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The alteration in river flow patterns, particularly those that originate in the Himalaya, has been caused by the increased temperature and rainfall variability brought on by climate change. Due to the impending intensification of extreme climate events, as predicted by the Intergovernmental Panel on Climate Change (IPCC) in its Sixth Assessment Report, it is more essential than ever to predict changes in streamflow for future periods. Despite the fact that some research has utilised machine-learning- and deep-learning-based models to predict streamflow patterns in response to climate change, very few studies have been undertaken for a mountainous catchment, with the number of studies for the western Himalaya being minimal. This study investigates the capability of five different machine learning (ML) models and one deep learning (DL) model, namely the Gaussian linear regression model (GLM), Gaussian generalised additive model (GAM), multivariate adaptive regression splines (MARSs), artificial neural network (ANN), random forest (RF), and 1D convolutional neural network (1D-CNN), in streamflow prediction over the Sutlej River basin in the western Himalaya during the periods 2041–2070 (2050s) and 2071–2100 (2080s). Bias-corrected data downscaled at a grid resolution of 0.25∘ × 0.25∘ from six general circulation models (GCMs) of the Coupled Model Intercomparison Project Phase 6 GCM framework under two greenhouse gas (GHG) trajectories (SSP245 and SSP585) were used for this purpose. Four different rainfall scenarios (R0, R1, R2, and R3) were applied to the models trained with daily data (1979–2009) at Kasol (the outlet of the basin) in order to better understand how catchment size and the geo-hydromorphological aspects of the basin affect runoff. The predictive power of each model was assessed using six statistical measures, i.e. the coefficient of determination (R2), the ratio of the root mean square error to the standard deviation of the measured data (RSR), the mean absolute error (MAE), the Kling–Gupta efficiency (KGE), the Nash–Sutcliffe efficiency (NSE), and the percent bias (PBIAS). The RF model with rainfall scenario R3, which outperformed other models during the training (R2 = 0.90; RSR = 0.32; KGE = 0.87; NSE = 0.87; PBIAS = 0.03) and testing (R2 = 0.78; RSR = 0.47; KGE = 0.82; NSE = 0.71; PBIAS = −0.31) period, therefore was chosen to simulate streamflow in the Sutlej River in the 2050s and 2080s under the SSP245 and SSP585 scenarios. Bias correction was further applied to the projected daily streamflow in order to generate a reliable times series of the discharge. The mean ensemble of the model results shows that the mean annual streamflow of the Sutlej River is expected to rise between 2050s and 2080s by 0.79 % to 1.43 % for SSP585 and by 0.87 % to 1.10 % for SSP245. In addition, streamflow will increase during the monsoon (9.70 % to 11.41 % and 11.64 % to 12.70 %) in the 2050s and 2080s under both emission scenarios, but it will decrease during the pre-monsoon (−10.36 % to −6.12 % and −10.0 % to −9.13 %), post-monsoon (−1.23 % to −0.22 % and −5.59 % to −2.83 %), and during the winter (−21.87 % to −21.52 % and −21.87 % to −21.11 %). This variability in streamflow is highly correlated with the pattern of precipitation and temperature predicted by CMIP6 GCMs for future emission scenarios and with physical processes operating within the catchment. Predicted declines in the Sutlej River streamflow over the pre-monsoon (April to June) and winter (December to March) seasons might have a significant impact on agriculture downstream of the river, which is already having problems due to water restrictions at this time of year. The present study will therefore assist in strategy planning to ensure the sustainable use of water resources downstream by acquiring knowledge of the nature and causes of unpredictable streamflow patterns.

This study presents an approach for modelling and mapping fluvial flooding, considering both land use/land cover (LULC) change and climate change, and applies it to the Brahmani River Basin in eastern India. Climate change projections were obtained from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and their impacts on the hydrology of the catchment were investigated using HEC-HMS and HEC-RAS software. Results reveal that changes in LULC types, specifically an increase in proportions of agricultural and built-up areas and a decrease in forest cover, as undergone between years 1985 and 2018, have increased peak discharge following a storm, thereby causing an increase in spatial extent of floods of different return periods. Moreover, downscaled climate change scenarios from two General Circulation Models were used to determine potential changes in river discharge according to two GHG emission scenarios from the latest IPCC: SSP245 and SSP585. The projections indicate that peak discharge and the spatial extent of flooded areas will increase for floods with return periods ranging from two to 100 years. This study demonstrates the important influence that changes in LULC have had on the susceptibility of the BRB to flooding, with climate change projected to further enhance the risk of flooding as the century progresses. The findings of this research will promote flood risk management in the basin and widen our global understanding of the human and environmental attributions of flood risk.

The alteration in river flow patterns, particularly those that originate in the Himalayas, has been caused by the increased temperature and rainfall variability brought on by climate change. Due to the impending intensification of extreme climate events, as predicted by the Intergovernmental Panel on Climate Change (IPCC) in its sixth assessment report, it is more essential than ever to predict changes in streamflow for future periods. Despite the fact that some research has utilised machine learning-based models to predict streamflow patterns in response to climate change, very few studies have been undertaken for a mountainous catchment, with the number of studies for the western Himalaya being so small as to be considered insignificant. This study investigates the capability of different machine learning (ML) models, including the Gaussian Linear Regression Model (GLM), Gaussian Generalized Additive Model (GAM), Multivariate Adaptive Regression Splines (MARS), Artificial Neural Network (ANN), and Random Forest (RF), in streamflow prediction over the Sutlej River Basin in western Himalaya during the periods 2041–2070 (2050s) and 2071–2100 (2080s) for two greenhouse gas trajectories (SSP245 and SSP585). Coupled Model Intercomparison Project Phase 6 (CMIP6) bias corrected data downscaled at grid resolution of 0.25°× 0.25° for 6 General Circulation Models (GCMs) were used for this purpose. Four different rainfall scenarios (R0, R1, R2, and R3) were applied to the models trained with daily data (1979–2009) at Kasol (the outlet of the basin) in order to better understand how catchment size and the geo-hydro-morphological aspects of the basin affect runoff. RF model with rainfall scenario R3 which outperformed other models during the training and testing period therefore was chosen to simulate streamflow in the Sutlej River in the 2050s and 2080s under the SSP245 and SSP585 scenarios. The mean ensemble of model results show that the mean annual streamflow of the Sutlej River is expected to rise between 2050s and 2080s by 5.51 to 6.04 % for SSP585 and by 6.65 to 6.75 % for SSP245. The seasonal streamflow also is expected to increase in the 2050s and 2080s under both emission scenarios, with the exception of the pre-monsoon, where a decline in streamflow is anticipated for SSP585 in the 2080s. However, under both the emission scenarios, there seemed to be significant variation in the streamflow simulations among the individual models for various time periods. It has been found that the pattern of this variability is highly correlated with the pattern of precipitation and temperature predicted by various GCMs for future emission scenarios. The present study will therefore assist in strategy planning for ensuring the sustainable use of water resources downstream by acquiring a knowledge of the nature and causes of unpredictable streamflow patterns.

The Lesser Himalayan watersheds are vulnerable to environmental degradation due to their unstable lithology and anthropogenic pressure. A Gaula watershed was considered for the vulnerability analysis using satellite data, compound factor (CF) and multi-criteria decision making (MCDM) based on Grey Relation Analysis (GRA) and Complex Proportion Assessment of Alternatives (COPRAS) models. For prioritzation, 29 variables (12 morphometric, 5 geologies, 6 soils, 5 land uses/land covers, and 1 slope) were selected and the MCDM methods were validated using indices of the percentage of change and intensity of change. A total of five sub-watersheds (SW) were delineated, SW2 reported highest drainage density (0.58). The sinuosity index of SW1, SW4, and SW5 have 1.33, 1.34, and 1.27, showing active tectonic activities. The COPRAS reported highest intensity of change (93.33), whereas GRA and CF reported percentage of changes were 86.66 and 80.0, respectively. Hence, these models are appropriate to investigate the interrelationships of different components.

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.

There has been an exponential growth in the number of people living in urban areas since the middle of the twentieth century, and by the end of 2018, more than half of the world population lived in cities. This rapid urbanisation has created unprecedented challenges, among which the provision of domestic water has received increasing attention. Water is a basic need for humans and is the basis for socio-economic development. However, in many developing countries, governments have difficulties keeping pace with the fast rate of urbanisation due to limited financial resources and a lack of technical expertise. This ultimately results in a number of water-related problems, such as the lack of provision of an adequate water supply and improper sanitation, degradation of ecosystems and stormwater management failures. Moreover, climate change is exacerbating these water-related problems by influencing the hydrological cycle.

Intense urbanization in the Indian cities has brought about phenomenal changes in the existing landuse patterns, whereby increasing trends of surface temperature and the presence and absence of green cover and water bodies, their shape, pattern, configuration significantly modifies the thermal properties of the city. The present study comprehendeds how different biophysical, anthropogenic, and landscape composition determine and interact with the surface temperature in different climatic zones in India, by taking Jaipur and Guwahati cities as examples. This study has also incorporated several landscape metrics of composition and configuration and spatial cluster analyses to identify the most fragmented landscape in the two cities. Such complex relationship among the landuse and landcover, bio-physical parameters and the surface temperature can be assessed by using the Generalised Additive Model (GAM). Result shows that Jaipur has experienced high temperature changes in city centre in comparison to Guwahati, which can be attributed to the differential presence of environmental sensitive land class in the city cores and peripheries. Results also indicate that medium patches of environmental sensitive land class, as well as close proximity and configuration with environmental non-sensitive class reduces the surface temperature in two heterogenous landscape. Thus, Policymakers and urban planners could be assisted by our finding to mitigate surface temperature in the city.

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

Water management planners face considerable uncertainty in determining water availability, as change in global climate and land use/land cover patterns have altered hydrologic conditions and led to change in the dynamics of the water cycle. Therefore, it calls for having a better understanding of hydrologic processes and surface/groundwater dynamics in hydrologic and water resources management studies. The water budget analysis of a region can be done with the help of hydrological models and geospatial techniques. In the present chapter, applicability of geospatial techniques in hydrological modeling is described in detail.

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.

Recently, study in past trends of climate variables gained significant consideration because of its contribution in adaptions and mitigation strategies for potential future changes in climate, primarily in the area of water resource management. Future inter-annual and inter-seasonal variations in maximum and minimum temperature may bring significant changes in hydrological systems and affect regional water resources. The present study has been performed to observe past (1970–2010) as well as future (2011–2100) spatial and temporal variability in temperature (maximum and minimum) over selected stations of Sutlej basin located in North-Western Himalayan region in India. The generation of future time series of temperature data at different stations is done using statistical downscaling technique. The non-parametric test methods, modified Mann-Kendall test and Cumulative Sum chart are used for detecting monotonic trend and sequential shift in time series of maximum and minimum temperature. Sen’s slope estimator test is used to detect the magnitude of change over a period of time on annual and seasonal basis. The cooling experienced in annual TMax and TMin at Kasol in past (1970–2010) would be replaced by warming in future as increasing trends are detected in TMax during 2020s and 2050s and in TMin during 2020s, 2050s and 2080s under A1B and A2 scenarios. Similar results of warming are also predicted at Sunni for annual TMin in future under both scenarios which witnessed cooling during 1970–2010. The rise in TMin at Rampur is predicted to be continued in future as increasing trends are obtained under both the scenarios. Seasonal trend analysis reveals large variability in trends of TMax and TMin over these stations for the future periods.

Oxygen ( $${\updelta }^{18}\hbox {O}$$ ) and hydrogen ( $${\updelta }^{2}\hbox {H}$$ and $$^{3}\hbox {H}$$ ) isotopes of water, along with their hydrochemistry, were used to identify the source of a newly emerged seepage water in the downstream of Lake Nainital, located in the Lesser Himalayan region of Uttarakhand, India. A total of 57 samples of water from 19 different sites, in and around the seepage site, were collected. Samples were analysed for chemical tracers like $$\hbox {Ca}^{++}$$ , $$\hbox {Mg}^{++}$$ , $$\hbox {Na}^{+}$$ , $$\hbox {K}^{+}$$ , $${\hbox {SO}_{4}}^{--}$$ and $$\hbox {Cl}^{-}$$ using an Ion Chromatograph (Dionex IC-5000). A Dual Inlet Isotope Ratio Mass Spectrometer (DIIRMS) and an Ultra-Low Level Liquid Scintillation Counter (ULLSC), were used in measurements of stable isotopes ( $${\updelta }^{2}\hbox {H}$$ and $${\updelta }^{18}\hbox {O}$$ ) and a radioisotope ( $$^{3}\hbox {H}$$ ), respectively. Results obtained in this study repudiate the possibility of any likely connection between seepage water and the lake water, and indicate that the source of seepage water is mainly due to locally recharged groundwater. The study suggests that environmental isotopes ( $${\updelta }^{2}\hbox {H}$$ , $${\updelta }^{18}\hbox {O}$$ and $$^{3}\hbox {H}$$ ) can effectively be used as ‘tracers’ in the detection of the source of seepage water in conjunction with other hydrochemical tracers, and can help in water resource management and planning.

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.

In the recent past, lake water is greatly deteriorated due to the global climate change and anthropogenic activities because they affect both quantity and quality of the available water. Lakes in mountainous regions are generally small, and therefore more sensitive to natural/human-induced perturbations. In this study, the effect of twentieth century global climate change and human interventions was analysed for Lake Nainital, located in a densely populated valley of Kumaun Himalaya. For this purpose, gridded (0.50 × 0.50 and 0.250 × 0.250) climatic (T Mean, T Max, T Min, DTR and rainfall) data of period 1901–2000 available on monthly time steps were employed for detecting an annual and seasonal pattern of change in the climatic variables in the lake region. The samples of water collected between February 1994 and July 1996 for different months from the lake were used for determining the physical and chemical characteristics of the lake water. The samples were analysed for: field parameters (temperature, pH, EC), major ions (Ca2+, Mg2+, Na+, K+, HCO3 −, SO4 2− and Cl−), and nutrients (NO3 − and PO4 −). Results derived from the chemical analysis of water reveal that anthropogenic activities, increased in recent past, have accelerated the deterioration and eutrophication processes of the lake. However, decrease in mean annual and monsoonal rainfall will affect the various components of water balance and may cause reduction in the lake volume in the future.

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.

Changes in the spatial extent of the Snow Cover Area (SCA) of the Bhagirathi River Basin (BRB) in recent decades have impacted the hydrology of the region and the water resources of communities located downstream. Previous studies focusing on the region have to date been limited to the effects of terrain variables, namely elevation, slope and aspect, on variations in the spatial extent of snow cover without accounting for the impact of climate change. This study first investigates temporal changes in SCA in the BRB during the period 2001-2019, and their linkages to climate variability, and then examines how future climate change could impact the SCA of the basin and its implications for water resources. The SCA and Terrestrial Water Storage Anomaly (TWSA) over the basin were calculated using the MODIS daily snow cover and GRACE/GRACE-FO products, respectively, while estimates of temperature and precipitation were obtained from satellite observations and a reanalysis dataset. Climate change projections, for their part, were obtained from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and were downscaled to the catchment scale. A trend analysis revealed an increase in the SCA of the study catchment at both the annual and seasonal scales during the period 2001-2019, correlating with an increase in precipitation over the same time period. The results of trend analysis of TWSA in the basin also corroborated the increase in the mean annual SCA. A correlation analysis also detected statistically significant positive correlation between the seasonal (post-monsoon (r = 0.49, p 0.05, respectively) and winter (r = -0.69, p < 0.05 and r = -0.10, p > 0.05, respectively) seasons. An analysis of climate change scenarios downscaled over the study region revealed a significant increase in Tmean, Tmax, Tmin and precipitation with respect to the base period (2001-2020) during the pre-monsoon and winter seasons in the 2030s under the SSP245 and SSP585 greenhouse gas emission scenarios. The Multiple Linear Regression (MLR) model established based on SCA relationships with temperature and precipitation predicted a reduction in the SCA of higher altitude zones (4000m and above) during the pre-monsoon, post-monsoon and winter under SSP585 and SSP245, thereby resulting in a decline in the Bhagirathi river flow, despite a projected increase in precipitation for the region. The study demonstrates the impact of projected changes in climate on the SCA of a Himalayan catchment, and the potential implications for regions where snowmelt is important to streamflow regimes.