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

The quantification of spatial propagation of extreme precipitation events is vital in water resources planning and disaster mitigation. However, quantifying these extreme events has always been challenging as many traditional methods are insufficient to capture the nonlinear interrelationships between extreme event time series. Therefore, it is crucial to develop suitable methods for analyzing the dynamics of extreme events over a river basin with a diverse climate and complicated topography. Over the last decade, complex network analysis emerged as a powerful tool to study the intricate spatiotemporal relationship between many variables in a compact way. In this study, we employ two nonlinear concepts of event synchronization and edit distance to investigate the extreme precipitation pattern in the Ganga river basin. We use the network degree to understand the spatial synchronization pattern of extreme rainfall and identify essential sites in the river basin with respect to potential prediction skills. The study also attempts to quantify the influence of precipitation seasonality and topography on extreme events. The findings of the study reveal that (1) the network degree is decreased in the southwest to northwest direction, (2) the timing of 50th percentile precipitation within a year influences the spatial distribution of degree, (3) the timing is inversely related to elevation, and (4) the lower elevation greatly influences connectivity of the sites. The study highlights that edit distance could be a promising alternative to analyze event-like data by incorporating event time and amplitude and constructing complex networks of climate extremes.

Disentangling increasing compound extremes at regional scale during Indian summer monsoon.

Compound extremes exhibit greater adverse impacts than their univariate counterparts. Studies have reported changes in frequency and the spatial extent of extremes in India; however, investigation of compound extremes is in the infancy state. This study investigates the historical variation of compound dry and hot extremes (CDHE) and compound wet and cold extremes (CWCE) during the Indian summer monsoon period from 1951 to 2019 using monthly data. Results are analyzed for 10 identified homogeneous regions for India. Our results unravelled that CDHE (CWCE) frequency has increased (decreased) by 1-3 events per decade for the recent period (1977-2019) relative to the base period (1951-1976). Overall, the increasing (decreasing) pattern of CDHE (CWCE) is high across North-central India, Western India, North-eastern India and South-eastern coastlines. Our findings help in identification of the parts of the country affected by frequent and widespread CDHE during the recent period, which is alarming. More detailed assessments are required to disentangle the complex physical process of compound extremes to improve risk management options.

ZrxCa30-xP70 thermoluminescent bio glass, structure and elasticity.

Phosphate glasses of calcium oxide have been well proved materials for various bio bones and dental implants. However, still there is a lot of scope and demand to produce efficient elastic bio implants and resource. In view of this, ZrxCa30-xP70 phosphate materials are prepared by using melt quenching method. Bio, physical, thermoluminescence and elastic techniques are used to characterize the samples. Additionally, simulated body fluid was prepared and it is used especially for bio techniques. Further, the glasses are taken for different dose (~0, 10, 20 & 50 kGy) of gamma irradiation around half an hour. And again similar techniques are used to characterize the samples. All the findings from bio, physical, thermoluminescence and elastic characterization results are analysed and took for better comparison with previous studies to develop various bio bone (or) bio dental resource. Structural reports suggests that the ZrxCa30-xP70 materials were glassy before immersion in SBF solution and immersed (~720 h) samples are showing partial ceramic nature. The weight loss and pH reports suggests them for alternative bio resource as a bio bones and dental implants. Observed thermal stability, microhardness and elastic modulus evaluations of ZrxCa30-xP70 materials in required standards are also additional advantage. Furthermore, thermoluminiscence (TL) under different γ-irradiation doses is reported for glasses with and without immersing in a simulated body fluid. The glasses lose TL intensity when immersed in simulated body fluid for nearly 720 h. This is useful to modulate bio-behaviour in terms of hydroxyapatite layer growth on the glass surface.

Wavelet entropy-based evaluation of intrinsic predictability of time series.

Intrinsic predictability is imperative to quantify inherent information contained in a time series and assists in evaluating the performance of different forecasting methods to get the best possible prediction. Model forecasting performance is the measure of the probability of success. Nevertheless, model performance or the model does not provide understanding for improvement in prediction. Intuitively, intrinsic predictability delivers the highest level of predictability for a time series and informative in unfolding whether the system is unpredictable or the chosen model is a poor choice. We introduce a novel measure, the Wavelet Entropy Energy Measure (WEEM), based on wavelet transformation and information entropy for quantification of intrinsic predictability of time series. To investigate the efficiency and reliability of the proposed measure, model forecast performance was evaluated via a wavelet networks approach. The proposed measure uses the wavelet energy distribution of a time series at different scales and compares it with the wavelet energy distribution of white noise to quantify a time series as deterministic or random. We test the WEEM using a wide variety of time series ranging from deterministic, non-stationary, and ones contaminated with white noise with different noise-signal ratios. Furthermore, a relationship is developed between the WEEM and Nash-Sutcliffe Efficiency, one of the widely known measures of forecast performance. The reliability of WEEM is demonstrated by exploring the relationship to logistic map and real-world data.