Bhikhari Tharu

Bhikhari Tharu. CSAM 2026;33:375-87. https://doi.org/10.29220/CSAM.2026.33.3.375

Bhikhari Tharu was not included as an author in the original publication

Global warming is likely to provoke extreme storms in the eastern United States (eUS), ultimately affecting the probabilistic distribution of the dates of daily maximum precipitation. In this study, probabilistic properties of timing of annual maximum precipitation (AMP) were studied using circular statistics at 583 sites in the eUS (1950–2019). A kernel circular density method was applied to examine distributional modes of timing of AMP. The results of circular median show that seasonality is pronounced across the eUS with many locations having their median date of occurrence in summer, and AMP seasonality is strong in the East North Central region. Similarly, results of circular density method applied to the distribution of AMP timing shows that around 90% of the sites have two or three modes of AMP seasonality in the eUS. Comparison of seasonality between two historical records of equal length (1950–1984 and 1985–2019) shows great spatial variability across the eUS. Temporal changes in seasonal modes for AMP dates revealed four different cases of seasonality changes: (i) weakening of seasonality, (ii) strengthening of seasonality, (iii) strong seasonality for both the old and recent periods, (iv) or uniform or no preferred seasonality for both periods. While a spatial coherence of seasonality changes was not observed, majority of sites showed strong seasonality (case iii) for old and recent periods mainly during summer and fall seasons.

Bhikhari Tharu, Keshav Pokhrel, Gokarna Aryal, Ram C. Kafle, Netra Khanal. CSAM 2021;28:119-34. https://doi.org/10.29220/CSAM.2021.28.2.119

Despite the importance of seasonality of extreme precipitation events to stormwater management, there are limited number of studies examining seasonality of daily and monthly precipitation extremes over the contiguous United States. In this study, a circular statistical method was used for spatio-temporal assessment of seasonality of daily and monthly precipitation extremes and their teleconnections with large-scale climate patterns over the contiguous United States. Historic precipitation time series over the period of 64 years (1951–2014) for 1108 sites was used for the analysis. Calendar dates for extreme precipitation were used to characterize seasonality within a circular statistics framework which includes indices reflecting the mean date and variability of occurrence of extreme events. The rainfall seasonality during negative and positive phases of the El Niño–Southern Oscillation, North Atlantic Oscillation, and Pacific Decadal Oscillation were also investigated. Results showed that extreme precipitation seasonality varied across the contiguous United States with distinct spatial pattern of seasonality (strong seasonality) in the western and mid-western regions and mixed spatial pattern in the eastern region. In addition, extreme precipitation seasonality during negative and positive phases of three climate indices revealed that large-scale climate variabilities have strong influence on the mean date of occurrence of extreme precipitation but generally weak influence on the strength of seasonality in the contiguous United States. Results from our study might be helpful for sustainable water resource management, flood risk mitigation, and prediction of future precipitation seasonality. 

Background: Seasonal influenza (SI) is an acute respiratory illness that exerts a severe impact on human life year-round. Yet, very few studies have been conducted to investigate its peak timing for different age groups. Objective: To evaluate the average peak calendar time and intensity for the incidence of SI for different age groups. Methods: The study uses laboratory-confirmed Influenza data from the Centers for Disease Control and Prevention (CDC) of the USA with age groups 2, 11, 34, 57 and 65 years during 2009–2018 for the analysis. A non-parametric method of estimation of a circular probability distribution called likelihood cross-validation method has been utilised. Results: The average peak date of incidence for age groups 2 and 11 is around the last week of December. However, the date shifts to the last week of January to the first week of February for other groups. Age groups 65 and 2 years experienced the most severe impact among all. Discussion: The average peak time for SI incidence is between the last week of December to January with a single peak time for every age group. However, the incidence seems to develop an additional moderate peak time for age group 65.

Changes in extreme precipitation are associated with changes in their probability distributions and the characteristics of quantiles derived from fitted distributions. In this study, the linear quantile regression method is employed to analyse spatio‐temporal trends of extreme precipitation and to study the impact of North Atlantic tropical cyclones in the distribution of extreme precipitation for the southeastern United States. Daily annual maximum precipitation over the period of 64 years (1950–2013) for 107 sites was used for the analysis. Our results show that changes in upper quantiles of the distributions of the extreme precipitation have occurred in the southeastern United States. Analysis of the potential changes in the distribution of the extreme precipitation by separating the historical record into two periods, that is, before and after 1981, reveals that upper‐quantile trends have increasing magnitude in most of the sites for the latest time period. Analysis of the impact of tropical cyclones in the extreme precipitation distribution shows that overall the heavy rainfall events in the recent decades may have been caused by tropical cyclones. Such results are particularly useful for water managers who are more concerned with extreme values rather than the averaged one. Hence, our study has significant implication in environmental and infrastructural assessment as well as disaster risk management.

Cholesterol level (CL) is growing concerned as health issue in human health since it is considered one of the causes in heart diseases. A study of cholesterol level can provide insight about its nature and characteristics. A cross-sectional study. National Health and Nutrition Examination Survey (NHANS) II was conducted on a probability sample of approximately 28,000 persons in the USA and cholesterol level is obtained from laboratory results. Samples were selected so that certain population groups thought to be at high risk of malnutrition. Study included 11,864 persons for CL cases with 9,602 males and 2,262 females with races: whites, blacks, and others. Non-parametric statistical tests and goodness of fit test have been used to identify probability distributions. The study concludes that the cholesterol level exhibits significant racial and gender differences in terms of probability distributions. The study has concluded that white people are relatively higher at risk than black people to have risk line and high risk cholesterol. The study clearly indicates that black males normally have higher cholesterol. Females have lower variation in cholesterol than males. There exists gender and racial discrepancies in cholesterol which has been identified as lognormal and gamma probability distributions. White individuals seem to be at a higher risk of having high risk cholesterol level than blacks. Females tend to have higher variation in cholesterol level than males.