Bulbul Jan

In terms of geography and climate, Pakistan is a country with a wide range of characteristics. It is mostly an agricultural country that is reliant on the Indus water system. To the north, there are steep plateaus, and to the south, the Arabian Sea, while the interior is covered in plateaus or agricultural plains. That’s why, it requires some more specific details to monitor or analyse climate data and to cope with it, and the Statistical Downscaling Model (SDSM) is employed in this paper. Using the identical statistical downscaling model, three predictor selection methods [stepwise regression, correlation and partial analyses] are evaluated in terms of the uncertainty assessments of their downscaled outcomes (SDSM). The analysis works with SDSM using daily data for rainfall and temperature for Bonji and Gupis cities. The potential future values of local climate variables, such as temperature and rainfall, for the chosen cities are generated in this research using statistical downscaling models. The models are (1) the statistical downscaling model (SDSM), which used the stochastic weather generator and regression models, and (2) Singular Spectrum Analysis (SSA) a powerful hydroclimatic data tool that explores crucial information from the constrained components and forecasts more analysis. Both the calibration and validation stages of the anticipated river flow values from these approaches show noteworthy results. The Singular Spectrum Analysis method yields comparatively better outcomes, according to statistical examinations. For the climate and hydrological circumstances of the Pakistan region in the future, this may be the best option.

Indeed, dengue is known as the most widely distributed and rapidly transmitted mosquito-borne infection in the world. Climate change plays a significant role in both the temporal and spatial distribution of vector-borne diseases. The main objective of this investigation was to examine the impact of seasonal factors and the relationship between climate and dengue risk in the vicinity of Lahore, Pakistan, from 2007 to 2018. This study employed the generalized linear model (GLM) approach in combination with negative Poisson and binomial distributions. Hotspot areas were identified using ArcGIS, which may have the potential for a high concentration of reported dengue cases in the Lahore area. Based on the results, it has been confirmed that there was a seasonal fluctuation in the number of dengue cases per month, which correlated with the seasonality of climatic variables. Furthermore, the best-fitting model included average temperature (< 22°C) and precipitation-both lagged by one month and controlled by year. According to this model, each 1°C increase in a month's average temperature was associated with a 12% decrease in dengue cases, whereas each 10 mm increase in precipitation was associated with a 5% increase in dengue cases in the following month. Many factors, including the climate, contribute to the incidence of dengue cases, yet their role remains incompletely understood. Understanding climate is essential for analyzing epidemic risk and improving protective measures. This study highlights the need to improve dengue surveillance, epidemiology, and community health structure to safeguard and control future dengue outbreaks.

Flood disasters occur when extreme flooding poses a significant threat to human life, infrastructure, and the environment. These disasters result from excessive rainfall, rapid snowmelt, glacier outbursts, or other hydrological events that cause rivers to overflow their banks, inundating surrounding areas. Flooding leads to loss of life, economic hardship, displacement of communities, and environmental degradation. Ghizer region is our focus study area, which is one of the mountainous regions of Gilgit-Baltistan, Pakistan that experiences several disasters, including geological, meteorological, hydrological, and manmade disasters. Among them, floods are the common natural disaster that damages the infrastructure every year. This research study examines the Ghizer River, using the Hydrologic Engineering Center River Analysis System (HEC-RAS) and the Automated Water Extraction Index (AWEI) as analysis models. The HEC-RAS steady flow modeling utilizes to predict river flow behavior in return periods, providing water surface profiles and floodplain inundation maps. The AWEI technique of remote sensing is used to assess the changing morphological pattern of Ghizer River over time. This study focuses on the communities that exhibit heightened vulnerability to flood occurrences in the vicinity of the Ghizer River and to investigate the river's morphological patterns. These advance approaches help for understanding of flood risk zones, early warning system, and disaster awareness. The study covers the period from 1990 to 2023 to analyze historical flood trends and river morphology changes over time.

In this study, we introduce a recent approach to implement the wavelet analysis of ionospheric parameters and anomaly detection during ionospheric irregularities over Pakistan. The most important aspect of the wavelet analysis is the timefrequency distribution representation of the signal. This study investigates the variability of ionospheric F₂ layer and utilizes critical frequency (foF₂) of F₂ layer during the year 2008–2017 in Pakistan region. The results of this study identify the most important bands of low frequency variations, which are very relevant for study of periodicities measurements of F₂ layer. Moreover, Morlet wavelet has revealed that generally the global wavelet power spectrum (GWPS) is the most prominent in the annual cycles. The multifaceted interactions of ionospheric fluctuations with different time scales, e.g. annual, biannual, become significant due to higher peaks appear during the periods 128–256, 256–512 and 512–1024 days in daily data sets. The wavelet trend revales a noticeablel power range between the 2–16 day bands, representing an annual fluctuation for daily observations of F₂ layer. The low deviation in a frequency range highlights the existence of seasonality in the F₂ layer during the study period. These results emphasize that foF₂ observations exhibit irregular distrabances with non-stationary power at different frequencies. The study will be helpful for stakeholders and government authorities because high frequency sky-waves users schedule their programs one month in advance, but they also need day by day information about the ionospheric conditions in order to revise programs.

The earlier period investigations of Neutrinos emission from deep space indicate the existence of this particle which has been detected in various laboratories of the world. These laboratories are responsible for recording neutrinos emission originated from deep space. One laboratory situated near Sudbury, Ontario, Canada known as Sudbury Neutrino Observatory (SNO). Another laboratory is in Japan known as Kamikanado. We obtained a set of observations from both labs. It is obvious that the observations recorded at SNO have been utilized in this manuscript. From the same communication, the neutrino behaviour could be better understood by analyzing the SNO observed data set-I from November 1999 to May 2001 of D2O, while dataset-II is from July 2001 to August 2003 of salt water with particulars recorded, Run start Time since midnight. The samples used in this paper are 250 entries from both the observations sets. In this presentation, the most spectacular exhibition of the neutrino flux in its frequency components of the particles have been depicted in the form of a Periodogram that identifies covariance structure in the neutrino flux reaching this biosphere and parametric values obtained in this paper have been tabulated in various table frame of work. This communication does claim the neutrino emission characteristics in the real world. It has also been known from the literature survey that the same kind of work has not been framed in the third world countries where no neutrino detection laboratories exist. This piece of information will be beneficial for the private and public organizations where the experts are trying to explore deep space emissions detection and their characterization like the present study.