Water vapour is highly variable over tropical region and sensitive to weather condition, monsoon onset, green house effect, and pollution level in Ganga River. In the present study, variability in water vapour derived from Global Positioning System (GPS) over Varanasi (25$^{\circ}$20$^{\prime}$N, 82$^{\circ}$59$^{\prime}$E) during the period 2007–2010 has been studied. The GPS-derived water vapour (WV) has been compared with those retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) and ECMWF. The GPS-WV data concurrent to MODIS and ECMWF timing has been correlated to perform further analysis. To study the accuracy of water vapour retrieved from the MODIS and ECMWF, root mean square error (RMSE), absolute error (AE), correlation and standard deviation in it are computed with respect to GPS-derived water vapour. Analysis shows an annual correlation $R^{2}$ = 86%, RMSE = 9.5 mm and AE (MODIS–GPS) = 7.0 mm in MODIS retrieval and annual correlation R$^{2}$ = 86%, RMSE = 6.1 mm and AE (ECMWF–GPS) = 2.4 mm in ECMWF reanalysis retrieval. Correlation of ECMWF and MODIS datasets with the GPS datasets are found to vary significantly with seasons. The correlation is high during monsoon season and low during spring season. Water vapour is found to be an indicator for the onset of monsoon.

$\bf{Highlights}$

$\bullet$ Accuracy of water vapor (WV) retrieved from the MODIS and ECMWF with respect to GPS WV.

$\bullet$ High Annual correlation of $R^{2}$ = 0.86 between both MODIS–GPS and ECMWF–GPS.

$\bullet$ The correlation is high during monsoon season and low during spring season.

$\bullet$ The performance of ECMWF is found to be better than that of MODIS.

The perturbation produced in the atmosphere/ionosphere associated with earthquake precursors during seismic activity of two major earthquakes which occurred on (1) 24 June 2019 in Indonesia (M = 7.3) and (2) on 19 August 2018 at Ndoi, Fiji (M = 8.2), are studied. Based on statistical analysis of total electron content (TEC) data, the presence of ionospheric perturbations 5 days before and after the main shock are found, which depends on the distance as well as direction of observation point from the epicentre. In general, ionospheric perturbations after the EQ at all the stations are found larger than that before the EQ. Probable mechanisms behind these perturbations associated with EQ are also being discussed. The ionospheric perturbations are observed at stations which are at larger distances from the epicentre, but not observed over other stations in different directions which are comparatively closer to the epicentre. These results suggest that seismic induced ionospheric anomaly is not isotropic in nature. Ozone data from three satellites: AIRS, OMI, and TOMS-like and MERRA-2 model are also analyzed 5 days before the EQ day and compared to the monthly average level. A strong link between anomalous variation in ionospheric TEC and atmospheric ozone data prior to both the EQs is noticed.

This study investigates presence/absence of homogeneity in 32-yr (1981–2012) rainfall record of four individual monsoon months (June–September) and monsoon season (JJAS) for 16 stations in Saraswati River basin, Gujarat, India. Temporal homogeneity is examined by Hartley, Link-Wallace, Bartlett, and Tukey tests, and spatial homogeneity is tested by Levene’s and Tukey tests. Coefficient of variation for rainfall in June (72–163%), July (48–100%), August (78–114%) and September (93–127%) indicate a large variability in comparison to that in JJAS period (45–60%). Correlation coefficient (r) finds moderately significant (r $\geq$ 0.7) to highly significant (0.7 > r $\geq$ 0.3) relationships in rainfall for 68 (57%), 120 (100%), 120 (100%), 109 (91%), and 120 (100%) pairs of stations in June, July, August, September, and JJAS, respectively. Distribution of rainfall is uniform and stable in July, and hence, kharif crops may be sown in July to mitigate impact of uncertain rainfall on agriculture. Dissimilar results of four homogeneity tests justify approach of adopting multiple statistical tests. Considering the likely findings of Link–Wallace and Tukey tests, both are recommended for testing homogeneity. Non-homogeneity is found at Paswadal (June, September, and JJAS), Navawas (August and JJAS), Palanpur (JJAS), and Pilucha (September) stations. The Levene’s test reveals spatial homogeneity in July, August, September, and JJAS; and non-homogeneity in June. Hierarchical cluster analysis delineates four clusters of rainfall stations in four months and JJAS with their geographically distinct locations and a remarkable difference in inter-annual rainfall dynamics.