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The change in the phase, from quiescence to surge, may perturb the glacier’s dynamical behavior. To understand these changes through geospatial technique, the fastest moving tidewater Monacobreen Glacier, Svalbard, was studied for the year 2016–2019. It was elucidated that the surge had initiated during 2017–2018 before it the glacier was in quiescence since 2001. Initially, the glacier radar zones, namely percolation refreeze, lower percolation, and clean ice, were identified using the multi-temporal SAR data. These zones were utilized to delineate the equilibrium line altitude, and it was found that it is continuously moving down during surging, unlike the quiescence phase. The accumulation area ratio was always more than 0.67 throughout the analysis. The seasonal change in glacier surface velocity for both the phases was estimated, adapting the most appropriate Offset Tracking approach using the SAR data. The mean velocity over the main trunk was found to vary from 0.5 to 4 m/day. Thereafter, Glen's flow law equation was used to estimate the ice-thickness and found that the glacier has an average thickness range of 216–326 m in quiescence and 136–244 m during the surging. The analysis depicted that the basal shear stresses are increasing with surging, and the viscosity is decreasing. It can be said that the transition of the glacier from quiescent to surging phase has entirely changed its dynamic behavior and characteristics. Further, in the absence of field observations, the geospatial technique may provide reasonable estimates of the glacier’s physical and dynamical parameters.

Indo Gangetic flood plains are subjected to recurring floods due to large low-lying land areas adjacent to major Himalayan Rivers. Recent floods in the Ganga River during the late withdrawal phase of Indian summer monsoon rains in 2019 had reportedly caused death of ∼200–500 people and damaged major infrastructures in different parts of Bihar state, India. This paper presents a detailed analysis of the spatial extent and temporal pattern of flood inundation areas in Patna district of Bihar using Sentinel-1 Synthetic Aperture Radar (SAR) data. The results obtained using K-means classification show that about 1268 km2 (20% of the studied portion) of the land area were inundated during the peak flooding conditions on 9th October 2019. The peak flooding lasts for more than 20 days, with a maximum water depth of 18 m in the main river. Owing to the continually changing nature of river morphology by recurring floods, we also analyzed the channel morphology and bar characteristics before, during, and after the floods. Geomorphic change assessment indicates that the total bar occupied area was decreased from 661 km2 for pre-flood time to 446 km2 in peak flood time, whereas it retains its original shape in post-flood images.

Land use/land cover has become a prime concern that urgently needs to be addressed in the study of global environmental change. In the present study, supported by the land use/land cover maps of the retrospective time periods 2000, 2010 and 2020, derived using Landsat TM and OLI datasets, respectively; we used the land-use transition matrix, Markov-CA chain model to derive detailed information of the spatio-temporal variation of the land use/land cover change. Additionally, we highlight decrease in forest land (19 km2 and 37.7 km2, i.e., 0.88% and 1.75% of the total area), rangeland (0.2 km2 and 1.9 km2, 0.01% and 0.09%), and perennial snow or ice (8 km2 and 9 km2, 0.37% and 0.42%); on the other hand, increase in agricultural land (19 km2 and 33.9 km2, 0.88% and 1.58%), urban or built-up land (4.44 km2 and 8.6 km2, 0.21% and 0.40%) and water (4.18 km2 and 6.28 km2, 0.19% and 0.29%), during 2010 and 2020 relative to baseline period 2000. Finally, based on the CA transition rules and the transition area matrix produced from the Markov-CA chain model based calibration process, the future land use/land cover maps were projected for 2050 and 2080.

Prioritization of river basin in semi-arid to sub-humid tropical region is important in the context of natural resources conservation and management. In this paper, the Gomti River basin was prioritized for soil–water resource conservation based on analysis of morphometric parameters and land use–land cover with RS and GIS techniques. Gomti River is a monsoon and groundwater-fed river; it is a tributary of Ganga River. The basin has been divided into seventeen sub-basins. ASTER (30 m) Digital Elevation Model data and LANDSAT—8 (30 m) satellite imagery were used to analyze the morphometric parameters and to generate land-use–land-cover data for the basin through supervised image classification technique. The linear, areal, and relief aspects of each sub-basins were determined. Morphometric analysis revealed that the basin is a fifth-order drainage system with a dendritic to sub-dendritic drainage pattern. Drainage patterns indicate that sub-surface materials are homogeneous and lack structural control over topography. The circulatory ratio value indicates the basin is an elongated shape. Low values of morphometric parameters indicate good permeability, infiltration, and coarse drainage texture, low relief, and elongated shape indicated good groundwater potential in the basin. The final prioritization result shows that the sub-basin 12 is at the highest priority with the greatest shortage of groundwater potential, so immediate water conservation measures are needed. Contrary, sub-basin 3 is at low priority that indicates the highest groundwater potential. This study suggests the primary priority to improve the water resource management in semi-arid regions at the basin level.

The spatiotemporal study of the glaciers under the changing environment is critical from the freshwater availability, especially in the perennial rivers flowing out of them during the lean season. The Mrigthuni Glacier, one of the major glaciers in the Pindar River basin in Uttarakhand, has been selected as a study glacier for the present analysis. The long-term (1951–2018) meteorological data analysis indicated that the temperature increased in both the maximum ablation and accumulation period; however, the precipitation decreased during the primary accumulation period. The change detected in climate patterns provided an impetus to study its impact on the glacier using geospatial techniques. It was found that the glacier front retreated by 831.4 ± 42 m at the rate of 32.94 m yr−1 from 1990 to 2015. Further, the glacier accumulation area ratio has reduced from 55.63 to 53.22%, with a 66 m upward shift in equilibrium line altitude (ELA). The glacier's mass balance has reduced from 0.23 to 0.28 ± 0.035 m w.e. during the analysis period, based on a general equation developed for northwestern Himalaya using the optical remote sensing data. Due to the limitations of optical data, Synthetic Aperture Radar (SAR) data was used to map glacier radar zones. The distinct percolation/refreeze, transient firn, and ablation zones were identified on the SAR data composite of the different seasons. Considering SAR-based ELA as the most accurate, the analysis confirmed the negative mass balance with a value of 0.62 ± 0.036 m w.e. Therefore, it was concluded that SAR-based analysis might be vital for accurate glacier dynamics studies especially the mass balance.

Most of the part of India is already under water-stressed condition. In this regard, the continuous monitoring of the water levels (WL) and storage capacity of reservoirs, lakes, and rivers is very important for the estimation and utilization of water resources effectively. The long term ground observed WL of many of the water bodies is not easily available, which may be very critical for proper water resources management. Satellite radar altimetry is the remote sensing technique, which is being used to study sea surface height for the last 25 years. The advancement in radar technology with time has provided the opportunity to exploit the technique to retrieve the WL of inland water bodies. In the current study, an attempt has been made to generate long term time series on WL of around 29 geometrically complicated inland water bodies in India. These water bodies are mainly large reservoirs namely Ban Sagar, Balimela, Bargi, Bhakra, Gandhi Sagar, Hasdeo, Indravati, Jalaput, Kadana, Kolab, Mahi Bajaj, Maithon, Massanjore, Pong, Ramganga, Ranapratap Sagar, Rihand, Sardar Sarovar, Shivaji Sagar, Tilaiya, Ujjani, and Ukai. The WL of these water bodies was retrieved for around two decades using the European Remote-Sensing Satellite – 2 (ERS-2), ENVISAT Radar Altimeter – 2 (ENVISAT RA-2), and Saral-AltiKa altimeters data through Ice-1 retracking algorithm. Further, an attempt has also been made to estimate the WL of gauged/ungauged lakes namely Mansarovar, Pangong, Chilika, Bhopal, and Rann of Kutch over which Saral-AltiKa pass was there. As after July 2016, the SARAL-AltiKa is operating in the drifting orbit, systematic repeated observation of WL data of all reservoirs was not possible. The data of drifted tracks of Saral-AltiKa were tested for WL estimation of Ban Sagar reservoir. As the ERS-2, ENVISAT RA-2 and Saral-AltiKa all were having almost the same passing tracks, a long term WL series of these lakes could be generated from 1997 - 2016. However, at present only Sentinel – 3 is in orbit, the continuous altimeter based WL monitoring of some of these reservoirs (Gandhi Sagar, Nathsagar, Ranapratap, Ujjani, and Ukai) was attempted through Sentinel-3A satellite data from 2016 to 2018. The accuracy of the retrieved WL was than validated against the observed WL. In most of the reservoirs, a systematic bias was found due to the different characteristics and geoid height of each reservoir. The coefficient of determination, R2, value for a majority of reservoirs was as good as 0.9. In the case of ERS-2, the values of R2 varied for 0.44 to 0.97 with root mean square error (RMSE) in the range of 0.63 to 2.72m. These statistics improved with the ENVISAT RA-2 data analysis, the R2 value reached more than 0.90 for around 11 reservoirs. The highest, 0.99, for Hasdeo and Shivaji Sagar Reservoirs with RMSE of 0.44 and 0.56, respectively. Further, the accuracy improved with the analysis of Saral-AltiKa data. The R2 was always more than 0.9 for each reservoir and the lowest RMSE reduced to 0.03. Therefore, it can be said that the accuracy and consistency of WL retrieval through satellite altimetry has improved with time. Furthermore, the altimeter based retrieved WL may be used in hydrological studies and can contribute to better water resources management.

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.

Synthetic Aperture Radar (SAR) remote sensing is a state-of-the-art tool for snow monitoring and snow parameters estimation. SAR remote sensing-based techniques, such as interferometric SAR (InSAR)...

Urban flooding and waterlogging are causing menace in many cities around the world from the perspective of day-to-day functioning, health and hygiene, communication, and the consequent damages they cause to urban environment. The present study is an attempt to understand the urban flood risks in parts of Bhubaneswar City, India, based on its hydrodynamic set-up and level of urbanisation. The Storm Water Management Model is used for peak flow analysis, and the flooding extent has been assessed while taking into consideration the elevation, slope, land use/land cover (LULC) and design Storm Water Drain (SWD) infrastructure of the city. The micro-watersheds for each SWD are delineated using digital surface model derived from airborne Light Detection and Ranging (LiDAR) data (1 m), and the LULC information is obtained from high-resolution optical remote sensing data. After the model simulation, it is estimated that peak runoff is relatively higher, i.e. 0.1–0.5 cumecs for a large number of micro-watersheds, even rising to more than 1.5 cumecs for some, indicating the severity of urban floods in the city. After integrating the simulated flooding pattern with the vulnerability associated with socio-economic characteristics of urban dwellers, the flood risk has been assessed. The study suggests that capacity of design SWD systems needs augmentation according to present and predicted flooding conditions for the city.

In view of an exponential increase in the negative impacts of flash-floods globally, the present work aims at the identification of flash-floods-prone river reaches in the Beas river basin, Himachal Pradesh, India using a multi-criteria indexing technique. The flood hazard index (FHI) was computed by implementing analytical hierarchy process (AHP) model on 6 hydrologic parameters influencing flood hazard, namely rainfall intensity, curve number (CN) grid, time of travel, slope, Manning's roughness coefficient and drainage density. The CN grid (empirical parameter to estimate direct surface runoff) was used as one of the parameters which depend upon the land use, hydrologic soil group and hydrologic conditions. It is imperative to mention that remote sensing and geographical information system (GIS) techniques played a crucial role in the preparation of these 6 parameter layers. The AHP model calculates the normalized weights for each parameter using pair-wise comparison matrices. The rainfall intensity and curve number were the factors having the highest normalized weight of 34.52 each. Subsequently, the estimated weights of the parameters and hazard level-wise rating scores were used in a GIS environment to generate FHI. The generated FHI raster was masked using floodplain layer within geomorphology map and river buffer to identify flash-floods-affected river reaches. The generated flash-floods map was validated by historical flash-floods ground points, field observations and remote sensing data. The results depicted that the river reaches in the north and east of the Beas basin are susceptible to flash-floods which are mainly governed by heavy rainfall intensity and high runoff characteristics. The river stretches namely Bahang–Manali (Beas), Kullu–Bhuntar (Beas) and Manikaran–Kheer-Ganga (Parvati) have been categorized into very high and high flash-floods zones. Decreasing trend of normalized differential vegetation index (NDVI) was observed for river reaches falling within the very high and high zones indicating the vegetation loss post successive flash-floods events. The river order 2 lies in the very high and high flash-floods zones, indicating the fact that the contribution of tributaries is significant to flood events. Flash-floods map will serve as catastrophic product, which will help policymakers to take suitable measures to reduce the risk of flash-floods.

Complexity and heterogeneity of urban areas lead to difficulty in urban weather simulations and climate modeling. Diversity and size of urban areas necessitate to downscale global climate models to urban scale (~ hundreds of meters) and to enhance urban parameterization in the models to realistically simulate urban weather conditions. Hence, in this study, a methodology has been developed to generate multi-class urban land use land cover (LULC) by employing Resourcesat-2 LISS IV data. Weather Research and Forecast (WRF) model which is also a mesoscale numerical weather prediction and regional climate model was utilized to downscale the meteorological parameters up to 0.5 km grid resolution. Multi-class urban LULC prepared with improved urban parameters and updated Land Surface Parameters (LSPs) was ingested in model for Delhi to evaluate the model performance in three dominant seasons, i.e., summer, monsoon and winter. Evaluation of model performance with ground observation data revealed that multi-class urban LULC along with updated LSPs provided improved RMSE values of 2.31° C, 1.79 m/s and 0.94 mbar as compared to ingestion of multi-class urban LULC only (RMSE values of 3.42° C, 3.72 m/s and 1.58 mbar) for temperature at 2 m, wind speed and surface pressure, respectively. Temperature is found to be highest in summer season (38.58° C) and lowest in winter season while relative humidity is highest in monsoon season (~ 88%) and lowest in summer season (~ 30%). The study highlights the importance of ingestion of updated LSPs along with updated multi-class urban LULC for enhanced model performance.

Drought is a stochastic natural hazard that is caused by intense and persistent shortage of precipitation. The initial shortage of rainfall subsequently impacts the agriculture and hydrology sectors. Marathwada region of India comes under highly drought prone area in the country. Recent times have shown the increase in occurrence of agricultural drought in the non-monsoon season. The deviation from normal rainfall in the month of October causes soil moisture deficit which triggers an agricultural drought in the early-Rabi season. The traditional remote sensing based agricultural drought monitoring indices lack in identifying the early-season (ES) drought. An attempt has been made in the present study, to map ES agricultural drought in the Aurangabad district of Marathwada region using remote sensing. The meteorological deficit in the month of October, has been assessed using Standardized Precipitation Index (SPI). Impact of meteorological fluctuations on agricultural system in terms of dryness/wetness was evaluated using the Shortwave Angel Slope Index (SASI) derived using MODIS (Terra) Level-3, 8 daily, surface reflectance data for the October months of 2001–2012. It was observed that the area experiences moderate to severe drought 5 times with 12 years of study period (2001–2012). SASI and its parameters were estimated for each week of October month. SASI maps were further classified in four categories viz. moist vegetation; dry vegetation; moist soil and dry soil. The detailed analyses if these maps indicate that agricultural stress occurs in this area even if there is no meteorological stress. However, whenever, there is meteorological stress the area under agricultural stress exceeds more than 50% of the study region. A frequency distribution map of ES drought was prepared to identify the most drought prone area of the district and to alternately identify the irrigated area of the district.

Many methods and techniques utilize a combination of different bandwidths in the visible, NIR (Near Infrared), and SWIR (Shortwave Infrared) regions for glacier ice mapping. In this study, we investigated and compared various techniques to map the clean ice area of the glaciers using Landsat 8 and Sentinel 2 images. The effectiveness of different techniques and effects of different sensors on the mapping accuracy was also assessed in the Dhauliganga basin of Pithoragarh district, Uttarakhand state of India. Also, the time series composite of Sentinel-1 SAR (Synthetic Aperture Radar) data was used as reference data for estimating the bare or clean glacier ice area. It was reported from the study that clean ice/snow areas mapped using band ratios Red/SWIR and NIR/SWIR from Landsat 8 were overestimated by 4.41% and 4.94% respectively which were larger as compared to Sentinel 2. The most rigorous and well-known technique for glacier mapping, NDSI (Normalized Difference Snow Index) was also checked for both sensors. Clean ice/snow areas mapped from NDSI were found to be 1.35% less as compared to Red/SWIR and 2.62% greater as compared to NIR/SWIR bands of the Landsat 8. However using the same bands from the Sentinel 2, NDSI overestimated 0.67% and 5.27% respectively over both the ratios.

The North West Himalayan basins have always been prone to hydro-meteorological disasters. Among them Beas Basin is one of the highly affected basins. Beas basin is prone to cloudburst which causes huge loss to life and property every year. Increase in these devastating events have been noticed in the recent years. Climatic change is considered as the major driver for this increased occurrence of these events in the recent past. The analysis of long-term hydrological extremes over the basin will help in understanding the pattern of the hydro-meteorological extremes and also predicting its nature in near and far future. The Variable Infiltration Capacity (VIC) model at the grid size of 0.025° × 0.025° has been used in the present study, for simulating the hydrological behaviour of the Beas Basin. The parameterization of the model inputs is derived from Remote Sensing based and field observed datasets. The model was forced with meteorological dataset of ERA-Interim for the past and present time period and CORDEX dataset for the future time period. The model was calibrated using observed discharge data of Nadaun and Sujanpur stations. The Nash-Sutcliffe model efficiency of calibrated model was achieved to be 0.77 and 0.72 and coefficient of determination (R2) was 0.80 and 0.72, respectively. The validation results of the model for the same stations shows the model efficiency to be 0.73 and 0.74 with coefficient of determination (R2) as 0.67 and 0.82, respectively. The well calibrated model was used to simulate the hydrological behaviour of historic period (1979–2000), present period (2001–2017), near future period (2018–2050) and far future period (2051–2099). The exceedance probability curve method has been utilized in estimating the flood peak value for the future time period. The flood peak discharge value for the future time period comes out to be 1050 m3/s. The hydro-meteorological extremes rate per year in each period was found to be 9, 9, 12 and 14, respectively. The hydro-meteorological extremes rate is showing increasing trend in near future and very high increase in far future. The study highlights the probability of occurrence of catastrophic events in coming future. The methodology and results of the present study can be beneficial for sustainable development of the basin to counter the effect of probable hydro-meteorological extremes in coming future.

The performance evaluation of surface irrigation projects has been hitherto done at regional or project level due to the non-availability of a detailed spatial database of irrigation projects. Recent advancements in the capabilities of remote sensing techniques and Geographical Information System (GIS) have opened avenues of field level mapping and database creation for irrigation projects. In view of this, the application of remote sensing and GIS in command area mapping and detailed performance evaluation at the sub-project level (division level) was attempted, in the present study. The command area of Upper Wardha irrigation project in Amravati district of Maharashtra, India was mapped for irrigation infrastructures (canals, hydraulic structures, etc.), crop acreage, and other infrastructures (viz. road, railway, settlement, etc.) using high resolution Cartosat-1, IRS P6 LISS-IV data, and temporal IRS P6 LISS-III data. The performance of the irrigation project was evaluated using eight comparative indicators classified under three groups, namely agricultural performance, water-use performance, and physical performance. Remote sensing data along with field observed data were used to derive inputs for these performance indicators. The results indicate that the project has a positive impact on agriculture and economic growth of the region; however, water has been excessively used in some parts of the command area while supply has been inadequate in the tail region of the command. The water supply in the command area was reliable, however, not equally distributed among all the distributaries. The overall performance of the irrigation project was evaluated, and the divisions, which need immediate attention to improve the efficiency, were identified in this study.

River discharge and water level data play a vital role for various hydrological applications worldwide. However, limited availability of in-situ data has drawn attention towards using remote sensing techniques to monitor river flow. Indeed, multi-mission satellite altimetry data has been used to generate stage-discharge rating curves through power-law relations and empirical methods. The validation of hydrodynamic model-based rating curves is missing. We investigate the potential of available altimetry series (Jason 2, Jason 3, Saral/AltiKa, Sentinel 3A and Sentinel 3B) over Mahanadi River to validate the estimated rating curves at virtual stations. The hydrodynamic model (HEC-RAS) was developed and 07 virtual stations were identified for Mahanadi River from Boudh to Mundali Barrage. During calibration (July–October, 2018) and validation (July–October, 2018), Root Mean Square Error (RMSE) and Nash-Sutcliffe Efficiency (NSE) between simulated and in-situ water level was found to be (0.46 m, 0.83) and (0.45 m, 0.76) respectively. The calibrated and validated model was used to generate rating curves at virtual stations. The RMSE ranging between 27 cm to 88 cm was observed between simulated and altimetry water levels, specifying the potential of all the altimeters with varying specifications to validate the rating curves. The rating curves estimated at virtual stations provide a cost-effective tool for monitoring river flows at additional locations, producing discharge time series for various hydrological applications and assessing of contribution of lateral tributaries.

A hydrological model conceptualizing a certain rainfall event of a watershed is capable of reflecting the hydrological situation and assessing its response not only for historical but also projected climate data in future. This works presents a futuristic flood discharge estimation using the established event based HEC-HMS model corresponding to the meteorological forcing from shared socioeconomic pathways (SSPs) of Coupled Model Intercomparison Project-6 (CMIP6). The hydrological model was setup for flood-prone Himalayan Beas river basin, India. The calibration and validation of the model was carried out for the rainfall induced flooding events of monsoon 2005 and 2010, respectively. The coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) were achieved to be 0.82 and 0.79 for calibration and, 0.84 and 0.80 for validation at Bhuntar station, respectively. An improved carbon simulated CMIP6 rainfall data holding of ACCESS-ESM1.5, after bias correction and downscaling, was used to simulate the flood hydrographs in the Beas basin till 2100. The peak discharges of each decade from 2021 to 2100 was estimated and analysed, for the SSP245 and SSP585 scenarios. For the climate projection scenario SSP245, the peak flood event was estimated to be in July 2068 with peak discharge of 4446.7 m3/s while a SSP585 scenario observed extreme flood event in July 2057 having a peak discharge of 4817.2 m3/s. The estimated discharge magnitudes from SSP245 and SSP585 schemes are comparable to the 562 years and 706 years return period discharges of the basin, respectively. The study also revealed that the frequency of flooding events are maximum in the endmost decade of 2091-2100, with an increasing trend towards the later decades.Keywords: Flood, Hydrological Model, CMIP6, HEC-HMS, Himalayas, Beas river, Climate data

Increasing rate of flash-floods in Himalayan river basin causes immediate damage to human lives, daily living and infrastructure. The present work proposed flood risk assessment framework by blending the hydrodynamic modelling outputs and risk evaluation concepts. The different notions of risk as in hazard, vulnerability and exposure were evaluated over flood-prone Beas river with focus at Bhuntar, Kullu and Manali. The hydrodynamic model (MIKE 11) was established for 56 km river stretch right from Manali to Bhuntar. The flood depth and flow velocity outputs from the calibrated and validated hydrodynamic model were used for the estimation of flood hazard rating. Vulnerability maps were generated using depth-damage curves prepared by Joint Research Centre, EU, for each exposure of agriculture, settlement and roads. The 100 year return period flood risk maps were prepared and analysed for all the three towns. Key interviews and community focus group discussions were held further to strengthen, compare and verify the achieved outcomes. For a 100 year return period flood risk assessment, a total of 0.054 km2, 0.226 km2 and 0.334 km2 area was flooded and extreme flood risk zones were identified with 4.7%, 6.8%, and 10.9% area of the total inundated area at the settlement regions of towns of Manali, Kullu and Bhuntar, respectively. The area on right bank of the river was inundated severely and got classified into extreme flood risk zones. The major settlements at all the towns under consideration are at the right bank due to relatively flat, low lying terrain leading to the dire risk. The outcome of the work can assist disaster managers and local administrations for flood disaster planning in advance, thus reducing human and economic loss. Further, flood risk map could serve as catastrophic product to define flood insurance rate for various exposures in floodplain.Keywords: Flood Risk Assessment, MIKE11, Hazard, Vulnerability, Hydrodynamic Modelling

Retrieval of snow density using hybrid polarimetric RISAT-1 synthetic aperture radar (SAR) data is implemented in this study. The C-band datasets were acquired in the fine resolution stripmap mode-1 (FRS-1) in which microwaves EM are transmitted in the right circular polarization from the sensor and are received in linear (horizontal or vertical) polarizations resulting in hybrid polarizations-RH and RV. The datasets were acquired for the date of February 23, 2014 covering the Dhundi region in Manali district of Himachal Pradesh. In this season, the area experiences regular snowfall, hence the whole area was covered with the dry snow layer. Hybrid decomposition technique along with the integral equation model (IEM) is utilized for snow density extraction of the snowpack in the study area. The modeled and theoretical approaches are used to retrieve the dielectric constant of the snowpack. Fresnel coefficients, utilized in the IEM modeling approach, are the function of snowpack dielectric constant and the local incidence angle of the incident wave from the sensor. The retrieved snow density map is generated for the area. The validation of the retrieved results is done using ground data collected during the period.

In the present study, Artificial Neural Networks (ANNs) and Genetic Programming (GP) soft-computing models were established to estimate streamflow in Tel River Basin, Odisha, India. In most of the ...

An integrated approach using remote sensing (RS) and geographical information system (GIS) and groundwater modelling (GM) has been used for waterlogged areas identification in the Rohtak district of Haryana State, India. Surface waterlogged areas were delineated using optical remote sensing satellite data-based normalized difference water index (NDWI) technique. Sentinel 2 MSS (optical data) images pertaining to pre-monsoon and post-monsoon seasons were acquired and processed to extract water pixels. To overcome the limitation of false positives and cloud penetration associated with optical images, water pixels were also extracted using synthetic aperture radar (SAR) images of Sentinel 1A. Thresholding of NDWI for optical images and sigma naught for SAR images was done using the respective histograms to distinguish water and terrestrial features. Surface waterlogged areas were delineated from the generated combined water body raster based on the visual interpretation technique in a GIS environment. Sub-surface waterlogging conditions were simulated for the same year using process-based groundwater model MODFLOW. A conceptual model was created to simulate groundwater system based on flow processes, hydrogeological characteristics, aquifer hydraulic properties, source and sinks and boundary conditions. Sub-surface waterlogged areas were delineated based on depth to water table classification criteria. It was found that surface waterlogged area varies from 0.55 to 1.0% of the district area from pre-monsoon to post-monsoon season, while the percentage of sub-surface waterlogged areas was higher, varying from 9.1 to 21.6%, respectively. Statistics also shows that most of the surface waterlogged areas do not fall under the areas plagued by sub-surface waterlogging.

The basin was calibrated through monthly discharge for the period (2002–2008) including 2 years as warm-up (2000–2001), after that model was validated on 5 years of hydrometeorological datasets (2009–2013) at two gauge sites located at Neemsar (upstream gauge) and Lucknow (downstream gauge). It is found that the most sensitive parameter for moisture condition II (CN2) was initial curve number. The p-factor and r-factor were obtained in calibration period at Neemsar 0.73 and 0.58 while at Lucknow values are 0.79 and 0.51, whereas in validation period values are 0.61, 0.45 and 1.22, 0.75, respectively. Three statistical parameters have been used to evaluate the SWAT model performance such as Coefficient of Determination (R 2), Nash–Sutcliff efficiency (NSE), percent bias (PBIAS). The NSE and R 2 values were observed as 0.85, 0.84 and 0.87, 0.86, respectively, in the time of calibration period and values is 0.76, 0.76 and 0.79, 0.83, respectively, in the time of validation period at two above said gauging stations. The PBIAS values during calibration and validation period are − 13.3, − 14.7 and − 4.0, − 15.7, respectively, at the same gauge site which indicates good model performance result.

The LULC change vis-à-vis climate change inherently encompassing human dimensions consequently impact hydrological processes. A slight change in it may affect the water yield, as both are explicitly linked through various hydrological processes. The future availability of water resources largely depends upon planning and management of land use in this changing environment. However, the continuous human interactions keep on modifying the land use land cover (LULC) to fulfil the enhanced demand especially due to significant increase in population and development towards better facilities. These changes consequently impact each and every hydrological process vis-à-vis water availability. It has now become a major concern for water resources planners and managers. Moreover, these changes in LULC pattern along with changing climate put forth a huge challenge in front of them. The present study investigates the capabilities of Variable Infiltration Capacity (VIC) model, which has been developed as soil-vegetation-atmosphere transfer schemes, to assess the runoff potential. Initially, the required model inputs were derived and simulated at the national scale. It was found that the model has assessed the runoff potential of major river basins with high accuracy. The successful implication of the model at the national scale offered an opportunity to carry out a detailed hydrological simulation to study the impact of LULC change on hydrological regime of a basin. For the analysis, the Pennar River Basin, located in Southern part of India, was selected. The LULC change detection analysis showed that there is human induced alteration to LULC pattern, as the area under urban landscape is increasing (0.14%) and natural forest is declining (0.7%). These changes resulted in increase of runoff potential by around 45%. The study on Indian River basin clearly indicates that the LULC change influence and alter the hydrological regime of the river basin. Keywords: Land use land cover change, Runoff potential, Hydrological modeling, Variable infiltration capacity model, Impact assessment

Flood vulnerability is a significant component in assessing the probable degree of damage to various exposures in hazard conditions. In this study, a semi-distributed event-based hydrological model and indicator-based method were applied to evaluate the sub-basin level flood vulnerability using the Geographical Information System (GIS). The flood peak discharge of each sub-basin corresponding to the 2014 extreme flood of the Jhelum river was related with different sub-basins characteristics (terrain, hydrological, land use and soil) using a theoretical framework under an indicator-based method. The calibrated (2014) and validated (1992, 1997) hydrological model showed Nash-Sutcliffe Efficiency (NSE) of 0.98 and (0.99, 0.99) at relatively upstream gauging station Sangam against optimized Curve Number (CN) scaling factor of 0.98. The Anantnag and Kulgam districts, exhibiting multiple sub-basins contributing to the Sangam gauging station, are falling into a highly vulnerable category located in the Jhelum basin's southern part, Greater Himalayan Range. It was also revealed that sub-basins at the upstream of the Jhelum basin are more vulnerable compared to downstream area, where sub-basin W810 (Greater Himalayan), Anantnag district draining at Sangam gauging site is found as most vulnerable among the all other sub-basins. However, hydrological characteristics control the most vulnerable sub-basin peak discharge rather than other characteristics such as terrain, soil, or Land Use. Outcomes of the study will be helpful in prioritizing the flood mitigation planning not only with respect to the hydrological boundary (sub-basin level) but also with administrative district boundaries. The proposed method is generic and can be applied to any flood-prone river basin.

Satellite data are making revolutionary changes in the field of archaeology, and nowadays they have been widely used by the scientific community for regular mapping and monitoring of cultural heritage sites. The development of synthetic aperture radar (SAR) sensors helps in monitoring the heritage site with higher accuracy. This study uses the Persistent Scatter Interferometric SAR (PSInSAR), which is an advanced technique of differential SAR interferometry to find the surface deformation of the Angkor Wat temple and its premises. Seven interferometric data from the Phased Array type L-band Synthetic Aperture Radar (PALSAR) of Advanced Land Observing Satellite-1 (ALOS-1) for the period 2006–2009 and ten interferometric data from the ALOS-2 PALSAR-2 for the period 2014–2018 have been used in this study to identify the deformation pattern. It is found from this study that the Angkor Wat temple and its outer walls are stable.

Climate change can adversely affect the elements of water balance in river basins. This paper studies the effects of climate change on hydro-climatological components of the Gomti River basin using SWAT hydrological model. Model performance has been evaluated through sensitivity analysis, calibration, and validation. During calibration and validation time period, the Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R2) values were obtained as 0.86, 0.87, and 0.79, 0.83, respectively. From the sensitivity analysis, it was found that SCS runoff curve number for antecedent moisture condition II (CN2) is the most sensitive parameter, followed by saturated hydraulic conductivity (SOL_K) and then available water capacity of the soil layer (SOL_AWC) etc. To assess the future climate change effects on hydrological variables, MIROC-ESM, MIROC-ESM-CHEM, MIROC_MIROC5, and MOHC_HADGEM2_ES Global Climate Models (GCMs) under RCP 4.5 and RCP 8.5 were applied in the calibrated SWAT model. The following trends were observed after applying the scenarios—the temperature is rising under RCP 4.5 (8.5) scenarios, near-century by + 0.24% (+ 0.65), mid-century by + 2.8% (+ 4.62), and end-century by + 5.25% (+ 10.98), respectively. Precipitation is declining under RCP 4.5 (8.5) by − 29.17% (− 29.65%), − 28.69% (− 28.88%), and − 23.82% (− 20.11%), respectively. Evapotranspiration under RCP 4.5 (RCP 8.5) is increasing by + 3.62% (+ 3.95%), + 4.30% (+ 5.38%), and + 5.49% (+ 6.03%), respectively. Consequently, streamflow under RCP 4.5 (RCP 8.5) is declining by − 31.27% (− 23.99%), − 27.45% (− 28.96%), and − 24.30% (− 18.03%), respectively in near, mid, and end century. The findings will be beneficial for managing future water resources in the Gomti River basin.

Glacier velocity is one of the significant parameter for the assessment of the glacier dynamics, which directly signifies the response of the glacier health with the respect to climate change. The present work focuses on analysing the spatio-temporal characteristics of the surface velocity in the Dhauliganga basin for seven glaciers. The cross-correlation method was applied on the panchromatic scenes of Landsat-7 Enhanced Thematic Mapper Plus (ETM +), Landsat-8 Operational Land Imager (OLI) and Sentinel-2A Multispectral Instrument (MSI) sensors to estimate the velocity. The mean velocity of the seven glaciers along the central flow line ranges from 59.57 to 5.34 m/year while the average of mean velocity ranges from 30.82 to 11.04 m/year during 1993–2018. Whereas, across the flow line, mean velocity ranges from 63.58 to 4.74 m/year. The lowest velocity was observed in the T1–T2 locations near the snout, where the velocity highly dominated by mass loss and debris cover; the error in velocity estimation ranges from ± 3.67 to ± 4.57 m/year. Decreasing trend of surface velocity was observed for all the glaciers. The present work demonstrates the application of cross-correlation technique to monitor the dynamics of a group of glaciers on a basin scale having similar climatic setting. The velocity profiles of various glaciers can be used as input to methods estimating ice thickness and volume and to prioritize the glaciers in view of glacier-dynamics linked events such as surges and retreat induced to rise to pro-glacial lakes and glacier melt contribution to the river flows.