed us to identify a pattern in a biological monitoring data set that might otherwise have gone un-noticed, and to demonstrate a large-scale temporal decline in biological condition. [ABSTRACT FROM AUTHOR]