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While studies over retreat (reduction in glacier mass and snow cover area) of the Gangotri Glacier have received worldwide attention; no research has so far been conducted to quantify the contributions of snowmelt and glacier melt to the total flow of the Bhagirathi (upper Ganga) River emanating from the Gangotri Glacier. In the present study, the fractional contributions of snowmelt, glacier melt and direct runoff to the Bhagirathi River were estimated on ten daily and monthly time intervals for the ablation period (May to September) of 2005 using environmental isotope (δ2H, δ18O and 3H) techniques. Hydrograph separation was carried out using a two-component and three-component isotope mixing models. The estimated average seasonal (ablation period) contributions of snowmelt, glacier melt and direct runoff to the Bhagirathi River were 59.6%, 36.8% and 3.6%, respectively. Also, the significant temporal variations in their contributions were observed. The monthly proportions of snowmelt in the river flow varied from 42.9% (July) to 91.9% (May), while the monthly ratios of glacier melt ranged between 8.1% (May) and 47.4% (July). The observed temporal variability in the estimated contributions of the snowmelt could be linked to the Snow Cover Area (SCA) of the catchment. The results of the hydrograph separation indicate that the snowmelt dominates the river flow during the ablation period. Environmental tritium (3H) data also supports this finding. The presence of 3H in the river (average value: 8.8TU) endorse that it is mainly derived from the melting of relatively modern snow (average value: 12.4TU). These results suggest that the stable isotopes of oxygen and hydrogen in combination with radioactive 3H can be effectively used for the identification of the sources of water that sustains the river flow in the glaciated catchment of the Gangotri Glacier, and may contribute to a more robust assessment of the hydrological budget in the glaciated catchments of the Himalayan Region.

Water resources are under severe stress in the highly populated Indus River Basin due to the increased consumption of water across different sectors and climate change. Coping with these challenges, requires a clear understanding on hydrological processes and anthropogenic activities, and how these are influencing recharging and spatiotemporal availability of groundwater in the basin. The present study aims to investigate the natural and anthropogenic impact on Terrestrial Water Storage (TWS) over the Indus River Basin by using a series of statistical methods and the observation data from the Gravity Recovery and Climate Experiment (GRACE) and Follow-On (GRACE-FO). Our results show that (i) TWS Anomaly (TWSA) experienced a significant decrease from 2002 to 2020, particularly in the MUIP; (ii) the UIB showed a weak decreasing trend in TWSA as a result of the accelerated glacier melting; (iii) there was significant loss of groundwater (1.57 mm/month) caused by ineffective water management and over-exploitation; and (iv) assisted by favorable meteorological conditions, the precipitation presented a positive trend against the weakness of the Westerlies, which exerted the positive influence on TWSA.

The Himalayan-Karakoram (HK) region is often referred as the ‘Water Tower of Asia’, as the melt water from the glaciers and snow cover of this mountainous region provide a continuous supply of water to the major rivers of South Asia, notably the Indus, Ganga, and Brahmaputra. Recent studies reveal that climate change has altered the accumulation of snow and the snowmelt processes of the region, leading to changes in the size and mass of glaciers, with potential impacts on the river flow downstream and the ecosystems and livelihoods depending on it. Consequently, mapping and monitoring glaciers, mainly through estimates of their spatial extent, recession rate, debris cover extent and surface velocity, are integral parts of water resource management. Because of the rugged topography and harsh climatic conditions of the HK region, estimating these parameters based on traditional techniques is tedious and challenging. The exploitation of geospatial data and information through Geo-intelligence (GI) has, thus, emerged as a data collection and processing method in the study of glaciers. It processes images from satellites and airborne vehicles with high-end mathematical and statistical algorithms. This chapter demonstrates the effectiveness of GI techniques to map and monitor the Sakchum Glacier of the HK region with the results suggesting that these techniques could potentially contribute to long-term planning and management of water resources in regions whose water supply depends on the melt water of glaciers.

Although the Ganga is an important fluvial system of India, the isotopic investigations of its water are limited and not reported for the whole length of the river. This limits the understanding of the hydrological processes of the river whose flow characteristics have been changed considerably over the years due to changes in the climate and land use/land cover patterns of the region. This study intends to fill this gap of data and knowledge. Hence, a robust isotope datasets were generated for a period of 2–3 years from 11 locations covering the entire length of the river (2250 km). These data were further analysed to study the controls on spatiotemporal patterns of river water isotopes and understand dominance of different hydrological processes affecting flow characteristics of the river in different reaches. The stable isotopes of oxygen and hydrogen in river water exhibited large spatial and temporal variation throughout the study periods. The most negative isotopic values (mean δ18O: −15‰ to −9.7‰) between 0 and 318 km in the mountainous region during pre-monsoon period attributes to the dominant glaciers melt contribution while the altitude effect in rainfall is mainly responsible during monsoon season. However, less negative isotopic values (mean δ18O: −9.7‰ to −4.3‰) between 318 and 1000 km correspond to the mixing of water from major tributaries. Other hydrological factors responsible for the increased isotopic values include evaporative enrichment and contribution of isotopically less negative groundwater. The observed relatively low isotopic values (δ18O: −4.3‰ to −6.9‰) in the downstream of 1000 km of the river are due to joining of tributaries originating from the Nepal Himalayas. Results substantiate that distinct isotopic values found in different reaches of the river are because of the variations in basin characteristics, hydro-meteorological processes, and water mixing. These findings would contribute in developing a better knowledge on hydrological behaviour of the Ganga River and help in taking appropriate measures for maintaining its sustainable flows.

The ensembles of two Global Climate Models (GCMs) namely, third generation Canadian Coupled Global Climate Model (CGCM3) and Hadley Center Coupled Model, version 3 (HadCM3) are used to project future precipitation in a part of North-Western (N-W) Himalayan region, India. Statistical downscaling method is used to downscale and generate future scenarios of precipitation at station scale from large scale climate variables obtained from GCMs. The observed historical precipitation data has been collected for three metrological stations, namely, Rampur, Sunni and Kasol falling in the basin for further analysis. The future trends and patterns in precipitation under scenarios A2 and A1B for CGCM3 model, and A2 and B2 for HadCM3 model are analyzed for these stations under three different time periods: 2020’s, 2050’s and 2080’s. An overall rise in mean annual precipitation under scenarios A2 and A1B for CGCM3 model have been noticed for future periods: 2020’s, 2050’s and 2080’s. Decrease, in precipitation has been found under A2 and B2 scenarios of HadCM3 model for 2050’s and slight increase for 2080’s periods. Based on the analysis of results, CGCM3 model has been found better for simulation of precipitation in comparison to HadCM3 model.Journal of Hydrology and Meteorology, Vol. 9(1) 2015, p.1-14

The sensitivity of Sutlej river sub-basin (middle catchment) which is located in N-W Himalaya is investigated for its hydrologic response to potential changes in climate variability. The predictors of two global climate models (GCMs) that are found to perform well over Indian sub-continents are downscaled, and future time series of temperature (maximum and minimum) and precipitation is generated using statistical downscaling model (SDSM) under A1B, A2, and B2 emission scenarios. An overall increase in mean annual temperature and precipitation is predicted under both the models for future periods. The predicted increase in temperature is relatively higher for HadCM3 model compares to CGCM3 model whereas it is opposite for precipitation. The model also predicts considerable shift in monthly pattern of temperature and precipitation. Further, soil and water assessment tool (SWAT) is employed to appraise future changes in stream flow and water balance of the sub-basin under projected climate scenarios. The simulation results show that in future, increase in mean annual stream flow are likely to range from 1.3 to 7.8 % for CGCM3 model and 0.3 to 3.4 % for HadCM3 model, respectively. However, decrease in mean monthly stream flow is predicted under scenarios of CGCM3 model (from January to May and from November to December) and HadCM3 model (from January to April and from October to December), respectively.

Ensembles of two Global Climate Models (GCMs), CGCM3 and HadCM3, are used to project future maximum temperature (T Max), minimum temperature (T Min) and precipitation in a part of Sutlej River Basin, northwestern Himalayan region, India. Large scale atmospheric variables of CGCM3 and HadCM3 under different emission scenarios and the National Centre for Environmental Prediction/National Centre for Atmospheric Research reanalysis datasets are downscaled using Statistical Downscaling Model (SDSM). Variability and changes in T Max, T Min and precipitation under scenarios A1B and A2 of CGCM3 model and A2 and B2 of HadCM3 model are presented for future periods: 2020s, 2050s and 2080s. The study reveals rise in annual average T Max, T Min and precipitation under scenarios A1B and A2 for CGCM3 model as well as under A2 and B2 scenarios for HadCM3 model in 2020s, 2050s and 2080s. Increase in mean monthly T Min is also observed for all months of the year under all scenarios of both the models. This is followed by decrease in T Max during June, July August and September. However, the model projects rise in precipitation in months of July, August and September under A1B and A2 scenarios of CGCM3 model and A2 and B2 of HadCM3 model for future periods.

Geospatial techniques which encompasses remote sensing and Geographical Information System (GIS) has found to be very useful in delineation of basin parameters which are of the great importance in morphological studies. In the present paper, geospatial techniques were applied to delineate basin parameters for the Bhagirathi River Basin, in the Western Himalaya. A total number of 14 parameters were obtained to study its morphometric character. A maximum 6th order of stream is encountered in the study area and pattern is mainly of dendritic and radial type. Further, the low value 0.655 of drainage density indicates that the basin is highly permeable. The value of mean bifurcation ratio 4.68 in the basin indicates a less structural disturbance in drainage pattern of the basin. Accordingly, the value of texture ratio reflect a very course drainage texture in the basin. This study would help the local people to plan and utilize the resources for sustainable development of the basin area based on watershed parameters.

Sutlej basin, a mountainous river basin is located in N-W Himalayan region. This basin has highest potential for hydropower generation as compared to other basins of Indus River system. Recent studies have revealed rise in mean annual surface temperature which will modify pattern of Sutlej River flow in this basin. The present paper has aimed for studying annual and seasonal patterns of river discharge at different gauging sites of Sutlej River basin (middle catchment), India. The study has been performed over three gauging sites, namely, Kasol, Sunni and Rampur located under different physiographic and climatic conditions. The daily historical records (1970-2010) of 41 years river discharge data have been employed for statistical analysis. The annual and seasonal Standardized Discharge Indices (SDI) has been derived in order to preserve uniformity and facilitate comparison between flows of Sutlej River at different sites. Mann-Kendall (MK) test, a non-parametric test method, has been applied to detect trend in annual and seasonal SDI for periods 1970-2010. Decadal (annual and seasonal) patterns in SDI have also been discussed. The results of annual and seasonal trend analysis have revealed decreasing trends in SDI at all the gauging sites. The trend in annual SDI is statistically significant (95% confidence level) at Rampur (0,04 cumec/year) and insignificant at Kasol (0,02 cumec/year) and Sunni (0,01 cumec/year) respectively. The study of annual decadal change in SDI at all the sites shows that reduction in river discharge has occurred in the decade of 2001-2010. Before this, continuous rise in annual discharge has been reported at all the sites from decades 1970-1980 to the last decade of 20th century (1991-2000). The decline in river flow may affect agriculture and electricity production as well as there may be problems related with drinking water. The present study is expected to be useful for planning water resources related projects that can be undertaken in the Sutlej basin.

In this study, past (1970-2005) as well as future long term (2011-2099) trends in various extreme events of temperature and precipitation have been investigated over selected hydro-meteorological stations in the Sutlej river basin. The ensembles of two Coupled Model Intercomparison Project (CMIP3) models: third generation Canadian Coupled Global Climate Model and Hadley Centre Coupled Model have been used for simulation of future daily time series of temperature (maximum and minimum) and precipitation under A2 emission scenario. Large scale atmospheric variables of both models and National Centre for Environmental Prediction/National Centre for Atmospheric Research reanalysis data sets have been downscaled using statistical downscaling technique at individual stations. A total number of 25 extreme indices of temperature (14) and precipitation (11) as specified by the Expert Team of the World Meteorological Organization and Climate Variability and Predictability are derived for the past and future periods. Trends in extreme indices are detected over time using the modified Mann-Kendall test method. The stations which have shown either decrease or no change in hot extreme events (i.e., maximum TMax, warm days, warm nights, maximum TMin, tropical nights, summer days and warm spell duration indicators) for 1970–2005 and increase in cold extreme events (cool days, cool nights, frost days and cold spell duration indicators) are predicted to increase and decrease respectively in the future. In addition, an increase in frequency and intensity of extreme precipitation events is also predicted.

The annual and seasonal trend analysis of different surface temperature parameters (average, maximum, minimum and diurnal temperature range) has been done for historical (1971–2005) and future periods (2011–2099) in the middle catchment of Sutlej river basin, India. The future time series of temperature data has been generated through statistical downscaling from large scale predictors of CGCM3 and HadCM3 models under A2 scenario. Modified Mann–Kendall test and Cumulative Sum (CUSUM) chart have been used for detecting trend and sequential shift in time series of temperature parameters. The results of annual trend analysis for period of 1971–2005 show increasing as well as decreasing trends in average (T Mean), maximum (T Max), minimum (T Min) temperature and increasing trends in Diurnal Temperature Range (DTR) at different stations. But the annual trend analysis of downscaled data has revealed statistically significant (95% confidence level) rising trends in T Mean, T Max, T Min and falling trend in DTR for the period 2011–2099. The decreasing trend in DTR is due to higher rate of increase in T Min compared to T Max.