Gangotri glacier dynamics from multi-sensor SAR and optical data

The present study has analyzed dynamics of Gangotri glacier using multiple remote sensing (RS) datasets and ground based observations. Interferometric Synthetic Aperture Radar (InSAR) data pairs from European Remote Sensing satellite (ERS 1/2) tandem pair for spring of 1996, Sentinel-1 SAR pairs and Japanese's Advance Land Observation System (ALOS) PALSAR-2 SAR data for Spring of 2015 were used to derive glacier-surface velocity at seasonal time scale using Differential InSAR (DInSAR) techniques. Bi-static TanDEM-X (Experimental) data was used for the 1st time to estimate glacier surface elevation changes for a period of 22, 44, 88 days during summer of 2012 using InSAR techniques in this study. Annual glacier velocity was also estimated using temporal panchromatic data of LANDSAT-5 (30 m), LANDSAT-7/8 (15 m), Sentinel-2 (10 m) and Indian Remote Sensing Satellite IRS-1C/1D panchromatic (5 m) data during 1998–2019 with feature tracking approach. This study has estimated glacier surface velocity and surface elevation changes for the major parts of Gangotri glacier and its tributary glaciers using medium to high resolution optical and SAR datasets, at annual and seasonal time scale, which is an improvement over earlier studies, wherein snout based glacier recession or only main glacier velocities were reported. The velocity and slope were used to assess glacier-ice thickness distribution using Glabtop-2, slope dependent and laminar flow based methods over the Gangotri group of glaciers. The estimated ice thickness was estimated in the range of 58–550 m for the complete glacier while few small areas in middle &amp; upper regions carry higher thickness of about 607 m. The estimated glacier-ice thickness was found in the range of 58–67 m at the snout region. The estimation was validated using 2014 field measurements from Terrestrial Laser Scanner (TLS) for the first time and correlation was found to be 0.799 at snout of the glacier.</p

Assessment of Eco-Environmental Vulnerability Using Remote Sensing and GIS Tools in Maharashtra Region, India

Maharashtra region is prone to various disasters such as drought, floods, cyclones and earthquake and has been exposed to extreme weather events like dry spells. Communities within these dry lands are poor and face extreme conditions of water stress. This study has been carried out to analyze and quantify climatic and anthropogenic effect on eco-environmental vulnerability dynamic change. To achieve that a numerical model is set up, consisting of eight factors that are elevation, land use, drought, slope, NDVI, soil-type, soil erosion (water), and population density index &amp; has been evaluated using the method of spatial principle component analysis (SPCA) on Remote Sensing and GIS platform. The integrated eco-environmental vulnerability index (EVI) of study area is estimated to analyse spatial-temporal dynamic vulnerability changes in the 11 years gap from 2000 and 2011. The results show that the study area has become eco-environmental vulnerable slightly (about 80% of the region) with an increased eco-environmental vulnerability integrated index (EVSI) value by more than 50% (i.e., about 74%) and the driving force of dynamic change is mainly caused by socio-economic activities. In addition the estimation has been regionalized into thirty-four districts to serve as a base for decision-making for eco-environmental recovering and rebuilding. It is found that the most vulnerable district in 2011 is Ratnagiri and the least one is Sangli. There are nine districts which shows more than 100% increase in EVSI value, with the highest increase in Hingoli(100.65%), indicating that the districts have become most environmental vulnerable in the study-period. The research concludes that the method, supported by G.I.S using SPCA can’t only represent distinctly the input spatial distribution of plain-mountain-belt feature, but also respect the whole river-valley as a single unit

Hydrological Modelling Using a Rainfall Simulator over an Experimental Hillslope Plot

Hydrological processes are complex to compute in hilly areas when compared to plain areas. The governing processes behind runoff generation on hillslopes are subsurface storm flow, saturation excess flow, overland flow, return flow and pipe storage. The simulations of the above processes in the soil matrix require detailed hillslope hydrological modelling. In the present study, a hillslope experimental plot has been designed to study the runoff generation processes on the plot scale. The setup is designed keeping in view the natural hillslope conditions prevailing in the Northwestern Himalayas, India where high intensity rainfall events occur frequently. A rainfall simulator was installed over the experimental hillslope plot to generate rainfall with an intensity of 100 mm/h, which represents the dominating rainfall intensity range in the region. Soil moisture sensors were also installed at variable depths from 100 to 1000 mm at different locations of the plot to observe the soil moisture regime. From the experimental observations it was found that once the soil is saturated, it remains at field capacity for the next 24–36 h. Such antecedent moisture conditions are most favorable for the generation of rapid stormflow from hillslopes. A dye infiltration test was performed on the undisturbed soil column to observe the macropore fraction variability over the vegetated hillslopes. The estimated macropore fractions are used as essential input for the hillslope hydrological model. The main objective of the present study was to develop and test a method for estimating runoff responses from natural rainfall over hillslopes of the Northwestern Himalayas using a portable rainfall simulator. Using the experimental data and the developed conceptual model, the overland flow and the subsurface flow through a macropore-dominated area have been estimated/analyzed. The surface and subsurface runoff estimated using the developed hillslope hydrological model compared well with the observed surface runoff for a rainfall intensity of 100 mm/h. The surface runoff hydrograph was very well predicted by the model, with correlation coefficient (R2) and Nash–Sutcliffe efficiency coefficient (E) as 0.95 and 0.91, respectively. The observed soil/macropore storage component was estimated with the help of water balance equation and compared with the model predicted macropore storage. The error in computing the soil/macropore storage was estimated as 0.38 mm i.e., 13%

Synergistic analysis of satellite, unmanned aerial vehicle, terrestrial laser scanner data and process-based modelling for understanding the dynamics and morphological changes around the snout of Gangotri Glacier, India

The glaciated areas of the Himalaya often experience mass movement, glacial lake outburst flood and other associated hazards, posing threat to the communities and infrastructure in the downstream areas. A large debris flow occurred during July 16–19, 2017 near the present snout of the Gangotri Glacier in the Garhwal Himalaya, India resulting in significant geomorphological changes in the vicinity. The present study assesses the applicability of multi-source remote sensing data from satellites, unmanned aerial vehicle (UAV) and terrestrial laser scanner (TLS) along with process-based modelling to understand and quantify glacier snout dynamics and morphological changes around Gangotri Glacier in the Garhwal Himalaya of India.Weshow that retreat rates of Gangotri Glacier snout along the lateral flow lines are (left flowline 29.6m year−1; right flowline 60.5 m year−1) are significantly higher compared to the central flow line (18.2myear−1) during2010–2020 leading to total loss in glacial ice area of 0.11 ± 0.015 km2. The snout dynamics and evolution of the new channel from the terminus of the adjoining Meru Glacier, connecting the moraine-dammed glacial lake and the Bhagirathi River, suggest that retreat of the Gangotri Glacier, intense precipitation and excessive seepage from the glacial lake were the important drivers of the debris flow in July 2017. The accumulated debris occupied an area of ~0.25 km2 near the snout of the Gangotri Glacier and shifted Bhagirathi River by 36–200 ± 9.35m towards northeast. The synergistic analysis of pre- and post-event satellite, UAV and TLS-based digital elevation models (DEMs) and satellite images indicate that about −8±0.066Å~106m3 of sediments were generated by the debris flow. The results from remote sensing data suggest that a significant portion (~60%) of the deposited debris has been transported to the downstream areas between July 16–19, 2017 and October 2, 2017. Regular monitoring of the area is recommended, especially in light of climate change using earth observation data and ground measurements. The multi-source integrated framework implemented in this study is generic and can be applied for any debris flow or landslide studies in glaciated terrain.</p