Anthropogenic impacts on widespread global soil moisture (SM) drying in the root zone layer during 1948–2005 were evaluated based on the Global Land Data Assimilation System version 2 (GLDAS‐2) and global climate models from the Coupled Model Intercomparison Project Phase 5 using trend analysis and optimal fingerprint methods. Both methods show agreement that natural forcing alone cannot drive significant SM drying. There is a high probability (≥90%) that the anthropogenic climate change signal is detectable in global SM drying. Specifically, anthropogenic greenhouse gas forcing can lead to global SM drying by 2.1 × 10−3 m3/m3, which is comparable to the drying trend seen in Global Land Data Assimilation System version 2 (2.4 × 10−3 m3/m3) over the past 58 years. Global SM drying is expected to continue in the future, given continuous greenhouse gas emissions.

No abstract

Financial time series analyses have played an important role in developing some of the fundamental economic theories. However, many of the published analyses of financial time series focus on long-term average behavior of a market, and thus shed little light on the temporal evolution of a market, which from time to time may be interrupted by stock crashes and financial crises. Consequently, in terms of complexity science, it is still unknown whether the market complexity during a stock crash decreases or increases. To answer this question, we have examined the temporal variation of permutation entropy (PE) in Chinese stock markets by computing PE from high-frequency composite indies of two stock markets: the Shanghai Stock Exchange (SSE) and the Shenzhen Stock Exchange (SZSE). We have found that PE decreased significantly in two significant time windows, each encompassing a rapid market rise and then a few gigantic stock crashes. One window started in the middle of 2006, long before the 2008 global financial crisis, and continued up to early 2011. The other window was more recent, started in the middle of 2014, and ended in the middle of 2016. Since both windows were at least one year long, and proceeded stock crashes by at least half a year, the decrease in PE can be invaluable warning signs for regulators and investors alike.

No abstract

Social media is a rich data source for analyzing the social impact of hazard processes and human behavior in disaster situations; it is used by rescue agencies for coordination and by local governments for the distribution of official information. In this paper, we propose a method for data mining in Twitter to retrieve messages related to an event. We describe an automated process for the collection of hashtags highly related to the event and specific only to it. We compare our method with existing keyword-based methods and prove that hashtags are good markers for the separation of similar, simultaneous incidents; therefore, the retrieved messages have higher relevancy. The method uses disaster databases to find the location of an event and to estimate the impact area. The proposed method can also be adapted to retrieve messages about other types of events with a known location, such as riots, festivals and exhibitions.

No abstract

This study visualized and analyzed the developing trends and hot topics in natural disaster research. 19694 natural disaster-related articles (January 1900 to June 2015) are indexed in the Web of Science database. The first step in this study is using complex networks to visualize and analyze these articles. CiteSpace and Gephi were employed to generate a countries collaboration network and a disciplines collaboration network, and then attached hot topics to countries and disciplines, respectively. The results show that USA, China, and Italy are the three major contributors to natural disaster research. “Prediction model”, “social vulnerability”, and “landslide inventory map” are three hot topics in recent years. They have attracted attention not only from large countries like China but also from small countries like Panama and Turkey. Comparing two hybrid networks provides details of natural disaster research. Scientists from USA and China use image data to research earthquakes. Indonesia and Germany collaboratively study tsunamis in the Indian Ocean. However, Indonesian studies focus on modeling and simulations, while German research focuses on early warning technology. This study also introduces an activity index (AI) and an attractive index (AAI) to generate time evolution trajectories of some major countries from 2000 to 2013 and evaluate their trends and performance. Four patterns of evolution are visible during this 14-year period. China and India show steadily rising contributions and impacts, USA and England show relatively decreasing research efforts and impacts, Japan and Australia show fluctuating activities and stable attraction, and Spain and Germany show fluctuating activities and increasing impacts.

No abstract