Constructing high-resolution groundwater drought at spatio-temporal scale using GRACE satellite data based on machine learning in the Indus Basin

The complicated phenomenon induced by inadequate precipitation is a drought that impacts water resources and human life. Traditional methods to assess groundwater drought events are hindered due to sparse groundwater observations on a spatio-temporal scale. These groundwater drought events are not well studied in the study area of the Indus Basin Irrigation System (IBIS) holistically. This study applied four machine learning models to the training datasets of Gravity Recovery and Climate Experiment (GRACE) Terrestrial Water Storage (TWS) and Groundwater Storage (GWS) data to improve resolution to 0.25 degrees from 1 degrees. The Extreme Gradient Boosting (XGBoost) model outperformed the four models and results showed Pearson correlation (R) (0.99), Nash Sutcliff Efficiency (NSE) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm). The GRACE Groundwater Drought Index (GGDI) was calculated by normalizing XGBoost-downscaled GWS. The trend characteristics, the temporal evolution, and spatial distribution of GGDI were analyzed across the IBIS from 2003 to 2016. The wavelet coherence approach was used to evaluate the relationship between teleconnection factors and GGDI. The XGBoost downscaling model can accurately reproduce local groundwater behavior, with the acceptable correlation of coefficient values for validation (ranging from 0.02 to 0.84). The accumulated Standardized Precipitation Evapotranspiration Index (SPEI) with the time of 1, 3, and 6 months, and selfcalibrated Palmer Drought Severity Index (sc-PDSI) were used to validate GGDI. The findings have demonstrated that GGDI has comparable drought patterns to SPEI-3 and SPEI-6 and sc-PDSI. The teleconnection factors have a significant impact on the GGDI shown by the wavelet coherence technique. The impact of the sea surface temperature index (namely, NINO3.4) on GGDI was observed significantly high among other teleconnection factors in the IBIS. The proposed framework can serve as a useful tool for drought monitoring and a better understanding of extreme hydroclimatic conditions in the IBIS and other similar climatic regions.