The catchments of Rishiganga and then Dhauliganga valleys in the Chamoli district of Uttarakhand were impacted by a catastrophic flood triggered due to a massive rockslide, caused by wedge failure on 7th February, 2021. It is estimated that the massive rockslide of * 23 million cubic meter volume containing base rock, deposited ice, and snow got detached from the northern slopes of the Trishul mountain range near Ronti Glacier and created a vertical fall of almost 1700 m before severely impacting the Ronti Gad valley located at 1.5 km downstream of Ronti Glacier snout. The huge detached mass of rock and ice (GLIMS ID: G079733E30381N) swiftly moved downstream through the glaciated valley entraining snow, debris, mud on its way, caused rapid fluidization, created massive water/slush waves, and washed away partially or completely the hydel power projects and bridges in its route. It is estimated that * 0.93 Peta Joules of potential energy led to the generation of a significant amount of kinetic and thermal energy, good enough to trigger above-mentioned processes. Post-event analysis of high-resolution satellite data shows flood water marks in the valley and on the rock outcrops reaching up to * 80-150 m height on the way to Raini Village. The mud and the slush produced through this process led to the formation of a dammed lake and temporarily blocked one of the tributaries of the Rishiganga joining from the northeast. This study provides an insight into the sequence of events as they unfolded, through multi-temporal satellite image analysis, aerial survey, seismological data in conjunction with various other geo-spatial and geo-visualization tools for unraveling the flood event that has happened on February 7, 2021. We also discuss the potential cause of rockslide and the process mechanism of this unique event, causing loss of lives and property besides widespread devastation.

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<p><strong>Abstract.</strong> The seasonal snow cover and permanent ice in form of Himalayan glaciers provide fresh water to many perineal rivers of Himalayas. The melt water from seasonal snow and glaciers, especially during of 15 March to 15 June acts as important source of water for drinking, hydropower and irrigation requirements of many areas in North India. This work has highlights the use of C-band Synthetic Aperture Radar (SAR) data from RISAT-1, Sentinel-1A and 1B satellites and ALOS-PALSAR-2 PolInSAR data for snow cover and glacier dynamics study for parts of North West Himalaya. Glacier velocity was derived using InSAR based method using 6 day temporal interval images from Sentinel-1 satellites and 14 day interval for PALSAR-2 satellite. High coherence was obtained for main glacier in both the data sets, which resulted accurate line of site (LOS) glacier velocity estimates for test glaciers. These InSAR data glacier velocity results are obtained after a gap of 21 years. Glacier facies was estimated using multi-temporal SAR image composition based classification. All these maps were verified by extensive ground surveys done at these sites during 2014–2017. The time series data of C-band SAR in VV/VH polarisation was also used to map snow cover in test basins of Bhagirathi and Beas River. The VV/VH data clearly shows difference between dry and wet snow, thus helping in improved snow cover mapping using SAR data. This study will help in refining algorithms to be used for such studies using upcoming NASA-ISRO SAR (NISAR) mission.</p>

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ABSTRACT:The water resources status and availability of any river basin is of primary importance for overall and sustainable development of any river basin. This study has been done in Beas river basin which is located in North Western Himalaya for assessing the status of water resources in present and future climate change scenarios. In this study hydrological modelling approach has been used for quantifying the water balance components of Beas river basin upto Pandoh. The variable infiltration capacity (VIC) model has been used in energy balance mode for Beas river basin at 1km grid scale. The VIC model has been run with snow elevation zones files to simulate the snow module of VIC. The model was run with National Centre for Environmental Prediction (NCEP) forcing data (Tmax, Tmin, Rainfall and wind speed at 0.5degree resolution) from 1 Jan. 1999 to 31 Dec 2006 for calibration purpose. The additional component of glacier melt was added into overall river runoff using semi-empirical approach utilizing air temperature and glacier type and extent data. The ground water component is computed from overall recharge of ground water by water balance approach. The overall water balance approach is validated with river discharge data provided by Bhakra Beas Management Board (BBMB) from 1994-2014. VIC routing module was used to assess pixel wise flow availability at daily, monthly and annual time scales. The mean monthly flow at Pandoh during study period varied from 19 -1581 m 3 /s from VIC and 50 to 1556 m 3 /sec from observation data, with minimum water flow occurring in month of January and maximum flow in month of August with annual R 2 of 0.68. The future climate change data is taken from CORDEX database. The climate model of NOAA-GFDL-ESM2M for IPCC RCP scenario 4.5 and 8.5 were used for South Asia at 0.44 deg. grid from year 2006 to 2100. The climate forcing data for VIC model was prepared using daily maximum and minimum near surface air temperature, daily precipitation and daily surface wind speed. The GFDL model also gives validation phase scenarios from 2006 to 2015, which are used to test the overall model performance with current data. The current assessment made by hydrological water balance based approach has given reasonable good results in Beas river basin. The main limitation of this study is lack of full representation of glacier melt flow using fully energy balance model. This component will be addressed in coming time and it will be integrated with tradition hydrological and snowmelt runoff models. The other limitation of current study is dependence on NCEP or other reanalysis of climate forcing data for hydrological modelling, this leads to mismatch between actual and simulated water balance components. This problem can be addressed if more ground based and fine resolution grid based hydro meteorological data are used as input forcing data for hydrological modelling.