Switch in chemical weathering caused by the mass balance variability in a Himalayan glacierized basin: a case of Chhota Shigri Glacier

Long-term data (2003–2015) on meltwater chemistry, mass balance and discharge of a benchmark glacier (Chhota Shigri Glacier, India) were studied to determine any association between these variables. To infer the factors governing the alteration of chemical weathering processes in glacierized basins, multi-annual records of the hydrochemical indices (Ca²⁺+Mg²⁺/Na⁺+K⁺) and the C-ratio were also examined. A succession of negative mass balance years has resulted in a decline in solute concentrations in the runoff, as discharge has increased. The (Ca²⁺+Mg²⁺/Na⁺+K⁺) and C-ratio are highest during periods of negative annual mass balance, when the spatial extent of the channelized drainage system increases. Conversely, these ratios are lowest in positive mass balance years, when the spatial extent of the channelized drainage system decreases, and chemical weathering in the distributed drainage system becomes more dominant. This paper is the first to show the inter-annual linkages between meltwater chemistry, mass balance and discharge for a valley glacier.     

Deposition of atmospheric pollutant and their chemical characterization in snow pit profile at Dokriani Glacier,Central Himalaya

The uncertainty in assessing the numerous atmospheric pollutants transported via wind from arid and semi-arid regions is affecting the glacial ecosystem. In our study area due to the complexity of the system, a prominent seasonal difference noticed among major ions(Ca~(2+), Mg~(2+), SO_4~(2-), and NO_3~-). There is a need for understanding the ions cycling as a whole and the directionality of the feedback loops in the system. Therefore, we provide an appraisal of our current hypothesis for seasonal difference in major ion concentration from snow samples for two corresponding years(2013 and 2015) at Dokriani Glacier. A systematic study of chemical compositionsin the shallow snow pit from Dokriani Glacier was undertaken for the pre-monsoon season to understand the cycling of major ions from atmosphere to solute acquisition process. The intimating connections of ions cycling in snow and its temporal behavior was observed and analyzed through various statistical tests. Among major ions, the SO_4~(2-)has the highest concentration among anions on an average considered as 14.21% in 2013 and 29.46% in 2015. On the other side Ca~(2+) is the dominant cation contributing 28.22% in 2013 and 15.3% in 2015 on average. The average ratio of Na+/Cl-was higher in 2013 whereas lower in 2015. The backward trajectory analysis suggests the possible sources of the ions transported from Central Asia through the Western Disturbance(WD) as a prominent source of winter precipitation mainly in the Central Himalaya. Ionicconcentration of Ca~(2+) in cations was highly dominated while in anion SO_4~(2-)played the major role. Factor analysis and correlation matrix suggested that, the precipitation chemistry is mostly influenced by anthropogenic, crustal, and sea salt sources over the studied region. The elemental cycling through ocean, atmosphere and biosphere opens up new ways to understand the geochemical processes operating at the glacierized catchments of the Himalaya. Moreover, increasing the field-based studies in the coming decades would also have the certain advantage in overcoming the conceptual and computational geochemical modelling difficulties.     

Quantification of improvements by using AWiFS over WiFS data

The Advanced WiFS sensor of RESOURCESAT- 1 satellite offers significantly improved specifications compared to the WiFS sensor onboard IRS IC, P3 and ID satellites. The improvements are in terms of spatial resolution, radiometry (quantisation levels) and number of spectral bands. In the present study, an attempt has been made to quantify the gains due to these enhanced specifications. The study has been carried out in a predominantly agricultural area. For the study reported here, one set of overlapping data acquired on the same day by WiFS and AWiFS sensors has been selected. This eliminates the need of atmospheric correction/ normalization for comparison. The effect of spatial resolution has been studied by applying ISODATA spectral clustering algorithm with number of clusters set at three different levels, viz., 10, 20 and 30. They are assumed to mimic first, second and third level classification, respectively. Output images were filtered using 3 × 3 majority filter. Homogeneous polygons having area less than 1/2 and 1 pixel of WiFS were recorded. This indicates the minimum loss by using WiFS data. A relative gain of 10 – 15 % is observed due to improvement in spatial resolution. For comparison of radiometry, local variance measure was used. It was observed that local variance is much larger for AWiFS data in comparison with WiFS data. This indicates presence of enhanced local contrast, hence heterogeneity, in AWiFS data over WiFS data. Separability analysis has been carried out to demonstrate improvements due to two additional spectral bands (Green and SWIR).     

Seismic microzonation of Dehradun City using geophysical and geotechnical characteristics in the upper 30 m of soil column

The understanding of geotechnical characteristics of near-surface material is of fundamental interest in seismic microzonation. Shear wave velocity (Vs), one of the most important soil properties for soil response modeling, has been evaluated through seismic profiling using the multichannel analysis of surface waves in the city of Dehradun situated along the foothills of northwest Himalaya. Fifty sites in the city have been investigated with survey lines between 72 and 96 m in length. Multiple 1-D and interpolated 2-D profiles have been generated up to a depth of 30–40 m. The Vs were used in the SHAKE2000 software in combination with seismic input motion of the recent Chamoli earthquake to obtain site response and amplification spectra. The estimated Vs are higher in the northern part of the study area (i.e., 200–700 m/s from the surface to a depth of about 30 m) as compared to the south and southwestern parts of the city (i.e., 180–400 m/s for the same depth range). The response spectra suggest that spectral acceleration values for two-story structures are three to eight times higher than peak ground acceleration at bedrock. The analysis also suggests peak amplification at 3–4, 2–2.5, and 1–1.5 Hz in the northern, central, and south-southwestern parts of the city, respectively. The spatial distributions of Vs and spectral accelerations provide valuable information for the seismic microzonation in different parts of the urban area of Dehradun.     

Major ion chemistry of two cratonic rivers in the tropics: Weathering rates and their controlling factors

Continental weathering plays a dominant role in regulating the global carbon cycle, soil chemistry and nutrient supply to oceans. The CO₂-mediated silicate weathering acts as a major CO₂ sink, whereas sulphuric acid-mediated carbonate dissolution releases CO₂ to the atmosphere–ocean system. In this study, dissolved major ions and silica concentrations of two tropical (Damodar and Subarnarekha) river systems from India have been measured to constrain the type and rate of chemical weathering for these basins. The total dissolved solids (TDS) of these rivers, a measure of total solute supply from all possible sources, are about 2–3 times higher than that of the global average for rivers. Mass balance calculations involving inverse modelling estimate that 63 ± 11% of total cations are derived from rock weathering, of which 27 ± 7% of cations are supplied through silicate weathering. The sulphide-S concentrations are estimated by comparing the water chemistry of these two rivers with that of a nearby river (Brahmani) with similar lithology but no signatures of sulphide oxidation. The outflows of Damodar and Subarnarekha rivers receive 17% and 55% of SO₄ through sulphide oxidation, respectively. The sulphide oxidation fluxes from the ore mining areas, such as upper Damodar (0.52 × 10⁹ mol/yr) and lower Subarnarekha (0.66 × 10⁹ mol/yr) basins, are disproportionally (~9 times) higher compared to their fractional areal coverage to the global drainage area. The corresponding CO₂ release rate (2.84 × 10⁴ mol/km²/yr) for the Damodar basin is lower by five times than its CO₂ uptake rate (1.38 × 10⁵ mol/km²/yr). The outcomes of this study underscore the dominance of sulphide oxidation in controlling the dissolved chemical (cationic and sulphur) fluxes.     

A geo-information system approach for forest fire likelihood based on causative and anti-causative factors

Innumerable forest fire spread models exist for taking a decision, but far less focus is on the real causative factors which initiate/ignite fire in an area. It has been observed that the majority of the forest fires in India are initiated due to anthropogenic factors. In this study, we develop a geo-information system approach for management of forest fire in Mudumalai Wildlife Sanctuary, Tamil Nadu, India, with the objective to develop a forest fire likelihood model, integrating GIS and knowledge-based approach for predicting fire-sensitive initiation areas considering major causative and anti-causative factors. Amongst the various causative factors investigated, it was found that wildlife-dependent factor (antler collection and poaching) contributed significantly to fire occurrence followed by management-dependent factors (uncontrolled tourism and grazing), with much less influence of demographic factors. Similarly, anti-causative factor (stationing of anti-poaching/ fire camps) was considered as quite significant.

The likelihood model so developed, envisaging various factors and flammability, accounted for different scenarios as a result of pair-wise comparison on an ordinal scale in a knowledge matrix. The inferential statistics computed indicated the robustness of the model and its insensitivity to moderate changes. It makes it possible for this forest fire likelihood model to predict and prevent a forest fire in an effective and scientific manner because it can assume forest fire likelihood in real time and present in proper time.     

Radon as seismic precursor: new data with well water of Jalpaiguri,India

This paper reports the measurement of radon concentration in well water at the site of Jalpaiguri (26°32′N, 88°46′E) near the active fault zone of West Bengal, India. Radon concentration has been measured in well water with the help of solid-state nuclear track detectors (SSNTD). The study indicates a positive correlation between radon anomaly and earthquake. The data of radon content in well water have been compared with that in soil gas at the same site.     

Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve

Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R ²) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale.