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This study analyses the variability of daytime-only, nighttime-only, and compound heat waves (HWs) and their impact on population exposure across India using shared socioeconomic pathways (SSPs) scenarios (SSP126, SSP245, SSP370, and SSP585) from the Coupled Model Intercomparison Project Phase 6 experiment. The research questions addressed are: (1) what effects might compound heatwaves have under climate change scenarios? (2) How are compound heatwaves expected to impact the population in the future? The outcomes indicate that the compound HWs may increase by 4.6 events annually in Northwest India (NWI) under the SSP585 scenario. In contrast, daytime-only HWs are expected to decline after 2060, except in the Himalayan region, possibly due to changes in monsoon patterns and increased evaporative cooling. It is anticipated that nighttime-only heatwaves will uniformly increase across all regions and scenarios, with the most substantial rises observed in the Central Northeast India (CNI) and NWI. Under the SSP370 scenario during 2061–2100, the population exposure to compound heatwaves and nighttime-only heatwaves is projected to increase substantially across all regions. Specifically, exposure to compound heatwaves is anticipated to exceed historical levels by more than 30 times in most regions. Both the CNI and NWI regions show the highest rise in compound and nighttime-only heatwave extremes. The outcomes provide a substantial scientific foundation for policymakers to inform and enhance heat action plans at the national, state, and local levels.

Due to climate change, the agricultural and socio-economic development over the eastern Himalayan region of India is greatly affected. The present study has been carried out to investigate the implications of climate change on regional crop water requirements (CWR) and crop irrigation requirement (CIR) of major crops (maize, wheat and, rice) over a Himalayan state, i.e., Sikkim. Daily climatic datasets such as rainfall, minimum temperature, maximum temperature, wind speed, sunshine hours, and relative humidity are used for this analysis along with crop and soil data. For future period (2021–2099), climatic datasets are collected from the four climate models (ACCESS1-0, CCSM4, CNRM-CM5 and MPI-ESM-LR) of CORDEX under two different scenarios, i.e., Representative Concentration Pathway (RCP) 4.5 and 8.5. CWR & CIR of maize, wheat and rice crops are projected for three-time windows, i.e. start term (2021–2046), mid-term (2047–2073), and end term (2074–2099) by taking 1998–2015 as baseline period. In addition, uncertainty and sensitivity analysis is carried out. The outcomes from the study suggest an increase in the CWR towards the end of the twenty-first century for rice and wheat over West (8% and 39%) and South (11% and 37%) Sikkim with respect to baseline period. In case of Maize, a decreasing trend is noticed over West (− 4%) and East (− 15%) Sikkim. For all the crops in East Sikkim, a declining trend is likely to occur. In most of the cases, the CIR has increased towards the end of the twenty-first century. The uncertainty analysis reveals RCP 4.5 as the possible scenario over the study area. The outcomes from the study facilitate the agricultural and water managers for adopting effective measures to ensure sustainability.

Understanding the devastating nature of drought, this work has assessed the variability in the Severity-Area-Frequency (SAF) curve using Standardised Precipitation Evapotranspiration Index (SPEI) as meteorological drought indicator over Maharashtra, India. The future meteorological outputs from 19 Global Circulation Models (GCMs) of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) under two Representative Concentration Pathway (RCP) 4.5 and 8.5 are used. The SAF curves are developed for five different seasons namely pre-monsoon, monsoon, post-monsoon, Kharif and Rabi. The uncertainty associated with GCMs and scenarios is assessed using possibility theory. The results reveal that the precipitation magnitude is expected to increase in pre-monsoon, monsoon, and Kharif seasons over most of the areas in Maharashtra. However, the temperature is likely to increase during all the seasons in future. The frequency of extreme drought condition during post-monsoon, pre-monsoon, and Rabi seasons shows an increment as compared to historical period. The Rabi season drought is noticed to be most pronounced and likely to affect significant portions of Maharashtra during all return periods. The SAF curve reveals that, in most of the cases, the percentage of drought affected area is expected to increase for high magnitude of severity.

Analysing the link between terrestrial ecosystem productivity (i.e., Net Primary Productivity: NPP) and extreme climate conditions is vital in the context of increasing threats due to climate change. To reveal the impact of changing extreme conditions on NPP, a copula-based probabilistic model was developed, and the study was carried out over 25 river basins and 10 vegetation types of India. Further, the resiliency of the terrestrial ecosystems to sustain the extreme disturbances was evaluated at annual scale, monsoon, and non-monsoon seasons. The results showed, 15 out of 25 river basins were at high risks, and terrestrial ecosystems in only 5 river basins were resilient to extreme climatic conditions. Moreover, at least 50% area under 4 out of 10 vegetation cover types was found to be facing high chances of a drastic reduction in NPP, and 8 out of 10 vegetation cover types were non-resilient with the changing extreme climate conditions.

The agricultural drought significantly affects the socio-economic sectors in the agrarian country like India. Though there is a larger variability in the drought characteristics, the time to propagation from meteorological to agricultural drought is not investigated at regional scale in India. The Standardised Precipitation Evapotranspiration Index (SPEI), and Standardised Soil moisture Index (SSI) are computed incorporating large-scale climatic oscillations and regional hydro-meteorological variables. The time to propagation is calculated based on three different approaches. In addition, the internal characteristics of agricultural drought propagation is computed. The important findings from the study suggest that the time of propagation varies between 5 to 7 months for drought initiation, 9 to 15 months for drought peak, and 10 to 20 months for drought termination. The internal drought development and recover periods varies from 3.1 to 6 months. Over most of the area, the instantaneous drought development and recovery speed magnitude varies between 0.20 and 0.60. Lastly, it is observed that the exclusion of physical covariates leads to underestimation of agricultural drought propagation characteristics over India. The results of the current study can be used to guide future early warning and monitoring systems for agricultural drought as well as the study of agricultural drought at the regional level.

Nowadays, monitoring and prediction of air quality parameters are becoming significantly important research topics in the context of increasing urbanization and industrialization. Therefore, efficient modelling of air quality parameters is essential because such an approach would enable to identify the existing and forthcoming implication of air pollution. In recent years, sharp rise in air pollution levels in Indian National Capital Territory of Delhi (NCT-Delhi) has made it the most polluted city of the world. Machine learning approaches are considered as an efficient and cost-effective method to model the air quality parameters and are widely used. However, current methods fail to incorporate long-term dependencies arising due to complex interaction of natural and anthropogenic factors. The present study is mainly aimed at predicting O3, PM2.5, NOx, and CO concentrations at a location in NCT-Delhi using the long short-term memory (LSTM) approach, which is considered as more efficient over other deep learning methods. Factors and parameters such as vehicular emissions, meteorological conditions, traffic data, and pollutant levels are employed in five different combinations. Performance evaluation of LSTM algorithms for hourly concentration prediction is carried out during 2008–2010, and it is found that LSTM models efficiently deal with the complexities and is immensely effective in ambient air quality forecasting. This paper can be considered as a significant motivation for carrying research on urban air pollution using latest LSTMs and helping the government and policymakers a better forecasting methodology for planning measures to curb ill impacts of degrading air quality.

In the context of climate change, the uncertainty associated with Global Climate Models (GCM) and scenarios needs to be assessed for effective management practices and decision-making. The present study focuses on modelling the GCM and scenario uncertainty using Reliability Ensemble Averaging (REA) and possibility theory in projecting streamflows over Wainganga river basin. A macro scale, semi-distributed, grid-based hydrological model is used to project the streamflows from 2020 to 2094. The observed meteorological data are collected from the India Meteorological Department (IMD) and the streamflow data is obtained from Central Water Commission (CWC) Hyderabad. In REA, meteorological data are weighted based on the performance and convergence criteria (GCM uncertainty). Whereas in possibility theory, based on the projection of different GCMs and scenarios during recent past (2006–2015) possibility values are assigned. Based on the possibility values most probable experiment and weighted mean possible CDF for the future periods are obtained. The result shows that there is no significant difference in the outcomes is observed between REA and possibility theory. The uncertainty associated with GCM is more significant than the scenario uncertainty. An increasing trend in the low and medium flows is predicted in annual and monsoon period. However, flows during the non-monsoon season are projected to increase significantly. Moreover, it is observed that streamflow generation not only depends on the change in precipitation but also depends on the previous state of physical characteristics of the region.

Climate change and global warming surge the frequency and severity of extreme weather events like heatwaves, cyclones, floods, etc. This study assesses future heatwave events in four Indian cities, i.e., Srinagar, Jaipur, Guwahati, and Visakhapatnam. It uses CMIP 6 projections with four SSP scenarios, i.e., SSP 126, 245, 370, and 585. The yearly value of the heatwave magnitude index is used to classify the events in cities. The forthcoming forecast is distributed into three identical periods of 27 years each, i.e., near- (2020–2046), mid- (2047–2073), and long-term periods (2074–2100). The outcomes from the study showed that heatwave events would increase across the cities for all periods under SSP 370 and 585 scenarios. It is computed that 104 extreme events are probable to be observed across these four cities. This study highlights the importance of adaptive techniques in dealing with the negative implications of predicted heatwave weather events.

Evaluating the impact of climate change at river basin level has become essential for proper management of the water resources. In the present study, Godavari River basin in India is taken as study area to project the monthly monsoon precipitation using statistical downscaling. The downscaling method used is a regression based downscaling termed as fuzzy clustering with multiple regression. Among the atmospheric variables simulated by global circulation/climate model (GCM) mean sea level pressure, specific humidity and 500 hPa geopotential height are used as predictors. 1 o  × 1 o gridded rainfall data over Godavari river basin are collected from India Meteorological Department (IMD). A statistical relationship is established between the predictors and predictand (monsoon rainfall) to project the monsoon rainfall for the future using the Canadian Earth System Model (CanESM2) over IMD grid points under the Representative Concentration Pathways 2.6, 4.5 and 8.5 (RCP 2.6, 4.5, 8.5) scenarios of Fifth Coupled Model Inter-Comparison Project (CMIP 5). Downscaling procedure is applied to all 25 IMD grid points over the basin to find out the spatial distribution of monsoon rainfall for the future scenarios. For 2.6 and 4.5 scenarios results show an increasing trend. For scenario 8.5 rainfall showed a mixed trend with rainfall decreasing in the first thirty years of prediction and then increasing gradually over the next sixty years.

In the context of climate change, it is essential to quantify the uncertainty for effective design and risk management practices. In the present study, we have accessed the climate model and flood return level uncertainties over a river basin. Six high-resolution global climate models (GCMs) with two Representative Concentration Pathways (RCPs) are used to project the future climate change impact on streamflow of Wainganga River basin. Uncertainty associated with the use of high-resolution multiple GCM is treated with reliability ensemble average (REA) followed by bias correction. The bias-corrected weighted outputs are used as input to variable infiltration capacity (VIC) model, a physically based hydrological model. Calibration and validation are carried out for the hydrological model, and the parameters of VIC are fixed through trial-and-error method. The uncertainty in flood return level associated with the future projected flows is dealt with the Bayesian analysis and modelled through Markov Chain Monte Carlo (MCMC) simulation technique using Metropolis–Hastings algorithm with the non-informative prior distribution. The study provides a robust framework, which will help in effective decision-making and adaptation strategies over the river basin.