The meteorological drought dynamics and its impacts on rice productivity has been evaluated for the Indian Summer Monsoon Rainfall (ISMR) season using the standardized precipitation index (SPI) over the middle Gangetic plains (MGP) of Bihar. The meteorological drought over the ISMR period was found to be a recurring phenomenon coinciding with the rice growing season over Bihar. The rice crop has an intensive water requirement; therefore, it is significantly impacted by the meteorological droughts. In the present study, spatiotemporal characteristics viz. intensity, frequency, and probability of meteorological drought has been assessed along with an investigation for significant trends and detection of regime shift points to identify the impact of drought on rice production. For the purpose, SPI-4 derived from high resolution gridded daily rainfall data (0.25° × 0.25°) from India Meteorological Department (IMD) has been considered to analyse the meteorological drought episodes over agro-climatic zones of Bihar from 1961 to 2019. The regime shifts were determined using the Rodionov test for the drought dynamics and production of rice in Bihar. A moderate to severe drought-prone zone was found over the zone BRZ3B; while zone BRZ2 and BRZ3A showed comparatively a greater number of mild drought events persisting with more than 70% probability of occurrence. An inkling of increasing dependency on groundwater is found, which is in turn governing the rice production regime. The present study shows there is a substantial need for climate resilience and food security policies incorporating the subtle linkage between SPI variability and crop production, especially over rice producing regions of the globe.

The rapid urbanisation impacts on environment, climate, agriculture, water resources trigger several problems to human beings. The present study is carried out to estimate intensity and trend of Urban Heat Island (UHI) as Surface UHI (SUHI) over towns/cities of the Gangetic plain of the state of Bihar, India, in which urban areas show relatively greater Land Surface Temperature (LST) than its rural surroundings especially during night times. The LST data (2001-14) of Moderate Resolution Imaging Spectroradiometer (MODIS) is used for five major towns/cities of Bihar namely, Bhagalpur, Gaya, Patna, Purnea and Muzzaffarpur. Each city is classified into Urban, Suburban and Rural zones as per land cover of the area. During winter months (January, February, November and December), UHI is more intense over all towns/cities. Mann-Kendall Test is applied on Surface Urban Heat Island Intensity (SUHII) in which MK-Test Statistic (S) shows a significant increasing trend. This trend would alarm a risk to increase in air pollution, heat related biohazards, energy demand in the region. This study shows the need of urban greening and proper town planning over the considered areas to mitigate the changes.

The meteorological drought is a recurring phenomenon for Bihar, a densely populated Indian state situated on the Eastern Gangetic Plain (EGP). Drought largely affects a wide population of the state since most people residing here predominantly depend on agriculture for livelihood. The linkage between Aerosol and Indian Summer Monsoon Rainfall (ISMR) or southwest monsoon rainfall is a complex process affecting the hydrological cycle, which in turn may cause meteorological drought over the study area. Therefore, the relation between the Standardized Precipitation Index (SPI); a metric for categorising drought intensity, and Aerosol Optical Depth (AOD) are examined from 2000 to 2019. The high resolution (0.25° × 0.25°) gridded rainfall data (2000–2019) of India Meteorological Department (IMD) and AOD data (2000–2019) at a resolution of 1° × 1° at 550 nm from Moderate Resolution Imaging Spectro-radiometer (MODIS) products are analysed. To understand the role of the aerosol on rainfall and to further investigate the influence on underlying cloud properties as a probable cause of meteorological drought, MODIS-derived cloud parameters namely, Cloud Top Temperature (CTT), Cloud Top Pressure (CTP), and Cloud Fraction (CF) at the resolution of 1° × 1° for the period of 2000–2019 have been examined. A strong inverse relationship between CTT/CTP and SPI whereas as a directly positive relationship between CTT/CTP and AOD is found thus a strong correlation between SPI and AOD is also well verified by cloud parameters. A possible linkage between aerosols and drought conditions through indirect and semi direct effects of aerosol cloud interactions was also found to be quite important for Bihar & EGP.

The global warming and its impact on the cryosphere is a matter of serious concern. The Sikkim and the Eastern Himalaya are a canvas of vivid landscapes and of different climate zones. The study of cryosphere needs more attention on long term climatic trends of surface air temperature. The Gurudongmar area is very much important because this area is surrounded by glaciers and as well as cold desert and TsoLhamo Lake nearby. The Gurudongmar lake (located at an altitude of 17,800 ft) has been studied by several researchers in the context of Glacial Lake Outburst Floods (GLOFs) and reported a high risk lake which is being largely affected by global warming and climate change. The present study is aimed to investigate the trend of temperature in recent past and in future time periods over the study area of Sikkim. The observed and model's simulated gridded temperature data is considered to inkling of rising trend in winter months of December-January-February (DJF) over the study area. An increase in temperature is found for the future time period. This can be linked to the increasing hazard risk and change in local cryosphere environment.

The current research aimed to evaluate the predictive skill of statistically downscaled National Aeronautics Space Administration (NASA) Earth Exchange Global Daily Downscaled Projection (NEX-GDDP) data in simulating the Indian summer monsoon rainfall (ISMR) for the period of 1961–2005 over the individual homogeneous monsoon regions of India (HMRI). For the purpose, five models are selected, as these models (in GCM) have shown better performance in the simulation of ISMR by the researcher. The spatial characteristics and statistical scores (annual cycle, percentage bias, Taylor score, probability distribution function) are used to evaluate the performance of each model in simulating rainfall over land points of individual HMRI. In the spatial analysis, it seems that models of NEX-GDDP can simulate the ISMR, pretty well in comparison to APHRODITE (observation), and show a moderate to significantly high correlation (grid point) over each of the HMRI particularly to core monsoon region, except over few parts of PI. The Taylor statistics suggest that the model CanESM performs very well over the regions of PI, NWI, and WCI. The models MPI-ESM-LR and NorESM perform well in simulating the ISMR over CNI, followed by ACCESS, CanESM, and CCSM4. The models have varying bias in predicting the rainfall; however, ACCESS does perform well and shows the minimum bias (ranges from ~ 1 to ~ 14% only) among others. The models CanESM and NorESM (except over CNI) performed relatively better. The NEX-GDDP models overcome the global climate models (GCMs) in the retrospective simulation of ISMR over the land points of India. It is concluded that the models have good predictability of JJAS rainfall but unable to catch daily rainfall variability.

Climate models are widely used for global and regional assessment of climate change. The present study aims to assess the ability of regional climate models (RCMs) of the Coordinated Regional Climate Downscaling Experiment (CORDEX) and their driving global climate models (GCMs) of Coupled Models Intercomparison Project phase 5 (CMIP5) in simulating the Indian summer monsoon rainfall (ISMR) over India (1979–2005). The assessment of CORDEX-RCMs driven by the boundary conditions from GCMs is necessary to know the capability of RCMs in the simulation of ISMR over India. The spatiotemporal analysis of ISMR is performed for past periods to understand its characteristics while attempt is made for future projected changes over the homogeneous rainfall region of India. The study reveals that RCM REMO2009 (MPI) and its driving GCM MPI-ESM-LR are close to the observed rainfall of Global Precipitation Climatology Centre (GPCC). Further, it is noticed that the biases in REMO2009 (MPI) and GCMs viz. MPI-ESM-LR and GFDL-CM3 are of comparable amplitude making them suitable for future projection of ISMR for 2016–2045 under Representative Concentration Pathways (RCPs) 4.5 and 8.5 at 99% and 95% confidence levels. In the simulation of REMO2009 (MPI) and its driving GCM (MPI-ESM-LR) under RCPs 4.5 and 8.5, an excess of rainfall is possible over the parts of Peninsular India (PI) and West Central India (WCI) while a deficit over the North West India (NWI). The simulation of the GCM, GFDL-CM3 depicts an excess of rainfall over NWI, PI, and deficit over WCI under both emission scenarios.

Meteorological drought in India arises due to significant deficiency of rainfall for abnormal periods over an area. The large spatial and temporal variability of Indian summer monsoon rainfall (ISMR) over the Eastern Gangetic Plain (EGP) of India triggers meteorological drought (further leading to agricultural and hydrological drought), with widespread effects on both agricultural production and water resources over the area. To assess meteorological drought over agro-climatic zones of the states of Uttar Pradesh (UP), Bihar and West Bengal (WB) in the EGP, high-resolution gridded rainfall data (1961–2013) at resolution of 0.25° × 0.25° of India Meteorological Department (IMD) and u, v wind at 850 hPa at resolution of 0.25 × 0.25° of ERA-40 (1961–2002) of the European Centre for Medium-Range Weather Forecasts (ECMWF) is considered. Over the agro-climatic zones, the seasonality index (SI) of summer monsoon rainfall, spatial and temporal distribution of the 4-month Standardized Precipitation Index (SPI-4), frequency and probability of drought occurrence is estimated. The severe drought-prone zones are found to be over agro-climatic zones 6, 8 and 10 of UP; 1, 2 and 3B of Bihar with more than 50% probability of drought occurrence. At a 95% confidence level, a significant decrease in rainfall (for the period 1961–2013) is found over these zones. Over the EGP, a low-level easterly wind at 850 hPa in July is shifted towards foothills of the Himalaya, while in August it is weakened during drought conditions. This low-level easterly wind may be responsible for less moisture incursion over the Gangetic Plain from the Bay of Bengal, and may be the probable cause of less rainfall over the EGP, leading to meteorological drought.

The aerosol optical depth (AOD) is an important physical parameter and dimensionless number. The possible link between AOD and variability of summer monsoon rainfall and surface temperature over the densely populated Gangetic Plain may be used to assess change in weather and climate over the Plain. For examining the impact of AOD on summer monsoon rainfall and surface temperature, monthly data of AOD for the period of 2000–2015 are obtained from a remotely sensed moderate resolution imaging spectro-radiometer sensor at 550 nm and at a surface resolution of $$1{^{\circ }}\times 1{^{\circ }}$$ . For the period of 2000–2015, rainfall and surface temperature data at a resolution of $$1{^{\circ }}\times 1{^{\circ }}$$ are obtained from Indian Meteorological Department (IMD) and surface wind data are obtained from National Centers for Environmental Prediction (NCEP). Summer monsoon rainfall and AOD are inversely related during 2000–2015. On an average, a difference in the mean monthly surface maximum and minimum temperatures increases (decreases) with a decrease (increase) of AOD. The high degree of correlation exists between AOD and a difference in $$T_{\mathrm{max}}$$ and $$T_{\mathrm{min}}$$ during January to June–July. In winter months, relative strength of negative vorticity over the Gangetic Plain and positive vorticity in the adjacent area may be cause of more dispersion of AOD in February in comparison with that in December and January and therefore more AOD is noticed in January and December.