A complex network approach to study the extreme precipitation patterns in a river basin

Motion states identification of underwater glider based on complex networks and graph convolutional networks

Underwater glider (UG) plays an important role in ocean observation and exploration for a more efficient and deeper understanding of complex ocean environment. Timely identifying the motion states of UG is conducive for timely attitude adjustment and detection of potential anomalies, thereby improving the working reliability of UG. Combining limited penetrable visibility graph (LPVG) and graph convolutional networks (GCN) with self-attention mechanisms, we propose a novel method for motion states identification of UG, which is called as visibility graph and self-attention mechanism-based graph convolutional network (VGSA-GCN). Based on the actual sea trial data of UG, we chose the attitude angle signals of motion states related sensors collected by the control system of UG as the research object and constructed complex networks based on the LPVG method from pitch angle, roll angle, and heading angle data in diving and climbing states. Then, we build a self-attention mechanism-based GCN framework and classify the graphs under different motion states constructed by a complex network. Compared with support vector machines, convolutional neural network, and GCN without self-attention pooling layer, the proposed VGSA-GCN method can more accurately distinguish the diving and climbing states of UG. Subsequently, we analyze the variation of the transitivity coefficient corresponding to these two motion states. The results suggest that the coordination of the various sensors in the attitude adjustment unit during diving becomes closer and more efficient, which corresponds to the higher network measure of the diving state compared to the climbing state.

A modified extreme event-based synchronicity measure for climate time series

Extreme event-based synchronicity is a specific measure of similarity of extreme event-like time series. It is capable to capture the nonlinear interactions between climatic extreme events. In this study, we proposed a modified extreme event-based synchronicity measure that incorporates two types of extreme events (positive and negative) simultaneously in climate anomalies to characterize the synchronization and time delays. Statistical significance of the modified extreme event synchronization measure is tested by Monte-Carlo simulations. The applications of the modified extreme event-based synchronicity measure on synthetic time series verified that it was superior to the traditional event synchronicity measure. Both synchronous and antisynchronous features between climate time series could be captured by the modified measure. It is potentially applied in investigating the interrelationship between climate extremes and climate index or constructing complex networks of climate variables. In addition, this modified extreme event-based synchronicity measure could be easily applied to other types of time series just by identifying the extreme events properly.

Climate indices and hydrological extremes: Deciphering the best fit model

The present work comprehensively reviews all the pertinent large-scale climate indices used to analyse the hydrological extremes in India; along with various non-linear models, which have utilized long-term past precipitation data, and global climate indices to produce forecasts at different temporal scales. We specifically enumerated various statistical operations that may provide better precision at modelling efficiency. Further, in the quest to discover the best-fit modelling technique for the Indian scenario, we compared various modelling techniques applied to decipher hydroclimatic tele-connections between extreme hydrological variables and the large-scale climate indices. Our analyses suggest that the global atmospheric phenomena have performed better than the traditional geospatial models pertaining to the accurate prediction of precipitation extremes for India. We also confirmed that the use of large-scale climate indices to predict the local scale hydrological dynamics had been steadily increasing owing to the advantage associated with it. We conclude that wavelet-based non-linear models are a better fit, and large-scale climate indices based hydrological extremes prediction is an essential requirement for deciphering the esoteric nature of the Indian monsoon. The present work aims to contribute towards efficient water resources management under the pre-text of Indian hydrological extremes, which will be crucial and critical day by day for boosting Indian rain-dependent agriculture, as well as water supply and security.