Trends in Extremes of Surface Humidity, Temperature, and Summertime Heat Stress in China

In the past half century, the mean summertime temperature in China has increased, with nights warm- ing more than days. Using surface station observations, we show that the frequency of extreme heat-stress events in China, caused by extremely hot and humid days as well as by heatwaves lasting for a few days, has increased over the period from 1951 to 1994 When humidity is high, hot weather can cause heat stress in humans. The increased heatrstress trend may pose a public health problem

A method of detecting emerging trends in research topics

Purpose:This paper proposes a method of detecting emerging trends in research topics from a more micro perspective.Design/methodology/approach:Through co-word clustering to identify research topics and analyzing position changes of topic words in the keywords life-cycle diagram during different time periods,we detected emerging trends in research topics from a more micro perspective.I'he method was applied to the field of nanotechnology to verify its effectiveness and practicability.Findings:The results show that this method can be used to detect emerging trends in research topics from a more micro perspective,as it divides keywords into five categories:Potential keywords,emerging keywords,hot keywords,stable keywords and recession keywords,through which more details of topic changes can be found.Research limitations:We used keywords provided by authors and database indexers for keywords extraction.But this approach may lead to the problem of 'indexer effect'.The method may have a limited effect when applied to a disciplinary domain such as mathematics,which evolves slowly.Practical implications:This study provides information analysts with insights into the way to better understand specialty areas of a discipline domain and formulate research policies and strategic plans.Originality/value:This study contributes to the current literature by proposing a new method,which can detect emerging trends in research topics from a more micro perspective.

Trend detection and stochastic simulation prediction of streamflow at Yingluoxia hydrological station, Heihe River Basin, China

Investigating long-term variation and prediction of streamflow are critical to regional water resource management and planning. Under the continuous influence of climate change and human activity, the trends of hydrologic time series are nonstationary, and consequently the established methods for hydrological frequency analysis are no longer applicable. Five methods,including the linear regression, nonlinear regression,change point analysis, wavelet analysis and HilbertHuang transformation, were first selected to detect and identify the deterministic and stochastic components of streamflow. The results indicated there was a significant long-term increasing trend. To test the applicability of these five methods, a comprehensive weighted index was then used to assess their performance. This index showed that the linear regression was the best method. Secondly,using the normality test for stochastic components separated by the linear regression method, a normal distribution requirement was satisfied. Next, the Monte Carlo stochastic simulation technique was used to simulate these stochastic components with normal distribution, and thus a new ensemble hydrological time series was obtained by combining the corresponding deterministic components. Finally, according to these outcomes, the streamflow at different frequencies in 2020 was predicted.