Traditional drought monitoring is based on observed data from both meteorological and hydrological stations. Due to the scarcity of station observation data, it is difficult to obtain accurate drought distribution characteristics, and also tedious to replicate the large-scale information of drought. Thus, Gravity Recovery and Climate Experiment (GRACE) data are utilized in monitoring and characterizing regional droughts where ground station data is limited. In this study, we analyzed and assessed the drought characteristics utilizing the GRACE Groundwater Drought Index (GGDI) over four major river basins in India during the period of 2003–2016. The spatial distribution, temporal evolution of drought, and trend characteristics were analyzed using GGDI. Then, the relationship between GGDI and climate factors were evaluated by the method of wavelet coherence. The results indicate the following points: GRACE’s quantitative results were consistent and robust for drought assessment; out of the four basins, severe drought was noticed in the Cauvery river basin between 2012 and 2015, with severity of −27 and duration of 42 months; other than Godavari river basin, the remaining three basins displayed significant negative trends at monthly and seasonal scales; the wavelet coherence method revealed that climate factors had a substantial effect on GGDI, and the impact of Southern Oscillation Index (SOI) on drought was significantly high, followed by Sea Surface Temperature (SST) Index (namely, NINO3.4) and Multivariate El Niño–Southern Oscillation Index (MEI) in all the basins. This study provides reliable and robust quantitative result of GRACE water storage variations that shares new insights for further drought investigation.

In this study monthly, annual and seasonal groundwater variation trends of Warangal district (2000–2015) were examined for forty observation wells using four variations of non-parametric Mann Kendal (MK) methods. Magnitudes of trends were computed using the Sen’s slope estimator. Results conclude that on a monthly time series, three observation wells (31, 32 and 37) experienced significant positive trends (positive Z-statistics), whereas other wells experienced significant negative trends among forty. Wells 2, 17, 22, 33, 38, 39, 40 noticed strong significant negative trends. Using MK-1/MK-2/MK-3/MK-4 for monthly series, around 23/33/23/23% of cases exhibited positive trends (both significant and insignificant) and 77/67/77/77 % of cases exhibited negative trends. Median of trend slopes for groundwater levels (GWLs) were negative in monthly, seasonal and annual timescale. Seasonally trend slope was observed as a decreasing trend in the pre-monsoon period. Annually trend slope was varying approximately from −0.4 mm/year to +0.4 mm/year. ARIMA models were fitted for each well to predict the parameters of the models and to forecast (2015) using monthly GWLs from 2000 to 2014. ARIMA(2,1,2), ARIMA(2,0,1), ARIMA(4,0,0), ARIMA(1,1,1), ARIMA(1,0,1), ARIMA(1,0,2) and ARIMA(2,0,0) fitted for most of the Wells. Using these models GWLs were predicted and the performance of the predicted levels was analyzed using the correlation coefficient (R²) and Akaike Information Criteria (AIC) values.

In the present study, groundwater level (GWL) trends were analysed using six statistical trend techniques on seasonal time scale and forecasted monthly using time series analysis (TSA). Groundwater data (piezometric level) of 14 observation wells are considered from 1996–2017 for Warangal district of Telangana State. Results showed a significant declining trend in five observation wells out of 14. Notably, two observation wells exhibited increasing trends. Out of four seasons considered, variation in trends was observed in post-monsoon kharif and rabi. Change points in trend were detected using sequential Mann–Kendall (SMK) test which showed a good agreement with trend variations obtained from Mann–Kendall test. Innovative trend analysis technique (ITAT) is applied to derive monotonic and non-monotonic trend series plots. Compared to ITAT, SMK test showed good agreement with other trend tests. Considering the piezometric data of 1996–2017, GWLs for the year 2017 were forecasted using autoregressive integrated moving average model (ARIMA). Results concluded that ARIMA (4,1,2) model exhibited the best forecasting of GWLs for observation well 1 with coefficient of determination (R²) as 0.86, root mean square error as 0.28 and Akaike information criteria as 377.5 that showed a good correlation with observed and forecasted GWLs.