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This study evaluates spatial trend and distribution history of selected metals in the complex coastal environment of Odisha, India. The main objective is to analyse metal enrichment and level of contamination in sediment deposits. It is also attempted to evaluate the past depositional environment and prevailing process leading to metal enrichment and contamination. Three sediment cores up to a depth of 10 m were collected from older beach deposit of Chandbali (CB) and paleochannels of Agiviligan (AG) and Kasturikaran (KK) villages located along River Baitarani, coastal Odisha. The study area, to a large extent, is a transition zone of terrestrial and marine sediments. The assessment of metal contamination is based on spatial and down-core variability of selected metals- Al2O3, MnO, Cr, Co, Cu, Zn, and Pb against their background values. Contamination is examined using enrichment factor (EF), contamination factor (CF) and geo-accumulation index (Igeo). The study exhibits a complex pattern of metal distribution over space and time. The sediments are enriched and contaminated with MnO, Cr, and Pb not only in the surface layers but also in the deeper layers such as CBL4 (–7.3 m), CBL5 (–7.9 m), CBL6 (–10 m), AGL3 (–6 m), AGL5 (–10 m) and KKL3 (–8 m). Metal enrichment in deeper layers can be associated with the past depositional environment, prevailing processes, geology and geomorphology of the region. In the surface layers, metal contamination can result from recent anthropogenic activities like untreated industrial waste from Ferro-alloys Corporation Ltd. (FACCOR) and domestic waste. There is a decline in contamination levels as we move from inland (CB) to coast (KK). The samples are also analysed for grain size and geochemistry to differentiate between sedimentary layers deposited under distinct depositional environments in the past. The results reveal the existence of humid and dry climatic phases along with oscillating sea levels in the past, influencing metal enrichment and contamination. [ABSTRACT FROM AUTHOR]

The study estimates the soil organic carbon stock under various cropping systems to assess their sequestration potential in a tropical monsoon region, namely the Chulliyar-Ikshumathi sub-watershed of Bharathapuzha in Kerala, India. The cropping systems selected are the paddy, vegetables, coconut-arecanut groves, homestead farms, and mango plantations. The standard soil analyzing procedures to collect and analyze 129 soil samples from 43 locations were followed. The carbon stock (CS) of 0–10, 10–20, and 20–30 cm depth layers at each sampling location was estimated following the Walkley-Black method. The sequestration potential of soil under each cropping system is estimated to suggest an optimal cropping system for the region. The CS decreased with depth, irrespective of cropping systems. Among the cropping systems, the mango plantations, which practiced zero/no-tillage, have stability in the distribution of CS, while paddy fields showed higher variability in the CS. Even though mango plantations have more carbon sequestration potential (CSP), the coconut-arecanut grove is a better candidate for carbon sequestration (CSeq) in this region owing to its wider areal extent and acceptability. This analysis reveals the importance of assessing CS at the watershed level for sustainable land use planning to achieve better sequestration of atmospheric carbon. [ABSTRACT FROM AUTHOR]

Rainfall is time concentric in nature. The spatial and temporal distribution of rainfall is changing over the Earth with recent anthropogenic warming. The study explores various characteristics of annual and seasonal concentration of rainfall across India using the precipitation concentration index (PCI) and its trends during the period of 1986–2015, based on a high‐resolution gauge‐based rainfall data (0.25 × 0.25°), obtained from the India Meteorological Department (IMD). An intercomparison is made with 11 other gridded rainfall datasets to infer whether these datasets can reasonably reproduce the spatiotemporal distribution of PCI and its trends in various homogenous rainfall regions of India or not. These datasets are categorized into gauge‐based (APHRODITE, GPCC, CPC), satellite‐derived (CHIRPS, PERSIANN‐CDR), and reanalysis (JRA‐55, MERRA‐2, NCEP‐2, PGF, ERA‐Interim, ERA‐5). On annual scale, about 8.52, 24.1, and 67.38% of area of India are under moderate, irregular, and strongly irregular rainfall distribution, respectively. Spatial variation of PCI in India is influenced by geographical factors such as latitude, longitude, and elevation. Significant increasing and decreasing trends in annual PCI have been observed in the northeast, eastern, and western coasts of India, respectively. Gridded data intercomparison suggests that the gauge‐based APHRODITE, GPCC, and satellite‐derived CHIRPS datasets better perform in capturing the temporal and spatial variation of PCI across India when compared to the IMD gridded dataset, whereas the ERA‐5 performs better among the reanalysis datasets. However, the rainfall datasets exhibited marked differences with the IMD while estimating the annual and seasonal trend and its magnitude across various regions of India. The JRA‐55 overestimated areas of positive trend and its magnitude on annual and seasonal scale. These findings have practical implications for hydroclimatic studies. [ABSTRACT FROM AUTHOR]

The subsurface sediment archives provide clues to the past climate of the Earth. To reconstruct the paleoclimate of Lower Baitarani Basin, a 1000 cm deep sediment core (JP) was collected from the flood plain located in Jhumpuri village of Jajpur, Odisha. A total number of eleven distinct layers were identified based on variations in the sediment colour and textural at varying depths. From each layer, a representative sample was collected and carried to the laboratory for further assessment. Each of these sub-samples was examined for particle size analysis, availability of total organic carbon (TOC), and elemental concentration. The physical and geochemical proxies as paleoproductivity, terrestrial flux, weathering, level of salinity, colour, and texture variations were utilised to examine the paleoclimate of the area. The colour variations of the subsurface sequences suggest that the deposition has occurred under both freshwater and stagnated/marshy conditions. The texture variations—clay, silty clay, and loamy fine sand—also indicate deviations in sediment supply and rate of sediment aggradation during climatic shift between warm-humid and cold-arid episodes. With the help of geochemical proxies to illustrate the terrestrial flux: Fe2O3, Al2O3, TiO2, MgO, CaO, Cu, Co, Ba, and Zn; paleoproductivity: TOC,CaO/Al2O3, Zn/Al2O3,Sr/Al2O3, Cu/Al2O3, and Ba/Al2O3; weathering: Fe2O3/Al2O3, TiO2/Al2O3, and MgO/Al2O3; and the salinity index, a relatively cold-dry climate is inferred during the sedimentation of basal layers. Subsequently, a shift to the relatively warm-humid phase is suggested which again was followed by a cold-dry, and warm-humid phases. In the recent deposits, the proxies have not shown any considerable variation indicating prolongation of the existing warm-humid phase. However, some indications of a climatic shift to colder climate is also marked in the surface layer (JPL1). [ABSTRACT FROM AUTHOR]

Sea Level Rise (SLR) is considered as one of the effects of climate change that leads to Salt Water Intrusion (SWI) into the coastal areas around the world, thereby contaminating the groundwater and making it unusable for household and irrigation purposes along the coastal areas. Therefore, this paper is an attempt to analyze the change in sea level and shoreline and the consequent changes on the quality of groundwater in lowland and midland areas of Ernakulam district, Kerala. The tidal gauge data were sourced from the Permanent Service for Mean Sea Level (PSML) to assess the changes in sea level during 1971-2007. Remote sensing data was utilized for estimating the shoreline changes along the coast of the district using Digital Shoreline Analysis System (DSAS). The physical and chemical properties of groundwater were sourced for 27 locations from Central Ground Water Board (CGWB). Ionic ratios were computed to assess the salinity levels in the sampling locations. Sodium percentage (Na %) and potential salinity were computed for determining SWI and the suitability of the groundwater usage for domestic and irrigation purpose. Spatial variation of pH, EC, TDS and ionic ratios were assessed in the GIS environment through IDW interpolation. Findings indicate a rising trend in sea level at the rate of 1.8 mm/yr along the coast of the study area. Shoreline accretion and erosion was also observed at different locations along the coast during 2002-2012, though the rate of change varied across the shoreline. In general, water has shown slight alkaline nature, with pH varying from 7.2 to 8.2 at some locations, particularly along the coast. Higher values of EC and TDS were also observed along the coastal aquifer and may be ascribed to SWI. Sodium percentage (Na %) and potential salinity suggest presence of salt water. However, groundwater in majority of the areas especially away from the coast is good for domestic and irrigation uses. This study showed the groundwater quality deterioration, mainly due to the intrusion of sea water, particularly along the coastal areas of the Ernakulam district. [ABSTRACT FROM AUTHOR]

Understanding the spatial and temporal occurrences of extreme rainfall events and their changes is crucial for reducing the risk associated with extreme events, especially under anthropogenic warming. This study presents a comparative analysis of twelve gridded rainfall datasets in representing the spatial and temporal variation of extreme rainfall events and trends in the recent past (1983–2015) in India. The selected datasets fall into four categories: gauge-based (APHRODITE, CPC, GPCC, REGEN), satellite-derived (CHIRPS, PERSIANN-CDR), reanalysis (ERA5, MERRA-2, PGF, and JRA-55), and product merged from these three types (WFDEI and MSWEP). For comparison, a gauge-based, high resolution (0.25° × 0.25°) daily gridded rainfall dataset, prepared by the India Meteorological Department (IMD) is used as a reference dataset. Eleven extreme climate indices, defined by the World Meteorological Organization's Expert Team on Climate Change Detection and Indices (ETCCDI) and the IMD are used to represent the magnitude, frequency, and duration of extreme rainfall events in India. The relative performance of these datasets in capturing extreme rainfall events is evaluated by computing model evaluation matrices i.e. correlation coefficient (CC) and Percentage Bias (PBIAS). Trends in extreme rainfall indices and their magnitudes are calculated using Mann-Kendell and Sen's slope methods respectively, to compare with the IMD dataset. Our study finds large uncertainties in representing extreme rainfall events where in most cases the datasets underestimate higher extreme events compared to the IMD data. GPCC and MSWEP better capture the magnitude, duration, and frequency of extreme rainfall events in India and are comparable to the pattern of the IMD dataset. CHIRPS and ERA5 perform better than other satellite and reanalysis rainfall datasets. All these selected datasets consistently underestimate extreme rainfall events over the northern Himalayan region. The gridded datasets are unable to capture the spatial pattern of observed trends in extreme rainfall indices in India when compared to the IMD dataset. • Extreme rainfall characteristics over India studied using gauge, satellite, reanalysis, and merged gridded datasets. • Elevan ETCCDI and IMD indices were used to study extreme rainfall events over India • GPCC, CHIRPS, ERA5, and MSWEP better performed in gauge, satellite, reanalysis, and merged datasets respectively. • Datasets are incongruous in representing the observed trends in extreme rainfall indices over India. [ABSTRACT FROM AUTHOR]

Aerosols are one of the key components of climate systems. They absorb and scatter both solar and terrestrial radiation and produce strong surface as well as atmospheric radiative forcing effects. Aerosol climatology includes the measurement of light extinction by aerosol scattering and absorption, by procedures such as aerosol optical depth (AOD), angstrom exponent (α), single-scattering albedo (ω), and size distribution. This article analyses the dynamics of seasonal AOD over the Indian subcontinent from 2001 to 2009 using the MODIS level 2 data set. The analysis carried out for winter, pre-monsoon, monsoon, and post-monsoon seasons is based on 8 days' composite AOD data for selected months representative of each season. The spatial variability of AOD has been shown to be 0.47 μm and 0.66 μm for fine- and coarse-mode aerosols, respectively, which illustrates the principle of relative difference. The dynamics of seasonally averaged AOD over the period under study represent an increasing tendency from 0.20 to 0.37 at 0.47 μm and from 0.16 to 0.26 at 0.66 μm during winter (2003–2009), whereas AOD in the pre-monsoon season ranged from 0.24 to 0.16 at 0.47 μm and from 0.24 to 0.16 at 0.66 μm (2005–2009). The monsoon season yielded an AOD of less than 0.15 throughout the study period, and the post-monsoon season recorded an increasing tendency from 0.18 to 0.29 at 0.47 μm and from 0.16 to 0.19 at 0.66 μm (2005–2009), reflecting a similar trend to that of the winter AOD curve. The spatial distribution of AOD shows that the northern part of India – especially the Indo-Gangetic plain – remains most affected by high AOD throughout the year. Such high AOD can be attributed to increasing anthropogenic emission due to an ever-increasing population, and urban, industrial, and other economic activities causing high concentrations of fine-mode organic and inorganic aerosol particles, along with coarse soil and mineral dust over the Indo-Gangetic plain. [ABSTRACT FROM PUBLISHER]

Glacier ice-thickness measurement and distribution is one of the essential variables to assess present status of glacier-water equivalent and its volumetric reserve as well as to model the future glacier dynamics under the climate changing scenario. Yet, substantial gaps in ice thickness information exist for the Himalayan glaciers. The present study provides a long-term assessment (1965–2016) of recessional and area change patterns, as well as the detailed field-based (2016–2017) Ground Penetrating Radar(GPR), derived ice-thickness measurement of the Menthosa Glacier, Lahaul Himalaya. Additionally, the study examines whether the modelled ice thickness from remote sensing data is consistent with the field-based GPR measurement and how can it be improved. The extensive field surveys coupled with the multi-temporal high (Corona KH-4A) to medium resolution (Landsat Enhanced Thematic Mapper+ (ETM+)/Operational Land Imager (OLI), Sentinel 2A-Multispectral Instrument (MSI)) remote sensing data and cross-sectional GPR surveyed profile measurements have been used to examine past half a century (1965–2016) glacier fluctuation and the recent ice-thickness estimations, respectively. The results show that the Menthosa Glacier receded by 301.5 ± 19.2 m during the past half a century (1965–2016) with an average annual retreat of 5.9 ± 0.4 m a−1, whereas glacier lost 0.09 km2 ice in the frontal section. Field measurement over the past one decade (2006–2017) also conforms to a continuous recessional pattern and substantial glacier degeneration particularly the extensive surface lowering and significant appearance of ice-cliffs in the ablation and lateral zones over this period. The GPR measurements (2017) show the minimum glacier ice thickness of 24 meters at 4691 m a.s.l. (in the lower part of ablation area) and maximum glacier ice thickness of 55 meters measured at 4758 m a.s.l. (in the upper left-side tributary part of ablation area). Moreover, the modelled ice thickness derived from remotely sensed data is having Root Mean Square Error (RMSE) between 38 to 72 ± 10 m as compared with GPR measured ice thickness. [ABSTRACT FROM AUTHOR]

The study investigates a high-resolution retrieval of AOD over complex surfaces and various aerosol regimes of South Asian countries such as China, Bangladesh and Nepal from Landsat 7/8/9 and Sentinel 2 images. To estimate AOD from these satellite images, unlike the conventional radiative transfer-based look-up-table (LUT) approach, this study uses SEMARA coupled model which combines a simplified and robust surface reflection estimation (SREM) model and a simplified aerosol retrieval algorithm (SARA). The SEMARA model uses top-of-atmospheric reflectance (TOA) and land surface reflectance (LSR), solar and sensors viewing geometries, including the in-situ AOD data from the Aerosol Robotic Network (AERONET) station to simulate AOD at 30 m and 10 m spatial resolutions. The results are compared with AERONET and MODIS-based AOD for the validation sites in the South Asian countries. A high degree of agreement with AERONET AOD is obtained over Beijing in China (R2 > 0.9) for all sensors, while it varies for the validation AERONET stations over Nepal and Bangladesh (R2 = 0.40–0.86). The accuracy metrices suggest a low uncertainty over China Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for Beijing: 0.07 and 0.13), while it remains higher for stations over Nepal and Bangladesh (MAE and RMSE: 0.33, and 0.41; 0.15–0.27, respectively). >85% of the modelled observations fall within expected error (EE) for China, while 20–75% of observations fall within EE for Nepal and Bangladesh. We have also obtained a proximity effect for the accuracy of the estimated AOD from the SEMARA model. The findings, in general, suggest that the complexity in surface cover and aerosol type produce a higher offset with respect to AERONET observations over Bangladesh and Nepal, and therefore further validation studies are needed for other parts of the globe to measure the consistency of the model. [Display omitted] • A computationally efficient coupled model of simplified surface reflectance and aerosol retrieval algorithm is used. • The estimation accuracy remains high over the validation sites in China and drops notably over Nepal and Bangladesh. • Precise the estimation of surface reflectance, higher is the accuracy of SEMARA-derived AOD • The outcome shows a proximity effect of the AERONET station to the level of accuracy of estimated AOD. [ABSTRACT FROM AUTHOR]

The countries around the world are dealing with air quality issues for decades due to their mode of production and energy usages. The outbreak of COVID-19 as a pandemic and consequent global economic shutdown, for the first time, provided a base for the real-time experiment of the effect of reduced emissions across the globe in abetting the air pollution issue. The present study dealt with the changes in Aerosol Optical Depth (AOD), a marker of air pollution, because of global economic shutdown due to the coronavirus pandemic. The study considered the countries in south and south-east Asia (SSEA), Europe and the USA for their extended period of lockdown due to coronavirus pandemic. Daily Aerosol Optical Depth (AOD) from Moderate-resolution imaging spectroradiometer (MODIS) and tropospheric column density of NO 2 and SO 2 from Ozone monitoring instrument (OMI) sensors, including meteorological data such as wind speed (WS) and relative humidity (RH) were analyzed during the pre-lockdown (2017–2019) and lockdown periods (2020). The average AOD, NO 2 and SO 2 during the lockdown period were statistically compared with their pre-lockdown average using Wilcoxon-signed-paired-rank test. The accuracy of the MODIS-derived AOD, including the changing pattern of AOD due to lockdown was estimated using AERONET data. The weekly anomaly of AOD, NO 2 and SO 2 was used for analyzing the space-time variation of aerosol load as restrictions were imposed by the concerned countries at the different points of time. Additionally, a random forest-based regression (RF) model was used to examine the effects of meteorological and emission parameters on the spatial variation of AOD. A significant reduction of AOD (−20%) was obtained for majority of the areas in SSEA, Europe and USA during the lockdown period. Yet, the clusters of increased AOD (30–60%) was obtained in the south-east part of SSEA, the western part of Europe and US regions. NO 2 reductions were measured up to 20–40%, while SO 2 emission increased up to 30% for a majority of areas in these regions. A notable space-time variation was observed in weekly anomaly. We found the evidence of the formation of new particles for causing high AOD under high RH and low WS, aided by the downward vertical wind flow. The RF model showed a distinguishable relative importance of emission and meteorological factors among these regions to account for the spatial variability of AOD. Our findings suggest that the continued lockdown might provide a temporary solution to air pollution; however, to combat persistent air quality issues, it needs switching over to the cleaner mode of production and energy. The findings of this study, thus, advocated for alternative energy policy at the global scale. Image 1 • AOD reduced to 20% in majority of the areas in SSEA, Europe and USA due to lockdown measures. • NO 2 reduction was measured up to 20–40%, while SO 2 increased up to 30% for majority of the areas during lockdown. • A complex interaction among SO 2 , WS and RH leads to high AOD by forming new particles. • Significant correlation was found between SO 2 concentration and residential mobility during lockdown period for USA. • A distinguishable influence on AOD was obtained for emission and meteorological factors using random forest regression. [ABSTRACT FROM AUTHOR]