Detailed investigation of climatic fluctuations is indispensable for constraining hydroclimatic risks, glacier-climate feedbacks, and water resource vulnerabilities. This study investigates divergent hydroclimatic trends in the glaciated and topographically complex Ravi Basin situated in the Northwestern Himalaya during 1980-2024. High-resolution gridded datasets, including reanalysis-based (TerraClimate) and satellite-derived products (CHIRPS), validated with limited in-situ data, were used to assess climatic shifts, elevation-dependent warming (EDW), change points, and rainfall extremes. The Mann-Kendall test, Sen's slope estimator, and Pettitt's test were applied to detect trends, estimate magnitudes, and identify temporal shifts. The findings derived from these high-resolution gridded datasets are interpreted in the context of limited in-situ observations within the Ravi Basin. The results reveal significant warming across the basin, with maximum temperature (Tmax) increasing at similar to 0.30 degrees C/decade and minimum temperature (Tmin) at similar to 0.48 degrees C/decade. A clear EDW is observed, with elevations above 3500 m experiencing intensified warming, particularly Tmin, reflecting a total increase of similar to 2.08 degrees C over 45 years, nearly double that of Tmax (similar to 1.08 degrees C). Change point analysis further detected a basin-wide shift in the late 1990s, marking an abrupt rise after the change year in Tmax (+0.64 to +0.73 degrees C) and Tmin (+1.15 to +1.20 degrees C). However, precipitation trends are heterogeneous and non-significant, with localized monsoonal declines. Rainfall extremes are observed mostly in low to mid-elevation. These findings emphasize divergent hydroclimatic trends, their elevation-dependent nature, and growing thermal stress on higher altitude, providing critical insights for targeted climate adaptation, hydrological planning, and a denser meteorological network in the Ravi Basin.

This research aims to assess air quality (particulate matter (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), benzene, and toluene) in a transitional location of four polluted cities in the Indo-Gangetic Basin, India. According to the Air Quality World Report, these cities were among the top 30 most polluted, posing a higher risk to public health due to exposure to air pollutants. Therefore, we have analyzed annual and seasonal variabilities and their relationships with meteorology and the air quality index (AQI) from 2017 to 2022. The highest annual mean concentrations were observed at Patna for PM2.5 (113.14 ± 13.6 μg/m3), NO2 (58.32 ± 24.6 μg/m3), SO2 (18.17 ± 12.8 μg/m3), CO (1.58 ± 0.3 mg/m3), O3 (42.90 ± 16.0 μg/m3), and benzene (2.38 ± 2.4 μg/m3), except Toluene and Xylene during the study period. All the observed air pollutants exceeded the NAAQs and WHO permissible limits in the study locations. Pearson's correlation analysis of PM2.5 showed negative correlations with temperature (−0.9), RH (−0.1), WS (−0.5), SR (−0.7), and RF (−0.3), and a positive correlation with WD (0.3) at Aurangabad. The AQI values were in the good (0-50) and satisfactory (51-100) categories at Muzaffarpur and Patna, while in the winter months (Nov-Feb), the AQI lies in the poor (101-200) category, and in the good to satisfactory categories in the rest of the seasons. HYSPLIT Backward trajectory analysis identified northwest India and the Indo-Gangetic plain as major aerosol sources for Bihar, with contributions of sea salt, mineral dust, and mixed aerosols from the Bay of Bengal, the Arabian Sea, and the Thar Desert to the study areas.

Aerosols play a critical role in modulating regional climate by influencing radiative forcing. Therefore, this study analysed the seasonal variabilities and long-term trends in aerosol optical properties (AOPs) and their direct radiative effects (ADRF) over Northwest India from 2005 to 2024 using multiple datasets (MERRA-2, OMI). The observed mean of surface albedo (SA), single scattering albedo (SSA), scattering aerosol optical thickness (AOT), and extinction AOT were 0.217 ± 0.008, 0.960 ± 0.008, 0.378 ± 0.099, and 0.345 ± 0.096, respectively, over Northwest India. The spatial distribution revealed that the Aravalli region (AR) exhibited the highest single scattering albedo (SSA), scattering optical thickness (AOT), and extinction AOT, as well as the lowest surface albedo (SA). Meanwhile, urban areas (Western-Indian-Gangetic plains) showed the opposite pattern (lowest) of AOP variation in Northwest India due to the influence of dust aerosols. Shortwave direct radiative forcing (SWDRF) varies from − 11.16 to -3.27 W/m² at the top of the atmosphere (TOA), -37.84 to -12.72 W/m² at the surface (SUR), and 7.97 to 26.87 W/m² at the atmosphere (ATM). In comparison, longwave direct radiative forcing (LWDRF) ranged from − 2.59 to -0.08 W/m² at TOA, 0.76 to 8.37 W/m² at SUR, and − 10.96 to -0.93 W/m² at ATM. The percentage change revealed a 1.28

The vast network of glaciers in the Himalayas serves as a vital source of freshwater for the main river systems. These are essential in determining a region's climate and hydrology. Ecological balance, agricultural output, and hydrological systems all depend on these glaciers. However, the stability of hydrological systems and long-term water availability have become major concerns in recent decades due to the acceleration of glacier melting brought on by climate change. In this study, globally available gridded satellite and reanalysis datasets, including ERA5, IMDAA, IMD, APHRODITE, and others, were evaluated to identify the most accurate dataset for the Bhilangana Basin. A thorough performance evaluation was conducted to assess the suitability of these datasets for the region. Furthermore, a hybrid rainfall dataset was developed using a bias correction approach to improve accuracy and reliability, ensuring a more robust representation of precipitation dynamics. The Spatial Processes in Hydrology (SPHY) model was utilized to examine the dynamics of snow-glacier melt during the years 2020–2023. The performance matrix revealed that the ERA5 dataset performed better than other datasets except the hybrid precipitation dataset. The average variation during 2000-2023 in snow q was found in the range of 15 to 26 percent, rain q from 12 to 58 percent, glacier q from 56 to 18 percent and base q from 8 to 18 percent. The analysis further revealed that 11 parameters were found to be critical in influencing the model's output e.g. Degree day factor for snow(DDFS), Glacier debris degree day factor(DDFG), Tcritical, Glacier melt frac runoff. The SPHY model's applicability for studying snow-glacier melt runoff dynamics and the significance of combining various climate datasets to precisely forecast the water resource scenarios in glaciated basins are further highlighted by this study.

Forest fires have become a significant research subject among national and international communities due to increasingly favorable climate conditions characterized by prolonged periods conducive to forest fires. However, several studies have been developed worldwide to assess forest fires' conditions, frequency, and intensity. Still, the literature is not transparent globally for forest fires impacting climate or the climate affecting the forest fires. Therefore, the primary objective of this paper is to conduct a comprehensive review using bibliometric analysis and synthesis of the effects of climate change on forest fire activity throughout 2003–2023. The bibliometric data has been collected from the Web of Science (WoS) Core Collection. The results reveal that the USA and Canada occupy the leading positions in maximum production of scientific output with the highest documents and contain the most productive authors' institutes and well-known scholars or authors. Co-authorship overlay analysis showed that Flannigan, M. D. (2.08

This study aims to analyze the temporal and spatial distribution of Aerosol Optical Properties across Northwest India using aerosol data from MODIS (Moderate Resolution Imaging Spectroradiometer) and OMI (Ozone Monitoring Instrument) sensors from 2003 to 2022. Therefore, this study investigated the decadal, interannual, and seasonal changes in aerosol optical properties, vegetation index, and meteorological parameters in the northwest Indian region (8 boxes). Using GIOVANNI (Goddard Earth Sciences Data and Information Services Center (GES DISC) Online Visualization and Analysis Infrastructure), we retrieved daily and monthly Aqua and Terra MODIS products of aerosol optical depth (AOD), Angstrom exponent (AE), normalized difference vegetation index (NDVI), and OMI aerosol index (AI) to examine the spatiotemporal variations by using statistical approaches. The results demonstrated that the decadal averages of aerosol properties showed values of AOD 0.35 (Aqua) and 0.34 (Terra) and AE 1.20 (Aqua) and 1.10 (Terra) with the highest levels during the post-monsoon. Notably, the mean interannual concentrations of AOD and NDVI consistently surpass 0.3, and AE and AI exceed 1 in most locations, underscoring the persistence of high aerosol loading. Also, the study revealed a negative decadal change in AOD of about -8.24 %, while AE, AI, and NDVI showed positive decadal changes of about 9.24 %, 15.09 %, and 12.67 %, respectively. In addition, aerosol optical properties and local meteorology strongly correlated (-0.8 to +0.8). Principal Component Analysis (PCA) identifies meteorological parameters as significant drivers, with the first three components explaining over 70 % of the variation in aerosol optical properties. The NOAA HYSPLIT trajectory model suggests that the long-distance dust transport from the Arabian Peninsula frequently penetrates Gujarat province and then to northwest India. The results contributed to air quality management strategies and provided valuable insights into regional climate and air quality with the influence of meteorology.

Groundwater plays a vital role in global climate change and substantial human needs. However, the groundwater potential zone (GWPZ) delineation is essential for fulfilling livelihood needs. In recent years, studies based on geographic information systems (GIS) have acquired much attention in groundwater exploration. Thus, in order to determine the groundwater potential zone in the state of Uttarakhand, we employ a multi-criteria decision analysis (MCDA) based analytical hierarchy process (AHP) model with overlay weighted linear combination approach in this study. For groundwater potential zone demarcation, nine thematic layers such as geology, geomorphology, LULC, drainage density, slope, rainfall, soil, TWI, and curvature were created using remote sensing (RS) images and conventional data for a geographic information system (GIS). Furthermore, the weight of the parameters has been determined using AHP technique and overlay analysis was determined using GIS tools. A thematic map was categorized as "very poor," "poor," "moderate," "good," "very good," and "excellent" in order to determine the groundwater potential zone. According to the results, the area covered by the 'very poor' categories is 187.43 km2 (0.16 %) followed by 'poor' 2109.66 km2 (3.99 %), 'moderate' 29024.06 km2 (54.78 %), 'good' 15151.13 km2 (28.67 %), 'very good' 6537.19 km2 (12.37 %), and 'excellent' 814.84 km2 (1.55 %) with the accuracy of 89.9 %. The 'very poor' and 'moderate' groundwater potential zones were observed as 0.16 % and 54.78 %, respectively, and the possibility for GPZ gradually increased from the northeast to the southwest. The findings of this study have implications for future research on sustainable groundwater use, basin management of agriculture, and the link between groundwater and climate change. (c) 2023 COSPAR. Published by Elsevier B.V. All rights reserved.

During the pre- and post-monsoon season, the eastern and western coasts are highly vulnerable to cyclones. The tropical cyclone “Tauktae” formed in the Arabian Sea on 14 May 2021 and moved along the west coast of India, and landfall occurred on 17 May 2021. During the cyclone, the maximum wind speed was 220 km/h with a pressure of 935 mb affecting meteorological, atmospheric parameters, and weather conditions of the northern and central parts of India causing devastating damage. Analysis of satellite, Argo, and ground data show pronounced changes in the oceanic, atmospheric, and meteorological parameters associated during the formation and landfall of the cyclone. During cyclone generation (before landfall), the air temperature (AT) was maximum (30.51 °C), and winds (220 km/h) were strong with negative omega values (0.3). The relative humidity (RH) and rainfall (RF) were observed to be higher at the location of the cyclone formation in the ocean and over the landfall location, with an average value of 81.28% and 21.45 mm/day, respectively. The concentration of total column ozone (TCO), CO volume mixing ratio (COVMR), H 2 O mass mixing ratio (H 2 O MMR), aerosol parameters (AOD, AE) and air quality parameter (PM) was increased over land and along the cyclone track, leading to a deterioration in the air quality. The strong wind mixes the air mass from the surroundings to the local anthropogenic emissions, and causing strong mixing of the aerosols. The detailed results show a pronounced change in the ocean, land, meteorological, and atmospheric parameters showing a strong land–ocean-atmosphere coupling associated with the cyclone.

This research focuses on a bibliometric analysis of research on aerosols' impact on the glaciers in the Himalayan glacier region published in journals from all subject categories based on the Science Citation Index Expanded, collected from the Web of Science and Scopus database between January 2002 and April 2022. The indexing phrases like "aerosol," "glacier," and "snow" are commonly used terms and have been utilized to collect the related publications for this investigation. The document selections were based on years of publication, authorship, the scientific output of authors, distribution of publication by country, categories of the subjects, and names of journals in which scholarly papers were published. The number of articles on aerosols accelerating the melting of glaciers shows a notable increase in recent years, along with more glacier melting results from countries involved in climate science research. People's Republic of China (382) was the country with the highest publication output on aerosols impacting the melting of glaciers. The USA (367) was the most cited country, with about 17,500 total citations and 80.40 average citations per year from January 2002 to April 2022. The results reveal that research trends in the glaciers on aerosols' impact on the glaciers have been attractive in recent years, and the number of articles in this field keeps increasing fast. This study offers opportunities to track research trends, identify collaboration prospects, and inform climate policy. Integrating data sources and engaging the public will further enhance the impact and relevance of this critical research field.

In recent years, there has been a rapid increase in scientific research into hydrogeochemical research on glacier meltwater. Nevertheless, systematic and quantitative analyses are lacking to investigate how this research field has developed over the years. As a result, this study is aimed at examining and evaluating recent research trends and frontiers in hydrogeochemical research on glacier meltwater throughout the previous 20 years (2002-2022) and at locating collaboration networks. This is the first global-scale study, and visualization of the key hotspots and trends in hydrogeochemical research has been presented here. The Web of Science Core Collection (WoSCC) database aided in the retrieval of research publications related to hydrogeochemical research of glacier meltwater published between 2002 and 2022. From the beginning of 2002 till July 2022, 6035 publications on the hydrogeochemical study of glacier meltwater were compiled. The result revealed that the number of published papers on the hydrogeochemical study of glacier meltwater at higher altitudes had grown exponentially, with USA and China being the main research countries. The number of publications produced from the USA and China accounts for about half (50%) of all publications from the top 10 countries. Kang SC, Schwikowski M, and Tranter M are highly influential authors in hydrogeochemical research of glacier meltwater. However, the research from developed nations, particularly the United States, emphasizes hydrogeochemical research more than those from developing countries. In addition, the research on glacier meltwater's role in streamflow components is limited, particularly in the high-altitude regions and needs to be enhanced.

This research was conducted in the urban area of Patna region, the capital and largest city of Bihar, which is part of the Indo-Gangetic alluvium plain. This study aims to identify the sources and processes controlling groundwater’s hydrochemical evolution in the Patna region’s urban area. In this research, we evaluated the interplay between several measures of groundwater quality, the various possible causes of groundwater pollution, and the resulting health risks. Twenty groundwater samples were taken from various locations and examined to determine the water quality. The average EC of the groundwater in the investigated area was 728 ± 331.84 µS/cm, with a range of around 300–1700 µS/cm. Positive loadings were seen for total dissolved solids (TDS), electrical conductivity (EC), calcium (Ca2+), magnesium (Mg2+), sodium (Na+), chloride (Cl−), and sulphate (SO42−) in principal component analysis (PCA), demonstrating that these variables accounted for 61.78

The study focuses on the hydro-geochemistry of Shaune Garang glacier’s meltwater concerning glacial geomorphology. Seventy-nine water samples (53 in 2016 and 26 in 2017) of ablation season were analysed. The cations were dominant in the order Ca2+ > Mg2+ > Na+ > K+, and the anions in the order HCO3− > SO42− > Cl− > NO3−. The result demonstrated that HCO3− were the abundant ions, accounting for 41.03 and 34.84% of the total ionic budget (TZ). The high ionic proportions of (Ca2+ + Mg2+) versus TZ+ and (Ca2+ + Mg2+) versus (Na+ + K+) were identified as the primary factors influencing dissolved ion chemistry in meltwater. Piper diagram shows that Ca2+–HCO3– type water is the most common, followed by Mg2+–HCO3–. In addition, a remote sensing approach has been used to find the possible source of the chemical constituents in the meltwater. The catchment geology has been mapped on various scales, including diverse rocks and unconsolidated surface materials containing “quartz and carbonate minerals”. Layered silicates (LS) and “hydroxyl-bearing minerals” are not as common as they used to be, but their availability varies greatly in the area where they are found. The distribution of LS minerals within the catchment are majorly found at lower altitudes, which implies the weathering mechanism due to the interaction of meltwater and parental rock. Multivariate analysis revealed that CO3 and SiO2 weathering, sulphate dissolution, and pyrite oxidation dominate dissolved ion concentrations. Chemometric analysis of meltwater hydro-geochemistry through principal component analysis explains 72.1% of the total variance of four PCs. PCs 1, 2, 3, and 4 explain 39.21%, 12.91%, 10.24%, and 9.74% of variance, respectively, in 2016. Similarly, in 2017, four PCs explain 69.91% of the total variance. PC 1, 2, 3, and 4 can explain 26.62%, 20.12%, 12.64%, and 10.52% of variance.

Himalayan glaciers are under the enhanced retreating process and filed observations are scarce due to the harsh terrain and inclement weather in the upper Himalayan region. Under these conditions, the use of remote sensing and GIS technique is the best option for long term change detection in glacial features. Glacier length, area, and volume change analyses have been done using Landsat data series (MSS, TM, ETM+, and OLI/TIRS) from 1980 to 2019. Glaciers of Baspa basin were digitized in 1980 and 75 glaciers were reported whereas in 2019, number of glaciers were 84. During the period of 1980 to 2019, the number of glaciers were increased by 9 in the Baspa basin but the volume has been reduced. The increase in number has been due to the conversion of larger glaciers into a few smaller glaciers under the melting process.The results showed a general trend of loss in the areas and volume of the glaciers with varying magnitudes at different glaciers of the Baspa basin and in different periods. The overall retreat during 1980–2019 has been 22.40 ± 4.46% with the average loss in the area as 1.11 ± 0.01 km2 a-1 in the entire Baspa basin. The smallest rate of loss in the glacier area has been observed as 1.05 ± 0.01 km2 a-1 from 2001 to 2013. The Baspa basin possessed a glacial volume of 16.26 km3 in the year 1980 that got reduced to 12.98 km3 by the year 2019. The total loss in 39 years (1980–2019) has been calculated as 3.28 km3. The rate of loss in volume is highest in the period 2013 to 2019 which is 0.116 km3 a-1 and the minimum rate of loss in volume was observed from 1994 to 2001 i.e. 0.035 km3 a-1.The results on changes in the areal cover of glaciers as well as the volume are important for quantification of future water availability on the basis which controls the water supply in the river system. It is also of use in the planning of the hydropower sectors as well as the existing power plants in terms of the potential to generate electricity.

The present research has been performed to analyze the chemical behavior of rainwater of the Shaune Garang catchment (32.19° N, 78.20° E) in the Baspa basin, located at a high elevation (4221 m above mean sea level) in the Himachal Himalaya, India. During the study period, sixteen rainwater samples were collected from the Shaune Garang catchment at five different sites. The volume-weighted mean (VWM) pH value of rainwater ranged between 4.59 and 6.73, with an average value of 5.47 ± 0.69, indicating the alkaline nature of rainfall. The total ionic strength in the rainwater ranged from 113.4 to 263.3 µeq/l with an average value of 169.1 ± 40.4 µeq/l. The major dominant cations were Ca2+ (43.10%) and Na+ (31.97%) and anions were Cl− (37.68%), SO42− (28.71%) and NO3− (23.85%) in rainwater. The ionic ratios were calculated among all the ions. The fraction of (NO3− +Cl−) with SO42− was measured as 2.3, which specifies sour faces of rainwater due to HNO3, H2SO4, and HCl. A multivariate statistical assessment of rainwater chemistry through Principal Component Analysis (PCA) shows the significance of four factors controlling 78.37% of the total variance, including four-component (PC1 explained 27.89%, PC2 explained 24.98%, PC3 explained 14.64%, PC4 explained 10.85%). However, the individual contribution of Factor 1(PC1) explains 27.89% of the total variance (78.37%) and displays a strong optimistic loading for Ca2+ and Cl−. Further, high loading of Ca2+ and NO3− and moderate loading of SO42− signify the contribution of burning fossil fuel and soil dust. Anthropogenic and natural pollutants influence the composition of rainwater in the pristine Himalayas due to local and long-distance transportation. The study area receives precipitation from the West and North-West, transporting dust and fossil fuel emissions from the Thar Desert and Northwestern countries.

In the present study, we analyze a field-based seven-year data series of surface mass-balance measurements collected during 2011/12 to 2017/18 on Naradu Glacier, western Himalaya, India. The average annual specific mass balance for the said period is − 0.85 m w.e. with the maximum ablation of − 1.15 m w.e. The analysis shows that the topographic features, south and southeast aspects and slopes between 7 to 24 degrees are the reasons behind the maximum ablation from a particular zone. The causes of surface mass balance variability have been analyzed through multiple linear regression analyses (MLRA) by taking temperature and precipitation as predictors. The MLRA demonstrates that 71% of the observed surface mass balance variance can be explained by temperature and precipitation. It clearly illustrates the importance of summer temperature, which alone explains 64% variance of surface mass balance. The seasonal analysis shows that most of the surface mass balance variability is described by summer temperature and winter precipitation as two predictor variables. Among monthly combinations, surface mass balance variance is best characterized by June temperature and September precipitation.